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Turbocharge Your RAG: Quickstart Guide

Lab Overview​

Get started with Retrieval Augmented Generation (RAG) in LangChain, learning how to enhance your AI applications with external knowledge.

Lab Materials​

Key Topics​

  • RAG fundamentals
  • Document processing
  • Vector stores
  • Retrieval strategies
  • Response generation

Features​

  • Document ingestion
  • Text chunking
  • Vector embeddings
  • 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​

  • Google Colab account
  • OpenAI API key
  • Basic understanding of:
    • LLMs
    • Vector embeddings
    • Python programming

Resources​