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feat: Weaviate操作向量库功能优化

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ageerle
2025-05-08 10:41:01 +08:00
parent 81c0bb5738
commit aa92d232bb
7 changed files with 217 additions and 62 deletions
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27
ruoyi-modules-api/ruoyi-knowledge-api/pom.xml
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@@ -48,17 +48,17 @@
</dependency>
<!-- milvus java sdk -->
<dependency>
<groupId>io.milvus</groupId>
<artifactId>milvus-sdk-java</artifactId>
<version>2.3.2</version>
</dependency>
<!-- <dependency>-->
<!-- <groupId>io.milvus</groupId>-->
<!-- <artifactId>milvus-sdk-java</artifactId>-->
<!-- <version>2.3.2</version>-->
<!-- </dependency>-->
<dependency>
<groupId>io.weaviate</groupId>
<artifactId>client</artifactId>
<version>4.0.0</version>
</dependency>
<!-- <dependency>-->
<!-- <groupId>io.weaviate</groupId>-->
<!-- <artifactId>client</artifactId>-->
<!-- <version>4.0.0</version>-->
<!-- </dependency>-->
<dependency>
@@ -86,7 +86,12 @@
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
<artifactId>langchain4j-open-ai</artifactId>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-ollama</artifactId>
</dependency>
</dependencies>

43
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/bo/QueryVectorBo.java Normal file
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@@ -0,0 +1,43 @@
package org.ruoyi.domain.bo;
import lombok.Data;
/**
* 查询向量所需参数
* @author ageer
*/
@Data
public class QueryVectorBo {
/**
* 查询内容
*/
private String query;
/**
* 知识库kid
*/
private String kid;
/**
* 查询向量返回条数
*/
private Integer maxResults;
/**
* 模型名称
*/
private String modelName;
/**
* 请求key
*/
private String apiKey;
/**
* 请求地址
*/
private String baseUrl;
}

49
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/domain/bo/StoreEmbeddingBo.java Normal file
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@@ -0,0 +1,49 @@
package org.ruoyi.domain.bo;
import lombok.Data;
import java.util.List;
/**
* 保存向量所需参数
* @author ageer
*/
@Data
public class StoreEmbeddingBo {
/**
* 切分文本块列表
*/
private List<String> chunkList;
/**
* 知识库kid
*/
private String kid;
/**
* 文档id
*/
private String docId;
/**
* 知识块id列表
*/
private List<String> fids;
/**
* 模型名称
*/
private String modelName;
/**
* 请求key
*/
private String apiKey;
/**
* 请求地址
*/
private String baseUrl;
}

9
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/VectorStoreService.java
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@@ -1,20 +1,23 @@
package org.ruoyi.service;
import org.ruoyi.domain.bo.QueryVectorBo;
import org.ruoyi.domain.bo.StoreEmbeddingBo;
import java.util.List;
/**
* @author ageer
* 向量库管理
* @author ageer
*/
public interface VectorStoreService {
void storeEmbeddings(List<String> chunkList, String kid,String docId,List<String> fids);
void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo);
void removeByDocId(String kid,String docId);
void removeByKid(String kid);
List<String> getQueryVector(String query, String kid);
List<String> getQueryVector(QueryVectorBo queryVectorBo);
void createSchema(String kid);

