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46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
import types
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import instructor
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from instructor.cache import AutoCache
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from openai.types.chat import ChatCompletionMessageParam
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from pydantic import BaseModel, Field # type: ignore[import-not-found]
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def test_auto_cache_prevents_duplicate_provider_calls(monkeypatch):
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_ = monkeypatch # unused fixture for parity with other tests
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"""Ensure that AutoCache prevents duplicate provider calls via patch layer."""
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class User(BaseModel):
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name: str = Field(...)
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call_counter = {"n": 0}
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# Fake provider completion function mimicking minimal OpenAI chat response
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def fake_completion(*_args, **_kwargs): # noqa: D401, ANN001
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call_counter["n"] += 1
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content = User(name="cached").model_dump_json()
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# Return minimal ChatCompletion-like object
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return types.SimpleNamespace(
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choices=[
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types.SimpleNamespace(
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message=types.SimpleNamespace(content=content),
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finish_reason="stop",
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)
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],
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usage={},
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)
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# Create Instructor client using from_litellm so we go through patch stack
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cache = AutoCache(maxsize=10)
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client = instructor.from_litellm(fake_completion, mode=instructor.Mode.JSON)
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messages: list[ChatCompletionMessageParam] = [{"role": "user", "content": "hello"}]
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# First call – provider should be invoked
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_ = client.create(messages=list(messages), response_model=User, cache=cache)
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assert call_counter["n"] == 1
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# Second call with identical inputs – should hit cache, no new provider call
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_ = client.create(messages=list(messages), response_model=User, cache=cache)
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assert call_counter["n"] == 1, "Cache miss – provider was called again"
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