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1、引言 由于目前比较火的chatGPT是预训练模型，而训练一个大模型是需要较长时间（参数越多学习时间越长，保守估计一般是几个月，不差钱的可以多用点GPU缩短这个时间），这就导致了它所学习的知识不会是最新的，最新的chatGPT-4o只能基于2023年6月之前的数据进行回答，距离目前已经快一年的时间，如果想让GPT基于近一年的时间回复问题，就需要RAG（检索增强生成）技术了。此外，对于公司内部的私有数据，为了数据安全、商业利益考虑，不能放到互联网上的数据，因此GPT也没有这部分的知识，如果需要GPT基于这部分私有的知识进行回答，也需要使用RAG技术。本文将通过实战代码示例，意在帮助没有大模型实战经验的Java工程师掌握使用LangChain4j框架进行大模型开发。2、基本概念2.1 什么是RAGRAG（Retrieval-Augmented Generation）的核心思想是：将传统的信息检索（IR）技术与现代的生成式大模型（如chatGPT）结合起来。 具体来说，RAG模型在生成答案之前，会首先从一个大型的文档库或知识库中检索到若干条相关的文档片段。再将这些检索到的片段作为额外的上下文信息，输入到生成模型中，从而生成更为准确和信息丰富的文本。1.接收请求：首先，系统接收到用户的请求（例如提出一个问题）。2.信息检索（R)：系统从一个大型文档库中检索出与查询最相关的文档片段。这一步的目标是找到那些可能包含答案或相关信息的文档。3.生成增强（A）：将检索到的文档片段与原始查询一起输入到大模型（如chatGPT）中，注意使用合适的提示词，比如原始的问题是XXX，检索到的信息是YYY，给大模型的输入应该类似于：请基于YYY回答XXXX。4.输出生成（G)：大模型基于输入的查询和检索到的文档片段生成最终的文本答案，并返回给用户。第2步骤中的信息检索，不一定必须使用向量数据库，可以是关系型数据库（如MySQL）或全文搜索引擎（如Elasticsearch, ES），但大模型应用场景广泛使用向量数据库的原因是：在大模型RAG的应用场景中，主要是要查询相似度高的某几个文档，而不是精确的查找某一条（MySQL、ES擅长）。相似度高的两个文档，可能不包含相同的关键词。例如，句子1: "他很高兴。" 句子2: "他感到非常快乐。" 虽然都是描述【他】很开心快乐的心情，但是不包含相同的关键词；包含相同的关键词的两个文档可能完全没有关联，例如：句子1: "他喜欢苹果。" 句子2: "苹果是一家大公司。" 虽然都包含相同的关键词【苹果】，但两句话的相似度很低。
2.2 LangChain4j简介LangChain4j是LangChiain的java版本，LangChain的Lang取自Large Language Model，代表大语言模型，Chain是链式执行，即把语言模型应用中的各功能模块化，串联起来，形成一个完整的工作流。它是面向大语言模型的开发框架，意在封装与LLM对接的细节，简化开发流程，提升基于LLM开发的效率。更多介绍，详见：https://github.com/langchain4j/langchain4j/blob/main/README.md2.3 大模型开发 vs. 传统JAVA开发开发前，开发人员关注数据准备（进行训练）、选择和微调模型（得到更好的效果，更能匹配业务预期），开发过程中（大多数时候），重点在于如何有效的与大模型（LLM）进行沟通，利用LLM的专业知识解决特定的业务问题，开发中更关注如何描述问题（提示工程 Propmt Engineering）进行有效的推理，关注如何将大模型的使用集成到现有的业务系统中。开发前，开发人员关注系统架构的选择（高并发、高可用），功能的拆解、模块化等设计。开发过程中（大多数时候）是根据特定的业务问题，设计特定的算法、数据存储等以实现业务逻辑，以编码为主。
3. 实战经验3.1 环境搭建3.1.1 向量库（Chroma）先安装python，
参考：https://docs.python.org/zh-cn/3/using/windows.html#the-full-installerpython--version 再安装chroma，参考：https://docs.trychroma.com/getting-startedchromarun brewinstallpython 或者下载安装：https://www.python.org/downloads/macos/python--version 安装chroma（同上），参考：https://docs.trychroma.com/getting-startedchromarun <properties><langchain4j.version>0.31.0</langchain4j.version></properties><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-core</artifactId><version>${langchain4j.version}</version></dependency><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j</artifactId><version>${langchain4j.version}</version></dependency><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-open-ai</artifactId><version>${langchain4j.version}</version></dependency><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-embeddings</artifactId><version>${langchain4j.version}</version></dependency><dependency><groupId>dev.langchain4j</groupId><artifactId>langchain4j-chroma</artifactId><version>${langchain4j.version}</version></dependency><dependency><groupId>io.github.amikos-tech</groupId><artifactId>chromadb-java-client</artifactId><version>${langchain4j.version}</version></dependency>3.2 