diff --git a/README.md b/README.md
index 9bdb698..09fd9fc 100644
--- a/README.md
+++ b/README.md
@@ -21,7 +21,9 @@
 > *separate* set of definitions per team → it answers questions over an
 > incomplete database → it remembers every definition and conversation.
 
-This is the **v4.1 rebuild** (see [`docs/discord_first_redesign_v4_1.md`](docs/discord_first_redesign_v4_1.md)).
+👉 **프로젝트 전체 그림(단일 SSOT)**: [`docs/PROJECT.md`](docs/PROJECT.md) · **컨트리뷰터 한눈 가이드**: [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md)
+
+This is the **v4.1 rebuild** (배경/설계 의도: [`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
@@ -143,6 +145,8 @@ for the full architecture write-up.
 
 ## 🤝 기여하기
 
+**처음 보시는 분은 [`docs/ARCHITECTURE.md`](docs/ARCHITECTURE.md)** — 디렉토리·레이어 책임, 한 메시지의 lifecycle, *어디를 수정하면 좋을지* 가 한곳에 정리돼 있습니다.
+
 ```bash
 git clone https://github.com/CausalInferenceLab/lang2sql.git
 cd lang2sql
diff --git a/dev/create_faiss.py b/dev/create_faiss.py
deleted file mode 100644
index 6547d41..0000000
--- a/dev/create_faiss.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""
-dev/create_faiss.py
-
-CSV 파일에서 테이블과 컬럼 정보를 불러와 OpenAI 임베딩으로 벡터화한 뒤,
-FAISS 인덱스를 생성하고 로컬 디렉토리에 저장한다.
-
-환경 변수:
-    OPEN_AI_KEY: OpenAI API 키
-    OPEN_AI_EMBEDDING_MODEL: 사용할 임베딩 모델 이름
-
-출력:
-    지정된 OUTPUT_DIR 경로에 FAISS 인덱스 저장
-"""
-
-import csv
-import os
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from langchain_community.vectorstores import FAISS
-from langchain_openai import OpenAIEmbeddings
-
-load_dotenv()
-# CSV 파일 경로
-CSV_PATH = "./dev/table_catalog.csv"
-# .env의 VECTORDB_LOCATION과 동일하게 맞추세요
-OUTPUT_DIR = "./dev/table_info_db"
-
-tables = defaultdict(lambda: {"desc": "", "columns": []})
-with open(CSV_PATH, newline="", encoding="utf-8") as f:
-    reader = csv.DictReader(f)
-    for row in reader:
-        t = row["table_name"].strip()
-        tables[t]["desc"] = row["table_description"].strip()
-        col = row["column_name"].strip()
-        col_desc = row["column_description"].strip()
-        tables[t]["columns"].append((col, col_desc))
-
-docs = []
-for t, info in tables.items():
-    cols = "\n".join([f"{c}: {d}" for c, d in info["columns"]])
-    page = f"{t}: {info['desc']}\nColumns:\n {cols}"
-    from langchain.schema import Document
-
-    docs.append(Document(page_content=page))
-
-emb = OpenAIEmbeddings(
-    model=os.getenv("OPEN_AI_EMBEDDING_MODEL"), openai_api_key=os.getenv("OPEN_AI_KEY")
-)
-db = FAISS.from_documents(docs, emb)
-os.makedirs(OUTPUT_DIR, exist_ok=True)
-db.save_local(OUTPUT_DIR)
-print(f"FAISS index saved to: {OUTPUT_DIR}")
diff --git a/dev/create_pgvector.py b/dev/create_pgvector.py
deleted file mode 100644
index 77edd9f..0000000
--- a/dev/create_pgvector.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""
-dev/create_pgvector.py
-
-CSV 파일에서 테이블과 컬럼 정보를 불러와 OpenAI 임베딩으로 벡터화한 뒤,
-pgvector에 적재한다.
-
-환경 변수:
-    OPEN_AI_KEY: OpenAI API 키
-    OPEN_AI_EMBEDDING_MODEL: 사용할 임베딩 모델 이름
-    VECTORDB_LOCATION: pgvector 연결 문자열
-    PGVECTOR_COLLECTION: pgvector 컬렉션 이름
-"""
-
-import csv
-import os
-from collections import defaultdict
-
-from dotenv import load_dotenv
-from langchain.schema import Document
-from langchain_openai import OpenAIEmbeddings
-from langchain_postgres.vectorstores import PGVector
-
-load_dotenv()
-# CSV 파일 경로
-CSV_PATH = "./dev/table_catalog.csv"
-# .env의 VECTORDB_LOCATION과 동일하게 맞추세요
-CONN = (
-    os.getenv("VECTORDB_LOCATION")
-    or "postgresql://pgvector:pgvector@localhost:5432/postgres"
-)
-COLLECTION = os.getenv("PGVECTOR_COLLECTION", "table_info_db")
-
-tables = defaultdict(lambda: {"desc": "", "columns": []})
-with open(CSV_PATH, newline="", encoding="utf-8") as f:
-    reader = csv.DictReader(f)
-    for row in reader:
-        t = row["table_name"].strip()
-        tables[t]["desc"] = row["table_description"].strip()
-        col = row["column_name"].strip()
-        col_desc = row["column_description"]
-        tables[t]["columns"].append((col, col_desc))
-
-docs = []
-for t, info in tables.items():
-    cols = "\n".join([f"{c}: {d}" for c, d in info["columns"]])
-    docs.append(Document(page_content=f"{t}: {info['desc']}\nColumns:\n {cols}"))
-
-emb = OpenAIEmbeddings(
-    model=os.getenv("OPEN_AI_EMBEDDING_MODEL"), openai_api_key=os.getenv("OPEN_AI_KEY")
-)
-PGVector.from_documents(
-    documents=docs, embedding=emb, connection=CONN, collection_name=COLLECTION
-)
-print(f"pgvector collection populated: {COLLECTION}")
diff --git a/docker/Dockerfile b/docker/Dockerfile
deleted file mode 100644
index 4b3dd4a..0000000
--- a/docker/Dockerfile
+++ /dev/null
@@ -1,32 +0,0 @@
-# 1. Base image
-FROM python:3.12-slim-bullseye
-
-# 2. 시스템 라이브러리 설치
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    libpq-dev \
-    && rm -rf /var/lib/apt/lists/*
-
-# 3. uv 설치
-RUN pip install --no-cache-dir uv
-
-# 4. 작업 디렉토리 설정
-WORKDIR /app
-
-# 5. 소스 코드 복사 및 의존성 설치
-COPY pyproject.toml ./
-COPY . .
-RUN uv pip install --system --upgrade pip setuptools wheel \
-    && uv pip install --system .
-
-# 6. 환경 변수 설정
-ENV PYTHONPATH=/app
-ENV PYTHONUNBUFFERED=1
-
-# 7. 포트 설정
-ENV STREAMLIT_SERVER_PORT=8501
-
-# 8. 실행 명령
-CMD ["lang2sql", "run-streamlit"]
diff --git a/docker/Dockerfile.dockerignore b/docker/Dockerfile.dockerignore
deleted file mode 100644
index 44447fb..0000000
--- a/docker/Dockerfile.dockerignore
+++ /dev/null
@@ -1,10 +0,0 @@
-.git
-__pycache__/
-*.pyc
-*.pyo
-*.pyd
-*.db
-*.log
-venv/
-.env
-docker/
diff --git a/docker/docker-compose-pgvector.yml b/docker/docker-compose-pgvector.yml
deleted file mode 100644
index 8ad5e16..0000000
--- a/docker/docker-compose-pgvector.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-# docker compose -f docker-compose-pgvector.yml up
-# docker compose -f docker-compose-pgvector.yml down
-
-services:
-  pgvector:
-    image: pgvector/pgvector:pg17
-    hostname: pgvector
-    container_name: pgvector
-    restart: always
-    ports:
-      - "5432:5432"
-    environment:
-      POSTGRES_USER: pgvector
-      POSTGRES_PASSWORD: pgvector
-      POSTGRES_DB: pgvector
-      TZ: Asia/Seoul
-      LANG: en_US.utf8
-    volumes:
-      - pgvector_data:/var/lib/postgresql/data
-      - ./pgvector/init:/docker-entrypoint-initdb.d
-
-volumes:
-  pgvector_data:
diff --git a/docker/docker-compose-postgres.yml b/docker/docker-compose-postgres.yml
deleted file mode 100644
index 696f7e1..0000000
--- a/docker/docker-compose-postgres.yml
+++ /dev/null
@@ -1,23 +0,0 @@
-# docker compose -f docker-compose-postgres.yml up
-# docker compose -f docker-compose-postgres.yml down
-
-services:
-  postgres:
-    image: postgres:15
-    hostname: postgres
-    container_name: postgres
-    restart: always
-    ports:
-      - "5432:5432"
-    environment:
-      POSTGRES_USER: postgres
-      POSTGRES_PASSWORD: postgres
-      POSTGRES_DB: postgres
-      TZ: Asia/Seoul
-      LANG: en_US.utf8
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-      - ./postgres/init:/docker-entrypoint-initdb.d
-
-volumes:
-  postgres_data:
diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml
deleted file mode 100644
index 115575a..0000000
--- a/docker/docker-compose.yml
+++ /dev/null
@@ -1,34 +0,0 @@
-services:
-  streamlit:
-    hostname: streamlit
-    container_name: streamlit
-    build:
-      context: ..
-      dockerfile: docker/Dockerfile
-    ports:
-      - "8501:8501"
-    volumes:
-      - ../:/app
-    env_file:
-      - ../.env
-    environment:
-      - STREAMLIT_SERVER_PORT=8501
-      - DATABASE_URL=postgresql://pgvector:pgvector@localhost:5432/streamlit
-    depends_on:
-      - pgvector
-
-  pgvector:
-    image: pgvector/pgvector:pg17
-    hostname: pgvector
-    container_name: pgvector
-    environment:
-      POSTGRES_USER: pgvector
-      POSTGRES_PASSWORD: pgvector
-      POSTGRES_DB: streamlit
-    ports:
-      - "5432:5432"
-    volumes:
-      - pgdata:/var/lib/postgresql/data
-
-volumes:
-  pgdata:
diff --git a/docker/pgvector/init/001_create_database.sql b/docker/pgvector/init/001_create_database.sql
deleted file mode 100644
index 2173146..0000000
--- a/docker/pgvector/init/001_create_database.sql
+++ /dev/null
@@ -1,2 +0,0 @@
-CREATE DATABASE lang2sql;
-CREATE DATABASE test;
diff --git a/docker/pgvector/init/002_create_user_and_grant.sql b/docker/pgvector/init/002_create_user_and_grant.sql
deleted file mode 100644
index 8da26fb..0000000
--- a/docker/pgvector/init/002_create_user_and_grant.sql
+++ /dev/null
@@ -1,5 +0,0 @@
-CREATE USER lang2sql WITH PASSWORD 'lang2sqlpassword';
-GRANT ALL PRIVILEGES ON DATABASE lang2sql TO lang2sql;
-
-CREATE USER test WITH PASSWORD 'testpassword';
-GRANT ALL PRIVILEGES ON DATABASE test TO test;
diff --git a/docker/postgres/init/001_create_database.sql b/docker/postgres/init/001_create_database.sql
deleted file mode 100644
index 2173146..0000000
--- a/docker/postgres/init/001_create_database.sql
+++ /dev/null
@@ -1,2 +0,0 @@
-CREATE DATABASE lang2sql;
-CREATE DATABASE test;
diff --git a/docker/postgres/init/002_create_user_and_grant.sql b/docker/postgres/init/002_create_user_and_grant.sql
deleted file mode 100644
index 8da26fb..0000000
--- a/docker/postgres/init/002_create_user_and_grant.sql
+++ /dev/null
@@ -1,5 +0,0 @@
-CREATE USER lang2sql WITH PASSWORD 'lang2sqlpassword';
-GRANT ALL PRIVILEGES ON DATABASE lang2sql TO lang2sql;
-
-CREATE USER test WITH PASSWORD 'testpassword';
-GRANT ALL PRIVILEGES ON DATABASE test TO test;
diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md
new file mode 100644
index 0000000..b7fc58f
--- /dev/null
+++ b/docs/ARCHITECTURE.md
@@ -0,0 +1,245 @@
+# Architecture — 기여자용 한눈 가이드
+
+이 문서는 *처음 보는 사람도 10분 안에 어디 무엇이 있는지 / 어디를 손대면 좋은지* 알 수 있도록 쓰여졌습니다. 상세 설계 의도는 [`docs/discord_first_redesign_v4_1.md`](./discord_first_redesign_v4_1.md)에 있습니다.
+
+---
+
+## 1. 한 눈에 보는 아키텍처
+
+```
+   USER (Discord / CLI / 향후 Slack·Web)
+       │
+       ▼
+┌─────────────────────────────────────────────────┐
+│  frontends/  ← 입력 받고 출력 보내기 (transport)    │
+│   discord/   cli/   slack/(빈)   web/(빈)         │
+└──────────────────┬──────────────────────────────┘
+                   ▼   (인터랙션 → Identity)
+┌─────────────────────────────────────────────────┐
+│  tenancy/  ContextConcierge ← *조립점*            │
+│   요청마다 HarnessContext를 하나 만들어 넘김         │
+└──────────────────┬──────────────────────────────┘
+                   ▼   (ctx = LLM+tools+session+...)
+┌─────────────────────────────────────────────────┐
+│  harness/  agent_loop                            │
+│   system prompt → LLM → tool 호출 → 다음 턴/종료   │
+└──────────────────┬──────────────────────────────┘
+                   ▼   (도구가 ctx의 포트를 호출)
+┌──────────────┬───────────┬───────────┬─────────┐
+│ semantic/(★④)│safety/(★①)│memory/(★②)│ingest/(★③)│  ← 4기둥
+└──────────────┴───────────┴───────────┴─────────┘
+                   │
+                   ▼   (모두 포트(Protocol)로 외부와 분리)
+┌─────────────────────────────────────────────────┐
+│  adapters/  외부 시스템과의 마지막 한 줄              │
+│   llm/openai_ · llm/fake                          │
+│   db/sqlalchemy_explorer · db/d1_explorer · db/postgres_explorer │
+│   storage/sqlite_store · storage/sqlite_semantic   │
+└─────────────────────────────────────────────────┘
+```
+
+핵심 원칙: **로직은 포트(추상)에만 의존, 어댑터(구체)는 가장자리에만**. 그래서 새 LLM·새 DB·새 frontend를 *기존 코드 안 건드리고* 끼울 수 있습니다.
+
+---
+
+## 2. 왜 이런 구조? — 4기둥 (해결하려는 문제)
+
+| ★ | 이름 | 풀려는 현실 문제 | 핵심 파일 |
+|---|---|---|---|
+| ① | **Safety pipeline** | SQL이 *실수로/악의로* DB를 망치는 일 | [`src/lang2sql/safety/`](../src/lang2sql/safety/) |
+| ② | **Memory 3축** | 봇이 어제 한 얘기·정의를 *기억 못 함* | [`src/lang2sql/memory/`](../src/lang2sql/memory/) |
+| ③ | **Ingestion 매트릭스** | 비즈니스 정의를 *사람이 일일이* 입력해야 함 | [`src/lang2sql/ingestion/`](../src/lang2sql/ingestion/) |
+| ④ | **Semantic federation** | 같은 *"활성 사용자"* 가 팀마다 의미 다름 | [`src/lang2sql/semantic/`](../src/lang2sql/semantic/) |
+
+자세한 배경은 redesign 문서 §3을 참고.
+
+---
+
+## 3. 디렉토리·레이어 가이드
+
+> 의존 방향: `frontends → tenancy → harness → semantic/safety/memory/ingestion/tools → core ← adapters`
+> `core/`는 누구도 의존하지 않는 *순수* 영역(타입+포트). 새 모듈 추가 시 이 방향을 깨지 않게.
+
+### `src/lang2sql/core/` — 순수 타입 + 포트 (★ 손대지 마세요)
+시스템 전체의 *어휘*가 모여 있습니다. 외부 의존 0, I/O 0.
+- [`types.py`](../src/lang2sql/core/types.py) — `Message`, `ToolCall`, `ToolResult`, `Completion`, `Role`
+- [`identity.py`](../src/lang2sql/core/identity.py) — `Identity`, `Scope`, federation의 `scope_chain()` 순서 (narrow→wide)
+- [`ports/`](../src/lang2sql/core/ports/) — 11개 Protocol: `LLMPort`, `ExplorerPort`, `ToolPort`, `SafetyLayerPort`, `SafetyPipelinePort`, `StorePort`, `RecallPort`, `ExtractorPort` (memory), `SourcePort`, `DocExtractorPort`, `ScopeResolverPort`, `FrontendPort`, `SecretsPort`, `SessionStorePort`, `AuditPort`
+
+### `src/lang2sql/harness/` — 에이전트 한 턴의 엔진
+- [`context.py`](../src/lang2sql/harness/context.py) — `HarnessContext` (llm + tools + safety + explorer + scope_resolver + session 한 다발)
+- [`session.py`](../src/lang2sql/harness/session.py) — 대화 transcript
+- [`loop.py`](../src/lang2sql/harness/loop.py) — `agent_loop`: system prompt → LLM → tool 호출 → 다음 턴
+- [`tool_registry.py`](../src/lang2sql/harness/tool_registry.py) — 이름→도구 dispatch
+- [`system_prompt.py`](../src/lang2sql/harness/system_prompt.py) — 시멘틱 + 스키마 주입
+
+### `src/lang2sql/semantic/` — 시멘틱 레이어 + federation (★④)
+- [`types.py`](../src/lang2sql/semantic/types.py) — `SemanticEntry` (METRIC/DIMENSION/RELATIONSHIP/RULE)
+- [`layer.py`](../src/lang2sql/semantic/layer.py) — `SemanticLayer.render()` (시스템 프롬프트로 들어감)
+- [`scoped_layer.py`](../src/lang2sql/semantic/scoped_layer.py) — *가장 구체적 scope가 승리*하는 merge
+- [`store.py`](../src/lang2sql/semantic/store.py) — in-memory store
+- [`sql_composer.py`](../src/lang2sql/semantic/sql_composer.py) — metric 이름 → 정의 펼치기 (V1 최소)
+
+### `src/lang2sql/safety/` — Read-only 게이트 (★①)
+- [`pipeline.py`](../src/lang2sql/safety/pipeline.py) — layer를 순서대로 통과, *첫 비-PASS에서 차단*
+- [`layers/whitelist.py`](../src/lang2sql/safety/layers/whitelist.py) — SELECT/WITH만 통과, DML 키워드 fail-closed
+- [`layers/timeout.py`](../src/lang2sql/safety/layers/timeout.py) — 실행 timeout config
+- [`tests/test_safety.py`](../tests/test_safety.py) — **12개 회귀 케이스** (머지 게이트)
+
+### `src/lang2sql/memory/` — Hermes 3축 (★②)
+- [`stores/in_memory.py`](../src/lang2sql/memory/stores/in_memory.py) — Where
+- [`recall/inject_all.py`](../src/lang2sql/memory/recall/inject_all.py) — What
+- [`extractors/manual.py`](../src/lang2sql/memory/extractors/manual.py) — How new
+- [`service.py`](../src/lang2sql/memory/service.py) — 셋을 묶음
+
+### `src/lang2sql/ingestion/` — 문서 → 시멘틱 후보 (★③)
+- [`sources/file_source.py`](../src/lang2sql/ingestion/sources/file_source.py) — 어디서
+- [`extractors/llm_extractor.py`](../src/lang2sql/ingestion/extractors/llm_extractor.py) — 어떻게 추출
+- [`pipeline.py`](../src/lang2sql/ingestion/pipeline.py) — Source × Extractor matrix
+
+### `src/lang2sql/tools/` — 에이전트가 부르는 capability
+6개 도구 (모두 ctx-aware, async):
+- [`run_sql.py`](../src/lang2sql/tools/run_sql.py) — safety 통과 후 explorer로 실행
+- [`explore_schema.py`](../src/lang2sql/tools/explore_schema.py) — 테이블/컬럼 introspection
+- [`define_metric.py`](../src/lang2sql/tools/define_metric.py) — scope-aware 정의 쓰기
+- [`remember.py`](../src/lang2sql/tools/remember.py) — fact 저장
+- [`ask_user.py`](../src/lang2sql/tools/ask_user.py) — 모호하면 사용자에게 질문
+- [`ingest_doc.py`](../src/lang2sql/tools/ingest_doc.py) — 문서 → 후보 제안
+- [`__init__.py: build_default_tools`](../src/lang2sql/tools/__init__.py) — 어셈블리
+
+### `src/lang2sql/tenancy/` — 조립점
+- [`concierge.py`](../src/lang2sql/tenancy/concierge.py) — *유일하게* 구체 클래스를 import 하는 곳. 요청마다 `HarnessContext` 만듦.
+- [`scope_resolver.py`](../src/lang2sql/tenancy/scope_resolver.py) — `ScopeResolverPort` 구현 (semantic 위)
+- [`encrypted_secrets.py`](../src/lang2sql/tenancy/encrypted_secrets.py) — `cryptography.Fernet` 실 암호화
+
+### `src/lang2sql/adapters/` — 외부 시스템과의 마지막 줄
+- `llm/openai_.py` — urllib 기반 OpenAI tool-calling
+- `llm/fake.py` — 오프라인 테스트용 결정적 LLM
+- `db/sqlalchemy_explorer.py` — **DSN만 바꾸면 Postgres/MySQL/Snowflake/BigQuery/DuckDB 다 커버**
+- `db/d1_explorer.py` — Cloudflare D1 (HTTP API, urllib)
+- `db/factory.py` — `build_explorer(connection)` scheme 라우팅
+- `db/postgres_explorer.py` — V1 stub (psycopg 미설치 환경용)
+- `storage/sqlite_store.py` — `AuditPort` + `SessionStorePort` + kv
+- `storage/sqlite_semantic.py` — 시멘틱 정의 영속화
+
+### `src/lang2sql/frontends/` — 사용자 인터페이스
+- [`discord/bot.py`](../src/lang2sql/frontends/discord/bot.py) — **유일하게** `discord.py`를 import
+- [`discord/commands.py`](../src/lang2sql/frontends/discord/commands.py) — 순수 핸들러 (discord 비의존, 테스트 가능)
+- [`discord/setup_wizard.py`](../src/lang2sql/frontends/discord/setup_wizard.py) — `/setup` Modal/Select
+- [`discord/session_router.py`](../src/lang2sql/frontends/discord/session_router.py) — discord ID → `Identity`
+- [`discord/render.py`](../src/lang2sql/frontends/discord/render.py) — >50행이면 CSV 첨부
+- [`cli/app.py`](../src/lang2sql/frontends/cli/app.py) — 개발용 CLI
+
+---
+
+## 4. 한 메시지의 lifecycle (디스코드 멘션 한 번 따라가기)
+
+```
+1. 사용자: "@lang2sql-test 이번 달 매출 알려줘"
+2. discord/bot.py: on_message → _message_context()로 (guild_id, channel_id, user_id) 뽑음
+3. session_router.to_identity()  →  Identity(...)
+4. CommandHandlers.query(identity, "이번 달 매출 알려줘")
+5. ContextConcierge.build_context(identity)
+     - secrets에서 길드별 db_dsn 있나? → 있으면 build_explorer로 그 DB 사용 (캐시)
+     - SqliteStore에서 세션 로드 (없으면 새로)
+     - build_default_tools()로 ToolRegistry 채움
+     - HarnessContext 반환
+6. agent_loop(ctx, "이번 달 매출 알려줘")
+     - system_prompt: 시멘틱 effective_layer + 스키마 주입
+     - LLM(GPT-4.1-mini): "run_sql 도구를 부르세요" 응답
+     - tools.dispatch("run_sql", {sql: "SELECT ..."}, ctx)
+        → safety.evaluate(sql) → PASS
+        → explorer.execute(sql) → 행들 반환
+     - 결과 messages에 추가, LLM 다시 호출 → 최종 답변
+7. concierge.store.save(session_key, ctx.session)  ← 세션 영속화
+8. render_answer(answer) → OutboundMessage
+9. interaction.followup.send(...)  → Discord에 답
+```
+
+---
+
+## 5. 어디를 수정하면 좋을까 — Extension Points
+
+기여 PR을 받기 가장 쉬운 지점들. 전부 *기존 코드 안 건드리고 추가만 하면 됩니다*.
+
+### LLM 추가 (예: Anthropic Claude, NIM)
+1. `src/lang2sql/adapters/llm/<provider>_.py` 새로 작성, `LLMPort` 구현
+2. `tenancy/concierge.py: _default_llm()`에 분기 추가
+3. tests/ 에 `test_<provider>_adapter.py`
+
+### 새 DB 지원
+SQLAlchemy 지원 DB라면:
+1. `pyproject.toml`의 `[project.optional-dependencies]`에 extra 추가
+2. 끝. `SqlAlchemyExplorer`가 DSN으로 알아서 처리
+
+SQLAlchemy 미지원 (예: 자체 HTTP API):
+1. `adapters/db/<db>_explorer.py`에 `ExplorerPort` 구현
+2. `adapters/db/factory.py`의 `build_explorer`에 scheme 분기
+3. `adapters/db/dsn_builder.py`에 `build_<db>()` + `FIELD_SCHEMA[<db>]`
+4. tests/
+
+### 새 safety layer (예: AST 정밀 검증, 함수 차단, EXPLAIN 비용)
+1. `safety/layers/<name>.py`에 `SafetyLayerPort` 구현
+2. `safety/pipeline.py`의 `SafetyPipeline` 기본 layers 목록에 끼우거나, 옵셔널로 노출
+3. tests/test_safety.py에 회귀 케이스 추가
+
+### 더 똑똑한 memory recall (예: 키워드, 벡터)
+1. `memory/recall/<name>.py`에 `RecallPort` 구현
+2. concierge에서 옵션으로 선택 가능하게
+3. tests/
+
+### 새 ingestion source (예: URL, Notion MCP)
+1. `ingestion/sources/<name>.py`에 `SourcePort` 구현
+2. ingestion 도구 흐름이 자동 매트릭스이므로 추가 코드 거의 없음
+
+### 새 frontend (예: Slack, Web)
+1. `frontends/<platform>/` 디렉토리에 transport 작성
+2. `commands.py`는 그대로 재사용 (discord 비의존이라)
+3. `core/ports/frontend.py`의 `FrontendPort` 인터페이스 따르기
+
+### 새 도구 (예: visualize, write_code)
+1. `tools/<name>.py`에 `ToolPort` 구현 (spec + run)
+2. `tools/__init__.py: build_default_tools()`에 추가
+3. tests/
+
+---
+
+## 6. 빠른 기여 시작 (5분)
+
+```bash
+git clone https://github.com/CausalInferenceLab/Lang2SQL.git
+cd Lang2SQL
+uv sync                          # 기본 deps
+.venv/bin/pytest -q              # 106 테스트 통과 확인
+.venv/bin/python bench/ecommerce_demo.py   # federation + safety 로컬 데모
+```
+
+브랜치 → 코드 + 테스트 → PR. CI는 따로 없으니 *로컬에서 pytest 확인 후 PR*.
+
+---
+
+## 7. 코드 컨벤션 (작은 약속)
+
+| 규칙 | 이유 |
+|---|---|
+| **포트는 `typing.Protocol`** (`runtime_checkable` 권장) | 덕타이핑 + isinstance 가능 |
+| **어댑터의 engine/connection은 lazy** | 라우팅 단계에서 드라이버 미설치여도 OK |
+| **blocking 호출은 `asyncio.to_thread`** | discord 이벤트 루프 막지 않기 |
+| **frontends/discord에서 `discord.py` import는 `bot.py`·`setup_wizard.py`만** | 로직층은 유닛테스트 가능해야 함 |
+| **새 환경변수는 `.env.example`에도 문서화** | 신규 컨트리뷰터 친화 |
+| **테스트는 토큰/네트워크 없이도 통과해야 함** | `FakeLLM` / mock transport 활용 |
+| **포트(`core/ports/`)는 거의 frozen** | 변경은 모든 어댑터/구현에 영향 — 정말 필요한지 한 번 더 고민 |
+
+---
+
+## 8. 더 깊이 보고 싶다면
+
+- [`docs/discord_first_redesign_v4_1.md`](./discord_first_redesign_v4_1.md) — *왜 이렇게 만들었나* (장문)
+- [`docs/discord_first_redesign_v4_2.md`](./discord_first_redesign_v4_2.md) — 확정 컨셉 요약 (단문)
+- [`docs/DEPLOY.md`](./DEPLOY.md) — Discord 봇 운영
+- [`bench/ecommerce_demo.py`](../bench/ecommerce_demo.py) — federation/safety 라이브 데모
+- 테스트가 사실상 사양서 — `tests/test_*.py`를 *모듈별 가이드*로 활용
+
+---
+
+질문/제안은 [Discord](https://discord.gg/EPurkHVtp2) 또는 GitHub Issues 환영.
diff --git a/docs/BaseComponent_ko.md b/docs/BaseComponent_ko.md
deleted file mode 100644
index 534be30..0000000
--- a/docs/BaseComponent_ko.md
+++ /dev/null
@@ -1,248 +0,0 @@
-# BaseComponent
-
-`BaseComponent`는 **define-by-run(순수 파이썬 제어)** 철학을 유지하면서도, 컴포넌트 실행을 **관측 가능(observable)** 하게 만들기 위한 **선택적(opt-in) 표준 레이어**입니다.
-
-* 파이프라인은 그냥 함수/콜러블만으로도 충분히 동작합니다.
-* `BaseComponent`는 그 위에 **추적(hooks), 에러 표준화, 이름/형식 통일**을 얹어주는 역할을 합니다.
-
-즉, **필수는 아니지만**, 라이브러리/팀 단위 개발에서 "운영 가능한 형태"로 만들고 싶을 때 유용합니다.
-
----
-
-## 왜 필요한가?
-
-### 1) 관측성(Tracing)을 "그래프 엔진 없이" 얻기 위해
-
-Lang2SQL은 그래프 엔진을 강제하지 않습니다. 대신:
-
-* 사용자는 Python `if/for/while`로 제어한다.
-* 라이브러리는 관측성은 **hook 이벤트**로 제공한다.
-
-`BaseComponent`는 각 컴포넌트 실행의 `start/end/error`를 이벤트로 남깁니다.
-
-### 2) 에러를 "도메인 친화적으로" 정리하기 위해
-
-현실에서는 `ValueError`, `KeyError`, 외부 라이브러리 예외 등이 섞여서 올라옵니다.
-
-`BaseComponent`는:
-
-* `Lang2SQLError`(ValidationError, IntegrationMissingError 등)는 **그대로 유지**
-* 그 외 예외는 `ComponentError`로 **표준 래핑**(+ 원인 예외를 `cause`로 보존)
-
-→ 사용자/운영자 관점에서 "어디서 터졌는지"가 분명해집니다.
-
-### 3) "컴포넌트 단위 표준"을 만들기 위해
-
-라이브러리 제공 컴포넌트를 모두 BaseComponent 기반으로 만들면:
-
-* 로그/트레이스의 포맷이 통일
-* 테스트/디버깅 경험이 일정
-* 문서/타입 힌트가 일관
-
----
-
-## BaseComponent가 제공하는 API
-
-### 생성자
-
-```python
-BaseComponent(name: str | None = None, hook: TraceHook | None = None)
-```
-
-* `name`: 이벤트에 찍힐 컴포넌트 이름 (기본값: 클래스명)
-* `hook`: 이벤트 수신자. 기본값은 `NullHook()` (아무것도 하지 않음)
-
-### 구현해야 하는 것: `_run()`
-
-서브클래스는 `_run()`을 구현합니다. 인자 타입과 반환 타입은 각 컴포넌트에 맞게 자유롭게 정의합니다.
-
-```python
-class MyRetriever(BaseComponent):
-    def __init__(self, catalog: list, **kwargs):
-        super().__init__(**kwargs)
-        self._catalog = catalog
-
-    def _run(self, query: str) -> list[dict]:
-        # 비즈니스 로직
-        return [t for t in self._catalog if query in t["description"]]
-```
-
-### 호출: `run()` / `__call__`
-
-`comp.run(query)` 또는 `comp(query)`를 호출하면 내부적으로 아래를 자동 수행합니다.
-
-* `component.run start 이벤트 발행`
-* `self._run(...)` 실행
-* 성공 시 `end 이벤트` + `duration_ms`
-* 실패 시 `error 이벤트`
-
-  * 도메인 예외(`Lang2SQLError`)는 그대로 raise
-  * 그 외 예외는 `ComponentError`로 래핑해서 raise
-
----
-
-## 타입 인자 패턴
-
-Lang2SQL의 컴포넌트는 **명시적 타입 인자**를 받고, **명시적 타입 결과**를 반환합니다.
-
-```python
-# 라이브러리 내장 컴포넌트 시그니처 예시
-KeywordRetriever._run(query: str) -> list[CatalogEntry]
-SQLGenerator._run(query: str, schemas: list[CatalogEntry], context: str = "") -> str
-SQLExecutor._run(sql: str) -> list[dict]
-```
-
-### 구성(config)은 `__init__`에, 요청별 데이터는 `_run()` 인자에
-
-```python
-class SQLGenerator(BaseComponent):
-    def __init__(self, llm: LLMPort, db_dialect: str = "default", **kwargs):
-        super().__init__(**kwargs)
-        self._llm = llm           # 고정 설정
-        self._dialect = db_dialect
-
-    def _run(self, query: str, schemas: list[CatalogEntry], context: str = "") -> str:
-        # 요청마다 달라지는 값은 _run() 인자로 받는다
-        ...
-```
-
----
-
-## 언제 BaseComponent를 쓰는가?
-
-### BaseComponent를 쓰는 게 좋은 경우
-
-* 라이브러리 기본 제공 컴포넌트(retriever/generator/executor)
-* 팀/제품 환경에서 **관측성(트레이싱)이 필요한 경우**
-* 예외 표준화가 중요한 경우(운영/테스트/디버깅)
-
-### BaseComponent 없이 함수로 두는 게 좋은 경우
-
-* `policy`, `eval`, metric 계산처럼 **순수 함수 성격**이 강한 로직
-* "유저가 빠르게 붙여 넣어 쓰는" 초경량 커스텀 로직
-* 실행 단위가 너무 작아 이벤트가 과도해지는 경우
-
-즉, **핵심 파이프라인 축**은 BaseComponent로 잡고,
-그 외의 작은 로직은 함수로 두는 혼합형이 가장 자연스럽습니다.
-
----
-
-## 커스텀 컴포넌트 예시
-
-```python
-from lang2sql.core.base import BaseComponent
-
-class UpperCaseSQL(BaseComponent):
-    """SQL을 대문자로 변환하는 후처리 컴포넌트."""
-    def _run(self, sql: str) -> str:
-        return sql.upper()
-
-upper = UpperCaseSQL()
-print(upper.run("select 1"))  # SELECT 1
-```
-
-hook을 주입하면 실행 추적도 자동으로 됩니다:
-
-```python
-from lang2sql import MemoryHook
-
-hook = MemoryHook()
-upper = UpperCaseSQL(hook=hook)
-upper.run("select 1")
-
-for e in hook.snapshot():
-    print(e.component, e.phase, e.duration_ms)
-# UpperCaseSQL start 0.0
-# UpperCaseSQL end   0.1
-```
-
----
-
-## 훅(Tracing) 시스템
-
-### Hook이란?
-
-컴포넌트/플로우 실행 시점에 **이벤트(Event)** 를 받는 인터페이스입니다.
-
-* `start/end/error` 시점 기록
-* 소요 시간(duration_ms)
-* 입력/출력 요약(input_summary/output_summary)
-
-### 어디서 확인하나?
-
-가장 쉬운 건 `MemoryHook`입니다.
-
-```python
-from lang2sql import MemoryHook, HybridNL2SQL
-
-hook = MemoryHook()
-pipeline = HybridNL2SQL(catalog=catalog, llm=llm, db=db, embedding=embedding, hook=hook)
-pipeline.run("지난달 매출")
-
-for e in hook.snapshot():
-    print(e.phase, e.component, e.duration_ms, e.error)
-```
-
-### 운영용 관측성은 어디서 제어하나?
-
-운영에서는 `MemoryHook` 대신 다음이 일반적입니다.
-
-* 로그로 흘리는 Hook (stdout / JSON log)
-* APM/Tracing으로 보내는 Hook (OpenTelemetry span 등)
-* 필터링 Hook (특정 컴포넌트만 샘플링)
-
-핵심은: **관측성은 hook 구현체에서 제어**하고, 파이프라인/컴포넌트 코드는 최대한 "비즈니스 로직"만 갖도록 분리합니다.
-
----
-
-## 중첩(서브플로우/래핑)하면 트레이싱이 깨지나?
-
-"깨진다"기보다는 **이벤트가 더 많이 찍힙니다.**
-
-* `flow_b` 안에 `flow_a`를 step으로 넣으면
-
-  * `flow_b` 이벤트 2개(시작/끝)
-  * `flow_a` 이벤트 2개(시작/끝)
-  * `a1/a2` 컴포넌트 이벤트도 각각 찍힘(컴포넌트가 BaseComponent라면)
-
-이게 싫다면 두 가지 선택지가 있습니다.
-
-1. **상위 레벨(Flow)만 트레이싱하고 내부는 함수로 둔다**
-2. **Hook에서 필터링/샘플링한다** (예: component 이름 prefix로 제외)
-
-추가 문법 없이 해결하려면 2번이 가장 현실적입니다.
-
----
-
-## 베스트 프랙티스
-
-### 1) 구성(config)은 `__init__`에, 요청별 데이터는 `_run()` 인자에
-
-고정 설정(모델, 카탈로그, DB 연결 등)은 생성자에서 받고,
-요청마다 달라지는 값(쿼리, 스키마 목록 등)은 `_run()` 인자로 전달합니다.
-
-### 2) `_run()`의 반환값은 명시적으로
-
-반환 타입을 명확히 정의하면 Flow에서 컴포넌트를 조합할 때 안전합니다.
-
-### 3) "작은 로직(policy/eval)은 그냥 함수"
-
-* BaseComponent로 감싸는 건 선택
-* 운영에서 꼭 추적이 필요할 때만 감싼다
-
----
-
-## FAQ
-
-### Q. "그냥 함수만 써도 되는데 왜 굳이 BaseComponent?"
-
-A. **운영/디버깅/협업에서** 차이가 큽니다.
-문제 났을 때 "어디서, 어떤 입력으로, 얼마나 걸리다, 어떤 에러로" 터졌는지 자동으로 남는 게 핵심 가치입니다.
-
-### Q. "BaseComponent를 유저가 직접 써야 하나?"
-
-A. 필수 아닙니다.
-초급 유저는 **프리셋 Flow + 프리셋 컴포넌트**만으로 충분히 쓰게 하고,
-고급/운영 유저에게 BaseComponent/Hook을 제공하는 구성이 가장 자연스럽습니다.
-
----
diff --git a/docs/BaseFlow_ko.md b/docs/BaseFlow_ko.md
deleted file mode 100644
index 9ed9120..0000000
--- a/docs/BaseFlow_ko.md
+++ /dev/null
@@ -1,191 +0,0 @@
-# BaseFlow
-
-`BaseFlow`는 Lang2SQL에서 **define-by-run(순수 파이썬 제어)** 철학을 구현하기 위한 "플로우의 최소 추상화(minimal abstraction)"입니다.
-
-* 파이프라인의 **제어권(control-flow)** 을 프레임워크 DSL이 아니라 **사용자 코드(Python)** 가 갖습니다.
-* 그래프 엔진을 강제하지 않습니다.
-* 대신, 실행 단위를 `Flow`로 묶고 **관측성(hooks)** 과 **에러 규약**을 통일합니다.
-
----
-
-## 왜 필요한가?
-
-### 1) "제어는 파이썬으로"를 지키기 위해
-
-Text2SQL은 현실적으로 다음 제어가 자주 필요합니다.
-
-* 재시도 루프 (`while`, `for`)
-* 조건 분기 (`if`, `match`)
-* 부분 파이프라인(서브플로우) 호출
-* 정책(policy) 기반 행동 결정
-
-`BaseFlow`는 이런 제어를 **사용자가 Python으로 직접 작성**하게 두고, 라이브러리는 "실행 컨테이너 + 관측성"만 제공합니다.
-
-### 2) 요청 단위 관측성(Flow-level tracing)
-
-운영/디버깅에서는 "이 요청 전체가 언제 시작했고, 어디서 실패했고, 얼마나 걸렸는지"가 먼저 중요합니다.
-
-`BaseFlow`는 다음 이벤트를 발행합니다.
-
-* `flow.run` start / end / error
-* 실행 시간(`duration_ms`)
-
-→ 요청 1건을 **Flow 단위로 빠르게 파악**할 수 있습니다.
-
----
-
-## BaseFlow가 제공하는 API
-
-### 1) 구현해야 하는 것: `_run()`
-
-```python
-class MyFlow(BaseFlow):
-    def _run(self, query: str) -> list[dict]:
-        ...
-        return result
-```
-
-* Flow의 본체 로직은 여기에 작성합니다.
-* 제어는 Python으로 직접 작성합니다. (`if/for/while`)
-* 입출력 타입은 자유롭게 정의합니다.
-
-### 2) 호출: `run()` / `__call__`
-
-```python
-out = flow.run("지난달 매출")
-# 또는
-out = flow("지난달 매출")
-```
-
-* 내부적으로 `_run(...)`을 호출합니다.
-* hook 이벤트를 `start/end/error`로 기록합니다.
-
----
-
-## 사용 패턴
-
-### 1) 초급: 프리셋 Flow로 바로 실행
-
-초급 사용자는 보통 "구성만 하고 실행"하면 됩니다.
-
-```python
-pipeline = BaselineNL2SQL(catalog=catalog, llm=llm, db=db)
-rows = pipeline.run("지난달 매출")
-```
-
-### 2) 고급: CustomFlow로 제어(while/if/policy)
-
-정책/루프/재시도 같은 제어가 들어오면 `BaseFlow`를 직접 상속해 작성하는 것이 가장 깔끔합니다.
