Merge pull request #216 from fangzhh/main

feat: 优化嵌入模型业务,使用策略模型加工厂模式动态加载嵌入模型,支持多供应商多嵌入模型动态接入;
This commit is contained in:
ageerle
2025-10-09 20:11:40 +08:00
committed by GitHub
25 changed files with 855 additions and 35 deletions

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@@ -1,6 +1,7 @@
package org.ruoyi.domain;
import com.alibaba.excel.annotation.ExcelProperty;
import com.baomidou.mybatisplus.annotation.TableId;
import com.baomidou.mybatisplus.annotation.TableName;
import lombok.Data;
@@ -81,6 +82,11 @@ public class ChatModel extends BaseEntity {
*/
private Integer priority;
/**
* 模型供应商
*/
private String ProviderName;
/**
* 备注
*/

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@@ -1,5 +1,6 @@
package org.ruoyi.domain.bo;
import com.alibaba.excel.annotation.ExcelProperty;
import io.github.linpeilie.annotations.AutoMapper;
import jakarta.validation.constraints.NotBlank;
import jakarta.validation.constraints.NotNull;
@@ -85,6 +86,10 @@ public class ChatModelBo extends BaseEntity {
@NotBlank(message = "密钥不能为空", groups = { AddGroup.class, EditGroup.class })
private String apiKey;
/**
* 模型供应商
*/
private String ProviderName;
/**
* 备注

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@@ -95,6 +95,12 @@ public class ChatModelVo implements Serializable {
@ExcelProperty(value = "优先级")
private Integer priority;
/**
* 模型供应商
*/
@ExcelProperty(value = "模型供应商")
private String ProviderName;
/**
* 备注
*/

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@@ -106,6 +106,10 @@
<artifactId>dashscope-sdk-java</artifactId>
<version>2.19.0</version>
</dependency>
<dependency>
<groupId>org.ruoyi</groupId>
<artifactId>ruoyi-chat-api</artifactId>
</dependency>
</dependencies>

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@@ -83,6 +83,11 @@ public class KnowledgeInfo extends BaseEntity {
*/
private String vectorModelName;
/**
* 向量化模型id
*/
private Long embeddingModelId;
/**
* 向量化模型名称
*/

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@@ -92,7 +92,11 @@ public class KnowledgeInfoBo extends BaseEntity {
/**
* 向量化模型名称
*/
@NotBlank(message = "向量模型不能为空", groups = { AddGroup.class, EditGroup.class })
private Long embeddingModelId;
/**
* 向量化模型名称
*/
private String embeddingModelName;

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@@ -31,7 +31,12 @@ public class QueryVectorBo {
private String vectorModelName;
/**
* 向量化模型名称
* 向量化模型ID
*/
private Long embeddingModelId;
/**
* 向量化模型ID
*/
private String embeddingModelName;

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@@ -36,6 +36,11 @@ public class StoreEmbeddingBo {
*/
private String vectorModelName;
/**
* 向量化模型id
*/
private Long embeddingModelId;
/**
* 向量化模型名称
*/

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@@ -101,6 +101,11 @@ public class KnowledgeInfoVo implements Serializable {
*/
private String vectorModelName;
/**
* 向量化模型id
*/
private Long embeddingModelId;
/**
* 向量化模型名称
*/

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@@ -0,0 +1,26 @@
package org.ruoyi.embedding;
import dev.langchain4j.model.embedding.EmbeddingModel;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.embedding.model.ModalityType;
import java.util.Set;
/**
* BaseEmbedModelService 接口,扩展了 EmbeddingModel 接口
* 该接口定义了嵌入模型服务的基本配置和功能方法
*/
public interface BaseEmbedModelService extends EmbeddingModel {
/**
* 根据配置信息配置嵌入模型
* @param config 包含模型配置信息的 ChatModelVo 对象
*/
void configure(ChatModelVo config);
/**
* 获取当前嵌入模型支持的所有模态类型
* @return 返回支持的模态类型集合
*/
Set<ModalityType> getSupportedModalities();
}

