README.md

metadata

title: LegalLens API
emoji: 🏛️
colorFrom: blue
colorTo: indigo
sdk: docker
app_file: Dockerfile
pinned: false

Legal RAG Analysis API

A FastAPI backend for legal case analysis using Retrieval-Augmented Generation (RAG) system with LegalBERT predictions and Gemini AI evaluation.

Overview

This API provides comprehensive legal case analysis by combining:

• LegalBERT model for initial verdict predictions
• RAG system with FAISS indexes for retrieving relevant legal documents
• Gemini AI for final evaluation and detailed explanations

Features

• Case Analysis: Analyze legal cases and predict verdicts
• RAG Integration: Retrieve relevant legal documents from multiple sources
• AI Evaluation: Get detailed legal explanations from Gemini AI
• Health Monitoring: Check system status across all components
• Model Status: Monitor loading status of ML models and indexes

API Endpoints

Core Endpoints

POST /api/v1/analyze-case

Analyze a legal case and provide verdict prediction with detailed explanation.

Request Body:

{
  "caseText": "The accused was found in possession of stolen property...",
  "useQueryGeneration": true
}

Response:

{
  "initialVerdict": "guilty",
  "initialConfidence": 0.85,
  "finalVerdict": "guilty", 
  "verdictChanged": false,
  "searchQuery": "stolen property, IPC section 411, criminal breach of trust",
  "geminiExplanation": "Based on the legal analysis...",
  "supportingSources": {...},
  "analysisLogs": {...}
}

GET /api/v1/health

Check the health status of all system components.

Response:

{
  "status": "healthy",
  "services": {
    "legal_bert": true,
    "rag": true,
    "gemini": true
  },
  "error": null
}

GET /api/v1/models/status

Get detailed status of all models and indexes.

Response:

{
  "legalBert": {
    "loaded": false,
    "device": "cpu"
  },
  "ragIndexes": {
    "loaded": false,
    "indexCount": 0
  },
  "gemini": {
    "configured": true
  }
}

Setup Instructions

Prerequisites

1. Gemini API Key: Required for AI analysis

   • Get from Google AI Studio
   • Add as GEMINI_API_KEY environment variable

2. Model Files (Optional for development):

   • LegalBERT model files in ./models/legalbert_model/
   • FAISS indexes in ./faiss_indexes/

Installation

1. Install Dependencies:

   pip install fastapi uvicorn pydantic pydantic-settings google-genai
   

2. For Full Functionality (ML Models):

   pip install torch transformers sentence-transformers faiss-cpu numpy
   

3. Run the Server:

   python -m uvicorn main:app --host 0.0.0.0 --port 5000 --reload
   

Project Structure

├── main.py                 # FastAPI application entry point
├── app/
│   ├── api/
│   │   └── routes.py       # API route definitions
│   ├── core/
│   │   └── config.py       # Configuration settings
│   ├── models/
│   │   └── schemas.py      # Pydantic models
│   └── services/
│       ├── legal_bert.py   # LegalBERT service
│       ├── rag_service.py  # RAG retrieval service
│       └── gemini_service.py # Gemini AI service
├── models/                 # LegalBERT model files (to be added)
└── faiss_indexes/          # FAISS indexes (to be added)

Development Mode

The API works in development mode without ML dependencies:

• Uses placeholder predictions for LegalBERT
• Provides mock RAG retrieval
• Full Gemini AI integration for analysis

Adding Model Files

To enable full functionality:

1. LegalBERT Model:

   • Place model files in ./models/legalbert_model/
   • Install torch and transformers

2. FAISS Indexes:

   • Add indexes to ./faiss_indexes/
   • Install faiss-cpu and sentence-transformers

Configuration

Key settings in app/core/config.py:

• Model paths
• FAISS index locations
• API configuration
• RAG parameters

Environment Variables

• GEMINI_API_KEY: Required for Gemini AI integration
• LEGAL_BERT_MODEL_PATH: Path to LegalBERT model
• FAISS_INDEXES_PATH: Base path for FAISS indexes

Usage Examples

Basic Case Analysis

import requests

response = requests.post('http://localhost:5000/api/v1/analyze-case', json={
    'caseText': 'The accused was caught stealing from a shop.',
    'useQueryGeneration': True
})

result = response.json()
print(f"Verdict: {result['finalVerdict']}")
print(f"Explanation: {result['geminiExplanation']}")

Health Check

import requests

health = requests.get('http://localhost:5000/api/v1/health')
print(health.json())

API Documentation

Once running, visit:

• Interactive API Docs: http://localhost:5000/docs
• OpenAPI Schema: http://localhost:5000/openapi.json

Legal Document Sources

The RAG system retrieves from:

• Indian Constitution articles
• IPC sections
• Case law precedents
• Legal statutes
• Q&A legal content

Notes

• The system is designed for Indian criminal law cases
• Placeholder implementations allow development without full ML setup
• All services include health monitoring for production deployment
• CORS is configured for frontend integration