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RAG Implementation with LangChain

Lab Overview​

Learn how to implement a comprehensive Retrieval Augmented Generation system using LangChain, with hands-on examples in Google Colab.

Lab Materials​

Two major hands-on workshops:

RAG with LangChain in Google Colab​

  • Setting up RAG pipeline
  • Integration with LangChain
  • Real-time implementation
  • Performance optimization

RAG Deployment with LangServe and Docker​

  • Containerized deployment
  • LangServe integration
  • Scalability considerations
  • Production best practices

Key Topics​

  • RAG architecture
  • Document processing
  • Vector embeddings
  • Query pipeline
  • Response generation

Features​

  • Document ingestion
  • Text chunking
  • Vector store integration
  • Semantic search
  • Context-aware responses

Technical Components​

  • Document loaders
  • Text splitters
  • Embedding models
  • Vector databases
  • LLM integration

Implementation Steps​

  1. Document preparation
  2. Text chunking and processing
  3. Embedding generation
  4. Vector store setup
  5. Query pipeline creation
  6. Response generation

Prerequisites​

  • OpenAI API key
  • Google Colab account
  • Docker Desktop installation
  • Basic understanding of:
    • LangChain concepts
    • Docker containers
    • Python programming

Resources​