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General

Building RAG with LlamaIndex

This guide walks through building a simple RAG (Retrieval-Augmented Generation) application using LlamaIndex to connect external data to an LLM.

Steps

  1. Ingest your documents into an index
  2. Create an embedding model and embed documents
  3. Configure a retriever to fetch relevant context
  4. Combine retrieved context with prompts to the LLM

Example

  • Use LlamaIndex connectors to load PDFs or a database
  • Build a vector index and run similarity search for queries
  • Use the retrieved context to craft a prompt for final generation