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https://gitcode.com/ageerle/ruoyi-ai.git
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本地向量化
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jiahao.he@vtradex.com
@@ -0,0 +1,38 @@
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package org.ruoyi.common.chat.entity.models;
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import lombok.Data;
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import java.util.List;
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/**
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* @program: RUOYIAI
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* @ClassName LocalModelsSearchRequest
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* @description:
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* @author: hejh
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* @create: 2025-03-15 17:22
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* @Version 1.0
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**/
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@Data
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public class LocalModelsSearchRequest {
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private List<String> text;
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private String model_name;
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private String delimiter;
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private int k;
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private int block_size;
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private int overlap_chars;
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// 构造函数、Getter 和 Setter
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public LocalModelsSearchRequest(List<String> text, String model_name, String delimiter, int k, int block_size, int overlap_chars) {
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this.text = text;
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this.model_name = model_name;
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this.delimiter = delimiter;
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this.k = k;
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this.block_size = block_size;
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this.overlap_chars = overlap_chars;
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}
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}
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@@ -0,0 +1,20 @@
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package org.ruoyi.common.chat.entity.models;
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import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
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import com.fasterxml.jackson.annotation.JsonProperty;
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import lombok.Data;
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import java.util.List;
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@Data
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@JsonIgnoreProperties(ignoreUnknown = true)
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public class LocalModelsSearchResponse {
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@JsonProperty("topKEmbeddings")
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private List<List<List<Double>>> topKEmbeddings; // 处理三层嵌套数组
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// 默认构造函数
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public LocalModelsSearchResponse() {}
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}
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@@ -0,0 +1,198 @@
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package org.ruoyi.common.chat.localModels;
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import io.micrometer.common.util.StringUtils;
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import lombok.extern.slf4j.Slf4j;
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import okhttp3.OkHttpClient;
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import org.ruoyi.common.chat.entity.models.LocalModelsSearchRequest;
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import org.ruoyi.common.chat.entity.models.LocalModelsSearchResponse;
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import org.springframework.stereotype.Service;
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import retrofit2.Call;
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import retrofit2.Callback;
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import retrofit2.Response;
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import retrofit2.Retrofit;
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import retrofit2.converter.jackson.JacksonConverterFactory;
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import java.util.List;
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import java.util.concurrent.CountDownLatch;
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@Slf4j
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@Service
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public class LocalModelsofitClient {
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private static final String BASE_URL = "http://127.0.0.1:5000"; // Flask 服务的 URL
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private static Retrofit retrofit = null;
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// 获取 Retrofit 实例
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public static Retrofit getRetrofitInstance() {
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if (retrofit == null) {
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OkHttpClient client = new OkHttpClient.Builder()
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.build();
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retrofit = new Retrofit.Builder()
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.baseUrl(BASE_URL)
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.client(client)
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.addConverterFactory(JacksonConverterFactory.create()) // 使用 Jackson 处理 JSON 转换
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.build();
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}
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return retrofit;
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}
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/**
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* 向 Flask 服务发送文本向量化请求
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*
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* @param queries 查询文本列表
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* @param modelName 模型名称
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* @param delimiter 文本分隔符
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* @param topK 返回的结果数
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* @param blockSize 文本块大小
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* @param overlapChars 重叠字符数
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* @return 返回计算得到的 Top K 嵌入向量列表
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*/
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public static List<List<Double>> getTopKEmbeddings(
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List<String> queries,
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String modelName,
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String delimiter,
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int topK,
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int blockSize,
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int overlapChars) {
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modelName = (!StringUtils.isEmpty(modelName)) ? modelName : "msmarco-distilbert-base-tas-b"; // 默认模型名称
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delimiter = (!StringUtils.isEmpty(delimiter) ) ? delimiter : "."; // 默认分隔符
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topK = (topK > 0) ? topK : 3; // 默认返回 3 个结果
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blockSize = (blockSize > 0) ? blockSize : 500; // 默认文本块大小为 500
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overlapChars = (overlapChars > 0) ? overlapChars : 50; // 默认重叠字符数为 50
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// 创建 Retrofit 实例
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Retrofit retrofit = getRetrofitInstance();
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// 创建 SearchService 接口
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SearchService service = retrofit.create(SearchService.class);
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// 创建请求对象 LocalModelsSearchRequest
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LocalModelsSearchRequest request = new LocalModelsSearchRequest(
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queries, // 查询文本列表
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modelName, // 模型名称
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delimiter, // 文本分隔符
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topK, // 返回的结果数
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blockSize, // 文本块大小
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overlapChars // 重叠字符数
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);
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final CountDownLatch latch = new CountDownLatch(1); // 创建一个 CountDownLatch
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final List<List<Double>>[] topKEmbeddings = new List[]{null}; // 使用数组来存储结果（因为 Java 不支持直接修改 List）
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// 发起异步请求
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service.vectorize(request).enqueue(new Callback<LocalModelsSearchResponse>() {
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@Override
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public void onResponse(Call<LocalModelsSearchResponse> call, Response<LocalModelsSearchResponse> response) {
