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Merge branch 'BerriAI:main' into litellm_fix_icons

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Artur Zdolinski 2025-03-19 15:40:49 +01:00 • committed by GitHub
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63 changed files with 722 additions and 252 deletions
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27
litellm/integrations/arize/_utils.py
View file

@ -1,31 +1,37 @@
import json
from typing import TYPE_CHECKING, Any, Optional
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
from litellm.types.utils import StandardLoggingPayload
if TYPE_CHECKING:
from opentelemetry.trace import Span as _Span
Span = _Span
else:
Span = Any
def set_attributes(span: Span, kwargs, response_obj):
from openinference.semconv.trace import (
from litellm.integrations._types.open_inference import (
MessageAttributes,
OpenInferenceSpanKindValues,
SpanAttributes,
)
try:
litellm_params = kwargs.get("litellm_params", {}) or {}
standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
"standard_logging_object"
)
#############################################
############ LLM CALL METADATA ##############
#############################################
metadata = litellm_params.get("metadata", {}) or {}
span.set_attribute(SpanAttributes.METADATA, str(metadata))
if standard_logging_payload and (
metadata := standard_logging_payload["metadata"]
):
span.set_attribute(SpanAttributes.METADATA, safe_dumps(metadata))
#############################################
########## LLM Request Attributes ###########
@ -62,13 +68,12 @@ def set_attributes(span: Span, kwargs, response_obj):
msg.get("content", ""),
)
standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
"standard_logging_object"
)
if standard_logging_payload and (model_params := standard_logging_payload["model_parameters"]):
if standard_logging_payload and (
model_params := standard_logging_payload["model_parameters"]
):
# The Generative AI Provider: Azure, OpenAI, etc.
span.set_attribute(
SpanAttributes.LLM_INVOCATION_PARAMETERS, json.dumps(model_params)
SpanAttributes.LLM_INVOCATION_PARAMETERS, safe_dumps(model_params)
)
if model_params.get("user"):
@ -80,7 +85,7 @@ def set_attributes(span: Span, kwargs, response_obj):
########## LLM Response Attributes ##########
# https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions
#############################################
if hasattr(response_obj, 'get'):
if hasattr(response_obj, "get"):
for choice in response_obj.get("choices", []):
response_message = choice.get("message", {})
span.set_attribute(

63
litellm/integrations/arize/arize.py
View file

@ -3,31 +3,38 @@ arize AI is OTEL compatible
this file has Arize ai specific helper functions
"""
import os
from typing import TYPE_CHECKING, Any
import os
from datetime import datetime
from typing import TYPE_CHECKING, Any, Optional, Union
from litellm.integrations.arize import _utils
from litellm.integrations.opentelemetry import OpenTelemetry
from litellm.types.integrations.arize import ArizeConfig
from litellm.types.services import ServiceLoggerPayload
if TYPE_CHECKING:
from litellm.types.integrations.arize import Protocol as _Protocol
from opentelemetry.trace import Span as _Span
from litellm.types.integrations.arize import Protocol as _Protocol
Protocol = _Protocol
Span = _Span
else:
Protocol = Any
Span = Any
class ArizeLogger:
class ArizeLogger(OpenTelemetry):
def set_attributes(self, span: Span, kwargs, response_obj: Optional[Any]):
ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
return
@staticmethod
def set_arize_attributes(span: Span, kwargs, response_obj):
_utils.set_attributes(span, kwargs, response_obj)
return
@staticmethod
def get_arize_config() -> ArizeConfig:
@ -43,11 +50,6 @@ class ArizeLogger:
space_key = os.environ.get("ARIZE_SPACE_KEY")
api_key = os.environ.get("ARIZE_API_KEY")
if not space_key:
raise ValueError("ARIZE_SPACE_KEY not found in environment variables")
if not api_key:
raise ValueError("ARIZE_API_KEY not found in environment variables")
grpc_endpoint = os.environ.get("ARIZE_ENDPOINT")
http_endpoint = os.environ.get("ARIZE_HTTP_ENDPOINT")
@ -55,13 +57,13 @@ class ArizeLogger:
protocol: Protocol = "otlp_grpc"
if grpc_endpoint:
protocol="otlp_grpc"
endpoint=grpc_endpoint
protocol = "otlp_grpc"
endpoint = grpc_endpoint
elif http_endpoint:
protocol="otlp_http"
endpoint=http_endpoint
protocol = "otlp_http"
endpoint = http_endpoint
else:
protocol="otlp_grpc"
protocol = "otlp_grpc"
endpoint = "https://otlp.arize.com/v1"
return ArizeConfig(
@ -71,4 +73,33 @@ class ArizeLogger:
endpoint=endpoint,
)
async def async_service_success_hook(
self,
payload: ServiceLoggerPayload,
parent_otel_span: Optional[Span] = None,
start_time: Optional[Union[datetime, float]] = None,
end_time: Optional[Union[datetime, float]] = None,
event_metadata: Optional[dict] = None,
):
"""Arize is used mainly for LLM I/O tracing, sending router+caching metrics adds bloat to arize logs"""
pass
async def async_service_failure_hook(
self,
payload: ServiceLoggerPayload,
error: Optional[str] = "",
parent_otel_span: Optional[Span] = None,
start_time: Optional[Union[datetime, float]] = None,
end_time: Optional[Union[float, datetime]] = None,
event_metadata: Optional[dict] = None,
):
"""Arize is used mainly for LLM I/O tracing, sending router+caching metrics adds bloat to arize logs"""
pass
def create_litellm_proxy_request_started_span(
self,
start_time: datetime,
headers: dict,
):
"""Arize is used mainly for LLM I/O tracing, sending Proxy Server Request adds bloat to arize logs"""
pass

