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@ -370,21 +370,26 @@ def test_computer_calls() -> None:
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def test_file_search() -> None:
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def test_file_search() -> None:
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pytest.skip() # TODO: set up infra
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pytest.skip() # TODO: set up infra
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llm = ChatOpenAI(model=MODEL_NAME)
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llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
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tool = {
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tool = {
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"type": "file_search",
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"type": "file_search",
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"vector_store_ids": [os.environ["OPENAI_VECTOR_STORE_ID"]],
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"vector_store_ids": [os.environ["OPENAI_VECTOR_STORE_ID"]],
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}
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}
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response = llm.invoke("What is deep research by OpenAI?", tools=[tool])
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input_message = {"role": "user", "content": "What is deep research by OpenAI?"}
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response = llm.invoke([input_message], tools=[tool])
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_check_response(response)
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_check_response(response)
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full: Optional[BaseMessageChunk] = None
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full: Optional[BaseMessageChunk] = None
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for chunk in llm.stream("What is deep research by OpenAI?", tools=[tool]):
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for chunk in llm.stream([input_message], tools=[tool]):
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assert isinstance(chunk, AIMessageChunk)
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assert isinstance(chunk, AIMessageChunk)
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full = chunk if full is None else full + chunk
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full = chunk if full is None else full + chunk
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assert isinstance(full, AIMessageChunk)
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assert isinstance(full, AIMessageChunk)
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_check_response(full)
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_check_response(full)
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next_message = {"role": "user", "content": "Thank you."}
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_ = llm.invoke([input_message, full, next_message])
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@pytest.mark.default_cassette("test_stream_reasoning_summary.yaml.gz")
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@pytest.mark.default_cassette("test_stream_reasoning_summary.yaml.gz")
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@pytest.mark.vcr
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@pytest.mark.vcr
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