106
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/impl/WeaviateVectorStoreImpl.java
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@@ -3,6 +3,7 @@ package org.ruoyi.service.impl;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.ollama.OllamaEmbeddingModel;
import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
import dev.langchain4j.model.output.Response;
import dev.langchain4j.store.embedding.EmbeddingMatch;
@@ -11,9 +12,12 @@ import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.filter.Filter;
import dev.langchain4j.store.embedding.filter.comparison.IsEqualTo;
import dev.langchain4j.store.embedding.weaviate.WeaviateEmbeddingStore;
import jakarta.annotation.PostConstruct;
import lombok.RequiredArgsConstructor;
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.service.VectorStoreService;
import org.springframework.stereotype.Service;
@@ -23,9 +27,11 @@ import java.util.HashMap;
import java.util.List;
import java.util.Map;
/**
* Weaviate向量库管理
* @author ageer
* Weaviate 向量库管理
*/
@Service
@Slf4j
@@ -37,38 +43,7 @@ public class WeaviateVectorStoreImpl implements VectorStoreService {
private final ConfigService configService;
@Override
public List<String> getQueryVector(String query, String kid) {
EmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder()
.apiKey("sk-xxx")
.baseUrl("https://api.pandarobot.chat/v1/")
.modelName(TEXT_EMBEDDING_3_SMALL)
.build();
// Filter simpleFilter = new IsEqualTo("kid", kid);
// createSchema(kid);
Embedding queryEmbedding = embeddingModel.embed("聊天补全模型").content();
EmbeddingSearchRequest embeddingSearchRequest = EmbeddingSearchRequest.builder()
.queryEmbedding(queryEmbedding)
.maxResults(2)
// 添加过滤条件
// .filter(simpleFilter)
.build();
List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(embeddingSearchRequest).matches();
List<String> results = new ArrayList<>();
matches.forEach(embeddingMatch -> {
results.add(embeddingMatch.embedded().text());
});
return results;
}
@Override
@PostConstruct
public void createSchema(String kid) {
String protocol = configService.getConfigValue("weaviate", "protocol");
String host = configService.getConfigValue("weaviate", "host");
@@ -84,24 +59,42 @@ public class WeaviateVectorStoreImpl implements VectorStoreService {
}
@Override
public void storeEmbeddings(List<String> chunkList,String kid,String docId,List<String> fids) {
EmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder()
.apiKey("sk-xxxx")
.baseUrl("https://api.pandarobot.chat/v1/")
.modelName(TEXT_EMBEDDING_3_SMALL)
.build();
chunkList.forEach(chunk -> {
public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getModelName(),
storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
for (int i = 0; i < storeEmbeddingBo.getChunkList().size(); i++) {
Map<String, Object> dataSchema = new HashMap<>();
dataSchema.put("kid", kid);
dataSchema.put("docId", docId);
dataSchema.put("fid", fids.get(0));
Response<Embedding> response = embeddingModel.embed(chunk);
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(chunk);
TextSegment segment = TextSegment.from(storeEmbeddingBo.getChunkList().get(i));
segment.metadata().putAll(dataSchema);
embeddingStore.add(embedding,segment);
}
}
@Override
public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getModelName(),
queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
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)
.build();
List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(embeddingSearchRequest).matches();
List<String> results = new ArrayList<>();
matches.forEach(embeddingMatch -> {
results.add(embeddingMatch.embedded().text());
});
return results;
}
@@ -128,4 +121,25 @@ public class WeaviateVectorStoreImpl implements VectorStoreService {
embeddingStore.removeAll(simpleFilterByAnd);
}
/**
* 获取向量模型
*/
public EmbeddingModel getEmbeddingModel(String modelName,String apiKey,String baseUrl) {
EmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder().build();
if(TEXT_EMBEDDING_3_SMALL.toString().equals(modelName)) {
embeddingModel = OpenAiEmbeddingModel.builder()
.apiKey(apiKey)
.baseUrl(baseUrl)
.modelName(TEXT_EMBEDDING_3_SMALL)
.build();
// TODO 添加枚举
}else if("quentinz/bge-large-zh-v1.5".equals(modelName)) {
embeddingModel = OllamaEmbeddingModel.builder()
.baseUrl(baseUrl)
.modelName(modelName)
.build();
}
return embeddingModel;
}
}

