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style: apply Black 23.12.1 formatting to modified files

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netbrah 2026-03-19 17:44:40 -04:00
parent 20917f69bb
commit 2a8ae70ad3
3 changed files with 47 additions and 47 deletions
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6
litellm/llms/bedrock/chat/converse_handler.py
View file

@ -335,9 +335,9 @@ class BedrockConverseLLM(BaseAWSLLM):
aws_external_id = optional_params.pop("aws_external_id", None)
optional_params.pop("aws_region_name", None)
litellm_params["aws_region_name"] = (
aws_region_name # [DO NOT DELETE] important for async calls
)
litellm_params[
"aws_region_name"
] = aws_region_name # [DO NOT DELETE] important for async calls
credentials: Credentials = self.get_credentials(
aws_access_key_id=aws_access_key_id,

12
litellm/llms/bedrock/chat/invoke_handler.py
View file

@ -553,9 +553,9 @@ class BedrockLLM(BaseAWSLLM):
content=None,
)
model_response.choices[0].message = _message # type: ignore
model_response._hidden_params["original_response"] = (
outputText # allow user to access raw anthropic tool calling response
)
model_response._hidden_params[
"original_response"
] = outputText # allow user to access raw anthropic tool calling response
if (
_is_function_call is True
and stream is not None
@ -888,9 +888,9 @@ class BedrockLLM(BaseAWSLLM):
): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
inference_params[k] = v
if stream is True:
inference_params["stream"] = (
True # cohere requires stream = True in inference params
)
inference_params[
"stream"
] = True # cohere requires stream = True in inference params
data = json.dumps({"prompt": prompt, **inference_params})
elif provider == "anthropic":
if self.is_claude_messages_api_model(model):

76
litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
View file

@ -498,9 +498,9 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
value = _remove_strict_from_schema(value)
for tool in value:
openai_function_object: Optional[ChatCompletionToolParamFunctionChunk] = (
None
)
openai_function_object: Optional[
ChatCompletionToolParamFunctionChunk
] = None
if "function" in tool: # tools list
_openai_function_object = ChatCompletionToolParamFunctionChunk( # type: ignore
**tool["function"]
@ -632,15 +632,15 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
_tools_list.append(search_tool)
if googleSearchRetrieval is not None:
retrieval_tool = Tools()
retrieval_tool[VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value] = (
googleSearchRetrieval
)
retrieval_tool[
VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value
] = googleSearchRetrieval
_tools_list.append(retrieval_tool)
if enterpriseWebSearch is not None:
enterprise_tool = Tools()
enterprise_tool[VertexToolName.ENTERPRISE_WEB_SEARCH.value] = (
enterpriseWebSearch
)
enterprise_tool[
VertexToolName.ENTERPRISE_WEB_SEARCH.value
] = enterpriseWebSearch
_tools_list.append(enterprise_tool)
if code_execution is not None:
code_tool = Tools()
@ -1087,16 +1087,16 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
param_description="thinking_budget",
)
if VertexGeminiConfig._is_gemini_3_or_newer(model):
optional_params["thinkingConfig"] = (
VertexGeminiConfig._map_reasoning_effort_to_thinking_level(
effort_value, model
)
optional_params[
"thinkingConfig"
] = VertexGeminiConfig._map_reasoning_effort_to_thinking_level(
effort_value, model
)
else:
optional_params["thinkingConfig"] = (
VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(
effort_value, model
)
optional_params[
"thinkingConfig"
] = VertexGeminiConfig._map_reasoning_effort_to_thinking_budget(
effort_value, model
)
elif param == "thinking":
# Validate no conflict with thinking_level
@ -1105,11 +1105,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
param_name="thinking",
param_description="thinking_budget",
)
optional_params["thinkingConfig"] = (
VertexGeminiConfig._map_thinking_param(
cast(AnthropicThinkingParam, value),
model=model,
)
optional_params[
"thinkingConfig"
] = VertexGeminiConfig._map_thinking_param(
cast(AnthropicThinkingParam, value),
model=model,
)
elif param == "modalities" and isinstance(value, list):
response_modalities = self.map_response_modalities(value)
@ -1468,10 +1468,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
_tool_response_chunk["provider_specific_fields"] = { # type: ignore
"thought_signature": thought_signature
}
_tool_response_chunk["id"] = (
_encode_tool_call_id_with_signature(
_tool_response_chunk["id"] or "", thought_signature
)
_tool_response_chunk[
"id"
] = _encode_tool_call_id_with_signature(
_tool_response_chunk["id"] or "", thought_signature
)
_tools.append(_tool_response_chunk)
cumulative_tool_call_idx += 1
@ -2281,28 +2281,28 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
## ADD METADATA TO RESPONSE ##
setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)
model_response._hidden_params["vertex_ai_grounding_metadata"] = (
grounding_metadata
)
model_response._hidden_params[
"vertex_ai_grounding_metadata"
] = grounding_metadata
setattr(
model_response, "vertex_ai_url_context_metadata", url_context_metadata
)
model_response._hidden_params["vertex_ai_url_context_metadata"] = (
url_context_metadata
)
model_response._hidden_params[
"vertex_ai_url_context_metadata"
] = url_context_metadata
setattr(model_response, "vertex_ai_safety_results", safety_ratings)
model_response._hidden_params["vertex_ai_safety_results"] = (
safety_ratings # older approach - maintaining to prevent regressions
)
model_response._hidden_params[
"vertex_ai_safety_results"
] = safety_ratings # older approach - maintaining to prevent regressions
## ADD CITATION METADATA ##
setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)
model_response._hidden_params["vertex_ai_citation_metadata"] = (
citation_metadata # older approach - maintaining to prevent regressions
)
model_response._hidden_params[
"vertex_ai_citation_metadata"
] = citation_metadata # older approach - maintaining to prevent regressions
## ADD TRAFFIC TYPE ##
traffic_type = completion_response.get("usageMetadata", {}).get(