litellm-mirror/litellm/proxy/pass_through_endpoints/success_handler.py
Krish Dholakia 92a7e8e3e9 LiteLLM Minor Fixes & Improvements (12/05/2024) (#7051)
* fix(cost_calculator.py): move to using `.get_model_info()` for cost per token calculations

ensures cost tracking is reliable - handles edge cases of parsing model cost map

* build(model_prices_and_context_window.json): add 'supports_response_schema' for select tgai models

Fixes https://github.com/BerriAI/litellm/pull/7037#discussion_r1872157329

* build(model_prices_and_context_window.json): remove 'pdf input' and 'vision' support from nova micro in model map

Bedrock docs indicate no support for micro - https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html

* fix(converse_transformation.py): support amazon nova tool use

* fix(opentelemetry): Add missing LLM request type attribute to spans (#7041)

* feat(opentelemetry): add LLM request type attribute to spans

* lint

* fix: curl usage (#7038)

curl -d, --data <data> is lowercase d
curl -D, --dump-header <filename> is uppercase D

references:
https://curl.se/docs/manpage.html#-d
https://curl.se/docs/manpage.html#-D

* fix(spend_tracking.py): handle empty 'id' in model response - when creating spend log

Fixes https://github.com/BerriAI/litellm/issues/7023

* fix(streaming_chunk_builder.py): handle initial id being empty string

Fixes https://github.com/BerriAI/litellm/issues/7023

* fix(anthropic_passthrough_logging_handler.py): add end user cost tracking for anthropic pass through endpoint

* docs(pass_through/): refactor docs location + add table on supported features for pass through endpoints

* feat(anthropic_passthrough_logging_handler.py): support end user cost tracking via anthropic sdk

* docs(anthropic_completion.md): add docs on passing end user param for cost tracking on anthropic sdk

* fix(litellm_logging.py): use standard logging payload if present in kwargs

prevent datadog logging error for pass through endpoints

* docs(bedrock.md): add rerank api usage example to docs

* bugfix/change dummy tool name format (#7053)

* fix viewing keys (#7042)

* ui new build

* build(model_prices_and_context_window.json): add bedrock region models to model cost map (#7044)

* bye (#6982)

* (fix) litellm router.aspeech  (#6962)

* doc Migrating Databases

* fix aspeech on router

* test_audio_speech_router

* test_audio_speech_router

* docs show supported providers on batches api doc

* change dummy tool name format

---------

Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: yujonglee <yujonglee.dev@gmail.com>

* fix: fix linting errors

* test: update test

* fix(litellm_logging.py): fix pass through check

* fix(test_otel_logging.py): fix test

* fix(cost_calculator.py): update handling for cost per second

* fix(cost_calculator.py): fix cost check

* test: fix test

* (fix) adding public routes when using custom header  (#7045)

* get_api_key_from_custom_header

* add test_get_api_key_from_custom_header

* fix testing use 1 file for test user api key auth

* fix test user api key auth

* test_custom_api_key_header_name

* build: update ui build

---------

Co-authored-by: Doron Kopit <83537683+doronkopit5@users.noreply.github.com>
Co-authored-by: lloydchang <lloydchang@gmail.com>
Co-authored-by: hgulersen <haymigulersen@gmail.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: yujonglee <yujonglee.dev@gmail.com>
2024-12-06 14:29:53 -08:00

128 lines
4.4 KiB
Python

import json
import re
import threading
from datetime import datetime
from typing import Optional, Union
import httpx
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.litellm_logging import (
get_standard_logging_object_payload,
)
from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
VertexLLM,
)
from litellm.proxy._types import PassThroughEndpointLoggingResultValues
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.types.utils import StandardPassThroughResponseObject
from litellm.utils import executor as thread_pool_executor
from .llm_provider_handlers.anthropic_passthrough_logging_handler import (
AnthropicPassthroughLoggingHandler,
)
from .llm_provider_handlers.vertex_passthrough_logging_handler import (
VertexPassthroughLoggingHandler,
)
class PassThroughEndpointLogging:
def __init__(self):
self.TRACKED_VERTEX_ROUTES = [
"generateContent",
"streamGenerateContent",
"predict",
]
# Anthropic
self.TRACKED_ANTHROPIC_ROUTES = ["/messages"]
async def pass_through_async_success_handler(
self,
httpx_response: httpx.Response,
response_body: Optional[dict],
logging_obj: LiteLLMLoggingObj,
url_route: str,
result: str,
start_time: datetime,
end_time: datetime,
cache_hit: bool,
**kwargs,
):
standard_logging_response_object: Optional[
PassThroughEndpointLoggingResultValues
] = None
if self.is_vertex_route(url_route):
vertex_passthrough_logging_handler_result = (
VertexPassthroughLoggingHandler.vertex_passthrough_handler(
httpx_response=httpx_response,
logging_obj=logging_obj,
url_route=url_route,
result=result,
start_time=start_time,
end_time=end_time,
cache_hit=cache_hit,
**kwargs,
)
)
standard_logging_response_object = (
vertex_passthrough_logging_handler_result["result"]
)
kwargs = vertex_passthrough_logging_handler_result["kwargs"]
elif self.is_anthropic_route(url_route):
anthropic_passthrough_logging_handler_result = (
AnthropicPassthroughLoggingHandler.anthropic_passthrough_handler(
httpx_response=httpx_response,
response_body=response_body or {},
logging_obj=logging_obj,
url_route=url_route,
result=result,
start_time=start_time,
end_time=end_time,
cache_hit=cache_hit,
**kwargs,
)
)
standard_logging_response_object = (
anthropic_passthrough_logging_handler_result["result"]
)
kwargs = anthropic_passthrough_logging_handler_result["kwargs"]
if standard_logging_response_object is None:
standard_logging_response_object = StandardPassThroughResponseObject(
response=httpx_response.text
)
thread_pool_executor.submit(
logging_obj.success_handler,
standard_logging_response_object, # Positional argument 1
start_time, # Positional argument 2
end_time, # Positional argument 3
cache_hit, # Positional argument 4
**kwargs, # Unpacked keyword arguments
)
await logging_obj.async_success_handler(
result=(
json.dumps(result)
if isinstance(result, dict)
else standard_logging_response_object
),
start_time=start_time,
end_time=end_time,
cache_hit=False,
**kwargs,
)
def is_vertex_route(self, url_route: str):
for route in self.TRACKED_VERTEX_ROUTES:
if route in url_route:
return True
return False
def is_anthropic_route(self, url_route: str):
for route in self.TRACKED_ANTHROPIC_ROUTES:
if route in url_route:
return True
return False