新增CMS+RAG+AI知识库模块/向量数据库检索增强生成及人工智能对话
This commit is contained in:
thinkgem
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/**
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* Copyright (c) 2013-Now http://jeesite.com All rights reserved.
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* No deletion without permission, or be held responsible to law.
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*/
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package com.jeesite.modules.cms.ai.config;
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import com.jeesite.common.datasource.DataSourceHolder;
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import org.springframework.ai.chat.client.ChatClient;
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import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Configuration;
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import org.springframework.context.annotation.Primary;
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import org.springframework.jdbc.core.JdbcTemplate;
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import javax.sql.DataSource;
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import java.sql.SQLException;
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/**
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* AI 聊天配置类
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* @author ThinkGem
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*/
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@Configuration
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public class CmsAiChatConfig {
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/**
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* PG向量库数据源
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* @author ThinkGem
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*/
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@Bean
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@Primary
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@ConditionalOnProperty(name = "jdbc.ds_pgvector.type")
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public JdbcTemplate pgVectorStoreJdbcTemplate() throws SQLException {
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DataSource dataSource = DataSourceHolder.getRoutingDataSource()
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.createDataSource("ds_pgvector");
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return new JdbcTemplate(dataSource);
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}
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/**
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* 聊天对话客户端
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* @author ThinkGem
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*/
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@Bean
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public ChatClient chatClient(ChatClient.Builder builder) {
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return builder
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.defaultSystem("你是我的知识库AI助手，请帮我解答我提出的相关问题。")
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.build();
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}
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// @Bean
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// public BatchingStrategy batchingStrategy() {
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// return new TokenCountBatchingStrategy(EncodingType.CL100K_BASE, Integer.MAX_VALUE, 0.1);
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// }
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}
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@@ -0,0 +1,113 @@
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/**
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* Copyright (c) 2013-Now http://jeesite.com All rights reserved.
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* No deletion without permission, or be held responsible to law.
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*/
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package com.jeesite.modules.cms.ai.service;
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import com.jeesite.common.collect.ListUtils;
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import com.jeesite.common.collect.MapUtils;
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import com.jeesite.common.lang.StringUtils;
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import com.jeesite.common.lang.TimeUtils;
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import com.jeesite.common.utils.PageUtils;
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import com.jeesite.modules.cms.entity.Article;
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import com.jeesite.modules.cms.service.ArticleVectorStore;
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import com.jeesite.modules.cms.utils.CmsUtils;
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import com.vladsch.flexmark.html2md.converter.FlexmarkHtmlConverter;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.ai.document.Document;
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import org.springframework.ai.transformer.splitter.TokenTextSplitter;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Service;
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import java.util.List;
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import java.util.Map;
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/**
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* CMS 文章向量库存储
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* @author ThinkGem
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*/
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@Service
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public class ArticleVectorStoreImpl implements ArticleVectorStore {
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protected Logger logger = LoggerFactory.getLogger(getClass());
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@Autowired
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private VectorStore vectorStore;
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/**
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* 保存文章到向量库
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* @author ThinkGem
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*/
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@Override
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public void save(Article article) {
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Map<String, Object> metadata = MapUtils.newHashMap();
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metadata.put("id", article.getId());
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metadata.put("siteCode", article.getCategory().getSite().getSiteCode());
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metadata.put("categoryCode", article.getCategory().getCategoryCode());
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metadata.put("categoryName", article.getCategory().getCategoryName());
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metadata.put("title", article.getTitle());
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metadata.put("href", article.getHref());
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metadata.put("keywords", article.getKeywords());
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metadata.put("description", article.getDescription());
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metadata.put("url", article.getUrl());
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metadata.put("status", article.getStatus());
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metadata.put("createBy", article.getCreateBy());
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metadata.put("createDate", article.getCreateDate());
