diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index e025c08b025..296a88f9a0b 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -599,9 +599,9 @@ def get_tracer_to_use_for_request(self, kwargs: dict) -> Tracer: def _get_dynamic_otel_headers_from_kwargs(self, kwargs) -> Optional[dict]: """Extract dynamic headers from kwargs if available.""" - standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( - kwargs.get("standard_callback_dynamic_params") - ) + standard_callback_dynamic_params: Optional[ + StandardCallbackDynamicParams + ] = kwargs.get("standard_callback_dynamic_params") if not standard_callback_dynamic_params: return None @@ -619,7 +619,9 @@ def _get_tracer_with_dynamic_headers(self, dynamic_headers: dict): # Prevents thread exhaustion by reusing providers for the same credential sets (e.g. per-team keys) cache_key = str(sorted(dynamic_headers.items())) if cache_key in self._tracer_provider_cache: - return self._tracer_provider_cache[cache_key].get_tracer(LITELLM_TRACER_NAME) + return self._tracer_provider_cache[cache_key].get_tracer( + LITELLM_TRACER_NAME + ) # Create a temporary tracer provider with dynamic headers temp_provider = TracerProvider(resource=self._get_litellm_resource(self.config)) @@ -1618,7 +1620,6 @@ def set_attributes( # noqa: PLR0915 for idx, choice in enumerate(response_obj.get("choices")): if choice.get("finish_reason"): - message = choice.get("message") tool_calls = message.get("tool_calls") if tool_calls: @@ -1631,7 +1632,9 @@ def set_attributes( # noqa: PLR0915 ) except Exception as e: - self.handle_callback_failure(callback_name=self.callback_name or "opentelemetry") + self.handle_callback_failure( + callback_name=self.callback_name or "opentelemetry" + ) verbose_logger.exception( "OpenTelemetry logging error in set_attributes %s", str(e) ) diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index f6d7e128580..6591e152a14 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -30,6 +30,8 @@ from litellm.llms.anthropic.chat.transformation import AnthropicConfig from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException from litellm.types.llms.bedrock import * + +from ..common_utils import is_claude_4_5_on_bedrock from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionAssistantMessage, @@ -306,9 +308,7 @@ def _is_nova_lite_2_model(self, model: str) -> bool: return "nova-2-lite" in model_without_region def _map_web_search_options( - self, - web_search_options: dict, - model: str + self, web_search_options: dict, model: str ) -> Optional[BedrockToolBlock]: """ Map web_search_options to Nova grounding systemTool. @@ -634,7 +634,7 @@ def _filter_unsupported_beta_headers_for_bedrock( Filtered list of beta headers """ filtered_betas = [] - + # 1. Filter out beta headers that are universally unsupported on Bedrock Converse for beta in beta_list: should_keep = True @@ -642,10 +642,10 @@ def _filter_unsupported_beta_headers_for_bedrock( if unsupported_pattern in beta.lower(): should_keep = False break - + if should_keep: filtered_betas.append(beta) - + return filtered_betas def _separate_computer_use_tools( @@ -808,11 +808,11 @@ def map_openai_params( if param == "web_search_options" and isinstance(value, dict): # Note: we use `isinstance(value, dict)` instead of `value and isinstance(value, dict)` # because empty dict {} is falsy but is a valid way to enable Nova grounding - grounding_tool = self._map_web_search_options(value, model) - if grounding_tool is not None: - optional_params = self._add_tools_to_optional_params( - optional_params=optional_params, tools=[grounding_tool] - ) + grounding_tool = self._map_web_search_options(value, model) + if grounding_tool is not None: + optional_params = self._add_tools_to_optional_params( + optional_params=optional_params, tools=[grounding_tool] + ) # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models # Nova Lite 2 handles token budgeting differently through reasoningConfig @@ -926,6 +926,7 @@ def _get_cache_point_block( ChatCompletionAssistantMessage, ], block_type: Literal["system"], + model: Optional[str] = None, ) -> Optional[SystemContentBlock]: pass @@ -939,6 +940,7 @@ def _get_cache_point_block( ChatCompletionAssistantMessage, ], block_type: Literal["content_block"], + model: Optional[str] = None, ) -> Optional[ContentBlock]: pass @@ -951,16 +953,26 @@ def _get_cache_point_block( ChatCompletionAssistantMessage, ], block_type: Literal["system", "content_block"], + model: Optional[str] = None, ) -> Optional[Union[SystemContentBlock, ContentBlock]]: - if message_block.get("cache_control", None) is None: + cache_control = message_block.get("cache_control", None) + if cache_control is None: return None + + cache_point = CachePointBlock(type="default") + if isinstance(cache_control, dict) and "ttl" in cache_control: + ttl = cache_control["ttl"] + if ttl in ["5m", "1h"] and model is not None: + if is_claude_4_5_on_bedrock(model): + cache_point["ttl"] = ttl + if block_type == "system": - return SystemContentBlock(cachePoint=CachePointBlock(type="default")) + return SystemContentBlock(cachePoint=cache_point) else: - return ContentBlock(cachePoint=CachePointBlock(type="default")) + return ContentBlock(cachePoint=cache_point) def _transform_system_message( - self, messages: List[AllMessageValues] + self, messages: List[AllMessageValues], model: Optional[str] = None ) -> Tuple[List[AllMessageValues], List[SystemContentBlock]]: system_prompt_indices = [] system_content_blocks: List[SystemContentBlock] = [] @@ -972,7 +984,7 @@ def _transform_system_message( SystemContentBlock(text=message["content"]) ) cache_block = self._get_cache_point_block( - message, block_type="system" + message, block_type="system", model=model ) if cache_block: system_content_blocks.append(cache_block) @@ -983,7 +995,7 @@ def _transform_system_message( SystemContentBlock(text=m["text"]) ) cache_block = self._get_cache_point_block( - m, block_type="system" + m, block_type="system", model=model ) if cache_block: system_content_blocks.append(cache_block) @@ -1137,13 +1149,13 @@ def _process_tools_and_beta( if beta not in seen: unique_betas.append(beta) seen.add(beta) - + # Filter out unsupported beta headers for Bedrock Converse API filtered_betas = self._filter_unsupported_beta_headers_for_bedrock( model=model, beta_list=unique_betas, ) - + additional_request_params["anthropic_beta"] = filtered_betas return bedrock_tools, anthropic_beta_list @@ -1196,9 +1208,11 @@ def _transform_request_helper( ) # Prepare and separate parameters - inference_params, additional_request_params, request_metadata = self._prepare_request_params( - optional_params, model - ) + ( + inference_params, + additional_request_params, + request_metadata, + ) = self._prepare_request_params(optional_params, model) original_tools = inference_params.pop("tools", []) @@ -1250,7 +1264,9 @@ async def _async_transform_request( litellm_params: dict, headers: Optional[dict] = None, ) -> RequestObject: - messages, system_content_blocks = self._transform_system_message(messages) + messages, system_content_blocks = self._transform_system_message( + messages, model=model + ) # Convert last user message to guarded_text if guardrailConfig is present messages = self._convert_consecutive_user_messages_to_guarded_text( @@ -1306,7 +1322,9 @@ def _transform_request( litellm_params: dict, headers: Optional[dict] = None, ) -> RequestObject: - messages, system_content_blocks = self._transform_system_message(messages) + messages, system_content_blocks = self._transform_system_message( + messages, model=model + ) # Convert last user message to guarded_text if guardrailConfig is present messages = self._convert_consecutive_user_messages_to_guarded_text( @@ -1484,7 +1502,9 @@ def apply_tool_call_transformation_if_needed( return message, returned_finish_reason - def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[ + def _translate_message_content( + self, content_blocks: List[ContentBlock] + ) -> Tuple[ str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], @@ -1501,9 +1521,9 @@ def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tupl """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None citationsContentBlocks: Optional[List[CitationsContentBlock]] = None for idx, content in enumerate(content_blocks): """ @@ -1557,7 +1577,7 @@ def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tupl return content_str, tools, reasoningContentBlocks, citationsContentBlocks - def _transform_response( # noqa: PLR0915 + def _transform_response( # noqa: PLR0915 self, model: str, response: httpx.Response, @@ -1630,9 +1650,9 @@ def _transform_response( # noqa: PLR0915 chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None citationsContentBlocks: Optional[List[CitationsContentBlock]] = None if message is not None: @@ -1651,15 +1671,17 @@ def _transform_response( # noqa: PLR0915 provider_specific_fields["citationsContent"] = citationsContentBlocks if provider_specific_fields: - chat_completion_message["provider_specific_fields"] = provider_specific_fields + chat_completion_message[ + "provider_specific_fields" + ] = provider_specific_fields if reasoningContentBlocks is not None: - chat_completion_message["reasoning_content"] = ( - self._transform_reasoning_content(reasoningContentBlocks) - ) - chat_completion_message["thinking_blocks"] = ( - self._transform_thinking_blocks(reasoningContentBlocks) - ) + chat_completion_message[ + "reasoning_content" + ] = self._transform_reasoning_content(reasoningContentBlocks) + chat_completion_message[ + "thinking_blocks" + ] = self._transform_thinking_blocks(reasoningContentBlocks) chat_completion_message["content"] = content_str if ( json_mode is True diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 65d237bdbdf..4c87f6fa994 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -446,6 +446,29 @@ def get_bedrock_base_model(model: str) -> str: return model +def is_claude_4_5_on_bedrock(model: str) -> bool: + """ + Check if the model is a Claude 4.5 model on Bedrock. + Claude 4.5 models support prompt caching with '5m' and '1h' TTL on Bedrock. + """ + model_lower = model.lower() + claude_4_5_patterns = [ + "sonnet-4.5", + "sonnet_4.5", + "sonnet-4-5", + "sonnet_4_5", + "haiku-4.5", + "haiku_4.5", + "haiku-4-5", + "haiku_4_5", + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", + ] + return any(pattern in model_lower for pattern in claude_4_5_patterns) + + # Import after standalone functions to avoid circular imports from litellm.llms.bedrock.count_tokens.bedrock_token_counter import BedrockTokenCounter @@ -815,21 +838,23 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]: # If it's already a list, return it if isinstance(anthropic_beta_header, list): return anthropic_beta_header - + # Try to parse as JSON array first (e.g., '["interleaved-thinking-2025-05-14", "claude-code-20250219"]') if isinstance(anthropic_beta_header, str): anthropic_beta_header = anthropic_beta_header.strip() - if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith("]"): + if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith( + "]" + ): try: parsed = json.loads(anthropic_beta_header) if isinstance(parsed, list): return [str(beta).strip() for beta in parsed] except json.JSONDecodeError: pass # Fall through to comma-separated parsing - + # Fall back to comma-separated values return [beta.strip() for beta in anthropic_beta_header.split(",")] - + return [] diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index b1c45ea83a2..90de67a822f 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -23,7 +23,10 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) -from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers +from litellm.llms.bedrock.common_utils import ( + get_anthropic_beta_from_headers, + is_claude_4_5_on_bedrock, +) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams @@ -54,7 +57,7 @@ class AmazonAnthropicClaudeMessagesConfig( # These will be filtered out to prevent 400 "invalid beta flag" errors UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS = [ "advanced-tool-use", # Bedrock Invoke doesn't support advanced-tool-use beta headers - "prompt-caching-scope" + "prompt-caching-scope", ] def __init__(self, **kwargs): @@ -116,15 +119,22 @@ def get_complete_url( ) def _remove_ttl_from_cache_control( - self, anthropic_messages_request: Dict + self, anthropic_messages_request: Dict, model: Optional[str] = None ) -> None: """ Remove `ttl` field from cache_control in messages. Bedrock doesn't support the ttl field in cache_control. + Update: Bedock supports `5m` and `1h` for Claude 4.5 models. + Args: anthropic_messages_request: The request dictionary to modify in-place + model: The model name to check if it supports ttl """ + is_claude_4_5 = False + if model: + is_claude_4_5 = self._is_claude_4_5_on_bedrock(model) + if "messages" in anthropic_messages_request: for message in anthropic_messages_request["messages"]: if isinstance(message, dict) and "content" in message: @@ -133,7 +143,14 @@ def _remove_ttl_from_cache_control( for item in content: if isinstance(item, dict) and "cache_control" in item: cache_control = item["cache_control"] - if isinstance(cache_control, dict) and "ttl" in cache_control: + if ( + isinstance(cache_control, dict) + and "ttl" in cache_control + ): + ttl = cache_control["ttl"] + if is_claude_4_5 and ttl in ["5m", "1h"]: + continue + cache_control.pop("ttl", None) def _supports_extended_thinking_on_bedrock(self, model: str) -> bool: @@ -155,10 +172,18 @@ def _supports_extended_thinking_on_bedrock(self, model: str) -> bool: # Supported models on Bedrock for extended thinking supported_patterns = [ - "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", # Opus 4.5 - "opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1", # Opus 4.1 - "opus-4", "opus_4", # Opus 4 - "sonnet-4", "sonnet_4", # Sonnet 4 + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", # Opus 4.5 + "opus-4.1", + "opus_4.1", + "opus-4-1", + "opus_4_1", # Opus 4.1 + "opus-4", + "opus_4", # Opus 4 + "sonnet-4", + "sonnet_4", # Sonnet 4 ] return any(pattern in model_lower for pattern in supported_patterns) @@ -175,10 +200,27 @@ def _is_claude_opus_4_5(self, model: str) -> bool: """ model_lower = model.lower() opus_4_5_patterns = [ - "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", ] return any(pattern in model_lower for pattern in opus_4_5_patterns) + def _is_claude_4_5_on_bedrock(self, model: str) -> bool: + """ + Check if the model is Claude 4.5 on Bedrock. + + Claude Sonnet 4.5, Haiku 4.5, and Opus 4.5 support 1-hour prompt caching. + + Args: + model: The model name + + Returns: + True if the model is Claude 4.5 + """ + return is_claude_4_5_on_bedrock(model) + def _supports_tool_search_on_bedrock(self, model: str) -> bool: """ Check if the model supports tool search on Bedrock. @@ -199,9 +241,15 @@ def _supports_tool_search_on_bedrock(self, model: str) -> bool: # Supported models for tool search on Bedrock supported_patterns = [ # Opus 4.5 - "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", # Sonnet 4.5 - "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5", + "sonnet-4.5", + "sonnet_4.5", + "sonnet-4-5", + "sonnet_4_5", ] return any(pattern in model_lower for pattern in supported_patterns) @@ -238,8 +286,7 @@ def _filter_unsupported_beta_headers_for_bedrock( beta_headers_to_remove.add(beta) has_advanced_tool_use = True break - - + # 2. Filter out extended thinking headers for models that don't support them extended_thinking_patterns = [ "extended-thinking", @@ -263,7 +310,6 @@ def _filter_unsupported_beta_headers_for_bedrock( beta_set.add("tool-search-tool-2025-10-19") beta_set.add("tool-examples-2025-10-29") - def _get_tool_search_beta_header_for_bedrock( self, model: str, @@ -290,7 +336,9 @@ def _get_tool_search_beta_header_for_bedrock( input_examples_used: Whether input examples are used beta_set: The set of beta headers to modify in-place """ - if tool_search_used and not (programmatic_tool_calling_used or input_examples_used): + if tool_search_used and not ( + programmatic_tool_calling_used or input_examples_used + ): beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER) if "opus-4" in model.lower() or "opus_4" in model.lower(): beta_set.add("tool-search-tool-2025-10-19") @@ -302,13 +350,13 @@ def _convert_output_format_to_inline_schema( ) -> None: """ Convert Anthropic output_format to inline schema in message content. - + Bedrock Invoke doesn't support the output_format parameter, so we embed the schema directly into the user message content as text instructions. - + This approach adds the schema to the last user message, instructing the model to respond in the specified JSON format. - + Args: output_format: The output_format dict with 'type' and 'schema' anthropic_messages_request: The request dict to modify in-place @@ -321,35 +369,32 @@ def _convert_output_format_to_inline_schema( schema = output_format.get("schema") if not schema: return - + # Get messages from the request messages = anthropic_messages_request.get("messages", []) if not messages: return - + # Find the last user message last_user_message_idx = None for idx in range(len(messages) - 1, -1, -1): if messages[idx].get("role") == "user": last_user_message_idx = idx break - + if last_user_message_idx is None: return - + last_user_message = messages[last_user_message_idx] content = last_user_message.get("content", []) - + # Ensure content is a list if isinstance(content, str): content = [{"type": "text", "text": content}] last_user_message["content"] = content - + # Add schema as text content to the message - schema_text = { - "type": "text", - "text": json.dumps(schema) - } + schema_text = {"type": "text", "text": json.dumps(schema)} content.append(schema_text) def transform_anthropic_messages_request( @@ -374,9 +419,9 @@ def transform_anthropic_messages_request( # 1. anthropic_version is required for all claude models if "anthropic_version" not in anthropic_messages_request: - anthropic_messages_request["anthropic_version"] = ( - self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION - ) + anthropic_messages_request[ + "anthropic_version" + ] = self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION # 2. `stream` is not allowed in request body for bedrock invoke if "stream" in anthropic_messages_request: @@ -386,8 +431,10 @@ def transform_anthropic_messages_request( if "model" in anthropic_messages_request: anthropic_messages_request.pop("model", None) - # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it) - self._remove_ttl_from_cache_control(anthropic_messages_request) + # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models) + self._remove_ttl_from_cache_control( + anthropic_messages_request=anthropic_messages_request, model=model + ) # 5. Convert `output_format` to inline schema (Bedrock invoke doesn't support output_format) output_format = anthropic_messages_request.pop("output_format", None) @@ -396,14 +443,14 @@ def transform_anthropic_messages_request( output_format=output_format, anthropic_messages_request=anthropic_messages_request, ) - + # 6. AUTO-INJECT beta headers based on features used anthropic_model_info = AnthropicModelInfo() tools = anthropic_messages_optional_request_params.get("tools") messages_typed = cast(List[AllMessageValues], messages) tool_search_used = anthropic_model_info.is_tool_search_used(tools) - programmatic_tool_calling_used = anthropic_model_info.is_programmatic_tool_calling_used( - tools + programmatic_tool_calling_used = ( + anthropic_model_info.is_programmatic_tool_calling_used(tools) ) input_examples_used = anthropic_model_info.is_input_examples_used(tools) @@ -436,8 +483,7 @@ def transform_anthropic_messages_request( if beta_set: anthropic_messages_request["anthropic_beta"] = list(beta_set) - - + return anthropic_messages_request def get_async_streaming_response_iterator( @@ -455,7 +501,7 @@ def get_async_streaming_response_iterator( ) # Convert decoded Bedrock events to Server-Sent Events expected by Anthropic clients. return self.bedrock_sse_wrapper( - completion_stream=completion_stream, + completion_stream=completion_stream, litellm_logging_obj=litellm_logging_obj, request_body=request_body, ) @@ -474,14 +520,14 @@ async def bedrock_sse_wrapper( from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( BaseAnthropicMessagesStreamingIterator, ) + handler = BaseAnthropicMessagesStreamingIterator( litellm_logging_obj=litellm_logging_obj, request_body=request_body, ) - + async for chunk in handler.async_sse_wrapper(completion_stream): yield chunk - class AmazonAnthropicClaudeMessagesStreamDecoder(AWSEventStreamDecoder): diff --git a/litellm/llms/github_copilot/chat/transformation.py b/litellm/llms/github_copilot/chat/transformation.py index d613c82402d..be8ad7d0877 100644 --- a/litellm/llms/github_copilot/chat/transformation.py +++ b/litellm/llms/github_copilot/chat/transformation.py @@ -1,5 +1,6 @@ from typing import List, Optional, Tuple + from litellm.exceptions import AuthenticationError from litellm.llms.openai.openai import OpenAIConfig from litellm.types.llms.openai import AllMessageValues @@ -29,9 +30,7 @@ def _get_openai_compatible_provider_info( api_key: Optional[str], custom_llm_provider: str, ) -> Tuple[Optional[str], Optional[str], str]: - dynamic_api_base = ( - self.authenticator.get_api_base() or GITHUB_COPILOT_API_BASE - ) + dynamic_api_base = self.authenticator.get_api_base() or GITHUB_COPILOT_API_BASE try: dynamic_api_key = self.authenticator.get_api_key() except GetAPIKeyError as e: @@ -140,7 +139,7 @@ def _has_vision_content(self, messages: List[AllMessageValues]) -> bool: """ Check if any message contains vision content (images). Returns True if any message has content with vision-related types, otherwise False. - + Checks for: - image_url content type (OpenAI format) - Content items with type 'image_url' diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py index 6293efe9e09..998c60ab60d 100644 --- a/litellm/types/llms/bedrock.py +++ b/litellm/types/llms/bedrock.py @@ -8,6 +8,7 @@ class CachePointBlock(TypedDict, total=False): type: Literal["default"] + ttl: str class SystemContentBlock(TypedDict, total=False): @@ -961,6 +962,7 @@ class BedrockGetBatchResponse(TypedDict, total=False): timeoutDurationInHours: Optional[int] clientRequestToken: Optional[str] + class BedrockToolBlock(TypedDict, total=False): toolSpec: Optional[ToolSpecBlock] systemTool: Optional[SystemToolBlock] # For Nova grounding