LlamaIndex
LlamaIndex is a framework for RAG: indexing documents, searching for relevant fragments and transferring them to LLM.
LlamaIndex (formerly GPT Index) is a Python framework specialized in Retrieval-Augmented Generation tasks. If LangChain is a universal agent orchestrator, then LlamaIndex does one thing very well: it takes documents (PDF, Notion, Confluence, web pages), splits them into chunks, creates embeddings, puts them in a vector database and answers questions about this data.
In my projects, LlamaIndex has taken the place of the standard tool for “corporate knowledge base search”. Workflow is standard: upload 200 PDFs with regulations → LlamaIndex indexes them → manager asks a question → receives an answer with a link to a specific document. Set up in a day.
Among the features: LlamaIndex is better than LangChain at managing context when working with large corpora of documents - there are advanced chunking strategies, metadata, rerankers. For production RAGs, I prefer LlamaIndex over LangChain precisely for its more mature work with documents.
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