ThinkDoc's retrieval system adopts a triple technology fusion architecture: firstly, traditional keyword-based retrieval ensures the basic search rate; secondly, semantic understanding models such as BERT improve the accuracy of query intent recognition; and finally, knowledge graphs are introduced for correlation reasoning to realize cross-document deep information mining. This hybrid architecture enables the system to understand natural language questions (e.g., '2024 new energy policy') as well as handle complex query requirements in specialized fields. All search results support source tracing, can be associated to specific locations in the original document while returning text content, and support multimedia forms of presentation such as tables and images.
This answer comes from the articleThinkDoc: Knowledge Base Platform for Intelligent Parsing and RetrievalThe
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