Module 14 Wrap-up: The Production Architect

You have learned that great AI logic is useless if it's slow, fragile, or hard to access. By mastering FastAPI, Caching, and Resilience patterns, you have built a bridge between your "Brain" (Model) and your "Users" (API). These are the non-negotiable skills for an AI software engineer.

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Hands-on Exercise: The Bulletproof API

1. The Goal

Create a FastAPI application that serves your RAG chain (from Module 7). The API must:

1. Handle a POST request with a user question.
2. Use a Cache to save responses.
3. Include a Retry wrapper around the OpenAI model.
4. Test: Send two identical requests and verify the second one is faster using your terminal logs.

2. The Implementation Plan

• Copy your rag_chain logic into a FastAPI file.
• Use set_llm_cache with a local sqlite file.
• Wrap the model object with with_retry.

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Module 14 Summary

• FastAPI: The asynchronous backend for serving AI at scale.
• API Design: Using Pydantic for request validation.
• Caching: Reducing costs and TTFT (Time to First Token).
• Resilience: Using retries to handle transient system errors.
• Streaming: Providing the best possible user experience for chat interfaces.

Coming Up Next...

In Module 15, we reach the finish line of development: Deployment. We will learn how to Dockerize our FastAPI server and push it to the cloud so anyone in the world can use your agent.

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Module 14 Checklist

• I have installed fastapi and uvicorn.
• I can describe why async is important for LLM requests.
• I have successfully saved an AI response to a local SQLite cache.
• I understand the danger of caching "Real-time" data.
• I have seen a StreamingResponse work in my local browser.