RAG2.0 Engine Design Challenges and Implementation

This presentation introduces RAG2.0 engine design challenges and implementation strategies. The speaker begins by outlining the standard RAG architecture consisting of four phases: Extraction, Indexing, Retrieval, and Generation. Current RAG faces several challenges including low vector recall rates, complex document structures, and semantic gaps between questions and answers.

The next-generation RAG2.0 architecture is presented with two main components: offline processing using deep document understanding models for multi-modal documents, and online processing incorporating knowledge graphs and advanced retrieval techniques. The speaker introduces Infiniflow, a product combining RAGFlow and Infinity database to provide enterprise-level retrieval capabilities.

Effective chunking strategies are discussed, including document structure recognition, text extraction with OCR handling, and specialized processing for tables and charts. The presentation demonstrates RAGFlow's superior performance compared to open-source RAG solutions through accuracy metrics.

Advanced retrieval techniques are covered including indexing databases with multiple index types (vector, sparse vector, full-text, tensor), benchmark comparisons, and database selection criteria. The importance of multi-path retrieval is emphasized through experimental results showing three-path retrieval with tensor re-ranking achieves the best performance.

Sorting models are explained including dual encoders, cross encoders, and delayed interaction encoders. ColBERT (tensor-based re-ranking) is highlighted as particularly effective, offering two orders of magnitude better efficiency than GPU-based cross encoders while maintaining comparable accuracy.

Advanced RAG and preprocessing techniques include RAPTOR for document clustering and summarization, Agentic RAG for complex query handling, and Graph RAG using knowledge graphs with node embeddings for multi-hop question answering.

Future developments in multi-modal RAG are discussed, including three processing approaches: traditional OCR-based methods, multi-modal encoder-decoder models, and direct patch embedding generation. The presentation concludes with predictions about memory-enhanced agents requiring sophisticated database capabilities for enterprise deployment.