Format / lint the repo

.gitignore

@@ -149,7 +149,10 @@ wandb/
 
 # asdf tool versions
 .tool-versions
+
+# ruff
 /.ruff_cache/
+.ruff_cache/
 
 *.pkl
 *.bin

libs/aws/langchain_aws/chat_models/bedrock.py

@@ -98,16 +98,15 @@ def _convert_one_message_to_text_llama3(message: BaseMessage) -> str:
         )
     elif isinstance(message, HumanMessage):
         message_text = (
-            f"<|start_header_id|>user" f"<|end_header_id|>{message.content}<|eot_id|>"
+            f"<|start_header_id|>user<|end_header_id|>{message.content}<|eot_id|>"
         )
     elif isinstance(message, AIMessage):
         message_text = (
-            f"<|start_header_id|>assistant"
-            f"<|end_header_id|>{message.content}<|eot_id|>"
+            f"<|start_header_id|>assistant<|end_header_id|>{message.content}<|eot_id|>"
         )
     elif isinstance(message, SystemMessage):
         message_text = (
-            f"<|start_header_id|>system" f"<|end_header_id|>{message.content}<|eot_id|>"
+            f"<|start_header_id|>system<|end_header_id|>{message.content}<|eot_id|>"
         )
     else:
         raise ValueError(f"Got unknown type {message}")
@@ -131,21 +130,15 @@ def _convert_one_message_to_text_llama4(message: BaseMessage) -> str:
             f"<|header_start|>{message.role}<|header_end|>{message.content}<|eot|>"
         )
     elif isinstance(message, HumanMessage):
-        message_text = (
-            f"<|header_start|>user<|header_end|>{message.content}<|eot|>"
-        )
+        message_text = f"<|header_start|>user<|header_end|>{message.content}<|eot|>"
     elif isinstance(message, AIMessage):
         message_text = (
             f"<|header_start|>assistant<|header_end|>{message.content}<|eot|>"
         )
     elif isinstance(message, SystemMessage):
-        message_text = (
-            f"<|header_start|>system<|header_end|>{message.content}<|eot|>"
-        )
+        message_text = f"<|header_start|>system<|header_end|>{message.content}<|eot|>"
     elif isinstance(message, ToolMessage):
-        message_text = (
-            f"<|header_start|>ipython<|header_end|>{message.content}<|eom|>"
-        )
+        message_text = f"<|header_start|>ipython<|header_end|>{message.content}<|eom|>"
     else:
         raise ValueError(f"Got unknown type {message}")
 
@@ -197,7 +190,7 @@ def convert_messages_to_prompt_anthropic(
     """
     if messages is None:
         return ""
-    
+
     messages = messages.copy()  # don't mutate the original list
     if len(messages) > 0 and not isinstance(messages[-1], AIMessage):
         messages.append(AIMessage(content=""))
@@ -234,21 +227,13 @@ def convert_messages_to_prompt_mistral(messages: List[BaseMessage]) -> str:
 
 def _convert_one_message_to_text_deepseek(message: BaseMessage) -> str:
     if isinstance(message, ChatMessage):
-        message_text = (
-            f"<|{message.role}|>{message.content}"
-        )
+        message_text = f"<|{message.role}|>{message.content}"
     elif isinstance(message, HumanMessage):
-        message_text = (
-            f"<|User|>{message.content}"
-        )
+        message_text = f"<|User|>{message.content}"
     elif isinstance(message, AIMessage):
-        message_text = (
-            f"<|Assistant|>{message.content}"
-        )
+        message_text = f"<|Assistant|>{message.content}"
     elif isinstance(message, SystemMessage):
-        message_text = (
-            f"<|System|>{message.content}"
-        )
+        message_text = f"<|System|>{message.content}"
     else:
         raise ValueError(f"Got unknown type {message}")
 
