流式传输部分响应

字段级流式传输提供了响应模型当前状态的增量快照，这些快照可以立即使用。这种方法在渲染 UI 组件等场景中尤其适用。

Instructor 通过使用 Partial[T] 支持此模式。这使我们能够动态创建一个新类，将原始模型的所有字段视为 Optional。

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: johndoe@email.com, Twitter: @TechGuru44
- Name: Jane Smith, Email: janesmith@email.com, Twitter: @DigitalDiva88
- Name: Alex Johnson, Email: alexj@email.com, 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)