95
litellm/integrations/opentelemetry.py
View file

@ -10,6 +10,7 @@ from litellm.types.services import ServiceLoggerPayload
from litellm.types.utils import (
ChatCompletionMessageToolCall,
Function,
StandardCallbackDynamicParams,
StandardLoggingPayload,
)
@ -311,6 +312,8 @@ class OpenTelemetry(CustomLogger):
)
_parent_context, parent_otel_span = self._get_span_context(kwargs)
self._add_dynamic_span_processor_if_needed(kwargs)
# Span 1: Requst sent to litellm SDK
span = self.tracer.start_span(
name=self._get_span_name(kwargs),
@ -341,6 +344,45 @@ class OpenTelemetry(CustomLogger):
if parent_otel_span is not None:
parent_otel_span.end(end_time=self._to_ns(datetime.now()))
def _add_dynamic_span_processor_if_needed(self, kwargs):
"""
Helper method to add a span processor with dynamic headers if needed.
This allows for per-request configuration of telemetry exporters by
extracting headers from standard_callback_dynamic_params.
"""
from opentelemetry import trace
standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = (
kwargs.get("standard_callback_dynamic_params")
)
if not standard_callback_dynamic_params:
return
# Extract headers from dynamic params
dynamic_headers = {}
# Handle Arize headers
if standard_callback_dynamic_params.get("arize_space_key"):
dynamic_headers["space_key"] = standard_callback_dynamic_params.get(
"arize_space_key"
)
if standard_callback_dynamic_params.get("arize_api_key"):
dynamic_headers["api_key"] = standard_callback_dynamic_params.get(
"arize_api_key"
)
# Only create a span processor if we have headers to use
if len(dynamic_headers) > 0:
from opentelemetry.sdk.trace import TracerProvider
provider = trace.get_tracer_provider()
if isinstance(provider, TracerProvider):
span_processor = self._get_span_processor(
dynamic_headers=dynamic_headers
)
provider.add_span_processor(span_processor)
def _handle_failure(self, kwargs, response_obj, start_time, end_time):
from opentelemetry.trace import Status, StatusCode
@ -443,14 +485,12 @@ class OpenTelemetry(CustomLogger):
self, span: Span, kwargs, response_obj: Optional[Any]
):
try:
if self.callback_name == "arize":
from litellm.integrations.arize.arize import ArizeLogger
ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
return
elif self.callback_name == "arize_phoenix":
if self.callback_name == "arize_phoenix":
from litellm.integrations.arize.arize_phoenix import ArizePhoenixLogger
ArizePhoenixLogger.set_arize_phoenix_attributes(span, kwargs, response_obj)
ArizePhoenixLogger.set_arize_phoenix_attributes(
span, kwargs, response_obj
)
return
elif self.callback_name == "langtrace":
from litellm.integrations.langtrace import LangtraceAttributes
@ -779,7 +819,7 @@ class OpenTelemetry(CustomLogger):
carrier = {"traceparent": traceparent}
return TraceContextTextMapPropagator().extract(carrier=carrier), None
def _get_span_processor(self):
def _get_span_processor(self, dynamic_headers: Optional[dict] = None):
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
OTLPSpanExporter as OTLPSpanExporterGRPC,
)
@ -799,10 +839,9 @@ class OpenTelemetry(CustomLogger):
self.OTEL_ENDPOINT,
self.OTEL_HEADERS,
)
_split_otel_headers = {}
if self.OTEL_HEADERS is not None and isinstance(self.OTEL_HEADERS, str):
_split_otel_headers = self.OTEL_HEADERS.split("=")
_split_otel_headers = {_split_otel_headers[0]: _split_otel_headers[1]}
_split_otel_headers = OpenTelemetry._get_headers_dictionary(
headers=dynamic_headers or self.OTEL_HEADERS
)
if isinstance(self.OTEL_EXPORTER, SpanExporter):
verbose_logger.debug(
@ -844,6 +883,25 @@ class OpenTelemetry(CustomLogger):
)
return BatchSpanProcessor(ConsoleSpanExporter())
@staticmethod
def _get_headers_dictionary(headers: Optional[Union[str, dict]]) -> Dict[str, str]:
"""
Convert a string or dictionary of headers into a dictionary of headers.
"""
_split_otel_headers: Dict[str, str] = {}
if headers:
if isinstance(headers, str):
# when passed HEADERS="x-honeycomb-team=B85YgLm96******"
# Split only on first '=' occurrence
parts = headers.split("=", 1)
if len(parts) == 2:
_split_otel_headers = {parts[0]: parts[1]}
else:
_split_otel_headers = {}
elif isinstance(headers, dict):
_split_otel_headers = headers
return _split_otel_headers
async def async_management_endpoint_success_hook(
self,
logging_payload: ManagementEndpointLoggingPayload,
@ -948,3 +1006,18 @@ class OpenTelemetry(CustomLogger):
)
management_endpoint_span.set_status(Status(StatusCode.ERROR))
management_endpoint_span.end(end_time=_end_time_ns)
def create_litellm_proxy_request_started_span(
self,
start_time: datetime,
headers: dict,
) -> Optional[Span]:
"""
Create a span for the received proxy server request.
"""
return self.tracer.start_span(
name="Received Proxy Server Request",
start_time=self._to_ns(start_time),
context=self.get_traceparent_from_header(headers=headers),
kind=self.span_kind.SERVER,
)

22
litellm/litellm_core_utils/get_llm_provider_logic.py
View file

@ -129,17 +129,15 @@ def get_llm_provider( # noqa: PLR0915
model, custom_llm_provider
)
if custom_llm_provider:
if (
model.split("/")[0] == custom_llm_provider
): # handle scenario where model="azure/*" and custom_llm_provider="azure"
model = model.replace("{}/".format(custom_llm_provider), "")
return model, custom_llm_provider, dynamic_api_key, api_base
if custom_llm_provider and (
model.split("/")[0] != custom_llm_provider
): # handle scenario where model="azure/*" and custom_llm_provider="azure"
model = custom_llm_provider + "/" + model
if api_key and api_key.startswith("os.environ/"):
dynamic_api_key = get_secret_str(api_key)
# check if llm provider part of model name
if (
model.split("/", 1)[0] in litellm.provider_list
and model.split("/", 1)[0] not in litellm.model_list_set
@ -573,11 +571,11 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915
dynamic_api_key = api_key or get_secret_str("GALADRIEL_API_KEY")
elif custom_llm_provider == "snowflake":
api_base = (
api_base
or get_secret("SNOWFLAKE_API_BASE")
or f"https://{get_secret('SNOWFLAKE_ACCOUNT_ID')}.snowflakecomputing.com/api/v2/cortex/inference:complete"
) # type: ignore
dynamic_api_key = api_key or get_secret("SNOWFLAKE_JWT")
api_base
or get_secret_str("SNOWFLAKE_API_BASE")
or f"https://{get_secret('SNOWFLAKE_ACCOUNT_ID')}.snowflakecomputing.com/api/v2/cortex/inference:complete"
) # type: ignore
dynamic_api_key = api_key or get_secret_str("SNOWFLAKE_JWT")
if api_base is not None and not isinstance(api_base, str):
raise Exception("api base needs to be a string. api_base={}".format(api_base))