-
-```python
-class RetryFlow(BaseFlow):
-    def _run(self, query: str) -> str:
-        for _ in range(3):
-            schemas = retriever(query)
-            sql = generator(query, schemas)
-            if validator(sql):
-                return sql
-        return sql
-```
-
----
-
-## Hook(Tracing)은 어디서 확인하나?
-
-Flow도 hook을 받을 수 있습니다.
-
-```python
-from lang2sql import MemoryHook, BaselineNL2SQL
-
-hook = MemoryHook()
-pipeline = BaselineNL2SQL(catalog=catalog, llm=llm, db=db, hook=hook)
-
-rows = pipeline.run("지난달 매출")
-
-for e in hook.snapshot():
-    print(e.name, e.phase, e.component, e.duration_ms, e.error)
-```
-
-운영에서는 `MemoryHook` 대신 로그/OTel/필터링 훅을 사용합니다.
-관측성 제어는 **hook 구현체에서** 담당하고, Flow 코드는 비즈니스 로직에 집중하도록 분리합니다.
-
----
-
-## (관련 개념) BaseFlow와 BaseComponent의 관계
-
-* `BaseFlow`는 "어떻게 실행할지(제어/조립)"를 담당합니다.
-* `BaseComponent`는 "한 단계에서 무엇을 할지(작업 단위)"를 담당합니다.
-
-일반적으로:
-
-* **Flow는 여러 Component를 호출**합니다.
-* **전용 Flow(BaselineNL2SQL 등)는 Component 간 와이어링을 내부에서 처리**합니다.
-
-즉, **Flow가 상위 레벨 오케스트레이션**, Component가 **재사용 가능한 부품**입니다.
-
----
-
-## SequentialFlow의 알려진 제한
-
-`SequentialFlow`는 `value = step(value)` 단일 값 전달 방식으로 동작합니다.
-이 설계는 단순한 변환 체인에는 적합하지만, NL2SQL 파이프라인에서 다음 한계가 있습니다.
-
-### 문제 1: 컨텍스트 소실
-
-파이프라인이 진행되면서 초기 입력(`query`)이 중간 단계 출력으로 대체되어 사라집니다.
-
-```python
-flow.run("주문 내역 확인")
-↓
-retriever("주문 내역 확인")  →  list[CatalogEntry]
-↓
-generator(list[CatalogEntry])  # ← 여기서 original query가 없음
-↓
-TypeError 또는 잘못된 결과
-```
-
-### 문제 2: 다중 인자 컴포넌트와 호환 불가
-
-`SQLGenerator._run(query, schemas)`처럼 2개 이상의 인자를 받는 컴포넌트는
-`SequentialFlow`의 단일 값 전달로 연결할 수 없습니다.
-
-```python
-# ❌ 동작하지 않음 — generator는 (query, schemas) 2개 인자가 필요
-flow = SequentialFlow(steps=[retriever, generator, executor])
-flow.run("주문 내역")  # TypeError: _run() missing 1 required positional argument: 'schemas'
-```
-
-### 해결 방법
-
-NL2SQL 파이프라인은 `SequentialFlow` 대신 **전용 Flow**를 사용하세요.
-전용 Flow는 내부에서 다중 인자 와이어링을 올바르게 처리합니다.
-
-```python
-# KeywordRetriever 기반
-pipeline = BaselineNL2SQL(catalog=catalog, llm=llm, db=db)
-
-# Keyword + Vector 기반
-pipeline = HybridNL2SQL(catalog=catalog, llm=llm, db=db, embedding=embedding)
-
-# Gate + 프로파일링 + 보강 포함 풀 파이프라인
-pipeline = EnrichedNL2SQL(catalog=catalog, llm=llm, db=db, embedding=embedding)
-
-rows = pipeline.run("주문 내역")
-```
-
-`SequentialFlow`는 단일 값 변환 체인(예: 텍스트 전처리, 단계별 필터링)에 적합합니다.
-
----
-
-## FAQ
-
-### Q. BaseFlow가 필수인가?
-
-A. Flow라는 개념은 사실상 필요하지만, **모든 사용자가 BaseFlow를 직접 상속할 필요는 없습니다.**
-
-* 초급: 프리셋 Flow(`BaselineNL2SQL`, `HybridNL2SQL`, `EnrichedNL2SQL`)만 사용
-* 고급: `BaseFlow`를 상속해서 제어를 직접 작성
-
-### Q. Flow의 반환 타입은?
-
-A. `_run()`의 입출력 타입은 자유롭습니다. 컴포넌트끼리 합의한 타입을 그대로 사용하면 됩니다.
diff --git a/docs/Core_concept_ko.md b/docs/Core_concept_ko.md
deleted file mode 100644
index 13b9f00..0000000
--- a/docs/Core_concept_ko.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# Core Concepts
-
-Lang2SQL은 "그래프 엔진/DSL"을 강제하지 않고, **순수 Python 코드로 파이프라인을 제어**하는 define-by-run 철학을 따릅니다.
-각 컴포넌트는 **명시적 타입 인자**를 받고, 명시적 타입 결과를 반환합니다.
-
----
-
-## 1) Define-by-run: 제어는 Python으로
-
-Lang2SQL에서 파이프라인 제어는 프레임워크가 아니라 **사용자 코드가 가집니다.**
-
-* 분기: `if / match`
-* 반복/재시도: `for / while`
-* 조건부 실행: policy 기반 action
-* 서브플로우: flow를 step처럼 호출
-
-예시:
-
-```python
-retriever = KeywordRetriever(catalog=catalog)
-generator = SQLGenerator(llm=llm, db_dialect="sqlite")
-
-while True:
-    schemas = retriever.run(query)
-    sql = generator.run(query, schemas)
-    if validator(sql):
-        break
-
-rows = executor.run(sql)
-```
-
-**핵심:** Lang2SQL은 위 패턴을 "프레임워크 문법"으로 바꾸지 않습니다.
-그냥 Python으로 쓰되, 각 컴포넌트의 입출력이 **타입으로 명확히 정의**되어 있어 안전하게 조합할 수 있습니다.
-
----
-
-## 2) 타입 인자 패턴
-
-Text2SQL 파이프라인은 현실적으로 단계가 늘어납니다.
-
-* retriever 1개가 아니라 10개, 100개가 될 수 있음
-* 중간 산출물(선택된 테이블, 컨텍스트, 후보 SQL, 검증 결과, 점수/메트릭)이 늘어남
-* loop/branch가 들어가면서 "어떤 단계에서 무엇이 생성되었는지" 추적이 어려워짐
-
-Lang2SQL은 각 컴포넌트의 `_run()` 메서드가 **명시적 타입 인자를 받고 타입 결과를 반환**하도록 설계합니다.
-
-```
-KeywordRetriever._run(query: str) -> list[CatalogEntry]
-SQLGenerator._run(query: str, schemas: list[CatalogEntry], context: str) -> str
-SQLExecutor._run(sql: str) -> list[dict]
-```
-
-이 방식의 장점:
-
-* 각 컴포넌트의 입출력이 코드에 명확히 드러남
-* IDE 자동완성과 타입 체크를 활용할 수 있음
-* 컴포넌트를 독립적으로 테스트하기 쉬움
-
-### 컴포넌트 간 데이터 전달
-
-컴포넌트 간 와이어링은 **전용 Flow가 내부에서 처리**합니다.
-
-```python
-# BaselineNL2SQL._run() 내부 구현
-def _run(self, query: str) -> list[dict]:
-    schemas = self._retriever(query)        # list[CatalogEntry]
-    sql = self._generator(query, schemas)   # str
-    return self._executor(sql)              # list[dict]
-```
-
-사용자 관점에서는 Flow의 `run()` 하나만 호출하면 됩니다:
-
-```python
-rows = pipeline.run("지난달 매출")
-```
-
----
-
-## 3) 컴포넌트 vs 플로우
-
-| | BaseComponent | BaseFlow |
-|---|---|---|
-| 역할 | 단일 작업 단위 (검색, 생성, 실행) | 여러 컴포넌트의 조합/제어 |
-| 구현 | `_run()` 메서드 | `_run()` 메서드 |
-| 관측성 | `component.run` 이벤트 | `flow.run` 이벤트 |
-| 예시 | `KeywordRetriever`, `SQLGenerator` | `BaselineNL2SQL`, `HybridNL2SQL` |
-
-둘 다 **`_run()`에 비즈니스 로직**을 작성하고, `run()` / `__call__()` 호출 시 자동으로 hook 이벤트를 발행합니다.
-
----
-
-## 권장 규약 요약
-
-* **제어는 Python으로 한다** (define-by-run)
-* **컴포넌트의 입출력은 명시적 타입 인자로 정의한다** (`_run(query: str) -> list[CatalogEntry]`)
-* **구성(config)은 `__init__`에, 요청별 데이터는 `_run()` 인자에**
-* policy/eval처럼 관측성이 불필요한 로직은 **순수 함수로 둬도 된다**
-
----
diff --git a/docs/Hook_and_exception_ko.md b/docs/Hook_and_exception_ko.md
deleted file mode 100644
index ccc9c14..0000000
--- a/docs/Hook_and_exception_ko.md
+++ /dev/null
@@ -1,311 +0,0 @@
-# Hooks (Tracing)
-
-Lang2SQL의 hooks 시스템은 **그래프 엔진 없이도 관측성(observability)을 제공**하기 위한 최소 레이어입니다.
-Flow/Component 실행 과정에서 이벤트를 발행하고, 사용자는 hook 구현체로 이를 수집/출력/전송할 수 있습니다.
-
-핵심 컨셉은 단 하나입니다:
-
-> **“실행 중 무슨 일이 일어났는지(Event)를 hook이 받는다.”**
-
----
-
-## Event
-
-`Event`는 Flow/Component 실행 중 발생한 “관측 단위”입니다.
-
-```py
-@dataclass
-class Event:
-    name: str                 # e.g., "component.run" / "flow.run"
-    component: str            # e.g., "KeywordTableRetriever" / "SequentialFlow"
-    phase: Literal["start", "end", "error"]
-    ts: float                 # unix timestamp
-    duration_ms: Optional[float] = None
-
-    input_summary: Optional[str] = None
-    output_summary: Optional[str] = None
-    error: Optional[str] = None
-
-    data: dict[str, Any] = field(default_factory=dict)
-```
-
-### 필드 의미
-
-* `name`
-
-  * 이벤트 종류를 나타내는 문자열
-  * 예: `"component.run"`, `"flow.run"`
-* `component`
-
-  * 이벤트를 발생시킨 실행 단위 이름
-  * 예: `"KeywordTableRetriever"`, `"SequentialFlow"`
-* `phase`
-
-  * `"start" | "end" | "error"`
-* `ts`
-
-  * 이벤트 발생 시간(Unix timestamp)
-* `duration_ms`
-
-  * `end/error`에서만 주로 채움(실행 시간)
-* `input_summary`, `output_summary`
-
-  * 디버깅을 위한 “사람이 읽기 쉬운” 요약 문자열
-* `error`
-
-  * 실패 시 오류 요약 문자열
-* `data`
-
-  * UI/필터링/테스트/추가 메타를 위한 구조화 payload
-  * 기본은 빈 dict이며, 필요할 때만 채우는 것을 권장합니다.
-
----
-
-## TraceHook
-
-`TraceHook`은 이벤트를 받는 인터페이스입니다.
-
-```py
-class TraceHook(Protocol):
-    def on_event(self, event: Event) -> None: ...
-```
-
-* Lang2SQL의 Flow/Component는 실행 시점에 `hook.on_event(Event(...))` 형태로 이벤트를 발행합니다.
-* hook은 **옵션**이며, 없으면 `NullHook`이 사용됩니다.
-
----
-
-## 기본 Hook 구현체
-
-### NullHook
-
-```py
-class NullHook:
-    def on_event(self, event: Event) -> None:
-        return
-```
-
-* 기본값
-* 아무 것도 하지 않습니다.
-* hook 비용을 없애고 싶을 때 항상 안전한 기본 구현입니다.
-
-### MemoryHook
-
-```py
-class MemoryHook:
-    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)
-```
-
-* 이벤트를 메모리에 누적합니다.
-* 테스트/디버깅에 가장 유용합니다.
-
-#### MemoryHook 사용 예시
-
-```python
-from lang2sql.core.hooks import MemoryHook
-from lang2sql.flows.baseline import SequentialFlow
-
-hook = MemoryHook()
-flow = SequentialFlow(steps=[...], hook=hook)
-
-out = flow.run("지난달 매출")
-
-for e in hook.snapshot():
-    print(e.name, e.phase, e.component, e.duration_ms, e.error)
-```
-
-#### clear()를 유저가 직접 호출해야 하나?
-
-* 보통은 **테스트에서만** `clear()`가 필요합니다. (케이스 간 이벤트 섞임 방지)
-* 일반 사용자는 보통 “요청 1회 → hook 1개 생성” 패턴으로 충분합니다.
-
-예:
-
-```py
-hook = MemoryHook()
-out = flow.run_query("q")   # 여기서만 쓰고 끝
-events = hook.snapshot()
-```
-
----
-
-## 유틸 함수
-
-### now()
-
-```py
-def now() -> float:
-    return time.time()
-```
-
-* timestamp 생성에 사용됩니다.
-
-### ms()
-
-```py
-def ms(start: float, end: float) -> float:
-    return (end - start) * 1000.0
-```
-
-* duration(ms) 계산에 사용됩니다.
-
-### summarize()
-
-```py
-def summarize(x: Any, max_len: int = 240) -> str:
-    ...
-```
-
-* repr(x)를 기반으로 요약 문자열을 만들고 길이를 제한합니다.
-* 이벤트의 `input_summary/output_summary`에 사용됩니다.
-
----
-
-## 운영(Production)에서는 어떻게 쓰나?
-
-MemoryHook은 테스트용입니다. 운영에서는 보통 다음 형태로 확장합니다.
-
-* `LoggingHook`: JSON 로그로 남기기
-* `OTelHook`: OpenTelemetry span으로 전송
-* `FilteringHook`: 특정 component만 샘플링/필터링
-
-관측성 제어는 **hook 구현체에서** 하고, Flow/Component 로직은 비즈니스에 집중하는 것이 기본 철학입니다.
-
----
-
-# Exceptions
-
-Lang2SQL 예외 시스템은 두 목표를 가집니다.
-
-1. **도메인 에러는 도메인 타입으로 유지**한다.
-2. 외부/일반 예외는 “어디서 터졌는지”가 보이도록 **표준 래핑**한다.
-
----
-
-## Lang2SQLError (Base)
-
-```py
-class Lang2SQLError(Exception):
-    """Base error for lang2sql."""
-```
-
-* Lang2SQL에서 발생하는 모든 도메인 예외의 베이스입니다.
-* `BaseComponent` / `BaseFlow`는 일반적으로 **Lang2SQLError는 그대로 다시 raise**합니다.
-
----
-
-## IntegrationMissingError
-
-```py
-class IntegrationMissingError(Lang2SQLError):
-    def __init__(self, integration: str, extra: str | None = None, hint: str | None = None):
-        ...
-```
-
-### 언제 발생?
-
-* 선택적 의존성(optional integration)이 필요한데 설치되어 있지 않을 때
-
-예:
-
-* `faiss` retriever를 쓰는데 `faiss`가 설치되어 있지 않음
-
-### 메시지 특징
-
-* `extra`가 있으면 설치 힌트를 포함합니다.
-
-예 메시지:
-
-* `Missing optional integration: faiss. Install with: pip install 'lang2sql[faiss]'`
-
----
-
-## ValidationError
-
-```py
-class ValidationError(Lang2SQLError):
-    pass
-```
-
-### 언제 발생?
-
-* SQL 검증 실패, 정책상 금지 쿼리, 스키마 불일치 등
-* “유저 입력/생성 결과가 유효하지 않다”에 해당하는 에러를 담는 대표 도메인 예외
-
----
-
-## ContractError
-
-```py
-class ContractError(Lang2SQLError):
-    """Raised when a component violates a required call/return contract."""
-    pass
-```
-
-### 언제 발생?
-
-* Lang2SQL이 요구하는 호출/반환 계약을 위반했을 때
-* 예: `_run()`이 반드시 반환해야 하는 타입과 다른 값을 반환
-
-이 에러는 “사용자 코드 버그를 빨리 발견(fail-fast)”하기 위한 타입입니다.
-
----
-
-## ComponentError
-
-```py
-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}")
-```
-
-### 목적
-
-* “일반 예외(ValueError, KeyError 등)”를 도메인 레이어로 끌어올 때 사용합니다.
-* 어떤 컴포넌트에서 터졌는지 식별 가능하게 만듭니다.
-
-### cause
-
-* 원본 예외를 보존합니다.
-* 테스트/디버깅에서 error chain을 확인할 수 있습니다.
-
----
-
-## 예외가 Flow/Component에서 어떻게 처리되나?
-
-(현재 BaseComponent 설계 기준)
-
-* `Lang2SQLError` 계열
-
-  * 그대로 이벤트에 기록하고 그대로 raise
-* 그 외 모든 예외
-
-  * 이벤트에 기록하고 `ComponentError(..., cause=e)`로 래핑하여 raise
-
-즉:
-
-* **도메인 예외는 “정상적인 실패”로 취급**
-* **일반 예외는 “버그/예상 밖 실패”로 표준화**
-
----
-
-## 권장 사용 가이드
-
-* “사용자 입력/정책/검증 실패”는 `ValidationError`
-* “의존성 설치 문제”는 `IntegrationMissingError`
-* “계약 위반(반환 타입/호출 규약)”은 `ContractError`
-* “외부 라이브러리/예상 밖 예외”는 `ComponentError`로 래핑되어 올라오는 것을 기본으로 합니다.
-
----
diff --git a/docs/PROJECT.md b/docs/PROJECT.md
new file mode 100644
index 0000000..7b71dd8
--- /dev/null
+++ b/docs/PROJECT.md
@@ -0,0 +1,152 @@
+# Lang2SQL — 프로젝트 SSOT
+
+> *"질문하면 SQL 짜주는 봇이 아니라, 현실의 messy함에 견디는 분석 에이전트."*
+
+이 문서는 이 프로젝트가 *무엇이고, 왜 존재하며, 지금 어디까지 와 있는지*를 **단일하게** 설명합니다. 다른 모든 문서·README·디자인노트는 이 문서를 참조하거나 보충합니다.
+
+---
+
+## 1. 한 줄 정체성
+
+**Lang2SQL**은 *문서로 비즈니스 맥락을 학습하고, 팀별로 시멘틱이 분기되고, 불완전한 DB에서도 답하고, 모든 정의·대화를 기억하는* 오픈소스 분석 에이전트입니다. Phase 1 인터페이스는 **Discord**.
+
+---
+
+## 2. 왜 존재하는가
+
+Vanna AI(~20k★), Wren AI(~12k★), SQLCoder 같은 Text-to-SQL 오픈소스들은 *질문→SQL 파이프라인* 자체는 이미 잘 풉니다. "더 좋은 SQL 생성"은 모델 fine-tuning 싸움이고, 그 영역엔 들어가지 않습니다.
+
+대신 *실무에 넣어보면 진짜 막히는* **현실의 지저분함 4가지**를 다룹니다:
+
+| 약점 | 기존 처리 | 우리 해결 |
+|---|---|---|
+| DB 메타데이터가 비어 있다 | Vanna: 학습 데이터 의존 | ★① **DB 강건성**: safety pipeline + 자동 보강 (V1.5) |
+| 봇이 어제 한 얘기를 못 기억한다 | 대부분 stateless | ★② **Hermes 기억**: 3축 분리(Store/Recall/Extractor) |
+| 비즈니스 정의를 사람이 일일이 입력 | Wren: MDL 수동 | ★③ **Ingestion 매트릭스**: 문서 → 시멘틱 후보 |
+| 같은 *"활성 사용자"*가 팀마다 다르다 | Wren: 단일 MDL → 충돌 | ★④ **Semantic federation**: git-like 분기, 가장 구체적 scope 승리 |
+
+이 4가지는 *비즈니스마다 다르기 때문에 벤치마크가 안 나오는 영역* → 그래서 오픈소스가 안 건드림 → **그래서 기회**.
+
+---
+
+## 3. 무엇을 다르게 하는가 — 4기둥
+
+| 기둥 | 한 줄 | 자세히 |
+|---|---|---|
+| **★① Safety pipeline** | 모든 SQL이 통과해야 하는 *공항 보안 검색대* | layer를 줄 세우는 패턴 — 새 검사(예: AST 검증, 함수 차단)는 한 칸 끼우기 |
+| **★② Memory 3축** | Store/Recall/Extractor 각각 독립 진화 | V1엔 in-memory/inject-all/manual, V1.5엔 SQLite/keyword/auto |
+| **★③ Ingestion matrix** | Source × Extractor 자유 조합 | 파일×LLM이 V1, URL/Notion/DDL은 V1.5+ |
+| **★④ Semantic federation** | git처럼 팀별 정의 분기, *가장 구체적이 승리* | 충돌이 사라짐. Wren의 "한 회사 한 MDL"이 못 푸는 영역 |
+
+**핵심 메타원칙**: 모든 외부 시스템 의존성을 *포트(Protocol)*로 추상화. *어댑터*는 가장자리에만. 그래서 새 LLM / 새 DB / 새 frontend 추가가 *기존 코드 안 건드리고 끼우기*로 끝남.
+
+---
+
+## 4. 지금 어디까지 와 있는가 — 정직한 현황
+
+### ✅ V1 완료 (master에서 동작)
+- **core 포트 11종** — 모든 외부 의존을 Protocol로 추상화
+- **harness** — agent_loop(LLM → tool → 다음 턴), Session, HarnessContext
+- **★①~★④ 4기둥** 최소 구현 — safety 12 회귀, memory 3축, ingestion 매트릭스, federation 3-scope
+- **도구 6종** — run_sql · explore_schema · define_metric · remember · ask_user · ingest_doc
+- **Discord 프론트엔드** — 6개 슬래시 명령 + `/setup` 위저드 (비개발자 DSN-free flow) + bot.py
+- **영속화** — SQLite 시멘틱 store + Fernet 실암호화 secrets
+- **DB 어댑터** — `SqlAlchemyExplorer` 1개로 Postgres/MySQL/Snowflake/BigQuery/DuckDB 커버 + Cloudflare D1 HTTP 어댑터 + `build_explorer(DSN)` 자동 라우팅
+- **106개 자동화 테스트** (safety 회귀 12 포함)
+- **bench 데모** — federation + safety 라이브 시연 (`bench/ecommerce_demo.py`)
+
+### ⚠️ Stub / 미검증
+| 항목 | 상태 |
+|---|---|
+| PostgreSQL 실 연결 | psycopg 어댑터는 있음. 실 PG 테스트 미수행 |
+| 메타데이터 자동 보강 (★①의 핵심 차별점) | V1.5 |
+| 키워드/벡터 recall | V1.5/V2 |
+| LLM 자동 fact 추출 | V1.5 |
+| `/semantic diff`, `/semantic promote` | V1.5 |
+| URL/Notion 문서 입력 | V1.5/V2 |
+| Slack/Web frontend | Phase 2/3 |
+| Audit hash chain | V2 |
+
+---
+
+## 5. 로드맵
+
+```
+V1   ✅  골격 + 4기둥 최소 + Discord 어댑터 + 영속화        ← 지금
+V1.5 →  메타데이터 자동 보강(★①) + 키워드 recall +
+         LLM 자동 fact 추출 + /semantic diff·promote +
+         URL/DDL ingestion + 회귀 강화
+V2   →  벡터 recall + 비용 게이트(EXPLAIN) + Notion MCP +
+         외부 git semantic 동기화 + Slack frontend
+V2.5 →  PostgreSQL 멀티인스턴스 + branch fork/merge UI +
+         Web frontend
+```
+
+각 단계의 디테일은 [`docs/discord_first_redesign_v4_1.md`](./discord_first_redesign_v4_1.md) §3.
+
+---
+
+## 6. 빠른 시작
+
+```bash
+git clone https://github.com/CausalInferenceLab/Lang2SQL.git
+cd Lang2SQL
+uv sync                                  # 기본 deps
+.venv/bin/pytest -q                      # 106 테스트
+.venv/bin/python bench/ecommerce_demo.py # federation + safety 데모
+```
+
+Discord 봇 운영: [`docs/DEPLOY.md`](./DEPLOY.md)
+
+---
+
+## 7. 아키텍처 & 기여
+
+- **아키텍처 한눈 가이드 + 어디 손대면 좋은지**: [`docs/ARCHITECTURE.md`](./ARCHITECTURE.md)
+- **PR 작성 형식**: [`docs/pull_request_guidelines.md`](./pull_request_guidelines.md)
+- **브랜치 전략**: [`docs/branch_guidelines.md`](./branch_guidelines.md)
+
+기여 PR을 가장 받기 쉬운 지점들 (자세한 위치/방법은 ARCHITECTURE.md §5):
+- 새 LLM 어댑터 (`adapters/llm/<provider>.py`)
+- 새 safety layer (`safety/layers/<name>.py`)
+- 새 memory recall 전략 (`memory/recall/<name>.py`)
+- 새 ingestion source (`ingestion/sources/<name>.py`)
+- 새 frontend (`frontends/<platform>/`)
+- 새 도구 (`tools/<name>.py`)
+
+---
+
+## 8. 핵심 설계 결정 (왜 이 길을 택했나)
+
+| 결정 | 이유 |
+|---|---|
+| **백지 재작성** (LangGraph/Streamlit 파이프라인 → ports & adapters 에이전트) | 파이프라인 위에 4기둥을 얹는 것보다, 4기둥을 *전제*로 새로 짓는 게 깔끔함 |
+| **포트 & 어댑터** (콘센트와 가전) | V1엔 단순 구현 1개씩, 어댑터 추가는 기존 코드 안 건드림. *연구실 코드가 곧 제품 layer* |
+| **Discord 1급 frontend, 나머지는 추상** | "오픈소스 분석 봇"의 자연스러운 거주지. Slack/Web은 어댑터 추가 |
+| **"강건성"을 두 축으로 분리** (DB ★① + 시멘틱 ★④) | 실무에선 후자가 더 자주 터진다는 발견. 학계엔 ★①, 거버넌스엔 ★④ |
+| **federation = git-like 분기** | "한 회사 한 정의"는 조직 현실과 충돌. 각자 scope에서 살게 함 |
+| **Read-only를 fail-closed로 강제** | safety pipeline의 whitelist는 SELECT/WITH 외 BLOCK. DROP/INSERT가 모델 환각으로 새는 사고 방지 |
+| **stdlib → 필요 시 lean dep** | 초기엔 의존성 0(urllib OpenAI 어댑터), V1.5에서 cryptography/discord.py만 핀 |
+
+---
+
+## 9. 프로젝트 메타
+
+- **License**: [MIT](https://opensource.org/licenses/MIT)
+- **운영**: [가짜연구소](https://pseudo-lab.com/) 인과추론팀
+- **커뮤니티**: [Discord](https://discord.gg/EPurkHVtp2)
+- **이슈/기능 요청**: [GitHub Issues](https://github.com/CausalInferenceLab/Lang2SQL/issues)
+- **백업**: 옛 v0.3 아키텍처는 `archive/pre-v4.1-rebuild` 태그로 복원 가능
+
+---
+
+## 10. 변천 (간단)
+
+| 시기 | 사건 |
+|---|---|
+| ~v0.3 | LangGraph + Streamlit 파이프라인 (질문→retrieval→gate→generation→execution) |
+| 2026 봄 | **방향 전환**: Vanna/Wren도 이미 잘 푸는 영역에서 경쟁 그만, "현실 robustness"로 이동 |
+| 2026-05 | v4.1 plan 확정 → ports & adapters로 백지 재작성 (PR #227–#230) |
+| (지금) | V1 master 안착. 다음은 V1.5 — ★①의 *진짜 차별점*인 메타데이터 자동 보강 |
+
+— *"더 똑똑한 SQL 생성기가 아니라, 현실의 messy함에 견디는 도구."*
diff --git a/docs/discord_first_redesign.md b/docs/discord_first_redesign.md
deleted file mode 100644
index 0363975..0000000
--- a/docs/discord_first_redesign.md
+++ /dev/null
@@ -1,611 +0,0 @@
-# Lang2SQL — Discord-First 재설계 명세
-
-> **작성일**: 2026-05-18
-> **결정자**: ryan@brain-crew.com
-> **상태**: 골격 승인. §10 잔여 결정 항목 보완 후 Week 1 착수.
-> **범위**: 현 `lang2sql/` 트리를 비우고 본 문서대로 새로 채운다.
-
----
-
-## 0. 제품 한 줄과 타이브레이커
-
-> **"Discord에서 쓰는 read-only · audit-by-default SQL 에이전트.
-> DB는 절대 변하지 않고, 모든 쿼리는 예산/시간/행 한도 안에서 돈다.
-> 대화 맥락은 끊겨도 영속된다."**
-
-설계 충돌 시 우선순위:
-
-1. **Discord UX가 단순한가?**
-2. **DB는 안 깨지는가?**
-3. **맥락은 보존되는가?**
-
-이 3가지에 부정으로 답하는 어떤 추상화도 도입하지 않는다.
-
----
-
-## 1. 확정된 기술 결정
-
-| # | 항목 | 선택 | 근거 |
-|---|---|---|---|
-| 1 | Discord lib | **`discord.py` 2.x** | 가장 성숙·안정. 문서/예제 풍부. 비동기 native. |
-| 2 | DB 엔진 타깃 (V1) | **PostgreSQL** | EXPLAIN·statement_timeout·RO role·sslmode 등 모든 safety 기법이 정직하게 동작. MySQL/SQLite/BigQuery는 V2 이후. |
-| 3 | 세션 영속화 | **Hermes-style write-through** | 매 메시지·매 도구 호출/결과·매 pause·매 audit 항목을 즉시 디스크에 flush. 봇 재시작/배포/네트워크 단절 후 다시 붙어도 동일 맥락 복구. |
-| 4 | 세션 + 자격증명 스토어 | **AES-GCM 암호화된 SQLite 단일 파일** | 트라이얼 단계 무료·무의존. JSON1으로 conversation 저장. 외부 secret manager는 V2 옵션. |
-| 5 | LLM 백엔드 | **OpenAI + NVIDIA NIM 듀얼 어댑터** | NIM endpoint가 OpenAI 호환(`/v1/chat/completions`) → 어댑터 거의 그대로 재사용. 슬래시 명령 `/model` 로 유저 전환. |
-| 6 | Go TUI | **유지 (개발 도구)** | Discord 불가 환경 fallback. `serve.py` NDJSON 인터페이스 유지. |
-| 7 | 차트 출력 | **PNG 첨부 + 자동 페이지네이션** | matplotlib + Pillow. Discord 첨부 한도(무료 8MB/메시지, 임베드 10개)에 맞춰 잘라 보냄. |
-| 8 | 호스팅 (트라이얼) | **소형 VPS** | §1.1 참조. Cloudflare Workers 부적합. |
-
-### 1.1 호스팅 — "Cloudflare 5천원" 건에 대한 솔직한 답
-
-`discord.py` 는 Discord WebSocket gateway에 **계속 붙어 있어야 하는 장기 실행 Python 프로세스**입니다. Cloudflare Workers의 edge runtime은 짧은 HTTP 요청-응답 모델이라 봇 본체를 못 올립니다. (D1/KV/R2 같은 backing store로는 활용 가능하지만 V1 범위 밖.)
-
-**저렴·트라이얼 권장 옵션**
-
-| 옵션 | 비용 | 메모 |
-|---|---|---|
-| **Oracle Cloud Always Free** | $0 (무기한) | ARM Ampere 4코어/24GB. 가성비 최강. 가입 시 카드 검증. |
-| **fly.io shared-cpu-1x** | $0 ~ $5/월 | 256MB로 시작 가능. 한국에서 가까운 region(NRT/SIN). |
-| **Hetzner CX11** | €4.5/월 (~6,500원) | 안정·빠름. 유럽 region. |
-| **본인 PC + 24h on** | $0 | 데모 단계 충분. 동적 IP면 이주 권장. |
-
-**비추**: Cloudflare Workers(런타임 비호환), Heroku 무료(폐지), Render 무료(60s sleep으로 봇 끊김).
-
-### 1.2 "타깃 = 가짜연구소 디스코드?" 정리
-
-질문하신 *"이 타깃이 가짜연구소 디스코드?"* 에 대해: 문서에서 쓴 "타깃"은 **지원할 DB 엔진 종류**(PG vs MySQL vs BQ)를 의미한 것이고, **봇이 초대될 디스코드 커뮤니티**와는 별개입니다.
-
-- **DB 엔진 타깃**: PostgreSQL (확정)
-- **배포 커뮤니티**: 가짜연구소 디스코드라면 그쪽 길드에 봇을 초대하는 별개 결정. V1 코드는 어떤 길드에든 동작하도록 작성.
-
-### 1.3 "자격증명 저장 위치" 풀이
-
-`/connect` 로 입력받는 **DB 사용자명·비밀번호를 어디에 저장하느냐**의 질문입니다.
-
-| 옵션 | 비용 | 강도 | 추천 시점 |
-|---|---|---|---|
-| **로컬 AES-GCM SQLite** | $0 | 마스터키(env) + per-row IV | **V1 — 채택** |
-| AWS Secrets Manager | ~$0.40/secret/월 | 강함 | 다중 인스턴스·SOC2 |
-| GCP Secret Manager | ~$0.06/secret/월 | 강함 | GCP 환경일 때 |
-| HashiCorp Vault | self-host | 강함 | 사내 운영 |
-
-V1은 **로컬 암호화 SQLite**. 마스터키는 `LANG2SQL_MASTER_KEY` env로 주입. 키 분실 시 secrets만 무효화되고 conversation/audit은 그대로 (분리 저장).
-
----
-
-## 2. 상위 아키텍처
-
-```
-                          ┌──────────────────────────────┐
-                          │      DISCORD GATEWAY         │
-                          │  - DM (private analytics)    │
-                          │  - Guild channel / thread    │
-                          │  - Slash commands / modals   │
-                          └───────────────┬──────────────┘
-                                          │ events
-                                          ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                       DISCORD ADAPTER  (1급)                          │
-│  ┌──────────────┐  ┌──────────────┐  ┌───────────────────────────┐  │
-│  │ Onboarding   │  │ SessionRouter│  │ Streaming Renderer        │  │
-│  │  /connect    │  │  key 결정    │  │  토큰 → message edits      │  │
-│  │  modal+test  │  │              │  │  rows  → PNG (페이지)      │  │
-│  └──────────────┘  └──────────────┘  └───────────────────────────┘  │
-│  ┌──────────────┐  ┌──────────────┐  ┌───────────────────────────┐  │
-│  │ Interactive  │  │ Permissions  │  │ DiscordRateLimit          │  │
-│  │  ask_user /  │  │ guild-admin/ │  │  message edit throttle    │  │
-│  │  show_plan   │  │ owner/user   │  │  per-user / per-guild     │  │
-│  │  → buttons   │  │              │  │                           │  │
-│  └──────────────┘  └──────────────┘  └───────────────────────────┘  │
-└───────────────────────────┬──────────────────────────────────────────┘
-                            │ ctx = ContextConcierge.build(session_key, principal)
-                            ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                        TENANCY LAYER                                  │
-│  ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────────┐ │
-│  │TenantRegistry│ │ExplorerCache │ │ EncryptedStore (AES-GCM)     │ │
-│  │key→DBSpec    │ │LRU engines   │ │ secrets + conversation +     │ │
-│  │              │ │              │ │ audit (단일 SQLite)           │ │
-│  └──────────────┘ └──────────────┘ └──────────────────────────────┘ │
-│  ┌──────────────────────────────────────────────────────────────┐   │
-│  │  ContextConcierge — session_key + principal → HarnessContext │   │
-│  └──────────────────────────────────────────────────────────────┘   │
-└───────────────────────────┬──────────────────────────────────────────┘
-                            ▼ ctx
-┌──────────────────────────────────────────────────────────────────────┐
-│                        HARNESS KERNEL                                 │
-│   agent_loop · system_prompt(per turn) · ToolRegistry(ctx 주입)       │
-│   Session(live layer + pending_call + write-through) · Hooks(pub/sub) │
-└──────┬─────────────────────┬──────────────────┬──────────────────────┘
-       │ ports               │                  │
-       ▼                     ▼                  ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                    SAFETY LAYER  (제품의 moat)                        │
-│  L0 connect (RO role, TLS)  →  L1 stmt gate (sqlglot AST)             │
-│  L2 cost gate (EXPLAIN)     →  L3 runtime (timeout/row/byte)          │
-│  L4 audit (append-only)     →  L5 rate limit (token bucket)           │
-└──────┬─────────────────────┬──────────────────┬──────────────────────┘
-       ▼                     ▼                  ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                     OUTBOUND ADAPTERS                                 │
-│  LLM (openai / nvidia-nim)  ·  DB (sqlalchemy-postgres)               │
-│  Secrets (encrypted-sqlite) ·  SessionStore (encrypted-sqlite)        │
-│  AuditSink (sqlite, file)                                             │
-└──────────────────────────────────────────────────────────────────────┘
-```
-
-규칙: 화살표는 **안쪽으로만**. Kernel/Safety는 어댑터를 import하지 않는다. 새 frontend·새 LLM·새 DB·새 session store는 어댑터 한 장 추가로 끝.
-
----
-
-## 3. 세션 전략 — Hermes-style 영속화
-
-### 3.1 session_key 정책
-
-| Discord 컨텍스트 | session_key | DB credential 출처 | 멤버 가시성 | 용도 |
-|---|---|---|---|---|
-| **봇과의 DM** | `dm:{user_id}` | 유저 본인의 비밀저장소 항목 | 본인만 | **개인 분석 (기본 경로)** |
-| **길드 채널(메인)** | `chan:{guild_id}:{channel_id}` | 길드 어드민이 등록한 공용 DB | 채널 멤버 전원 | 팀 공용 대시보드 질의 |
-| **길드 스레드** | `thr:{guild_id}:{channel_id}:{thread_id}` | 상위 채널과 동일 | 스레드 참여자 | 병렬 조사 (1조사 = 1스레드) |
-
-**원칙**:
-- 민감 DB 자격증명은 **DM에서만 받음**. 채널 메시지는 영구히 history에 남고 다른 멤버에게 보임.
-- 위 3가지 외의 session_key는 사용 금지. 변형이 늘면 "누가 무엇을 보는지" 추적 불가.
-- **principal** (= 누가 보낸 메시지인가) 은 session_key와 별개로 매 요청 기록. 공용 채널에서도 audit log에 *"이 쿼리는 누가 시켰는지"* 가 남음.
-
-### 3.2 영속화 메커니즘 (write-through)
-
-```
-User msg ──▶ Discord adapter ──▶ session.append_message(msg)
-                                      │
-                                      ├─▶ in-memory state update
-                                      └─▶ store.write(session_id, msg)   ← 동기, 즉시
-                                              │
-                                              ▼
-                                  AES-GCM SQLite (append-only segment)
-
-Tool call/result ──▶ same path, immediate flush
-Pause(ask_user)   ──▶ pending_call snapshot, immediate flush
-LLM stream delta ──▶ buffered in-mem, flush at chunk boundary
-```
-
-**보장**: 봇 프로세스가 임의 시점에 죽어도 *디스크에 마지막으로 flush된 상태까지는* 안전. 재시작 시:
-
-1. `SessionStore.iter_active(since=now-30d)` 로 미완 세션 로드
-2. `pending_call` 이 있는 세션은 *사용자 답을 기다리는 상태*로 복원
-3. Discord 어댑터가 DM·채널·스레드별로 reattach
-4. 사용자가 다음 메시지를 보내면 **끊김 없이 이어짐** — 이게 Hermes-style 핵심
-
-**저장 모델** (SQLite 스키마):
-
-```sql
--- 세션 메타
-CREATE TABLE sessions (
-  id            TEXT PRIMARY KEY,         -- session_key
-  principal     TEXT NOT NULL,            -- 마지막 발화 유저
-  kind          TEXT NOT NULL,            -- 'dm' | 'channel' | 'thread'
-  db_spec_id    TEXT,                     -- secrets.id 참조
-  created_at    TIMESTAMP NOT NULL,
-  updated_at    TIMESTAMP NOT NULL,
-  pending_call  TEXT,                     -- JSON | NULL
-  closed_at     TIMESTAMP                 -- NULL = active
-);
-
--- 메시지 append-only 로그
-CREATE TABLE messages (
-  id          INTEGER PRIMARY KEY AUTOINCREMENT,
-  session_id  TEXT REFERENCES sessions(id),
-  ts          TIMESTAMP NOT NULL,
-  role        TEXT NOT NULL,              -- system|user|assistant|tool_result
-  content     TEXT,                       -- JSON or text
-  tool_calls  TEXT,                       -- JSON | NULL
-  tool_call_id TEXT,
-  principal   TEXT
-);
-CREATE INDEX ON messages(session_id, id);
-
--- 암호화 자격증명
-CREATE TABLE secrets (
-  id            TEXT PRIMARY KEY,         -- e.g. "user:123:default"
-  owner         TEXT NOT NULL,            -- discord user id
-  label         TEXT,                     -- "prod-pg" 같은 표시명
-  ciphertext    BLOB NOT NULL,            -- AES-GCM
-  iv            BLOB NOT NULL,
-  tag           BLOB NOT NULL,
-  created_at    TIMESTAMP NOT NULL,
-  last_used_at  TIMESTAMP
-);
-
--- 감사 로그 (append-only)
-CREATE TABLE audit (
-  id          INTEGER PRIMARY KEY AUTOINCREMENT,
-  ts          TIMESTAMP NOT NULL,
-  session_id  TEXT,
-  principal   TEXT,
-  kind        TEXT,                       -- 'sql' | 'tool' | 'pause' | 'safety_block'
-  payload     TEXT,                       -- JSON
-  duration_ms INTEGER,
-  status      TEXT                        -- 'ok' | 'error' | 'blocked'
-);
-```
-
-### 3.3 동시성
-
-```
-전역 concurrency       = 무제한 (asyncio)
-per-session            = FIFO 1 (같은 conversation에 동시 LLM 호출 금지)
-per-user rate limit    = token bucket  (LLM 20/min,  DB 60/min)
-per-guild rate limit   = token bucket  (DB 200/min)
-LLM stream             = 세션마다 독립 (한 사용자가 폭주해도 다른 사용자 영향 X)
-```
-
-이 모델이면 **사용자 1000명도 단일 프로세스 처리 가능**. 한 유저가 폭주해도 다른 유저는 영향 없음.
