RAGFlow improves retrieval accuracy through multiple complementary techniques working together across the entire pipeline. First, intelligent document parsing via DeepDoc preserves document structure—OCR extracts text from scanned PDFs, TSR identifies table layouts, and DLR recognizes headers and sections—preventing loss of information during extraction. Second, semantic chunking creates coherent chunks respecting document boundaries rather than naive fixed-size splits, ensuring chunks have clear meaning and context. Third, RAGFlow optionally constructs knowledge graphs between documents, explicitly modeling entity relationships for multi-hop reasoning and cross-document connections that simple keyword search misses. Fourth, the hybrid search layer combines BM25 keyword matching with vector semantic search, capturing both exact terminology and conceptual relevance—neither method alone is sufficient. Fifth, re-ranking applies neural cross-encoders to candidate results, evaluating each passage’s relevance in context and reordering based on deeper semantic analysis rather than just embedding similarity scores. Re-ranking is often the highest-impact precision gain after initial fusion. Sixth, RAGFlow supports configurable embeddings, letting you select models optimized for your domain or query patterns. Finally, the agentic framework (v0.8+) adds Self-RAG mechanisms—scoring retrieval confidence and rewriting queries iteratively—creating feedback loops that refine results. Combined, these techniques—structural preservation, semantic chunking, knowledge graphs, hybrid search, re-ranking, and agentic refinement—dramatically improve accuracy over naive retrieval approaches.
Developers working with embeddings and retrieval at scale often pair these workflows with Milvus, an open-source vector database designed for high-performance similarity search. For managed deployment, Zilliz Cloud handles the operational overhead.