Structured outputs with Mistral, a complete guide w/ instructor¶

This guide demonstrates how to use Mistral with Instructor to generate structured outputs. You'll learn how to use function calling with Mistral Large to create type-safe responses.

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.

import os
from pydantic import BaseModel
from mistralai import Mistral
from instructor import from_mistral, Mode


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


# enables `response_model` in chat call
client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))

instructor_client = from_mistral(
    client=client,
    model="mistral-large-latest",
    mode=Mode.MISTRAL_TOOLS,
    max_tokens=1000,
)

resp = instructor_client.messages.create(
    response_model=UserDetails,
    messages=[{"role": "user", "content": "Jason is 10"}],
    temperature=0,
)

print(resp)