https://github.com/danieladdisonorg/ai-chatbot-for-lawyer
AI-powered chatbot designed specifically for legal professionals. It aims to streamline client interactions, provide preliminary legal guidance, and enhance law firm efficiency. The chatbot offers features such as legal document analysis, client intake automation, legal research assistance, appointment scheduling, multi-language support,.
https://github.com/danieladdisonorg/ai-chatbot-for-lawyer
aws chatbot docker langchain lawyer-and-law-agency openai-chatgpt python react
Last synced: 6 months ago
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AI-powered chatbot designed specifically for legal professionals. It aims to streamline client interactions, provide preliminary legal guidance, and enhance law firm efficiency. The chatbot offers features such as legal document analysis, client intake automation, legal research assistance, appointment scheduling, multi-language support,.
- Host: GitHub
- URL: https://github.com/danieladdisonorg/ai-chatbot-for-lawyer
- Owner: danieladdisonorg
- Created: 2025-06-20T11:41:47.000Z (12 months ago)
- Default Branch: master
- Last Pushed: 2025-06-20T15:59:00.000Z (12 months ago)
- Last Synced: 2025-06-20T16:50:56.867Z (12 months ago)
- Topics: aws, chatbot, docker, langchain, lawyer-and-law-agency, openai-chatgpt, python, react
- Language: Python
- Homepage:
- Size: 92.9 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI Chatbot for Lawyers
A sophisticated AI-powered chatbot designed specifically for legal professionals to streamline client interactions, provide preliminary legal guidance, and enhance law firm efficiency.
## 🏗️ System Architecture
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ AI CHATBOT FOR LAWYERS │
└─────────────────────────────────────────────────────────────────────────────┘
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ FRONTEND │ │ BACKEND │ │ AI SERVICES │
│ │ │ │ │ │
│ ┌─────────────┐ │ │ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │ Streamlit │ │◄──►│ │ RAG Pipeline│ │◄──►│ │ Groq LLM │ │
│ │ Interface │ │ │ │ │ │ │ │ llama3-70b │ │
│ └─────────────┘ │ │ └─────────────┘ │ │ └─────────────┘ │
│ │ │ │ │ │
│ ┌─────────────┐ │ │ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │ File Upload │ │ │ │ Document │ │ │ │ Ollama │ │
│ │ Component │ │ │ │ Processing │ │ │ │ Embeddings │ │
│ └─────────────┘ │ │ └─────────────┘ │ │ └─────────────┘ │
│ │ │ │ │ │
│ ┌─────────────┐ │ │ ┌─────────────┐ │ │ │
│ │ Chat │ │ │ │ Vector DB │ │ │ │
│ │ Interface │ │ │ │ Operations │ │ │ │
│ └─────────────┘ │ │ └─────────────┘ │ │ │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
▼
┌─────────────────┐
│ DATA STORAGE │
│ │
│ ┌─────────────┐ │
│ │ FAISS │ │
│ │ Vector DB │ │
│ └─────────────┘ │
│ │
│ ┌─────────────┐ │
│ │ PDF Files │ │
│ │ Storage │ │
│ └─────────────┘ │
└─────────────────┘
```
## 🔄 Complete Workflow Process
```
USER INTERACTION FLOW
═══════════════════════════════════════════════════════════════════════════════
Step 1: Document Upload
┌─────────────────────────────────────────────────────────────────────────────┐
│ 📄 User uploads PDF → 📁 Saved to pdfs/ directory │
└─────────────────────────────────────────────────────────────────────────────┘
│
▼
Step 2: Document Processing
┌─────────────────────────────────────────────────────────────────────────────┐
│ PDFPlumberLoader → Text Extraction → RecursiveCharacterTextSplitter │
│ │
│ Configuration: │
│ • Chunk Size: 1000 characters │
│ • Overlap: 200 characters │
