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Bid_bot

项目简介

Bid_bot 是一个允许用户输入投标文件的路径和招标文件的路径,然后自动提取投标文件中的要求,并检索到招标文件相应的响应内容的项目。

安装步骤

使用 Conda 创建环境

  1. 克隆仓库到本地:

    git clone https://github.com/JuneYaooo/Bid_bot.git
    cd Bid_bot
  2. 使用 Conda 创建环境:

    conda env create -f environment.yml
    conda activate bidenv

使用 Python 虚拟环境

Linux 或 macOS

  1. 克隆仓库到本地:

    git clone https://github.com/JuneYaooo/Bid_bot.git
    cd Bid_bot
  2. 创建并激活虚拟环境:

    python -m venv venv
    source venv/bin/activate
  3. 安装依赖项:

    pip install -r requirements.txt

Windows

  1. 克隆仓库到本地:

    git clone https://github.com/JuneYaooo/Bid_bot.git
    cd Bid_bot
  2. 创建并激活虚拟环境:

    python -m venv venv
    venv\Scripts\activate
  3. 安装依赖项:

    pip install -r requirements.txt

使用方法

  1. 配置环境变量: 在项目根目录下复制 .env.template 文件并重命名为 .env,然后根据需要填写配置项:

    cp .env.template .env

    .env 文件中的配置项说明:

    • OCR_ENABLED: 是否启用OCR(光学字符识别)功能,TrueFalse
    • SUMMARY_ENABLED: 是否启用模型检索后总结能力(如果出于隐私考虑,不想让模型读取自己的投标文件,可以不启用这个,不启用的状态下,只包含招标要求对投标文件的检索能力),TrueFalse
    • LLM_TYPE: 使用的语言模型类型,可以是 gptkimi
    • OPENAI_MODEL_NAME: OpenAI 模型名称,例如 gpt-4o
    • OPENAI_API_KEY: 你的 OpenAI API 密钥。
    • OPENAI_BASE_URL: OpenAI API 的基础URL。
    • MOONSHOT_MODEL_NAME: Moonshot 模型名称,例如 moonshot-v1-32k
    • MOONSHOT_API_KEY: 你的 Moonshot API 密钥。
    • MOONSHOT_BASE_URL: Moonshot API 的基础URL。
  2. 上传文件: 将你的招标文件和投标文件上传到当前目录的 ./data/raw_files 文件夹下。

  3. 运行主脚本:

    python main.py

    启动后,命令行界面会提示你输入项目名称和文件名,并询问是否更新解析结果。例如:

    Clients and project manager initialized.
    Enter project name: shidong_hospital
    Project 'data/workspace/shidong_hospital/config.json' already exists. Loading existing configuration.
    Project 'shidong_hospital' created at data/workspace/shidong_hospital
    Enter the file name for zhaobiao_file: 招标文件.pdf
    Do you want to update the parsing for 招标文件.pdf? (yes/no): no
    Enter the file name for toubiao_file: 投标文件.pdf
    Do you want to update the parsing for 投标文件.pdf? (yes/no): no
    Extracted requirements from tender document.
    

    如果项目之前已经解析过某个文件,系统会提示你是否更新解析结果;如果没有解析过,则会自动进行解析。

待优化列表

  • 单独把关键词放在记忆模块中
  • 添加本地 embedding 模型
  • 添加 UI 界面

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