$ARGUMENTS/
├── main.py          # Entry point — run your AI feature
├── program.py       # AI logic (DSPy module)
├── metrics.py       # How to measure if the AI is working
├── optimize.py      # Make the AI better automatically
├── evaluate.py      # Test the AI's quality
├── data.py          # Training/test data loading
└── requirements.txt # Dependencies

dspy>=2.5

import dspy

def load_data():
    """Load and prepare training/dev data.

    Returns:
        tuple: (trainset, devset) as lists of dspy.Example
    """
    # TODO: Replace with actual data loading
    examples = [
        dspy.Example(input_field="...", output_field="...").with_inputs("input_field"),
    ]

    split = int(0.8 * len(examples))
    return examples[:split], examples[split:]

import dspy

class MySignature(dspy.Signature):
    """Describe the task here."""
    # Adapt fields to user's task
    input_field: str = dspy.InputField(desc="description")
    output_field: str = dspy.OutputField(desc="description")

class MyProgram(dspy.Module):
    def __init__(self):
        self.predict = dspy.ChainOfThought(MySignature)

    def forward(self, **kwargs):
        return self.predict(**kwargs)

def metric(example, prediction, trace=None):
    """Score how good the AI's output is.

    Args:
        example: Expected output (ground truth)
        prediction: What the AI actually produced
        trace: Optional trace for optimization

    Returns:
        float: Score between 0 and 1
    """
    # TODO: Implement task-specific metric
    return prediction.output_field == example.output_field

import dspy
from dspy.evaluate import Evaluate
from program import MyProgram
from metrics import metric
from data import load_data

undefined

import dspy
from program import MyProgram
from metrics import metric
from data import load_data

undefined

import dspy
from program import MyProgram

undefined

$ARGUMENTS/
├── main.py          # Entry point — run your AI feature
├── program.py       # AI logic (DSPy module)
├── server.py        # FastAPI app — routes and startup
├── models.py        # Pydantic request/response schemas
├── config.py        # Environment configuration
├── metrics.py       # How to measure if the AI is working
├── optimize.py      # Make the AI better automatically
├── evaluate.py      # Test the AI's quality
├── data.py          # Training/test data loading
├── requirements.txt # Dependencies
├── Dockerfile
└── .env.example

from contextlib import asynccontextmanager
import dspy
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field

from program import MyProgram

@asynccontextmanager
async def lifespan(app: FastAPI):
    lm = dspy.LM("openai/gpt-4o-mini")
    dspy.configure(lm=lm)
    app.state.program = MyProgram()
    try:
        app.state.program.load("optimized.json")
    except FileNotFoundError:
        pass
    yield

app = FastAPI(title="My AI API", lifespan=lifespan)

class QueryRequest(BaseModel):
    input_field: str = Field(..., min_length=1)

class QueryResponse(BaseModel):
    output_field: str

@app.post("/query", response_model=QueryResponse)
async def query(request: QueryRequest):
    result = app.state.program(input_field=request.input_field)
    return QueryResponse(output_field=result.output_field)

@app.get("/health")
async def health():
    return {"status": "ok"}

dspy>=2.5
fastapi>=0.100
uvicorn[standard]
pydantic-settings>=2.0

AI_MODEL_NAME=openai/gpt-4o-mini
AI_API_KEY=your-api-key-here