<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
	<parent>
		<groupId>com.saa</groupId>
		<artifactId>saa-parent</artifactId>
		<version>0.0.1-SNAPSHOT</version>
	</parent>
	<groupId>com.saa</groupId>
	<artifactId>saa-embed2vector</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<name>saa-embed2vector</name>
	<description>saa-embed2vector</description>
	<properties>
		<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
		<maven.compiler.source>17</maven.compiler.source>
		<maven.compiler.target>17</maven.compiler.target>
	</properties>
	<dependencies>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-web</artifactId>
		</dependency>
		<dependency>
			<groupId>com.alibaba.cloud.ai</groupId>
			<artifactId>spring-ai-alibaba-starter-dashscope</artifactId>
		</dependency>

		<!-- 添加 Redis 向量数据库依赖 -->
		<dependency>
			<groupId>org.springframework.ai</groupId>
			<artifactId>spring-ai-starter-vector-store-redis</artifactId>
		</dependency>

		<dependency>
			<groupId>org.projectlombok</groupId>
			<artifactId>lombok</artifactId>
			<version>1.18.34</version>
			<optional>true</optional>
		</dependency>

		<dependency>
			<groupId>cn.hutool</groupId>
			<artifactId>hutool-all</artifactId>
			<version>5.8.16</version>
		</dependency>
		<dependency>
			<groupId>junit</groupId>
			<artifactId>junit</artifactId>
			<scope>test</scope>
		</dependency>

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


	<build>
		<plugins>
			<plugin>
				<groupId>org.apache.maven.plugins</groupId>
				<artifactId>maven-compiler-plugin</artifactId>
				<version>3.8.1</version>
				<configuration>
					<source>17</source>
					<target>17</target>
					<encoding>UTF-8</encoding>
				</configuration>
			</plugin>
			<plugin>
				<groupId>org.springframework.boot</groupId>
				<artifactId>spring-boot-maven-plugin</artifactId>
				<version>${spring-boot.version}</version>
			</plugin>
		</plugins>
	</build>

</project>

server.port=8089

server.servlet.encoding.charset=utf-8
server.servlet.encoding.enabled=true
server.servlet.encoding.force=true

spring.application.name=saa-embed-vector

spring.ai.dashscope.api-key=sk-09c7b571687b46d5a2e25a03fbddxxxx
spring.ai.dashscope.chat.options.model=qwen-plus
spring.ai.dashscope.embedding.options.model=text-embedding-v3


spring.data.redis.host=localhost
spring.data.redis.port=16379
spring.data.redis.username=default
spring.data.redis.password=
spring.ai.vectorstore.redis.initialize-schema=true
spring.ai.vectorstore.redis.index-name=custom-index
spring.ai.vectorstore.redis.prefix=custom-prefix

package com.saa.embed2vector.controller;

import com.alibaba.cloud.ai.dashscope.embedding.DashScopeEmbeddingOptions;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.embedding.EmbeddingRequest;
import org.springframework.ai.embedding.EmbeddingResponse;
import org.springframework.ai.vectorstore.SearchRequest;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import reactor.core.publisher.Flux;

import java.util.Arrays;
import java.util.List;
import java.util.function.Consumer;

import static org.springframework.ai.chat.memory.ChatMemory.CONVERSATION_ID;

@Slf4j
@RequestMapping("/")
@RestController
public class EmbedController {

    @Resource
    private EmbeddingModel embeddingModel;

    @Resource
    private VectorStore vectorStore;

    /**
     * 文本向量化
     *
     * @param msg
     * @return
     */
    @GetMapping("/text2embed")
    public EmbeddingResponse text2Embed(@RequestParam("msg") String msg) {
        //EmbeddingResponse embeddingResponse = embeddingModel.call(new EmbeddingRequest(List.of(msg), null));

        EmbeddingResponse embeddingResponse = embeddingModel.call(new EmbeddingRequest(List.of(msg),
                DashScopeEmbeddingOptions.builder().withModel("text-embedding-v3").build()));

        System.out.println(Arrays.toString(embeddingResponse.getResult().getOutput()));

        return embeddingResponse;
    }


    /**
     * 文本向量化 后存入向量数据库RedisStack
     */
    @GetMapping("/embed2vector/add")
    public void add() {
        List<Document> documents = List.of(
                new Document("i study LLM"),
                new Document("i love java")
        );

        vectorStore.add(documents);
    }


    /**
     * 从向量数据库RedisStack查找，进行相似度查找
     *
     * @param msg
     * @return
     */
    @GetMapping("/embed2vector/get")
    public List getAll(@RequestParam(name = "msg") String msg) {
        SearchRequest searchRequest = SearchRequest.builder()
                .query(msg)
                .topK(2)
                .build();

        List<Document> list = vectorStore.similaritySearch(searchRequest);

        System.out.println(list);

        return list;
    }
}