Python Archetypes Overview
Modern Python archetypes with UV package management, async patterns, AI/ML capabilities, and comprehensive observability
The Python ecosystem in P6M Archetypes provides cutting-edge templates for building high-performance microservices, AI-powered applications, and data processing platforms using the latest Python tooling and best practices.
Available Archetypes
Modern Microservices (UV-based)
gRPC Service (UV Basic)
Production-ready gRPC microservice with UV package manager, SQLAlchemy 2.0, Prometheus metrics, and Grafana dashboards.
REST Service (UV Basic)
FastAPI REST service with modern Python tooling, Pydantic validation, and comprehensive async support.
GraphQL Service (UV Basic)
GraphQL service with Strawberry framework, type-safe resolvers, and modern Python async patterns.
AI & Machine Learning Services
AI Chatbot Service
Production AI chatbot with configurable LLM backends (OpenAI, Mistral, Llama), vector databases, and enterprise features.
Multi-Step AI Agent
Complex AI agent system with workflow orchestration, state management, and multi-step reasoning capabilities.
Metaflow ML Project
ML workflow orchestration with Metaflow framework for experiment tracking and production ML pipelines.
Legacy & Alternative Services
gRPC Service (Traditional)
Traditional Python gRPC service with Poetry package management for teams not ready for UV migration.
Project Scaffolding
General Python project structure with modern tooling and best practices for various application types.
Technology Stack
Modern Python Tooling
Package Management
- UV (10-100x faster than pip)
- Poetry (traditional alternative)
- Modern pyproject.toml configuration
- Lock file generation and reproducible builds
Core Frameworks
- FastAPI for REST APIs
- gRPC with async support
- Strawberry GraphQL
- SQLAlchemy 2.0 with async
- Pydantic for data validation
AI/ML Stack
- OpenAI GPT-4, Mistral AI, Llama models
- Vector databases (Pinecone, Weaviate, Chroma)
- Knowledge graphs (Neo4j, Neptune)
- LangChain for agent orchestration
- Metaflow for ML workflows
Production Features
Observability & Monitoring
- Prometheus metrics with custom business metrics
- Grafana dashboards with pre-configured panels
- OpenTelemetry distributed tracing
- Structured logging with correlation IDs
Testing & Quality
- pytest with async support
- TestContainers for integration testing
- Code coverage with HTML reports
- Type checking with mypy
DevOps & Deployment
- Multi-stage Docker builds with UV optimization
- Docker Compose for complete development stack
- Kubernetes manifests with health checks
- GitHub Actions CI/CD workflows
AI/ML Capabilities
LLM Integration
Supported Models
- OpenAI: GPT-4, GPT-4 Turbo, GPT-4 Vision
- Mistral AI: 7B, 8x7B, Large models
- Meta Llama: 3.1 (8B, 70B, 405B), 3.2 (1B, 3B, 11B, 90B)
- CodeLlama: 7B, 13B, 70B for code generation
Enterprise Features
- Multi-model routing and fallbacks
- Cost optimization strategies
- Rate limiting and usage tracking
- Authentication and authorization
- Conversation management
Agent Capabilities
Reasoning Agents
- ReAct Agent for reason-and-act patterns
- Chain of Thought for step-by-step reasoning
- Graph of Thought with knowledge graphs
- Multi-Document Agent for information synthesis
Data Integration Agents
- SQL Agents for natural language to SQL
- Pandas Agent for data analysis
- Search Agent for web information retrieval
- Document Agent for PDF/Office processing
Quick Start
Generate a Python Service
# Generate a modern gRPC service with UV
archetect render git@github.com:p6m-archetypes/python-grpc-service-uv-basic.archetype.git my-service
# Generate an AI chatbot service
archetect render git@github.com:p6m-archetypes/python-chatbot.archetype.git my-chatbot
# Generate a FastAPI REST service
archetect render git@github.com:p6m-archetypes/python-rest-service-uv-basic.archetype.git my-api
Development Workflow
cd my-service
# Sync all packages with UV
find . -name "pyproject.toml" -exec sh -c 'cd "$(dirname "$1")" && uv sync' _ {} \;
# Start the complete stack
docker-compose up -d
# Run integration tests
./scripts/run-integration-tests.sh
# Access services
# - gRPC: localhost:50051
# - Health: http://localhost:9011/health
# - Metrics: http://localhost:9011/metrics
# - Grafana: http://localhost:3000
Best Practices
Modern Python Patterns
Async/Await Design
- Full async support throughout the stack
- Non-blocking I/O operations
- Concurrent request handling
- Connection pooling optimization
Type Safety
- Comprehensive type hints
- Pydantic data validation
- Runtime type checking
- IDE integration and IntelliSense
Performance & Scalability
- UV package manager for 10-100x faster dependency resolution
- Async/await patterns for maximum concurrency
- Connection pooling for database and external services
- Intelligent caching strategies with Redis integration
- Container optimization with multi-stage builds
AI/ML Best Practices
- Model routing based on query complexity and cost
- Vector database integration for RAG patterns
- Conversation state management for chatbots
- Rate limiting and usage tracking for cost control
- A/B testing framework for model performance
These Python archetypes provide a comprehensive foundation for building modern, high-performance applications ranging from traditional microservices to cutting-edge AI-powered systems with enterprise-grade features and scalability.