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@@ -0,0 +1,117 @@
package org.ruoyi.embedding;
import lombok.RequiredArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.service.IChatModelService;
import org.springframework.beans.factory.NoSuchBeanDefinitionException;
import org.springframework.context.ApplicationContext;
import org.springframework.stereotype.Service;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
/**
* 嵌入模型工厂服务类
* 负责创建和管理各种嵌入模型实例
*/
@Service
@RequiredArgsConstructor
@Slf4j
public class EmbeddingModelFactory {
private final ApplicationContext applicationContext;
private final IChatModelService chatModelService;
// 模型缓存使用ConcurrentHashMap保证线程安全
private final Map<Long, BaseEmbedModelService> modelCache = new ConcurrentHashMap<>();
/**
* 创建嵌入模型实例
* 如果模型已存在于缓存中,则直接返回;否则创建新的实例
*
* @param embeddingModelId 嵌入模型的唯一标识ID
* @return BaseEmbedModelService 嵌入模型服务实例
*/
public BaseEmbedModelService createModel(Long embeddingModelId) {
return modelCache.computeIfAbsent(embeddingModelId, id -> {
ChatModelVo modelConfig = chatModelService.queryById(id);
if (modelConfig == null) {
throw new IllegalArgumentException("未找到模型配置ID=" + id);
}
return createModelInstance(modelConfig.getProviderName(), modelConfig);
});
}
/**
* 检查模型是否支持多模态
*
* @param embeddingModelId 嵌入模型的唯一标识ID
* @return boolean 如果模型支持多模态则返回true否则返回false
*/
public boolean isMultimodalModel(Long embeddingModelId) {
return createModel(embeddingModelId) instanceof MultiModalEmbedModelService;
}
/**
* 创建多模态嵌入模型实例
*
* @param tenantId 租户ID
* @return MultiModalEmbedModelService 多模态嵌入模型服务实例
* @throws IllegalArgumentException 当模型不支持多模态时抛出
*/
public MultiModalEmbedModelService createMultimodalModel(Long tenantId) {
BaseEmbedModelService model = createModel(tenantId);
if (model instanceof MultiModalEmbedModelService) {
return (MultiModalEmbedModelService) model;
}
throw new IllegalArgumentException("该模型不支持多模态");
}
/**
* 刷新模型缓存
* 根据给定的嵌入模型ID从缓存中移除对应的模型
*
* @param embeddingModelId 嵌入模型的唯一标识ID
*/
public void refreshModel(Long embeddingModelId) {
// 从模型缓存中移除指定ID的模型
modelCache.remove(embeddingModelId);
}
/**
* 获取所有支持模型工厂的列表
*
* @return List<String> 支持的模型工厂名称列表
*/
public List<String> getSupportedFactories() {
return new ArrayList<>(applicationContext.getBeansOfType(BaseEmbedModelService.class)
.keySet());
}
/**
* 创建具体的模型实例
* 根据提供的工厂名称和配置信息创建并配置模型实例
*
* @param factory 工厂名称,用于标识模型类型
* @param config 模型配置信息
* @return BaseEmbedModelService 配置好的模型实例
* @throws IllegalArgumentException 当无法获取指定的模型实例时抛出
*/
private BaseEmbedModelService createModelInstance(String factory, ChatModelVo config) {
try {
// 从Spring上下文中获取模型实例
BaseEmbedModelService model = applicationContext.getBean(factory, BaseEmbedModelService.class);
// 配置模型参数
model.configure(config);
log.info("成功创建嵌入模型: factory={}, modelId={}", config.getProviderName(), config.getId());
return model;
} catch (NoSuchBeanDefinitionException e) {
throw new IllegalArgumentException("获取不到嵌入模型: " + factory, e);
}
}
}

