Yuxi-Know's innovativeness is mainly reflected in the breakthroughs in three technical dimensions:
- dual-engine knowledge processing: Using both knowledge graphs (structured data) and vector retrieval (unstructured documents) to cover a wider range of knowledge expressions. For example, we prioritize the use of Neo4j mapping when querying explicit relationships such as "Beijing's capital city", and invoke document vector retrieval when dealing with open-ended questions such as "trends in artificial intelligence".
- Dynamic enhancement of capabilities: When the local knowledge base is insufficient, networked retrieval is realized through the Tavily API (API key configuration is required), combined with RAG technology to ensure the timeliness of answers. This hybrid retrieval model is more comprehensive than the coverage of a single knowledge base system
- Reasoning Strengthening: The specially integrated DeepSeek-R1 model handles multi-step mathematical reasoning (e.g., price calculation problems), and its 250 billion-parameter pedestal model outperforms general-purpose language models on complex logic problems.
In addition, its open-source nature allows developers to customize the model configuration and extend the tool chain, which allows the system to be deeply customized according to specific scenario requirements, an advantage that is difficult to achieve with commercial closed-source systems.
This answer comes from the articleYuxi-Know: A Knowledge Graph-based Intelligent Q&A PlatformThe
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