{"repo":"m7mdhka/pydantic-ai-production-ready-template","free":true,"listed":false,"github":"https://github.com/m7mdhka/pydantic-ai-production-ready-template","clone":"git clone https://github.com/m7mdhka/pydantic-ai-production-ready-template.git","description":"🚀 Production-ready template for building AI applications with Pydantic AI, FastAPI, PostgreSQL, Redis, LiteLLM, and comprehensive monitoring. Includes admin panel, CI/CD, testing, and observability out of the box.","language":"Python","stars":87,"topics":["asynchronous","ci-cd","commitizen","fastapi","litellm","postgresql","prompt-versioning","pydantic-ai","redis","alembic"],"license":null,"category":"databases-storage","readme_excerpt":"Pydantic AI Production Ready Template A production-ready template for building applications with Pydantic AI, FastAPI, and modern Python tooling. Architecture Overview The application follows a layered architecture where user requests flow through multiple services before reaching the LLM provider. The flow is broken down into clear, manageable sections below. High-Level Flow 📥 Part 1: Request Entry & Security (Click to expand) What happens here: - FastAPI receives the HTTP request - Security middleware checks rate limits (stored in Redis) - Session middleware manages user sessions - JWT token is validated for authentication - Invalid requests are rejected immediately 📋 Part 2: Prompt Retrieval & Caching (Click to expand) What happens here: - Agent requests a prompt by slug/identifier - Prompt Service first checks Redis cache (fast) - If not cached, loads from PostgreSQL database - Caches the prompt in Redis for future requests - Uses active version from prompt versioning system 🔀 Part 3: LLM Routing via LiteLLM (Click to expand) What happens here: - Agent sends the request to LiteLLM Proxy - LiteLLM routes to the configured provider - Handles load balancing and failover - Tracks usage and costs in PostgreSQL - Returns the LLM response to the agent 📤 Part 4: Response Flow & Observability (Click to expand) What happens here: - Agent processes and structures the LLM response - Logfire captures logs, metrics, and traces - Response flows back through FastAPI - User receives t","default_branch":null,"files":null,"tree":[],"storefront":"/r/m7mdhka","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/m7mdhka/pydantic-ai-production-ready-template/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}