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@@ -0,0 +1,35 @@
package org.ruoyi.embedding;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.model.output.Response;
import org.ruoyi.embedding.model.MultiModalInput;
/**
* 多模态嵌入模型服务接口,继承自基础嵌入模型服务
* 该接口提供了处理图像、视频以及多模态数据并转换为嵌入向量的功能
*/
public interface MultiModalEmbedModelService extends BaseEmbedModelService {
/**
* 将图像数据转换为嵌入向量
* @param imageDataUrl 图像的地址必须是公开可访问的URL
* @return 包含嵌入向量的响应对象,可能包含状态信息和嵌入结果
*/
Response<Embedding> embedImage(String imageDataUrl);
/**
* 将视频数据转换为嵌入向量
* @param videoDataUrl 视频的地址必须是公开可访问的URL
* @return 包含嵌入向量的响应对象,可能包含状态信息和嵌入结果
*/
Response<Embedding> embedVideo(String videoDataUrl);
/**
* 处理多模态输入并返回嵌入向量的方法
*
* @param input 包含多种模态信息(如图像、文本等)的输入对象
* @return Response<Embedding> 包含嵌入向量的响应对象Embedding通常表示输入数据的向量表示
*/
Response<Embedding> embedMultiModal(MultiModalInput input);
}

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@@ -0,0 +1,14 @@
package org.ruoyi.embedding.impl;
import org.springframework.stereotype.Component;
/**
* @Author: Robust_H
* @Date: 2025-09-30-下午3:00
* @Description: 阿里百炼基础嵌入模型兼容openai
*/
@Component("alibailian")
public class AliBaiLianBaseEmbedProvider extends OpenAiEmbeddingProvider{
}