程序编写3.2.1 项目结构LangChain├──core│├──src││├──main│││├──java││││└──cn.jdl.tech_and_data.ka││││├──ChatWithMemory││││├──Constants││││├──Main││││├──RagChat││││└──Utils│││├──resources││││├──log4j2.xml││││└──笑话.txt││├──test│││└──java│├──target├──pom.xml├──parent[learn.langchain.parent]├──pom.xml 一般是公司内网的知识库中或互联网上进行数据采集，获取到的文本文件、WORD文档或PDF文件，本文使用resources目录下的【笑话.txt】作为知识采集的结果文件URLdocUrl=Main.class.getClassLoader().getResource("笑话.txt");if(docUrl==null){log.error("未获取到文件");}Documentdocument=getDocument(docUrl);if(document==null){log.error("加载文件失败");}privatestaticDocumentgetDocument(URLresource){Documentdocument=null;try{Pathpath=Paths.get(resource.toURI());document=FileSystemDocumentLoader.loadDocument(path);}catch(URISyntaxExceptione){log.error("加载文件发生异常",e);}returndocument;}3.2.3 文档切分使用dev.langchain4j.data.document.splitter.DocumentSplitters#recursize它有三个参数：分段大小（一个分段中最大包含多少个token）、重叠度（段与段之前重叠的token数）、分词器（将一段文本进行分词，得到token）其中，重叠度的设计是为了减少按大小拆分后切断原来文本的语义，使其尽量完整。
DocumentSplitter splitter = DocumentSplitters.recursive(150,10,new OpenAiTokenizer());
splitter.split(document);
Token是经过分词后的文本单位，即将一个文本分词后得到的词、子词等的个数，具体取决于分词器（Tokenizer），比如：我喜欢吃苹果，可以拆分成我/喜欢/吃/苹果，token数量=4, 也可以拆分成我/喜/欢/吃/苹果，token数量=5chatGPT使用的是BPE（Byte Pair Encoding）算法进行分词，参见：https://en.wikipedia.org/wiki/Byte_pair_encoding18:17:29.371[main]INFOTokenizerTest-待分词的文本:我喜欢吃苹果18:17:30.055[main]INFOcn.jdl.tech_and_data.ka.Utils-当前的模型是:gpt-4o18:17:31.933[main]INFOTokenizerTest-分词结果:我/喜欢/吃/苹果 由于与LLM交互的时候输入的文本对应的token长度是有限制的，输入过长的内容，LLM会无响应或直接该报错，因此不能将所有相关的知识都作为输入给到LLM，需要将知识文档进行拆分，存储到向量库，每次调用LLM时，先找出与提出的问题关联度最高的文档片段，作为参考的上下文输入给LLM。虽然根据响应，允许输入1048576个字符=1024K个字符=1M个字符，但官网文档给的32K tokens，而一般1个中文字符对应1-2个Token，因此字符串建议不大于64K，实际使用中，为了保障性能，也是要控制输入不要过长。| 模型名称 | Token 输入上限（最大长度） |
|---|
| GPT-3 (davinci) | 4096 tokens | | GPT-3.5 (text-davinci-003) | 4096 tokens | | GPT-4 (8k context) | 8192 tokens | | GPT-4 (32k context) | 32768 tokens | | LLaMA (7B) | 2048 tokens | | LLaMA (13B) | 2048 tokens | | LLaMA (30B) | 2048 tokens | | LLaMA (65B) | 2048 tokens | | 讯飞星火（SparkDesk） | 8192 tokens | | 文心一言（Ernie 3.0） | 4096 tokens | | 智源悟道（WuDao 2.0） | 2048 tokens | | 阿里巴巴 M6 | 2048 tokens | | 华为盘古（Pangu-Alpha） | 2048 tokens | | 言犀大模型（ChatJd） | 2048 tokens |
