참고소스 수정본

참고/instructor-main/examples/multiple_search_queries/diagram.py

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+import erdantic as erd
+
+from segment_search_queries import MultiSearch
+
+diagram = erd.create(MultiSearch)
+diagram.draw("examples/segment_search_queries/schema.png")

참고/instructor-main/examples/multiple_search_queries/segment_search_queries.py

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+import enum
+import instructor
+
+from openai import OpenAI
+from pydantic import Field, BaseModel
+
+client = instructor.from_openai(OpenAI())
+
+
+class SearchType(str, enum.Enum):
+    """Enumeration representing the types of searches that can be performed."""
+
+    VIDEO = "video"
+    EMAIL = "email"
+
+
+class Search(BaseModel):
+    """
+    Class representing a single search query which contains title, query and the search type
+    """
+
+    search_title: str = Field(..., description="Title of the request")
+    query: str = Field(..., description="Query to search for relevant content")
+    type: SearchType = Field(..., description="Type of search")
+
+    async def execute(self):
+        import asyncio
+
+        await asyncio.sleep(1)
+        print(
+            f"Searching for `{self.search_title}` with query `{self.query}` using `{self.type}`"
+        )
+
+
+class MultiSearch(BaseModel):
+    """
+    Class representing multiple search queries.
+    Make sure they contain all the required attributes
+
+    Args:
+        searches (List[Search]): The list of searches to perform.
+    """
+
+    searches: list[Search] = Field(..., description="List of searches")
+
+    def execute(self):
+        import asyncio
+
+        loop = asyncio.get_event_loop()
+
+        tasks = asyncio.gather(*[search.execute() for search in self.searches])
+        return loop.run_until_complete(tasks)
+
+
+def segment(data: str) -> MultiSearch:
+    """
+    Convert a string into multiple search queries using OpenAI's GPT-3 model.
+
+    Args:
+        data (str): The string to convert into search queries.
+
+    Returns:
+        MultiSearch: An object representing the multiple search queries.
+    """
+
+    completion = client.chat.completions.create(
+        model="gpt-4-0613",
+        temperature=0.1,
+        response_model=MultiSearch,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful assistant.",
+            },
+            {
+                "role": "user",
+                "content": f"Consider the data below:\n{data} and segment it into multiple search queries",
+            },
+        ],
+        max_tokens=1000,
+    )
+    return MultiSearch.from_response(completion)
+
+
+if __name__ == "__main__":
+    queries = segment(
+        "Please send me the video from last week about the investment case study and also documents about your GPDR policy?"
+    )
+
+    queries.execute()
+    # >>> Searching for `Video` with query `investment case study` using `SearchType.VIDEO`
+    # >>> Searching for `Documents` with query `GPDR policy` using `SearchType.EMAIL`