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@@ -0,0 +1,281 @@
package org.ruoyi.embedding.impl;
import com.fasterxml.jackson.databind.ObjectMapper;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.model.output.TokenUsage;
import lombok.extern.slf4j.Slf4j;
import okhttp3.*;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.embedding.MultiModalEmbedModelService;
import org.ruoyi.embedding.model.AliyunMultiModalEmbedRequest;
import org.ruoyi.embedding.model.AliyunMultiModalEmbedResponse;
import org.ruoyi.embedding.model.ModalityType;
import org.ruoyi.embedding.model.MultiModalInput;
import org.springframework.stereotype.Component;
import java.io.IOException;
import java.util.*;
import java.util.concurrent.TimeUnit;
/**
* 阿里云百炼多模态嵌入模型服务实现类
* 实现了MultiModalEmbedModelService接口提供文本、图像和视频的嵌入向量生成服务
*/
@Component("bailianMultiModel")
@Slf4j
public class AliBaiLianMultiEmbeddingProvider implements MultiModalEmbedModelService {
private ChatModelVo chatModelVo;
private final OkHttpClient okHttpClient;
/**
* 构造函数初始化HTTP客户端
* 设置连接超时、读取超时和写入超时时间
*/
public AliBaiLianMultiEmbeddingProvider() {
this.okHttpClient = new OkHttpClient.Builder()
.connectTimeout(30, TimeUnit.SECONDS)
.readTimeout(60, TimeUnit.SECONDS)
.writeTimeout(30, TimeUnit.SECONDS)
.build();
}
/**
* 图像嵌入向量生成
* @param imageDataUrl 图像数据的URL
* @return 包含图像嵌入向量的Response对象
*/
@Override
public Response<Embedding> embedImage(String imageDataUrl) {
return embedSingleModality("image", imageDataUrl);
}
/**
* 视频嵌入向量生成
* @param videoDataUrl 视频数据的URL
* @return 包含视频嵌入向量的Response对象
*/
@Override
public Response<Embedding> embedVideo(String videoDataUrl) {
return embedSingleModality("video", videoDataUrl);
}
/**
* 多模态嵌入向量生成
* 支持同时处理文本、图像和视频等多种模态的数据
* @param input 包含多种模态输入的对象
* @return 包含多模态嵌入向量的Response对象
*/
@Override
public Response<Embedding> embedMultiModal(MultiModalInput input) {
try {
// 构建请求内容
List<Map<String, Object>> contents = buildContentMap(input);
if (contents.isEmpty()) {
throw new IllegalArgumentException("至少提供一种模态的内容");
}
// 构建请求
AliyunMultiModalEmbedRequest request = buildRequest(contents, chatModelVo);
AliyunMultiModalEmbedResponse resp = executeRequest(request, chatModelVo);
// 转换为 embeddings
Response<List<Embedding>> response = toEmbeddings(resp);
List<Embedding> embeddings = response.content();
if (embeddings.isEmpty()) {
log.warn("阿里云混合模态嵌入返回为空");
return Response.from(Embedding.from(new float[0]), response.tokenUsage());
}
// 多模态通常取第一个向量作为代表,也可以根据业务场景返回多个
return Response.from(embeddings.get(0), response.tokenUsage());
} catch (Exception e) {
log.error("阿里云混合模态嵌入失败", e);
throw new IllegalArgumentException("阿里云混合模态嵌入失败", e);
}
}
/**
* 配置模型参数
* @param config 模型配置信息
*/
@Override
public void configure(ChatModelVo config) {
this.chatModelVo = config;
}
/**
* 获取支持的模态类型
* @return 支持的模态类型集合
*/
@Override
public Set<ModalityType> getSupportedModalities() {
return Set.of(ModalityType.TEXT, ModalityType.VIDEO, ModalityType.IMAGE);
}
/**
* 批量文本嵌入向量生成
* @param textSegments 文本段列表
* @return 包含所有文本嵌入向量的Response对象
*/
@Override
public Response<List<Embedding>> embedAll(List<TextSegment> textSegments) {
if (textSegments.isEmpty()) return Response.from(Collections.emptyList());
try {
List<Map<String, Object>> contents = new ArrayList<>();
for (TextSegment segment : textSegments) {
contents.add(Map.of("text", segment.text()));
}
AliyunMultiModalEmbedRequest request = buildRequest(contents, chatModelVo);
AliyunMultiModalEmbedResponse resp = executeRequest(request, chatModelVo);
return toEmbeddings(resp);
} catch (Exception e) {
log.error("阿里云文本嵌入失败", e);
throw new IllegalArgumentException("阿里云文本嵌入失败", e);
}
}
/**
* 单模态嵌入(图片/视频/单条文本)复用方法
* @param key 模态类型image/video/text
* @param dataUrl 数据URL
* @return 包含嵌入向量的Response对象
*/
public Response<Embedding> embedSingleModality(String key, String dataUrl) {
try {
AliyunMultiModalEmbedRequest request = buildRequest(List.of(Map.of(key, dataUrl)), chatModelVo);
