A sophisticated AI-powered chatbot designed specifically for legal professionals to streamline client interactions, provide preliminary legal guidance, and enhance law firm efficiency.
┌─────────────────────────────────────────────────────────────────────────────┐
│ 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 │ │
│ └─────────────┘ │
└─────────────────┘
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 (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 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 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
- 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
- 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
- Python 3.8+ or Node.js 16+
- Docker (optional)
- API keys for AI services
- Database setup (PostgreSQL/MongoDB)
- Clone the repository:
git clone https://github.com/danieladdisonorg/AI-Chatbot-for-Lawyer.git- Navigate to the project directory:
cd AI-Chatbot-for-Lawyer- Install dependencies:
npm install- Set up environment variables:
cp .env.example .env-
Configure your
.envfile with:- API keys for AI services
- Database connection strings
- Security tokens
-
Run the application:
npm start# 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=productionAI-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
- 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
// 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"
});// Analyze legal documents
const analysis = await legalBot.analyzeDocument({
documentPath: './contracts/sample-contract.pdf',
analysisType: 'contract-review'
});Run the test suite:
npm testRun integration tests:
npm run test:integrationdocker build -t legal-chatbot .docker run -p 3000:3000 legal-chatbotThe 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
POST /api/chat
Content-Type: application/json
{
"message": "string",
"context": "string",
"userId": "string"
}POST /api/analyze
Content-Type: multipart/form-data
{
"document": "file",
"analysisType": "string"
}- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Follow ESLint configuration
- Write comprehensive tests
- Update documentation for new features
- Ensure security best practices
This project is licensed under the MIT License - see the LICENSE file for details.
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.
- Documentation: Wiki
- Issues: GitHub Issues
- Email: support@legalchatbot.com
- Advanced natural language processing
- Integration with major legal databases
- Mobile application development
- Voice interaction capabilities
- Multi-tenant architecture
- Advanced analytics dashboard
- Daniel Addison - Initial work - @danieladdisonorg
- OpenAI for GPT API
- Legal technology community
- Beta testing law firms
- Open source contributors
Built with ❤️ for the legal community