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> 技术文档 > 颠覆搜索认知！基于Embedding的下一代搜索技术，让Elasticsearch沦为‘古董级’工具！_elastcisearch embedding

颠覆搜索认知！基于Embedding的下一代搜索技术，让Elasticsearch沦为‘古董级’工具！_elastcisearch embedding

网友: 技术文档 2025-07-30 12:59:04

前沿
目前，很多企业会利用大模型来做企业内部知识库系统，比如智能客服系统，做法是把企业内部的整理的产品手册、常见问题手册，做成智能知识库系统，客户可以直接向知识库系统用自然语言提问，知识库系统能理解客户的问题并基于内部知识给出客户想要的答案。

在实现这些系统我们需要首先我们先学习一个前置知识，就是文本向量化。

首先看几个应用场景

法律行业：输入“员工私自带走客户名单” → 自动关联《竞业禁止协议范本》《刑法第219条司法解释》（而不是机械匹配“员工+客户+名单”）。

电商搜索：用户拍照上传“复古风连衣裙” → 搜索出相似款，并推荐“赫本风”“茶歇裙”等标签（ES需要提前打标）。

代码仓库：用自然语言搜“Python异步任务崩溃后自动重启” → 直接定位到Celery+Supervisor的代码片段（而不是靠README里的关键词）。

什么是向量
一个二维向量可以理解为平面坐标轴中的一个坐标点(x，y)，在编程领域，一个二维向量就是一个大小为二的float类型的数组

文本向量化
所谓文本向量化是指，利用大模型可以把一个字、一个词或一段话映射为一个多维向量，比如我们可以直接在LangChain4j中来调用向量模型来对一句话进行向量化体验:

package com.example;import dev.langchain4j.data.embedding.Embedding;import dev.langchain4j.http.client.spring.restclient.SpringRestClientBuilder;import dev.langchain4j.model.openai.OpenAiEmbeddingModel;import dev.langchain4j.model.output.Response;import static dev.langchain4j.model.openai.OpenAiEmbeddingModelName.TEXT_EMBEDDING_3_SMALL;public class VectorDemo1 { public static void main(String[] args) {  OpenAiEmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder() .baseUrl(\"http://langchain4j.dev/demo/openai/v1\") .apiKey(\"demo\") .modelName(TEXT_EMBEDDING_3_SMALL) .httpClientBuilder(new SpringRestClientBuilder()) .logRequests(true) .logResponses(true) .build(); Response<Embedding> embed = embeddingModel.embed(\"你好，我叫幼稚园的孩子王\"); System.out.println(embed.content().toString()); System.out.println(embed.content().vector().length); }}

代码结果：

2025-06-05T15:56:27.128305800Z main ERROR Log4j API could not find a logging provider.2025-06-05 23:56:28 [main] dev.langchain4j.http.client.log.HttpRequestLogger.log()DEBUG: HTTP request:- method: POST- url: http://langchain4j.dev/demo/openai/v1/embeddings- headers: [Authorization: Beare...mo], [User-Agent: langchain4j-openai], [Content-Type: application/json]- body: { \"model\" : \"text-embedding-3-small\", \"input\" : [ \"你好，我叫幼稚园的孩子王\" ]}2025-06-05 23:56:28 [main] io.micrometer.common.util.internal.logging.InternalLoggerFactory.newDefaultFactory()DEBUG: Using SLF4J as the default logging framework2025-06-05 23:56:34 [main] io.micrometer.observation.SimpleObservation$SimpleScope.close()TRACE: NoOp observation used with SimpleScope2025-06-05 23:56:34 [main] dev.langchain4j.http.client.log.HttpResponseLogger.log()DEBUG: HTTP response:- status code: 200- headers: [Transfer-Encoding: chunked], [Server: Jetty(9.4.48.v20220622)], [Date: Thu, 05 Jun 2025 15:56:30 GMT], [Content-Type: text/html;charset=utf-8]- body: 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从结果可以知道\"你好，我叫幼稚园的孩子王\"这句话经过OpenAiEmbeddingModel向量化之后得到的一个长度为1536的float数组。注意，1536是固定的，不会随着句子长度而变化。