AliyunMultiModalEmbedResponse resp = executeRequest(request, chatModelVo);
Response<List<Embedding>> response = toEmbeddings(resp);
List<Embedding> embeddings = response.content();
if (embeddings.isEmpty()) {
log.warn("阿里云 {} 嵌入返回为空", key);
return Response.from(Embedding.from(new float[0]), response.tokenUsage());
}
return Response.from(embeddings.get(0), response.tokenUsage());
} catch (Exception e) {
log.error("阿里云 {} 嵌入失败", key, e);
throw new IllegalArgumentException("阿里云 " + key + " 嵌入失败", e);
}
}
/**
* 构建请求对象
* @param contents 请求内容列表
* @param chatModelVo 模型配置信息
* @return 构建好的请求对象
*/
private AliyunMultiModalEmbedRequest buildRequest(List<Map<String, Object>> contents, ChatModelVo chatModelVo) {
if (contents.isEmpty()) throw new IllegalArgumentException("请求内容不能为空");
return AliyunMultiModalEmbedRequest.create(chatModelVo.getModelName(), contents);
}
/**
* 执行 HTTP 请求并解析响应
* @param request 请求对象
* @param chatModelVo 模型配置信息
* @return API响应对象
* @throws IOException IO异常
*/
private AliyunMultiModalEmbedResponse executeRequest(AliyunMultiModalEmbedRequest request, ChatModelVo chatModelVo) throws IOException {
String jsonBody = request.toJson();
RequestBody body = RequestBody.create(jsonBody, MediaType.get("application/json"));
Request httpRequest = new Request.Builder()
.url(chatModelVo.getApiHost())
.addHeader("Authorization", "Bearer " + chatModelVo.getApiKey())
.post(body)
.build();
try (okhttp3.Response response = okHttpClient.newCall(httpRequest).execute()) {
if (!response.isSuccessful()) {
String err = response.body() != null ? response.body().string() : "无错误信息";
throw new IllegalArgumentException("API调用失败: " + response.code() + " - " + err, null);
}
ResponseBody responseBody = response.body();
if (responseBody == null) throw new IllegalArgumentException("响应体为空", null);
return parseEmbeddingsFromResponse(responseBody.string());
}
}
/**
* 解析嵌入向量列表
* @param responseBody API响应的JSON字符串
* @return 嵌入向量响应对象
* @throws IOException IO异常
*/
private AliyunMultiModalEmbedResponse parseEmbeddingsFromResponse(String responseBody) throws IOException {
ObjectMapper objectMapper1 = new ObjectMapper();
return objectMapper1.readValue(responseBody, AliyunMultiModalEmbedResponse.class);
}
/**
* 构建 API 请求内容 Map
* @param input 多模态输入对象
* @return 包含各种模态内容的Map列表
*/
private List<Map<String, Object>> buildContentMap(MultiModalInput input) {
List<Map<String, Object>> contents = new ArrayList<>();
if (input.getText() != null && !input.getText().isBlank()) {
contents.add(Map.of("text", input.getText()));
}
if (input.getImageUrl() != null && !input.getImageUrl().isBlank()) {
contents.add(Map.of("image", input.getImageUrl()));
}
if (input.getVideoUrl() != null && !input.getVideoUrl().isBlank()) {
contents.add(Map.of("video", input.getVideoUrl()));
}
if (input.getMultiImageUrls() != null && input.getMultiImageUrls().length > 0) {
contents.add(Map.of("multi_images", Arrays.asList(input.getMultiImageUrls())));
}
return contents;
}
/**
* 将 API 原始响应解析为 LangChain4j 的 Response<Embedding>
* @param resp API原始响应对象
* @return 包含嵌入向量和token使用情况的Response对象
*/
private Response<List<Embedding>> toEmbeddings(AliyunMultiModalEmbedResponse resp) {
if (resp == null || resp.output() == null || resp.output().embeddings() == null) {
return Response.from(Collections.emptyList());
}
// 转换 double -> float
List<Embedding> embeddings = resp.output().embeddings().stream()
.map(item -> {
float[] vector = new float[item.embedding().size()];
for (int i = 0; i < item.embedding().size(); i++) {
vector[i] = item.embedding().get(i).floatValue();
}
return Embedding.from(vector);
})
.toList();
// 构建 TokenUsage
TokenUsage tokenUsage = null;
if (resp.usage() != null) {
tokenUsage = new TokenUsage(
resp.usage().input_tokens(),
resp.usage().image_tokens(),
resp.usage().input_tokens() +resp.usage().image_tokens()
);
}
return Response.from(embeddings, tokenUsage);
}
}

