Introduction
Deploying AI applications requires handling heavy compute loads, managing expensive API calls, and ensuring high availability. We'll cover containerization and orchestration.
Deployment Architecture
A typical production stack includes an API Gateway, Application Layer (FastAPI), Service Layer (Redis/Queue), and Data Layer.
Containerization with Docker
Package your DSPy app for consistent deployment:
Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]Serving with FastAPI
Expose your DSPy module as a REST API:
Python
from fastapi import FastAPI
import dspy
app = FastAPI()
lm = dspy.LM(model="gpt-3.5-turbo")
dspy.settings.configure(lm=lm)
rag = ProductionRAG()
@app.post("/query")
async def query_endpoint(q: str):
return rag.forward(question=q)