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openai[patch]: VCR some tests (#31524)
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libs/partners/openai/tests/cassettes/test_mcp_builtin.yaml.gz
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libs/partners/openai/tests/cassettes/test_mcp_builtin.yaml.gz
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@ -381,7 +381,7 @@ def test_stream_reasoning_summary() -> None:
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assert isinstance(response_2, AIMessage)
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# TODO: VCR some of these
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@pytest.mark.vcr
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def test_code_interpreter() -> None:
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llm = ChatOpenAI(model="o4-mini", use_responses_api=True)
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llm_with_tools = llm.bind_tools(
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@ -420,8 +420,8 @@ def test_code_interpreter() -> None:
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_ = llm_with_tools.invoke([input_message, full, next_message])
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@pytest.mark.vcr
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def test_mcp_builtin() -> None:
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pytest.skip() # TODO: set up VCR
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llm = ChatOpenAI(model="o4-mini", use_responses_api=True)
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llm_with_tools = llm.bind_tools(
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@ -434,10 +434,14 @@ def test_mcp_builtin() -> None:
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}
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]
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)
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response = llm_with_tools.invoke(
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"What transport protocols does the 2025-03-26 version of the MCP spec "
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"(modelcontextprotocol/modelcontextprotocol) support?"
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)
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input_message = {
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"role": "user",
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"content": (
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"What transport protocols does the 2025-03-26 version of the MCP spec "
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"support?"
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),
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}
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response = llm_with_tools.invoke([input_message])
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approval_message = HumanMessage(
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[
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@ -453,6 +457,10 @@ def test_mcp_builtin() -> None:
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_ = llm_with_tools.invoke(
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[approval_message], previous_response_id=response.response_metadata["id"]
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)
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# Zero-data retention (e.g., as below) requires change in output format.
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# _ = llm_with_tools.invoke(
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# [input_message, response, approval_message]
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# )
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@pytest.mark.vcr()
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