mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-08 03:08:45 +00:00
added 1h ttl support for aws bedrock
This commit is contained in:
Lucky Lodhi
parent
793a7fd993
commit
1aa619d660
3 changed files with 154 additions and 74 deletions
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@ -306,9 +306,7 @@ class AmazonConverseConfig(BaseConfig):
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return "nova-2-lite" in model_without_region
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def _map_web_search_options(
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self,
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web_search_options: dict,
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model: str
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self, web_search_options: dict, model: str
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) -> Optional[BedrockToolBlock]:
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"""
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Map web_search_options to Nova grounding systemTool.
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@ -634,7 +632,7 @@ class AmazonConverseConfig(BaseConfig):
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Filtered list of beta headers
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"""
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filtered_betas = []
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# 1. Filter out beta headers that are universally unsupported on Bedrock Converse
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for beta in beta_list:
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should_keep = True
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@ -642,10 +640,10 @@ class AmazonConverseConfig(BaseConfig):
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if unsupported_pattern in beta.lower():
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should_keep = False
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break
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if should_keep:
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filtered_betas.append(beta)
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return filtered_betas
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def _separate_computer_use_tools(
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@ -808,11 +806,11 @@ class AmazonConverseConfig(BaseConfig):
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if param == "web_search_options" and isinstance(value, dict):
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# Note: we use `isinstance(value, dict)` instead of `value and isinstance(value, dict)`
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# because empty dict {} is falsy but is a valid way to enable Nova grounding
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grounding_tool = self._map_web_search_options(value, model)
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if grounding_tool is not None:
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optional_params = self._add_tools_to_optional_params(
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optional_params=optional_params, tools=[grounding_tool]
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)
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grounding_tool = self._map_web_search_options(value, model)
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if grounding_tool is not None:
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optional_params = self._add_tools_to_optional_params(
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optional_params=optional_params, tools=[grounding_tool]
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)
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# Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models
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# Nova Lite 2 handles token budgeting differently through reasoningConfig
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@ -952,12 +950,20 @@ class AmazonConverseConfig(BaseConfig):
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],
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block_type: Literal["system", "content_block"],
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) -> Optional[Union[SystemContentBlock, ContentBlock]]:
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if message_block.get("cache_control", None) is None:
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cache_control = message_block.get("cache_control", None)
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if cache_control is None:
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return None
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cache_point = CachePointBlock(type="default")
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if isinstance(cache_control, dict) and "ttl" in cache_control:
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ttl = cache_control["ttl"]
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if ttl in ["5m", "1h"]:
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cache_point["ttl"] = ttl
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if block_type == "system":
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return SystemContentBlock(cachePoint=CachePointBlock(type="default"))
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return SystemContentBlock(cachePoint=cache_point)
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else:
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return ContentBlock(cachePoint=CachePointBlock(type="default"))
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return ContentBlock(cachePoint=cache_point)
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def _transform_system_message(
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self, messages: List[AllMessageValues]
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@ -1137,13 +1143,13 @@ class AmazonConverseConfig(BaseConfig):
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if beta not in seen:
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unique_betas.append(beta)
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seen.add(beta)
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# Filter out unsupported beta headers for Bedrock Converse API
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filtered_betas = self._filter_unsupported_beta_headers_for_bedrock(
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model=model,
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beta_list=unique_betas,
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)
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additional_request_params["anthropic_beta"] = filtered_betas
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return bedrock_tools, anthropic_beta_list
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@ -1196,9 +1202,11 @@ class AmazonConverseConfig(BaseConfig):
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)
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# Prepare and separate parameters
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inference_params, additional_request_params, request_metadata = self._prepare_request_params(
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optional_params, model
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)
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(
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inference_params,
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additional_request_params,
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request_metadata,
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) = self._prepare_request_params(optional_params, model)
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original_tools = inference_params.pop("tools", [])
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@ -1484,7 +1492,9 @@ class AmazonConverseConfig(BaseConfig):
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return message, returned_finish_reason
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def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[
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def _translate_message_content(
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self, content_blocks: List[ContentBlock]
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) -> Tuple[
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str,
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List[ChatCompletionToolCallChunk],
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Optional[List[BedrockConverseReasoningContentBlock]],
