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# The Llama Cloud API key. | |
LLAMA_CLOUD_API_KEY= | |
# The provider for the AI models to use. | |
MODEL_PROVIDER=openai | |
# The name of LLM model to use. | |
MODEL=gpt-3.5-turbo | |
# Name of the embedding model to use. | |
EMBEDDING_MODEL=text-embedding-3-large | |
# Dimension of the embedding model to use. | |
EMBEDDING_DIM=1024 | |
# The OpenAI API key to use. | |
OPENAI_API_KEY= | |
# Temperature for sampling from the model. | |
# LLM_TEMPERATURE= | |
# Maximum number of tokens to generate. | |
# LLM_MAX_TOKENS= | |
# The number of similar embeddings to return when retrieving documents. | |
TOP_K=3 | |
# Custom system prompt. | |
# Example: | |
# SYSTEM_PROMPT="You are a helpful assistant who helps users with their questions." | |
# SYSTEM_PROMPT= | |
# Configuration for Pinecone vector store | |
# The Pinecone API key. | |
# PINECONE_API_KEY= | |
# PINECONE_ENVIRONMENT= | |
# PINECONE_INDEX_NAME= | |
# The address to start the backend app. | |
APP_HOST=0.0.0.0 | |
# The port to start the backend app. | |
APP_PORT=8000 | |
# Postgres database configuration | |
POSTGRES_USER=postgres | |
POSTGRES_PASSWORD=postgres | |
POSTGRES_DB_NAME=postgres | |
POSTGRES_DB_HOST=localhost | |
POSTGRES_DB_PORT=5432 | |
SQLALCHEMY_DATABASE_URL=postgresql+asyncpg://${POSTGRES_USER}:${POSTGRES_PASSWORD}@${POSTGRES_DB_HOST}:${POSTGRES_DB_PORT}/${POSTGRES_DB_NAME} |