RAG2.0 Engine Design Challenges and Implementation

The presentation begins with an introduction to the topic "RAG2.0 Engine Design Challenges and Implementation" and outlines six main sections: (1) pain points and solutions of RAG1.0, (2) effective chunking, (3) accurate retrieval, (4) advanced RAG and preprocessing, (5) Q&A, and (6) future development.

RAG1.0 Pain Points and Solutions – The standard RAG pipeline (Extraction, Indexing, Retrieval, Generation) suffers from low vector recall, complex document structures, and semantic gaps between queries and relevant documents. These issues hinder enterprise‑grade applications.

Next‑Generation RAG (RAG2.0) – RAG2.0 separates offline and online processing. Offline processing uses multimodal deep‑document‑understanding models to semantically split documents, ensuring high‑quality data. Online processing enriches knowledge graphs, applies hybrid search (vector, sparse, tensor), and generates answers with LLMs.

Effective Chunking – Chunking involves three steps: (1) document structure recognition (headers, footers, tables, figures), (2) OCR or text extraction with line‑break correction, and (3) final chunk output. Table processing uses a table‑structure model to extract header‑cell relationships, and other visual elements are handled by multimodal models.

Indexing Database and Benchmark – A custom index‑type database creates appropriate indexes per column type (vector, sparse vector, full‑text, tensor). Benchmarks show lower latency and higher QPS compared with popular open‑source vector databases and search engines.

Retrieval Model Comparison – Three retrieval model families are discussed: dual‑encoder (vector search), cross‑encoder (token‑wise interaction), and delayed‑interaction encoder (stores token‑level tensors for efficient re‑ranking). Experiments on the MLDR dataset demonstrate that blended search (BM25 + dense + sparse) outperforms single‑mode retrieval, and tensor‑based re‑ranking further improves nDCG@10.

ColBERT and Tensor Benefits – ColBERT stores token embeddings, enabling high‑quality re‑ranking with CPU efficiency. Quantization reduces storage overhead dramatically, making tensor‑based re‑ranking a practical component for both text and multimodal retrieval.

Future Directions – Anticipated trends include multimodal RAG (image‑to‑text pipelines, patch embeddings), agentic RAG that orchestrates multiple operators, and memory‑enhanced agents requiring databases that support vectors, tensors, and structured data. The talk concludes with a Q&A session addressing model choices, table storage formats, and GPU acceleration for cross‑encoders.