Multi-Provider LLM Integration
Build a production-ready FastAPI service with provider abstraction, streaming responses, and automatic failover between LLM providers.
Lab Overview
🛠Lab from the AI Platform Engineering Bootcamp. Used in Week 2. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):
- Week 2: LLM Integration and API Patterns (slug: llm-integration-102)
🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.
This hands-on lab teaches you to build a multi-provider LLM integration service using FastAPI and Python.
You'll learn to:
- Create an abstract LLM client interface for provider-agnostic code
- Implement OpenAI and Ollama providers with a unified interface
- Build automatic failover when the primary provider fails
- Implement streaming responses for better user experience
- Add structured JSON output with Pydantic validation
- Test the complete service with multiple scenarios
This lab builds the foundation for the AI Gateway you'll deploy in the next lab.
What You'll Learn
Design an abstract LLM client interface that decouples application code from specific providers
Implement OpenAI and Ollama provider adapters behind a unified interface
Build automatic provider failover so the service recovers when a primary provider is unavailable
Stream LLM responses through FastAPI using server-sent events for real-time output
Validate structured JSON responses with Pydantic models for type-safe output handling
Prerequisites
python-async-patterns
fastapi-basics
ollama-local-llm
Technologies Covered
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