Git Repository Guide

LlamaIndex

LlamaIndex

AI/ML MIT

When to use this

Use this when you need to connect LLMs to your own data — PDFs, databases, APIs, Notion, Slack. Specializes in indexing large document collections and querying them efficiently with RAG.

YouTube Tutorials

Click any card to watch on YouTube

What is LlamaIndex?

LlamaIndex (formerly GPT Index) is a data framework for building LLM applications. It provides tools for ingesting, structuring, and querying data from various sources so your LLM can answer questions about it.

Key Features

  • Data Connectors — Ingest from 100+ sources: PDFs, Word docs, Notion, Slack, databases, APIs, websites.
  • Indexes — Structure data into forms LLMs can query: vector stores, knowledge graphs, keyword tables.
  • Query Engines — High-level interfaces for Q&A over your data.
  • Chat Engines — Multi-turn conversational interfaces with memory.
  • Agents — LLM-powered agents that use tools and query engines.

When LlamaIndex > LangChain

LlamaIndex is purpose-built for data indexing and retrieval. If your primary goal is RAG (making an LLM answer questions about a large document corpus), LlamaIndex's indexing abstractions are more mature and flexible than LangChain's.

Getting Started

pip install llama-index

LlamaIndex supports all major LLM providers (OpenAI, Anthropic, Mistral, local via Ollama) and all major vector stores (Pinecone, Chroma, Qdrant, Weaviate).