Fix code formatting for line length compliance

Author: fede-kamel

libs/oci/langchain_oci/chat_models/oci_generative_ai.py

@@ -247,8 +247,13 @@ def chat_generation_info(self, response: Any) -> Dict[str, Any]:
         }
 
         # Include token usage if available
-        if hasattr(response.data.chat_response, "usage") and response.data.chat_response.usage:
-            generation_info["total_tokens"] = response.data.chat_response.usage.total_tokens
+        if (
+            hasattr(response.data.chat_response, "usage")
+            and response.data.chat_response.usage
+        ):
+            generation_info["total_tokens"] = (
+                response.data.chat_response.usage.total_tokens
+            )
 
         # Include tool calls if available
         if self.chat_tool_calls(response):
@@ -629,9 +634,14 @@ def chat_generation_info(self, response: Any) -> Dict[str, Any]:
         }
 
         # Include token usage if available
-        if hasattr(response.data.chat_response, "usage") and response.data.chat_response.usage:
-            generation_info["total_tokens"] = response.data.chat_response.usage.total_tokens
-            
+        if (
+            hasattr(response.data.chat_response, "usage")
+            and response.data.chat_response.usage
+        ):
+            generation_info["total_tokens"] = (
+                response.data.chat_response.usage.total_tokens
+            )
+
         if self.chat_tool_calls(response):
             generation_info["tool_calls"] = self.format_response_tool_calls(
                 self.chat_tool_calls(response)
@@ -777,8 +787,7 @@ def messages_to_oci_params(
         # continue calling tools even after receiving results.
 
         def _should_allow_more_tool_calls(
-            messages: List[BaseMessage],
-            max_tool_calls: int
+            messages: List[BaseMessage], max_tool_calls: int
         ) -> bool:
             """
             Determine if the model should be allowed to call more tools.
@@ -794,10 +803,7 @@ def _should_allow_more_tool_calls(
                 max_tool_calls: Maximum number of tool calls before forcing stop
             """
             # Count total tool calls made so far
-            tool_call_count = sum(
-                1 for msg in messages
-                if isinstance(msg, ToolMessage)
-            )
+            tool_call_count = sum(1 for msg in messages if isinstance(msg, ToolMessage))
 
             # Safety limit: prevent runaway tool calling
             if tool_call_count >= max_tool_calls:
@@ -806,12 +812,12 @@ def _should_allow_more_tool_calls(
             # Detect infinite loop: same tool called with same arguments in succession
             recent_calls = []
             for msg in reversed(messages):
-                if hasattr(msg, 'tool_calls') and msg.tool_calls:
+                if hasattr(msg, "tool_calls") and msg.tool_calls:
                     for tc in msg.tool_calls:
                         # Create signature: (tool_name, sorted_args)
                         try:
-                            args_str = json.dumps(tc.get('args', {}), sort_keys=True)
-                            signature = (tc.get('name', ''), args_str)
+                            args_str = json.dumps(tc.get("args", {}), sort_keys=True)
+                            signature = (tc.get("name", ""), args_str)
 
                             # Check if this exact call was made in last 2 calls
                             if signature in recent_calls[-2:]:
@@ -1153,9 +1159,7 @@ def _prepare_request(
             ) from ex
 
         oci_params = self._provider.messages_to_oci_params(
-            messages,
-            max_sequential_tool_calls=self.max_sequential_tool_calls,
-            **kwargs
+            messages, max_sequential_tool_calls=self.max_sequential_tool_calls, **kwargs
         )
 
         oci_params["is_stream"] = stream
@@ -1165,12 +1169,17 @@ def _prepare_request(
             _model_kwargs[self._provider.stop_sequence_key] = stop
 
         # Warn if using max_tokens with OpenAI models
-        if self.model_id and self.model_id.startswith("openai.") and "max_tokens" in _model_kwargs:
+        if (
+            self.model_id
+            and self.model_id.startswith("openai.")
+            and "max_tokens" in _model_kwargs
+        ):
             import warnings
+
             warnings.warn(
                 f"OpenAI models require 'max_completion_tokens' instead of 'max_tokens'.",
                 UserWarning,
-                stacklevel=2
+                stacklevel=2,
             )
 
         chat_params = {**_model_kwargs, **kwargs, **oci_params}
@@ -1234,7 +1243,11 @@ def bind_tools(
 
         # Add parallel tool calls support
         # Use bind-time parameter if provided, else fall back to class default
-        use_parallel = parallel_tool_calls if parallel_tool_calls is not None else self.parallel_tool_calls
+        use_parallel = (
+            parallel_tool_calls
+            if parallel_tool_calls is not None
+            else self.parallel_tool_calls
+        )
         if use_parallel:
             kwargs["is_parallel_tool_calls"] = True
 
@@ -1267,7 +1280,7 @@ def with_structured_output(
                 used. Note that if using "json_mode" then you must include instructions
                 for formatting the output into the desired schema into the model call.
                 If "json_schema" then it allows the user to pass a json schema (or pydantic)
-                to the model for structured output. 
+                to the model for structured output.
             include_raw:
                 If False then only the parsed structured output is returned. If
                 an error occurs during model output parsing it will be raised. If True
@@ -1323,18 +1336,18 @@ def with_structured_output(
                 if is_pydantic_schema
                 else schema
             )
-            
+
             response_json_schema = self._provider.oci_response_json_schema(
                 name=json_schema_dict.get("title", "response"),
                 description=json_schema_dict.get("description", ""),
                 schema=json_schema_dict,
-                is_strict=True
+                is_strict=True,
             )
-            
+
             response_format_obj = self._provider.oci_json_schema_response_format(
                 json_schema=response_json_schema
             )
-            
+
             llm = self.bind(response_format=response_format_obj)
             if is_pydantic_schema:
                 output_parser = PydanticOutputParser(pydantic_object=schema)