chore: import upstream snapshot with attribution

examples/batch-classification/run-cache.py

@@ -0,0 +1,98 @@
+import instructor
+import asyncio
+
+from openai import AsyncOpenAI
+from pydantic import BaseModel, Field, field_validator
+from enum import Enum
+
+client = instructor.from_openai(AsyncOpenAI(), mode=instructor.Mode.TOOLS)
+sem = asyncio.Semaphore(5)
+
+
+class QuestionType(Enum):
+    CONTACT = "CONTACT"
+    TIMELINE_QUERY = "TIMELINE_QUERY"
+    DOCUMENT_SEARCH = "DOCUMENT_SEARCH"
+    COMPARE_CONTRAST = "COMPARE_CONTRAST"
+    EMAIL = "EMAIL"
+    PHOTOS = "PHOTOS"
+    SUMMARY = "SUMMARY"
+
+
+# You can add more instructions and examples in the description
+# or you can put it in the prompt in `messages=[...]`
+class QuestionClassification(BaseModel):
+    """
+    Predict the type of question that is being asked.
+    Here are some tips on how to predict the question type:
+    CONTACT: Searches for some contact information.
+    TIMELINE_QUERY: "When did something happen?
+    DOCUMENT_SEARCH: "Find me a document"
+    COMPARE_CONTRAST: "Compare and contrast two things"
+    EMAIL: "Find me an email, search for an email"
+    PHOTOS: "Find me a photo, search for a photo"
+    SUMMARY: "Summarize a large amount of data"
+    """
+
+    # If you want only one classification, just change it to
+    #   `classification: QuestionType` rather than `classifications: List[QuestionType]``
+    chain_of_thought: str = Field(
+        ..., description="The chain of thought that led to the classification"
+    )
+    classification: list[QuestionType] = Field(
+        description=f"An accuracy and correct prediction predicted class of question. Only allowed types: {[t.value for t in QuestionType]}, should be used",
+    )
+
+    @field_validator("classification", mode="before")
+    def validate_classification(cls, v):
+        # sometimes the API returns a single value, just make sure it's a list
+        if not isinstance(v, list):
+            v = [v]
+        return v
+
+
+# Modify the classify function
+async def classify(data: str):
+    async with sem:  # some simple rate limiting
+        return data, await client.chat.completions.create(
+            model="gpt-4",
+            response_model=QuestionClassification,
+            max_retries=2,
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"Classify the following question: {data}",
+                },
+            ],
+        )
+
+
+async def main(questions: list[str]):
+    tasks = [classify(question) for question in questions]
+    resps = []
+    for task in asyncio.as_completed(tasks):
+        question, label = await task
+        resp = {
+            "question": question,
+            "classification": [c.value for c in label.classification],
+            "chain_of_thought": label.chain_of_thought,
+        }
+        resps.append(resp)
+    return resps
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    questions = [
+        "What was that ai app that i saw on the news the other day?",
+        "Can you find the trainline booking email?",
+        "What was the book I saw on amazon yesturday?",
+        "Can you speak german?",
+        "Do you have access to the meeting transcripts?",
+        "what are the recent sites I visited?",
+        "what did I do on Monday?",
+        "Tell me about todays meeting and how it relates to the email on Monday",
+    ]
+
+    asyncio.run(main(questions))