chore: import upstream snapshot with attribution
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import asyncio
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from openai import OpenAI
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from agents import Agent, FileSearchTool, Runner, trace
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async def main():
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vector_store_id: str | None = None
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if vector_store_id is None:
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print("### Preparing vector store:\n")
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# Create a new vector store and index a file
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client = OpenAI()
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text = "Arrakis, the desert planet in Frank Herbert's 'Dune,' was inspired by the scarcity of water as a metaphor for oil and other finite resources."
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file_upload = client.files.create(
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file=("example.txt", text.encode("utf-8")),
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purpose="assistants",
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)
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print(f"File uploaded: {file_upload.to_dict()}")
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vector_store = client.vector_stores.create(name="example-vector-store")
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print(f"Vector store created: {vector_store.to_dict()}")
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indexed = client.vector_stores.files.create_and_poll(
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vector_store_id=vector_store.id,
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file_id=file_upload.id,
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)
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print(f"Stored files in vector store: {indexed.to_dict()}")
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vector_store_id = vector_store.id
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# Create an agent that can search the vector store
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agent = Agent(
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name="File searcher",
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instructions="You are a helpful agent. You answer only based on the information in the vector store.",
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tools=[
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FileSearchTool(
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max_num_results=3,
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vector_store_ids=[vector_store_id],
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include_search_results=True,
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)
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],
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)
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with trace("File search example"):
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result = await Runner.run(
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agent, "Be concise, and tell me 1 sentence about Arrakis I might not know."
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)
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print("\n### Final output:\n")
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print(result.final_output)
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"""
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Arrakis, the desert planet in Frank Herbert's "Dune," was inspired by the scarcity of water
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as a metaphor for oil and other finite resources.
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"""
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print("\n### Output items:\n")
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print("\n".join([str(out.raw_item) + "\n" for out in result.new_items]))
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"""
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{"id":"...", "queries":["Arrakis"], "results":[...]}
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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