참고소스 수정본

참고/instructor-main/examples/logfire-fastapi/server.py

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+from pydantic import BaseModel
+from fastapi import FastAPI
+from openai import AsyncOpenAI
+import instructor
+import logfire
+import asyncio
+from collections.abc import Iterable
+from fastapi.responses import StreamingResponse
+
+
+class UserData(BaseModel):
+    query: str
+
+
+class MultipleUserData(BaseModel):
+    queries: list[str]
+
+
+class UserDetail(BaseModel):
+    name: str
+    age: int
+
+
+app = FastAPI()
+openai_client = AsyncOpenAI()
+logfire.configure(pydantic_plugin=logfire.PydanticPlugin(record="all"))
+logfire.instrument_fastapi(app)
+logfire.instrument_openai(openai_client)
+client = instructor.from_openai(openai_client)
+
+
+@app.post("/user", response_model=UserDetail)
+async def endpoint_function(data: UserData) -> UserDetail:
+    user_detail = await client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        response_model=UserDetail,
+        messages=[
+            {"role": "user", "content": f"Extract: `{data.query}`"},
+        ],
+    )
+    logfire.info("/User returning", value=user_detail)
+    return user_detail
+
+
+@app.post("/many-users", response_model=list[UserDetail])
+async def extract_many_users(data: MultipleUserData):
+    async def extract_user(query: str):
+        user_detail = await client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            response_model=UserDetail,
+            messages=[
+                {"role": "user", "content": f"Extract: `{query}`"},
+            ],
+        )
+        logfire.info("/User returning", value=user_detail)
+        return user_detail
+
+    coros = [extract_user(query) for query in data.queries]
+    return await asyncio.gather(*coros)
+
+
+@app.post("/extract", response_class=StreamingResponse)
+async def extract(data: UserData):
+    supressed_client = AsyncOpenAI()
+    logfire.instrument_openai(supressed_client, suppress_other_instrumentation=False)
+    client = instructor.from_openai(supressed_client)
+    users = await client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        response_model=Iterable[UserDetail],
+        stream=True,
+        messages=[
+            {"role": "user", "content": data.query},
+        ],
+    )
+
+    async def generate():
+        with logfire.span("Generating User Response Objects"):
+            async for user in users:
+                resp_json = user.model_dump_json()
+                logfire.info("Returning user object", value=resp_json)
+
+                yield resp_json
+
+    return StreamingResponse(generate(), media_type="text/event-stream")