Lovdata Chat Interface

A web-based chat interface that allows users to interact with Large Language Models (LLMs) equipped with Norwegian legal research tools from the Lovdata MCP server.

Overview

This project creates a chat interface where users can:

• Choose from multiple LLM providers (OpenAI, Anthropic, Google Gemini)
• Have conversations enhanced with Norwegian legal document search capabilities
• Access laws, regulations, and legal provisions through AI-powered semantic search
• Receive properly cited legal information with cross-references

Architecture

Backend (FastAPI)

• LLM Provider Layer: Abstract interface supporting multiple LLM providers with tool calling
• MCP Integration: Client connection to lovdata-ai MCP server
• Skill System: Norwegian legal research guidance and best practices
• Chat Management: Conversation history, streaming responses, session management

Frontend (Next.js)

• Chat Interface: Real-time messaging with streaming responses
• Model Selector: Dropdown to choose LLM provider and model
• Tool Visualization: Display when legal tools are being used
• Citation Rendering: Properly formatted legal references and cross-references

External Dependencies

• Lovdata MCP Server: Provides 15+ tools for Norwegian legal research
• PostgreSQL Database: Vector embeddings for semantic search
• LLM APIs: OpenAI, Anthropic, Google Gemini (with API keys)

Supported LLM Providers

Provider	Models	Tool Support	Notes
OpenAI	GPT-4, GPT-4o	✅ Native	Requires API key
Anthropic	Claude-3.5-Sonnet	✅ Native	Requires API key
Google	Gemini-1.5-Pro	✅ Function calling	Requires API key
Local	Ollama models	⚠️ Limited	Self-hosted option

MCP Tools Available

The interface integrates all tools from the lovdata-ai MCP server:

Law Document Tools

• get_law: Retrieve specific laws by ID or title
• list_laws: Browse laws with filtering and pagination
• get_law_content: Get HTML content of laws
• get_law_text: Get plain text content

Search Tools

• search_laws_fulltext: Full-text search in laws
• search_laws_semantic: Semantic search using vector embeddings
• search_provisions_fulltext: Full-text search in provisions
• search_provisions_semantic: Semantic search in provisions

Provision Tools

• get_provision: Get individual legal provisions
• list_provisions: List all provisions in a law
• get_provisions_batch: Bulk retrieval for RAG applications

Reference Tools

• get_cross_references: Find references from/to provisions
• resolve_reference: Parse legal reference strings (e.g., "lov/2014-06-20-42/§8")

Skills Integration

The system loads Norwegian legal research skills that ensure:

• Proper citation standards (Lovdata URL formatting)
• Appropriate legal terminology usage
• Clear distinction between information and legal advice
• Systematic amendment tracking
• Cross-reference analysis

Implementation Plan

Phase 1: Core Infrastructure

1. Project Structure Setup

   • Create backend (FastAPI) and frontend (Next.js) directories
   • Set up Python virtual environment and Node.js dependencies
   • Configure development tooling (linting, testing, formatting)

2. LLM Provider Abstraction

   • Create abstract base class for LLM providers
   • Implement OpenAI, Anthropic, and Google Gemini clients
   • Add tool calling support and response streaming
   • Implement provider switching logic

3. MCP Server Integration

   • Build MCP client to connect to lovdata-ai server
   • Create tool registry and execution pipeline
   • Add error handling and retry logic
   • Implement tool result formatting for LLM consumption

Phase 2: Chat Functionality

4. Backend API Development

   • Create chat session management endpoints
   • Implement conversation history storage
   • Add streaming response support
   • Build health check and monitoring endpoints

5. Skill System Implementation

   • Create skill loading and parsing system
   • Implement skill application to LLM prompts
   • Add skill validation and error handling
   • Create skill management API endpoints

Phase 3: Frontend Development

6. Chat Interface

   • Build responsive chat UI with message history
   • Implement real-time message streaming
   • Add message formatting for legal citations
   • Create conversation management (new chat, clear history)

7. Model Selection UI

   • Create LLM provider and model selector
   • Add API key management (secure storage)
   • Implement model switching during conversations
   • Add model capability indicators

8. Tool Usage Visualization

   • Display when MCP tools are being used
   • Show tool execution results in chat
   • Add legal citation formatting
   • Create expandable tool result views

Phase 4: Deployment & Production

9. Containerization

   • Create Dockerfiles for backend and frontend
   • Set up Docker Compose for development
   • Configure production Docker Compose
   • Add environment variable management

10. Deployment Configuration

    • Set up CI/CD pipeline (GitHub Actions)
    • Configure cloud deployment (Railway/Render)
    • Add reverse proxy configuration
    • Implement SSL certificate management

11. Monitoring & Error Handling

    • Add comprehensive logging
    • Implement error tracking and reporting
    • Create health check endpoints
    • Add rate limiting and abuse protection

12. Documentation

    • Create setup and deployment guides
    • Document API endpoints
    • Add user documentation
    • Create troubleshooting guides

Development Setup

Prerequisites

• Python 3.12+
• Node.js 18+
• Docker and Docker Compose
• API keys for desired LLM providers

Local Development

# Clone and setup
git clone <repository>
cd lovdata-chat

# Backend setup
cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

# Frontend setup
cd ../frontend
npm install

# Start development servers
docker-compose -f docker-compose.dev.yml up

Environment Variables

# Backend
LOVDATA_MCP_URL=http://localhost:8001
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
GOOGLE_API_KEY=your_key_here

# Frontend
NEXT_PUBLIC_API_URL=http://localhost:8000

Deployment Options

Cloud Deployment (Recommended)

• Frontend: Vercel or Netlify
• Backend: Railway, Render, or Fly.io
• Database: Use existing lovdata-ai PostgreSQL instance

Self-Hosted Deployment

• Docker Compose: Full stack containerization
• Reverse Proxy: Nginx or Caddy
• SSL: Let's Encrypt automatic certificates

Security Considerations

• API keys stored securely (environment variables, secret management)
• Rate limiting on chat endpoints
• Input validation and sanitization
• CORS configuration for frontend-backend communication
• Audit logging for legal tool usage

Performance Optimization

• Response streaming for real-time chat experience
• MCP tool result caching
• Conversation history pagination
• Lazy loading of legal document content
• CDN for static frontend assets

Future Enhancements

• User authentication and conversation persistence
• Advanced citation management and export
• Integration with legal research workflows
• Multi-language support beyond Norwegian
• Advanced analytics and usage tracking

Contributing

1. Follow the implementation plan phases
2. Ensure comprehensive testing for LLM integrations
3. Document API changes and new features
4. Maintain security best practices for API key handling

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Status: Planning phase complete. Ready for implementation.

Next Steps: Begin with Phase 1 - Project Structure Setup