langchain4j 学习系列(5)-RAG

继续我们的langchain4j之旅，今天来看看RAG如何实现，“RAG萌宠新手盆友们”建议先看看B站大佬的视频RAG 工作机制详解—哔哩哔哩_bilibili，核心步骤就是下面这3张图：

最简单的RAG hello-world
1、分片/索引

这里embedding模型，我们用ollamal加载“nomic-embed-text:latest”, 项目的yaml配置参考下面：

# Ollama配置
ollama:
  base-url: http://localhost:11434
  model: deepseek-v3.1:671b-cloud
  embedding-model: nomic-embed-text:latest
  timeout: 60

示例代码：

    EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();

    @Autowired
    @Qualifier("ollamaEmbeddingModel")
    OllamaEmbeddingModel ollamaEmbeddingModel;

    
    @GetMapping(value = "/embed/memory", produces = MediaType.APPLICATION_JSON_VALUE)
    public ResponseEntity<String> embedMemory() {
        try {
            TextSegment segment1 = TextSegment.from("我喜欢打乒乓球");
            Embedding embedding1 = ollamaEmbeddingModel.embed(segment1).content();
            embeddingStore.add(embedding1, segment1);

            TextSegment segment2 = TextSegment.from("张三是个程序员");
            Embedding embedding2 = ollamaEmbeddingModel.embed(segment2).content();
            embeddingStore.add(embedding2, segment2);

            return ResponseEntity.ok("{\"code\":\"success\"}");
        } catch (Exception e) {
            log.error("embed-in-memory", e);
            return ResponseEntity.ok("{\"error\":\"embed in-memory error: " + e.getMessage() + "\"}");
        }
    }

略做解释：

为了简单起见，这里分片我们略过，直接手动用二个句子，当成2个分片
使用langchang4j自带的“内存”向量存储（可以很容易替换成其它向量数据库存储）

2、召回验证

    @GetMapping(value = "/query/memory", produces = MediaType.APPLICATION_JSON_VALUE)
    public ResponseEntity<String> queryInMemory(@RequestParam(required = false) String query) {
        try {
            if (!StringUtils.hasText(query)) {
                query = "我最喜欢的运动是什么?";
            }

            Embedding queryEmbedding = ollamaEmbeddingModel.embed(query).content();
            EmbeddingSearchRequest embeddingSearchRequest = EmbeddingSearchRequest.builder()
                    .queryEmbedding(queryEmbedding)
                    .maxResults(1)
                    .build();
            List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(embeddingSearchRequest).matches();
            EmbeddingMatch<TextSegment> embeddingMatch = matches.get(0);
            return ResponseEntity.ok("{\"score\":" + embeddingMatch.score() + "\",\"text\":\"" + embeddingMatch.embedded().text() + "\"}");
        } catch (Exception e) {
            log.error("query-in-memory", e);
            return ResponseEntity.ok("{\"error\":\"" + e.getMessage() + "\"}");
        }
    }

验证方法：

先访问 /embed/memory

日志里能看到类似输出：

2025-12-03T20:49:25.134+08:00  INFO 2004 --- [langchain4j-study] [nio-8080-exec-2] d.l.http.client.log.LoggingHttpClient    : HTTP request:
- method: POST
- url: http://localhost:11434/api/embed
- headers: [Content-Type: application/json]
- body: {
  "model" : "nomic-embed-text:latest",
  "input" : [ "我喜欢打乒乓球" ]
}