17
litellm/litellm_core_utils/litellm_logging.py
View file

@ -29,6 +29,7 @@ from litellm.batches.batch_utils import _handle_completed_batch
from litellm.caching.caching import DualCache, InMemoryCache
from litellm.caching.caching_handler import LLMCachingHandler
from litellm.cost_calculator import _select_model_name_for_cost_calc
from litellm.integrations.arize.arize import ArizeLogger
from litellm.integrations.custom_guardrail import CustomGuardrail
from litellm.integrations.custom_logger import CustomLogger
from litellm.integrations.mlflow import MlflowLogger
@ -76,7 +77,6 @@ from litellm.types.utils import (
from litellm.utils import _get_base_model_from_metadata, executor, print_verbose
from ..integrations.argilla import ArgillaLogger
from ..integrations.arize.arize import ArizeLogger
from ..integrations.arize.arize_phoenix import ArizePhoenixLogger
from ..integrations.athina import AthinaLogger
from ..integrations.azure_storage.azure_storage import AzureBlobStorageLogger
@ -2658,13 +2658,13 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
)
for callback in _in_memory_loggers:
if (
isinstance(callback, OpenTelemetry)
isinstance(callback, ArizeLogger)
and callback.callback_name == "arize"
):
return callback # type: ignore
_otel_logger = OpenTelemetry(config=otel_config, callback_name="arize")
_in_memory_loggers.append(_otel_logger)
return _otel_logger # type: ignore
_arize_otel_logger = ArizeLogger(config=otel_config, callback_name="arize")
_in_memory_loggers.append(_arize_otel_logger)
return _arize_otel_logger # type: ignore
elif logging_integration == "arize_phoenix":
from litellm.integrations.opentelemetry import (
OpenTelemetry,
@ -2897,15 +2897,13 @@ def get_custom_logger_compatible_class( # noqa: PLR0915
if isinstance(callback, OpenTelemetry):
return callback
elif logging_integration == "arize":
from litellm.integrations.opentelemetry import OpenTelemetry
if "ARIZE_SPACE_KEY" not in os.environ:
raise ValueError("ARIZE_SPACE_KEY not found in environment variables")
if "ARIZE_API_KEY" not in os.environ:
raise ValueError("ARIZE_API_KEY not found in environment variables")
for callback in _in_memory_loggers:
if (
isinstance(callback, OpenTelemetry)
isinstance(callback, ArizeLogger)
and callback.callback_name == "arize"
):
return callback
@ -3258,6 +3256,7 @@ class StandardLoggingPayloadSetup:
additional_headers=None,
litellm_overhead_time_ms=None,
batch_models=None,
litellm_model_name=None,
)
if hidden_params is not None:
for key in StandardLoggingHiddenParams.__annotations__.keys():
@ -3372,6 +3371,7 @@ def get_standard_logging_object_payload(
response_cost=None,
litellm_overhead_time_ms=None,
batch_models=None,
litellm_model_name=None,
)
)
@ -3657,6 +3657,7 @@ def create_dummy_standard_logging_payload() -> StandardLoggingPayload:
additional_headers=None,
litellm_overhead_time_ms=None,
batch_models=None,
litellm_model_name=None,
)
# Convert numeric values to appropriate types

1
litellm/litellm_core_utils/llm_response_utils/response_metadata.py
View file

@ -44,6 +44,7 @@ class ResponseMetadata:
"additional_headers": process_response_headers(
self._get_value_from_hidden_params("additional_headers") or {}
),
"litellm_model_name": model,
}
self._update_hidden_params(new_params)

26
litellm/llms/bedrock/common_utils.py
View file

@ -336,13 +336,7 @@ class BedrockModelInfo(BaseLLMModelInfo):
return model
@staticmethod
def get_base_model(model: str) -> str:
"""
Get the base model from the given model name.
Handle model names like - "us.meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1"
AND "meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1"
"""
def get_non_litellm_routing_model_name(model: str) -> str:
if model.startswith("bedrock/"):
model = model.split("/", 1)[1]
@ -352,6 +346,18 @@ class BedrockModelInfo(BaseLLMModelInfo):
if model.startswith("invoke/"):
model = model.split("/", 1)[1]
return model
@staticmethod
def get_base_model(model: str) -> str:
"""
Get the base model from the given model name.
Handle model names like - "us.meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1"
AND "meta.llama3-2-11b-instruct-v1:0" -> "meta.llama3-2-11b-instruct-v1"
"""
model = BedrockModelInfo.get_non_litellm_routing_model_name(model=model)
model = BedrockModelInfo.extract_model_name_from_arn(model)
potential_region = model.split(".", 1)[0]
@ -386,12 +392,16 @@ class BedrockModelInfo(BaseLLMModelInfo):
Get the bedrock route for the given model.
"""
base_model = BedrockModelInfo.get_base_model(model)
alt_model = BedrockModelInfo.get_non_litellm_routing_model_name(model=model)
if "invoke/" in model:
return "invoke"
elif "converse_like" in model:
return "converse_like"
elif "converse/" in model:
return "converse"
elif base_model in litellm.bedrock_converse_models:
elif (
base_model in litellm.bedrock_converse_models
or alt_model in litellm.bedrock_converse_models
):
return "converse"
return "invoke"

12
litellm/llms/sagemaker/completion/handler.py
View file

@ -213,7 +213,7 @@ class SagemakerLLM(BaseAWSLLM):
sync_response = sync_handler.post(
url=prepared_request.url,
headers=prepared_request.headers, # type: ignore
json=data,
data=prepared_request.body,
stream=stream,
)
@ -308,7 +308,7 @@ class SagemakerLLM(BaseAWSLLM):
sync_response = sync_handler.post(
url=prepared_request.url,
headers=prepared_request.headers, # type: ignore
json=_data,
data=prepared_request.body,
timeout=timeout,
)
@ -356,7 +356,7 @@ class SagemakerLLM(BaseAWSLLM):
self,
api_base: str,
headers: dict,
data: dict,
data: str,
logging_obj,
client=None,
):
@ -368,7 +368,7 @@ class SagemakerLLM(BaseAWSLLM):
response = await client.post(
api_base,
headers=headers,
json=data,
data=data,
stream=True,
)
@ -440,7 +440,7 @@ class SagemakerLLM(BaseAWSLLM):
completion_stream = await self.make_async_call(
api_base=prepared_request.url,
headers=prepared_request.headers, # type: ignore
data=data,
data=prepared_request.body,
logging_obj=logging_obj,
)
streaming_response = CustomStreamWrapper(
@ -522,7 +522,7 @@ class SagemakerLLM(BaseAWSLLM):
response = await async_handler.post(
url=prepared_request.url,
headers=prepared_request.headers, # type: ignore
json=data,
data=prepared_request.body,
timeout=timeout,
)

47
litellm/model_prices_and_context_window_backup.json
View file

@ -1385,16 +1385,33 @@
"supports_prompt_caching": true
},
"azure/gpt-4o": {
"max_tokens": 4096,
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 4096,
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.000015,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
},
"azure/global/gpt-4o-2024-11-20": {
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
@ -1403,8 +1420,24 @@
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 16384,
"input_cost_per_token": 0.00000275,
"output_cost_per_token": 0.000011,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
},
"azure/global/gpt-4o-2024-08-06": {
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
@ -1421,12 +1454,14 @@
"max_output_tokens": 16384,
"input_cost_per_token": 0.00000275,
"output_cost_per_token": 0.000011,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
},
"azure/us/gpt-4o-2024-11-20": {

