SpringBoot AI + PgVector向量库 + Openai Embedding模型

Spring Boot 项目引入 下载仓库地址

  <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>org.springframework.ai</groupId>
                <artifactId>spring-ai-bom</artifactId>
                <version>${spring-ai.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
            <!-- 统一管理依赖版本 -->
            <dependency>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-dependencies</artifactId>
                <version>${spring.boot.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency> 
        </dependencies>
  </dependencyManagement>
 <repositories>
        <!--        aliyun -->
        <repository>
            <id>aliyun</id>
            <name>aliyun</name>
            <url>https://maven.aliyun.com/repository/public</url>
            <snapshots>
                <enabled>false</enabled>
            </snapshots>
            <releases>
                <enabled>true</enabled>
            </releases>
        </repository>
        <repository>
            <id>spring-snapshots</id>
            <name>Spring Snapshots</name>
            <url>https://repo.spring.io/snapshot</url>
            <snapshots>
                <enabled>true</enabled>
            </snapshots>
            <releases>
                <enabled>false</enabled>
            </releases>
        </repository>
        <repository>
            <id>spring-milestones</id>
            <name>Spring Milestones</name>
            <url>https://repo.spring.io/milestone</url>
            <snapshots>
                <enabled>false</enabled>
            </snapshots>
            <releases>
                <enabled>true</enabled>
            </releases>
        </repository>
    </repositories>

Spring Boot项目 引入 Spring Ai Pgvector和OpenAi库

 <!-- 添加 Spring Boot Data JPA 依赖 -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-jpa</artifactId>
        </dependency>

        <!-- 添加 PostgreSQL 依赖 -->
        <dependency>
            <groupId>org.postgresql</groupId>
            <artifactId>postgresql</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
            <version>${spring-ai.version}</version>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-jdbc</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-pgvector-store</artifactId>
        </dependency>

PgVector : 开启扩展+ vector_store向量表创建

CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS hstore;
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

CREATE TABLE IF NOT EXISTS vector_store (
	id uuid DEFAULT uuid_generate_v4() PRIMARY KEY,
	content text,
	metadata json,
	embedding vector(1536) // 1536 is the default embedding dimension
);

CREATE INDEX ON vector_store USING HNSW (embedding vector_cosine_ops);

Spring Boot : 配置application.yaml

spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/postgres
    username: postgres
    password: postgres798
  ai:
    openai:# 硅基流动api（免费模型）
      base-url: https://api.siliconflow.cn
      api-key: sk-xxxxxxxx
      chat:
        options:
          model: deepseek-ai/DeepSeek-V3
          max-tokens: 1024
          temperature: 0.0
          top-p: 1.0
      embedding:  # 硅基流动api（免费模型）
        enabled: true
        api-key: sk-xxxx
        base-url: https://api.siliconflow.cn
        options:
          model: BAAI/bge-large-zh-v1.5
          max-tokens: 512
          dimensions: 1024
    vectorstore:
      pgvector:
        distance-type: COSINE_DISTANCE
        dimensions: 1024
        max-document-batch-size: 10000 # Optional: Maximum number of documents per batch
        table-name: vector_store
        index-type: hnsw

Spring 使用 Embedding + Vector Store 【pgVector】 接口

package com.kong.ai.api;

import com.kong.ai.common.dto.R;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.embedding.EmbeddingResponse;
import org.springframework.ai.vectorstore.SearchRequest;
import org.springframework.ai.vectorstore.VectorStore;
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
public class EmbeddingController {

    private final EmbeddingModel embeddingModel;

    private final VectorStore vectorStore;

    @Autowired
    public EmbeddingController(EmbeddingModel embeddingModel,VectorStore vectorStore) {
        this.embeddingModel = embeddingModel;
        this.vectorStore = vectorStore;
    }

    @GetMapping("/ai/embedding")
    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        EmbeddingResponse embeddingResponse = this.embeddingModel.embedForResponse(List.of(message));
        Document.builder().text(message).build();
        return Map.of("embedding", embeddingResponse);
    }

    @GetMapping("/ai/embedding/vectorstore")
    public Map vectorStore(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        List<Document> documents = List.of(
                new Document(message));
        vectorStore.add(documents);
        return Map.of("embedding", "ok");
    }
    
    @GetMapping("/ai/embedding/vectorstore/query")
    public R vectorStoreQuery(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        List<Document> results = this.vectorStore.similaritySearch(SearchRequest.builder().query(message).topK(5).build());

        return R.success(Map.of("embedding", results));
    }
}