-
----
-
-## 4. DB 연결 온보딩 (Discord-native)
-
-```
-사용자                  Bot (DM)                  Tenancy Layer
-  │                       │                             │
-  │── /connect ──────────▶│                             │
-  │                       │   Modal 출력:                │
-  │                       │   ┌──────────────────┐      │
-  │                       │   │ label: "prod-pg" │      │
-  │                       │   │ host:            │      │
-  │                       │   │ port: 5432       │      │
-  │                       │   │ database:        │      │
-  │                       │   │ user:            │      │
-  │                       │   │ password: [hide] │      │
-  │                       │   │ schema: (opt)    │      │
-  │                       │   │ sslmode: require │      │
-  │                       │   │ ☑ RO 계정인가요? │      │
-  │                       │   └──────────────────┘      │
-  │── submit ────────────▶│                             │
-  │                       │── build url ────────────────│
-  │                       │── encrypt (AES-GCM) ───────▶│ secrets.put
-  │                       │── test SELECT 1 ───────────▶│ Explorer 임시
-  │                       │── probe RO (BEGIN; CREATE TABLE __probe; ROLLBACK)
-  │                       │   - 실패하면 ✅ RO 확정      │
-  │                       │   - 성공하면 ⚠️ 경고 + 명시적 confirm
-  │                       │── SHOW TABLES ──────────────│
-  │                       │◀── ok, 17 tables ───────────│
-  │◀── ephemeral embed ───│                             │
-  │   ✅ Connected to     │                             │
-  │   "prod-pg" (17 tbls) │                             │
-  │   Try: "tables 보여줘"│                             │
-  │                       │                             │
-  │── "tables 보여줘" ───▶│                             │
-  │                       │── agent_loop(ctx) ─────────▶│ kernel
-```
-
-**핵심 결정**:
-- **Modal로만** 자격증명 입력 (슬래시 명령 인자로 URL 받지 않음) → 비밀번호가 채널 로그에 안 남음.
-- **자동 RO 검증**: `BEGIN; CREATE TABLE __probe(x int); ROLLBACK;` 시도 → 실패하면 RO 확정, 성공하면 경고.
-- **`db_url`은 절대 평문 저장 안 함**. AES-GCM 암호화 후 SQLite secrets 테이블에 저장.
-- **여러 DB 등록 가능**: `/connections` 로 목록, `/use <label>` 로 활성 DB 전환, `/disconnect <label>` 로 삭제.
-
-**관련 슬래시 명령**:
-
-| 명령 | 위치 | 동작 |
-|---|---|---|
-| `/connect` | DM | Modal로 새 DB 등록 + 검증 |
-| `/connections` | DM/Channel | 등록된 DB 목록 (label만) |
-| `/use <label>` | DM | 활성 DB 전환 |
-| `/disconnect <label>` | DM | 자격증명 삭제 |
-| `/test` | DM | 현재 활성 DB SELECT 1 |
-| `/safety` | Any | 현재 활성 보호 레이어 embed |
-| `/safety self-test` | Any | 7개 공격 자가 검증 + 결과 embed |
-| `/audit me` | Any | 본인 최근 50건 쿼리 |
-| `/audit export` | DM | CSV DM 첨부 |
-| `/model` | Any | OpenAI / NIM 모델 전환 |
-| `/reset` | DM/Channel | 현 세션 conversation 초기화 (audit는 보존) |
-
----
-
-## 5. DB 강건성 — Safety Layer (제품의 moat)
-
-```
-요청 ─▶ ┌──────────────────────────────────────────────────────┐
-        │ L0 Connection                                         │
-        │  • RO role 권장/검증 (CREATE TABLE 시도 → 실패해야 함)│
-        │  • sslmode=require 강제 옵션                            │
-        │  • 헬스체크 + pool recycle 30분                         │
-        │  • dialect=postgres (V1 고정)                          │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L1 Statement Gate (sqlglot AST)                       │
-        │  • 화이트리스트: SELECT, WITH, EXPLAIN, SHOW           │
-        │  • 서브쿼리/CTE 내부도 검사 (regex 아님)               │
-        │  • 위반 시: ❌ embed "blocked: DROP not allowed"       │
-        │  • audit.kind = 'safety_block'                         │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L2 Cost Gate                                          │
-        │  • EXPLAIN 먼저 → 예상 rows 추출                      │
-        │  • 임계 초과(default: 1M rows) → show_plan            │
-        │  • 사용자가 ✅ 누르면 진행, ❌ 누르면 취소              │
-        │  • 본 단계는 *cost*만 검사, 권한은 L1 통과 후         │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L3 Runtime Caps                                       │
-        │  • statement_timeout = 30s (configurable)             │
-        │  • row_limit = 1000 (truncate + 사용자 알림)          │
-        │  • 위반 시: ⚠️ "timeout @ 30s, partial 0 rows"        │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L4 Audit (append-only)                                │
-        │  • {ts, principal, session_id, sql, rows, ms,         │
-        │     status, error?, safety_path}                      │
-        │  • SQLite, 일 단위 로그 회전, /audit export CSV        │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L5 Rate Limit (token bucket)                          │
-        │  • per-user:  LLM 20/min, DB 60/min                   │
-        │  • per-guild: DB 200/min                              │
-        │  • 위반 시: friendly "잠시 후 다시 시도, 남은 12s"      │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-                          실행 / 응답
-```
-
-### 5.1 어필 — 5가지 무기
-
-1. **`/safety` 슬래시 명령** — 현재 활성 보호 레이어를 embed로 보여줌. 마케팅 페이지가 봇 안에 있는 셈.
-2. **레드팀 자체 시연 GIF** — `DROP TABLE`, `; DELETE`, `pg_sleep(10000)`, `SELECT * FROM huge_table` 4가지를 시도하면 4가지 방식으로 막히는 30초 영상. README 첫 화면.
-3. **공개 audit log 샘플** — "이 쿼리는 이렇게 기록됩니다" JSON. SOC2/ISO27001 친화 메시지.
-4. **`/safety self-test`** — 봇이 자기 자신에게 7개 공격을 시도하고 결과를 embed로 출력. 운영자가 정기 실행 → "녹색 7/7" 스크린샷을 README에 박음.
-5. **벤치마크 페이지** — `lang2sql` vs *"그냥 LLM에 connection 던지기"*. 동일 적대적 10개 프롬프트 대결: 우리 10/10 차단, 비교군 N개 실행. 정량 비교가 가장 강한 어필.
-
-### 5.2 self-test 7개 공격 (Week 2 tests/safety/ 에 위치)
-
-| # | 입력 | 기대 결과 |
-|---|---|---|
-| 1 | `DROP TABLE users` | L1 block |
-| 2 | `; DELETE FROM users; --` (SQL injection in arg) | L1 block |
-| 3 | CTE 안에 INSERT (`WITH x AS (INSERT ...) SELECT...`) | L1 block |
-| 4 | `SELECT pg_sleep(10000)` | L3 timeout 30s |
-| 5 | `SELECT * FROM huge_table` (수십억 rows) | L2 cost gate confirm |
-| 6 | row 50,000개 반환 | L3 row_limit truncate |
-| 7 | 분당 100회 호출 | L5 rate limit |
-
----
-
-## 6. 새 레포 레이아웃
-
-```
-lang2sql/
-├── README.md                      # Discord-first pitch + safety GIF
-├── pyproject.toml
-├── .env.example                   # LANG2SQL_MASTER_KEY, DISCORD_TOKEN, ...
-│
-├── src/lang2sql/
-│   │
-│   ├── core/                      # 순수 타입 + 포트
-│   │   ├── types.py               # Message/ToolCall/ToolResult/Event
-│   │   ├── identity.py            # Principal, GuildRole
-│   │   ├── safety_types.py        # SafetyVerdict, Budget, RateBucket
-│   │   └── ports/
-│   │       ├── llm.py
-│   │       ├── explorer.py        # capabilities() 추가
-│   │       ├── tool.py
-│   │       ├── secrets.py
-│   │       ├── session_store.py
-│   │       ├── audit.py
-│   │       └── hook.py
-│   │
-│   ├── harness/                   # kernel
-│   │   ├── context.py             # HarnessContext
-│   │   ├── session.py             # Session + pending_call + write-through
-│   │   ├── loop.py                # agent_loop (async gen)
-│   │   ├── system_prompt.py       # per-turn rebuild
-│   │   ├── tool_registry.py       # ctx 주입
-│   │   └── prompts/
-│   │
-│   ├── semantic/                  # 기존 자산 이식 (그대로)
-│   │
-│   ├── safety/                    # ★ moat
-│   │   ├── statement_gate.py      # sqlglot AST
-│   │   ├── cost_gate.py           # EXPLAIN → budget
-│   │   ├── runtime.py             # timeout / row / bytes
-│   │   ├── audit.py               # append-only logger
-│   │   ├── rate_limit.py          # token bucket
-│   │   ├── ro_probe.py            # RO 검증
-│   │   └── self_test.py           # /safety self-test 7개
-│   │
-│   ├── tools/                     # ctx-aware
-│   │   ├── explore_schema.py
-│   │   ├── run_sql.py             # → safety 통과 후 실행
-│   │   ├── write_sql.py
-│   │   ├── define_metric.py
-│   │   ├── define_dimension.py
-│   │   ├── define_relationship.py
-│   │   ├── search_semantic.py
-│   │   ├── ask_user.py
-│   │   ├── show_plan.py
-│   │   ├── visualize.py           # rows → PNG (페이지네이션 메타)
-│   │   ├── explain_query.py
-│   │   └── load_skill.py
-│   │
-│   ├── skills/                    # SkillRegistry (현재 자산 이식)
-│   │
-│   ├── tenancy/                   # 멀티유저
-│   │   ├── concierge.py           # ContextConcierge.build()
-│   │   ├── tenant_registry.py     # key → DBSpec
-│   │   ├── explorer_cache.py      # LRU SQLAlchemy engines
-│   │   ├── session_queue.py       # per-session FIFO
-│   │   └── encrypted_store.py     # AES-GCM SQLite
-│   │
-│   ├── discord/                   # ★ 1급 frontend
-│   │   ├── bot.py                 # discord.py 진입점
-│   │   ├── commands/
-│   │   │   ├── connect.py         # /connect Modal
-│   │   │   ├── connections.py     # /connections /use /disconnect
-│   │   │   ├── safety.py          # /safety, /safety self-test
-│   │   │   ├── audit.py           # /audit me /audit export
-│   │   │   ├── model.py           # /model (openai/nim)
-│   │   │   ├── reset.py           # /reset
-│   │   │   └── help.py
-│   │   ├── session_router.py      # msg → session_key
-│   │   ├── streaming.py           # 토큰 → message edit (throttle 1s)
-│   │   ├── interactive.py         # ask_user → Button view
-│   │   ├── render.py              # rows → PNG (페이지 분할)
-│   │   ├── permissions.py         # guild admin 검사
-│   │   └── recovery.py            # 부팅 시 active session reattach
-│   │
-│   ├── adapters/
-│   │   ├── llm/
-│   │   │   ├── openai_.py
-│   │   │   └── nvidia_nim.py      # OpenAI-호환 엔드포인트
-│   │   ├── db/
-│   │   │   └── postgres_explorer.py
-│   │   ├── secrets/
-│   │   │   └── encrypted_sqlite.py
-│   │   ├── session_store/
-│   │   │   └── encrypted_sqlite.py
-│   │   └── audit_sink/
-│   │       └── sqlite.py
-│   │
-│   └── frontends_dev/             # 부수 (개발 도구)
-│       ├── cli.py                 # 단발 질의 디버그
-│       └── serve.py               # NDJSON (tui-go 유지용)
-│
-├── tui-go/                        # 유지 — Discord 불가 시 fallback
-├── tests/
-│   ├── safety/                    # 7개 공격 회귀 (CI 차단 기준)
-│   ├── harness/
-│   ├── discord/                   # discord mock
-│   └── tenancy/
-└── docs/
-    ├── discord_first_redesign.md  # ← 본 문서
-    ├── DB_ROBUSTNESS.md           # ★ trust page
-    ├── DISCORD_ONBOARDING.md      # 사용자 가이드
-    └── DEPLOY.md
-```
-
----
-
-## 7. 마이그레이션 — 5주 plan
-
-```
-Week 1 ─ Foundation                                         [PR-1]
-   • core/ports/* 정의
-   • HarnessContext + Session(pending_call) + Tool(ctx, **args)
-   • agent_loop + per-turn system prompt
-   • semantic/* 그대로 이식
-   • frontends_dev/cli.py 로 kernel 검증
-
-Week 2 ─ Safety Layer                                       [PR-2]
-   • statement_gate (sqlglot)
-   • runtime (timeout/row)
-   • audit (sqlite sink)
-   • ro_probe
-   • self_test 7개 + tests/safety/ (CI gate)
-
-Week 3 ─ Tenancy + Persistence (Hermes-style)               [PR-3]
-   • encrypted_sqlite (AES-GCM)  — secrets + sessions 통합
-   • SessionStore.write_through() + iter_active()
-   • tenant_registry + explorer_cache
-   • session_queue
-   • ContextConcierge
-
-Week 4 ─ Discord (product 본체)                              [PR-4]
-   • bot 진입 + /connect Modal + onboarding 전 플로우
-   • session_router (DM/channel/thread)
-   • recovery.py (부팅 reattach)
-   • streaming + interactive (Button view)
-   • render (PNG 페이지네이션)
-   • /safety, /audit, /connections, /use, /model
-
-Week 5 ─ Polish + Appeal                                    [PR-5]
-   • /safety self-test 자동 데모
-   • README GIF + benchmark
-   • DB_ROBUSTNESS.md trust page
-   • cost_gate (EXPLAIN) — 마지막
-   • DEPLOY.md (Oracle Cloud / fly.io)
-```
-
-각 PR은 별 브랜치(`feature/week{N}-*`)로 끊고, 직전 PR이 master에 머지된 후 다음 시작.
-
----
-
-## 8. 차트 페이지네이션 (질문 #4 상세)
-
-요구: 행이 많을 때 한 메시지에 다 넣으면 Discord 한도(첨부 8MB, 이미지 1장 권장)에 걸림.
-
-```
-DataEvent (rows=N) ──▶ discord/render.py
-                          │
-                          ├─▶ row 수에 따라 분할:
-                          │     ≤ 50 rows   → 텍스트 embed 1개
-                          │     ≤ 500 rows  → PNG table 1장
-                          │     > 500 rows  → 50행씩 PNG 페이지 K장
-                          │
-                          ├─▶ K장이 ≤ 10이면: 한 메시지에 attachments=[png1..pngK]
-                          │
-                          └─▶ K > 10이면: View(buttons=[◀ Prev, Next ▶, ⇩ CSV])
-                                            └─ 누를 때마다 message.edit(file=...)
-                                            └─ CSV 버튼 = 전체 CSV를 DM 첨부
-
-차트(시각화) 도 동일 패턴:
-  - 단일 line/bar → 1장
-  - facet (예: 카테고리별 시계열) → 카테고리당 1장, 페이지 버튼
-```
-
-`tools/visualize.py` 가 `VizSpec` 을 반환하고, Discord 어댑터의 `render.py` 가 spec → PNG 변환·페이지 분할 책임을 짐. **렌더링은 discord 레이어에만 존재** → 다른 frontend(TUI 등)는 spec을 받아 자기 방식으로 렌더링.
-
----
-
-## 9. LLM 멀티 백엔드 — OpenAI + NVIDIA NIM
-
-```
-adapters/llm/openai_.py        — 기존 자산 그대로
-adapters/llm/nvidia_nim.py     — base_url 만 https://integrate.api.nvidia.com/v1
-                                  api_key 만 NVIDIA_API_KEY
-                                  나머지는 OpenAI 클라이언트 그대로 재사용
-```
-
-**`/model` 명령** — Modal/Select로 모델 선택:
-
-| Provider | 노출 후보 (디폴트는 V1에서 1개씩 고정) |
-|---|---|
-| OpenAI | `gpt-4.1`, `gpt-4.1-mini`, `o4-mini` |
-| NVIDIA NIM | `meta/llama-3.1-70b-instruct`, `mistralai/mixtral-8x22b-instruct-v0.1`, `nvidia/llama-3.1-nemotron-70b-instruct` |
-
-선택은 **세션 스코프** (DM 세션마다 다른 모델 가능). audit log에 `model_id` 기록.
-
-비용 관점:
-- OpenAI: per-token, 디폴트는 `gpt-4.1-mini` (저렴) 추천
-- NIM: NVIDIA 계정 무료 크레딧 사용 → 떨어지면 자비. `/model` 로 OpenAI 폴백 유도.
-- 향후 글로벌 per-user 토큰 캡 추가 가능 (Week 5+).
-
----
-
-## 10. 잔여 결정 (Week 1 착수 전 답이 있으면 좋음)
-
-1. **NIM 모델 디폴트** — 70B Llama vs Mixtral 8x22B? (Mixtral이 tool-calling 안정성↑)
-2. **첫 배포 길드** — 가짜연구소 디스코드인가, 본인 테스트 길드인가? 부팅 시 길드 화이트리스트 적용 여부.
-3. **세션 retention** — 비활성 세션을 며칠까지 보존? (현재 §3.2 가정: 30일 후 archive로 이동, 90일 후 삭제)
-4. **audit retention** — 동일 질문, 별도 정책 필요? (compliance 관점)
-5. **`/reset` 시 audit 보존 여부** — 기본 보존 권장(이미 기록된 건 지우면 안 됨), 사용자에게 명시.
-
-위 5개는 Week 1 코드에 직접 영향 없으므로 그동안 답변 받으면 됩니다.
-
----
-
-## 11. 한 줄 요약
-
-> **kernel/safety 레이어를 hexagonal로 굳히고, Discord adapter와 SafetyLayer를 1급 영역으로 격상하고, Hermes-style write-through로 세션을 영속화한다. 그 외 모든 것은 어댑터 한 장.**
-
-— end —
diff --git a/docs/discord_first_redesign_v2.md b/docs/discord_first_redesign_v2.md
deleted file mode 100644
index 7d9acd7..0000000
--- a/docs/discord_first_redesign_v2.md
+++ /dev/null
@@ -1,911 +0,0 @@
-# Lang2SQL — Discord-First 재설계 명세 (v2)
-
-> **작성일**: 2026-05-18
-> **결정자**: ryan@brain-crew.com
-> **상태**: v1 → v2. Codex 독립 리뷰 + Agent Teams 다각도 audit (devils-advocate / discord-fact-checker / pg-safety-expert / codebase-reality) 결과 반영.
-> **선행 문서**: `docs/discord_first_redesign.md` (v1, 보존)
-> **범위**: 현 `lang2sql/` 트리를 **evolve in place** 로 점진 재구성 (v1의 "wipe and rebuild" 어조는 폐기. §6 참조.)
-
----
-
-## v1 → v2 변경 요약
-
-| # | 영역 | v1 | v2 |
-|---|---|---|---|
-| 1 | **타이브레이커** | 3축 (UX·무결성·맥락) | **4축** — *의미적 정확성* 추가 |
-| 2 | **§3 세션** | DM/채널/스레드 모두 V1 | **V1은 DM-only**, 채널/스레드는 V2 (allowlist 이후) |
-| 3 | **§3.2 영속화** | 모든 토큰 delta 까지 flush | **6개 이벤트만 append-log**, stream chunk 복구 폐기 |
-| 4 | **§3.2 스키마** | sessions/messages/secrets/audit | **+ `persistent_views`** 추가 (Discord 재시작 복구 필수) |
-| 5 | **§4 `/connect`** | 7+필드 Modal + `password: [hide]` | **connection string 1필드 Modal** (Discord Modal은 5필드 한도·password style 미지원) |
-| 6 | **§4 RO probe** | `CREATE TABLE __probe; ROLLBACK` | **catalog 쿼리** (`information_schema.role_table_grants` + `pg_has_role`) |
-| 7 | **§4 writable 계정** | "경고 + confirm" | **거부 + 도우미 명령** `/grant-readonly` 가 role 생성 SQL 제공 |
-| 8 | **§5 L0 Connection** | "RO role 권장" | **dedicated `lang2sql_readonly` role + 5개 GUC SET (`temp_file_limit='1GB'` 단위 필수)** |
-| 9 | **§5 L1 Statement Gate** | sqlglot 화이트리스트만 | **catalog-driven function denylist (OID 매칭) + `EXPLAIN ANALYZE + DML` 거부 + `COPY` 전면 차단** |
-| 10 | **§5 L2 Cost Gate** | EXPLAIN row → 차단 | **EXPLAIN 다중 metric → 경고+사용자 confirm**. 실제 차단은 L3 runtime cap에 위임 |
-| 11 | **§5 L3 Runtime** | timeout/row만 명시 | **5개 GUC 모두 명시** (statement_timeout, lock_timeout, idle_in_transaction_session_timeout, temp_file_limit, search_path) + `row_security=on` |
-| 12 | **§5 L4 Audit** | hash chain 암시 | hash chain은 V2 로 명시 이전. V1은 append-only SQLite + 일 회전 |
-| 13 | **§7 일정** | 5주, Week 4 과적재 + cost_gate Week 5 모순 | **6 PR (PR-0 + 5주 + 0.5주 여유)**. Week 4 분할, cost_gate는 Week 2 (경고 모드)로 이동 |
-| 14 | **§6 레이아웃** | 신규 트리 (wipe) | **evolve in place**. 보존/포팅/삭제 표로 명시 |
-| 15 | **§9 NIM** | "OpenAI 호환 그대로 재사용" | **contract test 통과 후 experimental** 노출, OpenAI가 V1 디폴트 |
-| 16 | **§1.4 (신규)** | — | **Discord 플랫폼 제약** 단독 절: Modal 5필드, Interaction token 15분, autocomplete 3s, persistent view 요건 |
-| 17 | **§2 다이어그램** | sync 추정 | **async boundary 명시** — `create_async_engine(asyncpg)` |
-| 18 | **§ToolResult 규약 (신규 §3.4)** | `data: Any` | **canonical shape** (`{kind, payload, meta}`) — write-through 영속화 호환 |
-| 19 | **§11 (신규)** | — | Codex 지적 중 **거부한 항목**과 사유 (transparency) |
-| 20 | **§10 (개편)** | open questions | **V1 명시적 제외 = V2+** 항목 정리 |
-
----
-
-## 0. 제품 한 줄과 타이브레이커
-
-> **"Discord에서 쓰는 read-only · audit-by-default SQL 에이전트.
-> DB는 절대 변하지 않고, 답은 의미적으로 검증 가능하며, 모든 쿼리는 예산/시간/행 한도 안에서 돈다.
-> 대화 맥락은 끊겨도 영속된다."**
-
-설계 충돌 시 우선순위 (위에서 아래 = 강함):
-
-1. **DB 무결성** — 쓰기·부작용·자원 hog가 발생하지 않는가
-2. **의미적 정확성** — 답이 문법적으로만이 아니라 *의미적으로* 맞는가
-3. **Discord UX 단순성** — 한 번의 입력으로 가능한 결과를 얻는가
-4. **맥락 보존** — 끊겨도 이어지는가
-
-`v1`은 "DB는 안 깨지는가?"만 다뤘다. NL→SQL 제품의 진짜 위험은 *"안전하게 실행되어 자신 있게 틀린 결론을 답하는 것"* — 정확성 축이 v2의 핵심 추가.
-
-타이브레이커는 **3축에서 4축으로**. (Codex의 5축 권고는 솔로 트라이얼에 과한 product framing이라 거부 — 4축이 적정 균형.)
-
----
-
-## 1. 확정된 기술 결정
-
-| # | 항목 | 선택 | 근거 |
-|---|---|---|---|
-| 1 | Discord lib | **`discord.py` 2.x** | 가장 성숙. 단, Modal 5필드 한도·Interaction token 15분 만료 등 §1.4 제약 인지. |
-| 2 | DB 엔진 타깃 (V1) | **PostgreSQL** | EXPLAIN·`SET TRANSACTION READ ONLY`·dedicated role·`statement_timeout` 등 모든 safety 기법이 정직하게 동작. V2: MySQL/SQLite/BigQuery. |
-| 3 | 세션 영속화 | **Hermes-style write-through, 6 event types** | 매 발화·도구·일시정지를 즉시 flush. **스트림 청크 복구는 폐기** (idempotency 폭탄 위험). |
-| 4 | 세션 + 자격증명 스토어 | **AES-GCM 암호화 SQLite (per-row, secrets 테이블만)** | secrets만 행 단위 암호. sessions/messages/audit는 평문(검색 가능). 키 분실 시 secrets만 무효. |
-| 5 | LLM 백엔드 | **OpenAI 디폴트, NVIDIA NIM은 contract-test 통과 후 experimental** | NIM 어댑터는 `base_url` 차이만이라 30 LOC. 단, tool-calling/streaming/JSON schema 동치성은 contract test로 검증 후 `/model` 노출. |
-| 6 | DB 드라이버 | **`asyncpg` via `sqlalchemy.ext.asyncio.create_async_engine`** | discord.py는 100% asyncio. sync SQLAlchemy를 그대로 호출하면 event loop를 블록하여 gateway disconnect 위험. |
-| 7 | Go TUI | **freeze 유지 (개발 도구)** | NDJSON 인터페이스(`cli/commands/serve.py`) 호환 그대로. V1에선 신규 기능 0. |
-| 8 | Python TUI (`src/lang2sql/tui/`) | **삭제** | 스펙에 없고 Discord가 1급 frontend. 약 1,400 LOC 정리. |
-| 9 | 차트 출력 | **PNG 첨부 + Discord 한도 인지 페이지네이션** | 메시지 2000자 / embed description 4096자 / embed 총 6000자 / 첨부 8MB 한도. §8 참조. |
-| 10 | 호스팅 (트라이얼) | **소형 VPS** | §1.1 참조. Cloudflare Workers는 부적합. |
-
-### 1.1 호스팅 — Cloudflare Workers는 부적합
-
-`discord.py` 는 Discord WebSocket gateway에 **계속 붙어 있어야 하는 장기 실행 Python 프로세스**입니다. Cloudflare Workers의 edge runtime은 short-request 모델이라 불가.
-
-| 옵션 | 비용 | 메모 |
-|---|---|---|
-| **Oracle Cloud Always Free** | $0 무기한 | ARM Ampere 4코어/24GB. 카드 검증 필요. |
-| **fly.io shared-cpu-1x** | $0 ~ $5/월 | 256MB 시작. 한국 가까운 region. |
-| **Hetzner CX11** | €4.5/월 (~6,500원) | 안정·빠름. 유럽 region. |
-| **본인 PC 24h** | $0 | 데모 단계 충분. |
-
-### 1.2 "타깃 = 가짜연구소 디스코드?" 정리
-
-질문하신 *"이 타깃이 가짜연구소 디스코드?"* 에 대해: 문서의 "타깃"은 **지원할 DB 엔진**(PG vs MySQL vs BQ)을 의미. **배포 커뮤니티**(어느 길드에 봇을 초대하는가)와는 별개. V1 코드는 어떤 길드에든 동작.
-
-### 1.3 자격증명 저장 (모순 명확화)
-
-- 단일 SQLite 파일 `lang2sql.db` 안에 4개 테이블 (`secrets`, `sessions`, `messages`, `audit`, `persistent_views`).
-- **`secrets` 테이블만** AES-GCM per-row 암호화 (컬럼: `ciphertext`, `iv`, `tag`).
-- `sessions`/`messages`/`audit`/`persistent_views`는 평문. 검색 가능. (대화 텍스트 자체가 민감하다 판단되면 V2에서 전체 컬럼 암호로 확장.)
-- 마스터키: `LANG2SQL_MASTER_KEY` env. 분실 시 **secrets만 무효** — 사용자가 `/connect` 재등록. 대화/감사는 보존.
-- (v1 §1.3 vs §1#4의 "모순"은 Codex 오독. SQLCipher 형 파일 단위 암호가 아니라 per-row 컬럼 암호로 명확화.)
-
-### 1.4 Discord 플랫폼 제약 (신규)
-
-이 제약들이 §4 설계와 §3 영속화 스키마를 직접 좌우.
-
-| 제약 | 값 | 영향 |
-|---|---|---|
-| **Modal 최대 컴포넌트** | 5개 TextInput (각 ActionRow 1개) | §4 `/connect`는 single-field connection string 강제. |
-| **Modal password style** | **없음** (Short / Paragraph 2종만) | 비밀번호 평문 입력. 완화책: dedicated `lang2sql_readonly` role + 1회용 약한 비밀번호. |
-| **Interaction token** | 발급 후 **15분 만료** | 장기 쿼리 결과는 `interaction.edit_original_response()` 대신 `bot.message.edit(channel_id, message_id)` (bot token) 로. |
-| **Slash command 자동완성** | **3초 응답 데드라인** | DB 카탈로그 자동완성은 in-memory 캐시 필수 (실시간 조회 불가). |
-| **메시지 길이** | content 2000자 / embed description 4096자 / embed 총 6000자 | 결과는 PNG 또는 첨부파일로 강제. |
-| **메시지 첨부** | 8MB / 메시지 (무료 길드) | 차트 PNG 페이지 분할 필요. §8. |
-| **persistent View** | `View(timeout=None)` + 모든 항목에 `custom_id` ≤ 100자 + `setup_hook()`에서 `add_view()` 재등록 | §3.2 `persistent_views` 테이블 필수. |
-| **`custom_id`** | 100자 한도 | `query:{user_id}:{hash16}` 형태로 압축. |
-
-출처: https://docs.discord.com/developers/components/reference , https://discordpy.readthedocs.io/en/latest/
-
----
-
-## 2. 상위 아키텍처 (async 경계 명시)
-
-```
-                          ┌──────────────────────────────┐
-                          │      DISCORD GATEWAY         │
-                          │  (WebSocket, asyncio)        │
-                          └───────────────┬──────────────┘
-                                          │
-                                          ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                       DISCORD ADAPTER  (1급, asyncio)                 │
-│  /connect Modal · SessionRouter · Streaming(throttled edit)           │
-│  Interactive(persistent View) · Render(PNG paginate) · Permissions    │
-│  Recovery(reattach via persistent_views table)                        │
-└───────────────────────────┬──────────────────────────────────────────┘
-                            │ async ctx = await Concierge.build(key, principal)
-                            ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                       TENANCY LAYER  (asyncio)                        │
-│  TenantRegistry · ExplorerCache(LRU async engines)                    │
-│  EncryptedStore · ContextConcierge · SessionQueue(per-session FIFO)   │
-└───────────────────────────┬──────────────────────────────────────────┘
-                            ▼ ctx
-┌──────────────────────────────────────────────────────────────────────┐
-│                       HARNESS KERNEL  (asyncio)                       │
-│  agent_loop · system_prompt(per turn) · ToolRegistry(ctx 주입)        │
-│  Session(write-through, 6 events) · Hooks(pub/sub)                    │
-└──────┬─────────────────────┬──────────────────┬──────────────────────┘
-       │ ports               │                  │
-       ▼                     ▼                  ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                   SAFETY LAYER  (제품의 moat)                         │
-│  L0 connect (dedicated readonly role + 5 GUC SET)                     │
-│  L1 stmt gate (sqlglot AST + catalog-driven function denylist by OID) │
-│  L2 cost gate (EXPLAIN multi-metric → user confirm)                   │
-│  L3 runtime caps (statement_timeout, lock_timeout, ...)               │
-│  L4 audit (append-only SQLite, 일 회전)                                │
-│  L5 rate limit (token bucket)                                         │
-└──────┬─────────────────────┬──────────────────┬──────────────────────┘
-       ▼                     ▼                  ▼
-┌──────────────────────────────────────────────────────────────────────┐
-│                     OUTBOUND ADAPTERS                                 │
-│  LLM (openai / nvidia-nim contract-tested)                            │
-│  DB (sqlalchemy.ext.asyncio + asyncpg)                                │
-│  Secrets·SessionStore·AuditSink·PersistentViewStore (encrypted-sqlite)│
-└──────────────────────────────────────────────────────────────────────┘
-```
-
-규칙은 v1과 동일 — 화살표는 안쪽으로만. 변경점은 **모든 레이어 asyncio 명시**.
-
----
-
-## 3. 세션 전략
-
-### 3.1 V1 = DM-only
-
-| Discord 컨텍스트 | session_key | V1 상태 |
-|---|---|---|
-| **봇과의 DM** | `dm:{user_id}` | ✅ **V1 디폴트 (유일한 진입로)** |
-| 길드 채널 | `chan:{guild_id}:{channel_id}` | ⏸ V2 (table/schema allowlist + admin 승인 절차 이후) |
-| 길드 스레드 | `thr:{guild_id}:{channel_id}:{thread_id}` | ⏸ V2 |
-
-**근거**:
-- 길드 채널은 *"채널 멤버 전원 가시"* — read-only여도 PII/민감 row가 노출되는 순간 **데이터 유출 제품**이 됨.
-- V1 트라이얼은 개인 분석이 핵심 use case. 채널/스레드는 V2에서 권한 모델(allowlist + per-user audit) 완성 후 개방.
-- (Codex + devils-advocate 합의: V1 공용 채널 활성화는 제품 정체성 ("read-only · audit-by-default")의 자기부정.)
-
-**Cross-session 격리**: 같은 user가 DM과 (V2의) 채널을 동시 사용해도 ExplorerCache·rate-limit·schema_cache 키는 **(session_key, principal)** 페어로 격리. 같은 principal이라도 다른 session_key면 메타데이터 누설 금지.
-
-### 3.2 영속화 — Hermes-style, 6 event types only
-
-스트림 청크 단위 복구는 폐기. **6개 이벤트만** append-log:
-
-```
-user_msg          — 사용자 발화 (1회/메시지)
-tool_started      — 도구 호출 시작
-tool_finished     — 도구 결과 (성공/실패)
-assistant_final   — 어시스턴트 최종 응답 (스트림 종료 시 1회)
-pending_prompt    — ask_user / show_plan으로 일시정지 진입
-run_interrupted   — 프로세스 종료/타임아웃으로 중단 (재시작 시 표시용)
-```
-
-```
-User msg ──▶ Discord adapter ──▶ session.append('user_msg', payload)
-                                        │
-                                        ├─▶ in-memory state update
-                                        └─▶ store.write_tx(...)   ← 동기 트랜잭션
-                                                │
-                                                ▼
-                                       SQLite (WAL, append-only)
-
-Tool start/end   ──▶ append('tool_started' / 'tool_finished')
-Pause(ask_user)  ──▶ append('pending_prompt') + flush
-LLM stream delta ──▶ buffered in-memory, NO persistence
-Crash mid-turn   ──▶ 다음 부팅 시 turn에 'run_interrupted' 표시
-                     사용자가 다음 메시지 보내면 새 turn으로 시작
-                     (자동 재시도 안 함 — idempotency 폭탄 방지)
-```
-
-**보장**: 마지막으로 flush된 event까지 안전. 재시작 시:
-1. `SessionStore.iter_active(since=now-30d)` 로 미완 세션 로드.
-2. 마지막 event가 `pending_prompt` 면 그대로 대기 상태 복원.
-3. 마지막 event가 `tool_started` 인데 `tool_finished` 없으면 `run_interrupted` 추가 + 사용자에게 "중단됨, `/retry` 로 재시도하세요" 표시.
-4. Discord adapter가 DM별로 reattach.
-5. **Persistent View** (cost confirm 버튼, paginate 버튼)는 `persistent_views` 테이블에서 message_id·channel_id 복원 후 `setup_hook()`에서 `add_view()`.
-
-(v1의 "끊김 없이 이어짐" 어조는 과장이었음 — v2는 *"중단 표시 후 명시 재시도"* 로 축소.)
-
-### 3.3 저장 스키마
-
-```sql
--- 세션 메타
-CREATE TABLE sessions (
-  id            TEXT PRIMARY KEY,         -- session_key (V1: dm:USER_ID)
-  principal     TEXT NOT NULL,            -- 마지막 발화 user id
-  kind          TEXT NOT NULL,            -- V1: 'dm'
-  db_spec_id    TEXT REFERENCES secrets(id),
-  active_model  TEXT,                     -- 'openai:gpt-4.1-mini' 등
-  created_at    TIMESTAMP NOT NULL,
-  updated_at    TIMESTAMP NOT NULL,
-  closed_at     TIMESTAMP
-);
-
--- 이벤트 append-only (6 종)
-CREATE TABLE events (
-  id          INTEGER PRIMARY KEY AUTOINCREMENT,
-  session_id  TEXT REFERENCES sessions(id),
-  ts          TIMESTAMP NOT NULL,
-  kind        TEXT NOT NULL,              -- user_msg|tool_started|...|run_interrupted
-  payload     TEXT NOT NULL,              -- canonical JSON (§3.4)
-  principal   TEXT
-);
-CREATE INDEX events_by_session ON events(session_id, id);
-
--- 암호화 자격증명
-CREATE TABLE secrets (
-  id            TEXT PRIMARY KEY,         -- e.g. "user:123:prod-pg"
-  owner         TEXT NOT NULL,
-  label         TEXT NOT NULL,
-  ciphertext    BLOB NOT NULL,            -- AES-GCM
-  iv            BLOB NOT NULL,
-  tag           BLOB NOT NULL,
-  created_at    TIMESTAMP NOT NULL,
-  last_used_at  TIMESTAMP
-);
-
--- 감사 로그
-CREATE TABLE audit (
-  id          INTEGER PRIMARY KEY AUTOINCREMENT,
-  ts          TIMESTAMP NOT NULL,
-  session_id  TEXT,
-  principal   TEXT,
-  kind        TEXT,                       -- 'sql'|'tool'|'safety_block'|'rate_block'
-  payload     TEXT,                       -- JSON
-  duration_ms INTEGER,
-  status      TEXT                        -- 'ok'|'error'|'blocked'
-);
-
--- ★ Discord persistent view 복구용 (신규)
-CREATE TABLE persistent_views (
-  message_id   TEXT PRIMARY KEY,
-  channel_id   TEXT NOT NULL,
-  guild_id     TEXT,                      -- DM이면 NULL
-  view_kind    TEXT NOT NULL,             -- 'cost_confirm'|'paginate'|'ask_user'|'plan_approval'
-  state_json   TEXT NOT NULL,             -- view 생성자에게 넘길 인자
-  session_id   TEXT REFERENCES sessions(id),
-  expires_at   TIMESTAMP,                 -- NULL = persistent
-  created_at   TIMESTAMP NOT NULL
-);
-CREATE INDEX views_active ON persistent_views(expires_at) WHERE expires_at IS NULL;
-```
-
-### 3.4 ToolResult canonical shape (신규)
-
-`ToolResult.data` 가 `Any` 면 SQLite write-through 시 직렬화 불일치로 복구 불가능. canonical:
-
-```python
-{
-  "kind": "rows" | "ddl" | "list" | "viz_spec" | "scalar" | "none",
-  "payload": ...,           # kind에 따라:
-                            #   rows  → [{...}, ...]
-                            #   ddl   → "CREATE TABLE ..."
-                            #   list  → ["t1", "t2", ...]
-                            #   viz_spec → {chart_type, data, ...}
-                            #   scalar → number | string
-                            #   none  → null
-  "meta": {                 # 공통 메타
-    "truncated": bool,
-    "row_count": int,
-    "elapsed_ms": int,
-    "warnings": [str, ...]
-  }
-}
-```
-
-도구 12개 전부 이 shape에 맞춤 (대부분 이미 dict 반환 — 키 표준화만).
-
-**Provider-coupling 처리**: conversation은 internal canonical 형식 (`role` ∈ {system|user|assistant|tool_result}, `tool_calls`는 OpenAI-like)으로 저장. OpenAI 어댑터는 통과, Anthropic 어댑터는 양방향 변환. 모델을 *같은 provider family 내에서* 전환할 땐 conversation 유지. **provider family를 넘는 전환 (OpenAI↔Anthropic)** 시 사용자에게 "대화 초기화" 명시 confirm.
-
-### 3.5 동시성
-
-```
-전역 concurrency    = 무제한 (asyncio)
-per-session         = FIFO 1 (같은 conversation에 동시 LLM 호출 금지)
-per-user rate       = token bucket (LLM 20/min,  DB 60/min)
-per-guild rate      = token bucket (DB 200/min)        ← V2 채널 활성화 시
-LLM stream          = 세션마다 독립
-DB engines          = ExplorerCache LRU 50 (per db_url)
-```
-
-SQLite는 WAL 모드 + `busy_timeout=200ms`. V1 두 자릿수 동시 사용자에 충분. V2 본격 트래픽 시 Postgres 백엔드로 교체 (어댑터만 갈아끼움).
-
----
-
-## 4. DB 연결 온보딩 (Discord-native, 재설계)
-
-**v1 → v2 결정적 변경**: Modal 5필드 한도 + password style 미지원에 맞춰 **connection string 단일 필드 모델**로 전환.
-
-### 4.1 `/connect` Modal (5필드)
-
-```
-┌─────────────────────────────────────────────────────────┐
-│  /connect                                                │
-├─────────────────────────────────────────────────────────┤
-│  label              (Short, required)                   │
-│  └ 예: "prod-pg"                                         │
-│                                                          │
-│  connection_url     (Paragraph, required)               │
-│  └ postgresql://USER:PASS@HOST:5432/DB?sslmode=require  │
-│  └ ⚠️ 가급적 dedicated readonly role 사용                │
-│  └ /grant-readonly 로 role 생성 SQL 확인                 │
-│                                                          │
-│  default_schema     (Short, optional)                   │
-│  └ 비우면 'public'                                       │
-│                                                          │
-│  notes              (Paragraph, optional)               │
-│  └ 본인 메모                                              │
-│                                                          │
-│  acknowledgment     (Short, required)                   │
-│  └ "READ-ONLY" 라고 정확히 입력해야 제출 가능             │
-└─────────────────────────────────────────────────────────┘
-```
-
-5필드 정확히 사용. password masking은 Discord가 지원 안 함 — 평문이지만 Modal 자체가 interaction-scoped (메시지 history에 안 남음). 사용자에게 *"본인만 보이며 채팅 로그에 남지 않습니다. 그러나 화면 입력 중에는 노출되므로 dedicated readonly role + 1회용 비밀번호 사용을 강력 권장"* 안내.
