LawGlance: AI-Powered Legal Assistant

Website: lawglance.com

LawGlance is a free, open-source legal AI assistant built on Retrieval-Augmented Generation (RAG). It delivers accurate, cited answers to legal questions for both laypeople and professionals, currently covering Indian law with plans to expand internationally.

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Legal Coverage

LawGlance currently indexes the following statutes:

• The Indian Constitution
• The Bharatiya Nyaya Sanhita, 2023
• The Bharatiya Nagarik Suraksha Sanhita, 2023
• The Bharatiya Sakshya Adhiniyam, 2023
• The Consumer Protection Act, 2019
• The Motor Vehicles Act, 1988
• The Information Technology Act, 2000
• The Protection of Children from Sexual Offences Act (POCSO), 2012
• The Sexual Harassment of Women at Workplace (Prevention, Prohibition and Redressal) Act, 2013

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Quick Start

Prerequisites

• Python 3.11+
• uv package manager
• An OpenAI API key
• Redis (optional, recommended for production)

Setup

1. Clone the repository

git clone https://github.com/lawglance/lawglance.git
cd lawglance

2. Install uv

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Restart your terminal to ensure the uv command is on your PATH.

3. Install dependencies

uv sync

4. Configure environment

Create a .env file in the project root:

OPENAI_API_KEY=your-api-key-here

5. Run the application

uv run streamlit run app.py

6. Open in browser

http://127.0.0.1:8501

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Architecture

LawGlance is powered by an agentic retrieval pipeline built with LangGraph, located in the backend/ directory. Rather than a fixed RAG chain, the agent autonomously decides when to invoke the retrieve_docs tool, iterates until sufficient context is gathered, and then produces a cited final answer.

Module	Responsibility
backend/graph.py / backend/nodes.py	LangGraph agent loop: llm_call → tool_node → final_answer
backend/tools.py	Vector-store retrieval tool
backend/retrieval.py	agent_invoke() — entry point called by app.py
backend/main.py	Optional FastAPI HTTP API wrapping the same agent

The original single-chain implementation (lawglance_main.py, chains.py) remains in the repository for reference but is no longer the active path.

FastAPI backend

Run the HTTP API standalone (useful for integrating LawGlance into other applications):

uv run uvicorn backend.main:app --reload

Query directly:

curl "http://127.0.0.1:8000/query?query=What+is+Article+21%3F"

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Redis Caching

Redis caches chat history and LLM responses, improving throughput for concurrent users. It is optional for local development but recommended for production deployments.

Installation

Ubuntu / Linux

sudo apt-get update && sudo apt-get install redis-server

macOS (Homebrew)

brew install redis

Windows — use WSL (Ubuntu), then:

sudo apt-get update && sudo apt-get install redis-server
redis-server

Verify

redis-cli ping
# Expected: PONG

The backend connects to redis://localhost:6379/0 by default. Override via the redis_url parameter in the configuration if needed.

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Technology Stack

Technology	Role
LangGraph	Agentic retrieval loop (backend/)
LangChain	Conversational AI pipelines, RAG orchestration
ChromaDB	Vector database for legal document embeddings
FastAPI	HTTP API layer (backend/main.py)
Streamlit	Chat UI (app.py)
Redis	Chat history and response caching
OpenAI API	Language model inference

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Roadmap

• Agentic framework — delivered. The LangGraph single-agent pipeline now powers the live application. A full multi-agent specialist team remains on the roadmap.
• Global legal coverage — extending the knowledge base to additional jurisdictions (Canada and others).
• Voice interaction — speech input for hands-free legal queries.
• Multi-lingual support — answers in languages beyond English.
• Advanced search — precision ranking and personalised suggestions.
• Legal document generation — contract and agreement templates via guided input.
• Case management — deadline tracking and matter organisation.

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Contributing

Contributions are welcome. To get started:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature/your-feature.
3. Commit your changes with a descriptive message.
4. Push to your fork and open a pull request against main.

Please read CONTRIBUTING.md before submitting.

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License

This project is licensed under the Apache License 2.0. See LICENSE.md for the full text.