文档拆分的方案langchain4j中提供了6种：3、基于段落的：按照连续的两个换行符（\n\n）分割5、基于句子的（使用Apache OpenNLP，只支持英文，所以可以忽略?）文档切分的流程如下，其中segments是最终输出的拆分结果，类型是：List<TextSegment>。先使用基于段落的方案将整个文档切成若干段（分段）：parts，再对每个段落part按照其他的某个方案（如：分句），并在满足【分段大小（一个分段中最大包含多少个token）】的条件下进行，同时计算重叠部分，按照【重叠度（段与段之前重叠的token数）】补充重叠信息。由于需要将已拆分的知识片段文本存储向量库以便后续可以进行检索，而向量库存储的数据是向量不是文本，因此需要将文本进行向量化，即将一个字符串转换为一个N维数组，这个过程在自然语言处理（NLP）领域称为文本嵌入（Words Embedding）。不同的LLM对于文本嵌入的实现是不同的，ChatGPT的实现是基于transformer架构的，相关实现存储在服务端，每次嵌入都需要访问OpenAI的HTTP接口。通过下面的例子可以看到OpenAi使用的模型是：text-embedding-ada-002，向量的维度是：1536。OpenAiEmbeddingModelembeddingModel=newOpenAiEmbeddingModel.OpenAiEmbeddingModelBuilder().apiKey(API_KEY).baseUrl(BASE_URL).build();log.info("当前的模型是:{}",embeddingModel.modelName());Stringtext="两只眼睛";Embeddingembedding=embeddingModel.embed(text).content();log.info("文本:{}的嵌入结果是:\n{}",text,embedding.vectorAsList());log.info("它是{}维的向量",embedding.dimension());16:33:34.096[main]INFOcn.jdl.tech_and_data.ka.OpenAIEmbeddingTest-当前的模型是:text-embedding-ada-00216:33:36.201[main]INFOcn.jdl.tech_and_data.ka.OpenAIEmbeddingTest-文本:两只眼睛的嵌入结果是:[-0.010065761,-0.0021892355,0.007137785,-0.004445936,-0.018269492,0.014046189,0.010794383,-0.021953074,-0.008244209,-0.027377252,0.0036970759,0.028308267,5.81463E-4,0.0021504434,-0.0068240734,0.012217891,0.020495834,0.0020880383,0.021278426,-0.024273867,-0.005393818,0.0036970759,0.011563482,-0.0073604193,-0.019861663,0.0036768364,0.025069952,0.0060010017,-0.011023763,-0.021089524,0.025123924,-0.013189386,-0.025677137,-0.011671426,0.011239651,0.013816809,-0.008217223,0.0146938525,0.016407462,0.005839086,0.008790675,0.015476446,8.72827E-4,0.0318974,-0.011212665,0.0010473924,-0.027566154,0.005366832,-0.020306932,0.022479301,-0.0064192843,-0.0074346308,-0.028740043,-0.012055975,-0.015071657,-9.799275E-4,-0.004068133,-0.011900807,-0.013783077,-0.005191423,-4.3978673E-4,0.0073199403,-0.023261894,0.0011991884,-0.030683031,0.0053330995,0.0060819597,-0.0063821785,-5.401408E-4,-0.011219411,0.029927425,0.020050565,0.005994255,-0.016717799,0.04725241,-0.013735851,-0.027984437,-0.037645407,-6.0507574E-4,-0.002486081,0.017365463,-0.031222751,-0.0086287595,0.01062572,-0.016960673,6.588368E-5,-0.007083813,0.029846467,-0.02215547,-0.025312826,0.009283168,0.013533456,-0.007562814,0.0075898,-0.023046006,0.031708498,0.015355009,0.04325849,-0.011597214,-0.017648814,0.004995775,-0.015004192,-0.031384666,0.0016655395,-0.014437486,-0.002553546,0.018755239,-0.002821719,0.016461432,-0.015328023,-0.016461432,0.004800127,0.022816624,-0.026878012,-0.023032513,8.2686654E-4,0.025474742,-0.011806356,-0.04123454,-0.011482524,0.0049249367,0.010861848,0.0069084046,-0.015058164