LlamaIndex

LlamaIndex is an open source Python framework focused specifically on connecting AI models to private data sources and building retrieval-augmented generation (RAG) applications. While LangChain is a broader LLM application framework, LlamaIndex has a tighter focus on data ingestion, indexing, and retrieval that makes it the preferred choice for teams building document Q&A systems and knowledge bases.

The data connectors in LlamaIndex support ingesting documents from a wide range of sources: PDFs, Word documents, databases, APIs, web pages, Slack, Notion, and many others. These connectors handle the complexity of extracting text from diverse formats and preparing it for AI processing, which reduces the amount of custom data handling code developers need to write.

The indexing capabilities support various approaches to structuring data for retrieval, from simple vector search to more sophisticated hybrid approaches that combine semantic similarity with keyword matching. The choice of indexing strategy significantly affects retrieval quality, and LlamaIndex provides tools for experimenting with different approaches.

Querying across indexed data is LlamaIndex's most polished feature. Complex queries that require reasoning across multiple retrieved chunks, combining information from multiple sources, or applying filters to retrieved results are well-supported.

LlamaIndex has expanded into multi-agent applications, providing tools for building systems where multiple AI agents collaborate to complete tasks, building on its strength in data retrieval.

LlamaCloud, the managed service built on LlamaIndex, provides hosted versions of data ingestion and retrieval infrastructure for teams that want to use LlamaIndex capabilities without managing the infrastructure themselves.