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(feat) Add usage tracking for streaming /anthropic passthrough routes (#6842)
* use 1 file for AnthropicPassthroughLoggingHandler * add support for anthropic streaming usage tracking * ci/cd run again * fix - add real streaming for anthropic pass through * remove unused function stream_response * working anthropic streaming logging * fix code quality * fix use 1 file for vertex success handler * use helper for _handle_logging_vertex_collected_chunks * enforce vertex streaming to use sse for streaming * test test_basic_vertex_ai_pass_through_streaming_with_spendlog * fix type hints * add comment * fix linting * add pass through logging unit testing
This commit is contained in:
Ishaan Jaff
GitHub
parent
aa6b133557
commit
5fd5bb615c
12 changed files with 688 additions and 295 deletions
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@ -4,114 +4,116 @@ from datetime import datetime
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from enum import Enum
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from typing import AsyncIterable, Dict, List, Optional, Union
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import httpx
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import litellm
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from litellm._logging import verbose_proxy_logger
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.anthropic.chat.handler import (
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ModelResponseIterator as AnthropicIterator,
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)
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from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
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ModelResponseIterator as VertexAIIterator,
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)
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from litellm.types.utils import GenericStreamingChunk
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from .llm_provider_handlers.anthropic_passthrough_logging_handler import (
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AnthropicPassthroughLoggingHandler,
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)
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from .llm_provider_handlers.vertex_passthrough_logging_handler import (
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VertexPassthroughLoggingHandler,
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)
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from .success_handler import PassThroughEndpointLogging
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from .types import EndpointType
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def get_litellm_chunk(
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model_iterator: VertexAIIterator,
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custom_stream_wrapper: litellm.utils.CustomStreamWrapper,
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chunk_dict: Dict,
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) -> Optional[Dict]:
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generic_chunk: GenericStreamingChunk = model_iterator.chunk_parser(chunk_dict)
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if generic_chunk:
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return custom_stream_wrapper.chunk_creator(chunk=generic_chunk)
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return None
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def get_iterator_class_from_endpoint_type(
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endpoint_type: EndpointType,
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) -> Optional[type]:
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if endpoint_type == EndpointType.VERTEX_AI:
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return VertexAIIterator
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return None
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async def chunk_processor(
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aiter_bytes: AsyncIterable[bytes],
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response: httpx.Response,
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request_body: Optional[dict],
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litellm_logging_obj: LiteLLMLoggingObj,
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endpoint_type: EndpointType,
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start_time: datetime,
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passthrough_success_handler_obj: PassThroughEndpointLogging,
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url_route: str,
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) -> AsyncIterable[bytes]:
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):
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"""
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- Yields chunks from the response
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- Collect non-empty chunks for post-processing (logging)
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"""
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collected_chunks: List[str] = [] # List to store all chunks
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try:
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async for chunk in response.aiter_lines():
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verbose_proxy_logger.debug(f"Processing chunk: {chunk}")
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if not chunk:
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continue
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iteratorClass = get_iterator_class_from_endpoint_type(endpoint_type)
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if iteratorClass is None:
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# Generic endpoint - litellm does not do any tracking / logging for this
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async for chunk in aiter_bytes:
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yield chunk
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else:
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# known streaming endpoint - litellm will do tracking / logging for this
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model_iterator = iteratorClass(
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sync_stream=False, streaming_response=aiter_bytes
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)
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custom_stream_wrapper = litellm.utils.CustomStreamWrapper(
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completion_stream=aiter_bytes, model=None, logging_obj=litellm_logging_obj
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)
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buffer = b""
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all_chunks = []
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async for chunk in aiter_bytes:
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buffer += chunk
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try:
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_decoded_chunk = chunk.decode("utf-8")
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_chunk_dict = json.loads(_decoded_chunk)
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litellm_chunk = get_litellm_chunk(
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model_iterator, custom_stream_wrapper, _chunk_dict
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)
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if litellm_chunk:
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all_chunks.append(litellm_chunk)
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except json.JSONDecodeError:
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pass
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finally:
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yield chunk # Yield the original bytes
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# Handle SSE format - pass through the raw SSE format
