SpringBoot 整合Redis Stack 构建本地向量数据库相似性查询的方法

docker run -d --name redis-stack -v /redis-data:/data -p 6379:6379 -p 8001:8001 redis/redis-stack:latest

version: '3'
services:
  redis-stack:
    image: redis/redis-stack
    ports:
      - 6379:6379
  redis-insight:
    image: redislabs/redisinsight:latest
    ports:
      - 5540:5540

<properties>
    <maven.compiler.source>17</maven.compiler.source>
    <maven.compiler.target>17</maven.compiler.target>
    <project.build.sourceencoding>utf-8</project.build.sourceencoding>
</properties>
<parent>
    <groupid>org.springframework.boot</groupid>
    <artifactid>spring-boot-starter-parent</artifactid>
    <version>3.2.4</version>
    <relativepath/>
</parent>
<dependencies>
    <dependency>
        <groupid>com.alibaba.cloud.ai</groupid>
        <artifactid>spring-ai-alibaba-starter</artifactid>
        <version>1.0.0-m2.1</version>
    </dependency>
    <dependency>
        <groupid>com.alibaba</groupid>
        <artifactid>fastjson</artifactid>
        <version>2.0.35</version>
    </dependency>
    <dependency>
        <groupid>com.squareup.okhttp3</groupid>
        <artifactid>okhttp</artifactid>
        <version>4.12.0</version>
    </dependency>
    <dependency>
        <groupid>org.springframework.boot</groupid>
        <artifactid>spring-boot-starter-web</artifactid>
    </dependency>
    <dependency>
        <groupid>org.projectlombok</groupid>
        <artifactid>lombok</artifactid>
        <scope>provided</scope>
    </dependency>
    <!-- https://mvnrepository.com/artifact/com.alibaba/dashscope-sdk-java -->
    <dependency>
        <groupid>com.alibaba</groupid>
        <artifactid>dashscope-sdk-java</artifactid>
        <version>2.16.9</version>
    </dependency>
    <dependency>
        <groupid>org.apache.httpcomponents</groupid>
        <artifactid>httpclient</artifactid>
        <version>4.5.13</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/io.milvus/milvus-sdk-java -->
    <dependency>
        <groupid>io.milvus</groupid>
        <artifactid>milvus-sdk-java</artifactid>
        <version>2.4.6</version>
    </dependency>
</dependencies>
<repositories>
    <repository>
        <id>spring-milestones</id>
        <name>spring milestones</name>
        <url>https://repo.spring.io/milestone</url>
        <snapshots>
            <enabled>false</enabled>
        </snapshots>
    </repository>
</repositories>

server:
  port: 8081
spring:
  ai:
    dashscope:
      api-key: 你的apikey
      embedding:
        options:
          model: text-embedding-v2

package com.congge.web;
import jakarta.annotation.resource;
import lombok.extern.slf4j.slf4j;
import org.springframework.ai.embedding.embeddingmodel;
import org.springframework.ai.embedding.embeddingresponse;
import org.springframework.beans.factory.annotation.autowired;
import org.springframework.web.bind.annotation.getmapping;
import org.springframework.web.bind.annotation.requestparam;
import org.springframework.web.bind.annotation.restcontroller;
import java.util.list;
import java.util.map;
@restcontroller
@slf4j
public class documentcontroller {
    private final embeddingmodel embeddingmodel;
    @autowired
    public documentcontroller(embeddingmodel embeddingmodel) {
        this.embeddingmodel = embeddingmodel;
    }
    @getmapping("/ai/embedding")
    public map embed(@requestparam(value = "message", defaultvalue = "tell me a joke") string message) {
        embeddingresponse embeddingresponse = this.embeddingmodel.embedforresponse(list.of(message));
        return map.of("embedding", embeddingresponse);
    }
    //localhost:8081/embedding
    @getmapping("/embedding")
    public object embeddingtest() {
        // 文本嵌入
        float[] embed = embeddingmodel.embed("你好，我是小码农叔叔");
        log.info("文本转换得到的向量: {}", embed);
        return embed;
    }
}

        <dependency>
            <groupid>org.springframework.ai</groupid>
            <artifactid>spring-ai-tika-document-reader</artifactid>
        </dependency>

@sneakythrows
@postmapping("read/multipart-file")
public void readmultipartfile(@requestparam multipartfile file) {
    // 从io流中读取文件
    resource resource = new inputstreamresource(file.getinputstream());
    list<document> documents = new tikadocumentreader(resource)
            .read();
}

/**
 * 从本地文件读取
 * @return
 */
@getmapping("/read/from-local-file")
public object readlocalfile() {
    filesystemresource resource = new filesystemresource("e:\\工作空间\\wzlocal\\java编码规范v1.0.pdf");
    list<document> documents = new tikadocumentreader(resource)
            .read();
    return documents;
}

