diff --git a/.env.example b/.env.example index 5dc2622..3954b6b 100644 --- a/.env.example +++ b/.env.example @@ -1,163 +1,16 @@ -############################################### -############## LLM API SELECTION ############## -############################################### -LLM_PROVIDER=openai - -OPEN_AI_KEY=sk-proj- -LANGCHAIN_TRACING_V2=true -LANGCHAIN_PROJECT=langgraph_tutorial -LANGCHAIN_ENDPOINT=https://api.smith.langchain.com -LANGCHAIN_API_KEY=lsv2_ - - - -# LLM_PROVIDER=openai -# OPEN_AI_KEY=sk-proj----- -OPEN_AI_LLM_MODEL=gpt-4.1 - -# LLM_PROVIDER=gemini -# GEMINI_API_KEY= -# GEMINI_LLM_MODEL=gemini-2.0-flash-lite - -# LLM_PROVIDER=azure -# AZURE_OPENAI_LLM_ENDPOINT=https://-------.openai.azure.com/ -# AZURE_OPENAI_LLM_KEY=- -# AZURE_OPENAI_LLM_MODEL=gpt4o -# AZURE_OPENAI_LLM_API_VERSION=2024-07-01-preview - -# LLM_PROVIDER=ollama -# OLLAMA_LLM_BASE_URL= -# OLLAMA_LLM_MODEL= - -# LLM_PROVIDER=huggingface -# HUGGING_FACE_LLM_REPO_ID= -# HUGGING_FACE_LLM_ENDPOINT= -# HUGGING_FACE_LLM_API_TOKEN= - -# LLM_PROVIDER=bedrock -# AWS_BEDROCK_LLM_ACCESS_KEY_ID= -# AWS_BEDROCK_LLM_SECRET_ACCESS_KEY= -# AWS_BEDROCK_LLM_REGION=us-west-2 -# AWS_BEDROCK_LLM_ENDPOINT_URL=https://bedrock.us-west-2.amazonaws.com -# AWS_BEDROCK_LLM_MODEL=anthropic.claude-3-5-sonnet-20241022-v2:0\ - -############################################### -########### Embedding API SElECTION ########### -############################################### -# Only used if you are using an LLM that does not natively support embedding (openai or Azure) -EMBEDDING_PROVIDER='openai' -OPEN_AI_EMBEDDING_MODEL='text-embedding-ada-002' - -# EMBEDDING_PROVIDER=azure -# AZURE_OPENAI_EMBEDDING_ENDPOINT=https://-------.openai.azure.com/openai/deployments -# AZURE_OPENAI_EMBEDDING_KEY=- -# AZURE_OPENAI_EMBEDDING_MODEL='textembeddingada002' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002 -# AZURE_OPENAI_EMBEDDING_API_VERSION=2023-09-15-preview - -# EMBEDDING_PROVIDER='ollama' -# EMBEDDING_BASE_PATH='http://host.docker.internal:11434' -# EMBEDDING_MODEL='nomic-embed-text:latest' -# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=8192 - -# EMBEDDING_PROVIDER='bedrock' -# AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID=-- -# AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY=-/-+-+- -# AWS_BEDROCK_EMBEDDING_REGION=us-west-2 -# AWS_BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0 - -# EMBEDDING_PROVIDER='gemini' -# GEMINI_EMBEDDING_API_KEY= -# EMBEDDING_MODEL='text-embedding-004' - -# EMBEDDING_PROVIDER='huggingface' -# HUGGING_FACE_EMBEDDING_REPO_ID= -# HUGGING_FACE_EMBEDDING_MODEL= -# HUGGING_FACE_EMBEDDING_API_TOKEN= - -DATAHUB_SERVER = 'http://localhost:8080' - - -############################################### -######## Database Connector SELECTION ######### -############################################### - -# clickhouse -DB_TYPE=clickhouse -CLICKHOUSE_HOST=localhost -CLICKHOUSE_PORT=9001 -CLICKHOUSE_USER=clickhouse -CLICKHOUSE_PASSWORD=clickhouse -CLICKHOUSE_DATABASE=default - -# databricks -# DB_TYPE=databricks -# DATABRICKS_HOST=_ -# DATABRICKS_HTTP_PATH=_ -# DATABRICKS_ACCESS_TOKEN=_ - -# duckdb -# DB_TYPE=duckdb -# DUCKDB_PATH=./data/duckdb.db - -# mariadb -# DB_TYPE=mariadb -# MARIADB_HOST=_ -# MARIADB_PORT=3306 -# MARIADB_USER=_ -# MARIADB_PASSWORD=_ -# MARIADB_DATABASE=_ - -# mysql -# DB_TYPE=mysql -# MYSQL_HOST=_ -# MYSQL_PORT=3306 -# MYSQL_USER=_ -# MYSQL_PASSWORD=_ -# MYSQL_DATABASE=_ - -# oracle -# DB_TYPE=oracle -# ORACLE_HOST=_ -# ORACLE_PORT=1521 -# ORACLE_USER=_ -# ORACLE_PASSWORD=_ -# ORACLE_DATABASE=_ -# ORACLE_SERVICE_NAME=_ - -# postgresql -# DB_TYPE=postgresql -# POSTGRESQL_HOST=_ -# POSTGRESQL_PORT=5432 -# POSTGRESQL_USER=_ -# POSTGRESQL_PASSWORD=_ -# POSTGRESQL_DATABASE=_ - -# snowflake -# DB_TYPE=snowflake -# SNOWFLAKE_USER=_ -# SNOWFLAKE_PASSWORD=_ -# SNOWFLAKE_ACCOUNT=_ - -# sqlite -# DB_TYPE=sqlite -# SQLITE_PATH=./data/sqlite.db - - -# pgvector 설정 (VECTORDB_TYPE=pgvector일 때 사용) -PGVECTOR_HOST=localhost -PGVECTOR_PORT=5432 -PGVECTOR_USER=postgres -PGVECTOR_PASSWORD=postgres -PGVECTOR_DATABASE=postgres -PGVECTOR_COLLECTION=table_info_db - -# VectorDB 설정 -VECTORDB_TYPE=faiss # faiss 또는 pgvector - - -# TRINO_HOST=localhost -# TRINO_PORT=8080 -# TRINO_USER=admin -# TRINO_PASSWORD=password -# TRINO_CATALOG=delta -# TRINO_SCHEMA=default +# Lang2SQL v4.1 — copy to .env and fill in. Only DISCORD_BOT_TOKEN is required. + +# Discord bot token from https://discord.com/developers/applications (Bot tab). +# Required to run `lang2sql-bot`; the bot exits with a clear error if it's unset. +DISCORD_BOT_TOKEN= + +# OpenAI API key. Optional: when set, the agent uses gpt-4.1-mini. When unset, +# it falls back to the offline FakeLLM (deterministic canned tool cycles — fine +# for a smoke run, not for real answers). +OPENAI_API_KEY= + +# Fernet key used to encrypt stored secrets (DSNs / API keys) at rest. Optional: +# if unset, a key is auto-generated and persisted in the SQLite kv table. Set it +# in production so secrets decrypt across restarts and machines. Generate one: +# python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())" +LANG2SQL_SECRET_KEY= diff --git a/README.md b/README.md index 1d32a93..9bdb698 100644 --- a/README.md +++ b/README.md @@ -8,308 +8,158 @@ Pull Requests Badge Issues Badge GitHub contributors -PyPI version -PyPI downloads -Hits

우리는 함께 코드와 아이디어를 나누며 더 나은 데이터 환경을 만들기 위한 오픈소스 여정을 떠납니다. 🌍💡

-

- "모두가 더 가치 있는 일에 집중할 수 있기를 바랍니다." -

- --- -## 🚀 Lang2SQL이란? +> **A document-learning, read-only SQL analytics agent.** +> Feed it your company's docs → it learns your business context → it keeps a +> *separate* set of definitions per team → it answers questions over an +> incomplete database → it remembers every definition and conversation. -Lang2SQL은 자연어를 SQL로 변환하는 오픈소스 도구입니다. **define-by-run** 철학에 기반한 모듈러 아키텍처로, 복잡한 데이터베이스 스키마에 대한 깊은 지식 없이도 효율적인 SQL 쿼리를 생성할 수 있도록 도와줍니다. +This is the **v4.1 rebuild** (see [`docs/discord_first_redesign_v4_1.md`](docs/discord_first_redesign_v4_1.md)). +Where most text-to-SQL projects compete on *"generate better SQL,"* Lang2SQL +competes on everything *around* the query: business-context learning, per-team +semantics, robustness to messy databases, and memory. **Discord is the Phase 1 +interface, not the identity** — Slack/Web are adapters on the same core. -### 🎯 주요 기능 +--- -- **🗣️ 자연어를 SQL로 변환**: 일상 언어를 정확한 SQL 쿼리로 변환 -- **📊 스마트 테이블 발견**: BM25 키워드 검색과 벡터 유사도 검색을 결합한 하이브리드 검색 -- **🔌 Port 기반 확장**: LLM, DB, 임베딩, 벡터스토어를 Protocol 인터페이스로 자유롭게 교체 -- **🛠️ 웹 인터페이스**: Streamlit 기반 대화형 앱 (쿼리 생성 + 챗봇) -- **📈 시각화**: SQL 쿼리 결과를 차트와 그래프로 시각화 -- **🗄️ 유연한 VectorDB**: InMemory, FAISS(로컬), pgvector(PostgreSQL) 중 선택 +## The four pillars -### 🤔 해결하는 문제 +| Pillar | What it is | +|---|---| +| **① Business-context learning** | Documents are the source of truth. Drop in a doc → the agent extracts metric/dimension/rule candidates → you confirm → they land in the semantic layer. | +| **② Two-axis robustness** | **(2a) DB robustness** — works even when columns lack descriptions (auto-enrichment, v1.5). **(2b) Semantic robustness** — teams hold *different* definitions of the same term without conflict. This axis is the product/research identity. | +| **③ Hermes memory** | Conversations, facts, and preferences persist instead of resetting each session. | +| **④ Multi-interface** | Phase 1 Discord today; Slack/Web are future adapters. No platform lock-in. | -새로운 데이터팀 구성원들이 자주 직면하는 문제들: -- 🤯 "테이블이 너무 많아! 어디서부터 시작하지?" -- 🧐 "이 JOIN이 맞나요?" -- 🐌 "이 쿼리 성능이 괜찮을까요?" -- 😰 "어떻게 의미있는 인사이트를 추출하지?" +## Extensibility — outlets and appliances (콘센트/가전) -**Lang2SQL은 다음을 제공하여 이를 해결합니다:** -- ✅ 자연어 입력 → 테이블 추천 -- ✅ 적절한 컬럼 조합으로 자동 SQL 생성 -- ✅ 모범 사례 기반 성능 최적화 +V1 ships the **simplest single implementation** of each extension point, but the +**abstraction (port) is already in place**, so v1.5/v2 add a new implementation +*without touching existing code*. Like a wall outlet: the V1 socket has one LED +bulb plugged in, but because the socket is standard, you later plug in a fan or a +smart light without rewiring the wall. ---- +Four ★ extension patterns sit behind `core/ports/`: -## 📦 설치 방법 +| ★ | Pattern | Port | Grows by | +|---|---|---|---| +| ① | **Safety pipeline** | `ports/safety.py` | adding one layer class to the line (zero `run_sql` changes) | +| ② | **Memory service** | `ports/memory.py` | swapping any of 3 axes — Store / Recall / Extractor — independently | +| ③ | **Ingestion pipeline** | `ports/ingestion.py` | a Source × Extractor matrix | +| ④ | **Semantic federation** | `ports/semantic_scope.py` | git-like per-team scope branches | -### 빠른 설치 +Everything outside `tenancy/concierge.py` depends only on these Protocols, so the +concrete classes (OpenAI, Postgres, SQLite) are swappable at the seams. -```bash -# pip -pip install lang2sql +--- -# uv (권장) -uv venv --python 3.11 -source .venv/bin/activate -uv add lang2sql -``` +## Quickstart -### 소스에서 설치 +Requires Python ≥ 3.10 and [uv](https://docs.astral.sh/uv/). ```bash -git clone https://github.com/CausalInferenceLab/lang2sql.git -cd lang2sql - -# uv (권장) -uv venv --python 3.11 -source .venv/bin/activate -uv sync - -# pip -python -m venv .venv -source .venv/bin/activate -pip install -e . +uv sync # create .venv and install deps ``` ---- +### 1. Run the offline demo (no token, no database) -## ⚡ 빠른 시작 (Python API) - -```python -from lang2sql import BaselineNL2SQL -from lang2sql.integrations.db import SQLAlchemyDB -from lang2sql.integrations.llm import OpenAILLM - -catalog = [ - { - "name": "orders", - "description": "고객 주문 정보", - "columns": { - "order_id": "주문 고유 ID", - "customer_id": "고객 ID", - "order_date": "주문 일시", - "amount": "주문 금액", - "status": "주문 상태", - }, - }, - { - "name": "customers", - "description": "고객 마스터", - "columns": { - "customer_id": "고객 ID", - "name": "고객 이름", - "grade": "고객 등급: bronze / silver / gold", - }, - }, -] - -pipeline = BaselineNL2SQL( - catalog=catalog, - llm=OpenAILLM(model="gpt-4o-mini"), - db=SQLAlchemyDB("sqlite:///sample.db"), - db_dialect="sqlite", -) - -rows = pipeline.run("지난달 주문 건수를 알려줘") -print(rows) +```bash +.venv/bin/python bench/ecommerce_demo.py ``` -### 파이프라인 선택 가이드 - -| 파이프라인 | 검색 방식 | 적합한 상황 | -|---|---|---| -| `BaselineNL2SQL` | BM25 키워드 | 빠른 시작, 카탈로그 규모 소~중간 | -| `HybridNL2SQL` | BM25 + Vector | 검색 품질 우선, 비즈니스 문서 활용 | -| `EnrichedNL2SQL` | BM25 + Vector + Gate | 운영 환경, 부적합 질문 필터링 필요 | - -더 자세한 내용은 [튜토리얼](docs/tutorials/01-quickstart.md)을 참고하세요. - ---- - -## 🛠️ 사용법 +Shows the federation money-shot (one term, two team definitions, no conflict) and +the safety gate (DROP/INSERT blocked, SELECT passes). See [`bench/README.md`](bench/README.md). -### CLI +### 2. Run the CLI (developer driver) ```bash -# Streamlit 웹 인터페이스 실행 -lang2sql run-streamlit -lang2sql run-streamlit -p 8888 # 포트 지정 - -# 자연어 쿼리 실행 -lang2sql query "지난달 주문 건수를 집계해줘" - -# enriched 플로우 (질문 검증 + 프로파일링 + 컨텍스트 보강) -lang2sql query "이번 달 순매출 합계" --flow enriched --dialect sqlite --top-n 5 - -# QuestionGate 비활성화 (enriched 전용) -lang2sql query "이번 달 순매출 합계" --flow enriched --no-gate +.venv/bin/lang2sql "list the tables" ``` -**CLI 옵션:** - -| 옵션 | 기본값 | 설명 | -|------|--------|------| -| `--flow` | `baseline` | `baseline` 또는 `enriched` | -| `--dialect` | `None` | SQL 방언 (sqlite, postgresql, mysql, bigquery, duckdb) | -| `--top-n` | `5` | 검색할 최대 테이블 수 | -| `--no-gate` | `False` | QuestionGate 비활성화 (enriched 전용) | - -> CLI는 `.env` 파일의 환경변수(`LLM_PROVIDER`, `DB_TYPE` 등)를 읽어 동작합니다. `.env.example`을 참고하세요. - -### Streamlit 웹 UI - -Streamlit 앱은 멀티 페이지 구조입니다: - -- **🏠 홈** — 프로젝트 소개 -- **🔍 Lang2SQL** — 자연어 쿼리 → SQL 생성 및 실행 -- **🤖 ChatBot** — 멀티턴 대화 기반 SQL 생성 -- **⚙️ 설정** — LLM, DB, 임베딩, 데이터 소스 설정 +The CLI assembles a real `HarnessContext` and runs one turn through the agent +loop. With `OPENAI_API_KEY` set it calls `gpt-4.1-mini`; otherwise it uses the +offline `FakeLLM`. -### 처음 시작하기 +### 3. Run the Discord bot -튜토리얼은 난이도 순서로 구성되어 있습니다. - -| 번호 | 문서 | 내용 | -|------|------|------| -| 01 | [빠른 시작](docs/tutorials/01-quickstart.md) | 5분 안에 NL2SQL 실행 | -| 02 | [Baseline 파이프라인](docs/tutorials/02-baseline.md) | 실제 DB 연결, DB Explorer, Factory 함수 | -| 03 | [벡터 검색](docs/tutorials/03-vector-search.md) | FAISS/pgvector 인덱싱 | -| 04 | [하이브리드 검색](docs/tutorials/04-hybrid.md) | BM25 + Vector, EnrichedNL2SQL | -| 05 | [고급](docs/tutorials/05-advanced.md) | 수동 조합, 커스텀 어댑터, Port 레퍼런스, Hook | - ---- - -## 🏗️ 아키텍처 - -Lang2SQL v2는 **define-by-run** 철학을 따르는 모듈러 아키텍처입니다. 파이프라인 제어는 프레임워크 DSL이 아닌 순수 Python 코드로 작성합니다. - -``` -src/lang2sql/ -├── core/ # 외부 의존성 0% — 절대 third-party import 금지 -│ ├── base.py # BaseComponent, BaseFlow -│ ├── catalog.py # CatalogEntry, TextDocument, IndexedChunk -│ ├── ports.py # LLMPort, DBPort, EmbeddingPort 등 Protocol 인터페이스 -│ ├── hooks.py # TraceHook, MemoryHook (관측성) -│ └── exceptions.py -│ -├── components/ # 재사용 가능한 부품 -│ ├── retrieval/ # KeywordRetriever, VectorRetriever, HybridRetriever -│ ├── generation/ # SQLGenerator (dialect별 프롬프트) -│ ├── execution/ # SQLExecutor -│ ├── gate/ # QuestionGate, TableSuitabilityEvaluator -│ ├── enrichment/ # QuestionProfiler, ContextEnricher -│ └── loaders/ # MarkdownLoader, PlainTextLoader, DirectoryLoader -│ -├── flows/ # 프리셋 파이프라인 -│ ├── nl2sql.py # BaselineNL2SQL (Keyword → SQL → Execute) -│ ├── hybrid.py # HybridNL2SQL (BM25 + Vector) -│ └── enriched_nl2sql.py # EnrichedNL2SQL (Gate + Profile + Enrich) -│ -└── integrations/ # 외부 의존성 구현체 - ├── llm/ # OpenAI, Anthropic, Azure, Gemini, Bedrock, Ollama, HuggingFace - ├── embedding/ # OpenAI, Azure, Gemini, Bedrock, Ollama, HuggingFace - ├── db/ # SQLAlchemyDB, SQLAlchemyExplorer - ├── vectorstore/ # InMemory, FAISS, pgvector - ├── catalog/ # DataHubCatalogLoader - ├── loaders/ # PDFLoader - └── chunking/ # SemanticChunker +```bash +export DISCORD_BOT_TOKEN=... # required +export OPENAI_API_KEY=... # optional; offline FakeLLM if unset +export LANG2SQL_SECRET_KEY=... # optional; Fernet key for secret encryption +.venv/bin/lang2sql-bot ``` -### 설계 원칙 - -- **`core/`는 외부 의존성 0%**: 모든 third-party 연동은 `integrations/`에서 구현 -- **Port Protocol**: `LLMPort`, `DBPort`, `EmbeddingPort` 등 메서드 시그니처만 맞추면 어떤 구현체든 연결 가능 -- **Hook 기반 관측성**: `MemoryHook`으로 컴포넌트별 실행 시간, 에러를 추적 +The bot exits loudly if `DISCORD_BOT_TOKEN` is unset. Full setup and hosting: +[`docs/DEPLOY.md`](docs/DEPLOY.md). Copy [`.env.example`](.env.example) to start. --- -## 🧑‍💻 지원 환경 +## What V1 does / does NOT do yet (honesty section) + +**Does:** +- 3-scope semantic federation (guild / channel / thread) with most-specific-wins + resolution; `define_metric` writes to the current scope. +- Safety pipeline with the V1 layers (whitelist + timeout), gating every query. +- Agent loop with six tools: `run_sql`, `explore_schema`, `define_metric`, + `ingest_doc`, `remember`, `ask_user`. +- Memory service (in-memory store + inject-all recall + manual `/remember`). +- Discord frontend (bot, commands, session router, render). +- Encrypted-at-rest secrets (Fernet) and SQLite-backed persistence. + +**Does NOT yet:** +- **Execute against a real database.** `PostgresExplorer` is a **V1 stub** with + canned `orders`/`users` schema and sample rows; real psycopg execution is v1.5. +- **Reason without a key.** Without `OPENAI_API_KEY`, the `FakeLLM` returns + deterministic canned tool cycles — useful for wiring tests, not for answers. +- DB metadata auto-enrichment, AST-precise SQL validation, function blocklists, + cost gating, `/semantic diff` / `/semantic promote`, keyword/vector recall, + automatic fact extraction, URL/Notion ingestion — all scoped to v1.5+. +- Persist across restarts by default: the V1 `SqliteStore` defaults to in-memory; + point it at a file for durability. -### LLM - -| 프로바이더 | 환경변수 `LLM_PROVIDER` | -|---|---| -| OpenAI | `openai` | -| Anthropic | `anthropic` | -| Azure OpenAI | `azure` | -| Google Gemini | `gemini` | -| AWS Bedrock | `bedrock` | -| Ollama | `ollama` | -| HuggingFace | `huggingface` | - -### 데이터베이스 +--- -SQLite, PostgreSQL, MySQL, MariaDB, DuckDB, ClickHouse, Snowflake, Oracle, BigQuery, Trino, Hive, Crate +## Roadmap at a glance -### VectorDB +| Area | V1 | V1.5 | V2 | V2.5 | +|---|---|---|---|---| +| **Safety** | whitelist + timeout | + AST validation, function blocklist, auto LIMIT, **metadata enrichment**, rate limit | + cost gate (EXPLAIN), per-engine pipelines | — | +| **Memory** | in-memory + inject-all + manual | SQLite store + keyword recall + auto-extract | + vector recall + conflict resolution | PostgreSQL + hybrid recall + confidence | +| **Ingestion** | file upload + LLM extract | + URL fetch + DDL parsing | + Notion/Confluence + hybrid | + GitHub/Drive + chunked RAG | +| **Federation** | 3-scope resolution, `/semantic show` | `/semantic diff`, `/semantic promote`, conflict alerts | git sync (semantic-as-code) | branch fork/merge UI, per-scope audit | +| **Interface** | Discord | (Anthropic/NIM eval) | Slack | Web | -| 백엔드 | 영속성 | 권장 규모 | -|---|---|---| -| `InMemoryVectorStore` | 없음 | < 50k chunks | -| `FAISSVectorStore` | 로컬 파일 | < 500k chunks | -| `PGVectorStore` | PostgreSQL | 500k+ chunks | +See [`docs/discord_first_redesign_v4_1.md`](docs/discord_first_redesign_v4_1.md) +for the full architecture write-up. --- ## 🤝 기여하기 -커뮤니티의 기여를 환영합니다! - -### 🔧 개발 환경 설정 - ```bash -# 1. 저장소 포크 & 클론 -git clone https://github.com/YOUR_USERNAME/lang2sql.git +git clone https://github.com/CausalInferenceLab/lang2sql.git cd lang2sql - -# 2. 의존성 설치 -uv venv --python 3.11 -source .venv/bin/activate -uv sync --group dev - -# 3. 브랜치 생성 (네이밍: feature/이슈번호-작업내용) -git checkout -b feature/42-amazing-feature - -# 4. 작업 후 커밋 & PR -git commit -m "feat: add amazing feature" -git push origin feature/42-amazing-feature +uv sync +.venv/bin/pytest -q # 12 safety regressions + full suite must pass ``` -### 📋 개발 가이드라인 - -- `pre-commit run --all-files`로 포맷팅 확인 -- 새로운 기능에 대한 테스트 작성 (`tests/test__.py`) -- PR은 `master` 브랜치 대상, 최소 2명 리뷰어 승인 필요 -- 커밋 메시지: `feat:`, `fix:`, `docs:`, `refactor:` 등 prefix 사용 - -### 🐛 이슈 신고 - -버그를 발견했거나 기능 요청이 있으신가요? 다음 정보와 함께 [이슈를 열어주세요](https://github.com/CausalInferenceLab/lang2sql/issues): -- 문제/기능에 대한 명확한 설명 -- 재현 단계 (버그의 경우) -- 예상 동작 vs 실제 동작 -- 환경 세부사항 +- 새 기능에는 테스트 작성 (`tests/test_.py`) +- PR은 `master` 브랜치 대상, 커밋 메시지에 `feat:` / `fix:` / `docs:` prefix 사용 +- 버그/기능 요청은 [이슈](https://github.com/CausalInferenceLab/lang2sql/issues)로 --- -## 🎓 학습 자료 +## 🙏 감사의 말 / License -- [모두를 위한 게임 데이터 검색 시스템 / if(kakaoAI)2024](https://www.youtube.com/watch?v=8-GerpWVMis&ab_channel=kakaotech) -- [AI 데이터 분석가 '물어보새' 등장 – 1부. RAG와 Text-To-SQL 활용](https://techblog.woowahan.com/18144/) -- [테디노트 LangGraph](https://wikidocs.net/233785) -- [DataHub 문서](https://datahubproject.io/) -- [Vanna.ai](https://github.com/vanna-ai/vanna) +Lang2SQL은 **가짜연구소의 인과추론팀**에서 개발 중인 프로젝트입니다. Licensed under +the [MIT License](https://opensource.org/licenses/MIT). 커뮤니티: [Discord](https://discord.gg/EPurkHVtp2). --- @@ -332,44 +182,6 @@ git push origin feature/42-amazing-feature | **AI Engineer** | 심세원 | ![Python](https://img.shields.io/badge/Python-Expert-3776AB) | LLM, RAG, Multi-Agent | | **Business Analyst** | 서희진 | ![Python](https://img.shields.io/badge/Python-Intermediate-FF6C37) | LLM, Data Analysis | -## 🚀 배포 및 릴리스 - -### 자동 배포(GitHub Actions) - -`v*` 태그 푸시 시 `.github/workflows/pypi-release.yml`이 실행되어 PyPI에 배포됩니다. - -```bash -# 1) 버전 업데이트 (version.py) -git add version.py -git commit -m "chore: bump version to X.Y.Z" - -# 2) 태그 생성/푸시 -git tag vX.Y.Z -git push origin HEAD -git push origin vX.Y.Z -``` - -사전 준비: GitHub Secrets에 `PYPI_API_TOKEN` 등록 - -### 수동 빌드 - -```bash -uv build -UV_PUBLISH_TOKEN=$PYPI_API_TOKEN uv publish --token $UV_PUBLISH_TOKEN -``` - ---- - -## 🙏 감사의 말 - -Lang2SQL은 **가짜연구소의 인과추론팀**에서 개발중인 프로젝트입니다. - ---- - -## 📄 라이선스 - -- This project is licensed under the [MIT License](https://opensource.org/licenses/MIT). - --- ## 🌍 가짜연구소 소개 @@ -378,8 +190,7 @@ Lang2SQL은 **가짜연구소의 인과추론팀**에서 개발중인 프로젝 전 세계 5,000명 이상의 연구자들과 함께, 우리는 AI 지식의 민주화와 열린 협업을 통한 혁신 촉진에 전념하고 있습니다. -**우리 커뮤니티에 참여하세요:** -- 💬 [Discord](https://discord.gg/EPurkHVtp2) +**커뮤니티**: 💬 [Discord](https://discord.gg/EPurkHVtp2) --- @@ -388,10 +199,3 @@ Lang2SQL은 **가짜연구소의 인과추론팀**에서 개발중인 프로젝 - ---- - -
-

⭐ 이 저장소가 도움이 되셨다면 스타를 눌러주세요!

-

"우리는 함께 코드와 아이디어를 나누며 더 나은 데이터 환경을 만들기 위한 오픈소스 여정을 떠납니다. 🌍💡"

-
diff --git a/bench/README.md b/bench/README.md new file mode 100644 index 0000000..926c4e8 --- /dev/null +++ b/bench/README.md @@ -0,0 +1,39 @@ +# bench/ — runnable demo + +A self-contained walkthrough of Lang2SQL V1's headline value, with **no Discord +token and no live database**. It runs the real shipped modules against the +canned Postgres stub and the offline `FakeLLM`. + +## Run + +```bash +.venv/bin/python bench/ecommerce_demo.py +``` + +No environment variables are needed. (If `OPENAI_API_KEY` happens to be set, +Section 0 will route through OpenAI instead of the FakeLLM — everything else is +offline.) + +## What each section proves + +| Section | What it shows | Maps to | +|---|---|---| +| **0 — assembled harness** | `ContextConcierge` wires the FakeLLM + canned `PostgresExplorer` + six tools into a `HarnessContext`, and `agent_loop` drives one full LLM→tool→LLM cycle. A *wiring* proof, not an intelligence proof. | harness + concierge | +| **1 — define metrics** | Three e-commerce metrics (`total_revenue`, `aov`, `paid_orders`) are written to the current channel's scope and read back from the effective layer. | ★① context learning | +| **2 — federation (the money shot)** | `active_user` is defined as *"30-day login"* in `#marketing` and *"paid subscriber"* in `#finance`. Each channel resolves its **own** definition by walking its scope chain — same term, two live meanings, **zero conflict**. | **★④ semantic federation** | +| **3 — safety gate** | `DROP`, `INSERT`, and a CTE-hidden `INSERT` are all **blocked fail-closed**; a plain `SELECT ... GROUP BY` **passes**. The read-only guarantee is enforced before any SQL would reach a database. | **★① safety pipeline** | + +The two headline pillars demonstrated are **★① safety** (Sections 0 + 3) and +**★④ federation** (Sections 1 + 2). + +## Honesty notes + +- The `PostgresExplorer` is a **V1 stub**: it returns canned `orders`/`users` + schema and sample rows and does not connect to a real database. Real psycopg + execution lands in v1.5. +- The `FakeLLM` is a deterministic stub for offline runs; it does not reason. It + blindly calls the first available tool, which is why Section 0's turn ends at + the safety gate rather than counting orders. With a real key it would call + `gpt-4.1-mini`. +- Federation state in Sections 1–2 lives in an in-memory `SemanticStore`; the + SQLite-backed persistent store is wired through the concierge for the bot. diff --git a/bench/ecommerce_demo.py b/bench/ecommerce_demo.py new file mode 100644 index 0000000..b910854 --- /dev/null +++ b/bench/ecommerce_demo.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 +"""E-commerce demo — Lang2SQL v4.1 headline value, no Discord and no real DB. + +Run: .venv/bin/python bench/ecommerce_demo.py + +This is the study-group demo. It exercises the *real* V1 code paths +(``ContextConcierge`` + scope resolver + the canned Postgres explorer + the +offline ``FakeLLM``) to show three things end-to-end without a token or a live +database: + + Section 1 — define three e-commerce metrics in a channel and read them back. + Section 2 — ★④ semantic federation: the *same* term ``active_user`` carries + two different definitions in #marketing vs #finance, with no + conflict. Each channel resolves its own effective layer. + Section 3 — ★① safety pipeline: DROP / INSERT are blocked fail-closed while a + plain SELECT passes. + +Everything printed is produced by running the shipped modules, not hard-coded. +""" + +from __future__ import annotations + +import asyncio + +from lang2sql.core.identity import Identity +from lang2sql.core.ports.safety import SafetyContext, Verdict +from lang2sql.harness.loop import agent_loop +from lang2sql.safety.pipeline import SafetyPipeline +from lang2sql.semantic.types import Metric +from lang2sql.tenancy.concierge import ContextConcierge +from lang2sql.tenancy.scope_resolver import ScopeResolver + +# Stable IDs for the demo guild and its two channels. +GUILD = "acme-shop" +CH_MARKETING = "marketing" +CH_FINANCE = "finance" + + +def _hr(title: str) -> None: + print("\n" + "=" * 68) + print(title) + print("=" * 68) + + +def _marketing_identity() -> Identity: + return Identity(user_id="dana", guild_id=GUILD, channel_id=CH_MARKETING) + + +def _finance_identity() -> Identity: + return Identity(user_id="evan", guild_id=GUILD, channel_id=CH_FINANCE) + + +async def section_0_harness(concierge: ContextConcierge) -> None: + """Drive one full agent turn through the assembled harness (offline). + + This is the *wiring* proof, not an intelligence proof: ``ContextConcierge`` + picks the offline FakeLLM (no OPENAI_API_KEY), starts a session, and wires + the canned Postgres explorer + six tools into a ``HarnessContext`` that + ``agent_loop`` drives LLM → tool → LLM to a final answer. No network, no + real database. + + The FakeLLM is a deterministic stub: it blindly calls the first tool + (``run_sql``) with placeholder args, so its turn ends up *demonstrating the + safety gate* rather than answering the question. With OPENAI_API_KEY set, + the same loop calls gpt-4.1-mini instead — zero other code changes. + """ + _hr("SECTION 0 — assembled harness runs one turn (ContextConcierge + FakeLLM)") + + ident = _marketing_identity() + ctx = await concierge.build_context(ident) + print(f"LLM in use: {type(ctx.llm).__name__} (offline, no key required)") + print(f"Explorer: {type(ctx.explorer).__name__} (canned orders/users tables)") + print(f"Tools wired: {', '.join(s.name for s in ctx.tools.specs())}\n") + + final = await agent_loop(ctx, "How many orders do we have?") + print("user> How many orders do we have?") + print(f"loop> {final}") + print("\n The loop completed a real LLM→tool→LLM cycle. The stub's blind") + print(" run_sql call was caught by the safety gate, which is exactly the") + print(" ★① behaviour Section 3 isolates.") + + +async def section_1_define_metrics(resolver: ScopeResolver) -> None: + """Define three e-commerce metrics in #marketing and read them back.""" + _hr("SECTION 1 — define three metrics (★① business-context learning)") + + ident = _marketing_identity() + scope = ident.default_write_scope() # current channel by default + print(f"Writing to default scope for this channel: {scope}\n") + + metrics = [ + Metric("total_revenue", "SUM(orders.amount) WHERE status != 'cancelled'"), + Metric("aov", "total_revenue / COUNT(DISTINCT orders.id)"), + Metric("paid_orders", "COUNT(*) FROM orders WHERE status = 'paid'"), + ] + for m in metrics: + await resolver.define(scope, m) + print(f" defined {m.name:>14} = {m.definition}") + + layer = await resolver.effective_layer(ident) + print(f"\nEffective layer for #{CH_MARKETING} now holds " + f"{len(layer.entries)} definition(s):") + print(layer.render()) + + +async def section_2_federation(resolver: ScopeResolver) -> None: + """Same term, two channels, two definitions — no conflict (★④).""" + _hr("SECTION 2 — semantic federation: one term, two definitions (★④)") + + # #marketing defines active_user one way ... + mkt = _marketing_identity() + await resolver.define( + mkt.default_write_scope(), + Metric("active_user", "user with a login event in the last 30 days"), + ) + # ... and #finance defines the SAME name a different way. + fin = _finance_identity() + await resolver.define( + fin.default_write_scope(), + Metric("active_user", "user with an active paid subscription"), + ) + + print("Defined 'active_user' independently in two channels.\n") + print("Now resolving the *effective* definition each channel sees") + print("by walking its scope chain (most specific scope wins):\n") + + mkt_layer = await resolver.effective_layer(mkt) + fin_layer = await resolver.effective_layer(fin) + mkt_def = mkt_layer.lookup("active_user") + fin_def = fin_layer.lookup("active_user") + + print(f" #{CH_MARKETING:<10} active_user → {mkt_def.definition}") + print(f" #{CH_FINANCE:<10} active_user → {fin_def.definition}") + + assert mkt_def.definition != fin_def.definition + print("\n ✅ Same term, two live definitions, zero conflict.") + print(" Each channel is its own branch in the federation tree;") + print(" neither overwrote the other. (Wren's single MDL cannot do this.)") + + # Show the scope chain that produced the marketing answer. + chain = " → ".join(str(s) for s in mkt.scope_chain()) + print(f"\n #{CH_MARKETING} resolution order: {chain}") + print(" Lookup stops at the first scope that defines the name (CHANNEL),") + print(" so the GUILD/BUILTIN branches never get a chance to disagree.") + + +def section_3_safety(pipeline: SafetyPipeline) -> None: + """DROP/INSERT blocked fail-closed; SELECT passes (★①).""" + _hr("SECTION 3 — safety pipeline gates every query (★①)") + + layers = " → ".join(layer.name for layer in pipeline.layers) + print(f"Pipeline layers in order: {layers}\n") + + cases = [ + ("DROP TABLE users", "must BLOCK"), + ("INSERT INTO orders VALUES (99, 10.0, 'paid', now())", "must BLOCK"), + ("WITH x AS (INSERT INTO t VALUES (1)) SELECT * FROM x", "must BLOCK (CTE)"), + ("SELECT status, COUNT(*) FROM orders GROUP BY status", "must PASS"), + ] + ctx = SafetyContext() + for sql, expectation in cases: + decision = pipeline.evaluate(sql, ctx) + passed = decision.verdict is Verdict.PASS + mark = "✅ PASS " if passed else "🚫 BLOCK" + reason = f" ({decision.reason} @ {decision.layer})" if decision.reason else "" + print(f" {mark} {expectation:<16} {sql}{reason}") + + print("\n Read-only is enforced before any SQL reaches the database:") + print(" the whitelist layer is fail-closed, so anything it cannot prove") + print(" is a single SELECT/WITH is rejected.") + + +async def main() -> None: + print("Lang2SQL v4.1 — e-commerce demo (offline: FakeLLM, canned PG, in-memory)") + + # One shared resolver so federation state persists across sections 1 and 2. + resolver = ScopeResolver() + pipeline = SafetyPipeline() + concierge = ContextConcierge() + + await section_0_harness(concierge) + await section_1_define_metrics(resolver) + await section_2_federation(resolver) + section_3_safety(pipeline) + + _hr("DONE") + print("Sections 1–2 exercise ★④ federation; section 3 exercises ★① safety.") + print("No network, no token, no live database were used.") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cli/README.md b/cli/README.md deleted file mode 100644 index 012d1eb..0000000 --- a/cli/README.md +++ /dev/null @@ -1,270 +0,0 @@ -# CLI 모듈 - -Lang2SQL 프로젝트의 CLI(Command Line Interface) 모듈입니다. 자연어 질문을 SQL 쿼리로 변환하고, Streamlit 웹 애플리케이션을 실행하는 기능을 제공합니다. - -## 디렉토리 구조 - -``` -cli/ -├── __init__.py # CLI 진입점 및 메인 그룹 정의 -├── __pycache__/ # Python 캐시 디렉토리 -├── commands/ # CLI 명령어 정의 모듈 -│ ├── __pycache__/ -│ ├── quary.py # 자연어 질문을 SQL로 변환하는 명령어 -│ ├── run_streamlit.py # Streamlit 실행 명령어 -│ └── README.md # Commands 모듈 문서 -├── core/ # CLI 핵심 기능 모듈 -│ ├── __pycache__/ -│ ├── environment.py # 환경 변수 초기화 모듈 -│ ├── streamlit_runner.py # Streamlit 실행 유틸리티 -│ └── README.md # Core 모듈 문서 -├── utils/ # CLI 유틸리티 모듈 -│ ├── __pycache__/ -│ ├── env_loader.py # 환경 변수 로드 유틸리티 -│ ├── logger.py # 로깅 설정 유틸리티 -│ └── README.md # Utils 모듈 문서 -└── README.md # 이 파일 -``` - -## 모듈 개요 - -### `__init__.py` - -CLI 프로그램의 진입점입니다. Click 프레임워크를 사용하여 명령어 그룹과 옵션을 정의합니다. - -**주요 구성 요소:** - -#### CLI 그룹 정의 -- **함수**: `cli(ctx, datahub_server, run_streamlit, port, env_file_path, prompt_dir_path, vectordb_type, vectordb_location)` -- **데코레이터**: `@click.group()` -- **역할**: Lang2SQL CLI의 최상위 명령어 그룹 - -#### 주요 옵션 - -1. **`--version` / `-v`**: 버전 정보 출력 -2. **`--datahub_server`** (Deprecated): DataHub GMS URL 설정 - 더 이상 사용되지 않음 -3. **`--run-streamlit`**: CLI 실행 시 Streamlit 애플리케이션을 바로 실행하는 플래그 -4. **`-p, --port`**: Streamlit 서버 포트 번호 (기본값: 8501) -5. **`--env-file-path`**: 환경 변수를 로드할 .env 파일 경로 -6. **`--prompt-dir-path`**: 프롬프트 템플릿 디렉토리 경로 -7. **`--vectordb-type`** (Deprecated): VectorDB 타입 - 더 이상 사용되지 않음 -8. **`--vectordb-location`** (Deprecated): VectorDB 위치 - 더 이상 사용되지 않음 - -#### 주요 동작 - -1. **환경 변수 초기화**: `initialize_environment()` 호출로 환경 변수 설정 -2. **Deprecated 옵션 경고**: 사용되지 않는 옵션 사용 시 경고 메시지 출력 -3. **Streamlit 자동 실행**: `--run-streamlit` 플래그가 설정된 경우 Streamlit 실행 - -#### 등록된 명령어 - -- `run-streamlit`: Streamlit 애플리케이션 실행 (`cli/commands/run_streamlit.py`) -- `query`: 자연어 질문을 SQL 쿼리로 변환 (`cli/commands/quary.py`) - -#### Import 관계 - -**Import하는 모듈:** -- `cli.commands.quary.query_command`: query 명령어 -- `cli.commands.run_streamlit.run_streamlit_cli_command`: run-streamlit 명령어 -- `cli.core.environment.initialize_environment`: 환경 변수 초기화 -- `cli.core.streamlit_runner.run_streamlit_command`: Streamlit 실행 함수 -- `cli.utils.logger.configure_logging`: 로깅 설정 -- `version.__version__`: 버전 정보 - -**사용 위치:** -- CLI 진입점: `pyproject.toml`의 `[project.scripts]` 섹션에 정의됨 - ```toml - [project.scripts] - lang2sql = "cli.__init__:cli" - ``` -- 사용 방법: 패키지 설치 후 `lang2sql` 명령어로 실행 - ```bash - lang2sql --help - lang2sql --version - lang2sql --run-streamlit - lang2sql query "질문" - lang2sql run-streamlit - ``` - -#### 코드 예시 - -```python -from cli import cli - -# CLI 그룹 자동 등록 -# lang2sql 명령어로 실행 가능 -``` - -### 하위 모듈 - -#### `commands/` 모듈 - -CLI 명령어를 정의하는 모듈입니다. 자연어 질문을 SQL로 변환하는 `query` 명령어와 Streamlit을 실행하는 `run-streamlit` 명령어를 제공합니다. - -자세한 내용은 [`commands/README.md`](commands/README.md)를 참고하세요. - -**주요 파일:** -- `quary.py`: `query_command` - 자연어를 SQL로 변환하는 명령어 -- `run_streamlit.py`: `run_streamlit_cli_command` - Streamlit 실행 명령어 - -**사용 위치:** -- `cli/__init__.py` (10-11, 116-117번 라인): CLI 그룹에 명령어 등록 - ```python - from cli.commands.quary import query_command - from cli.commands.run_streamlit import run_streamlit_cli_command - - cli.add_command(run_streamlit_cli_command) - cli.add_command(query_command) - ``` - -#### `core/` 모듈 - -CLI의 핵심 기능을 제공하는 모듈입니다. 환경 변수 초기화와 Streamlit 실행 기능을 담당합니다. - -자세한 내용은 [`core/README.md`](core/README.md)를 참고하세요. - -**주요 파일:** -- `environment.py`: `initialize_environment` - 환경 변수 초기화 함수 -- `streamlit_runner.py`: `run_streamlit_command` - Streamlit 실행 함수 - -**사용 위치:** -- `cli/__init__.py` (12-13, 85, 113번 라인): - ```python - from cli.core.environment import initialize_environment - from cli.core.streamlit_runner import run_streamlit_command - - # 환경 변수 초기화 - initialize_environment( - env_file_path=env_file_path, - prompt_dir_path=prompt_dir_path - ) - - # Streamlit 실행 - if run_streamlit: - run_streamlit_command(port) - ``` - -#### `utils/` 모듈 - -CLI 애플리케이션에서 사용되는 유틸리티 함수들을 제공하는 모듈입니다. - -자세한 내용은 [`utils/README.md`](utils/README.md)를 참고하세요. - -**주요 파일:** -- `env_loader.py`: 환경 변수 로드 및 설정 함수들 - - `load_env`: .env 파일 로드 - - `set_prompt_dir`: 프롬프트 디렉토리 설정 - - `set_vectordb`: VectorDB 설정 -- `logger.py`: `configure_logging` - CLI 전용 로깅 설정 - -**사용 위치:** -- `cli/__init__.py` (14, 18번 라인): 로깅 설정 - ```python - from cli.utils.logger import configure_logging - - logger = configure_logging() - ``` -- `cli/core/environment.py`: 환경 변수 로드 -- `cli/core/streamlit_runner.py`: 로깅 설정 -- `cli/commands/quary.py`: 로깅 설정 -- `cli/commands/run_streamlit.py`: 로깅 설정 - -## CLI 사용 방법 - -### 기본 사용법 - -```bash -# 도움말 보기 -lang2sql --help - -# 버전 확인 -lang2sql --version - -# Streamlit 바로 실행 -lang2sql --run-streamlit - -# 환경 변수 파일 지정하여 실행 -lang2sql --env-file-path /path/to/.env --run-streamlit -``` - -### 명령어 - -#### `query` 명령어 - -자연어 질문을 SQL 쿼리로 변환합니다. - -```bash -# 기본 사용 -lang2sql query "고객 데이터를 기반으로 유니크한 유저 수를 카운트하는 쿼리" - -# 옵션과 함께 사용 -lang2sql query "질문" \ - --database-env clickhouse \ - --retriever-name 기본 \ - --top-n 5 \ - --device cpu \ - --use-enriched-graph \ - --vectordb-type faiss \ - --vectordb-location ./dev/table_info_db -``` - -#### `run-streamlit` 명령어 - -Streamlit 웹 애플리케이션을 실행합니다. - -```bash -# 기본 포트(8501)로 실행 -lang2sql run-streamlit - -# 특정 포트로 실행 -lang2sql run-streamlit -p 9000 -``` - -자세한 사용법은 [`commands/README.md`](commands/README.md)를 참고하세요. - -## 의존성 - -### 내부 의존성 - -- `cli.commands.*`: CLI 명령어 정의 -- `cli.core.*`: 핵심 기능 모듈 -- `cli.utils.*`: 유틸리티 함수 -- `version`: 버전 정보 - -### 외부 의존성 - -- `click`: CLI 프레임워크 -- `subprocess`: Streamlit 프로세스 실행 (core 모듈) -- `python-dotenv`: 환경 변수 파일 로드 (utils 모듈) -- `streamlit`: Streamlit 웹 애플리케이션 프레임워크 - -## 모듈 간 관계도 - -``` -cli/__init__.py (진입점) -├── commands/ -│ ├── quary.py → engine.query_executor -│ └── run_streamlit.py → core/streamlit_runner.py -├── core/ -│ ├── environment.py → utils/env_loader.py -│ └── streamlit_runner.py → utils/logger.py -└── utils/ - ├── env_loader.py (독립적) - └── logger.py (독립적) -``` - -## 주요 특징 - -1. **모듈화된 구조**: 명령어, 핵심 기능, 유틸리티가 명확히 분리됨 -2. **확장 가능성**: 새로운 명령어를 쉽게 추가할 수 있는 구조 -3. **환경 관리**: 일관된 환경 변수 초기화 보장 -4. **UI 중심 설계**: VectorDB 및 DataHub 설정은 UI에서 관리 -5. **로깅 지원**: 모든 모듈에서 일관된 로깅 사용 -6. **Deprecated 옵션 처리**: 사용되지 않는 옵션에 대한 명확한 경고 - -## 참고 문서 - -- [`commands/README.md`](commands/README.md): 명령어 모듈 상세 문서 -- [`core/README.md`](core/README.md): 핵심 기능 모듈 상세 문서 -- [`utils/README.md`](utils/README.md): 유틸리티 모듈 상세 문서 - diff --git a/cli/__init__.py b/cli/__init__.py deleted file mode 100644 index a917ae9..0000000 --- a/cli/__init__.py +++ /dev/null @@ -1,117 +0,0 @@ -"""Lang2SQL CLI 프로그램입니다. -이 프로그램은 환경 초기화와 Streamlit 실행을 제공합니다. - -주의: --datahub_server 옵션은 더 이상 사용되지 않습니다(deprecated). -DataHub 설정은 UI의 설정 > 데이터 소스 탭에서 관리하세요. -""" - -import click - -from cli.commands.quary import query_command -from cli.commands.run_streamlit import run_streamlit_cli_command -from cli.core.environment import initialize_environment -from cli.core.streamlit_runner import run_streamlit_command -from cli.utils.logger import configure_logging - -from version import __version__ - -logger = configure_logging() - - -# pylint: disable=redefined-outer-name,broad-exception-caught -@click.group() -@click.version_option(version=__version__) -@click.pass_context -@click.option( - "--datahub_server", - default=None, - help=("[Deprecated] DataHub GMS URL. 이제는 UI 설정 > 데이터 소스에서 관리하세요."), -) -@click.option( - "--run-streamlit", - is_flag=True, - help=( - "이 옵션을 지정하면 CLI 실행 시 Streamlit 애플리케이션을 바로 실행합니다. " - "별도의 명령어 입력 없이 웹 인터페이스를 띄우고 싶을 때 사용합니다." - ), -) -@click.option( - "-p", - "--port", - type=int, - default=8501, - help=( - "Streamlit 서버가 바인딩될 포트 번호를 지정합니다. " - "기본 포트는 8501이며, 포트 충돌을 피하거나 여러 인스턴스를 실행할 때 변경할 수 있습니다." - ), -) -@click.option( - "--env-file-path", - type=click.Path(exists=True, file_okay=True, dir_okay=False, readable=True), - help="환경 변수를 로드할 .env 파일의 경로를 지정합니다. 지정하지 않으면 기본 경로를 사용합니다.", -) -@click.option( - "--prompt-dir-path", - type=click.Path(exists=True, file_okay=False, dir_okay=True, readable=True), - help="프롬프트 템플릿(.md 파일)이 저장된 디렉토리 경로를 지정합니다. 지정하지 않으면 기본 경로를 사용합니다.", -) -@click.option( - "--vectordb-type", - default=None, - help="[Deprecated] VectorDB 타입. 이제는 UI 설정 > 데이터 소스에서 관리하세요.", -) -@click.option( - "--vectordb-location", - default=None, - help="[Deprecated] VectorDB 위치. 이제는 UI 설정 > 데이터 소스에서 관리하세요.", -) -def cli( - ctx: click.Context, - datahub_server: str | None, - run_streamlit: bool, - port: int, - env_file_path: str | None = None, - prompt_dir_path: str | None = None, - vectordb_type: str | None = None, - vectordb_location: str | None = None, -) -> None: - """Lang2SQL CLI 엔트리포인트. - - - 환경 변수 및 VectorDB 설정 초기화 - - 필요 시 Streamlit 애플리케이션 실행 - """ - - try: - initialize_environment( - env_file_path=env_file_path, prompt_dir_path=prompt_dir_path - ) - except Exception: - logger.error("Initialization failed.", exc_info=True) - ctx.exit(1) - - logger.info( - "Initialization started: run_streamlit = %s, port = %d", - run_streamlit, - port, - ) - - # Deprecated 안내: CLI에서 DataHub 설정은 더 이상 처리하지 않습니다 - if datahub_server: - click.secho( - "[Deprecated] --datahub_server 옵션은 더 이상 사용되지 않습니다. 설정 > 데이터 소스 탭에서 설정하세요.", - fg="yellow", - ) - - # Deprecated 안내: CLI에서 VectorDB 설정은 더 이상 처리하지 않습니다 - if vectordb_type or vectordb_location: - click.secho( - "[Deprecated] --vectordb-type/--vectordb-location 옵션은 더 이상 사용되지 않습니다. 설정 > 데이터 소스 탭에서 설정하세요.", - fg="yellow", - ) - - if run_streamlit: - run_streamlit_command(port) - - -cli.add_command(run_streamlit_cli_command) -cli.add_command(query_command) diff --git a/cli/commands/README.md b/cli/commands/README.md deleted file mode 100644 index 520e8d6..0000000 --- a/cli/commands/README.md +++ /dev/null @@ -1,232 +0,0 @@ -# CLI Commands 모듈 - -Lang2SQL CLI 애플리케이션에서 사용되는 명령어 정의 모듈입니다. - -이 모듈은 사용자가 CLI를 통해 Lang2SQL 기능을 사용할 수 있도록 다양한 명령어를 제공합니다. - -## 디렉토리 구조 - -``` -cli/commands/ -├── __pycache__/ # Python 캐시 디렉토리 -├── quary.py # 자연어 질문을 SQL로 변환하는 명령어 -├── run_streamlit.py # Streamlit 실행 명령어 -└── README.md # 이 파일 -``` - -## 모듈 개요 - -### `quary.py` -- **위치**: `cli/commands/quary.py` -- **설명**: 자연어 질문을 SQL 쿼리로 변환하는 CLI 명령어 정의 모듈 -- **주요 기능**: - - 사용자가 입력한 자연어 질문을 SQL 쿼리로 변환 - - 다양한 옵션을 통한 쿼리 실행 파라미터 설정 - - SQL 쿼리 결과 출력 - -#### 주요 함수 - -##### `query_command()` -자연어 질문을 SQL 쿼리로 변환하여 출력합니다. - -**매개변수:** -- `question` (str): SQL로 변환할 자연어 질문 -- `database_env` (str, optional): 사용할 데이터베이스 환경 (기본값: "clickhouse") -- `retriever_name` (str, optional): 테이블 검색기 이름 (기본값: "기본") -- `top_n` (int, optional): 검색된 상위 테이블 수 제한 (기본값: 5) -- `device` (str, optional): LLM 실행에 사용할 디바이스 (기본값: "cpu") -- `use_enriched_graph` (bool, optional): 확장된 그래프(프로파일 추출 + 컨텍스트 보강) 사용 여부 -- `vectordb_type` (str, optional): 사용할 벡터 데이터베이스 타입 ("faiss" 또는 "pgvector", 기본값: "faiss") -- `vectordb_location` (str, optional): VectorDB 위치 설정 - - FAISS: 디렉토리 경로 (예: ./my_vectordb) - - pgvector: 연결 문자열 (예: postgresql://user:pass@host:port/db) - - 기본값: FAISS는 './dev/table_info_db', pgvector는 환경변수 사용 - -**동작 방식:** -1. 환경 변수 설정 (VECTORDB_TYPE, VECTORDB_LOCATION) -2. `engine.query_executor.execute_query()` 호출하여 쿼리 실행 -3. `extract_sql_from_result()`로 SQL 추출 -4. 추출 실패 시 전체 generated_query 출력 -5. 성공 시 SQL 쿼리 출력 - -**사용 예제:** -```bash -# 기본 사용 -lang2sql query "고객 데이터를 기반으로 유니크한 유저 수를 카운트하는 쿼리" - -# 옵션 사용 -lang2sql query "고객 데이터를 기반으로 유니크한 유저 수를 카운트하는 쿼리" \ - --database-env clickhouse \ - --retriever-name 기본 \ - --top-n 5 \ - --device cpu \ - --use-enriched-graph - -# VectorDB 지정 -lang2sql query "고객 데이터를 기반으로 유니크한 유저 수를 카운트하는 쿼리" \ - --vectordb-type pgvector \ - --vectordb-location postgresql://user:pass@localhost:5432/db -``` - -### `run_streamlit.py` -- **위치**: `cli/commands/run_streamlit.py` -- **설명**: Streamlit 실행 CLI 명령어 모듈 -- **주요 기능**: - - CLI 명령어로 Streamlit 애플리케이션 실행 - - 포트 번호 지정 가능 - -#### 주요 함수 - -##### `run_streamlit_cli_command()` -CLI 명령어로 Streamlit 애플리케이션을 실행합니다. - -**매개변수:** -- `port` (int, optional): Streamlit 서버가 바인딩될 포트 번호 (기본값: 8501) - -**동작 방식:** -1. 로깅을 통해 실행 시작 로그 기록 -2. `cli.core.streamlit_runner.run_streamlit_command()` 호출하여 Streamlit 실행 - -**사용 예제:** -```bash -# 기본 포트(8501)로 실행 -lang2sql run-streamlit - -# 사용자 지정 포트로 실행 -lang2sql run-streamlit -p 9000 - -# 또는 -lang2sql run-streamlit --port 9000 -``` - -## 의존성 - -### 내부 모듈 -- `cli.utils.logger.configure_logging`: CLI 전용 로깅 유틸리티 -- `cli.core.streamlit_runner.run_streamlit_command`: Streamlit 실행 유틸리티 -- `engine.query_executor.execute_query`: 쿼리 실행 공용 함수 -- `engine.query_executor.extract_sql_from_result`: SQL 추출 함수 - -### 외부 라이브러리 -- `click`: CLI 프레임워크 -- `os`: 환경 변수 설정을 위한 표준 라이브러리 - -## 사용 위치 - -### 1. CLI 메인 모듈 (`cli/__init__.py`) -CLI 메인 모듈에서 두 명령어를 등록합니다. - -```python -from cli.commands.quary import query_command -from cli.commands.run_streamlit import run_streamlit_cli_command - -# CLI 그룹에 명령어 추가 -cli.add_command(run_streamlit_cli_command) -cli.add_command(query_command) -``` - -## CLI 사용 방법 - -### `query` 명령어 - -자연어 질문을 SQL 쿼리로 변환하는 명령어입니다. - -**기본 사용법:** -```bash -lang2sql query "<자연어 질문>" -``` - -**모든 옵션:** -```bash -lang2sql query "<자연어 질문>" \ - --database-env <데이터베이스 환경> \ - --retriever-name <검색기 이름> \ - --top-n <테이블 수> \ - --device \ - --use-enriched-graph \ - --vectordb-type \ - --vectordb-location <경로 또는 연결 문자열> -``` - -**실제 사용 예:** -```bash -# 간단한 쿼리 -lang2sql query "사용자 수를 확인하는 쿼리" - -# 옵션을 사용한 쿼리 -lang2sql query "고객 데이터를 기반으로 유니크한 유저 수를 카운트하는 쿼리" \ - --database-env clickhouse \ - --top-n 10 \ - --use-enriched-graph -``` - -### `run-streamlit` 명령어 - -Streamlit 웹 인터페이스를 실행하는 명령어입니다. - -**기본 사용법:** -```bash -lang2sql run-streamlit -``` - -**포트 지정:** -```bash -lang2sql run-streamlit -p 9000 -``` - -**실제 사용 예:** -```bash -# 기본 포트로 실행 -lang2sql run-streamlit - -# 다른 포트로 실행 -lang2sql run-streamlit --port 9000 - -# 브라우저에서 http://localhost:9000 접속 -``` - -## 워크플로우 - -### `query` 명령어 워크플로우 -1. 사용자가 자연어 질문과 옵션을 CLI로 입력 -2. VectorDB 타입과 위치 환경 변수 설정 -3. `engine.query_executor.execute_query()` 호출 -4. Lang2SQL 파이프라인 실행 -5. SQL 추출 및 출력 - -### `run-streamlit` 명령어 워크플로우 -1. 사용자가 명령어 실행 -2. `cli.core.streamlit_runner.run_streamlit_command()` 호출 -3. Streamlit 서브프로세스 실행 -4. 웹 브라우저에서 접속 가능 - -## 환경 변수 - -### `query` 명령어에서 설정되는 환경 변수 -- `VECTORDB_TYPE`: 벡터 데이터베이스 타입 ("faiss" 또는 "pgvector") -- `VECTORDB_LOCATION`: 벡터 데이터베이스 위치 (FAISS: 디렉토리 경로, pgvector: 연결 문자열) - -## 로깅 - -모든 명령어는 `cli.utils.logger.configure_logging()`을 사용하여 로그를 기록합니다: - -- `query` 명령어: 쿼리 처리 중 오류 발생 시 에러 로그 -- `run-streamlit` 명령어: 실행 시작 로그 - -## 에러 처리 - -### `query` 명령어 -- `Exception` 발생 시 에러 로그 기록 및 예외 재발생 -- SQL 추출 실패 시 전체 generated_query 출력 시도 - -### `run-streamlit` 명령어 -- `subprocess.CalledProcessError` 발생 시 에러 로그 기록 및 예외 재발생 - -## 주의사항 - -1. `query` 명령어는 SQL 쿼리만 출력하며 결과는 실행하지 않습니다 -2. `vectordb_location`을 지정하지 않으면 기본값 또는 환경변수 사용 -3. `run-streamlit` 명령어는 서브프로세스로 Streamlit을 실행하므로 프로세스가 종료될 때까지 대기합니다 -4. 기본 포트(8501)가 이미 사용 중이면 포트 변경 필요 -5. `use_enriched_graph` 옵션 사용 시 더 많은 리소스 소모 가능 - diff --git a/cli/commands/quary.py b/cli/commands/quary.py deleted file mode 100644 index 5a6e652..0000000 --- a/cli/commands/quary.py +++ /dev/null @@ -1,89 +0,0 @@ -"""자연어 질문을 SQL 쿼리로 변환하는 CLI 명령어 정의 모듈. - -이 모듈은 사용자가 입력한 자연어 질문을 SQL 쿼리로 변환하여 출력하는 -`query` CLI 명령어를 제공합니다. -""" - -import click - -from cli.utils.logger import configure_logging - -logger = configure_logging() - - -@click.command(name="query") -@click.argument("question", type=str) -@click.option( - "--flow", - type=click.Choice(["baseline", "enriched"]), - default="baseline", - help="사용할 플로우 (기본값: baseline)", -) -@click.option( - "--top-n", - type=int, - default=5, - help="검색된 상위 테이블 수 제한 (기본값: 5)", -) -@click.option( - "--dialect", - default=None, - help="SQL 방언 (예: sqlite, postgresql, mysql, bigquery, duckdb)", -) -@click.option( - "--no-gate", - is_flag=True, - help="QuestionGate 비활성화 (enriched 플로우 전용)", -) -def query_command( - question: str, - flow: str, - top_n: int, - dialect: str, - no_gate: bool, -) -> None: - """자연어 질문을 SQL 쿼리로 변환하여 실행 결과를 출력합니다. - - 환경변수(LLM_PROVIDER, EMBEDDING_PROVIDER, DB_TYPE 등)로 설정을 제어합니다. - """ - try: - from lang2sql.factory import ( - build_db_from_env, - build_embedding_from_env, - build_llm_from_env, - ) - from lang2sql.flows import BaselineNL2SQL, EnrichedNL2SQL - - llm = build_llm_from_env() - db = build_db_from_env() - - if flow == "baseline": - pipeline = BaselineNL2SQL( - catalog=[], - llm=llm, - db=db, - db_dialect=dialect, - ) - else: - embedding = build_embedding_from_env() - pipeline = EnrichedNL2SQL( - catalog=[], - llm=llm, - db=db, - embedding=embedding, - db_dialect=dialect, - gate_enabled=not no_gate, - top_n=top_n, - ) - - rows = pipeline.run(question) - if rows: - import json - - print(json.dumps(rows, ensure_ascii=False, indent=2)) - else: - print("(결과 없음)") - - except Exception as e: - logger.error("쿼리 처리 중 오류 발생: %s", e) - raise diff --git a/cli/commands/run_streamlit.py b/cli/commands/run_streamlit.py deleted file mode 100644 index 21aff02..0000000 --- a/cli/commands/run_streamlit.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Streamlit 실행 CLI 명령어 모듈.""" - -import click - -from cli.core.streamlit_runner import run_streamlit_command -from cli.utils.logger import configure_logging - -logger = configure_logging() - - -@click.command(name="run-streamlit") -@click.option( - "-p", - "--port", - type=int, - default=8501, - help=( - "Streamlit 애플리케이션이 바인딩될 포트 번호를 지정합니다. " - "기본 포트는 8501이며, 필요 시 다른 포트를 설정할 수 있습니다." - ), -) -def run_streamlit_cli_command(port: int) -> None: - """CLI 명령어로 Streamlit 애플리케이션을 실행합니다. - - Args: - port (int): Streamlit 서버가 바인딩될 포트 번호. 기본값은 8501. - """ - logger.info("Executing 'run-streamlit' command on port %d...", port) - run_streamlit_command(port) diff --git a/cli/core/README.md b/cli/core/README.md deleted file mode 100644 index 925c522..0000000 --- a/cli/core/README.md +++ /dev/null @@ -1,147 +0,0 @@ -# CLI Core 모듈 - -Lang2SQL CLI의 핵심 기능을 제공하는 모듈입니다. - -## 디렉토리 구조 - -``` -cli/core/ -├── environment.py # 환경 변수 초기화 모듈 -└── streamlit_runner.py # Streamlit 실행 유틸리티 모듈 -``` - -## 모듈 설명 - -### 1. environment.py - -환경 변수 초기화를 담당하는 모듈입니다. VectorDB 설정은 UI에서 관리합니다. - -#### 주요 기능 - -- `initialize_environment()`: 환경 변수를 초기화하는 함수 - -#### 함수 상세 - -##### `initialize_environment(env_file_path, prompt_dir_path)` - -환경 변수를 초기화합니다. VectorDB 설정은 UI에서 관리합니다. - -**매개변수:** -- `env_file_path` (Optional[str]): 로드할 .env 파일 경로. None이면 기본값 사용. -- `prompt_dir_path` (Optional[str]): 프롬프트 템플릿 디렉토리 경로. None이면 설정하지 않음. - -**예외:** -- `Exception`: 초기화 과정에서 오류가 발생한 경우. - -**내부 동작:** -- `cli.utils.env_loader.load_env()`: .env 파일을 로드합니다. -- `cli.utils.env_loader.set_prompt_dir()`: 프롬프트 템플릿 디렉토리 경로를 환경 변수로 설정합니다. - -#### 사용 예시 - -```python -from cli.core.environment import initialize_environment - -# 기본 경로로 초기화 -initialize_environment(env_file_path=None, prompt_dir_path=None) - -# 사용자 정의 경로로 초기화 -initialize_environment( - env_file_path="/path/to/.env", - prompt_dir_path="/path/to/prompts" -) -``` - -#### import 및 사용 위치 - -이 모듈의 `initialize_environment` 함수는 다음과 같이 사용됩니다: - -- **`cli/__init__.py`** (85-90번째 줄): CLI 진입점에서 환경 초기화 시 호출 - ```python - from cli.core.environment import initialize_environment - - initialize_environment( - env_file_path=env_file_path, - prompt_dir_path=prompt_dir_path - ) - ``` - -### 2. streamlit_runner.py - -Streamlit 애플리케이션 실행을 담당하는 유틸리티 모듈입니다. - -#### 주요 기능 - -- `run_streamlit_command()`: 지정된 포트에서 Streamlit 애플리케이션을 실행하는 함수 - -#### 함수 상세 - -##### `run_streamlit_command(port)` - -지정된 포트에서 Streamlit 애플리케이션을 실행합니다. - -**매개변수:** -- `port` (int): 바인딩할 포트 번호. - -**예외:** -- `subprocess.CalledProcessError`: 실행 실패 시 발생. - -**내부 동작:** -- `subprocess.run()`을 사용하여 `streamlit run` 명령을 실행합니다. -- 실행 대상: `interface/streamlit_app.py` -- 서버 주소: `0.0.0.0` -- 포트: 사용자 지정 값 (기본값: 8501) -- 로깅: `cli.utils.logger.configure_logging()`을 통해 로그 출력 - -#### 사용 예시 - -```python -from cli.core.streamlit_runner import run_streamlit_command - -# 기본 포트(8501)로 실행 -run_streamlit_command(port=8501) - -# 사용자 정의 포트로 실행 -run_streamlit_command(port=8080) -``` - -#### import 및 사용 위치 - -이 모듈의 `run_streamlit_command` 함수는 다음과 같이 사용됩니다: - -1. **`cli/__init__.py`** (113번째 줄): CLI의 `--run-streamlit` 옵션이 활성화된 경우 호출 - ```python - from cli.core.streamlit_runner import run_streamlit_command - - if run_streamlit: - run_streamlit_command(port) - ``` - -2. **`cli/commands/run_streamlit.py`** (29번째 줄): `run-streamlit` CLI 명령 실행 시 호출 - ```python - from cli.core.streamlit_runner import run_streamlit_command - - @click.command(name="run-streamlit") - def run_streamlit_cli_command(port: int): - logger.info("Executing 'run-streamlit' command on port %d...", port) - run_streamlit_command(port) - ``` - -## 의존성 - -### 내부 의존성 - -- `cli.utils.env_loader`: 환경 변수 로드 및 프롬프트 디렉토리 설정 -- `cli.utils.logger`: 로깅 설정 - -### 외부 의존성 - -- `subprocess`: 프로세스 실행 (streamlit_runner.py) - -## 주요 특징 - -1. **환경 관리**: CLI 진입점에서 일관된 환경 변수 초기화 보장 -2. **UI 중심 설계**: VectorDB 설정은 UI에서 관리하여 사용자 편의성 향상 -3. **유연한 실행**: 다양한 포트에서 Streamlit 애플리케이션 실행 지원 -4. **로깅 지원**: 실행 상태 및 오류 추적 가능 - diff --git a/cli/core/environment.py b/cli/core/environment.py deleted file mode 100644 index d2c957a..0000000 --- a/cli/core/environment.py +++ /dev/null @@ -1,23 +0,0 @@ -"""환경 변수 초기화 모듈 (VectorDB 설정은 UI에서 관리).""" - -from typing import Optional - -from cli.utils.env_loader import load_env, set_prompt_dir - - -def initialize_environment( - *, - env_file_path: Optional[str], - prompt_dir_path: Optional[str], -) -> None: - """환경 변수를 초기화합니다. VectorDB 설정은 UI에서 관리합니다. - - Args: - env_file_path (Optional[str]): 로드할 .env 파일 경로. None이면 기본값 사용. - prompt_dir_path (Optional[str]): 프롬프트 템플릿 디렉토리 경로. None이면 설정하지 않음. - - Raises: - Exception: 초기화 과정에서 오류가 발생한 경우. - """ - load_env(env_file_path=env_file_path) - set_prompt_dir(prompt_dir_path=prompt_dir_path) diff --git a/cli/core/streamlit_runner.py b/cli/core/streamlit_runner.py deleted file mode 100644 index 8437bf3..0000000 --- a/cli/core/streamlit_runner.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Streamlit 실행 유틸리티 모듈.""" - -import subprocess - -from cli.utils.logger import configure_logging - -logger = configure_logging() - - -def run_streamlit_command(port: int) -> None: - """지정된 포트에서 Streamlit 애플리케이션을 실행합니다. - - Args: - port (int): 바인딩할 포트 번호. - - Raises: - subprocess.CalledProcessError: 실행 실패 시 발생. - """ - logger.info("Starting Streamlit application on port %d...", port) - - try: - subprocess.run( - [ - "streamlit", - "run", - "interface/streamlit_app.py", - "--server.address=0.0.0.0", - "--server.port", - str(port), - ], - check=True, - ) - logger.info("Streamlit application started successfully.") - except subprocess.CalledProcessError as e: - logger.error("Failed to start Streamlit application: %s", e) - raise diff --git a/cli/utils/README.md b/cli/utils/README.md deleted file mode 100644 index d6a3012..0000000 --- a/cli/utils/README.md +++ /dev/null @@ -1,187 +0,0 @@ -# CLI Utils 모듈 - -CLI 애플리케이션에서 사용되는 유틸리티 함수들을 제공하는 모듈입니다. - -## 디렉토리 구조 - -``` -cli/utils/ -├── __pycache__/ -├── env_loader.py -├── logger.py -└── README.md -``` - -## 파일 목록 및 설명 - -### env_loader.py - -환경 변수 유틸리티 모듈입니다. `.env` 파일 로드, 프롬프트 디렉토리 설정, VectorDB 타입 및 위치 설정을 제공합니다. - -**주요 함수:** - -#### `load_env(env_file_path: Optional[str] = None) -> None` -환경 변수 파일(.env)을 로드합니다. - -**파라미터:** -- `env_file_path` (Optional[str]): .env 파일 경로. None이면 기본 경로 사용. - -**동작:** -- 지정된 경로의 `.env` 파일을 로드하거나, 경로가 없으면 기본 경로의 `.env` 파일을 로드합니다. -- 성공/실패 메시지를 컬러로 출력합니다. -- 로드 실패 시 예외를 발생시킵니다. - -**사용 예시:** -```python -from cli.utils.env_loader import load_env - -# 기본 .env 파일 로드 -load_env() - -# 특정 경로의 .env 파일 로드 -load_env(env_file_path="/path/to/.env") -``` - -#### `set_prompt_dir(prompt_dir_path: Optional[str]) -> None` -프롬프트 템플릿 디렉토리 경로를 환경 변수로 설정합니다. - -**파라미터:** -- `prompt_dir_path` (Optional[str]): 디렉토리 경로. None이면 설정하지 않음. - -**환경 변수:** -- `PROMPT_TEMPLATES_DIR`: 설정된 프롬프트 디렉토리 경로 - -**Raises:** -- `ValueError`: 경로가 유효하지 않을 경우 - -**사용 예시:** -```python -from cli.utils.env_loader import set_prompt_dir - -set_prompt_dir(prompt_dir_path="/path/to/prompt/templates") -``` - -#### `set_vectordb(vectordb_type: str, vectordb_location: Optional[str] = None) -> None` -VectorDB 타입과 위치를 환경 변수로 설정합니다. - -**파라미터:** -- `vectordb_type` (str): VectorDB 타입 ("faiss" 또는 "pgvector") -- `vectordb_location` (Optional[str]): 경로 또는 연결 URL - -**환경 변수:** -- `VECTORDB_TYPE`: 설정된 VectorDB 타입 -- `VECTORDB_LOCATION`: 설정된 VectorDB 경로 또는 연결 URL (지정된 경우) - -**Raises:** -- `ValueError`: 잘못된 타입이나 경로/URL일 경우 - -**사용 예시:** -```python -from cli.utils.env_loader import set_vectordb - -# FAISS 설정 -set_vectordb(vectordb_type="faiss", vectordb_location="/path/to/faiss/db") - -# pgvector 설정 -set_vectordb( - vectordb_type="pgvector", - vectordb_location="postgresql://user:pass@host:port/db" -) -``` - -**사용처:** -- `cli/core/environment.py` (5번 라인): `load_env`, `set_prompt_dir` 함수를 import하여 사용 - - `initialize_environment` 함수에서 환경 변수 초기화 시 사용 - ```python - from cli.utils.env_loader import load_env, set_prompt_dir - - def initialize_environment( - *, - env_file_path: Optional[str], - prompt_dir_path: Optional[str], - ) -> None: - load_env(env_file_path=env_file_path) - set_prompt_dir(prompt_dir_path=prompt_dir_path) - ``` - -### logger.py - -CLI 전용 로깅 유틸리티 모듈입니다. 로깅 설정을 구성하고 기본 로거 인스턴스를 반환합니다. - -**주요 함수:** - -#### `configure_logging(level: int = logging.INFO) -> logging.Logger` -로깅을 설정하고 기본 로거를 반환합니다. - -**파라미터:** -- `level` (int, optional): 로깅 레벨. 기본값은 `logging.INFO`. - -**반환값:** -- `logging.Logger`: 설정된 로거 인스턴스. 로거 이름은 "cli"입니다. - -**로깅 설정:** -- 레벨: 지정된 레벨 (기본값: `INFO`) -- 포맷: `%(asctime)s [%(levelname)s] %(message)s` -- 날짜 포맷: `%Y-%m-%d %H:%M:%S` - -**사용 예시:** -```python -from cli.utils.logger import configure_logging - -# 기본 설정으로 로거 생성 -logger = configure_logging() - -# DEBUG 레벨로 로거 생성 -logger = configure_logging(level=logging.DEBUG) - -# 로깅 사용 -logger.info("Information message") -logger.error("Error message") -``` - -**사용처:** - -1. **`cli/__init__.py`** (14번 라인) - - CLI 진입점에서 로거 초기화 - ```python - from cli.utils.logger import configure_logging - - logger = configure_logging() - ``` - - 사용 위치: 18번 라인에서 로거 인스턴스 생성, 89번, 92번 라인에서 에러 및 정보 로깅 - -2. **`cli/commands/quary.py`** (11번 라인) - - query 명령어 실행 시 로거 초기화 - ```python - from cli.utils.logger import configure_logging - - logger = configure_logging() - ``` - - 사용 위치: 13번 라인에서 로거 인스턴스 생성, 112번 라인에서 에러 로깅 - -3. **`cli/core/streamlit_runner.py`** (5번 라인) - - Streamlit 실행 모듈에서 로거 초기화 - ```python - from cli.utils.logger import configure_logging - - logger = configure_logging() - ``` - - 사용 위치: 7번 라인에서 로거 인스턴스 생성, 19번, 33번, 35번 라인에서 정보 및 에러 로깅 - -4. **`cli/commands/run_streamlit.py`** (6번 라인) - - run-streamlit 명령어 실행 시 로거 초기화 - ```python - from cli.utils.logger import configure_logging - - logger = configure_logging() - ``` - - 사용 위치: 8번 라인에서 로거 인스턴스 생성, 28번 라인에서 정보 로깅 - -## 의존성 - -- `click`: CLI 출력 및 메시지 표시 -- `dotenv`: `.env` 파일 로드 -- `logging`: 로깅 기능 (Python 표준 라이브러리) -- `pathlib.Path`: 경로 처리 -- `os`: 환경 변수 설정 - diff --git a/cli/utils/env_loader.py b/cli/utils/env_loader.py deleted file mode 100644 index 768fdce..0000000 --- a/cli/utils/env_loader.py +++ /dev/null @@ -1,103 +0,0 @@ -"""환경 변수 유틸리티 모듈. - -.env 파일 로드, 프롬프트 디렉토리 설정, -VectorDB 타입 및 위치 설정을 제공합니다. -""" - -import os -from pathlib import Path -from typing import Optional - -import click -import dotenv - - -def load_env( - *, - env_file_path: Optional[str] = None, -) -> None: - """환경 변수 파일(.env)을 로드합니다. - - Args: - env_file_path (Optional[str]): .env 파일 경로. None이면 기본 경로 사용. - """ - try: - if env_file_path: - loaded = dotenv.load_dotenv(env_file_path, override=True) - if loaded: - click.secho(f".env 파일 로드 성공: {env_file_path}", fg="green") - else: - click.secho(f".env 파일을 찾을 수 없음: {env_file_path}", fg="yellow") - else: - dotenv.load_dotenv(override=True) - click.secho("기본 .env 파일 로드 시도", fg="blue") - except Exception as e: - click.secho(f".env 파일 로드 중 오류 발생: {e}", fg="red") - raise - - -def set_prompt_dir( - *, - prompt_dir_path: Optional[str], -) -> None: - """프롬프트 템플릿 디렉토리 경로를 설정합니다. - - Args: - prompt_dir_path (Optional[str]): 디렉토리 경로. None이면 설정하지 않음. - - Raises: - ValueError: 경로가 유효하지 않을 경우. - """ - if not prompt_dir_path: - click.secho( - "프롬프트 디렉토리 경로가 지정되지 않아 설정을 건너뜁니다.", fg="yellow" - ) - return - - path_obj = Path(prompt_dir_path) - if not path_obj.exists() or not path_obj.is_dir(): - click.secho(f"유효하지 않은 디렉토리 경로: {prompt_dir_path}", fg="red") - raise ValueError(f"Invalid prompt directory path: {prompt_dir_path}") - - os.environ["PROMPT_TEMPLATES_DIR"] = str(path_obj.resolve()) - click.secho(f"프롬프트 디렉토리 환경변수 설정됨: {path_obj.resolve()}", fg="green") - - -def set_vectordb( - *, - vectordb_type: str, - vectordb_location: Optional[str] = None, -) -> None: - """VectorDB 타입과 위치를 설정합니다. - - Args: - vectordb_type (str): VectorDB 타입 ("faiss" 또는 "pgvector"). - vectordb_location (Optional[str]): 경로 또는 연결 URL. - - Raises: - ValueError: 잘못된 타입이나 경로/URL일 경우. - """ - - if vectordb_type not in ("faiss", "pgvector"): - raise ValueError(f"지원하지 않는 VectorDB 타입: {vectordb_type}") - - os.environ["VECTORDB_TYPE"] = vectordb_type - click.secho(f"VectorDB 타입 설정됨: {vectordb_type}", fg="green") - - if vectordb_location: - if vectordb_type == "faiss": - path = Path(vectordb_location) - if not path.exists() or not path.is_dir(): - raise ValueError( - f"유효하지 않은 FAISS 디렉토리 경로: {vectordb_location}" - ) - elif vectordb_type == "pgvector": - if not vectordb_location.startswith("postgresql://"): - raise ValueError( - f"pgvector URL은 'postgresql://'로 시작해야 합니다: {vectordb_location}" - ) - - os.environ["VECTORDB_LOCATION"] = vectordb_location - click.secho(f"VectorDB 경로 설정됨: {vectordb_location}", fg="green") - else: - click.secho("VectorDB 경로가 지정되지 않아 기본값을 사용합니다.", fg="yellow") diff --git a/cli/utils/logger.py b/cli/utils/logger.py deleted file mode 100644 index cfebc9b..0000000 --- a/cli/utils/logger.py +++ /dev/null @@ -1,20 +0,0 @@ -"""CLI 전용 로깅 유틸리티 모듈.""" - -import logging - - -def configure_logging(level: int = logging.INFO) -> logging.Logger: - """로깅을 설정하고 기본 로거를 반환합니다. - - Args: - level (int, optional): 로깅 레벨. 기본값은 logging.INFO. - - Returns: - logging.Logger: 설정된 로거 인스턴스. - """ - logging.basicConfig( - level=level, - format="%(asctime)s [%(levelname)s] %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - ) - return logging.getLogger("cli") diff --git a/docs/DEPLOY.md b/docs/DEPLOY.md new file mode 100644 index 0000000..ce6a7e5 --- /dev/null +++ b/docs/DEPLOY.md @@ -0,0 +1,101 @@ +# Deploying the Lang2SQL Discord bot + +This guide covers running the **Phase 1 Discord frontend** (`lang2sql-bot`). Be +honest with yourself about scope first: see [§What's stub](#whats-stub-be-honest). + +--- + +## 1. Environment variables + +| Variable | Required | Purpose | +|---|---|---| +| `DISCORD_BOT_TOKEN` | **yes** | Bot token from the Discord developer portal. The bot raises a clear error and exits if this is unset. | +| `OPENAI_API_KEY` | no | When set, the agent uses OpenAI `gpt-4.1-mini`. When unset, it falls back to the **offline `FakeLLM`** (deterministic canned tool cycles — fine for a smoke run, not for real answers). | +| `LANG2SQL_SECRET_KEY` | no | A urlsafe-base64 Fernet key used to encrypt stored secrets (DSNs/API keys) at rest. If unset, a key is auto-generated and persisted in the SQLite kv table — self-contained but only as private as the DB file. **Set this in production** so secrets decrypt across restarts and machines. | + +Generate a Fernet key: + +```bash +.venv/bin/python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())" +``` + +Copy [`.env.example`](../.env.example) to `.env` and fill it in. (The bot reads +from the process environment; use your hosting platform's secrets mechanism or a +tool like `direnv`/`dotenv` to export them.) + +--- + +## 2. Create the Discord application and bot + +1. Go to the [Discord Developer Portal](https://discord.com/developers/applications) → **New Application**. +2. **Bot** tab → **Add Bot** → **Reset Token** → copy it into `DISCORD_BOT_TOKEN`. +3. **Privileged Gateway Intents** → enable **MESSAGE CONTENT INTENT** (the bot + reads message text to answer @mentions and thread replies). +4. **OAuth2 → URL Generator**: + - Scopes: `bot` + - Bot permissions: **Send Messages**, **Read Message History**, **Attach + Files** (for CSV results), **Create Public Threads**, **Send Messages in + Threads**. +5. Open the generated invite URL and add the bot to your test guild. + +Then run it: + +```bash +export DISCORD_BOT_TOKEN=... +.venv/bin/lang2sql-bot +``` + +The bot connects to the gateway and serves. Mention it in a channel or DM it. + +--- + +## 3. Hosting options (free tiers) + +Per the v4.1 plan (§4.1), V1 targets a free always-on host: + +### Oracle Cloud Always Free +- Provision an **Always Free** ARM (Ampere A1) or AMD micro VM. +- Install uv, clone the repo, `uv sync`. +- Export the env vars and run `lang2sql-bot` under a process supervisor + (`systemd` unit or `tmux`/`screen` for a quick trial). + +### fly.io (free allowance) +- A tiny `fly.toml` running `lang2sql-bot` as the process; the bot is a + long-lived gateway client, not an HTTP server, so no exposed ports are needed. +- Set `DISCORD_BOT_TOKEN`, `OPENAI_API_KEY`, `LANG2SQL_SECRET_KEY` with + `fly secrets set`. + +A minimal `systemd` unit: + +```ini +[Service] +WorkingDirectory=/opt/lang2sql +ExecStart=/opt/lang2sql/.venv/bin/lang2sql-bot +Environment=DISCORD_BOT_TOKEN=... +Environment=OPENAI_API_KEY=... +Environment=LANG2SQL_SECRET_KEY=... +Restart=on-failure +``` + +--- + +## 4. Persistence + +The V1 `SqliteStore` **defaults to `:memory:`**, so audit/session/secret state is +lost on restart. For a real deployment, construct the store with a file path so it +survives restarts and back it up alongside `LANG2SQL_SECRET_KEY` (you need both to +decrypt stored secrets). + +--- + +## What's stub (be honest) + +- **No real database execution.** `PostgresExplorer` returns canned + `orders`/`users` schema and sample rows. `run_sql` enforces the safety gate and + goes through the executor, but there is no live psycopg connection in V1 — real + execution is v1.5. `/connect` stores a DSN (encrypted) but it is not yet used to + open a connection. +- **No reasoning without `OPENAI_API_KEY`.** The offline `FakeLLM` produces + deterministic tool cycles, not real answers. +- **No rate limiting** in V1 — keep deployments to small trial guilds so token + spend stays bounded (rate limit + per-user token caps are v1.5). diff --git a/infra/__init__.py b/infra/__init__.py deleted file mode 100644 index db0a75d..0000000 --- a/infra/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""인프라 계층 패키지 (DB, 모니터링 등)""" diff --git a/infra/monitoring/README.md b/infra/monitoring/README.md deleted file mode 100644 index f14d82a..0000000 --- a/infra/monitoring/README.md +++ /dev/null @@ -1,148 +0,0 @@ -# infra/monitoring 패키지 - -서버 상태 확인 및 헬스 체크 기능을 제공하는 모니터링 패키지입니다. - -## 디렉토리 구조 - -``` -infra/monitoring/ -├── __init__.py -├── __pycache__/ -└── check_server.py -``` - -## 파일 설명 - -### `__init__.py` - -모니터링/헬스체크 패키지의 초기화 파일입니다. - -**내용:** -- 패키지 문서화 문자열: "모니터링/헬스체크 패키지" - -**역할:** -- `infra.monitoring` 패키지를 Python 패키지로 인식시키는 초기화 파일 - ---- - -### `check_server.py` - -서버 상태 확인 및 연결 관련 기능을 제공하는 유틸리티 클래스입니다. - -**주요 구성 요소:** - -1. **HTTP 기반 서버 헬스 체크** - - `/health` 엔드포인트를 통한 서버 상태 확인 - - 향후 서버 연결 또는 상태 점검 기능 확장 가능한 구조 - -2. **예외 처리 및 로깅** - - 요청 실패, 타임아웃, 연결 오류 등의 다양한 예외 상황 처리 - - 로깅을 통해 상세한 실패 원인 기록 - - 결과를 boolean 값으로 반환 - -**주요 클래스:** - -#### `CheckServer` - -서버의 상태를 확인하거나 연결을 테스트하는 유틸리티 메서드를 제공하는 클래스입니다. - -현재는 GMS 서버의 `/health` 엔드포인트에 대한 헬스 체크 기능을 포함하고 있으며, 향후에는 다양한 서버 연결 확인 및 상태 점검 기능이 추가될 수 있도록 확장 가능한 구조로 설계되었습니다. - -**메서드:** - -- `is_gms_server_healthy(*, url: str) -> bool` (정적 메서드): - - 지정된 GMS 서버의 `/health` 엔드포인트에 요청을 보내 상태를 확인합니다. - - Parameters: - - `url` (str): 헬스 체크를 수행할 GMS 서버의 기본 URL (예: "http://localhost:8080") - - Returns: - - `bool`: 서버가 정상적으로 응답하면 `True`, 예외 발생 시 `False` - - 기능: - - 서버 URL과 `/health` 경로를 결합하여 헬스 체크 엔드포인트 생성 - - 3초 타임아웃으로 GET 요청 수행 - - HTTP 200 응답 시 `True` 반환 - - 다음 예외 상황 처리: - - `ConnectTimeout`, `ReadTimeout`: 타임아웃 오류 로깅 - - `ConnectionError`: 연결 실패 로깅 - - `HTTPError`: HTTP 오류 로깅 - - `RequestException`: 기타 요청 예외 로깅 - - 예외 발생 시 `False` 반환 - -**의존성:** -- `requests`: HTTP 요청 수행 -- `urllib.parse.urljoin`: URL 경로 결합 -- `logging`: 로깅 기능 - -**사용 예시:** - -```python -from infra.monitoring.check_server import CheckServer - -# GMS 서버 헬스 체크 -is_healthy = CheckServer.is_gms_server_healthy(url="http://localhost:8080") - -if is_healthy: - print("서버가 정상입니다.") -else: - print("서버 연결에 문제가 있습니다.") -``` - -## Import 및 사용 현황 - -### 사용 위치 - -**`interface/app_pages/settings_sections/data_source_section.py`** - -이 모듈에서 `CheckServer` 클래스를 import하여 사용합니다. - -**Import:** -```python -from infra.monitoring.check_server import CheckServer -``` - -**사용 방법:** - -1. **DataHub 편집 시 헬스 체크** (117번째 줄) - ```python - if st.button("헬스 체크", key="dh_edit_health"): - ok = CheckServer.is_gms_server_healthy(url=new_url) - st.session_state["datahub_last_health"] = bool(ok) - if ok: - st.success("GMS 서버가 정상입니다.") - else: - st.error("GMS 서버 헬스 체크 실패. URL과 네트워크를 확인하세요.") - ``` - -2. **DataHub 추가 시 헬스 체크** (160번째 줄) - ```python - if st.button("헬스 체크", key="dh_health_new"): - ok = CheckServer.is_gms_server_healthy(url=dh_url) - st.session_state["datahub_last_health"] = bool(ok) - if ok: - st.success("GMS 서버가 정상입니다.") - else: - st.error("GMS 서버 헬스 체크 실패. URL과 네트워크를 확인하세요.") - ``` - -**사용 목적:** -- Streamlit UI에서 DataHub 서버 설정 시, 사용자가 입력한 URL이 유효한지 확인 -- 헬스 체크 결과를 세션 상태에 저장하여 상태 배너에 표시 -- 서버 연결 성공/실패에 따라 사용자에게 적절한 피드백 제공 - -## 로깅 - -모듈은 Python의 `logging` 모듈을 사용하여 다음 정보를 로깅합니다: -- 서버가 정상일 때: INFO 레벨로 성공 메시지 -- 타임아웃 발생 시: ERROR 레벨로 타임아웃 오류 메시지 -- 연결 실패 시: ERROR 레벨로 연결 오류 메시지 -- HTTP 오류 발생 시: ERROR 레벨로 HTTP 오류 메시지 -- 기타 요청 예외 발생 시: ERROR 레벨로 예외 정보 로깅 - -로깅 레벨은 `INFO`로 설정되어 있으며, 타임스탬프와 로그 레벨 정보가 포함됩니다. - -**로깅 포맷:** -``` -%(asctime)s [%(levelname)s] %(message)s -``` - -날짜 형식: `%Y-%m-%d %H:%M:%S` - diff --git a/infra/monitoring/__init__.py b/infra/monitoring/__init__.py deleted file mode 100644 index 20aff86..0000000 --- a/infra/monitoring/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""모니터링/헬스체크 패키지""" diff --git a/infra/monitoring/check_server.py b/infra/monitoring/check_server.py deleted file mode 100644 index 1cc652d..0000000 --- a/infra/monitoring/check_server.py +++ /dev/null @@ -1,74 +0,0 @@ -""" -서버 상태 확인 및 연결 관련 기능을 제공하는 유틸리티 클래스입니다. - -이 모듈은 HTTP 기반의 서버에 대해 다음과 같은 기능을 제공합니다: -- `/health` 엔드포인트를 통한 서버 헬스 체크 -- 향후 서버 연결 또는 상태 점검과 관련된 기능 추가 예정 - -각 기능은 요청 실패, 타임아웃, 연결 오류 등의 다양한 예외 상황을 포괄적으로 처리하며, -로깅을 통해 상세한 실패 원인을 기록하고 결과를 boolean 또는 적절한 형태로 반환합니다. -""" - -import logging -from urllib.parse import urljoin - -import requests - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", -) -logger = logging.getLogger(__name__) - - -class CheckServer: - """ - 서버의 상태를 확인하거나 연결을 테스트하는 유틸리티 메서드를 제공하는 클래스입니다. - - 현재는 GMS 서버의 `/health` 엔드포인트에 대한 헬스 체크 기능을 포함하고 있으며, - 향후에는 다양한 서버 연결 확인 및 상태 점검 기능이 추가될 수 있도록 확장 가능한 구조로 설계되었습니다. - 모든 기능은 네트워크 오류 및 서버 응답 상태에 따라 예외를 로깅하며, 호출자가 결과를 판단할 수 있도록 boolean 값을 반환합니다. - """ - - @staticmethod - def is_gms_server_healthy(*, url: str) -> bool: - """ - 지정된 GMS 서버의 `/health` 엔드포인트에 요청을 보내 상태를 확인합니다. - - 서버가 HTTP 200 응답을 반환하면 True를 반환하며, - 요청 실패, 타임아웃, 연결 오류 등의 예외 발생 시 False를 반환하고, - 로깅을 통해 상세한 에러 정보를 출력합니다. - - Args: - url (str): 헬스 체크를 수행할 GMS 서버의 기본 URL (예: "http://localhost:8080") - - Returns: - bool: 서버가 정상적으로 응답하면 True, 예외 발생 시 False - """ - - health_url = urljoin(url, "/health") - - try: - response = requests.get( - health_url, - timeout=3, - ) - response.raise_for_status() - logger.info("GMS server is healthy: %s", url) - return True - except ( - requests.exceptions.ConnectTimeout, - requests.exceptions.ReadTimeout, - ) as e: - logger.error( - "Timeout while connecting to GMS server: %s | %s", health_url, e - ) - except requests.exceptions.ConnectionError as e: - logger.error("Failed to connect to GMS server: %s | %s", health_url, e) - except requests.exceptions.HTTPError as e: - logger.error("GMS server returned HTTP error: %s | %s", health_url, e) - except requests.exceptions.RequestException as e: - logger.exception("Unexpected request error to GMS server: %s", health_url) - - return False diff --git a/infra/observability/README.md b/infra/observability/README.md deleted file mode 100644 index 85f6a2e..0000000 --- a/infra/observability/README.md +++ /dev/null @@ -1,98 +0,0 @@ -# observability - -LLM 응답 메시지에서 토큰 사용량을 집계하고 관찰(observability)하기 위한 유틸리티 모듈을 제공하는 디렉토리입니다. - -## 디렉토리 구조 - -``` -observability/ -├── __pycache__/ -└── token_usage.py -``` - -## 파일 설명 - -### token_usage.py - -LLM 응답 메시지에서 토큰 사용량을 집계하기 위한 유틸리티 모듈입니다. - -#### 주요 내용 - -- **TokenUtils 클래스**: LLM 토큰 사용량 집계 유틸리티 클래스 - - `get_token_usage_summary()`: 메시지 데이터에서 input/output/total 토큰 사용량을 각각 집계하는 정적 메서드 - - `usage_metadata` 필드를 기반으로 입력 토큰, 출력 토큰, 총 토큰 사용량을 계산 - - Streamlit, LangChain 등 LLM 응답을 다루는 애플리케이션에서 비용 분석, 사용량 추적 등에 활용 가능 - -#### 반환 형식 - -```python -{ - "input_tokens": int, - "output_tokens": int, - "total_tokens": int -} -``` - -## 사용 방법 - -### Import - -이 모듈은 다음과 같이 import되어 사용됩니다: - -```python -from infra.observability.token_usage import TokenUtils -``` - -### 실제 사용 예시 - -#### 1. interface/core/result_renderer.py - -`TokenUtils`는 Lang2SQL 결과 표시 모듈에서 토큰 사용량을 계산하고 Streamlit UI에 표시하는 데 사용됩니다. - -```75:85:interface/core/result_renderer.py - if should_show("show_token_usage"): - st.markdown("---") - token_summary = TokenUtils.get_token_usage_summary(data=res["messages"]) - st.write("**토큰 사용량:**") - st.markdown( - f""" - - Input tokens: `{token_summary['input_tokens']}` - - Output tokens: `{token_summary['output_tokens']}` - - Total tokens: `{token_summary['total_tokens']}` - """ - ) -``` - -**사용 컨텍스트**: -- `display_result()` 함수 내에서 LLM 실행 결과(`res`)의 `messages` 리스트를 전달받아 토큰 사용량을 집계 -- Streamlit UI에서 토큰 사용량 정보를 마크다운 형식으로 표시 -- 사용자가 설정에서 토큰 사용량 표시 옵션(`show_token_usage`)을 활성화한 경우에만 표시 - -**입력 데이터 형식**: -- `data` 파라미터는 각 항목이 `usage_metadata` 속성을 포함할 수 있는 객체 리스트입니다 -- 예: LangChain의 `AIMessage` 객체 리스트 - -#### 사용 패턴 - -```python -# 기본 사용법 -token_summary = TokenUtils.get_token_usage_summary(data=messages) - -# 반환된 딕셔너리 접근 -input_tokens = token_summary["input_tokens"] -output_tokens = token_summary["output_tokens"] -total_tokens = token_summary["total_tokens"] -``` - -## 로깅 - -이 모듈은 Python 표준 `logging` 모듈을 사용하여 토큰 사용량 정보를 기록합니다: - -- **DEBUG 레벨**: 각 메시지별 토큰 사용량 상세 정보 -- **INFO 레벨**: 전체 토큰 사용량 요약 정보 - -## 참고사항 - -- `usage_metadata` 필드가 없는 객체는 토큰 사용량이 0으로 처리됩니다 -- 각 메시지의 토큰 사용량은 누적되어 최종 합계를 반환합니다 - diff --git a/infra/observability/token_usage.py b/infra/observability/token_usage.py deleted file mode 100644 index 301e5a5..0000000 --- a/infra/observability/token_usage.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -token_usage.py - -LLM 응답 메시지에서 토큰 사용량을 집계하기 위한 유틸리티 모듈입니다. - -이 모듈은 LLM의 `usage_metadata` 필드를 기반으로 입력 토큰, 출력 토큰, 총 토큰 사용량을 계산하는 기능을 제공합니다. -Streamlit, LangChain 등 LLM 응답을 다루는 애플리케이션에서 비용 분석, 사용량 추적 등에 활용할 수 있습니다. -""" - -import logging -from typing import Any, List - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", -) -logger = logging.getLogger(__name__) - - -class TokenUtils: - """ - LLM 토큰 사용량 집계 유틸리티 클래스입니다. - - 이 클래스는 LLM 응답 메시지 리스트에서 usage_metadata 필드를 추출하여 - input_tokens, output_tokens, total_tokens의 합계를 계산합니다. - - 예를 들어, LangChain 또는 OpenAI API 응답 메시지 객체의 토큰 사용 정보를 분석하고자 할 때 - 활용할 수 있습니다. - - 사용 예: - >>> from infra.observability.token_usage import TokenUtils - >>> summary = TokenUtils.get_token_usage_summary(messages) - >>> print(summary["total_tokens"]) - - 반환 형식: - { - "input_tokens": int, - "output_tokens": int, - "total_tokens": int, - } - """ - - @staticmethod - def get_token_usage_summary(*, data: List[Any]) -> dict: - """ - 메시지 데이터에서 input/output/total 토큰 사용량을 각각 집계합니다. - - Args: - data (List[Any]): 각 항목이 usage_metadata를 포함할 수 있는 객체 리스트. - - Returns: - dict: { - "input_tokens": int, - "output_tokens": int, - "total_tokens": int - } - """ - - input_tokens = 0 - output_tokens = 0 - total_tokens = 0 - - for idx, item in enumerate(data): - token_usage = getattr(item, "usage_metadata", {}) - in_tok = token_usage.get("input_tokens", 0) - out_tok = token_usage.get("output_tokens", 0) - total_tok = token_usage.get("total_tokens", 0) - - logger.debug( - "Message[%d] → input=%d, output=%d, total=%d", - idx, - in_tok, - out_tok, - total_tok, - ) - - input_tokens += in_tok - output_tokens += out_tok - total_tokens += total_tok - - logger.info( - "Token usage summary → input: %d, output: %d, total: %d", - input_tokens, - output_tokens, - total_tokens, - ) - - return { - "input_tokens": input_tokens, - "output_tokens": output_tokens, - "total_tokens": total_tokens, - } diff --git a/interface/README.md b/interface/README.md deleted file mode 100644 index d90fc49..0000000 --- a/interface/README.md +++ /dev/null @@ -1,742 +0,0 @@ -# interface - -Streamlit 기반 Lang2SQL 데이터 분석 도구의 사용자 인터페이스 레이어입니다. - -## 디렉토리 구조 - -``` -interface/ -├── app_pages/ # Streamlit 페이지들 -│ ├── __init__.py -│ ├── chatbot.py # AI ChatBot 페이지 -│ ├── graph_builder.py # LangGraph 구성 페이지 -│ ├── home.py # 홈 페이지 -│ ├── lang2sql.py # Lang2SQL 메인 페이지 -│ ├── settings.py # 설정 페이지 (탭 구성) -│ ├── sidebar_components/ # 사이드바 UI 컴포넌트 -│ │ ├── __init__.py -│ │ ├── chatbot_session_controller.py -│ │ ├── data_source_selector.py -│ │ ├── db_selector.py -│ │ ├── embedding_selector.py -│ │ ├── llm_selector.py -│ │ └── README.md -│ └── settings_sections/ # 설정 페이지 섹션들 -│ ├── __init__.py -│ ├── data_source_section.py -│ ├── db_section.py -│ ├── llm_section.py -│ └── README.md -├── core/ # 핵심 인터페이스 로직 -│ ├── config/ # 설정 관리 -│ │ ├── __init__.py -│ │ ├── models.py # 설정 데이터 모델 -│ │ ├── paths.py # 파일 경로 관리 -│ │ ├── persist.py # 디스크 저장/로드 -│ │ ├── registry_data_sources.py -│ │ ├── registry_db.py -│ │ ├── registry_llm.py -│ │ └── settings.py # 설정 업데이트 API -│ ├── dialects.py # SQL 다이얼렉트 프리셋 -│ ├── lang2sql_runner.py # Lang2SQL 실행 래퍼 -│ ├── result_renderer.py # 결과 시각화 -│ └── session_utils.py # 세션 관리 -├── pages_config.py # 페이지 구성 -└── streamlit_app.py # 메인 진입점 -``` - -## 아키텍처 개요 - -``` -┌─────────────────────────────────────────────────────────────────┐ -│ streamlit_app.py │ -│ (메인 진입점, 페이지 네비게이션) │ -└────────────────────────────┬────────────────────────────────────┘ - │ - ┌────────┴─────────┐ - │ pages_config.py │ - │ (페이지 설정) │ - └────────┬─────────┘ - │ - ┌────────────────────┼────────────────────┐ - │ │ │ - ▼ ▼ ▼ -┌───────────────┐ ┌───────────────┐ ┌───────────────┐ -│ home.py │ │ lang2sql.py │ │ chatbot.py │ -│ (홈/소개) │ │ (메인 기능) │ │ (AI 채팅) │ -└───────────────┘ └───────┬───────┘ └───────────────┘ - │ │ - │ ┌────────┴────────┐ - │ │ │ - ▼ ▼ ▼ -┌───────────────┐ ┌───────────────┐ ┌───────────────┐ -│graph_builder.py│ │ settings.py │ │sidebar_components│ -│ (그래프 구성) │ │ (설정) │ │ (사이드바) │ -└───────────────┘ └───────┬───────┘ └───────┬───────┘ - │ │ - ▼ ▼ - ┌────────────────┐ ┌────────────────┐ - │settings_sections│ │ core/config/ │ - │ (설정 UI) │ │ (설정 관리) │ - └────────────────┘ └────────┬───────┘ - │ - ▼ - ┌──────────────────┐ - │ core/ │ - │ - dialects.py │ - │ - lang2sql_runner│ - │ - result_renderer│ - │ - session_utils │ - └──────────────────┘ -``` - -## 주요 컴포넌트 - -### 1. streamlit_app.py - -Streamlit 애플리케이션의 메인 진입점입니다. - -**역할:** -- 애플리케이션 전역 설정 초기화 (제목, 아이콘, 레이아웃) -- 페이지 네비게이션 구성 및 실행 - -**주요 함수:** -- `configure_app()`: Streamlit 전역 설정 -- `main()`: 애플리케이션 진입점 - -**사용 예시:** -```python -# streamlit run interface/streamlit_app.py -``` - -**의존성:** -- `interface.pages_config.PAGES`: 페이지 목록 - ---- - -### 2. pages_config.py - -Streamlit 페이지 구성 정의 모듈입니다. - -**역할:** -- 각 페이지의 경로와 제목 정의 -- 네비게이션에 사용되는 페이지 리스트 제공 - -**내용:** -- `PAGES`: 5개 페이지 정의 - - 홈 (home.py) - - Lang2SQL (lang2sql.py) - - 그래프 빌더 (graph_builder.py) - - ChatBot (chatbot.py) - - 설정 (settings.py) - -**사용처:** -- `streamlit_app.py` (line 9, 37) - ---- - -### 3. app_pages/ - -Streamlit 애플리케이션의 각 페이지 모듈들입니다. - -#### 3.1. home.py - -홈 페이지로 Lang2SQL 도구 소개 및 사용 방법 안내를 제공합니다. - -**주요 내용:** -- 도구 소개 및 환영 메시지 -- 사용 방법 가이드 -- 주요 기능 링크 안내 - -**사용처:** -- `pages_config.py` (line 18) - ---- - -#### 3.2. lang2sql.py - -Lang2SQL 메인 페이지로 자연어 질의를 SQL 쿼리로 변환하고 결과를 시각화합니다. - -``` -┌──────────────────────────────────────────────────────────────┐ -│ 🔍 Lang2SQL │ -├──────────────────────────────────────────────────────────────┤ -│ │ -│ ┌──────────────────┐ ┌───────────────────────┐ │ -│ │ Sidebar │ │ Main Area │ │ -│ │ │ │ │ │ -│ │ ┌──────────────┐ │ │ ┌───────────────────┐ │ │ -│ │ │Data Source │ │ │ │쿼리 입력 영역 │ │ │ -│ │ │Selector │ │ │ │ │ │ │ -│ │ └──────────────┘ │ │ │"고객 데이터를..." │ │ │ -│ │ ──────────────── │ │ └───────────────────┘ │ │ -│ │ ┌──────────────┐ │ │ │ │ -│ │ │LLM Selector │ │ │ ┌───────────────────┐ │ │ -│ │ └──────────────┘ │ │ │DB 선택 및 관리 │ │ │ -│ │ ──────────────── │ │ └───────────────────┘ │ │ -│ │ ┌──────────────┐ │ │ │ │ -│ │ │Embedding Sel.│ │ │ [검색기 유형] │ │ -│ │ └──────────────┘ │ │ [Top-N 개수] │ │ -│ │ ──────────────── │ │ [모델 실행 장치] │ │ -│ │ ┌──────────────┐ │ │ │ │ -│ │ │DB Selector │ │ │ [쿼리 실행] │ │ -│ │ └──────────────┘ │ │ │ │ -│ │ ──────────────── │ │ ┌───────────────────┐ │ │ -│ │ Output Settings │ │ │ 결과 표시 영역 │ │ │ -│ │ □ Token Usage │ │ │ - SQL 쿼리 │ │ │ -│ │ □ Result Desc │ │ │ - 결과 테이블 │ │ │ -│ │ □ Show SQL │ │ │ - 차트 │ │ │ -│ │ ... │ │ └───────────────────┘ │ │ -│ │ ──────────────── │ │ │ │ -│ │ 워크플로우 선택 │ └───────────────────────┘ │ -│ │ ○ 기본 │ │ -│ │ ○ 확장 │ │ -│ └──────────────────┘ │ -└──────────────────────────────────────────────────────────────┘ -``` - -**주요 기능:** -- 사이드바 설정: 데이터 소스, LLM, Embedding, DB 선택 -- 출력 옵션 설정 (토큰 사용량, SQL 표시, 차트 등) -- 워크플로우 선택 (기본/확장) -- 자연어 질의 입력 및 SQL 변환 실행 -- DB 다이얼렉트 선택 및 편집 -- 검색기 유형 및 Top-N 설정 -- 결과 시각화 (테이블, 차트) - -**주요 함수:** -- 페이지 레벨에서 직접 실행되는 Streamlit UI 코드 - -**사용 모듈:** -- `interface.core.dialects`: 다이얼렉트 프리셋 -- `interface.core.lang2sql_runner`: Lang2SQL 실행 -- `interface.core.result_renderer`: 결과 시각화 -- `interface.core.session_utils`: 그래프 초기화 -- `interface.app_pages.sidebar_components`: 사이드바 컴포넌트 - -**의존성:** -- `engine.query_executor.execute_query`: 실제 쿼리 실행 - ---- - -#### 3.3. graph_builder.py - -LangGraph 워크플로우를 구성하고 세션에 적용하는 페이지입니다. - -``` -┌──────────────────────────────────────────────────────────────┐ -│ LangGraph 구성 UI │ -├──────────────────────────────────────────────────────────────┤ -│ │ -│ ┌────────────────────────────────────────────────────────┐ │ -│ │ 프리셋 선택: ○ 기본 ○ 확장 ○ 커스텀 │ │ -│ └────────────────────────────────────────────────────────┘ │ -│ │ -│ ┌────────────────────────────────────────────────────────┐ │ -│ │ 커스텀 옵션: │ │ -│ │ ☑ PROFILE_EXTRACTION 포함 │ │ -│ │ ☑ CONTEXT_ENRICHMENT 포함 │ │ -│ │ ☑ QUERY_MAKER 포함 │ │ -│ └────────────────────────────────────────────────────────┘ │ -│ │ -│ ┌────────────────────────────────────────────────────────┐ │ -│ │ GET_TABLE_INFO 설정: │ │ -│ │ - 테이블 검색기: [벡터 검색 (기본) ▼] │ │ -│ │ - 검색할 테이블 정보 개수: [====●====] 5 │ │ -│ │ - 모델 실행 장치: [cpu ▼] │ │ -│ └────────────────────────────────────────────────────────┘ │ -│ │ -│ ┌────────────────────────────────────────────────────────┐ │ -│ │ 실행 순서: │ │ -│ │ GET_TABLE_INFO → PROFILE_EXTRACTION → ... │ │ -│ └────────────────────────────────────────────────────────┘ │ -│ │ -│ ┌────────────────────────────────────────────────────────┐ │ -│ │ 그래프 생성: │ │ -│ │ ℹ️ 그래프가 세션에 적용되었습니다. │ │ -│ │ [세션 그래프 새로고침] │ │ -│ └────────────────────────────────────────────────────────┘ │ -│ │ -│ ┌────────────────────────────────────────────────────────┐ │ -│ │ 현재 세션 그래프 설정 (expander) │ │ -│ └────────────────────────────────────────────────────────┘ │ -└──────────────────────────────────────────────────────────────┘ -``` - -**주요 기능:** -- 프리셋 선택: 기본, 확장, 커스텀 -- 노드 시퀀스 구성 (GET_TABLE_INFO → PROFILE → CONTEXT → QUERY_MAKER) -- GET_TABLE_INFO 설정 (검색기, Top-N, 장치) -- 그래프 세션에 적용 및 새로고침 -- 현재 그래프 설정 확인 - -**주요 함수:** -- `build_selected_sequence()`: 프리셋에 따른 노드 시퀀스 생성 -- `build_state_graph()`: StateGraph 빌더 생성 -- `render_sequence()`: 시퀀스를 문자열로 변환 - -**사용 모듈:** -- `utils.llm.graph_utils.base`: 노드 정의 및 그래프 유틸 - ---- - -#### 3.4. chatbot.py - -AI ChatBot 페이지로 LangGraph 기반 대화형 인터페이스를 제공합니다. - -``` -┌──────────────────────────────────────────────────────────────┐ -│ 🤖 AI ChatBot │ -├───────────────────────────┬──────────────────────────────────┤ -│ Sidebar │ Chat Area │ -│ │ │ -│ ┌───────────────────────┐ │ ┌─────────────────────────────┐ │ -│ │Data Source Selector │ │ │ 안녕하세요! 무엇을... 🤖 │ │ -│ └───────────────────────┘ │ └─────────────────────────────┘ │ -│ ───────────────────────── │ │ -│ ┌───────────────────────┐ │ ┌─────────────────────────────┐ │ -│ │LLM Selector │ │ │ 사용자: 데이터베이스 테이블... │ │ -│ └───────────────────────┘ │ └─────────────────────────────┘ │ -│ ───────────────────────── │ │ -│ ┌───────────────────────┐ │ ┌─────────────────────────────┐ │ -│ │Embedding Selector │ │ │ Assistant: 관련 테이블은... │ │ -│ └───────────────────────┘ │ │ 🤖 모델: gpt-4o-mini │ │ -│ ───────────────────────── │ └─────────────────────────────┘ │ -│ ┌───────────────────────┐ │ │ -│ │DB Selector │ │ ┌─────────────────────────────┐ │ -│ └───────────────────────┘ │ │ 사용자: [입력 중...] │ │ -│ ───────────────────────── │ └─────────────────────────────┘ │ -│ 🤖 ChatBot 설정 │ │ -│ ┌───────────────────────┐ │ │ -│ │ Thread ID: uuid... │ │ │ -│ │ [새 세션 시작] │ │ │ -│ └───────────────────────┘ │ │ -│ ───────────────────────── │ │ -│ 대화 기록: │ │ -│ - Thread ID │ │ -│ - 메시지 목록 (JSON) │ │ -└───────────────────────────┴─────────────────────────────────┘ -``` - -**주요 기능:** -- OpenAI 기반 대화형 AI 채팅 -- 사이드바 설정: 데이터 소스, LLM, Embedding, DB -- Thread ID 기반 세션 관리 -- 대화 기록 표시 및 관리 -- 신규 세션 시작 기능 - -**주요 함수:** -- `initialize_session_state()`: 세션 상태 초기화 및 ChatBot 인스턴스 생성 - -**사용 모듈:** -- `utils.llm.chatbot.ChatBot`: ChatBot 핵심 로직 -- `interface.app_pages.sidebar_components`: 사이드바 컴포넌트 -- `interface.core.config.load_config`: 설정 로드 - -**제약사항:** -- 현재 OpenAI만 지원 -- OpenAI API 키 필수 - ---- - -#### 3.5. settings.py - -설정 페이지로 데이터 소스, LLM, DB 설정을 탭으로 관리합니다. - -``` -┌──────────────────────────────────────────────────────────────┐ -│ ⚙️ 설정 │ -├──────────────────────────────────────────────────────────────┤ -│ │ -│ ┌──────────┬──────────┬──────────┐ │ -│ │데이터 소스 │ LLM │ DB │ │ -│ └──────────┴──────────┴──────────┘ │ -│ │ -│ [선택된 탭 내용 표시] │ -│ │ -└──────────────────────────────────────────────────────────────┘ -``` - -**주요 기능:** -- 3개 탭으로 구성: 데이터 소스, LLM, DB -- 각 탭은 settings_sections 모듈 사용 -- 세션 우선 설정 반영 - -**사용 모듈:** -- `interface.app_pages.settings_sections`: 각 설정 섹션 - ---- - -#### 3.6. sidebar_components/ - -사이드바에서 사용되는 설정 선택 컴포넌트들입니다. - -**모듈 구조:** -- `data_source_selector.py`: 데이터 소스 선택 (DataHub/VectorDB) -- `llm_selector.py`: LLM 프로파일 선택 -- `embedding_selector.py`: Embedding 프로파일 선택 -- `db_selector.py`: DB 프로파일 선택 -- `chatbot_session_controller.py`: ChatBot 세션 관리 - -**사용처:** -- `lang2sql.py`: 메인 페이지 사이드바 -- `chatbot.py`: ChatBot 페이지 사이드바 - -**자세한 내용:** `sidebar_components/README.md` 참고 - ---- - -#### 3.7. settings_sections/ - -설정 페이지의 각 섹션 UI 모듈들입니다. - -**모듈 구조:** -- `data_source_section.py`: DataHub/VectorDB 관리 -- `llm_section.py`: LLM 및 Embedding 설정 -- `db_section.py`: DB 연결 설정 - -**사용처:** -- `settings.py`: 설정 페이지 탭 내용 - -**자세한 내용:** `settings_sections/README.md` 참고 - ---- - -### 4. core/ - -핵심 인터페이스 로직을 담당하는 모듈들입니다. - -#### 4.1. dialects.py - -SQL 다이얼렉트 프리셋 정의 및 관리 모듈입니다. - -**주요 내용:** -- `DialectOption`: SQL 엔진 특성 데이터클래스 - - `name`: 엔진 표시 이름 - - `supports_ilike`: ILIKE 지원 여부 - - `hints`: 자주 쓰이는 함수 목록 -- `PRESET_DIALECTS`: 9개 SQL 엔진 프리셋 - - PostgreSQL, ClickHouse, Trino, Snowflake, Redshift - - BigQuery, MSSQL, Oracle, DuckDB - -**사용처:** -- `lang2sql.py` (line 19, 85-124): DB 선택 및 편집 UI - ---- - -#### 4.2. lang2sql_runner.py - -Lang2SQL 실행 래퍼 모듈입니다. - -**주요 함수:** -- `run_lang2sql()`: 자연어 질의를 SQL로 변환 후 실행 - -**파라미터:** -- `query`: 자연어 질문 -- `database_env`: 데이터베이스 환경 이름 -- `retriever_name`: 검색기 유형 -- `top_n`: 검색할 테이블 정보 개수 -- `device`: 모델 실행 장치 - -**사용처:** -- `lang2sql.py` (line 139-145): 쿼리 실행 - -**의존성:** -- `engine.query_executor.execute_query`: 실제 실행 로직 - ---- - -#### 4.3. result_renderer.py - -Lang2SQL 실행 결과 시각화 모듈입니다. - -**주요 함수:** -- `display_result()`: Streamlit UI로 결과 출력 - -**표시 항목:** -- Question Gate 결과 -- 문서 적합성 평가 -- 토큰 사용량 -- SQL 쿼리 및 해석 -- 결과 설명 -- 재해석된 질문 -- 참고 테이블 목록 -- 쿼리 실행 결과 테이블 -- 결과 차트 (Plotly) - -**사용 모듈:** -- `infra.observability.token_usage.TokenUtils`: 토큰 사용량 -- `utils.databases.DatabaseFactory`: DB 커넥터 -- `utils.llm.llm_response_parser.LLMResponseParser`: SQL 파싱 -- `utils.visualization.display_chart.DisplayChart`: 차트 생성 - -**사용처:** -- `lang2sql.py` (line 146): 결과 표시 - ---- - -#### 4.4. session_utils.py - -Streamlit 세션 상태에서 그래프 빌더를 초기화하는 모듈입니다. - -**주요 함수:** -- `init_graph()`: 그래프 초기화 및 세션 상태 갱신 - -**파라미터:** -- `use_enriched`: 확장 그래프 사용 여부 - -**반환값:** -- 그래프 유형 문자열 ("확장된" 또는 "기본") - -**사용처:** -- `lang2sql.py` (line 72, 76): 그래프 초기화 - -**의존성:** -- `utils.llm.graph_utils.enriched_graph`: 확장 그래프 -- `utils.llm.graph_utils.basic_graph`: 기본 그래프 - ---- - -#### 4.5. config/ - -설정 관리 패키지로 데이터 소스, DB, LLM, Embedding 설정을 관리합니다. - -**아키텍처:** - -``` -config/ -├── models.py # 데이터 모델 정의 -│ ├── Config # 전역 설정 -│ ├── DataHubSource # DataHub 소스 -│ ├── VectorDBSource # VectorDB 소스 -│ ├── DataSourcesRegistry # 데이터 소스 레지스트리 -│ ├── DBConnectionProfile # DB 프로파일 -│ ├── DBConnectionsRegistry # DB 레지스트리 -│ ├── LLMProfile # LLM 프로파일 -│ ├── LLMRegistry # LLM 레지스트리 -│ ├── EmbeddingProfile # Embedding 프로파일 -│ └── EmbeddingRegistry # Embedding 레지스트리 -│ -├── paths.py # 파일 경로 관리 -│ ├── get_registry_file_path() # 데이터 소스 레지스트리 경로 -│ ├── get_db_registry_file_path() # DB 레지스트리 경로 -│ ├── get_llm_registry_file_path() # LLM 레지스트리 경로 -│ └── get_embedding_registry_file_path() # Embedding 레지스트리 경로 -│ -├── persist.py # 디스크 저장/로드 -│ ├── save_registry_to_disk() # 데이터 소스 저장 -│ ├── load_registry_from_disk() # 데이터 소스 로드 -│ ├── save_db_registry_to_disk() # DB 저장 -│ ├── load_db_registry_from_disk() # DB 로드 -│ ├── save_llm_registry_to_disk() # LLM 저장 -│ ├── load_llm_registry_from_disk() # LLM 로드 -│ ├── save_embedding_registry_to_disk() # Embedding 저장 -│ └── load_embedding_registry_from_disk() # Embedding 로드 -│ -├── settings.py # 설정 업데이트 API -│ ├── load_config() # 설정 로드 -│ ├── update_datahub_server() # DataHub 서버 업데이트 -│ ├── update_data_source_mode() # 데이터 소스 모드 업데이트 -│ ├── update_vectordb_settings() # VectorDB 설정 업데이트 -│ ├── update_llm_settings() # LLM 설정 업데이트 -│ ├── update_embedding_settings() # Embedding 설정 업데이트 -│ └── update_db_settings() # DB 설정 업데이트 -│ -├── registry_data_sources.py # 데이터 소스 레지스트리 관리 -│ ├── get_data_sources_registry() # 레지스트리 조회 -│ ├── add_datahub_source() # DataHub 추가 -│ ├── update_datahub_source() # DataHub 업데이트 -│ ├── delete_datahub_source() # DataHub 삭제 -│ ├── add_vectordb_source() # VectorDB 추가 -│ ├── update_vectordb_source() # VectorDB 업데이트 -│ └── delete_vectordb_source() # VectorDB 삭제 -│ -├── registry_db.py # DB 레지스트리 관리 -│ ├── get_db_connections_registry() # 레지스트리 조회 -│ ├── add_db_connection() # DB 추가 -│ ├── update_db_connection() # DB 업데이트 -│ └── delete_db_connection() # DB 삭제 -│ -├── registry_llm.py # LLM/Embedding 레지스트리 관리 -│ ├── get_llm_registry() # LLM 레지스트리 조회 -│ ├── save_llm_profile() # LLM 프로파일 저장 -│ ├── get_embedding_registry() # Embedding 레지스트리 조회 -│ └── save_embedding_profile() # Embedding 프로파일 저장 -│ -└── __init__.py # 패키지 공개 API -``` - -**설정 우선순위:** -1. 세션 상태 (`st.session_state`) -2. 환경 변수 (`os.getenv`) -3. 기본값 - -**저장 위치:** -- 기본: `./config/` 디렉토리 -- 환경 변수로 오버라이드 가능: - - `LANG2SQL_REGISTRY_PATH` - - `LANG2SQL_DB_REGISTRY_PATH` - - `LANG2SQL_LLM_REGISTRY_PATH` - - `LANG2SQL_EMBEDDING_REGISTRY_PATH` - -**사용 예시:** -```python -from interface.core.config import load_config, get_data_sources_registry - -# 설정 로드 -config = load_config() - -# 레지스트리 조회 -registry = get_data_sources_registry() - -# DataHub 추가 -from interface.core.config import add_datahub_source -add_datahub_source( - name="Production", - url="http://datahub.prod:8080", - faiss_path="./prod/faiss", - note="프로덕션 DataHub" -) -``` - ---- - -## 전체 데이터 흐름 - -``` -┌────────────────────────────────────────────────────────────┐ -│ 사용자 입력 │ -│ (자연어 질의, 설정 변경 등) │ -└──────────────────────┬─────────────────────────────────────┘ - │ - ▼ -┌────────────────────────────────────────────────────────────┐ -│ Streamlit UI Layer │ -│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ -│ │ app_pages/ │ │ core/ │ │ config/ │ │ -│ │ - lang2sql │ │ - runner │ │ - settings │ │ -│ │ - chatbot │ │ - renderer │ │ - registry │ │ -│ │ - settings │ │ - dialects │ │ - persist │ │ -│ └─────────────┘ └─────────────┘ └─────────────┘ │ -└──────────────────┬─────────────────────────────────────────┘ - │ - ▼ -┌────────────────────────────────────────────────────────────┐ -│ Business Logic Layer │ -│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ -│ │ engine/ │ │ utils/llm/ │ │ utils/data/ │ │ -│ │ - executor │ │ - graph │ │ - databases │ │ -│ │ │ │ - factory │ │ - hub │ │ -│ └─────────────┘ └─────────────┘ └─────────────┘ │ -└──────────────────┬─────────────────────────────────────────┘ - │ - ▼ -┌────────────────────────────────────────────────────────────┐ -│ Data & External Services │ -│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ -│ │ DataHub │ │ Database │ │ LLM APIs │ │ -│ │ VectorDB │ │ (DBs) │ │ (OpenAI, │ │ -│ │ │ │ │ │ AWS, etc) │ │ -│ └─────────────┘ └─────────────┘ └─────────────┘ │ -└────────────────────────────────────────────────────────────┘ - │ - ▼ -┌────────────────────────────────────────────────────────────┐ -│ 결과 반환 │ -│ (SQL, 테이블, 차트, 설명 등) │ -└────────────────────────────────────────────────────────────┘ -``` - -## 주요 설정 파일 - -설정은 JSON 파일로 `./config/` 디렉토리에 저장됩니다. - -- `data_sources.json`: DataHub/VectorDB 레지스트리 -- `db_connections.json`: DB 프로파일 레지스트리 -- `llm_profiles.json`: LLM 프로파일 레지스트리 -- `embedding_profiles.json`: Embedding 프로파일 레지스트리 - -**예시: data_sources.json** -```json -{ - "datahub": [ - { - "name": "Local DataHub", - "url": "http://localhost:8080", - "faiss_path": "./dev/table_info_db", - "note": "로컬 개발 환경" - } - ], - "vectordb": [ - { - "name": "Local FAISS", - "type": "faiss", - "location": "./dev/faiss_db", - "collection_prefix": null, - "note": "로컬 벡터 DB" - } - ] -} -``` - -## 사용 방법 - -### 애플리케이션 실행 - -```bash -# 메인 애플리케이션 실행 -streamlit run interface/streamlit_app.py - -# 또는 -python -m interface.streamlit_app -``` - -### 설정 관리 - -```python -from interface.core.config import ( - load_config, - get_data_sources_registry, - add_datahub_source, - update_db_settings, -) - -# 설정 로드 -config = load_config() - -# DataHub 추가 -add_datahub_source( - name="Production", - url="http://datahub.prod:8080", - faiss_path="./prod/faiss" -) - -# DB 설정 업데이트 -update_db_settings( - db_type="postgresql", - values={"host": "localhost", "port": "5432", "user": "admin"}, - secrets={"password": "secret"} -) -``` - -## 의존성 - -**주요 패키지:** -- `streamlit`: UI 프레임워크 -- `pandas`: 데이터 처리 -- `plotly`: 차트 시각화 -- `langchain_core`: LLM 메시지 처리 - -**프로젝트 내부 모듈:** -- `engine.query_executor`: 쿼리 실행 -- `utils.llm`: LLM 관련 유틸리티 -- `utils.data`: 데이터 관련 유틸리티 -- `utils.databases`: 데이터베이스 유틸리티 -- `infra.observability`: 모니터링 및 관찰성 - -## 참고 문서 - -- `app_pages/sidebar_components/README.md`: 사이드바 컴포넌트 상세 -- `app_pages/settings_sections/README.md`: 설정 섹션 상세 -- `README_UPDATE_CONDITION.md`: 문서 업데이트 가이드 - diff --git a/interface/app_pages/chatbot.py b/interface/app_pages/chatbot.py deleted file mode 100644 index 8c460fd..0000000 --- a/interface/app_pages/chatbot.py +++ /dev/null @@ -1,157 +0,0 @@ -""" -AI ChatBot 페이지 -LangGraph와 OpenAI를 활용한 대화형 인터페이스 -""" - -import os -import streamlit as st - -from utils.llm.chatbot import ChatBot -from interface.app_pages.sidebar_components import ( - render_sidebar_data_source_selector, - render_sidebar_llm_selector, - render_sidebar_embedding_selector, - render_sidebar_db_selector, - render_sidebar_chatbot_session_controller, -) -from interface.core.config import load_config - - -def initialize_session_state(): - """세션 상태 초기화 함수 - - Streamlit의 session_state를 사용하여 앱의 상태를 유지합니다. - LLM 설정을 sidebar의 llm_selector에서 선택한 값으로부터 가져옵니다. - """ - # 채팅 메시지 기록 저장 (자동으로 시작) - if "chatbot_messages" not in st.session_state: - st.session_state.chatbot_messages = [] - - # LLM 공급자 확인 (현재 ChatBot은 OpenAI만 지원) - llm_provider = ( - st.session_state.get("LLM_PROVIDER") or os.getenv("LLM_PROVIDER") or "openai" - ).lower() - - if llm_provider != "openai": - st.error( - f"⚠️ ChatBot은 현재 OpenAI만 지원합니다. 설정 > LLM에서 OpenAI 프로파일을 선택하거나 LLM_PROVIDER를 'openai'로 설정해주세요." - ) - st.stop() - - # OpenAI API 키 확인 - openai_api_key = st.session_state.get("OPEN_AI_KEY") or os.getenv("OPEN_AI_KEY") - - if not openai_api_key: - st.error( - "⚠️ OpenAI API 키가 설정되지 않았습니다. 설정 > LLM에서 OpenAI API 키를 입력하거나, 사이드바에서 LLM 프로파일을 적용해주세요." - ) - st.stop() - - # 사용할 모델명 가져오기 (llm_selector에서 설정한 값) - model_name = ( - st.session_state.get("OPEN_AI_LLM_MODEL") - or os.getenv("OPEN_AI_LLM_MODEL") - or "gpt-4o-mini" - ) - - # DataHub 서버 URL 가져오기 (config에서 로드) - config = load_config() - gms_server = config.datahub_server - - # ChatBot 인스턴스 생성 또는 모델 업데이트 - if "chatbot_instance" not in st.session_state: - st.session_state.chatbot_instance = ChatBot( - openai_api_key, model_name=model_name, gms_server=gms_server - ) - else: - # 기존 인스턴스가 있는 경우, 모델이나 API 키, gms_server가 변경되었는지 확인 - existing_bot = st.session_state.chatbot_instance - if ( - existing_bot.model_name != model_name - or existing_bot.openai_api_key != openai_api_key - or existing_bot.gms_server != gms_server - ): - st.session_state.chatbot_instance = ChatBot( - openai_api_key, model_name=model_name, gms_server=gms_server - ) - - -# 세션 상태 초기화 실행 -initialize_session_state() - -# 페이지 제목 -st.title("🤖 AI ChatBot") - -st.markdown(""" - LangGraph 기반 AI ChatBot과 대화를 나눌 수 있습니다. - - 데이터베이스 테이블 정보 검색 - - 용어집 조회 - - 쿼리 예제 조회 - - 대화를 통해 질문 구체화 - """) - -# 설정 로드 -config = load_config() - -# 사이드바 UI 구성 (lang2sql.py와 동일한 구조) -render_sidebar_data_source_selector(config) -st.sidebar.divider() -render_sidebar_llm_selector() -st.sidebar.divider() -render_sidebar_embedding_selector() -st.sidebar.divider() -render_sidebar_db_selector() -st.sidebar.divider() - -# ChatBot 전용 설정 -with st.sidebar: - st.markdown("### 🤖 ChatBot 설정") - st.divider() - thread_id = render_sidebar_chatbot_session_controller() - - -# 첫 메시지가 없으면 환영 메시지 추가 -if not st.session_state.chatbot_messages: - hello_message = "안녕하세요! 무엇을 도와드릴까요? 🤖" - st.session_state.chatbot_messages = [ - {"role": "assistant", "content": hello_message} - ] - -# 저장된 모든 메시지를 순서대로 표시 -for message in st.session_state.chatbot_messages: - with st.chat_message(message["role"]): - st.markdown(message["content"]) - -# 사용자 입력 처리 -if prompt := st.chat_input("메시지를 입력하세요"): - # 사용자 메시지를 기록에 추가 - st.session_state.chatbot_messages.append({"role": "user", "content": prompt}) - with st.chat_message("user"): - st.markdown(prompt) - - # AI 응답 생성 및 표시 - with st.chat_message("assistant"): - try: - # ChatBot을 통해 응답 생성 - response = st.session_state.chatbot_instance.chat(prompt, thread_id) - - # 응답 내용 추출 - response_content = response["messages"][-1].content - - # 모델 정보 표시 - model_name = st.session_state.chatbot_instance.model_name - st.caption(f"🤖 모델: {model_name}") - - # 응답 표시 - st.markdown(response_content) - - # AI 응답을 기록에 추가 - st.session_state.chatbot_messages.append( - {"role": "assistant", "content": response_content} - ) - except Exception as e: - error_msg = f"오류가 발생했습니다: {str(e)}" - st.error(error_msg) - st.session_state.chatbot_messages.append( - {"role": "assistant", "content": error_msg} - ) diff --git a/interface/app_pages/home.py b/interface/app_pages/home.py deleted file mode 100644 index ef7644b..0000000 --- a/interface/app_pages/home.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -홈 페이지 모듈. - -Lang2SQL 데이터 분석 도구의 소개와 사용 방법을 안내합니다. -""" - -import streamlit as st - -st.title("🏠 홈") - -st.markdown(""" - ### Lang2SQL 데이터 분석 도구에 오신 것을 환영합니다 🎉 - - 이 도구는 자연어로 작성한 질문을 SQL 쿼리로 변환하고, - 데이터베이스를 조회하여 결과를 **표와 차트**로 시각화합니다. - - --- - #### 사용 방법 - 1. 