# Instructor

_Structured extraction in Python, powered by OpenAI's function calling api, designed for simplicity, transparency, and control._

---

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Dive into the world of Python-based structured extraction, by OpenAI's function calling API and Pydantic, the most widely used data validation library for Python. Instructor stands out for its simplicity, transparency, and user-centric design. Whether you're a seasoned developer or just starting out, you'll find Instructor's approach intuitive and steerable.

## Usage

```py hl_lines="5 13"
import instructor
from openai import OpenAI
from pydantic import BaseModel

# This enables response_model keyword
# from client.chat.completions.create
client = instructor.patch(OpenAI())

class UserDetail(BaseModel):
    name: str
    age: int

user = client.chat.completions.create(
    model="gpt-3.5-turbo",
    response_model=UserDetail,
    messages=[
        {"role": "user", "content": "Extract Jason is 25 years old"},
    ]
)

assert isinstance(user, UserDetail)
assert user.name == "Jason"
assert user.age == 25
```

**Using async clients**

For async clients you must use apatch vs patch like so:

```py
import instructor
from openai import AsyncOpenAI
from pydantic import BaseModel

aclient = instructor.apatch(AsyncOpenAI())

class UserExtract(BaseModel):
    name: str
    age: int

model = await aclient.chat.completions.create(
    model="gpt-3.5-turbo",
    response_model=UserExtract,
    messages=[
        {"role": "user", "content": "Extract jason is 25 years old"},
    ],
)

assert isinstance(model, UserExtract)
```

!!! note "Accessing the original response"

    If you want to access anything like usage or other metadata, the original response is available on the `Model._raw_response` attribute.

    ```python
    user: UserDetail = client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=UserDetail,
        messages=[
            {"role": "user", "content": "Extract Jason is 25 years old"},
        ]
    )

    from openai.types.chat.chat_completion import ChatCompletion

    assert isinstance(user._raw_response, ChatCompletion)
    ```

## Why use Instructor?

The question of using Instructor is fundamentally a question of why to use Pydantic.

1. **Powered by type hints** — Instructor is powered by Pydantic, which is powered by type hints. Schema validation, prompting is controleld by type annotations; less to learn, less code ot write, and integrates with your IDE.

2. **Powered by OpenAI** — Instructor is powered by OpenAI's function calling API. This means you can use the same API for both prompting and extraction.

3. **Customizable** — Pydantic is highly customizable. You can define your own validators, custom error messages, and more.

4. **Ecosystem** Pydantic is the most widely used data validation library for Python. It's used by FastAPI, Typer, and many other popular libraries.

5. **Battle Tested** — Pydantic is downloaded over 100M times per month, and supported by a large community of contributors.

6. **Easy Integration with CLI** - We offer a variety of CLI tools like `instructor jobs`, `instructor files` and `instructor usage` to track your OpenAI usage, fine-tuning jobs and more, just check out our [CLI Documentation](cli/index.md) to find out more.

## More Examples

If you'd like to see more check out our [cookbook](examples/index.md).

[Installing Instructor](installation.md) is a breeze. Just run `pip install instructor`.

## Contributing

If you want to help out checkout some of the issues marked as `good-first-issue` or `help-wanted`. Found [here](https://github.com/jxnl/instructor/labels/good%20first%20issue). They could be anything from code improvements, a guest blog post, or a new cook book.

## License

This project is licensed under the terms of the MIT License.