│ • Add Start Index: True │
└─────────────────────────────────────────────────────────────────────────────┘
│
▼
Step 3: Vector Embedding Creation
┌─────────────────────────────────────────────────────────────────────────────┐
│ Text Chunks → Ollama Embeddings (llama2:latest) → Vector Representations │
└─────────────────────────────────────────────────────────────────────────────┘
│
▼
Step 4: Vector Database Storage
┌─────────────────────────────────────────────────────────────────────────────┐
│ FAISS Database ← Vector Storage ← Save to vectorstore/db_faiss/ │
└─────────────────────────────────────────────────────────────────────────────┘
│
▼
Step 5: Query Processing
┌─────────────────────────────────────────────────────────────────────────────┐
│ 🔍 User Query → Similarity Search → Retrieve Top 4 Documents │
└─────────────────────────────────────────────────────────────────────────────┘
│
▼
Step 6: Context Generation & AI Response
┌─────────────────────────────────────────────────────────────────────────────┐
│ Context Assembly → Groq LLM (llama3-70b-8192) → 💬 Generated Response │
└─────────────────────────────────────────────────────────────────────────────┘
```
## 📊 RAG Pipeline Architecture
```
RAG (Retrieval-Augmented Generation) PIPELINE
═══════════════════════════════════════════════════════════════════════════════
INPUT PROCESSING RETRIEVAL GENERATION
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ 📄 PDF Upload │ │ 🔍 Query │ │ 🤖 AI Response │
│ │ │ Embedding │ │ Generation │
│ ┌─────────────┐ │ │ │ │ │
│ │PDFPlumber │ │ │ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │Loader │ │ │ │Similarity │ │ │ │Groq LLM │ │
│ └─────────────┘ │ │ │Search │ │ │ │llama3-70b │ │
│ │ │ │ │(k=4) │ │ │ │8192 │ │
│ ▼ │ │ └─────────────┘ │ │ └─────────────┘ │
│ ┌─────────────┐ │ │ │ │ │ ▲ │
│ │Text │ │ │ ▼ │ │ │ │
│ │Splitter │ │ │ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │1000 chars │ │ │ │Top 4 │ │ │ │Context + │ │
│ │200 overlap │ │ │ │Documents │ │ │ │Query │ │
│ └─────────────┘ │ │ └─────────────┘ │ │ │Assembly │ │
│ │ │ │ │ │ │ └─────────────┘ │
│ ▼ │ │ ▼ │ │ │
│ ┌─────────────┐ │ │ ┌─────────────┐ │ │ │
│ │Ollama │ │ │ │Context │ │──────────┤ │
│ │Embeddings │ │ │ │Generation │ │ │ │
│ │llama2 │ │ │ └─────────────┘ │ │ │
│ └─────────────┘ │ │ │ │ │
│ │ │ │ │ │ │
│ ▼ │ └─────────────────┘ └─────────────────┘
│ ┌─────────────┐ │ ▲ │
│ │FAISS Vector │ │ │ │
│ │Database │ │──────────────────────────┘ │
│ │Storage │ │ │
│ └─────────────┘ │ ▼
│ │ ┌─────────────────┐
└─────────────────┘ │ 📱 User │
│ Interface │
│ Display │
└─────────────────┘
```
## 🔧 Technical Implementation Flow
```
TECHNICAL STACK INTERACTION
═══════════════════════════════════════════════════════════════════════════════
frontend.py rag_pipeline.py vector_db.py
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Streamlit UI │ │ RAG Logic │ │ Vector Ops │
│ │ │ │ │ │
│ ┌─────────────┐ │ │ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │file_uploader│ │───────►│ │retrieve_docs│ │────────►│ │upload_pdf │ │
│ └─────────────┘ │ │ └─────────────┘ │ │ └─────────────┘ │
│ │ │ │ │ │ │ │
│ ┌─────────────┐ │ │ ▼ │ │ ▼ │
│ │text_area │ │───────►│ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │(user_query) │ │ │ │get_context │ │ │ │load_pdf │ │
│ └─────────────┘ │ │ └─────────────┘ │ │ └─────────────┘ │
│ │ │ │ │ │ │ │
│ ┌─────────────┐ │ │ ▼ │ │ ▼ │
│ │chat_message │ │◄───────│ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │(response) │ │ │ │ans_query │ │ │ │create_chunks│ │