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metadata.put("updateBy", article.getUpdateBy());
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metadata.put("updateDate", article.getUpdateDate());
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String content = article.getTitle() + ", " + article.getKeywords() + ", "
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+ article.getDescription() + ", " + StringUtils.toMobileHtml(
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article.getArticleData().getContent());
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String markdown = FlexmarkHtmlConverter.builder().build().convert(content);
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List<Document> documents = List.of(new Document(article.getId(), markdown, metadata));
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List<Document> splitDocuments = new TokenTextSplitter().apply(documents);
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this.delete(article); // 删除原数据
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ListUtils.pageList(splitDocuments, 64, params -> {
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vectorStore.add((List<Document>)params[0]); // 增加新数据
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return null;
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});
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}
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/**
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* 删除向量库文章
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* @author ThinkGem
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*/
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@Override
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public void delete(Article article) {
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if (StringUtils.isNotBlank(article.getId())) {
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vectorStore.delete(new FilterExpressionBuilder().eq("id", article.getId()).build());
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}
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}
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/**
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* 重建向量库文章
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* @author ThinkGem
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*/
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public String rebuild(Article article) {
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logger.debug("开始重建向量库。 siteCode: {}, categoryCode: {}",
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article.getCategory().getSite().getSiteCode(),
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article.getCategory().getCategoryCode());
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long start = System.currentTimeMillis();
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try{
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article.setIsQueryArticleData(true); // 查询文章内容
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PageUtils.findList(article, null, e -> {
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List<Article> list = CmsUtils.getArticleService().findList((Article) e);
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if (!list.isEmpty()) {
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list.forEach(this::save);
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return true;
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}
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return false;
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});
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}catch(Exception ex){
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logger.error("重建向量库失败", ex);
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return "重建向量库失败：" + ex.getMessage();
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}
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String message = "重建向量库完成！ 用时" + TimeUtils.formatTime(System.currentTimeMillis() - start) + "。";
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logger.debug(message);
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return message;
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}
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}
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@@ -0,0 +1,45 @@
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/**
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* Copyright (c) 2013-Now http://jeesite.com All rights reserved.
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* No deletion without permission, or be held responsible to law.
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*/
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package com.jeesite.modules.cms.ai.service;
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import com.jeesite.common.cache.CacheUtils;
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import com.jeesite.common.collect.ListUtils;
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import org.springframework.ai.chat.memory.ChatMemory;
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import org.springframework.ai.chat.messages.Message;
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import org.springframework.stereotype.Service;
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import java.util.List;
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/**
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* AI 对话消息存储
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* @author ThinkGem
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*/
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@Service
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public class CacheChatMemory implements ChatMemory {
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private static final String CMS_CHAT_MSG_CACHE = "cmsChatMsgCache";
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@Override
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public void add(String conversationId, List<Message> messages) {
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List<Message> conversationHistory = CacheUtils.get(CMS_CHAT_MSG_CACHE, conversationId);
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if (conversationHistory == null) {
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conversationHistory = ListUtils.newArrayList();
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}
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conversationHistory.addAll(messages);
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CacheUtils.put(CMS_CHAT_MSG_CACHE, conversationId, conversationHistory);
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}
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@Override
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public List<Message> get(String conversationId, int lastN) {
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List<Message> all = CacheUtils.get(CMS_CHAT_MSG_CACHE, conversationId);
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return all != null ? all.stream().skip(Math.max(0, all.size() - lastN)).toList() : List.of();
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}
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@Override
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public void clear(String conversationId) {
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CacheUtils.remove(CMS_CHAT_MSG_CACHE, conversationId);
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}
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}
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@@ -0,0 +1,115 @@
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/**
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* Copyright (c) 2013-Now http://jeesite.com All rights reserved.
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* No deletion without permission, or be held responsible to law.
