wehub-resource-sync
93 lines
3.0 KiB
Markdown
93 lines
3.0 KiB
Markdown
# Paralegal AI Assistant
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⚖️ An intelligent paralegal AI assistant that analyzes PDF documents and provides comprehensive answers through advanced RAG (Retrieval-Augmented Generation) with web search fallback capabilities.
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## Setup Instructions
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### Prerequisites
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- Python 3.13+
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- OpenAI API key
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- Firecrawl API key (optional)
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- Docker (for self-hosted Milvus)
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### Installation
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1. **Clone and navigate to the project:**
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```bash
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git clone <repository-url>
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cd paralegal-agent-crew
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```
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2. **Set up environment variables:**
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```bash
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cp .env.example .env
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```
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Edit `.env` and add your API keys:
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```env
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OPENAI_API_KEY=your_openai_api_key_here
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FIRECRAWL_API_KEY=your_firecrawl_api_key_here
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```
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3. **Install dependencies with UV:**
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```bash
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uv sync
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```
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### Option A: Using Local Milvus (Default)
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The project works with embedded Milvus out of the box. Simply run:
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```bash
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uv run streamlit run app.py
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```
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### Option B: Self-Hosted Milvus with Docker
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4. **Set up Milvus vector database:**
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**Quick Setup (Recommended):**
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```bash
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# Download and run Milvus installation script
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curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh -o standalone_embed.sh
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bash standalone_embed.sh start
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```
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**Alternative - Docker Compose:**
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```bash
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# Download docker-compose file
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wget https://github.com/milvus-io/milvus/releases/download/v2.0.2/milvus-standalone-docker-compose.yml -O docker-compose.yml
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# Start Milvus
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docker-compose up -d
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```
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5. **Update configuration for external Milvus:**
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Modify `config/settings.py` to point to your Milvus instance:
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```python
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milvus_host: str = "localhost"
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milvus_port: int = 19530
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```
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6. **Run the application:**
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```bash
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uv run streamlit run app.py
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```
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7. **Open your browser and go to `http://localhost:8501`**
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### Milvus Management
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- **Milvus WebUI**: Access at `http://127.0.0.1:9091/webui/`
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- **Stop Milvus**: `bash standalone_embed.sh stop`
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- **Delete Milvus**: `bash standalone_embed.sh delete`
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## About the Project
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This paralegal AI assistant combines multiple technologies to provide intelligent document analysis:
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- **Document Processing**: Extracts and chunks PDF documents for analysis
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- **Vector Database**: Uses Milvus with binary quantization for efficient similarity search
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- **Embeddings**: BGE-large-en-v1.5 model for high-quality text representations
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- **Intelligent Routing**: Automatically evaluates response quality and triggers web search when needed
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- **Web Search Integration**: Firecrawl integration for additional context from the web
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- **Workflow Management**: CrewAI-powered agentic workflows for complex query handling
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The system provides an interactive Streamlit interface where users can upload PDF documents, ask questions, and receive comprehensive answers with citations and sources. It automatically determines when to use document knowledge versus web search to provide the most accurate and complete responses. |