InstaGraph converts text or URLs into interactive, color-coded knowledge graphs, aiding users in visualizing complex relationships. It leverages OpenAI's GPT-3.5 for entity extraction and relationship mapping, making it suitable for researchers, students, and anyone needing to understand data connections.

How It Works

This Flask application processes user-provided text or URLs, feeding them to GPT-3.5 to identify entities and their relationships. The output is then rendered as a visually intuitive, color-coded knowledge graph. An optional Neo4j or FalkorDB backend can be configured for persistent graph storage.

Quick Start & Requirements

• Install via pip install -r requirements.txt after cloning the repository.
• Requires Python, pip, and an OpenAI API key.
• Optional: Neo4j or FalkorDB instance for graph database features.
• Run with python main.py.
• Docker images are available for development and production.
• Official documentation and demo links are not explicitly provided, but the README offers a clear setup guide.

Highlighted Details

• Dynamic text-to-graph conversion.
• Color-coded nodes and edges for enhanced visualization.
• Responsive design for cross-device usability.
• Supports optional Neo4j and FalkorDB integration.

Maintenance & Community

The project is primarily maintained by @yoheinakajima, who is open to contributions and seeking help with project management. A waitlist for a hosted version is available at instagraph.ai.

Licensing & Compatibility

Released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The project is actively under development, with several features listed as planned additions. The author notes limited time for maintenance and GitHub management, suggesting potential delays in issue resolution or feature implementation.