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*/
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package com.jeesite.modules.cms.ai.service;
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import com.jeesite.common.cache.CacheUtils;
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import com.jeesite.common.collect.MapUtils;
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import com.jeesite.common.idgen.IdGen;
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import com.jeesite.common.lang.DateUtils;
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import com.jeesite.common.lang.StringUtils;
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import com.jeesite.common.service.BaseService;
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import com.jeesite.modules.sys.utils.UserUtils;
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import org.springframework.ai.chat.client.ChatClient;
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import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
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import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
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import org.springframework.ai.chat.memory.ChatMemory;
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import org.springframework.ai.chat.messages.Message;
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import org.springframework.ai.chat.messages.UserMessage;
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import org.springframework.ai.chat.model.ChatResponse;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.ai.vectorstore.VectorStore;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Service;
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import reactor.core.publisher.Flux;
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import java.util.List;
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import java.util.Map;
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/**
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* AI 聊天服务类
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* @author ThinkGem
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*/
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@Service
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public class CmsAiChatService extends BaseService {
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private static final String CMS_CHAT_CACHE = "cmsChatCache";
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@Autowired
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private ChatClient chatClient;
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@Autowired
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private ChatMemory chatMemory;
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@Autowired
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private VectorStore vectorStore;
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/**
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* 获取聊天对话消息
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* @author ThinkGem
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*/
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public List<Message> getChatMessage(String conversationId) {
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return chatMemory.get(conversationId, 100);
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}
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private static String getChatCacheKey() {
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String key = UserUtils.getUser().getId();
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if (StringUtils.isBlank(key)) {
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key = UserUtils.getSession().getId().toString();
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}
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return key;
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}
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public Map<String, Map<String, Object>> getChatCacheMap() {
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Map<String, Map<String, Object>> cache = CacheUtils.get(CMS_CHAT_CACHE, getChatCacheKey());
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if (cache == null) {
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cache = MapUtils.newHashMap();
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}
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return cache;
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}
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/**
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* 新建或更新聊天对话
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* @author ThinkGem
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*/
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public Map<String, Object> saveChatConversation(String conversationId, String title) {
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if (StringUtils.isBlank(conversationId)) {
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conversationId = IdGen.nextId();
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}
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if (StringUtils.isBlank(title)) {
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title = "新对话 " + DateUtils.getTime();
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}
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Map<String, Object> map = MapUtils.newHashMap();
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map.put("id", conversationId);
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map.put("title", title);
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Map<String, Map<String, Object>> cache = getChatCacheMap();
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cache.put(conversationId, map);
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CacheUtils.put(CMS_CHAT_CACHE, getChatCacheKey(), cache);
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return map;
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}
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/**
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* 删除聊天对话
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* @author ThinkGem
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*/
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public void deleteChatConversation(String conversationId) {
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Map<String, Map<String, Object>> cache = getChatCacheMap();
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cache.remove(conversationId);
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CacheUtils.put(CMS_CHAT_CACHE, getChatCacheKey(), cache);
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chatMemory.clear(conversationId);
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}
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/**
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* 聊天对话，流输出
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* @author ThinkGem
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*/
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public Flux<ChatResponse> chatStream(String conversationId, String message) {
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return chatClient.prompt()
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.messages(new UserMessage(message))
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.advisors(
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new MessageChatMemoryAdvisor(chatMemory, conversationId, 1024),
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new QuestionAnswerAdvisor(vectorStore, SearchRequest.builder().similarityThreshold(0.6F).topK(6).build()))
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.stream()
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.chatResponse();
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}
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}
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@@ -0,0 +1,84 @@
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/**
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* Copyright (c) 2013-Now http://jeesite.com All rights reserved.
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* No deletion without permission, or be held responsible to law.