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if isinstance(chunk, bytes):
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chunk = chunk.decode("utf-8")
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# Process any remaining data in the buffer
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if buffer:
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try:
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_chunk_dict = json.loads(buffer.decode("utf-8"))
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# Store the chunk for post-processing
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if chunk.strip(): # Only store non-empty chunks
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collected_chunks.append(chunk)
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yield f"{chunk}\n"
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if isinstance(_chunk_dict, list):
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for _chunk in _chunk_dict:
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litellm_chunk = get_litellm_chunk(
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model_iterator, custom_stream_wrapper, _chunk
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)
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if litellm_chunk:
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all_chunks.append(litellm_chunk)
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elif isinstance(_chunk_dict, dict):
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litellm_chunk = get_litellm_chunk(
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model_iterator, custom_stream_wrapper, _chunk_dict
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)
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if litellm_chunk:
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all_chunks.append(litellm_chunk)
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except json.JSONDecodeError:
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pass
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complete_streaming_response: Optional[
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Union[litellm.ModelResponse, litellm.TextCompletionResponse]
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] = litellm.stream_chunk_builder(chunks=all_chunks)
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if complete_streaming_response is None:
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complete_streaming_response = litellm.ModelResponse()
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# After all chunks are processed, handle post-processing
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end_time = datetime.now()
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if passthrough_success_handler_obj.is_vertex_route(url_route):
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_model = passthrough_success_handler_obj.extract_model_from_url(url_route)
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complete_streaming_response.model = _model
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litellm_logging_obj.model = _model
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litellm_logging_obj.model_call_details["model"] = _model
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asyncio.create_task(
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litellm_logging_obj.async_success_handler(
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result=complete_streaming_response,
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start_time=start_time,
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end_time=end_time,
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)
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await _route_streaming_logging_to_handler(
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litellm_logging_obj=litellm_logging_obj,
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passthrough_success_handler_obj=passthrough_success_handler_obj,
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url_route=url_route,
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request_body=request_body or {},
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endpoint_type=endpoint_type,
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start_time=start_time,
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all_chunks=collected_chunks,
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end_time=end_time,
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)
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except Exception as e:
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verbose_proxy_logger.error(f"Error in chunk_processor: {str(e)}")
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raise
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async def _route_streaming_logging_to_handler(
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litellm_logging_obj: LiteLLMLoggingObj,
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passthrough_success_handler_obj: PassThroughEndpointLogging,
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url_route: str,
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request_body: dict,
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endpoint_type: EndpointType,
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start_time: datetime,
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all_chunks: List[str],
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end_time: datetime,
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):
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"""
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Route the logging for the collected chunks to the appropriate handler
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Supported endpoint types:
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- Anthropic
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- Vertex AI
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"""
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if endpoint_type == EndpointType.ANTHROPIC:
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await AnthropicPassthroughLoggingHandler._handle_logging_anthropic_collected_chunks(
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litellm_logging_obj=litellm_logging_obj,
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passthrough_success_handler_obj=passthrough_success_handler_obj,
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url_route=url_route,
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request_body=request_body,
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endpoint_type=endpoint_type,
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start_time=start_time,
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all_chunks=all_chunks,
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end_time=end_time,
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)
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elif endpoint_type == EndpointType.VERTEX_AI:
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await VertexPassthroughLoggingHandler._handle_logging_vertex_collected_chunks(
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litellm_logging_obj=litellm_logging_obj,
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passthrough_success_handler_obj=passthrough_success_handler_obj,
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url_route=url_route,
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request_body=request_body,
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endpoint_type=endpoint_type,
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start_time=start_time,
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all_chunks=all_chunks,
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end_time=end_time,
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)
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elif endpoint_type == EndpointType.GENERIC:
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# No logging is supported for generic streaming endpoints
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pass
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