Multiple Classification with Instructor
If you want to try outs via instructor hub, you can pull it by running
For multi-label classification, we introduce a new enum class and a different Pydantic model to handle multiple labels.
import openai
import instructor
from typing import List, Literal
from pydantic import BaseModel, Field
# Apply the patch to the OpenAI client
# enables response_model keyword
client = instructor.from_openai(openai.OpenAI())
LABELS = Literal["ACCOUNT", "BILLING", "GENERAL_QUERY"]
class MultiClassPrediction(BaseModel):
"""
A few-shot example of multi-label classification:
Examples:
- "My account is locked and I can't access my billing info.": ACCOUNT, BILLING
- "I need help with my subscription.": ACCOUNT
- "How do I change my payment method?": BILLING
- "Can you tell me the status of my order?": BILLING
- "I have a question about the product features.": GENERAL_QUERY
"""
labels: List[LABELS] = Field(
...,
description="Only select the labels that apply to the support ticket.",
)
def multi_classify(data: str) -> MultiClassPrediction:
return client.chat.completions.create(
model="gpt-4o-mini",
response_model=MultiClassPrediction,
messages=[
{
"role": "system",
"content": f"You are a support agent at a tech company. Only select the labels that apply to the support ticket.",
},
{
"role": "user",
"content": f"Classify the following support ticket: <text>{data}</text>",
},
],
) # type: ignore
if __name__ == "__main__":
ticket = "My account is locked and I can't access my billing info."
prediction = multi_classify(ticket)
assert {"ACCOUNT", "BILLING"} == {label for label in prediction.labels}
print("input:", ticket)
#> input: My account is locked and I can't access my billing info.
print("labels:", LABELS)
#> labels: typing.Literal['ACCOUNT', 'BILLING', 'GENERAL_QUERY']
print("prediction:", prediction)
#> prediction: labels=['ACCOUNT', 'BILLING']