LlamaIndex
Best for Simplest Python path to RAG—index a folder of PDFs in five lines, query with natural language, and integrate with any LLM or vector store.
When not Code-first like LangChain; steeper than no-code tools but less boilerplate than raw API calls.
Open-source data framework for connecting large language models to private data sources—PDFs, databases, APIs, Slack, Notion, SharePoint, and more. Handles ingestion, chunking, embedding, indexing, and query routing so developers can build production-grade RAG applications without building the plumbing from scratch. Python and TypeScript SDKs with a large integration ecosystem. Free and open-source under MIT license. Best for developers building retrieval-augmented generation systems, internal knowledge assistants, and document Q&A applications who need flexible, battle-tested data connectors beyond what basic vector search provides.
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On these task shortlists
- RAG over your documentsbest for beginners
Build a retrieval-augmented generation (RAG) system to answer questions over your own PDFs, wikis, and knowledge bases.
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