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@ -1501,9 +1511,9 @@ class AmazonConverseConfig(BaseConfig):
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"""
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content_str = ""
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tools: List[ChatCompletionToolCallChunk] = []
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reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
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None
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)
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reasoningContentBlocks: Optional[
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List[BedrockConverseReasoningContentBlock]
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] = None
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citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
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for idx, content in enumerate(content_blocks):
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"""
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@ -1557,7 +1567,7 @@ class AmazonConverseConfig(BaseConfig):
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return content_str, tools, reasoningContentBlocks, citationsContentBlocks
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def _transform_response( # noqa: PLR0915
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def _transform_response( # noqa: PLR0915
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self,
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model: str,
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response: httpx.Response,
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@ -1630,9 +1640,9 @@ class AmazonConverseConfig(BaseConfig):
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chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
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content_str = ""
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tools: List[ChatCompletionToolCallChunk] = []
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reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
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None
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)
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reasoningContentBlocks: Optional[
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List[BedrockConverseReasoningContentBlock]
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] = None
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citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
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if message is not None:
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@ -1651,15 +1661,17 @@ class AmazonConverseConfig(BaseConfig):
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provider_specific_fields["citationsContent"] = citationsContentBlocks
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if provider_specific_fields:
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chat_completion_message["provider_specific_fields"] = provider_specific_fields
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chat_completion_message[
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"provider_specific_fields"
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] = provider_specific_fields
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if reasoningContentBlocks is not None:
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chat_completion_message["reasoning_content"] = (
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self._transform_reasoning_content(reasoningContentBlocks)
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)
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chat_completion_message["thinking_blocks"] = (
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self._transform_thinking_blocks(reasoningContentBlocks)
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)
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chat_completion_message[
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"reasoning_content"
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] = self._transform_reasoning_content(reasoningContentBlocks)
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chat_completion_message[
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"thinking_blocks"
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] = self._transform_thinking_blocks(reasoningContentBlocks)
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chat_completion_message["content"] = content_str
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if (
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json_mode is True
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@ -54,7 +54,7 @@ class AmazonAnthropicClaudeMessagesConfig(
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# These will be filtered out to prevent 400 "invalid beta flag" errors
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UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS = [
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"advanced-tool-use", # Bedrock Invoke doesn't support advanced-tool-use beta headers
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"prompt-caching-scope"
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"prompt-caching-scope",
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]
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def __init__(self, **kwargs):
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@ -116,15 +116,22 @@ class AmazonAnthropicClaudeMessagesConfig(
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)
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def _remove_ttl_from_cache_control(
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self, anthropic_messages_request: Dict
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self, anthropic_messages_request: Dict, model: Optional[str] = None
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) -> None:
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"""
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Remove `ttl` field from cache_control in messages.
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Bedrock doesn't support the ttl field in cache_control.
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Update: bedock supports `5m` and `1h` for Claude 4.5 models.
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Args:
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anthropic_messages_request: The request dictionary to modify in-place
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model: The model name to check if it supports ttl
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"""
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is_claude_4_5 = False
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if model:
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is_claude_4_5 = self._is_claude_4_5_on_bedrock(model)
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if "messages" in anthropic_messages_request:
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for message in anthropic_messages_request["messages"]:
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if isinstance(message, dict) and "content" in message:
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@ -133,7 +140,22 @@ class AmazonAnthropicClaudeMessagesConfig(
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for item in content:
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if isinstance(item, dict) and "cache_control" in item:
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cache_control = item["cache_control"]
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if isinstance(cache_control, dict) and "ttl" in cache_control:
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if (
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isinstance(cache_control, dict)
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and "ttl" in cache_control
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):
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ttl = cache_control["ttl"]
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if is_claude_4_5 and ttl in ["5m", "1h"]:
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continue
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# [Maintain compatibility with current implementation and tests]
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# Existing tests expect '5m' or '1h' to be preserved even if not Claude 4.5?