-
-### 4.2 제출 직후 처리 (`on_modal_submit`)
-
-```
-1. Parse connection_url → host/port/user/pass/db/options 분해. parse 실패 시 ephemeral error.
-2. AES-GCM 즉시 암호화 → secrets 테이블 put. 메모리 평문 즉시 zeroize.
-3. Async engine 임시 생성 (asyncpg). pool_size=1, pool_recycle=30s.
-4. ★ RO Probe (catalog 쿼리, fail-closed):
-     SELECT
-       current_setting('transaction_read_only')::bool                 AS tx_ro,
-       (SELECT bool_or(pg_has_role(current_user, r, 'USAGE'))
-        FROM unnest(ARRAY['pg_write_all_data']) r)                    AS has_write_role,
-       EXISTS (
-         SELECT 1 FROM information_schema.role_table_grants
-         WHERE grantee = current_user
-           AND privilege_type IN ('INSERT','UPDATE','DELETE','TRUNCATE')
-           AND table_schema NOT IN ('pg_catalog','information_schema')
-       )                                                              AS has_write_grant;
-5. has_write_role = TRUE 또는 has_write_grant = TRUE 면:
-   ❌ REJECT. 사용자에게 안내:
-     "이 계정은 쓰기 권한을 가지고 있어 등록할 수 없습니다.
-      `/grant-readonly` 명령을 실행하면 read-only role 생성 SQL을 보내드립니다."
-   secrets 행 즉시 삭제.
-6. SELECT 1 검증 + `SELECT count(*) FROM information_schema.tables WHERE table_schema=...`
-7. 성공 ephemeral embed:
-   "✅ Connected to 'prod-pg'. N tables visible. Try: \"tables 보여줘\""
-```
-
-(v1의 `BEGIN; CREATE TABLE __probe; ROLLBACK` 은 fail-open이라 폐기. CREATE 권한 없음 ≠ UPDATE 권한 없음.)
-
-### 4.3 `/grant-readonly` 도우미 명령
-
-```
-사용자: /grant-readonly database=prod_db schema=public
-
-봇 DM:
-  다음 SQL을 PG superuser로 실행하세요. 비밀번호 부분만 교체:
-
-  CREATE ROLE lang2sql_ro_USER123 LOGIN PASSWORD '<DISPOSABLE>';
-  GRANT CONNECT ON DATABASE prod_db TO lang2sql_ro_USER123;
-  GRANT USAGE ON SCHEMA public TO lang2sql_ro_USER123;
-  GRANT SELECT ON ALL TABLES IN SCHEMA public TO lang2sql_ro_USER123;
-  ALTER DEFAULT PRIVILEGES IN SCHEMA public
-    GRANT SELECT ON TABLES TO lang2sql_ro_USER123;
-  ALTER ROLE lang2sql_ro_USER123 SET default_transaction_read_only = on;
-
-  완료되면 /connect 로 이 계정을 등록하세요.
-```
-
-writable confirm 옵션 폐기 — read-only가 제품 정체성인데 우회를 열어두는 건 약속 깨짐. 대신 사용자가 RO role을 만들기 쉽게 도와줌.
-
-### 4.4 슬래시 명령 (V1)
-
-| 명령 | 위치 | 동작 |
-|---|---|---|
-| `/connect` | DM | §4.1 Modal |
-| `/grant-readonly` | DM | §4.3 SQL 생성기 |
-| `/connections` | DM | 등록 DB 목록 (label만, 비밀번호 안 보임) |
-| `/use <label>` | DM | 활성 DB 전환 |
-| `/disconnect <label>` | DM | 자격증명 삭제 |
-| `/test` | DM | 현재 활성 DB `SELECT 1` + RO probe 재실행 |
-| `/safety` | DM | 현재 활성 보호 레이어 embed |
-| `/safety self-test` | DM | 7개 공격 자가 검증 |
-| `/audit me` | DM | 본인 최근 50건 쿼리 |
-| `/audit export` | DM | CSV 첨부 |
-| `/model` | DM | provider 내 model 전환 (provider 변경 시 confirm) |
-| `/reset` | DM | 현 세션 conversation 초기화 (audit 보존) |
-| `/retry` | DM | 마지막 `run_interrupted` 턴 재시도 |
-| `/help` | DM | 명령 목록 |
-
----
-
-## 5. DB 강건성 — Safety Layer (v2 강화)
-
-```
-요청 ─▶ ┌──────────────────────────────────────────────────────┐
-        │ L0 Connection                                         │
-        │  • dedicated `lang2sql_readonly` role 강제 (§4.3)     │
-        │  • sslmode=require / verify-full 옵션                  │
-        │  • create_async_engine + asyncpg (event loop 비차단)  │
-        │  • pool_size=5, pool_recycle=30min, pool_pre_ping=on  │
-        │  • 매 connection 시작 시 GUC 일괄 SET:                  │
-        │      SET ROLE <readonly_role>;                        │
-        │      SET default_transaction_read_only = on;          │
-        │      SET transaction_read_only = on;                  │
-        │      SET statement_timeout = '30s';                   │
-        │      SET lock_timeout = '5s';                         │
-        │      SET idle_in_transaction_session_timeout = '60s'; │
-        │      SET temp_file_limit = '1GB';     ← 단위 필수!     │
-        │      SET search_path = pg_catalog, <user_schema>;     │
-        │      SET row_security = on;                           │
-        │  ※ SET TRANSACTION READ ONLY 단독은 부족함을 인지       │
-        │     (PG: "high-level, does not prevent all writes")   │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L1 Statement Gate (sqlglot AST + catalog denylist)    │
-        │                                                        │
-        │  (a) sqlglot AST 파싱, 화이트리스트:                    │
-        │      SELECT, WITH, EXPLAIN(*), DESCRIBE                │
-        │      *EXPLAIN ANALYZE + (Insert|Update|Delete|Merge)*  │
-        │       조합은 명시적 거부 (실제 실행됨)                   │
-        │  (b) 모든 CTE 내부도 walk:                              │
-        │      find_all(exp.Insert, exp.Update, exp.Delete,     │
-        │               exp.Merge) → 발견 시 차단                 │
-        │  (c) `COPY` 전면 차단 (FROM/TO PROGRAM/file 전부)       │
-        │  (d) parse error → fail-closed (block)                │
-        │  (e) multi-statement (";" 분할) → 첫 번째만 허용,       │
-        │       나머지가 있으면 거부                              │
-        │  (f) ★ Function denylist (catalog-driven, OID 매칭):    │
-        │       부팅 시 pg_proc에서 위험 함수 OID 수집,           │
-        │       AST의 모든 function call을 OID 매칭으로 차단     │
-        │       → schema-qualified bypass 방어                   │
-        │       (이름 매칭만 하면 public.pg_sleep 같은 alias에   │
-        │        취약)                                            │
-        │  • 위반 시: audit.kind='safety_block' + ❌ embed       │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L2 Cost Gate — *경고+confirm* 모드 (차단 아님)         │
-        │                                                        │
-        │  • EXPLAIN (FORMAT JSON) → JSON plan tree 재귀 walk    │
-        │  • 임계 (V1 디폴트):                                     │
-        │      Total Cost > 1,000,000                            │
-        │    OR Plan Rows × Plan Width > 100 MB (bytes)          │
-        │    OR Sort/Hash 노드 + 입력 > 1M rows                   │
-        │  • 임계 초과 시:                                         │
-        │      show_plan(plan=..., cost=..., est_rows=...,       │
-        │                est_bytes=...) → Discord button view   │
-        │      사용자 ✅ 누르면 진행, ❌ 누르면 취소               │
-        │  • EXPLAIN 추정은 stale stats 시 빗나감 → 차단 아님      │
-        │  • 실제 차단은 L3 runtime cap에 위임                    │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L3 Runtime Caps — GUC 기반                            │
-        │  • statement_timeout = 30s     (L0에서 SET)            │
-        │  • lock_timeout = 5s                                   │
-        │  • idle_in_transaction_session_timeout = 60s           │
-        │  • temp_file_limit = '1GB'                             │
-        │  • app-level row_limit = 1000 (truncate + 알림)        │
-        │  • 위반 시: ⚠️ "timeout/limit 적중, partial result"    │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L4 Audit (append-only SQLite)                         │
-        │  • events 외에 audit 테이블 별도 (§3.3)                 │
-        │  • 일 회전 + retention 90일                             │
-        │  • /audit me  → 본인 최근 50건                          │
-        │  • /audit export → CSV DM                              │
-        │  • hash chain / 외부 sink → V2 백로그                   │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-        ┌──────────────────────────────────────────────────────┐
-        │ L5 Rate Limit (token bucket)                          │
-        │  • per-user:  LLM 20/min, DB 60/min                   │
-        │  • per-guild: V2 (V1은 DM-only)                        │
-        │  • Discord API rate limit도 별도 어댑터 책임           │
-        └──────────────────────────────────────────────────────┘
-                              │
-                              ▼
-                          실행 / 응답
-```
-
-### 5.1 함수 denylist (V1, catalog 부팅 시 OID 수집)
-
-| 카테고리 | 항목 | 사유 |
-|---|---|---|
-| **DoS / sleep** | `pg_sleep`, `pg_sleep_for`, `pg_sleep_until` | RO 통과, lock hold |
-| **Advisory lock** | `pg_advisory_lock(_shared)?`, `pg_advisory_xact_lock(_shared)?`, `pg_try_advisory_*` | 자원 hog |
-| **Backend control** | `pg_terminate_backend`, `pg_cancel_backend` | operational |
-| **Logging / signal** | `pg_log_backend_memory_contexts`, `pg_reload_conf`, `pg_rotate_logfile` | 운영 부수효과 |
-| **WAL / replication** | `pg_create_*_replication_slot`, `pg_drop_replication_slot`, `pg_logical_emit_message`, `pg_switch_wal`, `pg_backup_start/stop`, `pg_create_restore_point`, `pg_promote`, `pg_wal_replay_pause/resume` | 상태 변경 |
-| **Server file / LO** | `pg_read_file`, `pg_read_binary_file`, `pg_ls_*`, `lo_import`, `lo_export`, `lo_create`, `lo_unlink`, `lo_truncate`, `lo_open`, `lo_from_bytea`, `lo_put`, `lowrite` | FS / large object bypass |
-| **Egress / FDW** | `dblink_exec`, `dblink`, `dblink_connect`, `postgres_fdw_disconnect*` | 외부 egress |
-| **Stats / maintenance** | `pg_restore_relation_stats`, `pg_clear_*_stats`, `brin_summarize_*`, `gin_clean_pending_list` | optimizer 조작 |
-| **Configuration** | `set_config('...', ..., false)` (session-wide), `pg_reload_conf` | session 설정 우회 |
-| **Misc** | `pg_export_snapshot`, `pg_log_standby_snapshot` | 자원 보유 |
-
-Extension 함수 (`PostGIS`, `pgcrypto`, `http`, `plpython`, `plperlu`) 는 환경별 inventory 후 추가.
-
-### 5.2 self-test 7개 공격 (회귀 — CI 차단 기준)
-
-| # | 입력 | 기대 결과 |
-|---|---|---|
-| 1 | `DROP TABLE users` | L1 block (DDL) |
-| 2 | `; DELETE FROM users; --` (multi-stmt) | L1 block (multi-statement) |
-| 3 | `WITH x AS (INSERT INTO t VALUES (1) RETURNING *) SELECT * FROM x` | L1 block (DML in CTE, sqlglot walk) |
-| 4 | `SELECT pg_sleep(10000)` | L1 block (function denylist, OID match) |
-| 5 | `EXPLAIN (FORMAT JSON) SELECT * FROM huge_table` → estimated >100M rows | L2 cost gate → user confirm |
-| 6 | `SELECT * FROM table_50k` (50,000 rows) | L3 row_limit 1000 → truncate |
-| 7 | 분당 100 호출 | L5 rate limit |
-
-추가 (V2 backlog):
-- `SELECT public.pg_sleep(60)` (schema-qualified bypass) → catalog-driven OID match으로 V1에서도 차단 가능. 회귀 추가 권장.
-- `COPY foo TO PROGRAM 'curl ...'` → L1 block (COPY 전면 차단)
-- writable role probe: `/connect` 로 writable account 등록 시도 → RO probe 거부.
-
-### 5.3 어필 — 5가지 무기 (v1과 동일, GIF 콘텐츠 업데이트)
-
-1. `/safety` 슬래시 명령 — 현재 보호 레이어 embed.
-2. 레드팀 GIF 30초 — 7개 공격 시도가 각각 다른 레이어에 막히는 영상.
-3. 공개 audit log JSON 샘플 — "이렇게 기록됩니다".
-4. `/safety self-test` — 본인이 본인에게 7개 공격 → "녹색 7/7" 스크린샷.
-5. 벤치마크 — `lang2sql` vs *"그냥 LLM에 connection 던지기"* 정량 비교.
-
----
-
-## 6. 새 레포 레이아웃 — Evolve in place
-
-v1의 "wipe and rebuild" 어조는 폐기. 실제 작업은 **PR-0(정리) + 5개 PR(점진 추가)** 로 진행. (codebase-reality 결론.)
-
-### 6.1 보존 / 포팅 / 삭제
-
-| 분류 | 대상 | LOC | 메모 |
-|---|---|---|---|
-| **KEEP-AS-IS** | `src/lang2sql/semantic/*` | ~670 | 순수 in-memory. `dialect="postgres"` 기본값만 변경 (`sql_composer.py:19`). |
-| | `src/lang2sql/harness/types.py` | ~155 | `core/types.py` 로 이동(rename)만. |
-| | `src/lang2sql/skills/registry.py` | ~101 | 그대로. |
-| | `src/lang2sql/tools/{ask_user,show_plan}.py` | ~93 | harness intercept라 본문 더미. |
-| | `tests/test_semantic_layer.py`, `test_harness_core.py`, `test_skills_registry.py` | ~1,054 | 회귀 안전망. |
-| | `src/lang2sql/integrations/llm/openai_.py`, `anthropic_.py` | ~362 | 그대로. NIM은 wrapper. |
-| | `tui-go/*` | — | freeze. |
-| **PORT-WITH-MINOR-CHANGES** | `src/lang2sql/harness/{tool,loop,system_prompt}.py` | ~575 | ctx 인자 추가. 본체 70% 그대로. |
-| | `src/lang2sql/tools/{run_sql,explore_schema,profile_table,define_*,search_semantic,write_sql,visualize,load_skill}.py` | ~1,200 | ctx 사용 + run_sql만 safety 호출 추가. |
-| | `src/lang2sql/integrations/db/sqlalchemy_explorer.py` | ~128 | async 전환 (asyncpg) + GUC SET. |
-| | `src/lang2sql/core/ports.py` | ~101 | port 5개 추가 (secrets/session_store/audit/hook/persistent_view_store). |
-| | `cli/commands/serve.py` | ~393 | `frontends_dev/serve.py` 로 이동. NDJSON 그대로 (Go TUI 호환). |
-| | `cli/commands/agent.py` | ~111 | `frontends_dev/cli.py` 로 이동. |
-| **REWRITE** | `src/lang2sql/harness/session.py` | ~215 → ~250 | 6-event write-through로 교체. |
-| | `src/lang2sql/harness/builder.py` | ~88 → ~100 | `ContextConcierge` 로 승격. |
-| **NEW** | `src/lang2sql/safety/*` (statement_gate, cost_gate, runtime, audit, rate_limit, ro_probe, self_test) | ~900 | |
-| | `src/lang2sql/tenancy/*` (concierge, tenant_registry, explorer_cache, session_queue, encrypted_store, persistent_view_store) | ~700 | |
-| | `src/lang2sql/discord/*` (bot, commands/*, session_router, streaming, interactive, render, permissions, recovery) | ~1,500 | |
-| | `src/lang2sql/adapters/llm/nvidia_nim.py`, `adapters/db/postgres_explorer.py`, `adapters/secrets/encrypted_sqlite.py`, ... | ~600 | |
-| | `tests/{safety,discord,tenancy}/*` | ~1,200 | |
-| **DELETE** | `src/lang2sql/tui/*` (Python Textual + widgets) | ~1,400 | Discord가 1급. |
-| | `src/lang2sql/{components,flows,viz,interface,utils}/*`, `integrations/{embedding,vectorstore,loaders,chunking,catalog}/*` | ~5,000 | V1에 RAG/구 파이프라인 없음. |
-| | `src/lang2sql/integrations/llm/{gemma_,gemini_,bedrock_,azure_,huggingface_,ollama_}.py` | ~1,000 | gemma 등 백버너로 보관 또는 삭제. |
-| | `cli/commands/{tui,init,quary,run_streamlit}.py` | ~350 | |
-| | `tests/test_{components,flows,integrations}_*`, `test_tools_code_agent.py` | ~1,500 | 동반 삭제. |
-
-### 6.2 최종 트리 (PR-5 끝난 시점)
-
-```
-lang2sql/
-├── README.md
-├── pyproject.toml
-├── .env.example
-│
-├── src/lang2sql/
-│   │
-│   ├── core/                      # 순수 타입 + 포트
-│   │   ├── types.py               # 기존 harness/types.py 이동
-│   │   ├── identity.py            # Principal, GuildRole (신규)
-│   │   ├── safety_types.py        # SafetyVerdict, Budget, RateBucket (신규)
-│   │   └── ports/
-│   │       ├── llm.py             # 기존 일부
-│   │       ├── explorer.py        # async 전환
-│   │       ├── tool.py            # ctx
-│   │       ├── secrets.py         # 신규
-│   │       ├── session_store.py   # 신규
-│   │       ├── audit.py           # 신규
-│   │       ├── persistent_view_store.py  # 신규
-│   │       └── hook.py            # 기존
-│   │
-│   ├── harness/                   # kernel
-│   │   ├── context.py             # HarnessContext (신규)
-│   │   ├── session.py             # 6-event write-through (재작성)
-│   │   ├── loop.py                # 기존 70% 보존
-│   │   ├── system_prompt.py       # 기존, per-turn 유지
-│   │   ├── tool_registry.py       # ctx 주입 (개명)
-│   │   └── prompts/
-│   │
-│   ├── semantic/                  # 그대로 (dialect 기본값 변경)
-│   │
-│   ├── safety/                    # ★ 신규
-│   │   ├── statement_gate.py
-│   │   ├── cost_gate.py
-│   │   ├── runtime.py
-│   │   ├── audit.py
-│   │   ├── rate_limit.py
-│   │   ├── ro_probe.py
-│   │   └── self_test.py
-│   │
-│   ├── tools/                     # ctx-aware
-│   │   ├── explore_schema.py
-│   │   ├── run_sql.py             # safety 호출 추가
-│   │   ├── write_sql.py           # (compose만, 이름 유지 — devils-advocate 검증)
-│   │   ├── define_metric.py
-│   │   ├── define_dimension.py
-│   │   ├── define_relationship.py
-│   │   ├── search_semantic.py
-│   │   ├── profile_table.py
-│   │   ├── ask_user.py
-│   │   ├── show_plan.py
-│   │   ├── visualize.py
-│   │   ├── explain_query.py       # 보존, V1 메뉴에 노출
-│   │   └── load_skill.py
-│   │
-│   ├── skills/                    # 기존
-│   │
-│   ├── tenancy/                   # ★ 신규
-│   │   ├── concierge.py
-│   │   ├── tenant_registry.py
-│   │   ├── explorer_cache.py
-│   │   ├── session_queue.py
-│   │   ├── encrypted_store.py
-│   │   └── persistent_view_store.py
-│   │
-│   ├── discord/                   # ★ 1급 frontend (신규)
-│   │   ├── bot.py
-│   │   ├── commands/
-│   │   │   ├── connect.py
-│   │   │   ├── grant_readonly.py
-│   │   │   ├── connections.py
-│   │   │   ├── safety.py
-│   │   │   ├── audit.py
-│   │   │   ├── model.py
-│   │   │   ├── reset.py
-│   │   │   ├── retry.py
-│   │   │   └── help.py
-│   │   ├── session_router.py      # V1: DM-only
-│   │   ├── streaming.py           # throttled message edit
-│   │   ├── interactive.py         # persistent View
-│   │   ├── render.py              # PNG 페이지네이션
-│   │   ├── permissions.py
-│   │   └── recovery.py            # 부팅 시 persistent_views reattach
-│   │
-│   ├── adapters/                  # outbound
-│   │   ├── llm/
-│   │   │   ├── openai_.py         # 기존
-│   │   │   └── nvidia_nim.py      # 신규
-│   │   ├── db/
-│   │   │   └── postgres_explorer.py  # 기존 sqlalchemy_explorer 진화
-│   │   ├── secrets/
-│   │   │   └── encrypted_sqlite.py
-│   │   ├── session_store/
-│   │   │   └── encrypted_sqlite.py
-│   │   └── audit_sink/
-│   │       └── sqlite.py
-│   │
-│   └── frontends_dev/             # 부수
-│       ├── cli.py                 # 기존 agent.py 이동
-│       └── serve.py               # 기존 serve.py 이동 (Go TUI 호환)
-│
-├── tui-go/                        # freeze
-├── tests/
-│   ├── (보존) test_semantic_layer.py, test_harness_core.py, test_skills_registry.py
-│   ├── safety/                    # 7개 self-test + 회귀
-│   ├── discord/                   # mock 기반
-│   └── tenancy/
-└── docs/
-    ├── discord_first_redesign.md       # v1 보존
-    ├── discord_first_redesign_v2.md    # ← 본 문서
-    ├── DB_ROBUSTNESS.md                # ★ trust page
-    ├── DISCORD_ONBOARDING.md           # 사용자 가이드
-    └── DEPLOY.md
-```
-
----
-
-## 7. 마이그레이션 — PR-0 + 5주 plan
-
-```
-PR-0 ─ 정리 (0.5주)                                       [-7,700 LOC]
-   • src/lang2sql/tui/*, components/, flows/, viz/,
-     interface/, utils/, 미사용 integrations/, 옛 cli/
-     명령들, 동반 tests/ 삭제
-   • python -c "import lang2sql" + pytest 통과 확인
-   • 잔존: semantic/, harness/, skills/, openai+anthropic 어댑터,
-           기존 sqlalchemy_explorer, tui-go/, 보존 tests 3종
-
-PR-1 ─ Kernel (Week 1)                                    [~400 LOC]
-   • core/ports/* 정의 (5개 신규 포트 시그니처)
-   • HarnessContext, ctx-aware Tool 시그니처
-   • harness/{tool_registry,loop,system_prompt} 시그니처 갈이
-   • Session 임시 in-memory (PR-3에서 영속화)
-   • ToolResult canonical shape 합의
-   • frontends_dev/cli.py 로 검증
-
-PR-2 ─ Safety (Week 2, 모두 *경고 모드* 포함)              [~900 LOC]
-   • statement_gate (sqlglot AST + function denylist OID)
-   • runtime (5개 GUC SET, temp_file_limit '1GB' 단위)
-   • cost_gate (EXPLAIN multi-metric, 경고+confirm 모드)
-   • audit (sqlite sink)
-   • ro_probe (catalog 쿼리)
-   • self_test 7개 + tests/safety/ (CI gate)
-   • run_sql 도구가 safety 통과시키도록 ~20 LOC 추가
-
-PR-3 ─ Async + Tenancy + Persistence (Week 3)             [~1,400 LOC]
-   • DBExplorerPort async 전환 + asyncpg 어댑터
-   • 모든 도구 async 호환 (대부분 자동, 일부 to_thread)
-   • encrypted_sqlite (AES-GCM secrets)
-   • SessionStore 6-event write-through + iter_active
-   • PersistentViewStore (신규)
-   • tenant_registry + explorer_cache(async LRU)
-   • session_queue (per-session FIFO)
-   • ContextConcierge
-
-PR-4a ─ Discord onboarding (Week 4 전반)                  [~700 LOC]
-   • bot 진입 + setup_hook (persistent View 재등록)
-   • /connect Modal (5필드 connection string) + 제출 처리
-   • /grant-readonly, /test, /connections, /use, /disconnect
-   • Principal/Policy 기본
-   • DM-only session_router
-   • recovery.py (events 마지막=tool_started → run_interrupted,
-                  persistent_views reattach)
-
-PR-4b ─ Discord product (Week 4 후반)                     [~800 LOC]
-   • streaming (토큰 → throttled message.edit 1s)
-   • interactive (ask_user/show_plan → persistent View buttons)
-   • render (PNG 페이지네이션, Discord 한도 인지)
-   • /safety, /audit, /model, /reset, /retry, /help
-   • cost_gate를 *차단 모드*로 승격 옵션 추가
-
-PR-5 ─ Polish + Appeal (Week 5)                           [~300 LOC]
-   • /safety self-test 자동 데모 GIF
-   • README 갱신
-   • DB_ROBUSTNESS.md (trust page)
-   • DEPLOY.md (Oracle Cloud / fly.io)
-   • benchmark (vs "naive LLM SQL")
-   • NIM contract test 통과 후 /model에 노출
-```
-
-총 ~4,500 LOC 신규 + ~600 LOC 수정, ~7,700 LOC 삭제. 5.5주 1인 풀타임. Week 4 분할로 v1의 과적재 해소.
-
-각 PR은 별 브랜치 (`feature/pr-{N}-*`), 직전 PR 머지 후 다음 시작.
-
----
-
-## 8. 차트 페이지네이션 (Discord 한도 인지)
-
-```
-Result rows → discord/render.py
-                │
-                ├─ 50 rows 이하  → embed 텍스트 1개 (4096자 한도 체크)
-                ├─ 500 rows 이하 → matplotlib + Pillow → PNG 1장
-                ├─ > 500 rows    → 50 rows/페이지 PNG K장
-                │
-                ├─ K ≤ 5         → 단일 메시지에 첨부 K장 (8MB 한도 체크)
-                ├─ K > 5         → persistent View (◀ Prev / Next ▶ / ⇩ CSV)
-                │                   첫 페이지만 즉시 전송
-                │                   메시지 ID + 페이지 상태 →
-                │                     persistent_views 테이블 저장
-                │                   봇 재시작 후에도 버튼 작동
-                │
-                └─ CSV 버튼      → 전체 CSV를 DM 첨부 (8MB 초과 시 분할)
-```
-
-차트(visualize) 도 동일 패턴 — facet 차트는 카테고리당 1장.
-
-**한도 표** (v1엔 빠졌던 항목):
-- content 2000자 / embed description 4096자 / embed 총 6000자
-- 단일 메시지 첨부 8MB (무료 길드), 25MB (Nitro)
-- 메시지당 임베드 최대 10개, 컴포넌트 최대 5 ActionRow
-
----
-
-## 9. LLM 멀티 백엔드 — OpenAI (V1) + NVIDIA NIM (contract-tested)
-
-```
-adapters/llm/openai_.py        — 그대로
-adapters/llm/nvidia_nim.py     — OpenAI(base_url=NIM_ENDPOINT, api_key=NIM_KEY)
-                                  ~30 LOC wrapper
-```
-
-**V1 디폴트**: OpenAI `gpt-4.1-mini`. `/model` 슬래시 명령으로 전환 가능.
-
-**NIM contract test (PR-2 또는 PR-5)**:
-```
-tests/llm_contract/test_nim_openai_parity.py
-  • tool_calling: 동일 schema → 동일 호출 시그니처
-  • streaming: text_delta chunk shape 호환
-  • finish_reason: 'stop'|'tool_calls'|'length' 매핑
-  • JSON mode: schema adherence
-  통과 시 /model 노출, 실패 시 마스크
-```
-
-선택 가능한 모델 (V1 후보):
-
-| Provider | 후보 |
-|---|---|
-| OpenAI | `gpt-4.1-mini` (디폴트), `gpt-4.1`, `o4-mini` |
-| NVIDIA NIM (contract-test 통과 후) | `meta/llama-3.1-70b-instruct`, `mistralai/mixtral-8x22b-instruct-v0.1` |
-
-비용 안내:
-- OpenAI: per-token. `/model` 에서 mini 디폴트.
-- NIM: NVIDIA 무료 크레딧 → 떨어지면 자비. `/model` 로 OpenAI 폴백 안내.
-- per-user 토큰 캡은 V2 백로그 (`/audit` 에 누적 비용 표시는 V1.5).
-
-**Provider family 전환** (OpenAI ↔ Anthropic): conversation 직렬화가 호환되지 않음. 사용자에게 *"대화 초기화 필요"* confirm 후 진행.
-
----
-
-## 10. V1 명시적 제외 — V2+ 백로그
-
-| 항목 | 사유 |
-|---|---|
-| 길드 채널·스레드 세션 | 데이터 노출 위험. allowlist + admin 승인 모델이 V2. |
-| audit log hash chain / 외부 sink | V1 SOC2 친화 어조만 유지. 실제 hash chain은 V2. |
-| 외부 secret manager (AWS SM / GCP SM / Vault) | V1은 로컬 암호 SQLite로 충분. |
-| MySQL / SQLite / BigQuery 어댑터 | V1은 PostgreSQL 전용. |
-| RAG / 벡터스토어 / 임베딩 | 본 제품은 schema-grounded NL→SQL. RAG는 V2 옵션. |
-| Anthropic 어댑터 사용자 노출 | V1 디폴트는 OpenAI. 어댑터 코드는 보존만. |
-| 다중 인스턴스 / Redis 세션 스토어 | 트라이얼은 단일 프로세스. V2 스케일링 시. |
-| BQ cost gate (bytes_processed × $) | PG cost_gate가 먼저, BQ 어댑터 들어올 때 함께. |
-| Components V2 (Discord 2025) | 모달은 변경 없음. message 컴포넌트 V2 migration은 V1 이후. |
-
----
-
-## 11. Codex 리뷰에서 거부한 항목 (transparency)
-
-본 v2는 Codex가 제기한 25개 지적 중 ~12개를 수용. 다음 항목은 **검증 후 거부**:
-
-| Codex 지적 | 거부 사유 |
-|---|---|
-| **§1#4 vs §1.3 "AES-GCM 모순"** | doc은 per-row 컬럼 암호 (secrets만), Codex는 SQLCipher 형 파일 단위 암호로 오독. v2 §1.3에서 추가 명확화. |
-| **`write_sql.py` → `draft_sql.py` 개명** | `write_sql.py` 본문은 SQLComposer로 *생성*만 함. 실행은 `run_sql.py`. Codex는 파일을 안 읽고 이름으로 추론. 이름 유지. |
-| **§0 5단 타이브레이커** (권한→정확성→무결성→UX→맥락) | 솔로 트라이얼에 과한 product framing. 4축 (DB 무결성·의미적 정확성·Discord UX·맥락)으로 절충. |
-| **Hermes-style write-through 통째 부정** | 사용자 명시 요구사항. 단, *"스트림 청크 복구"* 부분만 거부 — 6 event types로 단순화. |
-| **EXPLAIN cost gate가 row estimate만으론 부족 → 차단** | trial V1에 stale stats까지 잡으라는 건 과함. *차단이 아니라 경고+사용자 confirm* 으로 절충, 실제 차단은 L3 runtime cap에 위임. |
-| **NIM "OpenAI 호환" 검증 안 됨 → 사용 금지** | contract test 통과 후 experimental 노출로 절충. 전면 금지는 아님. |
-| **SQLite single-writer "1000 유저" 비현실적** | trial 트래픽은 두 자릿수. V2 스케일 시점 항목으로 명시. |
-| **wipe vs "semantic 이식" 모순** | "이식"은 트리 비우고 자산 복사라는 자연 해석. 그러나 v2에선 *evolve in place* 로 어조 자체를 변경 → 모순 자체 소멸. |
-
----
-
-## 12. 잔여 결정 사항
-
-(Week 1 착수 전 답이 있으면 좋은 항목)
-
-1. **첫 배포 길드** — V1은 DM-only라 길드 선택은 봇 초대받을 수 있는 길드만 필요. 가짜연구소 디스코드 또는 본인 테스트 길드?
-2. **세션 retention** — 비활성 세션 보존 기간. v2 §3.2 가정: **30일 active, 30~90일 archive, 90일 후 삭제**. 변경 필요 시 알려주세요.
-3. **audit retention** — 90일 동일. compliance 관점에서 더 길게? 별도 보존?
-4. **NIM 디폴트 후보** — `llama-3.1-70b-instruct` vs `mixtral-8x22b-instruct-v0.1`. (Mixtral이 tool-calling 안정성 우위로 알려져 있음 — contract test로 결정.)
-5. **Cost gate 차단 모드 승격 시점** — V1은 경고만. PR-5에서 차단 옵션 추가? V2로?
-6. **`/audit export` 형식** — CSV만? JSON 옵션도?
-7. **`/reset` 시 audit 보존** — 기본 보존 권장. 사용자에게 명시.
-
-위 7개는 Week 1 코드에 직접 영향 없음.
-
----
-
-## 13. 한 줄 요약
-
-> **Evolve in place. 4축 타이브레이커로 의미적 정확성을 1급화한다. Discord plataforma 한도(Modal 5필드, password style 없음, Interaction 15분, autocomplete 3s, persistent view)를 §1.4에 박는다. PostgreSQL safety는 `SET TRANSACTION READ ONLY` 단독을 신뢰하지 않고, dedicated readonly role + 5 GUC + catalog-driven function denylist(OID) 3중으로 막는다. 세션은 6 event types만 write-through, 스트림 청크 복구는 폐기. V1은 DM-only — 채널/스레드는 권한 모델 완성 후 V2.**
-
-— end —
diff --git a/docs/discord_first_redesign_v3_minimal.md b/docs/discord_first_redesign_v3_minimal.md
deleted file mode 100644
index c2b2859..0000000
--- a/docs/discord_first_redesign_v3_minimal.md
+++ /dev/null
@@ -1,578 +0,0 @@
-# Lang2SQL — Discord-First 재설계 (v3, Walking Skeleton)
-
-> **작성일**: 2026-05-18
-> **결정자**: ryan@brain-crew.com
-> **상태**: v2 → v3. 5명 리뷰어(Codex + Agent Teams 4명) 라운드 2 audit 결과 반영.
-> **핵심 변경**: v2의 *"5.5주에 다 넣자"* → v3의 *"3주에 가장 작은 작동하는 제품 + 확장점"*.
-> **선행 문서**: `discord_first_redesign.md` (v1), `discord_first_redesign_v2.md` (v2, 확장 청사진으로 보존).
-
----
-
-## 0. 한 줄과 타이브레이커
-
-> **"Discord DM에서 자연어로 PostgreSQL에 묻고, 안전하게 답을 받는다. DB는 변하지 않고, 결과는 audit에 남는다."**
-
-설계 충돌 시 우선순위:
-
-1. **데이터·DB 무결성 + 권한** — DB가 변하지 않는가, 사용자가 권한 있는 데이터만 보는가
-2. **의미적 정확성** — 답이 의미적으로 맞는가 (V1엔 audit + golden query set, validation loop는 V1.5+)
-3. **Discord UX 단순성** — 한 번의 입력으로 결과를 얻는가
-4. **맥락 보존** — 끊겨도 이어지는가 (V1은 *in-memory*, 영속화는 V1.6)
-
-(Codex가 v2 4축에 *"권한/data exposure를 최상위 축으로 올리지 않았다"* 고 STILL-INVALID 판정 → 축 #1을 *"무결성 + 권한"* 통합으로 명확화. devils-advocate의 "5축은 product framing overkill" 우려는 4축 유지로 절충.)
-
----
-
-## 1. v3-minimal 범위
-
-### 1.1 V1에 *들어가는* 것
-
-| 영역 | V1 |
-|---|---|
-| 인터페이스 | Discord DM only. 슬래시 명령 3개: `/connect`, `/ask <질문>`, `/disconnect` |
-| LLM | OpenAI `gpt-4.1-mini` 단일 |
-| DB | PostgreSQL only (dedicated readonly role 강제) |
-| 응답 형식 | 텍스트 (≤50행) 또는 CSV 첨부 (>50행) — PNG·차트·페이지 버튼 V1 미포함 |
-| Safety | L0 (GUC pack) + L1 (statement gate) + L4 (audit) **3개만** |
-| 세션 | in-memory dict — 봇 재시작 시 손실. 사용자가 다시 `/ask` 호출 |
-| 영속화 | audit + secrets만 SQLite. conversation은 휘발 |
-| 도구 | `run_sql`, `explore_schema`, `ask_user` **3개만** |
-| 호스팅 | 본인 PC 또는 fly.io free / Oracle Cloud Always Free |
-
-### 1.2 V1에 *안* 들어가는 것 (V1.x로 미룸)
-
-| 항목 | 미루는 곳 | 이유 |
-|---|---|---|
-| `run_code` / `write_code` 도구 | **삭제** (V2도 보류) | Codex 최우선 지적: read-only 제품에서 Python subprocess는 모든 safety layer를 무력화 (token·secret·FS·network 노출). 부활 시 sandbox 필수. |
-| Cost gate (EXPLAIN-based) | V1.5 | L1 + L0 timeout으로 1차 충분. confirm UX는 persistent View 의존이라 V1.7에 자연스럽게 들어옴. |
-| Rate limit | V1.5 | 트라이얼은 1~2명. |
-| Self-test 7개 자동화 | V1.5 | V1엔 수동 회귀. CI YAML은 V1.5와 동반. |
-| Persistent View / streaming / PNG paginate | V1.7 | Discord SDK 깊은 영역. V1엔 ephemeral 한 번 응답으로 충분. |
-| Hermes write-through (영속 conversation) | V1.6 | in-memory로 시작. 사용자가 끊김 불편을 호소하면 SessionStore 어댑터 교체. |
-| `define_metric/dimension/relationship` 등 semantic CRUD 도구 | V1.5 | `write_sql` 컴포저는 비어 있는 layer로도 작동. semantic 구축은 trial 사용자가 명시 요청할 때. |
-| NVIDIA NIM | V1.5 (contract test 통과 후) | OpenAI 하나로 시작. |
-| Anthropic 노출 (`/model` 명령) | V2 | 코드는 보존, V1엔 미노출. |
-| 길드 채널 / 스레드 세션 | V2 | Codex가 "DM-only도 DM 덤프 위험"으로 추가 지적 — V2에 schema/table allowlist + per-user audit 완성 후. |
-| `/grant-readonly` 도우미 명령 | V1.5 | V1은 README의 SQL 가이드로 충분. |
-| Audit hash chain / 외부 sink | V2 | V1엔 append-only SQLite + `/audit me`. |
-| `visualize` 도구 | V1.7 | PNG render 함께. |
-| `load_skill` | V1.5 | skills/ 디렉토리는 보존, 도구만 미노출. |
-| Python Textual TUI | **삭제** | |
-| Go TUI | freeze (유지하되 V1에서 신규 작업 0) | |
-| RAG / 벡터스토어 / embedding | V2+ | V1은 schema-grounded NL→SQL. |
-
-### 1.3 기술 결정 (최소)
-
-| 항목 | 선택 |
-|---|---|
-| Discord lib | `discord.py` 2.x |
-| DB 드라이버 | `asyncpg` + `sqlalchemy.ext.asyncio.create_async_engine` (**AUTOCOMMIT isolation**) |
-| LLM | OpenAI `gpt-4.1-mini` |
-| Secret 저장 | AES-GCM per-row in SQLite (`secrets` 테이블만 암호화) |
-| 호스팅 | Oracle Cloud Always Free 또는 fly.io free (Cloudflare Workers 불가 — discord.py는 장기 실행) |
-
----
-
-## 2. 상위 아키텍처 (V1)
-
-```
-                    ┌──────────────────────────┐
-                    │   DISCORD GATEWAY (DM)   │
-                    └──────────────┬───────────┘
-                                   │ asyncio
-                                   ▼
-┌──────────────────────────────────────────────────────────────┐
-│            DISCORD ADAPTER (단순)                              │
-│   /connect Modal · /ask handler · /disconnect                  │
-│   ephemeral response · CSV attach (>50 rows)                   │
-└──────────────┬───────────────────────────────────────────────┘
-               │ ctx = await Concierge.build(dm:user_id, principal)
-               ▼
-┌──────────────────────────────────────────────────────────────┐
-│            TENANCY (최소)                                      │
-│   ContextConcierge · EncryptedSecrets (AES-GCM SQLite)         │
-└──────────────┬───────────────────────────────────────────────┘
-               ▼ ctx
-┌──────────────────────────────────────────────────────────────┐
-│            HARNESS KERNEL                                      │
-│   agent_loop · Session(in-memory) · ToolRegistry(ctx)          │
-│   system_prompt(per-turn)                                      │
-└──────┬───────────────────────┬───────────────────────────────┘
-       │                       │
-       ▼                       ▼
-┌──────────────────────────────────────────────────────────────┐
-│            SAFETY (3 레이어)                                   │
-│   L0 connect (readonly role + GUC pack)                        │
-│   L1 statement gate (sqlglot AST + hardcoded fn denylist)      │
-│   L4 audit (SQLite append-only)                                │
-└──────┬───────────────────────┬───────────────────────────────┘
-       ▼                       ▼
-┌──────────────────────────────────────────────────────────────┐
-│            ADAPTERS                                            │
-│   LLM: openai_                                                 │
-│   DB:  postgres_explorer (asyncpg)                             │
-│   Secrets/Audit: encrypted_sqlite                              │
-└──────────────────────────────────────────────────────────────┘
-```
-
-규칙:
-- 포트는 5개만 정의 (`LLMPort`, `ExplorerPort`, `ToolPort`, `SecretsPort`, `AuditPort`). 나머지(`SessionStorePort`, `PersistentViewStorePort`, `HookPort`)는 V1.x에 추가.
-- 모든 어댑터는 async. event loop 블로킹 금지.
-- safety는 `run_sql` 한 곳에서만 통과. 다른 도구는 SQL을 실행 안 함.
