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* update eval-driven-dev skill * fix: update skill update command to use correct repository path * address comments. * update eval driven dev
61 lines
1.8 KiB
Markdown
61 lines
1.8 KiB
Markdown
# Runnable Example: Standalone Function (No Server)
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**When the app is a plain Python function or module** — no web framework, no server, no infrastructure.
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**Approach**: Import and call the function directly from `run()`. This is the simplest case.
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```python
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# pixie_qa/run_app.py
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from pydantic import BaseModel
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import pixie
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class AppArgs(BaseModel):
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question: str
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class AppRunnable(pixie.Runnable[AppArgs]):
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"""Drives a standalone function for tracing and evaluation."""
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@classmethod
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def create(cls) -> "AppRunnable":
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return cls()
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async def run(self, args: AppArgs) -> None:
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from myapp.agent import answer_question
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await answer_question(args.question)
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```
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If the function is synchronous, wrap it with `asyncio.to_thread`:
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```python
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import asyncio
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async def run(self, args: AppArgs) -> None:
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from myapp.agent import answer_question
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await asyncio.to_thread(answer_question, args.question)
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```
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If the function depends on an external service (e.g., a vector store), the `wrap(purpose="input")` calls you added in Step 2a handle it automatically — the registry injects test data in eval mode.
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### When to use `setup()` / `teardown()`
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Most standalone functions don't need lifecycle methods. Use them only when the function requires a shared resource (e.g., a pre-loaded embedding model, a database connection):
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```python
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class AppRunnable(pixie.Runnable[AppArgs]):
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_model: SomeModel
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@classmethod
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def create(cls) -> "AppRunnable":
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return cls()
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async def setup(self) -> None:
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from myapp.models import load_model
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self._model = load_model()
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async def run(self, args: AppArgs) -> None:
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from myapp.agent import answer_question
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await answer_question(args.question, model=self._model)
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```
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