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fix(openai): revert some changes (#32462)

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Keep coverage on `output_version="v0"` (increasing coverage is being
managed in v0.4 branch).
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ccurme 2025-08-08 09:51:18 -03:00 committed by GitHub
parent 00244122bd
commit 02001212b0
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1 changed files with 19 additions and 66 deletions
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85
libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py
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@ -175,9 +175,7 @@ class FooDict(TypedDict):
def test_parsed_pydantic_schema() -> None:
llm = ChatOpenAI(
model=MODEL_NAME, use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
response = llm.invoke("how are ya", response_format=Foo)
parsed = Foo(**json.loads(response.text()))
assert parsed == response.additional_kwargs["parsed"]
@ -195,9 +193,7 @@ def test_parsed_pydantic_schema() -> None:
async def test_parsed_pydantic_schema_async() -> None:
llm = ChatOpenAI(
model=MODEL_NAME, use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
response = await llm.ainvoke("how are ya", response_format=Foo)
parsed = Foo(**json.loads(response.text()))
assert parsed == response.additional_kwargs["parsed"]
@ -217,9 +213,7 @@ async def test_parsed_pydantic_schema_async() -> None:
@pytest.mark.flaky(retries=3, delay=1)
@pytest.mark.parametrize("schema", [Foo.model_json_schema(), FooDict])
def test_parsed_dict_schema(schema: Any) -> None:
llm = ChatOpenAI(
model=MODEL_NAME, use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
response = llm.invoke("how are ya", response_format=schema)
parsed = json.loads(response.text())
assert parsed == response.additional_kwargs["parsed"]
@ -237,9 +231,7 @@ def test_parsed_dict_schema(schema: Any) -> None:
def test_parsed_strict() -> None:
llm = ChatOpenAI(
model=MODEL_NAME, use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
class InvalidJoke(TypedDict):
setup: Annotated[str, ..., "The setup of the joke"]
@ -266,9 +258,7 @@ def test_parsed_strict() -> None:
@pytest.mark.flaky(retries=3, delay=1)
@pytest.mark.parametrize("schema", [Foo.model_json_schema(), FooDict])
async def test_parsed_dict_schema_async(schema: Any) -> None:
llm = ChatOpenAI(
model=MODEL_NAME, use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
response = await llm.ainvoke("how are ya", response_format=schema)
parsed = json.loads(response.text())
assert parsed == response.additional_kwargs["parsed"]
@ -290,9 +280,7 @@ def test_function_calling_and_structured_output() -> None:
"""return x * y"""
return x * y
llm = ChatOpenAI(
model=MODEL_NAME, use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
bound_llm = llm.bind_tools([multiply], response_format=Foo, strict=True)
# Test structured output
response = llm.invoke("how are ya", response_format=Foo)
@ -336,9 +324,7 @@ def test_reasoning(output_version: Literal["v0", "responses/v1"]) -> None:
def test_stateful_api() -> None:
llm = ChatOpenAI(
model=MODEL_NAME, use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model=MODEL_NAME, use_responses_api=True)
response = llm.invoke("how are you, my name is Bobo")
assert "id" in response.response_metadata
@ -435,9 +421,7 @@ def test_stream_reasoning_summary(
@pytest.mark.vcr
def test_code_interpreter() -> None:
llm = ChatOpenAI(
model="o4-mini", use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model="o4-mini", use_responses_api=True)
llm_with_tools = llm.bind_tools(
[{"type": "code_interpreter", "container": {"type": "auto"}}]
)
@ -447,16 +431,13 @@ def test_code_interpreter() -> None:
}
response = llm_with_tools.invoke([input_message])
_check_response(response)
tool_outputs = [
block
for block in response.content
if isinstance(block, dict) and block.get("type") == "code_interpreter_call"
]
tool_outputs = response.additional_kwargs["tool_outputs"]
assert tool_outputs
assert any(output["type"] == "code_interpreter_call" for output in tool_outputs)
# Test streaming
# Use same container
tool_outputs = response.additional_kwargs["tool_outputs"]
assert len(tool_outputs) == 1
container_id = tool_outputs[0]["container_id"]
llm_with_tools = llm.bind_tools(
@ -468,11 +449,7 @@ def test_code_interpreter() -> None:
assert isinstance(chunk, AIMessageChunk)
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
tool_outputs = [
block
for block in full.content
if isinstance(block, dict) and block.get("type") == "code_interpreter_call"
]
tool_outputs = full.additional_kwargs["tool_outputs"]
assert tool_outputs
assert any(output["type"] == "code_interpreter_call" for output in tool_outputs)
@ -483,9 +460,7 @@ def test_code_interpreter() -> None:
@pytest.mark.vcr
def test_mcp_builtin() -> None:
llm = ChatOpenAI(
model="o4-mini", use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model="o4-mini", use_responses_api=True)
llm_with_tools = llm.bind_tools(
[
@ -514,8 +489,8 @@ def test_mcp_builtin() -> None:
"approve": True,
"approval_request_id": output["id"],
}
for output in response.content
if isinstance(output, dict) and output.get("type") == "mcp_approval_request"
for output in response.additional_kwargs["tool_outputs"]
if output["type"] == "mcp_approval_request"
]
)
_ = llm_with_tools.invoke(
@ -574,9 +549,7 @@ def test_mcp_builtin_zdr() -> None:
@pytest.mark.vcr()
def test_image_generation_streaming() -> None:
"""Test image generation streaming."""
llm = ChatOpenAI(
model="gpt-4.1", use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model="gpt-4.1", use_responses_api=True)
tool = {
"type": "image_generation",
# For testing purposes let's keep the quality low, so the test runs faster.
@ -623,13 +596,7 @@ def test_image_generation_streaming() -> None:
# At the moment, the streaming API does not pick up annotations fully.
# So the following check is commented out.
# _check_response(complete_ai_message)
tool_outputs = [
block
for block in complete_ai_message.content
if isinstance(block, dict) and block.get("type") == "image_generation_call"
]
assert len(tool_outputs) == 1
tool_output = tool_outputs[0]
tool_output = complete_ai_message.additional_kwargs["tool_outputs"][0]
assert set(tool_output.keys()).issubset(expected_keys)
@ -637,9 +604,7 @@ def test_image_generation_streaming() -> None:
def test_image_generation_multi_turn() -> None:
"""Test multi-turn editing of image generation by passing in history."""
# Test multi-turn
llm = ChatOpenAI(
model="gpt-4.1", use_responses_api=True, output_version="responses/v1"
)
llm = ChatOpenAI(model="gpt-4.1", use_responses_api=True)
# Test invocation
tool = {
"type": "image_generation",
@ -656,13 +621,7 @@ def test_image_generation_multi_turn() -> None:
]
ai_message = llm_with_tools.invoke(chat_history)
_check_response(ai_message)
tool_outputs = [
block
for block in ai_message.content
if isinstance(block, dict) and block.get("type") == "image_generation_call"
]
assert len(tool_outputs) == 1
tool_output = tool_outputs[0]
tool_output = ai_message.additional_kwargs["tool_outputs"][0]
# Example tool output for an image
# {
@ -711,13 +670,7 @@ def test_image_generation_multi_turn() -> None:
ai_message2 = llm_with_tools.invoke(chat_history)
_check_response(ai_message2)
tool_outputs2 = [
block
for block in ai_message2.content
if isinstance(block, dict) and block.get("type") == "image_generation_call"
]
assert len(tool_outputs2) == 1
tool_output2 = tool_outputs2[0]
tool_output2 = ai_message2.additional_kwargs["tool_outputs"][0]
assert set(tool_output2.keys()).issubset(expected_keys)