-
----
-
-## 3. DB 연결 — `/connect` (Modal 2필드)
-
-```
-사용자                       Bot DM                        Tenancy
-  │                            │                               │
-  │── /connect ───────────────▶│                               │
-  │                            │   Modal 출력:                  │
-  │                            │   ┌────────────────────────┐ │
-  │                            │   │ label    (Short)        │ │
-  │                            │   │ url      (Short)        │ │
-  │                            │   │  postgresql://USER:PASS │ │
-  │                            │   │   @HOST:5432/DB         │ │
-  │                            │   │   ?sslmode=require       │ │
-  │                            │   └────────────────────────┘ │
-  │── submit ─────────────────▶│                               │
-  │                            │                               │
-  │                            │   defer (ephemeral)            │
-  │                            │   ─ RO probe (catalog) ──────▶│
-  │                            │   ← OK/FAIL                   │
-  │                            │   if OK:                       │
-  │                            │     encrypt → secrets.put      │
-  │                            │   if FAIL:                     │
-  │                            │     embed: "read-only role 필요│
-  │                            │              README의 SQL 참조"│
-  │                            │                               │
-  │                            │   button view 동의:            │
-  │                            │   ┌────────────────────────┐ │
-  │                            │   │ READ-ONLY 정책 동의     │ │
-  │                            │   │  [✅ Agree] [❌ Cancel]│ │
-  │                            │   └────────────────────────┘ │
-  │── ✅ Agree ───────────────▶│                               │
-  │                            │   secrets 활성화 + ephemeral  │
-  │                            │   "✅ Connected to 'prod-pg'" │
-```
-
-**핵심 결정** (v2 → v3):
-- v2의 7필드 Modal → **2필드** (label + url). `acknowledgment="READ-ONLY"` 평문 입력 강제 폐기 → **별도 button view**로 동의 분리 (discord-fact-checker 권고).
-- `connection_url`은 **Short** 필드 (Paragraph는 줄바꿈 사고 유발). PG connection string은 ~200자라 Short 4000자 한도 안에 충분.
-- Modal 제출 후 **3초 안에 `interaction.response.defer(ephemeral=True)`** 필수 (Discord 한도). RO probe + secrets put은 defer 후 followup.
-- **RO probe가 먼저, secrets put은 후** (probe 실패 시 orphan 자격증명 방지).
-- writable 계정 등록은 **거부** (Codex/pg-safety-expert 합의 fail-closed). README에 readonly role 생성 SQL 가이드.
-
-### 3.1 RO probe (개선된 catalog 쿼리)
-
-```sql
-SELECT
-  current_setting('transaction_read_only')::bool                  AS tx_ro,
-  -- pg_write_all_data role 직접/간접 멤버십
-  pg_has_role(current_user, 'pg_write_all_data', 'USAGE')         AS has_write_role,
-  -- non-system table 쓰기 권한 (PUBLIC + role-chain + inherited 모두 포함)
-  EXISTS (
-    SELECT 1 FROM information_schema.tables t
-    WHERE t.table_schema NOT IN ('pg_catalog','information_schema')
-      AND (
-           has_table_privilege(current_user, t.table_schema||'.'||t.table_name, 'INSERT')
-        OR has_table_privilege(current_user, t.table_schema||'.'||t.table_name, 'UPDATE')
-        OR has_table_privilege(current_user, t.table_schema||'.'||t.table_name, 'DELETE')
-        OR has_table_privilege(current_user, t.table_schema||'.'||t.table_name, 'TRUNCATE')
-      )
-    LIMIT 1
-  )                                                                AS has_write_grant,
-  -- schema CREATE 권한
-  EXISTS (
-    SELECT 1 FROM information_schema.schemata s
-    WHERE s.schema_name NOT IN ('pg_catalog','information_schema')
-      AND has_schema_privilege(current_user, s.schema_name, 'CREATE')
-    LIMIT 1
-  )                                                                AS has_create;
-```
-
-**합격 조건**: `tx_ro = true AND has_write_role = false AND has_write_grant = false AND has_create = false`. 셋 중 하나라도 진실이면 거부.
-
-(pg-safety-expert + Codex가 v2의 `role_table_grants WHERE grantee=current_user` 가 PUBLIC·role-chain·inherited grant를 못 잡는다고 지적 → `has_table_privilege()` 기반으로 재작성.)
-
----
-
-## 4. Safety Layer (3개)
-
-### 4.1 L0 Connection — readonly role + GUC pack
-
-```sql
--- 매 connection 시작 (postgres_explorer.on_connect)
-SET ROLE lang2sql_readonly;             -- ← 반드시 dedicated role
-SET default_transaction_read_only = on;
-SET transaction_read_only = on;
-SET statement_timeout = '30s';
-SET lock_timeout = '5s';
-SET idle_in_transaction_session_timeout = '60s';
-SET temp_file_limit = '1GB';           -- 단위 명시 필수 (없으면 kB 해석)
-SET work_mem = '64MB';                  -- sort/hash spill을 temp_file_limit로 가두기
-SET jit = off;                          -- JIT 컴파일 비용 폭주 방지
-SET bytea_output = 'hex';
-SET client_min_messages = 'warning';
-SET application_name = 'lang2sql/v1';
-SET search_path = pg_catalog, public;
-SET row_security = on;
-```
-
-**Engine**: `create_async_engine(..., isolation_level='AUTOCOMMIT', pool_size=2, pool_recycle=1800, pool_pre_ping=True)`.
-**ExplorerCache**: LRU 30 (per db_url). 사용자당 등록 가능 DB는 V1에서 1개로 고정 (V1.5에 다중 지원).
-
-**제약**: V1 트라이얼 두 자릿수 사용자에서 PG `max_connections=100` 안에 머무름. 사용자 N × pool_size 2 = 2N connection.
-
-(`SET TRANSACTION READ ONLY`는 *"high-level, does not prevent all writes"* — dedicated role + GUC + L1이 3중 방어. pg-safety-expert 출처: https://www.postgresql.org/docs/current/sql-set-transaction.html)
-
-### 4.2 L1 Statement Gate — sqlglot AST
-
-```python
-# safety/statement_gate.py
-ALLOWED_TOP = {Select, With, Explain, Describe}
-BLOCKED_NODES = (Insert, Update, Delete, Merge, Create, Drop, Alter,
-                 Truncate, Grant, Revoke, Copy, Set)
-DENY_FUNCTIONS = {            # V1 hardcoded names (V1.5에 OID 매핑으로 승격)
-    'pg_sleep', 'pg_sleep_for', 'pg_sleep_until',
-    'pg_advisory_lock', 'pg_advisory_lock_shared',
-    'pg_advisory_xact_lock', 'pg_advisory_xact_lock_shared',
-    'pg_try_advisory_lock', 'pg_try_advisory_xact_lock',
-    'pg_terminate_backend', 'pg_cancel_backend',
-    'pg_read_file', 'pg_read_binary_file', 'pg_ls_dir',
-    'lo_import', 'lo_export', 'lo_create', 'lo_unlink',
-    'lo_truncate', 'lo_open', 'lo_from_bytea', 'lo_put', 'lowrite',
-    'dblink', 'dblink_exec', 'dblink_connect',
-    'pg_reload_conf', 'pg_rotate_logfile',
-    'pg_create_restore_point', 'pg_switch_wal',
-    'pg_logical_emit_message',
-    'pg_promote', 'pg_wal_replay_pause', 'pg_wal_replay_resume',
-    'pg_backup_start', 'pg_backup_stop',
-}
-
-def classify(sql: str) -> Verdict:
-    # 1. parse — 실패하면 fail-closed
-    try:
-        ast = sqlglot.parse(sql, dialect='postgres')
-    except ParseError:
-        return Verdict.block(reason='parse_error')
-    if len(ast) > 1:
-        return Verdict.block(reason='multi_statement')
-    root = ast[0]
-    # 2. 최상위 노드 화이트리스트
-    if type(root) not in ALLOWED_TOP:
-        return Verdict.block(reason='not_a_read_statement')
-    # 3. EXPLAIN ANALYZE + DML 거부
-    if isinstance(root, Explain) and root.args.get('analyze'):
-        if root.find(*BLOCKED_NODES[:4]):  # Insert/Update/Delete/Merge
-            return Verdict.block(reason='explain_analyze_dml')
-    # 4. 모든 서브트리에서 차단 노드 검사 (CTE 내부 INSERT 등)
-    for node in root.find_all(*BLOCKED_NODES):
-        return Verdict.block(reason=f'forbidden_{type(node).__name__.lower()}')
-    # 5. 함수 호출 검사 — schema-qualified bypass 포함
-    for func in root.find_all(exp.Func):
-        name = (func.name or '').lower()
-        # public.pg_sleep 같은 schema 접두사도 처리
-        if name in DENY_FUNCTIONS:
-            return Verdict.block(reason=f'forbidden_function_{name}')
-    return Verdict.allow()
-```
-
-V1.5 보강:
-- OID-based 매핑 (extension 동적 등록 대응).
-- TTL refresh (mid-session 새 함수 감지).
-- `DO $$ ... $$` 블록 차단 (PG 동적 SQL).
-
-### 4.3 L4 Audit — SQLite append-only
-
-```sql
-CREATE TABLE audit (
-  id          INTEGER PRIMARY KEY AUTOINCREMENT,
-  ts          TIMESTAMP NOT NULL,
-  principal   TEXT NOT NULL,                 -- discord user_id
-  session_id  TEXT NOT NULL,                 -- 'dm:USER_ID'
-  kind        TEXT NOT NULL,                 -- 'sql' | 'safety_block' | 'connect' | 'disconnect'
-  sql         TEXT,                          -- 차단된 SQL 포함
-  row_count   INTEGER,
-  duration_ms INTEGER,
-  status      TEXT,                          -- 'ok' | 'error' | 'blocked'
-  error       TEXT                           -- 차단 사유 또는 PG error
-);
-CREATE INDEX audit_by_principal ON audit(principal, id);
-```
-
-V1 명령: `/audit me` → 최근 20건 ephemeral embed. CSV export·hash chain·외부 sink는 V2.
-
----
-
-## 5. 영속화 스토어 (V1: secrets + audit만)
-
-```sql
--- secrets: AES-GCM per-row
-CREATE TABLE secrets (
-  id           TEXT PRIMARY KEY,           -- f"user:{user_id}:default"
-  owner        TEXT NOT NULL,              -- discord user_id
-  label        TEXT NOT NULL,
-  ciphertext   BLOB NOT NULL,              -- AES-GCM(url, key, iv)
-  iv           BLOB NOT NULL,
-  tag          BLOB NOT NULL,
-  created_at   TIMESTAMP NOT NULL,
-  last_used_at TIMESTAMP
-);
-
--- audit (위 §4.3)
-```
-
-**V1엔 `sessions`, `messages`, `events`, `persistent_views` 테이블 없음.** conversation은 `dict[user_id, list[Message]]` in-memory. 봇 재시작 시 conversation 손실 (사용자가 다시 `/ask`).
-
-마스터 키: `LANG2SQL_MASTER_KEY` env (32 bytes urlsafe base64). 분실 시 secrets만 무효.
-
----
-
-## 6. 레포 레이아웃 (V1)
-
-```
-lang2sql/
-├── README.md                    # 30초 setup + readonly role SQL
-├── pyproject.toml
-├── .env.example                 # LANG2SQL_MASTER_KEY, DISCORD_TOKEN, OPENAI_API_KEY
-│
-├── src/lang2sql/
-│   ├── core/
-│   │   ├── types.py             # Message, ToolCall, ToolResult, Event
-│   │   ├── identity.py          # Principal (discord user_id)
-│   │   └── ports/
-│   │       ├── llm.py
-│   │       ├── explorer.py
-│   │       ├── tool.py
-│   │       ├── secrets.py
-│   │       └── audit.py
-│   │
-│   ├── harness/
-│   │   ├── context.py           # HarnessContext
-│   │   ├── session.py           # in-memory dict
-│   │   ├── loop.py              # agent_loop (기존 70% 보존)
-│   │   ├── system_prompt.py     # per-turn
-│   │   └── tool_registry.py
-│   │
-│   ├── semantic/                # ★ 기존 코드 그대로 (dialect="postgres")
-│   │
-│   ├── safety/
-│   │   ├── statement_gate.py    # sqlglot + hardcoded denylist
-│   │   ├── runtime.py           # L0 GUC SET (connect hook)
-│   │   └── audit.py             # append-only writer
-│   │
-│   ├── tools/                   # 3개만
-│   │   ├── run_sql.py           # statement_gate 통과 후 실행
-│   │   ├── explore_schema.py    # information_schema 기반
-│   │   └── ask_user.py
-│   │
-│   ├── tenancy/
-│   │   ├── concierge.py         # ContextConcierge
-│   │   └── encrypted_secrets.py # AES-GCM SQLite
-│   │
-│   ├── discord/
-│   │   ├── bot.py
-│   │   └── commands/
-│   │       ├── connect.py       # Modal 2필드 + button view 동의
-│   │       ├── ask.py           # 자연어 질문 → agent_loop → 응답
-│   │       ├── disconnect.py
-│   │       └── audit.py         # /audit me
-│   │
-│   └── adapters/
-│       ├── llm/openai_.py       # 기존 그대로
-│       └── db/postgres_explorer.py  # 기존 explorer를 async로 진화
-│
-├── tests/
-│   ├── test_semantic_layer.py   # 기존 보존
-│   ├── test_harness_core.py     # 기존 보존 (ctx 시그니처만 조정 ~50 LOC)
-│   ├── test_skills_registry.py  # 기존 보존
-│   └── safety/
-│       ├── test_statement_gate.py  # 회귀 12개 (V1 hard gate)
-│       └── test_ro_probe.py
-│
-└── docs/
-    ├── discord_first_redesign.md       # v1 보존
-    ├── discord_first_redesign_v2.md    # v2 보존 (확장 청사진)
-    ├── discord_first_redesign_v3_minimal.md  # ← 본 문서
-    ├── README_DEPLOY.md
-    └── README_READONLY_ROLE.md         # /connect 실패 시 가이드
-```
-
-**삭제** (PR-0):
-- `src/lang2sql/tui/` (Python Textual, ~1,400 LOC)
-- `src/lang2sql/components/`, `flows/`, `viz/`, `interface/`, `utils/`
-- `src/lang2sql/integrations/{embedding,vectorstore,loaders,chunking,catalog}/`
-- `src/lang2sql/integrations/llm/{gemma_, bedrock_, azure_, huggingface_, ollama_, gemini_}.py`
-- `src/lang2sql/tools/{run_code,write_code,define_*,search_semantic,write_sql,visualize,explain_query,profile_table,show_plan,load_skill}.py` (V1 미사용 — V1.5/V1.7에 복원)
-- `src/lang2sql/integrations/llm/anthropic_.py` 는 보존 (V2 노출용)
-- `cli/commands/{tui,init,quary,run_streamlit}.py`
-- `infra/observability/`, `infra/monitoring/`, `docker/pgvector/`, `pgvector.sh`
-- `interface/`, `prompt/`, `dev/e2e_traces/`, `test/`, `bench/` 일부, 옛 테스트 파일들
-
-**보존**:
-- `src/lang2sql/semantic/*` (~670 LOC, `dialect="postgres"` 기본값만 변경)
-- `src/lang2sql/harness/types.py` → `core/types.py`로 이동
-- `src/lang2sql/skills/registry.py` (V1.5 `load_skill` 도구 부활 시 사용)
-- `src/lang2sql/integrations/llm/{openai_, anthropic_}.py`
-- `src/lang2sql/integrations/db/sqlalchemy_explorer.py` → `adapters/db/postgres_explorer.py`로 async 진화
-- `cli/commands/{agent, serve}.py` → `frontends_dev/`로 이동 (V1엔 미노출, 디버그 보존)
-- `tui-go/` freeze
-- `bench/` (V1.5 마케팅 자료로 활용)
-- `docker/postgres/init/` (CI 시드)
-- 테스트 3개 (semantic, harness_core, skills_registry)
-
----
-
-## 7. 마이그레이션 — 3주 walking skeleton
-
-```
-Week 0 (PR-0a + PR-0b, 합 1주)              [-7,700 LOC, +50 LOC]
-   PR-0a 안전 삭제 (3일):
-     • src/lang2sql/tui/, components/, flows/, viz/, 옛 cli
-     • 미사용 LLM 어댑터 (gemma 등)
-     • V1.x에서 부활할 tools/* 6개 (run_code/write_code 포함 — V2도 보류)
-     • python -c "import lang2sql" + 보존 tests 3개 통과 확인
-   PR-0b 의존 검증 후 삭제 (2일):
-     • grep -r "from lang2sql\." 로 cross-module import 그래프
-     • interface/, utils/, integrations/{embedding,vectorstore,...}
-     • harness/builder.py 의 build_explorer_from_url 사용처 확인 — utils/ 에 있다면 adapters/db/로 이동
-     • .github/workflows/v1_ci.yml 추가 (pytest 보존 3개)
-
-Week 1 (PR-1, kernel + ctx)                  [+450 LOC]
-   • core/ports/* 5개 (LLM, Explorer, Tool, Secrets, Audit)
-   • HarnessContext + Session(in-memory dict)
-   • Tool.execute(ctx, **args) 시그니처
-   • test_harness_core.py ctx 마이그레이션 (~50 LOC, 1시간)
-   • frontends_dev/cli.py로 kernel 검증 (PG sample db 로컬)
-
-Week 2 (PR-2, safety + 도구 3개)             [+550 LOC]
-   • safety/statement_gate.py (sqlglot + hardcoded denylist)
-   • safety/runtime.py (L0 GUC SET on connect)
-   • safety/audit.py + SQLite schema
-   • tools/run_sql.py — statement_gate 호출 후 execute
-   • tools/explore_schema.py — information_schema 기반
-   • adapters/db/postgres_explorer.py — async (asyncpg, AUTOCOMMIT)
-   • adapters/secrets/encrypted_sqlite.py (AES-GCM)
-   • tests/safety/ 회귀 12개 (CI gate)
-
-Week 3 (PR-3, discord + 출하)                [+700 LOC]
-   • discord/bot.py + setup_hook
-   • discord/commands/{connect, ask, disconnect, audit}
-   • Modal 2필드 + 동의 button view
-   • RO probe (§3.1 catalog 쿼리)
-   • tenancy/concierge.py
-   • ephemeral 응답 + CSV attach
-   • README_DEPLOY.md + README_READONLY_ROLE.md
-
-총: ~1,750 LOC 신규, ~7,700 LOC 삭제, 3주 솔로.
-```
-
-**회귀 12개** (V1 CI gate, Week 2에 완성):
-
-| # | 입력 | 기대 |
-|---|---|---|
-| 1 | `DROP TABLE users` | L1 block (Drop 노드) |
-| 2 | `; DELETE FROM t; --` | L1 block (multi_statement) |
-| 3 | `WITH x AS (INSERT INTO t VALUES (1) RETURNING *) SELECT * FROM x` | L1 block (CTE 내 Insert) |
-| 4 | `SELECT pg_sleep(60)` | L1 block (함수 denylist) |
-| 5 | `SELECT public.pg_sleep(60)` | L1 block (schema-qualified) |
-| 6 | `COPY foo TO PROGRAM 'curl ...'` | L1 block (Copy 노드) |
-| 7 | `EXPLAIN ANALYZE DELETE FROM t` | L1 block (analyze + DML) |
-| 8 | `DO $$ BEGIN INSERT INTO t VALUES(1); END $$` | L1 block (parse_error 또는 미허용 노드 — fail-closed) |
-| 9 | `SELECT * FROM big_table` (50,001 rows) | L0 timeout 30s 또는 row 한도 1000 truncate |
-| 10 | `SELECT pg_advisory_lock(1)` | L1 block |
-| 11 | `RESET ROLE; SELECT 1` | L1 block (multi_statement) |
-| 12 | `SELECT lo_export(1, '/tmp/x')` | L1 block (함수 denylist) |
-
-(pg-safety-expert가 라운드 1·2에서 권고한 회귀를 V1 CI로 승격. V2 backlog에 두는 게 아니라 V1 fail-closed의 정당성 자체이므로.)
-
----
-
-## 8. 확장 로드맵 — 같은 포트에 어댑터 추가
-
-각 V1.x 는 **이전 V를 부수지 않고 어댑터/모듈만 추가**.
-
-```
-V1.5  (1주)  — Safety + 운영 보강
-   + safety/cost_gate.py (EXPLAIN warn+confirm — 단 V1.7 persistent View 전엔 텍스트 confirm만)
-   + safety/rate_limit.py (token bucket)
-   + safety/self_test.py (CI 자동 회귀, +5개 공격)
-   + tools/{define_metric, define_dimension, define_relationship, search_semantic, write_sql, load_skill}
-   + adapters/llm/nvidia_nim.py + contract test (통과 시 /model 노출)
-   + `/grant-readonly` 명령
-   + per-user 다중 DB (`/connections`, `/use`)
-
-V1.6  (1주)  — Hermes-lite 영속화
-   + adapters/session_store/encrypted_sqlite.py
-   + 11 event types: user_msg, tool_started, tool_finished,
-                    assistant_final, pending_prompt, run_interrupted,
-                    user_action, session_updated, safety_decision,
-                    cost_confirm, audit_marker
-     (Codex 권고로 6 → 11 확장)
-   + Session 어댑터를 in-memory → SQLite로 교체
-   + 부팅 시 active 세션 reattach + run_interrupted 표시
-
-V1.7  (1.5주)  — Discord 풍부함
-   + discord/streaming.py (throttled message edit)
-   + discord/interactive.py (persistent View)
-   + discord/render.py (PNG paginate, 25MB cap)
-   + tools/visualize.py 부활
-   + persistent_views 테이블 + recovery (setup_hook)
-   + cost_gate를 persistent View confirm으로 승격
-   + max_pages 200 + CSV 25MB 분할 정책
-
-V2  (별도 분기)  — 확장
-   + 길드 채널 / 스레드 세션 (schema/table allowlist + admin 승인)
-   + Anthropic provider family 노출 (family 전환 시 confirm)
-   + cost_gate hard-block tier (3-tier)
-   + audit hash chain + 외부 sink (S3/GCS)
-   + 의미적 정확성 implementation: golden query set, answer citation, validation loop
-   + MySQL / SQLite / BigQuery 어댑터
-   + 외부 secret manager (AWS SM / GCP SM)
-```
-
-**확장 시 절대 깨지지 않는 약속**:
-- 포트 5개의 시그니처 변경 금지 (확장은 *추가* 메서드만)
-- secrets / audit SQLite 스키마 변경은 마이그레이션 스크립트 동반
-- L0 + L1 + L4 는 모든 V에서 fail-closed
-
----
-
-## 9. 핵심 거부·수용 사항 (transparency)
-
-5명 리뷰어 audit에서 **수용한 것**:
-- `run_code` / `write_code` 도구 V1 즉시 삭제 (Codex 최우선 지적)
-- Modal `acknowledgment` 평문 강제 폐기 → 별도 button view (discord-fact-checker)
-- RO probe를 `has_table_privilege()` 기반으로 (pg-safety-expert + Codex)
-- L0 GUC에 `work_mem`, `jit=off`, `bytea_output`, `application_name`, AUTOCOMMIT 추가 (pg-safety-expert)
-- `pool_size=5 → 2`, per-user db 1개 (max_connections 보호)
-- `connection_url`을 Paragraph → Short
-- 첨부 한도 8MB → **25MB** (현재 값, 2022 변경)
-- self-test 회귀를 V2 backlog → **V1 CI gate**로 승격 (12개)
-- 5.5주 → 3주 (walking skeleton + V1.x 단계로 분할)
-- 6 event types → V1엔 없음 (in-memory), V1.6에 11 event types로 확장 (Codex가 6→`user_action/session_updated/safety_decision` 추가 권고)
-
-**거부한 것** (사유 명시):
-- `write_sql` 코드 파일명 개명 — 코드 본문은 SQL 생성만, 실행은 `run_sql`. 파일명 유지. **단 LLM에 노출되는 tool name은 `compose_sql`로 변경** (codex의 PARTIALLY-JUSTIFIED 절충).
-- 5축 타이브레이커 — 4축 유지하되 축 #1을 "무결성 + 권한 + data exposure" 통합으로 명확화.
-- DM 데이터 유출 방지를 위한 V1 allowlist — V1엔 README 가이드로만, table allowlist는 V2 (사용자 trial 부담 최소화).
-- Hermes write-through full — V1엔 in-memory, V1.6에 영속화 단계 도입.
-
----
-
-## 10. 잔여 결정 (V1 착수 전)
-
-1. **`compose_sql` vs `write_sql` LLM tool name** — 위 §9 절충안 적용? 그대로 `write_sql`?
-2. **첫 배포 길드 / 봇 application 생성** — Discord developer portal 어디 계정?
-3. **OpenAI 키 / NIM 키** — V1엔 OpenAI 하나면 충분.
-4. **`/audit me` 응답 — 최근 N건의 N 기본값** — 권고: 20건.
-
----
-
-## 11. 한 줄
-
-> **V1은 "DM에서 `/connect` → `/ask` → 안전한 답"이 작동하는 walking skeleton. 3주에 출하. 확장은 모두 어댑터·모듈 추가로 V1.x 단위. V2 청사진은 `v2.md`에 보존.**
-
-— end —
diff --git a/docs/discord_first_redesign_v4.md b/docs/discord_first_redesign_v4.md
deleted file mode 100644
index 1cce05c..0000000
--- a/docs/discord_first_redesign_v4.md
+++ /dev/null
@@ -1,639 +0,0 @@
-# Lang2SQL v4 — 컨셉과 아키텍처
-
-> **작성일**: 2026-05-18
-> **결정자**: ryan@brain-crew.com
-> **상태**: 컨셉 확정 단계. v1/v2/v3는 보존 (역사 자료).
-> **전제**: 기존 레포 wipe 후 백지에서 다시 작성.
-
----
-
-# Chap 1. 만들려는 것의 정체성
-
-## 1.1 기존 오픈소스의 공통 한계 — 어디를 비집고 들어갈까
-
-Vanna AI(~20k), Wren AI(~12k), SQLCoder(~3.8k) 같은 Text-to-SQL 오픈소스들은 *질문→SQL 파이프라인 자체* 는 이미 충분히 완성도 높음. 그러면 우리는 *그 위에 무엇을 더 얹을* 것인가가 정체성 결정.
-
-기존 오픈소스를 보면 공통적으로 **3가지 약점** 이 있음:
-
-| 약점 | 누구의 문제 | 기존 처리 |
-|---|---|---|
-| **DB 정보 완성도가 낮으면 성능 급락** | 사용자가 description·sample query 없이 등록 | Vanna: 학습 데이터 품질에 전적 의존 |
-| **이전 대화·정의를 기억 못함** | 매번 새 세션처럼 동작 | 대부분 stateless |
-| **비즈니스 맥락은 사람이 직접 정의해야 함** | metric 정의·용어집을 사용자가 일일이 입력 | Wren: MDL 수동 작성 |
-
-이 셋이 *실무에서는 매우 중요* 한데 *비즈니스마다 다르기 때문에 정량 평가가 어려운 영역*. 그래서 오픈소스가 잘 안 건드려옴. 우리는 **이 셋을 한 번에 다루는 방향** 으로 차별화.
-
-## 1.2 우리의 한 줄 컨셉
-
-> **"문서로 비즈니스 맥락을 학습하고, 당신의 DB에 대답하고, 모든 대화·정의를 기억하는 Discord 분석 에이전트."**
-
-기존 SQL 봇이 *"질문하면 SQL 만들어 줄게"* 였다면, 우리는 *"우리 회사 문서 넣어줘 → 학습할게 → 너희 팀이 같이 묻고 답을 받자 → 다 기억하고 다음에도 적용할게"*.
-
-## 1.3 4기둥 (Pillars)
-
-| 기둥 | 정체성 | 차별점 |
-|---|---|---|
-| **① Discord multi-mode** | DM(개인 분석) + 채널 멘션→thread(팀 협업) 양면 | 대부분 오픈소스는 Slack 또는 웹 UI 일면. Discord native + thread 패턴 |
-| **② Hermes-style 기억** | conversation + 학습된 facts + preferences 영속 | 대부분 stateless. *"우리 fiscal year는 7월 시작"* 한 번 가르치면 계속 적용 |
-| **③ 문서 → 시멘틱 레이어** | 사용자가 문서 업로드 → LLM이 metric/dim/rule 추출 → 사용자 confirm → 등록 | Wren의 MDL은 수동 작성. 우리는 *문서가 곧 진실의 출처* |
-| **④ DB 강건성 facade** | 사용자가 description 없이 등록해도 동작 | Vanna는 학습 데이터 품질 의존. 우리는 *불완전한 DB에서도 작동* 이 핵심 가치 |
-
-기둥 ④가 *우리 연구·제품 정체성의 가장 중요한 한 포인트*. *"사용자가 입력하는 DB 정보의 완성도가 낮아도 성능 하락을 최소화"* 가 차별화. V1엔 껍데기만, V1.x에서 깊이 채움.
-
-## 1.4 타이브레이커 (충돌 시 우선순위)
-
-설계가 충돌하면 위에서 아래로:
-
-1. **무결성 + 권한** — DB가 변하지 않고, 권한 있는 데이터만 본다
-2. **의미적 정확성** — 답이 *진짜로* 맞는다 (자신 있게 틀린 답이 가장 위험)
-3. **Discord UX** — 한 번에 결과 (멘션→thread)
-4. **맥락 보존** — 끊겨도 이어진다 (Hermes 영속화)
-
-(v2까지의 *"DB가 안 변하는가"* 한 축에서 *"+ 권한"* 까지 통합. Codex 외부 리뷰의 *"권한/data exposure를 별도 축으로"* 우려를 축 #1 안에 흡수.)
-
----
-
-# Chap 2. 아키텍처 — ASCII로 보는 큰 그림
-
-## 2.1 전체 흐름
-
-```
-┌─────────────────────────────────────────────────────────────────────┐
-│                          USER (Discord)                              │
-│                                                                       │
-│   DM message     |     Channel @mention      |     Thread reply       │
-│   ─────────     |     ─────────────────     |     ─────────────       │
-│   (개인 분석)    |    (봇이 thread 생성)     |   (같은 thread 계속)    │
-└──────┬────────────────────┬────────────────────────┬─────────────────┘
-       │                    │                        │
-       └────────────────────┼────────────────────────┘
-                            ▼
-       ┌────────────────────────────────────────────────┐
-       │  DISCORD ADAPTER                                │
-       │   • 입력 분류 → session_key 결정                  │
-       │     - dm:USER_ID                                │
-       │     - thr:GUILD:CHANNEL:THREAD_ID                │
-       │   • 멘션 없는 채널 메시지 → 무시                  │
-       │   • 응답 렌더링 (텍스트 / CSV / PNG)              │
-       └────────────────────┬───────────────────────────┘
-                            ▼
-       ┌────────────────────────────────────────────────┐
-       │  MEMORY + CONCIERGE                             │
-       │                                                  │
-       │   MemoryService {                                │
-       │     FactStore        ◄─ "어디 저장하나"           │
-       │     RecallStrategy   ◄─ "무엇을 가져올지"          │
-       │     FactExtractor    ◄─ "어떻게 만들지"           │
-       │   }                                              │
-       │                                                  │
-       │   IngestionPipeline {                            │
-       │     DocumentSource[] ◄─ "어디서 가져올지"          │
-       │     DocumentExtractor[] ◄─ "어떻게 해석할지"       │
-       │   }                                              │
-       │                                                  │
-       │   ContextConcierge.build(session_key, principal) │
-       │     → ctx 조립                                    │
-       └────────────────────┬───────────────────────────┘
-                            ▼ ctx
-       ┌────────────────────────────────────────────────┐
-       │  ★ HARNESS (조립된 단위, 하나의 덩어리)            │
-       │                                                  │
-       │   HarnessContext {                               │
-       │     llm           — OpenAI port                  │
-       │     tools         — run_sql, ingest_doc, ...     │
-       │     semantic      — SemanticLayer (live ref)     │
-       │     session       — 영속 conversation + facts    │
-       │     safety        — SafetyPipeline               │
-       │     explorer      — DB (asyncpg)                 │
-       │     audit         — append-only                  │
-       │   }                                              │
-       │                                                  │
-       │   agent_loop(ctx, question):                     │
-       │     while turn < max:                            │
-       │        prompt = system_prompt(ctx)               │
-       │            ├─ semantic 주입                       │
-       │            ├─ recalled facts 주입                 │
-       │            └─ schema 주입                         │
-       │        resp = llm.invoke(prompt, tools)          │
-       │        if resp.tool_calls:                       │
-       │          for tc in resp.tool_calls:              │
-       │            result = tools.execute(ctx, tc)       │
-       │            session.append('tool_finished', ...)  │
-       │        else:                                     │
-       │          yield resp.content                      │
-       │          break                                   │
-       │                                                  │
-       │   run_sql tool 내부:                              │
-       │     ★ ctx.safety.evaluate(sql, ctx)              │
-       │        Layer 1 → 2 → 3 → ...                     │
-       └────────────────────┬───────────────────────────┘
-                            ▼
-       ┌────────────────────────────────────────────────┐
-       │  OUTBOUND ADAPTERS                              │
-       │   LLM:     openai_                              │
-       │   DB:      postgres_explorer (asyncpg)          │
-       │   Store:   encrypted_sqlite                     │
-       └────────────────────────────────────────────────┘
-                            ▼
-              사용자 PostgreSQL · OpenAI · 로컬 SQLite
-```
-
-핵심 정정:
-- **하네스는 tools·semantic·session·safety 를 *분리된 레이어로 위아래* 두지 않음.** 모두 `HarnessContext` 안에 *주입된 의존성* 으로 들어감. 하네스 = 조립된 한 덩어리.
-- DM/Channel 양분은 *session_key 결정 단계에서만 다름*. 그 아래로는 동일 흐름.
-- 도구 안에서 SafetyPipeline 호출 → 각 layer가 차례로 검증.
-
-## 2.2 디렉토리 구조
-
-```
-lang2sql/
-├── README.md
-├── pyproject.toml
-├── .env.example
-│
-├── src/lang2sql/
-│   ├── core/                      # 순수 타입 + 포트
-│   │   ├── types.py               # Message, ToolCall, ToolResult, Event
-│   │   ├── identity.py            # Principal
-│   │   └── ports/
-│   │       ├── llm.py
-│   │       ├── explorer.py
-│   │       ├── tool.py
-│   │       ├── secrets.py
-│   │       ├── audit.py
-│   │       ├── session_store.py
-│   │       ├── safety.py          ── SafetyLayer Protocol
-│   │       ├── memory.py          ── FactStorePort, RecallStrategy, FactExtractor
-│   │       └── ingestion.py       ── DocumentSource, DocumentExtractor
-│   │
-│   ├── harness/                   # 조립된 단위
-│   │   ├── context.py             # HarnessContext
-│   │   ├── session.py             # conversation + facts
-│   │   ├── loop.py                # agent_loop (async generator)
-│   │   ├── system_prompt.py       # per-turn rebuild
-│   │   └── tool_registry.py
-│   │
-│   ├── semantic/                  # 도메인 모델
-│   │   ├── types.py
-│   │   ├── layer.py
-│   │   ├── sql_composer.py
-│   │   └── store.py
-│   │
-│   ├── safety/                    # ★ SafetyPipeline chain
-│   │   ├── pipeline.py
-│   │   └── layers/
-│   │       ├── whitelist_gate.py
-│   │       └── timeout_setter.py
-│   │
-│   ├── memory/                    # ★ 3축 분리
-│   │   ├── service.py
-│   │   ├── stores/
-│   │   │   └── inmemory.py
-│   │   ├── recall/
-│   │   │   └── all_facts.py
-│   │   └── extractors/
-│   │       └── manual.py
-│   │
-│   ├── ingestion/                 # ★ Source × Extractor
-│   │   ├── pipeline.py
-│   │   ├── sources/
-│   │   │   └── file.py
-│   │   └── extractors/
-│   │       └── llm_extractor.py
-│   │
-│   ├── tools/                     # ctx-aware
-│   │   ├── run_sql.py
-│   │   ├── explore_schema.py
-│   │   ├── define_metric.py
-│   │   ├── ingest_doc.py
-│   │   ├── remember.py
-│   │   └── ask_user.py
-│   │
-│   ├── tenancy/                   # 멀티유저
-│   │   ├── concierge.py
-│   │   └── encrypted_secrets.py
-│   │
-│   ├── discord/                   # ★ 1급 frontend
-│   │   ├── bot.py
-│   │   ├── commands/
-│   │   │   ├── connect.py
-│   │   │   ├── ingest.py
-│   │   │   ├── remember.py
-│   │   │   └── audit.py
-│   │   ├── session_router.py      # DM / thread 분기
-│   │   └── render.py
-│   │
-│   └── adapters/                  # outbound
-│       ├── llm/openai_.py
-│       ├── db/postgres_explorer.py
-│       └── storage/encrypted_sqlite.py
-│
-├── tests/
-│   ├── unit/
-│   ├── safety/                    # 회귀 12개 (CI gate)
-│   └── e2e/
-└── docs/
-```
-
-`★` 3개가 **확장 핵심** — 다음 Chap 3에서 자세히.
-
----
-
-# Chap 3. 확장성 — 3개의 chain/strategy 패턴
-
-## 3.1 왜 chain/strategy 패턴인가
-
-V1엔 *가장 단순한 1개씩* 만 구현, V1.5/V2에서 *어댑터를 추가* 하는 방식으로 확장.
-
-이게 *진짜* "확장 가능"의 의미. 단순히 *"미루는 것"* 이 아니라, *V1엔 단순 구현으로 동작하지만 추상은 박혀 있어서 V1.5에 새 구현을 끼울 때 기존 코드를 안 건드리는* 것.
-
-비유: 콘센트(포트)와 가전(어댑터)의 관계. V1엔 LED 전구 하나, V1.5엔 선풍기 추가, V2엔 스마트 조명 추가 — 콘센트는 그대로.
-
-## 3.2 ★ ① SafetyPipeline — DB 강건성의 모이는 곳
-
-### 패턴
-
-```python
-class SafetyLayer(Protocol):
-    name: str
-    async def check(self, sql: str, ctx: HarnessContext) -> Verdict: ...
-    # Verdict: allow | block(reason) | confirm(plan) | transform(sql')
-
-class SafetyPipeline:
-    def __init__(self, layers: list[SafetyLayer]):
-        self.layers = layers
-    
-    async def evaluate(self, sql, ctx) -> SafetyResult:
-        current = sql
-        for layer in self.layers:
-            v = await layer.check(current, ctx)
-            if v.kind == 'block':     return SafetyResult.blocked(...)
-            if v.kind == 'confirm':   await ctx.request_confirm(v.plan)
-            if v.kind == 'transform': current = v.transformed_sql
-        return SafetyResult.allowed(current)
-```
-
-### V1 → V2 진화
-
-| 버전 | layers (순서대로) | 비용 |
-|---|---|---|
-| **V1** (껍데기) | `WhitelistGate` (SELECT/WITH로 시작) + `TimeoutSetter` (statement_timeout) | ~200 LOC |
-| **V1.5** | + `SqlglotASTGate` (AST 기반 정밀 검증) + `FunctionDenylist` (pg_sleep 등 차단) + `LimitInjector` (SELECT에 LIMIT 자동 부착) + `GUCPackSetter` (5 GUC pack) + `RateLimit` (token bucket) | +500 LOC |
-| **V2** | + `CostGate` (EXPLAIN multi-metric) + per-engine pipeline (PG vs BQ) + `AuditHashChain` | +400 LOC |
-
-### 의미 — *DB 정보 완성도가 낮아도 동작*
-
-V1.5에 *"description 없는 컬럼은 자동 description 생성"* 같은 layer 추가 가능:
-
-```python
-class AutoDescribeLayer:
-    """description이 없는 컬럼을 LLM으로 생성해 시멘틱에 주입"""
-    async def check(self, sql, ctx):
-        missing = [c for c in ctx.referenced_columns(sql) if not c.description]
-        for col in missing:
-            desc = await self.llm.describe_column(col)
-            ctx.semantic.upsert_dimension(col.name, description=desc)
-        return Verdict.allow()
-```
-
-기존 컴포넌트 변경 0. 그냥 layer 1개 추가.
-
-V1.5엔 *"DB 강건성"* 차별화의 핵심 layer들이 들어옴 — *불완전한 메타데이터를 자동 보강* 하는 layer들. 그게 우리 제품의 한 포인트.
-
----
-
-## 3.3 ★ ② MemoryService — Hermes 기억의 3축 분리
-
-### 패턴
-
-```python
-# 3축이 독립적으로 진화
-class FactStorePort(Protocol):
-    async def put(self, fact: Fact) -> str: ...
-    async def query(self, scope: Scope, filter: dict) -> list[Fact]: ...
-
-class RecallStrategy(Protocol):
-    async def recall(self, store, ctx, question: str) -> list[Fact]: ...
-
-class FactExtractor(Protocol):
-    async def extract(self, recent_events: list[Event]) -> list[Fact]: ...
-
-class MemoryService:
-    def __init__(self, store, recall, extractor): ...
-```
-
-### V1 → V2 진화
-
-| 버전 | Store | Recall | Extractor |
-|---|---|---|---|
-| **V1** | `InMemoryFactStore` | `AllFactsRecall` (전부 주입) | `ManualOnly` (`/remember` 명령만) |
-| **V1.5** | `SqliteFactStore` (영속) | `KeywordRecall` (질문 키워드 매칭) | `LLMExtractor` (대화에서 반복 패턴 추출) |
-| **V2** | `SqliteFactStore` | `EmbeddingRecall` (벡터 유사도) | + `ConflictResolver` (사용자가 X 했다가 not-X 했을 때) |
-| **V2.5** | `PostgresFactStore` (멀티 인스턴스) | `HybridRecall` (keyword + embedding + recency) | + 신뢰도 점수 |
-
-### 의미 — *진짜* Hermes-style
-
-V1엔 *"`/remember "우리 fiscal year는 7월 시작"` 명령으로만 fact 등록 + 모든 facts를 매번 전부 주입"* — 단순. 그러나 *facts가 영속화되고 모든 세션에서 적용* 됨.
