Streaming Partial Responses¶
Field level streaming provides incremental snapshots of the current state of the response model that are immediately useable. This approach is particularly relevant in contexts like rendering UI components.
Instructor supports this pattern by making use of Partial[T]
. This lets us dynamically create a new class that treats all of the original model's fields as Optional
.
If you want to try outs via instructor hub
, you can pull it by running
import instructor
from openai import OpenAI
from pydantic import BaseModel
from typing import List
client = instructor.from_openai(OpenAI())
text_block = """
In our recent online meeting, participants from various backgrounds joined to discuss the upcoming tech conference. The names and contact details of the participants were as follows:
- Name: John Doe, Email: [email protected], Twitter: @TechGuru44
- Name: Jane Smith, Email: [email protected], Twitter: @DigitalDiva88
- Name: Alex Johnson, Email: [email protected], Twitter: @CodeMaster2023
During the meeting, we agreed on several key points. The conference will be held on March 15th, 2024, at the Grand Tech Arena located at 4521 Innovation Drive. Dr. Emily Johnson, a renowned AI researcher, will be our keynote speaker.
The budget for the event is set at $50,000, covering venue costs, speaker fees, and promotional activities. Each participant is expected to contribute an article to the conference blog by February 20th.
A follow-up meetingis scheduled for January 25th at 3 PM GMT to finalize the agenda and confirm the list of speakers.
"""
class User(BaseModel):
name: str
email: str
twitter: str
class MeetingInfo(BaseModel):
users: List[User]
date: str
location: str
budget: int
deadline: str
PartialMeetingInfo = instructor.Partial[MeetingInfo]
extraction_stream = client.chat.completions.create(
model="gpt-4",
response_model=PartialMeetingInfo,
messages=[
{
"role": "user",
"content": f"Get the information about the meeting and the users {text_block}",
},
],
stream=True,
) # type: ignore
from rich.console import Console
console = Console()
for extraction in extraction_stream:
obj = extraction.model_dump()
console.clear()
console.print(obj)