/**
 * 分割后的文档列表  localhost:8081/doc/split
 * @param file
 * @return
 * @throws exception
 */
@postmapping("/doc/split")
public object docsplit(@requestparam multipartfile file) throws exception{
    inputstreamresource inputstreamresource = new inputstreamresource(file.getinputstream());
    list<document> documentlist = new tikadocumentreader(inputstreamresource).read();
    // 分割后的文档列表
    list<document> splitdoclist = new tokentextsplitter().split(documentlist);
    list<string> contentlist = splitdoclist.stream().map(document::getcontent).collect(collectors.tolist());
    return contentlist;
}

<dependency>
    <groupid>org.springframework.ai</groupid>
    <artifactid>spring-ai-tika-document-reader</artifactid>
    <version>1.0.0-m3</version>
</dependency>
<dependency>
    <groupid>org.springframework.ai</groupid>
    <artifactid>spring-ai-redis-store</artifactid>
    <version>1.0.0-m3</version>
</dependency>
<dependency>
    <groupid>redis.clients</groupid>
    <artifactid>jedis</artifactid>
</dependency>

spring:
  ai:
    dashscope:
      api-key: 你的apikey
      embedding:
        options:
          model: text-embedding-v2
    vectorstore:
      redis:
        uri: redis://ip:6379
  data:
    redis:
      database: 0
      timeout: 10s
      lettuce:
        pool:
          # 连接池最大连接数
          max-active: 200
          # 连接池最大阻塞等待时间（使用负值表示没有限制）
          max-wait: -1ms
          # 连接池中的最大空闲连接
          max-idle: 10
          # 连接池中的最小空闲连接
          min-idle: 0
      repositories:
        enabled: false
      host: redis地址

package com.congge.config;
import lombok.allargsconstructor;
import org.springframework.ai.autoconfigure.vectorstore.redis.redisvectorstoreautoconfiguration;
import org.springframework.ai.autoconfigure.vectorstore.redis.redisvectorstoreproperties;
import org.springframework.ai.embedding.embeddingmodel;
import org.springframework.ai.vectorstore.redisvectorstore;
import org.springframework.ai.vectorstore.vectorstore;
import org.springframework.boot.autoconfigure.enableautoconfiguration;
import org.springframework.boot.autoconfigure.data.redis.redisconnectiondetails;
import org.springframework.boot.context.properties.enableconfigurationproperties;
import org.springframework.context.annotation.bean;
import org.springframework.context.annotation.configuration;
import redis.clients.jedis.jedispooled;
@configuration
// 禁用springai提供的redisstack向量数据库的自动配置，会和redis的配置冲突。
@enableautoconfiguration(exclude = {redisvectorstoreautoconfiguration.class})
// 读取redisstack的配置信息
@enableconfigurationproperties({redisvectorstoreproperties.class})
@allargsconstructor
public class redisvectorconfig {
    /**
     * 创建redisstack向量数据库
     *
     * @param embeddingmodel 嵌入模型
     * @param properties     redis-stack的配置信息
     * @return vectorstore 向量数据库
     */
    @bean
    public vectorstore vectorstore(embeddingmodel embeddingmodel,
                                   redisvectorstoreproperties properties,
                                   redisconnectiondetails redisconnectiondetails) {
        redisvectorstore.redisvectorstoreconfig config = redisvectorstore.redisvectorstoreconfig.builder().withindexname(properties.getindex()).withprefix(properties.getprefix()).build();
        return new redisvectorstore(config, embeddingmodel,
                new jedispooled(redisconnectiondetails.getstandalone().gethost(),
                        redisconnectiondetails.getstandalone().getport()
                        , redisconnectiondetails.getusername(),
                        redisconnectiondetails.getpassword()),
                properties.isinitializeschema());
    }
}

/**
 * 文档保存到向量数据库   localhost:8081/doc/embedding
 * @param file
 * @return
 * @throws exception
 */
@postmapping("/doc/embedding")
public object docembedding(@requestparam multipartfile file) throws exception{
    tikadocumentreader tikadocumentreader = new tikadocumentreader(new inputstreamresource(file.getinputstream()));
    list<document> splitdocuments = new tokentextsplitter().apply(tikadocumentreader.read());
    // 存入向量数据库，这个过程会自动调用embeddingmodel,将文本变成向量存入
    vectorstore.add(splitdocuments);
    return true;
}

/**
 * 关键字检索文档  localhost:8081/doc/query?keyword=面向对象
 * @param keyword
 * @return
 */
@getmapping("/doc/query")
public list<document> query(@requestparam string keyword) {
    list<document> documentlist = vectorstore.similaritysearch(keyword);
    return documentlist;
}