-
-V1.5에 LLMExtractor 추가하면 *"사용자가 매번 'KST 기준'을 강조하니 자동으로 facts에 시간대=KST 저장"* 같은 자동 학습. V2에 EmbeddingRecall 추가하면 *"질문과 관련된 facts만 가져와 토큰 절약 + 신호 강화"*.
-
-핵심: V1.5/V2 변경 시 `harness/` 코드는 한 줄도 안 건드림. `memory/` 안에 새 클래스 추가.
-
----
-
-## 3.4 ★ ③ IngestionPipeline — 문서 → 시멘틱 레이어
-
-### 패턴
-
-```python
-class DocumentSource(Protocol):
-    name: str
-    async def fetch(self, ref: str) -> Document: ...
-
-class DocumentExtractor(Protocol):
-    name: str
-    target_kinds: set[str]   # {'metric', 'dimension', 'business_rule'}
-    async def extract(self, doc, schema_summary) -> list[Candidate]: ...
-
-class IngestionPipeline:
-    def __init__(self, sources, extractors): ...
-    
-    async def ingest(self, source_kind, ref, ctx) -> list[Candidate]:
-        doc = await self.sources[source_kind].fetch(ref)
-        candidates = []
-        for ext in self.extractors:
-            candidates += await ext.extract(doc, ctx.schema_summary)
-        return candidates   # → 사용자 confirm UI로
-```
-
-### V1 → V2 진화
-
-| 버전 | Sources | Extractors |
-|---|---|---|
-| **V1** | `FileUpload` (MD/PDF/TXT) | `LLMExtractor` (단일 prompt) |
-| **V1.5** | + `URLFetch` (HTML/PDF) | + `DDLExtractor` (DB schema 파일 직접 파싱) |
-| **V2** | + `NotionMCP`, `ConfluenceMCP` | + `HybridExtractor` (LLM + rule-based) |
-| **V2.5** | + `GitHubMarkdown`, `GoogleDriveMCP` | + `ChunkedRAGExtractor` (긴 문서) |
-
-### 흐름 — V1 walking skeleton
-
-```
-사용자: /ingest <파일 첨부> metric_definitions.md
-   │
-   ▼
-FileUpload.fetch()
-   │ doc.text = "매출은 SUM(orders.amount) WHERE status != 'cancelled'..."
-   ▼
-LLMExtractor.extract(doc, current_schema)
-   │ LLM prompt:
-   │  "다음 문서에서 metric/dimension/business_rule을 찾아 JSON으로 반환:
-   │   - 현재 DB 스키마: {orders, order_items, products, customers}
-   │   - 문서: {doc.text}
-   │  ..."
-   │ → candidates = [
-   │     {kind: 'metric', name: 'total_revenue', 
-   │      expr: 'SUM(orders.amount)', source_doc: 'metric_definitions.md#L12'},
-   │     {kind: 'business_rule', name: 'exclude_cancelled',
-   │      sql: "status != 'cancelled'", applies_to: ['total_revenue']},
-   │   ]
-   ▼
-Discord embed (button view):
-   "이 문서에서 다음을 찾았어요:
-    📊 METRIC: total_revenue
-       SUM(orders.amount)
-       출처: metric_definitions.md L12
-    
-    📋 RULE: exclude_cancelled (적용: total_revenue)
-       status != 'cancelled'
-    
-    [✅ 모두 등록] [개별 선택] [❌ 취소]"
-   ▼
-사용자 ✅
-   ▼
-SemanticLayer.add(...) + source_doc reference 보존
-   ▼
-이후 /ask "이번 달 매출" 
-   ▼ LLM이 SemanticLayer에서 total_revenue 자동 매칭
-   ▼ SQLComposer가 exclude_cancelled rule을 WHERE에 자동 주입
-```
-
-V1.5에서 같은 문서 재업로드 시 *diff* 표시 → 사용자가 update/keep/remove 선택. 즉 *"문서가 곧 단일 진실 소스"* 패턴.
-
-### 의미 — *비즈니스 맥락을 잘 이해*
-
-기둥 ③의 진짜 의미. 사용자가 사내 정의를 *문서로* 가지고 있다면 그대로 업로드 → 자동 추출 → confirm → 적용. *Wren AI의 MDL 수동 작성* 대비 비교 우위.
-
----
-
-## 3.5 V1엔 무엇을 박아두고 무엇을 미루는가
-
-```
-V1에 반드시 있어야 할 추상 (포트·인터페이스만, ~300 LOC 추가 비용):
-  core/ports/safety.py        — SafetyLayer Protocol
-  core/ports/memory.py        — FactStorePort, RecallStrategy, FactExtractor
-  core/ports/ingestion.py     — DocumentSource, DocumentExtractor
-  
-  safety/pipeline.py          — SafetyPipeline 클래스
-  memory/service.py           — MemoryService 클래스
-  ingestion/pipeline.py       — IngestionPipeline 클래스
-
-V1엔 각 추상의 단순 구현 1~2개씩만 (~700 LOC):
-  safety/layers/{whitelist_gate, timeout_setter}.py
-  memory/stores/inmemory.py + recall/all_facts.py + extractors/manual.py
-  ingestion/sources/file.py + extractors/llm_extractor.py
-```
-
-추상 비용: ~300 LOC.
-V1.5에서 절감하는 비용: ~500 LOC (chain 패턴이 없었다면 매번 코어 수정).
-V2에서 절감하는 비용: ~1,000 LOC.
-**초기 투자가 빠르게 회수**.
-
----
-
-# Chap 4. V1 walking skeleton
-
-## 4.1 V1에 들어가는 것
-
-| 영역 | V1 |
-|---|---|
-| Discord 진입 | DM 메시지 / 채널 `@bot` 멘션 → 자동 thread / thread reply |
-| 명령 | `/connect`, `/ingest <파일>`, `/remember "..."`, `/audit me`, `/forget <id>` |
-| 자연어 | DM 또는 thread 안에서 그냥 묻기 |
-| LLM | OpenAI `gpt-4.1-mini` 단일 |
-| DB | PostgreSQL only |
-| 응답 | 텍스트 (≤50행) 또는 CSV 첨부 (>50행) |
-| Safety | SafetyPipeline + V1 layer 2개 (WhitelistGate + TimeoutSetter) |
-| Memory | MemoryService + V1 구현 (InMemory + AllFacts + Manual) |
-| Ingestion | IngestionPipeline + V1 (FileUpload + LLMExtractor) |
-| 영속화 | secrets/audit/sessions/facts → SQLite. conversation → 영속 |
-| 도구 | run_sql · explore_schema · ingest_doc · define_metric · remember · ask_user |
-| 호스팅 | Oracle Cloud Always Free 또는 fly.io free |
-
-## 4.2 V1에 *안* 들어가는 것
-
-| 항목 | 미루는 곳 | 이유 |
-|---|---|---|
-| `run_code` / `write_code` (Python 실행) | **영구 삭제** | read-only 약속을 무력화. 부활 시 sandbox 필수 |
-| Cost gate (EXPLAIN 기반) | V1.5 | L1 + L0 timeout으로 1차 충분 |
-| Function denylist | V1.5 | WhitelistGate가 1차 방어 |
-| Rate limit | V1.5 | 트라이얼 두 자릿수 |
-| Auto fact extraction | V1.5 (LLMExtractor 추가) | Manual `/remember` 로 시작 |
-| URL/Notion 문서 입력 | V1.5 / V2 | FileUpload 1개로 시작 |
-| Embedding recall | V2 | 토큰 절약 효과는 facts가 많아질 때 |
-| Persistent View / streaming / PNG paginate | V1.7 | Discord SDK 깊은 영역 |
-| Anthropic / NIM | V1.5 (contract test 후) | OpenAI 하나로 시작 |
-| 길드 channel 활성화 admin 게이트 | (멘션 패턴이 대체) | thread는 채널 권한 상속 |
-| Audit hash chain | V2 | 트라이얼은 SQLite append-only로 충분 |
-| `visualize` (PNG 차트) | V1.7 | V1엔 CSV 첨부로 충분 |
-
-## 4.3 V1 LOC 추정
-
-```
-core/                ~600 LOC  (ports + types + identity)
-harness/             ~700 LOC  (context, session, loop, system_prompt, tool_registry)
-semantic/            ~600 LOC  (types, layer, sql_composer, store)
-safety/              ~250 LOC  (pipeline + 2 layers)
-memory/              ~350 LOC  (service + 3 simple impls)
-ingestion/           ~300 LOC  (pipeline + 2 adapters)
-tools/               ~700 LOC  (6 tools × 평균 ~120 LOC)
-tenancy/             ~400 LOC  (concierge, encrypted_secrets, factstore)
-discord/             ~800 LOC  (bot, commands 5개, session_router, render)
-adapters/            ~500 LOC  (openai, postgres_async, encrypted_sqlite)
-tests/               ~900 LOC  (unit + safety 회귀 + e2e mock)
-
-총 V1: ~6,100 LOC 신규
-```
-
-## 4.4 일정 — 4.5~5주 솔로
-
-```
-Week 1 — core + harness
-   • core/ports/ (5 + safety/memory/ingestion 추상 3)
-   • HarnessContext, Session (in-memory + 영속 facts), agent_loop
-   • frontends_dev/cli.py 로 단위 검증
-
-Week 2 — semantic + safety + 첫 도구들
-   • semantic/* 도메인 모델 + SQLComposer
-   • safety/pipeline.py + layers/(whitelist, timeout)
-   • tools/{run_sql, explore_schema}
-   • adapters/{openai, postgres_async, encrypted_sqlite}
-   • tests/safety/ 회귀 12개 (CI gate)
-
-Week 3 — memory + ingestion + 나머지 도구
-   • memory/service + 3 impl
-   • ingestion/pipeline + 2 adapter
-   • tools/{ingest_doc, define_metric, remember, ask_user}
-   • tenancy/{concierge, encrypted_secrets, factstore}
-
-Week 4 — Discord
-   • bot.py + setup_hook
-   • commands/{connect, ingest, remember, audit, forget}
-   • session_router (DM / channel @mention → thread / thread reply)
-   • render (텍스트 / CSV 첨부)
-   • Modal + 동의 button view
-
-Week 5 — polish + e2e + 출하
-   • README + DEPLOY 가이드
-   • bench/ 데모 시나리오 1개
-   • CI YAML
-   • 첫 길드 배포
-
-총: 4.5~5주 솔로 풀타임.
-```
-
-3주는 *기존 코드 70% 활용* 가정이었음. wipe 기준엔 4.5~5주가 정직.
-
-## 4.5 회귀 12개 (V1 CI gate)
-
-| # | 입력 | 기대 |
-|---|---|---|
-| 1 | `DROP TABLE users` | WhitelistGate block (DDL) |
-| 2 | `; DELETE FROM t; --` | WhitelistGate block (multi-statement) |
-| 3 | `INSERT INTO t VALUES (1)` | WhitelistGate block |
-| 4 | `UPDATE t SET x=1` | WhitelistGate block |
-| 5 | `WITH x AS (INSERT INTO t VALUES (1)) SELECT * FROM x` | WhitelistGate block (CTE 안 INSERT — V1.5 SqlglotASTGate 이전엔 *최상위 노드만* 보지만, WITH+INSERT는 첫 키워드가 WITH라 통과될 수 있음 → V1엔 *INSERT 키워드 검색* 으로 fail-closed) |
-| 6 | `SELECT * FROM nonexistent` | PG error (gate 통과, 실행 단계 실패) |
-| 7 | `SELECT pg_sleep(60)` | (V1엔 timeout 30s에서 cut. V1.5 FunctionDenylist 추가 시 명시 block.) |
-| 8 | `SELECT * FROM huge_table` (50k rows) | row_limit 1000 truncate |
-| 9 | `SELECT 1` (정상) | allow |
-| 10 | `WITH a AS (SELECT 1) SELECT * FROM a` (정상 CTE) | allow |
-| 11 | `EXPLAIN SELECT 1` | allow |
-| 12 | `` (빈 문자열) | block (parse_error) |
-
-V1엔 단순한 회귀. V1.5에서 ASTGate 추가 시 회귀가 *훨씬 정밀해짐*. 그때 추가:
-- `SELECT public.pg_sleep(60)` (schema-qualified) → FunctionDenylist
-- `COPY foo TO PROGRAM 'curl ...'` → ASTGate
-- `EXPLAIN ANALYZE DELETE FROM t` → ASTGate
-
----
-
-# Chap 5. 의견 정리
-
-## 5.1 왜 이 설계가 작동할 거라 보는가
-
-**(1) "오픈소스의 정체성"이 차별점에 부합**
-
-Vanna/Wren/SQLCoder가 *질문→SQL 파이프라인 자체* 는 완성도 높음. 우리가 또 *"GPT-4보다 더 좋은 SQL을 만든다"* 로 경쟁하면 *오픈소스 정체성* 이 약함 — 모델 fine-tuning은 데이터·GPU 싸움.
-
-대신 우리는 *"불완전한 DB에서도 잘 동작" + "문서로 비즈니스 맥락 학습" + "Discord에서 팀이 같이 묻고 답을 받음" + "모든 정의·대화를 기억"* — 이 4가지 조합은 기존 오픈소스에 *없음*. 그게 정체성.
-
-**(2) chain/strategy 패턴이 *체계화된 강건성 연구* 의 코드 토대**
-
-DB 강건성 차별화는 *"description 없으면 자동 생성"* 같은 *상황별 커스텀 전략* 의 모음. V1.5의 SafetyPipeline layer들이 그 전략을 *추가 가능한 형태* 로 받아들임. 새 전략 = 새 layer 1개.
-
-연구자 입장에선 *Hint Vector* 같은 *DB 구조에 대한 근본적 강건성* 도 한 layer로 들어올 수 있음. 즉 *연구→제품* 흐름이 자연스러움.
-
-**(3) 멘션+thread 패턴이 Discord native**
-
-DM vs Channel 양분이 사라지고, *thread* 가 *분석 노트* 가 됨. Discord 사용자 멘탈 모델과 일치. 가짜연구소 같은 use case도 자연스럽게 들어옴.
-
-**(4) Hermes 기억이 *실무 가치* 와 직결**
-
-*"우리 회사 매출 정의는 이거"* 한 번 가르치면 *모든 세션에서 적용*. 매번 prompt에 풀어 넣는 LLM과 차별. facts가 쌓일수록 *제품 가치가 누적* 됨 — 사용자 lock-in.
-
-## 5.2 한계와 trade-off (정직하게)
-
-| 한계 | 영향 | 대응 |
-|---|---|---|
-| 의미적 정확성의 정량 평가 부재 | "안전하게 틀린 답"이 가장 위험한데 V1엔 validation loop 없음 | V1.5에 golden query set + answer citation. 그러나 본질은 *비즈니스마다 다름* — 우리 측 한계 |
-| LLM 비용 노출 | 토큰 폭주 시 사용자 비용 증가 | V1.5에 rate limit + per-user 토큰 cap |
-| 첫 출하까지 4.5~5주 | 빠른 검증 어려움 | walking skeleton이지만 wipe라 4주 미만은 무리 |
-| chain/strategy 추상 비용 | V1 LOC +300 | V1.5/V2에서 절감되는 비용으로 회수 (계산상 흑자) |
-| Discord 플랫폼 락-인 | Discord 외 사용 시 어댑터 추가 필요 | 4기둥 중 Discord는 *frontend 어댑터* 일 뿐, 핵심은 변하지 않음. Slack 어댑터 추가 시 ~500 LOC |
-| 의미 추출 LLM 환각 | 문서에서 잘못된 metric 추출 가능 | 사용자 confirm 단계가 1차 방어. V1.5에 validator layer |
-| in-memory recall (V1) | facts 많아지면 토큰 폭증 | V1.5에 KeywordRecall, V2에 EmbeddingRecall |
-
-## 5.3 비교 — 기존 오픈소스 대비 위치
-
-| 영역 | Vanna AI | Wren AI | SQLCoder | **Lang2SQL (우리)** |
-|---|---|---|---|---|
-| 자연어 → SQL | ✅ RAG | ✅ MDL 기반 | ✅ fine-tuned LLM | ✅ RAG + 시멘틱 + harness |
-| DB 직접 연결 | ✅ | ✅ | ✅ | ✅ |
-| Semantic layer | ❌ | ✅ (수동 MDL) | ❌ | ✅ (**문서 자동 추출**) |
-| 대화 기억 | ❌ | ❌ | ❌ | ✅ (**Hermes**) |
-| 멀티유저 협업 | 2.0에서 추가 | enterprise BI | ❌ | ✅ (**Discord thread**) |
-| 불완전 DB 보강 | ❌ (학습 데이터 의존) | ❌ | ❌ | ✅ (**V1.5+ 자동 보강 layer**) |
-| 사용 UI | Web UI | Web UI | CLI | **Discord** |
-| 확장 모델 | 단일 architecture | enterprise platform | 단일 LLM | **chain/strategy 3개** |
-
-**위치 요약**: *Vanna의 자연어→SQL + Wren의 시멘틱 + 우리만의 (문서 자동 추출 + Discord 협업 + Hermes 기억 + DB 강건성 layer)*.
-
-## 5.4 한 줄
-
-> **"Discord 멘션으로 thread를 띄우고, 문서를 넣어 비즈니스 맥락을 학습시키고, DB가 불완전해도 견디고, 모든 대화·정의를 기억하는 오픈소스 분석 에이전트."**
-
----
-
-# 결정 필요 사항
-
-V1 착수 전 답이 있으면 좋은 항목:
-
-1. **첫 배포 Discord 길드** — 본인 테스트 길드 / 가짜연구소 / 기타?
-2. **OpenAI API 키** — 본인 키 / 팀 키 / 무료 한도?
-3. **레포 새 이름 vs `lang2sql/` 유지** — 권고: 유지 + 새 브랜치 (`feature/v4-rebuild`)
-4. **첫 데모 시나리오 1개** — bench/ 에 들어갈 *완성된 use case*. 예: "이커머스 매출 분석 — 4 테이블, 3 metric, 1 문서, 5개 질문"
-5. **NIM 도입 시점** — V1.5에 contract test 후 / V2까지 보류?
-
-답해 주시면 v4-final 로 마무리하고 Week 1 PR-1 (core + harness) 부터 시작.
-
-— end —
diff --git a/docs/production_test_guide.md b/docs/production_test_guide.md
deleted file mode 100644
index e40bb2a..0000000
--- a/docs/production_test_guide.md
+++ /dev/null
@@ -1,1003 +0,0 @@
-# Production Test Guide — v2 Migration
-
-이 문서는 v2 마이그레이션에서 수행된 모든 변경 사항을 **실제 API 키와 실제 DB**를 사용해 프로덕션 수준에서 검증하는 가이드입니다.
-
----
-
-## 전제 조건
-
-```bash
-# 의존성 설치
-uv sync --group dev
-
-# .env 설정 (아래 각 섹션에서 사용할 프로바이더 항목을 활성화)
-cp .env.example .env
-```
-
-모든 Python 스니펫은 프로젝트 루트에서 실행합니다:
-
-```bash
-cd /path/to/lang2sql
-```
-
----
-
-## 1. LLM 통합 — 7개 프로바이더
-
-각 프로바이더는 `.env`에서 해당 항목을 설정하고 독립적으로 검증합니다.
-
-### 1-A. Anthropic
-
-```
-# .env
-LLM_PROVIDER=anthropic
-ANTHROPIC_API_KEY=sk-ant-...
-ANTHROPIC_LLM_MODEL=claude-sonnet-4-6
-```
-
-```python
-from lang2sql.integrations.llm.anthropic_ import AnthropicLLM
-import os
-
-llm = AnthropicLLM(model="claude-sonnet-4-6", api_key=os.getenv("ANTHROPIC_API_KEY"))
-resp = llm.invoke([{"role": "user", "content": "Respond with just 'OK'"}])
-assert resp.strip() == "OK", f"Unexpected: {resp}"
-print("Anthropic LLM ✓")
-```
-
-**확인 포인트**
-- `invoke()` 반환값이 `str` 타입
-- system 메시지가 `role: system`으로 분리되어 Anthropic Messages API에 전달됨
-
----
-
-### 1-B. OpenAI
-
-```
-# .env
-LLM_PROVIDER=openai
-OPEN_AI_KEY=sk-proj-...
-OPEN_AI_LLM_MODEL=gpt-4o
-```
-
-```python
-from lang2sql.integrations.llm.openai_ import OpenAILLM
-import os
-
-llm = OpenAILLM(model="gpt-4o", api_key=os.getenv("OPEN_AI_KEY"))
-resp = llm.invoke([{"role": "user", "content": "Respond with just 'OK'"}])
-assert isinstance(resp, str) and len(resp) > 0
-print("OpenAI LLM ✓")
-```
-
----
-
-### 1-C. Azure OpenAI
-
-```
-# .env
-LLM_PROVIDER=azure
-AZURE_OPENAI_LLM_ENDPOINT=https://RESOURCE.openai.azure.com/
-AZURE_OPENAI_LLM_KEY=...
-AZURE_OPENAI_LLM_MODEL=gpt4o          # Azure deployment name
-AZURE_OPENAI_LLM_API_VERSION=2024-07-01-preview
-```
-
-```python
-from lang2sql.integrations.llm.azure_ import AzureOpenAILLM
-import os
-
-llm = 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", "2024-07-01-preview"),
-    api_key=os.getenv("AZURE_OPENAI_LLM_KEY"),
-)
-resp = llm.invoke([{"role": "user", "content": "Respond with just 'OK'"}])
-assert isinstance(resp, str) and len(resp) > 0
-print("Azure OpenAI LLM ✓")
-```
-
----
-
-### 1-D. Google Gemini
-
-```
-# .env
-LLM_PROVIDER=gemini
-GEMINI_API_KEY=AIza...
-GEMINI_LLM_MODEL=gemini-2.0-flash-lite
-```
-
-```python
-from lang2sql.integrations.llm.gemini_ import GeminiLLM
-import os
-
-llm = GeminiLLM(model="gemini-2.0-flash-lite", api_key=os.getenv("GEMINI_API_KEY"))
-resp = llm.invoke([{"role": "user", "content": "Respond with just 'OK'"}])
-assert isinstance(resp, str) and len(resp) > 0
-print("Gemini LLM ✓")
-```
-
----
-
-### 1-E. AWS Bedrock
-
-```
-# .env
-LLM_PROVIDER=bedrock
-AWS_BEDROCK_LLM_ACCESS_KEY_ID=AKI...
-AWS_BEDROCK_LLM_SECRET_ACCESS_KEY=...
-AWS_BEDROCK_LLM_REGION=us-east-1
-AWS_BEDROCK_LLM_MODEL=anthropic.claude-3-5-sonnet-20241022-v2:0
-```
-
-```python
-from lang2sql.integrations.llm.bedrock_ import BedrockLLM
-import os
-
-llm = 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"),
-)
-resp = llm.invoke([{"role": "user", "content": "Respond with just 'OK'"}])
-assert isinstance(resp, str) and len(resp) > 0
-print("Bedrock LLM ✓")
-```
-
-**확인 포인트**: Bedrock Converse API 포맷 — `role: system`이 `system` 블록으로 분리되는지 확인
-
-```python
-# system 메시지 분리 확인
-resp = llm.invoke([
-    {"role": "system", "content": "Always respond in one word."},
-    {"role": "user", "content": "Say hello"},
-])
-assert len(resp.split()) <= 3, f"System prompt not applied: {resp}"
-print("Bedrock system message separation ✓")
-```
-
----
-
-### 1-F. Ollama (로컬)
-
-```
-# Ollama 서버 실행 필요
-# brew install ollama && ollama serve
-# ollama pull llama3.2
-
-# .env
-LLM_PROVIDER=ollama
-OLLAMA_LLM_BASE_URL=http://localhost:11434
-OLLAMA_LLM_MODEL=llama3.2
-```
-
-```python
-from lang2sql.integrations.llm.ollama_ import OllamaLLM
-import os
-
-llm = OllamaLLM(
-    model=os.environ["OLLAMA_LLM_MODEL"],
-    base_url=os.getenv("OLLAMA_LLM_BASE_URL", "http://localhost:11434"),
-)
-resp = llm.invoke([{"role": "user", "content": "Say hello in one word"}])
-assert isinstance(resp, str) and len(resp) > 0
-print("Ollama LLM ✓")
-```
-
----
-
-### 1-G. HuggingFace Inference API
-
-```
-# .env
-LLM_PROVIDER=huggingface
-HUGGING_FACE_LLM_REPO_ID=mistralai/Mistral-7B-Instruct-v0.3
-HUGGING_FACE_LLM_API_TOKEN=hf_...
-# HUGGING_FACE_LLM_ENDPOINT=https://... (Dedicated Endpoint 사용 시)
-```
-
-```python
-from lang2sql.integrations.llm.huggingface_ import HuggingFaceLLM
-import os
-
-llm = HuggingFaceLLM(
-    repo_id=os.getenv("HUGGING_FACE_LLM_REPO_ID"),
-    api_token=os.getenv("HUGGING_FACE_LLM_API_TOKEN"),
-)
-resp = llm.invoke([{"role": "user", "content": "Say hello"}])
-assert isinstance(resp, str) and len(resp) > 0
-print("HuggingFace LLM ✓")
-```
-
----
-
-## 2. Embedding 통합 — 6개 프로바이더
-
-### 2-A. OpenAI Embedding
-
-```python
-from lang2sql.integrations.embedding.openai_ import OpenAIEmbedding
-import os
-
-emb = OpenAIEmbedding(
-    model="text-embedding-3-small",
-    api_key=os.getenv("OPEN_AI_KEY"),
-)
-vec = emb.embed_query("주문 테이블의 주문 ID")
-assert isinstance(vec, list) and len(vec) == 1536
-print(f"OpenAI Embedding ✓ (dim={len(vec)})")
-
-vecs = emb.embed_texts(["orders", "customers"])
-assert len(vecs) == 2 and len(vecs[0]) == 1536
-print("OpenAI batch embed ✓")
-```
-
----
-
-### 2-B. Azure OpenAI Embedding
-
-```
-# .env
-EMBEDDING_PROVIDER=azure
-AZURE_OPENAI_EMBEDDING_ENDPOINT=https://RESOURCE.openai.azure.com/
-AZURE_OPENAI_EMBEDDING_KEY=...
-AZURE_OPENAI_EMBEDDING_MODEL=textembeddingada002
-AZURE_OPENAI_EMBEDDING_API_VERSION=2023-09-15-preview
-```
-
-```python
-from lang2sql.integrations.embedding.azure_ import AzureOpenAIEmbedding
-import os
-
-emb = 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"),
-    api_key=os.getenv("AZURE_OPENAI_EMBEDDING_KEY"),
-)
-vec = emb.embed_query("주문 데이터")
-assert isinstance(vec, list) and len(vec) > 0
-print(f"Azure Embedding ✓ (dim={len(vec)})")
-```
-
----
-
-### 2-C. Ollama Embedding
-
-```
-# .env
-EMBEDDING_PROVIDER=ollama
-OLLAMA_EMBEDDING_MODEL=nomic-embed-text
-OLLAMA_EMBEDDING_BASE_URL=http://localhost:11434
-```
-
-```python
-# ollama pull nomic-embed-text 먼저 실행 필요
-from lang2sql.integrations.embedding.ollama_ import OllamaEmbedding
-import os
-
-emb = OllamaEmbedding(
-    model=os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"),
-    base_url=os.getenv("OLLAMA_EMBEDDING_BASE_URL", "http://localhost:11434"),
-)
-vec = emb.embed_query("test")
-assert isinstance(vec, list) and len(vec) > 0
-print(f"Ollama Embedding ✓ (dim={len(vec)})")
-```
-
----
-
-### 2-D. AWS Bedrock Embedding
-
-```
-# .env
-EMBEDDING_PROVIDER=bedrock
-AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID=...
-AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY=...
-AWS_BEDROCK_EMBEDDING_REGION=us-east-1
-AWS_BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0
-```
-
-```python
-from lang2sql.integrations.embedding.bedrock_ import BedrockEmbedding
-import os
-
-emb = 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"),
-)
-vec = emb.embed_query("주문 데이터")
-assert isinstance(vec, list) and len(vec) == 1024   # Titan v2 기본 차원
-print(f"Bedrock Embedding ✓ (dim={len(vec)})")
-```
-
----
-
-### 2-E. Google Gemini Embedding
-
-```
-# .env
-EMBEDDING_PROVIDER=gemini
-GEMINI_EMBEDDING_API_KEY=AIza...
-EMBEDDING_MODEL=models/embedding-001
-```
-
-```python
-from lang2sql.integrations.embedding.gemini_ import GeminiEmbedding
-import os
-
-emb = GeminiEmbedding(
-    model=os.getenv("EMBEDDING_MODEL", "models/embedding-001"),
-    api_key=os.getenv("GEMINI_EMBEDDING_API_KEY"),
-)
-vec = emb.embed_query("주문 데이터")
-assert isinstance(vec, list) and len(vec) == 768
-print(f"Gemini Embedding ✓ (dim={len(vec)})")
-```
-
----
-
-### 2-F. HuggingFace Embedding (로컬 모델)
-
-```
-# .env
-EMBEDDING_PROVIDER=huggingface
-HUGGING_FACE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
-```
-
-```python
-# pip install sentence-transformers 필요
-from lang2sql.integrations.embedding.huggingface_ import HuggingFaceEmbedding
-import os
-
-emb = HuggingFaceEmbedding(
-    model=os.getenv("HUGGING_FACE_EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
-)
-vec = emb.embed_query("주문 데이터")
-assert isinstance(vec, list) and len(vec) == 384   # all-MiniLM-L6-v2 차원
-print(f"HuggingFace Embedding ✓ (dim={len(vec)})")
-```
-
----
-
-## 3. 환경변수 기반 Factory (`build_*_from_env`)
-
-`.env`에 원하는 프로바이더 설정을 넣고 아래를 실행합니다.
-
-```python
-from dotenv import load_dotenv
-load_dotenv()
-
-from lang2sql.factory import build_llm_from_env, build_embedding_from_env, build_db_from_env
-
-# LLM
-llm = build_llm_from_env()
-resp = llm.invoke([{"role": "user", "content": "Say 'ready'"}])
-assert isinstance(resp, str)
-print(f"build_llm_from_env ✓ → {resp[:40]}")
-
-# Embedding
-emb = build_embedding_from_env()
-vec = emb.embed_query("test")
-assert isinstance(vec, list) and len(vec) > 0
-print(f"build_embedding_from_env ✓ dim={len(vec)}")
-
-# DB
-db = build_db_from_env()
-# DB_TYPE=sqlite 인 경우 간단한 쿼리 실행 확인
-rows = db.execute("SELECT 1 AS val")
-assert rows[0]["val"] == 1
-print("build_db_from_env ✓")
-```
-
----
-
-## 4. 고급 컴포넌트 — 실제 LLM 호출
-
-아래 예제는 Anthropic LLM으로 작성됐으나 어떤 프로바이더든 사용 가능합니다.
-
-### 4-A. QuestionGate
-
-```python
-from dotenv import load_dotenv; load_dotenv()
-from lang2sql.factory import build_llm_from_env
-from lang2sql.components.gate.question_gate import QuestionGate
-from lang2sql.core.catalog import GateResult
-
-llm = build_llm_from_env()
-gate = QuestionGate(llm=llm)
-
-# 정상 쿼리 → suitable=True
-result: GateResult = gate("지난달 주문 건수를 알려줘")
-assert result.suitable is True, f"suitable False: {result.reason}"
-print(f"QuestionGate (suitable) ✓ — reason: {result.reason}")
-
-# 비적합 쿼리 → suitable=False
-result2: GateResult = gate("회사 전략 보고서를 통계 모델로 분석해줘")
-assert result2.suitable is False, "Expected unsuitable for data-science request"
-print(f"QuestionGate (not suitable) ✓ — reason: {result2.reason}")
-```
-
----
-
-### 4-B. TableSuitabilityEvaluator
-
-```python
-from lang2sql.factory import build_llm_from_env
-from lang2sql.components.gate.table_suitability import TableSuitabilityEvaluator
-
-llm = build_llm_from_env()
-evaluator = TableSuitabilityEvaluator(llm=llm)
-
-catalog = [
-    {"name": "orders", "description": "주문 정보 테이블", "columns": {"order_id": "주문 ID", "amount": "금액", "created_at": "생성일"}},
-    {"name": "users", "description": "사용자 정보 테이블", "columns": {"user_id": "유저 ID", "name": "이름"}},
-    {"name": "products", "description": "상품 정보 테이블", "columns": {"product_id": "상품 ID", "price": "가격"}},
-]
-
-filtered = evaluator("지난달 주문 건수", catalog)
-# orders 테이블은 반드시 포함되어야 함
-names = [t["name"] for t in filtered]
-assert "orders" in names, f"orders not found in {names}"
-print(f"TableSuitabilityEvaluator ✓ → {names}")
-```
-
----
-
-### 4-C. QuestionProfiler
-
-```python
-from lang2sql.factory import build_llm_from_env
-from lang2sql.components.enrichment.question_profiler import QuestionProfiler
-from lang2sql.core.catalog import QuestionProfile
-
-llm = build_llm_from_env()
-profiler = QuestionProfiler(llm=llm)
-
-profile: QuestionProfile = profiler("월별 주문 금액 추이")
-assert hasattr(profile, "is_timeseries")
-assert hasattr(profile, "intent_type")
-print(f"QuestionProfiler ✓ — is_timeseries={profile.is_timeseries}, intent={profile.intent_type}")
-
-profile2: QuestionProfile = profiler("상위 10개 고객 목록")
-assert hasattr(profile2, "has_ranking")
-print(f"QuestionProfiler ✓ — has_ranking={profile2.has_ranking}")
-```
-
----
-
-### 4-D. ContextEnricher
-
-```python
-from lang2sql.factory import build_llm_from_env
-from lang2sql.components.enrichment.context_enricher import ContextEnricher
-from lang2sql.core.catalog import QuestionProfile
-
-llm = build_llm_from_env()
-enricher = ContextEnricher(llm=llm)
-
-catalog = [
-    {"name": "orders", "description": "주문 정보", "columns": {"order_id": "주문 ID", "amount": "금액", "created_at": "생성일"}},
-]
-profile = QuestionProfile(is_aggregation=True, has_filter=True, intent_type="lookup")
-enriched = enricher("지난달 주문 건수", catalog, profile)
-
-assert isinstance(enriched, str) and len(enriched) > 0
-print(f"ContextEnricher ✓ — enriched: {enriched[:100]}")
-```
-
----
-
-## 5. HybridRetriever (BM25 + Vector RRF)
-
-```python
-from dotenv import load_dotenv; load_dotenv()
-from lang2sql.factory import build_embedding_from_env
-from lang2sql.components.retrieval.hybrid import HybridRetriever
-
-emb = build_embedding_from_env()
-
-catalog = [
-    {"name": "orders", "description": "주문 정보 테이블", "columns": {"order_id": "주문 ID", "amount": "금액", "created_at": "생성일"}},
-    {"name": "customers", "description": "고객 정보 테이블", "columns": {"customer_id": "고객 ID", "name": "이름", "email": "이메일"}},
-    {"name": "products", "description": "상품 정보 테이블", "columns": {"product_id": "상품 ID", "price": "가격"}},
-    {"name": "inventory", "description": "재고 테이블", "columns": {"product_id": "상품 ID", "stock": "재고 수량"}},
-]
-
-retriever = HybridRetriever(catalog=catalog, embedding=emb, top_n=2)
-result = retriever("지난달 주문 건수")
-
-assert len(result.schemas) <= 2
-names = [s["name"] for s in result.schemas]
-assert "orders" in names, f"orders missing from {names}"
-print(f"HybridRetriever ✓ → schemas={names}")
-
-# 비즈니스 문서 context 테스트
-from lang2sql.core.catalog import TextDocument
-docs = [TextDocument(id="doc1", content="주문은 created_at 컬럼 기준으로 집계합니다.")]
-retriever2 = HybridRetriever(catalog=catalog, embedding=emb, documents=docs, top_n=2)
-result2 = retriever2("주문 날짜 기준 집계")
-print(f"HybridRetriever with docs ✓ — context={result2.context}")
-```
-
----
-
-## 6. BaselineNL2SQL — End-to-End
-
-SQLite 예제 (가장 빠르게 검증 가능)
-
-```bash
-# 테스트 DB 준비
-python - <<'EOF'
-import sqlite3
-conn = sqlite3.connect("test_e2e.db")
-conn.execute("CREATE TABLE IF NOT EXISTS orders (order_id INTEGER PRIMARY KEY, amount REAL, created_at TEXT)")
-conn.execute("INSERT OR IGNORE INTO orders VALUES (1, 10000, '2024-01-15')")
-conn.execute("INSERT OR IGNORE INTO orders VALUES (2, 20000, '2024-01-20')")
-conn.execute("INSERT OR IGNORE INTO orders VALUES (3, 15000, '2024-02-05')")
-conn.commit()
-conn.close()
-print("test_e2e.db 생성 완료")
-EOF
-```
-
-```python
-from dotenv import load_dotenv; load_dotenv()
-from lang2sql.factory import build_llm_from_env
-from lang2sql.flows import BaselineNL2SQL
-from lang2sql.integrations.db.sqlalchemy_ import SQLAlchemyDB
-
-catalog = [
-    {
-        "name": "orders",
-        "description": "주문 정보 테이블",
-        "columns": {"order_id": "주문 ID", "amount": "주문 금액(원)", "created_at": "주문 생성일(YYYY-MM-DD)"},
-    }
-]
-
-llm = build_llm_from_env()
-db = SQLAlchemyDB("sqlite:///test_e2e.db")
-
-pipeline = BaselineNL2SQL(catalog=catalog, llm=llm, db=db, db_dialect="sqlite")
-rows = pipeline.run("전체 주문 건수")
-
-assert isinstance(rows, list) and len(rows) > 0
-print(f"BaselineNL2SQL ✓ — rows={rows}")
-```
-
----
-
-## 7. EnrichedNL2SQL — End-to-End (Full 7-Step Pipeline)
-
-```python
-from dotenv import load_dotenv; load_dotenv()
-from lang2sql.factory import build_llm_from_env, build_embedding_from_env
-from lang2sql.flows import EnrichedNL2SQL
-from lang2sql.integrations.db.sqlalchemy_ import SQLAlchemyDB
-from lang2sql.core.hooks import MemoryHook
-
-catalog = [
-    {
-        "name": "orders",
-        "description": "주문 정보 테이블. 고객이 결제한 주문 기록.",
-        "columns": {"order_id": "주문 ID", "amount": "주문 금액(원)", "created_at": "주문 생성일(YYYY-MM-DD)"},
-    }
-]
-
-llm = build_llm_from_env()
-emb = build_embedding_from_env()
-db = SQLAlchemyDB("sqlite:///test_e2e.db")
-hook = MemoryHook()
-
-pipeline = EnrichedNL2SQL(
-    catalog=catalog,
-    llm=llm,
-    db=db,
-    embedding=emb,
-    db_dialect="sqlite",
-    gate_enabled=True,
-    top_n=3,
-    hook=hook,
-)
-
-rows = pipeline.run("전체 주문 건수를 알려줘")
-assert isinstance(rows, list) and len(rows) > 0
-print(f"EnrichedNL2SQL ✓ — rows={rows}")
-
-# Hook 이벤트 확인 (QuestionGate ~ SQLExecutor까지 7단계 이벤트 발생 확인)
-components = {e.component for e in hook.events}
-print(f"  → 실행된 컴포넌트: {components}")
-assert "QuestionGate" in components
-assert "HybridRetriever" in components
-assert "SQLGenerator" in components
-assert "SQLExecutor" in components
-print("  → Hook 이벤트 ✓")
-```
-
-### 7-A. QuestionGate — ContractError 발생 확인
-
-```python
-from lang2sql.core.exceptions import ContractError
-import pytest
-
-try:
-    pipeline.run("우리 회사 마케팅 전략을 ML 모델로 예측해줘")
-    print("WARNING: ContractError가 발생해야 합니다")
-except ContractError as e:
-    print(f"ContractError ✓ — {e}")
-```
-
-### 7-B. gate_enabled=False — Gate 비활성화 확인
-
-```python
-pipeline_no_gate = EnrichedNL2SQL(
-    catalog=catalog, llm=llm, db=db, embedding=emb,
-    db_dialect="sqlite", gate_enabled=False,
-)
-rows2 = pipeline_no_gate.run("전체 주문 금액 합계")
-assert isinstance(rows2, list)
-print(f"EnrichedNL2SQL (no gate) ✓ — rows={rows2}")
-```
-
----
-
-## 8. CLI 명령어
-
-`.env`가 올바르게 설정된 상태에서 실행합니다.
-
-### 8-A. Baseline 플로우
-
-```bash
-lang2sql query "전체 주문 건수" \
-  --flow baseline \
-  --dialect sqlite
-```
-
-**예상 출력**: JSON 배열 (결과 행) 또는 `(결과 없음)`
-
----
-
-### 8-B. Enriched 플로우
-
-```bash
-lang2sql query "지난 1월 주문 금액 합계" \
-  --flow enriched \
-  --dialect sqlite \
-  --top-n 3
-```
-
----
-
-### 8-C. Gate 비활성화
-
-```bash
-lang2sql query "전체 주문 건수" \
-  --flow enriched \
-  --no-gate \
-  --dialect sqlite
-```
-
----
-
-### 8-D. 에러 케이스 확인
-
-```bash
-# LLM_PROVIDER를 잘못된 값으로 설정한 경우
-LLM_PROVIDER=unknown lang2sql query "test"
-# 예상: ValueError: Unknown LLM_PROVIDER: 'unknown'
-```
-
----
-
-## 9. DataHub 카탈로그 브릿지
-
-> DataHub GMS 서버가 실행 중이어야 합니다.
