LocalAItable是一个强大的本地化AI表格处理工具，允许您通过本地大模型或云端API批量处理Excel/CSV表格数据，实现类似"多维表格"的智能化数据处理能力。

• 双模式AI支持：同时支持OpenAI API和本地部署的Ollama模型
• 表格数据处理：轻松导入/导出Excel和CSV文件，自动检测文件编码
• 批量AI生成：为表格中的数据批量生成AI内容，支持多线程并行处理
• 模板系统：强大的提示词模板管理，支持变量替换和条件逻辑
• 友好界面：直观的图形用户界面，无需编程经验即可操作
• 完全本地化：使用本地模型时，所有数据处理均在本地完成，保护数据隐私

• 文本摘要生成：批量将长文本内容转化为简洁摘要
• 数据提取与解析：从非结构化文本中提取结构化数据(如血压、日期等)
• 内容翻译：批量翻译表格中的文本内容
• 情感分析与分类：分析文本情感倾向或进行内容分类
• 关键词提取：从大量文本中提取关键词和核心概念
• 医疗数据处理：提取和整理医疗记录中的关键数据

• Python 3.8或更高版本
• 本地运行Ollama模型推荐8GB以上内存
• 支持Windows、macOS和Linux系统

1. 克隆仓库到本地

git clone https://github.com/yourusername/LocalAItable.git
cd LocalAItable

2. 安装依赖包

pip install -r requirements.txt

3. (可选) 设置OpenAI API密钥

   • 在程序界面中设置
   • 或设置环境变量 OPENAI_API_KEY

4. (可选) 安装并配置Ollama

   • 从Ollama官网下载并安装
   • 下载所需模型，如 ollama pull deepseek-r1:14b

1. 运行应用程序

python ai_column_generator.py

2. 导入数据

   • 点击"选择Excel/CSV文件"按钮
   • 如遇编码问题，可使用"手动指定编码打开"功能

3. 配置AI

   • 选择API类型(OpenAI或Ollama)
   • 配置相应API密钥或URL地址
   • 选择合适的AI模型

4. 选择处理列

   • 指定要处理的表格列(引用列)
   • 指定AI生成内容的保存列(目标列)

5. 设置提示词

   • 使用内置模板或创建自定义模板
   • 支持变量替换和条件逻辑

6. 生成内容

   • 点击"预览"按钮测试效果
   • 点击"生成并更新"按钮批量处理
   • 处理完成后，可导出更新后的表格文件

基础模板示例：

请根据以下内容生成一段简洁的摘要：

{引用内容}

条件逻辑模板：

请分析以下内容，{如果:关键词:重点关注这些关键词: {关键词}
}

{引用内容}

本项目基于MIT许可证开源 - 详见 LICENSE 文件

欢迎提交问题和功能建议！如果您想贡献代码，请先fork仓库并创建拉取请求。

如有问题或建议，请通过GitHub Issues与我们联系。

LocalAItable is a powerful local AI spreadsheet processing tool that allows you to batch process Excel/CSV spreadsheet data through local large language models or cloud APIs, achieving intelligent data processing capabilities similar to "multi-dimensional tables".

• Dual AI Support: Supports both OpenAI API and locally deployed Ollama models
• Spreadsheet Processing: Easily import/export Excel and CSV files with automatic encoding detection
• Batch AI Generation: Generate AI content for spreadsheet data in batch with multi-threading support
• Template System: Powerful prompt template management with variable substitution and conditional logic
• User-Friendly Interface: Intuitive graphical user interface requiring no programming experience
• Fully Localized: When using local models, all data processing is done locally to protect data privacy

• Text Summarization: Batch convert long text content into concise summaries
• Data Extraction & Parsing: Extract structured data from unstructured text (e.g., blood pressure, dates)
• Content Translation: Batch translate text content in spreadsheets
• Sentiment Analysis & Classification: Analyze text sentiment or classify content
• Keyword Extraction: Extract keywords and core concepts from large volumes of text
• Medical Data Processing: Extract and organize key data from medical records

• Python 3.8 or higher
• 8GB+ RAM recommended for running Ollama models locally
• Supports Windows, macOS, and Linux systems

1. Clone the repository

git clone https://github.com/yourusername/LocalAItable.git
cd LocalAItable

2. Install dependencies

pip install -r requirements.txt

3. (Optional) Set up OpenAI API key

   • Configure in the program interface
   • Or set the environment variable OPENAI_API_KEY

4. (Optional) Install and configure Ollama

   • Download and install from Ollama website
   • Download required models, e.g., ollama pull deepseek-r1:14b

1. Run the application

python ai_column_generator.py

2. Import data

   • Click the "Select Excel/CSV File" button
   • For encoding issues, use the "Open with Manual Encoding" feature

3. Configure AI

   • Select API type (OpenAI or Ollama)
   • Configure corresponding API key or URL
   • Choose an appropriate AI model

4. Select processing columns

   • Specify the spreadsheet columns to process (reference columns)
   • Specify the column to save AI-generated content (target column)

5. Set up prompts

   • Use built-in templates or create custom templates
   • Support variable substitution and conditional logic

6. Generate content

   • Click the "Preview" button to test the effect
   • Click "Generate and Update" for batch processing
   • After processing, export the updated spreadsheet file

Basic template example:

Please generate a concise summary based on the following content:

{引用内容}

Conditional logic template:

Please analyze the following content, {如果:关键词:with special attention to these keywords: {关键词}
}

{引用内容}

This project is open-sourced under the MIT License - see the LICENSE file for details

Issues and feature suggestions are welcome! If you'd like to contribute code, please fork the repository and create a pull request.

For questions or suggestions, please contact us through GitHub Issues.