Simple Synthetic Data Generation¶

What that people have been using instructor for is to generate synthetic data rather than extracting data itself. We can even use the J-Schemo extra fields to give specific examples to control how we generate data.

Consider the example below. We'll likely generate very simple names.

from typing import Iterable
from pydantic import BaseModel
import instructor
from openai import OpenAI


# Define the UserDetail model
class UserDetail(BaseModel):
    name: str
    age: int


# Patch the OpenAI client to enable the response_model functionality
client = instructor.from_openai(OpenAI())


def generate_fake_users(count: int) -> Iterable[UserDetail]:
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=Iterable[UserDetail],
        messages=[
            {"role": "user", "content": f"Generate a {count} synthetic users"},
        ],
    )


for user in generate_fake_users(5):
    print(user)
    #> name='Alice' age=25
    #> name='Bob' age=30
    #> name='Charlie' age=35
    #> name='David' age=40
    #> name='Eve' age=22

Leveraging Simple Examples¶

We might want to set examples as part of the prompt by leveraging Pydantics configuration. We can set examples directly in the JSON scheme itself.

from typing import Iterable
from pydantic import BaseModel, Field
import instructor
from openai import OpenAI


# Define the UserDetail model
class UserDetail(BaseModel):
    name: str = Field(examples=["Timothee Chalamet", "Zendaya"])
    age: int


# Patch the OpenAI client to enable the response_model functionality
client = instructor.from_openai(OpenAI())


def generate_fake_users(count: int) -> Iterable[UserDetail]:
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=Iterable[UserDetail],
        messages=[
            {"role": "user", "content": f"Generate a {count} synthetic users"},
        ],
    )


for user in generate_fake_users(5):
    print(user)
    #> name='John Doe' age=25
    #> name='Jane Smith' age=30
    #> name='Michael Johnson' age=22
    #> name='Emily Davis' age=28
    #> name='David Brown' age=35

By incorporating names of celebrities as examples, we have shifted towards generating synthetic data featuring well-known personalities, moving away from the simplistic, single-word names previously used.

Leveraging Complex Example¶

To effectively generate synthetic examples with more nuance, lets upgrade to the "gpt-4-turbo-preview" model, use model level examples rather than attribute level examples:

import instructor

from typing import Iterable
from pydantic import BaseModel, ConfigDict
from openai import OpenAI


# Define the UserDetail model
class UserDetail(BaseModel):
    """Old Wizards"""

    name: str
    age: int

    model_config = ConfigDict(
        json_schema_extra={
            "examples": [
                {"name": "Gandalf the Grey", "age": 1000},
                {"name": "Albus Dumbledore", "age": 150},
            ]
        }
    )


# Patch the OpenAI client to enable the response_model functionality
client = instructor.from_openai(OpenAI())


def generate_fake_users(count: int) -> Iterable[UserDetail]:
    return client.chat.completions.create(
        model="gpt-4-turbo-preview",
        response_model=Iterable[UserDetail],
        messages=[
            {"role": "user", "content": f"Generate `{count}` synthetic examples"},
        ],
    )


for user in generate_fake_users(5):
    print(user)
    #> name='Merlin' age=1000
    #> name='Saruman the White' age=700
    #> name='Radagast the Brown' age=600
    #> name='Elminster Aumar' age=1200
    #> name='Mordenkainen' age=850

Leveraging Descriptions¶

By adjusting the descriptions within our Pydantic models, we can subtly influence the nature of the synthetic data generated. This method allows for a more nuanced control over the output, ensuring that the generated data aligns more closely with our expectations or requirements.

For instance, specifying "Fancy French sounding names" as a description for the name field in our UserDetail model directs the generation process to produce names that fit this particular criterion, resulting in a dataset that is both diverse and tailored to specific linguistic characteristics.

import instructor

from typing import Iterable
from pydantic import BaseModel, Field
from openai import OpenAI


# Define the UserDetail model
class UserDetail(BaseModel):
    name: str = Field(description="Fancy French sounding names")
    age: int


# Patch the OpenAI client to enable the response_model functionality
client = instructor.from_openai(OpenAI())


def generate_fake_users(count: int) -> Iterable[UserDetail]:
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=Iterable[UserDetail],
        messages=[
            {"role": "user", "content": f"Generate `{count}` synthetic users"},
        ],
    )


for user in generate_fake_users(5):
    print(user)
    #> name='Jean Luc' age=30
    #> name='Claire Belle' age=25
    #> name='Pierre Leclair' age=40
    #> name='Amelie Rousseau' age=35
    #> name='Etienne Lefevre' age=28