🧠 Knowledge Graph From Text

A Streamlit web app that generates interactive knowledge graphs from plain text using Ollama AI models. You can upload a .txt file or paste text, and the app will create a graph where concepts are visualized as connected nodes.

📸 Preview

🚀 Features

Two input methods: Upload .txt file or paste text directly.
Ollama model integration: Select from available local models (e.g., Gemma, Mistral, LLaMA).
Automatic graph storage: Generated graphs are saved and can be reloaded anytime.
Interactive visualization: Zoom, drag, and explore relationships between concepts.
Optimized for speed: Uses hashed filenames to prevent regenerating the same graph.

📂 Project Structure

.
├── app.py            # Main Streamlit app 
├── Data              # Folder containing .html files.
├── n_sc.png          # Image file (doc)
├── README.md         # Project documentation and instructions(doc)
├── requirements.txt  # List of Python dependencies for the project(libraries)
├── sc.png            # Another image file (doc)
└── src
    ├── config
    │   └── folder_con.py       # Configuration for folder creation
    ├── graph
    │   ├── generate_kgraph.py  # Script to generate knowledge graphs
    │   └── visulization.py     # Script to visualize knowledge graphs (PyVis)
    ├── model
    │   └── model_info.py       # information about installed Ollama models/context len
    └── utils
        ├── file_op.py          # Utility functions for file operations (read/write)
        └── text_clean.py       # Utility functions for text preprocessing/cleaning

⚙️ Installation

A. Non-Docker Installation

Clone the repository

git clone https://github.com/ganeshnikhil/Kgraph.git
cd Kgraph

Create a virtual environment

python -m venv venv
source venv/bin/activate  # On Linux/Mac
venv\Scripts\activate     # On Windows

Install dependencies

pip install -r requirements.txt

Install Ollama (if not already installed) Follow instructions from: https://ollama.ai/download
Start Ollama server

ollama serve

Download a model (example with Gemma)

ollama pull gemma3:4b

Run the Streamlit app

streamlit run app.py

B. Docker Installation

Clone the repository

git clone https://github.com/ganeshnikhil/Kgraph.git
cd Kgraph

Build Docker images

docker-compose build

Start services (Ollama + Streamlit)

docker-compose up -d

Pull Ollama models inside the container

docker exec -it ollama ollama pull <model-name>

Access the Streamlit app Open your browser and go to: http://localhost:8501

▶️ Usage

Upload a .txt file or paste your text in the sidebar.
Select an Ollama model to use for generating the graph.
Click "Generate Knowledge Graph".
View, zoom, and explore the interactive visualization.
Load previously generated graphs from the sidebar.

📌 Notes

All generated graphs are saved in the Data/ directory as .html files.
Large input text may take more time to process depending on the model.
If no model is found, the app will fall back to Gemma.

📜 License

This project is licensed under the MIT License — see the LICENSE file for details.