infra: add more formatter rules to openai (#23189)

Turns on
https://docs.astral.sh/ruff/settings/#format_docstring-code-format and
https://docs.astral.sh/ruff/settings/#format_skip-magic-trailing-comma

```toml
[tool.ruff.format]
docstring-code-format = true
skip-magic-trailing-comma = true
```

libs/partners/openai/langchain_openai/__init__.py

@@ -1,11 +1,5 @@
-from langchain_openai.chat_models import (
+from langchain_openai.chat_models import AzureChatOpenAI, ChatOpenAI
-    AzureChatOpenAI,
+from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings
-    ChatOpenAI,
-)
-from langchain_openai.embeddings import (
-    AzureOpenAIEmbeddings,
-    OpenAIEmbeddings,
-)
 from langchain_openai.llms import AzureOpenAI, OpenAI
 
 __all__ = [

libs/partners/openai/langchain_openai/chat_models/__init__.py

@@ -1,7 +1,4 @@
 from langchain_openai.chat_models.azure import AzureChatOpenAI
 from langchain_openai.chat_models.base import ChatOpenAI
 
-__all__ = [
+__all__ = ["ChatOpenAI", "AzureChatOpenAI"]
-    "ChatOpenAI",
-    "AzureChatOpenAI",
-]

libs/partners/openai/langchain_openai/chat_models/azure.py

@@ -43,10 +43,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
 
         from langchain_openai import AzureChatOpenAI
 
-        AzureChatOpenAI(
+        AzureChatOpenAI(azure_deployment="35-turbo-dev", openai_api_version="2023-05-15")
-            azure_deployment="35-turbo-dev",
-            openai_api_version="2023-05-15",
-        )
 
     Be aware the API version may change.
 
@@ -60,7 +57,7 @@ class AzureChatOpenAI(BaseChatOpenAI):
 
     Any parameters that are valid to be passed to the openai.create call can be passed
     in, even if not explicitly saved on this class.
-    """
+    """  # noqa: E501
 
     azure_endpoint: Union[str, None] = None
     """Your Azure endpoint, including the resource.

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -63,10 +63,7 @@ from langchain_core.messages import (
     ToolMessageChunk,
 )
 from langchain_core.messages.ai import UsageMetadata
-from langchain_core.output_parsers import (
+from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
-    JsonOutputParser,
-    PydanticOutputParser,
-)
 from langchain_core.output_parsers.base import OutputParserLike
 from langchain_core.output_parsers.openai_tools import (
     JsonOutputKeyToolsParser,
@@ -182,9 +179,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     Returns:
         The dictionary.
     """
-    message_dict: Dict[str, Any] = {
+    message_dict: Dict[str, Any] = {"content": _format_message_content(message.content)}
-        "content": _format_message_content(message.content),
-    }
     if (name := message.name or message.additional_kwargs.get("name")) is not None:
         message_dict["name"] = name
 
@@ -388,10 +383,7 @@ class BaseChatOpenAI(BaseChatModel):
             "OPENAI_API_BASE"
         )
         values["openai_proxy"] = get_from_dict_or_env(
-            values,
+            values, "openai_proxy", "OPENAI_PROXY", default=""
-            "openai_proxy",
-            "OPENAI_PROXY",
-            default="",
         )
 
         client_params = {
@@ -586,10 +578,7 @@ class BaseChatOpenAI(BaseChatModel):
             generation_info = dict(finish_reason=res.get("finish_reason"))
             if "logprobs" in res:
                 generation_info["logprobs"] = res["logprobs"]
-            gen = ChatGeneration(
+            gen = ChatGeneration(message=message, generation_info=generation_info)
-                message=message,
-                generation_info=generation_info,
-            )
             generations.append(gen)
         llm_output = {
             "token_usage": token_usage,
@@ -849,10 +838,7 @@ class BaseChatOpenAI(BaseChatModel):
                     f"provided function was {formatted_functions[0]['name']}."
                 )
             kwargs = {**kwargs, "function_call": function_call}
-        return super().bind(
+        return super().bind(functions=formatted_functions, **kwargs)
-            functions=formatted_functions,
-            **kwargs,
-        )
 
     def bind_tools(
         self,
@@ -998,15 +984,20 @@ class BaseChatOpenAI(BaseChatModel):
                 from langchain_openai import ChatOpenAI
                 from langchain_core.pydantic_v1 import BaseModel
 
+
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''
+
                     answer: str
                     justification: str
 
+
                 llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
                 structured_llm = llm.with_structured_output(AnswerWithJustification)
 
-                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
 
                 # -> AnswerWithJustification(
                 #     answer='They weigh the same',
@@ -1019,15 +1010,22 @@ class BaseChatOpenAI(BaseChatModel):
                 from langchain_openai import ChatOpenAI
                 from langchain_core.pydantic_v1 import BaseModel
 
+
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''
+
                     answer: str
                     justification: str
 
+
                 llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
-                structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
+                structured_llm = llm.with_structured_output(
+                    AnswerWithJustification, include_raw=True
+                )
 
