In view of the problems of insufficient knowledge accuracy, lack of real-time performance and professional limitations in the application of large language models (LLMs) in professional fields, this study proposes a retrieval-augmented generation (RAG) question-answering system based on the LangChain framework and the Zhangheng-1 satellite electric field knowledge graph. By integrating the reasoning ability of RAG technology and LLMs, and using LangChain's modular components (including LLMs access, prompt word templates and task chain orchestration) and Milvus vector database, dynamic retrieval and generation optimization of professional knowledge are achieved. The experimental data comes from 41 core papers in the field of Zhangheng-1 satellite electric field, covering research directions such as electric field anomaly detection and data processing methods. The test results show that compared with the ordinary Qwen-Plus model, the enhanced version of RAG shows better professionalism and accuracy in scientific parameter description and method applicability analysis. The study confirms that RAG technology can effectively solve the knowledge limitations of LLMs in professional fields and provide a feasible technical solution for building a highly reliable professional knowledge question-answering system, which has important theoretical value and practical significance.
Key words
LangChain /
RAG /
Zhangheng-1 satellite /
question-answering system
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