2025-12-03T20:49:25.998+08:00  INFO 2004 --- [langchain4j-study] [nio-8080-exec-2] d.l.http.client.log.LoggingHttpClient    : HTTP response:
- status code: 200
- headers: [content-type: application/json; charset=utf-8], [date: Wed, 03 Dec 2025 12:49:25 GMT], [transfer-encoding: chunked]
- body: {"model":"nomic-embed-text:latest","embeddings":[[-0.012458213,0.0039172457,-0.12891848,0.017662216,0.025300438,0.024325758,-0.0036070538,-0.033644814,-0.0381311,-0.03634163,-0.05963595,0.054057397,-0.03499457,0.018935377,-0.008374583,-0.034164727,-0.0049227136,-0.06220498,-0.08091822,0.09595101,-0.05103614,-0.0136290835,-0.021773232,-0.013128546,0.15322413,0.012732275,0.015309959,0.040424943,-0.00033224112,0.01658926,-0.012258051,-0.013984016,-0.0032665338,0.0071487525,-0.06581794,-0.0094845425,-0.020760503,-0.015683724,0.077724874,-0.0058224904,0.009427068,0.044845946,0.02461985,-0.024601495,0.09705044,-0.0011949927,0.0046018264,0.024254085,0.081773125,-0.01725117,-0.015873862,0.02042173,0.011301929,-0.039436378,0.057020035,0.017206708,-0.020540748,-0.049172606,-0.023007197,0.018489506,0.05498273,0.037677817,0.004085807,0.024920072,0.038203474,-0.02519353,-0.008932274,0.033442993,0.037548456,-0.0081279045,0.05669283,-0.022172932,0.0041421456,0.05737762,-0.022708746,-0.03461972,0.015064465,0.043800466,0.012183848,-0.004566944,-0.0036377572,-0.013947094,0.08149233,-0.0016764933,0.0054556034,0.0058031534,0.015527312,-0.0035933307,-0.08838505,0.029139869,-0.006777496,0.0077084485,0.019112136,0.033412933,-0.0480321,-0.012102348,-0.004239011,0.061032787,-0.0579935,-0.009473412,-0.03933967,-0.0043621548,0.023765758,0.0032002365,0.0425869,0.026631407,0.021958495,0.012563806,-0.028455937,0.012312696,-0.002447975,0.051577017,-0.019399943,0.03240589,0.005346467,-0.037924852,0.025513943,-0.03641099,-0.01771762,0.030995507,-0.045378692,-0.0072853183,0.01496905,0.033930972,0.047220357,0.001891088,-0.043199636,0.025126396,0.0082712,-0.014336107,0.02299608,0.03695494,-0.031902455,-0.014891659,-0.04119217,-0.0126992455,-0.033002827,-0.04176258,0.01696001,0.012815302,-0.010139981,0.02541227,0.026224406,-0.08467736,-0.01036433,-0.021439519,0.034442775,-0.07415152,-0.07627749,0.015752453,0.007223294,0.01375651,0.0011124315,0.021325773,0.02891942,-0.018423207,-0.008344011,0.020883271,0.009631505,0.024712183,0.017345998,0.0029280796,-0.03804128,0.0068171904,0.0008997442,-0.06083421,-0.007373738,0.07257523,0.02059963,0.02534663,-0.04393039,-0.02154865,0.013769385,-0.004244279,-0.04271552,0.0022474776,-0.018932758,-0.046988882,-0.028639391,0.052725717,0.04184288,-0.032908622,0.013641719,-0.024019161,-0.06525277,-0.008747925,0.002658261,0.0054255393,-0.009330522,-0.030206269,0.010751471,-0.0045538982,-0.02103397,-0.008394027,-0.0061083557,-0.06730141,-0.016043691,0.016352192,-0.003351765,-0.042771764,-0.016877415,-