那么，我们通过这种向量模型得到一句话对应的向量有什么作用呢？非常有用，因为我们可以基于向量来判断两句话之间的相似度。

向量相似度

前面提到，向量相当于就是坐标点，如果两个坐标点靠得近，那么就表示这两个向量相似，所以，如果两句话对应的向量相似，那么就表示这两句话语义比较相似，当然这中间最关键的就是向量模型，因为向量是它生成的，向量模型也是经过大量机器学习训练之后产生的，向量模型效果越好，就表示它对于自然语言理解的程度越好，同时也就表示它生成出来的向量越准确，越能反映出语义的相似度。
一句话：向量模型效果 ⇨ 自然语言理解能力 ⇨ 向量质量 ⇨ 语义相似度表征

余弦相似度是衡量两个向量方向相似度的常用方法，值在-1到1之间，1表示完全相同，0表示无关，-1表示完全相反：

public class CosineSimilarity { /** * 计算两个向量的余弦相似度 * @param vectorA 向量A * @param vectorB 向量B * @return 余弦相似度 * @throws IllegalArgumentException 当向量长度不一致或为零向量时抛出异常 */ public static double cosineSimilarity(double[] vectorA, double[] vectorB) { // 检查向量长度是否相同 if (vectorA.length != vectorB.length) { throw new IllegalArgumentException(\"Vectors must have the same length\"); } // 计算点积 double dotProduct = 0.0; // 计算向量A的模 double normA = 0.0; // 计算向量B的模 double normB = 0.0; for (int i = 0; i < vectorA.length; i++) { dotProduct += vectorA[i] * vectorB[i]; normA += Math.pow(vectorA[i], 2); normB += Math.pow(vectorB[i], 2); } // 计算模的平方根 normA = Math.sqrt(normA); normB = Math.sqrt(normB); // 检查零向量 if (normA == 0 || normB == 0) { throw new IllegalArgumentException(\"Cannot compute cosine similarity for zero vectors\"); } return dotProduct / (normA * normB); } public static void main(String[] args) { // 示例坐标（可以是任意维度） double[] vector1 = {1, 2, 3}; double[] vector2 = {4, 5, 6}; try { double similarity = cosineSimilarity(vector1, vector2); System.out.println(\"余弦相似度: \" + similarity); } catch (IllegalArgumentException e) { System.out.println(\"计算错误: \" + e.getMessage()); } }}

执行结果：

余弦相似度: 0.9746318461970762表示这两个向量方向非常相似

向量数据库
对于向量模型生成出来的向量，我们可以持久化到向量数据库，并且能利用向量数据库来计算两个向量之间的相似度，或者根据一个向量查找跟这个向量最相似的向量。

在LangChain4j中，EmbeddingStore表示向量数据库，它有20个实现类：

  1. AstraDbEmbeddingStore
  2. AzureAiSearchEmbeddingStore
  3. CassandraEmbeddingStore
  4. ChromaEmbeddingStore
  5. ElasticsearchEmbeddingStore
  6. InMemoryEmbeddingStore
  7. InfinispanEmbeddingStore
  8. MemoryIdEmbeddingStore
  9. MilvusEmbeddingStore
  10. MinimalEmbeddingStore
  11. MongoDbEmbeddingStore
  12. Neo4jEmbeddingStore
  13. OpenSearchEmbeddingStore
  14. PgVectorEmbeddingStore
  15. PineconeEmbeddingStore
  16. QdrantEmbeddingStore
  17. RedisEmbeddingStore
  18. VearchEmbeddingStore
  19. VespaEmbeddingStore
  20. WeaviateEmbeddingStore

其中有我们熟悉的几个数据库都可以用来存储向量，比如Elasticsearch、MongoDb、Neo4j、Pg、Redis。

那么我们使用Redis来演示对于向量的增删查改，首先添加依赖：

<dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-redis</artifactId> <version>${langchain4j.version}</version></dependency>

