Enterprise-Grade RAG 2.0 System Construction and Document Parsing in Practice
This scenario guides the construction of an enterprise-grade RAG 2.0 system, focusing on solving issues of large model hallucination, knowledge freshness, and data security. Through layered architecture design, hybrid retrieval (vector + full-text + knowledge graph), and document parsing (OCR, layout analysis, table recognition), it achieves 'more comprehensive search, better ranking, and more accurate answers'. Practice shows that combining ontology constraints with GraphRAG can improve recall accuracy by 15-20%, and pre-processing document parsing significantly enhances knowledge base quality. Suitable for industries requiring high-precision knowledge Q&A, such as engineering manufacturing, finance, and law.
Steps
- 1
Deploy a document parsing platform (e.g., RAGFlow DeepDoc or PaddleOCR-VL) to perform layout analysis, table restoration, and structure extraction on documents such as PDFs, scanned files, and drawings.
- 2
Slice the parsed structured content (Markdown/JSON), build vector indexes (e.g., Infinity) and full-text indexes (e.g., Elasticsearch), and optionally integrate a knowledge graph.
- 3
Design an offline ingestion pipeline: document parsing → slicing → vectorization → index construction; and an online Q&A pipeline: query rewriting → hybrid retrieval → re-ranking → LLM generation.
- 4
Configure the query rewriting module (multi-turn rewriting, HyDE) and ranking module (coarse ranking + fine ranking + knowledge filtering) to improve retrieval recall and ranking accuracy.
- 5
Integrate an LLM (e.g., ERNIE-4.5) and design prompt templates, combining retrieval results to generate traceable answers while preserving original text position mapping.
- 6
Deploy privately on the enterprise intranet, connecting to knowledge bases, business systems, and Agent platforms, with permission control and operation logging.
Recommended tools
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