0.014862734,-0.02238862,-0.044956047,-0.016563887,-0.005472391,0.025998248,0.030923538,0.0114779575,-0.008590849,0.055101596,-0.045934986,-0.016732357,0.0017127717,-0.01398391,0.009828012,0.0013537256,-0.04638824,-0.09776861,0.032502655,0.03829003,0.021019036,-0.022623325,-0.0063213673,0.06002845,-0.0033687477,0.0025381974,-0.008064681,0.025479037,-0.031904727,-0.020291261,-0.040521063,0.106739186,0.06693403,-0.07955313,0.061153132,0.0010594398,0.098829836,-0.021282706,0.035282467,-0.0055606607,-0.006739511,0.010749998,-0.025116479,-0.030600592,-0.012364566,0.014683787,-0.027038267,0.007232086,0.022181682,0.024009116,-0.0074885017,-0.001418513,0.057900473,0.021429744,-0.052669637,-0.021409081,0.04249021,-0.00037090518,-0.004339607,0.005603592,-0.07505218,-0.009280494,-0.038106687,-0.0122500025,-0.01169743,-0.012411175,0.01995531,-0.009089314,0.010376189,0.033866882,0.05661189,0.004106048,0.055462204,-0.04531201,-0.006846949,0.022010498,-0.031004658,-0.061774857,0.012150931,0.030378802,-0.013861524,0.033796314,0.026777264,-0.0005726319,0.009243347,-0.049827427,-0.019435763,0.0015726548,0.0010323931,0.022491656,0.0013827641,-0.006789208,0.0655327,0.024350733,0.013471634,0.048357487,0.005508278,0.003927951,0.024350954,0.057273958,0.029133383,0.030226987,-0.022165632,0.015017607,0.016747091,0.08473727,-0.004313997,0.009891569,-0.031435654,-0.06068177,-0.0071860636,-0.015852293,0.04086709,-0.09022679,0.028333658,0.03241014,-0.013437933,0.026222765,0.0017604269,0.00016218411,-0.053620227,0.03844302,-0.041051988,-0.04597637,0.056273703,0.026004747,0.037739772,-0.001115858,-0.038443428,-0.03239269,0.03821732,0.044401653,-0.023431325,-0.0035764996,0.0440406,0.017453695,-0.010676289,-0.04152656,0.07972034,0.016837802,0.0046362695,0.036325917,-0.025703505,-0.023982164,-0.014884179,-0.06366106,0.02458128,0.02455616,-0.014446463,-0.020697251,0.01716094,-0.03154391,-0.02452164,0.010915117,0.014459898,0.016768359,0.036897838,0.0031146673,-0.009332912,0.043560322,0.01723151,-0.015456339,0.08636779,-0.0039273244,0.075721465,0.01884202,-0.011837652,0.016794553,0.020799648,-0.05813417,-0.037899256,0.005361145,0.0045944178,-0.018389683,0.00974621,-0.033685226,-0.0018594693,-0.044432137,0.022150835,-0.02758174,-0.021186957,-0.03605525,0.005528283,0.026348246,0.0138723,0.04552078,-0.018413119,-0.016553754,0.018572886,-0.0235327,-0.05108994,-0.024805017,-0.0030415633,0.045106664,-0.06671464,0.039470863,0.021384079,0.03922674,-0.024490539,-0.0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2025-12-03T20:49:26.046+08:00  INFO 2004 --- [langchain4j-study] [nio-8080-exec-2] d.l.http.client.log.LoggingHttpClient    : HTTP request:
- method: POST
- url: http://localhost:11434/api/embed
- headers: [Content-Type: application/json]
- body: {
  "model" : "nomic-embed-text:latest",
  "input" : [ "张三是个程序员" ]
}

