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Jason
89 lines
3.0 KiB
Markdown
89 lines
3.0 KiB
Markdown
# Welcome to OpenAI Function Call
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We offer a minially invasive extention of `Pydantic.BaseModel` named `OpenAISchema`. It only has two methods, one to generate the correct schema, and one to produce the class from the completion.
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This library is more, so a list of examples and a helper class so I'll keep the example as just structured extraction.
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If the OpenAI is a chef's knife of code, I hope you sell you a nice handle which comes with a little pamplet of cuttign techniques.
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It leverages the data validation capabilities of the Pydantic library to handle output parsing in a more structured and reliable manner.
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If you have any feedback, leave an issue or hit me up on [twitter](https://twitter.com/jxnlco).
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If you're looking for something more batteries included I strongly recommend [MarvinAI](https://www.askmarvin.ai/) which offers a high level api but does not provide as much access to prompting.
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!!! tip "Just rip it out!"
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If you don't want to install dependencies. I recommend literally ripping the `function_calls.py` into your own codebase. [[source code]](https://github.com/jxnl/openai_function_call/blob/main/openai_function_call/function_calls.py)
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## Installation
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```python
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pip install openai_function_call
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```
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## Usage
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Below are examples showcasing the use of function calls and schemas with OpenAI and Pydantic. In later docs we'll go over a wide array of more creative uses.
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### Example 1: Extraction
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!!! Tip
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Prompt are now sourced from docstrings and descriptions, so write clear and descriptive documentation!
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```python
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import openai
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from openai_function_call 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="users'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) # name="John Doe", age=30
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```
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### Example 2: Function Calls
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```python
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import openai
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from openai_function_call import openai_function
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@openai_function
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def sum(a:int, b:int) -> int:
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"""Sum description adds a + b"""
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return a + b
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completion = openai.ChatCompletion.create(
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model="gpt-3.5-turbo-0613",
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temperature=0,
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functions=[sum.openai_schema],
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function_call={"name": sum.openai_schema["name"]},
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messages=[
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{
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"role": "system",
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"content": "You must use the `sum` function instead of adding yourself.",
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},
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{
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"role": "user",
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"content": "What is 6+3",
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},
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],
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)
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result = sum.from_response(completion)
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print(result) # 9
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```
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