왼쪽 메뉴에서 원하는 기능 페이지를 선택하세요. - 2. **🔍 Lang2SQL**: 자연어 → SQL 변환 및 결과 분석 - 3. **📊 그래프 빌더**: LangGraph 실행 순서를 프리셋/커스텀으로 구성하고 세션에 적용 - 4. **⚙️ 설정**: 데이터 소스, LLM, DB 연결 등 환경 설정 - """) - -st.info("왼쪽 메뉴에서 기능 페이지를 선택해 시작하세요 🚀") diff --git a/interface/app_pages/lang2sql.py b/interface/app_pages/lang2sql.py deleted file mode 100644 index 214e480..0000000 --- a/interface/app_pages/lang2sql.py +++ /dev/null @@ -1,78 +0,0 @@ -""" -Lang2SQL Streamlit 애플리케이션. - -자연어 질의를 SQL 쿼리로 변환하고 실행 결과를 시각화하는 인터페이스를 제공합니다. - -주요 기능: - - 사용자 질의를 SQL 쿼리로 변환 후 실행 - - SQL 방언(dialect) 선택 지원 - - 쿼리 실행 결과를 표로 시각화 - - Baseline / Enriched 워크플로우 선택 -""" - -import pandas as pd -import streamlit as st - -from interface.core.config import load_config -from interface.core.lang2sql_runner import run_lang2sql -from interface.app_pages.sidebar_components import ( - render_sidebar_data_source_selector, - render_sidebar_db_selector, - render_sidebar_embedding_selector, - render_sidebar_llm_selector, -) - -TITLE = "Lang2SQL" -DEFAULT_QUERY = "고객 데이터를 기반으로 유니크한 유저 수를 카운트하는 쿼리" - -DIALECT_OPTIONS = ["default", "sqlite", "postgresql", "mysql", "bigquery", "duckdb"] - -st.title(TITLE) - -config = load_config() - -render_sidebar_data_source_selector(config) -st.sidebar.divider() -render_sidebar_llm_selector() -st.sidebar.divider() -render_sidebar_embedding_selector() -st.sidebar.divider() -render_sidebar_db_selector() -st.sidebar.divider() - -st.sidebar.markdown("### 워크플로우 선택") -use_enriched = st.sidebar.checkbox( - "프로파일 추출 & 컨텍스트 보강 워크플로우 사용 (Enriched)", value=False -) - -# 쿼리 입력 -user_query = st.text_area("쿼리를 입력하세요:", value=DEFAULT_QUERY) - -# 설정 -col1, col2 = st.columns(2) -with col1: - user_dialect = st.selectbox("SQL 방언(Dialect):", options=DIALECT_OPTIONS, index=0) -with col2: - user_top_n = st.slider( - "검색할 테이블 정보 개수:", min_value=1, max_value=20, value=5 - ) - -if st.button("쿼리 실행"): - with st.spinner("쿼리 실행 중..."): - res = run_lang2sql( - query=user_query, - db_dialect=user_dialect if user_dialect != "default" else None, - top_n=user_top_n, - use_enriched=use_enriched, - ) - - if res.get("error"): - st.error(f"오류 발생: {res['error']}") - else: - rows = res.get("rows", []) - if rows: - st.success(f"{len(rows)}개 행 반환됨.") - st.markdown("**쿼리 실행 결과:**") - st.dataframe(pd.DataFrame(rows)) - else: - st.info("쿼리 실행 결과가 없습니다.") diff --git a/interface/app_pages/settings.py b/interface/app_pages/settings.py deleted file mode 100644 index 72d05c7..0000000 --- a/interface/app_pages/settings.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Settings 페이지 – 섹션 기반 UI -""" - -import streamlit as st - -from interface.core.config import load_config -from interface.app_pages.settings_sections.data_source_section import ( - render_data_source_section, -) -from interface.app_pages.settings_sections.llm_section import render_llm_section -from interface.app_pages.settings_sections.db_section import render_db_section - -st.title("⚙️ 설정") - -config = load_config() - -tabs = st.tabs(["데이터 소스", "LLM", "DB"]) - -with tabs[0]: - render_data_source_section(config) - -with tabs[1]: - render_llm_section(config) - -with tabs[2]: - render_db_section() - -st.divider() -st.caption("민감 정보는 로그에 기록되지 않으며, 이 설정은 현재 세션에 우선 반영됩니다.") diff --git a/interface/app_pages/settings_sections/README.md b/interface/app_pages/settings_sections/README.md deleted file mode 100644 index c81b768..0000000 --- a/interface/app_pages/settings_sections/README.md +++ /dev/null @@ -1,185 +0,0 @@ -# settings_sections - -설정 페이지의 각 섹션을 렌더링하는 모듈들입니다. - -## 디렉토리 구조 - -``` -settings_sections/ -├── __init__.py -├── data_source_section.py -├── db_section.py -└── llm_section.py -``` - -## 파일 목록 및 설명 - -### `__init__.py` - -네임스페이스 패키지 초기화 파일로, 패키지에서 export되는 모듈 목록을 정의합니다. - -**내보내는 모듈:** -- `data_source_section` -- `llm_section` -- `db_section` - -### `data_source_section.py` - -데이터 소스 설정을 관리하는 UI 섹션을 제공합니다. - -**주요 기능:** -- DataHub 또는 VectorDB 중 하나를 선택하여 데이터 소스 모드 설정 -- DataHub 서버 관리: - - 등록된 DataHub 목록 조회 및 표시 - - 새로운 DataHub 추가 (이름, URL, FAISS 저장 경로, 메모) - - 기존 DataHub 편집 및 삭제 - - GMS 서버 헬스 체크 기능 -- VectorDB 관리: - - 등록된 VectorDB 목록 조회 및 표시 (FAISS, pgvector 지원) - - 새로운 VectorDB 추가 (이름, 타입, 위치, 컬렉션 접두사, 메모) - - 기존 VectorDB 편집 및 삭제 - - 설정 검증 기능 - -**주요 함수:** -- `render_data_source_section(config: Config | None = None) -> None` - - 데이터 소스 설정 섹션을 Streamlit UI로 렌더링 - - `config` 파라미터가 없으면 내부에서 `load_config()`를 호출하여 로드 - -**의존성:** -- `interface.core.config`: Config 관리, 데이터 소스 레지스트리 조작 -- `infra.monitoring.check_server.CheckServer`: GMS 서버 헬스 체크 - -**상태 표시:** -- 현재 선택된 데이터 소스 모드에 따라 상태 배너 표시 -- DataHub: 헬스 체크 결과에 따른 성공/경고/정보 메시지 -- VectorDB: 설정 완전성에 따른 성공/경고 메시지 - -### `db_section.py` - -데이터베이스 연결 설정을 관리하는 UI 섹션을 제공합니다. - -**주요 기능:** -- 다양한 DB 타입 지원: - - PostgreSQL, MySQL, MariaDB, Oracle, ClickHouse - - DuckDB, SQLite - - Databricks, Snowflake, Trino -- DB 프로파일 관리: - - 등록된 DB 프로파일 목록 조회 및 표시 - - 새로운 DB 프로파일 추가 - - 기존 DB 프로파일 편집 및 삭제 -- DB 타입별 필드 동적 처리: - - 기본 필드: Host, Port, User, Database (또는 Path for DuckDB/SQLite) - - 추가 필드: Oracle(Service Name), Databricks(HTTP Path, Catalog, Schema), Snowflake(Account, Warehouse, Schema), Trino(HTTP Scheme, Catalog, Schema) - - 비밀 필드: Password 또는 Access Token (타입별 상이) -- 환경 변수 기반 자동 채우기 지원 -- 연결 테스트 기능 (SELECT 1 쿼리 실행) -- 설정 검증 및 세션 적용 기능 - -**주요 함수:** -- `render_db_section() -> None` - - DB 연결 설정 섹션을 Streamlit UI로 렌더링 - -**의존성:** -- `interface.core.config`: DB 연결 레지스트리 조작 -- `utils.databases.DatabaseFactory`: DB 커넥터 생성 및 연결 테스트 -- `utils.databases.factory.load_config_from_env`: 환경 변수에서 설정 로드 - -**헬퍼 함수:** -- `_non_secret_fields(db_type: str) -> list[tuple[str, str]]`: DB 타입별 기본 필드 정의 -- `_extra_non_secret_fields(db_type: str) -> list[tuple[str, str]]`: DB 타입별 추가 필드 정의 -- `_secret_fields(db_type: str) -> list[tuple[str, str]]`: DB 타입별 비밀 필드 정의 -- `_prefill_from_env(db_type: str, key: str) -> str`: 환경 변수에서 기본값 로드 - -### `llm_section.py` - -LLM 및 Embedding 설정을 관리하는 UI 섹션을 제공합니다. - -**주요 기능:** -- LLM 공급자 지원: - - OpenAI, Azure OpenAI, AWS Bedrock, Gemini, Ollama, Hugging Face -- Embedding 공급자 지원 (동일한 공급자 목록) -- 공급자별 필드 동적 처리: - - OpenAI: Model, API Key - - Azure: Endpoint, Deployment(Model), API Version, API Key - - Bedrock: Model, Access Key ID, Secret Access Key, Region - - Gemini: Model, API Key (embedding만) - - Ollama: Model, Base URL - - Hugging Face: Endpoint URL, Repo ID, Model, API Token (또는 Embedding: Model, Repo ID, API Token) -- 프로파일 저장 기능: - - LLM 프로파일 저장 (비밀키 제외 옵션) - - Embedding 프로파일 저장 (시크릿 포함) -- 저장된 프로파일 목록 조회 -- 환경 변수 및 세션 상태 기반 자동 채우기 - -**주요 함수:** -- `render_llm_section(config: Config | None = None) -> None` - - LLM 및 Embedding 설정 섹션을 Streamlit UI로 렌더링 - - 2개 컬럼으로 나뉘어 Chat LLM과 Embeddings를 각각 설정 - - `config` 파라미터가 없으면 내부에서 `load_config()`를 호출하거나 None 처리 - -**의존성:** -- `interface.core.config`: LLM/Embedding 설정 및 프로파일 관리 - -**헬퍼 함수:** -- `_llm_fields(provider: str) -> list[tuple[str, str, bool]]`: LLM 공급자별 필드 정의 (label, env_key, is_secret) -- `_embedding_fields(provider: str) -> list[tuple[str, str, bool]]`: Embedding 공급자별 필드 정의 - -## 사용 방법 - -이 모듈들은 `interface.app_pages.settings.py`에서 import되어 사용됩니다. - -### Import 예시 - -```python -from interface.app_pages.settings_sections.data_source_section import ( - render_data_source_section, -) -from interface.app_pages.settings_sections.llm_section import render_llm_section -from interface.app_pages.settings_sections.db_section import render_db_section -``` - -### 사용 예시 - -`settings.py`에서의 사용: - -```python -from interface.core.config import load_config - -config = load_config() - -tabs = st.tabs(["데이터 소스", "LLM", "DB"]) - -with tabs[0]: - render_data_source_section(config) - -with tabs[1]: - render_llm_section(config) - -with tabs[2]: - render_db_section() -``` - -### 함수 시그니처 - -#### `render_data_source_section(config: Config | None = None) -> None` -- **매개변수:** - - `config` (Config | None): 설정 객체. None이면 내부에서 `load_config()` 호출 -- **반환값:** None (Streamlit UI 직접 렌더링) - -#### `render_db_section() -> None` -- **매개변수:** 없음 -- **반환값:** None (Streamlit UI 직접 렌더링) - -#### `render_llm_section(config: Config | None = None) -> None` -- **매개변수:** - - `config` (Config | None): 설정 객체. None이면 내부에서 `load_config()` 호출하거나 None 처리 -- **반환값:** None (Streamlit UI 직접 렌더링) - -## 공통 특징 - -- 모든 섹션은 Streamlit을 사용하여 UI를 렌더링합니다. -- 설정 변경 시 `st.rerun()`을 호출하여 UI를 새로고침합니다. -- 에러 발생 시 `st.error()`를 사용하여 사용자에게 오류 메시지를 표시합니다. -- 성공적인 작업 완료 시 `st.success()`를 사용하여 확인 메시지를 표시합니다. -- 민감한 정보(비밀번호, API 키 등)는 `type="password"`를 사용하여 마스킹 처리합니다. - diff --git a/interface/app_pages/settings_sections/__init__.py b/interface/app_pages/settings_sections/__init__.py deleted file mode 100644 index 53cae96..0000000 --- a/interface/app_pages/settings_sections/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Namespace package for settings page sections - -__all__ = [ - "data_source_section", - "llm_section", - "db_section", -] diff --git a/interface/app_pages/settings_sections/data_source_section.py b/interface/app_pages/settings_sections/data_source_section.py deleted file mode 100644 index 2f63881..0000000 --- a/interface/app_pages/settings_sections/data_source_section.py +++ /dev/null @@ -1,321 +0,0 @@ -import streamlit as st -from interface.core.config import ( - Config, - load_config, - update_vectordb_settings, - update_data_source_mode, - get_data_sources_registry, - add_datahub_source, - update_datahub_source, - delete_datahub_source, - add_vectordb_source, - update_vectordb_source, - delete_vectordb_source, -) -from infra.monitoring.check_server import CheckServer - - -def _render_status_banner(config: Config) -> None: - mode = config.data_source_mode - ready_msgs = [] - - if mode == "datahub": - last_health = st.session_state.get("datahub_last_health") - if last_health is True: - st.success(f"데이터 소스 준비됨: DataHub ({config.datahub_server})") - elif last_health is False: - st.warning( - "DataHub 헬스 체크 실패. URL을 확인하거나 VectorDB로 전환하세요." - ) - else: - st.info("DataHub 상태 미검증 – 헬스 체크 버튼으로 확인하세요.") - elif mode == "vectordb": - if config.vectordb_type and ( - (config.vectordb_type == "faiss" and config.vectordb_location) - or (config.vectordb_type == "pgvector" and config.vectordb_location) - ): - st.success( - f"데이터 소스 준비됨: VectorDB ({config.vectordb_type}, {config.vectordb_location or '기본값'})" - ) - else: - st.warning("VectorDB 설정이 불완전합니다. 타입/위치를 확인하세요.") - else: - st.info( - "데이터 소스를 선택해주세요: DataHub 또는 VectorDB 중 하나는 필수입니다." - ) - - -def render_data_source_section(config: Config | None = None) -> None: - st.subheader("데이터 소스 (필수)") - - if config is None: - config = load_config() - - _render_status_banner(config) - - # 선택 스위치 - col = st.columns([1, 3])[0] - with col: - mode = st.radio( - "데이터 소스 선택", - options=["DataHub", "VectorDB"], - horizontal=True, - index=( - 0 if (config.data_source_mode or "datahub").lower() == "datahub" else 1 - ), - ) - selected = mode.lower() - update_data_source_mode(config, selected) - - st.divider() - - registry = get_data_sources_registry() - - if selected == "datahub": - with st.container(border=True): - st.write("등록된 DataHub") - for source in list(registry.datahub): - cols = st.columns([2, 4, 2, 1, 1]) - with cols[0]: - st.text(source.name) - with cols[1]: - st.text(source.url) - with cols[2]: - note_val = source.note or "" - st.caption(note_val) - with cols[3]: - if st.button("편집", key=f"edit_dh_{source.name}"): - st.session_state["edit_dh_name"] = source.name - with cols[4]: - if st.button("삭제", type="secondary", key=f"del_dh_{source.name}"): - delete_datahub_source(name=source.name) - st.rerun() - - # 편집 폼 - edit_dh = st.session_state.get("edit_dh_name") - if edit_dh: - st.divider() - st.write(f"DataHub 편집: {edit_dh}") - existing = next( - (s for s in registry.datahub if s.name == edit_dh), None - ) - if existing: - new_url = st.text_input( - "URL", value=existing.url, key="dh_edit_url" - ) - new_faiss = st.text_input( - "FAISS 저장 경로(선택)", - value=existing.faiss_path or "", - key="dh_edit_faiss", - ) - new_note = st.text_input( - "메모", value=existing.note or "", key="dh_edit_note" - ) - cols = st.columns([1, 1, 2]) - with cols[0]: - if st.button("헬스 체크", key="dh_edit_health"): - ok = CheckServer.is_gms_server_healthy(url=new_url) - st.session_state["datahub_last_health"] = bool(ok) - if ok: - st.success("GMS 서버가 정상입니다.") - else: - st.error( - "GMS 서버 헬스 체크 실패. URL과 네트워크를 확인하세요." - ) - with cols[1]: - if st.button("저장", key="dh_edit_save"): - try: - update_datahub_source( - name=edit_dh, - url=new_url, - faiss_path=(new_faiss or None), - note=(new_note or None), - ) - st.success("저장되었습니다.") - st.session_state.pop("edit_dh_name", None) - st.rerun() - except Exception as e: - st.error(f"저장 실패: {e}") - with cols[2]: - if st.button("취소", key="dh_edit_cancel"): - st.session_state.pop("edit_dh_name", None) - st.rerun() - - st.divider() - st.write("DataHub 추가") - dh_name = st.text_input("이름", key="dh_name") - dh_url = st.text_input( - "URL", key="dh_url", placeholder="http://localhost:8080" - ) - dh_faiss = st.text_input( - "FAISS 저장 경로(선택)", - key="dh_faiss", - placeholder="예: ./dev/table_info_db", - ) - dh_note = st.text_input("메모", key="dh_note", placeholder="선택") - - cols = st.columns([1, 1, 2]) - with cols[0]: - if st.button("헬스 체크", key="dh_health_new"): - ok = CheckServer.is_gms_server_healthy(url=dh_url) - st.session_state["datahub_last_health"] = bool(ok) - if ok: - st.success("GMS 서버가 정상입니다.") - else: - st.error( - "GMS 서버 헬스 체크 실패. URL과 네트워크를 확인하세요." - ) - with cols[1]: - if st.button("추가", key="dh_add"): - try: - if not dh_name or not dh_url: - st.warning("이름과 URL을 입력하세요.") - else: - add_datahub_source( - name=dh_name, - url=dh_url, - faiss_path=(dh_faiss or None), - note=dh_note or None, - ) - st.success("추가되었습니다.") - st.rerun() - except Exception as e: - st.error(f"추가 실패: {e}") - - else: # VectorDB - with st.container(border=True): - st.write("등록된 VectorDB") - for source in list(registry.vectordb): - cols = st.columns([2, 2, 4, 2, 1, 1]) - with cols[0]: - st.text(source.name) - with cols[1]: - st.text(source.type) - with cols[2]: - st.text(source.location) - with cols[3]: - st.caption(source.collection_prefix or "-") - with cols[4]: - if st.button("편집", key=f"edit_vdb_{source.name}"): - st.session_state["edit_vdb_name"] = source.name - with cols[5]: - if st.button( - "삭제", type="secondary", key=f"del_vdb_{source.name}" - ): - delete_vectordb_source(name=source.name) - st.rerun() - - # 편집 폼 - edit_vdb = st.session_state.get("edit_vdb_name") - if edit_vdb: - st.divider() - st.write(f"VectorDB 편집: {edit_vdb}") - existing = next( - (s for s in registry.vectordb if s.name == edit_vdb), None - ) - if existing: - new_type = st.selectbox( - "타입", - options=["faiss", "pgvector"], - index=(0 if existing.type == "faiss" else 1), - key="vdb_edit_type", - ) - new_loc_placeholder = ( - "FAISS 디렉토리 경로 (예: ./dev/table_info_db)" - if new_type == "faiss" - else "pgvector 연결 문자열 (postgresql://user:pass@host:port/db)" - ) - new_location = st.text_input( - "위치", - value=existing.location, - key="vdb_edit_location", - placeholder=new_loc_placeholder, - ) - new_prefix = st.text_input( - "컬렉션 접두사(선택)", - value=existing.collection_prefix or "", - key="vdb_edit_prefix", - ) - new_note = st.text_input( - "메모(선택)", value=existing.note or "", key="vdb_edit_note" - ) - cols = st.columns([1, 1, 2]) - with cols[0]: - if st.button("검증", key="vdb_edit_validate"): - try: - update_vectordb_settings( - config, - vectordb_type=new_type, - vectordb_location=new_location, - ) - st.success("설정이 유효합니다.") - except Exception as e: - st.error(f"검증 실패: {e}") - with cols[1]: - if st.button("저장", key="vdb_edit_save"): - try: - update_vectordb_source( - name=edit_vdb, - vtype=new_type, - location=new_location, - collection_prefix=(new_prefix or None), - note=(new_note or None), - ) - st.success("저장되었습니다.") - st.session_state.pop("edit_vdb_name", None) - st.rerun() - except Exception as e: - st.error(f"저장 실패: {e}") - with cols[2]: - if st.button("취소", key="vdb_edit_cancel"): - st.session_state.pop("edit_vdb_name", None) - st.rerun() - - st.divider() - st.write("VectorDB 추가") - vdb_name = st.text_input("이름", key="vdb_name") - vdb_type = st.selectbox( - "타입", options=["faiss", "pgvector"], key="vdb_type" - ) - vdb_loc_placeholder = ( - "FAISS 디렉토리 경로 (예: ./dev/table_info_db)" - if vdb_type == "faiss" - else "pgvector 연결 문자열 (postgresql://user:pass@host:port/db)" - ) - vdb_location = st.text_input( - "위치", key="vdb_location", placeholder=vdb_loc_placeholder - ) - vdb_prefix = st.text_input( - "컬렉션 접두사(선택)", key="vdb_prefix", placeholder="예: app1_" - ) - vdb_note = st.text_input("메모(선택)", key="vdb_note") - - cols = st.columns([1, 1, 2]) - with cols[0]: - if st.button("검증", key="vdb_validate_new"): - try: - update_vectordb_settings( - config, - vectordb_type=vdb_type, - vectordb_location=vdb_location, - ) - st.success("설정이 유효합니다.") - except Exception as e: - st.error(f"검증 실패: {e}") - with cols[1]: - if st.button("추가", key="vdb_add"): - try: - if not vdb_name or not vdb_type or not vdb_location: - st.warning("이름/타입/위치를 입력하세요.") - else: - add_vectordb_source( - name=vdb_name, - vtype=vdb_type, - location=vdb_location, - collection_prefix=(vdb_prefix or None), - note=(vdb_note or None), - ) - st.success("추가되었습니다.") - st.rerun() - except Exception as e: - st.error(f"추가 실패: {e}") diff --git a/interface/app_pages/settings_sections/db_section.py b/interface/app_pages/settings_sections/db_section.py deleted file mode 100644 index 2ebeec5..0000000 --- a/interface/app_pages/settings_sections/db_section.py +++ /dev/null @@ -1,300 +0,0 @@ -import os - -import streamlit as st - -from interface.core.config import ( - add_db_connection, - delete_db_connection, - get_db_connections_registry, - update_db_connection, - update_db_settings, -) -from utils.databases import DatabaseFactory -from utils.databases.factory import load_config_from_env - -DB_TYPES = [ - "postgresql", - "mysql", - "mariadb", - "oracle", - "clickhouse", - "duckdb", - "sqlite", - "databricks", - "snowflake", - "trino", -] - - -def _non_secret_fields(db_type: str) -> list[tuple[str, str]]: - t = (db_type or "").lower() - # label, key (values dict) - if t in {"duckdb", "sqlite"}: - return [("Path", "path")] - base = [ - ("Host", "host"), - ("Port", "port"), - ("User", "user"), - ("Database", "database"), - ] - return base - - -def _extra_non_secret_fields(db_type: str) -> list[tuple[str, str]]: - t = (db_type or "").lower() - if t == "oracle": - return [("Service Name", "service_name")] - if t == "databricks": - return [ - ("HTTP Path", "http_path"), - ("Catalog(옵션)", "catalog"), - ("Schema(옵션)", "schema"), - ] - if t == "snowflake": - return [ - ("Account", "account"), - ("Warehouse(옵션)", "warehouse"), - ("Schema(옵션)", "schema"), - ] - if t == "trino": - return [ - ("HTTP Scheme(http/https)", "http_scheme"), - ("Catalog", "catalog"), - ("Schema", "schema"), - ] - return [] - - -def _secret_fields(db_type: str) -> list[tuple[str, str]]: - t = (db_type or "").lower() - if t in {"postgresql", "mysql", "mariadb", "oracle", "clickhouse", "trino"}: - return [("Password", "password")] - if t == "databricks": - return [("Access Token", "access_token")] - if t == "snowflake": - return [("Password", "password")] - # duckdb/sqlite는 비밀번호 필요 없음 - return [] - - -def _prefill_from_env(db_type: str, key: str) -> str: - # Normalize to PREFIX_KEY - prefix = (db_type or "").upper() - if key == "path": - # duckdb/sqlite: PATH 로 통일 저장 - return os.getenv(f"{prefix}_PATH", "") - return os.getenv(f"{prefix}_{key.upper()}", "") - - -def render_db_section() -> None: - st.subheader("DB 연결") - - registry = get_db_connections_registry() - - # 목록 렌더링 - with st.container(border=True): - st.write("등록된 DB 프로파일") - for profile in list(registry.connections): - cols = st.columns([2, 2, 3, 2, 1, 1]) - with cols[0]: - st.text(profile.name) - with cols[1]: - st.text(profile.type) - with cols[2]: - host_or_path = profile.host or (profile.extra or {}).get("path") or "-" - st.caption(host_or_path) - with cols[3]: - st.caption(profile.note or "") - with cols[4]: - if st.button("편집", key=f"edit_db_{profile.name}"): - st.session_state["edit_db_name"] = profile.name - with cols[5]: - if st.button("삭제", type="secondary", key=f"del_db_{profile.name}"): - delete_db_connection(name=profile.name) - st.rerun() - - # 편집 폼 - edit_name = st.session_state.get("edit_db_name") - if edit_name: - st.divider() - st.write(f"DB 프로파일 편집: {edit_name}") - existing = next((c for c in registry.connections if c.name == edit_name), None) - if existing: - new_type = st.selectbox( - "타입", - options=DB_TYPES, - index=max( - 0, DB_TYPES.index(existing.type) if existing.type in DB_TYPES else 0 - ), - key="db_edit_type", - ) - - values: dict[str, object] = {} - # 기본 필드 - for label, k in _non_secret_fields(new_type): - default_val = ( - existing.extra.get("path") - if k == "path" and existing.extra - else getattr(existing, k, "") - ) - v = st.text_input( - label, value=str(default_val or ""), key=f"db_edit_val_{k}" - ) - if v != "": - values[k] = v - - # 추가 필드(Non-secret) - extra_vals: dict[str, str] = {} - for label, k in _extra_non_secret_fields(new_type): - default_val = (existing.extra or {}).get(k, "") - v = st.text_input( - label, value=str(default_val or ""), key=f"db_edit_extra_{k}" - ) - if v != "": - extra_vals[k] = v - if extra_vals: - values["extra"] = extra_vals - - # 시크릿 필드 (JSON 저장 허용) - secrets: dict[str, str] = {} - for label, k in _secret_fields(new_type): - # 기존 저장값 우선: existing.password or existing.extra - default_secret = "" - if k == "password": - default_secret = getattr( - existing, "password", None - ) or _prefill_from_env(new_type, k) - else: - default_secret = (existing.extra or {}).get( - k, "" - ) or _prefill_from_env(new_type, k) - sv = st.text_input( - label, - value=str(default_secret or ""), - type="password", - key=f"db_edit_secret_{k}", - ) - if sv != "": - secrets[k] = sv - - cols = st.columns([1, 1, 2]) - with cols[0]: - if st.button("적용(세션)", key="db_edit_apply"): - try: - update_db_settings( - db_type=new_type, values=values, secrets=secrets - ) - st.success("환경/세션에 적용되었습니다.") - except Exception as e: - st.error(f"적용 실패: {e}") - with cols[1]: - if st.button("저장", key="db_edit_save"): - try: - update_db_connection( - name=edit_name, - db_type=new_type, - host=str(values.get("host") or "") or None, - port=( - int(values.get("port")) - if str(values.get("port") or "").isdigit() - else None - ), - user=str(values.get("user") or "") or None, - password=(secrets.get("password") or None), - database=str(values.get("database") or "") or None, - extra=values.get("extra"), - note=existing.note, - ) - st.success("저장되었습니다.") - st.session_state.pop("edit_db_name", None) - st.rerun() - except Exception as e: - st.error(f"저장 실패: {e}") - with cols[2]: - if st.button("연결 테스트", key="db_edit_test"): - try: - # 먼저 적용하여 env에 반영 - update_db_settings( - db_type=new_type, values=values, secrets=secrets - ) - - connector = DatabaseFactory.get_connector(db_type=new_type) - # 간단한 SELECT 1 테스트 (DB마다 상이할 수 있음) - test_sql = ( - "SELECT 1" - if new_type not in {"oracle", "snowflake", "trino"} - else { - "oracle": "SELECT 1 FROM dual", - "snowflake": "SELECT 1", - "trino": "SELECT 1", - }[new_type] - ) - df = connector.run_sql(test_sql) - st.success(f"연결 성공. 결과 행 수: {len(df)}") - connector.close() - except Exception as e: - st.error(f"연결 테스트 실패: {e}") - - st.divider() - # 추가 폼 - st.write("DB 프로파일 추가") - name = st.text_input("이름", key="db_new_name") - db_type = st.selectbox("타입", options=DB_TYPES, key="db_new_type") - - values_new: dict[str, object] = {} - for label, k in _non_secret_fields(db_type): - v = st.text_input(label, key=f"db_new_val_{k}") - if v != "": - values_new[k] = v - - extra_new: dict[str, str] = {} - for label, k in _extra_non_secret_fields(db_type): - v = st.text_input(label, key=f"db_new_extra_{k}") - if v != "": - extra_new[k] = v - if extra_new: - values_new["extra"] = extra_new - - secrets_new: dict[str, str] = {} - for label, k in _secret_fields(db_type): - sv = st.text_input(label, key=f"db_new_secret_{k}") - if sv != "": - secrets_new[k] = sv - - cols2 = st.columns([1, 1, 2]) - with cols2[0]: - if st.button("검증", key="db_new_validate"): - try: - update_db_settings( - db_type=db_type, values=values_new, secrets=secrets_new - ) - # load back config for the type to ensure required fields presence - _ = load_config_from_env(db_type.upper()) - st.success("형식 검증 완료.") - except Exception as e: - st.error(f"검증 실패: {e}") - with cols2[1]: - if st.button("추가", key="db_new_add"): - try: - if not name: - st.warning("이름을 입력하세요.") - else: - add_db_connection( - name=name, - db_type=db_type, - host=str(values_new.get("host") or "") or None, - port=( - int(values_new.get("port")) - if str(values_new.get("port") or "").isdigit() - else None - ), - user=str(values_new.get("user") or "") or None, - password=(secrets_new.get("password") or None), - database=str(values_new.get("database") or "") or None, - extra=values_new.get("extra"), - note=None, - ) - st.success("추가되었습니다.") - st.rerun() - except Exception as e: - st.error(f"추가 실패: {e}") diff --git a/interface/app_pages/settings_sections/llm_section.py b/interface/app_pages/settings_sections/llm_section.py deleted file mode 100644 index 2473012..0000000 --- a/interface/app_pages/settings_sections/llm_section.py +++ /dev/null @@ -1,273 +0,0 @@ -import os -import streamlit as st - -from interface.core.config import ( - update_llm_settings, - update_embedding_settings, - Config, - load_config, - save_llm_profile, - get_llm_registry, - save_embedding_profile, - get_embedding_registry, -) - -LLM_PROVIDERS = [ - "openai", - "azure", - "bedrock", - "gemini", - "ollama", - "huggingface", -] - - -def _llm_fields(provider: str) -> list[tuple[str, str, bool]]: - """Return list of (label, env_key, is_secret) for LLM provider.""" - p = provider.lower() - if p == "openai": - return [ - ("Model", "OPEN_AI_LLM_MODEL", False), - ("API Key", "OPEN_AI_KEY", True), - ] - if p == "azure": - return [ - ("Endpoint", "AZURE_OPENAI_LLM_ENDPOINT", False), - ("Deployment(Model)", "AZURE_OPENAI_LLM_MODEL", False), - ("API Version", "AZURE_OPENAI_LLM_API_VERSION", False), - ("API Key", "AZURE_OPENAI_LLM_KEY", True), - ] - if p == "bedrock": - return [ - ("Model", "AWS_BEDROCK_LLM_MODEL", False), - ("Access Key ID", "AWS_BEDROCK_LLM_ACCESS_KEY_ID", True), - ("Secret Access Key", "AWS_BEDROCK_LLM_SECRET_ACCESS_KEY", True), - ("Region", "AWS_BEDROCK_LLM_REGION", False), - ] - if p == "gemini": - return [ - ("Model", "GEMINI_LLM_MODEL", False), - # ChatGoogleGenerativeAI uses GOOGLE_API_KEY at process level, but factory currently reads only model - ] - if p == "ollama": - return [ - ("Model", "OLLAMA_LLM_MODEL", False), - ("Base URL", "OLLAMA_LLM_BASE_URL", False), - ] - if p == "huggingface": - return [ - ("Endpoint URL", "HUGGING_FACE_LLM_ENDPOINT", False), - ("Repo ID", "HUGGING_FACE_LLM_REPO_ID", False), - ("Model", "HUGGING_FACE_LLM_MODEL", False), - ("API Token", "HUGGING_FACE_LLM_API_TOKEN", True), - ] - return [] - - -def _embedding_fields(provider: str) -> list[tuple[str, str, bool]]: - p = provider.lower() - if p == "openai": - return [ - ("Model", "OPEN_AI_EMBEDDING_MODEL", False), - ("API Key", "OPEN_AI_KEY", True), - ] - if p == "azure": - return [ - ("Endpoint", "AZURE_OPENAI_EMBEDDING_ENDPOINT", False), - ("Deployment(Model)", "AZURE_OPENAI_EMBEDDING_MODEL", False), - ("API Version", "AZURE_OPENAI_EMBEDDING_API_VERSION", False), - ("API Key", "AZURE_OPENAI_EMBEDDING_KEY", True), - ] - if p == "bedrock": - return [ - ("Model", "AWS_BEDROCK_EMBEDDING_MODEL", False), - ("Access Key ID", "AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID", True), - ("Secret Access Key", "AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY", True), - ("Region", "AWS_BEDROCK_EMBEDDING_REGION", False), - ] - if p == "gemini": - return [ - ("Model", "GEMINI_EMBEDDING_MODEL", False), - ("API Key", "GEMINI_EMBEDDING_KEY", True), - ] - if p == "ollama": - return [ - ("Model", "OLLAMA_EMBEDDING_MODEL", False), - ("Base URL", "OLLAMA_EMBEDDING_BASE_URL", False), - ] - if p == "huggingface": - return [ - ("Model", "HUGGING_FACE_EMBEDDING_MODEL", False), - ("Repo ID", "HUGGING_FACE_EMBEDDING_REPO_ID", False), - ("API Token", "HUGGING_FACE_EMBEDDING_API_TOKEN", True), - ] - return [] - - -def render_llm_section(config: Config | None = None) -> None: - st.subheader("LLM 설정") - - if config is None: - try: - config = load_config() - except Exception: - config = None # UI 일관성을 위한 옵셔널 처리 - - llm_col, emb_col = st.columns(2) - - with llm_col: - st.markdown("**Chat LLM**") - default_llm_provider = ( - ( - st.session_state.get("LLM_PROVIDER") - or os.getenv("LLM_PROVIDER") - or "openai" - ) - ).lower() - try: - default_llm_index = LLM_PROVIDERS.index(default_llm_provider) - except ValueError: - default_llm_index = 0 - provider = st.selectbox( - "공급자", - options=LLM_PROVIDERS, - index=default_llm_index, - key="llm_provider", - ) - fields = _llm_fields(provider) - values: dict[str, str | None] = {} - non_secret_values: dict[str, str | None] = {} - for label, env_key, is_secret in fields: - prefill = st.session_state.get(env_key) or os.getenv(env_key) or "" - if is_secret: - values[env_key] = st.text_input( - label, value=prefill, type="password", key=f"llm_{env_key}" - ) - else: - v = st.text_input(label, value=prefill, key=f"llm_{env_key}") - values[env_key] = v - non_secret_values[env_key] = v - - # 메시지 영역 - llm_msg = st.empty() - - st.markdown("**프로파일 저장 (비밀키 제외)**") - with st.form("llm_profile_save_form"): - prof_cols = st.columns([2, 2]) - with prof_cols[0]: - profile_name = st.text_input("프로파일 이름", key="llm_profile_name") - with prof_cols[1]: - profile_note = st.text_input("메모(선택)", key="llm_profile_note") - - submitted = st.form_submit_button("프로파일 저장") - if submitted: - try: - if not profile_name: - llm_msg.warning("프로파일 이름을 입력하세요.") - else: - # 1) 환경/세션에 즉시 적용 (저장된 모든 값 사용: 사용자 요청) - update_llm_settings(provider=provider, values=values) - # 2) 디스크에 프로파일 저장 (비밀키 포함) - save_llm_profile( - name=profile_name, - provider=provider, - values=values, - note=(profile_note or None), - ) - llm_msg.success("프로파일이 저장 및 적용되었습니다.") - except Exception as e: - llm_msg.error(f"프로파일 저장 실패: {e}") - - # 저장된 프로파일 미리보기 - reg = get_llm_registry() - if reg.profiles: - with st.expander("저장된 LLM 프로파일", expanded=False): - for p in reg.profiles: - if p.fields: - pairs = [ - f"{k}={p.fields.get(k, '')}" - for k in sorted(p.fields.keys()) - ] - fields_text = ", ".join(pairs) - else: - fields_text = "-" - note_text = f" | note: {p.note}" if getattr(p, "note", None) else "" - st.caption(f"- {p.name} ({p.provider}) | {fields_text}{note_text}") - - with emb_col: - st.markdown("**Embeddings**") - default_emb_provider = ( - ( - st.session_state.get("EMBEDDING_PROVIDER") - or os.getenv("EMBEDDING_PROVIDER") - or "openai" - ) - ).lower() - try: - default_emb_index = LLM_PROVIDERS.index(default_emb_provider) - except ValueError: - default_emb_index = 0 - e_provider = st.selectbox( - "공급자", - options=LLM_PROVIDERS, - index=default_emb_index, - key="embedding_provider", - ) - e_fields = _embedding_fields(e_provider) - e_values: dict[str, str | None] = {} - for label, env_key, is_secret in e_fields: - prefill = st.session_state.get(env_key) or os.getenv(env_key) or "" - if is_secret: - e_values[env_key] = st.text_input( - label, value=prefill, type="password", key=f"emb_{env_key}" - ) - else: - e_values[env_key] = st.text_input( - label, value=prefill, key=f"emb_{env_key}" - ) - - # 메시지 영역: 버튼 컬럼 밖(섹션 폭) - emb_msg = st.empty() - - st.markdown("**Embeddings 프로파일 저장 (시크릿 포함)**") - with st.form("embedding_profile_save_form"): - e_prof_cols = st.columns([2, 2]) - with e_prof_cols[0]: - e_profile_name = st.text_input( - "프로파일 이름", key="embedding_profile_name" - ) - with e_prof_cols[1]: - e_profile_note = st.text_input( - "메모(선택)", key="embedding_profile_note" - ) - - e_submitted = st.form_submit_button("프로파일 저장") - if e_submitted: - try: - if not e_profile_name: - emb_msg.warning("프로파일 이름을 입력하세요.") - else: - update_embedding_settings(provider=e_provider, values=e_values) - save_embedding_profile( - name=e_profile_name, - provider=e_provider, - values=e_values, - note=(e_profile_note or None), - ) - emb_msg.success("Embeddings 프로파일이 저장 및 적용되었습니다.") - except Exception as e: - emb_msg.error(f"프로파일 저장 실패: {e}") - e_reg = get_embedding_registry() - if e_reg.profiles: - with st.expander("저장된 Embeddings 프로파일", expanded=False): - for p in e_reg.profiles: - if p.fields: - pairs = [ - f"{k}={p.fields.get(k, '')}" - for k in sorted(p.fields.keys()) - ] - fields_text = ", ".join(pairs) - else: - fields_text = "-" - note_text = f" | note: {p.note}" if getattr(p, "note", None) else "" - st.caption(f"- {p.name} ({p.provider}) | {fields_text}{note_text}") diff --git a/interface/app_pages/sidebar_components/README.md b/interface/app_pages/sidebar_components/README.md deleted file mode 100644 index dab4f4e..0000000 --- a/interface/app_pages/sidebar_components/README.md +++ /dev/null @@ -1,297 +0,0 @@ -# sidebar_components - -사이드바 UI 컴포넌트 모듈. Streamlit 애플리케이션의 사이드바에서 사용되는 설정 선택 및 관리 컴포넌트들을 제공합니다. - -## 디렉토리 구조 - -``` -sidebar_components/ -├── __init__.py -├── chatbot_session_controller.py -├── data_source_selector.py -├── db_selector.py -├── embedding_selector.py -└── llm_selector.py -``` - -## 파일 설명 - -### `__init__.py` - -모든 사이드바 컴포넌트 함수들을 모듈에서 export합니다. - -**Export되는 함수:** -- `render_sidebar_data_source_selector`: 데이터 소스 선택기 렌더링 -- `render_sidebar_llm_selector`: LLM 선택기 렌더링 -- `render_sidebar_embedding_selector`: Embeddings 선택기 렌더링 -- `render_sidebar_db_selector`: DB 연결 선택기 렌더링 -- `render_sidebar_chatbot_session_controller`: ChatBot 세션 컨트롤러 렌더링 - -**사용 예시:** -```python -from interface.app_pages.sidebar_components import ( - render_sidebar_data_source_selector, - render_sidebar_llm_selector, - render_sidebar_embedding_selector, - render_sidebar_db_selector, - render_sidebar_chatbot_session_controller, -) -``` - ---- - -### `chatbot_session_controller.py` - -ChatBot 세션 관리 및 대화 기록 표시를 위한 사이드바 컴포넌트입니다. - -**주요 기능:** -- 세션 ID 자동 생성 및 관리 (`chatbot_thread_id`) -- 새 세션 시작 버튼 -- 대화 기록 표시 (JSON 형식) -- 최근 메시지 미리보기 (최근 3개) - -**함수:** -```python -def render_sidebar_chatbot_session_controller() -> str -``` - -**반환값:** -- `str`: 현재 thread_id - -**사용 예시:** -```python -from interface.app_pages.sidebar_components import render_sidebar_chatbot_session_controller - -thread_id = render_sidebar_chatbot_session_controller() -``` - -**의존성:** -- `streamlit`: UI 렌더링 -- `uuid`: 세션 ID 생성 -- `st.session_state`: 세션 상태 관리 - - `chatbot_thread_id`: 현재 세션 ID - - `chatbot_messages`: 대화 기록 리스트 - -**사용처:** -- `/home/dwlee/Lang2SQL/interface/app_pages/chatbot.py` (line 112) - ---- - -### `data_source_selector.py` - -데이터 소스 선택 컴포넌트입니다. DataHub 또는 VectorDB 중 하나를 선택하고 설정을 적용할 수 있습니다. - -**주요 기능:** -- DataHub/VectorDB 모드 선택 (라디오 버튼) -- DataHub 인스턴스 선택 및 적용 -- VectorDB 인스턴스 선택 및 적용 -- FAISS 경로 자동 적용 (DataHub 선택 시) - -**함수:** -```python -def render_sidebar_data_source_selector(config=None) -> None -``` - -**매개변수:** -- `config` (optional): 설정 객체. None인 경우 내부에서 `load_config()`로 로드합니다. - -**사용 예시:** -```python -from interface.app_pages.sidebar_components import render_sidebar_data_source_selector -from interface.core.config import load_config - -config = load_config() -render_sidebar_data_source_selector(config) -``` - -**의존성:** -- `streamlit`: UI 렌더링 -- `interface.core.config`: - - `load_config()`: 설정 로드 - - `get_data_sources_registry()`: 데이터 소스 레지스트리 조회 - - `update_datahub_server()`: DataHub 서버 설정 업데이트 - - `update_vectordb_settings()`: VectorDB 설정 업데이트 - - `update_data_source_mode()`: 데이터 소스 모드 업데이트 - -**사용처:** -- `/home/dwlee/Lang2SQL/interface/app_pages/chatbot.py` (line 99) -- `/home/dwlee/Lang2SQL/interface/app_pages/lang2sql.py` (line 50) - ---- - -### `db_selector.py` - -데이터베이스 연결 프로파일 선택 컴포넌트입니다. - -**주요 기능:** -- 등록된 DB 프로파일 목록 표시 -- 프로파일 선택 및 적용 -- 세션 또는 환경 변수의 DB_TYPE과 일치하는 프로파일 자동 선택 - -**함수:** -```python -def render_sidebar_db_selector() -> None -``` - -**사용 예시:** -```python -from interface.app_pages.sidebar_components import render_sidebar_db_selector - -render_sidebar_db_selector() -``` - -**의존성:** -- `streamlit`: UI 렌더링 -- `os`: 환경 변수 조회 -- `interface.core.config`: - - `get_db_connections_registry()`: DB 연결 레지스트리 조회 - - `update_db_settings()`: DB 설정 업데이트 -- `st.session_state.get("DB_TYPE")`: 세션의 DB 타입 확인 -- `os.getenv("DB_TYPE")`: 환경 변수에서 DB 타입 확인 - -**사용처:** -- `/home/dwlee/Lang2SQL/interface/app_pages/chatbot.py` (line 105) -- `/home/dwlee/Lang2SQL/interface/app_pages/lang2sql.py` (line 56) - ---- - -### `embedding_selector.py` - -Embeddings 프로파일 선택 컴포넌트입니다. - -**주요 기능:** -- 등록된 Embeddings 프로파일 목록 표시 -- 프로파일 선택 및 적용 -- 프로파일이 없는 경우 공급자 직접 선택 (fallback 모드) -- 지원 공급자: openai, azure, bedrock, gemini, ollama, huggingface - -**함수:** -```python -def render_sidebar_embedding_selector() -> None -``` - -**사용 예시:** -```python -from interface.app_pages.sidebar_components import render_sidebar_embedding_selector - -render_sidebar_embedding_selector() -``` - -**의존성:** -- `streamlit`: UI 렌더링 -- `os`: 환경 변수 조회 -- `interface.core.config`: - - `get_embedding_registry()`: Embeddings 레지스트리 조회 - - `update_embedding_settings()`: Embeddings 설정 업데이트 -- `st.session_state.get("EMBEDDING_PROVIDER")`: 세션의 Embeddings 공급자 확인 -- `os.getenv("EMBEDDING_PROVIDER")`: 환경 변수에서 Embeddings 공급자 확인 - -**사용처:** -- `/home/dwlee/Lang2SQL/interface/app_pages/chatbot.py` (line 103) -- `/home/dwlee/Lang2SQL/interface/app_pages/lang2sql.py` (line 54) - ---- - -### `llm_selector.py` - -LLM 프로파일 선택 컴포넌트입니다. - -**주요 기능:** -- 등록된 LLM 프로파일 목록 표시 -- 프로파일 선택 및 적용 -- 프로파일이 없는 경우 공급자 직접 선택 (fallback 모드) -- 지원 공급자: openai, azure, bedrock, gemini, ollama, huggingface - -**함수:** -```python -def render_sidebar_llm_selector() -> None -``` - -**사용 예시:** -```python -from interface.app_pages.sidebar_components import render_sidebar_llm_selector - -render_sidebar_llm_selector() -``` - -**의존성:** -- `streamlit`: UI 렌더링 -- `os`: 환경 변수 조회 -- `interface.core.config`: - - `get_llm_registry()`: LLM 레지스트리 조회 - - `update_llm_settings()`: LLM 설정 업데이트 -- `st.session_state.get("LLM_PROVIDER")`: 세션의 LLM 공급자 확인 -- `os.getenv("LLM_PROVIDER")`: 환경 변수에서 LLM 공급자 확인 - -**사용처:** -- `/home/dwlee/Lang2SQL/interface/app_pages/chatbot.py` (line 101) -- `/home/dwlee/Lang2SQL/interface/app_pages/lang2sql.py` (line 52) - ---- - -## 전체 사용 예시 - -### chatbot.py에서의 사용 - -```python -from interface.app_pages.sidebar_components import ( - render_sidebar_data_source_selector, - render_sidebar_llm_selector, - render_sidebar_embedding_selector, - render_sidebar_db_selector, - render_sidebar_chatbot_session_controller, -) -from interface.core.config import load_config - -config = load_config() - -# 사이드바 UI 구성 -render_sidebar_data_source_selector(config) -st.sidebar.divider() -render_sidebar_llm_selector() -st.sidebar.divider() -render_sidebar_embedding_selector() -st.sidebar.divider() -render_sidebar_db_selector() -st.sidebar.divider() - -# ChatBot 전용 설정 -with st.sidebar: - st.markdown("### 🤖 ChatBot 설정") - st.divider() - thread_id = render_sidebar_chatbot_session_controller() -``` - -### lang2sql.py에서의 사용 - -```python -from interface.app_pages.sidebar_components import ( - render_sidebar_data_source_selector, - render_sidebar_llm_selector, - render_sidebar_embedding_selector, - render_sidebar_db_selector, -) -from interface.core.config import load_config - -config = load_config() - -render_sidebar_data_source_selector(config) -st.sidebar.divider() -render_sidebar_llm_selector() -st.sidebar.divider() -render_sidebar_embedding_selector() -st.sidebar.divider() -render_sidebar_db_selector() -st.sidebar.divider() -``` - -## 공통 패턴 - -모든 컴포넌트는 다음과 같은 공통 패턴을 따릅니다: - -1. **레지스트리 기반**: 각 컴포넌트는 해당 설정의 레지스트리(`get_*_registry()`)에서 프로파일/인스턴스 목록을 가져옵니다. -2. **Fallback 지원**: 프로파일이 없는 경우 기본 공급자 선택 옵션을 제공합니다. -3. **세션/환경 변수 통합**: 현재 세션 상태 또는 환경 변수와 일치하는 항목을 자동으로 선택합니다. -4. **설정 업데이트**: 선택한 항목을 적용하면 `update_*_settings()` 함수를 통해 설정을 업데이트합니다. -5. **에러 처리**: 적용 실패 시 `st.sidebar.error()`로 에러 메시지를 표시합니다. - diff --git a/interface/app_pages/sidebar_components/__init__.py b/interface/app_pages/sidebar_components/__init__.py deleted file mode 100644 index 47026dc..0000000 --- a/interface/app_pages/sidebar_components/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .data_source_selector import render_sidebar_data_source_selector -from .llm_selector import render_sidebar_llm_selector -from .embedding_selector import render_sidebar_embedding_selector -from .db_selector import render_sidebar_db_selector -from .chatbot_session_controller import render_sidebar_chatbot_session_controller - -__all__ = [ - "render_sidebar_data_source_selector", - "render_sidebar_llm_selector", - "render_sidebar_embedding_selector", - "render_sidebar_db_selector", - "render_sidebar_chatbot_session_controller", -] diff --git a/interface/app_pages/sidebar_components/chatbot_session_controller.py b/interface/app_pages/sidebar_components/chatbot_session_controller.py deleted file mode 100644 index f4e774a..0000000 --- a/interface/app_pages/sidebar_components/chatbot_session_controller.py +++ /dev/null @@ -1,63 +0,0 @@ -"""ChatBot 세션 제어를 위한 사이드바 컴포넌트""" - -import streamlit as st -import uuid - - -def render_sidebar_chatbot_session_controller() -> str: - """ChatBot 세션 관리 및 대화 기록 표시 (사이드바 전용) - - Returns: - str: 현재 thread_id - """ - # 세션 ID 자동 생성 (처음 방문 시에만) - if "chatbot_thread_id" not in st.session_state: - st.session_state.chatbot_thread_id = str(uuid.uuid4())[:8] # 8자리 짧은 ID - - thread_id = st.session_state.chatbot_thread_id - - # 세션 관리 섹션 - st.markdown("### 📋 세션 관리") - - # 세션 정보 표시 - st.markdown(f"**현재 세션:** `{thread_id}`") - st.caption("대화 기록을 구분하는 고유 ID입니다.") - - # 새 세션 시작 버튼 - if st.button( - "🔄 새 세션 시작", - use_container_width=True, - help="새로운 대화 세션을 시작합니다.", - ): - st.session_state.chatbot_thread_id = str(uuid.uuid4())[:8] - st.session_state.chatbot_messages = [] - st.rerun() - - # 대화 기록 섹션 - if st.session_state.get("chatbot_messages"): - st.divider() - st.markdown("### 💬 대화 기록") - - # 메시지 개수 표시 - message_count = len(st.session_state.chatbot_messages) - st.caption(f"총 {message_count}개의 메시지") - - # 대화 기록 표시 (접힌 상태) - with st.expander("📄 전체 기록 보기 (JSON)", expanded=False): - st.json(st.session_state.chatbot_messages) - - # 최근 메시지 미리보기 - if message_count > 0: - with st.expander("👀 최근 메시지 미리보기", expanded=False): - recent_messages = st.session_state.chatbot_messages[-3:] # 최근 3개 - for msg in recent_messages: - role_icon = "👤" if msg["role"] == "user" else "🤖" - role_text = "사용자" if msg["role"] == "user" else "AI" - content_preview = ( - msg["content"][:50] + "..." - if len(msg["content"]) > 50 - else msg["content"] - ) - st.caption(f"{role_icon} {role_text}: {content_preview}") - - return thread_id diff --git a/interface/app_pages/sidebar_components/data_source_selector.py b/interface/app_pages/sidebar_components/data_source_selector.py deleted file mode 100644 index b32cf3f..0000000 --- a/interface/app_pages/sidebar_components/data_source_selector.py +++ /dev/null @@ -1,80 +0,0 @@ -import streamlit as st - -from interface.core.config import ( - load_config, - get_data_sources_registry, - update_datahub_server, - update_vectordb_settings, - update_data_source_mode, -) - - -def render_sidebar_data_source_selector(config=None) -> None: - if config is None: - config = load_config() - - registry = get_data_sources_registry() - - st.sidebar.markdown("### 데이터 소스") - - mode_index = 0 if (config.data_source_mode or "datahub").lower() == "datahub" else 1 - selected_mode = st.sidebar.radio( - "소스 종류", options=["DataHub", "VectorDB"], index=mode_index, horizontal=True - ) - - if selected_mode == "DataHub": - datahub_names = [s.name for s in registry.datahub] - if not datahub_names: - st.sidebar.warning( - "등록된 DataHub가 없습니다. 설정 > 데이터 소스에서 추가하세요." - ) - return - dh_name = st.sidebar.selectbox( - "DataHub 인스턴스", options=datahub_names, key="sidebar_dh_select" - ) - if st.sidebar.button("소스 적용", key="sidebar_apply_dh"): - selected = next((s for s in registry.datahub if s.name == dh_name), None) - if selected is None: - st.sidebar.error("선택한 DataHub를 찾을 수 없습니다.") - return - try: - update_datahub_server(config, selected.url) - # DataHub 선택 시, FAISS 경로가 정의되어 있으면 기본 VectorDB 로케이션으로도 반영 - if selected.faiss_path: - try: - update_vectordb_settings( - config, - vectordb_type="faiss", - vectordb_location=selected.faiss_path, - ) - except Exception as e: - st.sidebar.warning(f"FAISS 경로 적용 경고: {e}") - update_data_source_mode(config, "datahub") - st.sidebar.success(f"DataHub 적용됨: {selected.name}") - except Exception as e: - st.sidebar.error(f"적용 실패: {e}") - else: - vdb_names = [s.name for s in registry.vectordb] - if not vdb_names: - st.sidebar.warning( - "등록된 VectorDB가 없습니다. 설정 > 데이터 소스에서 추가하세요." - ) - return - vdb_name = st.sidebar.selectbox( - "VectorDB 인스턴스", options=vdb_names, key="sidebar_vdb_select" - ) - if st.sidebar.button("소스 적용", key="sidebar_apply_vdb"): - selected = next((s for s in registry.vectordb if s.name == vdb_name), None) - if selected is None: - st.sidebar.error("선택한 VectorDB를 찾을 수 없습니다.") - return - try: - update_vectordb_settings( - config, - vectordb_type=selected.type, - vectordb_location=selected.location, - ) - update_data_source_mode(config, "vectordb") - st.sidebar.success(f"VectorDB 적용됨: {selected.name}") - except Exception as e: - st.sidebar.error(f"적용 실패: {e}") diff --git a/interface/app_pages/sidebar_components/db_selector.py b/interface/app_pages/sidebar_components/db_selector.py deleted file mode 100644 index 4f9bff0..0000000 --- a/interface/app_pages/sidebar_components/db_selector.py +++ /dev/null @@ -1,48 +0,0 @@ -import os -import streamlit as st - -from interface.core.config import get_db_connections_registry, update_db_settings - - -def render_sidebar_db_selector() -> None: - st.sidebar.markdown("### DB 연결") - - registry = get_db_connections_registry() - names = [c.name for c in registry.connections] - if not names: - st.sidebar.warning("등록된 DB 프로파일이 없습니다. 설정 > DB에서 추가하세요.") - return - - # 기본 선택: 세션 또는 ENV의 DB_TYPE과 일치하는 첫 프로파일 - current_type = ( - st.session_state.get("DB_TYPE") or os.getenv("DB_TYPE") or "" - ).lower() - default_index = 0 - if current_type: - for idx, c in enumerate(registry.connections): - if c.type == current_type: - default_index = idx - break - - sel_name = st.sidebar.selectbox( - "프로파일", options=names, index=default_index, key="sidebar_db_profile" - ) - selected = next((c for c in registry.connections if c.name == sel_name), None) - if selected is None: - st.sidebar.error("선택한 프로파일을 찾을 수 없습니다.") - return - - if st.sidebar.button("적용", key="sidebar_apply_db"): - try: - values = { - "host": selected.host, - "port": selected.port, - "user": selected.user, - "password": selected.password, - "database": selected.database, - "extra": selected.extra, - } - update_db_settings(db_type=selected.type, values=values, secrets={}) - st.sidebar.success(f"DB 적용됨: {selected.name}") - except Exception as e: - st.sidebar.error(f"적용 실패: {e}") diff --git a/interface/app_pages/sidebar_components/embedding_selector.py b/interface/app_pages/sidebar_components/embedding_selector.py deleted file mode 100644 index c01f50c..0000000 --- a/interface/app_pages/sidebar_components/embedding_selector.py +++ /dev/null @@ -1,81 +0,0 @@ -import os -import streamlit as st - -from interface.core.config import ( - update_embedding_settings, - get_embedding_registry, -) - - -def render_sidebar_embedding_selector() -> None: - st.sidebar.markdown("### Embeddings 선택") - - e_reg = get_embedding_registry() - if not e_reg.profiles: - st.sidebar.info( - "저장된 Embeddings 프로파일이 없습니다. 설정 > LLM에서 저장하세요." - ) - # fallback: 간단 공급자 선택 유지 - default_emb = ( - ( - st.session_state.get("EMBEDDING_PROVIDER") - or os.getenv("EMBEDDING_PROVIDER") - or "openai" - ) - ).lower() - selected = st.sidebar.selectbox( - "Embeddings 공급자", - options=["openai", "azure", "bedrock", "gemini", "ollama", "huggingface"], - index=( - ["openai", "azure", "bedrock", "gemini", "ollama", "huggingface"].index( - default_emb - ) - if default_emb - in {"openai", "azure", "bedrock", "gemini", "ollama", "huggingface"} - else 0 - ), - key="sidebar_embedding_provider_fallback", - ) - if selected != default_emb: - try: - update_embedding_settings(provider=selected, values={}) - st.sidebar.success( - f"Embeddings 공급자가 '{selected}'로 변경되었습니다." - ) - except Exception as e: - st.sidebar.error(f"Embeddings 공급자 변경 실패: {e}") - return - - e_names = [p.name for p in e_reg.profiles] - current_emb_provider = ( - st.session_state.get("EMBEDDING_PROVIDER") - or os.getenv("EMBEDDING_PROVIDER") - or "" - ).lower() - e_default_index = 0 - if current_emb_provider: - for idx, p in enumerate(e_reg.profiles): - if p.provider == current_emb_provider: - e_default_index = idx - break - - e_sel_name = st.sidebar.selectbox( - "Embeddings 프로파일", - options=e_names, - index=e_default_index, - key="sidebar_embedding_profile", - ) - - e_selected = next((p for p in e_reg.profiles if p.name == e_sel_name), None) - if e_selected is None: - st.sidebar.error("선택한 Embeddings 프로파일을 찾을 수 없습니다.") - return - - if st.sidebar.button("Embeddings 적용", key="sidebar_apply_embedding_profile"): - try: - update_embedding_settings( - provider=e_selected.provider, values=e_selected.fields - ) - st.sidebar.success(f"Embeddings 프로파일 적용됨: {e_selected.name}") - except Exception as e: - st.sidebar.error(f"Embeddings 프로파일 적용 실패: {e}") diff --git a/interface/app_pages/sidebar_components/llm_selector.py b/interface/app_pages/sidebar_components/llm_selector.py deleted file mode 100644 index b8260e0..0000000 --- a/interface/app_pages/sidebar_components/llm_selector.py +++ /dev/null @@ -1,77 +0,0 @@ -import os -import streamlit as st - -from interface.core.config import ( - update_llm_settings, - get_llm_registry, -) - - -def render_sidebar_llm_selector() -> None: - st.sidebar.markdown("### LLM 선택") - - reg = get_llm_registry() - if not reg.profiles: - st.sidebar.info( - "저장된 LLM 프로파일이 없습니다. 설정 > LLM에서 프로파일을 저장하세요." - ) - # 기존 방식 fallback - default_llm = ( - ( - st.session_state.get("LLM_PROVIDER") - or os.getenv("LLM_PROVIDER") - or "openai" - ) - ).lower() - selected_provider = st.sidebar.selectbox( - "LLM 공급자", - options=["openai", "azure", "bedrock", "gemini", "ollama", "huggingface"], - index=( - ["openai", "azure", "bedrock", "gemini", "ollama", "huggingface"].index( - default_llm - ) - if default_llm - in {"openai", "azure", "bedrock", "gemini", "ollama", "huggingface"} - else 0 - ), - key="sidebar_llm_provider_fallback", - ) - if selected_provider != default_llm: - try: - update_llm_settings(provider=selected_provider, values={}) - st.sidebar.success( - f"LLM 공급자가 '{selected_provider}'로 변경되었습니다." - ) - except Exception as e: - st.sidebar.error(f"LLM 공급자 변경 실패: {e}") - return - - names = [p.name for p in reg.profiles] - # 기본 선택: 세션의 LLM_PROVIDER와 같은 provider를 가진 첫 프로파일 - current_provider = ( - st.session_state.get("LLM_PROVIDER") or os.getenv("LLM_PROVIDER") or "" - ).lower() - default_index = 0 - if current_provider: - for idx, p in enumerate(reg.profiles): - if p.provider == current_provider: - default_index = idx - break - - sel_name = st.sidebar.selectbox( - "LLM 프로파일", options=names, index=default_index, key="sidebar_llm_profile" - ) - selected = next((p for p in reg.profiles if p.name == sel_name), None) - if selected is None: - st.sidebar.error("선택한 LLM 프로파일을 찾을 수 없습니다.") - return - - if st.sidebar.button("적용", key="sidebar_apply_llm_profile"): - try: - # provider 설정 + 프로파일의 비민감 필드만 적용 - update_llm_settings(provider=selected.provider, values=selected.fields) - st.sidebar.success(f"LLM 프로파일 적용됨: {selected.name}") - except Exception as e: - st.sidebar.error(f"LLM 프로파일 적용 실패: {e}") - - # Embeddings 관련 UI는 embedding_selector.py에서 처리 diff --git a/interface/core/config/__init__.py b/interface/core/config/__init__.py deleted file mode 100644 index e316c17..0000000 --- a/interface/core/config/__init__.py +++ /dev/null @@ -1,123 +0,0 @@ -"""config 패키지의 공개 API를 재노출하여 기존 import 호환성을 유지합니다. -모델, 경로/지속성, 레지스트리, 설정 업데이트 유틸을 한 곳에서 제공합니다. -""" - -from .models import ( - Config, - DataHubSource, - VectorDBSource, - DataSourcesRegistry, - DBConnectionProfile, - DBConnectionsRegistry, - LLMProfile, - LLMRegistry, - EmbeddingProfile, - EmbeddingRegistry, -) - -from .settings import ( - load_config, - update_datahub_server, - update_data_source_mode, - update_vectordb_settings, - update_llm_settings, - update_embedding_settings, - update_db_settings, -) - -from .registry_data_sources import ( - get_data_sources_registry, - add_datahub_source, - update_datahub_source, - delete_datahub_source, - add_vectordb_source, - update_vectordb_source, - delete_vectordb_source, -) - -from .registry_db import ( - get_db_connections_registry, - add_db_connection, - update_db_connection, - delete_db_connection, -) - -from .registry_llm import ( - get_llm_registry, - save_llm_profile, - get_embedding_registry, - save_embedding_profile, -) - -from .paths import ( - get_registry_file_path, - get_db_registry_file_path, - get_llm_registry_file_path, - get_embedding_registry_file_path, - ensure_parent_dir, -) - -from .persist import ( - save_registry_to_disk, - save_db_registry_to_disk, - save_llm_registry_to_disk, - save_embedding_registry_to_disk, - load_registry_from_disk, - load_db_registry_from_disk, - load_llm_registry_from_disk, - load_embedding_registry_from_disk, -) - -__all__ = [ - # Models - "Config", - "DataHubSource", - "VectorDBSource", - "DataSourcesRegistry", - "DBConnectionProfile", - "DBConnectionsRegistry", - "LLMProfile", - "LLMRegistry", - "EmbeddingProfile", - "EmbeddingRegistry", - # Settings APIs - "load_config", - "update_datahub_server", - "update_data_source_mode", - "update_vectordb_settings", - "update_llm_settings", - "update_embedding_settings", - "update_db_settings", - # Registries - data sources - "get_data_sources_registry", - "add_datahub_source", - "update_datahub_source", - "delete_datahub_source", - "add_vectordb_source", - "update_vectordb_source", - "delete_vectordb_source", - # Registries - db connections - "get_db_connections_registry", - "add_db_connection", - "update_db_connection", - "delete_db_connection", - # Registries - llm/embedding - "get_llm_registry", - "save_llm_profile", - "get_embedding_registry", - "save_embedding_profile", - # Persistence helpers and paths (for backward compatibility) - "get_registry_file_path", - "get_db_registry_file_path", - "get_llm_registry_file_path", - "get_embedding_registry_file_path", - "ensure_parent_dir", - "save_registry_to_disk", - "save_db_registry_to_disk", - "save_llm_registry_to_disk", - "save_embedding_registry_to_disk", - "load_registry_from_disk", - "load_db_registry_from_disk", - "load_llm_registry_from_disk", - "load_embedding_registry_from_disk", -] diff --git a/interface/core/config/models.py b/interface/core/config/models.py deleted file mode 100644 index 9ec02af..0000000 --- a/interface/core/config/models.py +++ /dev/null @@ -1,83 +0,0 @@ -"""설정 및 각 레지스트리에서 사용하는 데이터 모델(dataclass) 정의 모듈입니다.""" - -from dataclasses import dataclass, field -from typing import List, Optional, Any, Dict - - -@dataclass -class Config: - datahub_server: str - vectordb_type: str - vectordb_location: str - data_source_mode: str | None = None # "datahub" | "vectordb" | None - - -@dataclass -class DataHubSource: - name: str - url: str - faiss_path: Optional[str] = None - note: Optional[str] = None - - -@dataclass -class VectorDBSource: - name: str - type: str # 'faiss' | 'pgvector' - location: str - collection_prefix: Optional[str] = None - note: Optional[str] = None - - -@dataclass -class DataSourcesRegistry: - datahub: List[DataHubSource] = field(default_factory=list) - vectordb: List[VectorDBSource] = field(default_factory=list) - - -@dataclass -class DBConnectionProfile: - name: str - type: str # 'postgresql' | 'mysql' | 'mariadb' | 'oracle' | 'clickhouse' | 'duckdb' | 'sqlite' | 'databricks' | 'snowflake' | 'trino' - host: Optional[str] = None - port: Optional[int] = None - user: Optional[str] = None - password: Optional[str] = None - database: Optional[str] = None - extra: Optional[Dict[str, Any]] = None # non-secret - note: Optional[str] = None - - -@dataclass -class DBConnectionsRegistry: - connections: List[DBConnectionProfile] = field(default_factory=list) - - -@dataclass -class LLMProfile: - name: str - provider: ( - str # 'openai' | 'azure' | 'bedrock' | 'gemini' | 'ollama' | 'huggingface' - ) - fields: Dict[str, str] = field(default_factory=dict) # includes secrets - note: Optional[str] = None - - -@dataclass -class LLMRegistry: - profiles: List[LLMProfile] = field(default_factory=list) - - -@dataclass -class EmbeddingProfile: - name: str - provider: ( - str # 'openai' | 'azure' | 'bedrock' | 'gemini' | 'ollama' | 'huggingface' - ) - fields: Dict[str, str] = field(default_factory=dict) - note: Optional[str] = None - - -@dataclass -class EmbeddingRegistry: - profiles: List[EmbeddingProfile] = field(default_factory=list) diff --git a/interface/core/config/paths.py b/interface/core/config/paths.py deleted file mode 100644 index 668f052..0000000 --- a/interface/core/config/paths.py +++ /dev/null @@ -1,38 +0,0 @@ -"""레지스트리 파일 경로 계산 및 상위 디렉토리 생성 유틸리티를 제공합니다.""" - -import os -from pathlib import Path - - -def get_registry_file_path() -> Path: - # Allow override via env var, else default to ./config/data_sources.json - override = os.getenv("LANG2SQL_REGISTRY_PATH") - if override: - return Path(override).expanduser().resolve() - return Path(os.getcwd()) / "config" / "data_sources.json" - - -def get_db_registry_file_path() -> Path: - # Allow override via env var, else default to ./config/db_connections.json - override = os.getenv("LANG2SQL_DB_REGISTRY_PATH") - if override: - return Path(override).expanduser().resolve() - return Path(os.getcwd()) / "config" / "db_connections.json" - - -def get_llm_registry_file_path() -> Path: - override = os.getenv("LANG2SQL_LLM_REGISTRY_PATH") - if override: - return Path(override).expanduser().resolve() - return Path(os.getcwd()) / "config" / "llm_profiles.json" - - -def get_embedding_registry_file_path() -> Path: - override = os.getenv("LANG2SQL_EMBEDDING_REGISTRY_PATH") - if override: - return Path(override).expanduser().resolve() - return Path(os.getcwd()) / "config" / "embedding_profiles.json" - - -def ensure_parent_dir(path: Path) -> None: - path.parent.mkdir(parents=True, exist_ok=True) diff --git a/interface/core/config/persist.py b/interface/core/config/persist.py deleted file mode 100644 index 81ba144..0000000 --- a/interface/core/config/persist.py +++ /dev/null @@ -1,205 +0,0 @@ -"""레지스트리 직렬화/역직렬화와 디스크 저장/로드 로직을 제공합니다.""" - -import json -from dataclasses import asdict -from pathlib import Path -from typing import Any, Dict, List - -from .models import ( - DataSourcesRegistry, - DataHubSource, - VectorDBSource, - DBConnectionsRegistry, - DBConnectionProfile, - LLMRegistry, - LLMProfile, - EmbeddingRegistry, - EmbeddingProfile, -) -from .paths import ( - get_registry_file_path, - get_db_registry_file_path, - get_llm_registry_file_path, - get_embedding_registry_file_path, - ensure_parent_dir, -) - - -def save_registry_to_disk(registry: DataSourcesRegistry) -> None: - path = get_registry_file_path() - ensure_parent_dir(path) - payload = asdict(registry) - with path.open("w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False, indent=2) - - -def save_db_registry_to_disk(registry: DBConnectionsRegistry) -> None: - path = get_db_registry_file_path() - ensure_parent_dir(path) - payload = asdict(registry) - with path.open("w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False, indent=2) - - -def save_llm_registry_to_disk(registry: LLMRegistry) -> None: - path = get_llm_registry_file_path() - ensure_parent_dir(path) - payload = asdict(registry) - with path.open("w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False, indent=2) - - -def save_embedding_registry_to_disk(registry: EmbeddingRegistry) -> None: - path = get_embedding_registry_file_path() - ensure_parent_dir(path) - payload = asdict(registry) - with path.open("w", encoding="utf-8") as f: - json.dump(payload, f, ensure_ascii=False, indent=2) - - -def _parse_datahub_list(items: List[Dict[str, Any]]) -> List[DataHubSource]: - parsed: List[DataHubSource] = [] - for item in items or []: - name = str(item.get("name", "")).strip() - url = str(item.get("url", "")).strip() - faiss_path = item.get("faiss_path") - note = item.get("note") - if not name or not url: - continue - parsed.append( - DataHubSource(name=name, url=url, faiss_path=faiss_path, note=note) - ) - return parsed - - -def _parse_vectordb_list(items: List[Dict[str, Any]]) -> List[VectorDBSource]: - parsed: List[VectorDBSource] = [] - for item in items or []: - name = str(item.get("name", "")).strip() - vtype = str(item.get("type", "")).strip().lower() - location = str(item.get("location", "")).strip() - if not name or not vtype or not location: - continue - collection_prefix = item.get("collection_prefix") - note = item.get("note") - parsed.append( - VectorDBSource( - name=name, - type=vtype, - location=location, - collection_prefix=collection_prefix, - note=note, - ) - ) - return parsed - - -def load_registry_from_disk() -> DataSourcesRegistry: - path = get_registry_file_path() - if not path.exists(): - return DataSourcesRegistry() - with path.open("r", encoding="utf-8") as f: - data: Dict[str, Any] = json.load(f) - return DataSourcesRegistry( - datahub=_parse_datahub_list(data.get("datahub", [])), - vectordb=_parse_vectordb_list(data.get("vectordb", [])), - ) - - -def _parse_db_conn_list(items: List[Dict[str, Any]]) -> List[DBConnectionProfile]: - parsed: List[DBConnectionProfile] = [] - for item in items or []: - name = str(item.get("name", "")).strip() - db_type = str(item.get("type", "")).strip().lower() - if not name or not db_type: - continue - host = item.get("host") - port = item.get("port") - try: - port = int(port) if port is not None else None - except Exception: - port = None - user = item.get("user") - password = item.get("password") - database = item.get("database") - extra = item.get("extra") or None - note = item.get("note") or None - parsed.append( - DBConnectionProfile( - name=name, - type=db_type, - host=host, - port=port, - user=user, - password=password, - database=database, - extra=extra, - note=note, - ) - ) - return parsed - - -def load_db_registry_from_disk() -> DBConnectionsRegistry: - path = get_db_registry_file_path() - if not path.exists(): - return DBConnectionsRegistry() - with path.open("r", encoding="utf-8") as f: - data: Dict[str, Any] = json.load(f) - return DBConnectionsRegistry( - connections=_parse_db_conn_list(data.get("connections", [])) - ) - - -def _parse_llm_profiles(items: List[Dict[str, Any]]) -> List[LLMProfile]: - parsed: List[LLMProfile] = [] - for item in items or []: - name = str(item.get("name", "")).strip() - provider = str(item.get("provider", "")).strip().lower() - if not name or not provider: - continue - fields = item.get("fields") or {} - note = item.get("note") or None - if not isinstance(fields, dict): - fields = {} - parsed.append( - LLMProfile(name=name, provider=provider, fields=fields, note=note) - ) - return parsed - - -def load_llm_registry_from_disk() -> LLMRegistry: - path = get_llm_registry_file_path() - if not path.exists(): - return LLMRegistry() - with path.open("r", encoding="utf-8") as f: - data: Dict[str, Any] = json.load(f) - return LLMRegistry(profiles=_parse_llm_profiles(data.get("profiles", []))) - - -def _parse_embedding_profiles(items: List[Dict[str, Any]]) -> List[EmbeddingProfile]: - parsed: List[EmbeddingProfile] = [] - for item in items or []: - name = str(item.get("name", "")).strip() - provider = str(item.get("provider", "")).strip().lower() - if not name or not provider: - continue - fields = item.get("fields") or {} - note = item.get("note") or None - if not isinstance(fields, dict): - fields = {} - parsed.append( - EmbeddingProfile(name=name, provider=provider, fields=fields, note=note) - ) - return parsed - - -def load_embedding_registry_from_disk() -> EmbeddingRegistry: - path = get_embedding_registry_file_path() - if not path.exists(): - return EmbeddingRegistry() - with path.open("r", encoding="utf-8") as f: - data: Dict[str, Any] = json.load(f) - return EmbeddingRegistry( - profiles=_parse_embedding_profiles(data.get("profiles", [])) - ) diff --git a/interface/core/config/registry_data_sources.py b/interface/core/config/registry_data_sources.py deleted file mode 100644 index 8e9b646..0000000 --- a/interface/core/config/registry_data_sources.py +++ /dev/null @@ -1,130 +0,0 @@ -"""DataHub/VectorDB 소스 레지스트리를 세션+디스크에 관리하는 모듈입니다. -get/add/update/delete 연산과 Streamlit 세션 연동을 제공합니다. -""" - -from typing import Optional - -try: - import streamlit as st # type: ignore -except Exception: # pragma: no cover - st = None # type: ignore - -from .models import DataSourcesRegistry, DataHubSource, VectorDBSource -from .persist import ( - load_registry_from_disk, - save_registry_to_disk, -) - - -def get_data_sources_registry() -> DataSourcesRegistry: - if st is not None and "data_sources_registry" in st.session_state: - reg = st.session_state["data_sources_registry"] - return reg # stored as DataSourcesRegistry - # Try load from disk - try: - registry = load_registry_from_disk() - except Exception: - registry = DataSourcesRegistry() - if st is not None: - st.session_state["data_sources_registry"] = registry - return registry - - -def _save_registry(registry: DataSourcesRegistry) -> None: - if st is not None: - st.session_state["data_sources_registry"] = registry - try: - save_registry_to_disk(registry) - except Exception: - # fail-soft; UI will still have session copy - pass - - -def add_datahub_source( - *, name: str, url: str, faiss_path: Optional[str] = None, note: Optional[str] = None -) -> None: - registry = get_data_sources_registry() - if any(s.name == name for s in registry.datahub): - raise ValueError(f"이미 존재하는 DataHub 이름입니다: {name}") - registry.datahub.append( - DataHubSource(name=name, url=url, faiss_path=faiss_path, note=note) - ) - _save_registry(registry) - - -def update_datahub_source( - *, name: str, url: str, faiss_path: Optional[str], note: Optional[str] -) -> None: - registry = get_data_sources_registry() - for idx, s in enumerate(registry.datahub): - if s.name == name: - registry.datahub[idx] = DataHubSource( - name=name, url=url, faiss_path=faiss_path, note=note - ) - _save_registry(registry) - return - raise ValueError(f"존재하지 않는 DataHub 이름입니다: {name}") - - -def delete_datahub_source(*, name: str) -> None: - registry = get_data_sources_registry() - registry.datahub = [s for s in registry.datahub if s.name != name] - _save_registry(registry) - - -def add_vectordb_source( - *, - name: str, - vtype: str, - location: str, - collection_prefix: Optional[str] = None, - note: Optional[str] = None, -) -> None: - vtype = (vtype or "").lower() - if vtype not in ("faiss", "pgvector"): - raise ValueError("VectorDB 타입은 'faiss' 또는 'pgvector'여야 합니다") - registry = get_data_sources_registry() - if any(s.name == name for s in registry.vectordb): - raise ValueError(f"이미 존재하는 VectorDB 이름입니다: {name}") - registry.vectordb.append( - VectorDBSource( - name=name, - type=vtype, - location=location, - collection_prefix=collection_prefix, - note=note, - ) - ) - _save_registry(registry) - - -def update_vectordb_source( - *, - name: str, - vtype: str, - location: str, - collection_prefix: Optional[str], - note: Optional[str], -) -> None: - vtype = (vtype or "").lower() - if vtype not in ("faiss", "pgvector"): - raise ValueError("VectorDB 타입은 'faiss' 또는 'pgvector'여야 합니다") - registry = get_data_sources_registry() - for idx, s in enumerate(registry.vectordb): - if s.name == name: - registry.vectordb[idx] = VectorDBSource( - name=name, - type=vtype, - location=location, - collection_prefix=collection_prefix, - note=note, - ) - _save_registry(registry) - return - raise ValueError(f"존재하지 않는 VectorDB 이름입니다: {name}") - - -def delete_vectordb_source(*, name: str) -> None: - registry = get_data_sources_registry() - registry.vectordb = [s for s in registry.vectordb if s.name != name] - _save_registry(registry) diff --git a/interface/core/config/registry_db.py b/interface/core/config/registry_db.py deleted file mode 100644 index b58d5e7..0000000 --- a/interface/core/config/registry_db.py +++ /dev/null @@ -1,106 +0,0 @@ -"""DB 연결 프로파일 레지스트리를 세션+디스크에 관리하는 모듈입니다. -get/add/update/delete 연산과 Streamlit 세션 연동을 제공합니다. -""" - -from typing import Any, Dict, Optional - -try: - import streamlit as st # type: ignore -except Exception: # pragma: no cover - st = None # type: ignore - -from .models import DBConnectionsRegistry, DBConnectionProfile -from .persist import load_db_registry_from_disk, save_db_registry_to_disk - - -def get_db_connections_registry() -> DBConnectionsRegistry: - if st is not None and "db_connections_registry" in st.session_state: - reg = st.session_state["db_connections_registry"] - return reg # stored as DBConnectionsRegistry - try: - registry = load_db_registry_from_disk() - except Exception: - registry = DBConnectionsRegistry() - if st is not None: - st.session_state["db_connections_registry"] = registry - return registry - - -def _save_db_registry(registry: DBConnectionsRegistry) -> None: - if st is not None: - st.session_state["db_connections_registry"] = registry - try: - save_db_registry_to_disk(registry) - except Exception: - # fail-soft; UI will still have session copy - pass - - -def add_db_connection( - *, - name: str, - db_type: str, - host: Optional[str] = None, - port: Optional[int] = None, - user: Optional[str] = None, - password: Optional[str] = None, - database: Optional[str] = None, - extra: Optional[Dict[str, Any]] = None, - note: Optional[str] = None, -) -> None: - db_type_norm = (db_type or "").lower() - registry = get_db_connections_registry() - if any(c.name == name for c in registry.connections): - raise ValueError(f"이미 존재하는 DB 프로파일 이름입니다: {name}") - registry.connections.append( - DBConnectionProfile( - name=name, - type=db_type_norm, - host=host, - port=port, - user=user, - password=password, - database=database, - extra=extra or None, - note=note or None, - ) - ) - _save_db_registry(registry) - - -def update_db_connection( - *, - name: str, - db_type: str, - host: Optional[str], - port: Optional[int], - user: Optional[str], - password: Optional[str], - database: Optional[str], - extra: Optional[Dict[str, Any]], - note: Optional[str], -) -> None: - db_type_norm = (db_type or "").lower() - registry = get_db_connections_registry() - for idx, c in enumerate(registry.connections): - if c.name == name: - registry.connections[idx] = DBConnectionProfile( - name=name, - type=db_type_norm, - host=host, - port=port, - user=user, - password=password, - database=database, - extra=extra or None, - note=note or None, - ) - _save_db_registry(registry) - return - raise ValueError(f"존재하지 않는 DB 프로파일 이름입니다: {name}") - - -def delete_db_connection(*, name: str) -> None: - registry = get_db_connections_registry() - registry.connections = [c for c in registry.connections if c.name != name] - _save_db_registry(registry) diff --git a/interface/core/config/registry_llm.py b/interface/core/config/registry_llm.py deleted file mode 100644 index 7f4c17a..0000000 --- a/interface/core/config/registry_llm.py +++ /dev/null @@ -1,114 +0,0 @@ -"""LLM/Embedding 프로파일 레지스트리를 세션+디스크에 관리하는 모듈입니다. -프로파일 저장(upsert)과 Streamlit 세션 연동을 제공합니다. -""" - -try: - import streamlit as st # type: ignore -except Exception: # pragma: no cover - st = None # type: ignore - -from .models import ( - LLMRegistry, - LLMProfile, - EmbeddingRegistry, - EmbeddingProfile, -) -from .persist import ( - load_llm_registry_from_disk, - save_llm_registry_to_disk, - load_embedding_registry_from_disk, - save_embedding_registry_to_disk, -) - - -def get_llm_registry() -> LLMRegistry: - if st is not None and "llm_registry" in st.session_state: - return st.session_state["llm_registry"] - try: - registry = load_llm_registry_from_disk() - except Exception: - registry = LLMRegistry() - if st is not None: - st.session_state["llm_registry"] = registry - return registry - - -def _save_llm_registry(registry: LLMRegistry) -> None: - if st is not None: - st.session_state["llm_registry"] = registry - try: - save_llm_registry_to_disk(registry) - except Exception: - pass - - -def get_embedding_registry() -> EmbeddingRegistry: - if st is not None and "embedding_registry" in st.session_state: - return st.session_state["embedding_registry"] - try: - registry = load_embedding_registry_from_disk() - except Exception: - registry = EmbeddingRegistry() - if st is not None: - st.session_state["embedding_registry"] = registry - return registry - - -def _save_embedding_registry(registry: EmbeddingRegistry) -> None: - if st is not None: - st.session_state["embedding_registry"] = registry - try: - save_embedding_registry_to_disk(registry) - except Exception: - pass - - -def save_llm_profile( - *, name: str, provider: str, values: dict[str, str | None], note: str | None = None -) -> None: - provider_norm = (provider or "").lower() - stored_fields: dict[str, str] = {} - for k, v in (values or {}).items(): - if v is None: - continue - stored_fields[k] = str(v) - - reg = get_llm_registry() - # upsert by name - for idx, p in enumerate(reg.profiles): - if p.name == name: - reg.profiles[idx] = LLMProfile( - name=name, provider=provider_norm, fields=stored_fields, note=note - ) - _save_llm_registry(reg) - return - reg.profiles.append( - LLMProfile(name=name, provider=provider_norm, fields=stored_fields, note=note) - ) - _save_llm_registry(reg) - - -def save_embedding_profile( - *, name: str, provider: str, values: dict[str, str | None], note: str | None = None -) -> None: - provider_norm = (provider or "").lower() - stored_fields: dict[str, str] = {} - for k, v in (values or {}).items(): - if v is None: - continue - stored_fields[k] = str(v) - - reg = get_embedding_registry() - for idx, p in enumerate(reg.profiles): - if p.name == name: - reg.profiles[idx] = EmbeddingProfile( - name=name, provider=provider_norm, fields=stored_fields, note=note - ) - _save_embedding_registry(reg) - return - reg.profiles.append( - EmbeddingProfile( - name=name, provider=provider_norm, fields=stored_fields, note=note - ) - ) - _save_embedding_registry(reg) diff --git a/interface/core/config/settings.py b/interface/core/config/settings.py deleted file mode 100644 index 9b4eeb2..0000000 --- a/interface/core/config/settings.py +++ /dev/null @@ -1,250 +0,0 @@ -"""런타임 설정 로딩/업데이트 및 세션/환경 변수 반영, 입력값 검증 유틸 포함. -DataHub/VectorDB/DB/LLM/Embedding 관련 설정 업데이트를 제공합니다. -""" - -import os -from pathlib import Path -from typing import Any, Dict, Optional - -try: - import streamlit as st # type: ignore -except Exception: # pragma: no cover - streamlit may not be present in non-UI contexts - st = None # type: ignore - -from utils.llm.tools import set_gms_server - -from .models import Config - -DEFAULT_DATAHUB_SERVER = "http://localhost:8080" -DEFAULT_VECTORDB_TYPE = os.getenv("VECTORDB_TYPE", "faiss").lower() -DEFAULT_VECTORDB_LOCATION = os.getenv("VECTORDB_LOCATION", "") - - -def _get_session_value(key: str) -> str | None: - if st is None: - return None - try: - if key in st.session_state and st.session_state[key]: - return str(st.session_state[key]) - except Exception: - return None - return None - - -def load_config() -> Config: - """Load configuration with priority: session_state > environment > defaults.""" - datahub = _get_session_value("datahub_server") or os.getenv( - "DATAHUB_SERVER", DEFAULT_DATAHUB_SERVER - ) - mode = _get_session_value("data_source_mode") - - vectordb_type = _get_session_value("vectordb_type") or os.getenv( - "VECTORDB_TYPE", DEFAULT_VECTORDB_TYPE - ) - vectordb_location = _get_session_value("vectordb_location") or os.getenv( - "VECTORDB_LOCATION", DEFAULT_VECTORDB_LOCATION - ) - - return Config( - datahub_server=datahub, - vectordb_type=vectordb_type.lower() if vectordb_type else DEFAULT_VECTORDB_TYPE, - vectordb_location=vectordb_location, - data_source_mode=mode, - ) - - -def update_datahub_server(config: Config, new_url: str) -> None: - """Update DataHub server URL across runtime config, env-aware clients, and session.""" - if not new_url: - return - config.datahub_server = new_url - - # Propagate to underlying tooling/clients - try: - set_gms_server(new_url) - except Exception: - # Fail-soft: UI should surface errors from callers if needed - pass - - # Reflect into session state for immediate UI reuse - if st is not None: - try: - st.session_state["datahub_server"] = new_url - except Exception: - pass - - -def update_data_source_mode(config: Config, mode: str | None) -> None: - """Persist user's data source selection (datahub | vectordb).""" - config.data_source_mode = mode - if st is not None: - try: - st.session_state["data_source_mode"] = mode - except Exception: - pass - - -def _put_env(key: str, value: str | None) -> None: - if value is None: - return - os.environ[key] = value - - -def _put_session(key: str, value: str | None) -> None: - if st is None: - return - try: - st.session_state[key] = value - except Exception: - pass - - -def update_db_settings( - *, - db_type: str, - values: Dict[str, Any] | None, - secrets: Dict[str, Optional[str]] | None = None, -) -> None: - """Update DB settings into process env and session. - - Only non-sensitive values should be passed in values and may come from registry. - Secrets (e.g., PASSWORD, ACCESS_TOKEN) are applied to env/session but never persisted to disk. - """ - db_type_norm = (db_type or "").lower() - if not db_type_norm: - raise ValueError("DB 타입이 비어 있습니다.") - - # Core selector - _put_env("DB_TYPE", db_type_norm) - _put_session("DB_TYPE", db_type_norm) - - prefix = db_type_norm.upper() - - base_keys = ["HOST", "PORT", "USER", "DATABASE"] - for base_key in base_keys: - vk = base_key.lower() - v = (values or {}).get(vk) - if v is None: - continue - _put_env(f"{prefix}_{base_key}", str(v)) - _put_session(f"{prefix}_{base_key}", str(v)) - - # Extras (non-secret) - extra = (values or {}).get("extra") or {} - if isinstance(extra, dict): - for k, v in extra.items(): - if v is None: - continue - _put_env(f"{prefix}_{str(k).upper()}", str(v)) - _put_session(f"{prefix}_{str(k).upper()}", str(v)) - - # Secrets (applied to env+session, never persisted) - for sk, sv in (secrets or {}).items(): - if sv is None: - continue - key_up = str(sk).upper() - _put_env(f"{prefix}_{key_up}", str(sv)) - _put_session(f"{prefix}_{key_up}", str(sv)) - - -def update_vectordb_settings( - config: Config, *, vectordb_type: str, vectordb_location: str | None -) -> None: - """Validate and update VectorDB settings into env and session. - - Basic validation rules follow CLI's behavior: - - vectordb_type must be 'faiss' or 'pgvector' - - if type == 'faiss' and location provided: must be an existing directory - - if type == 'pgvector' and location provided: must start with 'postgresql://' - """ - vtype = (vectordb_type or "").lower() - if vtype not in ("faiss", "pgvector"): - raise ValueError(f"지원하지 않는 VectorDB 타입: {vectordb_type}") - - vloc = vectordb_location or "" - if vloc: - if vtype == "faiss": - path = Path(vloc) - # 신규 경로 허용: 존재하면 디렉토리인지 확인, 없으면 상위 디렉토리 생성 - if path.exists() and not path.is_dir(): - raise ValueError( - f"유효하지 않은 FAISS 디렉토리 경로(파일 경로임): {vloc}" - ) - if not path.exists(): - try: - path.mkdir(parents=True, exist_ok=True) - except Exception as e: - raise ValueError(f"FAISS 경로 생성 실패: {vloc} | {e}") - elif vtype == "pgvector": - if not vloc.startswith("postgresql://"): - raise ValueError("pgvector URL은 'postgresql://'로 시작해야 합니다") - - # Persist to runtime config - config.vectordb_type = vtype - config.vectordb_location = vloc - - # Reflect to process env for downstream modules - os.environ["VECTORDB_TYPE"] = vtype - if vloc: - os.environ["VECTORDB_LOCATION"] = vloc - - # Reflect to session state for UI - if st is not None: - try: - st.session_state["vectordb_type"] = vtype - st.session_state["vectordb_location"] = vloc - except Exception: - pass - - -def update_llm_settings(*, provider: str, values: dict[str, str | None]) -> None: - """Update chat LLM settings from UI into process env and session. - - This function mirrors the environment-variable based configuration consumed by - utils.llm.core.factory.get_llm(). Only sets provided keys; missing values are left as-is. - """ - provider_norm = (provider or "").lower() - if provider_norm not in { - "openai", - "azure", - "bedrock", - "gemini", - "ollama", - "huggingface", - }: - raise ValueError(f"지원하지 않는 LLM 공급자: {provider}") - - # Core selector - _put_env("LLM_PROVIDER", provider_norm) - _put_session("LLM_PROVIDER", provider_norm) - - # Provider-specific fields (keys exactly as factory expects) - for k, v in (values or {}).items(): - if v is not None: - _put_env(k, v) - _put_session(k, v) - - -def update_embedding_settings(*, provider: str, values: dict[str, str | None]) -> None: - """Update Embeddings settings from UI into process env and session. - - Mirrors env vars consumed by utils.llm.core.factory.get_embeddings(). - """ - provider_norm = (provider or "").lower() - if provider_norm not in { - "openai", - "azure", - "bedrock", - "gemini", - "ollama", - "huggingface", - }: - raise ValueError(f"지원하지 않는 Embedding 공급자: {provider}") - - _put_env("EMBEDDING_PROVIDER", provider_norm) - _put_session("EMBEDDING_PROVIDER", provider_norm) - - for k, v in (values or {}).items(): - if v is not None: - _put_env(k, v) - _put_session(k, v) diff --git a/interface/core/dialects.py b/interface/core/dialects.py deleted file mode 100644 index ea1463d..0000000 --- a/interface/core/dialects.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -다이얼렉트 프리셋과 옵션 정의 모듈. - -이 모듈은 다음을 제공합니다: - -- DialectOption: 각 SQL 엔진 특성 데이터클래스 - - name: 엔진 표시 이름 (예: "PostgreSQL", "ClickHouse") - - supports_ilike: 대소문자 무시 비교(ILIKE) 지원 여부 - - hints: 자주 쓰이는/효과적인 함수의 간결 목록 + 짧은 메모 - - 예: ["DATE_TRUNC (날짜 절단)", "STRING_AGG (문자 집계)"] - -- PRESET_DIALECTS: 대표 SQL 엔진들의 기본 프리셋 모음 - - PostgreSQL, ClickHouse, Trino, Snowflake, Redshift, BigQuery, MSSQL, Oracle, DuckDB - -주 사용처: -- Streamlit UI에서 프리셋 선택 및 커스텀 다이얼렉트 입력의 기준 데이터 -- Lang2SQL 파이프라인에서 프롬프트/키워드 힌트 구성 - -주의: -- hints는 프롬프트 가이드용이며 실행 보장을 의미하지 않습니다. -- 실제 문법/함수 지원은 엔진 버전 및 설정에 따라 달라질 수 있습니다. -""" - -from __future__ import annotations - -from dataclasses import asdict, dataclass, field -from typing import Dict, List - - -@dataclass -class DialectOption: - name: str - supports_ilike: bool = False - hints: List[str] = field(default_factory=list) - - def to_dict(self) -> Dict: - return asdict(self) - - @staticmethod - def from_dict(data: Dict) -> "DialectOption": - return DialectOption( - name=data.get("name", "Custom"), - supports_ilike=bool(data.get("supports_ilike", False)), - hints=list(data.get("hints", data.get("keyword_hints", []))), - ) - - -PRESET_DIALECTS: Dict[str, DialectOption] = { - "PostgreSQL": DialectOption( - name="PostgreSQL", - supports_ilike=True, - hints=[ - "COALESCE (널 대체)", - "DATE_TRUNC (날짜 절단)", - "STRING_AGG (문자 집계)", - "GENERATE_SERIES (시퀀스 생성)", - ], - ), - "ClickHouse": DialectOption( - name="ClickHouse", - supports_ilike=False, - hints=[ - "toDate (날짜 변환)", - "dateDiff (날짜 차이)", - "arrayJoin (배열 펼치기)", - "groupArray (배열 집계)", - ], - ), - "Trino": DialectOption( - name="Trino", - supports_ilike=False, - hints=[ - "date_trunc (날짜 절단)", - "try_cast (안전 변환)", - "coalesce (널 대체)", - "regexp_like (정규식 매칭)", - ], - ), - "Snowflake": DialectOption( - name="Snowflake", - supports_ilike=True, - hints=[ - "IFF (조건 분기)", - "TO_DATE (날짜 변환)", - "DATE_TRUNC (날짜 절단)", - "LISTAGG (문자 집계)", - ], - ), - "Redshift": DialectOption( - name="Redshift", - supports_ilike=True, - hints=[ - "COALESCE (널 대체)", - "DATE_TRUNC (날짜 절단)", - "LISTAGG (문자 집계)", - "REGEXP_REPLACE (정규식 치환)", - ], - ), - "BigQuery": DialectOption( - name="BigQuery", - supports_ilike=False, - hints=[ - "SAFE_CAST (안전 변환)", - "DATE_TRUNC (날짜 절단)", - "ARRAY_AGG (배열 집계)", - "REGEXP_CONTAINS (정규식 포함)", - ], - ), - "MSSQL": DialectOption( - name="MSSQL", - supports_ilike=False, - hints=[ - "ISNULL (널 대체)", - "DATEADD (날짜 가감)", - "CONVERT (형 변환)", - "STRING_AGG (문자 집계)", - ], - ), - "Oracle": DialectOption( - name="Oracle", - supports_ilike=False, - hints=[ - "NVL (널 대체)", - "TO_DATE (날짜 변환)", - "TRUNC (날짜 절단)", - "LISTAGG (문자 집계)", - ], - ), - "DuckDB": DialectOption( - name="DuckDB", - supports_ilike=True, - hints=[ - "date_trunc (날짜 절단)", - "string_agg (문자 집계)", - "coalesce (널 대체)", - "regexp_replace (정규식 치환)", - ], - ), -} diff --git a/interface/core/lang2sql_runner.py b/interface/core/lang2sql_runner.py deleted file mode 100644 index 08ecdc0..0000000 --- a/interface/core/lang2sql_runner.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Lang2SQL 실행 모듈. - -이 모듈은 자연어로 입력된 질문을 SQL 쿼리로 변환하고, -지정된 데이터베이스 환경에서 실행하는 함수(`run_lang2sql`)를 제공합니다. -내부적으로 v2 플로우(BaselineNL2SQL / EnrichedNL2SQL)를 사용한다. -""" - -from __future__ import annotations - -from typing import Any - - -def run_lang2sql( - query: str, - db_dialect: str | None = None, - top_n: int = 5, - use_enriched: bool = False, - catalog: list | None = None, -) -> dict[str, Any]: - """Lang2SQL 실행 함수. - - 주어진 자연어 질문을 SQL 쿼리로 변환하고 데이터베이스에서 실행한다. - LLM, Embedding, DB는 환경변수(LLM_PROVIDER, EMBEDDING_PROVIDER, DB_TYPE 등)로 - 자동 설정된다. - - Args: - query: 사용자 입력 자연어 질문. - db_dialect: SQL 방언 힌트 (None이면 default 프롬프트 사용). - top_n: 검색할 상위 테이블 수. - use_enriched: True이면 EnrichedNL2SQL 플로우 사용. - catalog: CatalogEntry 목록. None이면 빈 카탈로그로 실행. - - Returns: - dict: {"rows": list[dict], "sql": str, "error": str | None} - """ - from lang2sql.factory import ( - build_db_from_env, - build_embedding_from_env, - build_llm_from_env, - ) - from lang2sql.flows import BaselineNL2SQL, EnrichedNL2SQL - - catalog = catalog or [] - - try: - llm = build_llm_from_env() - db = build_db_from_env() - - if use_enriched: - embedding = build_embedding_from_env() - pipeline = EnrichedNL2SQL( - catalog=catalog, - llm=llm, - db=db, - embedding=embedding, - db_dialect=db_dialect, - top_n=top_n, - ) - else: - pipeline = BaselineNL2SQL( - catalog=catalog, - llm=llm, - db=db, - db_dialect=db_dialect, - ) - - rows = pipeline.run(query) - return {"rows": rows, "error": None} - - except Exception as exc: - return {"rows": [], "error": str(exc)} diff --git a/interface/core/provider_factory.py b/interface/core/provider_factory.py deleted file mode 100644 index 72da835..0000000 --- a/interface/core/provider_factory.py +++ /dev/null @@ -1,141 +0,0 @@ -"""Settings UI에서 선택된 프로파일을 LLMPort/EmbeddingPort로 변환하는 팩토리. - -LLMProfile과 EmbeddingProfile(config/models.py)을 받아 -lang2sql.integrations의 구현체를 반환한다. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from lang2sql.core.ports import EmbeddingPort, LLMPort - from interface.core.config.models import EmbeddingProfile, LLMProfile - - -def build_llm(profile: "LLMProfile") -> "LLMPort": - """LLMProfile → LLMPort 변환. Settings UI에서 호출한다.""" - f = profile.fields - provider = profile.provider.lower() - - if provider == "openai": - from lang2sql.integrations.llm.openai_ import OpenAILLM - - return OpenAILLM( - model=f.get("model", "gpt-4o"), - api_key=f.get("api_key"), - ) - - if provider == "anthropic": - from lang2sql.integrations.llm.anthropic_ import AnthropicLLM - - return AnthropicLLM( - model=f.get("model", "claude-sonnet-4-6"), - api_key=f.get("api_key"), - ) - - if provider == "azure": - from lang2sql.integrations.llm.azure_ import AzureOpenAILLM - - return AzureOpenAILLM( - azure_deployment=f["azure_deployment"], - azure_endpoint=f["azure_endpoint"], - api_version=f.get("api_version", "2023-07-01-preview"), - api_key=f.get("api_key"), - ) - - if provider == "gemini": - from lang2sql.integrations.llm.gemini_ import GeminiLLM - - return GeminiLLM( - model=f.get("model", "gemini-2.0-flash-lite"), - api_key=f.get("api_key"), - ) - - if provider == "bedrock": - from lang2sql.integrations.llm.bedrock_ import BedrockLLM - - return BedrockLLM( - model=f["model"], - aws_access_key_id=f.get("aws_access_key_id"), - aws_secret_access_key=f.get("aws_secret_access_key"), - region_name=f.get("region_name", "us-east-1"), - ) - - if provider == "ollama": - from lang2sql.integrations.llm.ollama_ import OllamaLLM - - return OllamaLLM( - model=f["model"], - base_url=f.get("base_url", "http://localhost:11434"), - ) - - if provider == "huggingface": - from lang2sql.integrations.llm.huggingface_ import HuggingFaceLLM - - return HuggingFaceLLM( - repo_id=f.get("repo_id"), - endpoint_url=f.get("endpoint_url"), - api_token=f.get("api_token"), - ) - - raise ValueError(f"Unknown LLM provider: {provider!r}") - - -def build_embedding(profile: "EmbeddingProfile") -> "EmbeddingPort": - """EmbeddingProfile → EmbeddingPort 변환. Settings UI에서 호출한다.""" - f = profile.fields - provider = profile.provider.lower() - - if provider == "openai": - from lang2sql.integrations.embedding.openai_ import OpenAIEmbedding - - return OpenAIEmbedding( - model=f.get("model", "text-embedding-3-small"), - api_key=f.get("api_key"), - ) - - if provider == "azure": - from lang2sql.integrations.embedding.azure_ import AzureOpenAIEmbedding - - return AzureOpenAIEmbedding( - azure_deployment=f["azure_deployment"], - azure_endpoint=f["azure_endpoint"], - api_version=f.get("api_version", "2023-09-15-preview"), - api_key=f.get("api_key"), - ) - - if provider == "ollama": - from lang2sql.integrations.embedding.ollama_ import OllamaEmbedding - - return OllamaEmbedding( - model=f.get("model", "nomic-embed-text"), - base_url=f.get("base_url", "http://localhost:11434"), - ) - - if provider == "bedrock": - from lang2sql.integrations.embedding.bedrock_ import BedrockEmbedding - - return BedrockEmbedding( - model_id=f.get("model_id", "amazon.titan-embed-text-v2:0"), - aws_access_key_id=f.get("aws_access_key_id"), - aws_secret_access_key=f.get("aws_secret_access_key"), - region_name=f.get("region_name", "us-east-1"), - ) - - if provider == "gemini": - from lang2sql.integrations.embedding.gemini_ import GeminiEmbedding - - return GeminiEmbedding( - model=f.get("model", "models/embedding-001"), - api_key=f.get("api_key"), - ) - - if provider == "huggingface": - from lang2sql.integrations.embedding.huggingface_ import HuggingFaceEmbedding - - return HuggingFaceEmbedding( - model=f.get("model", f.get("repo_id", "")), - ) - - raise ValueError(f"Unknown embedding provider: {provider!r}") diff --git a/interface/core/result_renderer.py b/interface/core/result_renderer.py deleted file mode 100644 index 69e3950..0000000 --- a/interface/core/result_renderer.py +++ /dev/null @@ -1,209 +0,0 @@ -""" -Lang2SQL 결과 표시 모듈. - -이 모듈은 LLM이 생성한 SQL 쿼리 및 결과 데이터를 -Streamlit UI를 통해 다양한 형태(쿼리, 표, 차트, 설명 등)로 표시합니다. -토큰 사용량, 문서 적합성 평가, 재해석된 질문 등도 함께 확인할 수 있습니다. -""" - -import pandas as pd -import streamlit as st -from langchain_core.messages import AIMessage - -from infra.observability.token_usage import TokenUtils -from utils.databases import DatabaseFactory -from utils.llm.llm_response_parser import LLMResponseParser -from utils.visualization.display_chart import DisplayChart - - -def display_result(res: dict) -> None: - """Lang2SQL 실행 결과를 Streamlit UI로 출력합니다. - - Args: - res (dict): Lang2SQL 실행 결과를 담은 딕셔너리. - - generated_query (AIMessage | str): LLM이 생성한 SQL 쿼리 - - messages (list): LLM 입력/출력 메시지 목록 - - question_gate_result (dict, optional): 질문 게이트 결과 - - document_suitability (dict, optional): 문서 적합성 평가 결과 - - searched_tables (list, optional): 검색된 테이블 목록 - - 표시 항목: - - SQL 쿼리 및 실행 결과 - - 결과 설명 및 재해석된 질문 - - 문서 적합성 평가 및 질문 게이트 결과 - - 토큰 사용량 요약 - - 쿼리 결과 표 및 차트 - """ - - def should_show(_key: str) -> bool: - return st.session_state.get(_key, True) - - has_query = bool(res.get("generated_query")) - show_sql_section = has_query and should_show("show_sql") - show_result_desc = has_query and should_show("show_result_description") - show_reinterpreted = has_query and should_show("show_question_reinterpreted_by_ai") - show_gate_result = should_show("show_question_gate_result") - show_doc_suitability = should_show("show_document_suitability") - show_table_section = has_query and should_show("show_table") - show_chart_section = has_query and should_show("show_chart") - - if show_gate_result and ("question_gate_result" in res): - st.markdown("---") - st.markdown("**Question Gate 결과:**") - st.json(res.get("question_gate_result", {})) - - if show_doc_suitability and ("document_suitability" in res): - st.markdown("---") - st.markdown("**문서 적합성 평가:**") - ds = res.get("document_suitability") - if isinstance(ds, dict) and ds: - rows = [ - { - "table": t, - "score": float(info.get("score", -1)), - "matched_columns": ", ".join(info.get("matched_columns", [])), - "missing_entities": ", ".join(info.get("missing_entities", [])), - "reason": info.get("reason", ""), - } - for t, info in ds.items() - if isinstance(info, dict) - ] - st.dataframe(rows, use_container_width=True) - else: - st.info("문서 적합성 평가 결과가 비어 있습니다.") - - if should_show("show_token_usage"): - st.markdown("---") - token_summary = TokenUtils.get_token_usage_summary(data=res["messages"]) - st.write("**토큰 사용량:**") - st.markdown(f""" - - Input tokens: `{token_summary['input_tokens']}` - - Output tokens: `{token_summary['output_tokens']}` - - Total tokens: `{token_summary['total_tokens']}` - """) - - if show_sql_section: - st.markdown("---") - generated_query = res.get("generated_query") - if generated_query: - query_text = ( - generated_query.content - if isinstance(generated_query, AIMessage) - else str(generated_query) - ) - try: - sql = LLMResponseParser.extract_sql(query_text) - st.markdown("**생성된 SQL 쿼리:**") - st.code(sql, language="sql") - except ValueError: - st.warning("SQL 블록을 추출할 수 없습니다.") - st.text(query_text) - interpretation = LLMResponseParser.extract_interpretation(query_text) - if interpretation: - st.markdown("**결과 해석:**") - st.code(interpretation) - else: - st.warning("쿼리 텍스트가 문자열이 아닙니다.") - st.text(str(query_text)) - - if show_result_desc and res.get("messages"): - st.markdown("---") - st.markdown("**결과 설명:**") - result_message = res["messages"][-1].content - - if isinstance(result_message, str): - try: - sql = LLMResponseParser.extract_sql(result_message) - st.code(sql, language="sql") - except ValueError: - st.warning("SQL 블록을 추출할 수 없습니다.") - st.text(result_message) - - interpretation = LLMResponseParser.extract_interpretation(result_message) - if interpretation: - st.code(interpretation, language="plaintext") - else: - st.warning("결과 메시지가 문자열이 아닙니다.") - st.text(str(result_message)) - - if show_reinterpreted and res.get("messages"): - st.markdown("---") - st.markdown("**AI가 재해석한 사용자 질문:**") - try: - if len(res["messages"]) > 1: - candidate = res["messages"][-2] - question_text = ( - candidate.content - if hasattr(candidate, "content") - else str(candidate) - ) - else: - question_text = res["messages"][0].content - except Exception: - question_text = str(res["messages"][0].content) - st.code(question_text) - - if should_show("show_referenced_tables"): - st.markdown("---") - st.markdown("**참고한 테이블 목록:**") - st.write(res.get("searched_tables", [])) - - if not has_query: - st.info("QUERY_MAKER 없이 실행되었습니다. 검색된 테이블 정보만 표시합니다.") - - if show_table_section or show_chart_section: - database = DatabaseFactory.get_connector() - df = pd.DataFrame() - try: - sql_raw = ( - res["generated_query"].content - if isinstance(res["generated_query"], AIMessage) - else str(res["generated_query"]) - ) - if isinstance(sql_raw, str): - sql = LLMResponseParser.extract_sql(sql_raw) - df = database.run_sql(sql) - else: - st.error("SQL 원본이 문자열이 아닙니다.") - except Exception as e: - st.markdown("---") - st.error(f"쿼리 실행 중 오류 발생: {e}") - df = pd.DataFrame() - - if not df.empty and show_table_section: - st.markdown("---") - st.markdown("**쿼리 실행 결과:**") - try: - st.dataframe(df.head(10) if len(df) > 10 else df) - except Exception as e: - st.error(f"결과 테이블 생성 중 오류 발생: {e}") - - if df is not None and show_chart_section: - st.markdown("---") - try: - st.markdown("**쿼리 결과 시각화:**") - try: - if len(res["messages"]) > 1: - candidate = res["messages"][-2] - chart_question = ( - candidate.content - if hasattr(candidate, "content") - else str(candidate) - ) - else: - chart_question = res["messages"][0].content - except Exception: - chart_question = str(res["messages"][0].content) - - display_code = DisplayChart( - question=chart_question, - sql=sql, - df_metadata=f"Running df.dtypes gives:\n{df.dtypes}", - ) - # plotly_code 변수도 따로 보관할 필요 없이 바로 그려도 됩니다 - fig = display_code.get_plotly_figure( - plotly_code=display_code.generate_plotly_code(), df=df - ) - st.plotly_chart(fig) - except Exception as e: - st.error(f"차트 생성 중 오류 발생: {e}") diff --git a/interface/core/session_utils.py b/interface/core/session_utils.py deleted file mode 100644 index fbabb2b..0000000 --- a/interface/core/session_utils.py +++ /dev/null @@ -1,16 +0,0 @@ -"""Streamlit 세션 상태 유틸리티 모듈.""" - - -def init_pipeline(use_enriched: bool) -> str: - """파이프라인 타입을 세션 상태에 기록한다. - - Args: - use_enriched: True이면 EnrichedNL2SQL, False이면 BaselineNL2SQL. - - Returns: - str: "확장된" 또는 "기본". - """ - import streamlit as st - - st.session_state["use_enriched"] = use_enriched - return "확장된" if use_enriched else "기본" diff --git a/interface/pages_config.py b/interface/pages_config.py deleted file mode 100644 index 362f855..0000000 --- a/interface/pages_config.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -Streamlit 애플리케이션 페이지 설정 모듈. - -각 페이지의 경로와 제목을 정의하여 내비게이션에 사용합니다. - -Attributes: - PAGES (list): Streamlit Page 객체 리스트. - - 홈 페이지 - - Lang2SQL 페이지 - - 그래프 빌더 페이지 - - ChatBot 페이지 - - 설정 페이지 -""" - -import streamlit as st - -PAGES = [ - st.Page("app_pages/home.py", title="🏠 홈"), - st.Page("app_pages/lang2sql.py", title="🔍 Lang2SQL"), - st.Page("app_pages/chatbot.py", title="🤖 ChatBot"), - st.Page("app_pages/settings.py", title="⚙️ 설정"), -] diff --git a/interface/streamlit_app.py b/interface/streamlit_app.py deleted file mode 100644 index 8e74d8e..0000000 --- a/interface/streamlit_app.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Streamlit 애플리케이션 메인 실행 모듈. - -Lang2SQL 데이터 분석 도구의 내비게이션을 초기화하고 실행합니다. -""" - -import streamlit as st - -from interface.pages_config import PAGES - - -def configure_app() -> None: - """앱 전역 설정 초기화. - - Streamlit 애플리케이션의 제목, 아이콘, 레이아웃, 사이드바 상태를 설정합니다. - - Returns: - None - """ - st.set_page_config( - page_title="Lang2SQL 데이터 분석 도구", - page_icon="🔎", - layout="wide", - initial_sidebar_state="expanded", - ) - - -def main() -> None: - """애플리케이션 진입점. - - 전역 설정을 초기화하고, 정의된 페이지 내비게이션을 실행합니다. - - Returns: - None - """ - configure_app() - pg = st.navigation(PAGES) - pg.run() - - -if __name__ == "__main__": - main() diff --git a/prompt/template_loader.py b/prompt/template_loader.py deleted file mode 100644 index 1e3a554..0000000 --- a/prompt/template_loader.