0
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litellm/proxy/_experimental/out/onboarding.txt
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15
litellm/proxy/auth/user_api_key_auth.py
View file

@ -51,7 +51,7 @@ from litellm.proxy.auth.oauth2_proxy_hook import handle_oauth2_proxy_request
from litellm.proxy.auth.route_checks import RouteChecks
from litellm.proxy.auth.service_account_checks import service_account_checks
from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
from litellm.proxy.utils import PrismaClient, ProxyLogging, _to_ns
from litellm.proxy.utils import PrismaClient, ProxyLogging
from litellm.types.services import ServiceTypes
user_api_key_service_logger_obj = ServiceLogging() # used for tracking latency on OTEL
@ -370,14 +370,11 @@ async def _user_api_key_auth_builder( # noqa: PLR0915
)
if open_telemetry_logger is not None:
parent_otel_span = open_telemetry_logger.tracer.start_span(
name="Received Proxy Server Request",
start_time=_to_ns(start_time),
context=open_telemetry_logger.get_traceparent_from_header(
headers=request.headers
),
kind=open_telemetry_logger.span_kind.SERVER,
parent_otel_span = (
open_telemetry_logger.create_litellm_proxy_request_started_span(
start_time=start_time,
headers=dict(request.headers),
)
)
### USER-DEFINED AUTH FUNCTION ###

5
litellm/proxy/proxy_server.py
View file

@ -33,9 +33,12 @@ from litellm.types.utils import (
if TYPE_CHECKING:
from opentelemetry.trace import Span as _Span
from litellm.integrations.opentelemetry import OpenTelemetry
Span = _Span
else:
Span = Any
OpenTelemetry = Any
def showwarning(message, category, filename, lineno, file=None, line=None):
@ -777,7 +780,7 @@ disable_spend_logs = False
jwt_handler = JWTHandler()
prompt_injection_detection_obj: Optional[_OPTIONAL_PromptInjectionDetection] = None
store_model_in_db: bool = False
open_telemetry_logger: Optional[Any] = None
open_telemetry_logger: Optional[OpenTelemetry] = None
### INITIALIZE GLOBAL LOGGING OBJECT ###
proxy_logging_obj = ProxyLogging(
user_api_key_cache=user_api_key_cache, premium_user=premium_user

27
litellm/router_strategy/lowest_tpm_rpm_v2.py
View file

@ -65,10 +65,13 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
# ------------
# Setup values
# ------------
dt = get_utc_datetime()
current_minute = dt.strftime("%H-%M")
model_id = deployment.get("model_info", {}).get("id")
rpm_key = f"{model_id}:rpm:{current_minute}"
deployment_name = deployment.get("litellm_params", {}).get("model")
rpm_key = f"{model_id}:{deployment_name}:rpm:{current_minute}"
local_result = self.router_cache.get_cache(
key=rpm_key, local_only=True
) # check local result first
@ -151,7 +154,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
dt = get_utc_datetime()
current_minute = dt.strftime("%H-%M")
model_id = deployment.get("model_info", {}).get("id")
rpm_key = f"{model_id}:rpm:{current_minute}"
deployment_name = deployment.get("litellm_params", {}).get("model")
rpm_key = f"{model_id}:{deployment_name}:rpm:{current_minute}"
local_result = await self.router_cache.async_get_cache(
key=rpm_key, local_only=True
) # check local result first
@ -228,8 +233,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
if standard_logging_object is None:
raise ValueError("standard_logging_object not passed in.")
model_group = standard_logging_object.get("model_group")
model = standard_logging_object["hidden_params"].get("litellm_model_name")
id = standard_logging_object.get("model_id")
if model_group is None or id is None:
if model_group is None or id is None or model is None:
return
elif isinstance(id, int):
id = str(id)
@ -244,7 +250,7 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
"%H-%M"
) # use the same timezone regardless of system clock
tpm_key = f"{id}:tpm:{current_minute}"
tpm_key = f"{id}:{model}:tpm:{current_minute}"
# ------------
# Update usage
# ------------
@ -276,6 +282,7 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
if standard_logging_object is None:
raise ValueError("standard_logging_object not passed in.")
model_group = standard_logging_object.get("model_group")
model = standard_logging_object["hidden_params"]["litellm_model_name"]
id = standard_logging_object.get("model_id")
if model_group is None or id is None:
return
@ -290,7 +297,7 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
"%H-%M"
) # use the same timezone regardless of system clock
tpm_key = f"{id}:tpm:{current_minute}"
tpm_key = f"{id}:{model}:tpm:{current_minute}"
# ------------
# Update usage
# ------------
@ -458,8 +465,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
id = m.get("model_info", {}).get(
"id"
) # a deployment should always have an 'id'. this is set in router.py
tpm_key = "{}:tpm:{}".format(id, current_minute)
rpm_key = "{}:rpm:{}".format(id, current_minute)
deployment_name = m.get("litellm_params", {}).get("model")
tpm_key = "{}:{}:tpm:{}".format(id, deployment_name, current_minute)
rpm_key = "{}:{}:rpm:{}".format(id, deployment_name, current_minute)
tpm_keys.append(tpm_key)
rpm_keys.append(rpm_key)
@ -576,8 +584,9 @@ class LowestTPMLoggingHandler_v2(CustomLogger):
id = m.get("model_info", {}).get(
"id"
) # a deployment should always have an 'id'. this is set in router.py
tpm_key = "{}:tpm:{}".format(id, current_minute)
rpm_key = "{}:rpm:{}".format(id, current_minute)
deployment_name = m.get("litellm_params", {}).get("model")
tpm_key = "{}:{}:tpm:{}".format(id, deployment_name, current_minute)
rpm_key = "{}:{}:rpm:{}".format(id, deployment_name, current_minute)
tpm_keys.append(tpm_key)
rpm_keys.append(rpm_key)

9
litellm/types/integrations/arize.py
View file

@ -1,4 +1,4 @@
from typing import TYPE_CHECKING, Literal, Any
from typing import TYPE_CHECKING, Any, Literal, Optional
from pydantic import BaseModel
@ -6,9 +6,10 @@ if TYPE_CHECKING:
Protocol = Literal["otlp_grpc", "otlp_http"]
else:
Protocol = Any
class ArizeConfig(BaseModel):
space_key: str
api_key: str
space_key: Optional[str] = None
api_key: Optional[str] = None
protocol: Protocol
endpoint: str