15
ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/chat/service/chat/impl/SseServiceImpl.java
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@@ -25,6 +25,7 @@ import org.ruoyi.common.core.utils.file.FileUtils;
import org.ruoyi.common.core.utils.file.MimeTypeUtils;
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.service.VectorStoreService;
import org.ruoyi.service.IChatModelService;
@@ -166,7 +167,10 @@ public class SseServiceImpl implements ISseService {
// 获取对话消息列表
List<Message> messages = chatRequest.getMessages();
String sysPrompt = chatModelVo.getSystemPrompt();
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。";
@@ -180,11 +184,20 @@ public class SseServiceImpl implements ISseService {
if(StringUtils.isNotEmpty(chatRequest.getKid())){
List<Message> knMessages = new ArrayList<>();
String content = messages.get(messages.size() - 1).getContent().toString();
List<String> nearestList = vectorStoreService.getQueryVector(content, chatRequest.getKid());
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);

30
ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/chat/service/knowledge/KnowledgeInfoServiceImpl.java
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@@ -3,6 +3,7 @@ package org.ruoyi.chat.service.knowledge;
import cn.hutool.core.collection.CollUtil;
import cn.hutool.core.util.RandomUtil;
import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
import com.baomidou.mybatisplus.core.conditions.query.QueryWrapper;
import com.baomidou.mybatisplus.core.toolkit.Wrappers;
import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
import lombok.RequiredArgsConstructor;
@@ -14,15 +15,19 @@ import org.ruoyi.common.core.utils.StringUtils;
import org.ruoyi.common.satoken.utils.LoginHelper;
import org.ruoyi.core.page.PageQuery;
import org.ruoyi.core.page.TableDataInfo;
import org.ruoyi.domain.ChatModel;
import org.ruoyi.domain.KnowledgeAttach;
import org.ruoyi.domain.KnowledgeFragment;
import org.ruoyi.domain.KnowledgeInfo;
import org.ruoyi.domain.bo.KnowledgeInfoBo;
import org.ruoyi.domain.bo.KnowledgeInfoUploadBo;
import org.ruoyi.domain.bo.StoreEmbeddingBo;
import org.ruoyi.domain.vo.ChatModelVo;
import org.ruoyi.domain.vo.KnowledgeInfoVo;
import org.ruoyi.mapper.KnowledgeAttachMapper;
import org.ruoyi.mapper.KnowledgeFragmentMapper;
import org.ruoyi.mapper.KnowledgeInfoMapper;
import org.ruoyi.service.IChatModelService;
import org.ruoyi.service.VectorStoreService;
import org.ruoyi.service.IKnowledgeInfoService;
import org.slf4j.Logger;
@@ -55,6 +60,8 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
private final KnowledgeAttachMapper attachMapper;
private final IChatModelService chatModelService;
/**
* 查询知识库
*/
@@ -219,10 +226,31 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
knowledgeAttach.setContent(content);
knowledgeAttach.setCreateTime(new Date());
attachMapper.insert(knowledgeAttach);
vectorStoreService.storeEmbeddings(chunkList,kid,docId,fids);
// 通过kid查询知识库信息
KnowledgeInfoVo knowledgeInfoVo = baseMapper.selectVoOne(Wrappers.<KnowledgeInfo>lambdaQuery()
.eq(KnowledgeInfo::getKid, kid));
// 通过向量模型查询模型信息
ChatModelVo chatModelVo = chatModelService.selectModelByName(knowledgeInfoVo.getVectorModel());
StoreEmbeddingBo storeEmbeddingBo = new StoreEmbeddingBo();
storeEmbeddingBo.setKid(kid);
storeEmbeddingBo.setDocId(docId);
storeEmbeddingBo.setFids(fids);
storeEmbeddingBo.setChunkList(chunkList);
storeEmbeddingBo.setModelName(knowledgeInfoVo.getVectorModel());
storeEmbeddingBo.setApiKey(chatModelVo.getApiKey());
storeEmbeddingBo.setBaseUrl(chatModelVo.getApiHost());
vectorStoreService.storeEmbeddings(storeEmbeddingBo);
}
/**
* 检查用户是否有删除知识库权限
*
* @param knowledgeInfoList 知识库列表
*/
public void check(List<KnowledgeInfoVo> knowledgeInfoList){
LoginUser loginUser = LoginHelper.getLoginUser();
for (KnowledgeInfoVo knowledgeInfoVo : knowledgeInfoList) {