│ └─────────────┘ │ │ └─────────────┘ │ │ └─────────────┘ │
│ │ │ │ │ │ │ │
└─────────────────┘ │ ▼ │ │ ▼ │
│ ┌─────────────┐ │ │ ┌─────────────┐ │
│ │ChatGroq │ │ │ │FAISS │ │
│ │llama3-70b │ │ │ │Database │ │
│ └─────────────┘ │ │ └─────────────┘ │
│ │ │ │
└─────────────────┘ └─────────────────┘
ENVIRONMENT VARIABLES
┌─────────────────────────────────────────────────────────────────────────────┐
│ GROK_API_KEY=your_groq_api_key │
│ OLLAMA_MODEL=llama2:latest │
│ FAISS_DB_PATH=vectorstore/db_faiss │
└─────────────────────────────────────────────────────────────────────────────┘
```
## 📈 Data Flow Diagram
```
DATA TRANSFORMATION PIPELINE
═══════════════════════════════════════════════════════════════════════════════
Raw PDF Document
│
▼
┌─────────────────┐
│ PDFPlumberLoader│ ──► Extract text content from PDF pages
└─────────────────┘
│
▼
┌─────────────────┐
│ Text Splitter │ ──► Split into manageable chunks
│ • Size: 1000 │ • Maintains context overlap
│ • Overlap: 200 │ • Adds start index for tracking
└─────────────────┘
│
▼
┌─────────────────┐
│ Ollama Model │ ──► Convert text to vector embeddings
│ llama2:latest │ • High-dimensional representations
└─────────────────┘ • Semantic similarity capture
│
▼
┌─────────────────┐
│ FAISS Database │ ──► Store vectors for fast retrieval
│ Vector Storage │ • Similarity search optimization
└─────────────────┘ • Persistent storage
│
▼
┌─────────────────┐
│ Query Processing│ ──► User query → Vector search
│ k=4 results │ • Find most relevant chunks
└─────────────────┘ • Rank by similarity score
│
▼
┌─────────────────┐
│ Context Assembly│ ──► Combine retrieved documents
│ Prompt Template │ • Structure for LLM input
└─────────────────┘ • Include query and context
│
▼
┌─────────────────┐
│ Groq LLM │ ──► Generate contextual response
│ llama3-70b-8192 │ • Temperature: 0.1 (focused)
└─────────────────┘ • Max tokens: 1000
│
▼
Final Answer
```
## 🚀 Features
- **Legal Document Analysis**: Automated review and analysis of legal documents
- **Client Intake Automation**: Streamlined client onboarding process
- **Legal Research Assistant**: Quick access to relevant case law and statutes
- **Appointment Scheduling**: Integrated calendar management for client meetings
- **Multi-language Support**: Serve diverse client populations
- **Secure Communication**: End-to-end encryption for sensitive legal discussions
- **Case Management Integration**: Seamless workflow with existing legal software
## 🛠️ Technology Stack
- **Backend**: Python/Node.js
- **AI/ML**: OpenAI GPT, LangChain, Vector Databases
- **Frontend**: React.js/Vue.js
- **Database**: PostgreSQL/MongoDB
- **Security**: OAuth 2.0, JWT, SSL/TLS encryption
- **Deployment**: Docker, AWS/Azure
## 📋 Prerequisites
- Python 3.8+ or Node.js 16+
- Docker (optional)
- API keys for AI services
- Database setup (PostgreSQL/MongoDB)
## 🔧 Installation
1. Clone the repository:
```bash
git clone https://github.com/danieladdisonorg/AI-Chatbot-for-Lawyer.git
```
2. Navigate to the project directory:
```bash
cd AI-Chatbot-for-Lawyer
```
3. Install dependencies:
```bash
npm install
```
4. Set up environment variables:
```bash
cp .env.example .env
```
5. Configure your `.env` file with:
- API keys for AI services
- Database connection strings
- Security tokens
6. Run the application:
```bash
npm start
```
## ⚙️ Configuration
### Environment Variables
```env
# AI Service Configuration
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
