feat(v1): V1 walking skeleton — core/harness/4기둥/tools/Discord/영속화

.env.example

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