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40 lines
1.6 KiB
40 lines
1.6 KiB
from swarm_models.openai_function_caller import OpenAIFunctionCaller
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from pydantic import BaseModel
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# Pydantic is a data validation library that provides data validation and parsing using Python type hints.
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# It is used here to define the data structure for making API calls to retrieve weather information.
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class WeatherAPI(BaseModel):
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city: str
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date: str
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# The WeatherAPI class is a Pydantic BaseModel that represents the data structure
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# for making API calls to retrieve weather information. It has two attributes: city and date.
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# Example usage:
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# Initialize the function caller
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function_caller = OpenAIFunctionCaller(
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system_prompt="You are a helpful assistant.",
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max_tokens=500,
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temperature=0.5,
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base_model=WeatherAPI,
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)
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# The OpenAIFunctionCaller class is used to interact with the OpenAI API and make function calls.
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# Here, we initialize an instance of the OpenAIFunctionCaller class with the following parameters:
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# - system_prompt: A prompt that sets the context for the conversation with the API.
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# - max_tokens: The maximum number of tokens to generate in the API response.
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# - temperature: A parameter that controls the randomness of the generated text.
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# - base_model: The base model to use for the API calls, in this case, the WeatherAPI class.
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# Run the function caller
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response = function_caller.run(
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"Get the weather forecast for New York City on July 4th, 2022."
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)
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# The run() method of the OpenAIFunctionCaller class is used to make a function call to the API.
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# It takes a string parameter that represents the user's request or query.
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print(response)
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