然后需要注意的是，普通的Redis是不支持向量存储和查询的，需要额外的redisearch模块，我这边是直接使用docker来运行一个带有redisearch模块的redis容器的，命令为：

 docker run --name outtest_redis -p 7001:6379 -v /usr/local/redis/data:/data -v /usr/local/redis/conf/redis.conf:/etc/redis/redis.conf -d redis redis-server /etc/redis/redis.conf

注意端口6379不要和你现有的Redis冲突了。
另外从Redis 6.0+（支持多用户 ACL）引入了访问控制列表（ACL），可以创建多用户。但默认仍保留一个 默认用户（用户名 default），密码通过 requirepass 设置。

连接时需要指定用户名和密码
redis向量数据库安装请查看文档 http://u5a.cn/OrOKc
然后就可以使用以下代码把向量存到redis中了：

RedisEmbeddingStore embeddingStore = RedisEmbeddingStore.builder() .host(\"localhost\") .port(7001) .user(\"default\") .password(\"xxx\") .dimension(1536) .build();Response<Embedding> embed = embeddingModel.embed(\"你好，我叫幼稚园的孩子王\");embeddingStore.add(embed.content());

dimension表示要存储的向量的维度，所以为1536，如果你不是使用OpenAiEmbeddingModel得到的向量，那么维度可能会不一样。
颠覆搜索认知！基于Embedding的下一代搜索技术，让Elasticsearch沦为‘古董级’工具！_elastcisearch embedding
可以使用以下命令来清空：

redis-cli FT.DROPINDEX embedding-index DD

匹配向量

在上面我们存储了\"你好，我叫幼稚园的孩子王\"的向量到Redis，接下来我们来使用“我的名字叫幼稚园的孩子王”来查找，看能不能查找到“我叫幼稚园的孩子王”，最好新建一个类，新增和查询分为两个类比较方便：

package com.example;import dev.langchain4j.data.embedding.Embedding;import dev.langchain4j.data.segment.TextSegment;import dev.langchain4j.http.client.spring.restclient.SpringRestClientBuilder;import dev.langchain4j.model.openai.OpenAiEmbeddingModel;import dev.langchain4j.model.output.Response;import dev.langchain4j.store.embedding.EmbeddingMatch;import dev.langchain4j.store.embedding.redis.RedisEmbeddingStore;import java.util.List;import static dev.langchain4j.model.openai.OpenAiEmbeddingModelName.TEXT_EMBEDDING_3_SMALL;public class VectorSearchDemo { public static void main(String[] args) { OpenAiEmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder() .baseUrl(\"http://langchain4j.dev/demo/openai/v1\") .apiKey(\"demo\") .modelName(TEXT_EMBEDDING_3_SMALL) .httpClientBuilder(new SpringRestClientBuilder()) .logRequests(true) .logResponses(true) .build(); RedisEmbeddingStore embeddingStore = RedisEmbeddingStore.builder() .host(\"localhost\") .port(7001) .user(\"default\") .password(\"xxx\") .dimension(1536) .build(); // 生成向量 Response<Embedding> embed = embeddingModel.embed(\"我叫幼稚园的孩子王\"); List<EmbeddingMatch<TextSegment>> result = embeddingStore.findRelevant(embed.content(), 4,0); for (EmbeddingMatch<TextSegment> embeddingMatch : result) { System.out.println(embeddingMatch.score()); } }}

