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Structured Outputs with Ollama

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

instructor hub pull --slug ollama --py >

Open-source LLMS are gaining popularity, and the release of Ollama's OpenAI compatibility later it has made 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 ollama. But before we proceed, let's first explore the concept of patching.


Instructor's patch enhances a openai api it 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.


Start by downloading Ollama, and then pull a model such as Llama 2 or Mistral.

Make sure you update your ollama to the latest version!

ollama pull llama2
from openai import OpenAI
from pydantic import BaseModel, Field
from typing import List

import instructor

class Character(BaseModel):
    name: str
    age: int
    fact: List[str] = Field(..., description="A list of facts about the character")

# enables `response_model` in create call
client = instructor.from_openai(
        api_key="ollama",  # required, but unused

resp =
            "role": "user",
            "content": "Tell me about the Harry Potter",
  "name": "Harry James Potter",
  "age": 37,
  "fact": [
    "He is the chosen one.",
    "He has a lightning-shaped scar on his forehead.",
    "He is the son of James and Lily Potter.",
    "He attended Hogwarts School of Witchcraft and Wizardry.",
    "He is a skilled wizard and sorcerer.",
    "He fought against Lord Voldemort and his followers.",
    "He has a pet owl named Snowy."