# Database Configuration
DATABASE_URL=your_database_connection_string
# Security
JWT_SECRET=your_jwt_secret
ENCRYPTION_KEY=your_encryption_key
# Application Settings
PORT=3000
NODE_ENV=production
```
## 🏗️ Project Structure
```
AI-Chatbot-for-Lawyer/
├── src/
│ ├── components/ # React components
│ ├── services/ # AI and API services
│ ├── utils/ # Utility functions
│ ├── models/ # Data models
│ └── config/ # Configuration files
├── public/ # Static assets
├── tests/ # Test files
├── docs/ # Documentation
├── docker/ # Docker configuration
└── scripts/ # Build and deployment scripts
```
## 🔒 Security & Compliance
- **GDPR Compliant**: Full data protection compliance
- **HIPAA Ready**: Healthcare information protection
- **Attorney-Client Privilege**: Secure communication channels
- **Data Encryption**: All sensitive data encrypted at rest and in transit
- **Audit Logging**: Comprehensive activity tracking
- **Access Controls**: Role-based permissions system
## 📚 Usage
### Basic Chat Interface
```javascript
// Initialize the chatbot
const legalBot = new LegalChatbot({
apiKey: process.env.OPENAI_API_KEY,
specialization: 'general-practice'
});
// Handle user queries
await legalBot.processQuery({
message: "What are the requirements for filing a trademark?",
context: "intellectual-property"
});
```
### Document Analysis
```javascript
// Analyze legal documents
const analysis = await legalBot.analyzeDocument({
documentPath: './contracts/sample-contract.pdf',
analysisType: 'contract-review'
});
```
## 🧪 Testing
Run the test suite:
```bash
npm test
```
Run integration tests:
```bash
npm run test:integration
```
## 🚀 Deployment
### Docker Deployment
```bash
docker build -t legal-chatbot .
```
```bash
docker run -p 3000:3000 legal-chatbot
```
### Cloud Deployment
The application is configured for deployment on major cloud platforms:
- **AWS**: ECS, Lambda, or EC2
- **Azure**: Container Instances or App Service
- **Google Cloud**: Cloud Run or Compute Engine
## 📖 API Documentation
### Chat Endpoint
```http
POST /api/chat
Content-Type: application/json
{
"message": "string",
"context": "string",
"userId": "string"
}
```
### Document Analysis Endpoint
```http
POST /api/analyze
Content-Type: multipart/form-data
{
"document": "file",
"analysisType": "string"
}
```
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
### Development Guidelines
- Follow ESLint configuration
- Write comprehensive tests
- Update documentation for new features
- Ensure security best practices
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## ⚠️ Disclaimer
This AI chatbot is designed to assist legal professionals and should not be considered a substitute for professional legal advice. Always consult with qualified attorneys for specific legal matters.
## 📞 Support
- **Documentation**: [Wiki](https://github.com/danieladdisonorg/AI-Chatbot-for-Lawyer/wiki)
- **Issues**: [GitHub Issues](https://github.com/danieladdisonorg/AI-Chatbot-for-Lawyer/issues)
- **Email**: support@legalchatbot.com
## 🗺️ Roadmap
- [ ] Advanced natural language processing
- [ ] Integration with major legal databases
- [ ] Mobile application development
- [ ] Voice interaction capabilities
- [ ] Multi-tenant architecture
- [ ] Advanced analytics dashboard
## 👥 Authors
- **Daniel Addison** - *Initial work* - [@danieladdisonorg](https://github.com/danieladdisonorg)
## 🙏 Acknowledgments
- OpenAI for GPT API
- Legal technology community
- Beta testing law firms
- Open source contributors
---
**Built with ❤️ for the legal community**