,0.022182455,0.020603778,-0.019133043,-2.224233E-4,0.0052892473,0.0039669354,0.04010113,-0.0034322762,0.02782252,0.016420955,-0.0103626065,0.02841621,-0.007900138,0.01867428,-0.019875156,-0.0020593656,0.0045336406,0.033246696,-0.019510847,-0.0082509555,0.0024405424,0.022600738,0.004155837,0.027134378,0.0050058947,-0.0032400014,0.014909741,-0.010227677,0.022317385,0.0035149206,-0.0036700899,0.0026716096,0.004617972,0.02202054,-0.030197285,0.0147613175,-0.01835045,0.0144105,0.031384666,-0.015894728,0.02841621,0.023315866,-0.01200875,0.008878379,-7.5349846E-4,0.0050295074,-0.0030477263,0.013385034,8.340347E-4,0.005464656,0.023639698,0.021426849,0.0020559924,-0.0013518278,-0.0071850107,-0.012319089,-0.04951923,-0.016326504,-0.0025973981,-0.0010473924,0.0013956799,-0.015867742,-0.008149759,-0.0035081743,0.009924085,0.0011915986,0.014032697,0.013128667,0.011064242,-0.0013341182,-0.6079396,-0.014235091,-0.015530418,-0.01925448,0.0027643738,0.02628432,0.012163919,0.018242506,-0.020239467,-0.03470394,-0.0027930464,-0.008595027,-0.0023983768,-8.711404E-4,-0.013762837,-0.01414064,-0.014073175,-0.009512549,0.0015761484,-0.022911076,-0.025110431,0.011475777,0.011327355,0.010295142,0.035999265,-0.0025147537,0.019632282,-0.0058289664,-0.019794198,0.044149023,-0.030575087,0.01907907,-0.0147613175,-0.0038454987,0.0539989,-0.009330394,-0.011037256,0.02071172,0.012845315,0.041207556,-0.015705826,-0.004182823,0.0073536728,-0.003724062,-0.00466857,-0.0070163487,0.012238131,0.0028099127,-0.008210477,-0.0083588995,-0.011934538,-0.006679024,0.011266637,-0.013978725,0.016528897,-0.0016950553,0.020306932,-0.022128483,0.01681225,-0.0022314012,0.02065775,0.005980762,-0.017324984,-0.013486232,0.027930465,0.010092747,-0.031735484,0.013884274,0.011745637,4.0204858E-4,0.013816809,0.008284688,0.00772473,0.024732629,0.012163919,0.019200508,0.018404422,-0.004941803,-0.004041147,-0.014302556,-0.010254663,0.002772807,-0.028038409,0.0015213332,0.002821719,-0.029603593,-0.018930648,0.0029701418,9.504116E-4,0.0036397309,-0.0016545764,0.009310154,0.0016579496,-0.011786116,-0.010112987,0.019052085,-0.037402533,0.0029667686,0.026635138,-9.687537E-5,-0.0017228846,0.0051745567,0.015503432,-0.008541055,0.01101027,0.022101497,-0.019133043,-0.020738706,0.042071104,-0.026081925,0.003548653,-0.01694718,0.008851393,7.215581E-5,0.01577329,-0.022830117,-0.004985655,0.012926273,0.021926088,0.020050565,0.042152062,-0.017136082,0.02814635,-0.0010060702,0.034218192,0.024260374,-0.056346674,-0.020374397,-0.003442396,-0.017149575,-0.01427557,0.0071175457,0.004705676,-0.021453835,-0.010086001,-0.00860852,-0.02269519,-0.023099978,0.013762837,-0.006625052,-0.017230533,-0.0010414892,0.023342852,-0.020819664,0.014343035,-0.0022448942,-0.017230533,-0.013492978,0.0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89,0.034865856,-0.013047709,2.5362583E-4,-0.00390959,-0.019092564]16:33:36.203[main]INFOcn.jdl.tech_and_data.ka.OpenAIEmbeddingTest-它是1536维的向量 