@@ -291,30 +276,22 @@ def convert_messages_to_prompt_writer(messages: List[BaseMessage]) -> str:
 
 def _convert_one_message_to_text_openai(message: BaseMessage) -> str:
     if isinstance(message, SystemMessage):
-        message_text = (
-            f"<|start|>system<|message|>{message.content}<|end|>"
-        )
+        message_text = f"<|start|>system<|message|>{message.content}<|end|>"
     elif isinstance(message, ChatMessage):
         # developer role messages
-        message_text = (
-            f"<|start|>{message.role}<|message|>{message.content}<|end|>"
-        )
+        message_text = f"<|start|>{message.role}<|message|>{message.content}<|end|>"
     elif isinstance(message, HumanMessage):
-        message_text = (
-            f"<|start|>user<|message|>{message.content}<|end|>"
-        )
+        message_text = f"<|start|>user<|message|>{message.content}<|end|>"
     elif isinstance(message, AIMessage):
         message_text = (
             f"<|start|>assistant<|channel|>final<|message|>{message.content}<|end|>"
         )
     elif isinstance(message, ToolMessage):
-      # TODO: Tool messages in the OpenAI format should use "<|start|>{toolname} to=assistant<|message|>"
-      # Need to extract the tool name from the ToolMessage content or tool_call_id
-      # For now using generic "to=assistant" format as placeholder until we implement tool calling
-      # Will be resolved in follow-up PR with full tool support
-        message_text = (
-            f"<|start|>to=assistant<|channel|>commentary<|message|>{message.content}<|end|>"
-        )
+        # TODO: Tool messages in the OpenAI format should use "<|start|>{toolname} to=assistant<|message|>"
+        # Need to extract the tool name from the ToolMessage content or tool_call_id
+        # For now using generic "to=assistant" format as placeholder until we implement tool calling
+        # Will be resolved in follow-up PR with full tool support
+        message_text = f"<|start|>to=assistant<|channel|>commentary<|message|>{message.content}<|end|>"
     else:
         raise ValueError(f"Got unknown type {message}")
 
@@ -373,7 +350,7 @@ def _format_data_content_block(block: dict) -> dict:
                     "type": "base64",
                     "media_type": block["mime_type"],
                     "data": block["data"],
-                }
+                },
             }
         else:
             error_message = "Image data only supported through in-line base64 format."
@@ -440,9 +417,7 @@ def _format_anthropic_messages(
     for i, message in enumerate(merged_messages):
         if message.type == "system":
             if system is not None:
-                raise ValueError(
-                    "Received multiple non-consecutive system messages."
-                )
+                raise ValueError("Received multiple non-consecutive system messages.")
             elif isinstance(message.content, str):
                 system = message.content
             elif isinstance(message.content, list):
@@ -482,9 +457,9 @@ def _format_anthropic_messages(
 
         if not isinstance(message.content, str):
             # parse as dict
-            assert isinstance(
-                message.content, list
-            ), "Anthropic message content must be str or list of dicts"
+            assert isinstance(message.content, list), (
+                "Anthropic message content must be str or list of dicts"
+            )
 
