rag-package 0.1.4

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: Support for both Ollama and local GGUF models 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
• 🚀 Ollama Integration: Easy setup with Ollama for improved installation experience

Quick Start

Installation

From PyPI (Recommended)

# Install the package (Ollama backend only)
pip install rag-package

# Or install with GGUF backend support (requires compilation)
pip install rag-package[gguf]

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 .

AI Backend Setup

The package supports two AI backends:

Option 1: Ollama (Recommended - Easier Setup)

1. Install Ollama: Download from ollama.ai
2. Pull a model:

   # For legal documents (recommended)
   ollama run hf.co/TheBloke/law-chat-GGUF:Q4_0
   
   # Or use a general model
   ollama pull llama3.2
   

3. Configure: The package will auto-detect Ollama and use it by default

Option 2: Local GGUF Models (Advanced)

1. Install GGUF support: pip install rag-package[gguf] (requires compilation tools)
2. Download models: Place GGUF files in the models/ directory
3. Recommended: Law Chat GGUF (Q4_0 variant)
4. Configure: Set backend: "llama-cpp" in your config file

Basic Usage

Web Interface

# Start the web server
rag serve

# Or using Python module
python -m uvicorn lawfirm_rag.api.fastapi_app:app --reload

# 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

Git Bash Usage

If you're using Git Bash on Windows and the rag command isn't found, use one of these options:

Option 1: Use Python module syntax

# Run any command through the Python module
python -m lawfirm_rag.cli.main serve
python -m lawfirm_rag.cli.main analyze document.pdf

Option 2: Add Scripts directory to Git Bash PATH

Add this line to your ~/.bashrc file:

# Adjust the Python version in the path if needed
export PATH="$PATH:/c/Users/$USERNAME/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0/LocalCache/local-packages/Python311/Scripts"

Then restart Git Bash or run source ~/.bashrc

Option 3: Create an alias

Add this line to your ~/.bashrc file:

alias rag="python -m lawfirm_rag.cli.main"

Then restart Git Bash or run source ~/.bashrc

AI Model Setup

The package supports multiple AI backends for flexibility and ease of use:

Ollama Backend (Recommended)

• Easy Installation: No compilation required
• Model Management: Built-in model downloading and management
• Better Performance: Optimized for local inference
• Setup: Install Ollama and pull models as shown above

Local GGUF Backend (Advanced)

• Direct Model Loading: Load GGUF files directly
• Full Control: Manual model management
• Setup:
  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)

Backend Selection

The package automatically detects the best available backend:

1. Ollama (if server is running and models are available)
2. Local GGUF (if models are found in the models directory)
3. Fallback (graceful degradation with limited functionality)

You can force a specific backend by creating a config.yaml file:

llm:
  backend: ollama  # or "llama-cpp" for GGUF files
  ollama:
    base_url: http://localhost:11434
    default_model: law-chat

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 multiple methods:

Configuration Files

• config.yaml: Main configuration file (project root)
• ~/.lawfirm-rag/config.yaml: User-level configuration
• .env: Environment variables and API keys

LLM Backend Configuration

# config.yaml
llm:
  backend: ollama  # "auto", "ollama", or "llama-cpp"
  ollama:
    base_url: http://localhost:11434
    default_model: law-chat
    default_embed_model: mxbai-embed-large
    timeout: 30
    max_retries: 3
  llama_cpp:
    model_path: ~/.lawfirm-rag/models/default.gguf
    n_ctx: 4096
    temperature: 0.7

Environment Variables

# Ollama Configuration
OLLAMA_BASE_URL=http://localhost:11434

# Legacy GGUF Model Settings (if using llama-cpp backend)
LAWFIRM_RAG_CONFIG_PATH=./config.yaml

# Optional: API keys for cloud models (future feature)
ANTHROPIC_API_KEY=your_key_here
OPENAI_API_KEY=your_key_here

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
from lawfirm_rag.core.ai_engine import create_ai_engine_from_config
from lawfirm_rag.utils.config import ConfigManager

# Initialize components with automatic backend detection
config_manager = ConfigManager()
config = config_manager.get_config()

processor = DocumentProcessor()
ai_engine = create_ai_engine_from_config(config)  # Auto-detects Ollama or GGUF
query_gen = QueryGenerator(ai_engine)

# Manual backend selection
ai_engine = AIEngine(backend_type="ollama", model_name="law-chat")
# or
ai_engine = AIEngine(backend_type="llama-cpp", model_path="path/to/model.gguf")

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"