一种基于多层索引的知识检索方法
By constructing a multi-layered index structure and combining vector similarity analysis of knowledge classification tags, document summaries, and text blocks, the problems of fuzzy knowledge base classification, excessively broad retrieval scope, and contextual breaks in existing technologies are solved, achieving efficient and accurate knowledge retrieval.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INSPUR GENERSOFT CO LTD
- Filing Date
- 2025-05-30
- Publication Date
- 2026-07-17
AI Technical Summary
The lack of an effective classification system in existing knowledge bases means that RAG technology needs to traverse multiple unrelated knowledge bases during retrieval, which increases retrieval complexity and time cost. Furthermore, traditional methods cannot guarantee the accuracy and contextual integrity of retrieval results.
A knowledge retrieval method based on multi-level indexing is adopted. By constructing a three-level index structure of knowledge classification tags, document summaries and text blocks, semantic analysis and vector similarity calculation are performed using a large language model. The query statement and tags are dynamically matched to narrow the search scope and quickly locate highly relevant documents and text blocks.
It significantly improves the accuracy and efficiency of knowledge retrieval, reduces redundant calculations, and ensures the contextual coherence of retrieval results and the quality of answer generation.
Smart Images
Figure CN120633843B_ABST