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*/
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package com.jeesite.modules.cms.ai.web;
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import com.jeesite.common.config.Global;
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import com.jeesite.common.web.BaseController;
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import com.jeesite.modules.cms.ai.service.CmsAiChatService;
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import org.springframework.ai.chat.messages.Message;
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import org.springframework.ai.chat.model.ChatResponse;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.http.MediaType;
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import org.springframework.web.bind.annotation.RequestMapping;
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import org.springframework.web.bind.annotation.RestController;
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import reactor.core.publisher.Flux;
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import java.util.Collection;
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import java.util.Comparator;
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import java.util.List;
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import java.util.Map;
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import java.util.stream.Collectors;
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/**
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* AI 聊天控制器类
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* @author ThinkGem
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*/
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@RestController
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@RequestMapping("${adminPath}/cms/chat")
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public class CmsAiChatController extends BaseController {
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@Autowired
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private CmsAiChatService cmsAiChatService;
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/**
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* 获取聊天对话消息
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* @author ThinkGem
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*/
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@RequestMapping("/message")
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public List<Message> message(String id) {
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return cmsAiChatService.getChatMessage(id);
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}
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/**
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* 聊天对话列表
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* @author ThinkGem
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*/
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@RequestMapping("/list")
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public Collection<Map<String, Object>> list() {
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return cmsAiChatService.getChatCacheMap().values().stream()
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.sorted(Comparator.comparing(map -> (String) map.get("id"),
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Comparator.reverseOrder())).collect(Collectors.toList());
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}
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/**
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* 新建或更新聊天对话
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* @author ThinkGem
|
||||
*/
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@RequestMapping("/save")
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public String save(String id, String title) {
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Map<String, Object> map = cmsAiChatService.saveChatConversation(id, title);
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return renderResult(Global.TRUE, "保存成功", map);
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}
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/**
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* 删除聊天对话
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* @author ThinkGem
|
||||
*/
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@RequestMapping("/delete")
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public String delete(String id) {
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cmsAiChatService.deleteChatConversation(id);
