# GraphRAG-Ollama-UI

**Repository Path**: magic_yuan_admin/GraphRAG-Ollama-UI

## Basic Information

- **Project Name**: GraphRAG-Ollama-UI
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: dev
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-18
- **Last Updated**: 2024-07-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

#  🕸️ GraphRAG Local with Ollama and Gradio UI

Welcome to **GraphRAG Local with Ollama and Interactive UI**! This is an adaptation of Microsoft's [GraphRAG](https://github.com/microsoft/graphrag), tailored to support local models using Ollama and featuring a new interactive user interface.

*NOTE: The app gained traction much quicker than I anticipated so I am frantically trying to get the bugs fixed and suggested improvements integrated. Right now it is functional but you may still run into some issues. This may mean you will need to update your version by the end of today if you encounter an error by chance. I am trying to be fluid with the adjustments. 

Changes being made right now:
- LLM agnostic: Use Ollama or set your own base URL and local model for LLM and Embedder
- Bug fixes on indexing and output file generation (missing _final/entities on some calls)
- Launch your own GraphRAG API server so you can use the functions in your own external app
- Dockerfile for easier deployment

Feel free to open an Issue if you run into an error and I will try to address it ASAP so you don't run into any downtime*

## 📄 Research Paper

For more details on the original GraphRAG implementation, please refer to the [GraphRAG paper](https://arxiv.org/pdf/2404.16130).

## 🌟 Features

- **Local Model Support:** Leverage local models with Ollama for LLM and embeddings.
- **Cost-Effective:** Eliminate dependency on costly OpenAI models.
- **Interactive UI:** User-friendly interface for managing data, running queries, and visualizing results.
- **Real-time Graph Visualization:** Visualize your knowledge graph in 3D using Plotly.
- **File Management:** Upload, view, edit, and delete input files directly from the UI.
- **Settings Management:** Easily update and manage your GraphRAG settings through the UI.
- **Output Exploration:** Browse and view indexing outputs and artifacts.
- **Logging:** Real-time logging for better debugging and monitoring.

![GraphRAG UI](ui.png)

## 📦 Installation and Setup

Follow these steps to set up and run GraphRAG Local with Ollama and Interactive UI:

1. **Create and activate a new conda environment:**
    ```bash
    conda create -n graphrag-ollama -y
    conda activate graphrag-ollama
    ```

2. **Install Ollama:**
    Visit [Ollama's website](https://ollama.com/) for installation instructions.

4. **Install the required packages:**
    ```bash
    pip install -r requirements.txt
    ```

4. **Launch the interactive UI:**
    ```bash
    gradio app.py
    ```
    or

    ```bash
    python app.py
    ```

6. **Using the UI:**
    - Once the UI is launched, you can perform all necessary operations through the interface.
    - This includes initializing the project, managing settings, uploading files, running indexing, and executing queries.
    - The UI provides a user-friendly way to interact with GraphRAG without needing to run command-line operations.

Note: The UI now handles all the operations that were previously done through command-line instructions, making the process more streamlined and user-friendly.

## 🛠️ Customization

Users can experiment by changing the models in the `settings.yaml` file. The LLM model expects language models like llama3, mistral, phi3, etc., and the embedding model section expects embedding models like mxbai-embed-large, nomic-embed-text, etc., which are provided by Ollama. You can find the complete list of models provided by Ollama [here](https://ollama.com/library).

## 📊 Visualization

The UI now includes a 3D graph visualization feature. To use it:

1. Run indexing on your data
2. Go to the "Indexing Outputs" tab
3. Select the latest output folder and navigate to the GraphML file
4. Click the "Visualize Graph" button

## 📚 Citations

- Original GraphRAG repository by Microsoft: [GraphRAG](https://github.com/microsoft/graphrag)
- Ollama: [Ollama](https://ollama.com/)

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