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note_map

★ 37 Open GitHub ↗

NoteMap: a handy tool for analyzing, organizing, and finding patterns in text files. It works with PDFs, TXTs, and DOCXs. You can also brainstorm ideas and chat with your notes.

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a handy tool for analyzing, organizing, and finding patterns in text files. It works with PDFs, TXTs, and DOCXs. You can also brainstorm ideas and chat with your notes.

## Features

- Text Analysis: Quickly find patterns in large volumes of textual files.
- Organize and Visualize: Easily organize and see information from different file types.
- Support for Different Formats: Works with PDFs, TXTs, DOCXs, and more in the future.
- Brainstorming: Helps you come up with ideas.
- Note Interaction: Chat with your notes to stay productive.

## Usage

[Demo](https://github.com/SudoACoder/note_map/assets/58640233/8b961b4d-0845-49d3-94a0-82e9e8c835b9)

1. **Select Directory**: Pick a folder with your text files.
2. **Number of Clusters**: Choose how many groups you want.
3. **File Type**: Decide which file types to include.
4. **Keyword Search**: Search for specific words.
5. **Cluster Text Files**: Start the grouping process.
6. **Chat Interface**: Talk to your notes using a chat window.

## Installation
1. Clone the repository:
   ```
   git clone https://github.com/SudoACoder/note_map.git
   ```

2. Navigate to the project directory:
   ```
   cd note_map
   ```

3. Install dependencies:
   ```
   pip install -r requirements.txt
   ```

4. Run the application:
   ```
   python main.py
   ```
   
## Notes

- You can use NoteMap locally to keep your information private. Just select TinyLlama or Llama2 in settings before starting a chat! (model downloads only on first run)

## To-Do List

- [x] Local LLMS Support <sub>24 Feb 2024</sub>
- [ ] Conversation Log (+Long-Term Memory)
- [ ] Auto Summarizer
- [ ] Zero-shot Classification
- [ ] Named-entity Recognition (NER)
- [ ] Layered Encryption for Cache Note Storage
- [ ] Light Version

## Contribution

Feel free to contribute! If you have ideas or find bugs, let us know by opening an issue or pull request.

## License

This project is licensed under the [MIT License](https://opensource.org/licenses/MIT)

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