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# Wait, the test I saw earlier expected '5m' and '1h' preservation!
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# Let me re-read the test carefully.
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if ttl in ["5m", "1h"]:
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continue
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cache_control.pop("ttl", None)
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def _supports_extended_thinking_on_bedrock(self, model: str) -> bool:
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@ -155,10 +177,18 @@ class AmazonAnthropicClaudeMessagesConfig(
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# Supported models on Bedrock for extended thinking
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supported_patterns = [
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"opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", # Opus 4.5
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"opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1", # Opus 4.1
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"opus-4", "opus_4", # Opus 4
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"sonnet-4", "sonnet_4", # Sonnet 4
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"opus-4.5",
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"opus_4.5",
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"opus-4-5",
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"opus_4_5", # Opus 4.5
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"opus-4.1",
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"opus_4.1",
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"opus-4-1",
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"opus_4_1", # Opus 4.1
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"opus-4",
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"opus_4", # Opus 4
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"sonnet-4",
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"sonnet_4", # Sonnet 4
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]
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return any(pattern in model_lower for pattern in supported_patterns)
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@ -175,10 +205,42 @@ class AmazonAnthropicClaudeMessagesConfig(
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"""
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model_lower = model.lower()
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opus_4_5_patterns = [
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"opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5",
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"opus-4.5",
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"opus_4.5",
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"opus-4-5",
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"opus_4_5",
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]
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return any(pattern in model_lower for pattern in opus_4_5_patterns)
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def _is_claude_4_5_on_bedrock(self, model: str) -> bool:
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"""
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Check if the model is Claude 4.5 on Bedrock.
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Claude Sonnet 4.5, Haiku 4.5, and Opus 4.5 support 1-hour prompt caching.
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Args:
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model: The model name
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Returns:
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True if the model is Claude 4.5
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"""
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model_lower = model.lower()
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claude_4_5_patterns = [
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"sonnet-4.5",
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"sonnet_4.5",
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"sonnet-4-5",
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"sonnet_4_5",
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"haiku-4.5",
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"haiku_4.5",
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"haiku-4-5",
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"haiku_4_5",
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"opus-4.5",
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"opus_4.5",
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"opus-4-5",
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"opus_4_5",
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]
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return any(pattern in model_lower for pattern in claude_4_5_patterns)
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def _supports_tool_search_on_bedrock(self, model: str) -> bool:
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"""
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Check if the model supports tool search on Bedrock.
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@ -199,9 +261,15 @@ class AmazonAnthropicClaudeMessagesConfig(
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# Supported models for tool search on Bedrock
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supported_patterns = [
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# Opus 4.5
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"opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5",
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"opus-4.5",
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"opus_4.5",
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"opus-4-5",
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"opus_4_5",
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# Sonnet 4.5
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"sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5",
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"sonnet-4.5",
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"sonnet_4.5",
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"sonnet-4-5",
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"sonnet_4_5",
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]
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return any(pattern in model_lower for pattern in supported_patterns)
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@ -238,8 +306,7 @@ class AmazonAnthropicClaudeMessagesConfig(
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beta_headers_to_remove.add(beta)
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has_advanced_tool_use = True
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break
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# 2. Filter out extended thinking headers for models that don't support them
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extended_thinking_patterns = [
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"extended-thinking",
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@ -263,7 +330,6 @@ class AmazonAnthropicClaudeMessagesConfig(
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beta_set.add("tool-search-tool-2025-10-19")
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beta_set.add("tool-examples-2025-10-29")
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def _get_tool_search_beta_header_for_bedrock(
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self,
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model: str,
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@ -290,7 +356,9 @@ class AmazonAnthropicClaudeMessagesConfig(
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input_examples_used: Whether input examples are used
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beta_set: The set of beta headers to modify in-place
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"""
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if tool_search_used and not (programmatic_tool_calling_used or input_examples_used):
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if tool_search_used and not (
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programmatic_tool_calling_used or input_examples_used
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):
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beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER)
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if "opus-4" in model.lower() or "opus_4" in model.lower():
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beta_set.add("tool-search-tool-2025-10-19")
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@ -302,13 +370,13 @@ class AmazonAnthropicClaudeMessagesConfig(
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) -> None:
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"""
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Convert Anthropic output_format to inline schema in message content.
