Upgrade Your RAG System with FastAPI: From Prototype to ProductionUpgrade Your RAG System with FastAPI: From Prototype to Production
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Uğurcan's avatar
Uğurcan Uzunkaya
• Mar 14
Same RAG system. This time we migrated it to a production-ready FastAPI backend. Live on stream again.
Three upgrades that actually matter:
In-memory storage is dead. Replaced with Weaviate running in Docker. Embeddings now persist across restarts.
No more blocking. The /api/embed endpoint offloads processing to a background task. The API stays responsive while heavy lifting happens in the background.
Idempotency built-in. Already embedded a PDF? The server detects it and skips re-processing. No duplicate vectors, no wasted Gemini API calls.
Added concurrency guards, pytest coverage, and ruff for linting. The kind of engineering decisions that don't show on the surface but matter at scale.
From a CLI prototype to a structured, testable, cloud-ready backend. All on camera.
Completed work
FastAPIPythonGoogle GeminiDevOpsAI Engineer
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