9
litellm/types/utils.py
View file

@ -1625,13 +1625,16 @@ class StandardLoggingAdditionalHeaders(TypedDict, total=False):
class StandardLoggingHiddenParams(TypedDict):
model_id: Optional[str]
model_id: Optional[
str
] # id of the model in the router, separates multiple models with the same name but different credentials
cache_key: Optional[str]
api_base: Optional[str]
response_cost: Optional[str]
litellm_overhead_time_ms: Optional[float]
additional_headers: Optional[StandardLoggingAdditionalHeaders]
batch_models: Optional[List[str]]
litellm_model_name: Optional[str] # the model name sent to the provider by litellm
class StandardLoggingModelInformation(TypedDict):
@ -1763,6 +1766,10 @@ class StandardCallbackDynamicParams(TypedDict, total=False):
# Humanloop dynamic params
humanloop_api_key: Optional[str]
# Arize dynamic params
arize_api_key: Optional[str]
arize_space_key: Optional[str]
# Logging settings
turn_off_message_logging: Optional[bool] # when true will not log messages

47
model_prices_and_context_window.json
View file

@ -1385,16 +1385,33 @@
"supports_prompt_caching": true
},
"azure/gpt-4o": {
"max_tokens": 4096,
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 4096,
"input_cost_per_token": 0.000005,
"output_cost_per_token": 0.000015,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
},
"azure/global/gpt-4o-2024-11-20": {
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
@ -1403,8 +1420,24 @@
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 16384,
"input_cost_per_token": 0.00000275,
"output_cost_per_token": 0.000011,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
},
"azure/global/gpt-4o-2024-08-06": {
"max_tokens": 16384,
"max_input_tokens": 128000,
"max_output_tokens": 16384,
"input_cost_per_token": 0.0000025,
"output_cost_per_token": 0.00001,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
@ -1421,12 +1454,14 @@
"max_output_tokens": 16384,
"input_cost_per_token": 0.00000275,
"output_cost_per_token": 0.000011,
"cache_read_input_token_cost": 0.00000125,
"litellm_provider": "azure",
"mode": "chat",
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_response_schema": true,
"supports_vision": true,
"supports_prompt_caching": true,
"supports_tool_choice": true
},
"azure/us/gpt-4o-2024-11-20": {

6
tests/litellm/caching/test_redis_cache.py
View file

@ -20,7 +20,8 @@ from litellm.caching.redis_cache import RedisCache
@pytest.mark.parametrize("namespace", [None, "test"])
@pytest.mark.asyncio
async def test_redis_cache_async_increment(namespace):
async def test_redis_cache_async_increment(namespace, monkeypatch):
monkeypatch.setenv("REDIS_HOST", "https://my-test-host")
redis_cache = RedisCache(namespace=namespace)
# Create an AsyncMock for the Redis client
mock_redis_instance = AsyncMock()
@ -46,7 +47,8 @@ async def test_redis_cache_async_increment(namespace):
@pytest.mark.asyncio
async def test_redis_client_init_with_socket_timeout():
async def test_redis_client_init_with_socket_timeout(monkeypatch):
monkeypatch.setenv("REDIS_HOST", "my-fake-host")
redis_cache = RedisCache(socket_timeout=1.0)
assert redis_cache.redis_kwargs["socket_timeout"] == 1.0
client = redis_cache.init_async_client()

84
tests/litellm/llms/azure_ai/chat/test_azure_ai_transformation.py
View file

@ -8,84 +8,24 @@ import pytest
sys.path.insert(
0, os.path.abspath("../../../../..")
) # Adds the parent directory to the system path
import litellm
from litellm.llms.azure_ai.chat.transformation import AzureAIStudioConfig
@pytest.mark.parametrize("is_async", [True, False])
@pytest.mark.asyncio
async def test_azure_ai_request_format(is_async):
async def test_get_openai_compatible_provider_info():
"""
Test that Azure AI requests are formatted correctly with the proper endpoint and parameters
for both synchronous and asynchronous calls
"""
litellm._turn_on_debug()
config = AzureAIStudioConfig()
# Set up the test parameters
api_key = "00xxx"
api_base = "https://my-endpoint-europe-berri-992.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-08-01-preview"
model = "azure_ai/gpt-4o-mini"
messages = [
{"role": "user", "content": "hi"},
{"role": "assistant", "content": "Hello! How can I assist you today?"},
{"role": "user", "content": "hi"},
]
api_base, dynamic_api_key, custom_llm_provider = (
config._get_openai_compatible_provider_info(
model="azure_ai/gpt-4o-mini",
api_base="https://my-base",
api_key="my-key",
custom_llm_provider="azure_ai",
)
)
if is_async:
# Mock AsyncHTTPHandler.post method for async test
with patch(
"litellm.llms.custom_httpx.llm_http_handler.AsyncHTTPHandler.post"
) as mock_post:
# Set up mock response
mock_post.return_value = AsyncMock()
# Call the acompletion function
try:
await litellm.acompletion(
custom_llm_provider="azure_ai",
api_key=api_key,
api_base=api_base,
model=model,
messages=messages,
)
except Exception as e:
# We expect an exception since we're mocking the response
pass
else:
# Mock HTTPHandler.post method for sync test
with patch(
"litellm.llms.custom_httpx.llm_http_handler.HTTPHandler.post"
) as mock_post:
# Set up mock response
mock_post.return_value = MagicMock()
# Call the completion function
try:
litellm.completion(
custom_llm_provider="azure_ai",
api_key=api_key,
api_base=api_base,
model=model,
messages=messages,
)
except Exception as e:
# We expect an exception since we're mocking the response
pass
# Verify the request was made with the correct parameters
mock_post.assert_called_once()
call_args = mock_post.call_args
print("sync request call=", json.dumps(call_args.kwargs, indent=4, default=str))
# Check URL
assert call_args.kwargs["url"] == api_base
# Check headers
assert call_args.kwargs["headers"]["api-key"] == api_key
# Check request body
request_body = json.loads(call_args.kwargs["data"])
assert (
request_body["model"] == "gpt-4o-mini"
) # Model name should be stripped of provider prefix
assert request_body["messages"] == messages
assert custom_llm_provider == "azure"

21
tests/litellm/llms/bedrock/test_bedrock_common_utils.py Normal file
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@ -0,0 +1,21 @@
import json
import os
import sys
import pytest
from fastapi.testclient import TestClient
sys.path.insert(
0, os.path.abspath("../../../..")
) # Adds the parent directory to the system path
from litellm.llms.bedrock.common_utils import BedrockModelInfo
def test_deepseek_cris():
bedrock_model_info = BedrockModelInfo
bedrock_route = bedrock_model_info.get_bedrock_route(
model="bedrock/us.deepseek.r1-v1:0"
)
assert bedrock_route == "converse"