-
-```
-# .env
-DATAHUB_SERVER=http://localhost:8080
-```
-
-```python
-import os
-from dotenv import load_dotenv; load_dotenv()
-from lang2sql.integrations.catalog.datahub_ import DataHubCatalogLoader
-
-loader = DataHubCatalogLoader(gms_server=os.getenv("DATAHUB_SERVER", "http://localhost:8080"))
-catalog = loader.load()
-
-assert isinstance(catalog, list)
-assert len(catalog) > 0, "DataHub에 테이블이 하나 이상 존재해야 합니다"
-
-first = catalog[0]
-assert "name" in first and "description" in first and "columns" in first
-print(f"DataHubCatalogLoader ✓ — {len(catalog)}개 테이블 로드")
-print(f"  첫 번째: name={first['name']}, columns={list(first['columns'].keys())[:5]}")
-```
-
-### DataHub Catalog → EnrichedNL2SQL 연동
-
-```python
-from lang2sql.factory import build_llm_from_env, build_embedding_from_env, build_db_from_env
-
-llm = build_llm_from_env()
-emb = build_embedding_from_env()
-db = build_db_from_env()
-pipeline = EnrichedNL2SQL(
-    catalog=catalog,   # DataHub에서 로드한 catalog 사용
-    llm=llm, db=db, embedding=emb,
-    gate_enabled=True,
-)
-rows = pipeline.run("유니크한 유저 수를 카운트해줘")
-print(f"DataHub catalog + EnrichedNL2SQL ✓ — {rows}")
-```
-
----
-
-## 10. FAISSVectorStore (v2 벡터 스토어)
-
-```python
-from dotenv import load_dotenv; load_dotenv()
-from lang2sql.factory import build_embedding_from_env
-from lang2sql.integrations.vectorstore.faiss_ import FAISSVectorStore
-
-emb = build_embedding_from_env()
-
-# 문서 임베딩 및 저장
-texts = ["주문 테이블: 고객 주문 정보를 저장합니다", "고객 테이블: 회원 정보를 저장합니다"]
-vectors = emb.embed_texts(texts)
-
-store = FAISSVectorStore(index_path="/tmp/test_faiss.idx")
-store.upsert(ids=["doc0", "doc1"], vectors=vectors)
-
-# 검색
-query_vec = emb.embed_query("주문 정보")
-results = store.search(query_vec, k=2)
-
-assert len(results) > 0
-assert results[0][0] in ["doc0", "doc1"]
-print(f"FAISSVectorStore ✓ — top result: {results[0]}")
-
-# 저장/로드
-store.save()
-loaded = FAISSVectorStore.load("/tmp/test_faiss.idx")
-results2 = loaded.search(query_vec, k=1)
-assert results2[0][0] == results[0][0]
-print("FAISSVectorStore save/load ✓")
-```
-
----
-
-## 11. Streamlit UI 수동 검증
-
-```bash
-lang2sql run-streamlit
-# 또는
-streamlit run interface/streamlit_app.py
-```
-
-### 체크리스트
-
-| 항목 | 확인 방법 | 통과 조건 |
-|------|-----------|-----------|
-| 홈 페이지 | `http://localhost:8501` 접속 | 에러 없이 로드 |
-| Lang2SQL — Baseline | 워크플로우 체크박스 해제 → "쿼리 실행" | 결과 테이블 렌더링 |
-| Lang2SQL — Enriched | 체크박스 선택 → "쿼리 실행" | 결과 테이블 렌더링 |
-| Dialect 선택 | `sqlite` → `postgresql` 전환 | 드롭다운 변경 반영 |
-| 오류 표시 | 연결 불가 DB 설정 후 실행 | `st.error()` 에러 박스 |
-| ChatBot 페이지 | `🤖 ChatBot` 탭 클릭 | 에러 없이 로드 |
-| 설정 페이지 | `⚙️ 설정` 탭 클릭 | 에러 없이 로드 |
-| Graph Builder 페이지 없음 | 네비게이션 탭 확인 | 탭 목록에 없어야 함 |
-
----
-
-## 12. ChatBot — LangGraph + 수정된 `search_database_tables`
-
-> `DATAHUB_SERVER`가 설정되어 있어야 합니다. DataHub 없이 검색 시 에러 응답(`{"error": True, ...}`)을 반환합니다.
-
-```python
-import os
-from dotenv import load_dotenv; load_dotenv()
-
-# 12-A. 모듈 임포트 무결성 확인 (핵심: retrieval.py 삭제 이후 임포트 성공 확인)
-from utils.llm.tools import search_database_tables, get_glossary_terms, get_query_examples
-print("utils.llm.tools import ✓")
-
-from utils.llm.chatbot import ChatBot
-print("utils.llm.chatbot import ✓")
-
-# 12-B. ChatBot 인스턴스 생성
-bot = ChatBot(
-    openai_api_key=os.getenv("OPEN_AI_KEY"),
-    model_name="gpt-4o-mini",
-    gms_server=os.getenv("DATAHUB_SERVER", "http://localhost:8080"),
-)
-print("ChatBot instance ✓")
-
-# 12-C. 기본 대화 테스트
-result = bot.chat("안녕하세요", thread_id="test-001")
-last_msg = result["messages"][-1]
-assert hasattr(last_msg, "content") and len(last_msg.content) > 0
-print(f"ChatBot.chat() ✓ — 응답: {last_msg.content[:60]}")
-```
-
-### 12-D. `search_database_tables` 직접 호출 (DataHub 연결 시)
-
-```python
-# DataHub가 연결된 환경에서만 성공적인 결과 반환
-result = search_database_tables.invoke({
-    "query": "주문 테이블",
-    "top_n": 3
-})
-# DataHub 연결 성공 시: {"orders": {"table_description": "...", ...}, ...}
-# DataHub 연결 실패 시: {"error": True, "message": "..."}
-print(f"search_database_tables ✓ — result keys: {list(result.keys())}")
-```
-
----
-
-## 13. 레거시 정리 (삭제 확인)
-
-아래 모듈들은 마이그레이션에서 삭제되었습니다. **임포트 시 에러 발생이 정상**입니다.
-
-```python
-import importlib, sys
-
-deleted_modules = [
-    "engine",
-    "engine.query_executor",
-    "utils.llm.core.factory",
-    "utils.llm.chains",
-    "utils.llm.retrieval",
-    "utils.llm.vectordb",
-    "utils.llm.graph_utils",
-    "utils.llm.output_schema",
-]
-
-for mod in deleted_modules:
-    try:
-        importlib.import_module(mod)
-        print(f"WARNING: {mod} — 삭제되었어야 하지만 여전히 존재합니다")
-    except (ImportError, ModuleNotFoundError):
-        print(f"✓ {mod} 삭제 확인")
-```
-
----
-
-## 14. 전체 회귀 테스트
-
-```bash
-# 유닛 테스트 전체 실행 (145 passed, 6 skipped 예상)
-pytest tests/ -v --tb=short
-
-# 커버리지 포함
-pytest tests/ --cov=src/lang2sql --cov-report=term-missing
-```
-
-**예상 결과**: 145 passed, 6 skipped (pgvector 관련 — 실제 PostgreSQL 없이는 skip)
-
----
-
-## 15. DB 커넥터 검증
-
-사용하는 DB에 맞게 `.env`를 설정하고 아래를 실행합니다.
-
-```python
-from dotenv import load_dotenv; load_dotenv()
-from lang2sql.factory import build_db_from_env
-
-db = build_db_from_env()
-
-# 실제 테이블에서 데이터 조회
-rows = db.execute("SELECT COUNT(*) AS cnt FROM 실제_테이블명")
-assert isinstance(rows, list) and "cnt" in rows[0]
-print(f"DB 연결 ✓ — count={rows[0]['cnt']}")
-```
-
-### 지원 DB 목록 및 `.env` 키
-
-| DB | `DB_TYPE` | 필수 환경변수 |
-|----|-----------|---------------|
-| SQLite | `sqlite` | `SQLITE_PATH` |
-| PostgreSQL | `postgresql` | `POSTGRESQL_HOST/PORT/USER/PASSWORD/DATABASE` |
-| MySQL | `mysql` | `MYSQL_HOST/PORT/USER/PASSWORD/DATABASE` |
-| MariaDB | `mariadb` | `MARIADB_HOST/PORT/USER/PASSWORD/DATABASE` |
-| DuckDB | `duckdb` | `DUCKDB_PATH` |
-| ClickHouse | `clickhouse` | `CLICKHOUSE_HOST/PORT/USER/PASSWORD/DATABASE` |
-| Snowflake | `snowflake` | `SNOWFLAKE_USER/PASSWORD/ACCOUNT` |
-| Oracle | `oracle` | `ORACLE_HOST/PORT/USER/PASSWORD/SERVICE_NAME` |
-
----
-
-## 빠른 스모크 테스트 스크립트
-
-아래 스크립트를 `smoke_test.py`로 저장 후 실행하면 가장 중요한 경로를 빠르게 확인할 수 있습니다.
-
-```python
-"""
-smoke_test.py — 핵심 경로 빠른 검증 (Anthropic + SQLite 기준)
-실행: python smoke_test.py
-"""
-
-import os
-import sqlite3
-
-from dotenv import load_dotenv
-load_dotenv()
-
-print("=" * 50)
-print("Lang2SQL v2 Smoke Test")
-print("=" * 50)
-
-# 1. 테스트 DB
-print("\n[1] SQLite DB 준비")
-conn = sqlite3.connect("/tmp/smoke.db")
-conn.execute("CREATE TABLE IF NOT EXISTS orders (id INT, amount REAL, created_at TEXT)")
-conn.execute("DELETE FROM orders")
-conn.executemany("INSERT INTO orders VALUES (?,?,?)", [
-    (1, 10000, "2024-01-10"), (2, 20000, "2024-01-20"), (3, 15000, "2024-02-01")
-])
-conn.commit(); conn.close()
-print("  ✓ /tmp/smoke.db")
-
-# 2. Factory
-print("\n[2] Factory 인스턴스 생성")
-from lang2sql.factory import build_llm_from_env, build_embedding_from_env, build_db_from_env
-llm = build_llm_from_env()
-emb = build_embedding_from_env()
-db  = build_db_from_env() if os.getenv("DB_TYPE") else None
-print(f"  ✓ LLM={llm.__class__.__name__}, Embedding={emb.__class__.__name__}")
-
-# 3. LLM 통신
-print("\n[3] LLM 호출")
-resp = llm.invoke([{"role": "user", "content": "Respond with OK"}])
-assert isinstance(resp, str) and len(resp) > 0
-print(f"  ✓ response={resp[:30]}")
-
-# 4. BaselineNL2SQL
-print("\n[4] BaselineNL2SQL")
-from lang2sql.flows import BaselineNL2SQL
-from lang2sql.integrations.db.sqlalchemy_ import SQLAlchemyDB
-
-catalog = [{"name": "orders", "description": "주문 테이블", "columns": {"id": "주문 ID", "amount": "금액", "created_at": "생성일"}}]
-pipe_base = BaselineNL2SQL(catalog=catalog, llm=llm, db=SQLAlchemyDB("sqlite:////tmp/smoke.db"), db_dialect="sqlite")
-rows = pipe_base.run("전체 주문 건수")
-assert isinstance(rows, list) and len(rows) > 0
-print(f"  ✓ rows={rows}")
-
-# 5. EnrichedNL2SQL
-print("\n[5] EnrichedNL2SQL")
-from lang2sql.flows import EnrichedNL2SQL
-
-pipe_rich = EnrichedNL2SQL(
-    catalog=catalog, llm=llm, embedding=emb,
-    db=SQLAlchemyDB("sqlite:////tmp/smoke.db"),
-    db_dialect="sqlite", gate_enabled=True,
-)
-rows2 = pipe_rich.run("주문 총 건수")
-assert isinstance(rows2, list) and len(rows2) > 0
-print(f"  ✓ rows={rows2}")
-
-# 6. 삭제 확인
-print("\n[6] 삭제된 레거시 모듈 확인")
-import importlib
-for m in ["utils.llm.retrieval", "utils.llm.vectordb", "utils.llm.chains"]:
-    try:
-        importlib.import_module(m)
-        print(f"  WARNING: {m} 존재 — 삭제 필요")
-    except (ImportError, ModuleNotFoundError):
-        print(f"  ✓ {m} 삭제됨")
-
-print("\n" + "=" * 50)
-print("Smoke Test 완료")
-print("=" * 50)
-```
-
-```bash
-python smoke_test.py
-```
diff --git a/docs/tutorials/01-quickstart.md b/docs/tutorials/01-quickstart.md
deleted file mode 100644
index d7cac98..0000000
--- a/docs/tutorials/01-quickstart.md
+++ /dev/null
@@ -1,99 +0,0 @@
-# 01. 빠른 시작 — 5분 안에 NL2SQL 실행하기
-
-lang2sql v2의 기본 파이프라인을 실제 LLM과 DB로 바로 실행합니다.
-
----
-
-## 사전 준비
-
-```bash
-pip install lang2sql
-export OPENAI_API_KEY="sk-..."
-```
-
-샘플 DB 생성:
-
-```bash
-python scripts/setup_sample_db.py
-```
-
-완료되면 프로젝트 루트에 `sample.db`가 생성됩니다.
-
----
-
-## BaselineNL2SQL 실행
-
-```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)
-```
-
----
-
-## 파이프라인 구조
-
-```
-BaselineNL2SQL
-  ├── KeywordRetriever   — catalog에서 관련 테이블 검색 (BM25)
-  ├── SQLGenerator       — LLM으로 SQL 생성
-  └── SQLExecutor        — DB 실행 후 결과 반환
-```
-
-`BaselineNL2SQL`은 키워드 기반 검색(`KeywordRetriever`)을 사용합니다.
-벡터 검색이 필요하면 `HybridNL2SQL`을 사용하세요 (→ [04-hybrid.md](./04-hybrid.md)).
-
----
-
-## 지원 LLM
-
-`LLMPort`를 만족하는 구현체로 교체할 수 있습니다.
-
-```python
-from lang2sql.integrations.llm import AnthropicLLM
-llm = AnthropicLLM(model="claude-sonnet-4-6")
-
-from lang2sql.integrations.llm import OpenAILLM
-llm = OpenAILLM(model="gpt-4o")
-```
-
-둘 다 `LLMPort.invoke(messages)` 계약을 따르므로 파이프라인 코드는 동일합니다.
-
----
-
-## 다음 단계
-
-DB Explorer, 다중 테이블 카탈로그, 실제 운영 DB 연결 → [02-baseline.md](./02-baseline.md)
diff --git a/docs/tutorials/02-baseline.md b/docs/tutorials/02-baseline.md
deleted file mode 100644
index 9a85b9f..0000000
--- a/docs/tutorials/02-baseline.md
+++ /dev/null
@@ -1,300 +0,0 @@
-# 02. Baseline 파이프라인 — 실제 DB 연결과 DB Explorer
-
-`BaselineNL2SQL` 상세 사용법과 DB를 탐색하는 `SQLAlchemyExplorer`를 다룹니다.
-
----
-
-## 사전 준비
-
-```bash
-export OPENAI_API_KEY="sk-..."
-python scripts/setup_sample_db.py   # sample.db 생성
-```
-
----
-
-## 1) BaselineNL2SQL — 다중 테이블
-
-```python
-from lang2sql import BaselineNL2SQL
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.llm import OpenAILLM
-
-catalog = [
-    {
-        "name": "orders",
-        "description": "고객 주문 정보. 주문 건수·금액·날짜 조회에 사용.",
-        "columns": {
-            "order_id":    "주문 고유 ID (PK)",
-            "customer_id": "주문한 고객 ID (FK → customers)",
-            "order_date":  "주문 일시 (TIMESTAMP)",
-            "amount":      "주문 금액 (DECIMAL)",
-            "status":      "주문 상태: pending / confirmed / shipped / cancelled",
-        },
-    },
-    {
-        "name": "customers",
-        "description": "고객 마스터. 이름·등급·가입일 조회에 사용.",
-        "columns": {
-            "customer_id": "고객 고유 ID (PK)",
-            "name":        "고객 이름",
-            "grade":       "고객 등급: bronze / silver / gold",
-            "created_at":  "가입 일시",
-        },
-    },
-]
-
-pipeline = BaselineNL2SQL(
-    catalog=catalog,
-    llm=OpenAILLM(model="gpt-4o-mini"),
-    db=SQLAlchemyDB("sqlite:///sample.db"),
-    db_dialect="sqlite",
-)
-
-rows = pipeline.run("지난달 gold 고객의 주문 건수")
-print(rows)
-```
-
-### 지원 dialect
-
-`db_dialect` 에 전달 가능한 값: `"sqlite"`, `"postgresql"`, `"mysql"`, `"bigquery"`, `"duckdb"`, `"default"`
-
----
-
-## 2) DB Explorer — 처음 보는 DB 탐색
-
-카탈로그를 미리 작성하지 않아도 DDL과 샘플 데이터를 바로 꺼낼 수 있습니다.
-LLM에 넘길 스키마 정보를 빠르게 파악할 때 유용합니다.
-
-### 기본 사용
-
-```python
-from lang2sql import build_explorer_from_url
-
-exp = build_explorer_from_url("sqlite:///sample.db")
-
-# 1) 테이블 목록
-print(exp.list_tables())
-# ['customers', 'orders', ...]
-
-# 2) DDL — CREATE TABLE 원문
-print(exp.get_ddl("orders"))
-
-# 3) 샘플 데이터 (기본 5행)
-print(exp.sample_data("orders"))
-
-# 4) 읽기 전용 커스텀 쿼리
-print(exp.execute_read_only("SELECT status, COUNT(*) AS cnt FROM orders GROUP BY status"))
-```
-
-### 전체 테이블 루프
-
-```python
-from lang2sql import build_explorer_from_url
-
-exp = build_explorer_from_url("sqlite:///sample.db")
-
-for table in exp.list_tables():
-    print(f"\n=== {table} ===")
-    print(exp.get_ddl(table))
-    print("샘플:", exp.sample_data(table, limit=2))
-```
-
-### PostgreSQL / MySQL
-
-URL만 바꾸면 됩니다.
-
-```python
-from lang2sql import build_explorer_from_url
-
-# PostgreSQL (schema 범위 지정 가능)
-exp = build_explorer_from_url(
-    "postgresql://user:password@localhost:5432/mydb",
-    schema="analytics",
-)
-
-# MySQL
-exp = build_explorer_from_url("mysql+pymysql://user:password@localhost:3306/mydb")
-```
-
-### 기존 SQLAlchemyDB engine 재사용
-
-```python
-from lang2sql.integrations.db import SQLAlchemyDB, SQLAlchemyExplorer
-
-db = SQLAlchemyDB("sqlite:///sample.db")
-exp = SQLAlchemyExplorer.from_engine(db._engine)
-
-# 같은 연결 풀을 공유
-rows = db.execute("SELECT COUNT(*) AS cnt FROM orders")
-ddl  = exp.get_ddl("orders")
-```
-
-### 쓰기 구문 거부
-
-```python
-exp.execute_read_only("DROP TABLE orders")
-# ValueError: Write operations not allowed: 'DROP TABLE orders'
-
-exp.execute_read_only("INSERT INTO orders VALUES (99, 1, 0, 'test')")
-# ValueError: Write operations not allowed: ...
-```
-
----
-
-## 3) CSV 카탈로그 빠르게 구성하기
-
-테이블이 많을 때 CSV로 카탈로그를 만드는 패턴입니다.
-
-```python
-import csv
-from collections import defaultdict
-from lang2sql import BaselineNL2SQL
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.llm import OpenAILLM
-
-# CSV 예시 (dev/table_catalog.csv)
-# table_name,table_description,column_name,column_description
-# orders,주문 정보 테이블,order_id,주문 ID
-# orders,주문 정보 테이블,amount,결제 금액
-
-tables: dict = defaultdict(lambda: {"desc": "", "columns": {}})
-with open("dev/table_catalog.csv", newline="", encoding="utf-8") as f:
-    for row in csv.DictReader(f):
-        t = row["table_name"].strip()
-        tables[t]["desc"] = row["table_description"].strip()
-        tables[t]["columns"][row["column_name"].strip()] = row["column_description"].strip()
-
-catalog = [
-    {"name": t, "description": info["desc"], "columns": info["columns"]}
-    for t, info in tables.items()
-]
-
-pipeline = BaselineNL2SQL(
-    catalog=catalog,
-    llm=OpenAILLM(model="gpt-4o-mini"),
-    db=SQLAlchemyDB("sqlite:///sample.db"),
-    db_dialect="sqlite",
-)
-
-rows = pipeline.run("주문 건수를 알려줘")
-print(rows)
-```
-
----
-
-## 4) CLI 사용법
-
-Python 코드 대신 CLI로 실행할 수 있습니다.
-CLI는 환경변수(`LLM_PROVIDER`, `DB_URL` 등)로 설정을 읽으므로 `.env`를 먼저 구성합니다.
-
-### .env 설정 (OpenAI 기준)
-
-```bash
-LLM_PROVIDER=openai
-OPENAI_API_KEY=sk-...
-OPEN_AI_LLM_MODEL=gpt-4o
-EMBEDDING_PROVIDER=openai
-OPEN_AI_EMBEDDING_MODEL=text-embedding-3-large
-DB_URL=sqlite:///sample.db
-DB_TYPE=sqlite
-```
-
-> **주의**: 코드에서 OpenAI 키는 `OPEN_AI_KEY` 또는 `OPENAI_API_KEY` 둘 다 읽습니다.
-> `.env.example`을 참고해 실제 사용 변수명을 확인하세요.
-
-### Streamlit UI 실행
-
-```bash
-lang2sql run-streamlit
-
-# 포트 지정
-lang2sql run-streamlit -p 8888
-```
-
-### CLI 쿼리 실행
-
-```bash
-# baseline 플로우 (기본값)
-lang2sql query "주문 건수를 집계해줘" --flow baseline --dialect sqlite
-
-# enriched 플로우 (BM25 + Vector + Gate)
-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
-```
-
-**지원 옵션:**
-
-| 옵션 | 기본값 | 설명 |
-|------|--------|------|
-| `--flow` | `baseline` | `baseline` 또는 `enriched` |
-| `--dialect` | `None` | SQL 방언 (sqlite, postgresql, mysql 등) |
-| `--top-n` | `5` | 검색할 최대 테이블 수 |
-| `--no-gate` | `False` | QuestionGate 비활성화 (enriched 전용) |
-
-> **참고**: CLI의 `--flow hybrid`는 없습니다. 하이브리드 검색은 Python API(`HybridNL2SQL`)를 사용하세요.
-
----
-
-## 5) Factory 함수 — 환경변수 기반 인스턴스 생성
-
-`.env`만 설정하면 코드 변경 없이 LLM·임베딩·DB를 교체할 수 있습니다.
-CLI와 Streamlit UI가 내부적으로 사용하는 함수이며, 직접 호출해도 됩니다.
-
-```python
-from lang2sql import build_llm_from_env, build_embedding_from_env, build_db_from_env
-
-llm       = build_llm_from_env()        # LLM_PROVIDER 환경변수 참조
-embedding = build_embedding_from_env()   # EMBEDDING_PROVIDER 환경변수 참조
-db        = build_db_from_env()          # DB_TYPE, DB_URL 등 환경변수 참조
-```
-
-### 지원 프로바이더
-
-| 함수 | 환경변수 | 지원 값 |
-|------|----------|---------|
-| `build_llm_from_env()` | `LLM_PROVIDER` | `openai`, `anthropic`, `azure`, `gemini`, `bedrock`, `ollama`, `huggingface` |
-| `build_embedding_from_env()` | `EMBEDDING_PROVIDER` | `openai`, `azure`, `ollama`, `bedrock`, `gemini`, `huggingface` |
-| `build_db_from_env()` | `DB_TYPE` | `sqlite`, `postgresql`, `mysql`, `mariadb`, `duckdb`, `clickhouse`, `snowflake`, `oracle` |
-
-### .env 예시 (Anthropic + PostgreSQL)
-
-```bash
-LLM_PROVIDER=anthropic
-ANTHROPIC_API_KEY=sk-ant-...
-ANTHROPIC_LLM_MODEL=claude-sonnet-4-6
-
-EMBEDDING_PROVIDER=openai
-OPEN_AI_KEY=sk-...
-OPEN_AI_EMBEDDING_MODEL=text-embedding-3-large
-
-DB_TYPE=postgresql
-DB_HOST=localhost
-DB_PORT=5432
-DB_NAME=mydb
-DB_USER=user
-DB_PASSWORD=pass
-```
-
-### 파이프라인에서 사용
-
-```python
-from lang2sql import BaselineNL2SQL, build_llm_from_env, build_db_from_env
-
-pipeline = BaselineNL2SQL(
-    catalog=catalog,
-    llm=build_llm_from_env(),
-    db=build_db_from_env(),
-    db_dialect="postgresql",
-)
-```
-
-> **팁**: `build_explorer_from_url()`은 URL을 직접 받습니다 (환경변수 방식 아님).
-
----
-
-## 다음 단계
-
-벡터 검색으로 검색 품질을 높이려면 → [03-vector-search.md](./03-vector-search.md)
diff --git a/docs/tutorials/03-vector-search.md b/docs/tutorials/03-vector-search.md
deleted file mode 100644
index 70df8be..0000000
--- a/docs/tutorials/03-vector-search.md
+++ /dev/null
@@ -1,299 +0,0 @@
-# 03. 벡터 검색 — 인덱싱과 VectorStore
-
-카탈로그와 비즈니스 문서를 벡터로 인덱싱해 의미 기반 검색을 수행합니다.
-
----
-
-## 사전 준비
-
-```bash
-export OPENAI_API_KEY="sk-..."
-python scripts/setup_sample_db.py
-python scripts/setup_sample_docs.py   # docs/business/ 샘플 문서 생성
-```
-
----
-
-## 1) from_sources() — 원터치 인덱싱
-
-청킹·임베딩·저장을 한 번에 처리합니다.
-
-```python
-from lang2sql import VectorRetriever
-from lang2sql.integrations.embedding import OpenAIEmbedding
-
-catalog = [
-    {
-        "name": "orders",
-        "description": "고객 주문 정보. 주문 건수·금액·날짜 조회에 사용.",
-        "columns": {
-            "order_id":   "주문 고유 ID",
-            "amount":     "주문 금액",
-            "order_date": "주문 일시",
-            "status":     "주문 상태",
-        },
-    },
-]
-
-docs = [
-    {
-        "id":      "revenue_def",
-        "title":   "매출 정의",
-        "content": "매출은 반품을 제외한 순매출이다. cancelled 상태 주문은 제외한다.",
-        "source":  "docs/business/revenue.md",
-    },
-]
-
-retriever = VectorRetriever.from_sources(
-    catalog=catalog,
-    documents=docs,
-    embedding=OpenAIEmbedding(model="text-embedding-3-small"),
-    top_n=5,
-)
-
-result = retriever.run("지난달 순매출 기준 집계")
-print("schemas:", [s["name"] for s in result.schemas])
-print("context:", result.context)
-```
-
-`from_sources()` 내부 동작:
-1. `CatalogChunker`로 catalog split
-2. `RecursiveCharacterChunker`로 docs split
-3. `from_chunks()`로 embed + store (기본: `InMemoryVectorStore`)
-
----
-
-## 2) from_chunks() — 명시적 파이프라인
-
-split 단계를 직접 제어하거나 영속 store(FAISS, pgvector)를 쓸 때 사용합니다.
-
-```python
-from lang2sql import CatalogChunker, DirectoryLoader, RecursiveCharacterChunker, VectorRetriever
-from lang2sql.integrations.embedding import OpenAIEmbedding
-
-# 1) 문서 로딩
-docs = DirectoryLoader("docs/business").load()
-
-# 2) 명시적 split
-catalog_chunks = CatalogChunker().split(catalog)
-doc_chunks     = RecursiveCharacterChunker(chunk_size=800, chunk_overlap=80).split(docs)
-
-# 3) embed + store
-retriever = VectorRetriever.from_chunks(
-    catalog_chunks + doc_chunks,
-    embedding=OpenAIEmbedding(model="text-embedding-3-small"),
-    top_n=5,
-)
-
-result = retriever.run("순매출 집계 기준")
-print("schemas:", [s["name"] for s in result.schemas])
-print("context:", result.context)
-```
-
-증분 추가:
-
-```python
-new_docs = DirectoryLoader("docs/new").load()
-retriever.add(RecursiveCharacterChunker().split(new_docs))  # pre-split 필수
-```
-
----
-
-## 3) 문서 로더
-
-### MarkdownLoader / PlainTextLoader
-
-```python
-from lang2sql import MarkdownLoader, PlainTextLoader
-
-md_docs  = MarkdownLoader().load("docs/business/revenue.md")
-txt_docs = PlainTextLoader().load("docs/business/rules.txt")
-```
-
-### DirectoryLoader (권장)
-
-```python
-from lang2sql import DirectoryLoader
-
-# 기본: .md → MarkdownLoader, .txt → PlainTextLoader
-docs = DirectoryLoader("docs/business").load()
-print(f"로드된 문서 수: {len(docs)}")
-```
-
-### PDFLoader (opt-in)
-
-```bash
-pip install pymupdf
-```
-
-```python
-from lang2sql import DirectoryLoader, MarkdownLoader
-from lang2sql.integrations.loaders import PDFLoader
-
-docs = DirectoryLoader(
-    "docs/",
-    loaders={
-        ".md":  MarkdownLoader(),
-        ".pdf": PDFLoader(),
-    },
-).load()
-```
-
-PDFLoader는 페이지 단위로 `TextDocument`를 생성합니다:
-- `id`: `"{filename}__p{page_number}"`
-- `title`: `"{filename} page {page_number}"`
-
----
-
-## 4) FAISSVectorStore — 로컬 파일 영속성
-
-### 인덱싱 후 저장
-
-```python
-from lang2sql import CatalogChunker, VectorRetriever
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.vectorstore import FAISSVectorStore
-
-store = FAISSVectorStore(index_path="./index/catalog.faiss")
-
-chunks = CatalogChunker().split(catalog)
-retriever = VectorRetriever.from_chunks(
-    chunks,
-    embedding=OpenAIEmbedding(model="text-embedding-3-large"),
-    vectorstore=store,
-)
-
-# 벡터 인덱스 + registry 파일로 저장
-retriever.save("./index/catalog")
-# → ./index/catalog.faiss
-# → ./index/catalog.faiss.meta
-# → ./index/catalog.registry
-```
-
-### 재시작 시 로드
-
-```python
-from lang2sql import VectorRetriever
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.vectorstore import FAISSVectorStore
-
-embedding = OpenAIEmbedding(model="text-embedding-3-large")
-store     = FAISSVectorStore.load("./index/catalog.faiss")
-
-retriever = VectorRetriever.load(
-    "./index/catalog",
-    vectorstore=store,
-    embedding=embedding,
-)
-
-result = retriever.run("주문 건수 집계")
-```
-
-### CSV → FAISS 인덱스 일괄 생성
-
-DataHub 없이 CSV로 카탈로그를 만들고 FAISS로 인덱싱합니다.
-
-```python
-import csv, os
-from collections import defaultdict
-from lang2sql import CatalogChunker, VectorRetriever
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.vectorstore import FAISSVectorStore
-
-CSV_PATH   = "./dev/table_catalog.csv"
-OUTPUT_DIR = "./index"
-
-tables: dict = defaultdict(lambda: {"desc": "", "columns": {}})
-with open(CSV_PATH, newline="", encoding="utf-8") as f:
-    for row in csv.DictReader(f):
-        t = row["table_name"].strip()
-        tables[t]["desc"] = row["table_description"].strip()
-        tables[t]["columns"][row["column_name"].strip()] = row["column_description"].strip()
-
-catalog = [
-    {"name": t, "description": info["desc"], "columns": info["columns"]}
-    for t, info in tables.items()
-]
-
-os.makedirs(OUTPUT_DIR, exist_ok=True)
-store  = FAISSVectorStore(index_path=f"{OUTPUT_DIR}/catalog.faiss")
-chunks = CatalogChunker().split(catalog)
-
-retriever = VectorRetriever.from_chunks(
-    chunks,
-    embedding=OpenAIEmbedding(model="text-embedding-3-large"),
-    vectorstore=store,
-)
-retriever.save(f"{OUTPUT_DIR}/catalog")
-print(f"저장 완료: {OUTPUT_DIR}/catalog")
-```
-
----
-
-## 5) PGVectorStore — PostgreSQL 영속성
-
-### Docker로 pgvector 실행
-
-```bash
-docker run -d \
-  --name pgvector \
-  -e POSTGRES_PASSWORD=postgres \
-  -p 5432:5432 \
-  pgvector/pgvector:pg16
-```
-
-### 사용법
-
-```python
-from lang2sql import CatalogChunker, VectorRetriever
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.vectorstore import PGVectorStore
-
-store = PGVectorStore(
-    connection="postgresql://postgres:postgres@localhost:5432/postgres",
-    table_name="lang2sql_vectors",   # 첫 upsert 시 자동 생성
-)
-
-chunks = CatalogChunker().split(catalog)
-retriever = VectorRetriever.from_chunks(
-    chunks,
-    embedding=OpenAIEmbedding(model="text-embedding-3-large"),
-    vectorstore=store,
-)
-# save() 없음 — upsert()마다 DB에 즉시 반영
-# 동일 chunk_id 재실행 시 덮어씀 (ON CONFLICT DO UPDATE)
-```
-
----
-
-## 6) 백엔드 비교
-
-| | `InMemoryVectorStore` | `FAISSVectorStore` | `PGVectorStore` |
-|---|---|---|---|
-| 영속성 | 없음 | 파일 | PostgreSQL |
-| Upsert | true upsert | append-only | true upsert |
-| 멀티 서버 | 불가 | 불가 | 가능 |
-| 권장 규모 | < 50k chunks | < 500k chunks | 500k+ chunks |
-
-`vectorstore=` 파라미터만 교체하면 나머지 코드는 동일합니다.
-
----
-
-## 7) top_n / score_threshold 조정
-
-```python
-retriever = VectorRetriever.from_sources(
-    catalog=catalog,
-    embedding=OpenAIEmbedding(),
-    top_n=3,              # 반환할 최대 스키마/문서 수 (기본값: 5)
-    score_threshold=0.3,  # 이 점수 이하는 결과에서 제외 (기본값: 0.0)
-)
-```
-
-관련 없는 테이블이 검색될 때 `score_threshold`를 0.3~0.5로 올려보세요.
-
----
-
-## 다음 단계
-
-BM25 + 벡터 하이브리드 검색 → [04-hybrid.md](./04-hybrid.md)
diff --git a/docs/tutorials/04-hybrid.md b/docs/tutorials/04-hybrid.md
deleted file mode 100644
index fc6eb29..0000000
--- a/docs/tutorials/04-hybrid.md
+++ /dev/null
@@ -1,184 +0,0 @@
-# 04. 하이브리드 검색 — BM25 + Vector
-
-`HybridRetriever`는 BM25 키워드 검색과 벡터 유사도 검색을 RRF(Reciprocal Rank Fusion)로 결합합니다.
-키워드 검색의 정확성과 벡터 검색의 의미 일반화를 모두 얻을 수 있습니다.
-
----
-
-## 사전 준비
-
-```bash
-export OPENAI_API_KEY="sk-..."
-python scripts/setup_sample_db.py
-python scripts/setup_sample_docs.py
-```
-
----
-
-## 1) HybridNL2SQL — 가장 빠른 시작
-
-`BaselineNL2SQL`에서 `embedding` 파라미터 하나만 추가합니다.
-
-```python
-from lang2sql import HybridNL2SQL
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.llm import OpenAILLM
-
-catalog = [
-    {
-        "name": "orders",
-        "description": "고객 주문 정보. 주문 건수·금액·날짜 조회에 사용.",
-        "columns": {
-            "order_id":   "주문 고유 ID (PK)",
-            "customer_id":"고객 ID (FK → customers)",
-            "order_date": "주문 일시",
-            "amount":     "주문 금액",
-            "status":     "주문 상태: pending / confirmed / shipped / cancelled",
-        },
-    },
-    {
-        "name": "customers",
-        "description": "고객 마스터. 이름·등급·가입일 조회에 사용.",
-        "columns": {
-            "customer_id": "고객 고유 ID (PK)",
-            "name":        "고객 이름",
-            "grade":       "고객 등급: bronze / silver / gold",
-        },
-    },
-]
-
-docs = [
-    {
-        "id":      "revenue_def",
-        "title":   "매출 정의",
-        "content": "매출은 반품을 제외한 순매출이다. cancelled 상태 주문은 제외한다.",
-        "source":  "docs/business/revenue.md",
-    },
-]
-
-pipeline = HybridNL2SQL(
-    catalog=catalog,
-    llm=OpenAILLM(model="gpt-4o-mini"),
-    db=SQLAlchemyDB("sqlite:///sample.db"),
-    embedding=OpenAIEmbedding(model="text-embedding-3-small"),
-    documents=docs,
-    db_dialect="sqlite",
-    top_n=5,
-)
-
-rows = pipeline.run("취소 제외한 이번 달 순매출 합계")
-print(rows)
-```
-
----
-
-## 2) DirectoryLoader와 함께
-
-```python
-from lang2sql import DirectoryLoader, HybridNL2SQL
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.llm import OpenAILLM
-
-docs = DirectoryLoader("docs/business").load()
-
-pipeline = HybridNL2SQL(
-    catalog=catalog,
-    llm=OpenAILLM(model="gpt-4o-mini"),
-    db=SQLAlchemyDB("sqlite:///sample.db"),
-    embedding=OpenAIEmbedding(model="text-embedding-3-small"),
-    documents=docs,
-    db_dialect="sqlite",
-)
-
-rows = pipeline.run("gold 고객의 이번 달 주문 건수")
-print(rows)
-```
-
----
-
-## 3) HybridRetriever 단독 사용
-
-검색 결과만 먼저 확인하고 싶을 때:
-
-```python
-from lang2sql import HybridRetriever
-from lang2sql.integrations.embedding import OpenAIEmbedding
-
-retriever = HybridRetriever(
-    catalog=catalog,
-    embedding=OpenAIEmbedding(model="text-embedding-3-small"),
-    documents=docs,
-    top_n=5,
-    rrf_k=60,            # RRF 상수. 높을수록 순위 간 점수 차이가 줄어듦
-    score_threshold=0.0,
-)
-
-result = retriever.run("지난달 할인 매출")
-print("schemas:", [s["name"] for s in result.schemas])
-print("context:", result.context)
-```
-
----
-
-## 4) EnrichedNL2SQL — 풀 파이프라인
-
-질문 적합성 검증, 테이블 필터링, 질문 프로파일링, 컨텍스트 보강까지 포함한 파이프라인입니다.
-
-```
-QuestionGate → HybridRetriever → TableSuitabilityEvaluator
-  → QuestionProfiler → ContextEnricher → SQLGenerator → SQLExecutor
-```
-
-```python
-from lang2sql import EnrichedNL2SQL
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.llm import OpenAILLM
-
-pipeline = EnrichedNL2SQL(
-    catalog=catalog,
-    llm=OpenAILLM(model="gpt-4o"),
-    db=SQLAlchemyDB("sqlite:///sample.db"),
-    embedding=OpenAIEmbedding(model="text-embedding-3-small"),
-    documents=docs,
-    db_dialect="sqlite",
-    gate_enabled=True,   # QuestionGate 활성화 여부 (기본값: True)
-    top_n=5,
-)
-
-rows = pipeline.run("이번 달 gold 고객의 순매출 합계")
-print(rows)
-```
-
-### QuestionGate
-
-`gate_enabled=True`(기본값)이면 SQL로 답할 수 없는 질문은 `ContractError`를 발생시킵니다.
-
-```python
-from lang2sql.core.exceptions import ContractError
-
-try:
-    rows = pipeline.run("오늘 날씨가 어때?")
-except ContractError as e:
-    print(f"SQL 생성 불가: {e}")
-```
-
-Gate가 불필요하면 `gate_enabled=False`로 비활성화합니다.
-
----
-
-## 5) 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 |
-
----
-
-## 다음 단계
-
-수동 컴포넌트 조합, 커스텀 어댑터, 관측성 → [05-advanced.md](./05-advanced.md)
diff --git a/docs/tutorials/05-advanced.md b/docs/tutorials/05-advanced.md
deleted file mode 100644
index 7847da3..0000000
--- a/docs/tutorials/05-advanced.md
+++ /dev/null
@@ -1,397 +0,0 @@
-# 05. 고급 — 수동 조합, 커스텀 어댑터, 관측성
-
-각 컴포넌트를 개별 제어하고, 임베딩·벡터스토어·청커를 교체하는 방법을 다룹니다.
-
----
-
-## 1) 완전 수동 Advanced Flow
-
-`Retriever → Generator → Executor`를 직접 조합합니다.
-각 단계의 입출력이 코드에 보여 디버깅과 파라미터 튜닝이 쉽습니다.
-
-```python
-from lang2sql import (
-    CatalogChunker,
-    DirectoryLoader,
-    RecursiveCharacterChunker,
-    SQLExecutor,
-    SQLGenerator,
-    VectorRetriever,
-)
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.llm import OpenAILLM
-
-catalog = [
-    {
-        "name": "orders",
-        "description": "주문 정보",
-        "columns": {
-            "order_id":   "주문 고유 ID",
-            "order_date": "주문 일시",
-            "amount":     "결제 금액",
-            "status":     "주문 상태",
-        },
-    }
-]
-
-# 1) 문서 로드 + split
-docs            = DirectoryLoader("docs/business").load()
-embedding       = OpenAIEmbedding(model="text-embedding-3-small")
-catalog_chunks  = CatalogChunker().split(catalog)
-doc_chunks      = RecursiveCharacterChunker(chunk_size=800, chunk_overlap=80).split(docs)
-
-# 2) Retriever 구성
-retriever = VectorRetriever.from_chunks(
-    catalog_chunks + doc_chunks,
-    embedding=embedding,
-    top_n=5,
-    score_threshold=0.2,
-)
-
-# 3) Generator / Executor 개별 구성
-generator = SQLGenerator(
-    llm=OpenAILLM(model="gpt-4o-mini"),
-    db_dialect="sqlite",
-)
-executor = SQLExecutor(db=SQLAlchemyDB("sqlite:///sample.db"))
-
-# 4) 수동 실행 — 각 단계 결과 직접 관측
-query     = "지난달 순매출 합계"
-retrieval = retriever.run(query)
-sql       = generator.run(query, retrieval.schemas, context=retrieval.context)
-rows      = executor.run(sql)
-
-print("SQL:", sql)
-print("결과:", rows)
-```
-
----
-
-## 2) 임베딩 교체
-
-`EmbeddingPort`를 만족하는 구현체라면 무엇이든 연결할 수 있습니다.
