mirror of
https://github.com/kennethreitz/instructor.git
synced 2026-06-05 22:50:18 +00:00
Jason Liu
105 lines
3.2 KiB
Markdown
105 lines
3.2 KiB
Markdown
# OpenAI Function Call and Pydantic Integration Module
|
|
|
|
This Python module provides a powerful and efficient approach to output parsing when interacting with OpenAI's Function Call API. It leverages the data validation capabilities of the Pydantic library to handle output parsing in a more structured and reliable manner. This README will guide you through the installation, usage, and contribution processes of this module.
|
|
If you have any feedback, leave an issue or hit me up on [twitter](https://twitter.com/jxnlco).
|
|
|
|
|
|
## Installation
|
|
|
|
To get started, clone the repository
|
|
|
|
```bash
|
|
git clone https://github.com/jxnl/openai_function_call.git
|
|
```
|
|
|
|
Next, install the necessary Python packages from the requirements.txt file:
|
|
|
|
```bash
|
|
pip install -r requirements.txt
|
|
```
|
|
|
|
## Contributing
|
|
|
|
Your contributions are welcome! If you have great examples or find neat patterns, clone the repo and add another example.
|
|
The goal is to find great patterns and cool examples to highlight.
|
|
|
|
If you encounter any issues or want to provide feedback, you can create an issue in this repository. You can also reach out to me on Twitter at @jxnlco.
|
|
|
|
### Poetry
|
|
|
|
We also use poetry if you'd like
|
|
|
|
```bash
|
|
poetry build
|
|
```
|
|
|
|
Note that there's no separate pip install command for this module. Simply copy and paste the module's code into your application.
|
|
|
|
## Usage
|
|
|
|
This module simplifies the interaction with the OpenAI API, enabling a more structured and predictable conversation with the AI. Below are examples showcasing the use of function calls and schemas with OpenAI and Pydantic.
|
|
|
|
### Example 1: Function Calls
|
|
|
|
```python
|
|
import openai
|
|
from openai_function_call import openai_function
|
|
|
|
@openai_function
|
|
def sum(a:int, b:int) -> int:
|
|
"""Sum description adds a + b"""
|
|
return a + b
|
|
|
|
completion = openai.ChatCompletion.create(
|
|
model="gpt-3.5-turbo-0613",
|
|
temperature=0,
|
|
functions=[sum.openai_schema],
|
|
messages=[
|
|
{
|
|
"role": "system",
|
|
"content": "You must use the `sum` function instead of adding yourself.",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "What is 6+3 use the `sum` function",
|
|
},
|
|
],
|
|
)
|
|
|
|
result = sum.from_response(completion)
|
|
print(result) # 9
|
|
```
|
|
|
|
### Example 2: Schema Extraction
|
|
|
|
```python
|
|
import openai
|
|
from openai_function_call import OpenAISchema
|
|
|
|
class UserDetails(OpenAISchema):
|
|
"""User Details"""
|
|
name: str = Field(..., description="User's name")
|
|
age: int = Field(..., description="User's age")
|
|
|
|
completion = openai.ChatCompletion.create(
|
|
model="gpt-3.5-turbo-0613",
|
|
functions=[UserDetails.openai_schema]
|
|
messages=[
|
|
{"role": "system", "content": "I'm going to ask for user details. Use UserDetails to parse this data."},
|
|
{"role": "user", "content": "My name is John Doe and I'm 30 years old."},
|
|
],
|
|
)
|
|
|
|
user_details = UserDetails.from_response(completion)
|
|
print(user_details) # UserDetails(name="John Doe", age=30)
|
|
```
|
|
|
|
## Advanced Usage
|
|
|
|
If you want to see more examples checkout the examples folder!
|
|
|
|
## License
|
|
|
|
This project is licensed under the terms of the MIT license.
|
|
|
|
For more details, refer to the LICENSE file in the repository. |