Skip to content

Why use Instructor?

Why use Pydantic?

Its hard to answer the question of why use Instructor without first answering why use Pydantic.:

  • Powered by type hints — with Pydantic, schema validation and serialization are controlled by type annotations; less to learn, less code to write, and integration with your IDE and static analysis tools.

  • Speed — Pydantic's core validation logic is written in Rust. As a result, Pydantic is among the fastest data validation libraries for Python.

  • JSON Schema — Pydantic models can emit JSON Schema, allowing for easy integration with other tools. [Learn more…]

  • Customisation — Pydantic allows custom validators and serializers to alter how data is processed in many powerful ways.

  • Ecosystem — around 8,000 packages on PyPI use Pydantic, including massively popular libraries like FastAPI, huggingface, Django Ninja, SQLModel, & LangChain.

  • Battle tested — Pydantic is downloaded over 70M times/month and is used by all FAANG companies and 20 of the 25 largest companies on NASDAQ. If you're trying to do something with Pydantic, someone else has probably already done it.

Our instructor.patch for the OpenAI class introduces three key enhancements:

  • Response Mode: Specify a Pydantic model to streamline data extraction.
  • Max Retries: Set your desired number of retry attempts for requests.
  • Validation Context: Provide a context object for enhanced validator access. A Glimpse into Instructor's Capabilities

Using Validators

Learn more about validators checkout our blog post Good llm validation is just good validation

With Instructor, your code becomes more efficient and readable. Here’s a quick peek:

Understanding the patch

Lets go over the patch function. And see how we can leverage it to make use of instructor

Step 1: Patch the client

First, import the required libraries and apply the patch function to the OpenAI module. This exposes new functionality with the response_model parameter.

import instructor
from openai import OpenAI
from pydantic import BaseModel

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

Step 2: Define the Pydantic Model

Create a Pydantic model to define the structure of the data you want to extract. This model will map directly to the information in the prompt.

from pydantic import BaseModel

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

Step 3: Extract

Use the method to send a prompt and extract the data into the Pydantic object. The response_model parameter specifies the Pydantic model to use for extraction. Its helpful to annotate the variable with the type of the response model. which will help your IDE provide autocomplete and spell check.

user: UserDetail =
        {"role": "user", "content": "Extract Jason is 25 years old"},

assert == "Jason"
assert user.age == 25

Understanding Validation

Validation can also be plugged into the same Pydantic model. Here, if the answer attribute contains content that violates the rule "don't say objectionable things," Pydantic will raise a validation error.

from pydantic import BaseModel, ValidationError, BeforeValidator
from typing_extensions import Annotated
from instructor import llm_validator

class QuestionAnswer(BaseModel):
    question: str
    answer: Annotated[
        BeforeValidator(llm_validator("don't say objectionable things"))

    qa = QuestionAnswer(
        question="What is the meaning of life?",
        answer="The meaning of life is to be evil and steal",
except ValidationError as e:

Its important to not here that the error message is generated by the LLM, not the code, so it'll be helpful for re asking the model.

1 validation error for QuestionAnswer
   Assertion failed, The statement is objectionable. (type=assertion_error)

Self Correcting on Validation Error

Here, the UserDetails model is passed as the response_model, and max_retries is set to 2.

import instructor

from openai import OpenAI
from pydantic import BaseModel, field_validator

# Apply the patch to the OpenAI client
client = instructor.patch(OpenAI())

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

    def validate_name(cls, v):
        if v.upper() != v:
            raise ValueError("Name must be in uppercase.")
        return v

model =
        {"role": "user", "content": "Extract jason is 25 years old"},

assert == "JASON"

As you can see, we've baked in a self correcting mechanism into the model. This is a powerful way to make your models more robust and less brittle without include a lot of extra code or prompt.