Put your AI behind an API. Use when you need to serve AI features as web endpoints, add AI to an existing backend, deploy AI for other services to call, wrap a DSPy program in REST or HTTP, build an AI microservice, or put a language model behind FastAPI or Flask. Also use for deploy AI model to production, AI REST API, serve DSPy program over HTTP, Docker AI service, AI endpoint for mobile app, h
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Put Your AI Behind an API
Wrap a DSPy program in a web API so other services or a frontend can call it over HTTP. Defaults to FastAPI but adapts to the user's existing framework.
Step 1: Gather context
Ask the user:
What DSPy program are you serving? (classification, RAG, extraction, pipeline, etc.)
Is it optimized? (do you have an optimized.json from /ai-improving-accuracy?)
What endpoints do you need? (single query, batch, health check, etc.)
**Do you have a
[Description truncada. Veja o README completo no GitHub.]
