How does LlamaIndex actually work?
Data connectors load sources in their native formats and split them into nodes. Each node is turned into vector embeddings and stored, commonly in a vector database, though other index structures exist for keyword or hierarchical access. A query engine then retrieves the most relevant chunks and synthesizes an answer, which is the RAG loop as a managed pipeline. The framework’s depth sits on this data side: parsing messy documents and tuning retrieval quality are its center of gravity, not orchestration.
LlamaIndex vs LangChain: what’s the difference?
LangChain is a broad component library spanning models, tools, chains, and integrations, while LlamaIndex concentrates on making your data usable to any of them. When retrieval quality decides whether the product works, teams often reach for LlamaIndex first, and when an application grows past single-shot retrieval into adaptive querying and retries, the same pipeline becomes agentic RAG inside whichever orchestration layer the team already runs. Choose by where your hard problem lives: data quality points to LlamaIndex, application complexity points to LangChain.