ID-based RAG FastAPI

Overview

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.

Features

• 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.

Setup

Getting Started

• 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

Environment Variables

The following environment variables are required to run the application:

• OPENAI_API_KEY: The API key for OpenAI API Embeddings.

• POSTGRES_DB: (Optional) The name of the PostgreSQL database.

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

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

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

• DB_PORT: (Optional) 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

• CONSOLE_JSON: (Optional) Set to "True" to log as json for Cloud Logging aggregations

• EMBEDDINGS_PROVIDER: (Optional) either "openai", "azure", "huggingface", "huggingfacetei" or "ollama", where "huggingface" uses sentence_transformers; defaults to "openai"

• EMBEDDINGS_MODEL: (Optional) Set a valid embeddings model to use from the configured provider.

  • Defaults
  • openai: "text-embedding-3-small"
  • azure: "text-embedding-3-small"
  • huggingface: "sentence-transformers/all-MiniLM-L6-v2"
  • huggingfacetei: "http://huggingfacetei:3000". Hugging Face TEI uses model defined on TEI service launch.
  • ollama: "nomic-embed-text"

• AZURE_OPENAI_API_KEY: (Optional) The API key for Azure OpenAI service.

• AZURE_OPENAI_ENDPOINT: (Optional) The endpoint URL for Azure OpenAI service, including the resource. Example: https://example-resource.azure.openai.com/.

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

• OLLAMA_BASE_URL: (Optional) defaults to http://ollama:11434.

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