README.md

metadata

title: 8674-Project
emoji: 📚
colorFrom: blue
colorTo: indigo
sdk: streamlit
app_file: src/streamlit_app.py
pinned: false

CogniMap Studio 🧠✨

Transform your documents into interactive, queryable mind maps using local AI.

CogniMap Studio is designed to run entirely on your own machine, making it perfect for private study and for users with limited VRAM (e.g., 8GB). It uses the power of Ollama to turn dense materials into visual, easy-to-explore learning tools.

A mind map generated from a document.

Features

📚 Upload Your Documents: Works seamlessly with PDF and TXT files.

🗺️ Auto-Generate Mind Maps: Automatically extracts key concepts and their relationships to build a visual map of your material.

✍️ Interact & Explore: A fully interactive and editable mind map powered by Cytoscape.js.

💬 Ask Your Documents Anything: Use a built-in RAG pipeline to chat with your materials, getting answers from a local LLM without your data ever leaving your computer.

Tech Stack

Category Technologies Backend Python, Flask, LangChain Frontend React, Cytoscape.js, Axios AI/ML Ollama, FAISS (CPU), Sentence-Transformers Models all-MiniLM-L6-v2 (Embeddings), mistral:instruct / llama3:8b (LLM)

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Getting Started

Follow these steps to get CogniMap Studio running on your local machine.

1. Prerequisites

Make sure you have the following installed:

Git: To clone the repository.

Python 3.9+: For the backend server.

Node.js & npm: For the frontend application.

Ollama: Install from the official Ollama website.

2. Clone the Repository

Bash

git clone https://github.com/your-username/your-repo-name.git cd your-repo-name

3. Set Up Ollama & AI Models

First, ensure the Ollama server is running. Then, pull the LLM you want to use. mistral:instruct is recommended for systems with 8GB VRAM.

Bash

Recommended for 8GB VRAM

ollama pull mistral:instruct

A more capable alternative

ollama pull llama3:8b-instruct

4. Set Up the Backend

Open a terminal in the project's root directory.

Bash

Navigate to the backend folder

cd backend

Create and activate a virtual environment

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

Install Python dependencies

pip install -r requirements.txt

5. Set Up the Frontend

Open a second terminal in the project's root directory.

Bash

Navigate to the frontend folder

cd frontend

Install Node.js dependencies

npm install

6. Launch the Application!

You'll need both terminals running simultaneously.

In your first terminal (backend):

Bash

Make sure you are in the backend/ directory with venv active

flask run The backend will be available at http://127.0.0.1:5000.

In your second terminal (frontend):

Bash

Make sure you are in the frontend/ directory

npm start The application will open in your browser at http://localhost:3000.

For Developers

Experimentation with JupyterLab

If you want to experiment with the backend logic (document parsing, embedding, etc.), you can use JupyterLab.

Bash

Install JupyterLab in the backend's virtual environment

pip install jupyterlab

Start the server

jupyter lab

Hugging Face Token

Ollama handles the model downloads, so a Hugging Face token is generally not required to run this application. However, if you plan to modify the code to download models directly from the Hub, you can log in via the terminal:

Bash

pip install huggingface_hub huggingface-cli login