Structured extraction in Python, powered by OpenAI's function calling API, designed for simplicity, transparency, and control.
Dive into the world of Python-based structured extraction, empowered by OpenAI's cutting-edge function calling API. 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 its results insightful.
Installing Instructor is a breeze. Simply run pip install instructor in your terminal and you're on your way to a smoother data handling experience!
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.
To 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:
import instructor
from openai import OpenAI
from pydantic import BaseModel
# Enables `response_model`
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 == 25If you're using openai<1.0.0 then make sure you pip install instructor<0.3.0
where you can patch a global client like so:
import openai
import instructor
instructor.patch()
user = openai.ChatCompletion.create(
...,
response_model=UserDetail,
)For async clients you must use apatch vs. patch, as shown:
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)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.chat.completions.create
client = instructor.patch(OpenAI())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: intUse the client.chat.completions.create 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. It is helpful to annotate the variable with the type of the response model which will help your IDE provide autocomplete and spell check.
user: UserDetail = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserDetail,
messages=[
{"role": "user", "content": "Extract Jason is 25 years old"},
]
)
assert user.name == "Jason"
assert user.age == 25Validation can also be plugged into the same Pydantic model.
In this example, if the answer attribute contains content that violates the rule "Do not 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[
str,
BeforeValidator(llm_validator("don't say objectionable things"))
]
try:
qa = QuestionAnswer(
question="What is the meaning of life?",
answer="The meaning of life is to be evil and steal",
)
except ValidationError as e:
print(e)It is important to note here that the error message is generated by the LLM, not the code. Thus, it is helpful for re-asking the model.
1 validation error for QuestionAnswer
answer
Assertion failed, The statement is objectionable. (type=assertion_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
@field_validator("name")
@classmethod
def validate_name(cls, v):
if v.upper() != v:
raise ValueError("Name must be in uppercase.")
return v
model = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserDetails,
max_retries=2,
messages=[
{"role": "user", "content": "Extract jason is 25 years old"},
],
)
assert model.name == "JASON"We invite you to contribute to evals in pytest as a way to monitor the quality of the OpenAI models and the instructor library. To get started check out the jxnl/instructor/tests/evals and contribute your own evals in the form of pytest tests. These evals will be run once a week and the results will be posted.
If you want to help, checkout some of the issues marked as good-first-issue or help-wanted found here. They could be anything from code improvements, a guest blog post, or a new cookbook.
We also provide some added CLI functionality for easy convinience:
-
instructor jobs: This helps with the creation of fine-tuning jobs with OpenAI. Simple useinstructor jobs create-from-file --helpto get started creating your first fine-tuned GPT3.5 model -
instructor files: Manage your uploaded files with ease. You'll be able to create, delete and upload files all from the command line -
instructor usage: Instead of heading to the OpenAI site each time, you can monitor your usage from the cli and filter by date and time period. Note that usage often takes ~5-10 minutes to update from OpenAI's side
This project is licensed under the terms of the MIT License.