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Jason Liu
55 lines
2.0 KiB
Markdown
55 lines
2.0 KiB
Markdown
# OpenAI Schema
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The `OpenAISchema` is an extension of `Pydantic.BaseModel` that offers a minimally invasive way to define schemas for OpenAI completions. It provides two main methods: `openai_schema` to generate the correct schema and `from_response` to create an instance of the class from the completion result.
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## Prompt Placement
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Our philosophy is to keep prompts close to the code. This is achieved by using docstrings and field descriptions to provide prompts and descriptions for your schema fields.
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## Structured Extraction
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You can directly use the `OpenAISchema` class in your `openai` API create calls by passing in the `openai_schema` class property and extracting the class out using the `from_response` method. This style of usage provides full control over configuration and prompting.
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```python
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import openai
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from instructor import OpenAISchema
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from pydantic import Field
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class UserDetails(OpenAISchema):
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"""Details of a user"""
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name: str = Field(..., description="User's full name")
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age: int
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completion = openai.ChatCompletion.create(
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model="gpt-3.5-turbo-0613",
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functions=[UserDetails.openai_schema],
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function_call={"name": UserDetails.openai_schema["name"]},
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messages=[
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{"role": "system", "content": "Extract user details from my requests"},
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{"role": "user", "content": "My name is John Doe and I'm 30 years old."},
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],
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)
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user_details = UserDetails.from_response(completion)
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print(user_details) # UserDetails(name='John Doe', age=30)
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```
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You can also use the `@openai_schema` decorator to decorate `BaseModels`, but you may lose some type hinting as a result.
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```python
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import openai
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from instructor import openai_schema
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from pydantic import Field, BaseModel
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@openai_schema
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class UserDetails(BaseModel):
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"""Details of a user"""
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name: str = Field(..., description="User's full name")
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age: int
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```
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## Code Reference
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For more information about the code, including the complete API reference, please refer to the `instructor` documentation.
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::: instructor.function_calls |