29
tests/llm_translation/test_azure_ai.py
View file

@ -265,3 +265,32 @@ class TestAzureAIRerank(BaseLLMRerankTest):
"api_base": os.getenv("AZURE_AI_COHERE_API_BASE"),
"api_key": os.getenv("AZURE_AI_COHERE_API_KEY"),
}
@pytest.mark.asyncio
async def test_azure_ai_request_format():
"""
Test that Azure AI requests are formatted correctly with the proper endpoint and parameters
for both synchronous and asynchronous calls
"""
from openai import AsyncAzureOpenAI, AzureOpenAI
litellm._turn_on_debug()
# Set up the test parameters
api_key = os.getenv("AZURE_API_KEY")
api_base = f"{os.getenv('AZURE_API_BASE')}/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview"
model = "azure_ai/gpt-4o"
messages = [
{"role": "user", "content": "hi"},
{"role": "assistant", "content": "Hello! How can I assist you today?"},
{"role": "user", "content": "hi"},
]
await litellm.acompletion(
custom_llm_provider="azure_ai",
api_key=api_key,
api_base=api_base,
model=model,
messages=messages,
)

76
tests/local_testing/test_arize_ai.py
View file

@ -1,6 +1,11 @@
import asyncio
import json
import logging
import os
import time
from unittest.mock import patch, Mock
import opentelemetry.exporter.otlp.proto.grpc.trace_exporter
from litellm import Choices
import pytest
from dotenv import load_dotenv
@ -30,6 +35,26 @@ async def test_async_otel_callback():
await asyncio.sleep(2)
@pytest.mark.asyncio()
async def test_async_dynamic_arize_config():
litellm.set_verbose = True
verbose_proxy_logger.setLevel(logging.DEBUG)
verbose_logger.setLevel(logging.DEBUG)
litellm.success_callback = ["arize"]
await litellm.acompletion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "hi test from arize dynamic config"}],
temperature=0.1,
user="OTEL_USER",
arize_api_key=os.getenv("ARIZE_SPACE_2_API_KEY"),
arize_space_key=os.getenv("ARIZE_SPACE_2_KEY"),
)
await asyncio.sleep(2)
@pytest.fixture
def mock_env_vars(monkeypatch):
monkeypatch.setenv("ARIZE_SPACE_KEY", "test_space_key")
@ -58,3 +83,52 @@ def test_get_arize_config_with_endpoints(mock_env_vars, monkeypatch):
config = ArizeLogger.get_arize_config()
assert config.endpoint == "grpc://test.endpoint"
assert config.protocol == "otlp_grpc"
@pytest.mark.skip(
reason="Works locally but not in CI/CD. We'll need a better way to test Arize on CI/CD"
)
def test_arize_callback():
litellm.callbacks = ["arize"]
os.environ["ARIZE_SPACE_KEY"] = "test_space_key"
os.environ["ARIZE_API_KEY"] = "test_api_key"
os.environ["ARIZE_ENDPOINT"] = "https://otlp.arize.com/v1"
# Set the batch span processor to quickly flush after a span has been added
# This is to ensure that the span is exported before the test ends
os.environ["OTEL_BSP_MAX_QUEUE_SIZE"] = "1"
os.environ["OTEL_BSP_MAX_EXPORT_BATCH_SIZE"] = "1"
os.environ["OTEL_BSP_SCHEDULE_DELAY_MILLIS"] = "1"
os.environ["OTEL_BSP_EXPORT_TIMEOUT_MILLIS"] = "5"
try:
with patch.object(
opentelemetry.exporter.otlp.proto.grpc.trace_exporter.OTLPSpanExporter,
"export",
new=Mock(),
) as patched_export:
litellm.completion(
model="openai/test-model",
messages=[{"role": "user", "content": "arize test content"}],
stream=False,
mock_response="hello there!",
)
time.sleep(1) # Wait for the batch span processor to flush
assert patched_export.called
finally:
# Clean up environment variables
for key in [
"ARIZE_SPACE_KEY",
"ARIZE_API_KEY",
"ARIZE_ENDPOINT",
"OTEL_BSP_MAX_QUEUE_SIZE",
"OTEL_BSP_MAX_EXPORT_BATCH_SIZE",
"OTEL_BSP_SCHEDULE_DELAY_MILLIS",
"OTEL_BSP_EXPORT_TIMEOUT_MILLIS",
]:
if key in os.environ:
del os.environ[key]
# Reset callbacks
litellm.callbacks = []

18
tests/local_testing/test_get_llm_provider.py
View file

@ -216,3 +216,21 @@ def test_bedrock_invoke_anthropic():
)
assert custom_llm_provider == "bedrock"
assert model == "invoke/anthropic.claude-3-5-sonnet-20240620-v1:0"
@pytest.mark.parametrize("model", ["xai/grok-2-vision-latest", "grok-2-vision-latest"])
def test_xai_api_base(model):
args = {
"model": model,
"custom_llm_provider": "xai",
"api_base": None,
"api_key": "xai-my-specialkey",
"litellm_params": None,
}
model, custom_llm_provider, dynamic_api_key, api_base = litellm.get_llm_provider(
**args
)
assert custom_llm_provider == "xai"
assert model == "grok-2-vision-latest"
assert api_base == "https://api.x.ai/v1"
assert dynamic_api_key == "xai-my-specialkey"

9
tests/local_testing/test_provider_specific_config.py
View file

@ -5,7 +5,7 @@
import os
import sys
import traceback
import json
import pytest
sys.path.insert(
@ -465,7 +465,8 @@ def test_sagemaker_default_region():
)
mock_post.assert_called_once()
_, kwargs = mock_post.call_args
args_to_sagemaker = kwargs["json"]
print(f"kwargs: {kwargs}")
args_to_sagemaker = json.loads(kwargs["data"])
print("Arguments passed to sagemaker=", args_to_sagemaker)
print("url=", kwargs["url"])
@ -517,7 +518,7 @@ def test_sagemaker_environment_region():
)
mock_post.assert_called_once()
_, kwargs = mock_post.call_args
args_to_sagemaker = kwargs["json"]
args_to_sagemaker = json.loads(kwargs["data"])
print("Arguments passed to sagemaker=", args_to_sagemaker)
print("url=", kwargs["url"])
@ -574,7 +575,7 @@ def test_sagemaker_config_region():
mock_post.assert_called_once()
_, kwargs = mock_post.call_args
args_to_sagemaker = kwargs["json"]
args_to_sagemaker = json.loads(kwargs["data"])
print("Arguments passed to sagemaker=", args_to_sagemaker)
print("url=", kwargs["url"])

