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userpu
67
langchain4j-ai-pinecone/pom.xml
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67
langchain4j-ai-pinecone/pom.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<parent>
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<groupId>com.iwe3</groupId>
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<artifactId>langchain4j-ai-java</artifactId>
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<version>1.0-SNAPSHOT</version>
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</parent>
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<artifactId>langchain4j-ai-pinecone</artifactId>
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<properties>
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<maven.compiler.source>17</maven.compiler.source>
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<maven.compiler.target>17</maven.compiler.target>
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<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
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</properties>
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<dependencies>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-pinecone</artifactId>
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</dependency>
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<!-- 接入阿里云百炼平台 -->
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
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</dependency>
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<!-- 简单的rag实现 -->
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-easy-rag</artifactId>
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</dependency>
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<!-- 解析pdf文档 -->
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-document-parser-apache-pdfbox</artifactId>
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</dependency>
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-test</artifactId>
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</dependency>
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<dependency>
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<groupId>org.projectlombok</groupId>
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<artifactId>lombok</artifactId>
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<optional>true</optional>
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</dependency>
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-web</artifactId>
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</dependency>
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<!--导入低阶依赖-->
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
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<version>1.9.1-beta17</version>
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</dependency>
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<!--导入高阶依赖-->
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-spring-boot-starter</artifactId>
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<version>1.9.1-beta17</version>
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</dependency>
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</dependencies>
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</project>
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package com.iwe3.langchain4j;
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import org.springframework.boot.SpringApplication;
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import org.springframework.boot.autoconfigure.SpringBootApplication;
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@SpringBootApplication
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public class PineConeApplication {
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public static void main(String[] args) {
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SpringApplication.run(PineConeApplication.class,args);
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}
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}
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package com.iwe3.langchain4j.config;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.model.embedding.EmbeddingModel;
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import dev.langchain4j.store.embedding.pinecone.PineconeEmbeddingStore;
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import dev.langchain4j.store.embedding.pinecone.PineconeServerlessIndexConfig;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Configuration;
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@Configuration
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public class EmbeddingStoreConfig {
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@Autowired
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private EmbeddingModel embeddingModel;
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@Bean
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public EmbeddingStore<TextSegment> embeddingStore() {
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// 创建向量存储
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return PineconeEmbeddingStore.builder()
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.apiKey(System.getenv("PINECONE_API_KEY"))
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.index("xiaoai-index") // 如果指定的索引不存在，将创建一个新的索引
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.nameSpace("xiaoai-namespace") // 如果指定的名称空间不存在，将创建一个新的名称空间
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.createIndex(PineconeServerlessIndexConfig.builder()
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.cloud("AWS") // 指定索引部署在 AWS 云服务上。
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.region("us-east-1") // 指定索引所在的 AWS 区域为 us-east-1。
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.dimension(embeddingModel.dimension()) // 指定索引的向量维度，该维度与 embeddingModel 生成的向量维度相同。
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.build())
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.build();
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}
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}
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package com.iwe3.langchain4j.config;
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import dev.langchain4j.model.chat.ChatModel;
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import dev.langchain4j.model.openai.OpenAiChatModel;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Configuration;
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@Configuration
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public class LLMConfig {
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/**
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* @Description: 普通对话接口 ChatModel
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*/
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@Bean(name = "qwen")
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public ChatModel chatModelQwen()
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{
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return OpenAiChatModel.builder()
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.apiKey(System.getenv("DASH_SCOPE_API_KEY"))
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.modelName("qwen-plus")
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.baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
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.build();
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}
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}
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11
langchain4j-ai-pinecone/src/main/resources/application.yml
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11
langchain4j-ai-pinecone/src/main/resources/application.yml
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server:
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port: 9011
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spring:
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application:
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name: langchain4j-ai-pinecone
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langchain4j:
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community:
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dashscope:
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embedding-model:
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model-name: text-embedding-v3
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api-key: ${DASH_SCOPE_API_KEY}
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package com.iwe3.langchain4j;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.model.embedding.EmbeddingModel;
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import dev.langchain4j.store.embedding.EmbeddingMatch;
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import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
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import dev.langchain4j.store.embedding.EmbeddingSearchResult;
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import org.junit.jupiter.api.Test;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.boot.test.context.SpringBootTest;
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@SpringBootTest
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class PineConeApplicationTest {
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@Autowired
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private EmbeddingModel embeddingModel;
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@Autowired
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private EmbeddingStore embeddingStore;
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/**
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* Pinecone - 相似度匹配
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*/
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@Test
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public void embeddingSearch() {
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// 提问，并将问题转成向量数据
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var queryEmbedding = embeddingModel.embed("你最喜欢的运动是什么？").content();
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// 创建搜索请求对象
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var searchRequest = EmbeddingSearchRequest.builder()
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.queryEmbedding(queryEmbedding)
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.maxResults(1) // 匹配最相似的一条记录
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.minScore(0.8)
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.build();
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// 根据搜索请求 searchRequest 在向量存储中进行相似度搜索
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EmbeddingSearchResult<TextSegment> searchResult = embeddingStore.search(searchRequest);
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// searchResult.matches(): 获取搜索结果中的匹配项列表。
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// .get(0): 从匹配项列表中获取第一个匹配项
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EmbeddingMatch<TextSegment> embeddingMatch = searchResult.matches().get(0);
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// 获取匹配项的相似度得分
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System.out.println(embeddingMatch.score()); // 输出：0.8144288515898701
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// 返回文本结果
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System.out.println(embeddingMatch.embedded().text());
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}
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/**
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* 将文本转换成向量，然后存储到 pinecone 中
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*
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* 参考:
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* https://docs.langchain4j.dev/tutorials/embedding-stores
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*/
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@Test
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public void testPineconeEmbedded() {
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// 将文本转换成向量
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var seg1 = TextSegment.from("我喜欢羽毛球");
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var em1 = embeddingModel.embed(seg1).content();
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embeddingStore.add(em1, seg1);// 存入向量数据库
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var seg2 = TextSegment.from("今天天气很好");
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var em2 = embeddingModel.embed(seg2).content();
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embeddingStore.add(em2, seg2);// 存入向量数据库
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}
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@Test
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public void testEmbeddingModel() {
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var embed = embeddingModel.embed("你好");
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System.out.println("向量维度：" + embed.content().vector().length);
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System.out.println("向量输出：" + embed.toString());
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}
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}
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