Overview
LlamaIndex is a data framework for building LLM applications. It structures and connects documents, databases, and other data sources to LLMs to enable retrieval-augmented generation (RAG) and high-quality question answering.
Key features
- Wide range of data connectors and index structures for diverse data sources.
- Seamless integrations with major LLM and embedding providers; plugin-friendly design.
- Tooling and CLI for building, evaluating, and benchmarking retrieval strategies.
Use cases
- Knowledge-base Q&A and document retrieval applications.
- Private data integration for on-premise LLM services and enterprise search.
- Prototyping, teaching, and benchmarking RAG systems.
Technical notes
- Implemented primarily in Python with modular core and integration packages.
- Components include loaders, indices, retrievers, and query engines with persistence options.
- Comprehensive documentation and examples for quick adoption and productionization.