-                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
                 # -> {
                 #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
                 #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
@@ -1041,16 +1039,21 @@ class BaseChatOpenAI(BaseChatModel):
                 from langchain_core.pydantic_v1 import BaseModel
                 from langchain_core.utils.function_calling import convert_to_openai_tool
 
+
                 class AnswerWithJustification(BaseModel):
                     '''An answer to the user question along with justification for the answer.'''
+
                     answer: str
                     justification: str
 
+
                 dict_schema = convert_to_openai_tool(AnswerWithJustification)
                 llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
                 structured_llm = llm.with_structured_output(dict_schema)
 
-                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
                 # -> {
                 #     'answer': 'They weigh the same',
                 #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
@@ -1231,14 +1234,32 @@ class ChatOpenAI(BaseChatOpenAI):
         .. code-block:: python
 
             messages = [
-                ("system", "You are a helpful translator. Translate the user sentence to French."),
+                (
+                    "system",
+                    "You are a helpful translator. Translate the user sentence to French.",
+                ),
                 ("human", "I love programming."),
             ]
             llm.invoke(messages)
 
         .. code-block:: python
 
-            AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 31, 'total_tokens': 36}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_43dfabdef1', 'finish_reason': 'stop', 'logprobs': None}, id='run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0', usage_metadata={'input_tokens': 31, 'output_tokens': 5, 'total_tokens': 36})
+            AIMessage(
+                content="J'adore la programmation.",
+                response_metadata={
+                    "token_usage": {
+                        "completion_tokens": 5,
+                        "prompt_tokens": 31,
+                        "total_tokens": 36,
+                    },
+                    "model_name": "gpt-4o",
+                    "system_fingerprint": "fp_43dfabdef1",
+                    "finish_reason": "stop",
+                    "logprobs": None,
+                },
+                id="run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0",
+                usage_metadata={"input_tokens": 31, "output_tokens": 5, "total_tokens": 36},
+            )
 
     Stream:
         .. code-block:: python
@@ -1248,13 +1269,19 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            AIMessageChunk(content='', id='run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0')
+            AIMessageChunk(content="", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
-            AIMessageChunk(content='J', id='run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0')
+            AIMessageChunk(content="J", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
-            AIMessageChunk(content="'adore", id='run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0')
+            AIMessageChunk(content="'adore", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
-            AIMessageChunk(content=' la', id='run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0')
+            AIMessageChunk(content=" la", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
-            AIMessageChunk(content=' programmation', id='run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0')
+            AIMessageChunk(
-            AIMessageChunk(content='.', id='run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0')
+                content=" programmation", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0"
-            AIMessageChunk(content='', response_metadata={'finish_reason': 'stop'}, id='run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0')
+            )
+            AIMessageChunk(content=".", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
+            AIMessageChunk(
+                content="",
+                response_metadata={"finish_reason": "stop"},
+                id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0",
+            )
 
         .. code-block:: python
 
@@ -1266,7 +1293,11 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            AIMessageChunk(content="J'adore la programmation.", response_metadata={'finish_reason': 'stop'}, id='run-bf917526-7f58-4683-84f7-36a6b671d140')
+            AIMessageChunk(
+                content="J'adore la programmation.",
+                response_metadata={"finish_reason": "stop"},
+                id="run-bf917526-7f58-4683-84f7-36a6b671d140",
+            )
 
     Async:
         .. code-block:: python
@@ -1281,41 +1312,75 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 31, 'total_tokens': 36}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_43dfabdef1', 'finish_reason': 'stop', 'logprobs': None}, id='run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0', usage_metadata={'input_tokens': 31, 'output_tokens': 5, 'total_tokens': 36})
+            AIMessage(
+                content="J'adore la programmation.",
+                response_metadata={
+                    "token_usage": {
+                        "completion_tokens": 5,
+                        "prompt_tokens": 31,
+                        "total_tokens": 36,
+                    },
+                    "model_name": "gpt-4o",
+                    "system_fingerprint": "fp_43dfabdef1",
+                    "finish_reason": "stop",
+                    "logprobs": None,
+                },
+                id="run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0",
+                usage_metadata={"input_tokens": 31, "output_tokens": 5, "total_tokens": 36},
+            )
 
     Tool calling:
         .. code-block:: python
 
             from langchain_core.pydantic_v1 import BaseModel, Field
 
+
             class GetWeather(BaseModel):
                 '''Get the current weather in a given location'''
 
-                location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+                location: str = Field(
+                    ..., description="The city and state, e.g. San Francisco, CA"
+                )
+
 
             class GetPopulation(BaseModel):
                 '''Get the current population in a given location'''
 
-                location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
+                location: str = Field(
+                    ..., description="The city and state, e.g. San Francisco, CA"
+                )
+
 
             llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
-            ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
+            ai_msg = llm_with_tools.invoke(
+                "Which city is hotter today and which is bigger: LA or NY?"
+            )
             ai_msg.tool_calls
 
