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https://github.com/kennethreitz/instructor.git
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Jason Liu
75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
import redis
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import functools
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import inspect
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import instructor
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from pydantic import BaseModel
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from openai import OpenAI
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client = instructor.patch(OpenAI())
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cache = redis.Redis("localhost")
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def instructor_cache(func):
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"""Cache a function that returns a Pydantic model"""
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return_type = inspect.signature(func).return_annotation
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if not issubclass(return_type, BaseModel):
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raise ValueError("The return type must be a Pydantic model")
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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key = f"{func.__name__}-{functools._make_key(args, kwargs, typed=False)}"
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# Check if the result is already cached
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if (cached := cache.get(key)) is not None:
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# Deserialize from JSON based on the return type
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if issubclass(return_type, BaseModel):
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return return_type.model_validate_json(cached)
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# Call the function and cache its result
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result = func(*args, **kwargs)
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serialized_result = result.model_dump_json()
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cache.set(key, serialized_result)
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return result
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return wrapper
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class UserDetail(BaseModel):
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name: str
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age: int
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@instructor_cache
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def extract(data) -> UserDetail:
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# Assuming client.chat.completions.create returns a UserDetail instance
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return client.chat.completions.create(
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model="gpt-3.5-turbo",
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response_model=UserDetail,
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messages=[
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{"role": "user", "content": data},
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],
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)
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def test_extract():
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import time
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start = time.perf_counter()
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model = extract("Extract jason is 25 years old")
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assert model.name.lower() == "jason"
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assert model.age == 25
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print(f"Time taken: {time.perf_counter() - start}")
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start = time.perf_counter()
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model = extract("Extract jason is 25 years old")
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assert model.name.lower() == "jason"
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assert model.age == 25
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print(f"Time taken: {time.perf_counter() - start}")
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if __name__ == "__main__":
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test_extract()
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# Time taken: 0.798335583996959
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# Time taken: 0.00017016706988215446
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