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@@ -0,0 +1,41 @@
package org.ruoyi.embedding.impl;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.ollama.OllamaEmbeddingModel;
import dev.langchain4j.model.output.Response;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.embedding.BaseEmbedModelService;
import org.ruoyi.embedding.model.ModalityType;
import org.springframework.stereotype.Component;
import java.util.List;
import java.util.Set;
/**
* @Author: Robust_H
* @Date: 2025-09-30-下午3:00
* @Description: Ollama嵌入模型
*/
@Component("ollama")
public class OllamaEmbeddingProvider implements BaseEmbedModelService {
private ChatModelVo chatModelVo;
@Override
public void configure(ChatModelVo config) {
this.chatModelVo = config;
}
@Override
public Set<ModalityType> getSupportedModalities() {
return Set.of(ModalityType.TEXT);
}
@Override
public Response<List<Embedding>> embedAll(List<TextSegment> textSegments) {
return OllamaEmbeddingModel.builder()
.baseUrl(chatModelVo.getApiHost())
.modelName(chatModelVo.getModelName())
.build()
.embedAll(textSegments);
}
}

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@@ -0,0 +1,43 @@
package org.ruoyi.embedding.impl;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
import dev.langchain4j.model.output.Response;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.embedding.BaseEmbedModelService;
import org.ruoyi.embedding.model.ModalityType;
import org.springframework.stereotype.Component;
import java.util.List;
import java.util.Set;
/**
* @Author: Robust_H
* @Date: 2025-09-30-下午3:59
* @Description: OpenAi嵌入模型
*/
@Component("openai")
public class OpenAiEmbeddingProvider implements BaseEmbedModelService {
protected ChatModelVo chatModelVo;
@Override
public void configure(ChatModelVo config) {
this.chatModelVo = config;
}
@Override
public Set<ModalityType> getSupportedModalities() {
return Set.of(ModalityType.TEXT);
}
@Override
public Response<List<Embedding>> embedAll(List<TextSegment> textSegments) {
return OpenAiEmbeddingModel.builder()
.baseUrl(chatModelVo.getApiHost())
.apiKey(chatModelVo.getApiKey())
.modelName(chatModelVo.getModelName())
.build()
.embedAll(textSegments);
}
}

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@@ -0,0 +1,18 @@
package org.ruoyi.embedding.impl;
import org.ruoyi.embedding.BaseEmbedModelService;
import org.ruoyi.embedding.model.ModalityType;
import org.springframework.stereotype.Component;
import java.util.Set;
/**
* @Author: Robust_H
* @Date: 2025-09-30-下午3:59
* @Description: 硅基流动(兼容 OpenAi
*/
@Component("siliconflow")
public class SiliconFlowEmbeddingProvider extends OpenAiEmbeddingProvider {
}

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@@ -0,0 +1,43 @@
package org.ruoyi.embedding.impl;
import dev.langchain4j.community.model.zhipu.ZhipuAiEmbeddingModel;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.output.Response;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.embedding.BaseEmbedModelService;
import org.ruoyi.embedding.model.ModalityType;
import org.springframework.stereotype.Component;
import java.util.List;
import java.util.Set;
/**
* @Author: Robust_H
* @Date: 2025-09-30-下午4:02
* @Description: 智谱AI
*/
@Component("zhipu")
public class ZhiPuAiEmbeddingProvider implements BaseEmbedModelService {
private ChatModelVo chatModelVo;
@Override
public void configure(ChatModelVo config) {
this.chatModelVo = config;
}
@Override
public Set<ModalityType> getSupportedModalities() {
return Set.of();
}
@Override
public Response<List<Embedding>> embedAll(List<TextSegment> textSegments) {
return ZhipuAiEmbeddingModel.builder()
.baseUrl(chatModelVo.getApiHost())
.apiKey(chatModelVo.getApiKey())
.model(chatModelVo.getModelName())
.build()
.embedAll(textSegments);
}
}

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@@ -0,0 +1,44 @@
package org.ruoyi.embedding.model;
import org.ruoyi.common.json.utils.JsonUtils;
import lombok.Data;
import java.util.List;
import java.util.Map;
/**
* @Author: Robust_H
* @Date: 2025-10-1-上午10:00
* @Description: 阿里云多模态嵌入请求
*/
@Data
public class AliyunMultiModalEmbedRequest {
private String model;
private Input input;
/**
* 表示输入数据的记录类(Record)
* 该类用于封装一个包含多个映射关系列表的输入数据结构
*
* @param contents 包含多个Map的列表每个Map中存储String类型的键和Object类型的值
*/
public record Input(List<Map<String, Object>> contents) { }
/**
* 创建请求对象
*/
public static AliyunMultiModalEmbedRequest create(String modelName, List<Map<String, Object>> contents) {
AliyunMultiModalEmbedRequest request = new AliyunMultiModalEmbedRequest();
request.setModel(modelName);
Input input = new Input(contents);
request.setInput(input);
return request;
}
/**
* 转换为JSON字符串
*/
public String toJson() {
return JsonUtils.toJsonString(this);
}
}