8
tests/local_testing/test_sagemaker.py
View file

@ -265,7 +265,7 @@ async def test_acompletion_sagemaker_non_stream():
# Assert
mock_post.assert_called_once()
_, kwargs = mock_post.call_args
args_to_sagemaker = kwargs["json"]
args_to_sagemaker = json.loads(kwargs["data"])
print("Arguments passed to sagemaker=", args_to_sagemaker)
assert args_to_sagemaker == expected_payload
assert (
@ -325,7 +325,7 @@ async def test_completion_sagemaker_non_stream():
# Assert
mock_post.assert_called_once()
_, kwargs = mock_post.call_args
args_to_sagemaker = kwargs["json"]
args_to_sagemaker = json.loads(kwargs["data"])
print("Arguments passed to sagemaker=", args_to_sagemaker)
assert args_to_sagemaker == expected_payload
assert (
@ -386,7 +386,7 @@ async def test_completion_sagemaker_prompt_template_non_stream():
# Assert
mock_post.assert_called_once()
_, kwargs = mock_post.call_args
args_to_sagemaker = kwargs["json"]
args_to_sagemaker = json.loads(kwargs["data"])
print("Arguments passed to sagemaker=", args_to_sagemaker)
assert args_to_sagemaker == expected_payload
@ -445,7 +445,7 @@ async def test_completion_sagemaker_non_stream_with_aws_params():
# Assert
mock_post.assert_called_once()
_, kwargs = mock_post.call_args
args_to_sagemaker = kwargs["json"]
args_to_sagemaker = json.loads(kwargs["data"])
print("Arguments passed to sagemaker=", args_to_sagemaker)
assert args_to_sagemaker == expected_payload
assert (

82
tests/local_testing/test_tpm_rpm_routing_v2.py
View file

@ -20,7 +20,7 @@ sys.path.insert(
from unittest.mock import AsyncMock, MagicMock, patch
from litellm.types.utils import StandardLoggingPayload
import pytest
from litellm.types.router import DeploymentTypedDict
import litellm
from litellm import Router
from litellm.caching.caching import DualCache
@ -47,12 +47,14 @@ def test_tpm_rpm_updated():
deployment_id = "1234"
deployment = "azure/chatgpt-v-2"
total_tokens = 50
standard_logging_payload = create_standard_logging_payload()
standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
standard_logging_payload["model_group"] = model_group
standard_logging_payload["model_id"] = deployment_id
standard_logging_payload["total_tokens"] = total_tokens
standard_logging_payload["hidden_params"]["litellm_model_name"] = deployment
kwargs = {
"litellm_params": {
"model": deployment,
"metadata": {
"model_group": model_group,
"deployment": deployment,
@ -62,10 +64,16 @@ def test_tpm_rpm_updated():
"standard_logging_object": standard_logging_payload,
}
litellm_deployment_dict: DeploymentTypedDict = {
"model_name": model_group,
"litellm_params": {"model": deployment},
"model_info": {"id": deployment_id},
}
start_time = time.time()
response_obj = {"usage": {"total_tokens": total_tokens}}
end_time = time.time()
lowest_tpm_logger.pre_call_check(deployment=kwargs["litellm_params"])
lowest_tpm_logger.pre_call_check(deployment=litellm_deployment_dict)
lowest_tpm_logger.log_success_event(
response_obj=response_obj,
kwargs=kwargs,
@ -74,8 +82,8 @@ def test_tpm_rpm_updated():
)
dt = get_utc_datetime()
current_minute = dt.strftime("%H-%M")
tpm_count_api_key = f"{deployment_id}:tpm:{current_minute}"
rpm_count_api_key = f"{deployment_id}:rpm:{current_minute}"
tpm_count_api_key = f"{deployment_id}:{deployment}:tpm:{current_minute}"
rpm_count_api_key = f"{deployment_id}:{deployment}:rpm:{current_minute}"
print(f"tpm_count_api_key={tpm_count_api_key}")
assert response_obj["usage"]["total_tokens"] == test_cache.get_cache(
@ -113,6 +121,7 @@ def test_get_available_deployments():
standard_logging_payload["model_group"] = model_group
standard_logging_payload["model_id"] = deployment_id
standard_logging_payload["total_tokens"] = total_tokens
standard_logging_payload["hidden_params"]["litellm_model_name"] = deployment
kwargs = {
"litellm_params": {
"metadata": {
@ -135,10 +144,11 @@ def test_get_available_deployments():
## DEPLOYMENT 2 ##
total_tokens = 20
deployment_id = "5678"
standard_logging_payload = create_standard_logging_payload()
standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
standard_logging_payload["model_group"] = model_group
standard_logging_payload["model_id"] = deployment_id
standard_logging_payload["total_tokens"] = total_tokens
standard_logging_payload["hidden_params"]["litellm_model_name"] = deployment
kwargs = {
"litellm_params": {
"metadata": {
@ -209,11 +219,12 @@ def test_router_get_available_deployments():
print(f"router id's: {router.get_model_ids()}")
## DEPLOYMENT 1 ##
deployment_id = 1
standard_logging_payload = create_standard_logging_payload()
standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
standard_logging_payload["model_group"] = "azure-model"
standard_logging_payload["model_id"] = str(deployment_id)
total_tokens = 50
standard_logging_payload["total_tokens"] = total_tokens
standard_logging_payload["hidden_params"]["litellm_model_name"] = "azure/gpt-turbo"
kwargs = {
"litellm_params": {
"metadata": {
@ -237,6 +248,9 @@ def test_router_get_available_deployments():
standard_logging_payload = create_standard_logging_payload()
standard_logging_payload["model_group"] = "azure-model"
standard_logging_payload["model_id"] = str(deployment_id)
standard_logging_payload["hidden_params"][
"litellm_model_name"
] = "azure/gpt-35-turbo"
kwargs = {
"litellm_params": {
"metadata": {
@ -293,10 +307,11 @@ def test_router_skip_rate_limited_deployments():
## DEPLOYMENT 1 ##
deployment_id = 1
total_tokens = 1439
standard_logging_payload = create_standard_logging_payload()
standard_logging_payload: StandardLoggingPayload = create_standard_logging_payload()
standard_logging_payload["model_group"] = "azure-model"
standard_logging_payload["model_id"] = str(deployment_id)
standard_logging_payload["total_tokens"] = total_tokens
standard_logging_payload["hidden_params"]["litellm_model_name"] = "azure/gpt-turbo"
kwargs = {
"litellm_params": {
"metadata": {
@ -699,3 +714,54 @@ def test_return_potential_deployments():
)
assert len(potential_deployments) == 1
@pytest.mark.asyncio
async def test_tpm_rpm_routing_model_name_checks():
deployment = {
"model_name": "gpt-3.5-turbo",
"litellm_params": {
"model": "azure/chatgpt-v-2",
"api_key": os.getenv("AZURE_API_KEY"),
"api_base": os.getenv("AZURE_API_BASE"),
"mock_response": "Hey, how's it going?",
},
}
router = Router(model_list=[deployment], routing_strategy="usage-based-routing-v2")
async def side_effect_pre_call_check(*args, **kwargs):
return args[0]
with patch.object(
router.lowesttpm_logger_v2,
"async_pre_call_check",
side_effect=side_effect_pre_call_check,
) as mock_object, patch.object(
router.lowesttpm_logger_v2, "async_log_success_event"
) as mock_logging_event:
response = await router.acompletion(
model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}]
)
mock_object.assert_called()
print(f"mock_object.call_args: {mock_object.call_args[0][0]}")
assert (
mock_object.call_args[0][0]["litellm_params"]["model"]
== deployment["litellm_params"]["model"]
)
await asyncio.sleep(1)
mock_logging_event.assert_called()
print(f"mock_logging_event: {mock_logging_event.call_args.kwargs}")
standard_logging_payload: StandardLoggingPayload = (
mock_logging_event.call_args.kwargs.get("kwargs", {}).get(
"standard_logging_object"
)
)
assert (
standard_logging_payload["hidden_params"]["litellm_model_name"]
== "azure/chatgpt-v-2"
)

