RAGFlow

Sweet spot: a team whose RAG quality is being capped by document parsing — they are ingesting complex PDFs and the answers are thin because the tables and figures are mangled. RAGFlow's DeepDoc is the most credible open alternative to commercial parsing services (LlamaParse, Reducto, Unstructured) and, self-hosted, keeps the data in your environment. Failure modes. Deployment is a real project — five services, a database, storage, and non-trivial config. If your docs are clean HTML or plain-text, this is overkill and LlamaIndex with a basic loader is simpler. The agent builder is serviceable but lags dedicated agent frameworks. And the Chinese-language heritage shows in some docs; expect to read between the lines occasionally. What to pilot. Take ten of your worst-performing PDFs — ones where current answers are thin or wrong. Ingest them into RAGFlow and ask the same ten questions you have been asking. If answer quality lifts meaningfully (especially on table / figure questions), commit to the infrastructure investment. If the parsing is only marginally better, the bottleneck is elsewhere and a lighter stack is fine.

RAGFlow is an open-source, end-to-end RAG engine from InfiniFlow. Its differentiator is document parsing: it uses a custom deep-layout model (DeepDoc) to extract structure from complex documents — tables with merged cells, figures, multi-column layouts, forms — where naive PDF loaders produce garbage. Output is a structured representation that preserves table relationships, heading hierarchy, and figure context, which downstream retrieval can exploit. Beyond parsing, RAGFlow ships a full RAG stack: chunking strategies per document type, keyword + vector + rerank hybrid retrieval, knowledge graph generation over a corpus, an agent builder, and a chat UI. It runs as a single Docker-compose deployment (Elasticsearch or Infinity as the backend, MinIO for files, Redis for caching, MySQL for metadata). The project has grown rapidly among teams that found LangChain / LlamaIndex's parsing insufficient for their document types — legal contracts, financial reports, medical charts. It is self-hostable, Apache-2-licensed, and has an active dev community. A companion hosted version exists but the self-hosted path is where most serious adoption happens because the parsing pipeline is compute-heavy and data-sensitive.