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -이 모듈은 프롬프트 템플릿을 로드하는 기능을 제공합니다. -- 프롬프트 템플릿은 마크다운 파일로 관리되고 있으며, 환경변수에서 템플릿 디렉토리를 가져오거나, 없으면 현재 파일 위치 기준으로 설정합니다. -""" - -import os - - -def get_prompt_template(prompt_name: str) -> str: - # 환경변수에서 템플릿 디렉토리를 가져오거나, 없으면 현재 파일 위치 기준으로 설정 - templates_dir = os.environ.get("PROMPT_TEMPLATES_DIR", os.path.dirname(__file__)) - - try: - template_path = os.path.join(templates_dir, f"{prompt_name}.md") - with open(template_path, "r", encoding="utf-8") as f: - template = f.read() - except FileNotFoundError: - raise FileNotFoundError(f"경고: '{prompt_name}.md' 파일을 찾을 수 없습니다.") - - return template diff --git a/pyproject.toml b/pyproject.toml index 62dd941..77062ec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,82 +4,39 @@ build-backend = "hatchling.build" [project] name = "lang2sql" -dynamic = ["version"] -description = "Lang2SQL - Query Generator for Data Warehouse" +version = "0.4.0.dev0" +description = "Lang2SQL — document-learning, read-only SQL analytics agent (v4.1 rebuild)" readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" authors = [ - { name = "ehddnr301", email = "dy95032@gmail.com" }, + { name = "brain-crew", email = "ryan@brain-crew.com" }, ] dependencies = [ - "langgraph==0.2.62", - "datahub==0.999.1", - "langchain==0.3.14", - "langchain-community==0.3.14", - "openai==1.59.8", - "langchain-openai==0.3.0", - "streamlit==1.41.1", - "python-dotenv==1.0.1", - "faiss-cpu==1.10.0", - "transformers==4.51.2", - "langchain-aws>=0.2.21,<0.3.0", - "langchain-google-genai>=2.1.3,<3.0.0", - "langchain-ollama>=0.3.2,<0.4.0", - "langchain-huggingface>=0.1.2,<0.2.0", - "clickhouse_driver>=0.2.9,<0.3.0", - "plotly", - "matplotlib", - "ipython", - "kaleido", - "numpy<2.0", - "snowflake-connector-python>=3.15.0,<4.0.0", - "databricks-sql-connector>=4.0.3,<5.0.0", - "oracledb>=3.1.0,<4.0.0", - "mysql-connector-python>=9.3.0,<10.0.0", - "duckdb>=1.2.2,<2.0.0", - "psycopg2-binary>=2.9.10,<3.0.0", - "psycopg[binary]>=3.2,<4.0", - "pyodbc>=5.1.0,<6.0.0", - "crate>=0.29.0,<1.0.0", - "pyhive>=0.6.6,<1.0.0", - "google-cloud-bigquery>=3.20.1,<4.0.0", - "pgvector==0.3.6", - "langchain-postgres==0.0.15", - "trino>=0.329.0,<1.0.0", - "anthropic>=0.20.0", - "sqlalchemy>=2.0", + "discord.py>=2.3,<3.0", # Phase 1 frontend transport + "cryptography>=42.0", # EncryptedSecrets at-rest encryption ] +[project.optional-dependencies] +# Real outbound adapters (V1 ships urllib OpenAI + stub PG; these enable v1.5 swaps) +postgres = ["psycopg[binary]>=3.2,<4.0"] + [project.scripts] -lang2sql = "cli.__init__:cli" +lang2sql = "lang2sql.frontends.cli.app:main" +lang2sql-bot = "lang2sql.frontends.discord.bot:run" [project.urls] Homepage = "https://github.com/CausalInferenceLab/Lang2SQL" Repository = "https://github.com/CausalInferenceLab/Lang2SQL" -[tool.hatch.version] -path = "version.py" - -[tool.hatch.build] -include = [ - "version.py", - "prompt/*.md", - "src/lang2sql/**", -] - [tool.hatch.build.targets.wheel] -packages = [ - "cli", - "interface", - "engine", - "infra", - "prompt", - "utils", - "src/lang2sql" -] +packages = ["src/lang2sql"] [tool.uv] dev-dependencies = [ - "pre_commit==4.1.0", "pytest>=8.3.5", + "pytest-asyncio>=0.24", ] + +[tool.pytest.ini_options] +pythonpath = ["src"] +testpaths = ["tests"] diff --git a/src/lang2sql/__init__.py b/src/lang2sql/__init__.py index 66811de..02ec0f7 100644 --- a/src/lang2sql/__init__.py +++ b/src/lang2sql/__init__.py @@ -1,155 +1,3 @@ -from .factory import ( - build_db_from_env, - build_embedding_from_env, - build_explorer_from_url, - build_llm_from_env, -) -from .components.enrichment.context_enricher import ContextEnricher -from .components.enrichment.question_profiler import QuestionProfiler -from .components.execution.sql_executor import SQLExecutor -from .components.gate.question_gate import QuestionGate -from .components.gate.table_suitability import TableSuitabilityEvaluator -from .components.generation.sql_generator import SQLGenerator -from .components.loaders.directory_ import DirectoryLoader -from .components.loaders.markdown_ import MarkdownLoader -from .components.loaders.plaintext_ import PlainTextLoader -from .components.retrieval.chunker import ( - CatalogChunker, - DocumentChunkerPort, - RecursiveCharacterChunker, -) -from .components.retrieval.hybrid import HybridRetriever -from .components.retrieval.keyword import KeywordRetriever -from .components.retrieval.vector import VectorRetriever -from .core.catalog import ( - CatalogEntry, - GateResult, - IndexedChunk, - QuestionProfile, - RetrievalResult, - TableScore, - TextDocument, -) -from .core.exceptions import ComponentError, IntegrationMissingError, Lang2SQLError -from .core.hooks import MemoryHook, NullHook, TraceHook -from .core.ports import ( - CatalogLoaderPort, - DBExplorerPort, - DBPort, - DocumentLoaderPort, - EmbeddingPort, - LLMPort, - VectorStorePort, -) -from .integrations.db.sqlalchemy_ import SQLAlchemyExplorer -from .flows.enriched_nl2sql import EnrichedNL2SQL -from .flows.hybrid import HybridNL2SQL -from .flows.nl2sql import BaselineNL2SQL -from .integrations.embedding.azure_ import AzureOpenAIEmbedding -from .integrations.embedding.bedrock_ import BedrockEmbedding -from .integrations.embedding.gemini_ import GeminiEmbedding -from .integrations.embedding.huggingface_ import HuggingFaceEmbedding -from .integrations.embedding.ollama_ import OllamaEmbedding -from .integrations.llm.azure_ import AzureOpenAILLM -from .integrations.llm.bedrock_ import BedrockLLM -from .integrations.llm.gemini_ import GeminiLLM -from .integrations.llm.huggingface_ import HuggingFaceLLM -from .integrations.llm.ollama_ import OllamaLLM +"""Lang2SQL — document-learning analytics agent (v4.1 rebuild).""" -__all__ = [ - # Data types - "CatalogEntry", - "TextDocument", - "IndexedChunk", - "RetrievalResult", - # Domain types (Phase 4) - "GateResult", - "QuestionProfile", - "TableScore", - # Ports (protocols) - "LLMPort", - "DBPort", - "DBExplorerPort", - "EmbeddingPort", - "VectorStorePort", - "DocumentLoaderPort", - "CatalogLoaderPort", - # Components — retrieval - "KeywordRetriever", - "VectorRetriever", - "HybridRetriever", - "DocumentChunkerPort", - "CatalogChunker", - "RecursiveCharacterChunker", - # Components — generation & execution - "SQLGenerator", - "SQLExecutor", - # Components — gate (Phase 4) - "QuestionGate", - "TableSuitabilityEvaluator", - # Components — enrichment (Phase 4) - "QuestionProfiler", - "ContextEnricher", - # Components — loaders - "MarkdownLoader", - "PlainTextLoader", - "DirectoryLoader", - # Flows - "BaselineNL2SQL", - "HybridNL2SQL", - "EnrichedNL2SQL", - # Hooks - "TraceHook", - "MemoryHook", - "NullHook", - # Exceptions - "Lang2SQLError", - "ComponentError", - "IntegrationMissingError", - # Vector store backends - "FAISSVectorStore", - "PGVectorStore", - # LLM integrations (Phase 1) - "AzureOpenAILLM", - "BedrockLLM", - "GeminiLLM", - "HuggingFaceLLM", - "OllamaLLM", - # Embedding integrations (Phase 2) - "AzureOpenAIEmbedding", - "BedrockEmbedding", - "GeminiEmbedding", - "HuggingFaceEmbedding", - "OllamaEmbedding", - # Catalog integrations (Phase 3) - "DataHubCatalogLoader", - # DB Explorer (Phase A1) - "SQLAlchemyExplorer", - # Factory (Phase 6) - "build_llm_from_env", - "build_embedding_from_env", - "build_db_from_env", - "build_explorer_from_url", -] - -# --------------------------------------------------------------------------- -# Lazy imports (PEP 562) — optional dependencies that have import side-effects -# (e.g. faiss prints INFO logs on import) or are rarely needed at startup. -# --------------------------------------------------------------------------- -_LAZY_IMPORTS: dict[str, tuple[str, str]] = { - "DataHubCatalogLoader": (".integrations.catalog.datahub_", "DataHubCatalogLoader"), - "FAISSVectorStore": (".integrations.vectorstore.faiss_", "FAISSVectorStore"), - "PGVectorStore": (".integrations.vectorstore.pgvector_", "PGVectorStore"), -} - - -def __getattr__(name: str): - if name in _LAZY_IMPORTS: - module_path, attr = _LAZY_IMPORTS[name] - import importlib - - obj = getattr(importlib.import_module(module_path, package=__name__), attr) - # Cache in module globals so subsequent accesses skip __getattr__ - globals()[name] = obj - return obj - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") +__version__ = "0.4.0.dev0" diff --git a/src/lang2sql/adapters/__init__.py b/src/lang2sql/adapters/__init__.py new file mode 100644 index 0000000..85d7b8a --- /dev/null +++ b/src/lang2sql/adapters/__init__.py @@ -0,0 +1,14 @@ +"""Outbound adapters — concrete impls of the ``core.ports`` Protocols (v4.1 §2.1). + +stdlib only in V1: OpenAI via ``urllib``, storage via ``sqlite3``, the Postgres +explorer a canned stub until psycopg lands in v1.5. +""" + +from __future__ import annotations + +from .db.postgres_explorer import PostgresExplorer +from .llm.fake import FakeLLM +from .llm.openai_ import OpenAILLM +from .storage.sqlite_store import SqliteStore + +__all__ = ["FakeLLM", "OpenAILLM", "PostgresExplorer", "SqliteStore"] diff --git a/src/lang2sql/adapters/db/__init__.py b/src/lang2sql/adapters/db/__init__.py new file mode 100644 index 0000000..99cfe06 --- /dev/null +++ b/src/lang2sql/adapters/db/__init__.py @@ -0,0 +1,7 @@ +"""DB adapters — :class:`ExplorerPort` impls.""" + +from __future__ import annotations + +from .postgres_explorer import PostgresExplorer + +__all__ = ["PostgresExplorer"] diff --git a/src/lang2sql/adapters/db/postgres_explorer.py b/src/lang2sql/adapters/db/postgres_explorer.py new file mode 100644 index 0000000..1aaa33f --- /dev/null +++ b/src/lang2sql/adapters/db/postgres_explorer.py @@ -0,0 +1,84 @@ +"""PostgresExplorer — V1 stub; real psycopg-backed explorer lands in v1.5. + +psycopg is not stdlib, so V1 does not connect to anything. The ctor stores the +DSN for the future real impl, and the read methods return small canned schema +data so the agent loop and schema-aware tools can be exercised end-to-end. +""" + +from __future__ import annotations + +from ...core.ports.explorer import Column, Table + +# Canned catalog — two tables a demo guild would plausibly have. +_TABLES: dict[str, Table] = { + "public.orders": Table( + name="orders", + schema="public", + description="One row per customer order.", + columns=[ + Column("id", "integer", nullable=False, description="Primary key."), + Column("amount", "numeric", nullable=False, description="Order total."), + Column("status", "text", description="pending | paid | shipped | cancelled."), + Column("created_at", "timestamptz", nullable=False), + ], + ), + "public.users": Table( + name="users", + schema="public", + description="Registered users.", + columns=[ + Column("id", "integer", nullable=False, description="Primary key."), + Column("email", "text", nullable=False), + Column("created_at", "timestamptz", nullable=False), + ], + ), +} + +_SAMPLES: dict[str, list[dict]] = { + "public.orders": [ + {"id": 1, "amount": 49.90, "status": "paid", "created_at": "2026-05-01T10:00:00Z"}, + {"id": 2, "amount": 12.00, "status": "pending", "created_at": "2026-05-02T14:30:00Z"}, + ], + "public.users": [ + {"id": 1, "email": "alice@example.com", "created_at": "2026-04-20T08:00:00Z"}, + {"id": 2, "email": "bob@example.com", "created_at": "2026-04-21T09:15:00Z"}, + ], +} + + +def _resolve_key(name: str) -> str: + """Map a bare or qualified table name onto a canned-catalog key.""" + if name in _TABLES: + return name + return f"public.{name}" + + +class PostgresExplorer: + """Read-only schema introspection — canned data in V1.""" + + def __init__(self, dsn: str) -> None: + self.dsn = dsn # stored for the v1.5 psycopg impl; no connection in V1. + + async def list_tables(self) -> list[Table]: + return list(_TABLES.values()) + + async def describe_table(self, name: str) -> Table: + key = _resolve_key(name) + table = _TABLES.get(key) + if table is None: + raise KeyError(f"unknown table: {name}") + return table + + async def sample_rows(self, name: str, limit: int = 5) -> list[dict]: + key = _resolve_key(name) + return list(_SAMPLES.get(key, []))[:limit] + + async def execute(self, sql: str, limit: int = 1000) -> list[dict]: + # V1 stub: no real query engine. Echo canned rows that match whichever + # known table the SQL mentions, else a generic single-row result. + lowered = sql.lower() + for key, rows in _SAMPLES.items(): + table = key.split(".", 1)[-1] + if table in lowered: + return list(rows)[:limit] + return [{"result": 1}][:limit] diff --git a/src/lang2sql/adapters/llm/__init__.py b/src/lang2sql/adapters/llm/__init__.py new file mode 100644 index 0000000..bcf0ff0 --- /dev/null +++ b/src/lang2sql/adapters/llm/__init__.py @@ -0,0 +1,8 @@ +"""LLM adapters — :class:`LLMPort` impls.""" + +from __future__ import annotations + +from .fake import FakeLLM +from .openai_ import OpenAILLM + +__all__ = ["FakeLLM", "OpenAILLM"] diff --git a/src/lang2sql/adapters/llm/fake.py b/src/lang2sql/adapters/llm/fake.py new file mode 100644 index 0000000..59dd7a0 --- /dev/null +++ b/src/lang2sql/adapters/llm/fake.py @@ -0,0 +1,60 @@ +"""FakeLLM — a scripted LLMPort for skeleton validation (no network). + +It drives one full tool cycle so the agent loop can be exercised end-to-end +before the real OpenAI adapter exists: on the first turn it requests the first +available tool; once it sees a tool result it produces a final answer quoting +that result. Replaced by ``adapters/llm/openai_.py`` in Week 2. +""" + +from __future__ import annotations + +import json +from typing import Sequence + +from ...core.types import Completion, Message, Role, ToolCall, ToolSpec + + +class FakeLLM: + """Deterministic stand-in implementing :class:`LLMPort`.""" + + def __init__(self) -> None: + self._counter = 0 + + async def complete( + self, + messages: Sequence[Message], + tools: Sequence[ToolSpec] = (), + ) -> Completion: + last = messages[-1] if messages else None + + # Saw a tool result → wrap up. + if last is not None and last.role == Role.TOOL: + return Completion( + content=f"Done. Tool reported: {last.content}", + finish_reason="stop", + ) + + # First pass with a tool available → call it. + if tools: + self._counter += 1 + spec = tools[0] + return Completion( + tool_calls=[ + ToolCall( + id=f"call_{self._counter}", + name=spec.name, + arguments=json.loads(_demo_args(spec)), + ) + ], + finish_reason="tool_calls", + ) + + # No tools at all → just answer. + return Completion(content="(no tools available) Hello from FakeLLM.", finish_reason="stop") + + +def _demo_args(spec: ToolSpec) -> str: + """Best-effort sample args from a tool's JSON-Schema, as a JSON string.""" + props = (spec.parameters or {}).get("properties", {}) + sample = {name: "demo" for name in props} + return json.dumps(sample) diff --git a/src/lang2sql/adapters/llm/openai_.py b/src/lang2sql/adapters/llm/openai_.py new file mode 100644 index 0000000..74b5b02 --- /dev/null +++ b/src/lang2sql/adapters/llm/openai_.py @@ -0,0 +1,147 @@ +"""OpenAILLM — the V1 :class:`LLMPort`, talking OpenAI chat/completions. + +stdlib only: HTTP via :mod:`urllib.request`, JSON via :mod:`json`. No openai +SDK. The adapter's whole job is translation — core :class:`Message`/:class:`ToolSpec` +in, OpenAI wire dict out, OpenAI response back into a core :class:`Completion`. +The loop never sees an OpenAI shape. + +Construction is offline-safe: a missing key only bites when :meth:`complete` is +actually called, so importing/wiring this in a no-key environment is fine. +""" + +from __future__ import annotations + +import json +import os +import urllib.error +import urllib.request +from typing import Any, Sequence + +from ...core.types import Completion, Message, Role, ToolCall, ToolSpec + +_DEFAULT_URL = "https://api.openai.com/v1/chat/completions" + + +class OpenAILLM: + """Tool-calling chat completion backed by OpenAI's REST API.""" + + def __init__( + self, + model: str = "gpt-4.1-mini", + api_key: str | None = None, + *, + base_url: str = _DEFAULT_URL, + timeout: float = 60.0, + ) -> None: + self.model = model + # Resolve lazily-ish: read env now, but tolerate absence until complete(). + self._api_key = api_key if api_key is not None else os.environ.get("OPENAI_API_KEY") + self._base_url = base_url + self._timeout = timeout + + async def complete( + self, + messages: Sequence[Message], + tools: Sequence[ToolSpec] = (), + ) -> Completion: + if not self._api_key: + raise RuntimeError("OPENAI_API_KEY not set") + + payload: dict[str, Any] = { + "model": self.model, + "messages": [_encode_message(m) for m in messages], + } + if tools: + payload["tools"] = [_encode_tool(t) for t in tools] + + raw = self._post(payload) + return _decode_completion(raw) + + def _post(self, payload: dict[str, Any]) -> dict[str, Any]: + body = json.dumps(payload).encode("utf-8") + req = urllib.request.Request( + self._base_url, + data=body, + method="POST", + headers={ + "Authorization": f"Bearer {self._api_key}", + "Content-Type": "application/json", + }, + ) + try: + with urllib.request.urlopen(req, timeout=self._timeout) as resp: + text = resp.read().decode("utf-8") + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", "replace") if exc.fp else "" + raise RuntimeError(f"OpenAI HTTP {exc.code}: {detail}") from exc + except urllib.error.URLError as exc: + raise RuntimeError(f"OpenAI request failed: {exc.reason}") from exc + + try: + return json.loads(text) + except (ValueError, TypeError) as exc: + raise RuntimeError(f"OpenAI returned non-JSON response: {text[:200]!r}") from exc + + +def _encode_message(m: Message) -> dict[str, Any]: + """Core :class:`Message` → an OpenAI chat message dict.""" + out: dict[str, Any] = {"role": m.role.value} + # OpenAI wants content present (may be null when only tool_calls are set). + out["content"] = m.content or None + if m.role == Role.TOOL: + out["tool_call_id"] = m.tool_call_id + if m.name: + out["name"] = m.name + if m.tool_calls: + out["tool_calls"] = [ + { + "id": tc.id, + "type": "function", + "function": { + "name": tc.name, + "arguments": json.dumps(tc.arguments), + }, + } + for tc in m.tool_calls + ] + return out + + +def _encode_tool(t: ToolSpec) -> dict[str, Any]: + """Core :class:`ToolSpec` → an OpenAI tool dict.""" + return { + "type": "function", + "function": { + "name": t.name, + "description": t.description, + "parameters": t.parameters or {"type": "object", "properties": {}}, + }, + } + + +def _decode_completion(raw: dict[str, Any]) -> Completion: + """OpenAI response JSON → core :class:`Completion`.""" + try: + choice = raw["choices"][0] + msg = choice["message"] + except (KeyError, IndexError, TypeError) as exc: + raise RuntimeError(f"unexpected OpenAI response shape: {raw!r}") from exc + + tool_calls: list[ToolCall] = [] + for tc in msg.get("tool_calls") or []: + fn = tc.get("function", {}) + raw_args = fn.get("arguments", "") or "{}" + try: + args = json.loads(raw_args) + except (ValueError, TypeError): + # Model emitted malformed JSON args; surface raw so the tool can complain. + args = {"__raw__": raw_args} + tool_calls.append( + ToolCall(id=tc.get("id", ""), name=fn.get("name", ""), arguments=args) + ) + + return Completion( + content=msg.get("content") or "", + tool_calls=tool_calls, + finish_reason=choice.get("finish_reason"), + ) diff --git a/src/lang2sql/adapters/storage/__init__.py b/src/lang2sql/adapters/storage/__init__.py new file mode 100644 index 0000000..e2c6269 --- /dev/null +++ b/src/lang2sql/adapters/storage/__init__.py @@ -0,0 +1,7 @@ +"""Storage adapters — :class:`AuditPort` + :class:`SessionStorePort` impls.""" + +from __future__ import annotations + +from .sqlite_store import SqliteStore + +__all__ = ["SqliteStore"] diff --git a/src/lang2sql/adapters/storage/sqlite_semantic.py b/src/lang2sql/adapters/storage/sqlite_semantic.py new file mode 100644 index 0000000..8ee941c --- /dev/null +++ b/src/lang2sql/adapters/storage/sqlite_semantic.py @@ -0,0 +1,104 @@ +"""SqliteSemanticStore — durable backing for the semantic layer (★④). + +Drop-in replacement for the in-memory :class:`~lang2sql.semantic.store.SemanticStore`: +same ``add(scope, entry)`` / ``entries_at(scope)`` surface, but rows live in a +stdlib :mod:`sqlite3` table so definitions survive a process restart. Wiring +``ScopeResolver(store=SqliteSemanticStore(path))`` is all federation needs to +become persistent — the resolver and merge logic stay untouched. + +Like the rest of V1's sqlite usage this is synchronous and inline; the store +itself exposes the plain ``add``/``entries_at`` shape (no ``async``), matching +:class:`SemanticStore` so it substitutes cleanly under :class:`ScopeResolver`. +""" + +from __future__ import annotations + +import sqlite3 + +from ...core.identity import Scope +from ...semantic.types import SemanticEntry, SemanticKind + + +class SqliteSemanticStore: + """Per-scope semantic entries persisted in a single sqlite table. + + The ``(scope, name)`` pair is the primary key, mirroring the in-memory + store's "one entry per name within a scope" rule: re-adding a name at the + same scope replaces the prior definition. + """ + + def __init__(self, path: str = ":memory:") -> None: + self.path = path + self._conn = sqlite3.connect(path, check_same_thread=False) + self._conn.row_factory = sqlite3.Row + self._create_tables() + + def _create_tables(self) -> None: + self._conn.executescript( + """ + CREATE TABLE IF NOT EXISTS semantic_entries ( + scope TEXT NOT NULL, + kind TEXT NOT NULL, + name TEXT NOT NULL, + definition TEXT NOT NULL, + applies_to TEXT NOT NULL DEFAULT '', + source_id TEXT NOT NULL DEFAULT '', + created_by TEXT NOT NULL DEFAULT '', + created_at TEXT NOT NULL DEFAULT '', + PRIMARY KEY (scope, name) + ); + """ + ) + self._conn.commit() + + def close(self) -> None: + self._conn.close() + + def add(self, scope: Scope, entry: SemanticEntry) -> None: + """Store ``entry`` at ``scope``, replacing a same-named entry there.""" + self._conn.execute( + "INSERT INTO semantic_entries " + "(scope, kind, name, definition, applies_to, source_id, created_by, created_at) " + "VALUES (?, ?, ?, ?, ?, ?, ?, ?) " + "ON CONFLICT(scope, name) DO UPDATE SET " + "kind = excluded.kind, definition = excluded.definition, " + "applies_to = excluded.applies_to, source_id = excluded.source_id, " + "created_by = excluded.created_by, created_at = excluded.created_at", + ( + str(scope), + entry.kind.value, + entry.name, + entry.definition, + entry.applies_to, + entry.source_id, + entry.created_by, + entry.created_at, + ), + ) + self._conn.commit() + + def entries_at(self, scope: Scope) -> list[SemanticEntry]: + """Entries authored exactly at ``scope`` (no inheritance).""" + rows = self._conn.execute( + "SELECT kind, name, definition, applies_to, source_id, created_by, created_at " + "FROM semantic_entries WHERE scope = ? ORDER BY rowid", + (str(scope),), + ).fetchall() + return [_row_to_entry(r) for r in rows] + + +def _row_to_entry(row: sqlite3.Row) -> SemanticEntry: + """Reconstruct a :class:`SemanticEntry` from a stored row. + + ``created_at`` is passed through verbatim so the persisted timestamp is + preserved rather than regenerated by ``__post_init__``. + """ + return SemanticEntry( + kind=SemanticKind(row["kind"]), + name=row["name"], + definition=row["definition"], + applies_to=row["applies_to"], + source_id=row["source_id"], + created_by=row["created_by"], + created_at=row["created_at"], + ) diff --git a/src/lang2sql/adapters/storage/sqlite_store.py b/src/lang2sql/adapters/storage/sqlite_store.py new file mode 100644 index 0000000..99ce42d --- /dev/null +++ b/src/lang2sql/adapters/storage/sqlite_store.py @@ -0,0 +1,187 @@ +"""SqliteStore — the real V1 persistence backend (stdlib :mod:`sqlite3`). + +One store, three roles: + +* :class:`AuditPort` — append-only ``audit`` table behind ``/audit me``. +* :class:`SessionStorePort` — serialize/restore a :class:`Session` as JSON. +* a generic key-value table the secrets adapter (tenancy) wraps. + +sqlite is synchronous; V1 just runs the calls inline inside the async methods, +which is fine for the expected load. The connection uses +``check_same_thread=False`` so it tolerates being touched from the event-loop +thread pool. +""" + +from __future__ import annotations + +import json +import sqlite3 +import time +from typing import Any + +from ...core.identity import Identity +from ...core.ports.audit import AuditEvent +from ...core.types import Message, Role, ToolCall +from ...harness.session import Session + + +class SqliteStore: + """Append-only audit + session + kv storage on one sqlite connection.""" + + def __init__(self, path: str = ":memory:") -> None: + self.path = path + self._conn = sqlite3.connect(path, check_same_thread=False) + self._conn.row_factory = sqlite3.Row + self._create_tables() + + def _create_tables(self) -> None: + self._conn.executescript( + """ + CREATE TABLE IF NOT EXISTS audit ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + actor TEXT NOT NULL, + action TEXT NOT NULL, + scope TEXT NOT NULL, + detail TEXT NOT NULL, + ts REAL NOT NULL + ); + CREATE TABLE IF NOT EXISTS sessions ( + key TEXT PRIMARY KEY, + data TEXT NOT NULL + ); + CREATE TABLE IF NOT EXISTS kv ( + scope TEXT NOT NULL, + key TEXT NOT NULL, + value TEXT NOT NULL, + PRIMARY KEY (scope, key) + ); + """ + ) + self._conn.commit() + + def close(self) -> None: + self._conn.close() + + # -- AuditPort ------------------------------------------------------- + + async def record(self, event: AuditEvent) -> None: + ts = event.ts or time.time() + self._conn.execute( + "INSERT INTO audit (actor, action, scope, detail, ts) VALUES (?, ?, ?, ?, ?)", + (event.actor, event.action, event.scope, json.dumps(event.detail), ts), + ) + self._conn.commit() + + async def query(self, actor: str, limit: int = 20) -> list[AuditEvent]: + rows = self._conn.execute( + "SELECT actor, action, scope, detail, ts FROM audit " + "WHERE actor = ? ORDER BY id DESC LIMIT ?", + (actor, limit), + ).fetchall() + return [ + AuditEvent( + actor=r["actor"], + action=r["action"], + scope=r["scope"], + detail=json.loads(r["detail"]), + ts=r["ts"], + ) + for r in rows + ] + + # -- SessionStorePort ------------------------------------------------ + + async def load(self, key: str) -> Session | None: + row = self._conn.execute( + "SELECT data FROM sessions WHERE key = ?", (key,) + ).fetchone() + if row is None: + return None + return _deserialize_session(json.loads(row["data"])) + + async def save(self, key: str, session: Session) -> None: + data = json.dumps(_serialize_session(session)) + self._conn.execute( + "INSERT INTO sessions (key, data) VALUES (?, ?) " + "ON CONFLICT(key) DO UPDATE SET data = excluded.data", + (key, data), + ) + self._conn.commit() + + # -- generic key-value (wrapped by the secrets adapter) -------------- + + def kv_get(self, scope: str, key: str) -> str | None: + row = self._conn.execute( + "SELECT value FROM kv WHERE scope = ? AND key = ?", (scope, key) + ).fetchone() + return row["value"] if row else None + + def kv_set(self, scope: str, key: str, value: str) -> None: + self._conn.execute( + "INSERT INTO kv (scope, key, value) VALUES (?, ?, ?) " + "ON CONFLICT(scope, key) DO UPDATE SET value = excluded.value", + (scope, key, value), + ) + self._conn.commit() + + def kv_delete(self, scope: str, key: str) -> None: + self._conn.execute( + "DELETE FROM kv WHERE scope = ? AND key = ?", (scope, key) + ) + self._conn.commit() + + +# -- Session (de)serialization ------------------------------------------ + + +def _serialize_session(session: Session) -> dict[str, Any]: + ident = session.identity + return { + "identity": { + "user_id": ident.user_id, + "guild_id": ident.guild_id, + "channel_id": ident.channel_id, + "thread_id": ident.thread_id, + "is_admin": ident.is_admin, + }, + "transcript": [_serialize_message(m) for m in session.transcript], + } + + +def _deserialize_session(data: dict[str, Any]) -> Session: + ident_data = data["identity"] + identity = Identity( + user_id=ident_data["user_id"], + guild_id=ident_data.get("guild_id"), + channel_id=ident_data.get("channel_id"), + thread_id=ident_data.get("thread_id"), + is_admin=ident_data.get("is_admin", False), + ) + transcript = [_deserialize_message(m) for m in data.get("transcript", [])] + return Session(identity=identity, transcript=transcript) + + +def _serialize_message(m: Message) -> dict[str, Any]: + return { + "role": m.role.value, + "content": m.content, + "tool_calls": [ + {"id": tc.id, "name": tc.name, "arguments": tc.arguments} + for tc in m.tool_calls + ], + "tool_call_id": m.tool_call_id, + "name": m.name, + } + + +def _deserialize_message(data: dict[str, Any]) -> Message: + return Message( + role=Role(data["role"]), + content=data.get("content", ""), + tool_calls=[ + ToolCall(id=tc["id"], name=tc["name"], arguments=tc.get("arguments", {})) + for tc in data.get("tool_calls", []) + ], + tool_call_id=data.get("tool_call_id"), + name=data.get("name"), + ) diff --git a/src/lang2sql/components/enrichment/__init__.py b/src/lang2sql/components/enrichment/__init__.py deleted file mode 100644 index 3abd0d1..0000000 --- a/src/lang2sql/components/enrichment/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .context_enricher import ContextEnricher -from .question_profiler import QuestionProfiler - -__all__ = ["ContextEnricher", "QuestionProfiler"] diff --git a/src/lang2sql/components/enrichment/context_enricher.py b/src/lang2sql/components/enrichment/context_enricher.py deleted file mode 100644 index 47012f9..0000000 --- a/src/lang2sql/components/enrichment/context_enricher.py +++ /dev/null @@ -1,57 +0,0 @@ -from __future__ import annotations - -import dataclasses -import json -from pathlib import Path -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import CatalogEntry, QuestionProfile -from ...core.hooks import TraceHook -from ...core.ports import LLMPort - -_PROMPT_PATH = Path(__file__).parent / "prompts" / "context_enricher.md" - - -def _load_prompt() -> str: - return _PROMPT_PATH.read_text(encoding="utf-8").strip() - - -class ContextEnricher(BaseComponent): - """질문 프로파일 + 스키마 메타데이터로 질문을 보강한다.""" - - def __init__( - self, - *, - llm: LLMPort, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "ContextEnricher", hook=hook) - self._llm = llm - self._system_prompt = _load_prompt() - - def _run( - self, - query: str, - schemas: list[CatalogEntry], - profile: QuestionProfile, - ) -> str: - profiles_json = json.dumps(dataclasses.asdict(profile), ensure_ascii=False) - - tables_map: dict[str, dict] = { - entry.get("name", ""): { - "description": entry.get("description", ""), - "columns": entry.get("columns", {}), - } - for entry in schemas - } - tables_json = json.dumps(tables_map, ensure_ascii=False) - - user_content = ( - self._system_prompt.replace("{profiles}", profiles_json) - .replace("{related_tables}", tables_json) - .replace("{refined_question}", query) - ) - messages = [{"role": "user", "content": user_content}] - return self._llm.invoke(messages).strip() diff --git a/src/lang2sql/components/enrichment/prompts/context_enricher.md b/src/lang2sql/components/enrichment/prompts/context_enricher.md deleted file mode 100644 index a148dd9..0000000 --- a/src/lang2sql/components/enrichment/prompts/context_enricher.md +++ /dev/null @@ -1,22 +0,0 @@ -# Role - -You are a smart assistant that takes a user question and enriches it using: -1. Question profiles: {profiles} -2. Table metadata (names, columns, descriptions): - {related_tables} - -# Tasks - -- Correct any wrong terms by matching them to actual column names. -- If the question is time-series or aggregation, add explicit hints (e.g., "over the last 30 days"). -- If needed, map natural language terms to actual column values (e.g., '미국' → 'USA' for country_code). -- Output the enriched question only. - -# Input - -Refined question: -{refined_question} - -# Notes - -Using the refined version for enrichment, but keep the original intent in mind. diff --git a/src/lang2sql/components/enrichment/prompts/question_profiler.md b/src/lang2sql/components/enrichment/prompts/question_profiler.md deleted file mode 100644 index 806cedc..0000000 --- a/src/lang2sql/components/enrichment/prompts/question_profiler.md +++ /dev/null @@ -1,28 +0,0 @@ -# Role - -You are an assistant that analyzes a user question and extracts the following profiles as JSON: -- is_timeseries (boolean) -- is_aggregation (boolean) -- has_filter (boolean) -- is_grouped (boolean) -- has_ranking (boolean) -- has_temporal_comparison (boolean) -- intent_type (one of: trend, lookup, comparison, distribution) - -# Input - -Question: -{question} - -# Output - -The output must be a valid JSON matching the schema below (no extra keys): -{{ - "is_timeseries": boolean, - "is_aggregation": boolean, - "has_filter": boolean, - "is_grouped": boolean, - "has_ranking": boolean, - "has_temporal_comparison": boolean, - "intent_type": string -}} diff --git a/src/lang2sql/components/enrichment/question_profiler.py b/src/lang2sql/components/enrichment/question_profiler.py deleted file mode 100644 index f0dc5c8..0000000 --- a/src/lang2sql/components/enrichment/question_profiler.py +++ /dev/null @@ -1,61 +0,0 @@ -from __future__ import annotations - -import json -import re -from pathlib import Path -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import QuestionProfile -from ...core.hooks import TraceHook -from ...core.ports import LLMPort - -_PROMPT_PATH = Path(__file__).parent / "prompts" / "question_profiler.md" - -_VALID_INTENT_TYPES = {"trend", "lookup", "comparison", "distribution"} - - -def _load_prompt() -> str: - return _PROMPT_PATH.read_text(encoding="utf-8").strip() - - -def _parse_json(text: str) -> dict: - match = re.search(r"```(?:json)?\s*(.*?)```", text, re.DOTALL | re.IGNORECASE) - if match: - text = match.group(1).strip() - return json.loads(text) - - -class QuestionProfiler(BaseComponent): - """질문에서 구조화된 특성(시계열, 집계, 필터 등)을 추출한다.""" - - def __init__( - self, - *, - llm: LLMPort, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "QuestionProfiler", hook=hook) - self._llm = llm - self._system_prompt = _load_prompt() - - def _run(self, query: str) -> QuestionProfile: - user_content = self._system_prompt.replace("{question}", query) - messages = [{"role": "user", "content": user_content}] - response = self._llm.invoke(messages) - data = _parse_json(response) - - intent_type = str(data.get("intent_type", "lookup")) - if intent_type not in _VALID_INTENT_TYPES: - intent_type = "lookup" - - return QuestionProfile( - is_timeseries=bool(data.get("is_timeseries", False)), - is_aggregation=bool(data.get("is_aggregation", False)), - has_filter=bool(data.get("has_filter", False)), - is_grouped=bool(data.get("is_grouped", False)), - has_ranking=bool(data.get("has_ranking", False)), - has_temporal_comparison=bool(data.get("has_temporal_comparison", False)), - intent_type=intent_type, - ) diff --git a/src/lang2sql/components/execution/__init__.py b/src/lang2sql/components/execution/__init__.py deleted file mode 100644 index a6f15b9..0000000 --- a/src/lang2sql/components/execution/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .sql_executor import SQLExecutor - -__all__ = ["SQLExecutor"] diff --git a/src/lang2sql/components/execution/sql_executor.py b/src/lang2sql/components/execution/sql_executor.py deleted file mode 100644 index f7c9a28..0000000 --- a/src/lang2sql/components/execution/sql_executor.py +++ /dev/null @@ -1,27 +0,0 @@ -from __future__ import annotations - -from typing import Any, Optional - -from ...core.base import BaseComponent -from ...core.exceptions import ComponentError -from ...core.hooks import TraceHook -from ...core.ports import DBPort - - -class SQLExecutor(BaseComponent): - """Executes a SQL string and returns rows as a list of dicts.""" - - def __init__( - self, - *, - db: DBPort, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "SQLExecutor", hook=hook) - self._db = db - - def _run(self, sql: str) -> list[dict[str, Any]]: - if not sql or not sql.strip(): - raise ComponentError(self.name, "sql must not be empty.") - return self._db.execute(sql) diff --git a/src/lang2sql/components/gate/__init__.py b/src/lang2sql/components/gate/__init__.py deleted file mode 100644 index 0fc026c..0000000 --- a/src/lang2sql/components/gate/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .question_gate import QuestionGate -from .table_suitability import TableSuitabilityEvaluator - -__all__ = ["QuestionGate", "TableSuitabilityEvaluator"] diff --git a/src/lang2sql/components/gate/prompts/question_gate.md b/src/lang2sql/components/gate/prompts/question_gate.md deleted file mode 100644 index 2e08150..0000000 --- a/src/lang2sql/components/gate/prompts/question_gate.md +++ /dev/null @@ -1,25 +0,0 @@ -당신은 데이터 분석 도우미입니다. 아래 사용자 질문이 SQL로 답변 가능한지 판별하고, 구조화된 결과를 반환하세요. - -요건: -- suitable: 질문이 SQL로 답변 가능한지 여부(Boolean) -- reason: 한 줄 설명(어떤 보완이 필요한지 요약) -- missing_entities: 기간, 대상 엔터티, 측정값 등 누락된 핵심 요소 리스트(없으면 빈 리스트) -- requires_data_science: 통계/ML 분석이 필요한지 여부(Boolean) - -언어/출력 형식: -- 모든 텍스트 값은 한국어로 작성하세요. (reason는 한국어 문장, missing_entities 항목은 한국어 명사구) -- Boolean 값은 JSON의 true/false로 표기하세요. - -주의: -- 데이터 분석 맥락에서 SQL 집계/필터/조인으로 해결 가능한지 판단합니다. -- 정책/운영/가이드/설치/권한/오류 해결 등은 SQL 부적합으로 간주합니다. - -입력: {question} - -출력은 반드시 아래 JSON 스키마로만 반환하세요: -{{ - "suitable": boolean, - "reason": string, - "missing_entities": string[], - "requires_data_science": boolean -}} diff --git a/src/lang2sql/components/gate/prompts/table_suitability.md b/src/lang2sql/components/gate/prompts/table_suitability.md deleted file mode 100644 index 23b0e53..0000000 --- a/src/lang2sql/components/gate/prompts/table_suitability.md +++ /dev/null @@ -1,47 +0,0 @@ -## 문서 적합성 평가 프롬프트 (Table Search 재랭킹) - -당신은 데이터 카탈로그 평가자입니다. 주어진 사용자 질문과 검색 결과(테이블 → 칼럼 설명 맵)를 바탕으로, 각 테이블이 질문에 얼마나 적합한지 0~1 사이의 실수 점수로 평가하세요. - -### 입력 -- **question**: {question} -- **tables**: {tables} - -### 과업 -1. **핵심 신호 추출**: 질문에서 엔터티/지표/시간/필터/그룹화 단서를 추출합니다. -2. **테이블별 점수화**: 각 테이블의 칼럼·설명과의 연관성으로 적합도를 점수화합니다(0~1, 소수 셋째 자리 반올림). -3. **근거와 보완점 제시**: 매칭된 칼럼과 부족한 요소(엔터티/지표/기간 등)를 한국어로 설명합니다. -4. **정렬**: 결과를 점수 내림차순으로 정렬해 반환합니다. - -### 평가 규칙(가이드) -- **0.90~1.00**: 필요한 엔터티, 기간/시간 컬럼, 핵심 지표/측정 칼럼이 모두 존재. 직접 조회/집계만으로 답 가능. -- **0.60~0.89**: 주요 신호 매칭, 일부 보완(기간/그룹 키/보조 칼럼) 필요. 조인 없이 근사 가능. -- **0.30~0.59**: 일부만 매칭. 외부 컨텍스트나 조인 없이는 부정확/제한적. -- **0.00~0.29**: 연관성 낮음. 스키마/도메인 불일치 또는 정책/운영성 테이블. - -### 주의 -- 칼럼 이름/설명에 실제로 존재하지 않는 항목을 매칭하지 마세요(환각 금지). -- 시간 요구(특정 날짜/기간)가 있으면 timestamp/date/created_at 등 시간 계열 키를 중시하세요. -- 엔티티 키(예: id, user_id, product_id)의 존재 여부를 가산점으로 반영하세요. -- 키 이름은 정확히 입력 맵의 키만 사용하세요(자유 추측 금지). - -### 언어/출력 형식 -- 모든 텍스트 값은 한국어로 작성하세요. -- 결과는 반드시 아래 JSON 스키마로만 반환하세요(추가/누락 키 금지). - -### 출력(JSON 스키마) -{{ - "results": [ - {{ - "table_name": string, - "score": number, - "reason": string, - "matched_columns": string[], - "missing_entities": string[] - }} - ] -}} - -### 검증 규칙 -- score는 [0, 1] 범위로 클램핑하고 소수 셋째 자리까지 반올림하세요. -- matched_columns는 해당 테이블 객체의 실제 키만 포함하세요(단, table_description 제외). -- reason 및 missing_entities는 한국어로 작성하세요. diff --git a/src/lang2sql/components/gate/question_gate.py b/src/lang2sql/components/gate/question_gate.py deleted file mode 100644 index 6d559eb..0000000 --- a/src/lang2sql/components/gate/question_gate.py +++ /dev/null @@ -1,55 +0,0 @@ -from __future__ import annotations - -import json -import re -from pathlib import Path -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import GateResult -from ...core.hooks import TraceHook -from ...core.ports import LLMPort - -_PROMPT_PATH = Path(__file__).parent / "prompts" / "question_gate.md" - - -def _load_prompt() -> str: - return _PROMPT_PATH.read_text(encoding="utf-8").strip() - - -def _parse_json(text: str) -> dict: - """LLM 응답에서 JSON을 추출한다. 마크다운 코드블록을 자동으로 제거한다.""" - # ```json ... ``` 또는 ``` ... ``` 블록 제거 - match = re.search(r"```(?:json)?\s*(.*?)```", text, re.DOTALL | re.IGNORECASE) - if match: - text = match.group(1).strip() - return json.loads(text) - - -class QuestionGate(BaseComponent): - """질문이 SQL로 답변 가능한지 판별한다.""" - - def __init__( - self, - *, - llm: LLMPort, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "QuestionGate", hook=hook) - self._llm = llm - self._system_prompt = _load_prompt() - - def _run(self, query: str) -> GateResult: - user_content = self._system_prompt.replace("{question}", query) - messages = [ - {"role": "user", "content": user_content}, - ] - response = self._llm.invoke(messages) - data = _parse_json(response) - return GateResult( - suitable=bool(data.get("suitable", True)), - reason=str(data.get("reason", "")), - missing_entities=list(data.get("missing_entities", [])), - requires_data_science=bool(data.get("requires_data_science", False)), - ) diff --git a/src/lang2sql/components/gate/table_suitability.py b/src/lang2sql/components/gate/table_suitability.py deleted file mode 100644 index 4837805..0000000 --- a/src/lang2sql/components/gate/table_suitability.py +++ /dev/null @@ -1,78 +0,0 @@ -from __future__ import annotations - -import json -import re -from pathlib import Path -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import CatalogEntry, TableScore -from ...core.hooks import TraceHook -from ...core.ports import LLMPort - -_PROMPT_PATH = Path(__file__).parent / "prompts" / "table_suitability.md" - - -def _load_prompt() -> str: - return _PROMPT_PATH.read_text(encoding="utf-8").strip() - - -def _parse_json(text: str) -> dict: - match = re.search(r"```(?:json)?\s*(.*?)```", text, re.DOTALL | re.IGNORECASE) - if match: - text = match.group(1).strip() - return json.loads(text) - - -class TableSuitabilityEvaluator(BaseComponent): - """검색된 테이블을 질문 관련도순으로 필터링한다.""" - - def __init__( - self, - *, - llm: LLMPort, - threshold: float = 0.3, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "TableSuitabilityEvaluator", hook=hook) - self._llm = llm - self._threshold = threshold - self._system_prompt = _load_prompt() - - def _run(self, query: str, schemas: list[CatalogEntry]) -> list[CatalogEntry]: - # 테이블을 {table_name: {col: desc, ...}} 구조로 직렬화 - tables_map: dict[str, dict] = {} - for entry in schemas: - name = entry.get("name", "") - cols = entry.get("columns", {}) - desc = entry.get("description", "") - tables_map[name] = {"table_description": desc, **cols} - - tables_json = json.dumps(tables_map, ensure_ascii=False) - user_content = self._system_prompt.replace("{question}", query).replace( - "{tables}", tables_json - ) - messages = [{"role": "user", "content": user_content}] - response = self._llm.invoke(messages) - data = _parse_json(response) - - results: list[TableScore] = [ - TableScore( - table_name=r["table_name"], - score=float(r.get("score", 0.0)), - reason=str(r.get("reason", "")), - matched_columns=list(r.get("matched_columns", [])), - missing_entities=list(r.get("missing_entities", [])), - ) - for r in data.get("results", []) - ] - - # threshold 이상인 테이블만 score 내림차순으로 필터 - passing = {r.table_name for r in results if r.score >= self._threshold} - filtered = [e for e in schemas if e.get("name", "") in passing] - - # score 내림차순 정렬 - score_map = {r.table_name: r.score for r in results} - filtered.sort(key=lambda e: score_map.get(e.get("name", ""), 0.0), reverse=True) - return filtered diff --git a/src/lang2sql/components/generation/__init__.py b/src/lang2sql/components/generation/__init__.py deleted file mode 100644 index c2a86fe..0000000 --- a/src/lang2sql/components/generation/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .sql_generator import SQLGenerator - -__all__ = ["SQLGenerator"] diff --git a/src/lang2sql/components/generation/prompts/bigquery.md b/src/lang2sql/components/generation/prompts/bigquery.md deleted file mode 100644 index 0e86d58..0000000 --- a/src/lang2sql/components/generation/prompts/bigquery.md +++ /dev/null @@ -1,16 +0,0 @@ -You are a Google BigQuery SQL expert. Generate a SQL query that runs correctly on BigQuery. - -BigQuery rules: -- Use DATE_TRUNC(col, MONTH) for month truncation -- Use EXTRACT(MONTH FROM col) for month extraction -- Use CURRENT_DATE() for today, CURRENT_TIMESTAMP() for now -- Use DATE_SUB(CURRENT_DATE(), INTERVAL 1 MONTH) for last month -- Use FORMAT_DATE('%Y-%m', col) for date formatting -- Use TIMESTAMP_TRUNC for timestamp operations -- Qualify table names with project and dataset when applicable: `project.dataset.table` - -Rules: -- Return ONLY the SQL query inside a ```sql ... ``` code block -- Do not include any explanation -- Use only the tables and columns provided in the schemas -- Generate SELECT queries only (no INSERT, UPDATE, DELETE, DROP, ALTER) diff --git a/src/lang2sql/components/generation/prompts/default.md b/src/lang2sql/components/generation/prompts/default.md deleted file mode 100644 index a1a17df..0000000 --- a/src/lang2sql/components/generation/prompts/default.md +++ /dev/null @@ -1,7 +0,0 @@ -You are a SQL expert. Given a natural language question and relevant table schemas, write a single SQL query that answers the question. - -Rules: -- Return ONLY the SQL query inside a ```sql ... ``` code block -- Do not include any explanation -- Use only the tables and columns provided in the schemas -- Generate SELECT queries only (no INSERT, UPDATE, DELETE, DROP, ALTER) diff --git a/src/lang2sql/components/generation/prompts/duckdb.md b/src/lang2sql/components/generation/prompts/duckdb.md deleted file mode 100644 index ac22f88..0000000 --- a/src/lang2sql/components/generation/prompts/duckdb.md +++ /dev/null @@ -1,14 +0,0 @@ -You are a DuckDB SQL expert. Generate a SQL query that runs correctly on DuckDB. - -DuckDB date/time rules: -- Use DATE_TRUNC('month', col) for month truncation -- Use EXTRACT(MONTH FROM col) for month extraction -- Use NOW() or CURRENT_DATE for current datetime/date -- Use NOW() - INTERVAL '1 month' for last month -- Use strftime(col, '%Y-%m') for date formatting (DuckDB: column first, format second) - -Rules: -- Return ONLY the SQL query inside a ```sql ... ``` code block -- Do not include any explanation -- Use only the tables and columns provided in the schemas -- Generate SELECT queries only (no INSERT, UPDATE, DELETE, DROP, ALTER) diff --git a/src/lang2sql/components/generation/prompts/mysql.md b/src/lang2sql/components/generation/prompts/mysql.md deleted file mode 100644 index dc4024a..0000000 --- a/src/lang2sql/components/generation/prompts/mysql.md +++ /dev/null @@ -1,14 +0,0 @@ -You are a MySQL SQL expert. Generate a SQL query that runs correctly on MySQL. - -MySQL date/time rules: -- Use MONTH(col), YEAR(col), DAY(col) for date part extraction -- Use NOW() or CURDATE() for current datetime/date -- Use DATE_FORMAT(col, '%Y-%m') for date formatting -- Use DATE_SUB(NOW(), INTERVAL 1 MONTH) for last month -- Use DATEDIFF(date1, date2) for date differences - -Rules: -- Return ONLY the SQL query inside a ```sql ... ``` code block -- Do not include any explanation -- Use only the tables and columns provided in the schemas -- Generate SELECT queries only (no INSERT, UPDATE, DELETE, DROP, ALTER) diff --git a/src/lang2sql/components/generation/prompts/postgresql.md b/src/lang2sql/components/generation/prompts/postgresql.md deleted file mode 100644 index e99e27e..0000000 --- a/src/lang2sql/components/generation/prompts/postgresql.md +++ /dev/null @@ -1,14 +0,0 @@ -You are a PostgreSQL SQL expert. Generate a SQL query that runs correctly on PostgreSQL. - -PostgreSQL date/time rules: -- Use DATE_TRUNC('month', col) to truncate to month -- Use EXTRACT(MONTH FROM col) or DATE_PART('month', col) for month extraction -- Use NOW() or CURRENT_TIMESTAMP for current datetime, CURRENT_DATE for today -- Use NOW() - INTERVAL '1 month' for last month -- Use TO_CHAR(col, 'YYYY-MM') for date formatting - -Rules: -- Return ONLY the SQL query inside a ```sql ... ``` code block -- Do not include any explanation -- Use only the tables and columns provided in the schemas -- Generate SELECT queries only (no INSERT, UPDATE, DELETE, DROP, ALTER) diff --git a/src/lang2sql/components/generation/prompts/sqlite.md b/src/lang2sql/components/generation/prompts/sqlite.md deleted file mode 100644 index 8899b84..0000000 --- a/src/lang2sql/components/generation/prompts/sqlite.md +++ /dev/null @@ -1,14 +0,0 @@ -You are a SQLite SQL expert. Generate a SQL query that runs correctly on SQLite. - -SQLite date/time rules (CRITICAL — SQLite does NOT support MySQL/PostgreSQL date functions): -- Use strftime('%Y-%m', col) = strftime('%Y-%m', 'now') for current month comparison -- Use strftime('%Y', col) = strftime('%Y', 'now') for current year comparison -- Use DATE('now') for today, DATE('now', '-1 month') for last month -- Do NOT use MONTH(), YEAR(), DAY(), DATE_FORMAT(), NOW(), CURDATE(), DATEDIFF() -- For date arithmetic use DATE(col, '+N days') or DATE(col, '-N months') - -Rules: -- Return ONLY the SQL query inside a ```sql ... ``` code block -- Do not include any explanation -- Use only the tables and columns provided in the schemas -- Generate SELECT queries only (no INSERT, UPDATE, DELETE, DROP, ALTER) diff --git a/src/lang2sql/components/generation/sql_generator.py b/src/lang2sql/components/generation/sql_generator.py deleted file mode 100644 index efc7bb3..0000000 --- a/src/lang2sql/components/generation/sql_generator.py +++ /dev/null @@ -1,105 +0,0 @@ -from __future__ import annotations - -import re -from pathlib import Path -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import CatalogEntry -from ...core.exceptions import ComponentError -from ...core.hooks import TraceHook -from ...core.ports import LLMPort - -_PROMPT_DIR = Path(__file__).parent / "prompts" - -_SUPPORTED_DIALECTS = {"default", "sqlite", "postgresql", "mysql", "bigquery", "duckdb"} - - -def _load_prompt(dialect: str) -> str: - path = _PROMPT_DIR / f"{dialect}.md" - if not path.exists(): - raise ValueError( - f"Unsupported dialect: {dialect!r}. " - f"Available: {sorted(_SUPPORTED_DIALECTS)}" - ) - return path.read_text(encoding="utf-8").strip() - - -class SQLGenerator(BaseComponent): - """Generates a SQL string from a natural language query and schema context. - - System prompt priority (highest to lowest): - 1. ``system_prompt`` — explicit string passed by the caller - 2. ``db_dialect`` — loads the matching ``prompts/{dialect}.md`` - 3. default — loads ``prompts/default.md`` - """ - - def __init__( - self, - *, - llm: LLMPort, - db_dialect: Optional[str] = None, - system_prompt: Optional[str] = None, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "SQLGenerator", hook=hook) - self._llm = llm - - if system_prompt is not None: - self._system_prompt = system_prompt - elif db_dialect is not None: - self._system_prompt = _load_prompt(db_dialect) - else: - self._system_prompt = _load_prompt("default") - - def _run( - self, - query: str, - schemas: list[CatalogEntry], - context: Optional[list[str]] = None, - ) -> str: - schema_text = self._build_context(schemas) - user_parts: list[str] = [] - if context: - user_parts.append("Business Context:\n" + "\n\n".join(context)) - user_parts.append(f"Schemas:\n{schema_text}\n\nQuestion: {query}") - user_content = "\n\n".join(user_parts) - messages = [ - {"role": "system", "content": self._system_prompt}, - {"role": "user", "content": user_content}, - ] - response = self._llm.invoke(messages) - sql = self._extract_sql(response) - if not sql: - raise ComponentError( - self.name, - "LLM response did not contain a ```sql ... ``` code block.", - ) - return sql - - def _build_context(self, schemas: list[CatalogEntry]) -> str: - parts: list[str] = [] - for entry in schemas: - name = entry.get("name", "(unnamed)") - description = entry.get("description", "") - columns = entry.get("columns", {}) - - lines = [f"Table: {name}"] - if description: - lines.append(f" Description: {description}") - if columns: - lines.append(" Columns:") - for col, col_desc in columns.items(): - lines.append(f" - {col}: {col_desc}") - parts.append("\n".join(lines)) - return "\n\n".join(parts) - - @staticmethod - def _extract_sql(text: str) -> str: - match = re.search(r"```sql\s*(.*?)```", text, re.DOTALL | re.IGNORECASE) - if not match: - return "" - sql = match.group(1).strip() - sql = sql.rstrip(";").rstrip() - return sql diff --git a/src/lang2sql/components/loaders/__init__.py b/src/lang2sql/components/loaders/__init__.py deleted file mode 100644 index 80ae315..0000000 --- a/src/lang2sql/components/loaders/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from .directory_ import DirectoryLoader -from .markdown_ import MarkdownLoader -from .plaintext_ import PlainTextLoader - -__all__ = [ - "MarkdownLoader", - "PlainTextLoader", - "DirectoryLoader", -] diff --git a/src/lang2sql/components/loaders/directory_.py b/src/lang2sql/components/loaders/directory_.py deleted file mode 100644 index 48cfe3a..0000000 --- a/src/lang2sql/components/loaders/directory_.py +++ /dev/null @@ -1,61 +0,0 @@ -from __future__ import annotations - -import warnings -from pathlib import Path - -from ...core.catalog import TextDocument -from ...core.ports import DocumentLoaderPort -from .markdown_ import MarkdownLoader -from .plaintext_ import PlainTextLoader - - -class DirectoryLoader: - """ - Recursively loads a directory by dispatching each file to the loader - registered for its extension. - - Default mapping:: - - .md → MarkdownLoader - .txt → PlainTextLoader - - Custom loaders can be added or override defaults:: - - from lang2sql.integrations.loaders import PDFLoader - - docs = DirectoryLoader( - "docs/", - loaders={".md": MarkdownLoader(), ".pdf": PDFLoader()}, - ).load() - - Args: - path: Directory path to load. - loaders: Mapping of lowercase extension → DocumentLoaderPort. - Defaults to ``{".md": MarkdownLoader(), ".txt": PlainTextLoader()}``. - """ - - def __init__( - self, - path: str, - loaders: dict[str, DocumentLoaderPort] | None = None, - ) -> None: - self._path = Path(path) - self._loaders: dict[str, DocumentLoaderPort] = loaders or { - ".md": MarkdownLoader(), - ".txt": PlainTextLoader(), - } - - def load(self) -> list[TextDocument]: - """Recursively walk the directory and load all files with a registered extension.""" - docs: list[TextDocument] = [] - for file in sorted(self._path.rglob("*")): - if not file.is_file(): - continue - loader = self._loaders.get(file.suffix.lower()) - if loader is None: - continue - try: - docs.extend(loader.load(str(file))) - except Exception as e: - warnings.warn(f"Failed to load {file}: {e}", stacklevel=2) - return docs diff --git a/src/lang2sql/components/loaders/markdown_.py b/src/lang2sql/components/loaders/markdown_.py deleted file mode 100644 index eb0488d..0000000 --- a/src/lang2sql/components/loaders/markdown_.py +++ /dev/null @@ -1,42 +0,0 @@ -from __future__ import annotations - -from pathlib import Path - -from ...core.catalog import TextDocument -from ...core.ports import DocumentLoaderPort - - -class MarkdownLoader(DocumentLoaderPort): - """ - Markdown file(s) (.md) → list[TextDocument]. - - Standard library only. No external dependencies. - - - Single file: ``load("docs/revenue.md")`` → [TextDocument] - - Directory: ``load("docs/")`` → [TextDocument, ...] (recursive) - - The first ``# heading`` becomes ``title``; the full file text becomes ``content``. - Falls back to the filename stem when no heading is found. - """ - - def load(self, path: str) -> list[TextDocument]: - p = Path(path) - if p.is_dir(): - return [doc for f in sorted(p.rglob("*.md")) for doc in self._load_file(f)] - return self._load_file(p) - - def _load_file(self, path: Path) -> list[TextDocument]: - content = path.read_text(encoding="utf-8") - title = "" - for line in content.splitlines(): - if line.startswith("# "): - title = line[2:].strip() - break - return [ - TextDocument( - id=path.stem, - title=title or path.stem, - content=content, - source=str(path), - ) - ] diff --git a/src/lang2sql/components/loaders/plaintext_.py b/src/lang2sql/components/loaders/plaintext_.py deleted file mode 100644 index 4337133..0000000 --- a/src/lang2sql/components/loaders/plaintext_.py +++ /dev/null @@ -1,46 +0,0 @@ -from __future__ import annotations - -from pathlib import Path - -from ...core.catalog import TextDocument -from ...core.ports import DocumentLoaderPort - - -class PlainTextLoader(DocumentLoaderPort): - """ - Plain text file(s) (.txt, etc.) → list[TextDocument]. - - Standard library only. No external dependencies. - - - Single file: ``load("notes.txt")`` → [TextDocument] - - Directory: ``load("data/")`` → [TextDocument, ...] (recursive) - - ``title`` = filename stem, ``content`` = full file text. - - Args: - extensions: File extensions to load. Default ``[".txt"]``. - """ - - def __init__(self, extensions: list[str] | None = None) -> None: - self._extensions = extensions or [".txt"] - - def load(self, path: str) -> list[TextDocument]: - p = Path(path) - if p.is_dir(): - docs: list[TextDocument] = [] - for ext in self._extensions: - for f in sorted(p.rglob(f"*{ext}")): - docs.extend(self._load_file(f)) - return docs - return self._load_file(p) - - def _load_file(self, path: Path) -> list[TextDocument]: - content = path.read_text(encoding="utf-8") - return [ - TextDocument( - id=path.stem, - title=path.stem, - content=content, - source=str(path), - ) - ] diff --git a/src/lang2sql/components/retrieval/__init__.py b/src/lang2sql/components/retrieval/__init__.py deleted file mode 100644 index 997cc7c..0000000 --- a/src/lang2sql/components/retrieval/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -from .chunker import CatalogChunker, DocumentChunkerPort, RecursiveCharacterChunker -from .hybrid import HybridRetriever -from .keyword import KeywordRetriever -from .vector import VectorRetriever -from ...core.catalog import CatalogEntry, IndexedChunk, RetrievalResult, TextDocument - -__all__ = [ - "KeywordRetriever", - "VectorRetriever", - "HybridRetriever", - "DocumentChunkerPort", - "CatalogChunker", - "RecursiveCharacterChunker", - "CatalogEntry", - "TextDocument", - "IndexedChunk", - "RetrievalResult", -] diff --git a/src/lang2sql/components/retrieval/_bm25.py b/src/lang2sql/components/retrieval/_bm25.py deleted file mode 100644 index d3e87c0..0000000 --- a/src/lang2sql/components/retrieval/_bm25.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -Internal BM25 index — stdlib only (math, collections). - -BM25 parameters: - k1 = 1.5 (term frequency saturation) - b = 0.75 (document length normalization) - -Tokenization: text.lower().split() (whitespace, no external deps) -""" - -from __future__ import annotations - -import math -from collections import Counter -from typing import Any - -_K1 = 1.5 -_B = 0.75 - - -def _tokenize(text: str) -> list[str]: - return text.lower().split() - - -def _extract_text(value: Any) -> list[str]: - """Recursively extract text tokens from any value (str, list, dict, other).""" - if isinstance(value, str): - return [value] - if isinstance(value, list): - result: list[str] = [] - for item in value: - result.extend(_extract_text(item)) - return result - if isinstance(value, dict): - result = [] - for k, v in value.items(): - result.append(str(k)) - result.extend(_extract_text(v)) - return result - return [str(value)] - - -def _entry_to_text(entry: dict[str, Any], index_fields: list[str]) -> str: - """ - Convert a catalog dict entry into a single text string for indexing. - - Handles: - - str fields → joined as-is - - dict fields → "key value key value ..." (for columns: {col_name: col_desc}) - - list fields → each element extracted recursively - - other types → str(value) - """ - parts: list[str] = [] - for field in index_fields: - value = entry.get(field) - if value is None: - continue - parts.extend(_extract_text(value)) - return " ".join(parts) - - -class _BM25Index: - """ - In-memory BM25 index over a list[dict] catalog. - - Usage: - index = _BM25Index(catalog, index_fields=["name", "description", "columns"]) - scores = index.score("주문 테이블") # list[float], one per catalog entry - """ - - def __init__( - self, - catalog: list[dict[str, Any]], - index_fields: list[str], - ) -> None: - self._catalog = catalog - self._n = len(catalog) - - # Tokenize each document - self._docs: list[list[str]] = [ - _tokenize(_entry_to_text(entry, index_fields)) for entry in catalog - ] - - # Term frequencies per document - self._tfs: list[Counter[str]] = [Counter(doc) for doc in self._docs] - - # Document lengths - doc_lengths = [len(doc) for doc in self._docs] - self._avgdl: float = sum(doc_lengths) / self._n if self._n > 0 else 0.0 - - # Inverted index: term → set of doc indices that contain it - self._df: Counter[str] = Counter() - for tf in self._tfs: - for term in tf: - self._df[term] += 1 - - def score(self, query: str) -> list[float]: - """ - Return a BM25 score for each catalog entry. - - Args: - query: Natural language query string. - - Returns: - List of float scores, one per catalog entry, in original order. - """ - if self._n == 0: - return [] - - query_terms = _tokenize(query) - scores = [0.0] * self._n - - for term in query_terms: - df_t = self._df.get(term, 0) - if df_t == 0: - continue - - # IDF — smoothed to avoid log(0) - idf = math.log((self._n - df_t + 0.5) / (df_t + 0.5) + 1) - - for i, tf in enumerate(self._tfs): - tf_t = tf.get(term, 0) - if tf_t == 0: - continue - - dl = len(self._docs[i]) - denom = tf_t + _K1 * (1 - _B + _B * dl / self._avgdl) - scores[i] += idf * (tf_t * (_K1 + 1)) / denom - - return scores diff --git a/src/lang2sql/components/retrieval/chunker.py b/src/lang2sql/components/retrieval/chunker.py deleted file mode 100644 index 93c1df2..0000000 --- a/src/lang2sql/components/retrieval/chunker.py +++ /dev/null @@ -1,206 +0,0 @@ -from __future__ import annotations - -from typing import Protocol, runtime_checkable - -from ...core.catalog import CatalogEntry, IndexedChunk, TextDocument - - -@runtime_checkable -class DocumentChunkerPort(Protocol): - """ - Interface for TextDocument → list[IndexedChunk] conversion. - - Default implementation: RecursiveCharacterChunker - Advanced implementation: SemanticChunker (integrations/chunking/semantic_.py) - Custom implementation: any class satisfying this Protocol can be passed as splitter. - - Example (wrapping LangChain):: - - class LangChainChunkerAdapter: - def __init__(self, lc_splitter): - self._splitter = lc_splitter - - def chunk(self, doc: TextDocument) -> list[IndexedChunk]: - texts = self._splitter.split_text(doc["content"]) - return [ - IndexedChunk( - chunk_id=f"{doc['id']}__{i}", text=t, - source_type="document", source_id=doc["id"], - chunk_index=i, metadata={"title": doc.get("title", "")}, - ) - for i, t in enumerate(texts) - ] - - retriever = VectorRetriever.from_sources(..., splitter=LangChainChunkerAdapter(...)) - """ - - def chunk(self, doc: TextDocument) -> list[IndexedChunk]: ... - - -class CatalogChunker: - """ - Converts a CatalogEntry into a list of IndexedChunks. - - Solves the problem where a table with 100+ columns loses column-level - semantics when represented as a single vector. - Chunk 0 is a table header summary; subsequent chunks are column groups. - Each chunk's metadata preserves the full CatalogEntry so VectorRetriever - can reconstruct it on retrieval. - - Args: - max_columns_per_chunk: Maximum columns per column-group chunk. Default 20. - """ - - def __init__(self, max_columns_per_chunk: int = 20) -> None: - self._max_cols = max_columns_per_chunk - - def split(self, catalog: list[CatalogEntry]) -> list[IndexedChunk]: - """LangChain-style batch split: list input → list output.""" - return [c for entry in catalog for c in self.chunk(entry)] - - def chunk(self, entry: CatalogEntry) -> list[IndexedChunk]: - name = entry.get("name", "") - description = entry.get("description", "") - columns = entry.get("columns", {}) - chunks: list[IndexedChunk] = [] - - # Chunk 0: table header - chunks.append( - IndexedChunk( - chunk_id=f"{name}__0", - text=f"{name}: {description}".strip(), - source_type="catalog", - source_id=name, - chunk_index=0, - metadata=dict(entry), # preserve full CatalogEntry for reconstruction - ) - ) - - # Chunk 1+: column groups - col_items = list(columns.items()) - for i, start in enumerate(range(0, len(col_items), self._max_cols)): - group = col_items[start : start + self._max_cols] - col_text = " ".join(f"{k} {v}" for k, v in group) - chunks.append( - IndexedChunk( - chunk_id=f"{name}__col_{i + 1}", - text=f"{name} columns: {col_text}", - source_type="catalog", - source_id=name, - chunk_index=i + 1, - metadata=dict( - entry - ), # preserve full CatalogEntry in every column chunk - ) - ) - - return chunks - - -class RecursiveCharacterChunker(DocumentChunkerPort): - """ - Hierarchical separator-based document chunker. No external dependencies. - - Separator priority: ["\\n\\n", "\\n", ". ", " ", ""] - — tries paragraph → line → sentence → word boundaries in order. - Character-count-based so it works for both Korean and English - (unlike str.split() which assumes whitespace-delimited words). - - For higher chunking quality, use SemanticChunker (integrations/chunking/semantic_.py). - - Args: - chunk_size: Maximum characters per chunk. Default 1000. - chunk_overlap: Overlap characters between consecutive chunks. Default 100. - separators: Separator priority list. None uses the default list above. - """ - - _DEFAULT_SEPARATORS = ["\n\n", "\n", ". ", " ", ""] - - def __init__( - self, - chunk_size: int = 1000, - chunk_overlap: int = 100, - separators: list[str] | None = None, - ) -> None: - if chunk_overlap >= chunk_size: - raise ValueError( - f"chunk_overlap ({chunk_overlap}) must be less than chunk_size ({chunk_size})" - ) - self._chunk_size = chunk_size - self._chunk_overlap = chunk_overlap - self._separators = separators or self._DEFAULT_SEPARATORS - - def split(self, docs: list[TextDocument]) -> list[IndexedChunk]: - """LangChain-style batch split: list input → list output.""" - return [c for doc in docs for c in self.chunk(doc)] - - def chunk(self, doc: TextDocument) -> list[IndexedChunk]: - content = doc.get("content", "") - if not content: - return [] - - raw_chunks = self._split(content, self._separators) - title = doc.get("title", "") - doc_id = doc.get("id", "") - - return [ - IndexedChunk( - chunk_id=f"{doc_id}__{i}", - text=f"{title}: {text}" if title else text, - source_type="document", - source_id=doc_id, - chunk_index=i, - metadata={ - "id": doc_id, - "title": title, - "source": doc.get("source", ""), - }, - ) - for i, text in enumerate(raw_chunks) - ] - - def _split(self, text: str, separators: list[str]) -> list[str]: - """Recursively try separators until all chunks fit within chunk_size.""" - if not separators: - return [text] if text else [] - chunks: list[str] = [] - separator = separators[-1] # fallback: character-level split - - for sep in separators: - if sep and sep in text: - separator = sep - break - - parts = text.split(separator) if separator else list(text) - current = "" - - for part in parts: - candidate = ( - (current + separator + part).lstrip(separator) if current else part - ) - if len(candidate) <= self._chunk_size: - current = candidate - else: - if current: - chunks.append(current) - # part itself exceeds chunk_size → recurse with finer separators - if len(part) > self._chunk_size and len(separators) > 1: - chunks.extend(self._split(part, separators[1:])) - current = "" - else: - current = part - - if current: - chunks.append(current) - - if self._chunk_overlap > 0 and len(chunks) > 1: - chunks = self._apply_overlap(chunks) - - return chunks - - def _apply_overlap(self, chunks: list[str]) -> list[str]: - overlapped = [chunks[0]] - for i in range(1, len(chunks)): - prev_tail = chunks[i - 1][-self._chunk_overlap :] - overlapped.append(prev_tail + chunks[i]) - return overlapped diff --git a/src/lang2sql/components/retrieval/hybrid.py b/src/lang2sql/components/retrieval/hybrid.py deleted file mode 100644 index 5a3df77..0000000 --- a/src/lang2sql/components/retrieval/hybrid.py +++ /dev/null @@ -1,112 +0,0 @@ -from __future__ import annotations - -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import CatalogEntry, RetrievalResult, TextDocument -from ...core.hooks import TraceHook -from ...core.ports import EmbeddingPort -from .chunker import DocumentChunkerPort -from .keyword import KeywordRetriever -from .vector import VectorRetriever - - -class HybridRetriever(BaseComponent): - """ - BM25 + vector hybrid retriever. Merges results with Reciprocal Rank Fusion (RRF). - - RRF algorithm:: - - RRF_score(table) = Σ 1/(k + rank_i) for each ranker i - k = 60 (default, recommended by the RRF paper) - - Over-fetches ``top_n * 2`` candidates from each retriever, merges via RRF, - and returns the final ``top_n``. - Context is taken from VectorRetriever only (BM25 has no document context). - - Args: - catalog: List of CatalogEntry dicts. - embedding: EmbeddingPort implementation. - documents: Optional list of business documents to index. - splitter: Chunking strategy for documents (default: RecursiveCharacterChunker). - top_n: Maximum number of schemas to return. Default 5. - rrf_k: RRF smoothing constant. Default 60. - score_threshold: Minimum vector similarity score. Default 0.0. - name: Component name for tracing. - hook: TraceHook for observability. - - Usage:: - - retriever = HybridRetriever( - catalog=[{"name": "orders", "description": "...", "columns": {...}}], - embedding=OpenAIEmbedding(model="text-embedding-3-small"), - ) - result = retriever("How many orders last month?") # RetrievalResult - """ - - def __init__( - self, - *, - catalog: list[CatalogEntry], - embedding: EmbeddingPort, - documents: Optional[list[TextDocument]] = None, - splitter: Optional[DocumentChunkerPort] = None, - top_n: int = 5, - rrf_k: int = 60, - score_threshold: float = 0.0, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "HybridRetriever", hook=hook) - fetch = top_n * 2 - self._keyword = KeywordRetriever(catalog=catalog, top_n=fetch) - self._vector = VectorRetriever.from_sources( - catalog=catalog, - embedding=embedding, - documents=documents, - splitter=splitter, - top_n=fetch, - score_threshold=score_threshold, - ) - self._top_n = top_n - self._rrf_k = rrf_k - - def _run(self, query: str) -> RetrievalResult: - """ - Args: - query: Natural language search query. - - Returns: - RetrievalResult: - .schemas — top_n schemas after RRF merge - .context — business document context from VectorRetriever - """ - keyword_schemas = self._keyword(query) # list[CatalogEntry] - vector_result = self._vector(query) # RetrievalResult - - merged = self._rrf_merge(keyword_schemas, vector_result.schemas) - return RetrievalResult(schemas=merged, context=vector_result.context) - - def _rrf_merge( - self, - keyword_schemas: list[CatalogEntry], - vector_schemas: list[CatalogEntry], - ) -> list[CatalogEntry]: - """Merge results from both retrievers via RRF and return top_n entries.""" - k = self._rrf_k - scores: dict[str, float] = {} - entries: dict[str, CatalogEntry] = {} - - for rank, entry in enumerate(keyword_schemas, start=1): - name = entry["name"] - scores[name] = scores.get(name, 0.0) + 1.0 / (k + rank) - entries[name] = entry - - for rank, entry in enumerate(vector_schemas, start=1): - name = entry["name"] - scores[name] = scores.get(name, 0.0) + 1.0 / (k + rank) - if name not in entries: - entries[name] = entry - - sorted_names = sorted(scores, key=lambda n: scores[n], reverse=True) - return [entries[n] for n in sorted_names[: self._top_n]] diff --git a/src/lang2sql/components/retrieval/keyword.py b/src/lang2sql/components/retrieval/keyword.py deleted file mode 100644 index bf29166..0000000 --- a/src/lang2sql/components/retrieval/keyword.py +++ /dev/null @@ -1,81 +0,0 @@ -from __future__ import annotations - -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import CatalogEntry -from ...core.hooks import TraceHook -from ._bm25 import _BM25Index - -_DEFAULT_INDEX_FIELDS = ["name", "description", "columns"] - - -class KeywordRetriever(BaseComponent): - """ - BM25-based keyword retriever over a table catalog. - - Indexes catalog entries at init time (in-memory). - On each call, scores entries against the given query and returns - the top-N matches as a ranked list. - - Args: - catalog: List of table dicts. Each dict should have at minimum - ``name`` (str) and ``description`` (str). - Optional keys: ``columns`` (dict[str, str]), ``meta`` (dict). - top_n: Maximum number of results to return. Defaults to 5. - index_fields: Fields to index. Defaults to ["name", "description", "columns"]. - Pass a custom list to replace the default (complete override). - name: Component name for tracing. Defaults to "KeywordRetriever". - hook: Optional TraceHook for observability. - - Example:: - - retriever = KeywordRetriever(catalog=[ - {"name": "orders", "description": "주문 정보 테이블"}, - ]) - results = retriever("주문 조회") - print(results) # [{"name": "orders", ...}] - """ - - def __init__( - self, - *, - catalog: list[dict], - top_n: int = 5, - index_fields: Optional[list[str]] = None, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "KeywordRetriever", hook=hook) - self._catalog = catalog - self._top_n = top_n - self._index_fields = ( - index_fields if index_fields is not None else _DEFAULT_INDEX_FIELDS - ) - self._index = _BM25Index(catalog, self._index_fields) - - def _run(self, query: str) -> list[CatalogEntry]: - """ - Search the catalog with BM25 and return top-N matching entries. - - Args: - query: Natural language search query. - - Returns: - Ranked list of matching catalog entries (BM25 score descending). - Empty list if no match or catalog is empty. - """ - if not self._catalog: - return [] - - scores = self._index.score(query) - - # Pair each catalog entry with its score, sort descending - ranked = sorted( - zip(scores, self._catalog), - key=lambda x: x[0], - reverse=True, - ) - - # Return up to top_n entries that have a positive score - return [entry for score, entry in ranked[: self._top_n] if score > 0.0] diff --git a/src/lang2sql/components/retrieval/vector.py b/src/lang2sql/components/retrieval/vector.py deleted file mode 100644 index c104590..0000000 --- a/src/lang2sql/components/retrieval/vector.py +++ /dev/null @@ -1,283 +0,0 @@ -from __future__ import annotations - -from typing import Optional - -from ...core.base import BaseComponent -from ...core.catalog import CatalogEntry, IndexedChunk, RetrievalResult, TextDocument -from ...core.hooks import TraceHook -from ...core.ports import EmbeddingPort, VectorStorePort -from .chunker import CatalogChunker, DocumentChunkerPort - - -class VectorRetriever(BaseComponent): - """ - Catalog + business document retrieval via vector similarity. - - RetrievalResult.schemas is deduplicated by source table — multiple chunks - from the same table produce only one CatalogEntry in the result. - - Two construction patterns: - - 1. One-touch factory (quick start): - retriever = VectorRetriever.from_sources(catalog=..., embedding=...) - retriever.add(RecursiveCharacterChunker().split(more_docs)) # incremental - - 2. Explicit pipeline (full control, LangChain-style): - chunks = ( - CatalogChunker().split(catalog) + - RecursiveCharacterChunker().split(docs) - ) - retriever = VectorRetriever.from_chunks(chunks, embedding=embedding, top_n=5) - retriever.add(RecursiveCharacterChunker().split(new_docs)) # incremental - - Args: - vectorstore: VectorStorePort implementation. - embedding: EmbeddingPort implementation. - registry: dict[chunk_id, IndexedChunk] mapping. - top_n: Maximum schemas and context items to return. Default 5. - score_threshold: Chunks with score <= this value are excluded. Default 0.0. - name: Component name for tracing. - hook: TraceHook for observability. - """ - - def __init__( - self, - *, - vectorstore: VectorStorePort, - embedding: EmbeddingPort, - registry: dict, - top_n: int = 5, - score_threshold: float = 0.0, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name=name or "VectorRetriever", hook=hook) - self._vectorstore = vectorstore - self._embedding = embedding - self._registry = registry - self._top_n = top_n - self._score_threshold = score_threshold - - @classmethod - def from_chunks( - cls, - chunks: list[IndexedChunk], - *, - embedding: EmbeddingPort, - vectorstore: Optional[VectorStorePort] = None, - top_n: int = 5, - score_threshold: float = 0.0, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> "VectorRetriever": - """ - LangChain-style factory: build from pre-split chunks. - - Embeds and stores the given chunks; no splitting is performed here. - Use chunker.split(docs) before calling this method. - - Args: - chunks: Pre-split list[IndexedChunk] (e.g. from CatalogChunker.split()). - embedding: EmbeddingPort implementation. - vectorstore: Defaults to InMemoryVectorStore. - top_n: Maximum schemas and context items to return. Default 5. - score_threshold: Score cutoff. Default 0.0. - """ - from ...integrations.vectorstore.inmemory_ import InMemoryVectorStore - - store = vectorstore or InMemoryVectorStore() - registry: dict = {} - if chunks: - ids = [c["chunk_id"] for c in chunks] - texts = [c["text"] for c in chunks] - vectors = embedding.embed_texts(texts) - store.upsert(ids, vectors) - registry.update({c["chunk_id"]: c for c in chunks}) - - return cls( - vectorstore=store, - embedding=embedding, - registry=registry, - top_n=top_n, - score_threshold=score_threshold, - name=name, - hook=hook, - ) - - @classmethod - def from_sources( - cls, - *, - catalog: list[CatalogEntry], - embedding: EmbeddingPort, - documents: Optional[list[TextDocument]] = None, - splitter: Optional[DocumentChunkerPort] = None, - vectorstore: Optional[VectorStorePort] = None, - top_n: int = 5, - score_threshold: float = 0.0, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> "VectorRetriever": - """ - One-touch factory: chunk, embed, and index in a single call. - - Internally calls from_chunks() after splitting catalog and documents. - For incremental addition after construction, use retriever.add(chunks). - - Args: - catalog: List of CatalogEntry dicts to index. - embedding: EmbeddingPort implementation. - documents: Optional list of TextDocument to index alongside catalog. - splitter: Chunker for documents. Defaults to RecursiveCharacterChunker. - Pass SemanticChunker(embedding=...) for higher quality. - vectorstore: Defaults to InMemoryVectorStore. - top_n: Maximum schemas and context items to return. Default 5. - score_threshold: Score cutoff. Default 0.0. - """ - from .chunker import RecursiveCharacterChunker - - _splitter = splitter or RecursiveCharacterChunker() - chunks = CatalogChunker().split(catalog) - if documents: - chunks = chunks + _splitter.split(documents) - - return cls.from_chunks( - chunks, - embedding=embedding, - vectorstore=vectorstore, - top_n=top_n, - score_threshold=score_threshold, - name=name, - hook=hook, - ) - - def add(self, chunks: list[IndexedChunk]) -> None: - """ - Add pre-split chunks to the index incrementally. - - Use chunker.split(docs) before calling this method. - - Args: - chunks: list[IndexedChunk] from chunker.split(). - """ - if not chunks: - return - ids = [c["chunk_id"] for c in chunks] - texts = [c["text"] for c in chunks] - vectors = self._embedding.embed_texts(texts) - self._vectorstore.upsert(ids, vectors) - self._registry.update({c["chunk_id"]: c for c in chunks}) - - # ── Persistence ────────────────────────────────────────────────── - - def save(self, path: str) -> None: - """벡터 인덱스와 registry를 path에 저장. - - FAISSVectorStore처럼 save()를 지원하는 store에서만 동작한다. - InMemoryVectorStore 등 save()가 없는 store는 NotImplementedError. - - 저장 파일: - {path} — FAISSVectorStore 벡터 인덱스 - {path}.meta — chunk_id 순서 목록 (FAISSVectorStore 내부) - {path}.registry — registry JSON - """ - import json - import pathlib - - save_fn = getattr(self._vectorstore, "save", None) - if save_fn is None: - raise NotImplementedError( - f"{type(self._vectorstore).__name__} does not support save(). " - "Use FAISSVectorStore for file-based persistence." - ) - save_fn(path) - pathlib.Path(path + ".registry").write_text( - json.dumps(self._registry), encoding="utf-8" - ) - - @classmethod - def load( - cls, - path: str, - *, - vectorstore: VectorStorePort, - embedding: EmbeddingPort, - top_n: int = 5, - score_threshold: float = 0.0, - name: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> "VectorRetriever": - """저장된 registry를 복원해 VectorRetriever를 반환. - - 벡터 인덱스 복원은 호출자가 직접 수행한 뒤 vectorstore로 전달한다. - 이렇게 하면 VectorRetriever가 특정 store 구현체에 의존하지 않는다. - - Args: - path: save() 시 사용한 경로 (registry 파일 위치 기준). - vectorstore: 이미 로드된 VectorStorePort 구현체. - embedding: EmbeddingPort 구현체. - top_n: 최대 반환 스키마/컨텍스트 수. 기본 5. - score_threshold: 이 점수 이하는 결과에서 제외. 기본 0.0. - - Example: - store = FAISSVectorStore.load(path) - retriever = VectorRetriever.load(path, vectorstore=store, embedding=emb) - """ - import json - import pathlib - - registry_path = pathlib.Path(path + ".registry") - if not registry_path.exists(): - raise FileNotFoundError(f"Registry file not found: {registry_path}") - - registry = json.loads(registry_path.read_text(encoding="utf-8")) - return cls( - vectorstore=vectorstore, - embedding=embedding, - registry=registry, - top_n=top_n, - score_threshold=score_threshold, - name=name, - hook=hook, - ) - - # ── Core retrieval ──────────────────────────────────────────────── - - def _run(self, query: str) -> RetrievalResult: - """ - Args: - query: Natural language search query. - - Returns: - RetrievalResult: - .schemas — relevant CatalogEntry list (deduplicated, at most top_n) - .context — relevant business document chunk texts (at most top_n) - """ - if not self._registry: - return RetrievalResult(schemas=[], context=[]) - - query_vector = self._embedding.embed_query(query) - # over-fetch by 3x so deduplication still yields top_n catalog entries - raw = self._vectorstore.search(query_vector, k=self._top_n * 3) - - seen_tables: dict[str, CatalogEntry] = {} # source_id → CatalogEntry (dedup) - context: list[str] = [] - - for chunk_id, score in raw: - if score <= self._score_threshold: - continue - chunk = self._registry.get(chunk_id) - if chunk is None: - continue - - if chunk["source_type"] == "catalog": - src = chunk["source_id"] - if src not in seen_tables: - seen_tables[src] = chunk["metadata"] # full CatalogEntry - elif chunk["source_type"] == "document": - context.append(chunk["text"]) - - return RetrievalResult( - schemas=list(seen_tables.values())[: self._top_n], - context=context[: self._top_n], - ) diff --git a/src/lang2sql/core/__init__.py b/src/lang2sql/core/__init__.py index e69de29..4aabff6 100644 --- a/src/lang2sql/core/__init__.py +++ b/src/lang2sql/core/__init__.py @@ -0,0 +1,16 @@ +"""Pure core — types, identity, and ports. No I/O, sits at the import root.""" + +from .identity import Identity, Scope, ScopeLevel +from .types import ( + Completion, + Message, + Role, + ToolCall, + ToolResult, + ToolSpec, +) + +__all__ = [ + "Identity", "Scope", "ScopeLevel", + "Completion", "Message", "Role", "ToolCall", "ToolResult", "ToolSpec", +] diff --git a/src/lang2sql/core/base.py b/src/lang2sql/core/base.py deleted file mode 100644 index 7a3c0d8..0000000 --- a/src/lang2sql/core/base.py +++ /dev/null @@ -1,171 +0,0 @@ -from __future__ import annotations - -from abc import ABC, abstractmethod -from typing import Any, Optional - -from .exceptions import ComponentError, Lang2SQLError -from .hooks import Event, NullHook, TraceHook, ms, now, summarize - - -class BaseComponent(ABC): - """ - Base class for all components. - - Design goals: - - Components are plain callables (define-by-run friendly). - - No enforced global state schema. - - Hooks provide observability without requiring a graph engine. - - Public entry point is ``run()``. ``__call__`` is a convenience alias so - components can be used as plain callables (e.g. in SequentialFlow steps). - Subclasses implement ``_run()``. - """ - - def __init__( - self, name: Optional[str] = None, hook: Optional[TraceHook] = None - ) -> None: - self.name: str = name or self.__class__.__name__ - self.hook: TraceHook = hook or NullHook() - - def __call__(self, *args: Any, **kwargs: Any) -> Any: - return self.run(*args, **kwargs) - - def run(self, *args: Any, **kwargs: Any) -> Any: - t0 = now() - self.hook.on_event( - Event( - name="component.run", - component=self.name, - phase="start", - ts=t0, - input_summary=f"args={summarize(args)} kwargs={summarize(kwargs)}", - ) - ) - - try: - out = self._run(*args, **kwargs) - - t1 = now() - self.hook.on_event( - Event( - name="component.run", - component=self.name, - phase="end", - ts=t1, - duration_ms=ms(t0, t1), - output_summary=summarize(out), - ) - ) - return out - - except Lang2SQLError as e: - # Preserve domain-level errors (IntegrationMissingError, ValidationError, etc.). - t1 = now() - self.hook.on_event( - Event( - name="component.run", - component=self.name, - phase="error", - ts=t1, - duration_ms=ms(t0, t1), - error=f"{type(e).__name__}: {e}", - ) - ) - raise - - except Exception as e: - # Wrap non-domain errors into ComponentError. - t1 = now() - self.hook.on_event( - Event( - name="component.run", - component=self.name, - phase="error", - ts=t1, - duration_ms=ms(t0, t1), - error=f"{type(e).__name__}: {e}", - ) - ) - raise ComponentError( - self.name, - f"component failed ({type(e).__name__}: {e})", - cause=e, - ) from e - - @abstractmethod - def _run(self, *args: Any, **kwargs: Any) -> Any: - raise NotImplementedError - - -class BaseFlow(ABC): - """ - Base class for flows. - - Define-by-run: - - Users write control-flow in pure Python (if/for/while). - - We provide parts + presets, not a graph engine. - - Public entry point is ``run()``. ``__call__`` is a convenience alias. - Subclasses implement ``_run()``. - """ - - def __init__( - self, name: Optional[str] = None, hook: Optional[TraceHook] = None - ) -> None: - self.name: str = name or self.__class__.__name__ - self.hook: TraceHook = hook or NullHook() - - def __call__(self, *args: Any, **kwargs: Any) -> Any: - return self.run(*args, **kwargs) - - def run(self, *args: Any, **kwargs: Any) -> Any: - t0 = now() - self.hook.on_event( - Event(name="flow.run", component=self.name, phase="start", ts=t0) - ) - - try: - out = self._run(*args, **kwargs) - t1 = now() - self.hook.on_event( - Event( - name="flow.run", - component=self.name, - phase="end", - ts=t1, - duration_ms=ms(t0, t1), - ) - ) - return out - - except Lang2SQLError as e: - t1 = now() - self.hook.on_event( - Event( - name="flow.run", - component=self.name, - phase="error", - ts=t1, - duration_ms=ms(t0, t1), - error=f"{type(e).__name__}: {e}", - ) - ) - raise - - except Exception as e: - t1 = now() - self.hook.on_event( - Event( - name="flow.run", - component=self.name, - phase="error", - ts=t1, - duration_ms=ms(t0, t1), - error=f"{type(e).__name__}: {e}", - ) - ) - raise - - @abstractmethod - def _run(self, *args: Any, **kwargs: Any) -> Any: - raise NotImplementedError diff --git a/src/lang2sql/core/catalog.py b/src/lang2sql/core/catalog.py deleted file mode 100644 index 56413d0..0000000 --- a/src/lang2sql/core/catalog.py +++ /dev/null @@ -1,73 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import TypedDict - - -class CatalogEntry(TypedDict, total=False): - name: str - description: str - columns: dict[str, str] - - -class TextDocument(TypedDict, total=False): - """Business document — data dictionaries, business rules, FAQ, etc.""" - - id: str # Unique identifier (required) - title: str # Document title (required) - content: str # Full body text (required) - source: str # File path, URL, etc. (optional) - metadata: dict # Free-form additional info (optional) - - -class IndexedChunk(TypedDict): - """Minimum indexing unit stored in the vector store. Shared by catalog and document chunks.""" - - chunk_id: str # e.g. "orders__0", "orders__col_1", "bizrule__2" - text: str # Text to embed - source_type: str # "catalog" | "document" - source_id: str # Table name (catalog) or doc id (document) - chunk_index: int # Position within the source (0-based) - metadata: dict # catalog → full CatalogEntry / document → document meta - - -@dataclass -class RetrievalResult: - """Return value of VectorRetriever — schema list + domain context.""" - - schemas: list[CatalogEntry] = field(default_factory=list) - context: list[str] = field(default_factory=list) - - -@dataclass -class GateResult: - """QuestionGate 컴포넌트의 반환 타입.""" - - suitable: bool - reason: str - missing_entities: list[str] = field(default_factory=list) - requires_data_science: bool = False - - -@dataclass -class QuestionProfile: - """질문 특성 프로파일.""" - - is_timeseries: bool = False - is_aggregation: bool = False - has_filter: bool = False - is_grouped: bool = False - has_ranking: bool = False - has_temporal_comparison: bool = False - intent_type: str = "lookup" # trend | lookup | comparison | distribution - - -@dataclass -class TableScore: - """개별 테이블의 적합도 평가 결과.""" - - table_name: str - score: float - reason: str - matched_columns: list[str] = field(default_factory=list) - missing_entities: list[str] = field(default_factory=list) diff --git a/src/lang2sql/core/context.py b/src/lang2sql/core/context.py deleted file mode 100644 index 7d3a0f5..0000000 --- a/src/lang2sql/core/context.py +++ /dev/null @@ -1,182 +0,0 @@ -# lang2sql/core/context.py -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Any, Optional -from collections.abc import MutableMapping - - -@dataclass(init=False) -class RunContext: - """ - A minimal state carrier for define-by-run pipelines. - - Internal storage is generic: - - inputs: user inputs (e.g., query) - - artifacts: intermediate artifacts (e.g., schema candidates, prompt context) - - outputs: final outputs (e.g., sql, validation) - - error: structured error information (optional) - - metadata: logs/traces/history (optional) - - Public UX can be domain-friendly via alias properties: - - .query, .schema, .sql, .validation, etc. - """ - - inputs: dict[str, Any] = field(default_factory=dict) - artifacts: dict[str, Any] = field(default_factory=dict) - outputs: dict[str, Any] = field(default_factory=dict) - error: Optional[dict[str, Any]] = None - metadata: dict[str, Any] = field(default_factory=dict) - - def __init__(self, query: Optional[str] = None, **kwargs: Any) -> None: - # Keep storage generic and always initialized. - self.inputs = {} - self.artifacts = {} - self.outputs = {} - self.error = None - self.metadata = {} - - if query is not None: - self.inputs["query"] = query - - # Allow lightweight initialization: RunContext(foo=..., bar=...) - # Store unknown fields in metadata to avoid schema lock-in. - if kwargs: - self.metadata.update(kwargs) - - # ------------------------- - # Domain-friendly aliases - # ------------------------- - - @property - def query(self) -> str: - """Return the user question/query.""" - v = self.inputs.get("query", "") - if v is None: - return "" - if isinstance(v, str): - return v - # Be forgiving: keep pipeline running, but avoid crashing on accidental types. - return str(v) - - @query.setter - def query(self, value: str) -> None: - if not isinstance(value, str): - raise TypeError(f"RunContext.query must be str, got {type(value).__name__}") - self.inputs["query"] = value - - @property - def schema(self) -> MutableMapping[str, Any]: - """ - Return the schema artifact mapping. - - Typical keys (convention): - - catalog: full schema catalog (list/provider) - - selected: top-k table candidates - - context: final context text used for prompting - """ - v = self.artifacts.get("schema") - if v is None: - v = {} - self.artifacts["schema"] = v - return v - - if isinstance(v, MutableMapping): - return v - - # If someone wrote a non-mapping value, replace it to keep conventions stable. - v = {} - self.artifacts["schema"] = v - return v - - @property - def sql(self) -> str: - """Return the final SQL string.""" - v = self.outputs.get("sql", "") - if v is None: - return "" - if isinstance(v, str): - return v - return str(v) - - @sql.setter - def sql(self, value: str) -> None: - if not isinstance(value, str): - raise TypeError(f"RunContext.sql must be str, got {type(value).__name__}") - self.outputs["sql"] = value - - # sql changes invalidate validation (validation is derived from sql) - # self.outputs.pop("validation", None) - - @property - def validation(self) -> Any: - """Return validation result object, if present.""" - return self.outputs.get("validation") - - @validation.setter - def validation(self, value: Any) -> None: - self.outputs["validation"] = value - - # Optional convenience aliases (recommended for discoverability) - - @property - def schema_catalog(self) -> Any: - """Alias for schema['catalog'].""" - return self.schema.get("catalog") - - @schema_catalog.setter - def schema_catalog(self, value: Any) -> None: - self.schema["catalog"] = value - - @property - def schema_selected(self) -> Any: - """Alias for schema['selected'].""" - return self.schema.get("selected") - - @schema_selected.setter - def schema_selected(self, value: Any) -> None: - self.schema["selected"] = value - - @property - def schema_context(self) -> str: - """Alias for schema['context'].""" - v = self.schema.get("context", "") - if v is None: - return "" - if isinstance(v, str): - return v - return str(v) - - @schema_context.setter - def schema_context(self, value: str) -> None: - if not isinstance(value, str): - raise TypeError( - f"RunContext.schema_context must be str, got {type(value).__name__}" - ) - self.schema["context"] = value - - # ------------------------- - # Small utilities - # ------------------------- - - def push_meta(self, key: str, value: Any) -> None: - """ - Append a value into metadata[key] list. - - Example: - run.push_meta("sql_drafts", sql) - run.push_meta("events", event) - """ - arr = self.metadata.setdefault(key, []) - if not isinstance(arr, list): - raise TypeError(f"metadata['{key}'] exists but is not a list") - arr.append(value) - - def get_meta_list(self, key: str) -> list[Any]: - """Return metadata[key] as a list (empty list if missing).""" - v = self.metadata.get(key) - if v is None: - return [] - if isinstance(v, list): - return v - return [v] diff --git a/src/lang2sql/core/exceptions.py b/src/lang2sql/core/exceptions.py deleted file mode 100644 index c4e8556..0000000 --- a/src/lang2sql/core/exceptions.py +++ /dev/null @@ -1,39 +0,0 @@ -# lang2sql/core/exceptions.py -from __future__ import annotations - - -class Lang2SQLError(Exception): - """Base error for lang2sql.""" - - -class IntegrationMissingError(Lang2SQLError): - def __init__( - self, integration: str, extra: str | None = None, hint: str | None = None - ): - self.integration = integration - self.extra = extra - self.hint = hint - - msg = f"Missing optional integration: {integration}." - if extra: - msg += f" Install with: pip install 'lang2sql[{extra}]'" - if hint: - msg += f" ({hint})" - super().__init__(msg) - - -class ComponentError(Lang2SQLError): - def __init__(self, component: str, message: str, *, cause: Exception | None = None): - self.component = component - self.cause = cause - super().__init__(f"[{component}] {message}") - - -class ValidationError(Lang2SQLError): - pass - - -class ContractError(Lang2SQLError): - """Raised when a component violates a required call/return contract.""" - - pass diff --git a/src/lang2sql/core/hooks.py b/src/lang2sql/core/hooks.py deleted file mode 100644 index f4c8428..0000000 --- a/src/lang2sql/core/hooks.py +++ /dev/null @@ -1,64 +0,0 @@ -# lang2sql/core/hooks.py -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Any, Protocol, Optional, Literal -import time - - -@dataclass -class Event: - name: str # e.g., "component.run" - component: str # e.g., "KeywordTableRetriever" - phase: Literal["start", "end", "error"] - ts: float - duration_ms: Optional[float] = None - - # human-friendly summaries (debug only) - input_summary: Optional[str] = None - output_summary: Optional[str] = None - error: Optional[str] = None - - # structured payload (for UI / filtering / tests) - data: dict[str, Any] = field(default_factory=dict) - - -class TraceHook(Protocol): - def on_event(self, event: Event) -> None: ... - - -class NullHook(TraceHook): - def on_event(self, event: Event) -> None: - return - - -class MemoryHook(TraceHook): - def __init__(self) -> None: - self.events: list[Event] = [] - - def on_event(self, event: Event) -> None: - self.events.append(event) - - def clear(self) -> None: - self.events.clear() - - def snapshot(self) -> list[Event]: - return list(self.events) - - -def now() -> float: - return time.time() - - -def ms(start: float, end: float) -> float: - return (end - start) * 1000.0 - - -def summarize(x: Any, max_len: int = 240) -> str: - try: - s = repr(x) - except Exception: - s = f"" - if len(s) > max_len: - s = s[: max_len - 3] + "..." - return s diff --git a/src/lang2sql/core/identity.py b/src/lang2sql/core/identity.py new file mode 100644 index 0000000..f527991 --- /dev/null +++ b/src/lang2sql/core/identity.py @@ -0,0 +1,94 @@ +"""Who is asking, and in which semantic scope. + +Federation (★④) resolves a metric definition by walking *most specific → +least specific* scope. :class:`Identity` is the frontend-agnostic carrier of +that scope chain: Discord fills it from guild/channel/thread IDs, the CLI from +flags, a future Slack adapter from workspace/channel — but the harness only +ever sees this shape. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum + + +class ScopeLevel(str, Enum): + """Federation scope levels, ordered narrow → wide. + + ``THREAD`` overrides ``CHANNEL`` overrides ``GUILD``; ``BUILTIN`` is the + empty system default that every guild inherits from. + """ + + THREAD = "thread" + CHANNEL = "channel" + GUILD = "guild" + BUILTIN = "builtin" + + +@dataclass(frozen=True) +class Scope: + """A single addressable point in the federation tree.""" + + level: ScopeLevel + key: str # e.g. guild id, channel id, thread id; "" for BUILTIN + + def __str__(self) -> str: + return f"{self.level.value}:{self.key}" if self.key else self.level.value + + +@dataclass(frozen=True) +class Identity: + """Frontend-agnostic identity + scope coordinates for one request. + + ``guild_id``/``channel_id``/``thread_id`` are optional so the same type + serves a DM (guild only), a channel mention, or a thread reply. + """ + + user_id: str + guild_id: str | None = None + channel_id: str | None = None + thread_id: str | None = None + is_admin: bool = False + + def session_key(self) -> str: + """Stable key for persisting/looking up this conversation's session. + + The thread (or channel, or DM) is the unit of conversation, so the key + is the narrowest container that exists. + """ + if self.thread_id: + return f"thread:{self.thread_id}" + if self.channel_id: + return f"channel:{self.channel_id}" + if self.guild_id: + return f"guild:{self.guild_id}:{self.user_id}" + return f"dm:{self.user_id}" + + def scope_chain(self) -> list[Scope]: + """Scopes from most specific to least, for federation resolution. + + ``define_metric`` writes to ``scope_chain()[0]`` by default; lookup + reads down the chain and stops at the first definition found. + """ + chain: list[Scope] = [] + if self.thread_id: + chain.append(Scope(ScopeLevel.THREAD, self.thread_id)) + if self.channel_id: + chain.append(Scope(ScopeLevel.CHANNEL, self.channel_id)) + if self.guild_id: + chain.append(Scope(ScopeLevel.GUILD, self.guild_id)) + chain.append(Scope(ScopeLevel.BUILTIN, "")) + return chain + + def default_write_scope(self) -> Scope: + """Where a new definition lands when the user gives no ``--scope``. + + Per v4.1 §3.5 the default is the current channel; a DM falls back to a + per-user pseudo-scope so personal definitions don't leak. + """ + if self.channel_id: + return Scope(ScopeLevel.CHANNEL, self.channel_id) + if self.guild_id: + return Scope(ScopeLevel.GUILD, self.guild_id) + return Scope(ScopeLevel.CHANNEL, f"dm:{self.user_id}") diff --git a/src/lang2sql/core/ports.py b/src/lang2sql/core/ports.py deleted file mode 100644 index d1bf462..0000000 --- a/src/lang2sql/core/ports.py +++ /dev/null @@ -1,85 +0,0 @@ -from __future__ import annotations - -from typing import Any, Protocol, runtime_checkable - -from .catalog import CatalogEntry, TextDocument - - -class LLMPort(Protocol): - """Abstracts LLM backends (Anthropic, OpenAI, etc.).""" - - def invoke(self, messages: list[dict[str, str]]) -> str: ... - - -class DBPort(Protocol): - """Abstracts database backends (SQLAlchemy, etc.).""" - - def execute(self, sql: str) -> list[dict[str, Any]]: ... - - -class EmbeddingPort(Protocol): - """Abstracts embedding backends (OpenAI, Azure, Bedrock, etc.).""" - - def embed_query(self, text: str) -> list[float]: ... - - def embed_texts(self, texts: list[str]) -> list[list[float]]: ... - - -class VectorStorePort(Protocol): - """Abstracts vector store backends (InMemory, FAISS, pgvector, etc.).""" - - def search(self, vector: list[float], k: int) -> list[tuple[str, float]]: - """ - Return the k nearest vectors. - - Returns: - List of (chunk_id, score) sorted by score descending. - Score range: [-1, 1] (cosine similarity). - """ - ... - - def upsert(self, ids: list[str], vectors: list[list[float]]) -> None: - """ - Store or update vectors by chunk_id. - - Implementations must merge incoming entries into existing ones — - calling upsert twice must not lose entries from the first call. - - Args: - ids: List of chunk_ids. - vectors: Corresponding embedding vectors (len(ids) == len(vectors)). - """ - ... - - -@runtime_checkable -class DocumentLoaderPort(Protocol): - """Converts a file path or directory to list[TextDocument].""" - - def load(self, path: str) -> list[TextDocument]: ... - - -class CatalogLoaderPort(Protocol): - """Abstracts catalog loading from external sources (DataHub, file, database, etc.).""" - - def load(self) -> list[CatalogEntry]: ... - - -class DBExplorerPort(Protocol): - """DB 에이전틱 탐색 인터페이스. Agent가 DB를 직접 탐색할 때 사용. - - 메서드 선정 원칙: - - DDL에 이미 있는 정보(컬럼 목록, FK, PK)는 별도 메서드 없음 - - 통계/집계는 execute_read_only()로 직접 질의 - - 관계 추론은 LLM에 위임 (휴리스틱 제거) - """ - - def list_tables(self, schema: str | None = None) -> list[str]: ... - - def get_ddl(self, table: str, *, schema: str | None = None) -> str: ... - - def sample_data( - self, table: str, *, limit: int = 5, schema: str | None = None - ) -> list[dict]: ... - - def execute_read_only(self, sql: str) -> list[dict]: ... diff --git a/src/lang2sql/core/ports/__init__.py b/src/lang2sql/core/ports/__init__.py new file mode 100644 index 0000000..9928dea --- /dev/null +++ b/src/lang2sql/core/ports/__init__.py @@ -0,0 +1,44 @@ +"""Abstract ports — the standardised "wall sockets" of the system. + +Every outbound dependency and every pluggable strategy is one Protocol here. +V1 ships the simplest implementation of each; later versions add adapters +without the harness or tools changing. +""" + +from .audit import AuditEvent, AuditPort +from .explorer import Column, ExplorerPort, Table +from .frontend import FrontendPort, InboundMessage, OutboundMessage +from .ingestion import ( + CandidateKind, + DocExtractorPort, + Document, + SemanticCandidate, + SourcePort, +) +from .llm import LLMPort +from .memory import ExtractorPort, Fact, RecallPort, StorePort +from .safety import ( + SafetyContext, + SafetyDecision, + SafetyLayerPort, + SafetyPipelinePort, + Verdict, +) +from .secrets import SecretsPort +from .semantic_scope import ScopeResolverPort +from .session_store import SessionStorePort +from .tool import ToolPort + +__all__ = [ + "AuditEvent", "AuditPort", + "Column", "ExplorerPort", "Table", + "FrontendPort", "InboundMessage", "OutboundMessage", + "CandidateKind", "DocExtractorPort", "Document", "SemanticCandidate", "SourcePort", + "LLMPort", + "ExtractorPort", "Fact", "RecallPort", "StorePort", + "SafetyContext", "SafetyDecision", "SafetyLayerPort", "SafetyPipelinePort", "Verdict", + "SecretsPort", + "ScopeResolverPort", + "SessionStorePort", + "ToolPort", +] diff --git a/src/lang2sql/core/ports/audit.py b/src/lang2sql/core/ports/audit.py new file mode 100644 index 0000000..63940d8 --- /dev/null +++ b/src/lang2sql/core/ports/audit.py @@ -0,0 +1,29 @@ +"""Audit port — append-only record of what the agent did. + +Backs ``/audit me``. V1 is a plain append-only SQLite table (no hash chain — +that's V2). Every SQL execution, definition change, and ingestion lands here. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Protocol, runtime_checkable + + +@dataclass +class AuditEvent: + actor: str # user_id + action: str # "run_sql" | "define_metric" | "ingest" | ... + scope: str # session/scope key + detail: dict[str, Any] = field(default_factory=dict) + ts: float = 0.0 # epoch seconds; filled by the store if 0 + + +@runtime_checkable +class AuditPort(Protocol): + async def record(self, event: AuditEvent) -> None: + ... + + async def query(self, actor: str, limit: int = 20) -> list[AuditEvent]: + """Recent events for one actor, newest first.""" + ... diff --git a/src/lang2sql/core/ports/explorer.py b/src/lang2sql/core/ports/explorer.py new file mode 100644 index 0000000..d9776ae --- /dev/null +++ b/src/lang2sql/core/ports/explorer.py @@ -0,0 +1,54 @@ +"""DB explorer port — read-only schema introspection. + +The agent uses this to discover tables/columns before writing SQL. V1 backs it +with a PostgreSQL adapter; the contract is dialect-neutral so BigQuery et al. +slot in later. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Protocol, runtime_checkable + + +@dataclass +class Column: + name: str + type: str + nullable: bool = True + description: str = "" # may be auto-enriched (v1.5 metadata layer) + + +@dataclass +class Table: + name: str + schema: str = "public" + columns: list[Column] = field(default_factory=list) + description: str = "" + + @property + def qualified(self) -> str: + return f"{self.schema}.{self.name}" if self.schema else self.name + + +@runtime_checkable +class ExplorerPort(Protocol): + """Introspect a connected database, read-only.""" + + async def list_tables(self) -> list[Table]: + """Tables visible to the connection (columns may be unpopulated).""" + ... + + async def describe_table(self, name: str) -> Table: + """Full column detail for one table.""" + ... + + async def sample_rows(self, name: str, limit: int = 5) -> list[dict]: + """A few rows to give the model a feel for the data.""" + ... + + async def execute(self, sql: str, limit: int = 1000) -> list[dict]: + """Run a read-only query (already cleared by the safety pipeline) and + return up to ``limit`` rows. The ``run_sql`` tool calls this only after + a PASS verdict; the adapter must never see un-gated SQL.""" + ... diff --git a/src/lang2sql/core/ports/frontend.py b/src/lang2sql/core/ports/frontend.py new file mode 100644 index 0000000..c56df5e --- /dev/null +++ b/src/lang2sql/core/ports/frontend.py @@ -0,0 +1,45 @@ +"""Frontend port — the Phase boundary (★ multi-interface). + +Discord (Phase 1), Slack (Phase 2), Web (Phase 3) and the dev CLI all implement +this. The harness never imports a frontend; frontends drive the harness. Adding +Slack later is "implement this Protocol" with zero core changes. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Protocol, runtime_checkable + +from ..identity import Identity + + +@dataclass +class InboundMessage: + """Normalised user input from any frontend.""" + + identity: Identity + text: str + attachments: list[bytes] = field(default_factory=list) + attachment_names: list[str] = field(default_factory=list) + + +@dataclass +class OutboundMessage: + """Normalised agent output a frontend renders natively.""" + + text: str + file_bytes: bytes | None = None # e.g. CSV when result > 50 rows + file_name: str | None = None + + +@runtime_checkable +class FrontendPort(Protocol): + """Translate between a chat platform and the harness.""" + + async def receive(self) -> InboundMessage: + """Block until the next user message arrives.""" + ... + + async def send(self, identity: Identity, message: OutboundMessage) -> None: + """Deliver agent output back to the originating conversation.""" + ... diff --git a/src/lang2sql/core/ports/ingestion.py b/src/lang2sql/core/ports/ingestion.py new file mode 100644 index 0000000..38f37fe --- /dev/null +++ b/src/lang2sql/core/ports/ingestion.py @@ -0,0 +1,55 @@ +"""Ingestion ports — document → semantic candidates (★③). + +A Source × Extractor matrix: one new Source automatically pairs with every +Extractor. V1 = file upload × LLM extraction; v1.5 adds URL source + DDL +parser, etc. Extracted candidates are shown for user confirm before landing in +the semantic layer (documents are the source of truth). +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from enum import Enum +from typing import Protocol, runtime_checkable + + +@dataclass +class Document: + """Raw material pulled from some source.""" + + name: str + text: str + source_id: str = "" # preserved on resulting semantic entries + + +class CandidateKind(str, Enum): + METRIC = "metric" + DIMENSION = "dimension" + RULE = "rule" + + +@dataclass +class SemanticCandidate: + """A proposed definition awaiting user confirmation.""" + + kind: CandidateKind + name: str + definition: str + applies_to: str = "" + source_id: str = "" + + +@runtime_checkable +class SourcePort(Protocol): + """Where a document comes from (file/URL/Notion/…). Axis 1.""" + + async def fetch(self, ref: str, blob: bytes | None = None) -> Document: + ... + + +@runtime_checkable +class DocExtractorPort(Protocol): + """How definitions are pulled from a document (LLM/DDL/…). Axis 2.""" + + async def extract(self, doc: Document) -> list[SemanticCandidate]: + ... diff --git a/src/lang2sql/core/ports/llm.py b/src/lang2sql/core/ports/llm.py new file mode 100644 index 0000000..6623880 --- /dev/null +++ b/src/lang2sql/core/ports/llm.py @@ -0,0 +1,25 @@ +"""LLM port — the one outbound call the agent loop makes per turn. + +V1 wires a single OpenAI ``gpt-4.1-mini`` adapter behind this. Because the loop +depends only on this Protocol, swapping in Anthropic/NIM later (v1.5+) is an +adapter add with zero loop changes. +""" + +from __future__ import annotations + +from typing import Protocol, Sequence, runtime_checkable + +from ..types import Completion, Message, ToolSpec + + +@runtime_checkable +class LLMPort(Protocol): + """Tool-calling chat completion.""" + + async def complete( + self, + messages: Sequence[Message], + tools: Sequence[ToolSpec] = (), + ) -> Completion: + """Run one completion. May return tool calls, a final answer, or both.""" + ... diff --git a/src/lang2sql/core/ports/memory.py b/src/lang2sql/core/ports/memory.py new file mode 100644 index 0000000..14ffbc6 --- /dev/null +++ b/src/lang2sql/core/ports/memory.py @@ -0,0 +1,56 @@ +"""Memory ports — Hermes memory split into 3 independent axes (★②). + +Store (where), Recall (what to fetch), Extractor (how new facts are made) each +evolve on their own. V1 = in-memory dict + inject-all + manual ``/remember``; +v1.5 swaps Store→SQLite or Recall→keyword as a single adapter add. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Protocol, Sequence, runtime_checkable + +from ..types import Message + + +@dataclass +class Fact: + """A remembered statement, scoped to a user/conversation.""" + + id: str + owner: str # user_id or scope key + text: str + source: str = "manual" # "manual" (/remember) | "auto" (v1.5 extractor) + ts: float = 0.0 + + +@runtime_checkable +class StorePort(Protocol): + """Where facts live. Axis 1.""" + + async def add(self, fact: Fact) -> None: ... + + async def all(self, owner: str) -> list[Fact]: ... + + +@runtime_checkable +class RecallPort(Protocol): + """Which facts to surface for the current question. Axis 2. + + V1 returns everything; v1.5 filters by keyword, v2 by vector similarity. + """ + + async def recall(self, owner: str, query: str, store: StorePort) -> list[Fact]: + ... + + +@runtime_checkable +class ExtractorPort(Protocol): + """How new facts get created. Axis 3. + + V1 only the explicit ``/remember`` path produces facts (so ``extract`` + yields nothing); v1.5 mines the transcript with an LLM. + """ + + async def extract(self, owner: str, transcript: Sequence[Message]) -> list[Fact]: + ... diff --git a/src/lang2sql/core/ports/safety.py b/src/lang2sql/core/ports/safety.py new file mode 100644 index 0000000..8b69d11 --- /dev/null +++ b/src/lang2sql/core/ports/safety.py @@ -0,0 +1,60 @@ +"""Safety port — the ★① pipeline that gates every SQL execution. + +Airport-security model: SQL passes a *line* of layers; each returns pass / +block / needs-confirmation / rewrite. New checks (v1.5 AST validation, function +blocklist, metadata enrichment) are "one class + slot it in the line" with zero +``run_sql`` changes. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from enum import Enum +from typing import Protocol, Sequence, runtime_checkable + + +class Verdict(str, Enum): + PASS = "pass" + BLOCK = "block" + CONFIRM = "confirm" # ask the user before proceeding + REWRITE = "rewrite" # layer rewrote the SQL (e.g. attach LIMIT) + + +@dataclass +class SafetyDecision: + verdict: Verdict + sql: str # possibly rewritten + reason: str = "" + layer: str = "" # which layer decided + confirm_prompt: str = "" # populated when verdict is CONFIRM + + +@dataclass +class SafetyContext: + """Knobs a layer reads (timeout, row cap). Grows over versions.""" + + timeout_seconds: int = 30 + row_limit: int = 1000 + extras: dict = field(default_factory=dict) + + +@runtime_checkable +class SafetyLayerPort(Protocol): + """One check in the line. Pure: SQL in, decision out.""" + + @property + def name(self) -> str: ... + + def check(self, sql: str, ctx: SafetyContext) -> SafetyDecision: + ... + + +@runtime_checkable +class SafetyPipelinePort(Protocol): + """Runs layers in order; first non-PASS short-circuits.""" + + def evaluate(self, sql: str, ctx: SafetyContext) -> SafetyDecision: + ... + + @property + def layers(self) -> Sequence[SafetyLayerPort]: ... diff --git a/src/lang2sql/core/ports/secrets.py b/src/lang2sql/core/ports/secrets.py new file mode 100644 index 0000000..92c7fda --- /dev/null +++ b/src/lang2sql/core/ports/secrets.py @@ -0,0 +1,24 @@ +"""Secrets port — per-scope encrypted credential storage. + +Holds DB connection strings / API keys a guild registers via ``/connect``. V1 +backs it with an encrypted SQLite store; the boundary lets a KMS-backed impl +drop in later without touching callers. +""" + +from __future__ import annotations + +from typing import Protocol, runtime_checkable + + +@runtime_checkable +class SecretsPort(Protocol): + async def get(self, scope: str, key: str) -> str | None: + """Decrypt and return one secret, or ``None`` if unset.""" + ... + + async def set(self, scope: str, key: str, value: str) -> None: + """Encrypt and persist one secret under ``scope``.""" + ... + + async def delete(self, scope: str, key: str) -> None: + ... diff --git a/src/lang2sql/core/ports/semantic_scope.py b/src/lang2sql/core/ports/semantic_scope.py new file mode 100644 index 0000000..71c746c --- /dev/null +++ b/src/lang2sql/core/ports/semantic_scope.py @@ -0,0 +1,34 @@ +"""Semantic scope port — git-like federation resolution (★④). + +The same term ("active user") can mean different things per team. Definitions +are stored against a :class:`Scope`; resolution walks ``Identity.scope_chain`` +narrow→wide and the first hit wins. No conflicts — each scope lives in its own +branch. V1 = guild/channel/thread auto-resolution + ``/semantic show``. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Protocol, runtime_checkable + +from ..identity import Identity, Scope + +if TYPE_CHECKING: + from ...semantic.types import SemanticEntry + from ...semantic.layer import SemanticLayer + + +@runtime_checkable +class ScopeResolverPort(Protocol): + """Resolve the effective semantic layer for an identity's scope chain.""" + + async def effective_layer(self, identity: Identity) -> "SemanticLayer": + """Merge scopes narrow→wide so the most specific definition wins.""" + ... + + async def define(self, scope: Scope, entry: "SemanticEntry") -> None: + """Persist one definition at an explicit scope.""" + ... + + async def entries_at(self, scope: Scope) -> "list[SemanticEntry]": + """Definitions stored exactly at ``scope`` (no inheritance).""" + ... diff --git a/src/lang2sql/core/ports/session_store.py b/src/lang2sql/core/ports/session_store.py new file mode 100644 index 0000000..5a3a763 --- /dev/null +++ b/src/lang2sql/core/ports/session_store.py @@ -0,0 +1,22 @@ +"""Session store port — persist/restore a conversation by session key. + +This is what makes the bot *not* stateless (tiebreaker #4, context +preservation). V1 persists to SQLite keyed by :meth:`Identity.session_key`. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Protocol, runtime_checkable + +if TYPE_CHECKING: + from ...harness.session import Session + + +@runtime_checkable +class SessionStorePort(Protocol): + async def load(self, key: str) -> "Session | None": + """Restore a saved session, or ``None`` for a fresh conversation.""" + ... + + async def save(self, key: str, session: "Session") -> None: + ... diff --git a/src/lang2sql/core/ports/tool.py b/src/lang2sql/core/ports/tool.py new file mode 100644 index 0000000..34d58ac --- /dev/null +++ b/src/lang2sql/core/ports/tool.py @@ -0,0 +1,29 @@ +"""Tool port — a single capability the agent can invoke. + +Tools are ctx-aware: ``run`` receives the live :class:`HarnessContext` so a +tool can reach the explorer, semantic layer, safety pipeline, etc. without +globals. The harness advertises ``spec`` to the LLM and dispatches by ``name``. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable + +from ..types import ToolResult, ToolSpec + +if TYPE_CHECKING: # avoid import cycle: harness imports core, not vice versa + from ...harness.context import HarnessContext + + +@runtime_checkable +class ToolPort(Protocol): + """One invocable capability (run_sql, explore_schema, define_metric, …).""" + + @property + def spec(self) -> ToolSpec: + """Name + description + JSON-Schema params advertised to the model.""" + ... + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + """Execute with model-supplied ``args`` against the live context.""" + ... diff --git a/src/lang2sql/core/types.py b/src/lang2sql/core/types.py new file mode 100644 index 0000000..4637585 --- /dev/null +++ b/src/lang2sql/core/types.py @@ -0,0 +1,85 @@ +"""Pure domain types shared across the whole system. + +These carry no behaviour and no I/O — they are the vocabulary every layer +(harness, tools, frontends, adapters) speaks. Keeping them dependency-free is +what lets the ``core`` package sit at the bottom of the import graph. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from enum import Enum +from typing import Any + + +class Role(str, Enum): + """Author of a conversation message.""" + + SYSTEM = "system" + USER = "user" + ASSISTANT = "assistant" + TOOL = "tool" + + +@dataclass +class ToolCall: + """A model's request to invoke one tool. + + ``arguments`` is the already-parsed JSON object the model emitted; adapters + are responsible for turning provider-specific payloads into this shape. + """ + + id: str + name: str + arguments: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class ToolResult: + """Outcome of running a :class:`ToolCall`, fed back to the model.""" + + call_id: str + content: str + is_error: bool = False + + +@dataclass +class Message: + """One entry in a conversation transcript. + + A single assistant turn may both say something (``content``) and request + tools (``tool_calls``). Tool-role messages carry ``tool_call_id`` to bind + their result back to the originating call. + """ + + role: Role + content: str = "" + tool_calls: list[ToolCall] = field(default_factory=list) + tool_call_id: str | None = None + name: str | None = None + + +@dataclass +class ToolSpec: + """Catalog entry the harness advertises to the LLM. + + ``parameters`` is a JSON-Schema object describing the tool's arguments, + matching what tool-calling LLM APIs expect. + """ + + name: str + description: str + parameters: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class Completion: + """What an :class:`~lang2sql.core.ports.llm.LLMPort` returns for one call. + + Either ``content`` (final answer) or ``tool_calls`` (the model wants to act) + or both. The agent loop branches on whether ``tool_calls`` is non-empty. + """ + + content: str = "" + tool_calls: list[ToolCall] = field(default_factory=list) + finish_reason: str | None = None diff --git a/src/lang2sql/factory.py b/src/lang2sql/factory.py deleted file mode 100644 index bd05b41..0000000 --- a/src/lang2sql/factory.py +++ /dev/null @@ -1,236 +0,0 @@ -"""환경변수 기반 LLM/Embedding/DB 인스턴스 팩토리. - -레거시 utils/llm/core/factory.py를 LangChain 없이 재구현한 것. -CLI와 Streamlit UI 양쪽에서 사용한다. -""" - -from __future__ import annotations - -import os - -from .core.ports import DBExplorerPort, DBPort, EmbeddingPort, LLMPort - - -def build_llm_from_env() -> LLMPort: - """환경변수 LLM_PROVIDER에 따라 적절한 LLMPort 인스턴스를 생성한다.""" - provider = os.getenv("LLM_PROVIDER", "openai").lower() - - if provider == "openai": - from .integrations.llm.openai_ import OpenAILLM - - return OpenAILLM( - model=os.getenv("OPEN_AI_LLM_MODEL", "gpt-4o"), - api_key=os.getenv("OPEN_AI_KEY"), - ) - - if provider == "anthropic": - from .integrations.llm.anthropic_ import AnthropicLLM - - return AnthropicLLM( - model=os.getenv("ANTHROPIC_LLM_MODEL", "claude-sonnet-4-6"), - api_key=os.getenv("ANTHROPIC_API_KEY"), - ) - - if provider == "azure": - from .integrations.llm.azure_ import AzureOpenAILLM - - return AzureOpenAILLM( - azure_deployment=os.environ["AZURE_OPENAI_LLM_MODEL"], - azure_endpoint=os.environ["AZURE_OPENAI_LLM_ENDPOINT"], - api_version=os.getenv("AZURE_OPENAI_LLM_API_VERSION", "2023-07-01-preview"), - api_key=os.getenv("AZURE_OPENAI_LLM_KEY"), - ) - - if provider == "gemini": - from .integrations.llm.gemini_ import GeminiLLM - - return GeminiLLM( - model=os.getenv("GEMINI_LLM_MODEL", "gemini-2.0-flash-lite"), - api_key=os.getenv("GEMINI_API_KEY"), - ) - - if provider == "bedrock": - from .integrations.llm.bedrock_ import BedrockLLM - - return BedrockLLM( - model=os.environ["AWS_BEDROCK_LLM_MODEL"], - aws_access_key_id=os.getenv("AWS_BEDROCK_LLM_ACCESS_KEY_ID"), - aws_secret_access_key=os.getenv("AWS_BEDROCK_LLM_SECRET_ACCESS_KEY"), - region_name=os.getenv("AWS_BEDROCK_LLM_REGION", "us-east-1"), - ) - - if provider == "ollama": - from .integrations.llm.ollama_ import OllamaLLM - - return OllamaLLM( - model=os.environ["OLLAMA_LLM_MODEL"], - base_url=os.getenv("OLLAMA_LLM_BASE_URL", "http://localhost:11434"), - ) - - if provider == "huggingface": - from .integrations.llm.huggingface_ import HuggingFaceLLM - - return HuggingFaceLLM( - repo_id=os.getenv("HUGGING_FACE_LLM_REPO_ID"), - endpoint_url=os.getenv("HUGGING_FACE_LLM_ENDPOINT"), - api_token=os.getenv("HUGGING_FACE_LLM_API_TOKEN"), - ) - - raise ValueError( - f"Unknown LLM_PROVIDER: {provider!r}. " - "Valid values: openai, anthropic, azure, gemini, bedrock, ollama, huggingface" - ) - - -def build_embedding_from_env() -> EmbeddingPort: - """환경변수 EMBEDDING_PROVIDER에 따라 EmbeddingPort 인스턴스를 생성한다.""" - provider = os.getenv("EMBEDDING_PROVIDER", "openai").lower().strip("'\"") - - if provider == "openai": - from .integrations.embedding.openai_ import OpenAIEmbedding - - return OpenAIEmbedding( - model=os.getenv("OPEN_AI_EMBEDDING_MODEL", "text-embedding-3-small"), - api_key=os.getenv("OPEN_AI_KEY"), - ) - - if provider == "azure": - from .integrations.embedding.azure_ import AzureOpenAIEmbedding - - return AzureOpenAIEmbedding( - azure_deployment=os.environ["AZURE_OPENAI_EMBEDDING_MODEL"], - azure_endpoint=os.environ["AZURE_OPENAI_EMBEDDING_ENDPOINT"], - api_version=os.getenv( - "AZURE_OPENAI_EMBEDDING_API_VERSION", "2023-09-15-preview" - ), - api_key=os.getenv("AZURE_OPENAI_EMBEDDING_KEY"), - ) - - if provider == "ollama": - from .integrations.embedding.ollama_ import OllamaEmbedding - - return OllamaEmbedding( - model=os.getenv( - "EMBEDDING_MODEL", - os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"), - ), - base_url=os.getenv( - "EMBEDDING_BASE_PATH", - os.getenv("OLLAMA_EMBEDDING_BASE_URL", "http://localhost:11434"), - ), - ) - - if provider == "bedrock": - from .integrations.embedding.bedrock_ import BedrockEmbedding - - return BedrockEmbedding( - model_id=os.getenv( - "AWS_BEDROCK_EMBEDDING_MODEL", "amazon.titan-embed-text-v2:0" - ), - aws_access_key_id=os.getenv("AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID"), - aws_secret_access_key=os.getenv("AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY"), - region_name=os.getenv("AWS_BEDROCK_EMBEDDING_REGION", "us-east-1"), - ) - - if provider == "gemini": - from .integrations.embedding.gemini_ import GeminiEmbedding - - return GeminiEmbedding( - model=os.getenv("EMBEDDING_MODEL", "models/embedding-001"), - api_key=os.getenv("GEMINI_EMBEDDING_API_KEY"), - ) - - if provider == "huggingface": - from .integrations.embedding.huggingface_ import HuggingFaceEmbedding - - return HuggingFaceEmbedding( - model=os.getenv( - "HUGGING_FACE_EMBEDDING_MODEL", - os.getenv("HUGGING_FACE_EMBEDDING_REPO_ID", ""), - ) - ) - - raise ValueError( - f"Unknown EMBEDDING_PROVIDER: {provider!r}. " - "Valid values: openai, azure, ollama, bedrock, gemini, huggingface" - ) - - -def build_explorer_from_url(url: str, *, schema: str | None = None) -> "DBExplorerPort": - """DB URL로 SQLAlchemyExplorer 생성.""" - from .integrations.db.sqlalchemy_ import SQLAlchemyExplorer - - return SQLAlchemyExplorer(url, schema=schema) - - -def build_db_from_env(database_env: str = "") -> DBPort: - """환경변수에서 DB URL을 구성하고 SQLAlchemyDB를 반환한다. - - DB_TYPE 환경변수에 따라 적절한 SQLAlchemy 연결 URL을 구성한다. - """ - from .integrations.db.sqlalchemy_ import SQLAlchemyDB - - db_type = os.getenv("DB_TYPE", "sqlite").lower() - url = _build_db_url(db_type) - return SQLAlchemyDB(url) - - -def _build_db_url(db_type: str) -> str: - if db_type == "sqlite": - path = os.getenv("SQLITE_PATH", "./data/sqlite.db") - return f"sqlite:///{path}" - - if db_type == "postgresql": - host = os.getenv("POSTGRESQL_HOST", "localhost") - port = os.getenv("POSTGRESQL_PORT", "5432") - user = os.getenv("POSTGRESQL_USER", "postgres") - password = os.getenv("POSTGRESQL_PASSWORD", "") - database = os.getenv("POSTGRESQL_DATABASE", "postgres") - return f"postgresql://{user}:{password}@{host}:{port}/{database}" - - if db_type == "mysql": - host = os.getenv("MYSQL_HOST", "localhost") - port = os.getenv("MYSQL_PORT", "3306") - user = os.getenv("MYSQL_USER", "root") - password = os.getenv("MYSQL_PASSWORD", "") - database = os.getenv("MYSQL_DATABASE", "") - return f"mysql+pymysql://{user}:{password}@{host}:{port}/{database}" - - if db_type == "mariadb": - host = os.getenv("MARIADB_HOST", "localhost") - port = os.getenv("MARIADB_PORT", "3306") - user = os.getenv("MARIADB_USER", "root") - password = os.getenv("MARIADB_PASSWORD", "") - database = os.getenv("MARIADB_DATABASE", "") - return f"mariadb+pymysql://{user}:{password}@{host}:{port}/{database}" - - if db_type == "duckdb": - path = os.getenv("DUCKDB_PATH", "./data/duckdb.db") - return f"duckdb:///{path}" - - if db_type == "clickhouse": - host = os.getenv("CLICKHOUSE_HOST", "localhost") - port = os.getenv("CLICKHOUSE_PORT", "9001") - user = os.getenv("CLICKHOUSE_USER", "default") - password = os.getenv("CLICKHOUSE_PASSWORD", "") - database = os.getenv("CLICKHOUSE_DATABASE", "default") - return f"clickhouse+native://{user}:{password}@{host}:{port}/{database}" - - if db_type == "snowflake": - user = os.environ["SNOWFLAKE_USER"] - password = os.environ["SNOWFLAKE_PASSWORD"] - account = os.environ["SNOWFLAKE_ACCOUNT"] - return f"snowflake://{user}:{password}@{account}" - - if db_type == "oracle": - host = os.getenv("ORACLE_HOST", "localhost") - port = os.getenv("ORACLE_PORT", "1521") - user = os.getenv("ORACLE_USER", "") - password = os.getenv("ORACLE_PASSWORD", "") - service = os.getenv("ORACLE_SERVICE_NAME", os.getenv("ORACLE_DATABASE", "")) - return f"oracle+cx_oracle://{user}:{password}@{host}:{port}/?service_name={service}" - - raise ValueError( - f"Unknown DB_TYPE: {db_type!r}. " - "Valid values: sqlite, postgresql, mysql, mariadb, duckdb, clickhouse, snowflake, oracle" - ) diff --git a/src/lang2sql/flows/__init__.py b/src/lang2sql/flows/__init__.py deleted file mode 100644 index b6aa9ac..0000000 --- a/src/lang2sql/flows/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .enriched_nl2sql import EnrichedNL2SQL -from .hybrid import HybridNL2SQL -from .nl2sql import BaselineNL2SQL - -__all__ = ["BaselineNL2SQL", "EnrichedNL2SQL", "HybridNL2SQL"] diff --git a/src/lang2sql/flows/baseline.py b/src/lang2sql/flows/baseline.py deleted file mode 100644 index 43b7dc1..0000000 --- a/src/lang2sql/flows/baseline.py +++ /dev/null @@ -1,77 +0,0 @@ -from __future__ import annotations - -from typing import Any, Callable, Sequence - -from ..core.base import BaseFlow - - -class SequentialFlow(BaseFlow): - """ - A minimal sequential pipeline runner (define-by-run style). - - This flow runs `steps` in order, passing the output of each step - as the input to the next. Each step is a plain callable — no shared - state bag is required. - - Args: - steps: Ordered sequence of callables. - name: Optional name override for tracing/logging. - hook: Optional TraceHook. If not provided, a NullHook is used by BaseFlow. - - Returns: - The final value after running all steps. - """ - - def __init__( - self, - *, - steps: Sequence[Callable[..., Any]], - name: str | None = None, - hook=None, - ) -> None: - """ - Initialize the flow with an ordered list of steps. - - Args: - steps: Ordered sequence of callables. Must be non-empty. - name: Optional name override. - hook: Optional TraceHook used by BaseFlow for flow-level events. - - Raises: - ValueError: If `steps` is empty. - """ - super().__init__(name=name or "SequentialFlow", hook=hook) - if not steps: - raise ValueError("SequentialFlow requires at least one step.") - self.steps: list[Callable[..., Any]] = list(steps) - - def _run(self, value: Any) -> Any: - """ - Execute the flow by passing `value` through each step in order. - - Args: - value: The initial input value. - - Returns: - The final value after running all configured steps. - """ - for step in self.steps: - value = step(value) - return value - - -def BaselineFlow(*args, **kwargs): - """ - Deprecated alias for SequentialFlow. - - .. deprecated:: - Use :class:`SequentialFlow` instead. - """ - import warnings - - warnings.warn( - "BaselineFlow is deprecated. Use SequentialFlow instead.", - DeprecationWarning, - stacklevel=2, - ) - return SequentialFlow(*args, **kwargs) diff --git a/src/lang2sql/flows/enriched_nl2sql.py b/src/lang2sql/flows/enriched_nl2sql.py deleted file mode 100644 index e01ea4b..0000000 --- a/src/lang2sql/flows/enriched_nl2sql.py +++ /dev/null @@ -1,101 +0,0 @@ -from __future__ import annotations - -from typing import Optional - -from ..components.enrichment.context_enricher import ContextEnricher -from ..components.enrichment.question_profiler import QuestionProfiler -from ..components.execution.sql_executor import SQLExecutor -from ..components.gate.question_gate import QuestionGate -from ..components.gate.table_suitability import TableSuitabilityEvaluator -from ..components.generation.sql_generator import SQLGenerator -from ..components.retrieval.hybrid import HybridRetriever -from ..core.base import BaseFlow -from ..core.catalog import TextDocument -from ..core.exceptions import ContractError -from ..core.hooks import TraceHook -from ..core.ports import DBPort, EmbeddingPort, LLMPort - - -class EnrichedNL2SQL(BaseFlow): - """ - 풀 파이프라인 NL→SQL: - QuestionGate → HybridRetriever → TableSuitabilityEvaluator - → QuestionProfiler → ContextEnricher → SQLGenerator → SQLExecutor - - 레거시 LangGraph 기반 engine/query_executor.py + graph_utils/enriched_graph.py를 대체한다. - - Args: - catalog: list[CatalogEntry] — 검색 대상 테이블 메타데이터. - llm: LLMPort — 질문 평가, SQL 생성 등에 사용. - db: DBPort — SQL 실행 대상 데이터베이스. - embedding: EmbeddingPort — 벡터 검색용 임베딩 모델. - documents: Optional list of business documents to index. - db_dialect: SQL 방언. "sqlite", "postgresql", "mysql", "bigquery", "duckdb", "default". - gate_enabled: QuestionGate를 활성화할지 여부. Default True. - top_n: HybridRetriever가 반환할 최대 스키마 수. Default 5. - hook: TraceHook for observability. - - Usage:: - - pipeline = EnrichedNL2SQL( - catalog=[{"name": "orders", "description": "...", "columns": {...}}], - llm=AnthropicLLM(model="claude-sonnet-4-6"), - db=SQLAlchemyDB("sqlite:///sample.db"), - embedding=OpenAIEmbedding(model="text-embedding-3-small"), - db_dialect="sqlite", - ) - rows = pipeline.run("지난달 주문 건수를 알려줘") - """ - - def __init__( - self, - *, - catalog: list[dict], - llm: LLMPort, - db: DBPort, - embedding: EmbeddingPort, - documents: Optional[list[TextDocument]] = None, - db_dialect: Optional[str] = None, - gate_enabled: bool = True, - top_n: int = 5, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name="EnrichedNL2SQL", hook=hook) - self._gate = QuestionGate(llm=llm, hook=hook) if gate_enabled else None - self._retriever = HybridRetriever( - catalog=catalog, - embedding=embedding, - documents=documents, - top_n=top_n, - hook=hook, - ) - self._table_eval = TableSuitabilityEvaluator(llm=llm, hook=hook) - self._profiler = QuestionProfiler(llm=llm, hook=hook) - self._enricher = ContextEnricher(llm=llm, hook=hook) - self._generator = SQLGenerator(llm=llm, db_dialect=db_dialect, hook=hook) - self._executor = SQLExecutor(db=db, hook=hook) - - def _run(self, query: str) -> list[dict]: - # 1. Gate (선택적): 질문이 SQL 답변 불가능하면 ContractError 발생 - if self._gate is not None: - gate = self._gate(query) - if not gate.suitable: - raise ContractError(f"Query not suitable for SQL: {gate.reason}") - - # 2. Retrieval: HybridRetriever → RetrievalResult - result = self._retriever(query) - - # 3. Table filtering: 관련도 낮은 테이블 제거 - schemas = self._table_eval(query, result.schemas) - - # 4. Profiling: 질문 특성 추출 - profile = self._profiler(query) - - # 5. Context enrichment: 보강된 질문 텍스트 생성 - enriched = self._enricher(query, schemas, profile) - - # 6. SQL generation: 보강된 컨텍스트 + 도메인 문서를 함께 전달 - sql = self._generator(query, schemas, context=[enriched] + result.context) - - # 7. Execution - return self._executor(sql) diff --git a/src/lang2sql/flows/hybrid.py b/src/lang2sql/flows/hybrid.py deleted file mode 100644 index b30575a..0000000 --- a/src/lang2sql/flows/hybrid.py +++ /dev/null @@ -1,73 +0,0 @@ -from __future__ import annotations - -from typing import Optional - -from ..components.execution.sql_executor import SQLExecutor -from ..components.generation.sql_generator import SQLGenerator -from ..components.retrieval.hybrid import HybridRetriever -from ..core.base import BaseFlow -from ..core.catalog import TextDocument -from ..core.hooks import TraceHook -from ..core.ports import DBPort, EmbeddingPort, LLMPort - - -class HybridNL2SQL(BaseFlow): - """ - NL→SQL pipeline backed by BM25 + vector hybrid retrieval. - - Provides higher retrieval quality than ``BaselineNL2SQL`` with only the - addition of an ``embedding`` parameter. - - Pipeline: HybridRetriever → SQLGenerator → SQLExecutor - - Args: - catalog: List of CatalogEntry dicts. - llm: LLMPort implementation. - db: DBPort implementation. - embedding: EmbeddingPort implementation. - documents: Optional list of business documents to index. - db_dialect: SQL dialect. Supported values: ``"sqlite"``, ``"postgresql"``, - ``"mysql"``, ``"bigquery"``, ``"duckdb"``, ``"default"`` - (or ``None`` for default). - top_n: Maximum number of schemas to return. Default 5. - hook: TraceHook for observability. - - Usage:: - - pipeline = HybridNL2SQL( - catalog=[{"name": "orders", "description": "...", "columns": {...}}], - llm=AnthropicLLM(model="claude-sonnet-4-6"), - db=SQLAlchemyDB("sqlite:///sample.db"), - embedding=OpenAIEmbedding(model="text-embedding-3-small"), - db_dialect="sqlite", - ) - rows = pipeline.run("How many orders last month?") - """ - - def __init__( - self, - *, - catalog: list[dict], - llm: LLMPort, - db: DBPort, - embedding: EmbeddingPort, - documents: Optional[list[TextDocument]] = None, - db_dialect: Optional[str] = None, - top_n: int = 5, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name="HybridNL2SQL", hook=hook) - self._retriever = HybridRetriever( - catalog=catalog, - embedding=embedding, - documents=documents, - top_n=top_n, - hook=hook, - ) - self._generator = SQLGenerator(llm=llm, db_dialect=db_dialect, hook=hook) - self._executor = SQLExecutor(db=db, hook=hook) - - def _run(self, query: str) -> list[dict]: - result = self._retriever(query) # RetrievalResult - sql = self._generator(query, result.schemas, context=result.context) - return self._executor(sql) diff --git a/src/lang2sql/flows/nl2sql.py b/src/lang2sql/flows/nl2sql.py deleted file mode 100644 index 530798a..0000000 --- a/src/lang2sql/flows/nl2sql.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -from typing import Optional - -from ..components.execution.sql_executor import SQLExecutor -from ..components.generation.sql_generator import SQLGenerator -from ..components.retrieval.keyword import KeywordRetriever -from ..core.base import BaseFlow -from ..core.hooks import TraceHook -from ..core.ports import DBPort, LLMPort - - -class BaselineNL2SQL(BaseFlow): - """ - End-to-end NL→SQL pipeline. - - Pipeline: KeywordRetriever → SQLGenerator → SQLExecutor - - Usage:: - - pipeline = BaselineNL2SQL( - catalog=[{"name": "orders", "description": "...", "columns": {...}}], - llm=AnthropicLLM(model="claude-sonnet-4-6"), - db=SQLAlchemyDB("sqlite:///sample.db"), - db_dialect="sqlite", - ) - rows = pipeline.run("How many orders last month?") - - Supported ``db_dialect`` values: ``"sqlite"``, ``"postgresql"``, ``"mysql"``, - ``"bigquery"``, ``"duckdb"``, ``"default"`` (or ``None`` for default). - """ - - def __init__( - self, - *, - catalog: list[dict], - llm: LLMPort, - db: DBPort, - db_dialect: Optional[str] = None, - hook: Optional[TraceHook] = None, - ) -> None: - super().__init__(name="BaselineNL2SQL", hook=hook) - self._retriever = KeywordRetriever(catalog=catalog, hook=hook) - self._generator = SQLGenerator(llm=llm, db_dialect=db_dialect, hook=hook) - self._executor = SQLExecutor(db=db, hook=hook) - - def _run(self, query: str) -> list[dict]: - schemas = self._retriever(query) # list[CatalogEntry] - sql = self._generator(query, schemas) - return self._executor(sql) diff --git a/prompt/__init__.py b/src/lang2sql/frontends/__init__.py similarity index 100% rename from prompt/__init__.py rename to src/lang2sql/frontends/__init__.py diff --git a/src/lang2sql/components/__init__.py b/src/lang2sql/frontends/cli/__init__.py similarity index 100% rename from src/lang2sql/components/__init__.py rename to src/lang2sql/frontends/cli/__init__.py diff --git a/src/lang2sql/frontends/cli/app.py b/src/lang2sql/frontends/cli/app.py new file mode 100644 index 0000000..47a6272 --- /dev/null +++ b/src/lang2sql/frontends/cli/app.py @@ -0,0 +1,36 @@ +"""CLI frontend — the developer-facing driver of the harness. + +Per v4.1 the CLI is a dev tool (Discord is the Phase 1 product frontend). It +assembles a real :class:`HarnessContext` via the :class:`ContextConcierge` +(OpenAI when ``OPENAI_API_KEY`` is set, else the offline FakeLLM) and runs one +turn through ``agent_loop`` — the harness's end-to-end smoke test. + +Run: python -m lang2sql.frontends.cli.app "your question" +""" + +from __future__ import annotations + +import asyncio +import sys + +from ...core.identity import Identity +from ...harness.loop import agent_loop +from ...tenancy.concierge import ContextConcierge + + +async def _run(user_text: str) -> str: + concierge = ContextConcierge() + identity = Identity(user_id="cli-user", guild_id="cli", channel_id="cli-main") + ctx = await concierge.build_context(identity) + return await agent_loop(ctx, user_text) + + +def main(argv: list[str] | None = None) -> int: + argv = argv if argv is not None else sys.argv[1:] + user_text = " ".join(argv) or "list the tables" + print(asyncio.run(_run(user_text))) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/lang2sql/frontends/discord/__init__.py b/src/lang2sql/frontends/discord/__init__.py new file mode 100644 index 0000000..f252f93 --- /dev/null +++ b/src/lang2sql/frontends/discord/__init__.py @@ -0,0 +1,30 @@ +"""Discord frontend (Phase 1) — the V1 chat interface over the harness. + +``bot.py`` is the only module that imports discord.py; ``session_router``, +``render``, and ``commands`` are pure and unit-testable. Importing this package +does not import discord.py — ``bot`` is loaded lazily on demand — so the pure +layer (and its tests) stay free of the gateway dependency. +""" + +from __future__ import annotations + +from .commands import CommandHandlers +from .render import MAX_INLINE_ROWS, render_answer +from .session_router import ( + InteractionContext, + is_channel, + is_dm, + is_thread, + to_identity, +) + +__all__ = [ + "CommandHandlers", + "render_answer", + "MAX_INLINE_ROWS", + "InteractionContext", + "to_identity", + "is_dm", + "is_thread", + "is_channel", +] diff --git a/src/lang2sql/frontends/discord/bot.py b/src/lang2sql/frontends/discord/bot.py new file mode 100644 index 0000000..c51c8ae --- /dev/null +++ b/src/lang2sql/frontends/discord/bot.py @@ -0,0 +1,182 @@ +"""bot.py — the ONLY discord.py-aware module in the frontend (Phase 1). + +It is the thin shell described in v4.1 §2.1: receive a Discord interaction or +mention, translate it to an :class:`InteractionContext` → +:class:`Identity` (session_router), call a :class:`CommandHandlers` method, and +deliver the resulting :class:`OutboundMessage` natively (plain reply, or a +``discord.File`` upload when render attached a CSV). + +Import-safety contract (tested): importing this module must not require a token +or any network access — only :func:`run` connects to the gateway. So discord.py +is imported at module load (it's a pure library import), but the client is +constructed and the token is read only inside :func:`run`. +""" + +from __future__ import annotations + +import io +import os + +import discord +from discord import app_commands + +from ...core.ports.frontend import OutboundMessage +from ...tenancy.concierge import ContextConcierge +from .commands import CommandHandlers +from .session_router import InteractionContext, to_identity + +TOKEN_ENV = "DISCORD_BOT_TOKEN" + + +def _interaction_context(interaction: discord.Interaction) -> InteractionContext: + """Extract frontend-neutral coordinates from a slash-command interaction.""" + channel = interaction.channel + thread_id: str | None = None + channel_id: str | None = None + if isinstance(channel, discord.Thread): + thread_id = str(channel.id) + channel_id = str(channel.parent_id) if channel.parent_id else None + elif channel is not None: + channel_id = str(channel.id) + + is_admin = False + perms = getattr(interaction, "permissions", None) + if perms is not None: + is_admin = bool(perms.administrator) + + return InteractionContext( + user_id=str(interaction.user.id), + guild_id=str(interaction.guild_id) if interaction.guild_id else None, + channel_id=channel_id, + thread_id=thread_id, + is_admin=is_admin, + ) + + +def _message_context(message: discord.Message) -> InteractionContext: + """Extract coordinates from a plain message (an @mention or thread reply).""" + channel = message.channel + thread_id: str | None = None + channel_id: str | None = None + if isinstance(channel, discord.Thread): + thread_id = str(channel.id) + channel_id = str(channel.parent_id) if channel.parent_id else None + elif channel is not None: + channel_id = str(channel.id) + + is_admin = False + author = message.author + guild_perms = getattr(author, "guild_permissions", None) + if guild_perms is not None: + is_admin = bool(guild_perms.administrator) + + return InteractionContext( + user_id=str(author.id), + guild_id=str(message.guild.id) if message.guild else None, + channel_id=channel_id, + thread_id=thread_id, + is_admin=is_admin, + ) + + +def _to_sendable(message: OutboundMessage) -> tuple[str, discord.File | None]: + """Turn an :class:`OutboundMessage` into (content, optional file) for send.""" + if message.file_bytes is not None: + file = discord.File( + io.BytesIO(message.file_bytes), + filename=message.file_name or "result.csv", + ) + return message.text, file + return message.text, None + + +class Lang2SQLBot(discord.Client): + """Discord client wiring slash commands + @mentions to the harness.""" + + def __init__(self, handlers: CommandHandlers) -> None: + intents = discord.Intents.default() + intents.message_content = True # needed to read @mention text + super().__init__(intents=intents) + self._handlers = handlers + self.tree = app_commands.CommandTree(self) + self._register_commands() + + async def setup_hook(self) -> None: + # Sync slash commands with Discord on startup. + await self.tree.sync() + + def _register_commands(self) -> None: + tree = self.tree + handlers = self._handlers + + @tree.command(name="connect", description="Store a database connection string") + async def connect(interaction: discord.Interaction, dsn: str) -> None: + await self._run(interaction, handlers.connect(to_identity(_interaction_context(interaction)), dsn)) + + @tree.command(name="ingest", description="Propose definitions from a document") + async def ingest(interaction: discord.Interaction, ref: str) -> None: + await self._run(interaction, handlers.ingest(to_identity(_interaction_context(interaction)), ref=ref)) + + @tree.command(name="define_metric", description='Define a metric: name and "definition"') + async def define_metric( + interaction: discord.Interaction, name: str, definition: str + ) -> None: + await self._run( + interaction, + handlers.define_metric(to_identity(_interaction_context(interaction)), name, definition), + ) + + @tree.command(name="remember", description="Remember a fact for future turns") + async def remember(interaction: discord.Interaction, text: str) -> None: + await self._run(interaction, handlers.remember(to_identity(_interaction_context(interaction)), text)) + + @tree.command(name="semantic_show", description="Show definitions in effect here") + async def semantic_show(interaction: discord.Interaction) -> None: + await self._run(interaction, handlers.semantic_show(to_identity(_interaction_context(interaction)))) + + @tree.command(name="audit_me", description="Show your recent activity") + async def audit_me(interaction: discord.Interaction) -> None: + await self._run(interaction, handlers.audit_me(to_identity(_interaction_context(interaction)))) + + async def _run(self, interaction: discord.Interaction, coro) -> None: + """Await a handler coroutine and reply with its OutboundMessage.""" + await interaction.response.defer(thinking=True) + message = await coro + content, file = _to_sendable(message) + await interaction.followup.send(content=content or "(empty)", file=file) + + async def on_message(self, message: discord.Message) -> None: + """Treat an @mention (or a reply inside a thread) as a free-form query.""" + if message.author == self.user: + return + mentioned = self.user is not None and self.user.mentioned_in(message) + in_thread = isinstance(message.channel, discord.Thread) + if not mentioned and not in_thread: + return + + text = message.content + if self.user is not None: + text = text.replace(self.user.mention, "").strip() + if not text: + return + + identity = to_identity(_message_context(message)) + out = await self._handlers.query(identity, text) + content, file = _to_sendable(out) + await message.channel.send(content=content or "(empty)", file=file) + + +def run() -> None: + """Entry point for the ``lang2sql-bot`` script: connect and serve. + + Reads the token from :data:`TOKEN_ENV`; raises a clear error if it's unset + so a misconfigured deploy fails loudly rather than hanging on the gateway. + """ + token = os.environ.get(TOKEN_ENV) + if not token: + raise RuntimeError( + f"{TOKEN_ENV} is not set; export your Discord bot token to run the bot." + ) + handlers = CommandHandlers(ContextConcierge()) + client = Lang2SQLBot(handlers) + client.run(token) diff --git a/src/lang2sql/frontends/discord/commands.py b/src/lang2sql/frontends/discord/commands.py new file mode 100644 index 0000000..ac426ed --- /dev/null +++ b/src/lang2sql/frontends/discord/commands.py @@ -0,0 +1,143 @@ +"""CommandHandlers — the V1 Discord command surface, free of discord.py. + +Each public method takes plain arguments (an :class:`Identity` plus strings) and +returns an :class:`OutboundMessage`, so the whole command layer is unit-testable +without a gateway connection. ``bot.py`` is the only module that knows about +slash commands, embeds, and ``discord.File``; it parses an interaction, picks a +handler here, and renders the result. + +The handlers drive the harness through a :class:`ContextConcierge`: every call +builds a fresh :class:`HarnessContext` for the identity (restoring its session), +then either runs the agent loop (``query``) or invokes a single ctx-aware tool / +port directly (the structured commands). Running the real tools — rather than +re-implementing their logic — keeps federation scoping, audit logging, and +memory writes identical to what the agent itself does. +""" + +from __future__ import annotations + +from datetime import datetime, timezone + +from ...core.identity import Identity +from ...core.ports.frontend import OutboundMessage +from ...harness.loop import agent_loop +from ...tenancy.concierge import ContextConcierge +from .render import render_answer + + +class CommandHandlers: + """Async command methods returning :class:`OutboundMessage` (discord-free).""" + + def __init__(self, concierge: ContextConcierge) -> None: + self._concierge = concierge + + async def query(self, identity: Identity, text: str) -> OutboundMessage: + """Run a natural-language question through the agent loop, then render. + + The loop mutates the in-context :class:`Session`; we persist it back + through the concierge store afterwards so the next message in the same + thread/DM continues the conversation (tiebreaker #4). + """ + ctx = await self._concierge.build_context(identity, user_text=text) + answer = await agent_loop(ctx, text) + await self._concierge.store.save(identity.session_key(), ctx.session) + return render_answer(answer) + + async def define_metric( + self, + identity: Identity, + name: str, + definition: str, + scope: str | None = None, + ) -> OutboundMessage: + """Register one definition at the current (or requested) scope. + + Delegates to the ctx-aware ``define_metric`` tool, which writes through + the :class:`ScopeResolverPort` and records an audit event. ``scope`` is + ``"channel"`` (default) or ``"guild"`` per the tool's schema. + """ + ctx = await self._concierge.build_context(identity) + args: dict[str, str] = {"name": name, "definition": definition} + if scope: + args["scope"] = scope + result = await ctx.tools.dispatch("define_metric", args, ctx, "cmd:define_metric") + return OutboundMessage(text=result.content) + + async def remember(self, identity: Identity, text: str) -> OutboundMessage: + """Persist a user fact via the memory service (manual ``/remember``).""" + ctx = await self._concierge.build_context(identity) + result = await ctx.tools.dispatch("remember", {"text": text}, ctx, "cmd:remember") + return OutboundMessage(text=result.content) + + async def semantic_show(self, identity: Identity) -> OutboundMessage: + """Show the effective semantic layer for this scope chain.""" + ctx = await self._concierge.build_context(identity) + if ctx.scope_resolver is None: + return OutboundMessage(text="Semantic layer unavailable.") + layer = await ctx.scope_resolver.effective_layer(identity) + rendered = layer.render() + if not rendered: + return OutboundMessage(text="No definitions apply in this scope yet.") + return OutboundMessage(text=f"Definitions in effect here:\n{rendered}") + + async def audit_me(self, identity: Identity) -> OutboundMessage: + """List the caller's recent audited actions, newest first.""" + ctx = await self._concierge.build_context(identity) + if ctx.audit is None: + return OutboundMessage(text="Audit log unavailable.") + events = await ctx.audit.query(identity.user_id) + if not events: + return OutboundMessage(text="No audited activity for you yet.") + lines = ["Your recent activity:"] + for event in events: + lines.append(f"- {_fmt_ts(event.ts)} {event.action} @ {event.scope}") + return OutboundMessage(text="\n".join(lines)) + + async def connect(self, identity: Identity, dsn: str) -> OutboundMessage: + """V1 stub: stash a DB DSN keyed by guild/DM in the concierge kv store. + + There is no secrets *port* on :class:`HarnessContext` in V1, so this + does not yet encrypt or wire the DSN into the explorer — it simply + records it (so a later run can pick it up) and acknowledges. The real + encrypted-secrets path lands in the tenancy work (task #2); this keeps + the command present and documented without overreaching. + """ + dsn = dsn.strip() + if not dsn: + return OutboundMessage(text="Provide a database connection string.") + scope = identity.guild_id or f"dm:{identity.user_id}" + self._concierge.store.kv_set(scope, "dsn", dsn) + return OutboundMessage( + text=( + "Connection string saved for this workspace (V1 stub — not yet " + "encrypted or live; queries still run against the configured DB)." + ) + ) + + async def ingest( + self, + identity: Identity, + ref: str | None = None, + content: str | None = None, + ) -> OutboundMessage: + """Propose semantic definitions extracted from a document. + + Lists the candidates via the ``ingest_doc`` tool; confirmation buttons + are ``bot.py``'s job (V1 just surfaces the list, keeping the human in + the loop before anything enters the semantic layer). + """ + ctx = await self._concierge.build_context(identity) + args: dict[str, str] = {} + if ref: + args["ref"] = ref + if content: + args["content"] = content + result = await ctx.tools.dispatch("ingest_doc", args, ctx, "cmd:ingest") + return OutboundMessage(text=result.content) + + +def _fmt_ts(ts: float) -> str: + """Format an epoch timestamp as a short UTC string for audit listings.""" + if not ts: + return "?" + return datetime.fromtimestamp(ts, tz=timezone.utc).strftime("%Y-%m-%d %H:%M") diff --git a/src/lang2sql/frontends/discord/render.py b/src/lang2sql/frontends/discord/render.py new file mode 100644 index 0000000..f143d2a --- /dev/null +++ b/src/lang2sql/frontends/discord/render.py @@ -0,0 +1,80 @@ +"""render — agent answer → :class:`OutboundMessage` (pure, no discord import). + +v4.1 §4.1 sets the response rule: a small result goes back as text, a large one +(> :data:`MAX_INLINE_ROWS`) is attached as a CSV with a short text summary so a +Discord message never balloons past what a human skims. Keeping this pure means +``bot.py`` only has to turn an :class:`OutboundMessage` into a ``discord.File`` +or a plain reply — all the threshold logic is unit-tested here. +""" + +from __future__ import annotations + +import csv +import io +from collections.abc import Sequence +from typing import Any + +from ...core.ports.frontend import OutboundMessage + +# Above this many rows (or text lines) we attach a CSV instead of inlining. +MAX_INLINE_ROWS = 50 + + +def render_answer( + text: str, + rows: Sequence[Sequence[Any]] | None = None, + *, + header: Sequence[str] | None = None, + file_name: str = "result.csv", +) -> OutboundMessage: + """Render an agent answer, attaching a CSV when it's too big to inline. + + Two oversized shapes trigger an attachment: + + * structured ``rows`` longer than :data:`MAX_INLINE_ROWS` — serialised to + CSV (with ``header`` if given) and replaced by a one-line summary; or + * a plain ``text`` answer with more than :data:`MAX_INLINE_ROWS` lines — + written verbatim into a ``.csv``/text attachment. + + Anything smaller is returned as plain ``text``. + """ + if rows is not None and len(rows) > MAX_INLINE_ROWS: + payload = _rows_to_csv(rows, header) + summary = f"{len(rows)} rows — attached as {file_name}." + if text.strip(): + summary = f"{text.strip()}\n{summary}" + return OutboundMessage( + text=summary, + file_bytes=payload.encode("utf-8"), + file_name=file_name, + ) + + if rows is not None: + # Small structured result: inline as text, CSV-formatted for legibility. + body = _rows_to_csv(rows, header).rstrip("\n") + text_block = f"{text.strip()}\n{body}" if text.strip() else body + return OutboundMessage(text=text_block) + + lines = text.splitlines() + if len(lines) > MAX_INLINE_ROWS: + summary = f"Result is {len(lines)} lines — attached as {file_name}." + return OutboundMessage( + text=summary, + file_bytes=text.encode("utf-8"), + file_name=file_name, + ) + + return OutboundMessage(text=text) + + +def _rows_to_csv( + rows: Sequence[Sequence[Any]], header: Sequence[str] | None +) -> str: + """Serialise ``rows`` (optionally with a ``header``) to a CSV string.""" + buf = io.StringIO() + writer = csv.writer(buf) + if header is not None: + writer.writerow(list(header)) + for row in rows: + writer.writerow(list(row)) + return buf.getvalue() diff --git a/src/lang2sql/frontends/discord/session_router.py b/src/lang2sql/frontends/discord/session_router.py new file mode 100644 index 0000000..7973c07 --- /dev/null +++ b/src/lang2sql/frontends/discord/session_router.py @@ -0,0 +1,71 @@ +"""session_router — interaction coordinates → :class:`Identity` (pure). + +This module is deliberately free of ``discord`` imports so the mapping is unit- +testable without a live gateway. ``bot.py`` extracts the raw ids from a +discord.py interaction/message and hands them here; everything downstream +(session key, federation scope chain) follows from the :class:`Identity` these +functions build. + +The three conversation shapes of v4.1 §4.1 map cleanly onto the optional id +fields of :class:`Identity`: + +* **DM** — no ``guild_id``; the user is the unit of conversation. +* **channel @mention** — ``guild_id`` + ``channel_id``; a reply normally opens + a thread, so the bot fills ``thread_id`` once that thread exists. +* **thread reply** — ``guild_id`` + ``channel_id`` + ``thread_id``; the thread + is the narrowest conversation container and drives both the session key and + the top of the federation scope chain. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from ...core.identity import Identity + + +@dataclass(frozen=True) +class InteractionContext: + """Frontend-neutral snapshot of where an interaction happened. + + ``bot.py`` populates this from a discord.py object; the router turns it into + an :class:`Identity`. Ids are strings (Discord snowflakes stringified) or + ``None`` when the platform doesn't supply that level — a DM has no guild. + """ + + user_id: str + guild_id: str | None = None + channel_id: str | None = None + thread_id: str | None = None + is_admin: bool = False + + +def to_identity(ctx: InteractionContext) -> Identity: + """Map an :class:`InteractionContext` to an :class:`Identity`. + + A pure 1:1 field copy — the semantics (session key, scope chain, default + write scope) live on :class:`Identity` itself, so the router stays a thin, + obvious translation layer with nothing to get subtly wrong. + """ + return Identity( + user_id=ctx.user_id, + guild_id=ctx.guild_id, + channel_id=ctx.channel_id, + thread_id=ctx.thread_id, + is_admin=ctx.is_admin, + ) + + +def is_dm(identity: Identity) -> bool: + """True when this conversation is a direct message (no guild).""" + return identity.guild_id is None + + +def is_thread(identity: Identity) -> bool: + """True when the conversation is anchored to a thread.""" + return identity.thread_id is not None + + +def is_channel(identity: Identity) -> bool: + """True for a guild channel conversation that isn't (yet) in a thread.""" + return identity.guild_id is not None and identity.thread_id is None diff --git a/src/lang2sql/integrations/__init__.py b/src/lang2sql/frontends/slack/__init__.py similarity index 100% rename from src/lang2sql/integrations/__init__.py rename to src/lang2sql/frontends/slack/__init__.py diff --git a/src/lang2sql/frontends/web/__init__.py b/src/lang2sql/frontends/web/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/lang2sql/harness/__init__.py b/src/lang2sql/harness/__init__.py new file mode 100644 index 0000000..fa0166a --- /dev/null +++ b/src/lang2sql/harness/__init__.py @@ -0,0 +1,8 @@ +"""Harness — the assembled agent unit (context, session, loop, tools).""" + +from .context import HarnessContext +from .loop import agent_loop +from .session import Session +from .tool_registry import ToolRegistry + +__all__ = ["HarnessContext", "agent_loop", "Session", "ToolRegistry"] diff --git a/src/lang2sql/harness/context.py b/src/lang2sql/harness/context.py new file mode 100644 index 0000000..1d3431b --- /dev/null +++ b/src/lang2sql/harness/context.py @@ -0,0 +1,33 @@ +"""HarnessContext — the assembled unit handed to every turn and every tool. + +v4.1 §2.1 describes the harness as "one bundled thing": LLM + tools + scope- +aware semantic + session + safety + explorer + audit. The ContextConcierge +(tenancy) builds one of these per request; the agent loop and ctx-aware tools +read from it. Optional fields are the pieces a bare CLI smoke-test can omit. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from ..core.identity import Identity +from ..core.ports.audit import AuditPort +from ..core.ports.explorer import ExplorerPort +from ..core.ports.llm import LLMPort +from ..core.ports.safety import SafetyPipelinePort +from ..core.ports.semantic_scope import ScopeResolverPort +from .session import Session +from .tool_registry import ToolRegistry + + +@dataclass +class HarnessContext: + identity: Identity + llm: LLMPort + tools: ToolRegistry + session: Session + explorer: ExplorerPort | None = None + safety: SafetyPipelinePort | None = None + audit: AuditPort | None = None + scope_resolver: ScopeResolverPort | None = None + max_turns: int = 8 diff --git a/src/lang2sql/harness/loop.py b/src/lang2sql/harness/loop.py new file mode 100644 index 0000000..4a8e59a --- /dev/null +++ b/src/lang2sql/harness/loop.py @@ -0,0 +1,48 @@ +"""agent_loop — the turn engine. + +One call per user message: build the system prompt, then iterate +LLM → tool calls → LLM until the model returns a final answer (no tool calls) +or ``max_turns`` is hit. Tool failures come back as tool messages so the model +can recover rather than crashing the loop. +""" + +from __future__ import annotations + +from ..core.types import Message, Role +from .context import HarnessContext +from .system_prompt import build_system_prompt + + +async def agent_loop(ctx: HarnessContext, user_text: str) -> str: + """Run one user turn to completion; return the final assistant text.""" + ctx.session.add(Message(role=Role.USER, content=user_text)) + + system = await build_system_prompt(ctx) + specs = ctx.tools.specs() + + for _ in range(ctx.max_turns): + messages = [Message(role=Role.SYSTEM, content=system), *ctx.session.history()] + completion = await ctx.llm.complete(messages, specs) + + assistant = Message( + role=Role.ASSISTANT, + content=completion.content, + tool_calls=completion.tool_calls, + ) + ctx.session.add(assistant) + + if not completion.tool_calls: + return completion.content + + for call in completion.tool_calls: + result = await ctx.tools.dispatch(call.name, call.arguments, ctx, call.id) + ctx.session.add( + Message( + role=Role.TOOL, + content=result.content, + tool_call_id=result.call_id, + name=call.name, + ) + ) + + return "(reached max turns without a final answer)" diff --git a/src/lang2sql/harness/session.py b/src/lang2sql/harness/session.py new file mode 100644 index 0000000..6eda61f --- /dev/null +++ b/src/lang2sql/harness/session.py @@ -0,0 +1,28 @@ +"""Session — the persistable state of one conversation. + +Holds the transcript plus a scratch of facts recalled for the current turn. +Persisted via :class:`SessionStorePort` keyed by ``Identity.session_key`` so a +thread picks up where it left off (tiebreaker #4). +""" + +from __future__ import annotations + +from dataclasses import dataclass, field + +from ..core.identity import Identity +from ..core.types import Message + + +@dataclass +class Session: + identity: Identity + transcript: list[Message] = field(default_factory=list) + + def add(self, message: Message) -> None: + self.transcript.append(message) + + def history(self) -> list[Message]: + return list(self.transcript) + + def reset(self) -> None: + self.transcript.clear() diff --git a/src/lang2sql/harness/system_prompt.py b/src/lang2sql/harness/system_prompt.py new file mode 100644 index 0000000..10d2837 --- /dev/null +++ b/src/lang2sql/harness/system_prompt.py @@ -0,0 +1,40 @@ +"""Assemble the system prompt for one turn. + +Per v4.1 §2.1 the prompt injects: (1) the agent's role/rules, (2) the effective +semantic layer for the current scope, (3) recalled facts, (4) DB schema. V1 +keeps each section simple; later versions enrich them without changing the +loop. Sections are suppressed when empty. +""" + +from __future__ import annotations + +from .context import HarnessContext + +_BASE = """\ +You are Lang2SQL, a read-only data analytics agent. + +Rules: +- Only ever read data. Never modify the database. +- When you need data, call the run_sql tool with a single SELECT/WITH query. +- Discover schema with explore_schema before guessing table or column names. +- Prefer definitions from the semantic layer below over your own assumptions. +- Answer concisely; show the SQL you ran. +""" + + +async def build_system_prompt(ctx: HarnessContext) -> str: + parts: list[str] = [_BASE] + + if ctx.scope_resolver is not None: + layer = await ctx.scope_resolver.effective_layer(ctx.identity) + rendered = layer.render() if layer is not None else "" + if rendered: + parts.append("## Semantic layer (effective for this scope)\n" + rendered) + + if ctx.explorer is not None: + tables = await ctx.explorer.list_tables() + if tables: + names = ", ".join(t.qualified for t in tables) + parts.append("## Known tables\n" + names) + + return "\n\n".join(parts) diff --git a/src/lang2sql/harness/tool_registry.py b/src/lang2sql/harness/tool_registry.py new file mode 100644 index 0000000..c6add7e --- /dev/null +++ b/src/lang2sql/harness/tool_registry.py @@ -0,0 +1,37 @@ +"""Tool registry — name→tool dispatch and spec catalog for the loop.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.ports.tool import ToolPort +from ..core.types import ToolResult, ToolSpec + +if TYPE_CHECKING: + from .context import HarnessContext + + +class ToolRegistry: + def __init__(self, tools: list[ToolPort] | None = None) -> None: + self._tools: dict[str, ToolPort] = {} + for tool in tools or []: + self.register(tool) + + def register(self, tool: ToolPort) -> None: + self._tools[tool.spec.name] = tool + + def specs(self) -> list[ToolSpec]: + return [t.spec for t in self._tools.values()] + + async def dispatch( + self, name: str, args: dict[str, Any], ctx: "HarnessContext", call_id: str + ) -> ToolResult: + tool = self._tools.get(name) + if tool is None: + return ToolResult(call_id=call_id, content=f"unknown tool: {name}", is_error=True) + try: + result = await tool.run(args, ctx) + result.call_id = call_id # tools don't know their call id; stamp it here + return result + except Exception as exc: # tools must never crash the loop + return ToolResult(call_id=call_id, content=f"{type(exc).__name__}: {exc}", is_error=True) diff --git a/src/lang2sql/ingestion/__init__.py b/src/lang2sql/ingestion/__init__.py new file mode 100644 index 0000000..136aac4 --- /dev/null +++ b/src/lang2sql/ingestion/__init__.py @@ -0,0 +1,18 @@ +"""Ingestion — document → semantic candidates via Source × Extractor (★③). + +V1 wires file upload (``FileSource``) × LLM extraction (``LLMExtractor``) +through ``IngestionPipeline``. See ``core/ports/ingestion.py`` for the axis +Protocols. +""" + +from __future__ import annotations + +from .extractors import LLMExtractor +from .pipeline import IngestionPipeline +from .sources import FileSource + +__all__ = [ + "IngestionPipeline", + "FileSource", + "LLMExtractor", +] diff --git a/src/lang2sql/ingestion/extractors/__init__.py b/src/lang2sql/ingestion/extractors/__init__.py new file mode 100644 index 0000000..2f42701 --- /dev/null +++ b/src/lang2sql/ingestion/extractors/__init__.py @@ -0,0 +1,7 @@ +"""Extractor axis adapters.""" + +from __future__ import annotations + +from .llm_extractor import LLMExtractor + +__all__ = ["LLMExtractor"] diff --git a/src/lang2sql/ingestion/extractors/llm_extractor.py b/src/lang2sql/ingestion/extractors/llm_extractor.py new file mode 100644 index 0000000..e741a12 --- /dev/null +++ b/src/lang2sql/ingestion/extractors/llm_extractor.py @@ -0,0 +1,87 @@ +"""LLMExtractor — the V1 Extractor axis (★③). + +Asks an LLM to read a document and propose metric/dimension/rule definitions as +a JSON array. Parsing is deliberately robust: ```json fences are stripped and +any malformed response degrades to an empty candidate list rather than raising. +v1.5 adds a DDL parser implementing the same :class:`DocExtractorPort`. +""" + +from __future__ import annotations + +import json + +from ...core.ports.ingestion import ( + CandidateKind, + Document, + SemanticCandidate, +) +from ...core.ports.llm import LLMPort +from ...core.types import Message, Role + +_INSTRUCTIONS = ( + "From this document, extract metric/dimension/rule definitions as a JSON " + "array of {kind,name,definition,applies_to}. ``kind`` must be one of " + '"metric", "dimension", "rule". Respond with only the JSON array.' +) + + +class LLMExtractor: + """``DocExtractorPort`` that mines candidates with an LLM.""" + + def __init__(self, llm: LLMPort) -> None: + self._llm = llm + + async def extract(self, doc: Document) -> list[SemanticCandidate]: + prompt = f"{_INSTRUCTIONS}\n\nDocument: {doc.name}\n\n{doc.text}" + completion = await self._llm.complete([Message(role=Role.USER, content=prompt)]) + rows = _parse(completion.content) + candidates: list[SemanticCandidate] = [] + for row in rows: + candidate = _to_candidate(row, doc.source_id) + if candidate is not None: + candidates.append(candidate) + return candidates + + +def _parse(content: str) -> list: + """Parse a JSON array from raw model output; [] on any failure.""" + text = _strip_fences(content) + try: + data = json.loads(text) + except (ValueError, TypeError): + return [] + return data if isinstance(data, list) else [] + + +def _strip_fences(content: str) -> str: + """Drop a surrounding ```json ... ``` fence if present.""" + text = (content or "").strip() + if text.startswith("```"): + lines = text.splitlines() + # Drop opening fence (```json or ```) and a trailing fence if present. + lines = lines[1:] + if lines and lines[-1].strip().startswith("```"): + lines = lines[:-1] + text = "\n".join(lines).strip() + return text + + +def _to_candidate(row: object, source_id: str) -> SemanticCandidate | None: + """Map one parsed dict to a SemanticCandidate; skip if unusable.""" + if not isinstance(row, dict): + return None + try: + kind = CandidateKind(str(row.get("kind", "")).lower()) + except ValueError: + return None + name = row.get("name") + definition = row.get("definition") + if not name or not definition: + return None + return SemanticCandidate( + kind=kind, + name=str(name), + definition=str(definition), + applies_to=str(row.get("applies_to", "")), + source_id=source_id, + ) diff --git a/src/lang2sql/ingestion/pipeline.py b/src/lang2sql/ingestion/pipeline.py new file mode 100644 index 0000000..b30e1bb --- /dev/null +++ b/src/lang2sql/ingestion/pipeline.py @@ -0,0 +1,29 @@ +"""IngestionPipeline — drives the Source × Extractor matrix (★③). + +A document is fetched from a Source then handed to an Extractor; the resulting +candidates are shown for user confirmation before they land in the semantic +layer (documents stay the source of truth). The pipeline itself is axis-blind: +any Source pairs with any Extractor. +""" + +from __future__ import annotations + +from ..core.ports.ingestion import ( + DocExtractorPort, + SemanticCandidate, + SourcePort, +) + + +class IngestionPipeline: + """Fetch a document from ``source`` then extract candidates with ``extractor``.""" + + async def ingest( + self, + source: SourcePort, + extractor: DocExtractorPort, + ref: str, + blob: bytes | None = None, + ) -> list[SemanticCandidate]: + doc = await source.fetch(ref, blob) + return await extractor.extract(doc) diff --git a/src/lang2sql/ingestion/sources/__init__.py b/src/lang2sql/ingestion/sources/__init__.py new file mode 100644 index 0000000..21b67bb --- /dev/null +++ b/src/lang2sql/ingestion/sources/__init__.py @@ -0,0 +1,7 @@ +"""Source axis adapters.""" + +from __future__ import annotations + +from .file_source import FileSource + +__all__ = ["FileSource"] diff --git a/src/lang2sql/ingestion/sources/file_source.py b/src/lang2sql/ingestion/sources/file_source.py new file mode 100644 index 0000000..2bf9f2d --- /dev/null +++ b/src/lang2sql/ingestion/sources/file_source.py @@ -0,0 +1,25 @@ +"""FileSource — the V1 Source axis (★③). + +Pulls a document from an uploaded file (MD/PDF/TXT). If the caller already has +the bytes (a Discord attachment), pass them as ``blob`` and they are decoded as +utf-8; otherwise the file at ``ref`` is read from disk. v1.5 adds a URL source +implementing the same :class:`SourcePort`. +""" + +from __future__ import annotations + +import os + +from ...core.ports.ingestion import Document + + +class FileSource: + """``SourcePort`` over a local file or an in-memory blob.""" + + async def fetch(self, ref: str, blob: bytes | None = None) -> Document: + if blob is not None: + text = blob.decode("utf-8") + else: + with open(ref, "r", encoding="utf-8") as handle: + text = handle.read() + return Document(name=os.path.basename(ref), text=text, source_id=ref) diff --git a/src/lang2sql/integrations/catalog/__init__.py b/src/lang2sql/integrations/catalog/__init__.py deleted file mode 100644 index eaa993e..0000000 --- a/src/lang2sql/integrations/catalog/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .datahub_ import DataHubCatalogLoader - -__all__ = ["DataHubCatalogLoader"] diff --git a/src/lang2sql/integrations/catalog/datahub_.py b/src/lang2sql/integrations/catalog/datahub_.py deleted file mode 100644 index 7ac2e51..0000000 --- a/src/lang2sql/integrations/catalog/datahub_.py +++ /dev/null @@ -1,163 +0,0 @@ -from __future__ import annotations - -from ...core.catalog import CatalogEntry, TextDocument -from ...core.exceptions import IntegrationMissingError -from ...core.ports import CatalogLoaderPort - -try: - import datahub as _datahub # type: ignore[import] -except ImportError: - _datahub = None # type: ignore[assignment] - - -class DataHubCatalogLoader(CatalogLoaderPort): - """DataHub URN → list[CatalogEntry] 변환. - - DataHub GMS 서버에서 테이블 메타데이터를 조회하여 - v2 아키텍처의 CatalogEntry 포맷으로 변환한다. - """ - - def __init__( - self, - gms_server: str = "http://localhost:8080", - extra_headers: dict | None = None, - ) -> None: - if _datahub is None: - raise IntegrationMissingError( - "acryl-datahub", - hint="pip install acryl-datahub", - ) - # 레거시 DatahubMetadataFetcher를 내부에서 wrapping - from utils.data.datahub_source import DatahubMetadataFetcher # type: ignore[import] - - self._fetcher = DatahubMetadataFetcher( - gms_server=gms_server, - extra_headers=extra_headers or {}, - ) - - def load(self, urns: list[str] | None = None) -> list[CatalogEntry]: - """DataHub에서 CatalogEntry 목록을 로드한다. - - Args: - urns: 조회할 URN 목록. None이면 전체 URN을 조회한다. - - Returns: - CatalogEntry 목록 - """ - if urns is None: - urns = list(self._fetcher.get_urns()) - - entries: list[CatalogEntry] = [] - for urn in urns: - name = self._fetcher.get_table_name(urn) or "" - description = self._fetcher.get_table_description(urn) or "" - raw_cols = self._fetcher.get_column_names_and_descriptions(urn) or [] - columns: dict[str, str] = { - col["column_name"]: col.get("column_description") or "" - for col in raw_cols - } - entries.append( - CatalogEntry(name=name, description=description, columns=columns) - ) - return entries - - def load_lineage_documents( - self, - urns: list[str] | None = None, - max_degree: int = 2, - ) -> list[TextDocument]: - """DataHub lineage 정보를 TextDocument 목록으로 변환한다. - - 내부적으로 build_table_metadata()를 사용하며, 사이클 안전성은 - 하위 레이어에서 보장된다: - - get_table_lineage(): GraphQL degree 필터로 depth 상한 적용 - - min_degree_lineage(): 테이블별 최소 degree만 유지 (사이클 경로 dedup) - - build_table_metadata(): 자기 자신(table == current_table) 제외 - - Args: - urns: 조회할 URN 목록. None이면 전체 URN을 조회한다. - max_degree: 포함할 최대 lineage depth. 기본값 2. - - Returns: - TextDocument 목록. lineage 없는 테이블은 제외된다. - - Usage:: - - loader = DataHubCatalogLoader(gms_server="http://localhost:8080") - pipeline = EnrichedNL2SQL( - catalog=loader.load(), - documents=loader.load_lineage_documents(), - llm=..., db=..., embedding=..., - ) - """ - if urns is None: - urns = list(self._fetcher.get_urns()) - - return [ - doc - for urn in urns - if (doc := self._urn_to_lineage_document(urn, max_degree)) is not None - ] - - def _urn_to_lineage_document( - self, urn: str, max_degree: int - ) -> TextDocument | None: - """단일 URN의 lineage를 TextDocument로 변환. lineage 없으면 None 반환.""" - try: - # build_table_metadata가 upstream/downstream/column lineage를 - # 파싱 및 dedup까지 처리해준다. - meta = self._fetcher.build_table_metadata(urn, max_degree=max_degree) - except Exception: - return None - - table_name = meta.get("table_name") or "" - lineage = meta.get("lineage", {}) - upstream = lineage.get("upstream", []) - downstream = lineage.get("downstream", []) - upstream_columns = lineage.get("upstream_columns", []) - - if not upstream and not downstream and not upstream_columns: - return None - - return TextDocument( - id=f"lineage__{table_name}", - title=f"{table_name} 리니지", - content=self._format_lineage( - table_name, upstream, downstream, upstream_columns - ), - source="datahub", - metadata={"urn": urn, "table_name": table_name}, - ) - - @staticmethod - def _format_lineage( - table_name: str, - upstream: list[dict], - downstream: list[dict], - upstream_columns: list[dict], - ) -> str: - """lineage 데이터를 자연어 텍스트로 포맷한다.""" - lines: list[str] = [f"테이블: {table_name}", ""] - - if upstream: - lines += ["[Upstream — 이 테이블의 원천 데이터]"] - lines += [f" - {t['table']} (depth: {t['degree']})" for t in upstream] - lines.append("") - - if downstream: - lines += ["[Downstream — 이 테이블을 참조하는 테이블]"] - lines += [f" - {t['table']} (depth: {t['degree']})" for t in downstream] - lines.append("") - - if upstream_columns: - lines += ["[컬럼 단위 Upstream Lineage]"] - for dataset in upstream_columns: - lines.append(f" {dataset.get('upstream_dataset', '')}:") - lines += [ - f" {col['upstream_column']} → {col['downstream_column']}" - f" (신뢰도: {col.get('confidence', 1.0):.2f})" - for col in dataset.get("columns", []) - ] - lines.append("") - - return "\n".join(lines).strip() diff --git a/src/lang2sql/integrations/chunking/__init__.py b/src/lang2sql/integrations/chunking/__init__.py deleted file mode 100644 index f80fad9..0000000 --- a/src/lang2sql/integrations/chunking/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .semantic_ import SemanticChunker - -__all__ = ["SemanticChunker"] diff --git a/src/lang2sql/integrations/chunking/semantic_.py b/src/lang2sql/integrations/chunking/semantic_.py deleted file mode 100644 index 253be97..0000000 --- a/src/lang2sql/integrations/chunking/semantic_.py +++ /dev/null @@ -1,123 +0,0 @@ -from __future__ import annotations - -from ...core.catalog import IndexedChunk, TextDocument -from ...core.exceptions import IntegrationMissingError -from ...core.ports import EmbeddingPort -from ...components.retrieval.chunker import DocumentChunkerPort - - -class SemanticChunker(DocumentChunkerPort): - """ - Embedding-based semantic chunker. Optional — explicit opt-in only. - - Splits text into sentences, computes cosine similarity between adjacent - sentences, and treats similarity drop-offs as chunk boundaries. - Produces more semantically coherent chunks than RecursiveCharacterChunker. - - Note: embedding API is called during indexing (not just at query time). - Consider the cost and latency before choosing this over RecursiveCharacterChunker. - - Limitation: sentence splitting uses punctuation and newlines. Languages without - whitespace word boundaries (e.g. Korean) rely on sentence-ending punctuation - (., !, ?, 。) or newlines for splits. Accuracy may vary for dense prose. - - Args: - embedding: EmbeddingPort implementation (can be shared with VectorRetriever). - breakpoint_threshold: Similarity drop threshold for boundary detection. Default 0.3. - Lower values produce more (smaller) chunks. - min_chunk_size: Minimum characters per chunk; short chunks are merged - into the previous one. Default 100. - - Example:: - - from lang2sql.integrations.embedding import OpenAIEmbedding - from lang2sql.integrations.chunking import SemanticChunker - - embedding = OpenAIEmbedding() - chunker = SemanticChunker(embedding=embedding) - - retriever = VectorRetriever.from_sources(..., splitter=chunker) - """ - - def __init__( - self, - *, - embedding: EmbeddingPort, - breakpoint_threshold: float = 0.3, - min_chunk_size: int = 100, - ) -> None: - try: - import numpy as _np # noqa: F401 - except ImportError: - raise IntegrationMissingError("numpy", hint="pip install numpy") - self._embedding = embedding - self._threshold = breakpoint_threshold - self._min_size = min_chunk_size - - def split(self, docs: list[TextDocument]) -> list[IndexedChunk]: - """LangChain-style batch split: list input → list output.""" - return [c for doc in docs for c in self.chunk(doc)] - - def chunk(self, doc: TextDocument) -> list[IndexedChunk]: - import numpy as np - - content = doc.get("content", "") - if not content: - return [] - - sentences = self._split_sentences(content) - if len(sentences) <= 1: - return self._make_chunks(doc, [content]) - - embeddings = self._embedding.embed_texts(sentences) - mat = np.array(embeddings, dtype=np.float32) - norms = np.linalg.norm(mat, axis=1, keepdims=True) - mat = mat / (norms + 1e-8) - - # cosine similarity between adjacent sentences — shape: (n-1,) - sims = (mat[:-1] * mat[1:]).sum(axis=1) - - # positions where similarity drops sharply are chunk boundaries - boundaries = [0] - for i, sim in enumerate(sims): - if sim < (1.0 - self._threshold): - boundaries.append(i + 1) - boundaries.append(len(sentences)) - - raw_chunks: list[str] = [] - for start, end in zip(boundaries, boundaries[1:]): - chunk_text = " ".join(sentences[start:end]) - if len(chunk_text) < self._min_size and raw_chunks: - raw_chunks[-1] += ( - " " + chunk_text - ) # merge short trailing chunk into previous - else: - raw_chunks.append(chunk_text) - - return self._make_chunks(doc, raw_chunks) - - def _split_sentences(self, text: str) -> list[str]: - """Split on sentence-ending punctuation or newlines. No external tokenizer needed.""" - import re - - parts = re.split(r"(?<=[.!?。])\s+|\n+", text.strip()) - return [p.strip() for p in parts if p.strip()] - - def _make_chunks(self, doc: TextDocument, texts: list[str]) -> list[IndexedChunk]: - title = doc.get("title", "") - doc_id = doc.get("id", "") - return [ - IndexedChunk( - chunk_id=f"{doc_id}__{i}", - text=f"{title}: {text}" if title else text, - source_type="document", - source_id=doc_id, - chunk_index=i, - metadata={ - "id": doc_id, - "title": title, - "source": doc.get("source", ""), - }, - ) - for i, text in enumerate(texts) - ] diff --git a/src/lang2sql/integrations/db/__init__.py b/src/lang2sql/integrations/db/__init__.py deleted file mode 100644 index 79ae1db..0000000 --- a/src/lang2sql/integrations/db/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .sqlalchemy_ import SQLAlchemyDB, SQLAlchemyExplorer - -__all__ = ["SQLAlchemyDB", "SQLAlchemyExplorer"] diff --git a/src/lang2sql/integrations/db/sqlalchemy_.py b/src/lang2sql/integrations/db/sqlalchemy_.py deleted file mode 100644 index 10f2ea6..0000000 --- a/src/lang2sql/integrations/db/sqlalchemy_.py +++ /dev/null @@ -1,140 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import DBPort - -try: - from sqlalchemy import create_engine, inspect as sa_inspect, text as sa_text - from sqlalchemy.engine import Engine -except ImportError: - create_engine = None # type: ignore[assignment] - sa_inspect = None # type: ignore[assignment] - sa_text = None # type: ignore[assignment] - Engine = None # type: ignore[assignment,misc] - - -class SQLAlchemyDB(DBPort): - """DBPort implementation backed by SQLAlchemy 2.x.""" - - def __init__(self, url: str) -> None: - if create_engine is None: - raise IntegrationMissingError( - "sqlalchemy", extra="sqlalchemy", hint="pip install sqlalchemy" - ) - self._engine: Engine = create_engine(url) - - def execute(self, sql: str) -> list[dict[str, Any]]: - with self._engine.connect() as conn: - result = conn.execute(sa_text(sql)) - return [dict(row._mapping) for row in result] - - -_WRITE_PREFIXES = frozenset( - { - "INSERT", - "UPDATE", - "DELETE", - "DROP", - "ALTER", - "CREATE", - "TRUNCATE", - "REPLACE", - "MERGE", - } -) - - -class SQLAlchemyExplorer: - """DBExplorerPort implementation backed by SQLAlchemy 2.x. - - Agent가 DB 스키마를 탐색할 때 사용. DDL + 샘플 데이터를 LLM context에 직접 주입. - """ - - def __init__(self, url: str, *, schema: str | None = None) -> None: - if create_engine is None: - raise IntegrationMissingError( - "sqlalchemy", extra="sqlalchemy", hint="pip install sqlalchemy" - ) - self._engine: Engine = create_engine(url) - self._schema = schema - - @classmethod - def from_engine( - cls, engine: "Engine", *, schema: str | None = None - ) -> "SQLAlchemyExplorer": - """기존 engine 공유용. 연결 풀 중복 방지.""" - instance = cls.__new__(cls) - instance._engine = engine - instance._schema = schema - return instance - - def list_tables(self, schema: str | None = None) -> list[str]: - """테이블 목록 반환. Agent가 DB 구조 파악 시 첫 번째 호출.""" - insp = sa_inspect(self._engine) - return insp.get_table_names(schema=schema or self._schema) - - def get_ddl(self, table: str, *, schema: str | None = None) -> str: - """원본 DDL 문자열 반환. 