         .. code-block:: python
 
-            [{'name': 'GetWeather',
+            [
-              'args': {'location': 'Los Angeles, CA'},
+                {
-              'id': 'call_6XswGD5Pqk8Tt5atYr7tfenU'},
+                    "name": "GetWeather",
-             {'name': 'GetWeather',
+                    "args": {"location": "Los Angeles, CA"},
-              'args': {'location': 'New York, NY'},
+                    "id": "call_6XswGD5Pqk8Tt5atYr7tfenU",
-              'id': 'call_ZVL15vA8Y7kXqOy3dtmQgeCi'},
+                },
-             {'name': 'GetPopulation',
+                {
-              'args': {'location': 'Los Angeles, CA'},
+                    "name": "GetWeather",
-              'id': 'call_49CFW8zqC9W7mh7hbMLSIrXw'},
+                    "args": {"location": "New York, NY"},
-             {'name': 'GetPopulation',
+                    "id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
-              'args': {'location': 'New York, NY'},
+                },
-              'id': 'call_6ghfKxV264jEfe1mRIkS3PE7'}]
+                {
+                    "name": "GetPopulation",
+                    "args": {"location": "Los Angeles, CA"},
+                    "id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
+                },
+                {
+                    "name": "GetPopulation",
+                    "args": {"location": "New York, NY"},
+                    "id": "call_6ghfKxV264jEfe1mRIkS3PE7",
+                },
+            ]
 
         Note that ``openai >= 1.32`` supports a ``parallel_tool_calls`` parameter
         that defaults to ``True``. This parameter can be set to ``False`` to
@@ -1324,16 +1389,19 @@ class ChatOpenAI(BaseChatOpenAI):
         .. code-block:: python
 
             ai_msg = llm_with_tools.invoke(
-                "What is the weather in LA and NY?",
+                "What is the weather in LA and NY?", parallel_tool_calls=False
-                parallel_tool_calls=False,
             )
             ai_msg.tool_calls
 
         .. code-block:: python
 
-            [{'name': 'GetWeather',
+            [
-            'args': {'location': 'Los Angeles, CA'},
+                {
-            'id': 'call_4OoY0ZR99iEvC7fevsH8Uhtz'}]
+                    "name": "GetWeather",
+                    "args": {"location": "Los Angeles, CA"},
+                    "id": "call_4OoY0ZR99iEvC7fevsH8Uhtz",
+                }
+            ]
 
         Like other runtime parameters, ``parallel_tool_calls`` can be bound to a model
         using ``llm.bind(parallel_tool_calls=False)`` or during instantiation by
@@ -1348,6 +1416,7 @@ class ChatOpenAI(BaseChatOpenAI):
 
             from langchain_core.pydantic_v1 import BaseModel, Field
 
+
             class Joke(BaseModel):
                 '''Joke to tell user.'''
 
@@ -1355,12 +1424,17 @@ class ChatOpenAI(BaseChatOpenAI):
                 punchline: str = Field(description="The punchline to the joke")
                 rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
 
+
             structured_llm = llm.with_structured_output(Joke)
             structured_llm.invoke("Tell me a joke about cats")
 
         .. code-block:: python
 
-            Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=None)
+            Joke(
+                setup="Why was the cat sitting on the computer?",
+                punchline="To keep an eye on the mouse!",
+                rating=None,
+            )
 
         See ``ChatOpenAI.with_structured_output()`` for more.
 
@@ -1368,7 +1442,9 @@ class ChatOpenAI(BaseChatOpenAI):
         .. code-block:: python
 
             json_llm = llm.bind(response_format={"type": "json_object"})
-            ai_msg = json_llm.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
+            ai_msg = json_llm.invoke(
+                "Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
+            )
             ai_msg.content
 
         .. code-block:: python
@@ -1391,7 +1467,7 @@ class ChatOpenAI(BaseChatOpenAI):
                         "type": "image_url",
                         "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
                     },
-                ],
+                ]
             )
             ai_msg = llm.invoke([message])
             ai_msg.content
@@ -1408,7 +1484,7 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            {'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}
+            {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
 
         When streaming, set the ``stream_usage`` kwarg:
 
@@ -1422,7 +1498,7 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            {'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}
+            {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
 
         Alternatively, setting ``stream_usage`` when instantiating the model can be
         useful when incorporating ``ChatOpenAI`` into LCEL chains-- or when using
@@ -1431,10 +1507,7 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            llm = ChatOpenAI(
+            llm = ChatOpenAI(model="gpt-4o", stream_usage=True)
-                model="gpt-4o",
-                stream_usage=True,
-            )
             structured_llm = llm.with_structured_output(...)
 
