Lang2SQL/.env.example at 13298d6f735082dc4e472697b1c6e2240c026a00 · CausalInferenceLab/Lang2SQL · GitHub

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###############################################
############## LLM API SELECTION ##############
###############################################
LLM_PROVIDER=openai

OPENAI_API_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=_

# postgres
# DB_TYPE=postgres
# POSTGRES_HOST=_
# POSTGRES_PORT=5432
# POSTGRES_USER=_
# POSTGRES_PASSWORD=_
# POSTGRES_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