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@@ -0,0 +1,44 @@
package org.ruoyi.embedding.model;
import java.util.List;
/**
* 阿里云多模态嵌入 API 响应数据模型
*/
public record AliyunMultiModalEmbedResponse(
Output output, // 输出结果对象
String request_id, // 请求唯一标识
String code, // 错误码
String message, // 错误消息
Usage usage // 用量信息
) {
/**
* 输出对象,包含嵌入向量结果
*/
public record Output(
List<EmbeddingItem> embeddings // 嵌入向量列表
) {
}
/**
* 单个嵌入向量条目
*/
public record EmbeddingItem(
int index, // 输入内容的索引
List<Double> embedding, // 生成的 1024 维向量
String type // 输入的类型 (text/image/video/multi_images)
) {
}
/**
* 用量统计信息
*/
public record Usage(
int input_tokens, // 本次请求输入的 Token 数量
int image_tokens, // 本次请求输入的图像 Token 数量
int image_count, // 本次请求输入的图像数量
int duration // 本次请求输入的视频时长(秒)
) {
}
}

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@@ -0,0 +1,8 @@
package org.ruoyi.embedding.model;
/**
* 模态类型
*/
public enum ModalityType {
TEXT, IMAGE, AUDIO, VIDEO, MULTI
}

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@@ -0,0 +1,71 @@
package org.ruoyi.embedding.model;
import cn.hutool.core.util.ArrayUtil;
import cn.hutool.core.util.StrUtil;
import lombok.Builder;
import lombok.Data;
/**
* @Author: Robust_H
* @Date: 2025-09-30-下午2:13
* @Description: 多模态输入
*/
@Data
@Builder
public class MultiModalInput {
private String text;
private byte[] imageData;
private byte[] videoData;
private String imageMimeType;
private String videoMimeType;
private String[] multiImageUrls;
private String imageUrl;
private String videoUrl;
/**
* 检查是否有文本内容
*/
public boolean hasText() {
return StrUtil.isNotBlank(text);
}
/**
* 检查是否有图片内容
*/
public boolean hasImage() {
return ArrayUtil.isNotEmpty(imageData) || StrUtil.isNotBlank(imageUrl);
}
/**
* 检查是否有视频内容
*/
public boolean hasVideo() {
return ArrayUtil.isNotEmpty(videoData) || StrUtil.isNotBlank(videoUrl);
}
/**
* 检查是否有多图片
*/
public boolean hasMultiImages() {
return ArrayUtil.isNotEmpty(multiImageUrls);
}
/**
* 检查是否有任何内容
*/
public boolean hasAnyContent() {
return hasText() || hasImage() || hasVideo() || hasMultiImages();
}
/**
* 获取内容的数量
*/
public int getContentCount() {
int count = 0;
if (hasText()) count++;
if (hasImage()) count++;
if (hasVideo()) count++;
if (hasMultiImages()) count++;
return count;
}
}