     Logprobs:
@@ -1446,11 +1519,55 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            {'content': [{'token': 'J', 'bytes': [74], 'logprob': -4.9617593e-06, 'top_logprobs': []},
+            {
-              {'token': "'adore", 'bytes': [39, 97, 100, 111, 114, 101], 'logprob': -0.25202933, 'top_logprobs': []},
+                "content": [
-              {'token': ' la', 'bytes': [32, 108, 97], 'logprob': -0.20141791, 'top_logprobs': []},
+                    {
-              {'token': ' programmation', 'bytes': [32, 112, 114, 111, 103, 114, 97, 109, 109, 97, 116, 105, 111, 110], 'logprob': -1.9361265e-07, 'top_logprobs': []},
+                        "token": "J",
-              {'token': '.', 'bytes': [46], 'logprob': -1.2233183e-05, 'top_logprobs': []}]}
+                        "bytes": [74],
+                        "logprob": -4.9617593e-06,
+                        "top_logprobs": [],
+                    },
+                    {
+                        "token": "'adore",
+                        "bytes": [39, 97, 100, 111, 114, 101],
+                        "logprob": -0.25202933,
+                        "top_logprobs": [],
+                    },
+                    {
+                        "token": " la",
+                        "bytes": [32, 108, 97],
+                        "logprob": -0.20141791,
+                        "top_logprobs": [],
+                    },
+                    {
+                        "token": " programmation",
+                        "bytes": [
+                            32,
+                            112,
+                            114,
+                            111,
+                            103,
+                            114,
+                            97,
+                            109,
+                            109,
+                            97,
+                            116,
+                            105,
+                            111,
+                            110,
+                        ],
+                        "logprob": -1.9361265e-07,
+                        "top_logprobs": [],
+                    },
+                    {
+                        "token": ".",
+                        "bytes": [46],
+                        "logprob": -1.2233183e-05,
+                        "top_logprobs": [],
+                    },
+                ]
+            }
 
     Response metadata
         .. code-block:: python
@@ -1460,13 +1577,17 @@ class ChatOpenAI(BaseChatOpenAI):
 
         .. code-block:: python
 
-            {'token_usage': {'completion_tokens': 5,
+            {
-              'prompt_tokens': 28,
+                "token_usage": {
-              'total_tokens': 33},
+                    "completion_tokens": 5,
-             'model_name': 'gpt-4o',
+                    "prompt_tokens": 28,
-             'system_fingerprint': 'fp_319be4768e',
+                    "total_tokens": 33,
-             'finish_reason': 'stop',
+                },
-             'logprobs': None}
+                "model_name": "gpt-4o",
+                "system_fingerprint": "fp_319be4768e",
+                "finish_reason": "stop",
+                "logprobs": None,
+            }
 
     """  # noqa: E501
 

libs/partners/openai/langchain_openai/embeddings/__init__.py

@@ -1,7 +1,4 @@
 from langchain_openai.embeddings.azure import AzureOpenAIEmbeddings
 from langchain_openai.embeddings.base import OpenAIEmbeddings
 
-__all__ = [
+__all__ = ["OpenAIEmbeddings", "AzureOpenAIEmbeddings"]
-    "OpenAIEmbeddings",
-    "AzureOpenAIEmbeddings",
-]

libs/partners/openai/langchain_openai/embeddings/azure.py

@@ -90,10 +90,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
             or os.getenv("OPENAI_ORGANIZATION")
         )
         values["openai_proxy"] = get_from_dict_or_env(
-            values,
+            values, "openai_proxy", "OPENAI_PROXY", default=""
-            "openai_proxy",
-            "OPENAI_PROXY",
-            default="",
         )
         values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
             "AZURE_OPENAI_ENDPOINT"

libs/partners/openai/langchain_openai/embeddings/base.py

@@ -239,16 +239,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             "OPENAI_API_BASE"
         )
         values["openai_api_type"] = get_from_dict_or_env(
-            values,
+            values, "openai_api_type", "OPENAI_API_TYPE", default=""
-            "openai_api_type",
-            "OPENAI_API_TYPE",
-            default="",
         )
         values["openai_proxy"] = get_from_dict_or_env(
-            values,
+            values, "openai_proxy", "OPENAI_PROXY", default=""
-            "openai_proxy",
-            "OPENAI_PROXY",
-            default="",
         )
         if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
             default_api_version = "2023-05-15"
@@ -520,10 +514,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         if not self.check_embedding_ctx_length:
             embeddings: List[List[float]] = []
             for text in texts:
-                response = self.client.create(
+                response = self.client.create(input=text, **self._invocation_params)
-                    input=text,
-                    **self._invocation_params,
-                )
                 if not isinstance(response, dict):
                     response = response.dict()
                 embeddings.extend(r["embedding"] for r in response["data"])
@@ -551,8 +542,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             embeddings: List[List[float]] = []
             for text in texts:
                 response = await self.async_client.create(
-                    input=text,
+                    input=text, **self._invocation_params
-                    **self._invocation_params,
                 )
                 if not isinstance(response, dict):
                     response = response.dict()

libs/partners/openai/langchain_openai/llms/__init__.py

@@ -1,7 +1,4 @@
 from langchain_openai.llms.azure import AzureOpenAI
 from langchain_openai.llms.base import OpenAI
 
