Java开发者LLM实战——使用LangChain4j构建本地RAG系统

‍1、引言

2.1 什么是RAG

RAG（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。

2.2 LangChain4j简介

2.3 大模型开发 vs. 传统JAVA开发

3.1 环境搭建

3.1.1 向量库（Chroma）

python --version

chroma run

brew install python

python --version

chroma run

<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

URL docUrl = Main.class.getClassLoader().getResource("笑话.txt");if(docUrl==null){log.error("未获取到文件");}Document document = getDocument(docUrl);if(document==null){log.error("加载文件失败");}

private static Document getDocument(URL resource) {Document document = null;try{Path path = Paths.get(resource.toURI());document = FileSystemDocumentLoader.loadDocument(path);}catch (URISyntaxException e){log.error("加载文件发生异常", e);}return document;}

3.2.3 文档切分



DocumentSplitter splitter = DocumentSplitters.recursive(150,10,new OpenAiTokenizer());
splitter.split(document);

18:17:29.371 [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

OpenAiEmbeddingModel embeddingModel = new OpenAiEmbeddingModel.OpenAiEmbeddingModelBuilder().apiKey(API_KEY).baseUrl(BASE_URL).build();log.info("当前的模型是: {}", embeddingModel.modelName());String text = "两只眼睛";Embedding embedding = 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, 0.006193277, -0.010315382, 0.0029718284, -0.010207438, -0.01617808, 0.0010229364, 0.00531286, 0.0104300715, -0.0050969725, -0.015881235, -0.014896248, -0.008871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3.2.5 向量库存储

Client client = new Client(CHROMA_URL);EmbeddingFunction embeddingFunction = new OpenAIEmbeddingFunction(API_KEY, OPEN_AI_MODULE_NAME);client.createCollection(CHROMA_DB_DEFAULT_COLLECTION_NAME,null,true, embeddingFunction);

EmbeddingStore<TextSegment> embeddingStore = ChromaEmbeddingStore.builder().baseUrl(CHROMA_URL).collectionName(CHROMA_DB_DEFAULT_COLLECTION_NAME).build();segments.forEach(segment->{Embedding embedding = embeddingModel.embed(segment).content();embeddingStore.add(embedding, segment);});

3.2.6 向量库检索

Embedding queryEmbedding = embeddingModel.embed(qryText).content();

EmbeddingSearchRequest embeddingSearchRequest = 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);TextSegment TextSegment = embeddingMatch.embedded();

3.2.7 与LLM交互

基于如下信息进行回答:\n{{context}}\n提问:\n{{question}}

PromptTemplate promptTemplate = PromptTemplate.from("基于如下信息进行回答:\n" +"{{context}}\n" +"提问:\n" +"{{question}}");Map<String, Object> variables = new HashMap<>();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：

OpenAiChatModel openAiChatModel =OpenAiChatModel.builder().apiKey(API_KEY).baseUrl(BASE_URL).modelName(OPEN_AI_MODULE_NAME).temperature(TEMPERATURE_NO_RANDOM).build();UserMessage userMessage = prompt.toUserMessage();Response<AiMessage> aiMessageResponse = openAiChatModel.generate(userMessage);String response = 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！]