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feat: 修复知识库上传失败

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ageerle
2025-05-14 13:53:49 +08:00
parent 4454be44c3
commit 7d3282c347
9 changed files with 139 additions and 93 deletions
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14
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/KnowledgeInfo.java
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@@ -1,5 +1,6 @@
package org.ruoyi.domain;
import com.alibaba.excel.annotation.ExcelProperty;
import com.baomidou.mybatisplus.annotation.*;
import lombok.Data;
import lombok.EqualsAndHashCode;
@@ -78,14 +79,19 @@ public class KnowledgeInfo extends BaseEntity {
private Long textBlockSize;
/**
* 向量库
* 向量库模型名称
*/
private String vector;
private String vectorModelName;
/**
* 向量模型
* 向量化模型名称
*/
private String vectorModel;
private String embeddingModelName;
/**
* 系统提示词
*/
private String systemPrompt;
/**
* 备注

14
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/bo/KnowledgeInfoBo.java
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@@ -83,16 +83,22 @@ public class KnowledgeInfoBo extends BaseEntity {
private Long textBlockSize;
/**
* 向量库
* 向量库模型名称
*/
@NotBlank(message = "向量库不能为空", groups = { AddGroup.class, EditGroup.class })
private String vector;
private String vectorModelName;
/**
* 向量模型
* 向量化模型名称
*/
@NotBlank(message = "向量模型不能为空", groups = { AddGroup.class, EditGroup.class })
private String vectorModel;
private String embeddingModelName;
/**
* 系统提示词
*/
private String systemPrompt;
/**
* 备注

9
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/bo/QueryVectorBo.java
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@@ -26,9 +26,14 @@ public class QueryVectorBo {
private Integer maxResults;
/**
* 模型名称
* 向量库模型名称
*/
private String modelName;
private String vectorModelName;
/**
* 向量化模型名称
*/
private String embeddingModelName;
/**
* 请求key

9
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/bo/StoreEmbeddingBo.java
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@@ -32,9 +32,14 @@ public class StoreEmbeddingBo {
private List<String> fids;
/**
* 模型名称
* 向量库模型名称
*/
private String modelName;
private String vectorModelName;
/**
* 向量化模型名称
*/
private String embeddingModelName;
/**
* 请求key

16
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/vo/KnowledgeInfoVo.java
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@@ -98,16 +98,20 @@ public class KnowledgeInfoVo implements Serializable {
private Integer textBlockSize;
/**
* 向量库
* 向量库模型名称
*/
@ExcelProperty(value = "向量库")
private String vector;
private String vectorModelName;
/**
* 向量模型
* 向量化模型名称
*/
@ExcelProperty(value = "向量模型")
private String vectorModel;
private String embeddingModelName;
/**
* 系统提示词
*/
private String systemPrompt;
/**
* 备注

10
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/VectorStoreService.java
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@@ -13,14 +13,14 @@ public interface VectorStoreService {
void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo);
void removeByDocId(String kid,String docId);
void removeByKid(String kid);
List<String> getQueryVector(QueryVectorBo queryVectorBo);
void createSchema(String kid,String modelName);
void removeByKidAndFid(String kid, String fid);
void removeByKid(String kid,String modelName);
void removeByDocId(String kid,String docId,String modelName);
void removeByKidAndFid(String kid, String fid,String modelName);
}