-__all__ = [
+__all__ = ["OpenAI", "AzureOpenAI"]
-    "OpenAI",
-    "AzureOpenAI",
-]

libs/partners/openai/langchain_openai/llms/azure.py

@@ -117,10 +117,7 @@ class AzureOpenAI(BaseOpenAI):
             "OPENAI_API_BASE"
         )
         values["openai_proxy"] = get_from_dict_or_env(
-            values,
+            values, "openai_proxy", "OPENAI_PROXY", default=""
-            "openai_proxy",
-            "OPENAI_PROXY",
-            default="",
         )
         values["openai_organization"] = (
             values["openai_organization"]

libs/partners/openai/langchain_openai/llms/base.py

@@ -173,10 +173,7 @@ class BaseOpenAI(BaseLLM):
             "OPENAI_API_BASE"
         )
         values["openai_proxy"] = get_from_dict_or_env(
-            values,
+            values, "openai_proxy", "OPENAI_PROXY", default=""
-            "openai_proxy",
-            "OPENAI_PROXY",
-            default="",
         )
         values["openai_organization"] = (
             values["openai_organization"]
@@ -365,11 +362,7 @@ class BaseOpenAI(BaseLLM):
                 if not system_fingerprint:
                     system_fingerprint = response.get("system_fingerprint")
         return self.create_llm_result(
-            choices,
+            choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
-            prompts,
-            params,
-            token_usage,
-            system_fingerprint=system_fingerprint,
         )
 
     async def _agenerate(
@@ -425,11 +418,7 @@ class BaseOpenAI(BaseLLM):
                 choices.extend(response["choices"])
                 _update_token_usage(_keys, response, token_usage)
         return self.create_llm_result(
-            choices,
+            choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
-            prompts,
-            params,
-            token_usage,
-            system_fingerprint=system_fingerprint,
         )
 
     def get_sub_prompts(

libs/partners/openai/pyproject.toml

@@ -78,6 +78,10 @@ select = [
   "T201", # print
 ]
 
+[tool.ruff.format]
+docstring-code-format = true
+skip-magic-trailing-comma = true
+
 [tool.mypy]
 disallow_untyped_defs = "True"
 

libs/partners/openai/tests/integration_tests/chat_models/test_azure.py

@@ -39,9 +39,7 @@ def _get_llm(**kwargs: Any) -> AzureChatOpenAI:
 @pytest.mark.scheduled
 @pytest.fixture
 def llm() -> AzureChatOpenAI:
-    return _get_llm(
+    return _get_llm(max_tokens=10)
-        max_tokens=10,
-    )
 
 
 def test_chat_openai(llm: AzureChatOpenAI) -> None:
@@ -106,21 +104,13 @@ def test_chat_openai_streaming_generation_info() -> None:
     class _FakeCallback(FakeCallbackHandler):
         saved_things: dict = {}
 
-        def on_llm_end(
+        def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
-            self,
-            *args: Any,
-            **kwargs: Any,
-        ) -> Any:
             # Save the generation
             self.saved_things["generation"] = args[0]
 
     callback = _FakeCallback()
     callback_manager = CallbackManager([callback])
-    chat = _get_llm(
+    chat = _get_llm(max_tokens=2, temperature=0, callback_manager=callback_manager)
-        max_tokens=2,
-        temperature=0,
-        callback_manager=callback_manager,
-    )
     list(chat.stream("hi"))
     generation = callback.saved_things["generation"]
     # `Hello!` is two tokens, assert that that is what is returned

libs/partners/openai/tests/integration_tests/chat_models/test_base.py

@@ -13,11 +13,7 @@ from langchain_core.messages import (
     ToolCall,
     ToolMessage,
 )
-from langchain_core.outputs import (
+from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
-    ChatGeneration,
-    ChatResult,
-    LLMResult,
-)
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
 
@@ -120,21 +116,13 @@ def test_chat_openai_streaming_generation_info() -> None:
     class _FakeCallback(FakeCallbackHandler):
         saved_things: dict = {}
 
-        def on_llm_end(
+        def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
-            self,
-            *args: Any,
-            **kwargs: Any,
-        ) -> Any:
             # Save the generation
             self.saved_things["generation"] = args[0]
 
     callback = _FakeCallback()
     callback_manager = CallbackManager([callback])
-    chat = ChatOpenAI(
+    chat = ChatOpenAI(max_tokens=2, temperature=0, callback_manager=callback_manager)
-        max_tokens=2,
-        temperature=0,
-        callback_manager=callback_manager,
-    )
     list(chat.stream("hi"))
     generation = callback.saved_things["generation"]
     # `Hello!` is two tokens, assert that that is what is returned
@@ -162,12 +150,7 @@ def test_chat_openai_streaming_llm_output_contains_model_name() -> None:
 def test_chat_openai_invalid_streaming_params() -> None:
     """Test that streaming correctly invokes on_llm_new_token callback."""
     with pytest.raises(ValueError):
-        ChatOpenAI(
+        ChatOpenAI(max_tokens=10, streaming=True, temperature=0, n=5)
-            max_tokens=10,
-            streaming=True,
-            temperature=0,
-            n=5,
-        )
 
 
 @pytest.mark.scheduled
@@ -225,17 +208,12 @@ async def test_async_chat_openai_bind_functions() -> None:
             default=None, title="Fav Food", description="The person's favorite food"
         )
 
