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* feat(pass_through_endpoints/): support logging anthropic/gemini pass through calls to langfuse/s3/etc. * fix(utils.py): allow disabling end user cost tracking with new param Allows proxy admin to disable cost tracking for end user - keeps prometheus metrics small * docs(configs.md): add disable_end_user_cost_tracking reference to docs * feat(key_management_endpoints.py): add support for restricting access to `/key/generate` by team/proxy level role Enables admin to restrict key creation, and assign team admins to handle distributing keys * test(test_key_management.py): add unit testing for personal / team key restriction checks * docs: add docs on restricting key creation * docs(finetuned_models.md): add new guide on calling finetuned models * docs(input.md): cleanup anthropic supported params Closes https://github.com/BerriAI/litellm/issues/6856 * test(test_embedding.py): add test for passing extra headers via embedding * feat(cohere/embed): pass client to async embedding * feat(rerank.py): add `/v1/rerank` if missing for cohere base url Closes https://github.com/BerriAI/litellm/issues/6844 * fix(main.py): pass extra_headers param to openai Fixes https://github.com/BerriAI/litellm/issues/6836 * fix(litellm_logging.py): don't disable global callbacks when dynamic callbacks are set Fixes issue where global callbacks - e.g. prometheus were overriden when langfuse was set dynamically * fix(handler.py): fix linting error * fix: fix typing * build: add conftest to proxy_admin_ui_tests/ * test: fix test * fix: fix linting errors * test: fix test * fix: fix pass through testing
171 lines
No EOL
6.2 KiB
Python
171 lines
No EOL
6.2 KiB
Python
import asyncio
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import json
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import threading
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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.proxy._types import PassThroughEndpointLoggingResultValues
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from litellm.types.utils import (
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GenericStreamingChunk,
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ModelResponse,
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StandardPassThroughResponseObject,
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)
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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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class PassThroughStreamingHandler:
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@staticmethod
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async def chunk_processor(
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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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):
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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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try:
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raw_bytes: List[bytes] = []
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async for chunk in response.aiter_bytes():
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raw_bytes.append(chunk)
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yield chunk
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# After all chunks are processed, handle post-processing
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end_time = datetime.now()
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await PassThroughStreamingHandler._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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raw_bytes=raw_bytes,
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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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@staticmethod
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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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raw_bytes: List[bytes],
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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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all_chunks = PassThroughStreamingHandler._convert_raw_bytes_to_str_lines(
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raw_bytes
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)
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standard_logging_response_object: Optional[
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PassThroughEndpointLoggingResultValues
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] = None
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kwargs: dict = {}
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if endpoint_type == EndpointType.ANTHROPIC:
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anthropic_passthrough_logging_handler_result = 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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standard_logging_response_object = anthropic_passthrough_logging_handler_result[
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"result"
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]
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kwargs = anthropic_passthrough_logging_handler_result["kwargs"]
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elif endpoint_type == EndpointType.VERTEX_AI:
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vertex_passthrough_logging_handler_result = (
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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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)
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standard_logging_response_object = vertex_passthrough_logging_handler_result[
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"result"
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]
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kwargs = vertex_passthrough_logging_handler_result["kwargs"]
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if standard_logging_response_object is None:
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standard_logging_response_object = StandardPassThroughResponseObject(
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response=f"cannot parse chunks to standard response object. Chunks={all_chunks}"
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)
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threading.Thread(
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target=litellm_logging_obj.success_handler,
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args=(
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standard_logging_response_object,
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start_time,
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end_time,
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False,
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),
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).start()
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await litellm_logging_obj.async_success_handler(
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result=standard_logging_response_object,
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start_time=start_time,
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end_time=end_time,
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cache_hit=False,
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**kwargs,
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)
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@staticmethod
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def _convert_raw_bytes_to_str_lines(raw_bytes: List[bytes]) -> List[str]:
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"""
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Converts a list of raw bytes into a list of string lines, similar to aiter_lines()
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Args:
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raw_bytes: List of bytes chunks from aiter.bytes()
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Returns:
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List of string lines, with each line being a complete data: {} chunk
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"""
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# Combine all bytes and decode to string
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combined_str = b"".join(raw_bytes).decode("utf-8")
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# Split by newlines and filter out empty lines
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lines = [line.strip() for line in combined_str.split("\n") if line.strip()]
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return lines |