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Creating objects using Pydantic
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| import openai | |
| from pydantic import BaseModel, Field | |
| class OpenAISchema(BaseModel): | |
| @classmethod | |
| @property | |
| def openai_schema(cls): | |
| schema = cls.schema() | |
| return { | |
| "name": schema["title"], | |
| "description": schema["description"], | |
| "parameters": schema | |
| } | |
| @classmethod | |
| def from_response(cls, completion, throw_error=True): | |
| message = completion.choices[0].message | |
| if throw_error: | |
| assert "function_call" in message, "No function call detected" | |
| assert message["function_call"]["name"] == cls.openai_schema["name"], "Function name does not match" | |
| function_call = message["function_call"] | |
| arguments = json.loads(function_call["arguments"]) | |
| return cls(**arguments) | |
| # Note: The docstring and the description are used as part of the prompt, detailed desc and docs | |
| # can be used to provide more information about the function | |
| class Arguments(OpenAISchema): | |
| """Arguments to add""" | |
| a: int = Field(..., description="First argument") | |
| b: int = Field(..., description="Second argument") | |
| completion = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo-0613", | |
| temperature=0, | |
| functions=[Arguments.openai_schema], | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": "You must use the `arguments` function instead of adding yourself.", | |
| }, | |
| { | |
| "role": "user", | |
| "content": "What is 6+3, use the arguments", | |
| }, | |
| ], | |
| ) | |
| Arguments.from_response(completion) | |
| # Arguments(a=6, b=3) |
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