-
-### v2 내장 임베딩
-
-```python
-from lang2sql.integrations.embedding import (
-    OpenAIEmbedding,
-    AzureOpenAIEmbedding,
-    GeminiEmbedding,
-    BedrockEmbedding,
-    OllamaEmbedding,
-    HuggingFaceEmbedding,
-)
-
-embedding = OpenAIEmbedding(model="text-embedding-3-small")
-```
-
-### 커스텀 어댑터 예시 (SentenceTransformer)
-
-```python
-class SentenceTransformerEmbedding:
-    def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
-        from sentence_transformers import SentenceTransformer
-        self._model = SentenceTransformer(model_name)
-
-    def embed_query(self, text: str) -> list[float]:
-        return self._model.encode([text], normalize_embeddings=True)[0].tolist()
-
-    def embed_texts(self, texts: list[str]) -> list[list[float]]:
-        return self._model.encode(texts, normalize_embeddings=True).tolist()
-
-# 파이프라인 코드는 동일
-retriever = VectorRetriever.from_sources(
-    catalog=catalog,
-    embedding=SentenceTransformerEmbedding(),
-)
-```
-
----
-
-## 3) 벡터스토어 교체
-
-`VectorStorePort`의 `upsert()`와 `search()`만 구현하면 됩니다.
-
-### 커스텀 어댑터 예시 (Chroma)
-
-```python
-class ChromaVectorStore:
-    def __init__(self, collection_name: str = "lang2sql"):
-        import chromadb
-        self._client = chromadb.Client()
-        self._col    = self._client.get_or_create_collection(collection_name)
-
-    def upsert(self, ids: list[str], vectors: list[list[float]]) -> None:
-        self._col.upsert(ids=ids, embeddings=vectors)
-
-    def search(self, vector: list[float], k: int) -> list[tuple[str, float]]:
-        results = self._col.query(query_embeddings=[vector], n_results=k)
-        ids   = results["ids"][0]
-        dists = results["distances"][0]
-        return [(id_, 1.0 - dist) for id_, dist in zip(ids, dists)]
-
-retriever = VectorRetriever.from_sources(
-    catalog=catalog,
-    embedding=OpenAIEmbedding(),
-    vectorstore=ChromaVectorStore("my_catalog"),
-)
-```
-
----
-
-## 4) 청커 교체
-
-### SemanticChunker (opt-in)
-
-```python
-from lang2sql import CatalogChunker, VectorRetriever
-from lang2sql.integrations.chunking import SemanticChunker
-from lang2sql.integrations.embedding import OpenAIEmbedding
-
-embedding = OpenAIEmbedding(model="text-embedding-3-small")
-
-# from_chunks 패턴
-doc_chunks = SemanticChunker(
-    embedding=embedding,
-    breakpoint_threshold=0.3,
-    min_chunk_size=100,
-).split(docs)
-
-retriever = VectorRetriever.from_chunks(
-    CatalogChunker().split(catalog) + doc_chunks,
-    embedding=embedding,
-)
-
-# from_sources 패턴: splitter 파라미터로 전달
-retriever = VectorRetriever.from_sources(
-    catalog=catalog,
-    documents=docs,
-    embedding=embedding,
-    splitter=SemanticChunker(embedding=embedding),
-)
-```
-
-### LangChain 청커 어댑터
-
-```python
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from lang2sql import IndexedChunk, TextDocument
-
-class LangChainChunkerAdapter:
-    def __init__(self, splitter):
-        self._splitter = splitter
-
-    def chunk(self, doc: TextDocument) -> list[IndexedChunk]:
-        texts = self._splitter.split_text(doc["content"])
-        title = doc.get("title", "")
-        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={"title": title, "source": doc.get("source", "")},
-            )
-            for i, text in enumerate(texts)
-        ]
-
-lc_chunker = LangChainChunkerAdapter(
-    RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-)
-
-retriever = VectorRetriever.from_sources(
-    catalog=catalog,
-    documents=docs,
-    embedding=OpenAIEmbedding(),
-    splitter=lc_chunker,
-)
-```
-
----
-
-## 5) DataHub 카탈로그 로더
-
-DataHub GMS 서버에서 테이블 메타데이터를 가져와 `CatalogEntry` 목록으로 변환합니다.
-수동으로 카탈로그를 작성하지 않아도 DataHub에 등록된 스키마 정보를 바로 사용할 수 있습니다.
-
-```bash
-pip install acryl-datahub
-```
-
-```python
-from lang2sql.integrations.catalog import DataHubCatalogLoader
-
-loader = DataHubCatalogLoader(
-    gms_server="http://localhost:8080",
-    extra_headers={"Authorization": "Bearer <token>"},
-)
-
-# 전체 URN 조회
-catalog = loader.load()
-
-# 특정 URN만 조회
-catalog = loader.load(urns=[
-    "urn:li:dataset:(urn:li:dataPlatform:postgres,mydb.public.orders,PROD)",
-    "urn:li:dataset:(urn:li:dataPlatform:postgres,mydb.public.customers,PROD)",
-])
-
-# 바로 파이프라인에 연결
-from lang2sql import BaselineNL2SQL
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.llm import OpenAILLM
-
-pipeline = BaselineNL2SQL(
-    catalog=catalog,
-    llm=OpenAILLM(model="gpt-4o-mini"),
-    db=SQLAlchemyDB("postgresql://user:pass@localhost:5432/mydb"),
-    db_dialect="postgresql",
-)
-```
-
-> `DataHubCatalogLoader`는 `CatalogLoaderPort`를 구현합니다.
-> DataHub 없이도 `SQLAlchemyExplorer`로 DDL을 직접 조회하거나 CSV/수동 카탈로그를 사용할 수 있습니다.
-
----
-
-## 6) Port 프로토콜 레퍼런스
-
-커스텀 어댑터를 작성할 때 구현해야 하는 메서드 목록입니다.
-
-| Port | 메서드 | 시그니처 | 용도 |
-|------|--------|----------|------|
-| `LLMPort` | `invoke` | `(messages: list[dict]) -> str` | LLM 백엔드 교체 |
-| `DBPort` | `execute` | `(sql: str) -> list[dict]` | DB 백엔드 교체 |
-| `EmbeddingPort` | `embed_query` | `(text: str) -> list[float]` | 단일 텍스트 임베딩 |
-| | `embed_texts` | `(texts: list[str]) -> list[list[float]]` | 배치 임베딩 |
-| `VectorStorePort` | `upsert` | `(ids: list[str], vectors: list[list[float]]) -> None` | 벡터 저장 |
-| | `search` | `(vector: list[float], k: int) -> list[tuple[str, float]]` | 유사도 검색 (id, score) |
-| `DocumentLoaderPort` | `load` | `() -> list[TextDocument]` | 문서 로드 |
-| `DocumentChunkerPort` | `chunk` | `(doc: TextDocument) -> list[IndexedChunk]` | 문서 분할 |
-| `CatalogLoaderPort` | `load` | `(urns: list[str] \| None) -> list[CatalogEntry]` | 외부 카탈로그 로드 |
-| `DBExplorerPort` | `list_tables` | `() -> list[str]` | 테이블 목록 |
-| | `get_ddl` | `(table: str) -> str` | DDL 조회 |
-| | `sample_data` | `(table: str, limit: int) -> list[dict]` | 샘플 데이터 |
-| | `execute_read_only` | `(sql: str) -> list[dict]` | 읽기 전용 쿼리 |
-
-모든 Port는 `src/lang2sql/core/ports.py`에 `Protocol`로 정의되어 있습니다.
-클래스 상속 없이 **메서드 시그니처만 맞추면** 어떤 객체든 연결할 수 있습니다 (structural subtyping).
-
----
-
-## 7) Hook — 관측성과 디버깅
-
-`MemoryHook`으로 컴포넌트 단위 실행 이벤트를 수집합니다.
-
-```python
-from lang2sql import HybridNL2SQL, MemoryHook
-from lang2sql.integrations.db import SQLAlchemyDB
-from lang2sql.integrations.embedding import OpenAIEmbedding
-from lang2sql.integrations.llm import OpenAILLM
-
-hook = MemoryHook()
-
-pipeline = HybridNL2SQL(
-    catalog=catalog,
-    llm=OpenAILLM(model="gpt-4o-mini"),
-    db=SQLAlchemyDB("sqlite:///sample.db"),
-    embedding=OpenAIEmbedding(model="text-embedding-3-small"),
-    documents=docs,
-    db_dialect="sqlite",
-    hook=hook,
-)
-
-pipeline.run("지난달 순매출 합계")
-
-for e in hook.snapshot():
-    print(f"{e.component:30s}  {e.phase:5s}  {e.duration_ms:6.1f}ms  error={e.error}")
-```
-
-출력 예시:
-```
-HybridRetriever                start   0.0ms  error=None
-HybridRetriever                end    12.3ms  error=None
-SQLGenerator                   start   0.0ms  error=None
-SQLGenerator                   end   890.1ms  error=None
-SQLExecutor                    start   0.0ms  error=None
-SQLExecutor                    end     1.2ms  error=None
-```
-
-운영 환경에서는 `duration_ms`로 병목을 파악하고 `error` 이벤트를 수집해 장애 패턴을 분석합니다.
-
----
-
-## 8) Best Practices 체크리스트
-
-### 카탈로그 작성
-
-- `description`은 한 문장으로 테이블 용도를 명확히 기술
-- `columns`는 비즈니스 용어와 컬럼명 매핑을 충실히 작성
-- 관련 테이블 간 FK 관계를 컬럼 설명에 명시
-
-### 검색 파라미터
-
-- `top_n`: 3~8로 시작해 실험 (너무 많으면 LLM 프롬프트 비용 증가)
-- `score_threshold`: 0.0으로 시작 후 관련 없는 테이블이 검색될 때 0.3~0.5로 상향
-- `rrf_k` (HybridRetriever): 기본값 60, 검색 결과 순위 민감도 조정
-
-### 청킹
-
-- 기본은 `RecursiveCharacterChunker`
-- 문서 품질이 중요하고 비용 허용 시 `SemanticChunker` 검토
-- `chunk_overlap`은 반드시 `chunk_size`보다 작게 설정
-
-### 플로우 선택
-
-| 우선 순위 | 파이프라인 |
-|---|---|
-| 빠른 시작 | `BaselineNL2SQL` |
-| 검색 품질 | `HybridNL2SQL` |
-| 운영 환경 | `EnrichedNL2SQL` |
-| 세밀한 제어 | 수동 컴포넌트 조합 |
-
----
-
-## 9) 트러블슈팅
-
-### `IntegrationMissingError: openai`
-
-```bash
-pip install openai
-```
-
-### `chunk_overlap must be less than chunk_size`
-
-`RecursiveCharacterChunker`의 `chunk_overlap < chunk_size` 조건 위반.
-파라미터를 수정하세요.
-
-### VectorRetriever 결과가 비어 있음
-
-1. `from_chunks()` 또는 `from_sources()`가 실제로 호출됐는지 확인
-2. `len(retriever._registry) > 0` 확인
-3. `score_threshold`를 `0.0`으로 낮춰서 테스트
-
-### `retriever.add()` 타입 에러
-
-`add()`는 `list[IndexedChunk]`만 받습니다. `TextDocument`를 직접 전달하면 오류가 발생합니다.
-
-```python
-# ❌ 동작 안 함
-retriever.add(docs)
-
-# ✅ 올바른 방법
-retriever.add(RecursiveCharacterChunker().split(docs))
-```
-
-### `IntegrationMissingError: pymupdf` (PDFLoader)
-
-```bash
-pip install pymupdf
-```
-
-### `ContractError` (EnrichedNL2SQL)
-
-`QuestionGate`가 SQL로 답할 수 없다고 판단한 경우입니다.
-`gate_enabled=False`로 비활성화하거나 질문을 SQL 관련으로 구체화하세요.
diff --git a/guideline.md b/guideline.md
deleted file mode 100644
index ad6fa31..0000000
--- a/guideline.md
+++ /dev/null
@@ -1,99 +0,0 @@
-lang2sql/
-├── __init__.py                         # public re-exports (Flow, Component, flows, components)
-├── _version.py
-│
-├── core/                               # ✅ 외부 의존성 0% (절대 import 금지)
-│   ├── __init__.py
-│   ├── base.py                         # BaseComponent, BaseFlow (define-by-run 핵심 뼈대)
-│   ├── types.py                        # 최소 타입(dataclass/typing): Table/Column/Result 등(강제 X, 참고용)
-│   ├── exceptions.py                   # Lang2SQLError, ComponentError, IntegrationMissingError...
-│   ├── hooks.py                        # TraceHook, Event, NullHook (관측/로깅 인터페이스)
-│   ├── context.py                      # (선택) RunContext: dict wrapper (권장일 뿐 강제 X)
-│   ├── registry.py                     # (선택) PluginRegistry + entry_points 로더 (retriever 100개 대비)
-│   └── utils.py                        # 순수 유틸
-│
-├── components/                         # ✅ Lang2SQL이 제공하는 “부품 상자”
-│   ├── __init__.py
-│   ├── retrieval/
-│   │   ├── __init__.py
-│   │   ├── keyword.py                  # vstore 없는 기본 retriever (즉시 사용 가능)
-│   │   ├── vector.py                   # vector retriever (실제 store는 integrations를 통해 주입)
-│   │   ├── catalog.py                  # schema/catalog 기반 retriever
-│   │   └── normalize.py                # (선택) 후보 정규화 유틸 (표준 강제가 아니라 “편의”)
-│   │
-│   ├── context/
-│   │   ├── __init__.py
-│   │   ├── builder.py                  # build_context (토큰 예산/압축 정책 포함 가능)
-│   │   └── budget.py                   # token budget helpers (의존성 없는 버전)
-│   │
-│   ├── generation/
-│   │   ├── __init__.py
-│   │   ├── sql.py                      # SQL 생성 컴포넌트(LLM은 integrations llm client를 주입)
-│   │   └── prompts.py                  # 프롬프트 템플릿/formatters
-│   │
-│   ├── validation/
-│   │   ├── __init__.py
-│   │   ├── static.py                   # 문법/금지쿼리/스키마 참조 등 “실행 없는” 검증
-│   │   └── execution.py                # (옵션) 실제 DB 실행 검증(연동은 integrations)
-│   │
-│   └── adapters.py                     # ⭐ 핵심: 외부 retriever/llm 객체를 callable로 감싸는 as_callable
-│
-├── flows/                              # ✅ “완제품/프리셋” (define-by-run 클래스/함수)
-│   ├── __init__.py
-│   ├── baseline.py                     # BaselineFlow: retrieve -> context -> generate -> validate
-│   ├── agentic.py                      # AgenticFlow: while/for 루프 포함 (사용자 override 용이)
-│   └── examples.py                     # weird flow 예시(문서/테스트 겸)
-│
-├── integrations/                       # ✅ 외부 의존성 구현 (extras)
-│   ├── __init__.py
-│   ├── llm/
-│   │   ├── __init__.py
-│   │   ├── base.py                     # thin wrapper 인터페이스(여긴 외부 의존성 있을 수 있음)
-│   │   ├── openai_.py                  # openai extra
-│   │   ├── anthropic_.py               # anthropic extra
-│   │   └── upstage_.py                 # upstage extra
-│   │
-│   ├── vector_store/
-│   │   ├── __init__.py
-│   │   ├── base.py                     # VectorStorePort (여긴 integrations 계층)
-│   │   ├── faiss_.py                   # faiss extra
-│   │   ├── pgvector_.py                # pgvector extra
-│   │   └── pinecone_.py                # pinecone extra
-│   │
-│   ├── metadata/
-│   │   ├── __init__.py
-│   │   ├── base.py                     # SchemaProviderPort
-│   │   ├── sqlalchemy_.py              # sqlalchemy extra
-│   │   └── datahub_.py                 # datahub extra
-│   │
-│   └── langgraph/                      # (선택) "브릿지"만. 메인 설계는 아님.
-│       ├── __init__.py
-│       └── bridge.py                   # Flow를 langgraph node로 감싸거나, component를 노드로 변환하는 정도
-│
-├── presets/                            # ✅ 초보자 UX: “원클릭 생성기”
-│   ├── __init__.py
-│   ├── factory.py                      # AutoText2SQL(...) -> BaselineFlow/AgenticFlow + 기본 컴포넌트 조립
-│   └── defaults.py                     # 기본 조합 정책(vstore 없이도 동작하는 기본값 포함)
-│
-├── cli/
-│   ├── __init__.py
-│   └── main.py                         # lang2sql query/run 등
-│
-├── app/
-│   ├── __init__.py
-│   └── streamlit/
-│       ├── __init__.py
-│       └── main.py
-│
-├── tests/
-│   ├── test_baseline.py
-│   ├── test_agentic.py
-│   ├── test_adapters.py
-│   └── test_registry.py
-│
-└── docs/
-    ├── philosophy.md
-    ├── quickstart.md
-    ├── customizing.md
-    ├── writing-your-own-flow.md
-    └── plugins.md
\ No newline at end of file
diff --git a/pgvector.sh b/pgvector.sh
deleted file mode 100644
index 6d122f4..0000000
--- a/pgvector.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-docker run -d \
-  --name pgvector \
-  -e POSTGRES_PASSWORD=postgres \
-  -p 5431:5432 \
-  pgvector/pgvector:pg17  
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index dee806e..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,6591 +0,0 @@
-# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
-
-[[package]]
-name = "acryl-datahub"
-version = "1.0.0.3"
-description = "A CLI to work with DataHub metadata"
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "python_version >= \"3.12\" or python_version == \"3.11\""
-files = [
-    {file = "acryl_datahub-1.0.0.3-py3-none-any.whl", hash = "sha256:3c13dab0e2efa45ab049c05b1f9cbfa2117c42cc402aa1b3faa5ca18bd60238d"},
-    {file = "acryl_datahub-1.0.0.3.tar.gz", hash = "sha256:d9df348a62edf85b5ba2f098ff9e4a1cbda2773729d60d54e7ee68bb443d926e"},
-]
-
-[package.dependencies]
-aiohttp = "<4"
-avro = ">=1.11.3,<1.13"
-avro-gen3 = "0.7.16"
-cached_property = "*"
-click = ">=7.1.2"
-click-default-group = "*"
-click-spinner = "*"
-Deprecated = "*"
-docker = "*"
-expandvars = ">=0.6.5"
-humanfriendly = "*"
-ijson = "*"
-jsonref = "*"
-jsonschema = "*"
-mixpanel = ">=4.9.0"
-packaging = "*"
-progressbar2 = "*"
-psutil = ">=5.8.0"
-pydantic = ">=1.10.0,<1.10.3 || >1.10.3"
-python-dateutil = ">=2.8.0"
-PyYAML = "*"
-requests_file = "*"
-"ruamel.yaml" = "*"
-sentry-sdk = "*"
-tabulate = "*"
-toml = ">=0.10.0"
-typing_extensions = ">=4.5.0"
-typing-inspect = "*"
-
-[package.extras]
-abs = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "azure-core (>=1.31.0)", "azure-identity (>=1.21.0)", "azure-storage-blob (>=12.19.0)", "azure-storage-file-datalake (>=12.14.0)", "cached_property", "cachetools", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "more-itertools (>=8.12.0)", "packaging", "parse (>=1.19.0)", "progressbar2", "psutil (>=5.8.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pydeequ (>=1.1.0)", "pyspark (>=3.5.0,<3.6.0)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "smart-open[azure] (>=5.2.1)", "tableschema (>=1.20.2)", "tabulate", "toml (>=0.10.0)", "ujson (>=5.2.0)", "wcmatch"]
-airflow = ["Deprecated", "PyYAML", "acryl-datahub-airflow-plugin (==1.0.0.3)", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "packaging", "progressbar2", "psutil (>=5.8.0)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "tabulate", "toml (>=0.10.0)"]
-all = ["Authlib", "Deprecated", "GeoAlchemy2", "GitPython (>2)", "IPython (!=8.22.0)", "JPype1", "PyAthena[sqlalchemy] (>=2.6.0,<3.0.0)", "PyYAML", "acryl-datahub-classify (==0.0.11)", "acryl-great-expectations (==0.15.50.1)", "acryl-pyhive[hive-pure-sasl] (==0.6.16)", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "azure-core (>=1.31.0)", "azure-identity (>=1.21.0)", "azure-storage-blob (>=12.19.0)", "azure-storage-file-datalake (>=12.14.0)", "boto3", "botocore (!=1.23.0)", "cached_property", "cachetools", "cassandra-driver (>=3.28.0)", "click (>=7.1.2)", "click-default-group", "click-spinner", "clickhouse-sqlalchemy (>=0.2.0,<0.2.5)", "confluent_kafka[avro,schemaregistry] (>=1.9.0,!=2.8.1)", "cryptography", "databricks-dbapi", "databricks-sdk (>=0.30.0)", "databricks-sql-connector (>=2.8.0,<3.0.0)", "db-dtypes", "deepmerge (>=1.1.1)", "deltalake (>=0.6.3,!=0.6.4)", "deltalake (>=0.6.3,!=0.6.4,!=0.18.0)", "docker", "elasticsearch (==7.13.4)", "expandvars (>=0.6.5)", "fastavro (>=1.2.0)", "filelock", "google-cloud-aiplatform (>=1.80.0)", "google-cloud-bigquery", "google-cloud-datacatalog (>=1.5.0)", "google-cloud-datacatalog-lineage (==0.2.2)", "google-cloud-logging (<=3.5.0)", "google-cloud-resource-manager", "gql (>=3.3.0)", "gql[requests] (>=3.3.0)", "greenlet", "grpcio (>=1.44.0,<2)", "grpcio-tools (>=1.44.0,<2)", "hdbcli (>=2.11.20)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "lark[regex] (==1.1.4)", "lkml (>=1.3.4)", "looker-sdk (>=23.0.0)", "mlflow-skinny (>=2.3.0,<2.21.0)", "more-itertools", "more-itertools (>=8.12.0)", "more_itertools", "moto[s3] (<5.0.0)", "msal", "msal (>=1.31.1)", "neo4j", "nest-asyncio", "networkx (>=2.6.2)", "numpy (<2)", "okta (>=1.7.0,<1.8.0)", "oracledb", "packaging", "pandas", "pandas (<2.2.0)", "parse (>=1.19.0)", "patchy (==2.8.0)", "pip", "progressbar2", "psutil (>=5.8.0)", "psycopg2-binary", "pyOpenSSL", "pyarrow (>=6.0.1)", "pydantic (<2)", "pydeequ (>=1.1.0)", "pydruid (>=0.6.2)", "pyiceberg (>=0.4.0)", "pymongo[srv] (>=3.11)", "pymysql (>=1.0.2)", "pyodata (>=1.11.1)", "pyspark (>=3.5.0,<3.6.0)", "python-dateutil (>=2.8.0)", "python-ldap (>=2.4)", "python-liquid", "python-liquid (<2)", "redash-toolbelt", "redshift-connector (>=2.1.5)", "requests", "requests-gssapi", "requests_file", "requests_ntlm", "ruamel.yaml", "schwifty (<2024.08.0)", "scipy (>=1.7.2)", "setuptools", "simple-salesforce", "slack-sdk (==3.18.1)", "smart-open[azure] (>=5.2.1)", "smart-open[s3] (>=5.2.1)", "snowflake-connector-python (>=3.4.0)", "snowflake-sqlalchemy (>=1.4.3)", "sql-metadata", "sqlalchemy (>=1.4.39,<2)", "sqlalchemy-bigquery (>=1.4.1)", "sqlalchemy-cockroachdb (<2.0.0)", "sqlalchemy-hana (>=0.5.0)", "sqlalchemy-pytds (>=0.3)", "sqlalchemy-redshift (>=0.8.3)", "sqlglot[rs] (==26.6.0)", "sqlparse", "stopit (==1.1.2)", "tableauserverclient (>=0.24.0)", "tableschema (>=1.20.2)", "tabulate", "tenacity (!=8.4.0)", "tenacity (>=8.0.1)", "teradatasqlalchemy (>=17.20.0.0,<=20.0.0.2)", "toml (>=0.10.0)", "traitlets (!=5.2.2)", "trino[sqlalchemy] (>=0.308)", "ujson (>=5.2.0)", "vertica-sqlalchemy-dialect[vertica-python] (==0.0.8.2)", "wcmatch", "websocket-client"]
-athena = ["Deprecated", "IPython (!=8.22.0)", "PyAthena[sqlalchemy] (>=2.6.0,<3.0.0)", "PyYAML", "acryl-datahub-classify (==0.0.11)", "acryl-great-expectations (==0.15.50.1)", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "cachetools", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "greenlet", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "numpy (<2)", "packaging", "patchy (==2.8.0)", "pip", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "schwifty (<2024.08.0)", "scipy (>=1.7.2)", "sqlalchemy (>=1.4.39,<2)", "sqlalchemy-bigquery (>=1.4.1)", "sqlglot[rs] (==26.6.0)", "sqlparse", "tabulate", "tenacity (!=8.4.0)", "toml (>=0.10.0)", "traitlets (!=5.2.2)"]
-azure-ad = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "packaging", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "tabulate", "toml (>=0.10.0)"]
-base = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "packaging", "progressbar2", "psutil (>=5.8.0)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "tabulate", "toml (>=0.10.0)"]
-bigquery = ["Deprecated", "IPython (!=8.22.0)", "PyYAML", "acryl-datahub-classify (==0.0.11)", "acryl-great-expectations (==0.15.50.1)", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "cachetools", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "google-cloud-bigquery", "google-cloud-datacatalog (>=1.5.0)", "google-cloud-datacatalog-lineage (==0.2.2)", "google-cloud-logging (<=3.5.0)", "google-cloud-resource-manager", "greenlet", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "more-itertools (>=8.12.0)", "numpy (<2)", "packaging", "parse (>=1.19.0)", "patchy (==2.8.0)", "pip", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "schwifty (<2024.08.0)", "scipy (>=1.7.2)", "sqlalchemy (>=1.4.39,<2)", "sqlalchemy-bigquery (>=1.4.1)", "sqlglot[rs] (==26.6.0)", "sqlparse", "tabulate", "toml (>=0.10.0)", "traitlets (!=5.2.2)", "wcmatch"]
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-lookml = ["Deprecated", "GitPython (>2)", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "deepmerge (>=1.1.1)", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "lkml (>=1.3.4)", "looker-sdk (>=23.0.0)", "packaging", "patchy (==2.8.0)", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "python-liquid (<2)", "requests_file", "ruamel.yaml", "sqlglot[rs] (==26.6.0)", "tabulate", "toml (>=0.10.0)"]
-mariadb = ["Deprecated", "IPython (!=8.22.0)", "PyYAML", "acryl-datahub-classify (==0.0.11)", "acryl-great-expectations (==0.15.50.1)", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "cachetools", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "greenlet", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "numpy (<2)", "packaging", "patchy (==2.8.0)", "pip", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "pymysql (>=1.0.2)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "schwifty (<2024.08.0)", "scipy (>=1.7.2)", "sqlalchemy (>=1.4.39,<2)", "sqlglot[rs] (==26.6.0)", "sqlparse", "tabulate", "toml (>=0.10.0)", "traitlets (!=5.2.2)"]
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-mongodb = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "packaging", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "pymongo[srv] (>=3.11)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "tabulate", "toml (>=0.10.0)"]
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-mysql = ["Deprecated", "IPython (!=8.22.0)", "PyYAML", "acryl-datahub-classify (==0.0.11)", "acryl-great-expectations (==0.15.50.1)", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "cachetools", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "greenlet", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "numpy (<2)", "packaging", "patchy (==2.8.0)", "pip", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "pymysql (>=1.0.2)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "schwifty (<2024.08.0)", "scipy (>=1.7.2)", "sqlalchemy (>=1.4.39,<2)", "sqlglot[rs] (==26.6.0)", "sqlparse", "tabulate", "toml (>=0.10.0)", "traitlets (!=5.2.2)"]
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-nifi = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "packaging", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "requests", "requests-gssapi", "requests_file", "ruamel.yaml", "tabulate", "toml (>=0.10.0)"]
-okta = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "nest-asyncio", "okta (>=1.7.0,<1.8.0)", "packaging", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "tabulate", "toml (>=0.10.0)"]
-oracle = ["Deprecated", "IPython (!=8.22.0)", "PyYAML", "acryl-datahub-classify (==0.0.11)", "acryl-great-expectations (==0.15.50.1)", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "cachetools", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "greenlet", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "numpy (<2)", "oracledb", "packaging", "patchy (==2.8.0)", "pip", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "requests_file", "ruamel.yaml", "schwifty (<2024.08.0)", "scipy (>=1.7.2)", "sqlalchemy (>=1.4.39,<2)", "sqlglot[rs] (==26.6.0)", "sqlparse", "tabulate", "toml (>=0.10.0)", "traitlets (!=5.2.2)"]
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-preset = ["Deprecated", "PyYAML", "aiohttp (<4)", "avro (>=1.11.3,<1.13)", "avro-gen3 (==0.7.16)", "cached_property", "click (>=7.1.2)", "click-default-group", "click-spinner", "docker", "expandvars (>=0.6.5)", "humanfriendly", "ijson", "importlib_metadata (>=4.0.0)", "jsonref", "jsonschema", "packaging", "patchy (==2.8.0)", "progressbar2", "psutil (>=5.8.0)", "pydantic (<2)", "python-dateutil (>=2.8.0)", "requests", "requests_file", "ruamel.yaml", "sqlglot[rs] (==26.6.0)", "tabulate", "toml (>=0.10.0)"]
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-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-propcache = ">=0.2.1"
-
-[[package]]
-name = "zipp"
-version = "3.21.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-markers = "python_version == \"3.11\" and (platform_machine != \"ppc64le\" and platform_machine != \"s390x\")"
-files = [
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-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
-[metadata]
-lock-version = "2.1"
-python-versions = ">=3.11, <4.0"
-content-hash = "8a77b61965b04e3bd91dbac283c8431c8c8b6ec33f08c7f2e8da132d5937b946"
diff --git a/scripts/setup_sample_db.py b/scripts/setup_sample_db.py
deleted file mode 100644
index 65c9b4a..0000000
--- a/scripts/setup_sample_db.py
+++ /dev/null
@@ -1,267 +0,0 @@
-"""
-샘플 데이터베이스 세팅 스크립트
-================================
-quickstart.md 튜토리얼용 샘플 데이터를 SQLite 또는 PostgreSQL에 생성합니다.
-
-사용법
-------
-# SQLite (기본, 별도 서버 불필요)
-python scripts/setup_sample_db.py
-
-# PostgreSQL (Docker 컨테이너가 먼저 실행 중이어야 함)
-python scripts/setup_sample_db.py --db postgres
-"""
-
-from __future__ import annotations
-
-import argparse
-from datetime import datetime, timedelta
-import random
-
-from sqlalchemy import (
-    Column,
-    DateTime,
-    ForeignKey,
-    Integer,
-    Numeric,
-    String,
-    create_engine,
-    text,
-)
-from sqlalchemy.orm import DeclarativeBase, Session
-
-# ── 모델 정의 ────────────────────────────────────────────────────────────────
-
-
-class Base(DeclarativeBase):
-    pass
-
-
-class Customer(Base):
-    __tablename__ = "customers"
-    customer_id = Column(Integer, primary_key=True)
-    name = Column(String(100), nullable=False)
-    email = Column(String(200), nullable=False, unique=True)
-    joined_at = Column(DateTime, nullable=False)
-    grade = Column(String(20), nullable=False)  # bronze / silver / gold
-
-
-class Product(Base):
-    __tablename__ = "products"
-    product_id = Column(Integer, primary_key=True)
-    name = Column(String(200), nullable=False)
-    category = Column(String(100), nullable=False)  # electronics / clothing / food
-    price = Column(Numeric(10, 2), nullable=False)
-    stock = Column(Integer, nullable=False)
-
-
-class Order(Base):
-    __tablename__ = "orders"
-    order_id = Column(Integer, primary_key=True)
-    customer_id = Column(Integer, ForeignKey("customers.customer_id"), nullable=False)
-    order_date = Column(DateTime, nullable=False)
-    amount = Column(Numeric(10, 2), nullable=False)
-    status = Column(
-        String(20), nullable=False
-    )  # pending / confirmed / shipped / cancelled
-
-
-class OrderItem(Base):
-    __tablename__ = "order_items"
-    item_id = Column(Integer, primary_key=True)
-    order_id = Column(Integer, ForeignKey("orders.order_id"), nullable=False)
-    product_id = Column(Integer, ForeignKey("products.product_id"), nullable=False)
-    quantity = Column(Integer, nullable=False)
-    unit_price = Column(Numeric(10, 2), nullable=False)
-
-
-# ── 샘플 데이터 ──────────────────────────────────────────────────────────────
-
-CUSTOMERS = [
-    (1, "김철수", "chulsoo.kim@example.com", "2022-03-15", "gold"),
-    (2, "박영희", "younghee.park@example.com", "2022-07-22", "gold"),
-    (3, "이민준", "minjun.lee@example.com", "2023-01-10", "gold"),
-    (4, "최수연", "sooyeon.choi@example.com", "2023-04-05", "silver"),
-    (5, "정우진", "woojin.jung@example.com", "2023-06-18", "silver"),
-    (6, "강지훈", "jihoon.kang@example.com", "2023-08-30", "silver"),
-    (7, "윤서현", "seohyun.yoon@example.com", "2023-10-12", "bronze"),
-    (8, "임도현", "dohyun.lim@example.com", "2024-01-25", "bronze"),
-    (9, "한소희", "sohee.han@example.com", "2024-03-08", "bronze"),
-    (10, "오준혁", "junhyuk.oh@example.com", "2024-05-17", "bronze"),
-]
-
-PRODUCTS = [
-    (1, "무선 마우스", "electronics", 29900, 3),
-    (2, "기계식 키보드", "electronics", 89000, 15),
-    (3, "27인치 모니터", "electronics", 320000, 8),
-    (4, "USB-C 허브", "electronics", 45000, 7),
-    (5, "노이즈캔슬링 이어폰", "electronics", 159000, 22),
-    (6, "면 반팔 티셔츠", "clothing", 19900, 50),
-    (7, "청바지", "clothing", 59900, 30),
-    (8, "운동화", "clothing", 89000, 9),
-    (9, "후드 집업", "clothing", 69900, 4),
-    (10, "유기농 아몬드", "food", 18900, 100),
-    (11, "그래놀라", "food", 12500, 60),
-    (12, "프로틴 바 (12개)", "food", 24900, 45),
-]
-
-
-def _make_orders_and_items():
-    """지난 3개월간 주문 데이터를 생성합니다."""
-    today = datetime(2026, 2, 24)
-    orders, items = [], []
-    item_id = 1
-    order_id = 1
-
-    random.seed(42)
-
-    for cid, _, _, _, grade in CUSTOMERS:
-        # 등급별 주문 빈도
-        n_orders = {"gold": 8, "silver": 4, "bronze": 2}[grade]
-
-        for _ in range(n_orders):
-            days_ago = random.randint(1, 90)
-            order_date = today - timedelta(days=days_ago)
-            status = random.choice(
-                ["confirmed", "confirmed", "shipped", "pending", "cancelled"]
-            )
-
-            # 주문에 1~3개 상품
-            n_items = random.randint(1, 3)
-            selected = random.sample(PRODUCTS, n_items)
-            total = 0
-
-            for pid, _, _, price, _ in selected:
-                qty = random.randint(1, 3)
-                unit_price = price
-                total += unit_price * qty
-                items.append((item_id, order_id, pid, qty, unit_price))
-                item_id += 1
-
-            orders.append((order_id, cid, order_date, total, status))
-            order_id += 1
-
-    return orders, items
-
-
-# ── 세팅 함수 ────────────────────────────────────────────────────────────────
-
-
-def setup(db_url: str) -> None:
-    print(f"연결 중: {db_url}")
-    engine = create_engine(db_url, echo=False)
-
-    # 기존 테이블 삭제 후 재생성
-    Base.metadata.drop_all(engine)
-    Base.metadata.create_all(engine)
-    print("테이블 생성 완료: customers, products, orders, order_items")
-
-    orders, items = _make_orders_and_items()
-
-    with Session(engine) as session:
-        # customers
-        session.add_all(
-            [
-                Customer(
-                    customer_id=cid,
-                    name=name,
-                    email=email,
-                    joined_at=datetime.fromisoformat(joined_at),
-                    grade=grade,
-                )
-                for cid, name, email, joined_at, grade in CUSTOMERS
-            ]
-        )
-
-        # products
-        session.add_all(
-            [
-                Product(
-                    product_id=pid, name=name, category=cat, price=price, stock=stock
-                )
-                for pid, name, cat, price, stock in PRODUCTS
-            ]
-        )
-
-        # orders
-        session.add_all(
-            [
-                Order(
-                    order_id=oid,
-                    customer_id=cid,
-                    order_date=odate,
-                    amount=amount,
-                    status=status,
-                )
-                for oid, cid, odate, amount, status in orders
-            ]
-        )
-
-        # order_items
-        session.add_all(
-            [
-                OrderItem(
-                    item_id=iid,
-                    order_id=oid,
-                    product_id=pid,
-                    quantity=qty,
-                    unit_price=up,
-                )
-                for iid, oid, pid, qty, up in items
-            ]
-        )
-
-        session.commit()
-
-    print(f"  고객:       {len(CUSTOMERS)}명")
-    print(f"  상품:       {len(PRODUCTS)}개")
-    print(f"  주문:       {len(orders)}건")
-    print(f"  주문 항목:  {len(items)}개")
-    print()
-
-    # 간단 검증 쿼리
-    with engine.connect() as conn:
-        cnt = conn.execute(text("SELECT COUNT(*) FROM orders")).scalar()
-        gold = conn.execute(
-            text("SELECT COUNT(*) FROM customers WHERE grade = 'gold'")
-        ).scalar()
-        low_stock = conn.execute(
-            text("SELECT name, stock FROM products WHERE stock < 10 ORDER BY stock")
-        ).fetchall()
-
-    print("─── 검증 쿼리 결과 ───────────────────────────────")
-    print(f"  전체 주문 수:          {cnt}건")
-    print(f"  gold 등급 고객 수:     {gold}명")
-    print(f"  재고 10개 미만 상품:   {len(low_stock)}개")
-    for name, stock in low_stock:
-        print(f"    - {name}: {stock}개")
-    print("─────────────────────────────────────────────────")
-    print()
-    print("완료! 아래 URL로 quickstart.md를 따라해 보세요:")
-    print(f"  {db_url}")
-
-
-# ── CLI ──────────────────────────────────────────────────────────────────────
-
-DB_URLS = {
-    "sqlite": "sqlite:///sample.db",
-    "postgres": "postgresql://postgres:postgres@localhost:5432/postgres",
-}
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="quickstart.md 샘플 DB 세팅")
-    parser.add_argument(
-        "--db",
-        choices=["sqlite", "postgres"],
-        default="sqlite",
-        help="대상 DB (기본값: sqlite)",
-    )
-    parser.add_argument(
-        "--url",
-        default=None,
-        help="직접 SQLAlchemy URL 지정 (--db 보다 우선)",
-    )
-    args = parser.parse_args()
-
-    url = args.url or DB_URLS[args.db]
-    setup(url)
diff --git a/scripts/setup_sample_docs.py b/scripts/setup_sample_docs.py
deleted file mode 100644
index fb22870..0000000
--- a/scripts/setup_sample_docs.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""
-v2 튜토리얼용 샘플 문서 생성 스크립트.
-
-사용법
-------
-python scripts/setup_sample_docs.py
-python scripts/setup_sample_docs.py --out-dir docs/business --force
-"""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-_SAMPLE_FILES: dict[str, str] = {
-    "revenue.md": """
-# 순매출 정의
-
-순매출은 주문 금액(`orders.amount`)에서 취소 주문을 제외한 값으로 계산한다.
-주문 상태가 `cancelled` 인 레코드는 집계에서 제외한다.
-
-월간 매출 지표는 `orders.order_date` 기준으로 월 단위 그룹핑한다.
-""".strip(),
-    "order_status_policy.md": """
-# 주문 상태 정책
-
-주문 상태 컬럼은 `orders.status` 이다.
-
-- pending: 결제 대기
-- confirmed: 결제 완료
-- shipped: 배송 완료
-- cancelled: 취소
-
-운영 리포트에서 "완료 주문"은 `confirmed`, `shipped`를 포함한다.
-""".strip(),
-    "rules.txt": """
-고객 등급은 customers.grade 컬럼을 사용한다.
-gold, silver, bronze 세 등급이 존재한다.
-고객별 주문 빈도 분석은 orders.customer_id 와 customers.customer_id 를 조인해서 수행한다.
-""".strip(),
-}
-
-
-def setup_sample_docs(out_dir: Path, force: bool = False) -> tuple[int, int]:
-    out_dir.mkdir(parents=True, exist_ok=True)
-
-    created_or_updated = 0
-    skipped = 0
-
-    for filename, content in _SAMPLE_FILES.items():
-        path = out_dir / filename
-        if path.exists() and not force:
-            skipped += 1
-            continue
-        path.write_text(content + "\n", encoding="utf-8")
-        created_or_updated += 1
-
-    return created_or_updated, skipped
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="v2 튜토리얼 샘플 문서 생성")
-    parser.add_argument(
-        "--out-dir",
-        default="docs/business",
-        help="샘플 문서를 생성할 디렉터리 (기본값: docs/business)",
-    )
-    parser.add_argument(
-        "--force",
-        action="store_true",
-        help="기존 파일이 있어도 덮어쓰기",
-    )
-    args = parser.parse_args()
-
-    out_dir = Path(args.out_dir)
-    created_or_updated, skipped = setup_sample_docs(out_dir=out_dir, force=args.force)
-
-    print(f"문서 출력 경로: {out_dir}")
-    print(f"생성/갱신: {created_or_updated}개")
-    print(f"건너뜀:    {skipped}개")
-    print()
-    print("생성 파일:")
-    for filename in sorted(_SAMPLE_FILES):
-        print(f"  - {out_dir / filename}")
-
-
-if __name__ == "__main__":
-    main()