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if (response.isSuccessful()) {
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LocalModelsSearchResponse searchResponse = response.body();
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if (searchResponse != null) {
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topKEmbeddings[0] = searchResponse.getTopKEmbeddings().get(0); // 获取结果
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log.info("Successfully retrieved embeddings");
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} else {
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log.error("Response body is null");
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}
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} else {
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log.error("Request failed. HTTP error code: " + response.code());
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}
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latch.countDown(); // 请求完成，减少计数
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}
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@Override
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public void onFailure(Call<LocalModelsSearchResponse> call, Throwable t) {
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t.printStackTrace();
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log.error("Request failed: ", t);
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latch.countDown(); // 请求失败，减少计数
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}
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});
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try {
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latch.await(); // 等待请求完成
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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return topKEmbeddings[0]; // 返回结果
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}
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// public static void main(String[] args) {
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// // 示例调用
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// List<String> queries = Arrays.asList("What is artificial intelligence?", "AI is transforming industries.");
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// String modelName = "msmarco-distilbert-base-tas-b";
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// String delimiter = ".";
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// int topK = 3;
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// int blockSize = 500;
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// int overlapChars = 50;
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//
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// List<List<Double>> topKEmbeddings = getTopKEmbeddings(queries, modelName, delimiter, topK, blockSize, overlapChars);
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//
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// // 打印结果
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// if (topKEmbeddings != null) {
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// System.out.println("Top K embeddings: ");
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// for (List<Double> embedding : topKEmbeddings) {
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// System.out.println(embedding);
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// }
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// } else {
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// System.out.println("No embeddings returned.");
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// }
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// }
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// public static void main(String[] args) {
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// // 创建 Retrofit 实例
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// Retrofit retrofit = LocalModelsofitClient.getRetrofitInstance();
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//
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// // 创建 SearchService 接口
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// SearchService service = retrofit.create(SearchService.class);
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//
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// // 创建请求对象 LocalModelsSearchRequest
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// LocalModelsSearchRequest request = new LocalModelsSearchRequest(
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// Arrays.asList("What is artificial intelligence?", "AI is transforming industries."), // 查询文本列表
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// "msmarco-distilbert-base-tas-b", // 模型名称
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// ".", // 分隔符
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// 3, // 返回的结果数
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// 500, // 文本块大小
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// 50 // 重叠字符数
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// );
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//
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// // 发起请求
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// service.vectorize(request).enqueue(new Callback<LocalModelsSearchResponse>() {
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// @Override
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// public void onResponse(Call<LocalModelsSearchResponse> call, Response<LocalModelsSearchResponse> response) {
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// if (response.isSuccessful()) {
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// LocalModelsSearchResponse searchResponse = response.body();
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// System.out.println("Response Body: " + response.body()); // Print the whole response body for debugging
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//
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// if (searchResponse != null) {
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// // If the response is not null, process it.
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// // Example: Extract the embeddings and print them
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// List<List<List<Double>>> topKEmbeddings = searchResponse.getTopKEmbeddings();
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// if (topKEmbeddings != null) {
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// // Print the Top K embeddings
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//
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// } else {
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// System.err.println("Top K embeddings are null");
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// }
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//
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// // If there is more information you want to process, handle it here
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//
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// } else {
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// System.err.println("Response body is null");
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// }
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// } else {
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// System.err.println("Request failed. HTTP error code: " + response.code());
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// log.error("Failed to retrieve data. HTTP error code: " + response.code());
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// }
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// }
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//
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// @Override
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// public void onFailure(Call<LocalModelsSearchResponse> call, Throwable t) {
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// // 请求失败，打印错误
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// t.printStackTrace();
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// log.error("Request failed: ", t);
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// }
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// });
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// }
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}
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@@ -0,0 +1,25 @@
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package org.ruoyi.common.chat.localModels;
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import org.ruoyi.common.chat.entity.models.LocalModelsSearchRequest;
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import org.ruoyi.common.chat.entity.models.LocalModelsSearchResponse;
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import retrofit2.Call;
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import retrofit2.http.Body;
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import retrofit2.http.POST;
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/**
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* @program: RUOYIAI
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* @ClassName SearchService
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* @description: 请求模型
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* @author: hejh
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* @create: 2025-03-15 17:27
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* @Version 1.0
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**/
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public interface SearchService {
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@POST("/vectorize") // 与 Flask 服务中的路由匹配
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Call<LocalModelsSearchResponse> vectorize(@Body LocalModelsSearchRequest request);
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}
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