运行结果如下：

0.9646165370941

这就是\"你好，我叫幼稚园的孩子王\"和“我叫幼稚园的孩子王”两句话之间的语义相似度分数，假如我们换成“基于Embedding的下一代搜索技术”，得到的分数为

0.561920404434

我们再演示几个场景：

package com.example;import dev.langchain4j.data.embedding.Embedding;import dev.langchain4j.data.segment.TextSegment;//import dev.langchain4j.model.openai.OpenAiEmbeddingModel;import dev.langchain4j.model.output.Response;import dev.langchain4j.store.embedding.EmbeddingMatch;import dev.langchain4j.store.embedding.redis.RedisEmbeddingStore;import java.util.List;import static dev.langchain4j.model.openai.OpenAiEmbeddingModelName.TEXT_EMBEDDING_3_SMALL;public class VectorDemo2 { public static void main(String[] args) { OpenAiEmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder() .baseUrl(\"http://langchain4j.dev/demo/openai/v1\") .apiKey(\"demo\") .modelName(TEXT_EMBEDDING_3_SMALL)  // .httpClientBuilder(new SpringRestClientBuilder()) .logRequests(true) .logResponses(true) .build(); RedisEmbeddingStore embeddingStore = RedisEmbeddingStore.builder() .host(\"localhost\") .port(7001) .user(\"default\") .password(\"xxxx\") .dimension(1536) .build(); // 生成向量 TextSegment textSegment1 = TextSegment.textSegment(\"客服电话是400-8558558\"); TextSegment textSegment2 = TextSegment.textSegment(\"客服工作时间是周一到周五\"); TextSegment textSegment3 = TextSegment.textSegment(\"客服投诉电话是400-8668668\"); Response<Embedding> embed1 = embeddingModel.embed(textSegment1); Response<Embedding> embed2 = embeddingModel.embed(textSegment2); Response<Embedding> embed3 = embeddingModel.embed(textSegment3); // 存储向量 embeddingStore.add(embed1.content(), textSegment1); embeddingStore.add(embed2.content(), textSegment2); embeddingStore.add(embed3.content(), textSegment3); // 生成向量 Response<Embedding> embed = embeddingModel.embed(\"客服电话多少\"); // 查询 List<EmbeddingMatch<TextSegment>> result = embeddingStore.findRelevant(embed.content(), 5,0); for (EmbeddingMatch<TextSegment> embeddingMatch : result) { if(embeddingMatch!=null){ System.out.println(embeddingMatch.embedded().text() + \",分数为：\" + embeddingMatch.score()); } } }}

执行结果如下：

2025-06-06 11:54:06 [main] dev.ai4j.openai4j.RequestLoggingInterceptor.logDebug()DEBUG: Request:- method: POST- url: http://langchain4j.dev/demo/openai/v1/embeddings- headers: [Authorization: Bearer ...], [User-Agent: langchain4j-openai]- body: { \"model\" : \"text-embedding-3-small\", \"input\" : [ \"客服电话是400-8558558\" ]}2025-06-06 11:54:08 [main] dev.ai4j.openai4j.ResponseLoggingInterceptor.logDebug()DEBUG: Response:- status code: 200- headers: [Date: Fri, 06 Jun 2025 03:54:07 GMT], [Content-Type: text/html;charset=utf-8], [Transfer-Encoding: chunked], [Server: Jetty(9.4.48.v20220622)]- body: 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11:54:08 [main] dev.ai4j.openai4j.RequestLoggingInterceptor.logDebug()DEBUG: Request:- method: POST- url: http://langchain4j.dev/demo/openai/v1/embeddings- headers: [Authorization: Bearer ...], [User-Agent: langchain4j-openai]- body: { \"model\" : \"text-embedding-3-small\", \"input\" : [ \"客服工作时间是周一到周五\" ]}2025-06-06 11:54:09 [main] dev.ai4j.openai4j.ResponseLoggingInterceptor.logDebug()DEBUG: Response:- status code: 200- headers: [Date: Fri, 06 Jun 2025 03:54:09 GMT], [Content-Type: text/html;charset=utf-8], [Transfer-Encoding: chunked], [Server: Jetty(9.4.48.v20220622)]....太多了日志客服电话是400-8558558,分数为：0.8621757626535客服投诉电话是400-8668668,分数为：0.8148611187935客服工作时间是周一到周五,分数为：0.779122173786

从这就更容易看出向量的好处，能够基于向量快速的得到和文本相似的文本，这样就能非常适合用来做RAG，也就是检索增强生成。
感谢各位看官的光顾，如果看到这句话 说明你还是花了点时间的 有问题多交流

笑话

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