RAG in Production - LangChain & FastAPI

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AI Summary

Summary: Integrating Vector Databases with Lang Chain and FastAPI

• Introduction
  • Demonstrates using vector databases with Lang Chain library and FastAPI.
  • Importance of an abstraction layer for robustness and scalability.
• Key Points
  • Common indexing mistakes:
    • Not using an API, leading to lack of standardized data interface.
    • Overlooking API’s role as a security layer.
    • Blocking code in Python, which can be avoided with asynchronous programming.
  • Lang Chain offers built-in solutions for asynchronous operations.
  • Efficiency in updating vector stores without reindexing for every document change.
• Implementation Steps
  • Set up PG Vector using Docker.
  • Install dependencies with pip install requirements.txt.
  • Create FastAPI app instance and configure environment variables.
  • Define endpoints for document management (add, get, delete) and chat functionality.
  • Use asynchronous programming for non-blocking operations.
• Code Walkthrough
  • main.py includes FastAPI, Lang Chain imports, and model definitions.
  • Endpoints:
    • ADD documents: Adds documents to vector store.
    • GET all IDs: Retrieves all document IDs.
    • GET documents by IDs: Fetches documents by their IDs.
    • DELETE documents: Removes documents from vector store.
    • Chat: Provides user interaction with the retriever.
  • Demonstrates slow API response simulation and the benefits of asynchronous code.
• Running the API
  • Use Uvicorn server to run the FastAPI app.
  • Five endpoints demonstrated for document and chat operations.
• Best Practices for Production
  • Use APIs for standardized data handling and security.
  • Avoid reindexing documents with each change; use a more granular approach.
  • Implement asynchronous code to prevent blocking and improve performance.
• Conclusion
  • Emphasizes the necessity of APIs, efficient indexing, and asynchronous programming for advanced Rec applications.