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layout default
title Chapter 8: Integration Examples
nav_order 8
parent OpenAI Python SDK Tutorial

Chapter 8: Integration Examples

Welcome to Chapter 8: Integration Examples. In this part of OpenAI Python SDK Tutorial: Production API Patterns, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter maps core SDK features to service-level integration patterns.

Example 1: FastAPI Summarization Endpoint

from fastapi import FastAPI
from openai import OpenAI

app = FastAPI()
client = OpenAI()

@app.post("/summarize")
def summarize(payload: dict):
    text = payload.get("text", "")
    resp = client.responses.create(
        model="gpt-5.2",
        input=f"Summarize in 3 bullet points:\n\n{text}",
    )
    return {"summary": resp.output_text, "request_id": resp.id}

Example 2: Retrieval-Enhanced Endpoint

  • retrieve top-k context from embeddings index
  • construct compact context block
  • generate answer with citation fields
  • return both answer and source metadata

Example 3: Tool-Gated Action Endpoint

  • classify requested action risk
  • require explicit confirmation for destructive operations
  • run tool with bounded timeout
  • log inputs and outputs for audit

Final Launch Checklist

  • contract tests for request/response schemas
  • regression eval set for output quality
  • budget alerts and rate-limit handling
  • incident runbook for degraded provider behavior

Final Summary

You now have an end-to-end blueprint for shipping Python SDK integrations that are reliable, observable, and migration-ready.

Related:

Depth Expansion Playbook

Source Code Walkthrough

examples/parsing_tools_stream.py

The GetWeather class in examples/parsing_tools_stream.py handles a key part of this chapter's functionality:

class GetWeather(BaseModel):
    city: str
    country: str


client = OpenAI()


with client.chat.completions.stream(
    model="gpt-4o-2024-08-06",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in SF and New York?",
        },
    ],
    tools=[
        # because we're using `.parse_stream()`, the returned tool calls
        # will be automatically deserialized into this `GetWeather` type
        openai.pydantic_function_tool(GetWeather, name="get_weather"),
    ],
    parallel_tool_calls=True,
) as stream:
    for event in stream:
        if event.type == "tool_calls.function.arguments.delta" or event.type == "tool_calls.function.arguments.done":
            rich.get_console().print(event, width=80)

print("----\n")
rich.print(stream.get_final_completion())

This class is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

examples/speech_to_text.py

The main function in examples/speech_to_text.py handles a key part of this chapter's functionality:

async def main() -> None:
    print("Recording for the next 10 seconds...")
    recording = await Microphone(timeout=10).record()
    print("Recording complete")
    transcription = await openai.audio.transcriptions.create(
        model="whisper-1",
        file=recording,
    )

    print(transcription.text)


if __name__ == "__main__":
    asyncio.run(main())

This function is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

examples/audio.py

The main function in examples/audio.py handles a key part of this chapter's functionality:

def main() -> None:
    # Create text-to-speech audio file
    with openai.audio.speech.with_streaming_response.create(
        model="tts-1",
        voice="alloy",
        input="the quick brown fox jumped over the lazy dogs",
    ) as response:
        response.stream_to_file(speech_file_path)

    # Create transcription from audio file
    transcription = openai.audio.transcriptions.create(
        model="whisper-1",
        file=speech_file_path,
    )
    print(transcription.text)

    # Create translation from audio file
    translation = openai.audio.translations.create(
        model="whisper-1",
        file=speech_file_path,
    )
    print(translation.text)


if __name__ == "__main__":
    main()

This function is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[GetWeather]
    B[main]
    C[main]
    A --> B
    B --> C
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