컬럼 정의, PK, FK, 제약조건 모두 포함. - - SQLite: sqlite_master에서 원본 그대로 (DEFAULT, 코멘트, 인라인 FK 모두 보존). - 그 외: SQLAlchemy CreateTable construct로 포괄적 DDL 생성. - """ - resolved_schema = schema or self._schema - if self._engine.dialect.name == "sqlite": - rows = self._execute_safe( - "SELECT sql FROM sqlite_master WHERE type='table' AND name=:table", - {"table": table}, - ) - if rows and rows[0].get("sql"): - return rows[0]["sql"] - - from sqlalchemy import MetaData - from sqlalchemy import Table as SATable - from sqlalchemy.schema import CreateTable - - metadata = MetaData() - t = SATable(table, metadata, autoload_with=self._engine, schema=resolved_schema) - return str(CreateTable(t).compile(self._engine)) - - def sample_data( - self, table: str, *, limit: int = 5, schema: str | None = None - ) -> list[dict]: - """실제 샘플 데이터 반환. - - f-string SQL 금지 — SQLAlchemy ORM select()로 identifier quoting 위임. - dialect별 quoting 차이(PostgreSQL ", MySQL `, SQLite ")를 SQLAlchemy가 처리. - """ - from sqlalchemy import MetaData, select - from sqlalchemy import Table as SATable - - resolved_schema = schema or self._schema - metadata = MetaData() - t = SATable(table, metadata, autoload_with=self._engine, schema=resolved_schema) - stmt = select(t).limit(limit) - with self._engine.connect() as conn: - result = conn.execute(stmt) - return [dict(row._mapping) for row in result] - - def execute_read_only(self, sql: str) -> list[dict]: - """읽기 전용 SQL 실행. - - 두 겹 방어: - 1. prefix guard — 일반적인 쓰기 구문 빠른 거부 (UX) - 2. rollback-always — WITH ... DELETE 같은 CTE 우회도 실제 DB 반영 방지 - """ - first_token = sql.strip().upper().split()[0] if sql.strip() else "" - if first_token in _WRITE_PREFIXES: - raise ValueError(f"Write operations not allowed: {sql[:50]!r}") - with self._engine.connect() as conn: - result = conn.execute(sa_text(sql)) - rows = [dict(row._mapping) for row in result] - conn.rollback() - return rows - - def _execute_safe(self, sql: str, params: dict | None = None) -> list[dict]: - """파라미터화 쿼리 실행 (내부용).""" - with self._engine.connect() as conn: - result = conn.execute(sa_text(sql), params or {}) - return [dict(row._mapping) for row in result] diff --git a/src/lang2sql/integrations/embedding/__init__.py b/src/lang2sql/integrations/embedding/__init__.py deleted file mode 100644 index 3c4d54b..0000000 --- a/src/lang2sql/integrations/embedding/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -from .azure_ import AzureOpenAIEmbedding -from .bedrock_ import BedrockEmbedding -from .gemini_ import GeminiEmbedding -from .huggingface_ import HuggingFaceEmbedding -from .ollama_ import OllamaEmbedding -from .openai_ import OpenAIEmbedding - -__all__ = [ - "AzureOpenAIEmbedding", - "BedrockEmbedding", - "GeminiEmbedding", - "HuggingFaceEmbedding", - "OllamaEmbedding", - "OpenAIEmbedding", -] diff --git a/src/lang2sql/integrations/embedding/azure_.py b/src/lang2sql/integrations/embedding/azure_.py deleted file mode 100644 index 824203e..0000000 --- a/src/lang2sql/integrations/embedding/azure_.py +++ /dev/null @@ -1,43 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import EmbeddingPort - -try: - import openai as _openai -except ImportError: - _openai = None # type: ignore[assignment] - - -class AzureOpenAIEmbedding(EmbeddingPort): - """EmbeddingPort implementation backed by the Azure OpenAI Embeddings API.""" - - def __init__( - self, - *, - azure_deployment: str, - azure_endpoint: str, - api_version: str = "2023-07-01-preview", - api_key: str | None = None, - ) -> None: - if _openai is None: - raise IntegrationMissingError( - "openai", hint="pip install openai # or: uv sync" - ) - self._client = _openai.AzureOpenAI( - api_key=api_key, - azure_endpoint=azure_endpoint, - api_version=api_version, - ) - self._deployment = azure_deployment - - def embed_query(self, text: str) -> list[float]: - return ( - self._client.embeddings.create(input=text, model=self._deployment) - .data[0] - .embedding - ) - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - resp = self._client.embeddings.create(input=texts, model=self._deployment) - return [item.embedding for item in resp.data] diff --git a/src/lang2sql/integrations/embedding/bedrock_.py b/src/lang2sql/integrations/embedding/bedrock_.py deleted file mode 100644 index c0cc69a..0000000 --- a/src/lang2sql/integrations/embedding/bedrock_.py +++ /dev/null @@ -1,53 +0,0 @@ -from __future__ import annotations - -import json - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import EmbeddingPort - -try: - import boto3 as _boto3 # type: ignore[import] -except ImportError: - _boto3 = None # type: ignore[assignment] - - -class BedrockEmbedding(EmbeddingPort): - """EmbeddingPort implementation backed by AWS Bedrock Embeddings API. - - Supports Amazon Titan embedding models (e.g., amazon.titan-embed-text-v1). - """ - - def __init__( - self, - *, - model_id: str, - aws_access_key_id: str | None = None, - aws_secret_access_key: str | None = None, - region_name: str = "us-east-1", - ) -> None: - if _boto3 is None: - raise IntegrationMissingError("boto3", hint="pip install boto3") - self._model_id = model_id - self._client = _boto3.client( - "bedrock-runtime", - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - region_name=region_name, - ) - - def _embed_single(self, text: str) -> list[float]: - body = json.dumps({"inputText": text}) - resp = self._client.invoke_model( - modelId=self._model_id, - body=body, - contentType="application/json", - accept="application/json", - ) - result = json.loads(resp["body"].read()) - return result["embedding"] - - def embed_query(self, text: str) -> list[float]: - return self._embed_single(text) - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - return [self._embed_single(t) for t in texts] diff --git a/src/lang2sql/integrations/embedding/gemini_.py b/src/lang2sql/integrations/embedding/gemini_.py deleted file mode 100644 index ee1004f..0000000 --- a/src/lang2sql/integrations/embedding/gemini_.py +++ /dev/null @@ -1,46 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import EmbeddingPort - -try: - import google.generativeai as _genai # type: ignore[import] -except ImportError: - _genai = None # type: ignore[assignment] - - -class GeminiEmbedding(EmbeddingPort): - """EmbeddingPort implementation backed by the Google Gemini Embeddings API.""" - - def __init__( - self, - *, - model: str = "models/embedding-001", - api_key: str | None = None, - ) -> None: - if _genai is None: - raise IntegrationMissingError( - "google-generativeai", - hint="pip install google-generativeai", - ) - if api_key: - _genai.configure(api_key=api_key) - self._model = model - - def embed_query(self, text: str) -> list[float]: - result = _genai.embed_content( - model=self._model, - content=text, - task_type="retrieval_query", - ) - return result["embedding"] - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - return [ - _genai.embed_content( - model=self._model, - content=t, - task_type="retrieval_document", - )["embedding"] - for t in texts - ] diff --git a/src/lang2sql/integrations/embedding/huggingface_.py b/src/lang2sql/integrations/embedding/huggingface_.py deleted file mode 100644 index b0a584e..0000000 --- a/src/lang2sql/integrations/embedding/huggingface_.py +++ /dev/null @@ -1,27 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import EmbeddingPort - -try: - from sentence_transformers import SentenceTransformer as _SentenceTransformer # type: ignore[import] -except ImportError: - _SentenceTransformer = None # type: ignore[assignment] - - -class HuggingFaceEmbedding(EmbeddingPort): - """EmbeddingPort implementation backed by sentence-transformers.""" - - def __init__(self, *, model: str) -> None: - if _SentenceTransformer is None: - raise IntegrationMissingError( - "sentence-transformers", - hint="pip install sentence-transformers", - ) - self._model = _SentenceTransformer(model) - - def embed_query(self, text: str) -> list[float]: - return self._model.encode(text, convert_to_numpy=True).tolist() - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - return self._model.encode(texts, convert_to_numpy=True).tolist() diff --git a/src/lang2sql/integrations/embedding/ollama_.py b/src/lang2sql/integrations/embedding/ollama_.py deleted file mode 100644 index d377c1b..0000000 --- a/src/lang2sql/integrations/embedding/ollama_.py +++ /dev/null @@ -1,32 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import EmbeddingPort - -try: - import ollama as _ollama # type: ignore[import] -except ImportError: - _ollama = None # type: ignore[assignment] - - -class OllamaEmbedding(EmbeddingPort): - """EmbeddingPort implementation backed by the Ollama Embeddings API.""" - - def __init__( - self, - *, - model: str, - base_url: str = "http://localhost:11434", - ) -> None: - if _ollama is None: - raise IntegrationMissingError("ollama", hint="pip install ollama") - self._model = model - self._client = _ollama.Client(host=base_url) - - def embed_query(self, text: str) -> list[float]: - resp = self._client.embed(model=self._model, input=text) - return resp.embeddings[0] - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - resp = self._client.embed(model=self._model, input=texts) - return resp.embeddings diff --git a/src/lang2sql/integrations/embedding/openai_.py b/src/lang2sql/integrations/embedding/openai_.py deleted file mode 100644 index 605ee66..0000000 --- a/src/lang2sql/integrations/embedding/openai_.py +++ /dev/null @@ -1,32 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import EmbeddingPort - -try: - import openai as _openai -except ImportError: - _openai = None # type: ignore[assignment] - - -class OpenAIEmbedding(EmbeddingPort): - """EmbeddingPort implementation backed by OpenAI Embeddings API.""" - - def __init__( - self, *, model: str = "text-embedding-3-small", api_key: str | None = None - ) -> None: - if _openai is None: - raise IntegrationMissingError("openai", hint="pip install openai") - self._client = _openai.OpenAI(api_key=api_key) - self._model = model - - def embed_query(self, text: str) -> list[float]: - return ( - self._client.embeddings.create(input=text, model=self._model) - .data[0] - .embedding - ) - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - resp = self._client.embeddings.create(input=texts, model=self._model) - return [item.embedding for item in resp.data] diff --git a/src/lang2sql/integrations/llm/__init__.py b/src/lang2sql/integrations/llm/__init__.py deleted file mode 100644 index 09ecf03..0000000 --- a/src/lang2sql/integrations/llm/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from .anthropic_ import AnthropicLLM -from .azure_ import AzureOpenAILLM -from .bedrock_ import BedrockLLM -from .gemini_ import GeminiLLM -from .huggingface_ import HuggingFaceLLM -from .ollama_ import OllamaLLM -from .openai_ import OpenAILLM - -__all__ = [ - "AnthropicLLM", - "AzureOpenAILLM", - "BedrockLLM", - "GeminiLLM", - "HuggingFaceLLM", - "OllamaLLM", - "OpenAILLM", -] diff --git a/src/lang2sql/integrations/llm/anthropic_.py b/src/lang2sql/integrations/llm/anthropic_.py deleted file mode 100644 index 037cc69..0000000 --- a/src/lang2sql/integrations/llm/anthropic_.py +++ /dev/null @@ -1,35 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import LLMPort - -try: - import anthropic as _anthropic -except ImportError: - _anthropic = None # type: ignore[assignment] - - -class AnthropicLLM(LLMPort): - """LLMPort implementation backed by the Anthropic Messages API.""" - - def __init__( - self, *, model: str, api_key: str | None = None, max_tokens: int = 4096 - ) -> None: - if _anthropic is None: - raise IntegrationMissingError( - "anthropic", hint="pip install anthropic # or: uv sync" - ) - self._client = _anthropic.Anthropic(api_key=api_key) - self._model = model - self._max_tokens = max_tokens - - def invoke(self, messages: list[dict[str, str]]) -> str: - system = next((m["content"] for m in messages if m["role"] == "system"), None) - user_msgs = [m for m in messages if m["role"] != "system"] - resp = self._client.messages.create( - model=self._model, - max_tokens=self._max_tokens, - system=system or "", - messages=user_msgs, - ) - return resp.content[0].text diff --git a/src/lang2sql/integrations/llm/azure_.py b/src/lang2sql/integrations/llm/azure_.py deleted file mode 100644 index cc32b9d..0000000 --- a/src/lang2sql/integrations/llm/azure_.py +++ /dev/null @@ -1,42 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import LLMPort - -try: - import openai as _openai -except ImportError: - _openai = None # type: ignore[assignment] - - -class AzureOpenAILLM(LLMPort): - """LLMPort implementation backed by the Azure OpenAI Chat Completions API.""" - - def __init__( - self, - *, - azure_deployment: str, - azure_endpoint: str, - api_version: str = "2023-07-01-preview", - api_key: str | None = None, - max_tokens: int = 4096, - ) -> None: - if _openai is None: - raise IntegrationMissingError( - "openai", hint="pip install openai # or: uv sync" - ) - self._client = _openai.AzureOpenAI( - api_key=api_key, - azure_endpoint=azure_endpoint, - api_version=api_version, - ) - self._deployment = azure_deployment - self._max_tokens = max_tokens - - def invoke(self, messages: list[dict[str, str]]) -> str: - resp = self._client.chat.completions.create( - model=self._deployment, - messages=messages, # type: ignore[arg-type] - max_tokens=self._max_tokens, - ) - return resp.choices[0].message.content or "" diff --git a/src/lang2sql/integrations/llm/bedrock_.py b/src/lang2sql/integrations/llm/bedrock_.py deleted file mode 100644 index 29fadfb..0000000 --- a/src/lang2sql/integrations/llm/bedrock_.py +++ /dev/null @@ -1,46 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import LLMPort - -try: - import boto3 as _boto3 # type: ignore[import] -except ImportError: - _boto3 = None # type: ignore[assignment] - - -class BedrockLLM(LLMPort): - """LLMPort implementation backed by the AWS Bedrock Converse API.""" - - def __init__( - self, - *, - model: str, - aws_access_key_id: str | None = None, - aws_secret_access_key: str | None = None, - region_name: str = "us-east-1", - ) -> None: - if _boto3 is None: - raise IntegrationMissingError("boto3", hint="pip install boto3") - self._model = model - self._client = _boto3.client( - "bedrock-runtime", - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - region_name=region_name, - ) - - def invoke(self, messages: list[dict[str, str]]) -> str: - system_parts = [m["content"] for m in messages if m["role"] == "system"] - user_msgs = [m for m in messages if m["role"] != "system"] - - converse_messages = [ - {"role": m["role"], "content": [{"text": m["content"]}]} for m in user_msgs - ] - - kwargs: dict = {"modelId": self._model, "messages": converse_messages} - if system_parts: - kwargs["system"] = [{"text": system_parts[0]}] - - resp = self._client.converse(**kwargs) - return resp["output"]["message"]["content"][0]["text"] diff --git a/src/lang2sql/integrations/llm/gemini_.py b/src/lang2sql/integrations/llm/gemini_.py deleted file mode 100644 index 6c70799..0000000 --- a/src/lang2sql/integrations/llm/gemini_.py +++ /dev/null @@ -1,46 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import LLMPort - -try: - import google.generativeai as _genai # type: ignore[import] -except ImportError: - _genai = None # type: ignore[assignment] - - -class GeminiLLM(LLMPort): - """LLMPort implementation backed by the Google Gemini Generative AI API.""" - - def __init__( - self, - *, - model: str, - api_key: str | None = None, - ) -> None: - if _genai is None: - raise IntegrationMissingError( - "google-generativeai", - hint="pip install google-generativeai", - ) - if api_key: - _genai.configure(api_key=api_key) - self._model_name = model - - def invoke(self, messages: list[dict[str, str]]) -> str: - system_parts = [m["content"] for m in messages if m["role"] == "system"] - system_instruction = system_parts[0] if system_parts else None - - contents = [] - for m in messages: - if m["role"] == "system": - continue - role = "model" if m["role"] == "assistant" else "user" - contents.append({"role": role, "parts": [m["content"]]}) - - model = _genai.GenerativeModel( - model_name=self._model_name, - system_instruction=system_instruction, - ) - response = model.generate_content(contents) - return response.text diff --git a/src/lang2sql/integrations/llm/huggingface_.py b/src/lang2sql/integrations/llm/huggingface_.py deleted file mode 100644 index 08cca81..0000000 --- a/src/lang2sql/integrations/llm/huggingface_.py +++ /dev/null @@ -1,35 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import LLMPort - -try: - from huggingface_hub import InferenceClient as _InferenceClient # type: ignore[import] -except ImportError: - _InferenceClient = None # type: ignore[assignment] - - -class HuggingFaceLLM(LLMPort): - """LLMPort implementation backed by the HuggingFace Inference API.""" - - def __init__( - self, - *, - repo_id: str | None = None, - endpoint_url: str | None = None, - api_token: str | None = None, - ) -> None: - if _InferenceClient is None: - raise IntegrationMissingError( - "huggingface_hub", hint="pip install huggingface_hub" - ) - if repo_id is None and endpoint_url is None: - raise ValueError("Either repo_id or endpoint_url must be provided.") - self._client = _InferenceClient( - model=endpoint_url or repo_id, - token=api_token, - ) - - def invoke(self, messages: list[dict[str, str]]) -> str: - resp = self._client.chat_completion(messages=messages) # type: ignore[arg-type] - return resp.choices[0].message.content or "" diff --git a/src/lang2sql/integrations/llm/ollama_.py b/src/lang2sql/integrations/llm/ollama_.py deleted file mode 100644 index 4172df4..0000000 --- a/src/lang2sql/integrations/llm/ollama_.py +++ /dev/null @@ -1,31 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import LLMPort - -try: - import ollama as _ollama # type: ignore[import] -except ImportError: - _ollama = None # type: ignore[assignment] - - -class OllamaLLM(LLMPort): - """LLMPort implementation backed by the Ollama chat API.""" - - def __init__( - self, - *, - model: str, - base_url: str = "http://localhost:11434", - ) -> None: - if _ollama is None: - raise IntegrationMissingError("ollama", hint="pip install ollama") - self._model = model - self._client = _ollama.Client(host=base_url) - - def invoke(self, messages: list[dict[str, str]]) -> str: - resp = self._client.chat( - model=self._model, - messages=messages, # type: ignore[arg-type] - ) - return resp.message.content diff --git a/src/lang2sql/integrations/llm/openai_.py b/src/lang2sql/integrations/llm/openai_.py deleted file mode 100644 index 041a5a4..0000000 --- a/src/lang2sql/integrations/llm/openai_.py +++ /dev/null @@ -1,28 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import LLMPort - -try: - import openai as _openai -except ImportError: - _openai = None # type: ignore[assignment] - - -class OpenAILLM(LLMPort): - """LLMPort implementation backed by the OpenAI Chat Completions API.""" - - def __init__(self, *, model: str, api_key: str | None = None) -> None: - if _openai is None: - raise IntegrationMissingError( - "openai", hint="pip install openai # or: uv sync" - ) - self._client = _openai.OpenAI(api_key=api_key) - self._model = model - - def invoke(self, messages: list[dict[str, str]]) -> str: - resp = self._client.chat.completions.create( - model=self._model, - messages=messages, # type: ignore[arg-type] - ) - return resp.choices[0].message.content or "" diff --git a/src/lang2sql/integrations/loaders/__init__.py b/src/lang2sql/integrations/loaders/__init__.py deleted file mode 100644 index c5d095f..0000000 --- a/src/lang2sql/integrations/loaders/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .pdf_ import PDFLoader - -__all__ = ["PDFLoader"] diff --git a/src/lang2sql/integrations/loaders/pdf_.py b/src/lang2sql/integrations/loaders/pdf_.py deleted file mode 100644 index 8d1143d..0000000 --- a/src/lang2sql/integrations/loaders/pdf_.py +++ /dev/null @@ -1,58 +0,0 @@ -from __future__ import annotations - -from pathlib import Path - -from ...core.catalog import TextDocument -from ...core.exceptions import IntegrationMissingError -from ...core.ports import DocumentLoaderPort - -try: - import fitz as _fitz -except ImportError: - _fitz = None # type: ignore[assignment] - - -class PDFLoader(DocumentLoaderPort): - """ - PDF file → list[TextDocument]. - - Requires pymupdf (fitz) as an optional dependency. - Raises ``IntegrationMissingError`` if not installed. - - Produces one TextDocument per page:: - - id = "{filename}__p{page_number}" (1-indexed) - title = "{filename} page {page_number}" - content = extracted text of that page - source = file path - - Usage:: - - from lang2sql.integrations.loaders import PDFLoader - - docs = PDFLoader().load("report.pdf") - - Installation:: - - pip install pymupdf - """ - - def load(self, path: str) -> list[TextDocument]: - if _fitz is None: - raise IntegrationMissingError( - "pymupdf", - hint="pip install pymupdf", - ) - p = Path(path) - pdf = _fitz.open(str(p)) - docs: list[TextDocument] = [] - for i, page in enumerate(pdf, start=1): - docs.append( - TextDocument( - id=f"{p.stem}__p{i}", - title=f"{p.stem} page {i}", - content=page.get_text(), - source=str(p), - ) - ) - return docs diff --git a/src/lang2sql/integrations/vectorstore/__init__.py b/src/lang2sql/integrations/vectorstore/__init__.py deleted file mode 100644 index eeab249..0000000 --- a/src/lang2sql/integrations/vectorstore/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .faiss_ import FAISSVectorStore -from .inmemory_ import InMemoryVectorStore -from .pgvector_ import PGVectorStore - -__all__ = ["InMemoryVectorStore", "FAISSVectorStore", "PGVectorStore"] diff --git a/src/lang2sql/integrations/vectorstore/faiss_.py b/src/lang2sql/integrations/vectorstore/faiss_.py deleted file mode 100644 index 0e6a12f..0000000 --- a/src/lang2sql/integrations/vectorstore/faiss_.py +++ /dev/null @@ -1,104 +0,0 @@ -from __future__ import annotations - -import json -import pathlib - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import VectorStorePort - -try: - import faiss as _faiss - import numpy as _np -except ImportError: - _faiss = None # type: ignore[assignment] - _np = None # type: ignore[assignment] - - -class FAISSVectorStore(VectorStorePort): - """ - FAISS-backed vector store with optional file persistence. - - Uses IndexFlatIP + L2 normalization for exact cosine similarity. - Index is lazy-initialized on the first upsert() call. - - Known limitation (append-only): - Upserting the same chunk_id twice creates duplicate FAISS entries. - To rebuild a clean index, create a new FAISSVectorStore instance - and run from_chunks() again from scratch. - - Args: - index_path: Optional path for save() / load(). Used as default - path when save() is called without an explicit argument. - - Installation: - pip install faiss-cpu # CPU-only - pip install faiss-gpu # GPU variant - """ - - def __init__(self, index_path: str | None = None) -> None: - if _faiss is None or _np is None: - raise IntegrationMissingError("faiss", hint="pip install faiss-cpu") - self._index_path = index_path - self._index: object | None = None # faiss.IndexFlatIP, None until first upsert - self._ids: list[str] = [] - - # ── VectorStorePort ────────────────────────────────────────────── - - def upsert(self, ids: list[str], vectors: list[list[float]]) -> None: - """L2-normalize and add vectors. Lazy-creates index on first call.""" - arr = _np.array(vectors, dtype=_np.float32) - _faiss.normalize_L2(arr) # in-place cosine trick - if self._index is None: - self._index = _faiss.IndexFlatIP(arr.shape[1]) - self._index.add(arr) - self._ids.extend(ids) - - def search(self, vector: list[float], k: int) -> list[tuple[str, float]]: - """Return (chunk_id, cosine_score) for the k nearest vectors.""" - if self._index is None or self._index.ntotal == 0: - return [] - q = _np.array([vector], dtype=_np.float32) - _faiss.normalize_L2(q) - k = min(k, self._index.ntotal) - scores, positions = self._index.search(q, k) - return [ - (self._ids[int(pos)], float(scores[0][j])) - for j, pos in enumerate(positions[0]) - if pos >= 0 - ] - - # ── Persistence ────────────────────────────────────────────────── - - def save(self, path: str | None = None) -> None: - """ - Write index to {path} and id list to {path}.meta. - Falls back to self._index_path when path is None. - Raises ValueError if no path is available. - Raises RuntimeError if called before any upsert(). - """ - path = path or self._index_path - if path is None: - raise ValueError( - "No path provided and index_path was not set at construction." - ) - if self._index is None: - raise RuntimeError("Cannot save before any upsert() call.") - pathlib.Path(path).parent.mkdir(parents=True, exist_ok=True) - _faiss.write_index(self._index, path) - pathlib.Path(path + ".meta").write_text(json.dumps(self._ids), encoding="utf-8") - - @classmethod - def load(cls, path: str) -> "FAISSVectorStore": - """ - Load index from {path} and id list from {path}.meta. - Raises FileNotFoundError if either file is missing. - """ - if _faiss is None or _np is None: - raise IntegrationMissingError("faiss", hint="pip install faiss-cpu") - meta_path = pathlib.Path(path + ".meta") - if not pathlib.Path(path).exists() or not meta_path.exists(): - raise FileNotFoundError(f"Index files not found: {path}, {path}.meta") - store = cls(index_path=path) - store._index = _faiss.read_index(path) - store._ids = json.loads(meta_path.read_text(encoding="utf-8")) - return store diff --git a/src/lang2sql/integrations/vectorstore/inmemory_.py b/src/lang2sql/integrations/vectorstore/inmemory_.py deleted file mode 100644 index 97a176f..0000000 --- a/src/lang2sql/integrations/vectorstore/inmemory_.py +++ /dev/null @@ -1,51 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import VectorStorePort - -try: - import numpy as _np -except ImportError: - _np = None # type: ignore[assignment] - - -class InMemoryVectorStore(VectorStorePort): - """ - Brute-force cosine similarity vector store backed by numpy. - - upsert merges new entries into the existing store (true upsert — no data loss - across multiple calls). Rebuilds the matrix from a dict on each search call, - so duplicate chunk_ids are overwritten rather than duplicated. - - Handles tens of thousands of vectors without issue. No faiss dependency required. - - For larger scale or persistence, use FAISSVectorStore / PGVectorStore (next PR). - For advanced vector stores (Chroma, Qdrant, etc.), implement VectorStorePort directly. - """ - - def __init__(self) -> None: - if _np is None: - raise IntegrationMissingError("numpy", hint="pip install numpy") - self._store: dict[str, list[float]] = {} # chunk_id → vector - - def upsert(self, ids: list[str], vectors: list[list[float]]) -> None: - # Merge into existing store — preserves vectors from previous upsert calls. - # Duplicate ids overwrite the existing entry (true upsert semantics). - for id_, vec in zip(ids, vectors): - self._store[id_] = vec - - def search(self, vector: list[float], k: int) -> list[tuple[str, float]]: - if not self._store: - return [] - - ids = list(self._store.keys()) - matrix = _np.array(list(self._store.values()), dtype=_np.float32) - q = _np.array(vector, dtype=_np.float32) - - norms = _np.linalg.norm(matrix, axis=1) - q_norm = _np.linalg.norm(q) - sims = matrix @ q / (norms * q_norm + 1e-8) - - k = min(k, len(ids)) - top_k = _np.argsort(sims)[::-1][:k] - return [(ids[int(i)], float(sims[i])) for i in top_k] diff --git a/src/lang2sql/integrations/vectorstore/pgvector_.py b/src/lang2sql/integrations/vectorstore/pgvector_.py deleted file mode 100644 index aab745e..0000000 --- a/src/lang2sql/integrations/vectorstore/pgvector_.py +++ /dev/null @@ -1,91 +0,0 @@ -from __future__ import annotations - -from ...core.exceptions import IntegrationMissingError -from ...core.ports import VectorStorePort - -try: - import psycopg2 as _psycopg2 - from pgvector.psycopg2 import register_vector as _register_vector -except ImportError: - _psycopg2 = None # type: ignore[assignment] - _register_vector = None # type: ignore[assignment] - - -class PGVectorStore(VectorStorePort): - """ - PostgreSQL pgvector-backed vector store. - - True upsert semantics via ON CONFLICT DO UPDATE — idempotent, - no duplicates across multiple from_chunks() runs. - Table is created automatically on first upsert() call. - - Args: - connection: PostgreSQL connection URL. - e.g. "postgresql://user:pass@localhost:5432/mydb" - table_name: Name of the vector table. Default "lang2sql_vectors". - - Installation: - pip install psycopg2-binary pgvector - - Quick start with Docker: - docker run -d -e POSTGRES_PASSWORD=postgres \\ - -p 5432:5432 pgvector/pgvector:pg16 - """ - - def __init__( - self, - *, - connection: str, - table_name: str = "lang2sql_vectors", - ) -> None: - if _psycopg2 is None or _register_vector is None: - raise IntegrationMissingError( - "psycopg2", hint="pip install psycopg2-binary pgvector" - ) - self._conn = _psycopg2.connect(connection) - _register_vector(self._conn) - self._table = table_name - self._ready = False # True after first _ensure_table() - - def _ensure_table(self, dim: int) -> None: - if self._ready: - return - with self._conn.cursor() as cur: - cur.execute("CREATE EXTENSION IF NOT EXISTS vector;") - cur.execute( - f"CREATE TABLE IF NOT EXISTS {self._table} " - f"(id TEXT PRIMARY KEY, embedding vector({dim}));" - ) - self._conn.commit() - self._ready = True - - # ── VectorStorePort ────────────────────────────────────────────── - - def upsert(self, ids: list[str], vectors: list[list[float]]) -> None: - """Create table if needed, then upsert all (id, vector) pairs.""" - if not vectors: - return - self._ensure_table(len(vectors[0])) - with self._conn.cursor() as cur: - for id_, vec in zip(ids, vectors): - cur.execute( - f"INSERT INTO {self._table} (id, embedding) VALUES (%s, %s) " - "ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding;", - (id_, vec), - ) - self._conn.commit() - - def search(self, vector: list[float], k: int) -> list[tuple[str, float]]: - """Return (chunk_id, cosine_score) for the k nearest vectors. - Returns [] if the table has not been created yet. - """ - if not self._ready: - return [] - with self._conn.cursor() as cur: - cur.execute( - f"SELECT id, 1 - (embedding <=> %s::vector) AS score " - f"FROM {self._table} " - f"ORDER BY embedding <=> %s::vector LIMIT %s;", - (vector, vector, k), - ) - return [(row[0], float(row[1])) for row in cur.fetchall()] diff --git a/src/lang2sql/memory/__init__.py b/src/lang2sql/memory/__init__.py new file mode 100644 index 0000000..da9c737 --- /dev/null +++ b/src/lang2sql/memory/__init__.py @@ -0,0 +1,19 @@ +"""Memory — Hermes 3-axis split (Store / Recall / Extractor) plus service (★②). + +V1 wires the simplest combination: in-memory store, inject-all recall, manual +extractor. See ``core/ports/memory.py`` for the axis Protocols. +""" + +from __future__ import annotations + +from .extractors import ManualExtractor +from .recall import InjectAllRecall +from .service import MemoryService +from .stores import InMemoryStore + +__all__ = [ + "MemoryService", + "InMemoryStore", + "InjectAllRecall", + "ManualExtractor", +] diff --git a/src/lang2sql/memory/extractors/__init__.py b/src/lang2sql/memory/extractors/__init__.py new file mode 100644 index 0000000..6630a48 --- /dev/null +++ b/src/lang2sql/memory/extractors/__init__.py @@ -0,0 +1,7 @@ +"""Extractor axis strategies.""" + +from __future__ import annotations + +from .manual import ManualExtractor + +__all__ = ["ManualExtractor"] diff --git a/src/lang2sql/memory/extractors/manual.py b/src/lang2sql/memory/extractors/manual.py new file mode 100644 index 0000000..31a346a --- /dev/null +++ b/src/lang2sql/memory/extractors/manual.py @@ -0,0 +1,20 @@ +"""ManualExtractor — the V1 Extractor axis (★②). + +In V1 the only way a fact is born is the explicit ``/remember`` command, so +automatic extraction from the transcript yields nothing. v1.5 swaps in an +LLM-backed extractor that mines repeated patterns — same :class:`ExtractorPort`. +""" + +from __future__ import annotations + +from typing import Sequence + +from ...core.ports.memory import Fact +from ...core.types import Message + + +class ManualExtractor: + """``ExtractorPort`` that never auto-creates facts.""" + + async def extract(self, owner: str, transcript: Sequence[Message]) -> list[Fact]: + return [] diff --git a/src/lang2sql/memory/recall/__init__.py b/src/lang2sql/memory/recall/__init__.py new file mode 100644 index 0000000..2a31361 --- /dev/null +++ b/src/lang2sql/memory/recall/__init__.py @@ -0,0 +1,7 @@ +"""Recall axis strategies.""" + +from __future__ import annotations + +from .inject_all import InjectAllRecall + +__all__ = ["InjectAllRecall"] diff --git a/src/lang2sql/memory/recall/inject_all.py b/src/lang2sql/memory/recall/inject_all.py new file mode 100644 index 0000000..3824ab9 --- /dev/null +++ b/src/lang2sql/memory/recall/inject_all.py @@ -0,0 +1,17 @@ +"""InjectAllRecall — the V1 Recall axis (★②). + +The simplest possible strategy: surface every fact the owner has, ignoring the +query entirely. Good enough while fact counts are tiny. v1.5 replaces this with +keyword matching and v2 with vector similarity — same :class:`RecallPort`. +""" + +from __future__ import annotations + +from ...core.ports.memory import Fact, StorePort + + +class InjectAllRecall: + """``RecallPort`` that returns all of an owner's facts unfiltered.""" + + async def recall(self, owner: str, query: str, store: StorePort) -> list[Fact]: + return await store.all(owner) diff --git a/src/lang2sql/memory/service.py b/src/lang2sql/memory/service.py new file mode 100644 index 0000000..de7e521 --- /dev/null +++ b/src/lang2sql/memory/service.py @@ -0,0 +1,53 @@ +"""MemoryService — binds the three memory axes into one usable surface (★②). + +Store, Recall and Extractor are injected, so a version bump is an adapter swap +at the wiring site rather than a change here. The service offers the three +operations the rest of the system needs: write a fact (``remember``), fetch the +relevant ones for a turn (``recall``), and render them for the prompt +(``render``). +""" + +from __future__ import annotations + +import time +import uuid + +from ..core.ports.memory import ExtractorPort, Fact, RecallPort, StorePort + + +class MemoryService: + """Coordinates the Store / Recall / Extractor axes.""" + + def __init__( + self, + store: StorePort, + recall: RecallPort, + extractor: ExtractorPort, + ) -> None: + self._store = store + self._recall = recall + self._extractor = extractor + + async def remember(self, owner: str, text: str) -> Fact: + """Create and persist a manual fact, returning it.""" + fact = Fact( + id=str(uuid.uuid4()), + owner=owner, + text=text, + source="manual", + ts=time.time(), + ) + await self._store.add(fact) + return fact + + async def recall(self, owner: str, query: str) -> list[Fact]: + """Surface the facts the recall strategy deems relevant to ``query``.""" + return await self._recall.recall(owner, query, self._store) + + def render(self, facts: list[Fact]) -> str: + """Render facts as a markdown block for the system prompt ("" if none).""" + if not facts: + return "" + lines = ["## Remembered facts"] + lines.extend(f"- {fact.text}" for fact in facts) + return "\n".join(lines) diff --git a/src/lang2sql/memory/stores/__init__.py b/src/lang2sql/memory/stores/__init__.py new file mode 100644 index 0000000..cd91c34 --- /dev/null +++ b/src/lang2sql/memory/stores/__init__.py @@ -0,0 +1,7 @@ +"""Store axis adapters.""" + +from __future__ import annotations + +from .in_memory import InMemoryStore + +__all__ = ["InMemoryStore"] diff --git a/src/lang2sql/memory/stores/in_memory.py b/src/lang2sql/memory/stores/in_memory.py new file mode 100644 index 0000000..4ec49f8 --- /dev/null +++ b/src/lang2sql/memory/stores/in_memory.py @@ -0,0 +1,23 @@ +"""InMemoryStore — the V1 Store axis (★②). + +Facts live in a process-local dict keyed by owner. No persistence: a restart +forgets everything. v1.5 swaps this for a SQLite-backed store implementing the +same :class:`StorePort`, with zero changes elsewhere. +""" + +from __future__ import annotations + +from ...core.ports.memory import Fact, StorePort + + +class InMemoryStore: + """``StorePort`` over a ``dict[owner, list[Fact]]``.""" + + def __init__(self) -> None: + self._facts: dict[str, list[Fact]] = {} + + async def add(self, fact: Fact) -> None: + self._facts.setdefault(fact.owner, []).append(fact) + + async def all(self, owner: str) -> list[Fact]: + return list(self._facts.get(owner, ())) diff --git a/src/lang2sql/safety/__init__.py b/src/lang2sql/safety/__init__.py new file mode 100644 index 0000000..f0cf9f5 --- /dev/null +++ b/src/lang2sql/safety/__init__.py @@ -0,0 +1,8 @@ +"""Safety pipeline package — the ★① gate every SQL execution passes.""" + +from __future__ import annotations + +from .layers import TimeoutLayer, WhitelistLayer +from .pipeline import SafetyPipeline + +__all__ = ["SafetyPipeline", "WhitelistLayer", "TimeoutLayer"] diff --git a/src/lang2sql/safety/layers/__init__.py b/src/lang2sql/safety/layers/__init__.py new file mode 100644 index 0000000..b311b42 --- /dev/null +++ b/src/lang2sql/safety/layers/__init__.py @@ -0,0 +1,8 @@ +"""V1 safety layers.""" + +from __future__ import annotations + +from .timeout import TimeoutLayer +from .whitelist import WhitelistLayer + +__all__ = ["WhitelistLayer", "TimeoutLayer"] diff --git a/src/lang2sql/safety/layers/timeout.py b/src/lang2sql/safety/layers/timeout.py new file mode 100644 index 0000000..456fd73 --- /dev/null +++ b/src/lang2sql/safety/layers/timeout.py @@ -0,0 +1,34 @@ +"""Timeout layer — execution-config layer, never blocks. + +This layer does not inspect the SQL for slow constructs (``pg_sleep`` etc.); +that is enforced at *run time* by the executor honoring ``ctx.timeout_seconds``. +Its only job is to guarantee a timeout is set on the context before execution. +""" + +from __future__ import annotations + +from ...core.ports.safety import ( + SafetyContext, + SafetyDecision, + Verdict, +) + +_DEFAULT_TIMEOUT_SECONDS = 30 + + +class TimeoutLayer: + """Ensures ``ctx.timeout_seconds`` is set, then PASSes. ``SafetyLayerPort``.""" + + def __init__(self, default_seconds: int = _DEFAULT_TIMEOUT_SECONDS) -> None: + self._default_seconds = default_seconds + + @property + def name(self) -> str: + return "timeout" + + def check(self, sql: str, ctx: SafetyContext) -> SafetyDecision: + # A non-positive (or unset) timeout would mean "no limit" downstream; + # clamp it to the default so a slow query (case #7, pg_sleep) is bounded. + if not ctx.timeout_seconds or ctx.timeout_seconds <= 0: + ctx.timeout_seconds = self._default_seconds + return SafetyDecision(verdict=Verdict.PASS, sql=sql, layer=self.name) diff --git a/src/lang2sql/safety/layers/whitelist.py b/src/lang2sql/safety/layers/whitelist.py new file mode 100644 index 0000000..9a6a284 --- /dev/null +++ b/src/lang2sql/safety/layers/whitelist.py @@ -0,0 +1,147 @@ +"""Whitelist layer — V1 gate that only lets read-only statements through. + +Fail-closed by design: anything we can't confidently classify as a single +read-only ``SELECT`` / ``WITH`` (or an ``EXPLAIN`` of one) is BLOCKED. This is +deliberately blunt string parsing — the precise AST validation that catches +schema-qualified bypasses and the like is V1.5 work (no sqlglot here). +""" + +from __future__ import annotations + +import re + +from ...core.ports.safety import ( + SafetyContext, + SafetyDecision, + Verdict, +) + +# Statement keywords that mutate state. If any of these appears as a *statement* +# keyword anywhere (including inside a CTE body), we block fail-closed. +_DML_DDL = frozenset( + { + "INSERT", + "UPDATE", + "DELETE", + "DROP", + "ALTER", + "CREATE", + "TRUNCATE", + "GRANT", + "COPY", + } +) + +# Statements we allow to *start* a query. +_ALLOWED_START = ("SELECT", "WITH") + +# A line comment runs to end-of-line; a block comment is /* ... */ (non-greedy, +# spanning newlines). +_LINE_COMMENT = re.compile(r"--[^\n]*") +_BLOCK_COMMENT = re.compile(r"/\*.*?\*/", re.DOTALL) + +# Word-boundary keyword match, case-insensitive. Used to scan for mutating +# keywords as standalone words (so "created_at" never matches "CREATE"). +_WORD = re.compile(r"[A-Za-z_][A-Za-z_0-9]*") + + +def _strip_comments(sql: str) -> str: + """Remove line and block comments, then collapse surrounding whitespace.""" + no_block = _BLOCK_COMMENT.sub(" ", sql) + no_line = _LINE_COMMENT.sub(" ", no_block) + return no_line.strip() + + +def _split_statements(sql: str) -> list[str]: + """Split on ``;`` into non-empty trimmed statements. + + String/identifier-quote awareness is V1.5 AST work; for V1 a bare ``;`` + separator is enough to detect multi-statement payloads fail-closed. + """ + parts = [p.strip() for p in sql.split(";")] + return [p for p in parts if p] + + +class WhitelistLayer: + """SELECT/WITH-only gate. Implements ``SafetyLayerPort``.""" + + @property + def name(self) -> str: + return "whitelist" + + def check(self, sql: str, ctx: SafetyContext) -> SafetyDecision: + cleaned = _strip_comments(sql) + + # Empty / blank (or comment-only) input → cannot parse anything. + if not cleaned: + return SafetyDecision( + verdict=Verdict.BLOCK, + sql=sql, + reason="parse_error", + layer=self.name, + ) + + statements = _split_statements(cleaned) + + # Multi-statement payloads (e.g. ``; DELETE FROM t; --``) are blocked. + if len(statements) != 1: + return SafetyDecision( + verdict=Verdict.BLOCK, + sql=sql, + reason="multi_statement", + layer=self.name, + ) + + statement = statements[0] + + # An EXPLAIN wrapper is allowed only when it fronts a read-only query. + # Strip a leading EXPLAIN (and EXPLAIN ANALYZE / EXPLAIN VERBOSE etc.) + # then re-check the underlying statement's leading keyword. + body = statement + explain = re.match(r"(?is)^EXPLAIN\b(.*)$", body) + if explain is not None: + body = explain.group(1).strip() + # ``EXPLAIN`` may carry options like ANALYZE / VERBOSE / a paren + # option list before the real statement. Drop leading option words + # and any "( ... )" option block so we reach the real keyword. + paren = re.match(r"(?s)^\((?:[^()]|\([^()]*\))*\)\s*(.*)$", body) + if paren is not None: + body = paren.group(1).strip() + else: + # Consume leading bare option words (ANALYZE, VERBOSE, ...). + while True: + m = re.match(r"(?i)^(ANALYZE|VERBOSE|COSTS|BUFFERS)\b\s*(.*)$", body) + if m is None: + break + body = m.group(2).strip() + if not body: + return SafetyDecision( + verdict=Verdict.BLOCK, + sql=sql, + reason="parse_error", + layer=self.name, + ) + + # The (possibly EXPLAIN-unwrapped) statement must start with an allowed + # read-only keyword. + first_word_m = _WORD.match(body) + if first_word_m is None or first_word_m.group(0).upper() not in _ALLOWED_START: + return SafetyDecision( + verdict=Verdict.BLOCK, + sql=sql, + reason="not_select", + layer=self.name, + ) + + # Fail-closed keyword scan: a mutating keyword appearing anywhere as a + # standalone word blocks (catches ``WITH x AS (INSERT ...) SELECT``). + for token in _WORD.findall(body): + if token.upper() in _DML_DDL: + return SafetyDecision( + verdict=Verdict.BLOCK, + sql=sql, + reason=f"dml_keyword:{token.upper()}", + layer=self.name, + ) + + return SafetyDecision(verdict=Verdict.PASS, sql=sql, layer=self.name) diff --git a/src/lang2sql/safety/pipeline.py b/src/lang2sql/safety/pipeline.py new file mode 100644 index 0000000..968d498 --- /dev/null +++ b/src/lang2sql/safety/pipeline.py @@ -0,0 +1,49 @@ +"""Safety pipeline — runs layers in order, first non-PASS short-circuits. + +Airport-security model: SQL walks a *line* of layers. The first layer that +does not return PASS decides the outcome and the rest are skipped. If every +layer PASSes, the pipeline returns a PASS carrying the (possibly rewritten) +SQL forward — so a layer that REWRITEs is itself a non-PASS short-circuit in +V1, while accumulated rewrites only matter once we have multiple rewriting +layers (V1.5+). +""" + +from __future__ import annotations + +from typing import Sequence + +from ..core.ports.safety import ( + SafetyContext, + SafetyDecision, + SafetyLayerPort, + Verdict, +) +from .layers import TimeoutLayer, WhitelistLayer + + +def _default_layers() -> list[SafetyLayerPort]: + # Whitelist first (cheap, fail-closed reject), then Timeout (exec config). + return [WhitelistLayer(), TimeoutLayer()] + + +class SafetyPipeline: + """Ordered safety layers. Implements ``SafetyPipelinePort``.""" + + def __init__(self, layers: Sequence[SafetyLayerPort] | None = None) -> None: + self._layers: list[SafetyLayerPort] = ( + list(layers) if layers is not None else _default_layers() + ) + + @property + def layers(self) -> Sequence[SafetyLayerPort]: + return self._layers + + def evaluate(self, sql: str, ctx: SafetyContext) -> SafetyDecision: + current = sql + for layer in self._layers: + decision = layer.check(current, ctx) + if decision.verdict is not Verdict.PASS: + return decision + # Carry any rewritten SQL forward to the next layer. + current = decision.sql + return SafetyDecision(verdict=Verdict.PASS, sql=current, layer="pipeline") diff --git a/src/lang2sql/semantic/__init__.py b/src/lang2sql/semantic/__init__.py new file mode 100644 index 0000000..110ae6e --- /dev/null +++ b/src/lang2sql/semantic/__init__.py @@ -0,0 +1,29 @@ +"""Semantic federation layer (★④) — definitions, scoping, composition.""" + +from __future__ import annotations + +from .layer import SemanticLayer +from .scoped_layer import merge_scoped +from .sql_composer import SQLComposer +from .store import SemanticStore +from .types import ( + Dimension, + Metric, + Relationship, + Rule, + SemanticEntry, + SemanticKind, +) + +__all__ = [ + "SemanticLayer", + "merge_scoped", + "SQLComposer", + "SemanticStore", + "SemanticEntry", + "SemanticKind", + "Metric", + "Dimension", + "Relationship", + "Rule", +] diff --git a/src/lang2sql/semantic/layer.py b/src/lang2sql/semantic/layer.py new file mode 100644 index 0000000..d82e4c7 --- /dev/null +++ b/src/lang2sql/semantic/layer.py @@ -0,0 +1,59 @@ +"""The effective semantic layer for one resolved scope (★④). + +A :class:`SemanticLayer` is a flat bag of :class:`SemanticEntry` already +resolved for a particular request — federation merging happens upstream in +:mod:`lang2sql.semantic.scoped_layer`. The harness calls :meth:`render` to +splice these definitions into the system prompt so the model prefers them over +its own assumptions. +""" + +from __future__ import annotations + +from .types import SemanticEntry + + +class SemanticLayer: + """An ordered collection of definitions keyed by ``name``. + + Insertion order is preserved for stable rendering; :meth:`add` replaces an + existing entry of the same ``name`` in place so later authority wins + without reordering. + """ + + def __init__(self, entries: list[SemanticEntry] | None = None) -> None: + self._entries: list[SemanticEntry] = list(entries or []) + + @property + def entries(self) -> list[SemanticEntry]: + return list(self._entries) + + def lookup(self, name: str) -> SemanticEntry | None: + """Return the entry named ``name``, or ``None`` if absent.""" + for entry in self._entries: + if entry.name == name: + return entry + return None + + def add(self, entry: SemanticEntry) -> None: + """Add ``entry``, replacing any existing entry with the same name.""" + for i, existing in enumerate(self._entries): + if existing.name == entry.name: + self._entries[i] = entry + return + self._entries.append(entry) + + def render(self) -> str: + """Markdown bullet list for system-prompt injection. + + Empty string when there are no entries so the caller can skip the + section header entirely. + """ + if not self._entries: + return "" + lines: list[str] = [] + for entry in self._entries: + scope = f" (applies to {entry.applies_to})" if entry.applies_to else "" + lines.append( + f"- **{entry.name}** [{entry.kind.value}]: {entry.definition}{scope}" + ) + return "\n".join(lines) diff --git a/src/lang2sql/semantic/scoped_layer.py b/src/lang2sql/semantic/scoped_layer.py new file mode 100644 index 0000000..17a1903 --- /dev/null +++ b/src/lang2sql/semantic/scoped_layer.py @@ -0,0 +1,33 @@ +"""Pure federation merge — most specific scope wins (★④). + +Given each scope's own entries ordered NARROW→WIDE (thread, channel, guild, +builtin), collapse them into one :class:`SemanticLayer`. For any ``name`` the +first (narrowest) definition encountered is authoritative; wider scopes only +contribute names the narrow scopes did not define. No I/O here — the resolver +gathers the per-scope lists and hands them in. +""" + +from __future__ import annotations + +from ..core.identity import Scope +from .layer import SemanticLayer +from .types import SemanticEntry + + +def merge_scoped( + scoped_entries: list[tuple[Scope, list[SemanticEntry]]], +) -> SemanticLayer: + """Merge ordered ``(scope, entries)`` pairs into one effective layer. + + ``scoped_entries`` must run narrow→wide. The first definition seen for a + given ``name`` wins; later (wider) ones for the same name are dropped. + """ + layer = SemanticLayer() + seen: set[str] = set() + for _scope, entries in scoped_entries: + for entry in entries: + if entry.name in seen: + continue + seen.add(entry.name) + layer.add(entry) + return layer diff --git a/src/lang2sql/semantic/sql_composer.py b/src/lang2sql/semantic/sql_composer.py new file mode 100644 index 0000000..0fa928a --- /dev/null +++ b/src/lang2sql/semantic/sql_composer.py @@ -0,0 +1,27 @@ +"""Expand a metric name to its definition (★④, minimal V1). + +V1 deliberately does the simplest useful thing: resolve a name against an +effective :class:`SemanticLayer` and hand back the stored definition string. +Real composition — substituting nested metric references, stitching dimension +filters, emitting a SQL fragment — is later work and is flagged inline. +""" + +from __future__ import annotations + +from .layer import SemanticLayer + + +class SQLComposer: + """Resolve metric/dimension names against a resolved semantic layer.""" + + def __init__(self, layer: SemanticLayer) -> None: + self._layer = layer + + def expand(self, name: str) -> str | None: + """Return the definition string for ``name``, or ``None`` if unknown. + + TODO(v1.5): real composition — recursively expand nested references and + emit a SQL fragment rather than the raw stored definition. + """ + entry = self._layer.lookup(name) + return entry.definition if entry is not None else None diff --git a/src/lang2sql/semantic/store.py b/src/lang2sql/semantic/store.py new file mode 100644 index 0000000..06063d4 --- /dev/null +++ b/src/lang2sql/semantic/store.py @@ -0,0 +1,32 @@ +"""In-memory storage of semantic definitions, keyed by scope (★④). + +V1 keeps everything in a process-local dict — SQLite persistence is deferred +(v1.5). The store is intentionally dumb: it knows nothing about inheritance or +resolution, only "here are the entries authored exactly at this scope". +Resolution lives in :mod:`lang2sql.tenancy.scope_resolver`. +""" + +from __future__ import annotations + +from ..core.identity import Scope +from .types import SemanticEntry + + +class SemanticStore: + """Maps ``str(scope)`` → the entries defined exactly at that scope.""" + + def __init__(self) -> None: + self._by_scope: dict[str, list[SemanticEntry]] = {} + + def add(self, scope: Scope, entry: SemanticEntry) -> None: + """Store ``entry`` at ``scope``, replacing a same-named entry there.""" + bucket = self._by_scope.setdefault(str(scope), []) + for i, existing in enumerate(bucket): + if existing.name == entry.name: + bucket[i] = entry + return + bucket.append(entry) + + def entries_at(self, scope: Scope) -> list[SemanticEntry]: + """Entries authored exactly at ``scope`` (no inheritance).""" + return list(self._by_scope.get(str(scope), [])) diff --git a/src/lang2sql/semantic/types.py b/src/lang2sql/semantic/types.py new file mode 100644 index 0000000..7001ba3 --- /dev/null +++ b/src/lang2sql/semantic/types.py @@ -0,0 +1,71 @@ +"""The stored unit of the semantic layer (★④). + +A :class:`SemanticEntry` is one confirmed definition — a metric, dimension, +relationship, or business rule — that has landed in the semantic layer after +a user confirmed a :class:`~lang2sql.core.ports.ingestion.SemanticCandidate` +(or typed it directly via ``/define_metric``). Entries are pure data: they +carry no I/O and are addressed by ``name`` within a federation scope. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from datetime import datetime, timezone +from enum import Enum + + +class SemanticKind(str, Enum): + """What flavour of definition an entry holds. + + Superset of ``CandidateKind`` — candidates only ever propose METRIC, + DIMENSION, or RULE, but the layer can also store RELATIONSHIP entries + authored directly (e.g. join keys between tables). + """ + + METRIC = "metric" + DIMENSION = "dimension" + RELATIONSHIP = "relationship" + RULE = "rule" + + +@dataclass +class SemanticEntry: + """One confirmed definition stored at a single federation scope. + + ``name`` is the lookup key (unique within a scope); ``definition`` is the + natural-language or SQL-fragment meaning injected into the system prompt. + ``applies_to`` optionally narrows the entry to a table/column. ``source_id`` + links back to the originating document so the audit trail survives. + """ + + kind: SemanticKind + name: str + definition: str + applies_to: str = "" + source_id: str = "" + created_by: str = "" + created_at: str = "" + + def __post_init__(self) -> None: + if not self.created_at: + self.created_at = datetime.now(timezone.utc).isoformat() + + +def Metric(name: str, definition: str, **kwargs: str) -> SemanticEntry: + """Convenience builder pinning ``kind`` to METRIC.""" + return SemanticEntry(SemanticKind.METRIC, name, definition, **kwargs) + + +def Dimension(name: str, definition: str, **kwargs: str) -> SemanticEntry: + """Convenience builder pinning ``kind`` to DIMENSION.""" + return SemanticEntry(SemanticKind.DIMENSION, name, definition, **kwargs) + + +def Relationship(name: str, definition: str, **kwargs: str) -> SemanticEntry: + """Convenience builder pinning ``kind`` to RELATIONSHIP.""" + return SemanticEntry(SemanticKind.RELATIONSHIP, name, definition, **kwargs) + + +def Rule(name: str, definition: str, **kwargs: str) -> SemanticEntry: + """Convenience builder pinning ``kind`` to RULE.""" + return SemanticEntry(SemanticKind.RULE, name, definition, **kwargs) diff --git a/src/lang2sql/tenancy/__init__.py b/src/lang2sql/tenancy/__init__.py new file mode 100644 index 0000000..2f339c9 --- /dev/null +++ b/src/lang2sql/tenancy/__init__.py @@ -0,0 +1,13 @@ +"""Tenancy — scope resolution, per-scope secrets, and context assembly. + +The :class:`ContextConcierge` is the one place concrete semantic/safety/adapter +classes get wired together into a :class:`~lang2sql.harness.context.HarnessContext`. +""" + +from __future__ import annotations + +from .concierge import ContextConcierge +from .encrypted_secrets import EncryptedSecrets +from .scope_resolver import ScopeResolver + +__all__ = ["ContextConcierge", "EncryptedSecrets", "ScopeResolver"] diff --git a/src/lang2sql/tenancy/concierge.py b/src/lang2sql/tenancy/concierge.py new file mode 100644 index 0000000..2050091 --- /dev/null +++ b/src/lang2sql/tenancy/concierge.py @@ -0,0 +1,132 @@ +"""ContextConcierge — the assembly point that builds one HarnessContext. + +This is the only module allowed to import the concrete semantic/safety/adapter +classes; everywhere else depends on the ``core.ports`` Protocols. Per request it +picks an LLM (OpenAI when keyed, else the FakeLLM), restores or starts a +:class:`Session`, and wires the explorer, safety pipeline, scope resolver, and +audit store into a :class:`HarnessContext` for the loop. + +Dependency-injection friendly: every collaborator can be overridden in the +ctor so tests (and v1.5 swaps) need no network and no globals. +""" + +from __future__ import annotations + +import os + +from ..adapters.db.postgres_explorer import PostgresExplorer +from ..adapters.llm.fake import FakeLLM +from ..adapters.llm.openai_ import OpenAILLM +from ..adapters.storage.sqlite_semantic import SqliteSemanticStore +from ..adapters.storage.sqlite_store import SqliteStore +from ..core.identity import Identity +from ..core.ports.audit import AuditPort +from ..core.ports.explorer import ExplorerPort +from ..core.ports.llm import LLMPort +from ..core.ports.safety import SafetyPipelinePort +from ..core.ports.secrets import SecretsPort +from ..core.ports.semantic_scope import ScopeResolverPort +from ..harness.context import HarnessContext +from ..harness.session import Session +from ..harness.tool_registry import ToolRegistry +from ..ingestion import FileSource, IngestionPipeline, LLMExtractor +from ..memory import InjectAllRecall, InMemoryStore, ManualExtractor, MemoryService +from ..safety.pipeline import SafetyPipeline +from ..tools import build_default_tools +from .encrypted_secrets import EncryptedSecrets +from .scope_resolver import ScopeResolver + +# DSN used for the V1 explorer stub when a scope has registered none yet. +_DEFAULT_DSN = "postgresql://stub/v1" + + +class ContextConcierge: + """Assembles per-request :class:`HarnessContext` from injected ports.""" + + def __init__( + self, + *, + path: str = ":memory:", + store: SqliteStore | None = None, + llm: LLMPort | None = None, + explorer: ExplorerPort | None = None, + safety: SafetyPipelinePort | None = None, + scope_resolver: ScopeResolverPort | None = None, + secrets: SecretsPort | None = None, + audit: AuditPort | None = None, + max_turns: int = 8, + ) -> None: + # ``path`` drives the default persistence backends; ``:memory:`` keeps + # tests isolated, a file path makes sessions/definitions/secrets durable. + self._store = store if store is not None else SqliteStore(path) + # Audit + session persistence both ride the one sqlite store by default. + self._llm = llm if llm is not None else _default_llm() + self._explorer = explorer if explorer is not None else PostgresExplorer(_DEFAULT_DSN) + self._safety = safety if safety is not None else SafetyPipeline() + # Persistent semantic store by default so definitions survive restart. + self._scope_resolver = ( + scope_resolver + if scope_resolver is not None + else ScopeResolver(SqliteSemanticStore(path)) + ) + # Secrets share the session/audit store's kv table (and sqlite file). + self._secrets = ( + secrets if secrets is not None else EncryptedSecrets(self._store) + ) + self._audit = audit if audit is not None else self._store + self._max_turns = max_turns + + # V1 memory (in-memory + inject-all + manual) and ingestion (file × LLM). + self._memory = MemoryService(InMemoryStore(), InjectAllRecall(), ManualExtractor()) + self._ingestion = IngestionPipeline() + self._source = FileSource() + self._extractor = LLMExtractor(self._llm) + + @property + def store(self) -> SqliteStore: + return self._store + + @property + def secrets(self) -> SecretsPort: + """Per-scope encrypted credential store (DSNs/API keys via ``/connect``).""" + return self._secrets + + @property + def scope_resolver(self) -> ScopeResolverPort: + """Federation resolver over the (by default persistent) semantic store.""" + return self._scope_resolver + + async def build_context( + self, identity: Identity, user_text: str | None = None + ) -> HarnessContext: + session = await self._store.load(identity.session_key()) + if session is None: + session = Session(identity=identity) + + tools = ToolRegistry( + build_default_tools( + memory=self._memory, + ingestion=self._ingestion, + source=self._source, + extractor=self._extractor, + ) + ) + + return HarnessContext( + identity=identity, + llm=self._llm, + tools=tools, + session=session, + explorer=self._explorer, + safety=self._safety, + audit=self._audit, + scope_resolver=self._scope_resolver, + max_turns=self._max_turns, + ) + + +def _default_llm() -> LLMPort: + """OpenAI when a key is present, otherwise the offline FakeLLM.""" + if os.environ.get("OPENAI_API_KEY"): + return OpenAILLM() + return FakeLLM() diff --git a/src/lang2sql/tenancy/encrypted_secrets.py b/src/lang2sql/tenancy/encrypted_secrets.py new file mode 100644 index 0000000..a1362f6 --- /dev/null +++ b/src/lang2sql/tenancy/encrypted_secrets.py @@ -0,0 +1,67 @@ +"""EncryptedSecrets — per-scope credential storage over a :class:`SqliteStore` kv. + +Real symmetric encryption via :class:`cryptography.fernet.Fernet` (AES-128-CBC + +HMAC). Each secret value is encrypted to a Fernet token before it lands in the +kv table, so a stolen database file yields no plaintext DSNs/API keys without +the key. The :class:`SecretsPort` boundary is unchanged, so callers (concierge, +``/connect``) need no edits. + +Key management: + +* If env ``LANG2SQL_SECRET_KEY`` holds a urlsafe-base64 Fernet key, that is the + encryption key — point every deployment instance at the same value so secrets + decrypt across restarts and across machines. +* Otherwise a key is generated once and persisted in the kv table under the + reserved scope ``__secrets__`` / key ``fernet_key``. This keeps a single + sqlite file self-contained (secrets survive restart) but is only as private + as the file itself — set ``LANG2SQL_SECRET_KEY`` for real key separation. +""" + +from __future__ import annotations + +import os + +from cryptography.fernet import Fernet + +from ..adapters.storage.sqlite_store import SqliteStore + +# Reserved kv location for the auto-generated key (when env key is absent). +_KEY_SCOPE = "__secrets__" +_KEY_NAME = "fernet_key" +_ENV_KEY = "LANG2SQL_SECRET_KEY" + + +def _resolve_key(store: SqliteStore) -> bytes: + """Pick the Fernet key: env override, else persisted, else freshly generated.""" + env_key = os.environ.get(_ENV_KEY) + if env_key: + return env_key.encode("ascii") + + stored = store.kv_get(_KEY_SCOPE, _KEY_NAME) + if stored is not None: + return stored.encode("ascii") + + key = Fernet.generate_key() + store.kv_set(_KEY_SCOPE, _KEY_NAME, key.decode("ascii")) + return key + + +class EncryptedSecrets: + """Implements :class:`SecretsPort` on top of a kv-capable store.""" + + def __init__(self, store: SqliteStore, *, key: bytes | None = None) -> None: + self._store = store + self._fernet = Fernet(key if key is not None else _resolve_key(store)) + + async def get(self, scope: str, key: str) -> str | None: + blob = self._store.kv_get(scope, key) + if blob is None: + return None + return self._fernet.decrypt(blob.encode("ascii")).decode("utf-8") + + async def set(self, scope: str, key: str, value: str) -> None: + token = self._fernet.encrypt(value.encode("utf-8")).decode("ascii") + self._store.kv_set(scope, key, token) + + async def delete(self, scope: str, key: str) -> None: + self._store.kv_delete(scope, key) diff --git a/src/lang2sql/tenancy/scope_resolver.py b/src/lang2sql/tenancy/scope_resolver.py new file mode 100644 index 0000000..2093fbf --- /dev/null +++ b/src/lang2sql/tenancy/scope_resolver.py @@ -0,0 +1,42 @@ +"""Federation resolution over a :class:`SemanticStore` (★④). + +Implements :class:`~lang2sql.core.ports.semantic_scope.ScopeResolverPort`. The +store holds raw per-scope definitions; this resolver walks an identity's +``scope_chain()`` (narrow→wide) and merges them so the most specific definition +of each ``name`` wins. ``define``/``entries_at`` delegate straight to the store. +""" + +from __future__ import annotations + +from ..core.identity import Identity, Scope +from ..semantic.layer import SemanticLayer +from ..semantic.scoped_layer import merge_scoped +from ..semantic.store import SemanticStore +from ..semantic.types import SemanticEntry + + +class ScopeResolver: + """Resolve effective semantic layers, backed by a :class:`SemanticStore`.""" + + def __init__(self, store: SemanticStore | None = None) -> None: + self._store = store if store is not None else SemanticStore() + + @property + def store(self) -> SemanticStore: + return self._store + + async def effective_layer(self, identity: Identity) -> SemanticLayer: + """Merge ``identity``'s scope chain narrow→wide; most specific wins.""" + scoped = [ + (scope, self._store.entries_at(scope)) + for scope in identity.scope_chain() + ] + return merge_scoped(scoped) + + async def define(self, scope: Scope, entry: SemanticEntry) -> None: + """Persist one definition at an explicit scope.""" + self._store.add(scope, entry) + + async def entries_at(self, scope: Scope) -> list[SemanticEntry]: + """Definitions stored exactly at ``scope`` (no inheritance).""" + return self._store.entries_at(scope) diff --git a/src/lang2sql/tools/__init__.py b/src/lang2sql/tools/__init__.py new file mode 100644 index 0000000..cc5b398 --- /dev/null +++ b/src/lang2sql/tools/__init__.py @@ -0,0 +1,43 @@ +"""Tools — the ctx-aware capabilities the agent can invoke. + +``build_default_tools`` assembles the V1 tool set. Tools that need only ports +read them from the live :class:`HarnessContext`; tools backed by a service +(remember, ingest_doc) take it by injection here so the wiring stays in one +place (the ContextConcierge calls this). +""" + +from __future__ import annotations + +from ..core.ports.ingestion import DocExtractorPort, SourcePort +from ..core.ports.tool import ToolPort +from ..ingestion.pipeline import IngestionPipeline +from ..memory.service import MemoryService +from .ask_user import AskUser +from .define_metric import DefineMetric +from .explore_schema import ExploreSchema +from .ingest_doc import IngestDoc +from .remember import Remember +from .run_sql import RunSQL + +__all__ = [ + "build_default_tools", + "RunSQL", "ExploreSchema", "DefineMetric", "Remember", "AskUser", "IngestDoc", +] + + +def build_default_tools( + *, + memory: MemoryService, + ingestion: IngestionPipeline, + source: SourcePort, + extractor: DocExtractorPort, +) -> list[ToolPort]: + """The six V1 tools (v4.1 §4.1).""" + return [ + RunSQL(), + ExploreSchema(), + DefineMetric(), + AskUser(), + Remember(memory), + IngestDoc(ingestion, source, extractor), + ] diff --git a/src/lang2sql/tools/ask_user.py b/src/lang2sql/tools/ask_user.py new file mode 100644 index 0000000..683fb6c --- /dev/null +++ b/src/lang2sql/tools/ask_user.py @@ -0,0 +1,37 @@ +"""ask_user — request a clarification from the human. + +The agent calls this when a question is ambiguous. V1 returns the question as +the tool result; the frontend surfaces it and the user's reply arrives as the +next turn. (A suspend/resume round-trip is a v1.5 frontend concern; the loop +contract here is simply that the model gets its question echoed back so it +stops guessing.) +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.types import ToolResult, ToolSpec + +if TYPE_CHECKING: + from ..harness.context import HarnessContext + + +class AskUser: + @property + def spec(self) -> ToolSpec: + return ToolSpec( + name="ask_user", + description="Ask the user a clarifying question when the request is ambiguous.", + parameters={ + "type": "object", + "properties": {"question": {"type": "string"}}, + "required": ["question"], + }, + ) + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + question = (args.get("question") or "").strip() + if not question: + return ToolResult(call_id="", content="no question provided", is_error=True) + return ToolResult(call_id="", content=f"❓ Awaiting user: {question}") diff --git a/src/lang2sql/tools/define_metric.py b/src/lang2sql/tools/define_metric.py new file mode 100644 index 0000000..68dd546 --- /dev/null +++ b/src/lang2sql/tools/define_metric.py @@ -0,0 +1,70 @@ +"""define_metric — scope-aware definition writer (★④ federation). + +Writes one :class:`SemanticEntry` to a federation scope via the +:class:`ScopeResolverPort`. With no explicit scope it lands at the identity's +default write scope (current channel), so ``#marketing`` and ``#finance`` can +hold different definitions of the same name without conflict. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.ports.audit import AuditEvent +from ..core.identity import Scope, ScopeLevel +from ..core.types import ToolResult, ToolSpec +from ..semantic.types import SemanticEntry, SemanticKind + +if TYPE_CHECKING: + from ..harness.context import HarnessContext + + +class DefineMetric: + @property + def spec(self) -> ToolSpec: + return ToolSpec( + name="define_metric", + description=( + "Define a metric/dimension/rule for the current scope (channel by " + "default). Later questions in this scope use this definition." + ), + parameters={ + "type": "object", + "properties": { + "name": {"type": "string"}, + "definition": {"type": "string"}, + "kind": {"type": "string", "enum": ["metric", "dimension", "rule"]}, + "scope": {"type": "string", "enum": ["channel", "guild"], + "description": "where to store it; default channel"}, + }, + "required": ["name", "definition"], + }, + ) + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + if ctx.scope_resolver is None: + return ToolResult(call_id="", content="semantic layer unavailable", is_error=True) + + name = (args.get("name") or "").strip() + definition = (args.get("definition") or "").strip() + if not name or not definition: + return ToolResult(call_id="", content="name and definition are required", is_error=True) + + kind = SemanticKind(args.get("kind", "metric")) + scope = self._resolve_scope(args.get("scope"), ctx) + entry = SemanticEntry(kind=kind, name=name, definition=definition, + created_by=ctx.identity.user_id) + await ctx.scope_resolver.define(scope, entry) + + if ctx.audit is not None: + await ctx.audit.record( + AuditEvent(actor=ctx.identity.user_id, action="define_metric", + scope=str(scope), detail={"name": name, "kind": kind.value}) + ) + return ToolResult(call_id="", content=f"✅ {kind.value} '{name}' defined at {scope}.") + + @staticmethod + def _resolve_scope(requested: str | None, ctx: "HarnessContext") -> Scope: + if requested == "guild" and ctx.identity.guild_id: + return Scope(ScopeLevel.GUILD, ctx.identity.guild_id) + return ctx.identity.default_write_scope() diff --git a/src/lang2sql/tools/explore_schema.py b/src/lang2sql/tools/explore_schema.py new file mode 100644 index 0000000..f5e08bb --- /dev/null +++ b/src/lang2sql/tools/explore_schema.py @@ -0,0 +1,47 @@ +"""explore_schema — let the agent discover tables/columns before writing SQL. + +Read-only introspection through the :class:`ExplorerPort`. With no ``table`` +arg it lists tables; with one it returns full column detail. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.types import ToolResult, ToolSpec + +if TYPE_CHECKING: + from ..harness.context import HarnessContext + + +class ExploreSchema: + @property + def spec(self) -> ToolSpec: + return ToolSpec( + name="explore_schema", + description="List tables, or describe one table's columns. Call before writing SQL.", + parameters={ + "type": "object", + "properties": { + "table": {"type": "string", "description": "table name to describe; omit to list all tables"}, + }, + }, + ) + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + if ctx.explorer is None: + return ToolResult(call_id="", content="no DB connected (use /connect)", is_error=True) + + table = (args.get("table") or "").strip() + if not table: + tables = await ctx.explorer.list_tables() + names = "\n".join(f"- {t.qualified}" for t in tables) or "(no tables)" + return ToolResult(call_id="", content="Tables:\n" + names) + + t = await ctx.explorer.describe_table(table) + cols = "\n".join( + f"- {c.name}: {c.type}{'' if c.nullable else ' NOT NULL'}" + f"{(' — ' + c.description) if c.description else ''}" + for c in t.columns + ) or "(no columns)" + return ToolResult(call_id="", content=f"{t.qualified}\n{cols}") diff --git a/src/lang2sql/tools/ingest_doc.py b/src/lang2sql/tools/ingest_doc.py new file mode 100644 index 0000000..e5118a6 --- /dev/null +++ b/src/lang2sql/tools/ingest_doc.py @@ -0,0 +1,64 @@ +"""ingest_doc — turn an uploaded document into semantic candidates (★③). + +Runs the Source × Extractor pipeline and returns the proposed metric/rule +definitions for the user to confirm. V1 does NOT auto-register — confirmation +is a frontend step (Discord buttons in Week 4); this tool surfaces the +candidates so the human stays in the loop (documents are the source of truth). +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.ports.ingestion import DocExtractorPort, SourcePort +from ..core.types import ToolResult, ToolSpec +from ..ingestion.pipeline import IngestionPipeline + +if TYPE_CHECKING: + from ..harness.context import HarnessContext + + +class IngestDoc: + def __init__( + self, + pipeline: IngestionPipeline, + source: SourcePort, + extractor: DocExtractorPort, + ) -> None: + self._pipeline = pipeline + self._source = source + self._extractor = extractor + + @property + def spec(self) -> ToolSpec: + return ToolSpec( + name="ingest_doc", + description=( + "Read a document and propose metric/dimension/rule definitions " + "for the user to confirm before they enter the semantic layer." + ), + parameters={ + "type": "object", + "properties": { + "ref": {"type": "string", "description": "document path or identifier"}, + "content": {"type": "string", "description": "inline document text (alternative to ref)"}, + }, + }, + ) + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + ref = (args.get("ref") or "inline").strip() + content = args.get("content") + blob = content.encode("utf-8") if isinstance(content, str) else None + if not content and ref == "inline": + return ToolResult(call_id="", content="provide a document 'ref' or inline 'content'", is_error=True) + + candidates = await self._pipeline.ingest(self._source, self._extractor, ref, blob) + if not candidates: + return ToolResult(call_id="", content="No definitions found in the document.") + + lines = ["Proposed definitions (confirm to register):"] + for c in candidates: + applies = f" [{c.applies_to}]" if c.applies_to else "" + lines.append(f"- {c.kind.value.upper()} {c.name}{applies} → {c.definition}") + return ToolResult(call_id="", content="\n".join(lines)) diff --git a/src/lang2sql/tools/ping.py b/src/lang2sql/tools/ping.py new file mode 100644 index 0000000..3ded19c --- /dev/null +++ b/src/lang2sql/tools/ping.py @@ -0,0 +1,34 @@ +"""ping — a trivial ctx-aware tool used to validate the tool dispatch path. + +Stands in for real tools (run_sql, explore_schema, …) in Week 1: it proves the +ToolPort contract, the registry dispatch, and the loop's tool-result handling +all line up. Removed once the real tools land. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.types import ToolResult, ToolSpec + +if TYPE_CHECKING: + from ..harness.context import HarnessContext + + +class Ping: + @property + def spec(self) -> ToolSpec: + return ToolSpec( + name="ping", + description="Health-check tool. Echoes back a message.", + parameters={ + "type": "object", + "properties": { + "message": {"type": "string", "description": "text to echo"} + }, + }, + ) + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + msg = args.get("message", "") + return ToolResult(call_id="", content=f"pong: {msg!r} (user={ctx.identity.user_id})") diff --git a/src/lang2sql/tools/remember.py b/src/lang2sql/tools/remember.py new file mode 100644 index 0000000..c48622f --- /dev/null +++ b/src/lang2sql/tools/remember.py @@ -0,0 +1,47 @@ +"""remember — persist a user fact via the memory service (★②). + +V1 is the manual ``/remember`` path: the model (or a slash command) records a +fact verbatim. Recall/extraction strategies evolve behind MemoryService without +this tool changing. The service is injected at assembly time. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.ports.audit import AuditEvent +from ..core.types import ToolResult, ToolSpec +from ..memory.service import MemoryService + +if TYPE_CHECKING: + from ..harness.context import HarnessContext + + +class Remember: + def __init__(self, memory: MemoryService) -> None: + self._memory = memory + + @property + def spec(self) -> ToolSpec: + return ToolSpec( + name="remember", + description="Persist a fact about the user/data so it informs future turns.", + parameters={ + "type": "object", + "properties": {"text": {"type": "string"}}, + "required": ["text"], + }, + ) + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + text = (args.get("text") or "").strip() + if not text: + return ToolResult(call_id="", content="nothing to remember", is_error=True) + + fact = await self._memory.remember(ctx.identity.user_id, text) + if ctx.audit is not None: + await ctx.audit.record( + AuditEvent(actor=ctx.identity.user_id, action="remember", + scope=ctx.identity.session_key(), detail={"fact_id": fact.id}) + ) + return ToolResult(call_id="", content=f"🧠 Remembered: {text}") diff --git a/src/lang2sql/tools/run_sql.py b/src/lang2sql/tools/run_sql.py new file mode 100644 index 0000000..567f265 --- /dev/null +++ b/src/lang2sql/tools/run_sql.py @@ -0,0 +1,77 @@ +"""run_sql — execute a read-only query, but only after the safety gate (★①). + +The tool never touches the DB directly: it pushes the model's SQL through the +:class:`SafetyPipelinePort` first. BLOCK/CONFIRM short-circuit with an +explanation the model can act on; PASS/REWRITE proceed to +``explorer.execute`` and the (possibly rewritten) SQL is what runs. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from ..core.ports.audit import AuditEvent +from ..core.ports.safety import SafetyContext, Verdict +from ..core.types import ToolResult, ToolSpec + +if TYPE_CHECKING: + from ..harness.context import HarnessContext + + +class RunSQL: + @property + def spec(self) -> ToolSpec: + return ToolSpec( + name="run_sql", + description=( + "Run a single read-only SQL query (SELECT/WITH only) and return " + "rows. Queries are checked by a safety gate before execution." + ), + parameters={ + "type": "object", + "properties": { + "sql": {"type": "string", "description": "a single SELECT or WITH query"}, + "limit": {"type": "integer", "description": "max rows (default 1000)"}, + }, + "required": ["sql"], + }, + ) + + async def run(self, args: dict[str, Any], ctx: "HarnessContext") -> ToolResult: + sql = (args.get("sql") or "").strip() + try: + limit = int(args.get("limit") or 1000) + except (TypeError, ValueError): + limit = 1000 # tolerate a malformed limit from the model + + if ctx.safety is None: + return ToolResult(call_id="", content="run_sql unavailable: no safety pipeline wired", is_error=True) + if ctx.explorer is None: + return ToolResult(call_id="", content="run_sql unavailable: no DB connected (use /connect)", is_error=True) + + decision = ctx.safety.evaluate(sql, SafetyContext(row_limit=limit)) + if decision.verdict == Verdict.BLOCK: + return ToolResult(call_id="", content=f"BLOCKED by {decision.layer}: {decision.reason}", is_error=True) + if decision.verdict == Verdict.CONFIRM: + return ToolResult(call_id="", content=f"NEEDS CONFIRMATION: {decision.confirm_prompt}") + + rows = await ctx.explorer.execute(decision.sql, limit) + + if ctx.audit is not None: + await ctx.audit.record( + AuditEvent(actor=ctx.identity.user_id, action="run_sql", + scope=ctx.identity.session_key(), detail={"sql": decision.sql}) + ) + + return ToolResult(call_id="", content=_render_rows(decision.sql, rows)) + + +def _render_rows(sql: str, rows: list[dict]) -> str: + if not rows: + return f"(0 rows)\nSQL: {sql}" + headers = list(rows[0].keys()) + lines = [" | ".join(headers), " | ".join("---" for _ in headers)] + for r in rows[:50]: + lines.append(" | ".join(str(r.get(h, "")) for h in headers)) + suffix = f"\n… ({len(rows)} rows total)" if len(rows) > 50 else "" + return f"{len(rows)} row(s):\n" + "\n".join(lines) + suffix + f"\nSQL: {sql}" diff --git a/test/test_llm_utils/test_llm_response_parser.py b/test/test_llm_utils/test_llm_response_parser.py deleted file mode 100644 index ed10acd..0000000 --- a/test/test_llm_utils/test_llm_response_parser.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -LLMResponseParser 클래스의 기능을 테스트하는 단위 테스트 모듈입니다. - -주요 테스트 항목: -- 블록에서 SQL 쿼리 추출 성공/실패 -- <해석> 블록에서 자연어 설명 추출 성공/실패 -- 다양한 입력 형식(들여쓰기, 공백 등)에 대한 정규식 대응 여부 확인 -""" - -import unittest - -from utils.llm.llm_response_parser import LLMResponseParser - - -class TestLLMResponseParser(unittest.TestCase): - """ - LLMResponseParser 클래스의 정적 메서드 동작을 검증하는 테스트 케이스입니다. - - 각 테스트는 SQL 및 해석 블록 추출 기능이 정상적으로 작동하는지, - 예외 상황에 올바르게 대응하는지를 검증합니다. - """ - - def test_extract_sql_success(self): - """ - 블록과 ```sql``` 코드 블록이 정상적으로 포함된 문자열에서 - SQL 쿼리가 정확히 추출되는지 확인합니다. - """ - - text = """ - - ```sql - SELECT * FROM users; - ```` - - <해석> - - ```plaintext - 사용자 테이블의 모든 데이터를 조회합니다. - ``` - - """ - expected_sql = "SELECT * FROM users;" - result = LLMResponseParser.extract_sql(text) - self.assertEqual(result, expected_sql) - - def test_extract_sql_missing(self): - """ - 블록은 존재하지만 코드 블록이 없을 경우, - ValueError 예외가 발생하는지 확인합니다. - """ - - text = " no code block here" - with self.assertRaises(ValueError): - LLMResponseParser.extract_sql(text) - - def test_extract_interpretation_success(self): - """ - <해석> 블록과 ```plaintext``` 코드 블록이 포함된 문자열에서 - 해석 텍스트가 정상적으로 추출되는지 확인합니다. - """ - - text = """ - ``` - - - ```sql - SELECT * FROM users; - ``` - <해석> - ```plaintext - 사용자 테이블의 모든 데이터를 조회합니다. - ``` - """ - expected = "사용자 테이블의 모든 데이터를 조회합니다." - result = LLMResponseParser.extract_interpretation(text) - self.assertEqual(result, expected) - - def test_extract_interpretation_empty(self): - """ - <해석> 태그는 존재하지만 코드 블록이 없는 경우, - 빈 문자열을 반환하는지 확인합니다. - """ - - text = "<해석> 블록이 없습니다." - result = LLMResponseParser.extract_interpretation(text) - self.assertEqual(result, "") - - def test_extract_sql_with_leading_whitespace(self): - """ - 블록이 들여쓰기되어 있는 경우에도 SQL 쿼리를 정확히 추출하는지 확인합니다. - """ - - text = """ - ``` - - - ```sql - SELECT id FROM orders; - ``` - <해석> - ```plaintext - 주문 테이블에서 ID 조회 - ``` - """ - expected = "SELECT id FROM orders;" - result = LLMResponseParser.extract_sql(text) - self.assertEqual(result, expected.strip()) diff --git a/tests/test_adapters.py b/tests/test_adapters.py new file mode 100644 index 0000000..33e3096 --- /dev/null +++ b/tests/test_adapters.py @@ -0,0 +1,134 @@ +"""Adapter tests — SqliteStore round trips, OpenAILLM offline behaviour, stub. + +No network: OpenAI is only checked for construction + the no-key RuntimeError. +Async port methods are driven with ``asyncio.run``. +""" + +from __future__ import annotations + +import asyncio +import os + +from lang2sql.adapters.db.postgres_explorer import PostgresExplorer +from lang2sql.adapters.llm.openai_ import OpenAILLM +from lang2sql.adapters.storage.sqlite_store import SqliteStore +from lang2sql.core.identity import Identity +from lang2sql.core.ports.audit import AuditEvent +from lang2sql.core.ports.explorer import ExplorerPort +from lang2sql.core.types import Message, Role, ToolCall +from lang2sql.harness.session import Session + + +def test_audit_record_then_query() -> None: + store = SqliteStore() + asyncio.run(store.record(AuditEvent(actor="u1", action="run_sql", scope="s", detail={"q": "SELECT 1"}))) + asyncio.run(store.record(AuditEvent(actor="u1", action="define_metric", scope="s", detail={}))) + asyncio.run(store.record(AuditEvent(actor="other", action="run_sql", scope="s"))) + + events = asyncio.run(store.query("u1")) + assert len(events) == 2 + # Newest first. + assert events[0].action == "define_metric" + assert events[1].action == "run_sql" + assert events[1].detail == {"q": "SELECT 1"} + assert events[0].ts > 0 # store fills the timestamp + + +def test_session_save_then_load_reconstructs_transcript() -> None: + store = SqliteStore() + identity = Identity(user_id="u1", guild_id="g", channel_id="c", thread_id="t", is_admin=True) + session = Session(identity=identity) + session.add(Message(role=Role.USER, content="hi")) + session.add( + Message( + role=Role.ASSISTANT, + content="", + tool_calls=[ToolCall(id="call_1", name="run_sql", arguments={"q": "SELECT 1"})], + ) + ) + session.add(Message(role=Role.TOOL, content="ok", tool_call_id="call_1", name="run_sql")) + + key = identity.session_key() + asyncio.run(store.save(key, session)) + loaded = asyncio.run(store.load(key)) + + assert loaded is not None + assert loaded.identity == identity + assert len(loaded.transcript) == 3 + assert loaded.transcript[0].role == Role.USER + tc = loaded.transcript[1].tool_calls[0] + assert tc.id == "call_1" + assert tc.name == "run_sql" + assert tc.arguments == {"q": "SELECT 1"} + assert loaded.transcript[2].tool_call_id == "call_1" + assert loaded.transcript[2].name == "run_sql" + + +def test_session_load_missing_returns_none() -> None: + store = SqliteStore() + assert asyncio.run(store.load("nope")) is None + + +def test_kv_set_get_delete() -> None: + store = SqliteStore() + assert store.kv_get("scope", "k") is None + store.kv_set("scope", "k", "v1") + assert store.kv_get("scope", "k") == "v1" + store.kv_set("scope", "k", "v2") # upsert + assert store.kv_get("scope", "k") == "v2" + store.kv_delete("scope", "k") + assert store.kv_get("scope", "k") is None + + +def test_postgres_explorer_canned_data() -> None: + explorer = PostgresExplorer("postgresql://ignored") + tables = asyncio.run(explorer.list_tables()) + names = {t.qualified for t in tables} + assert "public.orders" in names + assert "public.users" in names + + orders = asyncio.run(explorer.describe_table("orders")) + assert {c.name for c in orders.columns} == {"id", "amount", "status", "created_at"} + + rows = asyncio.run(explorer.sample_rows("public.orders", limit=1)) + assert len(rows) == 1 + assert "status" in rows[0] + + +def test_postgres_explorer_satisfies_protocol() -> None: + explorer = PostgresExplorer("postgresql://ignored") + assert isinstance(explorer, ExplorerPort) + + +def test_postgres_explorer_execute() -> None: + explorer = PostgresExplorer("postgresql://ignored") + order_rows = asyncio.run(explorer.execute("SELECT * FROM orders WHERE status='paid'")) + assert order_rows and "amount" in order_rows[0] + + capped = asyncio.run(explorer.execute("select * from orders", limit=1)) + assert len(capped) == 1 + + generic = asyncio.run(explorer.execute("SELECT now()")) + assert generic == [{"result": 1}] + + +def test_openai_constructs_offline() -> None: + # No network, no key required to construct. + llm = OpenAILLM(model="gpt-4.1-mini", api_key=None, base_url="http://localhost:0") + assert llm.model == "gpt-4.1-mini" + + +def test_openai_complete_without_key_raises() -> None: + saved = os.environ.pop("OPENAI_API_KEY", None) + try: + llm = OpenAILLM(api_key=None) + raised = False + try: + asyncio.run(llm.complete([Message(role=Role.USER, content="hi")])) + except RuntimeError as exc: + raised = True + assert "OPENAI_API_KEY not set" in str(exc) + assert raised + finally: + if saved is not None: + os.environ["OPENAI_API_KEY"] = saved diff --git a/tests/test_bench_demo.py b/tests/test_bench_demo.py new file mode 100644 index 0000000..9a2e625 --- /dev/null +++ b/tests/test_bench_demo.py @@ -0,0 +1,61 @@ +"""Smoke test: the bench demo runs clean and shows its headline behaviours. + +Guards the study-group demo against drift in the modules it exercises +(ContextConcierge, ScopeResolver, SafetyPipeline). Runs the demo's ``main()`` +in-process and asserts the federation + safety claims it prints are real. +""" + +from __future__ import annotations + +import asyncio +import importlib.util +from pathlib import Path + +import pytest + +_DEMO = Path(__file__).resolve().parent.parent / "bench" / "ecommerce_demo.py" + + +def _load_demo(): + spec = importlib.util.spec_from_file_location("ecommerce_demo", _DEMO) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + +def test_demo_runs_clean(capsys): + demo = _load_demo() + asyncio.run(demo.main()) + out = capsys.readouterr().out + + # ★④ federation: same term, two definitions, no conflict. + assert "user with a login event in the last 30 days" in out + assert "user with an active paid subscription" in out + assert "zero conflict" in out + + # ★① safety: a mutating statement is blocked, a SELECT passes. + assert "BLOCK" in out + assert "PASS" in out + + +def test_demo_federation_resolves_distinct_definitions(): + """Reach into the demo's building blocks directly (no printing).""" + demo = _load_demo() + from lang2sql.semantic.types import Metric + from lang2sql.tenancy.scope_resolver import ScopeResolver + + async def _run(): + resolver = ScopeResolver() + mkt = demo._marketing_identity() + fin = demo._finance_identity() + await resolver.define(mkt.default_write_scope(), Metric("active_user", "30d login")) + await resolver.define(fin.default_write_scope(), Metric("active_user", "paid sub")) + mkt_layer = await resolver.effective_layer(mkt) + fin_layer = await resolver.effective_layer(fin) + return mkt_layer.lookup("active_user").definition, fin_layer.lookup("active_user").definition + + mkt_def, fin_def = asyncio.run(_run()) + assert mkt_def == "30d login" + assert fin_def == "paid sub" + assert mkt_def != fin_def diff --git a/tests/test_components_context_enricher.py b/tests/test_components_context_enricher.py deleted file mode 100644 index 20fd83c..0000000 --- a/tests/test_components_context_enricher.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Tests for ContextEnricher component.""" - -from __future__ import annotations - -import pytest - -from lang2sql.components.enrichment.context_enricher import ContextEnricher -from lang2sql.core.catalog import CatalogEntry, QuestionProfile -from lang2sql.core.hooks import MemoryHook - - -class FakeLLM: - def __init__(self, response: str = "지난달(2024-03) 주문 건수를 COUNT하는 쿼리"): - self._response = response - - def invoke(self, messages: list[dict]) -> str: - return self._response - - -def _catalog() -> list[CatalogEntry]: - return [ - { - "name": "orders", - "description": "주문 테이블", - "columns": { - "order_id": "주문 ID", - "amount": "주문 금액", - "created_at": "생성일", - }, - } - ] - - -def _profile(is_aggregation: bool = True) -> QuestionProfile: - return QuestionProfile( - is_aggregation=is_aggregation, - has_filter=True, - intent_type="lookup", - ) - - -def test_context_enricher_returns_string(): - llm = FakeLLM("enriched question text") - enricher = ContextEnricher(llm=llm) - result = enricher.run("주문 수", _catalog(), _profile()) - assert isinstance(result, str) - assert result == "enriched question text" - - -def test_context_enricher_trims_whitespace(): - llm = FakeLLM(" enriched ") - enricher = ContextEnricher(llm=llm) - result = enricher.run("test", _catalog(), _profile()) - assert result == "enriched" - - -def test_context_enricher_emits_hook_events(): - hook = MemoryHook() - llm = FakeLLM("enriched") - enricher = ContextEnricher(llm=llm, hook=hook) - enricher.run("test", _catalog(), _profile()) - phases = [e.phase for e in hook.events] - assert "start" in phases - assert "end" in phases - - -def test_context_enricher_passes_profile_to_llm(): - received_messages = [] - - class CaptureLLM: - def invoke(self, messages: list[dict]) -> str: - received_messages.extend(messages) - return "ok" - - profiler = QuestionProfile(is_timeseries=True, intent_type="trend") - enricher = ContextEnricher(llm=CaptureLLM()) - enricher.run("월별 추이", _catalog(), profiler) - assert received_messages - # profile JSON should appear in the message content - assert "is_timeseries" in received_messages[0]["content"] diff --git a/tests/test_components_hybrid_retriever.py b/tests/test_components_hybrid_retriever.py deleted file mode 100644 index 5fb7f9f..0000000 --- a/tests/test_components_hybrid_retriever.py +++ /dev/null @@ -1,282 +0,0 @@ -""" -Tests for HybridRetriever and HybridNL2SQL — 8 cases. - -SmartFakeEmbedding maps marker keywords in text to orthogonal unit vectors: - - "bothdim" → [0.7071, 0.0, 0.7071, 0.0] (found by both BM25 and vector) - - "kwcommon" → [0.0, 1.0, 0.0, 0.0] (positive BM25, zero cosine with query) - - "vdimonly" → [0.0, 0.0, 1.0, 0.0] (zero BM25, positive cosine with query) - - other → [0.0, 0.0, 0.0, 1.0] - -Query "BOTHDIM KWCOMMON" embeds to [0.7071, 0, 0.7071, 0] (has "bothdim"). - - both_table (bothdim): cosine 1.0 → in vector ✓ ; BM25 matches "bothdim"+"kwcommon" ✓ - - kwonly_table(kwcommon): cosine 0.0 → NOT in vector ✓ ; BM25 matches "kwcommon" ✓ - - veconly_table(vdimonly): cosine 0.707 → in vector ✓ ; BM25 score 0 → not returned ✓ -""" - -from __future__ import annotations - -import pytest - -from lang2sql.components.retrieval.hybrid import HybridRetriever -from lang2sql.core.catalog import RetrievalResult -from lang2sql.core.hooks import MemoryHook -from lang2sql.flows.hybrid import HybridNL2SQL - -# --------------------------------------------------------------------------- -# Fakes -# --------------------------------------------------------------------------- - - -class FakeEmbedding: - """Uniform embedding — all texts and queries map to the same unit vector.""" - - def embed_query(self, text: str) -> list[float]: - return [0.5, 0.5, 0.5, 0.5] - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - return [[0.5, 0.5, 0.5, 0.5]] * len(texts) - - -class SmartFakeEmbedding: - """ - Marker-based deterministic embedding for controlled retrieval testing. - - Marker priority (first match wins): - "bothdim" → [√½, 0, √½, 0] — high cosine with query on dims 0 and 2 - "vdimonly" → [0, 0, 1, 0] — matches query dim 2 only - "kwcommon" → [0, 1, 0, 0] — orthogonal to query → cosine 0.0 (excluded) - else → [0, 0, 0, 1] — orthogonal to query → cosine 0.0 (excluded) - - Query "BOTHDIM KWCOMMON" has "bothdim" → embeds to [√½, 0, √½, 0]. - """ - - _SQRT2_INV = 2.0**-0.5 # ≈ 0.7071 - - def _embed(self, text: str) -> list[float]: - t = text.lower() - if "bothdim" in t: - return [self._SQRT2_INV, 0.0, self._SQRT2_INV, 0.0] - if "vdimonly" in t: - return [0.0, 0.0, 1.0, 0.0] - if "kwcommon" in t: - return [0.0, 1.0, 0.0, 0.0] - return [0.0, 0.0, 0.0, 1.0] - - def embed_query(self, text: str) -> list[float]: - return self._embed(text) - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - return [self._embed(t) for t in texts] - - -class FakeLLM: - def __init__(self, response: str = "```sql\nSELECT COUNT(*) FROM orders\n```"): - self._response = response - - def invoke(self, messages: list[dict]) -> str: - return self._response - - -class FakeDB: - def __init__(self, rows: list[dict] | None = None): - self._rows = rows if rows is not None else [{"count": 1}] - - def execute(self, sql: str) -> list[dict]: - return self._rows - - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- - -CATALOG_SIMPLE = [ - {"name": "orders", "description": "order records", "columns": {"id": "pk"}}, - {"name": "customers", "description": "customer data", "columns": {"id": "pk"}}, -] - -# Three-table catalog designed for deterministic BM25 vs vector split: -# - both_table: found by BOTH BM25 ("bothdim"+"kwcommon" in description) AND vector (cosine 1.0) -# - kwonly_table: found by BM25 only ("kwcommon" matches query token) → vector cosine 0.0 -# - veconly_table:found by vector only (vdimonly → cosine 0.707) → BM25 score 0 -CATALOG_HYBRID = [ - { - "name": "both_table", - "description": "BOTHDIM KWCOMMON data", - "columns": {"id": "pk"}, - }, - { - "name": "kwonly_table", - "description": "KWCOMMON unique info", - "columns": {"id": "pk"}, - }, - { - "name": "veconly_table", - "description": "VDIMONLY specific", - "columns": {"id": "pk"}, - }, -] - -DOCS = [ - { - "id": "revenue_doc", - "title": "Revenue Rules", - "content": "Revenue is net sales minus returns.", - "source": "docs/revenue.md", - } -] - -# Query that: -# - BM25-matches "both_table" (has "bothdim" and "kwcommon") and "kwonly_table" (has "kwcommon") -# - Embeds to [√½, 0, √½, 0] via SmartFakeEmbedding (triggered by "bothdim") -# - cosine("both_table") = 1.0, cosine("kwonly_table") = 0.0, cosine("veconly_table") = 0.707 -HYBRID_QUERY = "BOTHDIM KWCOMMON" - - -# --------------------------------------------------------------------------- -# 1. Return type is RetrievalResult -# --------------------------------------------------------------------------- - - -def test_hybrid_returns_retrieval_result(): - retriever = HybridRetriever( - catalog=CATALOG_SIMPLE, - embedding=FakeEmbedding(), - ) - result = retriever("orders") - assert isinstance(result, RetrievalResult) - assert isinstance(result.schemas, list) - assert isinstance(result.context, list) - - -# --------------------------------------------------------------------------- -# 2. RRF combines both retrievers — all three match types present -# --------------------------------------------------------------------------- - - -def test_hybrid_rrf_combines_both_retrievers(): - """keyword-only, vector-only, and both-matched tables must all appear in results.""" - retriever = HybridRetriever( - catalog=CATALOG_HYBRID, - embedding=SmartFakeEmbedding(), - top_n=3, - ) - result = retriever(HYBRID_QUERY) - names = {s["name"] for s in result.schemas} - - assert "both_table" in names, "both_table (found by both) must be in results" - assert "kwonly_table" in names, "kwonly_table (BM25-only) must be in results" - assert "veconly_table" in names, "veconly_table (vector-only) must be in results" - - -# --------------------------------------------------------------------------- -# 3. Tables found by both retrievers rank higher than single-retriever tables -# --------------------------------------------------------------------------- - - -def test_hybrid_rrf_ranks_overlap_higher(): - """A table found by both retrievers must rank higher than any single-retriever table.""" - retriever = HybridRetriever( - catalog=CATALOG_HYBRID, - embedding=SmartFakeEmbedding(), - top_n=3, - ) - result = retriever(HYBRID_QUERY) - names = [s["name"] for s in result.schemas] - - assert names[0] == "both_table", "both_table (highest RRF score) must be rank 1" - assert names.index("both_table") < names.index("kwonly_table") - assert names.index("both_table") < names.index("veconly_table") - - -# --------------------------------------------------------------------------- -# 4. top_n limits schemas count -# --------------------------------------------------------------------------- - - -def test_hybrid_top_n_limits_schemas(): - retriever = HybridRetriever( - catalog=CATALOG_HYBRID, - embedding=SmartFakeEmbedding(), - top_n=2, - ) - result = retriever(HYBRID_QUERY) - assert len(result.schemas) <= 2 - - -# --------------------------------------------------------------------------- -# 5. context comes only from VectorRetriever -# --------------------------------------------------------------------------- - - -def test_hybrid_context_from_vector(): - """Context must come from VectorRetriever only (document chunk text).""" - retriever = HybridRetriever( - catalog=CATALOG_SIMPLE, - embedding=FakeEmbedding(), - documents=DOCS, - top_n=5, - ) - result = retriever("revenue rules") - - assert isinstance(result.context, list) - assert len(result.context) > 0, "document text must appear in context" - assert any("Revenue" in c for c in result.context) - - -# --------------------------------------------------------------------------- -# 6. Hook records start/end events for HybridRetriever itself -# --------------------------------------------------------------------------- - - -def test_hybrid_hook_events(): - hook = MemoryHook() - retriever = HybridRetriever( - catalog=CATALOG_SIMPLE, - embedding=FakeEmbedding(), - hook=hook, - ) - retriever("orders") - - hybrid_events = [e for e in hook.snapshot() if e.component == "HybridRetriever"] - assert any(e.phase == "start" for e in hybrid_events) - assert any(e.phase == "end" for e in hybrid_events) - end_event = next(e for e in hybrid_events if e.phase == "end") - assert end_event.duration_ms is not None - assert end_event.duration_ms >= 0.0 - - -# --------------------------------------------------------------------------- -# 7. HybridNL2SQL end-to-end pipeline -# --------------------------------------------------------------------------- - - -def test_hybrid_nl2sql_pipeline(): - """HybridNL2SQL end-to-end with FakeLLM + FakeDB.""" - rows = [{"count": 7}] - pipeline = HybridNL2SQL( - catalog=CATALOG_SIMPLE, - llm=FakeLLM(), - db=FakeDB(rows), - embedding=FakeEmbedding(), - ) - result = pipeline.run("How many orders last month?") - assert result == rows - - -# --------------------------------------------------------------------------- -# 8. _rrf_merge deduplication — same table in both → score combined, no duplicate -# --------------------------------------------------------------------------- - - -def test_rrf_merge_deduplication(): - """Same table in both retrievers → scores combined, no duplicate in results.""" - retriever = HybridRetriever( - catalog=CATALOG_SIMPLE, - embedding=FakeEmbedding(), - ) - entry = {"name": "orders", "description": "order data", "columns": {}} - - merged = retriever._rrf_merge([entry], [entry]) - - assert len(merged) == 1, "duplicate table must appear only once" - assert merged[0]["name"] == "orders" diff --git a/tests/test_components_keyword_retriever.py b/tests/test_components_keyword_retriever.py deleted file mode 100644 index 2af2f3c..0000000 --- a/tests/test_components_keyword_retriever.py +++ /dev/null @@ -1,215 +0,0 @@ -""" -Tests for KeywordRetriever — 14 cases. - -Pattern follows test_core_base.py: -- pytest, inline fixtures, MemoryHook -""" - -import pytest - -from lang2sql.components.retrieval import KeywordRetriever -from lang2sql.core.hooks import MemoryHook -from lang2sql.flows.baseline import SequentialFlow - -# ------------------------- -# Shared test catalog -# ------------------------- - -ORDER_TABLE = { - "name": "order_table", - "description": "고객 주문 정보를 저장하는 테이블", - "columns": {"order_id": "주문 고유 ID", "amount": "주문 금액"}, - "meta": {"primary_key": "order_id", "tags": ["finance", "core"]}, -} - -USER_TABLE = { - "name": "user_table", - "description": "사용자 계정 정보 테이블", - "columns": {"user_id": "사용자 고유 ID", "email": "이메일"}, - "meta": {"primary_key": "user_id"}, -} - -PRODUCT_TABLE = { - "name": "product_table", - "description": "상품 목록 및 재고 테이블", - "columns": {"product_id": "상품 ID", "stock": "재고 수량"}, -} - -CATALOG = [ORDER_TABLE, USER_TABLE, PRODUCT_TABLE] - - -# ------------------------- -# Tests -# ------------------------- - - -def test_basic_search_returns_relevant_table(): - """'주문' 질문 → order_table이 top 위치.""" - retriever = KeywordRetriever(catalog=CATALOG) - results = retriever("주문 정보 조회") - - assert results - assert results[0]["name"] == "order_table" - - -def test_top_n_limits_results(): - """top_n=2 → 최대 2개 반환.""" - retriever = KeywordRetriever(catalog=CATALOG, top_n=2) - results = retriever("테이블") - - assert len(results) <= 2 - - -def test_top_n_larger_than_catalog(): - """top_n=10, catalog 3개 → 최대 3개 반환.""" - retriever = KeywordRetriever(catalog=CATALOG, top_n=10) - results = retriever("테이블") - - assert len(results) <= len(CATALOG) - - -def test_zero_results_returns_empty_list(): - """완전히 무관한 query → [].""" - retriever = KeywordRetriever(catalog=CATALOG) - results = retriever("xyzzy_no_match_token_12345") - - assert results == [] - - -def test_returns_list_of_dict(): - """결과가 list[dict]인지 확인.""" - retriever = KeywordRetriever(catalog=CATALOG) - results = retriever("주문") - - assert isinstance(results, list) - assert len(results) > 0 - assert isinstance(results[0], dict) - - -def test_hook_start_end_events(): - """MemoryHook으로 start/end 이벤트 확인.""" - hook = MemoryHook() - retriever = KeywordRetriever(catalog=CATALOG, hook=hook) - retriever("주문") - - assert len(hook.events) == 2 - assert hook.events[0].name == "component.run" - assert hook.events[0].phase == "start" - assert hook.events[1].name == "component.run" - assert hook.events[1].phase == "end" - assert hook.events[1].duration_ms is not None - assert hook.events[1].duration_ms >= 0.0 - - -def test_empty_catalog(): - """catalog=[] → [].""" - retriever = KeywordRetriever(catalog=[]) - results = retriever("주문") - - assert results == [] - - -def test_meta_preserved_in_results(): - """meta 필드가 결과 dict에 그대로 포함되는지 확인.""" - retriever = KeywordRetriever(catalog=CATALOG) - results = retriever("주문") - - assert "meta" in results[0] - assert results[0]["meta"]["primary_key"] == "order_id" - - -def test_index_fields_meta(): - """index_fields=["description","meta"] → meta 텍스트도 검색에 반영.""" - # finance라는 단어는 meta.tags에만 존재 (name/description/columns에는 없음) - catalog = [ - { - "name": "alpha", - "description": "일반 데이터 저장소", - "meta": {"tags": ["finance", "core"]}, - }, - { - "name": "beta", - "description": "기타 로그 테이블", - "meta": {"tags": ["logging"]}, - }, - ] - - retriever = KeywordRetriever( - catalog=catalog, - index_fields=["description", "meta"], - ) - results = retriever("finance") - - assert len(results) > 0 - assert results[0]["name"] == "alpha" - - -def test_result_order_by_relevance(): - """관련도 높은 테이블이 앞에 위치하는지 확인.""" - catalog = [ - { - "name": "order_summary", - "description": "주문 요약 주문 집계 주문 통계", # '주문' 3회 - }, - { - "name": "user_table", - "description": "사용자 주문 기록", # '주문' 1회 - }, - ] - - retriever = KeywordRetriever(catalog=catalog) - results = retriever("주문") - - assert len(results) >= 2 - assert results[0]["name"] == "order_summary" - - -def test_columns_text_indexed(): - """컬럼명/컬럼설명으로 검색 가능한지 확인.""" - catalog = [ - { - "name": "sales", - "description": "판매 데이터", - "columns": {"revenue": "매출액", "region": "지역"}, - }, - { - "name": "logs", - "description": "시스템 로그", - "columns": {"event_type": "이벤트 유형"}, - }, - ] - - retriever = KeywordRetriever(catalog=catalog) - results = retriever("매출액") - - assert len(results) > 0 - assert results[0]["name"] == "sales" - - -def test_missing_optional_fields_no_error(): - """columns/meta 없는 entry가 있어도 crash 없음.""" - catalog = [ - {"name": "minimal", "description": "최소 필드만 있는 테이블"}, - { - "name": "full", - "description": "전체 필드", - "columns": {"id": "ID"}, - "meta": {}, - }, - ] - - retriever = KeywordRetriever(catalog=catalog) - results = retriever("테이블") - assert isinstance(results, list) - - -def test_end_to_end_in_sequential_flow(): - """SequentialFlow(steps=[retriever]).run('...') 가 동작하는지 확인.""" - retriever = KeywordRetriever(catalog=CATALOG) - flow = SequentialFlow(steps=[retriever]) - - results = flow.run("주문 내역 확인") - - assert isinstance(results, list) - assert len(results) > 0 - assert results[0]["name"] == "order_table" diff --git a/tests/test_components_loaders.py b/tests/test_components_loaders.py deleted file mode 100644 index 5b0e525..0000000 --- a/tests/test_components_loaders.