69
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/impl/VectorStoreServiceImpl.java
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@@ -1,5 +1,7 @@
package org.ruoyi.service.impl;
import cn.hutool.core.util.RandomUtil;
import com.google.protobuf.ServiceException;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
@@ -16,6 +18,7 @@ import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;
import dev.langchain4j.store.embedding.weaviate.WeaviateEmbeddingStore;
import lombok.RequiredArgsConstructor;
import lombok.SneakyThrows;
import lombok.extern.slf4j.Slf4j;
import org.ruoyi.common.core.service.ConfigService;
import org.ruoyi.domain.bo.QueryVectorBo;
@@ -40,11 +43,10 @@ public class VectorStoreServiceImpl implements VectorStoreService {
private final ConfigService configService;
Map<String,EmbeddingStore<TextSegment>> storeMap = new HashMap<>();
private EmbeddingStore<TextSegment> embeddingStore;
@Override
public void createSchema(String kid,String modelName) {
EmbeddingStore<TextSegment> embeddingStore;
switch (modelName) {
case "weaviate" -> {
String protocol = configService.getConfigValue("weaviate", "protocol");
@@ -84,88 +86,83 @@ public class VectorStoreServiceImpl implements VectorStoreService {
embeddingStore = new InMemoryEmbeddingStore<>();
}
}
storeMap.put(kid,embeddingStore);
}
@Override
public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
EmbeddingStore<TextSegment> store = storeMap.get(storeEmbeddingBo.getKid());
EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getModelName(),
createSchema(storeEmbeddingBo.getKid(),storeEmbeddingBo.getVectorModelName());
EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
for (int i = 0; i < storeEmbeddingBo.getChunkList().size(); i++) {
List<String> chunkList = storeEmbeddingBo.getChunkList();
for (int i = 0; i < chunkList.size(); i++) {
Map<String, Object> dataSchema = new HashMap<>();
dataSchema.put("kid", storeEmbeddingBo.getKid());
dataSchema.put("docId", storeEmbeddingBo.getKid());
dataSchema.put("fid", storeEmbeddingBo.getFids().get(i));
Response<Embedding> response = embeddingModel.embed(storeEmbeddingBo.getChunkList().get(i));
Embedding embedding = response.content();
TextSegment segment = TextSegment.from(storeEmbeddingBo.getChunkList().get(i));
Embedding embedding = embeddingModel.embed(chunkList.get(i)).content();
TextSegment segment = TextSegment.from(chunkList.get(i));
segment.metadata().putAll(dataSchema);
store.add(embedding,segment);
embeddingStore.add(embedding,segment);
}
}
@Override
public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
EmbeddingStore<TextSegment> store = storeMap.get(queryVectorBo.getKid());
EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getModelName(),
createSchema(queryVectorBo.getKid(),queryVectorBo.getVectorModelName());
EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
Filter simpleFilter = new IsEqualTo("kid", queryVectorBo.getKid());
// Filter simpleFilter = new IsEqualTo("kid", queryVectorBo.getKid());
Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
EmbeddingSearchRequest embeddingSearchRequest = EmbeddingSearchRequest.builder()
.queryEmbedding(queryEmbedding)
.maxResults(queryVectorBo.getMaxResults())
// 添加过滤条件
.filter(simpleFilter)
// .filter(simpleFilter)
.build();
List<EmbeddingMatch<TextSegment>> matches = store.search(embeddingSearchRequest).matches();
List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(embeddingSearchRequest).matches();
List<String> results = new ArrayList<>();
matches.forEach(embeddingMatch -> results.add(embeddingMatch.embedded().text()));
return results;
}
@Override
public void removeByKid(String kid) {
EmbeddingStore<TextSegment> store = storeMap.get(kid);
public void removeByKid(String kid,String modelName) {
createSchema(kid,modelName);
// 根据条件删除向量数据
Filter simpleFilter = new IsEqualTo("kid", kid);
store.removeAll(simpleFilter);
embeddingStore.removeAll(simpleFilter);
}
@Override
public void removeByDocId(String kid, String docId) {
EmbeddingStore<TextSegment> store = storeMap.get(kid);
public void removeByDocId(String kid, String docId,String modelName) {
createSchema(kid,modelName);
// 根据条件删除向量数据
Filter simpleFilterByDocId = new IsEqualTo("docId", docId);
store.removeAll(simpleFilterByDocId);
embeddingStore.removeAll(simpleFilterByDocId);
}
@Override
public void removeByKidAndFid(String kid, String fid) {
EmbeddingStore<TextSegment> store = storeMap.get(kid);
public void removeByKidAndFid(String kid, String fid,String modelName) {
createSchema(kid,modelName);
// 根据条件删除向量数据
Filter simpleFilterByKid = new IsEqualTo("kid", kid);
Filter simpleFilterFid = new IsEqualTo("fid", fid);
Filter simpleFilterByAnd = Filter.and(simpleFilterFid, simpleFilterByKid);
store.removeAll(simpleFilterByAnd);
embeddingStore.removeAll(simpleFilterByAnd);
}
/**
* 获取向量模型
*/
public EmbeddingModel getEmbeddingModel(String modelName,String apiKey,String baseUrl) {
EmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder().build();
@SneakyThrows
public EmbeddingModel getEmbeddingModel(String modelName, String apiKey, String baseUrl) {
EmbeddingModel embeddingModel;
if(TEXT_EMBEDDING_3_SMALL.toString().equals(modelName)) {
embeddingModel = OpenAiEmbeddingModel.builder()
.apiKey(apiKey)
.baseUrl(baseUrl)
.modelName(TEXT_EMBEDDING_3_SMALL)
.modelName(modelName)
.build();
// TODO 添加枚举
}else if("quentinz/bge-large-zh-v1.5".equals(modelName)) {
@@ -173,6 +170,14 @@ public class VectorStoreServiceImpl implements VectorStoreService {
.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;
}

