Use default 88 line length in ruff formatting

Author: paxiaatucsdedu

libs/oci/langchain_oci/chat_models/oci_data_science.py

@@ -138,7 +138,11 @@ class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment):
             AIMessage(
                 content="Bonjour le monde!",
                 response_metadata={
-                    "token_usage": {"prompt_tokens": 40, "total_tokens": 50, "completion_tokens": 10},
+                    "token_usage": {
+                        "prompt_tokens": 40,
+                        "total_tokens": 50,
+                        "completion_tokens": 10,
+                    },
                     "model_name": "odsc-llm",
                     "system_fingerprint": "",
                     "finish_reason": "stop",
@@ -194,11 +198,16 @@ class Joke(BaseModel):
 
 
             structured_llm = chat.with_structured_output(Joke, method="json_mode")
-            structured_llm.invoke("Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys")
+            structured_llm.invoke(
+                "Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys"
+            )
 
         .. code-block:: python
 
-            Joke(setup="Why did the cat get stuck in the tree?", punchline="Because it was chasing its tail!")
+            Joke(
+                setup="Why did the cat get stuck in the tree?",
+                punchline="Because it was chasing its tail!",
+            )
 
         See ``ChatOCIModelDeployment.with_structured_output()`` for more.
 
@@ -240,7 +249,11 @@ def _construct_json_body(self, messages: list, params: dict) -> dict:
         .. code-block:: python
 
             {
-                "token_usage": {"prompt_tokens": 40, "total_tokens": 50, "completion_tokens": 10},
+                "token_usage": {
+                    "prompt_tokens": 40,
+                    "total_tokens": 50,
+                    "completion_tokens": 10,
+                },
                 "model_name": "odsc-llm",
                 "system_fingerprint": "",
                 "finish_reason": "stop",
@@ -264,7 +277,8 @@ def validate_openai(cls, values: Any) -> Any:
         """Checks if langchain_openai is installed."""
         if not importlib.util.find_spec("langchain_openai"):
             raise ImportError(
-                "Could not import langchain_openai package. Please install it with `pip install langchain_openai`."
+                "Could not import langchain_openai package. "
+                "Please install it with `pip install langchain_openai`."
             )
         return values
 
@@ -291,7 +305,9 @@ def _default_params(self) -> Dict[str, Any]:
             "stream": self.streaming,
         }
 
-    def _headers(self, is_async: Optional[bool] = False, body: Optional[dict] = None) -> Dict:
+    def _headers(
+        self, is_async: Optional[bool] = False, body: Optional[dict] = None
+    ) -> Dict:
         """Construct and return the headers for a request.
 
         Args:
@@ -343,13 +359,17 @@ def _generate(
                 response = chat.invoke(messages)
         """  # noqa: E501
         if self.streaming:
-            stream_iter = self._stream(messages, stop=stop, run_manager=run_manager, **kwargs)
+            stream_iter = self._stream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
             return generate_from_stream(stream_iter)
 
         requests_kwargs = kwargs.pop("requests_kwargs", {})
         params = self._invocation_params(stop, **kwargs)
         body = self._construct_json_body(messages, params)
-        res = self.completion_with_retry(data=body, run_manager=run_manager, **requests_kwargs)
+        res = self.completion_with_retry(
+            data=body, run_manager=run_manager, **requests_kwargs
+        )
         return self._process_response(res.json())
 
     def _stream(
@@ -397,7 +417,9 @@ def _stream(
         params = self._invocation_params(stop, **kwargs)
         body = self._construct_json_body(messages, params)  # request json body
 
-        response = self.completion_with_retry(data=body, run_manager=run_manager, stream=True, **requests_kwargs)
+        response = self.completion_with_retry(
+            data=body, run_manager=run_manager, stream=True, **requests_kwargs
+        )
         default_chunk_class = AIMessageChunk
         for line in self._parse_stream(response.iter_lines()):
             chunk = self._handle_sse_line(line, default_chunk_class)
@@ -447,7 +469,9 @@ async def _agenerate(
 
         """  # noqa: E501
         if self.streaming:
-            stream_iter = self._astream(messages, stop=stop, run_manager=run_manager, **kwargs)
+            stream_iter = self._astream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
             return await agenerate_from_stream(stream_iter)
 
         requests_kwargs = kwargs.pop("requests_kwargs", {})
@@ -571,14 +595,19 @@ def with_structured_output(
                 else JsonOutputParser()
             )
         else:
-            raise ValueError(f"Unrecognized method argument. Expected `json_mode`.Received: `{method}`.")
+            raise ValueError(
+                f"Unrecognized method argument. Expected `json_mode`."
+                f"Received: `{method}`."
+            )
 
         if include_raw:
             parser_assign = RunnablePassthrough.assign(
                 parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
             )
             parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
-            parser_with_fallback = parser_assign.with_fallbacks([parser_none], exception_key="parsing_error")
+            parser_with_fallback = parser_assign.with_fallbacks(
+                [parser_none], exception_key="parsing_error"
+            )
             return RunnableMap(raw=llm) | parser_with_fallback
         else:
             return llm | output_parser
@@ -661,7 +690,9 @@ def _process_stream_response(
             if not isinstance(choice, dict):
                 raise TypeError("Endpoint response is not well formed.")
         except (KeyError, IndexError, TypeError) as e:
-            raise ValueError("Error while formatting response payload for chat model of type") from e
+            raise ValueError(
+                "Error while formatting response payload for chat model of type"
+            ) from e
 
         chunk = _convert_delta_to_message_chunk(choice["delta"], default_chunk_cls)
         default_chunk_cls = chunk.__class__
@@ -673,7 +704,9 @@ def _process_stream_response(
         if usage is not None:
             gen_info.update({"usage": usage})
 
-        return ChatGenerationChunk(message=chunk, generation_info=gen_info if gen_info else None)
+        return ChatGenerationChunk(
+            message=chunk, generation_info=gen_info if gen_info else None
+        )
 
     def _process_response(self, response_json: dict) -> ChatResult:
         """Formats response in OpenAI spec.
@@ -698,7 +731,9 @@ def _process_response(self, response_json: dict) -> ChatResult:
             if not isinstance(choices, list):
                 raise TypeError("Endpoint response is not well formed.")
         except (KeyError, TypeError) as e:
-            raise ValueError("Error while formatting response payload for chat model of type") from e
+            raise ValueError(
+                "Error while formatting response payload for chat model of type"
+            ) from e
 
         for choice in choices:
             message = _convert_dict_to_message(choice["message"])