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return renderResult(Global.TRUE, "删除成功", id);
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||||
}
|
||||
|
||||
/**
|
||||
* 聊天对话，流输出
|
||||
* @author ThinkGem
|
||||
*/
|
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@RequestMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
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||||
public Flux<ChatResponse> stream(String id, String message) {
|
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return cmsAiChatService.chatStream(id, message);
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||||
}
|
||||
|
||||
}
|
||||
12
modules/cms-ai/src/main/resources/application-assistant.yml
Normal file
12
modules/cms-ai/src/main/resources/application-assistant.yml
Normal file
@@ -0,0 +1,12 @@
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||||
|
||||
## 重要提示（Tip）：
|
||||
|
||||
## 请勿在该配置文件中添加其它任何配置（添加也不会生效）。
|
||||
## 该文件，仅仅是为了让 jeesite-cms-ai.yml 文件，
|
||||
## 在 IDEA 中有一个自动完成及帮助提示，并无其它用意。
|
||||
## 参数配置请在 jeesite-cms-ai.yml 文件中添加。
|
||||
|
||||
spring:
|
||||
config:
|
||||
import:
|
||||
- classpath:config/jeesite-cms-ai.yml
|
||||
125
modules/cms-ai/src/main/resources/config/jeesite-cms-ai.yml
Normal file
125
modules/cms-ai/src/main/resources/config/jeesite-cms-ai.yml
Normal file
@@ -0,0 +1,125 @@
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# 温馨提示：不建议直接修改此文件，为了平台升级方便，建议将需要修改的参数值，复制到application.yml里进行覆盖该参数值。
|
||||
|
||||
spring:
|
||||
ai:
|
||||
|
||||
# 云上大模型（使用该模型，请开启 enabled 参数）
|
||||
openai:
|
||||
base-url: https://api.siliconflow.cn
|
||||
api-key: ${SFLOW_APP_KEY}
|
||||
#base-url: https://ai.gitee.com
|
||||
#api-key: ${GITEE_APP_KEY}
|
||||
# 聊天对话模型
|
||||
chat:
|
||||
enabled: true
|
||||
options:
|
||||
model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
|
||||
#model: DeepSeek-R1-Distill-Qwen-14B
|
||||
max-tokens: 1024
|
||||
temperature: 0.6
|
||||
top-p: 0.7
|
||||
frequency-penalty: 0
|
||||
logprobs: true
|
||||
# 向量库知识库模型（注意：不同的模型维度不同）
|
||||
embedding:
|
||||
enabled: true
|
||||
options:
|
||||
model: BAAI/bge-m3
|
||||
#model: bge-large-zh-v1.5
|
||||
dimensions: 512
|
||||
|
||||
# 本地大模型配置（使用该模型，请开启 enabled 参数）
|
||||
ollama:
|
||||
base-url: http://localhost:11434
|
||||
# 聊天对话模型
|
||||
chat:
|
||||
enabled: false
|
||||
options:
|
||||
#model: qwen2.5
|
||||
model: deepseek-r1:7b
|
||||
max-tokens: 1024
|
||||
temperature: 0.6
|
||||
top-p: 0.7
|
||||
frequency-penalty: 0
|
||||
# 向量库知识库模型（注意：不同的模型维度不同）
|
||||
embedding:
|
||||
enabled: false
|
||||
# 维度 dimensions 设置为 384
|
||||
#model: all-minilm:33m
|
||||
# 维度 dimensions 设置为 768
|
||||
#model: nomic-embed-text
|
||||
# 维度 dimensions 设置为 1024
|
||||
model: bge-m3
|
||||
|
||||
# 向量数据库配置
|
||||
vectorstore:
|
||||
|
||||
# Postgresql 向量数据库（PG 连接配置，见下文，需要手动建表）
|
||||
pgvector:
|
||||
initialize-schema: false
|
||||
id-type: TEXT
|
||||
index-type: HNSW
|
||||
distance-type: COSINE_DISTANCE
|
||||
#table-name: vector_store_384
|
||||
#dimensions: 384
|
||||
#table-name: vector_store_786
|
||||
#dimensions: 768
|
||||
table-name: vector_store_1024
|
||||
dimensions: 1024
|
||||
batching-strategy: TOKEN_COUNT
|
||||
max-document-batch-size: 10000
|
||||
|
||||
# # ES 向量数据库（ES 连接配置，见下文）
|
||||
# elasticsearch:
|
||||
# initialize-schema: true
|
||||
# index-name: vector-index
|
||||
# dimensions: 1024
|
||||
# similarity: cosine
|
||||
# batching-strategy: TOKEN_COUNT
|
||||
|
||||
# # Milvus 向量数据库（字符串长度不超过65535）
|
||||
# milvus:
|
||||
# initialize-schema: true
|
||||
# client:
|
||||
# host: "localhost"
|
||||
# port: 19530
|
||||
# username: "root"
|
||||
# password: "milvus"
|
||||
# database-name: "default2"
|
||||
# collection-name: "vector_store2"
|
||||
# embedding-dimension: 384
|
||||
# index-type: HNSW
|
||||
# metric-type: COSINE
|
||||
|
||||
# ========= Postgresql 向量数据库数据源 =========
|
||||
|
||||
jdbc:
|
||||
ds_pgvector:
|
||||
type: postgresql
|
||||
driver: org.postgresql.Driver
|
||||
url: jdbc:postgresql://127.0.0.1:5433/jeesite-ai
|
||||
username: postgres
|
||||
password: postgres
|
||||
testSql: SELECT 1
|
||||
|
||||
# ========= ES 向量数据库连接配置 =========
|
||||
|
||||
spring.elasticsearch:
|
||||
enabled: true
|
||||
socket-timeout: 120s
|
||||
connection-timeout: 120s
|
||||
uris: http://127.0.0.1:9200
|
||||
username: elastic
|
||||
password: elastic
|
||||
|
||||
# 对话消息存缓存，可自定义存数据库
|
||||
j2cache:
|
||||
caffeine:
|
||||
region:
|
||||
# 对话消息的超期时间，默认 30天，根据需要可以设置更久。
|
||||
cmsChatCache: 100000, 30d
|
||||
cmsChatMsgCache: 100000, 30d
|
||||
|
||||
#logging:
|
||||
# level:
|
||||
# org.springframework: debug
|
||||
@@ -0,0 +1 @@
|
||||
5.11.0
|
||||
Reference in New Issue
Block a user