API Matcher

A gRPC service that matches natural language queries to API endpoints using embeddings and vector similarity search.

Features

[Previous features section remains the same...]

Architecture

[Previous architecture section remains the same...]

Prerequisites

• Rust 1.70 or higher
• curl (for downloading models)
• gRPCurl (for testing gRPC endpoints)
• Docker (for server and development modes)
• At least 4GB of free disk space for models and Docker images

Operation Modes

1. Standalone Mode

[Previous standalone mode section remains the same...]

2. Docker Server Mode

Basic Docker Operations

# Build Docker image
docker build -t api-matcher .

# Run container
docker run -d \
  --name api-matcher \
  -p 50030:50030 \
  -v api-matcher-data:/app/data \
  -v $(pwd)/logs:/app/logs \
  api-matcher

# Stop container
docker stop api-matcher

# Remove container
docker rm api-matcher

# Remove image
docker rmi api-matcher

Docker Management

# Stop all running containers
docker stop $(docker ps -a -q)

# Remove all stopped containers
docker rm $(docker ps -a -q)

# Clean up system (remove unused containers, networks, images)
docker system prune

# Remove everything (use with caution)
docker system prune -a

Logging

# View real-time logs
docker logs -f api-matcher

# Show last 100 lines with timestamps
docker logs -f -t --tail 100 api-matcher

# Save logs to file
docker logs api-matcher > matcher_logs.txt

Volume Management

# Run with persistent data and logs
docker run -d \
  -p 50030:50030 \
  -v api-matcher-data:/app/data \
  -v $(pwd)/logs:/app/logs \
  --name api-matcher \
  api-matcher

# Check volume status
docker volume ls

Debugging

# Check container status
docker ps
docker stats api-matcher

# Interactive shell access
docker exec -it api-matcher /bin/bash

# Check resource usage
docker stats

Environment Variables

• MODEL_VERSION: Specify model version (default: paraphrase-multilingual-MiniLM-L12-v2)
• CONFIG_PATH: Custom config file path (default: /app/endpoints.yaml)
• MODEL_PATH: Custom model path (default: /app/models/multilingual-MiniLM)
• LOG_LEVEL: Set logging level (default: info)

Volumes

• /app/data: Vector database storage
• /app/logs: Application logs
• /app/config: Configuration files

3. Development Mode

With Auto-Reload

# Start development environment
docker compose -f docker-compose.dev.yml up -d

# View logs in development
docker compose -f docker-compose.dev.yml logs -f matcher

# Stop development environment
docker compose -f docker-compose.dev.yml down

Development Docker Compose

version: '3.8'
services:
  matcher:
    build: 
      context: .
      target: development
    volumes:
      - .:/usr/src/app
      - /usr/src/app/target
    ports:
      - "50030:50030"
    environment:
      - RUST_LOG=debug
    command: cargo watch -x run -- --server

Troubleshooting

Common Docker Issues

1. Container won't start

# Check logs for errors
docker logs api-matcher

# Verify port availability
lsof -i :50030

# Check container status
docker ps -a

2. Performance Issues

# Monitor resource usage
docker stats api-matcher

# Check available disk space
docker system df

3. Clean Up Resources

# Remove unused resources
docker system prune

# Remove specific container and image
docker stop api-matcher && \
docker rm api-matcher && \
docker rmi api-matcher

Model Download Issues

# Manually download models
docker exec -it api-matcher bash
curl -L [model-url] -o [model-path]

# Verify model files
docker exec api-matcher ls -l /app/models/multilingual-MiniLM

Production Deployment

Resource Requirements

• Minimum 2 CPU cores
• 4GB RAM
• 10GB disk space
• Docker 20.10 or newer

Security Considerations

• Run container as non-root user
• Use Docker secrets for sensitive data
• Enable Docker content trust
• Regular security updates

Monitoring

# Basic health check
docker inspect api-matcher

# Resource monitoring
docker stats api-matcher

# Log monitoring
docker logs -f api-matcher

Contributing

1. Fork the repository
2. Create your 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

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• sentence-transformers for the embedding models
• LanceDB for the vector database
• tonic for the gRPC implementation