ID-based RAG FastAPI

This project integrates Langchain with FastAPI in an Asynchronous, Scalable manner, providing a framework for document indexing and retrieval, using PostgreSQL/pgvector.

Files are organized into embeddings by file_id. The primary use case is for integration with LibreChat, but this simple API can be used for any ID-based use case.

The main reason to use the ID approach is to work with embeddings on a file-level. This makes for targeted queries when combined with file metadata stored in a database, such as is done by LibreChat.

The API will evolve over time to employ different querying/re-ranking methods, embedding models, and vector stores.

• Document Management: Methods for adding, retrieving, and deleting documents.
• Vector Store: Utilizes Langchain's vector store for efficient document retrieval.
• Asynchronous Support: Offers async operations for enhanced performance.

• Configure .env file based on section below
• Setup pgvector database:
  • Run an existing PSQL/PGVector setup, or,
  • Docker: docker compose up (also starts RAG API)
    • or, use docker just for DB: docker compose -f ./db-compose.yaml
• Run API:
  • Docker: docker compose up (also starts PSQL/pgvector)
    • or, use docker just for RAG API: docker compose -f ./api-compose.yaml
  • Local:

pip install -r requirements.txt
uvicorn main:app --host 0.0.0.0 --port 8000

The following environment variables are required to run the application:

• OPENAI_API_KEY: The API key for OpenAI API Embeddings.

• POSTGRES_DB: The name of the PostgreSQL database.

• POSTGRES_USER: The username for connecting to the PostgreSQL database.

• POSTGRES_PASSWORD: The password for connecting to the PostgreSQL database.

• DB_HOST: The hostname or IP address of the PostgreSQL database server.

• DB_PORT: The port number of the PostgreSQL database server.

• JWT_SECRET: (Optional) The secret key used for verifying JWT tokens for requests.

  • The secret is only used for verification. This basic approach assumes a signed JWT from elsewhere.
  • Omit to run API without requiring authentication

• COLLECTION_NAME: (Optional) The name of the collection in the vector store. Default value is "testcollection".

• CHUNK_SIZE: (Optional) The size of the chunks for text processing. Default value is "1500".

• CHUNK_OVERLAP: (Optional) The overlap between chunks during text processing. Default value is "100".

• RAG_UPLOAD_DIR: (Optional) The directory where uploaded files are stored. Default value is "./uploads/".

• PDF_EXTRACT_IMAGES: (Optional) A boolean value indicating whether to extract images from PDF files. Default value is "False".

• DEBUG_RAG_API: (Optional) Set to "True" to show more verbose logging output in the server console, and to enable postgresql database routes

• EMBEDDINGS_PROVIDER: (Optional) either "openai" or "huggingface", which uses sentence_transformers; defaults to "openai"

• EMBEDDINGS_MODEL: (Optional) openai default: "text-embedding-3-small", huggingface default: "sangmini/msmarco-cotmae-MiniLM-L12_en-ko-ja"

• HF_TOKEN: (Optional) if needed for huggingface option.

Make sure to set these environment variables before running the application. You can set them in a .env file or as system environment variables.