基于LangChain的检索增强生成知识库问答系统设计与实现

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中图分类号：TP391 文献标识码：A 文章编号：1006-8228（2026）02-51-06

Abstract：Curentlylargelanguagemodelsarewidelyusedinintellgentquestionanswering，whichdemonstratestronglanguage understandingandgenerationcapabiliesingeneralscenarios.However，inspecificdomainapplicationssuchasfinanceandlaw， limitationsintrainingdataoftenhinderacurateanswerstospecializedquestions，sometimesevenleadingto"halucination" responses.Toaddresstheseisses，thispaperintroducesRetrieval-AugmentedGeneration（RAG）technologyBasedonthe mainstreamlarge languagemodelframeworkLangChain，adomain-specific knowledgebasequestionansweringsystemisdeveloped. Thissystemefectivelyimprovesthequestionansweringperformanceoflargemodelsinspecificdomainsbyconstructingadomain knowledgevectorlibraryandemployingamethodofconcatenating prompt words.ExperimentalresultsshowthatusingRAG technologysignificantlyimprovestheaccuracyandrelevanceofthesysteminansweringdomain-speificquestions，validatingthe feasibility of the system.

Keywords：Large LanguageModel;Retrieval-Augmented Generation;LangChain；Question Answering System

0引言

近年来，大语言模型（LargeLanguageModel，LLM）在自然语言领域取得了突破性进展。（剩余7835字）