-    chat = ChatOpenAI(
+    chat = ChatOpenAI(max_tokens=30, n=1, streaming=True).bind_functions(
-        max_tokens=30,
+        functions=[Person], function_call="Person"
-        n=1,
+    )
-        streaming=True,
-    ).bind_functions(functions=[Person], function_call="Person")
 
     prompt = ChatPromptTemplate.from_messages(
-        [
+        [("system", "Use the provided Person function"), ("user", "{input}")]
-            ("system", "Use the provided Person function"),
-            ("user", "{input}"),
-        ]
     )
 
     chain = prompt | chat
@@ -420,13 +398,9 @@ async def test_astream() -> None:
     llm = ChatOpenAI(temperature=0, max_tokens=5)
     await _test_stream(llm.astream("Hello"), expect_usage=False)
     await _test_stream(
-        llm.astream("Hello", stream_options={"include_usage": True}),
+        llm.astream("Hello", stream_options={"include_usage": True}), expect_usage=True
-        expect_usage=True,
-    )
-    await _test_stream(
-        llm.astream("Hello", stream_usage=True),
-        expect_usage=True,
     )
+    await _test_stream(llm.astream("Hello", stream_usage=True), expect_usage=True)
     llm = ChatOpenAI(
         temperature=0,
         max_tokens=5,
@@ -437,16 +411,9 @@ async def test_astream() -> None:
         llm.astream("Hello", stream_options={"include_usage": False}),
         expect_usage=False,
     )
-    llm = ChatOpenAI(
+    llm = ChatOpenAI(temperature=0, max_tokens=5, stream_usage=True)
-        temperature=0,
-        max_tokens=5,
-        stream_usage=True,
-    )
     await _test_stream(llm.astream("Hello"), expect_usage=True)
-    await _test_stream(
+    await _test_stream(llm.astream("Hello", stream_usage=False), expect_usage=False)
-        llm.astream("Hello", stream_usage=False),
-        expect_usage=False,
-    )
 
 
 async def test_abatch() -> None:
@@ -538,10 +505,7 @@ def test_response_metadata_streaming() -> None:
         full = chunk if full is None else full + chunk
     assert all(
         k in cast(BaseMessageChunk, full).response_metadata
-        for k in (
+        for k in ("logprobs", "finish_reason")
-            "logprobs",
-            "finish_reason",
-        )
     )
     assert "content" in cast(BaseMessageChunk, full).response_metadata["logprobs"]
 
@@ -554,10 +518,7 @@ async def test_async_response_metadata_streaming() -> None:
         full = chunk if full is None else full + chunk
     assert all(
         k in cast(BaseMessageChunk, full).response_metadata
-        for k in (
+        for k in ("logprobs", "finish_reason")
-            "logprobs",
-            "finish_reason",
-        )
     )
     assert "content" in cast(BaseMessageChunk, full).response_metadata["logprobs"]
 
@@ -693,9 +654,7 @@ def test_openai_structured_output() -> None:
 
 def test_openai_proxy() -> None:
     """Test ChatOpenAI with proxy."""
-    chat_openai = ChatOpenAI(
+    chat_openai = ChatOpenAI(openai_proxy="http://localhost:8080")
-        openai_proxy="http://localhost:8080",
-    )
     mounts = chat_openai.client._client._client._mounts
     assert len(mounts) == 1
     for key, value in mounts.items():

libs/partners/openai/tests/integration_tests/chat_models/test_base_standard.py

@@ -30,11 +30,8 @@ class TestOpenAIStandard(ChatModelIntegrationTests):
         message = HumanMessage(
             content=[
                 {"type": "text", "text": "describe the weather in this image"},
-                {
+                {"type": "image_url", "image_url": {"url": image_url}},
-                    "type": "image_url",
+            ]
-                    "image_url": {"url": image_url},
-                },
-            ],
         )
         expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
             "input_tokens"
@@ -50,7 +47,7 @@ class TestOpenAIStandard(ChatModelIntegrationTests):
                     "type": "image_url",
                     "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
                 },
-            ],
+            ]
         )
         expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
             "input_tokens"
@@ -63,11 +60,8 @@ class TestOpenAIStandard(ChatModelIntegrationTests):
         message = HumanMessage(
             content=[
                 {"type": "text", "text": "how many dice are in this image"},
-                {
+                {"type": "image_url", "image_url": {"url": image_url}},
-                    "type": "image_url",
+            ]
-                    "image_url": {"url": image_url},
-                },
-            ],
         )
         expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
             "input_tokens"
@@ -83,7 +77,7 @@ class TestOpenAIStandard(ChatModelIntegrationTests):
                     "type": "image_url",
                     "image_url": {"url": f"data:image/png;base64,{image_data}"},
                 },
-            ],
+            ]
         )
         expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
             "input_tokens"

libs/partners/openai/tests/integration_tests/llms/test_azure.py

@@ -30,9 +30,7 @@ def _get_llm(**kwargs: Any) -> AzureOpenAI:
 
 @pytest.fixture
 def llm() -> AzureOpenAI:
-    return _get_llm(
+    return _get_llm(max_tokens=10)
-        max_tokens=10,
-    )
 