2025-12-03T20:49:26.078+08:00  INFO 2004 --- [langchain4j-study] [nio-8080-exec-2] d.l.http.client.log.LoggingHttpClient    : HTTP response:
- status code: 200
- headers: [content-type: application/json; charset=utf-8], [date: Wed, 03 Dec 2025 12:49:26 GMT], [transfer-encoding: chunked]
- body: 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也就是每个分片，都会调用1次ollama的embedding模型，然后返回1个N维向量化的数值列表。

再访问 /query/memory

在运行完下面这行代码后

EmbeddingSearchRequest embeddingSearchRequest = EmbeddingSearchRequest.builder()
        .queryEmbedding(queryEmbedding)
        .maxResults(1)
        .build();

日志里能看出类似下面的输出：

2025-12-03T20:55:50.338+08:00  INFO 2004 --- [langchain4j-study] [nio-8080-exec-3] d.l.http.client.log.LoggingHttpClient    : HTTP request:
- method: POST
- url: http://localhost:11434/api/embed
- headers: [Content-Type: application/json]
- body: {
  "model" : "nomic-embed-text:latest",
  "input" : [ "我最喜欢的运动是什么?" ]
}

2025-12-03T20:55:50.373+08:00  INFO 2004 --- [langchain4j-study] [nio-8080-exec-3] d.l.http.client.log.LoggingHttpClient    : HTTP response:
- status code: 200
- headers: [content-type: application/json; charset=utf-8], [date: Wed, 03 Dec 2025 12:55:50 GMT], [transfer-encoding: chunked]
- body: 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3、重排/生成

private interface Assistant {
    String chat(String userMessage);
}

/**
 * 基于RAG的AI聊天
 *
 * @param query
 * @return
 */
@GetMapping(value = "/query/bot", produces = MediaType.APPLICATION_JSON_VALUE)
public ResponseEntity<String> bot(@RequestParam(required = false) String query) {
    try {
        if (!StringUtils.hasText(query)) {
            query = "张三的职业是什么？";
        }

        ContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
                .embeddingStore(embeddingStore)
                .embeddingModel(ollamaEmbeddingModel)
                .maxResults(3)
                .minScore(0.6)
                .build();

        Assistant assistant = AiServices.builder(Assistant.class)
                .chatModel(ollamaChatModel)
                .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
                .contentRetriever(retriever)
                .build();

        return ResponseEntity.ok(assistant.chat(query));
    } catch (Exception e) {
        log.error("bot", e);
        return ResponseEntity.ok("{\"error\":\"" + e.getMessage() + "\"}");
    }
}

日志输出：

2025-12-03T21:06:00.218+08:00  INFO 16956 --- [langchain4j-study] [nio-8080-exec-6] d.l.http.client.log.LoggingHttpClient    : HTTP request:
- method: POST
- url: http://localhost:11434/api/embed
- headers: [Content-Type: application/json]
- body: {
  "model" : "nomic-embed-text:latest",
  "input" : [ "张三的职业是什么？" ]
}

2025-12-03T21:06:00.255+08:00  INFO 16956 --- [langchain4j-study] [nio-8080-exec-6] d.l.http.client.log.LoggingHttpClient    : HTTP response:
- status code: 200
- headers: [content-type: application/json; charset=utf-8], [date: Wed, 03 Dec 2025 13:06:00 GMT], [transfer-encoding: chunked]
- body: 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2025-12-03T21:06:00.306+08:00  INFO 16956 --- [langchain4j-study] [nio-8080-exec-6] d.l.http.client.log.LoggingHttpClient    : HTTP request:
- method: POST
- url: http://localhost:11434/api/chat
- headers: [Content-Type: application/json]
- body: {
  "model" : "deepseek-v3.1:671b-cloud",
  "messages" : [ {
    "role" : "user",
    "content" : "张三的职业是什么？\n\nAnswer using the following information:\n张三是个程序员\n\n我喜欢打乒乓球"
  } ],
  "options" : {
    "stop" : [ ]
  },
  "stream" : false,
  "tools" : [ ]
}

2025-12-03T21:06:02.708+08:00  INFO 16956 --- [langchain4j-study] [nio-8080-exec-6] d.l.http.client.log.LoggingHttpClient    : HTTP response:
- status code: 200
- headers: [content-type: text/plain; charset=utf-8], [date: Wed, 03 Dec 2025 13:06:02 GMT], [transfer-encoding: chunked]
- body: {"model":"deepseek-v3.1:671b-cloud","remote_model":"deepseek-v3.1:671b","remote_host":"https://ollama.com:443","created_at":"2025-12-03T13:06:02.704211517Z","message":{"role":"assistant","content":"根据提供的信息，张三的职业是程序员。  \n（信息源：第一句话“张三是个程序员”）"},"done":true,"done_reason":"stop","total_duration":1059949995,"prompt_eval_count":21,"eval_count":22}

从日志上看，先做了1次RAG检索(即：召回)，然后将返回的结果进行了排序，将得分最高的"张三是个程序员"，排到了第1位（即：重排），然后再把用户问题+重排后的结果，拼在一起发给LLM，最终得出了答案（即：增强生成)

文档示例代码：GitHub - yjmyzz/langchain4j-study at day05

posted @ 2025-12-03 21:23 菩提树下的杨过阅读(0) 评论(0) 收藏举报

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langchain4j 学习系列(5)-RAG

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