             # populate content
             content = []
@@ -514,19 +489,28 @@ def _format_anthropic_messages(
                             # Handle list content inside tool_result
                             processed_list = []
                             for list_item in content_item:
-                                if isinstance(list_item, dict) and list_item.get("type") == "image_url":
+                                if (
+                                    isinstance(list_item, dict)
+                                    and list_item.get("type") == "image_url"
+                                ):
                                     # Process image in list
-                                    source = _format_image(list_item["image_url"]["url"])
-                                    processed_list.append({"type": "image", "source": source})
+                                    source = _format_image(
+                                        list_item["image_url"]["url"]
+                                    )
+                                    processed_list.append(
+                                        {"type": "image", "source": source}
+                                    )
                                 else:
                                     # Keep other items as is
                                     processed_list.append(list_item)
                             # Add processed list to tool_result
-                            tool_blocks.append({
-                                "type": "tool_result",
-                                "tool_use_id": item.get("tool_use_id"),
-                                "content": processed_list
-                            })
+                            tool_blocks.append(
+                                {
+                                    "type": "tool_result",
+                                    "tool_use_id": item.get("tool_use_id"),
+                                    "content": processed_list,
+                                }
+                            )
                         else:
                             # For other content types, keep as is
                             tool_blocks.append(item)
@@ -788,9 +772,9 @@ def set_beta_use_converse_api(cls, values: Dict) -> Any:
             response = bedrock_client.get_inference_profile(
                 inferenceProfileIdentifier=model_id
             )
-            if 'models' in response and len(response['models']) > 0:
-                model_arn = response['models'][0]['modelArn']
-                resolved_base_model = model_arn.split('/')[-1]
+            if "models" in response and len(response["models"]) > 0:
+                model_arn = response["models"][0]["modelArn"]
+                resolved_base_model = model_arn.split("/")[-1]
                 values["beta_use_converse_api"] = "nova" in resolved_base_model
         return values
 
@@ -877,7 +861,7 @@ def _stream(
         added_model_name = False
         # Track guardrails trace information for callback handling
         guardrails_trace_info = None
-        
+
         for chunk in self._prepare_input_and_invoke_stream(
             prompt=prompt,
             system=system,
@@ -902,7 +886,7 @@ def _stream(
                     if services_trace.get("signal") and run_manager:
                         # Store trace info for potential callback
                         guardrails_trace_info = services_trace
-                    
+
                     usage_metadata = generation_info.pop("usage_metadata", None)
                     response_metadata = generation_info
                     if not added_model_name:
@@ -924,7 +908,7 @@ def _stream(
                         generation_chunk.text, chunk=generation_chunk
                     )
                 yield generation_chunk
-        
+
         # If guardrails intervened during streaming, notify the callback handler
         if guardrails_trace_info and run_manager:
             run_manager.on_llm_error(
@@ -987,7 +971,9 @@ def _generate(
                     else:
                         system = self.system_prompt_with_tools
             elif provider == "openai":
-                formatted_messages = ChatPromptAdapter.format_messages(provider, messages)
+                formatted_messages = ChatPromptAdapter.format_messages(
+                    provider, messages
+                )
             else:
                 prompt = ChatPromptAdapter.convert_messages_to_prompt(
                     provider=provider, messages=messages, model=self._get_base_model()
@@ -1116,13 +1102,10 @@ def bind_tools(
 
             # Disallow forced tool use when thinking is enabled on specific Claude models
             base_model = self._get_base_model()
-            if (
-                any(
-                    x in base_model
-                    for x in ("claude-3-7-", "claude-opus-4-", "claude-sonnet-4-")
-                )
-                and thinking_in_params(self.model_kwargs or {})
-            ):
+            if any(
+                x in base_model
+                for x in ("claude-3-7-", "claude-opus-4-", "claude-sonnet-4-")
+            ) and thinking_in_params(self.model_kwargs or {}):
                 forced = False
                 if isinstance(tool_choice, bool):
                     forced = bool(tool_choice)

libs/aws/langchain_aws/chat_models/bedrock_converse.py

@@ -467,9 +467,9 @@ class Joke(BaseModel):
     additionalModelResponseFieldPaths.
     """
 
-    supports_tool_choice_values: Optional[
-        Sequence[Literal["auto", "any", "tool"]]
-    ] = None
+    supports_tool_choice_values: Optional[Sequence[Literal["auto", "any", "tool"]]] = (
+        None
+    )
     """Which types of tool_choice values the model supports.
 