78
ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/chat/service/chat/impl/SseServiceImpl.java
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@@ -2,6 +2,7 @@ package org.ruoyi.chat.service.chat.impl;
import cn.dev33.satoken.stp.StpUtil;
import cn.hutool.core.collection.CollectionUtil;
import com.baomidou.mybatisplus.core.toolkit.Wrappers;
import com.google.protobuf.ServiceException;
import jakarta.servlet.http.HttpServletRequest;
import lombok.RequiredArgsConstructor;
@@ -29,6 +30,8 @@ import org.ruoyi.common.redis.utils.RedisUtils;
import org.ruoyi.domain.bo.ChatSessionBo;
import org.ruoyi.domain.bo.QueryVectorBo;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.domain.vo.KnowledgeInfoVo;
import org.ruoyi.service.IKnowledgeInfoService;
import org.ruoyi.service.VectorStoreService;
import org.ruoyi.service.IChatModelService;
import org.ruoyi.service.IChatSessionService;
@@ -67,6 +70,8 @@ public class SseServiceImpl implements ISseService {
private final IChatSessionService chatSessionService;
private final IKnowledgeInfoService knowledgeInfoService;
private ChatModelVo chatModelVo;
@@ -148,50 +153,61 @@ public class SseServiceImpl implements ISseService {
}
}
/**
* 构建消息列表
*/
private void buildChatMessageList(ChatRequest chatRequest){
chatModelVo = chatModelService.selectModelByName(chatRequest.getModel());
String sysPrompt;
chatModelVo = chatModelService.selectModelByName(chatRequest.getModel());
// 获取对话消息列表
List<Message> messages = chatRequest.getMessages();
String sysPrompt = chatModelVo.getSystemPrompt();
// 查询向量库相关信息加入到上下文
if(StringUtils.isNotEmpty(chatRequest.getKid())){
List<Message> knMessages = new ArrayList<>();
String content = messages.get(messages.size() - 1).getContent().toString();
// 通过kid查询知识库信息
KnowledgeInfoVo knowledgeInfoVo = knowledgeInfoService.queryById(Long.valueOf(chatRequest.getKid()));
// 查询向量模型配置信息
ChatModelVo chatModel = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModelName());
if(StringUtils.isEmpty(sysPrompt)){
// TODO 系统默认提示词,后续会增加提示词管理
sysPrompt ="你是一个由RuoYI-AI开发的人工智能助手，名字叫熊猫助手。你擅长中英文对话，能够理解并处理各种问题，提供安全、有帮助、准确的回答。" +
"当前时间："+ DateUtils.getDate()+
"#注意：回复之前注意结合上下文和工具返回内容进行回复。";
QueryVectorBo queryVectorBo = new QueryVectorBo();
queryVectorBo.setQuery(content);
queryVectorBo.setKid(chatRequest.getKid());
queryVectorBo.setApiKey(chatModel.getApiKey());
queryVectorBo.setBaseUrl(chatModel.getApiHost());
queryVectorBo.setVectorModelName(knowledgeInfoVo.getVectorModelName());
queryVectorBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModelName());
queryVectorBo.setMaxResults(knowledgeInfoVo.getRetrieveLimit());
List<String> nearestList = vectorStoreService.getQueryVector(queryVectorBo);
for (String prompt : nearestList) {
Message userMessage = Message.builder().content(prompt).role(Message.Role.USER).build();
knMessages.add(userMessage);
}
messages.addAll(knMessages);
// 设置知识库系统提示词
sysPrompt = knowledgeInfoVo.getSystemPrompt();
if(StringUtils.isEmpty(sysPrompt)){
sysPrompt ="###角色设定\n" +
"你是一个智能知识助手，专注于利用上下文中的信息来提供准确和相关的回答。\n" +
"###指令\n" +
"当用户的问题与上下文知识匹配时，利用上下文信息进行回答。如果问题与上下文不匹配，运用自身的推理能力生成合适的回答。\n" +
"###限制\n" +
"确保回答清晰简洁，避免提供不必要的细节。始终保持语气友好" +
"当前时间："+ DateUtils.getDate();
}
}else {
sysPrompt = chatModelVo.getSystemPrompt();
if(StringUtils.isEmpty(sysPrompt)){
sysPrompt ="你是一个由RuoYI-AI开发的人工智能助手，名字叫熊猫助手。你擅长中英文对话，能够理解并处理各种问题，提供安全、有帮助、准确的回答。" +
"当前时间："+ DateUtils.getDate()+
"#注意：回复之前注意结合上下文和工具返回内容进行回复。";
}
}
// 设置系统默认提示词
Message sysMessage = Message.builder().content(sysPrompt).role(Message.Role.SYSTEM).build();
messages.add(0,sysMessage);
chatRequest.setSysPrompt(sysPrompt);
// 查询向量库相关信息加入到上下文
if(StringUtils.isNotEmpty(chatRequest.getKid())){
List<Message> knMessages = new ArrayList<>();
String content = messages.get(messages.size() - 1).getContent().toString();
QueryVectorBo queryVectorBo = new QueryVectorBo();
queryVectorBo.setQuery(content);
queryVectorBo.setKid(chatRequest.getKid());
queryVectorBo.setApiKey(chatModelVo.getApiKey());
queryVectorBo.setBaseUrl(chatModelVo.getApiHost());
queryVectorBo.setModelName(chatModelVo.getModelName());
// TODO 查询向量返回条数,这里应该查询知识库配置
queryVectorBo.setMaxResults(3);
List<String> nearestList = vectorStoreService.getQueryVector(queryVectorBo);
for (String prompt : nearestList) {
Message userMessage = Message.builder().content(prompt).role(Message.Role.USER).build();
knMessages.add(userMessage);
}
// TODO 提示词,这里应该查询知识库配置
Message userMessage = Message.builder().content(content + (!nearestList.isEmpty() ? "\n\n注意：回答问题时，须严格根据我给你的系统上下文内容原文进行回答，请不要自己发挥,回答时保持原来文本的段落层级" : "")).role(Message.Role.USER).build();
knMessages.add(userMessage);
messages.addAll(knMessages);
}
// 用户对话内容
String chatString = null;
// 获取用户对话信息