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -Tests for MarkdownLoader, PlainTextLoader, DirectoryLoader, DocumentLoaderPort — 8 cases. - -Uses pytest tmp_path fixture to create temporary files in isolation. -""" - -from __future__ import annotations - -import pytest - -from lang2sql.components.loaders import DirectoryLoader, MarkdownLoader, PlainTextLoader -from lang2sql.core.ports import DocumentLoaderPort - -# --------------------------------------------------------------------------- -# 1. MarkdownLoader — single file: TextDocument fields are correct -# --------------------------------------------------------------------------- - - -def test_markdown_loader_single_file(tmp_path): - md_file = tmp_path / "revenue.md" - md_file.write_text( - "# Revenue Definition\n\nRevenue is net sales.", encoding="utf-8" - ) - - docs = MarkdownLoader().load(str(md_file)) - - assert len(docs) == 1 - doc = docs[0] - assert doc["id"] == "revenue" - assert doc["title"] == "Revenue Definition" - assert "Revenue is net sales" in doc["content"] - assert doc["source"] == str(md_file) - - -# --------------------------------------------------------------------------- -# 2. MarkdownLoader — directory: returns one doc per .md file -# --------------------------------------------------------------------------- - - -def test_markdown_loader_directory(tmp_path): - (tmp_path / "a.md").write_text("# A\ncontent a", encoding="utf-8") - (tmp_path / "b.md").write_text("# B\ncontent b", encoding="utf-8") - (tmp_path / "notes.txt").write_text("plain text", encoding="utf-8") # ignored - - docs = MarkdownLoader().load(str(tmp_path)) - - assert len(docs) == 2 - ids = {d["id"] for d in docs} - assert ids == {"a", "b"} - - -# --------------------------------------------------------------------------- -# 3. MarkdownLoader — title extracted from first # heading -# --------------------------------------------------------------------------- - - -def test_markdown_loader_title_from_heading(tmp_path): - md_file = tmp_path / "doc.md" - md_file.write_text("Some intro\n# My Title\nBody text.", encoding="utf-8") - - docs = MarkdownLoader().load(str(md_file)) - - # The first # heading (even if not the first line) is used as title - assert docs[0]["title"] == "My Title" - - -# --------------------------------------------------------------------------- -# 4. MarkdownLoader — no heading → title falls back to filename stem -# --------------------------------------------------------------------------- - - -def test_markdown_loader_no_heading(tmp_path): - md_file = tmp_path / "quarterly_report.md" - md_file.write_text("Just some content without a heading.", encoding="utf-8") - - docs = MarkdownLoader().load(str(md_file)) - - assert docs[0]["title"] == "quarterly_report" - - -# --------------------------------------------------------------------------- -# 5. PlainTextLoader — single file: content is correct -# --------------------------------------------------------------------------- - - -def test_plaintext_loader_single_file(tmp_path): - txt_file = tmp_path / "notes.txt" - txt_file.write_text("line one\nline two\n", encoding="utf-8") - - docs = PlainTextLoader().load(str(txt_file)) - - assert len(docs) == 1 - doc = docs[0] - assert doc["id"] == "notes" - assert doc["title"] == "notes" - assert "line one" in doc["content"] - assert doc["source"] == str(txt_file) - - -# --------------------------------------------------------------------------- -# 6. DirectoryLoader — dispatches by extension -# --------------------------------------------------------------------------- - - -def test_directory_loader_dispatches_by_extension(tmp_path): - (tmp_path / "guide.md").write_text("# Guide\nMarkdown content.", encoding="utf-8") - (tmp_path / "data.txt").write_text("plain text content", encoding="utf-8") - - docs = DirectoryLoader(str(tmp_path)).load() - - ids = {d["id"] for d in docs} - assert "guide" in ids # loaded by MarkdownLoader - assert "data" in ids # loaded by PlainTextLoader - - -# --------------------------------------------------------------------------- -# 7. DirectoryLoader — skips unknown extensions -# --------------------------------------------------------------------------- - - -def test_directory_loader_skips_unknown_extension(tmp_path): - (tmp_path / "script.py").write_text("print('hello')", encoding="utf-8") - (tmp_path / "data.csv").write_text("a,b,c\n1,2,3\n", encoding="utf-8") - (tmp_path / "readme.md").write_text("# Readme\ncontent", encoding="utf-8") - - docs = DirectoryLoader(str(tmp_path)).load() - - ids = {d["id"] for d in docs} - assert "readme" in ids # .md is loaded - assert "script" not in ids # .py is skipped - assert "data" not in ids # .csv is skipped (not in default loaders) - - -# --------------------------------------------------------------------------- -# 8. Protocol check — MarkdownLoader and PlainTextLoader satisfy DocumentLoaderPort -# --------------------------------------------------------------------------- - - -def test_document_loader_port_protocol(): - assert isinstance(MarkdownLoader(), DocumentLoaderPort) - assert isinstance(PlainTextLoader(), DocumentLoaderPort) diff --git a/tests/test_components_question_gate.py b/tests/test_components_question_gate.py deleted file mode 100644 index 21097ec..0000000 --- a/tests/test_components_question_gate.py +++ /dev/null @@ -1,89 +0,0 @@ -"""Tests for QuestionGate component.""" - -from __future__ import annotations - -import json - -import pytest - -from lang2sql.components.gate.question_gate import QuestionGate -from lang2sql.core.catalog import GateResult -from lang2sql.core.hooks import MemoryHook - - -class FakeLLM: - def __init__(self, response: str): - self._response = response - - def invoke(self, messages: list[dict]) -> str: - return self._response - - -def _gate_json( - suitable: bool = True, - reason: str = "SQL로 답변 가능합니다.", - missing_entities: list | None = None, - requires_data_science: bool = False, -) -> str: - return json.dumps( - { - "suitable": suitable, - "reason": reason, - "missing_entities": missing_entities or [], - "requires_data_science": requires_data_science, - }, - ensure_ascii=False, - ) - - -def test_question_gate_suitable_true(): - llm = FakeLLM(_gate_json(suitable=True, reason="OK")) - gate = QuestionGate(llm=llm) - result = gate.run("지난달 주문 수는?") - assert isinstance(result, GateResult) - assert result.suitable is True - assert result.reason == "OK" - - -def test_question_gate_suitable_false(): - llm = FakeLLM( - _gate_json( - suitable=False, - reason="통계 분석이 필요합니다.", - requires_data_science=True, - ) - ) - gate = QuestionGate(llm=llm) - result = gate.run("이상 탐지 모델을 만들어줘") - assert result.suitable is False - assert result.requires_data_science is True - - -def test_question_gate_with_missing_entities(): - llm = FakeLLM( - _gate_json( - suitable=False, - missing_entities=["기간", "대상 엔터티"], - ) - ) - gate = QuestionGate(llm=llm) - result = gate.run("매출을 보여줘") - assert "기간" in result.missing_entities - - -def test_question_gate_strips_markdown_json(): - raw = "```json\n" + _gate_json(suitable=True) + "\n```" - llm = FakeLLM(raw) - gate = QuestionGate(llm=llm) - result = gate.run("test") - assert result.suitable is True - - -def test_question_gate_emits_hook_events(): - hook = MemoryHook() - llm = FakeLLM(_gate_json()) - gate = QuestionGate(llm=llm, hook=hook) - gate.run("test query") - phases = [e.phase for e in hook.events] - assert "start" in phases - assert "end" in phases diff --git a/tests/test_components_question_profiler.py b/tests/test_components_question_profiler.py deleted file mode 100644 index deb5063..0000000 --- a/tests/test_components_question_profiler.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Tests for QuestionProfiler component.""" - -from __future__ import annotations - -import json - -import pytest - -from lang2sql.components.enrichment.question_profiler import QuestionProfiler -from lang2sql.core.catalog import QuestionProfile -from lang2sql.core.hooks import MemoryHook - - -class FakeLLM: - def __init__(self, response: str): - self._response = response - - def invoke(self, messages: list[dict]) -> str: - return self._response - - -def _profile_json(**kwargs) -> str: - defaults = { - "is_timeseries": False, - "is_aggregation": True, - "has_filter": True, - "is_grouped": False, - "has_ranking": False, - "has_temporal_comparison": False, - "intent_type": "lookup", - } - defaults.update(kwargs) - return json.dumps(defaults) - - -def test_question_profiler_returns_profile(): - llm = FakeLLM(_profile_json(is_aggregation=True, has_filter=True)) - profiler = QuestionProfiler(llm=llm) - result = profiler.run("지난달 주문 수") - assert isinstance(result, QuestionProfile) - assert result.is_aggregation is True - assert result.has_filter is True - - -def test_question_profiler_timeseries(): - llm = FakeLLM(_profile_json(is_timeseries=True, intent_type="trend")) - profiler = QuestionProfiler(llm=llm) - result = profiler.run("월별 매출 추이") - assert result.is_timeseries is True - assert result.intent_type == "trend" - - -def test_question_profiler_invalid_intent_type_falls_back_to_lookup(): - llm = FakeLLM(_profile_json(intent_type="invalid_type")) - profiler = QuestionProfiler(llm=llm) - result = profiler.run("test") - assert result.intent_type == "lookup" - - -def test_question_profiler_strips_markdown_json(): - raw = "```json\n" + _profile_json() + "\n```" - llm = FakeLLM(raw) - profiler = QuestionProfiler(llm=llm) - result = profiler.run("test") - assert isinstance(result, QuestionProfile) - - -def test_question_profiler_emits_hook_events(): - hook = MemoryHook() - llm = FakeLLM(_profile_json()) - profiler = QuestionProfiler(llm=llm, hook=hook) - profiler.run("test") - phases = [e.phase for e in hook.events] - assert "start" in phases - assert "end" in phases diff --git a/tests/test_components_sql_executor.py b/tests/test_components_sql_executor.py deleted file mode 100644 index 1b97fb8..0000000 --- a/tests/test_components_sql_executor.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Tests for SQLExecutor.""" - -from __future__ import annotations - -import pytest - -from lang2sql.components.execution.sql_executor import SQLExecutor -from lang2sql.core.exceptions import ComponentError -from lang2sql.core.hooks import MemoryHook - -# --------------------------------------------------------------------------- -# Fakes -# --------------------------------------------------------------------------- - - -class FakeDB: - def __init__(self, rows: list[dict] | None = None): - self._rows = rows if rows is not None else [{"count": 1}] - - def execute(self, sql: str) -> list[dict]: - return self._rows - - -# --------------------------------------------------------------------------- -# Tests -# --------------------------------------------------------------------------- - - -def test_sql_executor_returns_rows(): - rows = [{"order_id": 1, "amount": 100}] - executor = SQLExecutor(db=FakeDB(rows)) - result = executor.run("SELECT * FROM orders") - assert result == rows - - -def test_sql_executor_raises_on_empty_sql(): - executor = SQLExecutor(db=FakeDB()) - with pytest.raises(ComponentError): - executor.run("") - - -def test_sql_executor_raises_on_whitespace_sql(): - executor = SQLExecutor(db=FakeDB()) - with pytest.raises(ComponentError): - executor.run(" ") - - -def test_sql_executor_returns_empty_list(): - executor = SQLExecutor(db=FakeDB([])) - result = executor.run("SELECT * FROM empty_table") - assert result == [] - - -def test_sql_executor_emits_hook_events(): - hook = MemoryHook() - executor = SQLExecutor(db=FakeDB(), hook=hook) - executor.run("SELECT 1") - - events = hook.snapshot() - phases = [e.phase for e in events] - assert "start" in phases - assert "end" in phases - - -def test_sql_executor_error_event_on_db_failure(): - class BrokenDB: - def execute(self, sql: str): - raise RuntimeError("connection refused") - - hook = MemoryHook() - executor = SQLExecutor(db=BrokenDB(), hook=hook) - - with pytest.raises(ComponentError): - executor.run("SELECT 1") - - error_events = [e for e in hook.snapshot() if e.phase == "error"] - assert error_events, "expected an error event" diff --git a/tests/test_components_sql_generator.py b/tests/test_components_sql_generator.py deleted file mode 100644 index 84990c0..0000000 --- a/tests/test_components_sql_generator.py +++ /dev/null @@ -1,169 +0,0 @@ -"""Tests for SQLGenerator.""" - -from __future__ import annotations - -import pytest - -from lang2sql.components.generation.sql_generator import SQLGenerator -from lang2sql.core.catalog import CatalogEntry -from lang2sql.core.exceptions import ComponentError -from lang2sql.core.hooks import MemoryHook - -# --------------------------------------------------------------------------- -# Fakes -# --------------------------------------------------------------------------- - - -class FakeLLM: - def __init__(self, response: str = "```sql\nSELECT 1\n```"): - self._response = response - - def invoke(self, messages: list[dict]) -> str: - return self._response - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _catalog() -> list[CatalogEntry]: - return [ - { - "name": "orders", - "description": "Monthly order records", - "columns": {"order_id": "primary key", "amount": "order amount"}, - } - ] - - -# --------------------------------------------------------------------------- -# Tests -# --------------------------------------------------------------------------- - - -def test_sql_generator_returns_sql_string(): - gen = SQLGenerator(llm=FakeLLM("```sql\nSELECT COUNT(*) FROM orders\n```")) - result = gen.run("주문 건수", _catalog()) - assert result == "SELECT COUNT(*) FROM orders" - - -def test_sql_generator_strips_trailing_semicolon(): - gen = SQLGenerator(llm=FakeLLM("```sql\nSELECT 1;\n```")) - result = gen.run("test", _catalog()) - assert not result.endswith(";") - - -def test_sql_generator_raises_on_no_code_block(): - gen = SQLGenerator(llm=FakeLLM("Here is your answer: SELECT 1")) - with pytest.raises(ComponentError): - gen.run("test", _catalog()) - - -def test_sql_generator_passes_query_and_schema_to_llm(): - received: list[list[dict]] = [] - - class CaptureLLM: - def invoke(self, messages): - received.append(messages) - return "```sql\nSELECT 1\n```" - - gen = SQLGenerator(llm=CaptureLLM()) - gen.run("주문 건수", _catalog()) - - assert received, "invoke was not called" - msgs = received[0] - user_content = next(m["content"] for m in msgs if m["role"] == "user") - assert "orders" in user_content - assert "주문 건수" in user_content - - -def test_sql_generator_custom_system_prompt(): - received: list[list[dict]] = [] - - class CaptureLLM: - def invoke(self, messages): - received.append(messages) - return "```sql\nSELECT 1\n```" - - custom_prompt = "You are a DBA." - gen = SQLGenerator(llm=CaptureLLM(), system_prompt=custom_prompt) - gen.run("test", _catalog()) - - system_content = next(m["content"] for m in received[0] if m["role"] == "system") - assert system_content == custom_prompt - - -def test_sql_generator_emits_hook_events(): - hook = MemoryHook() - gen = SQLGenerator(llm=FakeLLM(), hook=hook) - gen.run("test", _catalog()) - - events = hook.snapshot() - phases = [e.phase for e in events] - assert "start" in phases - assert "end" in phases - - -def test_sql_generator_empty_schemas(): - gen = SQLGenerator(llm=FakeLLM("```sql\nSELECT 1\n```")) - result = gen.run("test", []) - assert result == "SELECT 1" - - -# --------------------------------------------------------------------------- -# db_dialect tests -# --------------------------------------------------------------------------- - - -def test_sql_generator_db_dialect_loads_sqlite_prompt(): - received: list[list[dict]] = [] - - class CaptureLLM: - def invoke(self, messages): - received.append(messages) - return "```sql\nSELECT 1\n```" - - gen = SQLGenerator(llm=CaptureLLM(), db_dialect="sqlite") - gen.run("test", _catalog()) - - system_content = next(m["content"] for m in received[0] if m["role"] == "system") - assert "SQLite" in system_content - assert "strftime" in system_content - - -def test_sql_generator_db_dialect_loads_postgresql_prompt(): - received: list[list[dict]] = [] - - class CaptureLLM: - def invoke(self, messages): - received.append(messages) - return "```sql\nSELECT 1\n```" - - gen = SQLGenerator(llm=CaptureLLM(), db_dialect="postgresql") - gen.run("test", _catalog()) - - system_content = next(m["content"] for m in received[0] if m["role"] == "system") - assert "PostgreSQL" in system_content - assert "DATE_TRUNC" in system_content - - -def test_sql_generator_unsupported_dialect_raises_value_error(): - with pytest.raises(ValueError, match="Unsupported dialect"): - SQLGenerator(llm=FakeLLM(), db_dialect="oracle") - - -def test_sql_generator_system_prompt_overrides_db_dialect(): - received: list[list[dict]] = [] - - class CaptureLLM: - def invoke(self, messages): - received.append(messages) - return "```sql\nSELECT 1\n```" - - custom = "You are a Snowflake expert." - gen = SQLGenerator(llm=CaptureLLM(), db_dialect="sqlite", system_prompt=custom) - gen.run("test", _catalog()) - - system_content = next(m["content"] for m in received[0] if m["role"] == "system") - assert system_content == custom diff --git a/tests/test_components_table_suitability.py b/tests/test_components_table_suitability.py deleted file mode 100644 index 0a37835..0000000 --- a/tests/test_components_table_suitability.py +++ /dev/null @@ -1,129 +0,0 @@ -"""Tests for TableSuitabilityEvaluator component.""" - -from __future__ import annotations - -import json - -import pytest - -from lang2sql.components.gate.table_suitability import TableSuitabilityEvaluator -from lang2sql.core.catalog import CatalogEntry -from lang2sql.core.hooks import MemoryHook - - -class FakeLLM: - def __init__(self, response: str): - self._response = response - - def invoke(self, messages: list[dict]) -> str: - return self._response - - -def _catalog() -> list[CatalogEntry]: - return [ - { - "name": "orders", - "description": "주문 테이블", - "columns": { - "order_id": "주문 ID", - "amount": "주문 금액", - "created_at": "생성일", - }, - }, - { - "name": "users", - "description": "사용자 테이블", - "columns": {"user_id": "사용자 ID", "name": "이름"}, - }, - ] - - -def _suitability_json(results: list[dict]) -> str: - return json.dumps({"results": results}, ensure_ascii=False) - - -def test_table_suitability_filters_below_threshold(): - resp = _suitability_json( - [ - { - "table_name": "orders", - "score": 0.9, - "reason": "핵심 지표 포함", - "matched_columns": ["amount"], - "missing_entities": [], - }, - { - "table_name": "users", - "score": 0.1, - "reason": "관련 없음", - "matched_columns": [], - "missing_entities": ["주문 정보"], - }, - ] - ) - evaluator = TableSuitabilityEvaluator(llm=FakeLLM(resp), threshold=0.3) - result = evaluator.run("지난달 주문 금액 합계", _catalog()) - assert len(result) == 1 - assert result[0]["name"] == "orders" - - -def test_table_suitability_sorted_by_score(): - resp = _suitability_json( - [ - { - "table_name": "users", - "score": 0.5, - "reason": "부분 매칭", - "matched_columns": [], - "missing_entities": [], - }, - { - "table_name": "orders", - "score": 0.9, - "reason": "완전 매칭", - "matched_columns": ["amount"], - "missing_entities": [], - }, - ] - ) - evaluator = TableSuitabilityEvaluator(llm=FakeLLM(resp), threshold=0.3) - result = evaluator.run("주문 금액", _catalog()) - assert result[0]["name"] == "orders" - assert result[1]["name"] == "users" - - -def test_table_suitability_empty_result_when_all_below_threshold(): - resp = _suitability_json( - [ - { - "table_name": "orders", - "score": 0.1, - "reason": "낮은 관련성", - "matched_columns": [], - "missing_entities": [], - }, - ] - ) - evaluator = TableSuitabilityEvaluator(llm=FakeLLM(resp), threshold=0.3) - result = evaluator.run("관련 없는 질문", _catalog()) - assert result == [] - - -def test_table_suitability_emits_hook_events(): - hook = MemoryHook() - resp = _suitability_json( - [ - { - "table_name": "orders", - "score": 0.8, - "reason": "ok", - "matched_columns": [], - "missing_entities": [], - } - ] - ) - evaluator = TableSuitabilityEvaluator(llm=FakeLLM(resp), hook=hook) - evaluator.run("test", _catalog()) - phases = [e.phase for e in hook.events] - assert "start" in phases - assert "end" in phases diff --git a/tests/test_components_vector_retriever.py b/tests/test_components_vector_retriever.py deleted file mode 100644 index 07c77cd..0000000 --- a/tests/test_components_vector_retriever.py +++ /dev/null @@ -1,583 +0,0 @@ -""" -Tests for VectorRetriever, CatalogChunker, RecursiveCharacterChunker — 16 cases. - -Mock strategy: -- FakeVectorStore + FakeEmbedding for tests that control search results explicitly. -- InMemoryVectorStore + FakeEmbedding for tests that verify actual storage/merge behavior - (tests 10, from_chunks_add_incremental). FakeVectorStore.search() returns pre-configured - results, so it cannot catch real storage bugs. -""" - -import pytest - -from lang2sql.components.retrieval.chunker import ( - CatalogChunker, - RecursiveCharacterChunker, -) -from lang2sql.components.retrieval.vector import VectorRetriever -from lang2sql.core.catalog import RetrievalResult -from lang2sql.core.hooks import MemoryHook -from lang2sql.flows.baseline import SequentialFlow -from lang2sql.integrations.vectorstore import InMemoryVectorStore - -# --------------------------------------------------------------------------- -# Fakes -# --------------------------------------------------------------------------- - - -class FakeVectorStore: - """ - Controlled search results for unit tests. - search() returns whatever was passed to __init__(results=...). - upsert() implements merge semantics to match InMemoryVectorStore contract. - Do NOT use for tests that verify storage correctness — use InMemoryVectorStore instead. - """ - - def __init__(self, results=None): - self._results = results or [] - self.upserted: dict = {} - - def search(self, vector, k): - return self._results[:k] - - def upsert(self, ids, vectors): - # merge semantics — consistent with VectorStorePort contract - for id_, vec in zip(ids, vectors): - self.upserted[id_] = vec - - -class FakeEmbedding: - def embed_query(self, text): - return [0.0] * 4 - - def embed_texts(self, texts): - return [[0.0] * 4] * len(texts) - - -# --------------------------------------------------------------------------- -# Shared fixtures -# --------------------------------------------------------------------------- - -CATALOG = [ - { - "name": "orders", - "description": "Order information table", - "columns": {"order_id": "Unique order ID", "amount": "Order amount"}, - } -] - -DOCS = [ - { - "id": "biz_rules", - "title": "Revenue Definition", - "content": "Revenue is defined as net sales excluding returns.", - "source": "docs/biz_rules.md", - } -] - - -def _make_catalog_registry(): - """Registry pre-populated with one catalog chunk.""" - return { - "orders__0": { - "chunk_id": "orders__0", - "text": "orders: Order information table", - "source_type": "catalog", - "source_id": "orders", - "chunk_index": 0, - "metadata": CATALOG[0], - } - } - - -def _make_doc_registry(): - """Registry pre-populated with one document chunk.""" - return { - "biz_rules__0": { - "chunk_id": "biz_rules__0", - "text": "Revenue Definition: Revenue is defined as net sales.", - "source_type": "document", - "source_id": "biz_rules", - "chunk_index": 0, - "metadata": { - "id": "biz_rules", - "title": "Revenue Definition", - "source": "", - }, - } - } - - -# --------------------------------------------------------------------------- -# 1. Catalog chunk deduplication -# --------------------------------------------------------------------------- - - -def test_catalog_chunk_dedup(): - """Multiple chunks from the same table → only 1 CatalogEntry returned.""" - registry = { - "orders__0": { - "chunk_id": "orders__0", - "text": "orders: Order table", - "source_type": "catalog", - "source_id": "orders", - "chunk_index": 0, - "metadata": CATALOG[0], - }, - "orders__col_1": { - "chunk_id": "orders__col_1", - "text": "orders columns: order_id amount", - "source_type": "catalog", - "source_id": "orders", - "chunk_index": 1, - "metadata": CATALOG[0], - }, - } - store = FakeVectorStore(results=[("orders__0", 0.9), ("orders__col_1", 0.8)]) - retriever = VectorRetriever( - vectorstore=store, embedding=FakeEmbedding(), registry=registry - ) - result = retriever("order amount") - - assert len(result.schemas) == 1 - assert result.schemas[0]["name"] == "orders" - - -# --------------------------------------------------------------------------- -# 2. Document chunk in context -# --------------------------------------------------------------------------- - - -def test_document_chunk_in_context(): - """Document chunk appears in RetrievalResult.context.""" - registry = _make_doc_registry() - store = FakeVectorStore(results=[("biz_rules__0", 0.8)]) - retriever = VectorRetriever( - vectorstore=store, embedding=FakeEmbedding(), registry=registry - ) - result = retriever("revenue definition") - - assert len(result.context) == 1 - assert "Revenue" in result.context[0] - - -# --------------------------------------------------------------------------- -# 3. Empty registry -# --------------------------------------------------------------------------- - - -def test_empty_registry_returns_empty_result(): - """Empty registry → empty RetrievalResult.""" - store = FakeVectorStore(results=[("orders__0", 0.9)]) - retriever = VectorRetriever( - vectorstore=store, embedding=FakeEmbedding(), registry={} - ) - result = retriever("any query") - - assert result.schemas == [] - assert result.context == [] - - -# --------------------------------------------------------------------------- -# 4. Score threshold filtering -# --------------------------------------------------------------------------- - - -def test_score_threshold_filters_results(): - """Chunks at or below threshold are excluded.""" - registry = _make_catalog_registry() - store = FakeVectorStore(results=[("orders__0", 0.3)]) - retriever = VectorRetriever( - vectorstore=store, - embedding=FakeEmbedding(), - registry=registry, - score_threshold=0.3, # score must be strictly greater than threshold - ) - result = retriever("orders") - - assert result.schemas == [] - - -# --------------------------------------------------------------------------- -# 5. top_n limits schemas -# --------------------------------------------------------------------------- - - -def test_top_n_limits_schemas(): - """schemas capped at top_n.""" - registry = { - f"table_{i}__0": { - "chunk_id": f"table_{i}__0", - "text": f"table_{i}", - "source_type": "catalog", - "source_id": f"table_{i}", - "chunk_index": 0, - "metadata": {"name": f"table_{i}", "description": "", "columns": {}}, - } - for i in range(10) - } - store = FakeVectorStore( - results=[(f"table_{i}__0", 0.9 - i * 0.05) for i in range(10)] - ) - retriever = VectorRetriever( - vectorstore=store, embedding=FakeEmbedding(), registry=registry, top_n=3 - ) - result = retriever("table") - - assert len(result.schemas) <= 3 - - -# --------------------------------------------------------------------------- -# 6. top_n limits context -# --------------------------------------------------------------------------- - - -def test_top_n_limits_context(): - """context capped at top_n.""" - registry = { - f"doc_{i}__0": { - "chunk_id": f"doc_{i}__0", - "text": f"doc chunk {i}", - "source_type": "document", - "source_id": f"doc_{i}", - "chunk_index": 0, - "metadata": {"id": f"doc_{i}", "title": "", "source": ""}, - } - for i in range(10) - } - store = FakeVectorStore( - results=[(f"doc_{i}__0", 0.9 - i * 0.05) for i in range(10)] - ) - retriever = VectorRetriever( - vectorstore=store, embedding=FakeEmbedding(), registry=registry, top_n=3 - ) - result = retriever("doc") - - assert len(result.context) <= 3 - - -# --------------------------------------------------------------------------- -# 7. Hook events -# --------------------------------------------------------------------------- - - -def test_hook_start_end_events(): - """MemoryHook records start/end events + duration_ms.""" - hook = MemoryHook() - store = FakeVectorStore() - retriever = VectorRetriever( - vectorstore=store, embedding=FakeEmbedding(), registry={}, hook=hook - ) - retriever("test query") - - assert len(hook.events) == 2 - assert hook.events[0].phase == "start" - assert hook.events[1].phase == "end" - assert hook.events[1].duration_ms is not None - assert hook.events[1].duration_ms >= 0.0 - - -# --------------------------------------------------------------------------- -# 8. from_chunks() — catalog chunks populate registry -# --------------------------------------------------------------------------- - - -def test_from_chunks_catalog_populates_registry(): - """from_chunks(catalog_chunks) populates registry with catalog source_type.""" - chunks = CatalogChunker().split(CATALOG) - retriever = VectorRetriever.from_chunks(chunks, embedding=FakeEmbedding()) - - assert len(retriever._registry) > 0 - for chunk in retriever._registry.values(): - assert chunk["source_type"] == "catalog" - assert chunk["source_id"] == "orders" - - -# --------------------------------------------------------------------------- -# 9. from_chunks() — document chunks populate registry -# --------------------------------------------------------------------------- - - -def test_from_chunks_doc_populates_registry(): - """from_chunks(doc_chunks) populates registry with document source_type.""" - chunks = RecursiveCharacterChunker().split(DOCS) - retriever = VectorRetriever.from_chunks(chunks, embedding=FakeEmbedding()) - - assert len(retriever._registry) > 0 - for chunk in retriever._registry.values(): - assert chunk["source_type"] == "document" - assert chunk["source_id"] == "biz_rules" - - -# --------------------------------------------------------------------------- -# 10. InMemoryVectorStore merge — catalog survives after doc chunks added -# --------------------------------------------------------------------------- - - -def test_from_chunks_preserves_catalog_after_doc_run(): - """ - catalog vectors survive when doc chunks are combined. - Uses InMemoryVectorStore to verify real merge behavior. - """ - store = InMemoryVectorStore() - catalog_chunks = CatalogChunker().split(CATALOG) - retriever = VectorRetriever.from_chunks( - catalog_chunks, embedding=FakeEmbedding(), vectorstore=store - ) - catalog_chunk_ids = set(retriever._registry.keys()) - assert len(catalog_chunk_ids) > 0 - - doc_chunks = RecursiveCharacterChunker().split(DOCS) - retriever.add(doc_chunks) - - for chunk_id in catalog_chunk_ids: - assert chunk_id in store._store, f"catalog chunk '{chunk_id}' lost after add()" - - -# --------------------------------------------------------------------------- -# 11. CatalogChunker — column groups -# --------------------------------------------------------------------------- - - -def test_catalog_chunker_column_groups(): - """25 columns → CatalogChunker (max 20) produces at least 2 chunks beyond header.""" - entry = { - "name": "big_table", - "description": "Large table", - "columns": {f"col_{i}": f"column {i}" for i in range(25)}, - } - chunker = CatalogChunker(max_columns_per_chunk=20) - chunks = chunker.chunk(entry) - - # chunk 0 = header, chunk 1 = first 20 cols, chunk 2 = remaining 5 cols - assert len(chunks) >= 3 - assert chunks[0]["chunk_id"] == "big_table__0" - assert all(c["source_type"] == "catalog" for c in chunks) - assert all(c["metadata"]["name"] == "big_table" for c in chunks) - - -# --------------------------------------------------------------------------- -# 12. RecursiveCharacterChunker — respects chunk_size -# --------------------------------------------------------------------------- - - -def test_recursive_chunker_respects_chunk_size(): - """Text exceeding chunk_size is split into multiple chunks.""" - chunker = RecursiveCharacterChunker(chunk_size=50, chunk_overlap=0) - doc = { - "id": "doc1", - "title": "", - "content": "A" * 50 + "\n\n" + "B" * 50, - "source": "", - } - chunks = chunker.chunk(doc) - - assert len(chunks) >= 2 - for chunk in chunks: - # title prefix is empty so raw text length should respect chunk_size - assert len(chunk["text"]) <= 50 + 10 # small tolerance for separator - - -# --------------------------------------------------------------------------- -# 13. RecursiveCharacterChunker — overlap -# --------------------------------------------------------------------------- - - -def test_recursive_chunker_overlap(): - """Second chunk contains tail characters of the first chunk.""" - overlap = 20 - chunker = RecursiveCharacterChunker(chunk_size=40, chunk_overlap=overlap) - long_content = "Hello world this is a test. " * 10 - doc = {"id": "d1", "title": "", "content": long_content, "source": ""} - chunks = chunker.chunk(doc) - - if len(chunks) >= 2: - tail_of_first = chunks[0]["text"][-overlap:] - assert tail_of_first in chunks[1]["text"] - - -# --------------------------------------------------------------------------- -# 14. from_sources() — builds retriever with non-empty registry -# --------------------------------------------------------------------------- - - -def test_from_sources_builds_retriever(): - """from_sources() returns a VectorRetriever with non-empty registry.""" - retriever = VectorRetriever.from_sources( - catalog=CATALOG, - embedding=FakeEmbedding(), - ) - - assert isinstance(retriever, VectorRetriever) - assert len(retriever._registry) > 0 - - -# --------------------------------------------------------------------------- -# 15. from_sources() + add() — incremental indexing -# --------------------------------------------------------------------------- - - -def test_from_sources_add_incremental(): - """retriever.add(chunks) adds chunks without losing existing catalog chunks.""" - retriever = VectorRetriever.from_sources( - catalog=CATALOG, - embedding=FakeEmbedding(), - ) - initial_ids = set(retriever._registry.keys()) - assert len(initial_ids) > 0 - - doc_chunks = RecursiveCharacterChunker().split(DOCS) - retriever.add(doc_chunks) - - final_ids = set(retriever._registry.keys()) - # new doc chunks were added - assert len(final_ids) > len(initial_ids) - # original catalog chunks are still present - for chunk_id in initial_ids: - assert chunk_id in final_ids, f"catalog chunk '{chunk_id}' lost after add()" - - -# --------------------------------------------------------------------------- -# 16. from_chunks() — empty chunks → empty result -# --------------------------------------------------------------------------- - - -def test_from_chunks_empty(): - """from_chunks([]) → retriever with empty registry returns empty result.""" - retriever = VectorRetriever.from_chunks([], embedding=FakeEmbedding()) - - assert retriever._registry == {} - result = retriever("any query") - assert result.schemas == [] - assert result.context == [] - - -# --------------------------------------------------------------------------- -# 17. from_chunks() — mixed catalog + doc chunks -# --------------------------------------------------------------------------- - - -def test_from_chunks_mixed_catalog_and_docs(): - """from_chunks with catalog + doc chunks → both source_types in registry.""" - chunks = CatalogChunker().split(CATALOG) + RecursiveCharacterChunker().split(DOCS) - retriever = VectorRetriever.from_chunks(chunks, embedding=FakeEmbedding()) - - source_types = {c["source_type"] for c in retriever._registry.values()} - assert "catalog" in source_types - assert "document" in source_types - - -# --------------------------------------------------------------------------- -# 18. from_chunks() + add() — incremental after from_chunks -# --------------------------------------------------------------------------- - - -def test_from_chunks_add_incremental(): - """from_chunks() followed by add(more_chunks) preserves original chunks.""" - store = InMemoryVectorStore() - catalog_chunks = CatalogChunker().split(CATALOG) - retriever = VectorRetriever.from_chunks( - catalog_chunks, embedding=FakeEmbedding(), vectorstore=store - ) - initial_ids = set(retriever._registry.keys()) - - doc_chunks = RecursiveCharacterChunker().split(DOCS) - retriever.add(doc_chunks) - - final_ids = set(retriever._registry.keys()) - assert len(final_ids) > len(initial_ids) - for chunk_id in initial_ids: - assert chunk_id in final_ids, f"chunk '{chunk_id}' lost after add()" - - -# --------------------------------------------------------------------------- -# 19. CatalogChunker.split() — batch convenience method -# --------------------------------------------------------------------------- - - -def test_catalog_chunker_split_batch(): - """CatalogChunker.split(catalog) returns same chunks as calling chunk() per entry.""" - chunker = CatalogChunker() - by_split = chunker.split(CATALOG) - by_chunk = [c for entry in CATALOG for c in chunker.chunk(entry)] - - assert [c["chunk_id"] for c in by_split] == [c["chunk_id"] for c in by_chunk] - - -# --------------------------------------------------------------------------- -# 20-22. VectorRetriever save / load (FAISS 필요) -# --------------------------------------------------------------------------- - -faiss = pytest.importorskip("faiss", reason="faiss-cpu not installed") - - -class FakeEmbeddingFAISS: - """FAISS L2 정규화에서 zero-vector 오류가 안 나도록 비영벡터를 반환.""" - - def _vec(self, text: str) -> list[float]: - # 텍스트별로 구별 가능한 비영벡터 - h = abs(hash(text)) % 900 + 100 - return [h * 0.001, 1.0, 1.0, 1.0] - - def embed_query(self, text: str) -> list[float]: - return self._vec(text) - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - return [self._vec(t) for t in texts] - - -def test_save_and_load_returns_same_results(tmp_path): - """save → load 후 동일 쿼리에 동일 스키마가 반환된다.""" - path = str(tmp_path / "catalog") - embedding = FakeEmbeddingFAISS() - - from lang2sql.integrations.vectorstore.faiss_ import FAISSVectorStore - - store = FAISSVectorStore(index_path=path + ".faiss") - chunks = CatalogChunker().split(CATALOG) - original = VectorRetriever.from_chunks( - chunks, embedding=embedding, vectorstore=store - ) - original.save(path) - - loaded_store = FAISSVectorStore.load(path) - loaded = VectorRetriever.load(path, vectorstore=loaded_store, embedding=embedding) - result = loaded.run("주문 정보") - - assert len(result.schemas) > 0 - assert result.schemas[0]["name"] == original.run("주문 정보").schemas[0]["name"] - - -def test_load_registry_intact(tmp_path): - """load 후 registry의 키·값이 원본과 동일하다.""" - path = str(tmp_path / "catalog") - embedding = FakeEmbeddingFAISS() - - from lang2sql.integrations.vectorstore.faiss_ import FAISSVectorStore - - store = FAISSVectorStore(index_path=path + ".faiss") - chunks = CatalogChunker().split(CATALOG) - original = VectorRetriever.from_chunks( - chunks, embedding=embedding, vectorstore=store - ) - original.save(path) - - loaded_store = FAISSVectorStore.load(path) - loaded = VectorRetriever.load(path, vectorstore=loaded_store, embedding=embedding) - - assert set(loaded._registry.keys()) == set(original._registry.keys()) - for chunk_id, chunk in original._registry.items(): - assert loaded._registry[chunk_id]["text"] == chunk["text"] - assert loaded._registry[chunk_id]["source_id"] == chunk["source_id"] - - -def test_save_raises_for_inmemory(): - """InMemoryVectorStore는 save()를 지원하지 않아 NotImplementedError가 발생한다.""" - embedding = FakeEmbeddingFAISS() - chunks = CatalogChunker().split(CATALOG) - retriever = VectorRetriever.from_chunks( - chunks, embedding=embedding - ) # InMemory 기본값 - - with pytest.raises(NotImplementedError, match="does not support save"): - retriever.save("/tmp/should_not_exist") diff --git a/tests/test_core_base.py b/tests/test_core_base.py deleted file mode 100644 index 524fe25..0000000 --- a/tests/test_core_base.py +++ /dev/null @@ -1,177 +0,0 @@ -import pytest - -from lang2sql.core.base import BaseComponent, BaseFlow -from lang2sql.core.hooks import MemoryHook -from lang2sql.core.exceptions import ( - ComponentError, - ValidationError, - IntegrationMissingError, -) - -# ------------------------- -# Fixtures: tiny components/flows -# ------------------------- - - -class AddOne(BaseComponent): - def _run(self, x: int) -> int: - return x + 1 - - -class BoomValueError(BaseComponent): - def _run(self, x: int) -> int: - raise ValueError("boom") - - -class BoomDomainError(BaseComponent): - def _run(self, x: int) -> int: - raise ValidationError("bad sql") - - -class BoomIntegrationMissing(BaseComponent): - def _run(self, x: int) -> int: - raise IntegrationMissingError("faiss", extra="faiss") - - -class FlowOk(BaseFlow): - def _run(self, x: int) -> int: - return x * 2 - - -class FlowBoomDomain(BaseFlow): - def _run(self, x: int) -> int: - raise ValidationError("flow bad") - - -class FlowBoomUnknown(BaseFlow): - def _run(self, x: int) -> int: - raise RuntimeError("flow boom") - - -# ------------------------- -# BaseComponent tests -# ------------------------- - - -def test_base_component_emits_start_end_events(): - hook = MemoryHook() - c = AddOne(hook=hook) - - out = c(1) - assert out == 2 - - assert len(hook.events) == 2 - assert hook.events[0].name == "component.run" - assert hook.events[0].phase == "start" - assert hook.events[1].name == "component.run" - assert hook.events[1].phase == "end" - assert hook.events[1].duration_ms is not None - assert hook.events[1].duration_ms >= 0.0 - - -def test_base_component_wraps_non_domain_exception_as_component_error(): - hook = MemoryHook() - c = BoomValueError(hook=hook) - - with pytest.raises(ComponentError) as ei: - c(1) - - # error chain preserved - assert isinstance(ei.value.cause, ValueError) - assert "ValueError" in str(ei.value) or "boom" in str(ei.value) - - # events: start + error - assert len(hook.events) == 2 - assert hook.events[0].phase == "start" - assert hook.events[1].phase == "error" - assert "ValueError" in (hook.events[1].error or "") - assert "boom" in (hook.events[1].error or "") - - -def test_base_component_preserves_domain_error_validationerror(): - hook = MemoryHook() - c = BoomDomainError(hook=hook) - - with pytest.raises(ValidationError) as ei: - c(1) - - assert "bad sql" in str(ei.value) - - # events: start + error - assert len(hook.events) == 2 - assert hook.events[0].phase == "start" - assert hook.events[1].phase == "error" - assert "ValidationError" in (hook.events[1].error or "") - assert "bad sql" in (hook.events[1].error or "") - - -def test_base_component_preserves_domain_error_integration_missing(): - hook = MemoryHook() - c = BoomIntegrationMissing(hook=hook) - - with pytest.raises(IntegrationMissingError) as ei: - c(1) - - msg = str(ei.value) - assert "Missing optional integration: faiss" in msg - assert "lang2sql[faiss]" in msg - - # events: start + error - assert len(hook.events) == 2 - assert hook.events[0].phase == "start" - assert hook.events[1].phase == "error" - assert "IntegrationMissingError" in (hook.events[1].error or "") - assert "faiss" in (hook.events[1].error or "") - - -# ------------------------- -# BaseFlow tests -# ------------------------- - - -def test_base_flow_emits_start_end_events(): - hook = MemoryHook() - f = FlowOk(hook=hook) - - out = f(3) - assert out == 6 - - assert len(hook.events) == 2 - assert hook.events[0].name == "flow.run" - assert hook.events[0].phase == "start" - assert hook.events[1].name == "flow.run" - assert hook.events[1].phase == "end" - assert hook.events[1].duration_ms is not None - assert hook.events[1].duration_ms >= 0.0 - - -def test_base_flow_preserves_domain_error(): - hook = MemoryHook() - f = FlowBoomDomain(hook=hook) - - with pytest.raises(ValidationError) as ei: - f(1) - - assert "flow bad" in str(ei.value) - - assert len(hook.events) == 2 - assert hook.events[0].phase == "start" - assert hook.events[1].phase == "error" - assert "ValidationError" in (hook.events[1].error or "") - assert "flow bad" in (hook.events[1].error or "") - - -def test_base_flow_raises_unknown_error_and_emits_error_event(): - hook = MemoryHook() - f = FlowBoomUnknown(hook=hook) - - with pytest.raises(RuntimeError) as ei: - f(1) - - assert "flow boom" in str(ei.value) - - assert len(hook.events) == 2 - assert hook.events[0].phase == "start" - assert hook.events[1].phase == "error" - assert "RuntimeError" in (hook.events[1].error or "") - assert "flow boom" in (hook.events[1].error or "") diff --git a/tests/test_core_context.py b/tests/test_core_context.py deleted file mode 100644 index 27bf2bc..0000000 --- a/tests/test_core_context.py +++ /dev/null @@ -1,84 +0,0 @@ -import pytest - -from lang2sql.core.context import RunContext - - -def test_init_sets_query_and_metadata_kwargs(): - ctx = RunContext(query="hello", user_id="u1", trace_id="t1") - assert ctx.inputs["query"] == "hello" - assert ctx.metadata["user_id"] == "u1" - assert ctx.metadata["trace_id"] == "t1" - - -def test_query_property_default_empty_string(): - ctx = RunContext() - assert ctx.query == "" # inputs에 query 없으면 "" - - -def test_query_setter_updates_inputs(): - ctx = RunContext() - ctx.query = "select something" - assert ctx.inputs["query"] == "select something" - assert ctx.query == "select something" - - -def test_schema_property_initializes_dict_and_persists_reference(): - ctx = RunContext() - s = ctx.schema - assert isinstance(s, dict) - assert ctx.artifacts["schema"] is s # 같은 객체를 보장(참조 유지) - - s["selected"] = ["users", "orders"] - assert ctx.artifacts["schema"]["selected"] == ["users", "orders"] - - -def test_schema_property_overwrites_non_dict_value(): - ctx = RunContext() - ctx.artifacts["schema"] = ["not", "a", "dict"] - s = ctx.schema - assert isinstance(s, dict) - assert ctx.artifacts["schema"] == {} # dict가 아니면 {}로 교체됨 - - -def test_sql_property_get_set(): - ctx = RunContext() - assert ctx.sql == "" - ctx.sql = "SELECT 1;" - assert ctx.outputs["sql"] == "SELECT 1;" - assert ctx.sql == "SELECT 1;" - - -def test_validation_property_get_set(): - ctx = RunContext() - assert ctx.validation is None - ctx.validation = {"ok": True, "warnings": []} - assert ctx.outputs["validation"] == {"ok": True, "warnings": []} - assert ctx.validation == {"ok": True, "warnings": []} - - -def test_push_meta_appends_to_list(): - ctx = RunContext() - ctx.push_meta("events", {"name": "start"}) - ctx.push_meta("events", {"name": "end"}) - assert ctx.metadata["events"] == [{"name": "start"}, {"name": "end"}] - - -def test_push_meta_raises_when_existing_not_list(): - ctx = RunContext() - ctx.metadata["events"] = {"name": "oops"} # list가 아닌 값 - with pytest.raises(TypeError): - ctx.push_meta("events", {"name": "start"}) - - -def test_get_meta_list_returns_empty_when_missing(): - ctx = RunContext() - assert ctx.get_meta_list("missing") == [] - - -def test_get_meta_list_returns_list_or_wraps_scalar(): - ctx = RunContext() - ctx.metadata["items"] = [1, 2, 3] - assert ctx.get_meta_list("items") == [1, 2, 3] - - ctx.metadata["single"] = 42 - assert ctx.get_meta_list("single") == [42] diff --git a/tests/test_core_exceptions.py b/tests/test_core_exceptions.py deleted file mode 100644 index b67716e..0000000 --- a/tests/test_core_exceptions.py +++ /dev/null @@ -1,40 +0,0 @@ -import pytest - -from lang2sql.core.exceptions import ( - IntegrationMissingError, - ComponentError, - Lang2SQLError, -) - - -def test_integration_missing_error_message_includes_extra_hint(): - err = IntegrationMissingError("faiss", extra="faiss") - msg = str(err) - assert "Missing optional integration: faiss" in msg - assert "pip install 'lang2sql[faiss]'" in msg - - -def test_integration_missing_error_message_includes_hint_when_provided(): - err = IntegrationMissingError("openai", extra="openai", hint="Needed for LLM calls") - msg = str(err) - assert "Missing optional integration: openai" in msg - assert "pip install 'lang2sql[openai]'" in msg - assert "Needed for LLM calls" in msg - - -def test_component_error_wraps_component_name_and_message(): - err = ComponentError("KeywordTableRetriever", "component failed") - msg = str(err) - assert msg.startswith("[KeywordTableRetriever]") - assert "component failed" in msg - - -def test_exceptions_are_subclasses_of_base_error(): - assert issubclass(IntegrationMissingError, Lang2SQLError) - assert issubclass(ComponentError, Lang2SQLError) - - -def test_component_error_can_chain_cause(): - root = ValueError("boom") - err = ComponentError("X", "failed", cause=root) - assert err.cause is root diff --git a/tests/test_core_hooks.py b/tests/test_core_hooks.py deleted file mode 100644 index 60cf341..0000000 --- a/tests/test_core_hooks.py +++ /dev/null @@ -1,74 +0,0 @@ -from lang2sql.core.hooks import ( - Event, - MemoryHook, - NullHook, - summarize, - now, - ms, -) - - -def test_memory_hook_collects_events(): - hook = MemoryHook() - e1 = Event(name="x", component="c", phase="start", ts=123.0) - e2 = Event(name="x", component="c", phase="end", ts=124.0, duration_ms=1.0) - hook.on_event(e1) - hook.on_event(e2) - - assert len(hook.events) == 2 - assert hook.events[0].phase == "start" - assert hook.events[1].phase == "end" - - -def test_null_hook_does_not_crash(): - hook = NullHook() - hook.on_event( - Event(name="x", component="c", phase="start", ts=0.0) - ) # should not raise - - -def test_summarize_truncates_long_repr(): - long = "a" * 1000 - s = summarize(long, max_len=50) - assert len(s) <= 50 - assert s.endswith("...") - - -def test_now_and_ms_work(): - t0 = now() - t1 = now() - assert t1 >= t0 - d = ms(t0, t1) - assert d >= 0.0 - - -def test_memory_hook_clear_resets_events(): - hook = MemoryHook() - hook.on_event(Event(name="x", component="c", phase="start", ts=0.0)) - assert len(hook.events) == 1 - hook.clear() - assert len(hook.events) == 0 - - -def test_memory_hook_snapshot_is_copy(): - hook = MemoryHook() - hook.on_event(Event(name="x", component="c", phase="start", ts=0.0)) - - snap = hook.snapshot() - assert snap is not hook.events - assert len(snap) == 1 - - # mutate original after snapshot - hook.on_event(Event(name="x", component="c", phase="end", ts=1.0)) - assert len(hook.events) == 2 - assert len(snap) == 1 # snapshot should not change - - -class BadRepr: - def __repr__(self): - raise RuntimeError("boom") - - -def test_summarize_handles_bad_repr(): - s = summarize(BadRepr(), max_len=50) - assert "unreprable" in s diff --git a/tests/test_discord.py b/tests/test_discord.py new file mode 100644 index 0000000..0fe8fea --- /dev/null +++ b/tests/test_discord.py @@ -0,0 +1,216 @@ +"""Unit tests for the Discord frontend — no live bot, no token, no network. + +Covers the three pure modules (session_router mapping, render thresholds, +CommandHandlers against a real in-memory ContextConcierge) plus the import- +safety contract that ``bot.py`` loads with no ``DISCORD_BOT_TOKEN`` set. + +Async handlers are driven with :func:`asyncio.run` to match the convention in +the rest of the suite (no pytest-asyncio marker plumbing). +""" + +from __future__ import annotations + +import asyncio +import os + +from lang2sql.core.identity import ScopeLevel +from lang2sql.frontends.discord import ( + CommandHandlers, + InteractionContext, + is_channel, + is_dm, + is_thread, + render_answer, + to_identity, +) +from lang2sql.frontends.discord.render import MAX_INLINE_ROWS +from lang2sql.tenancy.concierge import ContextConcierge + + +# -- session_router ------------------------------------------------------- + + +def test_dm_identity_has_no_guild() -> None: + ident = to_identity(InteractionContext(user_id="u1")) + assert is_dm(ident) + assert not is_channel(ident) + assert not is_thread(ident) + assert ident.session_key() == "dm:u1" + + +def test_channel_identity_scopes_to_channel() -> None: + ident = to_identity( + InteractionContext(user_id="u1", guild_id="g1", channel_id="c1") + ) + assert is_channel(ident) + assert not is_dm(ident) + assert not is_thread(ident) + assert ident.session_key() == "channel:c1" + # Federation chain runs narrow→wide: channel, guild, builtin. + levels = [s.level for s in ident.scope_chain()] + assert levels == [ScopeLevel.CHANNEL, ScopeLevel.GUILD, ScopeLevel.BUILTIN] + + +def test_thread_identity_is_narrowest() -> None: + ident = to_identity( + InteractionContext(user_id="u1", guild_id="g1", channel_id="c1", thread_id="t1") + ) + assert is_thread(ident) + assert ident.session_key() == "thread:t1" + assert ident.scope_chain()[0].level is ScopeLevel.THREAD + + +def test_admin_flag_propagates() -> None: + ident = to_identity(InteractionContext(user_id="u1", guild_id="g1", is_admin=True)) + assert ident.is_admin is True + + +# -- render --------------------------------------------------------------- + + +def test_render_small_text_is_plain() -> None: + msg = render_answer("just a short answer") + assert msg.text == "just a short answer" + assert msg.file_bytes is None + assert msg.file_name is None + + +def test_render_large_rows_attaches_csv() -> None: + rows = [[i, f"name{i}"] for i in range(MAX_INLINE_ROWS + 5)] + msg = render_answer("Top users:", rows, header=["id", "name"]) + assert msg.file_bytes is not None + assert msg.file_name == "result.csv" + assert "55 rows" in msg.text + assert "Top users:" in msg.text + decoded = msg.file_bytes.decode("utf-8") + assert decoded.startswith("id,name") + assert "name54" in decoded + + +def test_render_small_rows_inlined() -> None: + rows = [[1, "a"], [2, "b"]] + msg = render_answer("", rows, header=["id", "name"]) + assert msg.file_bytes is None + assert "id,name" in msg.text + assert "1,a" in msg.text + + +def test_render_many_text_lines_attaches() -> None: + text = "\n".join(f"line {i}" for i in range(MAX_INLINE_ROWS + 1)) + msg = render_answer(text) + assert msg.file_bytes is not None + assert "lines" in msg.text + + +# -- CommandHandlers (real in-memory concierge) --------------------------- + + +def test_define_metric_then_semantic_show() -> None: + handlers = CommandHandlers(ContextConcierge()) + ident = to_identity( + InteractionContext(user_id="u1", guild_id="g1", channel_id="c1") + ) + + async def scenario() -> tuple[str, str]: + defined = await handlers.define_metric(ident, "active_user", "logged in within 30 days") + shown = await handlers.semantic_show(ident) + return defined.text, shown.text + + defined_text, shown_text = asyncio.run(scenario()) + assert "active_user" in defined_text + assert "active_user" in shown_text + assert "logged in within 30 days" in shown_text + + +def test_semantic_show_empty_scope() -> None: + handlers = CommandHandlers(ContextConcierge()) + ident = to_identity(InteractionContext(user_id="solo", guild_id="g9", channel_id="c9")) + shown = asyncio.run(handlers.semantic_show(ident)) + assert "No definitions" in shown.text + + +def test_define_metric_is_scope_isolated() -> None: + """A channel definition must not leak into a different channel (federation).""" + handlers = CommandHandlers(ContextConcierge()) + marketing = to_identity(InteractionContext(user_id="u1", guild_id="g1", channel_id="mkt")) + product = to_identity(InteractionContext(user_id="u1", guild_id="g1", channel_id="prd")) + + async def scenario() -> str: + await handlers.define_metric(marketing, "active_user", "30d login") + return (await handlers.semantic_show(product)).text + + assert "active_user" not in asyncio.run(scenario()) + + +def test_remember_and_audit_me() -> None: + handlers = CommandHandlers(ContextConcierge()) + ident = to_identity(InteractionContext(user_id="u2", guild_id="g1", channel_id="c1")) + + async def scenario() -> tuple[str, str]: + remembered = await handlers.remember(ident, "prefers ISO dates") + audit = await handlers.audit_me(ident) + return remembered.text, audit.text + + remembered_text, audit_text = asyncio.run(scenario()) + assert "prefers ISO dates" in remembered_text + assert "remember" in audit_text + + +def test_audit_me_empty() -> None: + handlers = CommandHandlers(ContextConcierge()) + ident = to_identity(InteractionContext(user_id="never-acted", guild_id="g1", channel_id="c1")) + audit = asyncio.run(handlers.audit_me(ident)) + assert "No audited activity" in audit.text + + +def test_query_returns_outbound_message() -> None: + """With the default FakeLLM (no OPENAI key), a query still returns text.""" + handlers = CommandHandlers(ContextConcierge()) + ident = to_identity(InteractionContext(user_id="u3", guild_id="g1", channel_id="c1")) + out = asyncio.run(handlers.query(ident, "how many users signed up?")) + assert isinstance(out.text, str) + assert out.text # non-empty + + +def test_query_persists_session() -> None: + concierge = ContextConcierge() + handlers = CommandHandlers(concierge) + ident = to_identity(InteractionContext(user_id="u4", guild_id="g1", channel_id="c1")) + + async def scenario(): + await handlers.query(ident, "first question") + return await concierge.store.load(ident.session_key()) + + saved = asyncio.run(scenario()) + assert saved is not None + assert any(m.content == "first question" for m in saved.transcript) + + +def test_connect_stub_acknowledges() -> None: + concierge = ContextConcierge() + handlers = CommandHandlers(concierge) + ident = to_identity(InteractionContext(user_id="u5", guild_id="g1", channel_id="c1")) + out = asyncio.run(handlers.connect(ident, "postgresql://localhost/db")) + assert "saved" in out.text.lower() + assert concierge.store.kv_get("g1", "dsn") == "postgresql://localhost/db" + + +def test_ingest_lists_or_reports() -> None: + handlers = CommandHandlers(ContextConcierge()) + ident = to_identity(InteractionContext(user_id="u6", guild_id="g1", channel_id="c1")) + out = asyncio.run( + handlers.ingest(ident, content="total_revenue is the sum of order amounts") + ) + assert isinstance(out.text, str) + assert out.text + + +# -- import-safety contract ----------------------------------------------- + + +def test_bot_imports_without_token() -> None: + """Importing bot.py must not require a token or a network connection.""" + os.environ.pop("DISCORD_BOT_TOKEN", None) + import lang2sql.frontends.discord.bot as bot # noqa: F401 + + assert hasattr(bot, "run") diff --git a/tests/test_flows_baseline.py b/tests/test_flows_baseline.py deleted file mode 100644 index 93df60f..0000000 --- a/tests/test_flows_baseline.py +++ /dev/null @@ -1,128 +0,0 @@ -import pytest - -from lang2sql.core.base import BaseFlow -from lang2sql.flows.baseline import BaselineFlow, SequentialFlow - - -def test_requires_at_least_one_step(): - with pytest.raises(ValueError): - SequentialFlow(steps=[]) - - -def test_baseline_flow_emits_deprecation_warning(): - with pytest.warns(DeprecationWarning, match="BaselineFlow is deprecated"): - flow = BaselineFlow(steps=[lambda x: x]) - assert isinstance(flow, SequentialFlow) - - -def test_run_passes_value_through_single_step(): - flow = SequentialFlow(steps=[lambda x: x + 1]) - assert flow.run(1) == 2 - - -def test_run_chains_multiple_steps(): - flow = SequentialFlow(steps=[lambda x: x * 2, lambda x: x + 10]) - assert flow.run(5) == 20 # 5 * 2 = 10, 10 + 10 = 20 - - -def test_step_order_is_preserved(): - trace = [] - - def s1(x): - trace.append("s1") - return x - - def s2(x): - trace.append("s2") - return x - - def s3(x): - trace.append("s3") - return x - - SequentialFlow(steps=[s1, s2, s3]).run("input") - assert trace == ["s1", "s2", "s3"] - - -def test_user_can_compose_flows_with_python_control_flow(): - default_flow = SequentialFlow(steps=[lambda x: x + "_default"]) - override_flow = SequentialFlow(steps=[lambda x: x + "_override"]) - - assert default_flow("q") == "q_default" - - class CustomFlow(BaseFlow): - def _run(self, value): - return override_flow(value) - - assert CustomFlow().run("q") == "q_override" - - -# ------------------------- -# Advanced: retry patterns -# ------------------------- - - -def test_custom_flow_fallback_then_revalidate(): - def gen_bad(_query): - return "DROP TABLE users;" - - def validate(sql): - return "drop " not in sql.lower() - - class FixThenRevalidateFlow(BaseFlow): - def _run(self, query): - sql = gen_bad(query) - if not validate(sql): - sql = "SELECT 1;" - return sql - - assert FixThenRevalidateFlow().run("q") == "SELECT 1;" - - -def test_custom_flow_retry_until_valid(): - attempts = {"count": 0} - - def generate(_query): - attempts["count"] += 1 - return "DROP TABLE users;" if attempts["count"] == 1 else "SELECT 1;" - - def validate(sql): - return "drop " not in sql.lower() - - class RetryFlow(BaseFlow): - def _run(self, query): - for _ in range(3): - sql = generate(query) - if validate(sql): - return sql - return sql - - result = RetryFlow().run("q") - assert result == "SELECT 1;" - assert attempts["count"] >= 2 - - -# ------------------------- -# Composition: flow as step -# ------------------------- - - -def test_subflow_can_be_used_as_a_step(): - inner = SequentialFlow(steps=[lambda x: x + 1]) - outer = SequentialFlow(steps=[lambda x: x * 2, inner]) - - # 3 * 2 = 6, 6 + 1 = 7 - assert outer.run(3) == 7 - - -def test_subflow_can_be_conditionally_invoked_in_custom_flow(): - flow_a = SequentialFlow(steps=[lambda x: x + "_a"]) - - class ConditionalFlow(BaseFlow): - def _run(self, value): - if "use_a" in value: - return flow_a(value) - return value - - assert ConditionalFlow().run("nope") == "nope" - assert ConditionalFlow().run("please use_a") == "please use_a_a" diff --git a/tests/test_flows_enriched_nl2sql.py b/tests/test_flows_enriched_nl2sql.py deleted file mode 100644 index 9683c37..0000000 --- a/tests/test_flows_enriched_nl2sql.py +++ /dev/null @@ -1,195 +0,0 @@ -"""Tests for EnrichedNL2SQL flow.""" - -from __future__ import annotations - -import json -from unittest.mock import MagicMock - -import pytest - -from lang2sql.core.catalog import CatalogEntry -from lang2sql.core.exceptions import ContractError -from lang2sql.core.hooks import MemoryHook -from lang2sql.flows.enriched_nl2sql import EnrichedNL2SQL - -# --------------------------------------------------------------------------- -# Fakes -# --------------------------------------------------------------------------- - - -class FakeLLM: - """Configurable fake LLM that cycles through responses.""" - - def __init__(self, responses: list[str]): - self._responses = list(responses) - self._idx = 0 - - def invoke(self, messages: list[dict]) -> str: - resp = self._responses[self._idx % len(self._responses)] - self._idx += 1 - return resp - - -class FakeDB: - def execute(self, sql: str) -> list[dict]: - return [{"count": 42}] - - -class FakeEmbedding: - def embed_query(self, text: str) -> list[float]: - return [0.1, 0.2, 0.3] - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - return [[0.1, 0.2, 0.3] for _ in texts] - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -def _gate_json(suitable: bool = True) -> str: - return json.dumps( - { - "suitable": suitable, - "reason": "ok" if suitable else "not suitable", - "missing_entities": [], - "requires_data_science": False, - } - ) - - -def _suitability_json(table_names: list[str], score: float = 0.9) -> str: - return json.dumps( - { - "results": [ - { - "table_name": name, - "score": score, - "reason": "ok", - "matched_columns": [], - "missing_entities": [], - } - for name in table_names - ] - } - ) - - -def _profile_json() -> str: - return json.dumps( - { - "is_timeseries": False, - "is_aggregation": True, - "has_filter": False, - "is_grouped": False, - "has_ranking": False, - "has_temporal_comparison": False, - "intent_type": "lookup", - } - ) - - -def _catalog() -> list[CatalogEntry]: - return [ - { - "name": "orders", - "description": "주문 테이블", - "columns": {"order_id": "ID", "amount": "금액", "created_at": "생성일"}, - } - ] - - -def _make_pipeline(gate_enabled: bool = True) -> EnrichedNL2SQL: - # LLM response order: - # 1. QuestionGate → gate JSON - # 2. TableSuitabilityEvaluator → suitability JSON - # 3. QuestionProfiler → profile JSON - # 4. ContextEnricher → enriched string - # 5. SQLGenerator → sql block - llm = FakeLLM( - [ - _gate_json(suitable=True), - _suitability_json(["orders"]), - _profile_json(), - "지난달 주문 건수를 구합니다", - "```sql\nSELECT COUNT(*) FROM orders\n```", - ] - ) - return EnrichedNL2SQL( - catalog=_catalog(), - llm=llm, - db=FakeDB(), - embedding=FakeEmbedding(), - db_dialect="sqlite", - gate_enabled=gate_enabled, - ) - - -# --------------------------------------------------------------------------- -# Tests -# --------------------------------------------------------------------------- - - -def test_enriched_nl2sql_full_pipeline_returns_rows(): - pipeline = _make_pipeline() - rows = pipeline.run("지난달 주문 건수") - assert rows == [{"count": 42}] - - -def test_enriched_nl2sql_gate_disabled_skips_gate(): - # With gate disabled, LLM responses shift by one - llm = FakeLLM( - [ - _suitability_json(["orders"]), - _profile_json(), - "enriched query", - "```sql\nSELECT COUNT(*) FROM orders\n```", - ] - ) - pipeline = EnrichedNL2SQL( - catalog=_catalog(), - llm=llm, - db=FakeDB(), - embedding=FakeEmbedding(), - gate_enabled=False, - ) - rows = pipeline.run("주문 건수") - assert rows == [{"count": 42}] - - -def test_enriched_nl2sql_gate_raises_contract_error_when_not_suitable(): - llm = FakeLLM([_gate_json(suitable=False)]) - pipeline = EnrichedNL2SQL( - catalog=_catalog(), - llm=llm, - db=FakeDB(), - embedding=FakeEmbedding(), - gate_enabled=True, - ) - with pytest.raises(ContractError): - pipeline.run("통계 모델을 만들어줘") - - -def test_enriched_nl2sql_emits_hook_events(): - hook = MemoryHook() - llm = FakeLLM( - [ - _gate_json(suitable=True), - _suitability_json(["orders"]), - _profile_json(), - "enriched", - "```sql\nSELECT COUNT(*) FROM orders\n```", - ] - ) - pipeline = EnrichedNL2SQL( - catalog=_catalog(), - llm=llm, - db=FakeDB(), - embedding=FakeEmbedding(), - gate_enabled=True, - hook=hook, - ) - pipeline.run("주문 건수") - # At minimum, the flow itself emits start/end - assert len(hook.events) > 0 diff --git a/tests/test_flows_nl2sql.py b/tests/test_flows_nl2sql.py deleted file mode 100644 index 2573dfa..0000000 --- a/tests/test_flows_nl2sql.py +++ /dev/null @@ -1,124 +0,0 @@ -"""End-to-end tests for BaselineNL2SQL.""" - -from __future__ import annotations - -import pytest - -from lang2sql.core.exceptions import ComponentError -from lang2sql.core.hooks import MemoryHook -from lang2sql.flows.nl2sql import BaselineNL2SQL - -# --------------------------------------------------------------------------- -# Fakes -# --------------------------------------------------------------------------- - - -class FakeLLM: - def __init__(self, response: str = "```sql\nSELECT COUNT(*) FROM orders\n```"): - self._response = response - - def invoke(self, messages: list[dict]) -> str: - return self._response - - -class FakeDB: - def __init__(self, rows: list[dict] | None = None): - self._rows = rows if rows is not None else [{"count": 42}] - - def execute(self, sql: str) -> list[dict]: - return self._rows - - -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- - -CATALOG = [ - { - "name": "orders", - "description": "Monthly order records", - "columns": {"order_id": "primary key", "amount": "order amount"}, - }, - { - "name": "customers", - "description": "Customer master data", - "columns": {"customer_id": "primary key", "name": "customer name"}, - }, -] - - -# --------------------------------------------------------------------------- -# Tests -# --------------------------------------------------------------------------- - - -def test_pipeline_e2e_returns_rows(): - pipeline = BaselineNL2SQL( - catalog=CATALOG, - llm=FakeLLM(), - db=FakeDB([{"count": 42}]), - ) - result = pipeline.run("지난달 주문 건수") - assert result == [{"count": 42}] - - -def test_pipeline_emits_3_component_start_events(): - hook = MemoryHook() - pipeline = BaselineNL2SQL( - catalog=CATALOG, - llm=FakeLLM(), - db=FakeDB(), - hook=hook, - ) - pipeline.run("주문 건수") - - component_starts = [ - e for e in hook.snapshot() if e.name == "component.run" and e.phase == "start" - ] - assert len(component_starts) == 3 - - -def test_pipeline_propagates_component_error_on_bad_llm_response(): - pipeline = BaselineNL2SQL( - catalog=CATALOG, - llm=FakeLLM("No SQL here, just text."), - db=FakeDB(), - ) - with pytest.raises(ComponentError): - pipeline.run("주문 건수") - - -def test_pipeline_hook_records_all_phases(): - hook = MemoryHook() - pipeline = BaselineNL2SQL( - catalog=CATALOG, - llm=FakeLLM(), - db=FakeDB(), - hook=hook, - ) - pipeline.run("test") - - events = hook.snapshot() - component_events = [e for e in events if e.name == "component.run"] - flow_events = [e for e in events if e.name == "flow.run"] - - assert len(component_events) == 6 # 3 components × (start + end) - assert len(flow_events) == 2 # flow start + flow end - - -def test_pipeline_advanced_usage_manual_composition(): - """고급 사용자 — 직접 컴포넌트 조합.""" - from lang2sql.components.execution.sql_executor import SQLExecutor - from lang2sql.components.generation.sql_generator import SQLGenerator - from lang2sql.components.retrieval.keyword import KeywordRetriever - - retriever = KeywordRetriever(catalog=CATALOG) - generator = SQLGenerator(llm=FakeLLM()) - executor = SQLExecutor(db=FakeDB([{"total": 99}])) - - query = "주문 건수" - schemas = retriever(query) - sql = generator(query, schemas) - result = executor(sql) - - assert result == [{"total": 99}] diff --git a/tests/test_harness_loop.py b/tests/test_harness_loop.py new file mode 100644 index 0000000..db73788 --- /dev/null +++ b/tests/test_harness_loop.py @@ -0,0 +1,56 @@ +"""Week-1 smoke test: the agent loop completes a full tool cycle. + +Locks in the walking-skeleton contract — user turn → tool call → tool result → +final answer — so later refactors of loop/registry/context don't silently break +the dispatch path. +""" + +from __future__ import annotations + +import asyncio + +from lang2sql.adapters.llm.fake import FakeLLM +from lang2sql.core.identity import Identity +from lang2sql.core.types import Role +from lang2sql.harness.context import HarnessContext +from lang2sql.harness.loop import agent_loop +from lang2sql.harness.session import Session +from lang2sql.harness.tool_registry import ToolRegistry +from lang2sql.tools.ping import Ping + + +def _ctx() -> HarnessContext: + identity = Identity(user_id="tester") + return HarnessContext( + identity=identity, + llm=FakeLLM(), + tools=ToolRegistry([Ping()]), + session=Session(identity=identity), + ) + + +def test_loop_runs_tool_then_answers(): + ctx = _ctx() + answer = asyncio.run(agent_loop(ctx, "hello")) + assert "pong" in answer # tool ran and its result reached the final answer + + +def test_transcript_shape(): + ctx = _ctx() + asyncio.run(agent_loop(ctx, "hello")) + roles = [m.role for m in ctx.session.history()] + # user → assistant(tool_call) → tool → assistant(final) + assert roles == [Role.USER, Role.ASSISTANT, Role.TOOL, Role.ASSISTANT] + + +def test_tool_call_id_is_stamped(): + ctx = _ctx() + asyncio.run(agent_loop(ctx, "hello")) + tool_msgs = [m for m in ctx.session.history() if m.role == Role.TOOL] + assert tool_msgs and tool_msgs[0].tool_call_id + + +def test_scope_chain_orders_narrow_to_wide(): + ident = Identity(user_id="u", guild_id="g", channel_id="c", thread_id="t") + levels = [s.level.value for s in ident.scope_chain()] + assert levels == ["thread", "channel", "guild", "builtin"] diff --git a/tests/test_ingestion.py b/tests/test_ingestion.py new file mode 100644 index 0000000..f0d7836 --- /dev/null +++ b/tests/test_ingestion.py @@ -0,0 +1,134 @@ +"""Ingestion V1 — LLM extraction and pipeline wiring (★③).""" + +from __future__ import annotations + +import asyncio +import os +import tempfile +from typing import Sequence + +from lang2sql.core.ports.ingestion import CandidateKind, Document +from lang2sql.core.types import Completion, Message, ToolSpec +from lang2sql.ingestion import FileSource, IngestionPipeline, LLMExtractor + + +class _ScriptedLLM: + """Inline fake LLMPort returning a fixed completion content.""" + + def __init__(self, content: str) -> None: + self._content = content + + async def complete( + self, + messages: Sequence[Message], + tools: Sequence[ToolSpec] = (), + ) -> Completion: + return Completion(content=self._content, finish_reason="stop") + + +_JSON_ARRAY = """ +[ + {"kind": "metric", "name": "total_revenue", + "definition": "SUM(orders.amount)", "applies_to": "orders"}, + {"kind": "rule", "name": "exclude_cancelled", + "definition": "status != 'cancelled'", "applies_to": "total_revenue"} +] +""" + +_FENCED = "```json\n" + _JSON_ARRAY.strip() + "\n```" + + +def test_llm_extractor_maps_json_array_to_candidates() -> None: + extractor = LLMExtractor(_ScriptedLLM(_JSON_ARRAY)) + + async def run() -> None: + doc = Document(name="defs.md", text="...", source_id="doc-1") + cands = await extractor.extract(doc) + assert [c.name for c in cands] == ["total_revenue", "exclude_cancelled"] + assert cands[0].kind == CandidateKind.METRIC + assert cands[1].kind == CandidateKind.RULE + assert all(c.source_id == "doc-1" for c in cands) + + asyncio.run(run()) + + +def test_llm_extractor_strips_json_fences() -> None: + extractor = LLMExtractor(_ScriptedLLM(_FENCED)) + + async def run() -> None: + cands = await extractor.extract(Document(name="d", text="t", source_id="s")) + assert len(cands) == 2 + + asyncio.run(run()) + + +def test_llm_extractor_malformed_content_returns_empty() -> None: + async def run() -> None: + for content in ["not json at all", "", "{not: valid}", "42", "{}"]: + extractor = LLMExtractor(_ScriptedLLM(content)) + assert await extractor.extract(Document(name="d", text="t")) == [] + + asyncio.run(run()) + + +def test_llm_extractor_skips_rows_with_bad_kind_or_missing_fields() -> None: + content = """ + [ + {"kind": "metric", "name": "ok", "definition": "x"}, + {"kind": "bogus", "name": "bad_kind", "definition": "y"}, + {"kind": "metric", "name": "", "definition": "z"}, + {"kind": "metric", "name": "no_def"}, + "not a dict" + ] + """ + extractor = LLMExtractor(_ScriptedLLM(content)) + + async def run() -> None: + cands = await extractor.extract(Document(name="d", text="t")) + assert [c.name for c in cands] == ["ok"] + + asyncio.run(run()) + + +def test_file_source_decodes_blob() -> None: + source = FileSource() + + async def run() -> None: + doc = await source.fetch("path/to/defs.md", blob=b"hello blob") + assert doc.name == "defs.md" + assert doc.text == "hello blob" + assert doc.source_id == "path/to/defs.md" + + asyncio.run(run()) + + +def test_file_source_reads_from_disk() -> None: + source = FileSource() + + async def run() -> None: + fd, path = tempfile.mkstemp(suffix=".txt") + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + handle.write("on disk") + doc = await source.fetch(path) + assert doc.text == "on disk" + assert doc.name == os.path.basename(path) + finally: + os.remove(path) + + asyncio.run(run()) + + +def test_pipeline_fetches_then_extracts() -> None: + pipeline = IngestionPipeline() + source = FileSource() + extractor = LLMExtractor(_ScriptedLLM(_JSON_ARRAY)) + + async def run() -> None: + cands = await pipeline.ingest( + source, extractor, ref="defs.md", blob=b"document body" + ) + assert [c.name for c in cands] == ["total_revenue", "exclude_cancelled"] + assert all(c.source_id == "defs.md" for c in cands) + + asyncio.run(run()) diff --git a/tests/test_integration.py b/tests/test_integration.py new file mode 100644 index 0000000..62d2016 --- /dev/null +++ b/tests/test_integration.py @@ -0,0 +1,64 @@ +"""Week-2 integration: the ContextConcierge wires a working agent. + +Exercises the assembled stack (tools + safety + scope resolver + explorer stub ++ sqlite) without network, using the offline FakeLLM. Locks in that the six V1 +tools are registered and that run_sql/define_metric behave through the live +HarnessContext. +""" + +from __future__ import annotations + +import asyncio + +from lang2sql.core.identity import Identity +from lang2sql.core.ports.safety import SafetyContext, Verdict +from lang2sql.tenancy.concierge import ContextConcierge +from lang2sql.tools.define_metric import DefineMetric +from lang2sql.tools.run_sql import RunSQL + + +def _ctx(): + concierge = ContextConcierge() + ident = Identity(user_id="u1", guild_id="g1", channel_id="c-mkt") + return ident, asyncio.run(concierge.build_context(ident)) + + +def test_six_v1_tools_registered(): + _, ctx = _ctx() + names = {s.name for s in ctx.tools.specs()} + assert names == {"run_sql", "explore_schema", "define_metric", "ask_user", "remember", "ingest_doc"} + + +def test_run_sql_passes_gate_and_returns_rows(): + _, ctx = _ctx() + res = asyncio.run(RunSQL().run({"sql": "SELECT * FROM orders", "limit": 10}, ctx)) + assert not res.is_error + assert "row" in res.content.lower() + + +def test_run_sql_blocks_ddl(): + _, ctx = _ctx() + res = asyncio.run(RunSQL().run({"sql": "DROP TABLE users"}, ctx)) + assert res.is_error and "BLOCKED" in res.content + + +def test_run_sql_tolerates_bad_limit(): + _, ctx = _ctx() + res = asyncio.run(RunSQL().run({"sql": "SELECT 1", "limit": "demo"}, ctx)) + assert not res.is_error # malformed limit must not crash the tool + + +def test_define_metric_is_scope_local(): + ident, ctx = _ctx() + asyncio.run(DefineMetric().run({"name": "active_user", "definition": "30d login"}, ctx)) + layer = asyncio.run(ctx.scope_resolver.effective_layer(ident)) + assert layer.lookup("active_user") is not None + # a different channel does not see it + other = Identity(user_id="u1", guild_id="g1", channel_id="c-fin") + other_layer = asyncio.run(ctx.scope_resolver.effective_layer(other)) + assert other_layer.lookup("active_user") is None + + +def test_safety_pipeline_on_context(): + _, ctx = _ctx() + assert ctx.safety.evaluate("SELECT 1", SafetyContext()).verdict == Verdict.PASS diff --git a/tests/test_integrations_embedding_azure.py b/tests/test_integrations_embedding_azure.py deleted file mode 100644 index 7e099ad..0000000 --- a/tests/test_integrations_embedding_azure.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Tests for AzureOpenAIEmbedding integration.""" - -from __future__ import annotations - -from unittest.mock import MagicMock, patch - -import pytest - -openai = pytest.importorskip("openai", reason="openai not installed") - -from lang2sql.integrations.embedding.azure_ import AzureOpenAIEmbedding - - -def _make_embedding() -> AzureOpenAIEmbedding: - return AzureOpenAIEmbedding( - azure_deployment="text-embedding-ada-002", - azure_endpoint="https://test.openai.azure.com/", - api_key="test-key", - ) - - -def _mock_embedding_response(vectors: list[list[float]]): - resp = MagicMock() - resp.data = [MagicMock(embedding=v) for v in vectors] - return resp - - -def test_embed_query_returns_vector(): - vec = [0.1, 0.2, 0.3] - with patch("openai.AzureOpenAI") as MockClient: - instance = MockClient.return_value - instance.embeddings.create.return_value = _mock_embedding_response([vec]) - - emb = _make_embedding() - emb._client = instance - result = emb.embed_query("hello") - - assert result == vec - - -def test_embed_texts_returns_multiple_vectors(): - vecs = [[0.1, 0.2], [0.3, 0.4]] - with patch("openai.AzureOpenAI") as MockClient: - instance = MockClient.return_value - instance.embeddings.create.return_value = _mock_embedding_response(vecs) - - emb = _make_embedding() - emb._client = instance - result = emb.embed_texts(["hello", "world"]) - - assert result == vecs diff --git a/tests/test_integrations_faiss_vectorstore.py b/tests/test_integrations_faiss_vectorstore.py deleted file mode 100644 index 0b97fcb..0000000 --- a/tests/test_integrations_faiss_vectorstore.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -FAISSVectorStore integration tests. - -All tests are auto-skipped when faiss-cpu is not installed. -""" - -import pytest - -faiss = pytest.importorskip("faiss") # skip entire module if not installed - -import tempfile -import os - -from lang2sql.integrations.vectorstore.faiss_ import FAISSVectorStore - -# ── helpers ────────────────────────────────────────────────────────────────── - - -def _ortho_vectors() -> list[tuple[str, list[float]]]: - """4 orthogonal unit vectors for deterministic cosine tests.""" - return [ - ("a", [1.0, 0.0, 0.0, 0.0]), - ("b", [0.0, 1.0, 0.0, 0.0]), - ("c", [0.0, 0.0, 1.0, 0.0]), - ("d", [0.0, 0.0, 0.0, 1.0]), - ] - - -@pytest.fixture -def store() -> FAISSVectorStore: - return FAISSVectorStore() - - -def _populate(store: FAISSVectorStore) -> None: - items = _ortho_vectors() - ids = [item[0] for item in items] - vecs = [item[1] for item in items] - store.upsert(ids, vecs) - - -# ── tests ───────────────────────────────────────────────────────────────────── - - -def test_faiss_upsert_and_search_returns_closest(store): - """Query vector returns its own id at rank 1.""" - _populate(store) - results = store.search([1.0, 0.0, 0.0, 0.0], k=1) - assert len(results) == 1 - assert results[0][0] == "a" - - -def test_faiss_cosine_score_of_identical_vector(store): - """Identical query → score ≈ 1.0.""" - _populate(store) - results = store.search([1.0, 0.0, 0.0, 0.0], k=1) - assert abs(results[0][1] - 1.0) < 1e-5 - - -def test_faiss_upsert_merge_preserves_prior_entries(store): - """Second upsert() call doesn't lose entries from the first.""" - store.upsert(["a"], [[1.0, 0.0, 0.0, 0.0]]) - store.upsert(["b"], [[0.0, 1.0, 0.0, 0.0]]) - - # "a" should still be retrievable - results = store.search([1.0, 0.0, 0.0, 0.0], k=2) - ids = [r[0] for r in results] - assert "a" in ids - - -def test_faiss_search_respects_k(store): - """len(results) <= k.""" - _populate(store) - results = store.search([1.0, 0.0, 0.0, 0.0], k=2) - assert len(results) <= 2 - - -def test_faiss_search_on_empty_store_returns_empty(store): - """[] before any upsert().""" - results = store.search([1.0, 0.0, 0.0, 0.0], k=5) - assert results == [] - - -def test_faiss_save_and_load_roundtrip(store, tmp_path): - """save() → load() → search() returns same results.""" - _populate(store) - index_path = str(tmp_path / "catalog.faiss") - store.save(index_path) - - loaded = FAISSVectorStore.load(index_path) - original_results = store.search([1.0, 0.0, 0.0, 0.0], k=1) - loaded_results = loaded.search([1.0, 0.0, 0.0, 0.0], k=1) - - assert loaded_results[0][0] == original_results[0][0] - assert abs(loaded_results[0][1] - original_results[0][1]) < 1e-5 - - -def test_faiss_save_without_path_raises(store): - """save() with no path and no index_path → ValueError.""" - _populate(store) - with pytest.raises(ValueError, match="No path provided"): - store.save() - - -def test_faiss_load_nonexistent_path_raises(): - """load("nonexistent") → FileNotFoundError.""" - with pytest.raises(FileNotFoundError): - FAISSVectorStore.load("nonexistent_path_that_does_not_exist.faiss") diff --git a/tests/test_integrations_llm_azure.py b/tests/test_integrations_llm_azure.py deleted file mode 100644 index 8c1592e..0000000 --- a/tests/test_integrations_llm_azure.py +++ /dev/null @@ -1,53 +0,0 @@ -"""Tests for AzureOpenAILLM integration.""" - -from __future__ import annotations - -from unittest.mock import MagicMock, patch - -import pytest - -openai = pytest.importorskip("openai", reason="openai not installed") - -from lang2sql.integrations.llm.azure_ import AzureOpenAILLM - - -def _make_llm() -> AzureOpenAILLM: - return AzureOpenAILLM( - azure_deployment="gpt-4o", - azure_endpoint="https://test.openai.azure.com/", - api_key="test-key", - ) - - -def test_azure_llm_invoke_returns_string(): - mock_resp = MagicMock() - mock_resp.choices[0].message.content = "SELECT 1" - - with patch("openai.AzureOpenAI") as MockClient: - instance = MockClient.return_value - instance.chat.completions.create.return_value = mock_resp - - llm = _make_llm() - llm._client = instance - result = llm.invoke([{"role": "user", "content": "hello"}]) - - assert result == "SELECT 1" - - -def test_azure_llm_missing_dependency_raises(): - import sys - - with patch.dict(sys.modules, {"openai": None}): - # Re-import to trigger the ImportError guard - import importlib - - import lang2sql.integrations.llm.azure_ as mod - - importlib.reload(mod) - with pytest.raises(Exception): - mod.AzureOpenAILLM( - azure_deployment="x", - azure_endpoint="https://x.openai.azure.com/", - ) - # Reload back - importlib.reload(mod) diff --git a/tests/test_integrations_llm_ollama.py b/tests/test_integrations_llm_ollama.py deleted file mode 100644 index 95adf17..0000000 --- a/tests/test_integrations_llm_ollama.py +++ /dev/null @@ -1,26 +0,0 @@ -"""Tests for OllamaLLM integration.""" - -from __future__ import annotations - -from unittest.mock import MagicMock, patch - -import pytest - -ollama = pytest.importorskip("ollama", reason="ollama not installed") - -from lang2sql.integrations.llm.ollama_ import OllamaLLM - - -def test_ollama_llm_invoke_returns_string(): - mock_resp = MagicMock() - mock_resp.message.content = "SELECT 1" - - with patch("ollama.Client") as MockClient: - instance = MockClient.return_value - instance.chat.return_value = mock_resp - - llm = OllamaLLM(model="llama3", base_url="http://localhost:11434") - llm._client = instance - result = llm.invoke([{"role": "user", "content": "hello"}]) - - assert result == "SELECT 1" diff --git a/tests/test_integrations_pgvector_vectorstore.py b/tests/test_integrations_pgvector_vectorstore.py deleted file mode 100644 index 789872b..0000000 --- a/tests/test_integrations_pgvector_vectorstore.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -PGVectorStore integration tests. - -Requires a live PostgreSQL instance with pgvector installed. -Skipped when TEST_POSTGRES_URL env variable is not set. - -Example: - TEST_POSTGRES_URL="postgresql://postgres:postgres@localhost:5432/test" \\ - pytest tests/test_integrations_pgvector_vectorstore.py -v -""" - -import os -import pytest -from uuid import uuid4 - -pytestmark = pytest.mark.skipif( - not os.getenv("TEST_POSTGRES_URL"), - reason="TEST_POSTGRES_URL not set — skipping pgvector integration tests", -) - -from lang2sql.integrations.vectorstore.pgvector_ import PGVectorStore - -# ── helpers ────────────────────────────────────────────────────────────────── - - -def _unique_table() -> str: - return f"test_{uuid4().hex[:8]}" - - -def _make_store(table_name: str) -> PGVectorStore: - url = os.environ["TEST_POSTGRES_URL"] - return PGVectorStore(connection=url, table_name=table_name) - - -def _drop_table(store: PGVectorStore, table_name: str) -> None: - with store._conn.cursor() as cur: - cur.execute(f"DROP TABLE IF EXISTS {table_name};") - store._conn.commit() - - -# ── tests ───────────────────────────────────────────────────────────────────── - - -def test_pgvector_upsert_and_search(): - """Query vector returns its own id.""" - table = _unique_table() - store = _make_store(table) - try: - store.upsert(["a"], [[1.0, 0.0, 0.0, 0.0]]) - results = store.search([1.0, 0.0, 0.0, 0.0], k=1) - assert len(results) == 1 - assert results[0][0] == "a" - finally: - _drop_table(store, table) - store._conn.close() - - -def test_pgvector_upsert_is_idempotent(): - """Same id upserted twice → exactly one row in DB.""" - table = _unique_table() - store = _make_store(table) - try: - store.upsert(["a"], [[1.0, 0.0, 0.0, 0.0]]) - store.upsert(["a"], [[0.5, 0.5, 0.0, 0.0]]) # overwrite same id - - with store._conn.cursor() as cur: - cur.execute(f"SELECT COUNT(*) FROM {table} WHERE id = 'a';") - count = cur.fetchone()[0] - assert count == 1 - finally: - _drop_table(store, table) - store._conn.close() - - -def test_pgvector_search_score_in_range(): - """Score ∈ [-1, 1].""" - table = _unique_table() - store = _make_store(table) - try: - store.upsert( - ["a", "b", "c"], - [ - [1.0, 0.0, 0.0, 0.0], - [0.0, 1.0, 0.0, 0.0], - [0.0, 0.0, 1.0, 0.0], - ], - ) - results = store.search([1.0, 0.0, 0.0, 0.0], k=3) - for _, score in results: - assert -1.0 <= score <= 1.0 + 1e-6 - finally: - _drop_table(store, table) - store._conn.close() - - -def test_pgvector_search_respects_k(): - """len(results) <= k.""" - table = _unique_table() - store = _make_store(table) - try: - store.upsert( - ["a", "b", "c", "d"], - [ - [1.0, 0.0, 0.0, 0.0], - [0.0, 1.0, 0.0, 0.0], - [0.0, 0.0, 1.0, 0.0], - [0.0, 0.0, 0.0, 1.0], - ], - ) - results = store.search([1.0, 0.0, 0.0, 0.0], k=2) - assert len(results) <= 2 - finally: - _drop_table(store, table) - store._conn.close() - - -def test_pgvector_table_created_automatically(): - """Table exists in information_schema after first upsert().""" - table = _unique_table() - store = _make_store(table) - try: - store.upsert(["x"], [[1.0, 0.0]]) - with store._conn.cursor() as cur: - cur.execute( - "SELECT COUNT(*) FROM information_schema.tables " - "WHERE table_name = %s;", - (table,), - ) - count = cur.fetchone()[0] - assert count == 1 - finally: - _drop_table(store, table) - store._conn.close() - - -def test_pgvector_search_empty_store_returns_empty(): - """[] before any upsert().""" - table = _unique_table() - store = _make_store(table) - try: - results = store.search([1.0, 0.0, 0.0, 0.0], k=5) - assert results == [] - finally: - store._conn.close() diff --git a/tests/test_integrations_sqlalchemy_explorer.py b/tests/test_integrations_sqlalchemy_explorer.py deleted file mode 100644 index 60dd9b2..0000000 --- a/tests/test_integrations_sqlalchemy_explorer.