     Inferred if not specified. Inferred as ('auto', 'any', 'tool') if a 'claude-3'

libs/aws/langchain_aws/chat_models/sagemaker_endpoint.py

@@ -1,4 +1,5 @@
 """Sagemaker Chat Model."""
+
 import io
 import logging
 from typing import Any, Dict, Iterator, List, Mapping, Optional
@@ -136,16 +137,16 @@ class ChatSagemakerEndpoint(BaseChatModel):
             EC2 instance, credentials from IMDS will be used.
 
         client: boto3 client for Sagemaker Endpoint
-        
+
         endpoint_name: The name of the endpoint from the deployed Sagemaker model.
 
-        content_handler: Implementation for model specific ChatContentHandler 
+        content_handler: Implementation for model specific ChatContentHandler
 
 
     Example:
         .. code-block:: python
 
-            from langchain_aws.chat_models.sagemaker_endpoint import 
+            from langchain_aws.chat_models.sagemaker_endpoint import
             ChatSagemakerEndpoint
             endpoint_name = (
                 "my-endpoint-name"
@@ -161,7 +162,7 @@ class ChatSagemakerEndpoint(BaseChatModel):
                 region_name=region_name,
                 credentials_profile_name=credentials_profile_name
             )
-        
+
             # Usage with Inference Component
             se = ChatSagemakerEndpoint(
                 endpoint_name=endpoint_name,
@@ -190,13 +191,13 @@ class ChatSagemakerEndpoint(BaseChatModel):
     Must be unique within an AWS Region."""
 
     inference_component_name: Optional[str] = None
-    """Optional name of the inference component to invoke 
+    """Optional name of the inference component to invoke
     if specified with endpoint name."""
 
     region_name: Optional[str] = ""
-    """The aws region, e.g., `us-west-2`. 
+    """The aws region, e.g., `us-west-2`.
 
-    Falls back to AWS_REGION or AWS_DEFAULT_REGION env variable or region specified in 
+    Falls back to AWS_REGION or AWS_DEFAULT_REGION env variable or region specified in
     ~/.aws/config in case it is not provided here.
     """
 
@@ -205,14 +206,14 @@ class ChatSagemakerEndpoint(BaseChatModel):
 
     Profile should either have access keys or role information specified.
     If not specified, the default credential profile or, if on an EC2 instance,
-    credentials from IMDS will be used. 
+    credentials from IMDS will be used.
     See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
     """
 
     aws_access_key_id: Optional[SecretStr] = Field(
         default_factory=secret_from_env("AWS_ACCESS_KEY_ID", default=None)
     )
-    """AWS access key id. 
+    """AWS access key id.
 
     If provided, aws_secret_access_key must also be provided.
     If not specified, the default credential profile or, if on an EC2 instance,
@@ -225,7 +226,7 @@ class ChatSagemakerEndpoint(BaseChatModel):
     aws_secret_access_key: Optional[SecretStr] = Field(
         default_factory=secret_from_env("AWS_SECRET_ACCESS_KEY", default=None)
     )
-    """AWS secret_access_key. 
+    """AWS secret_access_key.
 
     If provided, aws_access_key_id must also be provided.
     If not specified, the default credential profile or, if on an EC2 instance,
@@ -238,9 +239,9 @@ class ChatSagemakerEndpoint(BaseChatModel):
     aws_session_token: Optional[SecretStr] = Field(
         default_factory=secret_from_env("AWS_SESSION_TOKEN", default=None)
     )
-    """AWS session token. 
+    """AWS session token.
 
-    If provided, aws_access_key_id and aws_secret_access_key must 
+    If provided, aws_access_key_id and aws_secret_access_key must
     also be provided. Not required unless using temporary credentials.
     See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
 
@@ -275,7 +276,7 @@ class ContentHandler(ChatContentHandler):
                 def transform_input(self, prompt: List[Dict[str, Any]], model_kwargs: Dict) -> bytes:
                     input_str = json.dumps({prompt: prompt, **model_kwargs})
                     return input_str.encode('utf-8')
-                
+
                 def transform_output(self, output: bytes) -> BaseMessage:
                     response_json = json.loads(output.read().decode("utf-8"))
                     return response_json[0]["generated_text"]