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⚖️ LLM-Powered Legal Knowledge Graphs for Indian Courts

Ontology-driven. LLM-boosted. Graph-native.
Creating the future of legal intelligence, one node at a time.

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🚧 Project Status: Work In Progress

This project is currently under active development.

🧪 Expect rapid changes, restructuring, and experimental branches.

🧠 We're still refining prompt engineering, triple extraction accuracy, and ontology alignment.

🧵 The codebase will grow to support:

High Court + Tribunal integration

Vector-based case retrieval

GraphRAG-powered legal QA

Timeline, Bench, Provision, and Citation mapping

We welcome early feedback, issues, and pull requests — but be advised that the API and structure may change frequently.

🧠 Project Overview

This project aims to build ontology-aligned Knowledge Graphs (KGs) for the Supreme Court of India by extracting structured information from legal judgments using Large Language Models (LLMs).

It leverages:

• A curated legal ontology (from NyOn)
• Neo4j graph database
• LLM pipelines (for entity/relation extraction)
• Vector search for retrieval
• Graph Reasoning (GraphRAG/Knowledge-augmented NLP)

🧬 The ultimate goal:
To build a foundation for AI-assisted legal research, argument mining, and decision reasoning in the Indian legal system.

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📚 Table of Contents

• 🧠 Project Overview
• 📦 Requirements
• 🛠️ Installation & Setup
• 🚀 Usage
• 🧩 Sample Queries
• 📐 Ontology Acknowledgement
• 🤝 Contributing
• 📄 License

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📦 Requirements

To run this project locally, make sure you have the following:

• Python 3.10+
• Neo4j 5.11+ (with vector index support)
• Neo4j APOC plugin installed
• OpenAI or HuggingFace API key (for LLM-based extraction)
• requirements.txt includes:
  • neo4j
  • openai
  • langchain
  • langchain-community
  • langchain-core
  • langchain-experimental
  • langchain-google-gena
  • langchain-huggingfac
  • langchain-neo4
  • langchain-openai
  • langchain-text-splitter
  • tqdm
  • pydantic
  • python-dotenv

pip install -r requirements.txt

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🛠️ Installation & Setup

1. Clone the repository

   git clone https://github.com/jahab/OntoLogyBasedKGCreation.git
   cd OntoLogyBasedKGCreation

2. Configure .env Create a .env file with:

   NEO4J_URI=bolt://localhost:7687
   NEO4J_USERNAME=neo4j
   NEO4J_PASSWORD=admin@123
   OPENAI_API_KEY=sk-...
   GOOGLE_API_KEY=GI...
   HF_TOKEN=hf_...
   

3. Install docker compose cli

4. Run docker compose up --build

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🚀 Usage

(WIP with UI)

Services

• Neo4j Browser: http://localhost:7474/browser/preview
• Qdrant Dashboard: http://localhost:6333
• Graph API Endpoint: http://0.0.0.0:4044/

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🔹 Create Graph

Endpoint:

POST http://0.0.0.0:4044/create_graph
# Request Body
{
  "pdf_file": "35346_2009_39_1501_24473_Judgement_29-Oct-2020.pdf",
  "model_provider": "google",
  "embedding_provider": "google",
  "embedding_model": "models/text-embedding-004",
  "extraction_model": "gemini-2.5-flash"
}

# Response Body
{"task_id":"3d1fb599-38bf-4945-8a67-49eac7aad4d6"}

🔹 Check Graph Creation Status

Endpoint:

POST http://0.0.0.0:4044/status

# Request Body
{
  "task_id": "3d1fb599-38bf-4945-8a67-49eac7aad4d6"
}

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📐 Ontology Acknowledgement

This project is powered by the excellent NyOn ontology Developed by semintelligence, NyOn provides a structured legal schema to represent core legal concepts such as:

• CourtCase, Court, Judge, Decision, LegalProvision, Bench, Party, and more.

We are deeply grateful for their contribution to the open legal knowledge ecosystem. 🙏

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🤝 Contributing

Want to shape the future of legal AI in India? We welcome contributions in the form of:

• Ontology extensions
• New entity extractors
• Fine-tuned LLMs
• RAG pipelines
• Legal document parsers

Feel free to submit a PR or open an issue!

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📄 License

GNU GENERAL PUBLIC LICENSE

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🚀 Vision

Build a legal co-pilot for India. Assist lawyers, judges, researchers, and policy makers with deep insights, structured search, and AI-powered reasoning.

Stay tuned for High Court expansion, timeline visualizations, and ChatRAG integrations. 🧾🔍⚖️

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