Knowledge Graph Visualizer

This project is a full-stack application for extracting, visualizing knowledge graphs from PDF. It leverages AI and graph technologies to help users explore relationships within their documents.

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Features

• Upload PDFs/CSVs: Extracts text and data from uploaded files.
• AI-Powered Knowledge Graphs: Uses LLMs to generate and query knowledge graphs from document content.
• Interactive Visualization: Visualizes knowledge graphs in the browser using Cytoscape.js.
• Conversational Interface: Chat with your documents to extract insights.

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Tech Stack

• Frontend: Next.js (React, TypeScript, Cytoscape.js)
• Backend: FastAPI (Python)
• Database: SQLAlchemy (default: PostgreSQL)
• AI/LLM: OpenAI API (configurable via environment variables)

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Prerequisites

• Node.js (v18+ recommended)
• npm (v9+ recommended)
• Python (v3.9+ recommended)
• pip (Python package manager)
• OpenAI API Key (for LLM features)

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LOCAL Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/ai-dashboard.git
cd ai-dashboard

2. Backend Setup

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt  # If requirements.txt exists, otherwise install FastAPI, SQLAlchemy, etc.
cp .env.example .env             # Create and edit your .env file with your OpenAI key and DB settings
uvicorn main:app --reload        # Starts FastAPI on http://localhost:8000

3. Frontend Setup

cd ../frontend
npm install
npm run dev                      # Starts Next.js on http://localhost:3000

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Environment Variables

• Backend (backend/.env):

  • OPENAI_API_KEY=your_openai_key
  • DATABASE_URL=sqlite:///./test.db (or your preferred DB)

• Frontend: (Set this to the URL of your backend API)

  • NEXT_PUBLIC_API_URL=http://localhost:8000

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Usage

1. Start both backend and frontend servers as above.
2. Open http://localhost:3000 in your browser.
3. Upload a PDF or CSV, visualize the knowledge graph, and interact via chat.

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