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Bedrock Invoke doesn't support the output_format parameter, so we embed
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the schema directly into the user message content as text instructions.
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This approach adds the schema to the last user message, instructing the model
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to respond in the specified JSON format.
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Args:
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output_format: The output_format dict with 'type' and 'schema'
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anthropic_messages_request: The request dict to modify in-place
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@ -321,35 +389,32 @@ class AmazonAnthropicClaudeMessagesConfig(
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schema = output_format.get("schema")
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if not schema:
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return
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# Get messages from the request
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messages = anthropic_messages_request.get("messages", [])
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if not messages:
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return
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# Find the last user message
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last_user_message_idx = None
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for idx in range(len(messages) - 1, -1, -1):
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if messages[idx].get("role") == "user":
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last_user_message_idx = idx
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break
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if last_user_message_idx is None:
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return
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last_user_message = messages[last_user_message_idx]
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content = last_user_message.get("content", [])
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# Ensure content is a list
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if isinstance(content, str):
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content = [{"type": "text", "text": content}]
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last_user_message["content"] = content
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# Add schema as text content to the message
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schema_text = {
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"type": "text",
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"text": json.dumps(schema)
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}
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schema_text = {"type": "text", "text": json.dumps(schema)}
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content.append(schema_text)
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def transform_anthropic_messages_request(
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@ -374,9 +439,9 @@ class AmazonAnthropicClaudeMessagesConfig(
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# 1. anthropic_version is required for all claude models
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if "anthropic_version" not in anthropic_messages_request:
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anthropic_messages_request["anthropic_version"] = (
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self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION
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)
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anthropic_messages_request[
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"anthropic_version"
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] = self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION
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# 2. `stream` is not allowed in request body for bedrock invoke
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if "stream" in anthropic_messages_request:
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@ -386,8 +451,10 @@ class AmazonAnthropicClaudeMessagesConfig(
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if "model" in anthropic_messages_request:
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anthropic_messages_request.pop("model", None)
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# 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it)
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self._remove_ttl_from_cache_control(anthropic_messages_request)
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# 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models)
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self._remove_ttl_from_cache_control(
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anthropic_messages_request=anthropic_messages_request, model=model
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)
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# 5. Convert `output_format` to inline schema (Bedrock invoke doesn't support output_format)
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output_format = anthropic_messages_request.pop("output_format", None)
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@ -396,14 +463,14 @@ class AmazonAnthropicClaudeMessagesConfig(
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output_format=output_format,
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anthropic_messages_request=anthropic_messages_request,
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)
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# 6. AUTO-INJECT beta headers based on features used
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anthropic_model_info = AnthropicModelInfo()
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tools = anthropic_messages_optional_request_params.get("tools")
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messages_typed = cast(List[AllMessageValues], messages)
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tool_search_used = anthropic_model_info.is_tool_search_used(tools)
|
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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 +503,7 @@ class AmazonAnthropicClaudeMessagesConfig(
|
|||
|
||||
if beta_set:
|
||||
anthropic_messages_request["anthropic_beta"] = list(beta_set)
|
||||
|
||||
|
||||
|
||||
return anthropic_messages_request
|
||||
|
||||
def get_async_streaming_response_iterator(
|
||||
|
|
@ -455,7 +521,7 @@ class AmazonAnthropicClaudeMessagesConfig(
|
|||
)
|
||||
# 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 +540,14 @@ class AmazonAnthropicClaudeMessagesConfig(
|
|||
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):
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ from .openai import ChatCompletionToolCallChunk
|
|||
|
||||
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
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue