Structured Outputs with Mistral Large¶

If you want to try this example using instructor hub, you can pull it by running

instructor hub pull --slug mistral --py > mistral_example.py

Mistral Large is the flagship model from Mistral AI, supporting 32k context windows and functional calling abilities. Mistral Large's addition of function calling makes it possible to obtain structured outputs using JSON schema.

By the end of this blog post, you will learn how to effectively utilize Instructor with Mistral Large.

Patching¶

Instructor's patch enhances the mistral api with the following features:

response_model in create calls that returns a pydantic model
max_retries in create calls that retries the call if it fails by using a backoff strategy

Learn More

To learn more, please refer to the docs. To understand the benefits of using Pydantic with Instructor, visit the tips and tricks section of the why use Pydantic page.

Mistral Client¶

The Mistral client employs a different client than OpenAI, making the patching process slightly different than other examples

Getting access

If you want to try this out for yourself check out the Mistral AI website. You can get started here.

import instructor

from pydantic import BaseModel
from mistralai.client import MistralClient

# enables `response_model` in chat call
client = MistralClient()

patched_chat = instructor.patch(create=client.chat, mode=instructor.Mode.MISTRAL_TOOLS)

if __name__ == "__main__":

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

    resp = patched_chat(
        model="mistral-large-latest",
        response_model=UserDetails,
        messages=[
            {
                "role": "user",
                "content": f'Extract the following entities: "Jason is 20"',
            },
        ],
    )
    print(resp)
    #> name='Jason' age=20