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Tests for SQLAlchemyExplorer (Phase A1).""" - -from __future__ import annotations - -import pytest -from sqlalchemy import create_engine, text - -# --------------------------------------------------------------------------- -# Fixture: SQLite in-memory DB with FK schema -# --------------------------------------------------------------------------- - - -@pytest.fixture() -def engine(): - eng = create_engine("sqlite:///:memory:") - with eng.connect() as conn: - conn.execute(text(""" - CREATE TABLE customers ( - id INTEGER PRIMARY KEY, - name TEXT NOT NULL, - email TEXT UNIQUE - ) - """)) - conn.execute(text(""" - CREATE TABLE orders ( - id INTEGER PRIMARY KEY, - customer_id INTEGER NOT NULL REFERENCES customers(id), - amount REAL, - status TEXT DEFAULT 'pending' - ) - """)) - conn.execute( - text("INSERT INTO customers VALUES (1, 'Alice', 'alice@example.com')") - ) - conn.execute(text("INSERT INTO customers VALUES (2, 'Bob', 'bob@example.com')")) - conn.execute(text("INSERT INTO orders VALUES (1, 1, 99.9, 'shipped')")) - conn.execute(text("INSERT INTO orders VALUES (2, 2, 42.0, 'pending')")) - conn.commit() - return eng - - -@pytest.fixture() -def explorer(engine): - from lang2sql.integrations.db.sqlalchemy_ import SQLAlchemyExplorer - - return SQLAlchemyExplorer.from_engine(engine) - - -# --------------------------------------------------------------------------- -# Tests -# --------------------------------------------------------------------------- - - -def test_list_tables(explorer): - tables = explorer.list_tables() - assert set(tables) == {"customers", "orders"} - - -def test_get_ddl_sqlite(explorer): - ddl = explorer.get_ddl("orders") - # 원본 DDL에 REFERENCES 절 포함 확인 - assert "REFERENCES" in ddl - assert "customer_id" in ddl - - -def test_get_ddl_contains_all_columns(explorer): - ddl = explorer.get_ddl("customers") - for col in ("id", "name", "email"): - assert col in ddl - - -def test_sample_data(explorer): - rows = explorer.sample_data("customers", limit=1) - assert len(rows) == 1 - assert "name" in rows[0] - assert "email" in rows[0] - - -def test_sample_data_default_limit(explorer): - rows = explorer.sample_data("customers") - # 2행 삽입, limit=5(기본값) → 모두 반환 - assert len(rows) == 2 - - -def test_sample_data_empty_table(engine): - from lang2sql.integrations.db.sqlalchemy_ import SQLAlchemyExplorer - - with engine.connect() as conn: - conn.execute(text("CREATE TABLE empty_tbl (x INTEGER)")) - conn.commit() - - exp = SQLAlchemyExplorer.from_engine(engine) - assert exp.sample_data("empty_tbl") == [] - - -def test_execute_read_only_select(explorer): - rows = explorer.execute_read_only("SELECT id, name FROM customers ORDER BY id") - assert len(rows) == 2 - assert rows[0]["name"] == "Alice" - - -def test_execute_read_only_rejects_insert(explorer): - with pytest.raises(ValueError, match="Write operations not allowed"): - explorer.execute_read_only( - "INSERT INTO customers VALUES (3, 'Eve', 'eve@x.com')" - ) - - -def test_execute_read_only_rejects_drop(explorer): - with pytest.raises(ValueError, match="Write operations not allowed"): - explorer.execute_read_only("DROP TABLE customers") - - -def test_execute_read_only_rejects_cte_delete(explorer): - # SQLite는 CTE + DELETE를 지원하지 않으므로 rollback만 검증 - # prefix guard는 통과하지만 실제 변경이 없음을 확인 - initial = explorer.execute_read_only("SELECT COUNT(*) as cnt FROM customers") - initial_count = initial[0]["cnt"] - - # rollback-always 검증: SELECT는 정상 동작, 데이터 변경 없음 - rows = explorer.execute_read_only("SELECT * FROM customers WHERE id = 1") - assert len(rows) == 1 - - after = explorer.execute_read_only("SELECT COUNT(*) as cnt FROM customers") - assert after[0]["cnt"] == initial_count - - -def test_from_engine_shares_data(engine): - from lang2sql.integrations.db.sqlalchemy_ import SQLAlchemyExplorer - - exp1 = SQLAlchemyExplorer.from_engine(engine) - exp2 = SQLAlchemyExplorer.from_engine(engine) - - rows1 = exp1.sample_data("customers") - rows2 = exp2.sample_data("customers") - assert rows1 == rows2 - - -def test_integration_with_sqlalchemydb(engine): - from lang2sql.integrations.db.sqlalchemy_ import SQLAlchemyDB, SQLAlchemyExplorer - - # SQLAlchemyDB와 같은 engine을 SQLAlchemyExplorer가 공유 - explorer = SQLAlchemyExplorer.from_engine(engine) - - tables = explorer.list_tables() - assert "customers" in tables - - ddl = explorer.get_ddl("customers") - assert "id" in ddl diff --git a/tests/test_memory.py b/tests/test_memory.py new file mode 100644 index 0000000..add301b --- /dev/null +++ b/tests/test_memory.py @@ -0,0 +1,77 @@ +"""Memory V1 — service round trip and inject-all behaviour (★②).""" + +from __future__ import annotations + +import asyncio + +from lang2sql.memory import ( + InMemoryStore, + InjectAllRecall, + ManualExtractor, + MemoryService, +) + + +def _service() -> MemoryService: + return MemoryService(InMemoryStore(), InjectAllRecall(), ManualExtractor()) + + +def test_remember_then_recall_round_trip() -> None: + svc = _service() + + async def run() -> None: + fact = await svc.remember("u1", "revenue excludes cancelled orders") + assert fact.id + assert fact.source == "manual" + assert fact.ts > 0 + + facts = await svc.recall("u1", "anything") + assert [f.text for f in facts] == ["revenue excludes cancelled orders"] + + asyncio.run(run()) + + +def test_inject_all_returns_every_fact_for_owner() -> None: + svc = _service() + + async def run() -> None: + await svc.remember("u1", "fact a") + await svc.remember("u1", "fact b") + await svc.remember("u2", "other owner") + + facts = await svc.recall("u1", "ignored query") + assert {f.text for f in facts} == {"fact a", "fact b"} + + asyncio.run(run()) + + +def test_recall_empty_owner_is_empty_and_renders_blank() -> None: + svc = _service() + + async def run() -> None: + facts = await svc.recall("nobody", "q") + assert facts == [] + assert svc.render(facts) == "" + + asyncio.run(run()) + + +def test_render_produces_markdown_block() -> None: + svc = _service() + + async def run() -> None: + await svc.remember("u1", "fact a") + rendered = svc.render(await svc.recall("u1", "q")) + assert "## Remembered facts" in rendered + assert "- fact a" in rendered + + asyncio.run(run()) + + +def test_manual_extractor_yields_nothing() -> None: + extractor = ManualExtractor() + + async def run() -> None: + assert await extractor.extract("u1", []) == [] + + asyncio.run(run()) diff --git a/tests/test_persistence.py b/tests/test_persistence.py new file mode 100644 index 0000000..ea8dc79 --- /dev/null +++ b/tests/test_persistence.py @@ -0,0 +1,156 @@ +"""Persistence tests — SqliteSemanticStore durability + Fernet secrets (★④ §4.1). + +Two guarantees under test: (1) semantic definitions and secrets survive a fresh +store instance pointed at the same sqlite file, and (2) secrets are stored as +real Fernet ciphertext, not recoverable without the key. +""" + +from __future__ import annotations + +import asyncio +import os + +import pytest +from cryptography.fernet import Fernet, InvalidToken + +from lang2sql.adapters.storage.sqlite_semantic import SqliteSemanticStore +from lang2sql.adapters.storage.sqlite_store import SqliteStore +from lang2sql.core.identity import Identity, Scope, ScopeLevel +from lang2sql.semantic.types import Metric, SemanticKind +from lang2sql.tenancy.encrypted_secrets import EncryptedSecrets +from lang2sql.tenancy.scope_resolver import ScopeResolver + + +def test_semantic_store_add_entries_at_round_trip() -> None: + store = SqliteSemanticStore() + scope = Scope(ScopeLevel.CHANNEL, "c1") + store.add(scope, Metric("revenue", "sum of order totals", created_by="u1")) + + entries = store.entries_at(scope) + assert len(entries) == 1 + assert entries[0].name == "revenue" + assert entries[0].kind is SemanticKind.METRIC + assert entries[0].definition == "sum of order totals" + assert entries[0].created_by == "u1" + # created_at is preserved, not regenerated. + assert entries[0].created_at != "" + + # Re-adding the same name at the same scope replaces it. + store.add(scope, Metric("revenue", "net revenue")) + entries = store.entries_at(scope) + assert len(entries) == 1 + assert entries[0].definition == "net revenue" + + # A different scope is isolated. + assert store.entries_at(Scope(ScopeLevel.CHANNEL, "other")) == [] + + +def test_semantic_store_survives_new_instance_on_same_file(tmp_path) -> None: + db = str(tmp_path / "semantic.db") + scope = Scope(ScopeLevel.GUILD, "g1") + + writer = SqliteSemanticStore(db) + writer.add(scope, Metric("aov", "avg order value", source_id="doc-7")) + created_at = writer.entries_at(scope)[0].created_at + writer.close() + + # A fresh instance on the same path sees the persisted definition. + reader = SqliteSemanticStore(db) + entries = reader.entries_at(scope) + assert len(entries) == 1 + assert entries[0].name == "aov" + assert entries[0].definition == "avg order value" + assert entries[0].source_id == "doc-7" + assert entries[0].created_at == created_at + reader.close() + + +def test_scope_resolver_effective_layer_over_sqlite_store() -> None: + store = SqliteSemanticStore() + # Narrow (channel) overrides wide (guild) for the same name. + store.add(Scope(ScopeLevel.GUILD, "g1"), Metric("revenue", "guild-wide def")) + store.add(Scope(ScopeLevel.CHANNEL, "c1"), Metric("revenue", "channel def")) + store.add(Scope(ScopeLevel.GUILD, "g1"), Metric("churn", "guild churn")) + + resolver = ScopeResolver(store) + identity = Identity(user_id="u1", guild_id="g1", channel_id="c1") + layer = asyncio.run(resolver.effective_layer(identity)) + + by_name = {e.name: e.definition for e in layer.entries} + assert by_name["revenue"] == "channel def" # most specific wins + assert by_name["churn"] == "guild churn" # inherited from guild + + +def test_encrypted_secrets_round_trip_and_ciphertext(tmp_path) -> None: + # Explicit key so the test is independent of env / generated state. + key = Fernet.generate_key() + store = SqliteStore() + secrets = EncryptedSecrets(store, key=key) + + assert asyncio.run(secrets.get("guild:1", "dsn")) is None + asyncio.run(secrets.set("guild:1", "dsn", "postgresql://u:p@host/db")) + assert asyncio.run(secrets.get("guild:1", "dsn")) == "postgresql://u:p@host/db" + + # What actually lands in kv is a Fernet token, never the plaintext. + blob = store.kv_get("guild:1", "dsn") + assert blob is not None + assert "postgresql" not in blob + assert blob != "postgresql://u:p@host/db" + # It decrypts only with the right key. + assert Fernet(key).decrypt(blob.encode("ascii")).decode() == "postgresql://u:p@host/db" + + asyncio.run(secrets.delete("guild:1", "dsn")) + assert asyncio.run(secrets.get("guild:1", "dsn")) is None + + +def test_encrypted_secrets_wrong_key_fails() -> None: + store = SqliteStore() + asyncio.run(EncryptedSecrets(store, key=Fernet.generate_key()).set("s", "k", "v")) + + # A different key cannot decrypt the stored token. + attacker = EncryptedSecrets(store, key=Fernet.generate_key()) + with pytest.raises(InvalidToken): + asyncio.run(attacker.get("s", "k")) + + +def test_encrypted_secrets_survive_new_instance_via_persisted_key(tmp_path) -> None: + db = str(tmp_path / "secrets.db") + # No env key -> a key is generated and persisted in the kv table. + saved = os.environ.pop("LANG2SQL_SECRET_KEY", None) + try: + store = SqliteStore(db) + asyncio.run(EncryptedSecrets(store).set("guild:1", "dsn", "postgresql://x")) + store.close() + + # A fresh store + secrets on the same file reuses the persisted key. + reopened = SqliteStore(db) + secrets = EncryptedSecrets(reopened) + assert asyncio.run(secrets.get("guild:1", "dsn")) == "postgresql://x" + reopened.close() + finally: + if saved is not None: + os.environ["LANG2SQL_SECRET_KEY"] = saved + + +def test_concierge_path_persists_definitions_and_secrets(tmp_path) -> None: + from lang2sql.adapters.llm.fake import FakeLLM + from lang2sql.tenancy.concierge import ContextConcierge + + db = str(tmp_path / "concierge.db") + saved = os.environ.pop("LANG2SQL_SECRET_KEY", None) + try: + c1 = ContextConcierge(path=db, llm=FakeLLM()) + scope = Scope(ScopeLevel.CHANNEL, "c1") + asyncio.run(c1.scope_resolver.define(scope, Metric("revenue", "sum totals"))) + asyncio.run(c1.secrets.set("guild:1", "dsn", "postgresql://secret")) + c1.store.close() + c1.scope_resolver.store.close() + + c2 = ContextConcierge(path=db, llm=FakeLLM()) + assert [e.name for e in asyncio.run(c2.scope_resolver.entries_at(scope))] == [ + "revenue" + ] + assert asyncio.run(c2.secrets.get("guild:1", "dsn")) == "postgresql://secret" + finally: + if saved is not None: + os.environ["LANG2SQL_SECRET_KEY"] = saved diff --git a/tests/test_safety.py b/tests/test_safety.py new file mode 100644 index 0000000..669f9db --- /dev/null +++ b/tests/test_safety.py @@ -0,0 +1,125 @@ +"""V1 safety pipeline regressions — the 12 CI-gate cases from §4.5. + +Gate-time vs exec-time distinction +---------------------------------- +The Whitelist + Timeout pipeline only decides whether SQL is *allowed to run*. +It does NOT execute SQL, so anything whose failure surfaces only when the query +actually runs against the DB is an EXECUTION-time concern and PASSes the gate: + + * #6 SELECT * FROM nonexistent -> PG raises at run time (gate PASSes) + * #7 SELECT pg_sleep(60) -> bounded by ctx.timeout_seconds at run time + * #8 SELECT * FROM huge_table -> truncated by row_limit at run time + +The Timeout layer is itself an exec-config layer: it sets ctx.timeout_seconds +and PASSes; it never inspects SQL for slow constructs. So #7's protection shows +up as "PASS + timeout configured", not as a BLOCK. + +Only the statement-shape violations (#1-5, #12) BLOCK; #9/#10/#11 PASS. +""" + +from __future__ import annotations + +from lang2sql.core.ports.safety import SafetyContext, Verdict +from lang2sql.safety import SafetyPipeline + + +def _verdict(sql: str) -> Verdict: + pipeline = SafetyPipeline() + return pipeline.evaluate(sql, SafetyContext()).verdict + + +def _decision(sql: str): + pipeline = SafetyPipeline() + return pipeline.evaluate(sql, SafetyContext()) + + +# --- BLOCK cases (#1-5, #12) ------------------------------------------------- + + +def test_case_01_drop_table_blocks(): + assert _verdict("DROP TABLE users") is Verdict.BLOCK + + +def test_case_02_multi_statement_blocks(): + assert _verdict("; DELETE FROM t; --") is Verdict.BLOCK + + +def test_case_03_insert_blocks(): + assert _verdict("INSERT INTO t VALUES (1)") is Verdict.BLOCK + + +def test_case_04_update_blocks(): + assert _verdict("UPDATE t SET x=1") is Verdict.BLOCK + + +def test_case_05_cte_insert_fail_closed_blocks(): + # WITH starts the statement but an INSERT keyword lurks in the CTE body. + sql = "WITH x AS (INSERT INTO t VALUES (1)) SELECT * FROM x" + decision = _decision(sql) + assert decision.verdict is Verdict.BLOCK + assert "INSERT" in decision.reason + + +def test_case_12_empty_string_parse_error_blocks(): + decision = _decision("") + assert decision.verdict is Verdict.BLOCK + assert decision.reason == "parse_error" + + +# --- EXECUTION-time concerns: PASS the gate (#6, #7, #8) --------------------- + + +def test_case_06_nonexistent_table_passes_gate(): + # Resolution failure is a run-time PG error, not a gate decision. + assert _verdict("SELECT * FROM nonexistent") is Verdict.PASS + + +def test_case_07_pg_sleep_passes_gate_with_timeout_configured(): + sql = "SELECT pg_sleep(60)" + ctx = SafetyContext() + ctx.timeout_seconds = 0 # simulate "unset" to prove the layer clamps it + decision = SafetyPipeline().evaluate(sql, ctx) + assert decision.verdict is Verdict.PASS + # Timeout layer must have ensured a positive bound for run-time enforcement. + assert ctx.timeout_seconds == 30 + + +def test_case_08_huge_table_passes_gate(): + # row_limit truncation happens at execution time, not at the gate. + assert _verdict("SELECT * FROM huge_table") is Verdict.PASS + + +# --- PASS cases (#9, #10, #11) ----------------------------------------------- + + +def test_case_09_select_one_passes(): + assert _verdict("SELECT 1") is Verdict.PASS + + +def test_case_10_cte_select_passes(): + assert _verdict("WITH a AS (SELECT 1) SELECT * FROM a") is Verdict.PASS + + +def test_case_11_explain_select_passes(): + assert _verdict("EXPLAIN SELECT 1") is Verdict.PASS + + +# --- Extra guards (foreshadow V1.5; verify V1 fail-closed today) ------------- + + +def test_explain_analyze_delete_blocks(): + # §4.5 notes EXPLAIN ANALYZE DELETE as a V1.5 regression; V1's fail-closed + # keyword scan + leading-keyword check already blocks it. + assert _verdict("EXPLAIN ANALYZE DELETE FROM t") is Verdict.BLOCK + + +def test_default_timeout_set_on_pass(): + ctx = SafetyContext() + SafetyPipeline().evaluate("SELECT 1", ctx) + assert ctx.timeout_seconds == 30 + + +def test_pipeline_exposes_default_layers(): + pipeline = SafetyPipeline() + names = [layer.name for layer in pipeline.layers] + assert names == ["whitelist", "timeout"] diff --git a/tests/test_semantic.py b/tests/test_semantic.py new file mode 100644 index 0000000..acf0f0c --- /dev/null +++ b/tests/test_semantic.py @@ -0,0 +1,115 @@ +"""Tests for the semantic federation layer (★④). + +Plain functions named ``test_*`` — runnable without pytest via the validation +one-liner in the spawn brief. +""" + +from __future__ import annotations + +import asyncio + +from lang2sql.core.identity import Identity, Scope, ScopeLevel +from lang2sql.semantic import ( + Metric, + SemanticEntry, + SemanticKind, + SemanticLayer, + SemanticStore, + merge_scoped, +) +from lang2sql.tenancy.scope_resolver import ScopeResolver + + +def test_render_empty_is_blank() -> None: + assert SemanticLayer().render() == "" + + +def test_render_lists_entries() -> None: + layer = SemanticLayer( + [ + SemanticEntry(SemanticKind.METRIC, "active_user", "30d login"), + SemanticEntry( + SemanticKind.DIMENSION, "region", "billing country", applies_to="users" + ), + ] + ) + rendered = layer.render() + assert "**active_user** [metric]: 30d login" in rendered + assert "**region** [dimension]: billing country (applies to users)" in rendered + # one bullet line per entry + assert len(rendered.splitlines()) == 2 + assert all(line.startswith("- ") for line in rendered.splitlines()) + + +def test_layer_add_replaces_same_name() -> None: + layer = SemanticLayer([SemanticEntry(SemanticKind.METRIC, "rev", "gross")]) + layer.add(SemanticEntry(SemanticKind.METRIC, "rev", "net")) + assert len(layer.entries) == 1 + assert layer.lookup("rev").definition == "net" + + +def test_scoped_merge_narrow_wins() -> None: + """active_user defined at channel must override the guild definition.""" + guild = Scope(ScopeLevel.GUILD, "g1") + channel = Scope(ScopeLevel.CHANNEL, "c1") + merged = merge_scoped( + [ + (channel, [Metric(name="active_user", definition="7d core action")]), + (guild, [Metric(name="active_user", definition="30d login")]), + ] + ) + assert merged.lookup("active_user").definition == "7d core action" + + +def test_scoped_merge_wider_fills_gaps() -> None: + guild = Scope(ScopeLevel.GUILD, "g1") + channel = Scope(ScopeLevel.CHANNEL, "c1") + merged = merge_scoped( + [ + (channel, [Metric(name="active_user", definition="7d")]), + (guild, [Metric(name="revenue", definition="net")]), + ] + ) + assert merged.lookup("active_user").definition == "7d" + assert merged.lookup("revenue").definition == "net" + + +def test_resolver_effective_layer_guild_channel() -> None: + store = SemanticStore() + resolver = ScopeResolver(store) + identity = Identity(user_id="u1", guild_id="g1", channel_id="c1") + + async def scenario() -> SemanticLayer: + await resolver.define( + Scope(ScopeLevel.GUILD, "g1"), + Metric(name="active_user", definition="30d login"), + ) + await resolver.define( + Scope(ScopeLevel.GUILD, "g1"), + Metric(name="revenue", definition="gross"), + ) + # channel override of active_user only + await resolver.define( + Scope(ScopeLevel.CHANNEL, "c1"), + Metric(name="active_user", definition="7d core action"), + ) + return await resolver.effective_layer(identity) + + layer = asyncio.run(scenario()) + assert layer.lookup("active_user").definition == "7d core action" # channel wins + assert layer.lookup("revenue").definition == "gross" # inherited from guild + + +def test_resolver_entries_at_no_inheritance() -> None: + store = SemanticStore() + resolver = ScopeResolver(store) + + async def scenario() -> list[SemanticEntry]: + await resolver.define( + Scope(ScopeLevel.GUILD, "g1"), + Metric(name="revenue", definition="gross"), + ) + # channel has nothing of its own + return await resolver.entries_at(Scope(ScopeLevel.CHANNEL, "c1")) + + assert asyncio.run(scenario()) == [] diff --git a/tests/test_tenancy.py b/tests/test_tenancy.py new file mode 100644 index 0000000..be148ac --- /dev/null +++ b/tests/test_tenancy.py @@ -0,0 +1,79 @@ +"""Tenancy tests — EncryptedSecrets round trip + ContextConcierge wiring. + +No network: with no OPENAI_API_KEY the concierge falls back to FakeLLM. +""" + +from __future__ import annotations + +import asyncio +import os + +from lang2sql.adapters.llm.fake import FakeLLM +from lang2sql.adapters.storage.sqlite_store import SqliteStore +from lang2sql.core.identity import Identity +from lang2sql.core.types import Message, Role +from lang2sql.harness.context import HarnessContext +from lang2sql.harness.session import Session +from lang2sql.tenancy.concierge import ContextConcierge +from lang2sql.tenancy.encrypted_secrets import EncryptedSecrets + + +def test_encrypted_secrets_round_trip() -> None: + store = SqliteStore() + secrets = EncryptedSecrets(store) + + assert asyncio.run(secrets.get("guild:1", "dsn")) is None + asyncio.run(secrets.set("guild:1", "dsn", "postgresql://u:p@host/db")) + assert asyncio.run(secrets.get("guild:1", "dsn")) == "postgresql://u:p@host/db" + + # Stored value is obfuscated, not plaintext. + assert store.kv_get("guild:1", "dsn") != "postgresql://u:p@host/db" + + asyncio.run(secrets.set("guild:1", "dsn", "postgresql://new")) # overwrite + assert asyncio.run(secrets.get("guild:1", "dsn")) == "postgresql://new" + + asyncio.run(secrets.delete("guild:1", "dsn")) + assert asyncio.run(secrets.get("guild:1", "dsn")) is None + + +def test_build_context_populates_llm_and_session() -> None: + saved = os.environ.pop("OPENAI_API_KEY", None) + try: + concierge = ContextConcierge() + identity = Identity(user_id="u1", guild_id="g", channel_id="c") + ctx = asyncio.run(concierge.build_context(identity, user_text="how many orders?")) + + assert isinstance(ctx, HarnessContext) + assert isinstance(ctx.llm, FakeLLM) # no key → fallback + assert isinstance(ctx.session, Session) + assert ctx.session.identity == identity + assert ctx.explorer is not None + assert ctx.safety is not None + assert ctx.scope_resolver is not None + assert ctx.audit is not None + finally: + if saved is not None: + os.environ["OPENAI_API_KEY"] = saved + + +def test_build_context_restores_saved_session() -> None: + store = SqliteStore() + identity = Identity(user_id="u1", channel_id="c", thread_id="t") + prior = Session(identity=identity) + prior.add(Message(role=Role.USER, content="earlier turn")) + asyncio.run(store.save(identity.session_key(), prior)) + + concierge = ContextConcierge(store=store, llm=FakeLLM()) + ctx = asyncio.run(concierge.build_context(identity)) + + assert len(ctx.session.transcript) == 1 + assert ctx.session.transcript[0].content == "earlier turn" + + +def test_injected_overrides_are_used() -> None: + store = SqliteStore() + fake = FakeLLM() + concierge = ContextConcierge(store=store, llm=fake) + ctx = asyncio.run(concierge.build_context(Identity(user_id="u1"))) + assert ctx.llm is fake + assert ctx.audit is store diff --git a/utils/data/README.md b/utils/data/README.md deleted file mode 100644 index f9294c4..0000000 --- a/utils/data/README.md +++ /dev/null @@ -1,449 +0,0 @@ -# utils/data 패키지 - -DataHub와의 상호작용을 위한 데이터 관련 유틸리티 패키지입니다. - -## 디렉토리 구조 - -``` -utils/data/ -├── __pycache__/ -├── datahub_services/ -│ ├── __pycache__/ -│ ├── base_client.py # 기본 클라이언트 -│ ├── glossary_service.py # 용어집 서비스 -│ ├── metadata_service.py # 메타데이터 서비스 -│ ├── query_service.py # 쿼리 서비스 -│ ├── __init__.py # 패키지 초기화 및 exports -│ └── README.md # 서비스 상세 문서 -├── datahub_source.py # 통합 메타데이터 페처 -└── queries.py # GraphQL 쿼리 모음 -``` - -## 파일별 상세 내용 - -### `datahub_source.py` - -DataHub 메타데이터 페처 - 리팩토링된 버전으로 기존 `DatahubMetadataFetcher`의 모든 기능을 유지하면서 내부적으로는 분리된 서비스 모듈들을 사용합니다. - -**클래스:** -- `DatahubMetadataFetcher`: 기존 인터페이스를 유지하는 통합 페처 - -**초기화:** -```python -from utils.data.datahub_source import DatahubMetadataFetcher - -fetcher = DatahubMetadataFetcher(gms_server="http://localhost:8080", extra_headers={}) -``` - -**주요 메서드:** - -#### 메타데이터 관련 메서드 - -##### `get_urns()` -필터를 적용하여 데이터셋의 URN 목록을 가져옵니다. - -**반환값:** -- URN 목록 - -##### `get_table_name(urn)` -URN에 대한 테이블 이름을 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `str`: 테이블 이름 (database.table 형태) 또는 None - -##### `get_table_description(urn)` -URN에 대한 테이블 설명을 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `str`: 테이블 설명 또는 None - -##### `get_column_names_and_descriptions(urn)` -URN에 대한 컬럼 이름 및 설명을 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `list`: 컬럼 정보 리스트 (각 항목: column_name, column_description, column_type) - -##### `get_table_lineage(urn, counts=100, direction="DOWNSTREAM", degree_values=None)` -URN에 대한 DOWNSTREAM/UPSTREAM lineage entity를 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN -- `counts` (int): 가져올 엔티티 수 (기본값: 100) -- `direction` (str): 리니지 방향 ("DOWNSTREAM" 또는 "UPSTREAM", 기본값: "DOWNSTREAM") -- `degree_values` (list): 필터링할 degree 값 리스트 (기본값: ["1", "2"]) - -**반환값:** -- `tuple`: (urn, lineage_result) - -##### `get_column_lineage(urn)` -URN에 대한 UPSTREAM lineage의 column source를 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `dict`: 컬럼 리니지 정보 - - `downstream_dataset`: 다운스트림 데이터셋 이름 - - `lineage_by_upstream_dataset`: 업스트림 데이터셋별 컬럼 매핑 - -##### `min_degree_lineage(lineage_result)` -lineage 중 최소 degree만 가져옵니다. - -**파라미터:** -- `lineage_result`: `get_table_lineage`의 반환값 - -**반환값:** -- `dict`: 테이블별 최소 degree 매핑 - -##### `build_table_metadata(urn, max_degree=2, sort_by_degree=True)` -테이블 단위로 통합 메타데이터를 생성합니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN -- `max_degree` (int): 최대 degree 제한 (기본값: 2) -- `sort_by_degree` (bool): degree 기준 정렬 여부 (기본값: True) - -**반환값:** -- `dict`: 통합 메타데이터 - - `table_name`: 테이블 이름 - - `description`: 테이블 설명 - - `columns`: 컬럼 정보 리스트 - - `lineage`: 리니지 정보 - -##### `get_urn_info(urn)` -특정 URN에 대한 모든 관련 정보를 가져옵니다. - -**파라미터:** -- `urn` (str): 조회할 데이터셋 URN - -**반환값:** -- `dict`: URN에 대한 전체 메타데이터 정보 - -##### `_print_urn_details(metadata)` -URN 메타데이터를 보기 좋게 출력하는 내부 함수입니다. - -#### 용어집 관련 메서드 - -##### `get_root_glossary_nodes()` -DataHub에서 루트 용어집 노드를 가져옵니다. - -**반환값:** -- `dict`: 루트 용어집 노드 정보 - -##### `get_glossary_node_by_urn(urn)` -특정 URN의 용어집 노드 및 그 자식 항목을 가져옵니다. - -**파라미터:** -- `urn` (str): 용어집 노드의 URN - -**반환값:** -- `dict`: 용어집 노드 정보와 자식 항목 - -##### `get_node_basic_info(node, index)` -용어집 노드의 기본 정보를 딕셔너리로 반환합니다. - -**파라미터:** -- `node` (dict): 용어집 노드 정보 -- `index` (int): 노드의 인덱스 - -**반환값:** -- `dict`: 노드의 기본 정보 - -##### `get_child_entity_info(entity, index)` -자식 엔티티(용어 또는 노드)의 정보를 딕셔너리로 반환합니다. - -**파라미터:** -- `entity` (dict): 자식 엔티티 정보 -- `index` (int): 엔티티의 인덱스 - -**반환값:** -- `dict`: 엔티티 정보 - -##### `process_node_details(node)` -노드의 상세 정보를 처리하고 딕셔너리로 반환합니다. - -**파라미터:** -- `node` (dict): 용어집 노드 정보 - -**반환값:** -- `dict`: 노드의 상세 정보 - -##### `process_glossary_nodes(result)` -용어집 노드 결과를 처리하고 딕셔너리로 반환합니다. - -**파라미터:** -- `result` (dict): API 응답 결과 - -**반환값:** -- `dict`: 처리된 용어집 노드 데이터 - -##### `get_glossary_data()` -DataHub에서 전체 용어집 데이터를 가져와 처리합니다. - -**반환값:** -- `dict`: 처리된 용어집 데이터 - -##### `get_glossary_terms_by_urn(dataset_urn)` -특정 데이터셋 URN의 glossary terms를 조회합니다. - -**파라미터:** -- `dataset_urn` (str): 데이터셋 URN - -**반환값:** -- `dict`: glossary terms 정보 - -#### 쿼리 관련 메서드 - -##### `get_queries(start=0, count=10, query="*", filters=None)` -DataHub에서 쿼리 목록을 가져옵니다. - -**파라미터:** -- `start` (int): 시작 인덱스 (기본값: 0) -- `count` (int): 반환할 쿼리 수 (기본값: 10) -- `query` (str): 필터링에 사용할 쿼리 문자열 (기본값: "*") -- `filters` (list): 추가 필터 (기본값: None) - -**반환값:** -- `dict`: 쿼리 목록 정보 - -##### `process_queries(result)` -쿼리 목록 결과를 처리하고 간소화된 형태로 반환합니다. - -**파라미터:** -- `result` (dict): API 응답 결과 - -**반환값:** -- `dict`: 처리된 쿼리 목록 데이터 - -##### `get_query_data(start=0, count=10, query="*", filters=None)` -DataHub에서 쿼리 목록을 가져와 처리합니다. - -**파라미터:** -- `start` (int): 시작 인덱스 (기본값: 0) -- `count` (int): 반환할 쿼리 수 (기본값: 10) -- `query` (str): 필터링에 사용할 쿼리 문자열 (기본값: "*") -- `filters` (list): 추가 필터 (기본값: None) - -**반환값:** -- `dict`: 처리된 쿼리 목록 데이터 - -##### `get_queries_by_urn(dataset_urn)` -특정 데이터셋 URN과 연관된 쿼리들을 조회합니다. - -**파라미터:** -- `dataset_urn` (str): 데이터셋 URN - -**반환값:** -- `dict`: 연관된 쿼리 목록 - -**의존성:** -- `utils.data.datahub_services.base_client.DataHubBaseClient`: 기본 클라이언트 -- `utils.data.datahub_services.metadata_service.MetadataService`: 메타데이터 서비스 -- `utils.data.datahub_services.query_service.QueryService`: 쿼리 서비스 -- `utils.data.datahub_services.glossary_service.GlossaryService`: 용어집 서비스 - -**사용 예시:** -```python -from utils.data.datahub_source import DatahubMetadataFetcher - -# 페처 초기화 -fetcher = DatahubMetadataFetcher(gms_server="http://localhost:8080") - -# 테이블 메타데이터 조회 -urn = "urn:li:dataset:(urn:li:dataPlatform:postgres,db.schema.table,TABLE)" -metadata = fetcher.get_urn_info(urn) - -# 용어집 조회 -glossary_data = fetcher.get_glossary_data() - -# 쿼리 조회 -queries = fetcher.get_query_data(start=0, count=10) -``` - -**import 되어 사용되는 위치:** -- `utils/llm/tools/datahub.py`: `from utils.data.datahub_source import DatahubMetadataFetcher` - ---- - -### `queries.py` - -DataHub GraphQL 쿼리 모음 파일입니다. DataHub GMS 서버와 통신하기 위한 모든 GraphQL 쿼리 문자열을 포함합니다. - -**주요 쿼리:** - -#### `ROOT_GLOSSARY_NODES_QUERY` -루트 용어집 노드를 조회하는 쿼리입니다. 4단계 계층 구조까지 포함합니다. - -**사용 위치:** -- `utils.data.datahub_services.glossary_service.GlossaryService.get_root_glossary_nodes()` - -#### `GLOSSARY_NODE_QUERY` -특정 URN의 용어집 노드 상세 정보를 조회하는 쿼리입니다. 자식 노드, 용어, 소유권, 권한 등의 상세 정보를 포함합니다. - -**파라미터:** -- `urn` (str): 용어집 노드 URN - -**사용 위치:** -- `utils.data.datahub_services.glossary_service.GlossaryService.get_glossary_node_by_urn()` - -#### `LIST_QUERIES_QUERY` -쿼리 목록을 조회하는 쿼리입니다. URN, 이름, 설명, SQL 문, 데이터셋 정보 등을 포함합니다. - -**파라미터:** -- `input` (dict): ListQueriesInput - - `start` (int): 시작 인덱스 - - `count` (int): 반환할 항목 수 - - `query` (str): 검색 쿼리 - - `filters` (list, optional): 추가 필터 - -**사용 위치:** -- `utils.data.datahub_services.query_service.QueryService.get_queries()` - -#### `QUERIES_BY_URN_QUERY` -특정 URN과 연관된 쿼리를 조회하는 단순화된 쿼리입니다. - -**파라미터:** -- `input` (dict): ListQueriesInput - -**사용 위치:** -- `utils.data.datahub_services.query_service.QueryService.get_queries_by_urn()` - -#### `GLOSSARY_TERMS_BY_URN_QUERY` -특정 데이터셋 URN의 glossary terms를 조회하는 쿼리입니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**사용 위치:** -- `utils.data.datahub_services.glossary_service.GlossaryService.get_glossary_terms_by_urn()` -- `utils.data.datahub_services.query_service.QueryService.get_glossary_terms_by_urn()` - -**import 되어 사용되는 위치:** -- `utils.data.datahub_services.glossary_service`: `from utils.data.queries import ...` -- `utils.data.datahub_services.query_service`: `from utils.data.queries import ...` - ---- - -### `datahub_services/` - -DataHub와의 상호작용을 위한 서비스 모듈들을 제공하는 서브패키지입니다. - -**주요 구성요소:** -- `base_client.py`: 기본 연결 및 통신 -- `glossary_service.py`: 용어집 서비스 -- `metadata_service.py`: 메타데이터 서비스 -- `query_service.py`: 쿼리 서비스 -- `__init__.py`: 패키지 초기화 및 exports - -상세한 문서는 [datahub_services/README.md](datahub_services/README.md)를 참조하세요. - ---- - -## 전체 사용 예시 - -### 예시 1: 기본 사용법 - -```python -from utils.data.datahub_source import DatahubMetadataFetcher - -# 페처 초기화 -fetcher = DatahubMetadataFetcher(gms_server="http://localhost:8080") - -# 테이블 메타데이터 조회 -urn = "urn:li:dataset:(urn:li:dataPlatform:postgres,db.schema.table,TABLE)" -metadata = fetcher.get_urn_info(urn) - -# 용어집 조회 -glossary_data = fetcher.get_glossary_data() - -# 쿼리 조회 -queries = fetcher.get_query_data(start=0, count=10) -``` - -### 예시 2: 분리된 서비스 사용 - -```python -from utils.data.datahub_services import ( - DataHubBaseClient, - MetadataService, - QueryService, - GlossaryService -) - -# 클라이언트 초기화 -client = DataHubBaseClient(gms_server="http://localhost:8080") - -# 서비스 초기화 -metadata_service = MetadataService(client) -query_service = QueryService(client) -glossary_service = GlossaryService(client) - -# 메타데이터 조회 -urn = "urn:li:dataset:(...)" -metadata = metadata_service.build_table_metadata(urn) - -# 쿼리 조회 -queries = query_service.get_query_data(start=0, count=10) - -# 용어집 조회 -glossary_data = glossary_service.get_glossary_data() -``` - -### 예시 3: 개별 메서드 사용 - -```python -from utils.data.datahub_source import DatahubMetadataFetcher - -fetcher = DatahubMetadataFetcher(gms_server="http://localhost:8080") - -# 테이블 정보 조회 -urn = "urn:li:dataset:(...)" -table_name = fetcher.get_table_name(urn) -description = fetcher.get_table_description(urn) -columns = fetcher.get_column_names_and_descriptions(urn) - -# 리니지 조회 -downstream_lineage = fetcher.get_table_lineage(urn, direction="DOWNSTREAM") -upstream_lineage = fetcher.get_table_lineage(urn, direction="UPSTREAM") -column_lineage = fetcher.get_column_lineage(urn) - -# 최소 degree 필터링 -min_degree = fetcher.min_degree_lineage(downstream_lineage) -``` - ---- - -## 의존성 - -### 외부 패키지 -- `requests`: HTTP 요청 처리 -- `datahub`: DataHub Python SDK - - `datahub.emitter.rest_emitter.DatahubRestEmitter` - - `datahub.ingestion.graph.client.DatahubClientConfig` - - `datahub.ingestion.graph.client.DataHubGraph` - - `datahub.metadata.schema_classes` - -### 내부 패키지 -- `utils.data.queries`: GraphQL 쿼리 정의 모듈 -- `utils.data.datahub_services.*`: DataHub 서비스 레이어 - ---- - -## 참고 사항 - -1. `DatahubMetadataFetcher`는 하위 호환성을 위해 분리된 서비스들을 내부적으로 사용합니다. -2. 모든 서비스는 `DataHubBaseClient`를 필요로 합니다. -3. GMS 서버 URL은 초기화 시 유효성 검사가 수행됩니다. -4. GraphQL 쿼리는 `queries.py`에 중앙 집중식으로 정의되어 있습니다. -5. 오류 발생 시 dict 형태로 `{"error": True, "message": "..."}` 구조로 반환됩니다. -6. `utils/llm/tools/datahub.py`에서 `DatahubMetadataFetcher`를 import하여 LLM 도구로 사용됩니다. - diff --git a/utils/data/datahub_services/README.md b/utils/data/datahub_services/README.md deleted file mode 100644 index 203c305..0000000 --- a/utils/data/datahub_services/README.md +++ /dev/null @@ -1,527 +0,0 @@ -# DataHub Services 패키지 - -DataHub와의 상호작용을 위한 서비스 모듈들을 제공하는 패키지입니다. - -## 디렉토리 구조 - -``` -datahub_services/ -├── __init__.py # 패키지 초기화 및 exports -├── base_client.py # 기본 클라이언트 -├── glossary_service.py # 용어집 서비스 -├── metadata_service.py # 메타데이터 서비스 -└── query_service.py # 쿼리 서비스 -``` - -## 파일별 상세 내용 - -### `__init__.py` - -패키지의 진입점이며, 주요 서비스 클래스들을 export합니다. - -**Export되는 클래스:** -- `DataHubBaseClient`: 기본 연결 및 통신 클라이언트 -- `MetadataService`: 메타데이터, 리니지, URN 관련 서비스 -- `QueryService`: 쿼리 관련 서비스 -- `GlossaryService`: 용어집 관련 서비스 - -**사용 방법:** -```python -from utils.data.datahub_services import ( - DataHubBaseClient, - MetadataService, - QueryService, - GlossaryService -) - -# 클라이언트 초기화 -client = DataHubBaseClient(gms_server="http://localhost:8080") - -# 서비스 초기화 -metadata_service = MetadataService(client) -query_service = QueryService(client) -glossary_service = GlossaryService(client) -``` - ---- - -### `base_client.py` - -DataHub GMS 서버와의 기본 연결 및 통신 기능을 제공하는 클라이언트입니다. - -**클래스:** -- `DataHubBaseClient` - -**주요 메서드:** - -#### `__init__(self, gms_server="http://localhost:8080", extra_headers={})` -DataHub 클라이언트를 초기화합니다. - -**파라미터:** -- `gms_server` (str): DataHub GMS 서버 URL -- `extra_headers` (dict): 추가 HTTP 헤더 - -**사용 예시:** -```python -from utils.data.datahub_services import DataHubBaseClient - -client = DataHubBaseClient(gms_server="http://localhost:8080") -``` - -#### `_is_valid_gms_server(self, gms_server)` -GMS 서버 주소의 유효성을 검사합니다. - -**파라미터:** -- `gms_server` (str): 검사할 GMS 서버 URL - -**반환값:** -- `bool`: 서버가 유효한 경우 True - -#### `execute_graphql_query(self, query, variables=None)` -GraphQL 쿼리를 실행합니다. - -**파라미터:** -- `query` (str): GraphQL 쿼리 문자열 -- `variables` (dict, optional): 쿼리 변수 - -**반환값:** -- `dict`: GraphQL 응답 또는 오류 정보 - -**사용 예시:** -```python -query = "{ health { status } }" -result = client.execute_graphql_query(query) -``` - -#### `get_datahub_graph(self)` -DataHub Graph 클라이언트를 반환합니다. - -**반환값:** -- DataHub Graph 객체 - -#### `get_urns(self)` -필터를 적용하여 데이터셋의 URN 목록을 가져옵니다. - -**반환값:** -- URN 목록 - -**의존성:** -- `requests`: HTTP 요청 처리 -- `datahub.emitter.rest_emitter.DatahubRestEmitter`: DataHub REST emitter - ---- - -### `glossary_service.py` - -DataHub의 용어집(Glossary) 관련 기능을 제공하는 서비스입니다. - -**클래스:** -- `GlossaryService` - -**초기화:** -```python -from utils.data.datahub_services import DataHubBaseClient, GlossaryService - -client = DataHubBaseClient(gms_server="http://localhost:8080") -glossary_service = GlossaryService(client) -``` - -**주요 메서드:** - -#### `get_root_glossary_nodes(self)` -DataHub에서 루트 용어집 노드를 가져옵니다. - -**반환값:** -- `dict`: 루트 용어집 노드 정보 - -#### `get_glossary_node_by_urn(self, urn)` -특정 URN의 용어집 노드 및 그 자식 항목을 가져옵니다. - -**파라미터:** -- `urn` (str): 용어집 노드의 URN - -**반환값:** -- `dict`: 용어집 노드 정보와 자식 항목 - -#### `get_node_basic_info(self, node, index)` -용어집 노드의 기본 정보를 딕셔너리로 반환합니다. - -**파라미터:** -- `node` (dict): 용어집 노드 정보 -- `index` (int): 노드의 인덱스 - -**반환값:** -- `dict`: 노드의 기본 정보 (index, name, description, child_count 등) - -#### `get_child_entity_info(self, entity, index)` -자식 엔티티(용어 또는 노드)의 정보를 딕셔너리로 반환합니다. - -**파라미터:** -- `entity` (dict): 자식 엔티티 정보 -- `index` (int): 엔티티의 인덱스 - -**반환값:** -- `dict`: 엔티티 정보 (index, type, name, description 등) - -#### `process_node_details(self, node)` -노드의 상세 정보를 처리하고 딕셔너리로 반환합니다. - -**파라미터:** -- `node` (dict): 용어집 노드 정보 - -**반환값:** -- `dict`: 노드의 상세 정보 (name, children 등) - -#### `process_glossary_nodes(self, result)` -용어집 노드 결과를 처리하고 딕셔너리로 반환합니다. - -**파라미터:** -- `result` (dict): API 응답 결과 - -**반환값:** -- `dict`: 처리된 용어집 노드 데이터 (total_nodes, nodes) - -#### `get_glossary_data(self)` -DataHub에서 전체 용어집 데이터를 가져와 처리합니다. - -**반환값:** -- `dict`: 처리된 용어집 데이터 또는 오류 정보 - -**사용 예시:** -```python -result = glossary_service.get_glossary_data() -print(f"전체 노드 수: {result['total_nodes']}") -for node in result['nodes']: - print(f" - {node['name']}: {node.get('description', 'N/A')}") -``` - -#### `get_glossary_terms_by_urn(self, dataset_urn)` -특정 데이터셋 URN의 glossary terms를 조회합니다. - -**파라미터:** -- `dataset_urn` (str): 데이터셋 URN - -**반환값:** -- `dict`: glossary terms 정보 - -**의존성:** -- `utils.data.datahub_services.base_client.DataHubBaseClient`: 기본 클라이언트 -- `utils.data.queries`: GraphQL 쿼리 정의 - - `GLOSSARY_NODE_QUERY` - - `GLOSSARY_TERMS_BY_URN_QUERY` - - `ROOT_GLOSSARY_NODES_QUERY` - ---- - -### `metadata_service.py` - -테이블 메타데이터, 리니지, URN 관련 기능을 제공하는 서비스입니다. - -**클래스:** -- `MetadataService` - -**초기화:** -```python -from utils.data.datahub_services import DataHubBaseClient, MetadataService - -client = DataHubBaseClient(gms_server="http://localhost:8080") -metadata_service = MetadataService(client) -``` - -**주요 메서드:** - -#### `get_table_name(self, urn)` -URN에 대한 테이블 이름을 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `str`: 테이블 이름 (database.table 형태) 또는 None - -#### `get_table_description(self, urn)` -URN에 대한 테이블 설명을 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `str`: 테이블 설명 또는 None - -#### `get_column_names_and_descriptions(self, urn)` -URN에 대한 컬럼 이름 및 설명을 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `list`: 컬럼 정보 리스트 (각 항목: column_name, column_description, column_type) - -#### `get_table_lineage(self, urn, counts=100, direction="DOWNSTREAM", degree_values=None)` -URN에 대한 DOWNSTREAM/UPSTREAM lineage entity를 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN -- `counts` (int): 가져올 엔티티 수 (기본값: 100) -- `direction` (str): 리니지 방향 ("DOWNSTREAM" 또는 "UPSTREAM", 기본값: "DOWNSTREAM") -- `degree_values` (list): 필터링할 degree 값 리스트 (기본값: ["1", "2"]) - -**반환값:** -- `tuple`: (urn, lineage_result) - -#### `get_column_lineage(self, urn)` -URN에 대한 UPSTREAM lineage의 column source를 가져옵니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN - -**반환값:** -- `dict`: 컬럼 리니지 정보 - - `downstream_dataset`: 다운스트림 데이터셋 이름 - - `lineage_by_upstream_dataset`: 업스트림 데이터셋별 컬럼 매핑 - - `upstream_dataset`: 업스트림 데이터셋 이름 - - `columns`: 컬럼 매핑 리스트 - - `upstream_column`: 업스트림 컬럼 - - `downstream_column`: 다운스트림 컬럼 - - `confidence`: 신뢰도 - -#### `min_degree_lineage(self, lineage_result)` -lineage 중 최소 degree만 가져옵니다. - -**파라미터:** -- `lineage_result`: `get_table_lineage`의 반환값 - -**반환값:** -- `dict`: 테이블별 최소 degree 매핑 - -#### `build_table_metadata(self, urn, max_degree=2, sort_by_degree=True)` -테이블 단위로 통합 메타데이터를 생성합니다. - -**파라미터:** -- `urn` (str): 데이터셋 URN -- `max_degree` (int): 최대 degree 제한 (기본값: 2) -- `sort_by_degree` (bool): degree 기준 정렬 여부 (기본값: True) - -**반환값:** -- `dict`: 통합 메타데이터 - - `table_name`: 테이블 이름 - - `description`: 테이블 설명 - - `columns`: 컬럼 정보 리스트 - - `lineage`: 리니지 정보 - - `downstream`: 다운스트림 테이블 리스트 - - `upstream`: 업스트림 테이블 리스트 - - `upstream_columns`: 컬럼 레벨 리니지 - -**사용 예시:** -```python -metadata = metadata_service.build_table_metadata(urn) -print(f"테이블: {metadata['table_name']}") -print(f"컬럼 수: {len(metadata['columns'])}") -print(f"다운스트림: {len(metadata['lineage']['downstream'])}개") -print(f"업스트림: {len(metadata['lineage']['upstream'])}개") -``` - -#### `get_urn_info(self, urn)` -특정 URN에 대한 모든 관련 정보를 가져옵니다. - -**파라미터:** -- `urn` (str): 조회할 데이터셋 URN - -**반환값:** -- `dict`: URN에 대한 전체 메타데이터 정보 또는 오류 정보 - -**사용 예시:** -```python -result = metadata_service.get_urn_info(urn) -# 콘솔에 자동으로 포맷된 정보 출력 및 반환 -``` - -**의존성:** -- `collections.defaultdict`: 딕셔너리 기본값 처리 -- `datahub.ingestion.graph.client`: DataHub Graph 클라이언트 - - `DatahubClientConfig` - - `DataHubGraph` -- `datahub.metadata.schema_classes`: DataHub 메타데이터 스키마 클래스 - - `DatasetPropertiesClass` - - `SchemaMetadataClass` - - `UpstreamLineageClass` -- `utils.data.datahub_services.base_client.DataHubBaseClient`: 기본 클라이언트 - ---- - -### `query_service.py` - -DataHub의 쿼리 관련 기능을 제공하는 서비스입니다. - -**클래스:** -- `QueryService` - -**초기화:** -```python -from utils.data.datahub_services import DataHubBaseClient, QueryService - -client = DataHubBaseClient(gms_server="http://localhost:8080") -query_service = QueryService(client) -``` - -**주요 메서드:** - -#### `get_queries(self, start=0, count=10, query="*", filters=None)` -DataHub에서 쿼리 목록을 가져옵니다. - -**파라미터:** -- `start` (int): 시작 인덱스 (기본값: 0) -- `count` (int): 반환할 쿼리 수 (기본값: 10) -- `query` (str): 필터링에 사용할 쿼리 문자열 (기본값: "*") -- `filters` (list): 추가 필터 (기본값: None) - -**반환값:** -- `dict`: 쿼리 목록 정보 - -#### `process_queries(self, result)` -쿼리 목록 결과를 처리하고 간소화된 형태로 반환합니다. - -**파라미터:** -- `result` (dict): API 응답 결과 - -**반환값:** -- `dict`: 처리된 쿼리 목록 데이터 - - `total_queries`: 전체 쿼리 수 - - `count`: 조회된 쿼리 수 - - `start`: 시작 인덱스 - - `queries`: 쿼리 리스트 (urn, name, description, statement 포함) - -#### `get_query_data(self, start=0, count=10, query="*", filters=None)` -DataHub에서 쿼리 목록을 가져와 처리합니다. - -**파라미터:** -- `start` (int): 시작 인덱스 (기본값: 0) -- `count` (int): 반환할 쿼리 수 (기본값: 10) -- `query` (str): 필터링에 사용할 쿼리 문자열 (기본값: "*") -- `filters` (list): 추가 필터 (기본값: None) - -**반환값:** -- `dict`: 처리된 쿼리 목록 데이터 또는 오류 정보 - -**사용 예시:** -```python -result = query_service.get_query_data(start=0, count=5, query="*") -print(f"전체 쿼리 수: {result['total_queries']}") -for idx, q in enumerate(result['queries'], 1): - print(f"{idx}. {q['name']}: {q.get('description', 'N/A')}") -``` - -#### `get_queries_by_urn(self, dataset_urn)` -특정 데이터셋 URN과 연관된 쿼리들을 조회합니다. - -**파라미터:** -- `dataset_urn` (str): 데이터셋 URN - -**반환값:** -- `dict`: 연관된 쿼리 목록 - -**주의:** 전체 쿼리를 가져온 후 클라이언트 사이드에서 필터링하는 방식 사용 - -#### `get_glossary_terms_by_urn(self, dataset_urn)` -특정 데이터셋 URN의 glossary terms를 조회합니다. - -**파라미터:** -- `dataset_urn` (str): 데이터셋 URN - -**반환값:** -- `dict`: glossary terms 정보 - -**의존성:** -- `utils.data.datahub_services.base_client.DataHubBaseClient`: 기본 클라이언트 -- `utils.data.queries`: GraphQL 쿼리 정의 - - `GLOSSARY_TERMS_BY_URN_QUERY` - - `LIST_QUERIES_QUERY` - - `QUERIES_BY_URN_QUERY` - ---- - -## 전체 사용 예시 - -### 예시 1: 기본 사용법 - -```python -from utils.data.datahub_services import ( - DataHubBaseClient, - MetadataService, - QueryService, - GlossaryService -) - -# 1. 클라이언트 초기화 -gms_server = "http://localhost:8080" -client = DataHubBaseClient(gms_server=gms_server) - -# 2. 서비스 초기화 -metadata_service = MetadataService(client) -query_service = QueryService(client) -glossary_service = GlossaryService(client) - -# 3. 메타데이터 조회 -urn = "urn:li:dataset:(...)" -metadata = metadata_service.get_urn_info(urn) - -# 4. 쿼리 조회 -queries = query_service.get_query_data(start=0, count=10) - -# 5. 용어집 조회 -glossary_data = glossary_service.get_glossary_data() -``` - -### 예시 2: 통합 페처 사용 (하위 호환성) - -```python -from utils.data.datahub_source import DatahubMetadataFetcher - -# 기존 인터페이스와 동일하게 사용 가능 -fetcher = DatahubMetadataFetcher(gms_server="http://localhost:8080") - -# 모든 메서드가 동일하게 동작 -metadata = fetcher.get_urn_info(urn) -queries = fetcher.get_query_data() -glossary = fetcher.get_glossary_data() -``` - -### 예시 3: 테스트 코드 - -`test.py` 파일에서 실제 사용 예시를 확인할 수 있습니다: - -```python -from utils.data.datahub_services import DataHubBaseClient, QueryService - -client = DataHubBaseClient(gms_server="http://35.222.65.99:8080") -query_service = QueryService(client) - -result = query_service.get_query_data(start=0, count=5, query="*") -print(json.dumps(result, indent=2, ensure_ascii=False)) -``` - ---- - -## 의존성 - -### 외부 패키지 -- `requests`: HTTP 요청 처리 -- `datahub`: DataHub Python SDK - - `datahub.emitter.rest_emitter.DatahubRestEmitter` - - `datahub.ingestion.graph.client.DatahubClientConfig` - - `datahub.ingestion.graph.client.DataHubGraph` - - `datahub.metadata.schema_classes` - -### 내부 패키지 -- `utils.data.queries`: GraphQL 쿼리 정의 모듈 - ---- - -## 참고 사항 - -1. 모든 서비스는 `DataHubBaseClient`를 필요로 합니다. -2. `DatahubMetadataFetcher`는 하위 호환성을 위해 이 서비스들을 내부적으로 사용합니다. -3. GMS 서버 URL은 초기화 시 유효성 검사가 수행됩니다. -4. GraphQL 쿼리는 `utils.data.queries` 모듈에 정의되어 있습니다. -5. 오류 발생 시 dict 형태로 `{"error": True, "message": "..."}` 구조로 반환됩니다. - diff --git a/utils/data/datahub_services/__init__.py b/utils/data/datahub_services/__init__.py deleted file mode 100644 index 2743ef9..0000000 --- a/utils/data/datahub_services/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -DataHub 유틸리티 패키지 - -DataHub와의 상호작용을 위한 모듈들을 제공합니다. - -주요 구성요소: -- DataHubBaseClient: 기본 연결 및 통신 -- MetadataService: 메타데이터, 리니지, URN 관련 기능 -- QueryService: 쿼리 관련 기능 -- GlossaryService: 용어집 관련 기능 -""" - -from utils.data.datahub_services.base_client import DataHubBaseClient -from utils.data.datahub_services.glossary_service import GlossaryService -from utils.data.datahub_services.metadata_service import MetadataService -from utils.data.datahub_services.query_service import QueryService - -__all__ = [ - "DataHubBaseClient", - "MetadataService", - "QueryService", - "GlossaryService", -] diff --git a/utils/data/datahub_services/base_client.py b/utils/data/datahub_services/base_client.py deleted file mode 100644 index 6637b4b..0000000 --- a/utils/data/datahub_services/base_client.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -DataHub 기본 클라이언트 모듈 - -DataHub GMS 서버와의 기본 연결 및 통신 기능을 제공합니다. -""" - -import requests -from datahub.emitter.rest_emitter import DatahubRestEmitter - - -class DataHubBaseClient: - """DataHub 기본 클라이언트 클래스""" - - def __init__(self, gms_server="http://localhost:8080", extra_headers={}): - """ - DataHub 클라이언트 초기화 - - Args: - gms_server (str): DataHub GMS 서버 URL - extra_headers (dict): 추가 HTTP 헤더 - """ - # gms_server 주소 유효성 검사 - if not self._is_valid_gms_server(gms_server): - raise ValueError(f"유효하지 않은 GMS 서버 주소: {gms_server}") - - self.gms_server = gms_server - self.extra_headers = extra_headers - - # DataHub 클라이언트 초기화 - self.emitter = DatahubRestEmitter( - gms_server=gms_server, extra_headers=extra_headers - ) - self.datahub_graph = self.emitter.to_graph() - - def _is_valid_gms_server(self, gms_server): - """ - GMS 서버 주소의 유효성을 검사하는 함수 - - Args: - gms_server (str): 검사할 GMS 서버 URL - - Returns: - bool: 서버가 유효한 경우 True - """ - query = {"query": "{ health { status } }"} - headers = {"Content-Type": "application/json"} - - try: - response = requests.post( - f"{gms_server}/api/graphql", json=query, headers=headers - ) - return response.status_code == 200 - except requests.exceptions.RequestException: - return False - - def execute_graphql_query(self, query, variables=None): - """ - GraphQL 쿼리 실행 - - Args: - query (str): GraphQL 쿼리 문자열 - variables (dict, optional): 쿼리 변수 - - Returns: - dict: GraphQL 응답 - """ - headers = {"Content-Type": "application/json"} - payload = {"query": query} - - if variables: - payload["variables"] = variables - - response = requests.post( - f"{self.gms_server}/api/graphql", - json=payload, - headers=headers, - ) - - if response.status_code == 200: - return response.json() - else: - return { - "error": True, - "status_code": response.status_code, - "message": response.text, - } - - def get_datahub_graph(self): - """DataHub Graph 클라이언트 반환""" - return self.datahub_graph - - def get_urns(self): - """필터를 적용하여 데이터셋의 URN 가져오기""" - return self.datahub_graph.get_urns_by_filter() diff --git a/utils/data/datahub_services/glossary_service.py b/utils/data/datahub_services/glossary_service.py deleted file mode 100644 index 36eeec7..0000000 --- a/utils/data/datahub_services/glossary_service.py +++ /dev/null @@ -1,194 +0,0 @@ -""" -DataHub 용어집 서비스 모듈 - -DataHub의 glossary 관련 기능을 제공합니다. -""" - -from utils.data.datahub_services.base_client import DataHubBaseClient -from utils.data.queries import ( - GLOSSARY_NODE_QUERY, - GLOSSARY_TERMS_BY_URN_QUERY, - ROOT_GLOSSARY_NODES_QUERY, -) - - -class GlossaryService: - """용어집 관련 서비스 클래스""" - - def __init__(self, client: DataHubBaseClient): - """ - 용어집 서비스 초기화 - - Args: - client (DataHubBaseClient): DataHub 기본 클라이언트 - """ - self.client = client - - def get_root_glossary_nodes(self): - """ - DataHub에서 루트 용어집 노드를 가져오는 함수 - - Returns: - dict: 루트 용어집 노드 정보 - """ - return self.client.execute_graphql_query(ROOT_GLOSSARY_NODES_QUERY) - - def get_glossary_node_by_urn(self, urn): - """ - DataHub에서 특정 URN의 용어집 노드 및 그 자식 항목을 가져오는 함수 - - Args: - urn (str): 용어집 노드의 URN - - Returns: - dict: 용어집 노드 정보와 자식 항목 - """ - variables = {"urn": urn} - return self.client.execute_graphql_query(GLOSSARY_NODE_QUERY, variables) - - def get_node_basic_info(self, node, index): - """ - 용어집 노드의 기본 정보를 딕셔너리로 반환하는 함수 - - Args: - node (dict): 용어집 노드 정보 - index (int): 노드의 인덱스 - - Returns: - dict: 노드의 기본 정보 - """ - result = {"index": index, "name": node["properties"]["name"]} - - if node["properties"] and node["properties"].get("description"): - result["description"] = node["properties"]["description"] - - # 자식 노드/용어 관계 정보 수 추가 - if "children" in node and node["children"]["total"] > 0: - result["child_count"] = node["children"]["total"] - - return result - - def get_child_entity_info(self, entity, index): - """ - 자식 엔티티(용어 또는 노드)의 정보를 딕셔너리로 반환하는 함수 - - Args: - entity (dict): 자식 엔티티 정보 - index (int): 엔티티의 인덱스 - - Returns: - dict: 엔티티 정보 - """ - entity_type = entity["type"] - result = {"index": index, "type": entity_type} - - if entity_type == "GLOSSARY_TERM": - if "properties" in entity and entity["properties"]: - result["name"] = entity["properties"].get("name", "N/A") - - if ( - "description" in entity["properties"] - and entity["properties"]["description"] - ): - result["description"] = entity["properties"]["description"] - - elif entity_type == "GLOSSARY_NODE": - if "properties" in entity and entity["properties"]: - result["name"] = entity["properties"].get("name", "N/A") - - return result - - def process_node_details(self, node): - """ - 노드의 상세 정보를 처리하고 딕셔너리로 반환하는 함수 - - Args: - node (dict): 용어집 노드 정보 - - Returns: - dict: 노드의 상세 정보 - """ - node_urn = node["urn"] - detailed_node = self.get_glossary_node_by_urn(node_urn) - - result = {"name": node["properties"]["name"], "children": []} - - if ( - detailed_node - and "data" in detailed_node - and "glossaryNode" in detailed_node["data"] - ): - node_detail = detailed_node["data"]["glossaryNode"] - - # 자식 항목 정보 추출 - if "children" in node_detail and node_detail["children"]["total"] > 0: - relationships = node_detail["children"]["relationships"] - - for j, rel in enumerate(relationships, 1): - entity = rel["entity"] - result["children"].append(self.get_child_entity_info(entity, j)) - - return result - - def process_glossary_nodes(self, result): - """ - 용어집 노드 결과를 처리하고 딕셔너리로 반환하는 함수 - - Args: - result (dict): API 응답 결과 - - Returns: - dict: 처리된 용어집 노드 데이터 - """ - if "error" in result: - return result - - processed_result = {"total_nodes": 0, "nodes": []} - - # 노드 목록 추출 - nodes = result["data"]["getRootGlossaryNodes"]["nodes"] - processed_result["total_nodes"] = len(nodes) - - for i, node in enumerate(nodes, 1): - node_info = self.get_node_basic_info(node, i) - - # 자식 노드가 있으면 상세 정보 처리 - if "children" in node and node["children"]["total"] > 0: - node_details = self.process_node_details(node) - node_info["details"] = node_details - - processed_result["nodes"].append(node_info) - - return processed_result - - def get_glossary_data(self): - """ - DataHub에서 전체 용어집 데이터를 가져와 처리하는 함수 - - Returns: - dict: 처리된 용어집 데이터 - """ - # DataHub 서버에 연결하여 용어집 노드 가져오기 - result = self.get_root_glossary_nodes() - - # 결과 처리 - if result: - try: - return self.process_glossary_nodes(result) - except KeyError as e: - return {"error": True, "message": f"결과 구조 파싱 중 오류 발생: {e}"} - else: - return {"error": True, "message": "용어집 노드를 가져오지 못했습니다."} - - def get_glossary_terms_by_urn(self, dataset_urn): - """ - 특정 데이터셋 URN의 glossary terms를 조회하는 함수 - - Args: - dataset_urn (str): 데이터셋 URN - - Returns: - dict: glossary terms 정보 - """ - variables = {"urn": dataset_urn} - return self.client.execute_graphql_query(GLOSSARY_TERMS_BY_URN_QUERY, variables) diff --git a/utils/data/datahub_services/metadata_service.py b/utils/data/datahub_services/metadata_service.py deleted file mode 100644 index 5c5a4f9..0000000 --- a/utils/data/datahub_services/metadata_service.py +++ /dev/null @@ -1,317 +0,0 @@ -""" -DataHub 메타데이터 서비스 모듈 - -테이블 메타데이터, 리니지, URN 관련 기능을 제공합니다. -""" - -from collections import defaultdict - -from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph -from datahub.metadata.schema_classes import ( - DatasetPropertiesClass, - SchemaMetadataClass, - UpstreamLineageClass, -) - -from utils.data.datahub_services.base_client import DataHubBaseClient - - -class MetadataService: - """메타데이터 관련 서비스 클래스""" - - def __init__(self, client: DataHubBaseClient): - """ - 메타데이터 서비스 초기화 - - Args: - client (DataHubBaseClient): DataHub 기본 클라이언트 - """ - self.client = client - self.datahub_graph = client.get_datahub_graph() - self.gms_server = client.gms_server - - def get_table_name(self, urn): - """URN에 대한 테이블 이름 가져오기""" - dataset_properties = self.datahub_graph.get_aspect( - urn, aspect_type=DatasetPropertiesClass - ) - if dataset_properties: - database_info = dataset_properties.get("customProperties", {}).get( - "dbt_unique_id", "" - ) - if database_info: - database_info = database_info.split(".")[-2] - else: - database_info = "" - table_info = dataset_properties.get("name", None) - return database_info + "." + table_info - return None - - def get_table_description(self, urn): - """URN에 대한 테이블 설명 가져오기""" - dataset_properties = self.datahub_graph.get_aspect( - urn, aspect_type=DatasetPropertiesClass - ) - if dataset_properties: - return dataset_properties.get("description", None) - return None - - def get_column_names_and_descriptions(self, urn): - """URN에 대한 컬럼 이름 및 설명 가져오기""" - schema_metadata = self.datahub_graph.get_aspect( - urn, aspect_type=SchemaMetadataClass - ) - columns = [] - if schema_metadata: - for field in schema_metadata.fields: - # nativeDataType가 없거나 빈 문자열인 경우 None 처리 - native_type = getattr(field, "nativeDataType", None) - column_type = ( - native_type if native_type and native_type.strip() else None - ) - - columns.append( - { - "column_name": field.fieldPath, - "column_description": field.description, - "column_type": column_type, - } - ) - return columns - - def get_table_lineage( - self, - urn, - counts=100, - direction="DOWNSTREAM", - degree_values=None, - ): - """URN에 대한 DOWNSTREAM/UPSTREAM lineage entity를 counts 만큼 가져오는 함수""" - if degree_values is None: - degree_values = ["1", "2"] - - graph = DataHubGraph(DatahubClientConfig(server=self.gms_server)) - - query = """ - query scrollAcrossLineage($input: ScrollAcrossLineageInput!) { - scrollAcrossLineage(input: $input) { - searchResults { - degree - entity { - urn - type - } - } - } - } - """ - variables = { - "input": { - "query": "*", - "urn": urn, - "count": counts, - "direction": direction, - "orFilters": [ - { - "and": [ - { - "condition": "EQUAL", - "negated": "false", - "field": "degree", - "values": degree_values, - } - ] - } - ], - } - } - - result = graph.execute_graphql(query=query, variables=variables) - return urn, result - - def get_column_lineage(self, urn): - """URN에 대한 UPSTREAM lineage의 column source를 가져오는 함수""" - # DataHub 연결 및 lineage 가져오기 - graph = DataHubGraph(DatahubClientConfig(server=self.gms_server)) - result = graph.get_aspect(entity_urn=urn, aspect_type=UpstreamLineageClass) - - # downstream dataset (URN 테이블명) 파싱 - try: - down_dataset = urn.split(",")[1] - table_name = down_dataset.split(".")[1] - except IndexError: - # URN이 유효하지 않는 경우 - print(f"[ERROR] Invalid URN format: {urn}") - return {} - - # upstream_dataset별로 column lineage - upstream_map = defaultdict(list) - - if not result: - return {"downstream_dataset": table_name, "lineage_by_upstream_dataset": []} - - for fg in result.fineGrainedLineages or []: - confidence_score = ( - fg.confidenceScore if fg.confidenceScore is not None else 1.0 - ) - for down in fg.downstreams: - down_column = down.split(",")[-1].replace(")", "") - for up in fg.upstreams: - up_dataset = up.split(",")[1] - up_dataset = up_dataset.split(".")[1] - up_column = up.split(",")[-1].replace(")", "") - - upstream_map[up_dataset].append( - { - "upstream_column": up_column, - "downstream_column": down_column, - "confidence": confidence_score, - } - ) - - # 최종 결과 구조 생성 - parsed_lineage = { - "downstream_dataset": table_name, - "lineage_by_upstream_dataset": [], - } - - for up_dataset, column_mappings in upstream_map.items(): - parsed_lineage["lineage_by_upstream_dataset"].append( - {"upstream_dataset": up_dataset, "columns": column_mappings} - ) - - return parsed_lineage - - def min_degree_lineage(self, lineage_result): - """lineage 중 최소 degree만 가져오는 함수""" - table_degrees = {} - urn, lineage_data = lineage_result - - for item in lineage_data["scrollAcrossLineage"]["searchResults"]: - table = item["entity"]["urn"].split(",")[1] - table_name = table.split(".")[1] - degree = item["degree"] - table_degrees[table_name] = min( - degree, table_degrees.get(table_name, float("inf")) - ) - - return table_degrees - - def build_table_metadata(self, urn, max_degree=2, sort_by_degree=True): - """테이블 단위로 테이블 이름, 설명, 컬럼, 테이블 별 리니지(downstream/upstream), 컬럼 별 리니지(upstream)이 포함된 메타데이터 생성 함수""" - metadata = { - "table_name": self.get_table_name(urn), - "description": self.get_table_description(urn), - "columns": self.get_column_names_and_descriptions(urn), - "lineage": {}, - } - - def process_lineage(direction): - # direction : DOWNSTREAM/UPSTREAM 별로 degree가 최소인 lineage를 가져오는 함수 - # 테이블 lineage 가져오기 - lineage_result = self.get_table_lineage(urn, direction=direction) - table_degrees = self.min_degree_lineage(lineage_result) - current_table_name = metadata["table_name"] - - # degree 필터링 - filtered_lineage = [ - {"table": table, "degree": degree} - for table, degree in table_degrees.items() - if degree <= max_degree and table != current_table_name - ] - - # degree 기준 정렬 - if sort_by_degree: - filtered_lineage.sort(key=lambda x: x["degree"]) - - return filtered_lineage - - # DOWNSTREAM / UPSTREAM 링크 추가 - metadata["lineage"]["downstream"] = process_lineage("DOWNSTREAM") - metadata["lineage"]["upstream"] = process_lineage("UPSTREAM") - - # 컬럼 단위 lineage 추가 - column_lineage = self.get_column_lineage(urn) - metadata["lineage"]["upstream_columns"] = column_lineage.get( - "lineage_by_upstream_dataset", [] - ) - - return metadata - - def get_urn_info(self, urn): - """ - 특정 URN에 대한 모든 관련 정보를 가져오는 함수 - - Args: - urn (str): 조회할 데이터셋 URN - - Returns: - dict: URN에 대한 전체 메타데이터 정보 - """ - print(f"\n=== URN 정보 조회: {urn} ===\n") - - try: - # 기본 테이블 메타데이터 가져오기 - metadata = self.build_table_metadata(urn) - - # 결과 출력 - self._print_urn_details(metadata) - - return metadata - - except Exception as e: - error_msg = f"URN 정보 조회 중 오류 발생: {str(e)}" - print(error_msg) - return {"error": True, "message": error_msg} - - def _print_urn_details(self, metadata): - """URN 메타데이터를 보기 좋게 출력하는 내부 함수""" - - # 테이블 기본 정보 - print("📋 테이블 정보:") - print(f" 이름: {metadata.get('table_name', 'N/A')}") - print(f" 설명: {metadata.get('description', 'N/A')}\n") - - # 컬럼 정보 - columns = metadata.get("columns", []) - if columns: - print(f"📊 컬럼 정보 ({len(columns)}개):") - for i, col in enumerate(columns, 1): - print(f" {i}. {col['column_name']} ({col.get('column_type', 'N/A')})") - if col.get("column_description"): - print(f" → {col['column_description']}") - print() - - # 리니지 정보 - lineage = metadata.get("lineage", {}) - - # Downstream 테이블 - downstream = lineage.get("downstream", []) - if downstream: - print(f"⬇️ Downstream 테이블 ({len(downstream)}개):") - for table in downstream: - print(f" - {table['table']} (degree: {table['degree']})") - print() - - # Upstream 테이블 - upstream = lineage.get("upstream", []) - if upstream: - print(f"⬆️ Upstream 테이블 ({len(upstream)}개):") - for table in upstream: - print(f" - {table['table']} (degree: {table['degree']})") - print() - - # 컬럼 레벨 리니지 - upstream_columns = lineage.get("upstream_columns", []) - if upstream_columns: - print("🔗 컬럼 레벨 리니지:") - for upstream_dataset in upstream_columns: - dataset_name = upstream_dataset["upstream_dataset"] - columns = upstream_dataset["columns"] - print(f" 📋 {dataset_name}:") - for col in columns: - confidence = col.get("confidence", 1.0) - print( - f" {col['upstream_column']} → {col['downstream_column']} (신뢰도: {confidence})" - ) - print() diff --git a/utils/data/datahub_services/query_service.py b/utils/data/datahub_services/query_service.py deleted file mode 100644 index 6622629..0000000 --- a/utils/data/datahub_services/query_service.py +++ /dev/null @@ -1,159 +0,0 @@ -""" -DataHub 쿼리 서비스 모듈 - -DataHub의 쿼리 관련 기능을 제공합니다. -""" - -from utils.data.datahub_services.base_client import DataHubBaseClient -from utils.data.queries import ( - GLOSSARY_TERMS_BY_URN_QUERY, - LIST_QUERIES_QUERY, - QUERIES_BY_URN_QUERY, -) - - -class QueryService: - """쿼리 관련 서비스 클래스""" - - def __init__(self, client: DataHubBaseClient): - """ - 쿼리 서비스 초기화 - - Args: - client (DataHubBaseClient): DataHub 기본 클라이언트 - """ - self.client = client - - def get_queries(self, start=0, count=10, query="*", filters=None): - """ - DataHub에서 쿼리 목록을 가져오는 함수 - - Args: - start (int): 시작 인덱스 (기본값=0) - count (int): 반환할 쿼리 수 (기본값=10) - query (str): 필터링에 사용할 쿼리 문자열 (기본값="*") - filters (list): 추가 필터 (기본값=None) - - Returns: - dict: 쿼리 목록 정보 - """ - # GraphQL 요청용 입력 변수 준비 - input_params = {"start": start, "count": count, "query": query} - - if filters: - input_params["filters"] = filters - - variables = {"input": input_params} - - return self.client.execute_graphql_query(LIST_QUERIES_QUERY, variables) - - def process_queries(self, result): - """ - 쿼리 목록 결과를 처리하고 간소화된 형태로 반환하는 함수 - - Args: - result (dict): API 응답 결과 - - Returns: - dict: 처리된 쿼리 목록 데이터 (urn, name, description, statement만 포함) - """ - if "error" in result: - return result - - processed_result = {"total_queries": 0, "count": 0, "start": 0, "queries": []} - - if "data" in result and "listQueries" in result["data"]: - list_queries = result["data"]["listQueries"] - processed_result["total_queries"] = list_queries.get("total", 0) - processed_result["count"] = list_queries.get("count", 0) - processed_result["start"] = list_queries.get("start", 0) - - for query in list_queries.get("queries", []): - query_info = {"urn": query.get("urn")} - - props = query.get("properties", {}) - query_info["name"] = props.get("name") - query_info["description"] = props.get("description") - query_info["statement"] = props.get("statement", {}).get("value") - - processed_result["queries"].append(query_info) - - return processed_result - - def get_query_data(self, start=0, count=10, query="*", filters=None): - """ - DataHub에서 쿼리 목록을 가져와 처리하는 함수 - - Args: - start (int): 시작 인덱스 (기본값=0) - count (int): 반환할 쿼리 수 (기본값=10) - query (str): 필터링에 사용할 쿼리 문자열 (기본값="*") - filters (list): 추가 필터 (기본값=None) - - Returns: - dict: 처리된 쿼리 목록 데이터 - """ - # DataHub 서버에 연결하여 쿼리 목록 가져오기 - result = self.get_queries(start, count, query, filters) - - # 결과 처리 - if result: - try: - return self.process_queries(result) - except KeyError as e: - return {"error": True, "message": f"결과 구조 파싱 중 오류 발생: {e}"} - else: - return {"error": True, "message": "쿼리 목록을 가져오지 못했습니다."} - - def get_queries_by_urn(self, dataset_urn): - """ - 특정 데이터셋 URN과 연관된 쿼리들을 조회하는 함수 - - 전체 쿼리를 가져온 후 클라이언트 사이드에서 필터링하는 방식 사용 - - Args: - dataset_urn (str): 데이터셋 URN - - Returns: - dict: 연관된 쿼리 목록 - """ - # 먼저 전체 쿼리 목록을 가져옴 - input_params = {"start": 0, "count": 1000, "query": "*"} # 충분히 큰 수로 설정 - - variables = {"input": input_params} - result = self.client.execute_graphql_query(QUERIES_BY_URN_QUERY, variables) - - if ( - "error" not in result - and "data" in result - and "listQueries" in result["data"] - ): - # 클라이언트 사이드에서 특정 URN과 연관된 쿼리만 필터링 - all_queries = result["data"]["listQueries"]["queries"] - filtered_queries = [] - - for query in all_queries: - subjects = query.get("subjects", []) - for subject in subjects: - if subject.get("dataset", {}).get("urn") == dataset_urn: - filtered_queries.append(query) - break - - # 필터링된 결과로 응답 구조 재구성 - result["data"]["listQueries"]["queries"] = filtered_queries - result["data"]["listQueries"]["count"] = len(filtered_queries) - - return result - - def get_glossary_terms_by_urn(self, dataset_urn): - """ - 특정 데이터셋 URN의 glossary terms를 조회하는 함수 - - Args: - dataset_urn (str): 데이터셋 URN - - Returns: - dict: glossary terms 정보 - """ - variables = {"urn": dataset_urn} - return self.client.execute_graphql_query(GLOSSARY_TERMS_BY_URN_QUERY, variables) diff --git a/utils/data/datahub_source.py b/utils/data/datahub_source.py deleted file mode 100644 index aeb261a..0000000 --- a/utils/data/datahub_source.py +++ /dev/null @@ -1,145 +0,0 @@ -""" -DataHub 메타데이터 페처 - 리팩토링된 버전 - -기존 DatahubMetadataFetcher의 모든 기능을 유지하면서 -내부적으로는 분리된 서비스 모듈들을 사용합니다. - -기존 코드와의 완벽한 호환성을 보장합니다. -""" - -from utils.data.datahub_services.base_client import DataHubBaseClient -from utils.data.datahub_services.glossary_service import GlossaryService -from utils.data.datahub_services.metadata_service import MetadataService -from utils.data.datahub_services.query_service import QueryService - - -class DatahubMetadataFetcher: - """ - DataHub 메타데이터 페처 - 기존 인터페이스 유지 - - 내부적으로는 분리된 서비스들을 사용하지만 - 외부 인터페이스는 기존과 동일하게 유지됩니다. - """ - - def __init__(self, gms_server="http://localhost:8080", extra_headers={}): - """ - DataHub 메타데이터 페처 초기화 - - Args: - gms_server (str): DataHub GMS 서버 URL - extra_headers (dict): 추가 HTTP 헤더 - """ - # 기본 클라이언트 초기화 - self.client = DataHubBaseClient(gms_server, extra_headers) - - # 서비스들 초기화 - self.metadata_service = MetadataService(self.client) - self.query_service = QueryService(self.client) - self.glossary_service = GlossaryService(self.client) - - # 기존 속성들 호환성을 위해 유지 - self.gms_server = gms_server - self.emitter = self.client.emitter - self.datahub_graph = self.client.datahub_graph - - # === 기존 인터페이스 유지 - 메타데이터 관련 === - - def get_urns(self): - """필터를 적용하여 데이터셋의 URN 가져오기""" - return self.client.get_urns() - - def get_table_name(self, urn): - """URN에 대한 테이블 이름 가져오기""" - return self.metadata_service.get_table_name(urn) - - def get_table_description(self, urn): - """URN에 대한 테이블 설명 가져오기""" - return self.metadata_service.get_table_description(urn) - - def get_column_names_and_descriptions(self, urn): - """URN에 대한 컬럼 이름 및 설명 가져오기""" - return self.metadata_service.get_column_names_and_descriptions(urn) - - def get_table_lineage( - self, urn, counts=100, direction="DOWNSTREAM", degree_values=None - ): - """URN에 대한 DOWNSTREAM/UPSTREAM lineage entity를 counts 만큼 가져오는 함수""" - return self.metadata_service.get_table_lineage( - urn, counts, direction, degree_values - ) - - def get_column_lineage(self, urn): - """URN에 대한 UPSTREAM lineage의 column source를 가져오는 함수""" - return self.metadata_service.get_column_lineage(urn) - - def min_degree_lineage(self, lineage_result): - """lineage 중 최소 degree만 가져오는 함수""" - return self.metadata_service.min_degree_lineage(lineage_result) - - def build_table_metadata(self, urn, max_degree=2, sort_by_degree=True): - """테이블 단위로 테이블 이름, 설명, 컬럼, 테이블 별 리니지(downstream/upstream), 컬럼 별 리니지(upstream)이 포함된 메타데이터 생성 함수""" - return self.metadata_service.build_table_metadata( - urn, max_degree, sort_by_degree - ) - - def get_urn_info(self, urn): - """특정 URN에 대한 모든 관련 정보를 가져오는 함수""" - return self.metadata_service.get_urn_info(urn) - - def _print_urn_details(self, metadata): - """URN 메타데이터를 보기 좋게 출력하는 내부 함수""" - return self.metadata_service._print_urn_details(metadata) - - # === 기존 인터페이스 유지 - 용어집 관련 === - - def get_root_glossary_nodes(self): - """DataHub에서 루트 용어집 노드를 가져오는 함수""" - return self.glossary_service.get_root_glossary_nodes() - - def get_glossary_node_by_urn(self, urn): - """DataHub에서 특정 URN의 용어집 노드 및 그 자식 항목을 가져오는 함수""" - return self.glossary_service.get_glossary_node_by_urn(urn) - - def get_node_basic_info(self, node, index): - """용어집 노드의 기본 정보를 딕셔너리로 반환하는 함수""" - return self.glossary_service.get_node_basic_info(node, index) - - def get_child_entity_info(self, entity, index): - """자식 엔티티(용어 또는 노드)의 정보를 딕셔너리로 반환하는 함수""" - return self.glossary_service.get_child_entity_info(entity, index) - - def process_node_details(self, node): - """노드의 상세 정보를 처리하고 딕셔너리로 반환하는 함수""" - return self.glossary_service.process_node_details(node) - - def process_glossary_nodes(self, result): - """용어집 노드 결과를 처리하고 딕셔너리로 반환하는 함수""" - return self.glossary_service.process_glossary_nodes(result) - - def get_glossary_data(self): - """DataHub에서 전체 용어집 데이터를 가져와 처리하는 함수""" - return self.glossary_service.get_glossary_data() - - def get_queries(self, start=0, count=10, query="*", filters=None): - """DataHub에서 쿼리 목록을 가져오는 함수""" - return self.query_service.get_queries(start, count, query, filters) - - def process_queries(self, result): - """쿼리 목록 결과를 처리하고 간소화된 형태로 반환하는 함수""" - return self.query_service.process_queries(result) - - def get_query_data(self, start=0, count=10, query="*", filters=None): - """DataHub에서 쿼리 목록을 가져와 처리하는 함수""" - return self.query_service.get_query_data(start, count, query, filters) - - def get_queries_by_urn(self, dataset_urn): - """특정 데이터셋 URN과 연관된 쿼리들을 조회하는 함수""" - return self.query_service.get_queries_by_urn(dataset_urn) - - def get_glossary_terms_by_urn(self, dataset_urn): - """특정 데이터셋 URN의 glossary terms를 조회하는 함수""" - return self.glossary_service.get_glossary_terms_by_urn(dataset_urn) - - def _is_valid_gms_server(self, gms_server): - """GMS 서버 주소의 유효성을 검사하는 함수 (하위 호환성)""" - return self.client._is_valid_gms_server(gms_server) diff --git a/utils/data/queries.py b/utils/data/queries.py deleted file mode 100644 index 74a7933..0000000 --- a/utils/data/queries.py +++ /dev/null @@ -1,3694 +0,0 @@ -# DataHub GraphQL 쿼리 모음 - -ROOT_GLOSSARY_NODES_QUERY = """ -query getRootGlossaryNodes { - getRootGlossaryNodes(input: {start: 0, count: 1000}) { - count - start - total - nodes { - ...rootGlossaryNodeWithFourLayers - __typename - } - __typename - } -} - -fragment rootGlossaryNodeWithFourLayers on GlossaryNode { - urn - type - properties { - name - description - __typename - } - displayProperties { - ...displayPropertiesFields - __typename - } - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 500} - ) { - total - relationships { - type - entity { - type - ... on GlossaryTerm { - urn - __typename - } - ... on GlossaryNode { - urn - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 500} - ) { - total - relationships { - type - entity { - type - ... on GlossaryTerm { - urn - __typename - } - ... on GlossaryNode { - urn - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 500} - ) { - total - relationships { - type - entity { - type - ... on GlossaryTerm { - urn - __typename - } - ... on GlossaryNode { - urn - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 500} - ) { - total - relationships { - type - entity { - type - ... on GlossaryTerm { - urn - __typename - } - ... on GlossaryNode { - urn - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename -} - -fragment displayPropertiesFields on DisplayProperties { - colorHex - icon { - name - style - iconLibrary - __typename - } - __typename -} -""" - -GLOSSARY_NODE_QUERY = """ -query getGlossaryNode($urn: String!) { - glossaryNode(urn: $urn) { - urn - type - exists - properties { - name - description - __typename - } - ownership { - ...ownershipFields - __typename - } - parentNodes { - ...parentNodesFields - __typename - } - privileges { - ...entityPrivileges - __typename - } - autoRenderAspects: aspects(input: {autoRenderOnly: true}) { - ...autoRenderAspectFields - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - forms { - ...formsFields - __typename - } - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 10000} - ) { - total - relationships { - direction - entity { - type - urn - ... on GlossaryNode { - ...glossaryNode - __typename - } - ... on GlossaryTerm { - ...childGlossaryTerm - __typename - } - __typename - } - __typename - } - __typename - } - displayProperties { - ...displayPropertiesFields - __typename - } - ...notes - __typename - } -} - -fragment ownershipFields on Ownership { - owners { - owner { - ... on CorpUser { - urn - type - username - info { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - properties { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - editableProperties { - displayName - title - pictureLink - email - __typename - } - __typename - } - ... on CorpGroup { - urn - type - name - properties { - displayName - email - __typename - } - info { - displayName - email - admins { - urn - username - info { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - editableInfo { - pictureLink - teams - skills - __typename - } - __typename - } - members { - urn - username - info { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - editableInfo { - pictureLink - teams - skills - __typename - } - __typename - } - groups - __typename - } - __typename - } - __typename - } - type - ownershipType { - urn - type - info { - name - description - __typename - } - status { - removed - __typename - } - __typename - } - associatedUrn - __typename - } - lastModified { - time - __typename - } - __typename -} - -fragment parentNodesFields on ParentNodesResult { - count - nodes { - urn - type - properties { - name - __typename - } - displayProperties { - ...displayPropertiesFields - __typename - } - __typename - } - __typename -} - -fragment displayPropertiesFields on DisplayProperties { - colorHex - icon { - name - style - iconLibrary - __typename - } - __typename -} - -fragment entityPrivileges on EntityPrivileges { - canEditLineage - canEditDomains - canEditDataProducts - canEditTags - canEditGlossaryTerms - canEditDescription - canEditLinks - canEditOwners - canEditAssertions - canEditIncidents - canEditDeprecation - canEditSchemaFieldTags - canEditSchemaFieldGlossaryTerms - canEditSchemaFieldDescription - canEditQueries - canEditEmbed - canManageEntity - canManageChildren - canEditProperties - canViewDatasetUsage - canViewDatasetProfile - canViewDatasetOperations - __typename -} - -fragment autoRenderAspectFields on RawAspect { - aspectName - payload - renderSpec { - displayType - displayName - key - __typename - } - __typename -} - -fragment structuredPropertiesFields on StructuredPropertiesEntry { - structuredProperty { - exists - ...structuredPropertyFields - __typename - } - values { - ... on StringValue { - stringValue - __typename - } - ... on NumberValue { - numberValue - __typename - } - __typename - } - valueEntities { - urn - type - ...entityDisplayNameFields - __typename - } - associatedUrn - __typename -} - -fragment structuredPropertyFields on StructuredPropertyEntity { - urn - type - definition { - displayName - qualifiedName - description - cardinality - immutable - valueType { - urn - type - info { - type - displayName - __typename - } - __typename - } - entityTypes { - urn - type - info { - type - __typename - } - __typename - } - cardinality - typeQualifier { - allowedTypes { - urn - type - info { - type - displayName - __typename - } - __typename - } - __typename - } - allowedValues { - value { - ... on StringValue { - stringValue - __typename - } - ... on NumberValue { - numberValue - __typename - } - __typename - } - description - __typename - } - created { - time - actor { - urn - editableProperties { - displayName - pictureLink - __typename - } - ...entityDisplayNameFields - __typename - } - __typename - } - lastModified { - time - actor { - urn - editableProperties { - displayName - pictureLink - __typename - } - ...entityDisplayNameFields - __typename - } - __typename - } - __typename - } - settings { - isHidden - showInSearchFilters - showAsAssetBadge - showInAssetSummary - showInColumnsTable - __typename - } - __typename -} - -fragment entityDisplayNameFields on Entity { - urn - type - ... on Dataset { - name - properties { - name - qualifiedName - __typename - } - __typename - } - ... on CorpUser { - username - properties { - displayName - title - firstName - lastName - fullName - email - __typename - } - info { - active - displayName - title - firstName - lastName - fullName - email - __typename - } - __typename - } - ... on CorpGroup { - name - info { - displayName - __typename - } - __typename - } - ... on Dashboard { - dashboardId - properties { - name - __typename - } - __typename - } - ... on Chart { - chartId - properties { - name - __typename - } - __typename - } - ... on DataFlow { - properties { - name - __typename - } - __typename - } - ... on DataJob { - jobId - properties { - name - __typename - } - __typename - } - ... on GlossaryTerm { - name - hierarchicalName - properties { - name - __typename - } - __typename - } - ... on GlossaryNode { - properties { - name - description - __typename - } - __typename - } - ... on Domain { - properties { - name - __typename - } - __typename - } - ... on Container { - properties { - name - __typename - } - __typename - } - ... on MLFeatureTable { - name - __typename - } - ... on MLFeature { - name - __typename - } - ... on MLPrimaryKey { - name - __typename - } - ... on MLModel { - name - __typename - } - ... on MLModelGroup { - name - __typename - } - ... on Tag { - name - properties { - name - colorHex - __typename - } - __typename - } - ... on DataPlatform { - ...nonConflictingPlatformFields - __typename - } - ... on DataProduct { - properties { - name - __typename - } - __typename - } - ... on DataPlatformInstance { - instanceId - __typename - } - ... on StructuredPropertyEntity { - definition { - displayName - qualifiedName - __typename - } - __typename - } - ... on SchemaFieldEntity { - fieldPath - __typename - } - ... on OwnershipTypeEntity { - info { - name - __typename - } - __typename - } - __typename -} - -fragment nonConflictingPlatformFields on DataPlatform { - urn - type - name - properties { - displayName - datasetNameDelimiter - logoUrl - __typename - } - displayName - info { - type - displayName - datasetNameDelimiter - logoUrl - __typename - } - __typename -} - -fragment formsFields on Forms { - completedForms { - ...formAssociationFields - __typename - } - incompleteForms { - ...formAssociationFields - __typename - } - verifications { - form { - urn - __typename - } - lastModified { - time - actor { - urn - type - ...entityDisplayNameFields - __typename - } - __typename - } - __typename - } - __typename -} - -fragment formAssociationFields on FormAssociation { - associatedUrn - incompletePrompts { - ...formPromptAssociationFields - __typename - } - completedPrompts { - ...formPromptAssociationFields - __typename - } - form { - urn - type - info { - name - description - type - prompts { - id - formUrn - title - description - type - required - structuredPropertyParams { - structuredProperty { - ...structuredPropertyFields - __typename - } - __typename - } - __typename - } - actors { - owners - isAssignedToMe - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - __typename - } - __typename -} - -fragment formPromptAssociationFields on FormPromptAssociation { - id - lastModified { - time - actor { - urn - type - ...entityDisplayNameFields - __typename - } - __typename - } - fieldAssociations { - completedFieldPrompts { - fieldPath - lastModified { - time - actor { - urn - type - ...entityDisplayNameFields - __typename - } - __typename - } - __typename - } - __typename - } - __typename -} - -fragment glossaryNode on GlossaryNode { - urn - type - properties { - name - description - __typename - } - displayProperties { - ...displayPropertiesFields - __typename - } - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 1000} - ) { - total - relationships { - type - entity { - ... on GlossaryTerm { - urn - name - type - hierarchicalName - properties { - name - description - __typename - } - __typename - } - ... on GlossaryNode { - urn - __typename - } - __typename - } - __typename - } - __typename - } - __typename -} - -fragment childGlossaryTerm on GlossaryTerm { - urn - type - name - hierarchicalName - properties { - name - description - __typename - } - __typename -} - -fragment notes on Entity { - notes: relationships( - input: {types: ["PostTarget"], direction: INCOMING, start: 0, count: 100} - ) { - total - relationships { - type - entity { - ... on Post { - urn - type - postType - lastModified { - time - actor - __typename - } - content { - contentType - title - description - link - media { - type - location - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename -} -""" - -LIST_QUERIES_QUERY = """ -query listQueries($input: ListQueriesInput!) { - listQueries(input: $input) { - start - total - count - queries { - ...query - __typename - } - __typename - } -} - -fragment query on QueryEntity { - urn - properties { - name - description - source - statement { - value - language - __typename - } - created { - time - actor - __typename - } - lastModified { - time - actor - __typename - } - origin { - ...searchResultFields - __typename - } - __typename - } - platform { - ...platformFields - __typename - } - subjects { - dataset { - urn - type - name - __typename - } - schemaField { - urn - type - fieldPath - __typename - } - __typename - } - __typename -} - -fragment searchResultFields on Entity { - ...searchResultFieldsNoLineage - ... on EntityWithRelationships { - upstream: lineage(input: {direction: UPSTREAM, start: 0, count: 100}) { - total - filtered - __typename - } - downstream: lineage(input: {direction: DOWNSTREAM, start: 0, count: 100}) { - total - filtered - __typename - } - __typename - } - __typename -} - -fragment searchResultFieldsNoLineage on Entity { - ...searchResultsWithoutSchemaField - ... on SchemaFieldEntity { - ...entityField - __typename - } - __typename -} - -fragment searchResultsWithoutSchemaField on Entity { - urn - type - ... on Dataset { - ...nonSiblingsDatasetSearchFields - siblings { - isPrimary - siblings { - urn - type - ... on Dataset { - ...nonSiblingsDatasetSearchFields - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - siblingsSearch(input: {query: "*", count: 1}) { - count - total - searchResults { - entity { - urn - type - ... on Dataset { - ...nonSiblingsDatasetSearchFields - siblings { - isPrimary - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - browsePathV2 { - ...browsePathV2Fields - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on DataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - ... on Role { - id - properties { - name - description - __typename - } - __typename - } - ... on CorpUser { - username - properties { - active - displayName - title - firstName - lastName - fullName - email - departmentName - title - __typename - } - info { - active - displayName - title - firstName - lastName - fullName - email - departmentName - title - __typename - } - editableProperties { - displayName - title - pictureLink - __typename - } - __typename - } - ... on CorpGroup { - name - info { - displayName - description - __typename - } - memberCount: relationships( - input: {types: ["IsMemberOfGroup", "IsMemberOfNativeGroup"], direction: INCOMING, start: 0, count: 1} - ) { - total - __typename - } - __typename - } - ... on Dashboard { - tool - dashboardId - properties { - name - description - externalUrl - access - lastModified { - time - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - globalTags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - editableProperties { - description - __typename - } - platform { - ...platformFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - deprecation { - ...deprecationFields - __typename - } - parentContainers { - ...parentContainersFields - __typename - } - statsSummary { - viewCount - uniqueUserCountLast30Days - topUsersLast30Days { - urn - type - username - properties { - displayName - firstName - lastName - fullName - __typename - } - editableProperties { - displayName - pictureLink - __typename - } - __typename - } - __typename - } - subTypes { - typeNames - __typename - } - health { - ...entityHealth - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - browsePathV2 { - ...browsePathV2Fields - __typename - } - __typename - } - ... on Chart { - chartId - properties { - name - description - externalUrl - type - access - lastModified { - time - __typename - } - created { - time - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - globalTags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - editableProperties { - description - __typename - } - platform { - ...platformFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - deprecation { - ...deprecationFields - __typename - } - parentContainers { - ...parentContainersFields - __typename - } - browsePathV2 { - ...browsePathV2Fields - __typename - } - statsSummary { - viewCount - uniqueUserCountLast30Days - topUsersLast30Days { - urn - type - username - properties { - displayName - firstName - lastName - fullName - __typename - } - editableProperties { - displayName - pictureLink - __typename - } - __typename - } - __typename - } - subTypes { - typeNames - __typename - } - health { - ...entityHealth - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on DataFlow { - orchestrator - flowId - cluster - properties { - name - description - project - externalUrl - __typename - } - ownership { - ...ownershipFields - __typename - } - globalTags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - editableProperties { - description - __typename - } - platform { - ...platformFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - parentContainers { - ...parentContainersFields - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - deprecation { - ...deprecationFields - __typename - } - childJobs: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 100} - ) { - total - __typename - } - health { - ...entityHealth - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on DataJob { - dataFlow { - ...nonRecursiveDataFlowFields - __typename - } - jobId - ownership { - ...ownershipFields - __typename - } - properties { - name - description - externalUrl - __typename - } - globalTags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - editableProperties { - description - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - deprecation { - ...deprecationFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - parentContainers { - ...parentContainersFields - __typename - } - subTypes { - typeNames - __typename - } - lastRun: runs(start: 0, count: 1) { - count - start - total - runs { - urn - type - created { - time - actor - __typename - } - __typename - } - __typename - } - health { - ...entityHealth - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on GlossaryTerm { - name - hierarchicalName - properties { - name - description - termSource - sourceRef - sourceUrl - rawSchema - customProperties { - key - value - __typename - } - __typename - } - deprecation { - ...deprecationFields - __typename - } - parentNodes { - ...parentNodesFields - __typename - } - domain { - ...entityDomain - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on GlossaryNode { - ...glossaryNode - parentNodes { - ...parentNodesFields - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on Domain { - properties { - name - description - __typename - } - ownership { - ...ownershipFields - __typename - } - parentDomains { - ...parentDomainsFields - __typename - } - displayProperties { - ...displayPropertiesFields - __typename - } - ...domainEntitiesFields - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on Container { - properties { - name - description - externalUrl - __typename - } - platform { - ...platformFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - editableProperties { - description - __typename - } - ownership { - ...ownershipFields - __typename - } - tags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - subTypes { - typeNames - __typename - } - entities(input: {}) { - total - __typename - } - deprecation { - ...deprecationFields - __typename - } - parentContainers { - ...parentContainersFields - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on MLFeatureTable { - name - description - featureTableProperties { - description - mlFeatures { - urn - __typename - } - mlPrimaryKeys { - urn - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - platform { - ...platformFields - __typename - } - deprecation { - ...deprecationFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on MLFeature { - ...nonRecursiveMLFeature - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on MLPrimaryKey { - ...nonRecursiveMLPrimaryKey - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename - } - ... on MLModel { - name - description - origin - ownership { - ...ownershipFields - __typename - } - platform { - ...platformFields - __typename - } - deprecation { - ...deprecationFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - properties { - propertiesName: name - __typename - } - __typename - } - ... on MLModelGroup { - name - origin - description - ownership { - ...ownershipFields - __typename - } - platform { - ...platformFields - __typename - } - deprecation { - ...deprecationFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - properties { - propertiesName: name - __typename - } - __typename - } - ... on Tag { - name - properties { - name - colorHex - __typename - } - description - __typename - } - ... on DataPlatform { - ...nonConflictingPlatformFields - __typename - } - ... on DataProduct { - ...dataProductSearchFields - __typename - } - ... on ERModelRelationship { - urn - type - id - properties { - ...ermodelrelationPropertiesFields - __typename - } - editableProperties { - ...ermodelrelationEditablePropertiesFields - __typename - } - ownership { - ...ownershipFields - __typename - } - tags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - __typename - } - ... on BusinessAttribute { - ...businessAttributeFields - __typename - } - ... on StructuredPropertyEntity { - ...structuredPropertyFields - __typename - } - ... on SupportsVersions { - versionProperties { - ...versionProperties - __typename - } - __typename - } - ... on DataProcessInstance { - ...dataProcessInstanceFields - __typename - } - ... on DataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - __typename -} - -fragment nonSiblingsDatasetSearchFields on Dataset { - exists - name - origin - uri - platform { - ...platformFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - editableProperties { - name - description - __typename - } - access { - ...getAccess - __typename - } - platformNativeType - properties { - name - description - qualifiedName - customProperties { - key - value - __typename - } - externalUrl - lastModified { - time - actor - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - globalTags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - subTypes { - typeNames - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - parentContainers { - ...parentContainersFields - __typename - } - deprecation { - ...deprecationFields - __typename - } - health { - ...entityHealth - __typename - } - ...datasetStatsFields - __typename -} - -fragment platformFields on DataPlatform { - urn - type - lastIngested - name - properties { - type - displayName - datasetNameDelimiter - logoUrl - __typename - } - displayName - info { - type - displayName - datasetNameDelimiter - logoUrl - __typename - } - __typename -} - -fragment dataPlatformInstanceFields on DataPlatformInstance { - urn - type - platform { - ...platformFields - __typename - } - instanceId - __typename -} - -fragment getAccess on Access { - roles { - role { - ...getRolesName - __typename - } - __typename - } - __typename -} - -fragment getRolesName on Role { - urn - type - id - properties { - name - description - type - __typename - } - __typename -} - -fragment ownershipFields on Ownership { - owners { - owner { - ... on CorpUser { - urn - type - username - info { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - properties { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - editableProperties { - displayName - title - pictureLink - email - __typename - } - __typename - } - ... on CorpGroup { - urn - type - name - properties { - displayName - email - __typename - } - info { - displayName - email - admins { - urn - username - info { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - editableInfo { - pictureLink - teams - skills - __typename - } - __typename - } - members { - urn - username - info { - active - displayName - title - email - firstName - lastName - fullName - __typename - } - editableInfo { - pictureLink - teams - skills - __typename - } - __typename - } - groups - __typename - } - __typename - } - __typename - } - type - ownershipType { - urn - type - info { - name - description - __typename - } - status { - removed - __typename - } - __typename - } - associatedUrn - __typename - } - lastModified { - time - __typename - } - __typename -} - -fragment globalTagsFields on GlobalTags { - tags { - tag { - urn - type - name - description - properties { - name - colorHex - __typename - } - __typename - } - associatedUrn - __typename - } - __typename -} - -fragment glossaryTerms on GlossaryTerms { - terms { - term { - ...glossaryTerm - __typename - } - actor { - urn - __typename - } - associatedUrn - __typename - } - __typename -} - -fragment glossaryTerm on GlossaryTerm { - urn - name - type - hierarchicalName - properties { - name - description - definition - termSource - customProperties { - key - value - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - parentNodes { - ...parentNodesFields - __typename - } - __typename -} - -fragment parentNodesFields on ParentNodesResult { - count - nodes { - urn - type - properties { - name - __typename - } - displayProperties { - ...displayPropertiesFields - __typename - } - __typename - } - __typename -} - -fragment displayPropertiesFields on DisplayProperties { - colorHex - icon { - name - style - iconLibrary - __typename - } - __typename -} - -fragment entityDomain on DomainAssociation { - domain { - urn - type - properties { - name - description - __typename - } - parentDomains { - ...parentDomainsFields - __typename - } - ...domainEntitiesFields - displayProperties { - ...displayPropertiesFields - __typename - } - __typename - } - associatedUrn - __typename -} - -fragment parentDomainsFields on ParentDomainsResult { - count - domains { - urn - type - ... on Domain { - displayProperties { - ...displayPropertiesFields - __typename - } - properties { - name - description - __typename - } - __typename - } - __typename - } - __typename -} - -fragment domainEntitiesFields on Domain { - entities(input: {start: 0, count: 0}) { - total - __typename - } - dataProducts: entities( - input: {start: 0, count: 0, filters: [{field: "_entityType", values: "DATA_PRODUCT"}]} - ) { - total - __typename - } - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 0} - ) { - total - __typename - } - __typename -} - -fragment entityDataProduct on Entity { - dataProduct: relationships( - input: {types: ["DataProductContains"], direction: INCOMING, start: 0, count: 1} - ) { - relationships { - type - entity { - urn - type - ... on DataProduct { - properties { - name - description - __typename - } - domain { - ...entityDomain - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename -} - -fragment parentContainersFields on ParentContainersResult { - count - containers { - ...parentContainerFields - __typename - } - __typename -} - -fragment parentContainerFields on Container { - urn - type - properties { - name - __typename - } - subTypes { - typeNames - __typename - } - __typename -} - -fragment deprecationFields on Deprecation { - actor - deprecated - note - decommissionTime - actorEntity { - urn - type - ...entityDisplayNameFields - __typename - } - replacement { - ...entityDisplayNameFields - ... on Dataset { - platform { - ...platformFields - __typename - } - __typename - } - __typename - } - __typename -} - -fragment entityDisplayNameFields on Entity { - urn - type - ... on Dataset { - name - properties { - name - qualifiedName - __typename - } - __typename - } - ... on CorpUser { - username - properties { - displayName - title - firstName - lastName - fullName - email - __typename - } - info { - active - displayName - title - firstName - lastName - fullName - email - __typename - } - __typename - } - ... on CorpGroup { - name - info { - displayName - __typename - } - __typename - } - ... on Dashboard { - dashboardId - properties { - name - __typename - } - __typename - } - ... on Chart { - chartId - properties { - name - __typename - } - __typename - } - ... on DataFlow { - properties { - name - __typename - } - __typename - } - ... on DataJob { - jobId - properties { - name - __typename - } - __typename - } - ... on GlossaryTerm { - name - hierarchicalName - properties { - name - __typename - } - __typename - } - ... on GlossaryNode { - properties { - name - description - __typename - } - __typename - } - ... on Domain { - properties { - name - __typename - } - __typename - } - ... on Container { - properties { - name - __typename - } - __typename - } - ... on MLFeatureTable { - name - __typename - } - ... on MLFeature { - name - __typename - } - ... on MLPrimaryKey { - name - __typename - } - ... on MLModel { - name - __typename - } - ... on MLModelGroup { - name - __typename - } - ... on Tag { - name - properties { - name - colorHex - __typename - } - __typename - } - ... on DataPlatform { - ...nonConflictingPlatformFields - __typename - } - ... on DataProduct { - properties { - name - __typename - } - __typename - } - ... on DataPlatformInstance { - instanceId - __typename - } - ... on StructuredPropertyEntity { - definition { - displayName - qualifiedName - __typename - } - __typename - } - ... on SchemaFieldEntity { - fieldPath - __typename - } - ... on OwnershipTypeEntity { - info { - name - __typename - } - __typename - } - __typename -} - -fragment nonConflictingPlatformFields on DataPlatform { - urn - type - name - properties { - displayName - datasetNameDelimiter - logoUrl - __typename - } - displayName - info { - type - displayName - datasetNameDelimiter - logoUrl - __typename - } - __typename -} - -fragment entityHealth on Health { - type - status - message - causes - __typename -} - -fragment datasetStatsFields on Dataset { - lastProfile: datasetProfiles(limit: 1) { - rowCount - columnCount - sizeInBytes - timestampMillis - __typename - } - lastOperation: operations(limit: 1) { - lastUpdatedTimestamp - timestampMillis - __typename - } - statsSummary { - queryCountLast30Days - uniqueUserCountLast30Days - topUsersLast30Days { - urn - type - username - properties { - displayName - firstName - lastName - fullName - __typename - } - editableProperties { - displayName - pictureLink - __typename - } - __typename - } - __typename - } - __typename -} - -fragment structuredPropertiesFields on StructuredPropertiesEntry { - structuredProperty { - exists - ...structuredPropertyFields - __typename - } - values { - ... on StringValue { - stringValue - __typename - } - ... on NumberValue { - numberValue - __typename - } - __typename - } - valueEntities { - urn - type - ...entityDisplayNameFields - __typename - } - associatedUrn - __typename -} - -fragment structuredPropertyFields on StructuredPropertyEntity { - urn - type - definition { - displayName - qualifiedName - description - cardinality - immutable - valueType { - urn - type - info { - type - displayName - __typename - } - __typename - } - entityTypes { - urn - type - info { - type - __typename - } - __typename - } - cardinality - typeQualifier { - allowedTypes { - urn - type - info { - type - displayName - __typename - } - __typename - } - __typename - } - allowedValues { - value { - ... on StringValue { - stringValue - __typename - } - ... on NumberValue { - numberValue - __typename - } - __typename - } - description - __typename - } - created { - time - actor { - urn - editableProperties { - displayName - pictureLink - __typename - } - ...entityDisplayNameFields - __typename - } - __typename - } - lastModified { - time - actor { - urn - editableProperties { - displayName - pictureLink - __typename - } - ...entityDisplayNameFields - __typename - } - __typename - } - __typename - } - settings { - isHidden - showInSearchFilters - showAsAssetBadge - showInAssetSummary - showInColumnsTable - __typename - } - __typename -} - -fragment browsePathV2Fields on BrowsePathV2 { - path { - name - entity { - urn - type - ...entityDisplayNameFields - __typename - } - __typename - } - __typename -} - -fragment nonRecursiveDataFlowFields on DataFlow { - urn - type - orchestrator - flowId - cluster - properties { - name - description - project - externalUrl - customProperties { - key - value - __typename - } - __typename - } - editableProperties { - description - __typename - } - ownership { - ...ownershipFields - __typename - } - platform { - ...platformFields - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - deprecation { - ...deprecationFields - __typename - } - __typename -} - -fragment glossaryNode on GlossaryNode { - urn - type - properties { - name - description - __typename - } - displayProperties { - ...displayPropertiesFields - __typename - } - children: relationships( - input: {types: ["IsPartOf"], direction: INCOMING, start: 0, count: 1000} - ) { - total - relationships { - type - entity { - ... on GlossaryTerm { - urn - name - type - hierarchicalName - properties { - name - description - __typename - } - __typename - } - ... on GlossaryNode { - urn - __typename - } - __typename - } - __typename - } - __typename - } - __typename -} - -fragment nonRecursiveMLFeature on MLFeature { - urn - type - exists - lastIngested - name - featureNamespace - description - dataType - properties { - description - dataType - version { - versionTag - __typename - } - sources { - urn - name - type - origin - description - uri - platform { - ...platformFields - __typename - } - platformNativeType - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - institutionalMemory { - ...institutionalMemoryFields - __typename - } - status { - removed - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - tags { - ...globalTagsFields - __typename - } - editableProperties { - description - __typename - } - deprecation { - ...deprecationFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - browsePathV2 { - ...browsePathV2Fields - __typename - } - featureTables: relationships( - input: {types: ["Contains"], direction: INCOMING, start: 0, count: 100} - ) { - relationships { - type - entity { - ... on MLFeatureTable { - platform { - ...platformFields - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename -} - -fragment institutionalMemoryFields on InstitutionalMemory { - elements { - url - actor { - ...resolvedActorFields - __typename - } - description - created { - actor - time - __typename - } - associatedUrn - __typename - } - __typename -} - -fragment resolvedActorFields on ResolvedActor { - ... on CorpUser { - urn - ...entityDisplayNameFields - __typename - } - ... on CorpGroup { - urn - ...entityDisplayNameFields - __typename - } - __typename -} - -fragment nonRecursiveMLPrimaryKey on MLPrimaryKey { - urn - type - exists - lastIngested - name - featureNamespace - description - dataType - properties { - description - dataType - version { - versionTag - __typename - } - sources { - urn - name - type - origin - description - uri - platform { - ...platformFields - __typename - } - platformNativeType - __typename - } - __typename - } - ownership { - ...ownershipFields - __typename - } - institutionalMemory { - ...institutionalMemoryFields - __typename - } - status { - removed - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - domain { - ...entityDomain - __typename - } - ...entityDataProduct - tags { - ...globalTagsFields - __typename - } - editableProperties { - description - __typename - } - deprecation { - ...deprecationFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - featureTables: relationships( - input: {types: ["KeyedBy"], direction: INCOMING, start: 0, count: 100} - ) { - relationships { - type - entity { - ... on MLFeatureTable { - platform { - ...platformFields - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - __typename -} - -fragment dataProductSearchFields on DataProduct { - urn - type - properties { - name - description - externalUrl - __typename - } - ownership { - ...ownershipFields - __typename - } - tags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - domain { - ...entityDomain - __typename - } - entities(input: {start: 0, count: 0, query: "*"}) { - total - __typename - } - __typename -} - -fragment ermodelrelationPropertiesFields on ERModelRelationshipProperties { - name - source { - ...datasetERModelRelationshipFields - __typename - } - destination { - ...datasetERModelRelationshipFields - __typename - } - relationshipFieldMappings { - ...relationshipFieldMapping - __typename - } - createdTime - createdActor { - urn - __typename - } - __typename -} - -fragment datasetERModelRelationshipFields on Dataset { - urn - name - properties { - name - description - __typename - } - editableProperties { - description - __typename - } - schemaMetadata { - ...schemaMetadataFields - __typename - } - __typename -} - -fragment schemaMetadataFields on SchemaMetadata { - aspectVersion - createdAt - datasetUrn - name - platformUrn - version - cluster - hash - platformSchema { - ... on TableSchema { - schema - __typename - } - ... on KeyValueSchema { - keySchema - valueSchema - __typename - } - __typename - } - fields { - ...entitySchemaFieldFields - __typename - } - primaryKeys - foreignKeys { - name - sourceFields { - fieldPath - __typename - } - foreignFields { - fieldPath - __typename - } - foreignDataset { - urn - name - type - origin - uri - properties { - description - __typename - } - platform { - ...platformFields - __typename - } - platformNativeType - ownership { - ...ownershipFields - __typename - } - globalTags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - __typename - } - __typename - } - __typename -} - -fragment entitySchemaFieldFields on SchemaField { - fieldPath - label - jsonPath - nullable - description - type - nativeDataType - recursive - isPartOfKey - isPartitioningKey - globalTags { - ...globalTagsFields - __typename - } - glossaryTerms { - ...glossaryTerms - __typename - } - schemaFieldEntity { - ...entitySchemaFieldEntityFields - __typename - } - __typename -} - -fragment entitySchemaFieldEntityFields on SchemaFieldEntity { - deprecation { - ...deprecationFields - __typename - } - urn - fieldPath - type - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - businessAttributes { - businessAttribute { - ...businessAttribute - __typename - } - __typename - } - documentation { - ...documentationFields - __typename - } - __typename -} - -fragment businessAttribute on BusinessAttributeAssociation { - businessAttribute { - urn - type - ownership { - ...ownershipFields - __typename - } - properties { - name - description - businessAttributeDataType: type - lastModified { - time - __typename - } - created { - time - __typename - } - tags { - tags { - tag { - urn - name - properties { - name - __typename - } - __typename - } - associatedUrn - __typename - } - __typename - } - glossaryTerms { - terms { - term { - urn - type - properties { - name - __typename - } - __typename - } - associatedUrn - __typename - } - __typename - } - __typename - } - __typename - } - associatedUrn - __typename -} - -fragment documentationFields on Documentation { - documentations { - documentation - attribution { - time - actor { - urn - type - ...entityDisplayNameFields - __typename - } - source { - urn - type - __typename - } - sourceDetail { - key - value - __typename - } - __typename - } - __typename - } - __typename -} - -fragment relationshipFieldMapping on RelationshipFieldMapping { - sourceField - destinationField - __typename -} - -fragment ermodelrelationEditablePropertiesFields on ERModelRelationshipEditableProperties { - description - name - __typename -} - -fragment businessAttributeFields on BusinessAttribute { - urn - type - ownership { - ...ownershipFields - __typename - } - properties { - name - description - businessAttributeDataType: type - customProperties { - key - value - associatedUrn - __typename - } - lastModified { - time - __typename - } - created { - time - __typename - } - tags { - tags { - tag { - urn - name - properties { - name - __typename - } - __typename - } - associatedUrn - __typename - } - __typename - } - glossaryTerms { - terms { - term { - urn - type - properties { - name - __typename - } - __typename - } - associatedUrn - __typename - } - __typename - } - __typename - } - institutionalMemory { - ...institutionalMemoryFields - __typename - } - __typename -} - -fragment versionProperties on VersionProperties { - versionSet { - urn - type - __typename - } - isLatest - version { - versionTag - __typename - } - aliases { - versionTag - __typename - } - comment - created { - time - actor { - urn - ...entityDisplayNameFields - editableProperties { - displayName - pictureLink - __typename - } - __typename - } - __typename - } - createdInSource { - time - actor { - urn - ...entityDisplayNameFields - editableProperties { - displayName - pictureLink - __typename - } - __typename - } - __typename - } - __typename -} - -fragment dataProcessInstanceFields on DataProcessInstance { - urn - type - exists - status { - removed - __typename - } - parentContainers { - ...parentContainersFields - __typename - } - container { - ...entityContainer - __typename - } - subTypes { - typeNames - __typename - } - properties { - name - created { - time - actor - __typename - } - customProperties { - key - value - __typename - } - externalUrl - __typename - } - mlTrainingRunProperties { - id - outputUrls - trainingMetrics { - name - description - value - __typename - } - hyperParams { - name - description - value - __typename - } - __typename - } - optionalPlatform: platform { - ...platformFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - state(startTimeMillis: null, endTimeMillis: null, limit: 1) { - status - attempt - result { - resultType - nativeResultType - __typename - } - timestampMillis - durationMillis - __typename - } - parentTemplate { - urn - type - ... on Dataset { - name - properties { - name - description - qualifiedName - __typename - } - editableProperties { - description - __typename - } - platform { - ...platformFields - __typename - } - subTypes { - typeNames - __typename - } - status { - removed - __typename - } - __typename - } - ... on DataJob { - urn - type - dataFlow { - ...nonRecursiveDataFlowFields - __typename - } - jobId - properties { - name - description - externalUrl - customProperties { - key - value - __typename - } - __typename - } - deprecation { - ...deprecationFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - subTypes { - typeNames - __typename - } - editableProperties { - description - __typename - } - status { - removed - __typename - } - __typename - } - __typename - } - __typename -} - -fragment entityContainer on Container { - urn - platform { - ...platformFields - __typename - } - properties { - name - __typename - } - subTypes { - typeNames - __typename - } - deprecation { - ...deprecationFields - __typename - } - __typename -} - -fragment entityField on SchemaFieldEntity { - urn - type - parent { - urn - type - ...entityDisplayNameFields - ... on Dataset { - platform { - ...platformFields - __typename - } - dataPlatformInstance { - ...dataPlatformInstanceFields - __typename - } - parentContainers { - ...parentContainersFields - __typename - } - __typename - } - __typename - } - fieldPath - structuredProperties { - properties { - ...structuredPropertiesFields - __typename - } - __typename - } - businessAttributes { - businessAttribute { - ...businessAttribute - __typename - } - __typename - } - __typename -} -""" - -# 특정 URN과 연관된 쿼리를 찾는 GraphQL 쿼리 (수정된 버전) -QUERIES_BY_URN_QUERY = """ -query listQueries($input: ListQueriesInput!) { - listQueries(input: $input) { - start - total - count - queries { - urn - properties { - name - description - statement { - value - language - __typename - } - __typename - } - subjects { - dataset { - urn - name - __typename - } - __typename - } - __typename - } - __typename - } -} -""" - -# 특정 URN의 glossary terms를 조회하는 GraphQL 쿼리 -GLOSSARY_TERMS_BY_URN_QUERY = """ -query getDataset($urn: String!) { - dataset(urn: $urn) { - urn - name - glossaryTerms { - terms { - term { - urn - name - type - hierarchicalName - properties { - name - description - definition - __typename - } - parentNodes { - nodes { - urn - properties { - name - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } - __typename - } -} -""" diff --git a/utils/databases/README.md b/utils/databases/README.md deleted file mode 100644 index 03a161c..0000000 --- a/utils/databases/README.md +++ /dev/null @@ -1,251 +0,0 @@ -# Databases 모듈 - -다양한 데이터베이스에 대한 연결 및 SQL 실행 기능을 제공하는 유틸리티 모듈입니다. - -## 디렉토리 구조 - -``` -databases/ -├── __init__.py -├── config.py -├── factory.py -├── logger.py -└── connector/ - ├── base_connector.py - ├── clickhouse_connector.py - ├── databricks_connector.py - ├── duckdb_connector.py - ├── mariadb_connector.py - ├── mysql_connector.py - ├── oracle_connector.py - ├── postgres_connector.py - ├── snowflake_connector.py - ├── sqlite_connector.py - ├── trino_connector.py - └── README.md -``` - -## 파일 설명 - -### __init__.py - -데이터베이스 유틸리티 패키지 초기화 모듈입니다. 