 
 @pytest.mark.scheduled

libs/partners/openai/tests/integration_tests/llms/test_base.py

@@ -6,9 +6,7 @@ from langchain_core.callbacks import CallbackManager
 from langchain_core.outputs import LLMResult
 
 from langchain_openai import OpenAI
-from tests.unit_tests.fake.callbacks import (
+from tests.unit_tests.fake.callbacks import FakeCallbackHandler
-    FakeCallbackHandler,
-)
 
 
 def test_stream() -> None:

libs/partners/openai/tests/unit_tests/chat_models/test_base.py

@@ -35,11 +35,7 @@ def test_function_message_dict_to_function_message() -> None:
     content = json.dumps({"result": "Example #1"})
     name = "test_function"
     result = _convert_dict_to_message(
-        {
+        {"role": "function", "name": name, "content": content}
-            "role": "function",
-            "name": name,
-            "content": content,
-        }
     )
     assert isinstance(result, FunctionMessage)
     assert result.name == name
@@ -131,10 +127,7 @@ def test__convert_dict_to_message_tool_call() -> None:
     raw_tool_calls: list = [
         {
             "id": "call_wm0JY6CdwOMZ4eTxHWUThDNz",
-            "function": {
+            "function": {"arguments": "oops", "name": "GenerateUsername"},
-                "arguments": "oops",
-                "name": "GenerateUsername",
-            },
             "type": "function",
         },
         {
@@ -158,14 +151,14 @@ def test__convert_dict_to_message_tool_call() -> None:
                 args="oops",
                 id="call_wm0JY6CdwOMZ4eTxHWUThDNz",
                 error="Function GenerateUsername arguments:\n\noops\n\nare not valid JSON. Received JSONDecodeError Expecting value: line 1 column 1 (char 0)",  # noqa: E501
-            ),
+            )
         ],
         tool_calls=[
             ToolCall(
                 name="GenerateUsername",
                 args={"name": "Sally", "hair_color": "green"},
                 id="call_abc123",
-            ),
+            )
         ],
     )
     assert result == expected_output
@@ -186,11 +179,7 @@ def mock_completion() -> dict:
         "choices": [
             {
                 "index": 0,
-                "message": {
+                "message": {"role": "assistant", "content": "Bar Baz", "name": "Erick"},
-                    "role": "assistant",
-                    "content": "Bar Baz",
-                    "name": "Erick",
-                },
                 "finish_reason": "stop",
             }
         ],
@@ -208,11 +197,7 @@ def test_openai_invoke(mock_completion: dict) -> None:
         return mock_completion
 
     mock_client.create = mock_create
-    with patch.object(
+    with patch.object(llm, "client", mock_client):
-        llm,
-        "client",
-        mock_client,
-    ):
         res = llm.invoke("bar")
         assert res.content == "Bar Baz"
     assert completed
@@ -229,11 +214,7 @@ async def test_openai_ainvoke(mock_completion: dict) -> None:
         return mock_completion
 
     mock_client.create = mock_create
-    with patch.object(
+    with patch.object(llm, "async_client", mock_client):
-        llm,
-        "async_client",
-        mock_client,
-    ):
         res = await llm.ainvoke("bar")
         assert res.content == "Bar Baz"
     assert completed
@@ -261,14 +242,8 @@ def test_openai_invoke_name(mock_completion: dict) -> None:
     mock_client = MagicMock()
     mock_client.create.return_value = mock_completion
 
-    with patch.object(
+    with patch.object(llm, "client", mock_client):
-        llm,
+        messages = [HumanMessage(content="Foo", name="Katie")]
-        "client",
-        mock_client,
-    ):
-        messages = [
-            HumanMessage(content="Foo", name="Katie"),
-        ]
         res = llm.invoke(messages)
         call_args, call_kwargs = mock_client.create.call_args
         assert len(call_args) == 0  # no positional args
@@ -303,12 +278,7 @@ def test_format_message_content() -> None:
 
     content = [
         {"type": "text", "text": "What is in this image?"},
-        {
+        {"type": "image_url", "image_url": {"url": "url.com"}},
-            "type": "image_url",
-            "image_url": {
-                "url": "url.com",
-            },
-        },
     ]
     assert content == _format_message_content(content)
 

libs/partners/openai/tests/unit_tests/fake/callbacks.py

@@ -136,123 +136,55 @@ class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
         """Whether to ignore retriever callbacks."""
         return self.ignore_retriever_
 
-    def on_llm_start(
+    def on_llm_start(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_llm_start_common()
 
-    def on_llm_new_token(
+    def on_llm_new_token(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_llm_new_token_common()
 
-    def on_llm_end(
+    def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_llm_end_common()
 
-    def on_llm_error(
+    def on_llm_error(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_llm_error_common(*args, **kwargs)
 
-    def on_retry(
+    def on_retry(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_retry_common()
 
-    def on_chain_start(
+    def on_chain_start(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_chain_start_common()
 
-    def on_chain_end(
+    def on_chain_end(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_chain_end_common()
 
-    def on_chain_error(
+    def on_chain_error(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_chain_error_common()
 
-    def on_tool_start(
+    def on_tool_start(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_tool_start_common()
 
-    def on_tool_end(
+    def on_tool_end(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_tool_end_common()
 
