rag-package 0.1.2

AI-powered document analysis and query generation tool with RAG capabilities

LawFirm RAG Package

A comprehensive Python package for document analysis and query generation using Retrieval-Augmented Generation (RAG) with local AI models. Currently optimized for legal documents with support for Westlaw, LexisNexis, and Casetext query generation.

Features

• 📄 Document Processing: Extract and analyze text from various legal document formats
• 🤖 AI-Powered Analysis: Local GGUF model support for document summarization and legal issue identification
• 🔍 Query Generation: Generate optimized search queries for legal databases (Westlaw, LexisNexis, Casetext)
• 🌐 Web Interface: Modern web UI for document upload and analysis
• 🔧 CLI Tools: Command-line interface for batch processing
• 💾 Model Management: Download, load, and manage AI models with progress tracking
• 🏗️ Pip Installable: Clean package structure for easy installation and distribution

Quick Start

Installation

From PyPI (Recommended)

# Install directly from PyPI
pip install lawfirm-rag-package

From GitHub Repository

# Clone the repository
git clone https://github.com/DannyMExe/rag-package.git
cd rag-package

# Install the package in development mode
pip install -e .

Basic Usage

Web Interface

# Start the web server
rag serve

# Open http://localhost:8000/app in your browser

CLI Usage

# Analyze documents
rag analyze document.pdf --type summary

# Generate queries for legal databases
rag query document.pdf --database westlaw

# Process multiple files
rag analyze *.pdf --output results.json

# Additional options
rag serve --port 8080
rag analyze document.pdf --type summary

AI Model Setup

The package supports local GGUF models for privacy and offline operation:

1. Via Web Interface: Use the Model Management section to download models
2. Manual Download: Place GGUF files in the models/ directory
3. Recommended Model: Law Chat GGUF (Q4_0 variant recommended)

Architecture

lawfirm_rag/
├── core/           # Core processing modules
│   ├── ai_engine.py       # GGUF model handling
│   ├── document_processor.py  # Document text extraction
│   ├── query_generator.py     # Legal database query generation
│   └── storage.py            # Document storage layer
├── api/            # FastAPI web server
├── cli/            # Command-line interface
├── web/            # Frontend assets
└── utils/          # Utilities and configuration

Legal Database Support

Westlaw

• Syntax: Terms and Connectors
• Operators: &, |, /s, /p, /n, !, %
• Example: negligen! /p \"motor vehicle\" /s injur! & damag!

LexisNexis

• Syntax: Boolean operators
• Operators: AND, OR, NOT, W/n, PRE/n
• Example: negligence AND \"motor vehicle\" AND damages

Casetext

• Syntax: Natural language + Boolean
• Features: Supports both natural language and boolean queries

Development

Project Structure

This project uses Task Master for development workflow management:

# View current tasks
task-master list

# Get next task to work on
task-master next

# Mark task complete
task-master set-status --id=X --status=done

Running Tests

# Install development dependencies
pip install -e \".[dev]\"

# Run tests
pytest

# Run with coverage
pytest --cov=lawfirm_rag

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

Configuration

Configuration is managed through:

• .taskmasterconfig: AI model settings and parameters
• .env: API keys and sensitive configuration (see .env.example)

Environment Variables

# AI Provider API Keys (optional - for cloud models)
ANTHROPIC_API_KEY=your_key_here
OPENAI_API_KEY=your_key_here
PERPLEXITY_API_KEY=your_key_here

# Local model settings
OLLAMA_BASE_URL=http://localhost:11434/api

API Reference

FastAPI Endpoints

• POST /upload - Upload documents for analysis
• POST /analyze - Analyze uploaded documents
• POST /query - Generate database queries
• GET /health - Service health check
• POST /models/load - Load AI models
• GET /models/loaded - Get loaded model status

Python API

from lawfirm_rag.core import DocumentProcessor, AIEngine, QueryGenerator

# Initialize components
processor = DocumentProcessor()
ai_engine = AIEngine(\"path/to/model.gguf\")
query_gen = QueryGenerator(ai_engine)

# Process document
text = processor.extract_text(\"document.pdf\")
summary = ai_engine.analyze_document(text, \"summary\")
query = query_gen.generate_query(text, \"westlaw\")

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with FastAPI for the web framework
• Uses llama-cpp-python for local AI model support
• Task management powered by Task Master AI

Support

For questions, issues, or contributions:

• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

[project.urls] Homepage = "https://github.com/DannyMExe/rag-package" Documentation = "https://lawfirm-rag.readthedocs.io" Repository = "https://github.com/DannyMExe/rag-package" "Bug Tracker" = "https://github.com/DannyMExe/rag-package/issues" Changelog = "https://github.com/DannyMExe/rag-package/blob/main/CHANGELOG.md"