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README
Apache-2.0

RAG知识库系统

项目简介

RAG知识库系统是一个基于检索增强生成(Retrieval-Augmented Generation)技术的知识管理系统。它允许用户上传文档,并通过自然语言查询来检索相关信息。系统结合了向量搜索和大语言模型,能够提供准确且上下文相关的回答。

运行说明

siliconflow申请开通,获取API密钥,实现deepseek在线算力。

deepseek算力免费申请链接:https://cloud.siliconflow.cn/i/QhRxqgS6 请用这个链接申请,可以送1百万个Token。

在项目根目录创建一个.env文件来存储您的API密钥和代理设置。为了方便使用,我们提供了一个示例文件env_example,您可以复制并重命名为.env,然后根据您的实际情况修改其中的值:

# 复制示例文件并重命名为.env
cp env_example .env

# 然后编辑.env文件,填入您的实际配置

.env文件内容示例:

SILICONFLOW_API_KEY=your_api_key_here
HTTP_PROXY=http://your_proxy_server:port
HTTPS_PROXY=http://your_proxy_server:port

需要下载huggingface的模型,需要科学上网。上面的代理设置可以在.env文件中配置。

大约下载6G的模型数据,共两个模型,下载时间较长。

win11运行成功的。Linux生产环境未测。

本项目会持续更新,欢迎大家贡献代码。

近期会更新成正式项目代码。

项目联系沟通QQ: 250128146 Email: xu@daqid.com

主要功能

  • 文档上传:支持上传.docx格式的文档
  • 知识检索:通过自然语言查询检索相关知识
  • 上下文管理:维护用户会话上下文,提供连贯的问答体验
  • 相关度评分:显示检索结果的相关度百分比

使用指南

  1. 安装依赖:
    pip install -r requirements.txt
    
  2. 启动应用:
    python app.py
    
  3. 访问系统:
  4. 使用系统:
    • 点击“上传文档”按钮上传文档
    • 在搜索框输入查询内容进行检索

贡献指南

欢迎贡献代码!请遵循以下步骤:

  1. Fork本项目
  2. 创建新分支 (git checkout -b feature/your-feature)
  3. 提交更改 (git commit -m 'Add some feature')
  4. 推送到分支 (git push origin feature/your-feature)
  5. 创建Pull Request

依赖项

  • Flask
  • ChromaDB
  • python-docx
  • numpy
  • BAAI/bge-m3 (嵌入模型)
  • BAAI/bge-reranker-v2-m3 (重排模型)

http://192.168.90.208:8072/admin/odoodd/syncLingwu?passc=

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简介

RAG Deepseek本地知识库 展开 收起
Python 等 3 种语言
Apache-2.0
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