-    def on_tool_error(
+    def on_tool_error(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_tool_error_common()
 
-    def on_agent_action(
+    def on_agent_action(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_agent_action_common()
 
-    def on_agent_finish(
+    def on_agent_finish(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_agent_finish_common()
 
-    def on_text(
+    def on_text(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_text_common()
 
-    def on_retriever_start(
+    def on_retriever_start(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_retriever_start_common()
 
-    def on_retriever_end(
+    def on_retriever_end(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_retriever_end_common()
 
-    def on_retriever_error(
+    def on_retriever_error(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_retriever_error_common()
 
     def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":
@@ -291,102 +223,46 @@ class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixi
         """Whether to ignore agent callbacks."""
         return self.ignore_agent_
 
-    async def on_retry(
+    async def on_retry(self, *args: Any, **kwargs: Any) -> Any:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
         self.on_retry_common()
 
-    async def on_llm_start(
+    async def on_llm_start(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_llm_start_common()
 
-    async def on_llm_new_token(
+    async def on_llm_new_token(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_llm_new_token_common()
 
-    async def on_llm_end(
+    async def on_llm_end(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_llm_end_common()
 
-    async def on_llm_error(
+    async def on_llm_error(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_llm_error_common(*args, **kwargs)
 
-    async def on_chain_start(
+    async def on_chain_start(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_chain_start_common()
 
-    async def on_chain_end(
+    async def on_chain_end(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_chain_end_common()
 
-    async def on_chain_error(
+    async def on_chain_error(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_chain_error_common()
 
-    async def on_tool_start(
+    async def on_tool_start(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_tool_start_common()
 
-    async def on_tool_end(
+    async def on_tool_end(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_tool_end_common()
 
-    async def on_tool_error(
+    async def on_tool_error(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_tool_error_common()
 
-    async def on_agent_action(
+    async def on_agent_action(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_agent_action_common()
 
-    async def on_agent_finish(
+    async def on_agent_finish(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_agent_finish_common()
 
-    async def on_text(
+    async def on_text(self, *args: Any, **kwargs: Any) -> None:
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> None:
         self.on_text_common()
 
     def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler":

libs/partners/openai/tests/unit_tests/llms/test_base.py

@@ -45,13 +45,7 @@ def mock_completion() -> dict:
     }
 
 
-@pytest.mark.parametrize(
+@pytest.mark.parametrize("model", ["gpt-3.5-turbo-instruct", "text-davinci-003"])
-    "model",
-    [
-        "gpt-3.5-turbo-instruct",
-        "text-davinci-003",
-    ],
-)
 def test_get_token_ids(model: str) -> None:
     OpenAI(model=model).get_token_ids("foo")
     return

libs/partners/openai/tests/unit_tests/test_secrets.py

@@ -93,10 +93,7 @@ def test_azure_openai_api_key_masked_when_passed_from_env(
     """Test that the API key is masked when passed from an environment variable."""
     monkeypatch.setenv("AZURE_OPENAI_API_KEY", "secret-api-key")
     monkeypatch.setenv("AZURE_OPENAI_AD_TOKEN", "secret-ad-token")
-    model = model_class(
+    model = model_class(azure_endpoint="endpoint", api_version="version")
-        azure_endpoint="endpoint",
-        api_version="version",
-    )
     print(model.openai_api_key, end="")  # noqa: T201
     captured = capsys.readouterr()
 
@@ -112,8 +109,7 @@ def test_azure_openai_api_key_masked_when_passed_from_env(
     "model_class", [AzureChatOpenAI, AzureOpenAI, AzureOpenAIEmbeddings]
 )
 def test_azure_openai_api_key_masked_when_passed_via_constructor(
-    model_class: Type,
+    model_class: Type, capsys: CaptureFixture
-    capsys: CaptureFixture,
 ) -> None:
     """Test that the API key is masked when passed via the constructor."""
     model = model_class(
@@ -172,8 +168,7 @@ def test_openai_api_key_masked_when_passed_from_env(
 
 @pytest.mark.parametrize("model_class", [ChatOpenAI, OpenAI, OpenAIEmbeddings])
 def test_openai_api_key_masked_when_passed_via_constructor(
-    model_class: Type,
+    model_class: Type, capsys: CaptureFixture
-    capsys: CaptureFixture,
 ) -> None:
     """Test that the API key is masked when passed via the constructor."""
     model = model_class(openai_api_key="secret-api-key")

libs/partners/openai/tests/unit_tests/test_token_counts.py

@@ -12,17 +12,8 @@ _EXPECTED_NUM_TOKENS = {
     "gpt-3.5-turbo": 12,
 }
 
-_MODELS = models = [
+_MODELS = models = ["ada", "babbage", "curie", "davinci"]
-    "ada",
+_CHAT_MODELS = ["gpt-4", "gpt-4-32k", "gpt-3.5-turbo"]
-    "babbage",
-    "curie",
-    "davinci",
-]
-_CHAT_MODELS = [
-    "gpt-4",
-    "gpt-4-32k",
-    "gpt-3.5-turbo",
-]
 
 
 @pytest.mark.parametrize("model", _MODELS)