{"repo":"iohwdd/knobot","free":true,"listed":false,"github":"https://github.com/iohwdd/knobot","clone":"git clone https://github.com/iohwdd/knobot.git","description":"基于 Langchain4j 与 Spring Boot 构建的对话系统，支持用户上传私有文档并自动构建知识库，实现上下文记忆和个性化问答能力。集成向量数据库与大模型，提供语义检索与多轮对话功能，支持知识库管理、聊天记录存储等模块。","language":"Java","stars":19,"topics":["java","vue","agent","chat","langchain4j"],"license":null,"category":"chat-messaging","readme_excerpt":"开发中 环境配置 1. 大模型api-key获取：阿里百炼平台 https://bailian.console.aliyun.com/?spm=5176.29597918.J SEsSjsNv72yRuRFS2VknO.2.635b7ca0Mz7cuE&tab=model#/api-key 2. 搜索引擎api-key获取：Searchapi（免费额度100次） https://www.searchapi.io/ 3. 向量数据库pgvector安装： docker pull ankane/pgvector 创建库名为 vecdb ，向量表在成功启动时自动创建。以上三点内容的配置信息统一在 application-ai.yml 中配置。 4. mysql初始化：脚本位于 knobot-service/src/main/resources/init.sql 5. oss对象存储：创建好Bucket与密钥对 https://ram.console.aliyun.com/profile/access-keys?spm=5176.7933691.nav-v2-dropdown-my-aliyun.5.29852c47zR5EjH ,配置好 application-oss.yml 6. 前端启动：代码在 https://github.com/iohwdd/knobot frontend ，依次执行 npm install npm run dev 即可 页面效果","default_branch":null,"files":null,"tree":[],"storefront":"/r/iohwdd","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/iohwdd/knobot/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}