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| # Legal RAG Analysis API | |
| ## Overview | |
| This is a FastAPI-based legal case analysis system that combines multiple AI technologies to provide comprehensive legal verdict predictions. The system uses a Retrieval-Augmented Generation (RAG) approach with LegalBERT for initial predictions and Gemini AI for final evaluation and explanation. | |
| ## User Preferences | |
| Preferred communication style: Simple, everyday language. | |
| Coding style: camelCase for function names, clear variable names, efficiency over boilerplate, no comments unless asked. | |
| Prefers to add model files and dependencies later after basic structure is ready. | |
| ## System Architecture | |
| The application follows a microservices-inspired architecture with clear separation of concerns: | |
| ### Backend Framework | |
| - **FastAPI** - Chosen for its high performance, automatic API documentation, and excellent type hinting support | |
| - **Python 3.x** - Primary language for ML/AI integration and legal domain processing | |
| - **Uvicorn** - ASGI server for production-ready deployment | |
| ### AI/ML Pipeline Architecture | |
| The system implements a three-stage analysis pipeline: | |
| 1. **Initial Prediction** - LegalBERT model for binary classification (guilty/not guilty) | |
| 2. **Knowledge Retrieval** - RAG system using FAISS for retrieving relevant legal documents | |
| 3. **Final Evaluation** - Gemini AI for contextual analysis and explanation generation | |
| ## Key Components | |
| ### 1. LegalBERT Service (`app/services/legal_bert.py`) | |
| - **Purpose**: Provides initial verdict predictions using a fine-tuned BERT model for legal texts | |
| - **Technology**: Transformers library with PyTorch backend | |
| - **Input**: Raw case text | |
| - **Output**: Binary verdict (guilty/not guilty) with confidence scores | |
| ### 2. RAG Service (`app/services/rag_service.py`) | |
| - **Purpose**: Retrieves relevant legal documents to support case analysis | |
| - **Technology**: | |
| - FAISS for vector similarity search | |
| - Sentence-BERT (BGE-Large) for text embeddings | |
| - Multiple legal document indexes (Constitution, IPC, case law, statutes) | |
| - **Features**: Parallel index querying, chunk deduplication, relevance filtering | |
| ### 3. Gemini Service (`app/services/gemini_service.py`) | |
| - **Purpose**: Generates search queries and provides final legal analysis | |
| - **Technology**: Google Gemini AI API | |
| - **Functions**: | |
| - Query generation from case facts | |
| - Final verdict evaluation with legal reasoning | |
| - Explanation generation in natural language | |
| ### 4. API Layer (`app/api/routes.py`) | |
| - **Endpoints**: | |
| - `POST /api/v1/analyze-case` - Main analysis endpoint | |
| - `GET /api/v1/health` - Service health monitoring | |
| - **Features**: Error handling, logging, service orchestration | |
| ## Data Flow | |
| 1. **Request Processing**: Case text received via POST request | |
| 2. **Initial Analysis**: LegalBERT processes text and returns preliminary verdict | |
| 3. **Query Generation**: Gemini generates optimized search query from case facts | |
| 4. **Knowledge Retrieval**: RAG system searches multiple legal document indexes | |
| 5. **Final Evaluation**: Gemini analyzes initial verdict against retrieved legal context | |
| 6. **Response Assembly**: Combined results with explanations returned to client | |
| ## External Dependencies | |
| ### AI/ML Models | |
| - **LegalBERT Model**: Custom fine-tuned model for legal verdict prediction | |
| - **Sentence Transformer**: BAAI/bge-large-en-v1.5 for text embeddings | |
| - **Gemini AI**: Google's generative AI for natural language processing | |
| ### Vector Databases | |
| - **FAISS Indexes**: Multiple pre-built indexes for different legal document types: | |
| - Constitution documents | |
| - Indian Penal Code (IPC) | |
| - Case law precedents | |
| - Statutes and regulations | |
| - Q&A legal content | |
| ### Python Libraries | |
| - FastAPI, Uvicorn (web framework) | |
| - Transformers, PyTorch (ML models) | |
| - Sentence-Transformers (embeddings) | |
| - FAISS (vector search) | |
| - Google GenAI (external API) | |
| ## Deployment Strategy | |
| ### Development Setup | |
| - Local development with hot reload enabled | |
| - Model files and indexes loaded from configurable paths | |
| - Environment-based configuration management | |
| ### Configuration Management | |
| - Centralized settings in `app/core/config.py` | |
| - Environment variable support for sensitive data (API keys) | |
| - Flexible path configuration for model and index files | |
| ### Health Monitoring | |
| - Service-level health checks for all components | |
| - Graceful degradation when external services are unavailable | |
| - Comprehensive logging for debugging and monitoring | |
| ### CORS Configuration | |
| - Permissive CORS setup for development | |
| - Can be restricted for production deployment | |
| ## Current Development Status (January 2025) | |
| ### ✅ Completed | |
| - FastAPI backend structure with proper routing and middleware | |
| - Placeholder implementations for all services (LegalBERT, RAG, Gemini) | |
| - Full Gemini AI integration for query generation and case evaluation | |
| - Health monitoring endpoints for all components | |
| - CORS configuration for frontend integration | |
| - API documentation with comprehensive endpoints | |
| - Camel case naming conventions as per user preference | |
| ### 🔄 Ready for Model Integration | |
| - LegalBERT service structure ready for torch/transformers integration | |
| - RAG service prepared for FAISS indexes and sentence-transformers | |
| - Configuration paths set for model files and indexes | |
| - Graceful degradation when model files are missing | |
| ### 📁 Directory Structure for Model Files | |
| - `./models/legalbert_model/` - LegalBERT model files (to be added) | |
| - `./faiss_indexes/` - FAISS vector indexes and chunks (to be added) | |
| ### 🔗 API Endpoints Working | |
| - `POST /api/v1/analyze-case` - Full case analysis with Gemini evaluation | |
| - `GET /api/v1/health` - Service health monitoring | |
| - `GET /api/v1/models/status` - Model loading status | |
| - `GET /` - Basic API info | |
| ## Next Steps for Full Functionality | |
| 1. Add LegalBERT model files to `./models/legalbert_model/` | |
| 2. Install ML dependencies: `torch`, `transformers`, `sentence-transformers`, `faiss-cpu` | |
| 3. Add FAISS indexes and chunk files to `./faiss_indexes/` | |
| 4. All placeholder implementations will automatically switch to real ML models | |
| ## Notes | |
| - The system gracefully handles missing model files during development | |
| - All services include health check mechanisms for monitoring | |
| - The RAG system supports parallel querying of multiple legal document indexes | |
| - Query generation is optimized for Indian criminal law terminology | |
| - The architecture supports easy addition of new legal document indexes | |
| - API follows camelCase conventions and clean code principles as requested |