LlamaIndex
RAG & knowledgeA data framework for connecting language models to your own documents and structured data.
LlamaIndex specializes in the data side of retrieval: loading documents, splitting them, building indexes, and retrieving the right context at query time. It supports a wide range of loaders and index structures, so messy real-world sources become something a model can use.
It scales from a few lines for a quick prototype to fine-grained control over chunking, retrieval, and reranking for production. A managed service, LlamaCloud, handles parsing and indexing for teams that would rather not.
Where it's ideally used
The right framework when retrieval quality is the hard part of the project and you want deep control over ingestion and indexing.
Where it doesn't fit
More framework than you need for one small, static document set — a simple embed-and-search script will do.