111
tests/logging_callback_tests/test_arize_logging.py Normal file
View file

@ -0,0 +1,111 @@
import os
import sys
import time
from unittest.mock import Mock, patch
import json
import opentelemetry.exporter.otlp.proto.grpc.trace_exporter
from typing import Optional
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system-path
from litellm.integrations._types.open_inference import SpanAttributes
from litellm.integrations.arize.arize import ArizeConfig, ArizeLogger
from litellm.integrations.custom_logger import CustomLogger
from litellm.main import completion
import litellm
from litellm.types.utils import Choices, StandardCallbackDynamicParams
import pytest
import asyncio
def test_arize_set_attributes():
"""
Test setting attributes for Arize
"""
from unittest.mock import MagicMock
from litellm.types.utils import ModelResponse
span = MagicMock()
kwargs = {
"role": "user",
"content": "simple arize test",
"model": "gpt-4o",
"messages": [{"role": "user", "content": "basic arize test"}],
"standard_logging_object": {
"model_parameters": {"user": "test_user"},
"metadata": {"key": "value", "key2": None},
},
}
response_obj = ModelResponse(
usage={"total_tokens": 100, "completion_tokens": 60, "prompt_tokens": 40},
choices=[Choices(message={"role": "assistant", "content": "response content"})],
)
ArizeLogger.set_arize_attributes(span, kwargs, response_obj)
assert span.set_attribute.call_count == 14
span.set_attribute.assert_any_call(
SpanAttributes.METADATA, json.dumps({"key": "value", "key2": None})
)
span.set_attribute.assert_any_call(SpanAttributes.LLM_MODEL_NAME, "gpt-4o")
span.set_attribute.assert_any_call(SpanAttributes.OPENINFERENCE_SPAN_KIND, "LLM")
span.set_attribute.assert_any_call(SpanAttributes.INPUT_VALUE, "basic arize test")
span.set_attribute.assert_any_call("llm.input_messages.0.message.role", "user")
span.set_attribute.assert_any_call(
"llm.input_messages.0.message.content", "basic arize test"
)
span.set_attribute.assert_any_call(
SpanAttributes.LLM_INVOCATION_PARAMETERS, '{"user": "test_user"}'
)
span.set_attribute.assert_any_call(SpanAttributes.USER_ID, "test_user")
span.set_attribute.assert_any_call(SpanAttributes.OUTPUT_VALUE, "response content")
span.set_attribute.assert_any_call(
"llm.output_messages.0.message.role", "assistant"
)
span.set_attribute.assert_any_call(
"llm.output_messages.0.message.content", "response content"
)
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_TOTAL, 100)
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, 60)
span.set_attribute.assert_any_call(SpanAttributes.LLM_TOKEN_COUNT_PROMPT, 40)
class TestArizeLogger(CustomLogger):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.standard_callback_dynamic_params: Optional[
StandardCallbackDynamicParams
] = None
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
print("logged kwargs", json.dumps(kwargs, indent=4, default=str))
self.standard_callback_dynamic_params = kwargs.get(
"standard_callback_dynamic_params"
)
@pytest.mark.asyncio
async def test_arize_dynamic_params():
"""verify arize ai dynamic params are recieved by a callback"""
test_arize_logger = TestArizeLogger()
litellm.callbacks = [test_arize_logger]
await litellm.acompletion(
model="gpt-4o",
messages=[{"role": "user", "content": "basic arize test"}],
mock_response="test",
arize_api_key="test_api_key_dynamic",
arize_space_key="test_space_key_dynamic",
)
await asyncio.sleep(2)
assert test_arize_logger.standard_callback_dynamic_params is not None
assert (
test_arize_logger.standard_callback_dynamic_params.get("arize_api_key")
== "test_api_key_dynamic"
)
assert (
test_arize_logger.standard_callback_dynamic_params.get("arize_space_key")
== "test_space_key_dynamic"
)

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@ -1 +1 @@
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7
ui/litellm-dashboard/src/components/SSOSettings.tsx
View file

@ -8,10 +8,11 @@ import { getModelDisplayName } from "./key_team_helpers/fetch_available_models_t
interface SSOSettingsProps {
accessToken: string | null;
possibleUIRoles?: Record<string, Record<string, string>> | null;
userID?: string;
userID: string;
userRole: string;
}
const SSOSettings: React.FC<SSOSettingsProps> = ({ accessToken, possibleUIRoles }) => {
const SSOSettings: React.FC<SSOSettingsProps> = ({ accessToken, possibleUIRoles, userID, userRole }) => {
const [loading, setLoading] = useState<boolean>(true);
const [settings, setSettings] = useState<any>(null);
const [isEditing, setIsEditing] = useState<boolean>(false);
@ -36,7 +37,7 @@ const SSOSettings: React.FC<SSOSettingsProps> = ({ accessToken, possibleUIRoles
// Fetch available models
if (accessToken) {
try {
const modelResponse = await modelAvailableCall(accessToken, null, null);
const modelResponse = await modelAvailableCall(accessToken, userID, userRole);
if (modelResponse && modelResponse.data) {
const modelNames = modelResponse.data.map((model: { id: string }) => model.id);
setAvailableModels(modelNames);

2
ui/litellm-dashboard/src/components/view_users.tsx
View file

@ -348,7 +348,7 @@ const ViewUserDashboard: React.FC<ViewUserDashboardProps> = ({
</TabPanel>
<TabPanel>
<SSOSettings accessToken={accessToken} possibleUIRoles={possibleUIRoles} userID={userID}/>
<SSOSettings accessToken={accessToken} possibleUIRoles={possibleUIRoles} userID={userID} userRole={userRole}/>
</TabPanel>
</TabPanels>
</TabGroup>