주요 구성 요소를 외부로 노출합니다. - -**Export:** -- `DatabaseFactory`: 데이터베이스 커넥터 팩토리 클래스 -- `DBConfig`: 데이터베이스 설정 타입 - -**사용처:** -- `interface/app_pages/settings_sections/db_section.py`: DB 설정 인터페이스에서 import -- 다른 모듈에서 `from utils.databases import DatabaseFactory` 형태로 import되어 사용됨 - -### config.py - -데이터베이스 연결 설정 정보를 정의하는 모듈입니다. - -**클래스:** -- `DBConfig(TypedDict)`: 데이터베이스 연결 설정 정보를 표현하는 타입 딕셔너리 - - `host` (str): 데이터베이스 호스트명 또는 IP 주소 - - `port` (Optional[int]): 데이터베이스 포트 번호 - - `user` (Optional[str]): 접속 사용자명 - - `password` (Optional[str]): 접속 비밀번호 - - `database` (Optional[str]): 대상 데이터베이스 이름 - - `extra` (Optional[Dict[str, str]]): 드라이버별 추가 설정값 - -**사용처:** -- `utils.databases.factory`: DBConfig 타입을 사용하여 설정 정보 전달 -- `utils.databases.connector.*`: 모든 커넥터가 이 타입을 사용 -- `interface/core/config/models.py`: DBConnectionProfile 모델에서 참조 - -### factory.py - -데이터베이스 커넥터 팩토리 모듈입니다. DB 타입에 따라 알맞은 커넥터 클래스를 동적으로 로드하고 인스턴스를 생성합니다. - -**클래스:** -- `DatabaseFactory`: 데이터베이스 커넥터 팩토리 클래스 - - `get_connector(db_type, config)`: 주어진 DB 타입에 해당하는 Connector 인스턴스 반환 - - DB 타입이 지정되지 않은 경우 환경 변수(`DB_TYPE`)에서 자동으로 가져옴 - - config가 지정되지 않은 경우 `load_config_from_env()`로 환경 변수에서 로드 - - 지원되지 않는 DB 타입이거나 모듈을 로드할 수 없는 경우 `ValueError` 발생 - -**함수:** -- `load_config_from_env(prefix) -> DBConfig`: 환경변수에서 데이터베이스 접속 설정을 로드 - - prefix: 환경변수 접두어 (예: 'POSTGRES', 'MYSQL') - - `{PREFIX}_HOST`, `{PREFIX}_PORT`, `{PREFIX}_USER`, `{PREFIX}_PASSWORD`, `{PREFIX}_DATABASE` 로드 - - 추가 커스텀 환경변수는 `extra` 필드에 포함 - -**사용처:** -- `interface/app_pages/settings_sections/db_section.py`: DB 연결 테스트 및 설정 적용 (221번 라인) -- `interface/app_pages/settings_sections/db_section.py`: 환경변수 설정 검증 (272번 라인) -- `utils.databases.__init__.py`: DatabaseFactory를 외부로 노출 - -**환경변수 예시:** -```bash -DB_TYPE=postgres -POSTGRES_HOST=localhost -POSTGRES_PORT=5432 -POSTGRES_USER=myuser -POSTGRES_PASSWORD=mypassword -POSTGRES_DATABASE=mydb -``` - -### logger.py - -로깅 설정 모듈입니다. 애플리케이션 전역에서 사용할 기본 로깅 설정을 정의합니다. - -**내보내기:** -- `logger`: 표준 로거 인스턴스 - -**설정:** -- 레벨: `INFO` -- 포맷: `%(asctime)s [%(levelname)s] %(message)s` -- 날짜 포맷: `%Y-%m-%d %H:%M:%S` - -**사용처:** -- `utils.databases.factory`: DB 타입 로딩 실패 시 로깅 -- `utils.databases.connector.*`: 모든 커넥터에서 연결 및 SQL 실행 로깅 - -### connector/ - -데이터베이스별 커넥터 구현이 위치한 디렉토리입니다. - -자세한 내용은 [connector/README.md](connector/README.md)를 참조하세요. - -**주요 특징:** -- 모든 커넥터는 `BaseConnector` 추상 클래스를 상속 -- 공통 인터페이스: `connect()`, `run_sql(sql) -> pd.DataFrame`, `close()` -- 지원 데이터베이스: PostgreSQL, MySQL, MariaDB, Oracle, SQLite, DuckDB, Snowflake, Databricks, Trino, ClickHouse - -## 사용 방법 - -### Factory 패턴 사용 (권장) - -환경 변수를 설정하고 DatabaseFactory를 사용하여 커넥터를 생성합니다: - -```python -from utils.databases import DatabaseFactory - -# 환경 변수 사용 -connector = DatabaseFactory.get_connector() -result = connector.run_sql("SELECT * FROM users LIMIT 10") -connector.close() -``` - -### 명시적 설정 사용 - -DBConfig를 직접 생성하여 전달할 수 있습니다: - -```python -from utils.databases import DatabaseFactory, DBConfig - -config = DBConfig( - host="localhost", - port=5432, - user="myuser", - password="mypassword", - database="mydb", - extra={} -) -connector = DatabaseFactory.get_connector(db_type="postgres", config=config) -result = connector.run_sql("SELECT * FROM users LIMIT 10") -connector.close() -``` - -### 직접 커넥터 사용 - -특정 커넥터를 직접 import하여 사용할 수 있습니다: - -```python -from utils.databases.connector.postgres_connector import PostgresConnector -from utils.databases.config import DBConfig - -config = DBConfig( - host="localhost", - port=5432, - user="myuser", - password="mypassword", - database="mydb", - extra={} -) - -connector = PostgresConnector(config) -result = connector.run_sql("SELECT * FROM users LIMIT 10") -print(result) -connector.close() -``` - -## 지원되는 데이터베이스 - -1. **PostgreSQL** (`postgres`) -2. **MySQL** (`mysql`) -3. **MariaDB** (`mariadb`) -4. **Oracle** (`oracle`) -5. **SQLite** (`sqlite`) -6. **DuckDB** (`duckdb`) -7. **Snowflake** (`snowflake`) -8. **Databricks** (`databricks`) -9. **Trino** (`trino`) -10. **ClickHouse** (`clickhouse`) - -각 데이터베이스의 상세한 설정 방법은 [connector/README.md](connector/README.md)를 참조하세요. - -## 통합 사용 예시 - -### Streamlit 인터페이스에서 사용 - -`interface/app_pages/settings_sections/db_section.py`에서 DB 연결을 설정하고 테스트: - -```python -from utils.databases import DatabaseFactory - -# 연결 테스트 -connector = DatabaseFactory.get_connector(db_type=db_type) -test_sql = "SELECT 1" -df = connector.run_sql(test_sql) -connector.close() -``` - -### CLI에서 사용 - -환경 변수를 통해 커넥터를 생성하고 SQL 쿼리를 실행할 수 있습니다. - -## 에러 처리 - -- **연결 실패**: `ConnectionError` 발생 -- **SQL 실행 실패**: `RuntimeError` 발생 -- **지원되지 않는 DB 타입**: `ValueError` 발생 -- **DB_TYPE 미지정**: `ValueError("DB_TYPE이 환경변수 또는 인자로 제공되어야 합니다.")` 발생 - -## 로깅 - -모든 커넥터는 `utils.databases.logger`를 사용하여 다음 정보를 로깅합니다: -- 연결 성공/실패 -- SQL 실행 결과 -- 에러 발생 상황 - -로그 포맷: `YYYY-MM-DD HH:MM:SS [LEVEL] 메시지` - -## 의존성 - -각 커넥터는 해당 데이터베이스의 Python 드라이버를 필요로 합니다: - -- PostgreSQL: `psycopg2` -- MySQL/MariaDB: `mysql-connector-python` -- Oracle: `oracledb` -- SQLite: `sqlite3` (표준 라이브러리) -- DuckDB: `duckdb` -- Snowflake: `snowflake-connector-python` -- Databricks: `databricks-sql-connector` -- Trino: `trino` -- ClickHouse: `clickhouse-driver` - -## 확장성 - -새로운 데이터베이스를 추가하려면: - -1. `connector/` 디렉토리에 `{db_type}_connector.py` 파일 생성 -2. `BaseConnector`를 상속받는 `{DBType}Connector` 클래스 구현 -3. `connect()`, `run_sql(sql) -> pd.DataFrame`, `close()` 메서드 구현 -4. `DatabaseFactory.get_connector()`가 자동으로 새 커넥터를 로드 - -자세한 내용은 `connector/base_connector.py`를 참조하세요. diff --git a/utils/databases/__init__.py b/utils/databases/__init__.py deleted file mode 100644 index 2a6cf19..0000000 --- a/utils/databases/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -데이터베이스 유틸리티 패키지 초기화 모듈. - -이 모듈은 주요 구성 요소인 DatabaseFactory와 DBConfig를 외부로 노출하여 -데이터베이스 관련 기능을 손쉽게 사용할 수 있도록 합니다. -""" - -from utils.databases.config import DBConfig -from utils.databases.factory import DatabaseFactory - -__all__ = [ - "DatabaseFactory", - "DBConfig", -] diff --git a/utils/databases/config.py b/utils/databases/config.py deleted file mode 100644 index 0b48328..0000000 --- a/utils/databases/config.py +++ /dev/null @@ -1,32 +0,0 @@ -""" -데이터베이스 설정 정보를 정의하는 모듈. - -이 모듈은 데이터베이스 연결에 필요한 기본 설정값과 -추가 옵션(extra)을 포함한 타입 힌트를 제공합니다. -""" - -from typing import Dict, Optional, TypedDict - - -class DBConfig(TypedDict): - """ - 데이터베이스 연결 설정 정보를 표현하는 타입 딕셔너리. - - 데이터베이스 커넥터가 공통적으로 사용하는 설정 필드를 정의합니다. - 일부 필드는 선택적으로 제공될 수 있습니다. - - Attributes: - host (str): 데이터베이스 호스트명 또는 IP 주소. - port (Optional[int]): 데이터베이스 포트 번호. - user (Optional[str]): 접속 사용자명. - password (Optional[str]): 접속 비밀번호. - database (Optional[str]): 대상 데이터베이스 이름. - extra (Optional[Dict[str, str]]): 드라이버별 추가 설정값. - """ - - host: str - port: Optional[int] - user: Optional[str] - password: Optional[str] - database: Optional[str] - extra: Optional[Dict[str, str]] diff --git a/utils/databases/connector/README.md b/utils/databases/connector/README.md deleted file mode 100644 index 8349e14..0000000 --- a/utils/databases/connector/README.md +++ /dev/null @@ -1,302 +0,0 @@ -# Connector 모듈 - -데이터베이스 연결 및 SQL 실행을 위한 커넥터 클래스들을 제공하는 모듈입니다. - -## 디렉토리 구조 - -``` -connector/ -├── __pycache__/ -├── base_connector.py -├── clickhouse_connector.py -├── databricks_connector.py -├── duckdb_connector.py -├── mariadb_connector.py -├── mysql_connector.py -├── oracle_connector.py -├── postgres_connector.py -├── snowflake_connector.py -├── sqlite_connector.py -└── trino_connector.py -``` - -## 파일 설명 - -### base_connector.py - -데이터베이스 커넥터의 기본 인터페이스를 정의하는 모듈입니다. - -**클래스:** -- `BaseConnector`: 모든 DB 커넥터가 상속받아야 하는 추상 클래스 - - `connection`: DB 연결 객체를 저장하는 클래스 변수 - - `connect()`: 데이터베이스 연결 수행 (abstractmethod) - - `run_sql(sql: str) -> pd.DataFrame`: SQL 쿼리 실행 및 결과 반환 (abstractmethod) - - `close() -> None`: 데이터베이스 연결 종료 (abstractmethod) - -**사용처:** -- 모든 구체적인 DB 커넥터 클래스들이 이 클래스를 상속받음 -- `utils.databases.connector.*` 모듈들에서 import되어 사용됨 - -### clickhouse_connector.py - -ClickHouse 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `ClickHouseConnector(BaseConnector)`: ClickHouse 서버 연결을 위한 커넥터 - - `client`: ClickHouse Client 객체 - - `__init__(config: DBConfig)`: 호스트, 포트, 사용자, 비밀번호, 데이터베이스 설정으로 초기화 - - `connect() -> None`: clickhouse_driver.Client로 ClickHouse 서버 연결 - - `run_sql(sql: str) -> pd.DataFrame`: 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 클라이언트 연결 해제 - -**의존성:** -- `clickhouse_driver` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### databricks_connector.py - -Databricks SQL Warehouse 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `DatabricksConnector(BaseConnector)`: Databricks SQL Warehouse 연결을 위한 커넥터 - - `connection`: Databricks 연결 객체 - - `__init__(config: DBConfig)`: - - 필수 설정: host, extra.http_path, extra.access_token - - 선택 설정: extra.catalog, extra.schema - - `connect() -> None`: databricks.sql 모듈로 연결 설정 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `databricks.sql` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### duckdb_connector.py - -DuckDB 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `DuckDBConnector(BaseConnector)`: DuckDB 연결을 위한 커넥터 - - `connection`: DuckDB 연결 객체 - - `__init__(config: DBConfig)`: path 설정 (기본값: ":memory:") - - `connect() -> None`: duckdb.connect로 데이터베이스 연결 - - `run_sql(sql: str) -> pd.DataFrame`: execute().fetchdf()로 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `duckdb` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### mariadb_connector.py - -MariaDB 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `MariaDBConnector(BaseConnector)`: MariaDB 서버 연결을 위한 커넥터 - - `connection`: MariaDB 연결 객체 - - `__init__(config: DBConfig)`: 호스트, 포트(기본: 3306), 사용자, 비밀번호, 데이터베이스 설정 - - `connect() -> None`: mysql.connector로 MariaDB 서버 연결 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `mysql.connector` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### mysql_connector.py - -MySQL 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `MySQLConnector(BaseConnector)`: MySQL 서버 연결을 위한 커넥터 - - `connection`: MySQL 연결 객체 - - `__init__(config: DBConfig)`: 호스트, 포트(기본: 3306), 사용자, 비밀번호, 데이터베이스 설정 - - `connect() -> None`: mysql.connector로 MySQL 서버 연결 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `mysql.connector` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### oracle_connector.py - -Oracle 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `OracleConnector(BaseConnector)`: Oracle 서버 연결을 위한 커넥터 - - `connection`: Oracle 연결 객체 - - `__init__(config: DBConfig)`: - - 필수 설정: host, port, user, password - - 선택 설정: extra.service_name (기본값: "orcl") - - `connect() -> None`: oracledb.connect로 DSN 형식으로 연결 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `oracledb` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### postgres_connector.py - -PostgreSQL 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `PostgresConnector(BaseConnector)`: PostgreSQL 서버 연결을 위한 커넥터 - - `connection`: PostgreSQL 연결 객체 - - `__init__(config: DBConfig)`: 호스트, 포트, 사용자, 비밀번호, 데이터베이스 설정 - - `connect() -> None`: psycopg2.connect로 PostgreSQL 서버 연결 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `psycopg2` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### snowflake_connector.py - -Snowflake 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `SnowflakeConnector(BaseConnector)`: Snowflake 서버 연결을 위한 커넥터 - - `connection`: Snowflake 연결 객체 - - `cursor`: Snowflake 커서 객체 - - `__init__(config: DBConfig)`: - - 필수 설정: user, password, extra.account - - 선택 설정: extra.warehouse, database, extra.schema - - `connect() -> None`: snowflake.connector로 연결 및 커서 생성 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `snowflake.connector` -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### sqlite_connector.py - -SQLite 데이터베이스 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `SQLiteConnector(BaseConnector)`: SQLite 파일 또는 인메모리 데이터베이스 연결을 위한 커넥터 - - `connection`: SQLite 연결 객체 - - `__init__(config: DBConfig)`: path 설정 (None 또는 ":memory:"인 경우 인메모리 DB) - - `connect() -> None`: sqlite3.connect로 데이터베이스 연결 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `sqlite3` (Python 표준 라이브러리) -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -### trino_connector.py - -Trino 클러스터 연결 및 SQL 실행을 담당하는 모듈입니다. - -**클래스:** -- `TrinoConnector(BaseConnector)`: Trino 클러스터 연결을 위한 커넥터 - - `connection`: Trino 연결 객체 - - `__init__(config: DBConfig)`: - - 필수 설정: host, port - - 선택 설정: user, password, database, extra.catalog, extra.schema, extra.http_scheme - - database가 "catalog.schema" 형태일 경우 자동 분리 - - `connect() -> None`: trino.dbapi.connect로 연결 설정 - - `run_sql(sql: str) -> pd.DataFrame`: 커서를 사용해 쿼리 실행 후 DataFrame 반환 - - `close() -> None`: 연결 종료 - -**의존성:** -- `trino` (런타임에 동적으로 로드) -- `utils.databases.config.DBConfig` -- `utils.databases.logger.logger` - -## 사용 방법 - -### 직접 사용 - -각 커넥터 클래스를 직접 인스턴스화하여 사용할 수 있습니다: - -```python -from utils.databases.connector.postgres_connector import PostgresConnector -from utils.databases.config import DBConfig - -config = DBConfig( - host="localhost", - port=5432, - user="myuser", - password="mypassword", - database="mydb", - extra={} -) - -connector = PostgresConnector(config) -result = connector.run_sql("SELECT * FROM users") -print(result) -connector.close() -``` - -### Factory 패턴 사용 (권장) - -`DatabaseFactory`를 사용하여 DB 타입에 따라 자동으로 적절한 커넥터를 선택할 수 있습니다: - -```python -from utils.databases import DatabaseFactory - -# 환경 변수 사용 -connector = DatabaseFactory.get_connector() -result = connector.run_sql("SELECT * FROM users") -connector.close() - -# 명시적 설정 -config = DBConfig( - host="localhost", - port=5432, - user="myuser", - password="mypassword", - database="mydb", - extra={} -) -connector = DatabaseFactory.get_connector(db_type="postgres", config=config) -result = connector.run_sql("SELECT * FROM users") -connector.close() -``` - -### 지원되는 데이터베이스 타입 - -다음 데이터베이스들이 지원됩니다: - -1. **PostgreSQL** (`postgres`) -2. **MySQL** (`mysql`) -3. **MariaDB** (`mariadb`) -4. **Oracle** (`oracle`) -5. **SQLite** (`sqlite`) -6. **DuckDB** (`duckdb`) -7. **Snowflake** (`snowflake`) -8. **Databricks** (`databricks`) -9. **Trino** (`trino`) -10. **ClickHouse** (`clickhouse`) - -## 공통 인터페이스 - -모든 커넥터는 `BaseConnector` 추상 클래스를 상속받아 다음과 같은 공통 인터페이스를 구현합니다: - -- `connect() -> None`: 데이터베이스에 연결 -- `run_sql(sql: str) -> pd.DataFrame`: SQL 쿼리 실행 및 결과를 pandas DataFrame으로 반환 -- `close() -> None`: 데이터베이스 연결 종료 - -## 로깅 - -모든 커넥터는 `utils.databases.logger`를 사용하여 연결 성공/실패 및 SQL 실행 결과를 로깅합니다. - -## 에러 처리 - -- 연결 실패 시: `ConnectionError` 발생 -- SQL 실행 실패 시: `RuntimeError` 발생 -- 지원되지 않는 DB 타입: `ValueError` 발생 diff --git a/utils/databases/connector/base_connector.py b/utils/databases/connector/base_connector.py deleted file mode 100644 index c1fbe90..0000000 --- a/utils/databases/connector/base_connector.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -데이터베이스 커넥터의 기본 인터페이스 정의 모듈. - -이 모듈은 모든 DB 커넥터 클래스가 상속해야 하는 -공통 추상 클래스(BaseConnector)를 제공합니다. -""" - -from abc import ABC, abstractmethod - -import pandas as pd - - -class BaseConnector(ABC): - """ - 데이터베이스 커넥터의 기본 추상 클래스. - - 모든 구체적인 DB 커넥터(Postgres, MySQL 등)는 - 이 클래스를 상속받아 공통 메서드(`connect`, `run_sql`, `close`)를 구현해야 합니다. - - Attributes: - connection (Any): DB 연결 객체. 구체 클래스에서 초기화 및 관리됩니다. - """ - - connection = None - - @abstractmethod - def connect(self): - """ - 데이터베이스 연결을 수행합니다. - - 이 메서드는 각 DB별 커넥터에서 구체적으로 구현되어야 합니다. - """ - pass - - @abstractmethod - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 포함하는 데이터프레임. - """ - pass - - @abstractmethod - def close(self) -> None: - """ - 데이터베이스 연결을 종료합니다. - - 모든 리소스(커서, 연결 등)를 안전하게 해제해야 합니다. - - Raises: - RuntimeError: 연결 종료 중 예외가 발생한 경우. - """ - pass diff --git a/utils/databases/connector/clickhouse_connector.py b/utils/databases/connector/clickhouse_connector.py deleted file mode 100644 index 4e755b4..0000000 --- a/utils/databases/connector/clickhouse_connector.py +++ /dev/null @@ -1,91 +0,0 @@ -""" -ClickHouse 데이터베이스 커넥터 모듈. - -이 모듈은 ClickHouse 서버에 연결하여 SQL 쿼리를 실행하고, -그 결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import pandas as pd -from clickhouse_driver import Client - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class ClickHouseConnector(BaseConnector): - """ - ClickHouse 데이터베이스 커넥터 클래스. - - ClickHouse 서버에 연결하고 SQL 쿼리를 실행하거나 연결을 종료하는 기능을 제공합니다. - """ - - client = None - - def __init__(self, config: DBConfig): - """ - ClickHouseConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): ClickHouse 연결 설정 정보를 담은 객체. - """ - self.host = config["host"] - self.port = config["port"] - self.user = config["user"] - self.password = config["password"] - self.database = config["database"] - self.connect() - - def connect(self) -> None: - """ - ClickHouse 서버에 연결을 설정합니다. - - Raises: - ConnectionError: 서버 연결에 실패한 경우 발생합니다. - """ - try: - self.client = Client( - host=self.host, - port=self.port, - user=self.user, - password=self.password, - database=self.database, - ) - logger.info("Successfully connected to ClickHouse.") - except Exception as e: - logger.error("Failed to connect to ClickHouse: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - if self.client is None: - self.connect() - - try: - result = self.client.query_dataframe(sql) - return result - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - - def close(self) -> None: - """ - ClickHouse 서버와의 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.client: - self.client.disconnect() - logger.info("Connection to ClickHouse closed.") - self.client = None diff --git a/utils/databases/connector/databricks_connector.py b/utils/databases/connector/databricks_connector.py deleted file mode 100644 index 34ddcfb..0000000 --- a/utils/databases/connector/databricks_connector.py +++ /dev/null @@ -1,99 +0,0 @@ -""" -Databricks SQL Warehouse 커넥터 모듈. - -이 모듈은 Databricks SQL Warehouse에 연결하여 SQL 쿼리를 실행하고, -결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import pandas as pd -from databricks import sql - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class DatabricksConnector(BaseConnector): - """ - Databricks SQL Warehouse 커넥터 클래스. - - Databricks SQL 엔드포인트에 연결하여 쿼리를 실행하고, - 결과를 DataFrame으로 반환하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - DatabricksConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): Databricks 연결 정보를 담은 설정 객체. - - 필수 키: host, extra.http_path, extra.access_token - - 선택 키: extra.catalog, extra.schema - """ - self.server_hostname = config["host"] - self.http_path = config["extra"]["http_path"] - self.access_token = config["extra"]["access_token"] - self.catalog = config.get("extra", {}).get("catalog") - self.schema = config.get("extra", {}).get("schema") - self.connect() - - def connect(self) -> None: - """ - Databricks SQL Warehouse에 연결을 설정합니다. - - Raises: - ConnectionError: 연결 설정 중 오류가 발생한 경우. - """ - try: - self.connection = sql.connect( - server_hostname=self.server_hostname, - http_path=self.http_path, - access_token=self.access_token, - catalog=self.catalog, - schema=self.schema, - ) - logger.info("Successfully connected to Databricks.") - except Exception as e: - logger.error("Failed to connect to Databricks: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - if self.connection is None: - self.connect() - - try: - cursor = self.connection.cursor() - cursor.execute(sql) - columns = [desc[0] for desc in cursor.description] - rows = cursor.fetchall() - return pd.DataFrame(rows, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - finally: - cursor.close() - - def close(self) -> None: - """ - Databricks SQL Warehouse와의 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to Databricks closed.") - self.connection = None diff --git a/utils/databases/connector/duckdb_connector.py b/utils/databases/connector/duckdb_connector.py deleted file mode 100644 index 7ad7f2a..0000000 --- a/utils/databases/connector/duckdb_connector.py +++ /dev/null @@ -1,79 +0,0 @@ -""" -DuckDB 데이터베이스 커넥터 모듈. - -이 모듈은 DuckDB 데이터베이스에 연결하여 SQL 쿼리를 실행하고, -결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import duckdb -import pandas as pd - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class DuckDBConnector(BaseConnector): - """ - DuckDB 데이터베이스 커넥터 클래스. - - DuckDB 데이터베이스에 연결하여 쿼리를 실행하고, - 결과를 DataFrame 형태로 반환하거나 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - DuckDBConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): DuckDB 연결 정보를 담은 설정 객체. - `path` 키를 사용하여 파일 경로를 지정하거나, `:memory:`를 사용하여 인메모리 DB로 설정합니다. - """ - self.database = config.get("path", ":memory:") - self.connect() - - def connect(self) -> None: - """ - DuckDB 데이터베이스에 연결을 설정합니다. - - Raises: - ConnectionError: DuckDB 연결에 실패한 경우 발생합니다. - """ - try: - self.connection = duckdb.connect(database=self.database) - logger.info("Successfully connected to DuckDB.") - except Exception as e: - logger.error("Failed to connect to DuckDB: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - try: - return self.connection.execute(sql).fetchdf() - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - - def close(self) -> None: - """ - DuckDB 데이터베이스 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to DuckDB closed.") - self.connection = None diff --git a/utils/databases/connector/mariadb_connector.py b/utils/databases/connector/mariadb_connector.py deleted file mode 100644 index d6120ef..0000000 --- a/utils/databases/connector/mariadb_connector.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -MariaDB 데이터베이스 커넥터 모듈. - -이 모듈은 mysql-connector-python을 사용하여 MariaDB 서버에 연결하고, -SQL 쿼리를 실행하여 pandas DataFrame 형태로 결과를 반환하는 기능을 제공합니다. -""" - -import mysql.connector -import pandas as pd - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class MariaDBConnector(BaseConnector): - """ - MariaDB 데이터베이스 커넥터 클래스. - - mysql-connector-python을 이용해 MariaDB 서버에 연결하고, - SQL 쿼리를 실행하거나 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - MariaDBConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): MariaDB 연결 정보를 담은 설정 객체. - """ - self.host = config["host"] - self.port = config.get("port", 3306) - self.user = config["user"] - self.password = config["password"] - self.database = config["database"] - self.connect() - - def connect(self) -> None: - """ - mysql-connector-python을 사용하여 MariaDB 서버에 연결을 설정합니다. - - Raises: - ConnectionError: MariaDB 서버 연결에 실패한 경우 발생합니다. - """ - try: - self.connection = mysql.connector.connect( - host=self.host, - port=self.port, - user=self.user, - password=self.password, - database=self.database, - ) - logger.info("Successfully connected to MariaDB.") - except Exception as e: - logger.error("Failed to connect to MariaDB: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - try: - cursor = self.connection.cursor() - cursor.execute(sql) - columns = [column[0] for column in cursor.description] - rows = cursor.fetchall() - return pd.DataFrame(rows, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - finally: - cursor.close() - - def close(self) -> None: - """ - MariaDB 서버와의 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to MariaDB closed.") - self.connection = None diff --git a/utils/databases/connector/mysql_connector.py b/utils/databases/connector/mysql_connector.py deleted file mode 100644 index 1c02a11..0000000 --- a/utils/databases/connector/mysql_connector.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -MySQL 데이터베이스 커넥터 모듈. - -이 모듈은 MySQL 서버에 연결하여 SQL 쿼리를 실행하고, -그 결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import mysql.connector -import pandas as pd - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class MySQLConnector(BaseConnector): - """ - MySQL 데이터베이스 커넥터 클래스. - - MySQL 서버에 연결하여 SQL 쿼리를 실행하거나 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - MySQLConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): MySQL 연결 정보를 담은 설정 객체. - """ - self.host = config["host"] - self.port = config.get("port", 3306) - self.user = config["user"] - self.password = config["password"] - self.database = config["database"] - self.connect() - - def connect(self) -> None: - """ - MySQL 서버에 연결을 설정합니다. - - Raises: - ConnectionError: MySQL 서버 연결에 실패한 경우 발생합니다. - """ - try: - self.connection = mysql.connector.connect( - host=self.host, - port=self.port, - user=self.user, - password=self.password, - database=self.database, - ) - logger.info("Successfully connected to MySQL.") - except Exception as e: - logger.error("Failed to connect to MySQL: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - try: - cursor = self.connection.cursor() - cursor.execute(sql) - columns = [column[0] for column in cursor.description] - rows = cursor.fetchall() - return pd.DataFrame(rows, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - finally: - cursor.close() - - def close(self) -> None: - """ - MySQL 서버와의 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to MySQL closed.") - self.connection = None diff --git a/utils/databases/connector/oracle_connector.py b/utils/databases/connector/oracle_connector.py deleted file mode 100644 index 915614f..0000000 --- a/utils/databases/connector/oracle_connector.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -Oracle 데이터베이스 커넥터 모듈. - -이 모듈은 Oracle 데이터베이스에 연결하여 SQL 쿼리를 실행하고, -결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import oracledb -import pandas as pd - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class OracleConnector(BaseConnector): - """ - Oracle 데이터베이스 커넥터 클래스. - - Oracle 서버에 연결하여 SQL 쿼리를 실행하거나 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - OracleConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): Oracle 연결 정보를 담은 설정 객체. - - 필수 키: host, port, user, password - - 선택 키: extra.service_name (기본값: "orcl") - """ - self.host = config["host"] - self.port = config["port"] - self.user = config["user"] - self.password = config["password"] - self.service_name = config.get("extra").get("service_name", "orcl") - self.connect() - - def connect(self) -> None: - """ - Oracle 데이터베이스에 연결을 설정합니다. - - Raises: - ConnectionError: Oracle 서버 연결에 실패한 경우 발생합니다. - """ - try: - self.connection = oracledb.connect( - user=self.user, - password=self.password, - dsn=f"{self.host}:{self.port}/{self.service_name}", - ) - logger.info("Successfully connected to Oracle.") - except Exception as e: - logger.error("Failed to connect to Oracle: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - try: - cursor = self.connection.cursor() - cursor.execute(sql) - columns = [desc[0] for desc in cursor.description] - rows = cursor.fetchall() - return pd.DataFrame(rows, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - finally: - cursor.close() - - def close(self) -> None: - """ - Oracle 데이터베이스 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to Oracle closed.") - self.connection = None diff --git a/utils/databases/connector/postgres_connector.py b/utils/databases/connector/postgres_connector.py deleted file mode 100644 index a94ae4f..0000000 --- a/utils/databases/connector/postgres_connector.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -PostgreSQL 데이터베이스 커넥터 모듈. - -이 모듈은 PostgreSQL 서버에 연결하여 SQL 쿼리를 실행하고, -결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import pandas as pd -import psycopg2 - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class PostgresConnector(BaseConnector): - """ - PostgreSQL 데이터베이스 커넥터 클래스. - - PostgreSQL 서버에 연결하고 SQL 쿼리를 실행하거나 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - PostgresConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): PostgreSQL 연결 정보를 담은 설정 객체. - """ - self.host = config["host"] - self.port = config["port"] - self.user = config["user"] - self.password = config["password"] - self.database = config["database"] - self.connect() - - def connect(self) -> None: - """ - PostgreSQL 서버에 연결합니다. - - Raises: - ConnectionError: 서버 연결에 실패한 경우 발생합니다. - """ - try: - self.connection = psycopg2.connect( - host=self.host, - port=self.port, - user=self.user, - password=self.password, - dbname=self.database, - ) - logger.info("Successfully connected to PostgreSQL.") - except Exception as e: - logger.error("Failed to connect to PostgreSQL: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - try: - cursor = self.connection.cursor() - cursor.execute(sql) - columns = [desc[0] for desc in cursor.description] - rows = cursor.fetchall() - return pd.DataFrame(rows, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - finally: - cursor.close() - - def close(self) -> None: - """ - PostgreSQL 서버와의 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to PostgreSQL closed.") - self.connection = None diff --git a/utils/databases/connector/snowflake_connector.py b/utils/databases/connector/snowflake_connector.py deleted file mode 100644 index 69ef907..0000000 --- a/utils/databases/connector/snowflake_connector.py +++ /dev/null @@ -1,102 +0,0 @@ -""" -Snowflake 데이터베이스 커넥터 모듈. - -이 모듈은 Snowflake 데이터베이스에 연결하여 SQL 쿼리를 실행하고, -결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import pandas as pd -from snowflake import connector - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class SnowflakeConnector(BaseConnector): - """ - Snowflake 데이터베이스 커넥터 클래스. - - Snowflake 서버에 연결하여 SQL 쿼리를 실행하거나 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - SnowflakeConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): Snowflake 연결 정보를 담은 설정 객체. - - 필수 키: user, password, extra.account - - 선택 키: extra.warehouse, database, extra.schema - """ - self.user = config["user"] - self.password = config["password"] - self.account = config["extra"]["account"] - self.warehouse = config.get("extra", {}).get("warehouse") - self.database = config.get("database") - self.schema = config.get("extra", {}).get("schema") - self.connect() - - def connect(self) -> None: - """ - Snowflake 데이터베이스에 연결을 설정합니다. - - Raises: - ConnectionError: Snowflake 서버 연결에 실패한 경우 발생합니다. - """ - try: - self.connection = connector.connect( - user=self.user, - password=self.password, - account=self.account, - warehouse=self.warehouse, - database=self.database, - schema=self.schema, - ) - logger.info("Successfully connected to Snowflake.") - self.cursor = self.connection.cursor() - except Exception as e: - logger.error("Failed to connect to Snowflake: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - if self.connection is None: - self.connect() - - cursor = self.connection.cursor() - - try: - self.cursor.execute(sql) - columns = [col[0] for col in self.cursor.description] - data = self.cursor.fetchall() - return pd.DataFrame(data, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - finally: - cursor.close() - - def close(self) -> None: - """ - Snowflake 데이터베이스 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to Snowflake closed.") - self.connection = None diff --git a/utils/databases/connector/sqlite_connector.py b/utils/databases/connector/sqlite_connector.py deleted file mode 100644 index ca5b138..0000000 --- a/utils/databases/connector/sqlite_connector.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -SQLite 데이터베이스 커넥터 모듈. - -이 모듈은 SQLite 데이터베이스에 연결하여 SQL 쿼리를 실행하고, -그 결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import sqlite3 - -import pandas as pd - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class SQLiteConnector(BaseConnector): - """ - SQLite 데이터베이스 커넥터 클래스. - - SQLite 파일 또는 인메모리 데이터베이스에 연결하여 - SQL 쿼리를 실행하거나 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - SQLiteConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): SQLite 연결 정보를 담은 설정 객체. - - `path` 키를 사용하여 SQLite 파일 경로를 지정합니다. - - 값이 None 또는 ":memory:"인 경우 인메모리 데이터베이스를 생성합니다. - """ - self.database = config.get("path", ":memory:") - self.connect() - - def connect(self) -> None: - """ - SQLite 데이터베이스에 연결을 설정합니다. - - Raises: - ConnectionError: 데이터베이스 연결에 실패한 경우 발생합니다. - """ - try: - self.connection = sqlite3.connect(self.database) - logger.info("Successfully connected to SQLite (%s).", self.database) - except Exception as e: - logger.error("Failed to connect to SQLite: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - if self.connection is None: - self.connect() - - try: - cursor = self.connection.cursor() - cursor.execute(sql) - columns = [col[0] for col in cursor.description] - rows = cursor.fetchall() - return pd.DataFrame(rows, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query: %s", e) - raise - finally: - cursor.close() - - def close(self) -> None: - """ - SQLite 데이터베이스 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to SQLite closed.") - self.connection = None diff --git a/utils/databases/connector/trino_connector.py b/utils/databases/connector/trino_connector.py deleted file mode 100644 index 521c74e..0000000 --- a/utils/databases/connector/trino_connector.py +++ /dev/null @@ -1,132 +0,0 @@ -""" -Trino 데이터베이스 커넥터 모듈. - -이 모듈은 Trino 클러스터에 연결하여 SQL 쿼리를 실행하고, -그 결과를 pandas DataFrame 형태로 반환하는 기능을 제공합니다. -""" - -import pandas as pd - -from utils.databases.config import DBConfig -from utils.databases.connector.base_connector import BaseConnector -from utils.databases.logger import logger - - -class TrinoConnector(BaseConnector): - """ - Trino 데이터베이스 커넥터 클래스. - - Trino 클러스터에 연결하여 SQL 쿼리를 실행하거나 - 연결을 종료하는 기능을 제공합니다. - """ - - connection = None - - def __init__(self, config: DBConfig): - """ - TrinoConnector 인스턴스를 초기화합니다. - - Args: - config (DBConfig): Trino 연결 정보를 담은 설정 객체. - - 필수 키: host, port - - 선택 키: user, password, database, extra.catalog, extra.schema, extra.http_scheme - - database가 "catalog.schema" 형태일 경우 자동으로 분리되어 설정됩니다. - """ - # pylint: disable=import-outside-toplevel - try: - import trino - - self.trino = trino - except ImportError as e: - logger.error( - "Trino 드라이버가 설치되어 있지 않습니다. pip install trino 명령을 실행하세요." - ) - raise ImportError("Trino 라이브러리가 설치되어 있지 않습니다.") from e - - self.host = config["host"] - self.port = config["port"] or 8080 - self.user = config.get("user") or "anonymous" - self.password = config.get("password") - self.database = config.get("database") # e.g., catalog.schema - self.extra = config.get("extra") or {} - self.http_scheme = self.extra.get("http_scheme", "http") - self.catalog = self.extra.get("catalog") - self.schema = self.extra.get("schema") - - # If database given as "catalog.schema", split into fields - if self.database and (not self.catalog or not self.schema): - if "." in self.database: - db_catalog, db_schema = self.database.split(".", 1) - self.catalog = self.catalog or db_catalog - self.schema = self.schema or db_schema - - self.connect() - - def connect(self) -> None: - """ - Trino 클러스터에 연결을 설정합니다. - - Raises: - ImportError: trino 드라이버를 불러오지 못한 경우 발생합니다. - ConnectionError: Trino 서버 연결에 실패한 경우 발생합니다. - """ - try: - auth = None - if self.password and self.http_scheme == "https": - auth = self.trino.auth.BasicAuthentication(self.user, self.password) - - self.connection = self.trino.dbapi.connect( - host=self.host, - port=self.port, - user=self.user, - http_scheme=self.http_scheme, - catalog=self.catalog, - schema=self.schema, - auth=auth, - ) - logger.info("Successfully connected to Trino.") - except Exception as e: - logger.error("Failed to connect to Trino: %s", e) - raise - - def run_sql(self, sql: str) -> pd.DataFrame: - """ - SQL 쿼리를 실행하고 결과를 pandas DataFrame으로 반환합니다. - - Args: - sql (str): 실행할 SQL 쿼리 문자열. - - Returns: - pd.DataFrame: 쿼리 결과를 담은 DataFrame 객체. - - Raises: - RuntimeError: SQL 실행 중 오류가 발생한 경우. - """ - try: - cursor = self.connection.cursor() - cursor.execute(sql) - columns = ( - [desc[0] for desc in cursor.description] if cursor.description else [] - ) - rows = cursor.fetchall() if cursor.description else [] - return pd.DataFrame(rows, columns=columns) - except Exception as e: - logger.error("Failed to execute SQL query on Trino: %s", e) - raise - finally: - try: - cursor.close() - logger.info("Cursor closed successfully.") - except Exception as e: # pylint: disable=broad-exception-caught - logger.error("Failed to close cursor: %s", e) - - def close(self) -> None: - """ - Trino 클러스터와의 연결을 종료합니다. - - 연결이 존재할 경우 안전하게 닫고 리소스를 해제합니다. - """ - if self.connection: - self.connection.close() - logger.info("Connection to Trino closed.") - self.connection = None diff --git a/utils/databases/factory.py b/utils/databases/factory.py deleted file mode 100644 index 4bfcb5b..0000000 --- a/utils/databases/factory.py +++ /dev/null @@ -1,115 +0,0 @@ -""" -데이터베이스 커넥터 팩토리 모듈. - -이 모듈은 DB 타입에 따라 알맞은 커넥터 클래스를 동적으로 로드하여 -해당 DB에 연결할 수 있는 인스턴스를 생성하는 기능을 제공합니다. -환경변수로부터 접속 설정을 자동으로 로드하는 유틸리티 함수도 포함합니다. -""" - -import importlib -import inspect -import os -from typing import Optional - -from utils.databases.config import DBConfig -from utils.databases.logger import logger - - -class DatabaseFactory: - """ - 데이터베이스 커넥터 팩토리 클래스. - - DB 타입에 따라 알맞은 Connector 클래스를 동적으로 로드하고, - 해당 인스턴스를 반환하는 기능을 제공합니다. - """ - - @staticmethod - def get_connector(db_type: Optional[str] = None, config: Optional[DBConfig] = None): - """ - 주어진 DB 타입에 해당하는 Connector 인스턴스를 반환합니다. - - 지정된 DB 타입에 맞는 커넥터 모듈을 동적으로 로드하고, - 해당 모듈 내의 Connector 클래스를 탐색하여 인스턴스를 생성합니다. - DB 타입이 지정되지 않은 경우 환경 변수(DB_TYPE)에서 자동으로 가져옵니다. - - Args: - db_type (Optional[str]): 데이터베이스 타입 문자열 (예: 'postgres', 'mysql', 'trino'). - config (Optional[DBConfig]): 데이터베이스 연결 설정 객체. - 지정되지 않은 경우 환경 변수에서 자동으로 로드됩니다. - - Returns: - BaseConnector: 주어진 DB 타입에 해당하는 Connector 인스턴스. - - Raises: - ValueError: DB_TYPE이 지정되지 않았거나, - 지원되지 않는 DB 타입이거나, - 모듈 또는 Connector 클래스를 찾을 수 없는 경우. - """ - if not db_type: - db_type = os.getenv("DB_TYPE") - if not db_type: - raise ValueError("DB_TYPE이 환경변수 또는 인자로 제공되어야 합니다.") - db_type = db_type.lower() - - if not config: - config = load_config_from_env(db_type.upper()) - - try: - module_name = f"utils.databases.connector.{db_type}_connector" - module = importlib.import_module(module_name) - - connector_class = None - for name, cls in inspect.getmembers(module, inspect.isclass): - if name.lower() == f"{db_type}connector": - connector_class = cls - break - if connector_class is None: - raise ValueError(f"No matching Connector class found for {db_type}") - except (ImportError, AttributeError) as e: - logger.error( - "지원되지 않는 DB 타입이거나 모듈을 로드할 수 없습니다: %s", - db_type, - ) - raise ValueError(f"Unsupported DB type: {db_type}") from e - - return connector_class(config) - - -def load_config_from_env(prefix: str) -> DBConfig: - """ - 환경변수에서 데이터베이스 접속 설정을 로드합니다. - - Args: - prefix (str): 환경변수 접두어 (예: 'POSTGRES', 'MYSQL'). - - Returns: - DBConfig: 환경변수에서 로드된 설정 정보를 담은 DBConfig 객체. - """ - base_keys = { - "HOST", - "PORT", - "USER", - "PASSWORD", - "DATABASE", - } - config = { - "host": os.getenv(f"{prefix}_HOST"), - "port": ( - int(os.getenv(f"{prefix}_PORT")) if os.getenv(f"{prefix}_PORT") else None - ), - "user": os.getenv(f"{prefix}_USER"), - "password": os.getenv(f"{prefix}_PASSWORD"), - "database": os.getenv(f"{prefix}_DATABASE"), - } - - extra = {} - for key, value in os.environ.items(): - if ( - key.startswith(f"{prefix}_") - and key.split("_", 1)[1].upper() not in base_keys - ): - extra[key[len(prefix) + 1 :].lower()] = value - if extra: - config["extra"] = extra - - return DBConfig(**config) diff --git a/utils/databases/logger.py b/utils/databases/logger.py deleted file mode 100644 index f9d0404..0000000 --- a/utils/databases/logger.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -로깅 설정 모듈. - -이 모듈은 애플리케이션 전역에서 사용할 기본 로깅 설정을 정의하고, -표준 로거 인스턴스(logger)를 제공합니다. -""" - -import logging - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", -) -logger = logging.getLogger(__name__) diff --git a/utils/llm/README.md b/utils/llm/README.md deleted file mode 100644 index 993ee98..0000000 --- a/utils/llm/README.md +++ /dev/null @@ -1,329 +0,0 @@ -## utils.llm 개요 - -Lang2SQL 파이프라인에서 LLM, 검색(RAG), 그래프 워크플로우, DB 실행, 시각화 등 보조 유틸리티를 모아둔 패키지입니다. 이 문서는 depth(계층)별로 기능과 통합 흐름을 정리합니다. - -## 디렉토리 구조 - -``` -utils/llm/ -├── README.md # 이 파일 -├── chains.py # LangChain 체인 생성 모듈 -├── retrieval.py # 테이블 메타 검색 및 재순위화 -├── llm_response_parser.py # LLM 응답에서 SQL 블록 추출 -├── chatbot.py # LangGraph ChatBot 구현 -├── core/ # LLM/Embedding 팩토리 모듈 -│ ├── __init__.py -│ ├── factory.py # LLM 및 Embedding 모델 생성 팩토리 -│ └── README.md # [상세 문서](./core/README.md) -├── graph_utils/ # LangGraph 워크플로우 모듈 -│ ├── __init__.py -│ ├── base.py # 공통 상태 및 노드 함수 -│ ├── basic_graph.py # 기본 워크플로우 그래프 -│ ├── enriched_graph.py # 확장된 워크플로우 그래프 -│ ├── profile_utils.py # 프로파일 유틸리티 함수 -│ └── README.md # [상세 문서](./graph_utils/README.md) -├── vectordb/ # 벡터 데이터베이스 모듈 -│ ├── __init__.py -│ ├── factory.py # VectorDB 팩토리 -│ ├── faiss_db.py # FAISS 벡터DB 구현 -│ ├── pgvector_db.py # pgvector 벡터DB 구현 -│ └── README.md # [상세 문서](./vectordb/README.md) -├── tools/ # DataHub 메타데이터 및 ChatBot 도구 -│ ├── __init__.py -│ ├── datahub.py # DataHub 메타데이터 수집 -│ ├── chatbot_tool.py # LangGraph ChatBot용 Tool 함수들 -│ └── README.md # [상세 문서](./tools/README.md) -└── output_schema/ # LLM 구조화 출력 Pydantic 모델 - ├── document_suitability.py # 문서 적합성 평가 모델 - ├── question_suitability.py # 질문 적합성 판단 모델 - └── README.md # [상세 문서](./output_schema/README.md) -``` - -## 모듈 상세 설명 - -### 최상위 유틸리티 파일 - -#### `chains.py` -**목적**: LangChain 기반 체인 생성 모듈 - -**주요 함수:** -- `create_query_maker_chain(llm)`: SQL 쿼리 생성 체인 -- `create_profile_extraction_chain(llm)`: 질문 프로파일 추출 체인 -- `create_query_enrichment_chain(llm)`: 질문 컨텍스트 보강 체인 -- `create_question_gate_chain(llm)`: SQL 적합성 판별 체인 -- `create_document_suitability_chain(llm)`: 문서 적합성 평가 체인 - -**내부 모델:** -- `QuestionProfile`: 자연어 질문의 특징을 구조화하는 Pydantic 모델 - -**의존성:** -- `utils.llm.core.get_llm`: LLM 인스턴스 생성 -- `utils.llm.output_schema`: 구조화 출력 모델 - -**사용처:** -- `utils/llm/graph_utils/base.py`: 각 노드에서 체인 호출 - -#### `retrieval.py` -**목적**: 테이블 메타데이터 검색 및 재순위화 - -**주요 함수:** -- `search_tables(query, retriever_name, top_n, device)`: 테이블 메타데이터 검색 - - `retriever_name`: "기본" 또는 "Reranker" - - `top_n`: 반환할 상위 결과 개수 -- `get_retriever(retriever_name, top_n, device)`: 검색기 생성 -- `load_reranker_model(device)`: 한국어 reranker 모델 로드 - -**의존성:** -- `utils.llm.vectordb.get_vector_db`: 벡터DB 인스턴스 -- `ko-reranker`: 한국어 재순위화 모델 - -**사용처:** -- `utils/llm/graph_utils/base.py`: `get_table_info_node`에서 호출 -- `utils/llm/tools/chatbot_tool.py`: `search_database_tables`에서 호출 - -#### `llm_response_parser.py` -**목적**: LLM 응답에서 ``, `<해석>` 블록 추출 - -**사용처:** -- `engine/query_executor.py`: SQL 추출 함수 - -### 하위 디렉토리 모듈 - -#### `core/` - LLM/Embedding 팩토리 -**목적**: 다양한 제공자(OpenAI, Azure, Bedrock, Gemini, Ollama, HuggingFace)의 LLM과 Embedding 모델을 통일된 인터페이스로 사용 - -**주요 기능:** -- `get_llm(**kwargs)`: 환경변수 기반 LLM 인스턴스 생성 -- `get_embeddings()`: 환경변수 기반 Embedding 인스턴스 생성 -- 제공자별 전용 함수들 제공 - -**사용처:** -- `utils/llm/chains.py`: `get_llm()` 호출 -- `utils/llm/vectordb/faiss_db.py`: `get_embeddings()` 호출 -- `utils/llm/vectordb/pgvector_db.py`: `get_embeddings()` 호출 - -**상세 문서**: [core/README.md](./core/README.md) - -#### `graph_utils/` - LangGraph 워크플로우 -**목적**: LangGraph를 사용하여 자연어 질문을 SQL 쿼리로 변환하는 워크플로우 구현 - -**주요 컴포넌트:** -- `base.py`: 공통 상태(`QueryMakerState`) 및 노드 함수들 -- `basic_graph.py`: 기본 워크플로우 (QUESTION_GATE → GET_TABLE_INFO → EVALUATE_DOCUMENT_SUITABILITY → QUERY_MAKER) -- `enriched_graph.py`: 확장 워크플로우 (PROFILE_EXTRACTION, CONTEXT_ENRICHMENT 추가) - -**워크플로우 노드:** -- `question_gate_node`: SQL 적합성 판별 -- `get_table_info_node`: 벡터 검색으로 테이블 정보 수집 -- `document_suitability_node`: 문서 적합성 평가 -- `profile_extraction_node`: 질문 프로파일 추출 -- `context_enrichment_node`: 질문 컨텍스트 보강 -- `query_maker_node`: SQL 쿼리 생성 - -**사용처:** -- `engine/query_executor.py`: 그래프 선택 및 실행 -- `interface/core/session_utils.py`: Streamlit 세션 그래프 초기화 - -**상세 문서**: [graph_utils/README.md](./graph_utils/README.md) - -#### `vectordb/` - 벡터 데이터베이스 -**목적**: 테이블 메타데이터를 벡터화하여 저장하고 검색 - -**주요 기능:** -- `get_vector_db()`: 환경변수 기반 벡터DB 인스턴스 반환 -- FAISS: 로컬 디스크 기반 벡터DB -- pgvector: PostgreSQL 기반 벡터DB - -**사용처:** -- `utils/llm/retrieval.py`: 테이블 검색 시 벡터DB 사용 - -**상세 문서**: [vectordb/README.md](./vectordb/README.md) - -#### `tools/` - DataHub 메타데이터 및 ChatBot 도구 -**목적**: DataHub 메타데이터 수집 및 LangGraph ChatBot용 Tool 함수 제공 - -**주요 기능:** -- `get_info_from_db()`: DataHub에서 테이블 메타데이터를 LangChain Document로 수집 -- `get_metadata_from_db()`: 전체 메타데이터 딕셔너리 반환 -- `search_database_tables()`: 벡터 검색 기반 테이블 정보 검색 Tool -- `get_glossary_terms()`: 용어집 정보 조회 Tool -- `get_query_examples()`: 쿼리 예제 조회 Tool - -**사용처:** -- `utils/llm/vectordb/faiss_db.py`: 벡터DB 초기화 시 메타데이터 수집 -- `utils/llm/vectordb/pgvector_db.py`: 벡터DB 초기화 시 메타데이터 수집 -- `utils/llm/chatbot.py`: ChatBot 도구로 사용 - -**상세 문서**: [tools/README.md](./tools/README.md) - -#### `output_schema/` - 구조화 출력 모델 -**목적**: LLM 구조화 출력을 위한 Pydantic 모델 정의 - -**주요 모델:** -- `QuestionSuitability`: SQL 생성 적합성 판단 결과 -- `DocumentSuitability`: 단일 테이블 적합성 평가 결과 -- `DocumentSuitabilityList`: 문서 적합성 평가 결과 리스트 - -**사용처:** -- `utils/llm/chains.py`: 체인 생성 시 구조화 출력 모델로 사용 - -**상세 문서**: [output_schema/README.md](./output_schema/README.md) - -### 통합 흐름(End-to-End) - -1. **사용자 질문 입력** → `engine/query_executor.execute_query()` 호출 -2. **그래프 선택 및 컴파일** → `graph_utils/basic_graph` 또는 `graph_utils/enriched_graph` 선택 -3. **QUESTION_GATE 노드** → `chains.create_question_gate_chain()`으로 SQL 적합성 판별 -4. **GET_TABLE_INFO 노드** → `retrieval.search_tables()` 호출 - - `vectordb.get_vector_db()`로 벡터DB 로드 - - 유사도 검색 또는 Reranker로 재순위화 - - 관련 테이블/컬럼 메타데이터 반환 -5. **EVALUATE_DOCUMENT_SUITABILITY 노드** → `chains.create_document_suitability_chain()`으로 문서 적합성 평가 -6. **PROFILE_EXTRACTION 노드** (확장 그래프만) → `chains.create_profile_extraction_chain()`으로 질문 특성 추출 - - `graph_utils/profile_utils.profile_to_text()`로 텍스트 변환 -7. **CONTEXT_ENRICHMENT 노드** (확장 그래프만) → `chains.create_query_enrichment_chain()`으로 질문 컨텍스트 보강 -8. **QUERY_MAKER 노드** → `chains.create_query_maker_chain()`으로 SQL 생성 - - DB 가이드/메타데이터 기반으로 SQL 생성 (`` 코드블록 포함) -9. **SQL 추출** → `llm_response_parser.extract_sql()`로 최종 SQL 추출 -10. **실행 및 시각화** (선택적) → `infra/db/connect_db.run_sql()` 실행, `utils/visualization/display_chart`로 시각화 - -### 환경 변수 요약 - -#### LLM 관련 -- **`LLM_PROVIDER`**: LLM 제공자 선택 (`openai`, `azure`, `bedrock`, `gemini`, `ollama`, `huggingface`) -- 제공자별 환경변수 (상세는 [core/README.md](./core/README.md) 참고) - - OpenAI: `OPEN_AI_KEY`, `OPEN_AI_LLM_MODEL` - - Azure: `AZURE_OPENAI_LLM_KEY`, `AZURE_OPENAI_LLM_ENDPOINT`, `AZURE_OPENAI_LLM_MODEL`, `AZURE_OPENAI_LLM_API_VERSION` - - AWS Bedrock: `AWS_BEDROCK_LLM_MODEL`, `AWS_BEDROCK_LLM_ACCESS_KEY_ID`, `AWS_BEDROCK_LLM_SECRET_ACCESS_KEY`, `AWS_BEDROCK_LLM_REGION` - - Gemini: `GEMINI_LLM_MODEL` - - Ollama: `OLLAMA_LLM_MODEL`, `OLLAMA_LLM_BASE_URL` - - HuggingFace: `HUGGING_FACE_LLM_MODEL`, `HUGGING_FACE_LLM_REPO_ID`, `HUGGING_FACE_LLM_ENDPOINT`, `HUGGING_FACE_LLM_API_TOKEN` - -#### Embedding 관련 -- **`EMBEDDING_PROVIDER`**: Embedding 제공자 선택 (`openai`, `azure`, `bedrock`, `gemini`, `ollama`, `huggingface`) -- 제공자별 환경변수 (상세는 [core/README.md](./core/README.md) 참고) - -#### VectorDB 관련 -- **`VECTORDB_TYPE`**: 벡터DB 타입 선택 (`faiss` 또는 `pgvector`, 기본: `faiss`) -- **FAISS**: `VECTORDB_LOCATION` (기본: `./dev/table_info_db`) -- **pgvector**: `PGVECTOR_HOST`, `PGVECTOR_PORT`, `PGVECTOR_USER`, `PGVECTOR_PASSWORD`, `PGVECTOR_DATABASE`, `PGVECTOR_COLLECTION` -- 상세는 [vectordb/README.md](./vectordb/README.md) 참고 - -#### DataHub 관련 -- **`DATAHUB_SERVER`**: DataHub GMS 서버 URL (예: `http://localhost:8080`) -- 상세는 [tools/README.md](./tools/README.md) 참고 - -#### ClickHouse 관련 (SQL 실행 시) -- `CLICKHOUSE_HOST`, `CLICKHOUSE_PORT`, `CLICKHOUSE_DATABASE`, `CLICKHOUSE_USER`, `CLICKHOUSE_PASSWORD` - -### 핵심 사용 예시 - -#### 1. 기본 쿼리 실행 - -```python -from engine.query_executor import execute_query, extract_sql_from_result - -res = execute_query( - query="지난달 매출 추이 보여줘", - database_env="postgres", - retriever_name="Reranker", - top_n=5, - device="cpu", - use_enriched_graph=True, -) - -sql = extract_sql_from_result(res) -``` - -#### 2. 체인 직접 사용 - -```python -from utils.llm.core import get_llm -from utils.llm.chains import create_query_maker_chain - -llm = get_llm() -chain = create_query_maker_chain(llm) - -result = chain.invoke({ - "user_input": "지난달 매출", - "user_database_env": "postgres", - "searched_tables": {...} -}) -``` - -#### 3. 벡터 검색 직접 사용 - -```python -from utils.llm.retrieval import search_tables - -tables = search_tables( - query="고객 정보", - retriever_name="Reranker", - top_n=5, - device="cpu" -) -``` - -#### 4. 그래프 빌더 직접 사용 - -```python -from utils.llm.graph_utils import basic_builder, enriched_builder - -# 기본 그래프 -graph = basic_builder.compile() - -# 확장 그래프 -graph = enriched_builder.compile() - -# 그래프 실행 -result = graph.invoke({ - "messages": [HumanMessage(content="지난달 매출 보여줘")], - "user_database_env": "postgres", - # ... 기타 상태 정보 -}) -``` - -### 파일간 의존 관계 - -#### 상위 레벨 의존성 -``` -engine/query_executor.py -├── utils/llm/graph_utils/ -│ ├── basic_graph.py -│ ├── enriched_graph.py -│ └── base.py -│ ├── utils/llm/chains.py -│ │ ├── utils/llm/core/get_llm() -│ │ └── utils/llm/output_schema/ -│ └── utils/llm/retrieval.py -│ └── utils/llm/vectordb/get_vector_db() -│ ├── utils/llm/core/get_embeddings() -│ └── utils/llm/tools/get_info_from_db() -└── utils/llm/llm_response_parser.py -``` - -#### 주요 import 관계 - -**graph_utils 모듈:** -- `graph_utils/base.py` → `chains.py`, `retrieval.py` 사용 -- `graph_utils/basic_graph.py` → `graph_utils/base.py` 사용 -- `graph_utils/enriched_graph.py` → `graph_utils/base.py` 사용 - -**chains 모듈:** -- `chains.py` → `core/get_llm()`, `output_schema/` 사용 - -**retrieval 모듈:** -- `retrieval.py` → `vectordb/get_vector_db()` 사용 - -**vectordb 모듈:** -- `vectordb/faiss_db.py` → `core/get_embeddings()`, `tools/get_info_from_db()` 사용 -- `vectordb/pgvector_db.py` → `core/get_embeddings()`, `tools/get_info_from_db()` 사용 - -**tools 모듈:** -- `tools/datahub.py` → DataHub 메타데이터 수집 -- `tools/chatbot_tool.py` → `retrieval/search_tables()` 사용 - -**외부 의존성:** -- `engine/query_executor.py` → `graph_utils/` 사용 -- `interface/core/session_utils.py` → `graph_utils/` 사용 -- `interface/app_pages/graph_builder.py` → `graph_utils/base.py` 사용 diff --git a/utils/llm/chatbot.py b/utils/llm/chatbot.py deleted file mode 100644 index d3b30b8..0000000 --- a/utils/llm/chatbot.py +++ /dev/null @@ -1,212 +0,0 @@ -""" -LangGraph 기반 ChatBot 모델 -OpenAI의 ChatGPT 모델을 사용하여 대화 기록을 유지하는 챗봇 구현 -""" - -from typing import Annotated, Sequence, TypedDict - -from langchain_core.messages import BaseMessage, SystemMessage -from langchain_openai import ChatOpenAI -from langgraph.checkpoint.memory import MemorySaver -from langgraph.graph import START, StateGraph -from langgraph.graph.message import add_messages -from langgraph.prebuilt import ToolNode - -from utils.llm.tools import ( - search_database_tables, - get_glossary_terms, - get_query_examples, -) - - -class ChatBotState(TypedDict): - """ - 챗봇 상태 - 사용자 질문을 SQL로 변환 가능한 구체적인 질문으로 만들어가는 과정 추적 - """ - - # 기본 메시지 (MessagesState와 동일) - messages: Annotated[Sequence[BaseMessage], add_messages] - - # datahub 서버 정보 - gms_server: str - - -class ChatBot: - """ - LangGraph를 사용한 대화형 챗봇 클래스 - OpenAI API를 통해 다양한 GPT 모델을 사용할 수 있으며, - MemorySaver를 통해 대화 기록을 관리합니다. - """ - - def __init__( - self, - openai_api_key: str, - model_name: str = "gpt-4o-mini", - gms_server: str = "http://localhost:8080", - ): - """ - ChatBot 인스턴스 초기화 - - Args: - openai_api_key: OpenAI API 키 - model_name: 사용할 모델명 (기본값: gpt-4o-mini) - gms_server: DataHub GMS 서버 URL (기본값: http://localhost:8080) - """ - self.openai_api_key = openai_api_key - self.model_name = model_name - self.gms_server = gms_server - # SQL 생성을 위한 데이터베이스 메타데이터 조회 도구 - self.tools = [ - search_database_tables, # 데이터베이스 테이블 정보 검색 - get_glossary_terms, # 용어집 조회 도구 - get_query_examples, # 쿼리 예제 조회 도구 - ] - self.llm = self._setup_llm() # LLM 인스턴스 설정 - self.app = self._setup_workflow() # LangGraph 워크플로우 설정 - - def _setup_llm(self): - """ - OpenAI ChatGPT LLM 인스턴스 생성 - Tool을 바인딩하여 LLM이 필요시 tool을 호출할 수 있도록 설정합니다. - - Returns: - ChatOpenAI: Tool이 바인딩된 LLM 인스턴스 - """ - llm = ChatOpenAI( - temperature=0.0, # SQL 생성은 정확성이 중요하므로 0으로 설정 - openai_api_key=self.openai_api_key, - model_name=self.model_name, - ) - # Tool을 LLM에 바인딩하여 함수 호출 기능 활성화 - return llm.bind_tools(self.tools) - - def _setup_workflow(self): - """ - LangGraph 워크플로우 설정 - 대화 기록을 관리하고 LLM과 통신하는 그래프 구조를 생성합니다. - Tool 호출 기능을 포함하여 LLM이 필요시 도구를 사용할 수 있도록 합니다. - - Returns: - CompiledGraph: 컴파일된 LangGraph 워크플로우 - """ - # ChatBotState를 사용하는 StateGraph 생성 - workflow = StateGraph(state_schema=ChatBotState) - - def call_model(state: ChatBotState): - """ - LLM 모델을 호출하는 노드 함수 - LLM이 응답을 생성하거나 tool 호출을 결정합니다. - - Args: - state: 현재 메시지 상태 - - Returns: - dict: LLM 응답이 포함된 상태 업데이트 - """ - # 질문 구체화 전문 어시스턴트 시스템 메시지 - sys_msg = SystemMessage(content="""# 역할 -당신은 사용자의 모호한 질문을 명확하고 구체적인 질문으로 만드는 전문 AI 어시스턴트입니다. - -# 주요 임무 -- 사용자의 자연어 질문을 이해하고 의도를 정확히 파악합니다 -- 대화를 통해 날짜, 지표, 필터 조건 등 구체적인 정보를 수집합니다 -- 단계별로 사용자와 대화하며 명확하고 구체적인 질문으로 다듬어갑니다 - -# 작업 프로세스 -1. 사용자의 최초 질문에서 의도 파악 -2. 질문을 명확히 하기 위해 필요한 정보 식별 (날짜, 지표, 대상, 조건 등) -3. **도구를 적극 활용하여 데이터베이스 스키마, 테이블 정보, 용어집 등을 확인** -4. 부족한 정보를 자연스럽게 질문하여 수집 -5. 수집된 정보를 바탕으로 질문을 점진적으로 구체화 -6. 충분히 구체화되면 최종 질문 확정 - -# 도구 사용 가이드 -- **search_database_tables**: 사용자와의 대화를 데이터와 연관짓기 위해 관련 테이블을 적극적으로 확인할 수 있는 도구 -- **get_glossary_terms**: 사용자가 사용한 용어의 정확한 의미를 확인할 때 사용가능한 도구 -- **get_query_examples**: 조직내 저장된 쿼리 예제를 조회하여 참고할 수 있는 도구 -- 답변하기 전에 최대한 많은 도구를 적극 활용하여 정보를 수집하세요 -- 불확실한 정보가 있다면 추측하지 말고 도구를 사용하여 확인하세요 - -# 예시 -- 모호한 질문: "KPI가 궁금해" -- 대화 후 구체화: "2025-01-02 날짜의 신규 유저가 발생시킨 매출이 궁금해" - -# 주의사항 -- 항상 친절하고 명확하게 대화합니다 -- 이전 대화 맥락을 고려하여 일관성 있게 응답합니다 -- 한 번에 너무 많은 것을 물어보지 않고 단계적으로 진행합니다 -- **중요: 사용자가 말한 내용이 충분히 구체화되지 않거나 의도가 명확히 파악되지 않을 경우, 추측하지 말고 모든 도구(get_glossary_terms, get_query_examples, search_database_tables)를 적극적으로 사용하여 맥락을 파악하세요** -- 도구를 통해 수집한 정보를 바탕으로 사용자에게 구체적인 방향성과 옵션을 제안하세요 -- 불확실한 정보가 있다면 추측하지 말고 도구를 사용하여 확인한 후 답변하세요 - ---- -다음은 사용자와의 대화입니다:""") - # 시스템 메시지를 대화의 맨 앞에 추가 - messages = [sys_msg] + state["messages"] - response = self.llm.invoke(messages) - return {"messages": response} - - def route_model_output(state: ChatBotState): - """ - LLM 출력에 따라 다음 노드를 결정하는 라우팅 함수 - Tool 호출이 필요한 경우 'tools' 노드로, 아니면 대화를 종료합니다. - - Args: - state: 현재 메시지 상태 - - Returns: - str: 다음에 실행할 노드 이름 ('tools' 또는 '__end__') - """ - messages = state["messages"] - last_message = messages[-1] - # LLM이 tool을 호출하려고 하는 경우 (tool_calls가 있는 경우) - if hasattr(last_message, "tool_calls") and last_message.tool_calls: - return "tools" - # Tool 호출이 없으면 대화 종료 - return "__end__" - - # 워크플로우 구조 정의 - workflow.add_edge(START, "model") # 시작 -> model 노드 - workflow.add_node("model", call_model) # LLM 호출 노드 - workflow.add_node("tools", ToolNode(self.tools)) # Tool 실행 노드 - - # model 노드 이후 조건부 라우팅 - workflow.add_conditional_edges("model", route_model_output) - # Tool 실행 후 다시 model로 돌아가서 최종 응답 생성 - workflow.add_edge("tools", "model") - - # MemorySaver를 사용하여 대화 기록 저장 기능 추가 - return workflow.compile(checkpointer=MemorySaver()) - - def chat(self, message: str, thread_id: str): - """ - 사용자 메시지에 대한 응답 생성 - - Args: - message: 사용자 입력 메시지 - thread_id: 대화 세션을 구분하는 고유 ID - - Returns: - dict: LLM 응답을 포함한 결과 딕셔너리 - """ - config = {"configurable": {"thread_id": thread_id}} - - # 상태 준비 - input_state = { - "messages": [{"role": "user", "content": message}], - "gms_server": self.gms_server, # DataHub 서버 URL을 상태에 포함 - } - - return self.app.invoke(input_state, config) - - def update_model(self, model_name: str): - """ - 사용 중인 LLM 모델 변경 - 모델 변경 시 LLM 인스턴스와 워크플로우를 재설정합니다. - - Args: - model_name: 변경할 모델명 - """ - self.model_name = model_name - self.llm = self._setup_llm() # 새 모델로 LLM 재설정 - self.app = self._setup_workflow() # 워크플로우 재생성 diff --git a/utils/llm/core/README.md b/utils/llm/core/README.md deleted file mode 100644 index c23e75c..0000000 --- a/utils/llm/core/README.md +++ /dev/null @@ -1,251 +0,0 @@ -# core - -LLM(Large Language Model)과 Embedding 모델을 생성하는 팩토리 패턴을 구현한 모듈입니다. 환경변수를 통해 다양한 제공자(OpenAI, Azure, Bedrock, Gemini, Ollama, HuggingFace)의 LLM과 Embedding 모델을 통일된 인터페이스로 사용할 수 있도록 합니다. - -## 디렉토리 구조 - -``` -core/ -├── __init__.py -├── factory.py -└── README.md -``` - -## 파일 설명 - -### `__init__.py` -core 모듈의 공개 인터페이스를 정의합니다. - -**주요 내보내기:** -- **LLM 팩토리 함수들:** - - `get_llm`: 환경변수(`LLM_PROVIDER`)에 따라 적절한 LLM 제공자를 선택하여 반환 - - `get_llm_openai`: OpenAI LLM 인스턴스 생성 - - `get_llm_azure`: Azure OpenAI LLM 인스턴스 생성 - - `get_llm_bedrock`: AWS Bedrock LLM 인스턴스 생성 - - `get_llm_gemini`: Google Gemini LLM 인스턴스 생성 - - `get_llm_ollama`: Ollama LLM 인스턴스 생성 - - `get_llm_huggingface`: HuggingFace LLM 인스턴스 생성 - -- **Embedding 팩토리 함수들:** - - `get_embeddings`: 환경변수(`EMBEDDING_PROVIDER`)에 따라 적절한 Embedding 제공자를 선택하여 반환 - - `get_embeddings_openai`: OpenAI Embedding 인스턴스 생성 - - `get_embeddings_azure`: Azure OpenAI Embedding 인스턴스 생성 - - `get_embeddings_bedrock`: AWS Bedrock Embedding 인스턴스 생성 - - `get_embeddings_gemini`: Google Gemini Embedding 인스턴스 생성 - - `get_embeddings_ollama`: Ollama Embedding 인스턴스 생성 - - `get_embeddings_huggingface`: HuggingFace Embedding 인스턴스 생성 - -### `factory.py` -LLM과 Embedding 모델을 생성하는 팩토리 함수들의 구현을 포함합니다. - -**주요 내용:** - -#### LLM 팩토리 함수들 - -- **`get_llm(**kwargs) -> BaseLanguageModel`** - - 환경변수 `LLM_PROVIDER`를 확인하여 적절한 LLM 제공자를 선택합니다. - - 지원하는 제공자: `openai`, `azure`, `bedrock`, `gemini`, `ollama`, `huggingface` - - 각 제공자별 전용 함수를 호출하여 LLM 인스턴스를 반환합니다. - -- **제공자별 LLM 생성 함수들:** - - `get_llm_openai(**kwargs)`: `ChatOpenAI` 인스턴스 생성 - - 환경변수: `OPEN_AI_LLM_MODEL`, `OPEN_AI_KEY` - - `get_llm_azure(**kwargs)`: `AzureChatOpenAI` 인스턴스 생성 - - 환경변수: `AZURE_OPENAI_LLM_KEY`, `AZURE_OPENAI_LLM_ENDPOINT`, `AZURE_OPENAI_LLM_MODEL`, `AZURE_OPENAI_LLM_API_VERSION` - - `get_llm_bedrock(**kwargs)`: `ChatBedrockConverse` 인스턴스 생성 - - 환경변수: `AWS_BEDROCK_LLM_MODEL`, `AWS_BEDROCK_LLM_ACCESS_KEY_ID`, `AWS_BEDROCK_LLM_SECRET_ACCESS_KEY`, `AWS_BEDROCK_LLM_REGION` - - `get_llm_gemini(**kwargs)`: `ChatGoogleGenerativeAI` 인스턴스 생성 - - 환경변수: `GEMINI_LLM_MODEL` - - `get_llm_ollama(**kwargs)`: `ChatOllama` 인스턴스 생성 - - 환경변수: `OLLAMA_LLM_MODEL`, `OLLAMA_LLM_BASE_URL` (선택적) - - `get_llm_huggingface(**kwargs)`: `ChatHuggingFace` 인스턴스 생성 - - 환경변수: `HUGGING_FACE_LLM_MODEL`, `HUGGING_FACE_LLM_REPO_ID`, `HUGGING_FACE_LLM_ENDPOINT`, `HUGGING_FACE_LLM_API_TOKEN` - -#### Embedding 팩토리 함수들 - -- **`get_embeddings() -> Optional[BaseLanguageModel]`** - - 환경변수 `EMBEDDING_PROVIDER`를 확인하여 적절한 Embedding 제공자를 선택합니다. - - 지원하는 제공자: `openai`, `azure`, `bedrock`, `gemini`, `ollama`, `huggingface` - - 각 제공자별 전용 함수를 호출하여 Embedding 인스턴스를 반환합니다. - -- **제공자별 Embedding 생성 함수들:** - - `get_embeddings_openai()`: `OpenAIEmbeddings` 인스턴스 생성 - - 환경변수: `OPEN_AI_EMBEDDING_MODEL`, `OPEN_AI_KEY` - - `get_embeddings_azure()`: `AzureOpenAIEmbeddings` 인스턴스 생성 - - 환경변수: `AZURE_OPENAI_EMBEDDING_KEY`, `AZURE_OPENAI_EMBEDDING_ENDPOINT`, `AZURE_OPENAI_EMBEDDING_MODEL`, `AZURE_OPENAI_EMBEDDING_API_VERSION` - - `get_embeddings_bedrock()`: `BedrockEmbeddings` 인스턴스 생성 - - 환경변수: `AWS_BEDROCK_EMBEDDING_MODEL`, `AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID`, `AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY`, `AWS_BEDROCK_EMBEDDING_REGION` - - `get_embeddings_gemini()`: `GoogleGenerativeAIEmbeddings` 인스턴스 생성 - - 환경변수: `GEMINI_EMBEDDING_MODEL`, `GEMINI_EMBEDDING_KEY` - - `get_embeddings_ollama()`: `OllamaEmbeddings` 인스턴스 생성 - - 환경변수: `OLLAMA_EMBEDDING_MODEL`, `OLLAMA_EMBEDDING_BASE_URL` - - `get_embeddings_huggingface()`: `HuggingFaceEndpointEmbeddings` 인스턴스 생성 - - 환경변수: `HUGGING_FACE_EMBEDDING_MODEL`, `HUGGING_FACE_EMBEDDING_REPO_ID`, `HUGGING_FACE_EMBEDDING_API_TOKEN` - -## 사용 방법 - -### 1. `utils/llm/chains.py`에서의 사용 - -LangChain 체인을 생성할 때 LLM 인스턴스를 가져옵니다: - -```python -from utils.llm.core import get_llm - -# 환경변수 LLM_PROVIDER에 따라 적절한 LLM 반환 -llm = get_llm() - -# 체인 생성 시 사용 -chain = prompt | llm | output_parser -``` - -**사용 위치:** `/home/dwlee/Lang2SQL/utils/llm/chains.py`의 모듈 레벨에서 `llm = get_llm()`로 전역 LLM 인스턴스 생성 - -**사용되는 체인:** -- Query Maker Chain -- Query Enrichment Chain -- Profile Extraction Chain -- Question Gate Chain -- Document Suitability Chain - -### 2. `utils/llm/vectordb/faiss_db.py`에서의 사용 - -FAISS 벡터 데이터베이스를 생성하거나 로드할 때 Embedding 모델을 가져옵니다: - -```python -from utils.llm.core import get_embeddings - -# 환경변수 EMBEDDING_PROVIDER에 따라 적절한 Embedding 반환 -embeddings = get_embeddings() - -# FAISS 벡터 DB 생성 -db = FAISS.from_documents(documents, embeddings) - -# 또는 로드 -db = FAISS.load_local(vectordb_path, embeddings, allow_dangerous_deserialization=True) -``` - -**사용 위치:** `/home/dwlee/Lang2SQL/utils/llm/vectordb/faiss_db.py`의 `get_faiss_vector_db()` 함수 - -### 3. `utils/llm/vectordb/pgvector_db.py`에서의 사용 - -PGVector 벡터 데이터베이스를 생성하거나 연결할 때 Embedding 모델을 가져옵니다: - -```python -from utils.llm.core import get_embeddings - -# 환경변수 EMBEDDING_PROVIDER에 따라 적절한 Embedding 반환 -embeddings = get_embeddings() - -# PGVector 벡터 스토어 생성 -vector_store = PGVector.from_documents( - documents=documents, - embedding=embeddings, - collection_name=collection_name, - connection_string=connection_string, -) -``` - -**사용 위치:** `/home/dwlee/Lang2SQL/utils/llm/vectordb/pgvector_db.py`의 `get_pgvector_db()` 함수 - -## 환경변수 설정 - -이 모듈을 사용하려면 다음 환경변수들을 설정해야 합니다: - -### LLM 설정 - -```bash -# LLM 제공자 선택 (필수) -export LLM_PROVIDER="openai" # 또는 azure, bedrock, gemini, ollama, huggingface - -# OpenAI 설정 -export OPEN_AI_LLM_MODEL="gpt-4o" -export OPEN_AI_KEY="your-api-key" - -# Azure OpenAI 설정 -export AZURE_OPENAI_LLM_KEY="your-key" -export AZURE_OPENAI_LLM_ENDPOINT="your-endpoint" -export AZURE_OPENAI_LLM_MODEL="your-deployment-name" -export AZURE_OPENAI_LLM_API_VERSION="2023-07-01-preview" - -# AWS Bedrock 설정 -export AWS_BEDROCK_LLM_MODEL="anthropic.claude-3-sonnet-20240229-v1:0" -export AWS_BEDROCK_LLM_ACCESS_KEY_ID="your-access-key" -export AWS_BEDROCK_LLM_SECRET_ACCESS_KEY="your-secret-key" -export AWS_BEDROCK_LLM_REGION="us-east-1" - -# Gemini 설정 -export GEMINI_LLM_MODEL="gemini-pro" - -# Ollama 설정 -export OLLAMA_LLM_MODEL="llama2" -export OLLAMA_LLM_BASE_URL="http://localhost:11434" # 선택적 - -# HuggingFace 설정 -export HUGGING_FACE_LLM_MODEL="meta-llama/Llama-2-7b-chat-hf" -export HUGGING_FACE_LLM_REPO_ID="your-repo-id" -export HUGGING_FACE_LLM_ENDPOINT="your-endpoint" -export HUGGING_FACE_LLM_API_TOKEN="your-token" -``` - -### Embedding 설정 - -```bash -# Embedding 제공자 선택 (필수) -export EMBEDDING_PROVIDER="openai" # 또는 azure, bedrock, gemini, ollama, huggingface - -# OpenAI Embedding 설정 -export OPEN_AI_EMBEDDING_MODEL="text-embedding-ada-002" -export OPEN_AI_KEY="your-api-key" - -# Azure OpenAI Embedding 설정 -export AZURE_OPENAI_EMBEDDING_KEY="your-key" -export AZURE_OPENAI_EMBEDDING_ENDPOINT="your-endpoint" -export AZURE_OPENAI_EMBEDDING_MODEL="your-deployment-name" -export AZURE_OPENAI_EMBEDDING_API_VERSION="2023-07-01-preview" - -# AWS Bedrock Embedding 설정 -export AWS_BEDROCK_EMBEDDING_MODEL="amazon.titan-embed-text-v1" -export AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID="your-access-key" -export AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY="your-secret-key" -export AWS_BEDROCK_EMBEDDING_REGION="us-east-1" - -# Gemini Embedding 설정 -export GEMINI_EMBEDDING_MODEL="models/embedding-001" -export GEMINI_EMBEDDING_KEY="your-api-key" - -# Ollama Embedding 설정 -export OLLAMA_EMBEDDING_MODEL="nomic-embed-text" -export OLLAMA_EMBEDDING_BASE_URL="http://localhost:11434" - -# HuggingFace Embedding 설정 -export HUGGING_FACE_EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" -export HUGGING_FACE_EMBEDDING_REPO_ID="your-repo-id" -export HUGGING_FACE_EMBEDDING_API_TOKEN="your-token" -``` - -## 설계 패턴 - -이 모듈은 **팩토리 패턴(Factory Pattern)**을 사용하여 구현되었습니다: - -1. **통일된 인터페이스**: `get_llm()`과 `get_embeddings()` 함수를 통해 다양한 제공자를 동일한 방식으로 사용 가능 -2. **환경변수 기반 설정**: 코드 변경 없이 환경변수만 수정하여 다른 제공자로 전환 가능 -3. **확장성**: 새로운 제공자를 추가하려면 `factory.py`에 해당 제공자의 생성 함수만 추가하면 됨 - -## 지원하는 제공자 - -### LLM 제공자 -- **OpenAI**: GPT 모델 시리즈 -- **Azure OpenAI**: Azure에서 호스팅되는 OpenAI 모델 -- **AWS Bedrock**: Claude, Llama 등 다양한 모델 -- **Google Gemini**: Gemini 프로/플래시 모델 -- **Ollama**: 로컬에서 실행되는 오픈소스 LLM -- **HuggingFace**: HuggingFace Hub/Endpoint를 통한 모델 접근 - -### Embedding 제공자 -- **OpenAI**: text-embedding-ada-002 등 -- **Azure OpenAI**: Azure에서 호스팅되는 OpenAI Embedding 모델 -- **AWS Bedrock**: Amazon Titan Embedding 모델 -- **Google Gemini**: Gemini Embedding 모델 -- **Ollama**: 로컬에서 실행되는 Embedding 모델 -- **HuggingFace**: HuggingFace Hub/Endpoint를 통한 Embedding 모델 - diff --git a/utils/llm/core/__init__.py b/utils/llm/core/__init__.py deleted file mode 100644 index 76e54fe..0000000 --- a/utils/llm/core/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -# Legacy LLM core module — factory removed (replaced by src/lang2sql/factory.py) - -__all__ = [] diff --git a/utils/llm/llm_response_parser.py b/utils/llm/llm_response_parser.py deleted file mode 100644 index df94924..0000000 --- a/utils/llm/llm_response_parser.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -LLM 응답 텍스트에서 특정 마크업 태그(``, `<해석>`)에 포함된 콘텐츠 블록을 추출하는 유틸리티 모듈입니다. - -이 모듈은 OpenAI, LangChain 등에서 생성된 LLM 응답 문자열에서 Markdown 코드 블록을 파싱하여, -SQL 쿼리 및 자연어 해석 설명을 분리하여 사용할 수 있도록 정적 메서드 형태의 API를 제공합니다. - -지원되는 태그: - - : SQL 코드 블록 (```sql ... ```) - - <해석>: 자연어 해석 블록 (```plaintext ... ```) -""" - -import re - - -class LLMResponseParser: - """ - LLM 응답 문자열에서 특정 태그(, <해석>)에 포함된 블록을 추출하는 유틸리티 클래스입니다. - - 주요 기능: - - 태그 내 ```sql ... ``` 블록에서 SQL 쿼리 추출 - - <해석> 태그 내 ```plaintext ... ``` 블록에서 자연어 해석 추출 - """ - - @staticmethod - def extract_sql(text: str) -> str: - """ - 태그 내부의 SQL 코드 블록만 추출합니다. - - Args: - text (str): 전체 LLM 응답 문자열. - - Returns: - str: SQL 쿼리 문자열 (```sql ... ``` 내부 텍스트). - - Raises: - ValueError: 태그 또는 SQL 코드 블록을 찾을 수 없는 경우. - """ - match = re.search(r"\s*```sql\n(.*?)```", text, re.DOTALL) - if match: - return match.group(1).strip() - raise ValueError("SQL 블록을 추출할 수 없습니다.") - - @staticmethod - def extract_interpretation(text: str) -> str: - """ - <해석> 태그 내부의 해석 설명 텍스트만 추출합니다. - - Args: - text (str): 전체 LLM 응답 문자열. - - Returns: - str: 해석 설명 텍스트. 블록이 존재하지 않으면 빈 문자열을 반환합니다. - """ - match = re.search(r"<해석>\s*```plaintext\n(.*?)```", text, re.DOTALL) - if match: - return match.group(1).strip() - return "" diff --git a/utils/llm/tools/README.md b/utils/llm/tools/README.md deleted file mode 100644 index fa7153d..0000000 --- a/utils/llm/tools/README.md +++ /dev/null @@ -1,284 +0,0 @@ -## utils.llm.tools 개요 - -Lang2SQL 파이프라인에서 DataHub 메타데이터 수집 및 검색, 그리고 LangGraph ChatBot에서 사용하는 도구(Tool) 함수들을 제공하는 모듈입니다. 병렬 처리 기반의 효율적인 메타데이터 수집과 LangChain Tool 인터페이스를 통한 챗봇 기능을 지원합니다. - -### 파일 구조 - -``` -utils/llm/tools/ -├── __init__.py # 모듈 진입점, 6개 함수 export -├── datahub.py # DataHub 메타데이터 수집 및 처리 -└── chatbot_tool.py # LangGraph ChatBot용 Tool 함수들 -``` - -### 각 파일 상세 설명 - -#### __init__.py - -**목적**: tools 모듈의 공개 인터페이스 정의 - -**Export 함수들**: - -**datahub 모듈에서**: -- `set_gms_server`: GMS 서버 설정 -- `get_info_from_db`: LangChain Document 리스트로 테이블/컬럼 정보 반환 -- `get_metadata_from_db`: 전체 메타데이터 딕셔너리 리스트 반환 - -**chatbot_tool 모듈에서**: -- `search_database_tables`: 벡터 검색 기반 테이블 정보 검색 -- `get_glossary_terms`: 용어집 정보 조회 -- `get_query_examples`: 쿼리 예제 조회 - -#### datahub.py - -**목적**: DataHub에서 테이블 및 컬럼 메타데이터를 수집하고 LangChain Document 형식으로 변환 - -**주요 함수**: - -1. **`set_gms_server(gms_server: str)`** - - 환경변수 `DATAHUB_SERVER`를 설정하고 DatahubMetadataFetcher 초기화 - - 유효하지 않은 서버 URL 시 ValueError 발생 - -2. **`get_info_from_db(max_workers: int = 8) -> List[Document]`** - - DataHub에서 모든 테이블 메타데이터를 수집하여 LangChain Document 리스트 반환 - - 각 Document에는 테이블명, 설명, 컬럼 정보가 포함 - - 형식: `"{테이블명}: {설명}\nColumns:\n {컬럼명}: {컬럼설명}"` - - `parallel_process()`로 병렬 처리 (기본 8 워커) - - 반환: LangChain Document 리스트 - -3. **`get_metadata_from_db() -> List[Dict]`** - - DataHub에서 전체 메타데이터를 딕셔너리 형태로 수집 - - 각 테이블의 상세 메타데이터 반환 - - 반환: 메타데이터 딕셔너리 리스트 - -**내부 헬퍼 함수**: - -- **`parallel_process()`**: ThreadPoolExecutor 기반 병렬 처리 유틸리티 - - tqdm 진행률 표시 지원 - - `max_workers`: 동시 실행 워커 수 (기본: 8) - -- **`_get_fetcher()`**: 환경변수 기반 DatahubMetadataFetcher 인스턴스 생성 - -- **`_process_urn()`**: URN에서 테이블명과 설명 추출 - -- **`_get_table_info()`**: 병렬 처리로 모든 테이블 정보 수집 - -- **`_get_column_info()`**: 특정 테이블의 컬럼 정보 수집 - -- **`_extract_dataset_name_from_urn()`**: URN에서 데이터셋 이름 추출 - -**의존성**: -- `langchain.schema.Document`: LangChain Document 타입 -- `utils.data.datahub_source.DatahubMetadataFetcher`: DataHub 메타데이터 페처 -- `concurrent.futures.ThreadPoolExecutor`: 병렬 처리 -- `tqdm`: 진행률 표시 - -**사용처**: -- `utils/llm/vectordb/faiss_db.py`: FAISS 벡터DB 초기화 시 메타데이터 수집 -- `utils/llm/vectordb/pgvector_db.py`: pgvector 벡터DB 초기화 시 메타데이터 수집 - -**특징**: -- 병렬 처리로 성능 최적화 -- tqdm 진행률 표시 -- 자동 재시도/에러 핸들링 -- LangChain Document 형식 지원 - -#### chatbot_tool.py - -**목적**: LangGraph ChatBot에서 사용하는 LangChain Tool 함수들 - -**주요 함수 (모두 `@tool` 데코레이터 사용)**: - -1. **`search_database_tables(query, retriever_name, top_n, device)`** - - **목적**: 자연어 쿼리를 기반으로 관련 테이블 정보 검색 - - **파라미터**: - - `query`: 검색할 자연어 질문 - - `retriever_name`: "기본" 또는 "Reranker" (기본: "기본") - - `top_n`: 반환할 테이블 개수 (기본: 5) - - `device`: "cpu" 또는 "cuda" (기본: "cpu") - - **반환**: `{테이블명: {table_description, 컬럼명: 컬럼설명, ...}}` - - **내부**: `utils.llm.retrieval.search_tables()` 호출 - - **사용 시기**: - - SQL 쿼리 생성 전 스키마 정보 필요 - - "어떤 테이블을 사용해야 해?" - - "고객 관련 테이블 정보를 알려줘" - -2. **`get_glossary_terms(gms_server)`** - - **목적**: DataHub 용어집(Glossary) 정보 조회 - - **파라미터**: - - `gms_server`: DataHub GMS 서버 URL (기본: "http://35.222.65.99:8080") - - **반환**: `[{name, description, children: [...]}, ...]` - - **내부**: `GlossaryService.get_glossary_data()` 호출 - - **특징**: - - 조직 특화 용어/비즈니스 정의 제공 - - `_simplify_glossary_data()`로 필수 필드만 추출 - - **사용 시기**: - - 모호한 용어 이해 필요 - - "용어집을 보여줘" - - 조직 내부 용어 확인 - -3. **`get_query_examples(gms_server, start, count, query)`** - - **목적**: DataHub에 저장된 SQL 쿼리 예제 조회 - - **파라미터**: - - `gms_server`: DataHub GMS 서버 URL (기본: "http://35.222.65.99:8080") - - `start`: 시작 위치 (기본: 0) - - `count`: 반환 개수 (기본: 10) - - `query`: 검색 쿼리 (기본: "*") - - **반환**: `[{name, description, statement}, ...]` - - **내부**: `QueryService.get_query_data()` 호출 - - **특징**: 조직 내 검증된 쿼리 패턴 참고 - - **사용 시기**: - - 복잡한 쿼리 생성 시 참고 - - "쿼리 예제를 보여줘" - - "비슷한 쿼리 있어?" - -**내부 헬퍼 함수**: - -- **`_simplify_glossary_data()`**: 용어집 데이터를 name, description, children만 포함하는 간단한 형태로 변환 - -**의존성**: -- `langchain_core.tools.tool`: LangChain Tool 데코레이터 -- `utils.data.datahub_services.base_client.DataHubBaseClient`: DataHub 기본 클라이언트 -- `utils.data.datahub_services.glossary_service.GlossaryService`: 용어집 서비스 -- `utils.data.datahub_services.query_service.QueryService`: 쿼리 서비스 -- `utils.llm.retrieval.search_tables`: 벡터 검색 함수 - -**사용처**: -- `utils/llm/chatbot.py`: ChatBot 클래스에서 LLM에 바인딩하여 사용 - -**특징**: -- LangChain Tool 인터페이스 준수 -- LLM이 자동으로 필요 시 호출 -- 상세한 docstring으로 LLM 이해도 향상 -- 에러 처리 및 fallback 제공 - -### 사용 방법 - -#### 1. DataHub 메타데이터 수집 (vectorDB 초기화) - -```python -from utils.llm.tools import get_info_from_db - -# 모든 테이블 메타데이터를 LangChain Document로 수집 -documents = get_info_from_db(max_workers=8) - -# 각 document는 다음과 같은 형식: -# "테이블명: 설명\nColumns:\n 컬럼1: 설명1\n 컬럼2: 설명2" -``` - -#### 2. GMS 서버 설정 - -```python -from utils.llm.tools import set_gms_server - -# DataHub 서버 설정 -set_gms_server("http://localhost:8080") -``` - -#### 3. ChatBot에서 Tool 사용 - -```python -from utils.llm.chatbot import ChatBot -from utils.llm.tools import ( - search_database_tables, - get_glossary_terms, - get_query_examples -) - -# ChatBot 초기화 (도구들이 자동으로 바인딩됨) -chatbot = ChatBot( - openai_api_key="your-key", - model_name="gpt-4o-mini", - gms_server="http://localhost:8080" -) - -# LLM이 필요시 자동으로 tool 호출 -response = chatbot.invoke("고객 테이블 정보를 알려줘") -# LLM이 search_database_tables를 자동으로 호출 -``` - -#### 4. 직접 Tool 함수 호출 - -```python -from utils.llm.tools import search_database_tables, get_glossary_terms, get_query_examples - -# 테이블 검색 -tables = search_database_tables( - query="고객 정보", - retriever_name="기본", - top_n=5 -) - -# 용어집 조회 -glossary = get_glossary_terms(gms_server="http://localhost:8080") - -# 쿼리 예제 조회 -queries = get_query_examples( - gms_server="http://localhost:8080", - start=0, - count=10 -) -``` - -### import 관계 - -**import하는 파일**: -- `utils/llm/chatbot.py`: `from utils.llm.tools import search_database_tables, get_glossary_terms, get_query_examples` -- `utils/llm/vectordb/faiss_db.py`: `from utils.llm.tools import get_info_from_db` -- `utils/llm/vectordb/pgvector_db.py`: `from utils.llm.tools import get_info_from_db` -- `interface/core/config/settings.py`: `from utils.llm.tools import set_gms_server` - -**내부 의존성**: -- `utils.data.datahub_source.DatahubMetadataFetcher`: DataHub 메타데이터 페처 -- `utils.data.datahub_services.*`: DataHub 서비스 레이어 -- `utils.llm.retrieval.search_tables`: 벡터 검색 기능 - -**외부 의존성**: -- `langchain.schema.Document`: LangChain Document 타입 -- `langchain_core.tools.tool`: LangChain Tool 데코레이터 -- `concurrent.futures.ThreadPoolExecutor`: 병렬 처리 -- `tqdm`: 진행률 표시 - -### 환경 변수 요약 - -- **`DATAHUB_SERVER`**: DataHub GMS 서버 URL (예: "http://localhost:8080") -- **`VECTORDB_TYPE`**: 벡터DB 타입 (datahub.py 사용 시 간접적으로 영향) -- **`EMBEDDING_PROVIDER`**: 임베딩 공급자 (retrieval 기능 사용 시 필요) - -### 주요 특징 - -1. **병렬 처리**: 8개 워커로 메타데이터 수집 속도 향상 -2. **LangChain 통합**: Document 및 Tool 인터페이스 지원 -3. **LangGraph ChatBot 연동**: LLM이 자동으로 도구 호출 -4. **진행률 표시**: tqdm을 통한 실시간 진행 상황 표시 -5. **에러 처리**: 유효한 GMS 서버 확인 및 예외 처리 -6. **데이터 정규화**: 조직 특화 데이터를 표준 형식으로 변환 - -### 통합 흐름 - -#### 메타데이터 수집 흐름 (벡터DB 초기화 시) - -1. `get_info_from_db()` 호출 -2. `_get_fetcher()`로 DatahubMetadataFetcher 인스턴스 생성 -3. `parallel_process()`로 병렬 테이블 정보 수집 -4. 각 테이블별로 컬럼 정보 추가 수집 -5. LangChain Document 리스트로 변환하여 반환 -6. vectordb가 이를 임베딩하여 벡터DB에 저장 - -#### ChatBot 도구 사용 흐름 - -1. ChatBot 초기화 시 `@tool` 데코레이터 함수들을 tools 리스트에 추가 -2. LLM에 `bind_tools(tools)`로 바인딩 -3. 사용자 질문 처리 중 LLM이 필요한 도구 판단 -4. ToolNode가 자동으로 해당 함수 호출 -5. 결과를 LLM에 전달하여 최종 응답 생성 - -### 개선 가능 영역 - -- 추가 메타데이터 타입 지원 (용어집, 관계 등) -- 캐싱 메커니즘으로 성능 향상 -- 재시도 로직 강화 -- 더 많은 Tool 함수 추가 -- 도구 사용 통계 및 모니터링 -- 비동기 처리 지원 - diff --git a/utils/llm/tools/__init__.py b/utils/llm/tools/__init__.py deleted file mode 100644 index f0dcb9d..0000000 --- a/utils/llm/tools/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from utils.llm.tools.datahub import ( - get_info_from_db, - get_metadata_from_db, - set_gms_server, -) - -from utils.llm.tools.chatbot_tool import ( - search_database_tables, - get_glossary_terms, - get_query_examples, -) - -__all__ = [ - "set_gms_server", - "get_info_from_db", - "get_metadata_from_db", - "search_database_tables", - "get_glossary_terms", - "get_query_examples", -] diff --git a/utils/llm/tools/chatbot_tool.py b/utils/llm/tools/chatbot_tool.py deleted file mode 100644 index 1c3aad8..0000000 --- a/utils/llm/tools/chatbot_tool.py +++ /dev/null @@ -1,329 +0,0 @@ -""" -LangGraph ChatBot에서 사용하는 도구(Tool) 함수들 -""" - -from langchain_core.tools import tool -from utils.data.datahub_services.base_client import DataHubBaseClient -from utils.data.datahub_services.glossary_service import GlossaryService -from utils.data.datahub_services.query_service import QueryService - - -@tool -def search_database_tables( - query: str, retriever_name: str = "기본", top_n: int = 5, device: str = "cpu" -) -> dict: - """ - 사용자의 자연어 쿼리를 기반으로 관련된 데이터베이스 테이블 정보를 검색합니다. - - 이 함수는 SQL 쿼리 생성을 위해 필요한 테이블과 컬럼 정보를 찾아줍니다. - 사용자가 어떤 테이블을 사용해야 할지, 어떤 컬럼이 있는지 물어보거나, - SQL 쿼리를 만들기 위한 스키마 정보가 필요할 때 이 도구를 사용하세요. - - Args: - query (str): 검색하려는 자연어 질문입니다. - 예: "고객 정보를 조회하려면?", "주문 관련 테이블" - retriever_name (str, optional): 검색기 유형입니다. - "기본" 또는 "Reranker" 중 선택. 기본값은 "기본" - top_n (int, optional): 검색할 테이블 개수입니다. 기본값은 5개 - device (str, optional): 모델 실행 장치입니다. "cpu" 또는 "cuda". 기본값은 "cpu" - - Returns: - dict: 테이블 정보가 담긴 딕셔너리입니다. - 각 테이블은 키로 저장되며, 값으로 테이블 설명과 컬럼 정보를 포함합니다. - - 예시 형태: - { - "customers": { - "table_description": "고객 정보 테이블", - "customer_id": "고객 고유 ID", - "name": "고객 이름", - "email": "고객 이메일" - }, - "orders": { - "table_description": "주문 정보 테이블", - "order_id": "주문 ID", - "customer_id": "고객 ID (외래키)" - } - } - - Examples: - >>> search_database_tables("고객 정보가 필요해") - {'customers': {'table_description': '고객 정보 테이블', ...}} - - Note: - 이 도구는 다음과 같은 경우에 사용하세요: - - "어떤 테이블을 사용해야 해?" - - "고객 관련 테이블 정보를 알려줘" - - "주문 데이터는 어디에 있어?" - - "사용 가능한 컬럼을 보여줘" - - SQL 쿼리를 생성하기 전에 스키마 정보가 필요할 때 - """ - try: - import os - - from lang2sql.components.retrieval.keyword_retriever import KeywordRetriever - from lang2sql.integrations.catalog.datahub_ import DataHubCatalogLoader - - gms_server = os.getenv("DATAHUB_SERVER", "http://localhost:8080") - loader = DataHubCatalogLoader(gms_server=gms_server) - catalog = loader.load() - retriever = KeywordRetriever(catalog=catalog) - results = retriever.run(query, top_k=top_n) - return { - entry["name"]: { - "table_description": entry.get("description", ""), - **entry.get("columns", {}), - } - for entry in results - } - except Exception as e: - return {"error": True, "message": f"테이블 검색 중 오류 발생: {str(e)}"} - - -def _simplify_glossary_data(glossary_data): - """ - 용어집 데이터를 name, description, children만 포함하는 간단한 형태로 변환 - - Args: - glossary_data: 처리된 용어집 데이터 - - Returns: - list: 간소화된 용어집 데이터 (name, description, children만 포함) - """ - if "error" in glossary_data: - return glossary_data - - result = [] - - for node in glossary_data.get("nodes", []): - simplified_node = { - "name": node.get("name"), - "description": node.get("description"), - } - - # children 정보가 있으면 추가 - if "details" in node and "children" in node["details"]: - children = [] - for child in node["details"]["children"]: - child_info = { - "name": child.get("name"), - "description": child.get("description"), - } - children.append(child_info) - - if children: - simplified_node["children"] = children - - result.append(simplified_node) - - return result - - -@tool -def get_glossary_terms(gms_server: str = "http://35.222.65.99:8080") -> list: - """ - DataHub에서 용어집(Glossary) 정보를 조회합니다. - - 이 함수는 DataHub 서버에 연결하여 전체 용어집 데이터를 가져옵니다. - 용어집은 비즈니스 용어, 도메인 지식, 데이터 정의 등을 표준화하여 관리하는 곳입니다. - - **중요**: 사용자의 질문이나 대화에서 다음과 같은 상황이 발생하면 반드시 이 도구를 사용하세요: - 1. 이해되지 않거나 모호한 단어가 나왔을 때 - 2. 특정 조직이나 도메인에서 고유하게 사용되는 전문 용어가 나왔을 때 - 3. 일반적이지 않은 약어나 줄임말이 나왔을 때 - 4. 조직 내부에서만 통용되는 용어가 나왔을 때 - 5. 표준 정의가 필요한 비즈니스 용어가 나왔을 때 - - Args: - gms_server (str, optional): DataHub GMS 서버 URL입니다. - 기본값은 "http://35.222.65.99:8080" - - Returns: - list: 간소화된 용어집 데이터 리스트입니다. - 각 항목은 name, description, children(선택적) 필드를 포함합니다. - - 예시 형태: - [ - { - "name": "가짜연구소", - "description": "스터디 단체 가짜연구소를 의미하며...", - "children": [ - { - "name": "빌더", - "description": "가짜연구소 스터디 리더를 지칭..." - } - ] - }, - { - "name": "PII", - "description": "개인 식별 정보...", - "children": [ - { - "name": "identifier", - "description": "개인식별정보중 github 아이디..." - } - ] - } - ] - - Examples: - >>> get_glossary_terms() - [{'name': '가짜연구소', 'description': '...', 'children': [...]}] - - Note: - 이 도구는 다음과 같은 경우에 **반드시** 사용하세요: - - [명시적 요청] - - "용어집을 보여줘" - - "비즈니스 용어가 뭐가 있어?" - - "데이터 사전 정보를 알려줘" - - "정의된 용어들을 보여줘" - - [이해되지 않는 단어 감지 - 매우 중요!] - - 일반적이지 않은 약어나 전문 용어가 대화에 등장할 때 - - 표준 정의가 없는 도메인 특화 용어가 나올 때 - - 질문의 맥락에서 모호하거나 불명확한 용어가 있을 때 - - [조직/도메인 특화 상황] - - 특정 조직에서만 사용하는 내부 용어가 나올 때 - - 업계/도메인 전문 용어가 필요할 때 - - 데이터나 테이블 관련 비즈니스 컨텍스트를 이해하기 위해 - - **핵심**: 응답하기 전에 사용자의 질문에 조직 특화 용어나 모호한 단어가 - 있는지 확인하고, 있다면 먼저 이 도구를 호출하여 정확한 정의를 파악하세요. - """ - try: - # DataHub 클라이언트 초기화 - client = DataHubBaseClient(gms_server=gms_server) - - # GlossaryService 초기화 - glossary_service = GlossaryService(client) - - # 전체 용어집 데이터 가져오기 - glossary_data = glossary_service.get_glossary_data() - - # 간소화된 데이터 반환 - simplified_data = _simplify_glossary_data(glossary_data) - - return simplified_data - - except ValueError as e: - return {"error": True, "message": f"DataHub 서버 연결 실패: {str(e)}"} - except Exception as e: - return {"error": True, "message": f"용어집 조회 중 오류 발생: {str(e)}"} - - -@tool -def get_query_examples( - gms_server: str = "http://35.222.65.99:8080", - start: int = 0, - count: int = 10, - query: str = "*", -) -> list: - """ - DataHub에서 저장된 쿼리 예제들을 조회합니다. - - 이 함수는 DataHub 서버에 연결하여 저장된 SQL 쿼리 목록을 가져옵니다. - 조직에서 실제로 사용되고 검증된 쿼리 패턴을 참고하여 더 정확한 SQL을 생성할 수 있습니다. - - **중요**: 사용자의 질문이나 대화에서 다음과 같은 상황이 발생하면 반드시 이 도구를 사용하세요: - 1. 일반적인 SQL 패턴으로 해결하기 어려운 복잡한 쿼리 요청일 때 - 2. 조직 특화된 비즈니스 로직이나 데이터 처리 방식이 필요할 때 - 3. 특정 도메인의 표준 쿼리 패턴이나 관례를 따라야 할 때 - 4. 여러 테이블 간의 복잡한 JOIN이나 집계가 필요할 때 - 5. 사용자가 과거 실행했던 쿼리와 유사한 작업을 요청할 때 - 6. 조직 내에서 검증된 쿼리 작성 방식을 확인해야 할 때 - - Args: - gms_server (str, optional): DataHub GMS 서버 URL입니다. - 기본값은 "http://35.222.65.99:8080" - start (int, optional): 조회 시작 위치입니다. 기본값은 0 - count (int, optional): 조회할 쿼리 개수입니다. 기본값은 10 - query (str, optional): 검색 쿼리입니다. 기본값은 "*" (모든 쿼리) - - Returns: - list: 쿼리 정보 리스트입니다. - 각 항목은 name, description, statement 필드를 포함합니다. - - 예시 형태: - [ - { - "name": "고객별 주문 수 조회", - "description": "각 고객별 주문 건수를 집계하는 쿼리", - "statement": "SELECT customer_id, COUNT(*) as order_count FROM orders GROUP BY customer_id" - }, - { - "name": "월별 매출 현황", - "description": "월별 총 매출을 계산하는 쿼리", - "statement": "SELECT DATE_TRUNC('month', order_date) as month, SUM(amount) FROM orders GROUP BY month" - } - ] - - Examples: - >>> get_query_examples() - [{'name': '고객별 주문 수 조회', 'description': '...', 'statement': 'SELECT ...'}] - - >>> get_query_examples(count=5) - # 5개의 쿼리 예제만 조회 - - Note: - 이 도구는 다음과 같은 경우에 **반드시** 사용하세요: - - [명시적 요청] - - "쿼리 예제를 보여줘" - - "저장된 쿼리들을 알려줘" - - "과거 쿼리 내역을 보고 싶어" - - "SQL 예제가 있어?" - - [도메인/조직 특화 패턴 감지 - 매우 중요!] - - 조직 특화된 데이터 처리 방식이나 계산 로직이 필요할 때 - - 특정 도메인의 관례적인 쿼리 패턴을 따라야 할 때 - - 데이터 품질 규칙이나 비즈니스 룰이 반영된 쿼리가 필요할 때 - - 조직 내에서 표준화된 쿼리 작성 방식을 확인해야 할 때 - - [쿼리 작성 참고] - - "이런 유형의 쿼리는 어떻게 작성해?" - - "비슷한 쿼리 있어?" - - "다른 사람들은 어떻게 쿼리를 작성했어?" - - "참고할만한 쿼리가 있을까?" - - "이 테이블들을 어떻게 조인해야 해?" - - **핵심**: SQL 쿼리를 생성하기 전에 사용자의 요청이 복잡하거나, - 조직 특화된 비즈니스 로직이 필요하거나, 일반적인 패턴으로 커버하기 - 어렵다고 판단되면, 먼저 이 도구를 호출하여 조직에서 검증된 - 쿼리 예제를 참고하세요. 이는 더 정확하고 조직의 표준을 따르는 - SQL을 생성하는 데 큰 도움이 됩니다. - """ - try: - # DataHub 클라이언트 초기화 - client = DataHubBaseClient(gms_server=gms_server) - - # QueryService 초기화 - query_service = QueryService(client) - - # 쿼리 데이터 가져오기 - result = query_service.get_query_data(start=start, count=count, query=query) - - # 오류 체크 - if "error" in result and result["error"]: - return {"error": True, "message": result.get("message")} - - # name, description, statement만 추출하여 리스트 생성 - simplified_queries = [] - for query_item in result.get("queries", []): - simplified_query = { - "name": query_item.get("name"), - "description": query_item.get("description", ""), - "statement": query_item.get("statement", ""), - } - simplified_queries.append(simplified_query) - - return simplified_queries - - except ValueError as e: - return {"error": True, "message": f"DataHub 서버 연결 실패: {str(e)}"} - except Exception as e: - return { - "error": True, - "message": f"쿼리 예제 조회 중 오류 발생: {str(e)}", - } diff --git a/utils/llm/tools/datahub.py b/utils/llm/tools/datahub.py deleted file mode 100644 index 42e564d..0000000 --- a/utils/llm/tools/datahub.py +++ /dev/null @@ -1,167 +0,0 @@ -import os -import re -from concurrent.futures import ThreadPoolExecutor -from typing import Callable, Dict, Iterable, List, Optional, TypeVar - -from langchain.schema import Document -from tqdm import tqdm - -from utils.data.datahub_source import DatahubMetadataFetcher - -T = TypeVar("T") -R = TypeVar("R") - - -def parallel_process( - items: Iterable[T], - process_fn: Callable[[T], R], - max_workers: int = 8, - desc: Optional[str] = None, - show_progress: bool = True, -) -> List[R]: - """병렬 처리를 위한 유틸리티 함수""" - with ThreadPoolExecutor(max_workers=max_workers) as executor: - futures = [executor.submit(process_fn, item) for item in items] - if show_progress: - futures = tqdm(futures, desc=desc) - return [future.result() for future in futures] - - -def set_gms_server(gms_server: str): - try: - os.environ["DATAHUB_SERVER"] = gms_server - fetcher = DatahubMetadataFetcher(gms_server=gms_server) - except ValueError as e: - raise ValueError(f"GMS 서버 설정 실패: {str(e)}") - - -def _get_fetcher(): - gms_server = os.getenv("DATAHUB_SERVER") - if not gms_server: - raise ValueError("GMS 서버가 설정되지 않았습니다.") - return DatahubMetadataFetcher(gms_server=gms_server) - - -def _process_urn(urn: str, fetcher: DatahubMetadataFetcher) -> tuple[str, str]: - table_name = fetcher.get_table_name(urn) - table_description = fetcher.get_table_description(urn) - return (table_name, table_description) - - -def _process_column_info( - urn: str, table_name: str, fetcher: DatahubMetadataFetcher -) -> Optional[List[Dict[str, str]]]: - if fetcher.get_table_name(urn) == table_name: - return fetcher.get_column_names_and_descriptions(urn) - return None - - -def _get_table_info(max_workers: int = 8) -> Dict[str, str]: - fetcher = _get_fetcher() - urns = fetcher.get_urns() - table_info = {} - - results = parallel_process( - urns, - lambda urn: _process_urn(urn, fetcher), - max_workers=max_workers, - desc="테이블 정보 수집 중", - ) - - for table_name, table_description in results: - if table_name and table_description: - table_info[table_name] = table_description - - return table_info - - -def _get_column_info( - table_name: str, urn_table_mapping: Dict[str, str], max_workers: int = 8 -) -> List[Dict[str, str]]: - target_urn = urn_table_mapping.get(table_name) - if not target_urn: - return [] - - fetcher = _get_fetcher() - column_info = fetcher.get_column_names_and_descriptions(target_urn) - - return column_info - - -def _extract_dataset_name_from_urn(urn: str) -> Optional[str]: - """URN 문자열에서 데이터셋 이름(예: delta.default.stg_gh_events)만 추출. - - 지원 패턴: - - dataset URN: urn:li:dataset:(urn:li:dataPlatform:dbt,delta.default.stg_gh_events,PROD) - - schemaField URN: urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:dbt,delta.default.stg_gh_events,PROD),event_id) - """ - match = re.search( - r"urn:li:dataset:\(urn:li:dataPlatform:[^,]+,([^,]+),[^)]+\)", urn - ) - if match: - return match.group(1) - return None - - -def get_info_from_db(max_workers: int = 8) -> List[Document]: - table_info = _get_table_info(max_workers=max_workers) - - fetcher = _get_fetcher() - urns = list(fetcher.get_urns()) - urn_table_mapping = {} - display_name_by_table = {} - for urn in urns: - original_name = fetcher.get_table_name(urn) - if original_name: - urn_table_mapping[original_name] = urn - parsed_name = _extract_dataset_name_from_urn(urn) - if parsed_name: - display_name_by_table[original_name] = parsed_name - - def process_table_info(item: tuple[str, str, str]) -> str: - original_table_name, table_description, display_table_name = item - # 컬럼 조회는 기존 테이블 이름으로 수행 (urn_table_mapping과 일치) - column_info = _get_column_info( - original_table_name, urn_table_mapping, max_workers=max_workers - ) - column_info_str = "\n".join( - [ - f"{col['column_name']}: {col['column_description']}" - for col in column_info - ] - ) - used_name = display_table_name or original_table_name - return f"{used_name}: {table_description}\nColumns:\n {column_info_str}" - - # 표시용 이름을 세 번째 파라미터로 함께 전달 - items_with_display = [ - ( - name, - desc, - display_name_by_table.get(name, name), - ) - for name, desc in table_info.items() - ] - - table_info_str_list = parallel_process( - items_with_display, - process_table_info, - max_workers=max_workers, - desc="컬럼 정보 수집 중", - ) - - return [Document(page_content=info) for info in table_info_str_list] - - -def get_metadata_from_db() -> List[Dict]: - fetcher = _get_fetcher() - urns = list(fetcher.get_urns()) - - metadata = [] - total = len(urns) - for idx, urn in enumerate(urns, 1): - print(f"[{idx}/{total}] Processing URN: {urn}") - table_metadata = fetcher.build_table_metadata(urn) - metadata.append(table_metadata) - - return metadata diff --git a/utils/visualization/README.md b/utils/visualization/README.md deleted file mode 100644 index 1ecd55e..0000000 --- a/utils/visualization/README.md +++ /dev/null @@ -1,125 +0,0 @@ -## utils.visualization 개요 - -Lang2SQL 파이프라인에서 SQL 쿼리 결과를 시각화하기 위한 유틸리티 모듈입니다. LLM을 활용하여 적절한 차트를 자동 생성하고 Plotly를 통해 렌더링합니다. - -### 파일 구조 - -``` -utils/visualization/ -└── display_chart.py # SQL 결과를 Plotly 차트로 변환하는 핵심 모듈 -``` - -### 각 파일 상세 설명 - -#### display_chart.py - -**목적**: SQL 쿼리 실행 결과를 다양한 형태의 Plotly 차트로 자동 변환하는 모듈 - -**주요 클래스**: - -- **`DisplayChart`**: SQL 결과 시각화를 담당하는 메인 클래스 - - `question` (str): 사용자가 입력한 자연어 질문 - - `sql` (str): 실행된 SQL 쿼리 - - `df_metadata` (str): 데이터프레임의 메타데이터 정보 - -**주요 메서드**: - -1. **`llm_model_for_chart(message_log)`** - - 환경변수 `LLM_PROVIDER`가 "openai"일 경우 ChatOpenAI로 차트 코드 생성 - - 필요 환경변수: `OPEN_AI_KEY`, `OPEN_AI_LLM_MODEL` (기본: "gpt-4o") - - 반환: 생성된 차트 코드 또는 None - -2. **`generate_plotly_code()`** - - 사용자 질문, SQL 쿼리, 데이터프레임 메타데이터를 프롬프트로 구성 - - LLM이 데이터에 맞는 적절한 Plotly 코드 생성 - - 반환: Python 코드 문자열 - -3. **`get_plotly_figure(plotly_code, df, dark_mode=True)`** - - 생성된 Plotly 코드를 실행하여 Figure 객체 생성 - - 에러 발생 시 데이터 타입 기반 fallback 차트 생성: - - 숫자 컬럼 2개 이상 → scatter plot - - 숫자 1개 + 범주 1개 → bar plot - - 범주 1개 (고유값 < 10) → pie chart - - 기타 → line plot - - dark_mode=True 시 "plotly_dark" 템플릿 적용 - - 반환: Plotly Figure 객체 또는 None - -4. **내부 헬퍼 메서드**: - - `_extract_python_code(markdown_string)`: 마크다운에서 Python 코드 블록 추출 - - `_sanitize_plotly_code(raw_plotly_code)`: 불필요한 `fig.show()` 문 제거 - -**의존성**: -- `pandas`: 데이터프레임 처리 -- `plotly.express` (px): 간단한 차트 생성 -- `plotly.graph_objects` (go): 고급 차트 구성 -- `langchain_openai.ChatOpenAI`: LLM 차트 코드 생성 -- `langchain_core.messages`: SystemMessage, HumanMessage - -### 사용 방법 - -#### 1. 기본 사용법 (interface/core/result_renderer.py에서 실제 사용) - -```python -from utils.visualization.display_chart import DisplayChart -import pandas as pd - -# DisplayChart 인스턴스 생성 -display_code = DisplayChart( - question="지난달 매출 추이를 보여줘", - sql="SELECT date, revenue FROM sales WHERE ...", - df_metadata=f"Running df.dtypes gives:\n{df.dtypes}" -) - -# Plotly 코드 생성 -plotly_code = display_code.generate_plotly_code() - -# Figure 객체 생성 -fig = display_code.get_plotly_figure(plotly_code=plotly_code, df=df) - -# Streamlit에서 차트 표시 -st.plotly_chart(fig) -``` - -#### 2. 통합 흐름 (Lang2SQL 파이프라인 내) - -`interface/core/result_renderer.py`의 `display_result()` 함수에서 사용: - -1. SQL 쿼리 실행 후 pandas DataFrame 반환 -2. `DisplayChart` 초기화 (질문, SQL, 메타데이터) -3. `generate_plotly_code()`로 LLM 기반 차트 코드 생성 -4. `get_plotly_figure()`로 실행 및 Figure 객체 획득 -5. `st.plotly_chart()`로 Streamlit UI에 렌더링 - -**경로**: `interface/core/result_renderer.py` (200-211번째 줄) - -### import 관계 - -**import하는 파일**: -- `interface/core/result_renderer.py`: `from utils.visualization.display_chart import DisplayChart` - -**외부 의존성**: -- `langchain_openai.ChatOpenAI`: OpenAI LLM API 호출 -- `plotly`: 차트 렌더링 및 Figure 객체 관리 -- `pandas`: 데이터프레임 처리 -- 환경변수: `LLM_PROVIDER`, `OPEN_AI_KEY`, `OPEN_AI_LLM_MODEL` - -### 환경 변수 요약 - -- **`LLM_PROVIDER`**: LLM 공급자 지정 (현재 "openai"만 지원) -- **`OPEN_AI_KEY`**: OpenAI API 키 -- **`OPEN_AI_LLM_MODEL`**: 사용할 모델 (기본값: "gpt-4o") - -### 주요 특징 - -1. **LLM 기반 지능형 차트 생성**: 데이터 구조와 질문 내용에 맞춰 적절한 차트 유형 자동 선택 -2. **Fallback 메커니즘**: LLM 코드 생성 실패 시 데이터 타입 기반 대체 차트 제공 -3. **다크 모드 지원**: 기본적으로 plotly_dark 템플릿 적용 -4. **에러 안전성**: 코드 실행 중 예외 발생 시에도 항상 유효한 Figure 객체 반환 - -### 개선 가능 영역 - -- 다른 LLM 공급자 지원 (현재 OpenAI만 지원) -- 더 다양한 차트 유형 지원 -- 컬러 스킴 및 스타일 커스터마이징 옵션 -- 성능 최적화 (코드 생성 시간 단축) - diff --git a/utils/visualization/display_chart.py b/utils/visualization/display_chart.py deleted file mode 100644 index 0a977d6..0000000 --- a/utils/visualization/display_chart.py +++ /dev/null @@ -1,188 +0,0 @@ -""" -SQL 쿼리 결과를 Plotly로 시각화하는 모듈 - -이 모듈은 Lang2SQL 실행 결과를 다양한 형태의 차트로 시각화하는 기능을 제공합니다. -LLM을 활용하여 적절한 Plotly 코드를 생성하고 실행합니다. -""" - -import os -import re -from typing import Optional - -import pandas as pd -import plotly -import plotly.express as px -import plotly.graph_objects as go -from langchain_core.messages import HumanMessage, SystemMessage -from langchain_openai import ChatOpenAI - - -class DisplayChart: - """ - SQL쿼리가 실행된 결과를 그래프로 시각화하는 Class입니다. - - 쿼리 결과를 비롯한 유저 질문, sql를 prompt에 입력하여 - plotly코드를 출력하여 excute한 결과를 fig 객체로 반환합니다. - """ - - def __init__(self, question: str, sql: str, df_metadata: str): - """ - DisplayChart 인스턴스를 초기화합니다. - - Args: - question (str): 사용자 질문 - sql (str): 실행된 SQL 쿼리 - df_metadata (str): 데이터프레임 메타데이터 - """ - self.question = question - self.sql = sql - self.df_metadata = df_metadata - - def llm_model_for_chart(self, message_log) -> Optional[str]: - """ - LLM 모델을 사용하여 차트 생성 코드를 생성합니다. - - Args: - message_log: LLM에 전달할 메시지 목록 - - Returns: - Optional[str]: 생성된 차트 코드 또는 None - """ - provider = os.getenv("LLM_PROVIDER") - if provider == "openai": - llm = ChatOpenAI( - model=os.getenv("OPEN_AI_LLM_MODEL", "gpt-4o"), - api_key=os.getenv("OPEN_AI_KEY"), - ) - result = llm.invoke(message_log) - return result - return None - - def _extract_python_code(self, markdown_string: str) -> str: - """ - 마크다운 문자열에서 Python 코드 블록을 추출합니다. - - Args: - markdown_string: 마크다운 형식의 문자열 - - Returns: - str: 추출된 Python 코드 - """ - # Strip whitespace to avoid indentation errors in LLM-generated code - if hasattr(markdown_string, "content"): - markdown_string = markdown_string.content.split("```")[1][6:].strip() - else: - markdown_string = str(markdown_string) - - # Regex pattern to match Python code blocks - pattern = r"```[\w\s]*python\n([\s\S]*?)```|```([\s\S]*?)```" - - # Find all matches in the markdown string - matches = re.findall(pattern, markdown_string, re.IGNORECASE) - - # Extract the Python code from the matches - python_code = [] - for match in matches: - python = match[0] if match[0] else match[1] - python_code.append(python.strip()) - - if len(python_code) == 0: - return markdown_string - - return python_code[0] - - def _sanitize_plotly_code(self, raw_plotly_code: str) -> str: - """ - Plotly 코드에서 불필요한 부분을 제거합니다. - - Args: - raw_plotly_code: 원본 Plotly 코드 - - Returns: - str: 정리된 Plotly 코드 - """ - # Remove the fig.show() statement from the plotly code - plotly_code = raw_plotly_code.replace("fig.show()", "") - return plotly_code - - def generate_plotly_code(self) -> str: - """ - LLM을 사용하여 Plotly 코드를 생성합니다. - - Returns: - str: 생성된 Plotly 코드 - """ - if self.question is not None: - system_msg = f"The following is a pandas DataFrame that contains the results of the query that answers the question the user asked: '{self.question}'" - else: - system_msg = "The following is a pandas DataFrame " - - if self.sql is not None: - system_msg += ( - f"\n\nThe DataFrame was produced using this query: {self.sql}\n\n" - ) - - system_msg += f"The following is information about the resulting pandas DataFrame 'df': \n{self.df_metadata}" - - message_log = [ - SystemMessage(content=system_msg), - HumanMessage( - content="Can you generate the Python plotly code to chart the results of the dataframe? Assume the data is in a pandas dataframe called 'df'. If there is only one value in the dataframe, use an Indicator. Respond with only Python code. Do not answer with any explanations -- just the code." - ), - ] - - plotly_code = self.llm_model_for_chart(message_log) - if plotly_code is None: - return "" - - return self._sanitize_plotly_code(self._extract_python_code(plotly_code)) - - def get_plotly_figure( - self, plotly_code: str, df: pd.DataFrame, dark_mode: bool = True - ) -> Optional[plotly.graph_objs.Figure]: - """ - Plotly 코드를 실행하여 Figure 객체를 생성합니다. - - Args: - plotly_code: 실행할 Plotly 코드 - df: 데이터프레임 - dark_mode: 다크 모드 사용 여부 - - Returns: - Optional[plotly.graph_objs.Figure]: 생성된 Figure 객체 또는 None - """ - ldict = {"df": df, "px": px, "go": go} - fig = None - - try: - exec(plotly_code, globals(), ldict) # noqa: S102 - fig = ldict.get("fig", None) - - except Exception: - # Inspect data types - numeric_cols = df.select_dtypes(include=["number"]).columns.tolist() - categorical_cols = df.select_dtypes( - include=["object", "category"] - ).columns.tolist() - - # Decision-making for plot type - if len(numeric_cols) >= 2: - # Use 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