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@@ -28,6 +28,8 @@ import lombok.extern.slf4j.Slf4j;
import org.ruoyi.common.core.service.ConfigService;
import org.ruoyi.domain.bo.QueryVectorBo;
import org.ruoyi.domain.bo.StoreEmbeddingBo;
import org.ruoyi.embedding.BaseEmbedModelService;
import org.ruoyi.embedding.EmbeddingModelFactory;
import org.ruoyi.service.VectorStoreService;
import org.springframework.stereotype.Service;
import java.util.*;
@@ -48,6 +50,8 @@ public class VectorStoreServiceImpl implements VectorStoreService {
// private EmbeddingStore<TextSegment> embeddingStore;
private WeaviateClient client;
private final EmbeddingModelFactory embeddingModelFactory;
@Override
public void createSchema(String kid, String modelName) {
@@ -98,18 +102,16 @@ public class VectorStoreServiceImpl implements VectorStoreService {
@Override
public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
createSchema(storeEmbeddingBo.getKid(), storeEmbeddingBo.getVectorModelName());
EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
BaseEmbedModelService model = embeddingModelFactory.createModel(storeEmbeddingBo.getEmbeddingModelId());
List<String> chunkList = storeEmbeddingBo.getChunkList();
List<String> fidList = storeEmbeddingBo.getFids();
String kid = storeEmbeddingBo.getKid();
String docId = storeEmbeddingBo.getDocId();
log.info("向量存储条数记录: " + chunkList.size());
long startTime = System.currentTimeMillis();
for (int i = 0; i < chunkList.size(); i++) {
String text = chunkList.get(i);
String fid = fidList.get(i);
Embedding embedding = embeddingModel.embed(text).content();
Embedding embedding = model.embed(text).content();
Map<String, Object> properties = Map.of(
"text", text,
"fid",fid,
@@ -137,9 +139,8 @@ public class VectorStoreServiceImpl implements VectorStoreService {
@Override
public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
createSchema(queryVectorBo.getKid(), queryVectorBo.getVectorModelName());
EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
BaseEmbedModelService model = embeddingModelFactory.createModel(queryVectorBo.getEmbeddingModelId());
Embedding queryEmbedding = model.embed(queryVectorBo.getQuery()).content();
float[] vector = queryEmbedding.vector();
List<String> vectorStrings = new ArrayList<>();
for (float v : vector) {
@@ -246,28 +247,4 @@ public class VectorStoreServiceImpl implements VectorStoreService {
log.error("删除失败: {}", result.getError());
}
}
/**
* 获取向量模型
*/
@SneakyThrows
public EmbeddingModel getEmbeddingModel(String modelName, String apiKey, String baseUrl) {
EmbeddingModel embeddingModel;
if ("quentinz/bge-large-zh-v1.5".equals(modelName)) {
embeddingModel = OllamaEmbeddingModel.builder()
.baseUrl(baseUrl)
.modelName(modelName)
.build();
} else if ("baai/bge-m3".equals(modelName)) {
embeddingModel = OpenAiEmbeddingModel.builder()
.apiKey(apiKey)
.baseUrl(baseUrl)
.modelName(modelName)
.build();
} else {
throw new ServiceException("未找到对应向量化模型!");
}
return embeddingModel;
}
}

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@@ -290,7 +290,7 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
.eq(KnowledgeInfo::getId, kid));
// 通过向量模型查询模型信息
ChatModelVo chatModelVo = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModelName());
ChatModelVo chatModelVo = chatModelService.queryById(knowledgeInfoVo.getEmbeddingModelId());
StoreEmbeddingBo storeEmbeddingBo = new StoreEmbeddingBo();
storeEmbeddingBo.setKid(kid);
@@ -298,7 +298,7 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
storeEmbeddingBo.setFids(fids);
storeEmbeddingBo.setChunkList(chunkList);
storeEmbeddingBo.setVectorModelName(knowledgeInfoVo.getVectorModelName());
storeEmbeddingBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModelName());
storeEmbeddingBo.setEmbeddingModelId(knowledgeInfoVo.getEmbeddingModelId());
storeEmbeddingBo.setApiKey(chatModelVo.getApiKey());
storeEmbeddingBo.setBaseUrl(chatModelVo.getApiHost());
vectorStoreService.storeEmbeddings(storeEmbeddingBo);

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@@ -0,0 +1,13 @@
-- 为 chat_model 表添加 provider_name 字段
-- 变更日期: 2025-10-04
-- 负责人: Robust_H
-- 说明: 嵌入模型供应商 (用于实现动态选择嵌入模型实现类)
ALTER TABLE `ruoyi-ai`.chat_model
ADD COLUMN `provider_name` varchar(20) DEFAULT NULL COMMENT '模型供应商' AFTER `model_name`;
-- 修改 knowledge_info 中的 embedding_model_nameembedding_model_id
-- 变更日期: 2025-10-04
-- 负责人: Robust_H
-- 说明: 用于区分多个供应商实现同一嵌入模型的情况
ALTER TABLE `ruoyi-ai`.knowledge_info
ADD COLUMN `embedding_model_id` bigint DEFAULT NULL COMMENT '模型id' AFTER `embedding_model_name`;