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refactor(bedrock): record the native guardrail trace where the Bedrock response is parsed

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
kerry 2026-10-27 18:04:51 +00:00
parent 09db332fb7
commit d52ba903dd
6 changed files with 4 additions and 7 deletions
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1
litellm/litellm_core_utils/litellm_logging.py
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@ -2969,7 +2969,6 @@ class Logging(LiteLLMLoggingBaseClass):
result = self._handle_a2a_response_logging(result=result)
logging_result: Final = self.normalize_logging_result(result=result)
self.record_native_bedrock_guardrail(logging_result)
if cache_hit is None and _served_from_cache_at_no_charge(logging_result):
self.model_call_details["cache_hit"] = True

2
litellm/llms/bedrock/chat/converse_transformation.py
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@ -2859,6 +2859,8 @@ class AmazonConverseConfig(BaseConfig):
# Add "trace" from Bedrock guardrails - if user has opted in to returning it
if "trace" in completion_response:
setattr(model_response, "trace", completion_response["trace"])
if logging_obj is not None:
logging_obj.record_native_bedrock_guardrail(model_response)
# Add service_tier if present in Bedrock response
# Map Bedrock serviceTier (object) to OpenAI service_tier (string)

1
litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude_transformation.py
View file

@ -426,4 +426,5 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
trace: Final = object_mapping(body.get(BEDROCK_INVOKE_TRACE_FIELD))
if trace is not None:
setattr(response, BEDROCK_INVOKE_TRACE_FIELD, trace)
logging_obj.record_native_bedrock_guardrail(response)
return response

1
litellm/llms/custom_httpx/llm_http_handler.py
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@ -2705,6 +2705,7 @@ class BaseLLMHTTPHandler:
raw_response=response,
logging_obj=logging_obj,
)
logging_obj.record_native_bedrock_guardrail(initial_response)
return await self._finalize_anthropic_messages_response(
initial_response=initial_response,

1
litellm/proxy/common_request_processing.py
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@ -3260,7 +3260,6 @@ class ProxyBaseLLMRequestProcessing:
if is_unbilled_non_inference_call_from_params(logging_obj.call_type, logging_obj.litellm_params, response)
else computed_cost_for_headers
)
logging_obj.record_native_bedrock_guardrail(response)
request_metadata_bucket: Final[object] = get_or_create_metadata_bucket(self.data)[1] # pyright: ignore[reportUnknownVariableType, reportUnknownMemberType] # self.data is an untyped dict
guardrail_cost_for_headers: Final = guardrail_information_cost(
_guardrail_entries_for_headers(request_metadata_bucket, logging_obj) # pyright: ignore[reportUnknownArgumentType] # bucket comes from the untyped request dict

5
litellm/responses/litellm_completion_transformation/handler.py
View file

@ -8,7 +8,6 @@ from typing import Final
from pydantic import TypeAdapter
import litellm
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.responses.additional_tools import hoist_additional_tools
from litellm.responses.litellm_completion_transformation.compaction_items import holds_compaction_summary
from litellm.responses.litellm_completion_transformation.streaming_iterator import (
@ -134,10 +133,6 @@ class LiteLLMCompletionTransformationHandler:
litellm_completion_response: Final[ModelResponse | litellm.CustomStreamWrapper] = await litellm.acompletion(
**acompletion_args,
)
logging_obj: Final[object] = kwargs.get("litellm_logging_obj") # pyright: ignore[reportUnknownVariableType, reportUnknownMemberType] # kwargs is untyped
if isinstance(litellm_completion_response, ModelResponse) and isinstance(logging_obj, LiteLLMLoggingObj):
logging_obj.record_native_bedrock_guardrail(litellm_completion_response)
if isinstance(litellm_completion_response, ModelResponse):
responses_api_response: Final[ResponsesAPIResponse] = (
LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response(