13
ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/chat/service/knowledge/KnowledgeInfoServiceImpl.java
View File

@@ -102,8 +102,6 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
lqw.eq(bo.getOverlapChar() != null, KnowledgeInfo::getOverlapChar, bo.getOverlapChar());
lqw.eq(bo.getRetrieveLimit() != null, KnowledgeInfo::getRetrieveLimit, bo.getRetrieveLimit());
lqw.eq(bo.getTextBlockSize() != null, KnowledgeInfo::getTextBlockSize, bo.getTextBlockSize());
lqw.eq(StringUtils.isNotBlank(bo.getVector()), KnowledgeInfo::getVector, bo.getVector());
lqw.eq(StringUtils.isNotBlank(bo.getVectorModel()), KnowledgeInfo::getVectorModel, bo.getVectorModel());
return lqw;
}
@@ -161,7 +159,7 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
}
baseMapper.insert(knowledgeInfo);
if (knowledgeInfo != null) {
vectorStoreService.createSchema(String.valueOf(knowledgeInfo.getId()),bo.getVector());
vectorStoreService.createSchema(String.valueOf(knowledgeInfo.getId()),bo.getVectorModelName());
}
}else {
baseMapper.updateById(knowledgeInfo);
@@ -177,7 +175,7 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
check(knowledgeInfoList);
// 删除向量库信息
knowledgeInfoList.forEach(knowledgeInfoVo -> {
vectorStoreService.removeByKid(String.valueOf(knowledgeInfoVo.getId()));
vectorStoreService.removeByKid(String.valueOf(knowledgeInfoVo.getId()),knowledgeInfoVo.getVectorModelName());
});
// 删除附件和知识片段
fragmentMapper.deleteByMap(map);
@@ -231,17 +229,18 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
// 通过kid查询知识库信息
KnowledgeInfoVo knowledgeInfoVo = baseMapper.selectVoOne(Wrappers.<KnowledgeInfo>lambdaQuery()
.eq(KnowledgeInfo::getKid, kid));
.eq(KnowledgeInfo::getId, kid));
// 通过向量模型查询模型信息
ChatModelVo chatModelVo = chatModelService.selectModelByName(knowledgeInfoVo.getVectorModel());
ChatModelVo chatModelVo = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModelName());
StoreEmbeddingBo storeEmbeddingBo = new StoreEmbeddingBo();
storeEmbeddingBo.setKid(kid);
storeEmbeddingBo.setDocId(docId);
storeEmbeddingBo.setFids(fids);
storeEmbeddingBo.setChunkList(chunkList);
storeEmbeddingBo.setModelName(knowledgeInfoVo.getVectorModel());
storeEmbeddingBo.setVectorModelName(knowledgeInfoVo.getVectorModelName());
storeEmbeddingBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModelName());
storeEmbeddingBo.setApiKey(chatModelVo.getApiKey());
storeEmbeddingBo.setBaseUrl(chatModelVo.getApiHost());
vectorStoreService.storeEmbeddings(storeEmbeddingBo);