3.2.5 向量库存储向量数据库，也称为向量存储或向量搜索引擎，是一种专门设计用于存储和管理向量（固定长度的数字列表）及其他数据项的数据库。这些向量是数据点在高维空间中的数学表示，其中每个维度对应数据的一个特征。向量数据库的主要目的是通过近似最近邻（ANN）算法实现高效的相似性搜索。在使用向量库前，需要先启动chromdb，再通过LangChain4j封装的SDK连接到向量库，并创建数据存储容器，即集合（Collection）中（相当于MySQL的表）Clientclient=newClient(CHROMA_URL);EmbeddingFunctionembeddingFunction=newOpenAIEmbeddingFunction(API_KEY,OPEN_AI_MODULE_NAME);client.createCollection(CHROMA_DB_DEFAULT_COLLECTION_NAME,null,true,embeddingFunction); 嵌入完成后，通过SDK连接到向量库，将向量（Embedding）与文本段(TextSegment)绑定，一并存储到向量库中。EmbeddingStore<TextSegment>embeddingStore=ChromaEmbeddingStore.builder().baseUrl(CHROMA_URL).collectionName(CHROMA_DB_DEFAULT_COLLECTION_NAME).build();segments.forEach(segment->{Embeddingembedding=embeddingModel.embed(segment).content();embeddingStore.add(embedding,segment);});3.2.6 向量库检索为了在向量库中查询到相似的知识片段，作为查询的文本也需要进行向量化，方法同上。EmbeddingqueryEmbedding=embeddingModel.embed(qryText).content(); 检索时，向量库通过ANN算法查找与查询向量（queryEmbedding）距离最近的若个（取决于查询入参）个文本片段。EmbeddingSearchRequestembeddingSearchRequest=EmbeddingSearchRequest.builder().queryEmbedding(queryEmbedding).maxResults(1).build();EmbeddingSearchResult<TextSegment>embeddedEmbeddingSearchResult=embeddingStore.search(embeddingSearchRequest);List<EmbeddingMatch<TextSegment>>embeddingMatcheList=embeddedEmbeddingSearchResult.matches();EmbeddingMatch<TextSegment>embeddingMatch=embeddingMatcheList.get(0);TextSegmentTextSegment=embeddingMatch.embedded(); 查询方法有4个入参：查询文本嵌入向量(queryEmbedding）、最大查询数量（最多查询多少个距离最近的向量）、最小分值（通过该值过滤一些候选值）、元数据过滤器（根据元数据过滤一些候选值）3.2.7 与LLM交互定义Prompt模板，告知LLM通过给定的上下文知识（context，即：查询向量库获取到的相关知识）回答提出的问题（question）：基于如下信息进行回答:\n{{context}}\n提问:\n{{question}}PromptTemplatepromptTemplate=PromptTemplate.from("基于如下信息进行回答:\n"+"{{context}}\n"+"提问:\n"+"{{question}}");Map<String,Object>variables=newHashMap<>();variables.put("context",textSegment.text());variables.put("question",QUESTION);prompt=promptTemplate.apply(variables);将Prompt信息组合到请求LLM的入参中：OpenAiChatModelopenAiChatModel=OpenAiChatModel.builder().apiKey(API_KEY).baseUrl(BASE_URL).modelName(OPEN_AI_MODULE_NAME).temperature(TEMPERATURE_NO_RANDOM).build();UserMessageuserMessage=prompt.toUserMessage();Response<AiMessage>aiMessageResponse=openAiChatModel.generate(userMessage);Stringresponse=aiMessageResponse.content();3.3测试验证 17:47:57.060 [main] INFOcn.jdl.tech_and_data.ka.Utils - 当前的模型是:gpt-4o17:47:57.067 [main] INFOcn.jdl.tech_and_data.ka.RagChat - [without RAG]userMessage=UserMessage { name = null contents = [TextContent { text = "请给我讲一个关于冰淇淋的笑话" }] }17:48:00.129 [main] INFOcn.jdl.tech_and_data.ka.RagChat - 不使用RAG，直接询问AI:[请给我讲一个关于冰淇淋的笑话] 得到的回答是:[当然！这里有一个关于冰淇淋的笑话，希望你会喜欢：
为什么冰淇淋总是很开心？
因为它有很多“甜蜜”的朋友！]17:48:00.269 [main] INFOcn.jdl.tech_and_data.ka.RagChat - version of chroma db is : 0.5.017:48:02.550 [main] INFOcn.jdl.tech_and_data.ka.RagChat - text:听说夏天和冰淇淋更配哦？不存在的，冰淇淋在我嘴里早就化了，连味道都没来得及品尝！, embeddingId=bd89a581-e764-47fc-967c-68899d6be90417:48:03.811 [main] INFOcn.jdl.tech_and_data.ka.RagChat - text:有一次，一个女孩因为太想要一支冰激凌而得不到，结果她竟然在超市的冰激凌货架前大声呼唤：“天啊，我渴望你！你能把冰激凌赐给我吗？”你猜怎么着？她得到了那支冰激凌，但是当她回家后发现里面全是空的！, embeddingId=6f84ab62-635f-4bd0-a3d2-ff55d00c4b0117:48:05.534 [main] INFOcn.jdl.tech_and_data.ka.RagChat - text:冰淇淋的冷，你绝对想象不到，它不仅冷静而且淡定的在我嘴里说着：“我是你的心头痛。”, embeddingId=c9d15213-2146-4f7b-8b23-c95d858b4ded17:48:06.843 [main] INFOcn.jdl.tech_and_data.ka.RagChat - text:半价冰淇淋妈妈带着小明去买冰淇淋，小明兴致勃勃地挑选了一个自己喜欢的口味。妈妈结账时，发现价格是双倍的，于是问店员为什么。店员说：“这个冰淇淋是两种口味的双球冰淇淋。” 小明听了，恍然大悟：“难怪它长了两只眼睛！”, embeddingId=042ee87a-bc0c-4ba2-aea5-dbef45c32d3317:48:08.452 [main] INFOcn.jdl.tech_and_data.ka.RagChat - text:有一次我家的冰箱坏了，里面的冰激凌都开始融化。我看到了一个很棒的创意——把所有的冰激凌球放在一个碗里，然后放入冰箱冷冻。结果我得到了一个巨大的冰激凌球混合物，这可是一道奇特的甜点！
麒麟飞到北极会变成什么？ 答案：冰淇淋。, embeddingId=e7436694-821e-4559-89d3-2f09be2e172a17:48:08.453 [main] INFOcn.jdl.tech_and_data.ka.RagChat - 通过关键词:【两只眼睛】查询向量库17:48:10.252 [main] INFOcn.jdl.tech_and_data.ka.RagChat - 查询到的文档信息是 matchedTextSegment: TextSegment { text = "半价冰淇淋妈妈带着小明去买冰淇淋，小明兴致勃勃地挑选了一个自己喜欢的口味。妈妈结账时，发现价格是双倍的，于是问店员为什么。店员说：“这个冰淇淋是两种口味的双球冰淇淋。” 小明听了，恍然大悟：“难怪它长了两只眼睛！”" metadata = {absolute_directory_path=/Users/qihaizhi/newJavaEngineerOrientation/LangChain/core/target/classes, file_name=笑话.txt, index=3} }17:48:10.258 [main] INFOcn.jdl.tech_and_data.ka.RagChat - [with RAG]userMessage=UserMessage { name = null contents = [TextContent { text = "请基于如下信息进行回答，尽量使用信息中的所有词语:半价冰淇淋妈妈带着小明去买冰淇淋，小明兴致勃勃地挑选了一个自己喜欢的口味。妈妈结账时，发现价格是双倍的，于是问店员为什么。店员说：“这个冰淇淋是两种口味的双球冰淇淋。” 小明听了，恍然大悟：“难怪它长了两只眼睛！”提问:请给我讲一个关于冰淇淋的笑话" }] }17:48:16.663 [main] INFOcn.jdl.tech_and_data.ka.RagChat - 使用RAG，询问AI:[请给我讲一个关于冰淇淋的笑话]， 得到的回答是:[有一天，妈妈带着小明去买冰淇淋。小明兴致勃勃地挑选了一个自己喜欢的口味。妈妈结账时，发现价格是双倍的，于是问店员为什么。店员说：“这个冰淇淋是两种口味的双球冰淇淋。” 小明听了，恍然大悟：“难怪它长了两只眼睛！”
这个笑话是不是很有趣呢？冰淇淋不仅美味，还能带来欢乐！]
4、总结与展望通过本文的实战示例，详细介绍了如何使用LangChain4j框架来实现基于RAG技术的本地大模型应用。通过不断探索和优化，RAG技术在大模型应用中的潜力将得到更充分的发挥，为各种业务场景提供更智能和高效的解决方案。希望本文能为Java工程师们提供一个清晰的实战指南，帮助大家在大模型开发的道路上走得更远。 |
