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Jason Liu
75 lines
2.4 KiB
Markdown
75 lines
2.4 KiB
Markdown
# Streaming Partial Responses
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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.
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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`.
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If you want to try outs via `instructor hub`, you can pull it by running
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```bash
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instructor hub pull --slug partial_streaming --py > partial_streaming.py
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```
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```python
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import instructor
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from openai import OpenAI
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from pydantic import BaseModel
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from typing import List
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client = instructor.patch(OpenAI())
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text_block = """
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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:
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- Name: John Doe, Email: johndoe@email.com, Twitter: @TechGuru44
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- Name: Jane Smith, Email: janesmith@email.com, Twitter: @DigitalDiva88
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- Name: Alex Johnson, Email: alexj@email.com, Twitter: @CodeMaster2023
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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.
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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.
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A follow-up meetingis scheduled for January 25th at 3 PM GMT to finalize the agenda and confirm the list of speakers.
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"""
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class User(BaseModel):
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name: str
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email: str
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twitter: str
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class MeetingInfo(BaseModel):
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users: List[User]
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date: str
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location: str
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budget: int
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deadline: str
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PartialMeetingInfo = instructor.Partial[MeetingInfo]
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extraction_stream = client.chat.completions.create(
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model="gpt-4",
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response_model=PartialMeetingInfo,
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messages=[
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{
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"role": "user",
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"content": f"Get the information about the meeting and the users {text_block}",
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},
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],
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stream=True,
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) # type: ignore
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from rich.console import Console
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console = Console()
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for extraction in extraction_stream:
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obj = extraction.model_dump()
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console.clear()
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console.print(obj)
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```
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