mirror of
https://github.com/BerriAI/litellm.git
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Merge branch 'main' into litellm-fix-vertexaibeta
This commit is contained in:
Tiger Yu
GitHub
commit
26630cd263
73 changed files with 3482 additions and 782 deletions
369
litellm/utils.py
369
litellm/utils.py
|
|
@ -48,8 +48,10 @@ from tokenizers import Tokenizer
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import litellm
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import litellm._service_logger # for storing API inputs, outputs, and metadata
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import litellm.litellm_core_utils
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import litellm.litellm_core_utils.json_validation_rule
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from litellm.caching import DualCache
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from litellm.litellm_core_utils.core_helpers import map_finish_reason
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from litellm.litellm_core_utils.exception_mapping_utils import get_error_message
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from litellm.litellm_core_utils.llm_request_utils import _ensure_extra_body_is_safe
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from litellm.litellm_core_utils.redact_messages import (
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redact_message_input_output_from_logging,
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|
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@ -579,7 +581,7 @@ def client(original_function):
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else:
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return False
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def post_call_processing(original_response, model):
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def post_call_processing(original_response, model, optional_params: Optional[dict]):
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try:
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if original_response is None:
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pass
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@ -594,11 +596,47 @@ def client(original_function):
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pass
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else:
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if isinstance(original_response, ModelResponse):
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model_response = original_response.choices[
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model_response: Optional[str] = original_response.choices[
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0
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].message.content
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### POST-CALL RULES ###
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rules_obj.post_call_rules(input=model_response, model=model)
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].message.content # type: ignore
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if model_response is not None:
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### POST-CALL RULES ###
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rules_obj.post_call_rules(
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input=model_response, model=model
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)
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### JSON SCHEMA VALIDATION ###
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if (
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optional_params is not None
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and "response_format" in optional_params
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and isinstance(
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optional_params["response_format"], dict
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)
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and "type" in optional_params["response_format"]
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and optional_params["response_format"]["type"]
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== "json_object"
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and "response_schema"
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in optional_params["response_format"]
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and isinstance(
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optional_params["response_format"][
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"response_schema"
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],
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dict,
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)
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and "enforce_validation"
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in optional_params["response_format"]
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and optional_params["response_format"][
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"enforce_validation"
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]
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is True
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):
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# schema given, json response expected, and validation enforced
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litellm.litellm_core_utils.json_validation_rule.validate_schema(
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schema=optional_params["response_format"][
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"response_schema"
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],
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response=model_response,
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)
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except Exception as e:
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raise e
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@ -867,7 +905,11 @@ def client(original_function):
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return result
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### POST-CALL RULES ###
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post_call_processing(original_response=result, model=model or None)
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post_call_processing(
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original_response=result,
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model=model or None,
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optional_params=kwargs,
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)
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# [OPTIONAL] ADD TO CACHE
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if (
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|
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@ -1316,7 +1358,9 @@ def client(original_function):
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).total_seconds() * 1000 # return response latency in ms like openai
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### POST-CALL RULES ###
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post_call_processing(original_response=result, model=model)
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post_call_processing(
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original_response=result, model=model, optional_params=kwargs
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)
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# [OPTIONAL] ADD TO CACHE
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if (
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@ -1847,9 +1891,10 @@ def supports_system_messages(model: str, custom_llm_provider: Optional[str]) ->
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Parameters:
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model (str): The model name to be checked.
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custom_llm_provider (str): The provider to be checked.
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Returns:
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bool: True if the model supports function calling, False otherwise.
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bool: True if the model supports system messages, False otherwise.
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Raises:
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Exception: If the given model is not found in model_prices_and_context_window.json.
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@ -1867,6 +1912,43 @@ def supports_system_messages(model: str, custom_llm_provider: Optional[str]) ->
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)
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def supports_response_schema(model: str, custom_llm_provider: Optional[str]) -> bool:
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"""
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Check if the given model + provider supports 'response_schema' as a param.
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Parameters:
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model (str): The model name to be checked.
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custom_llm_provider (str): The provider to be checked.
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Returns:
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bool: True if the model supports response_schema, False otherwise.
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Does not raise error. Defaults to 'False'. Outputs logging.error.
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"""
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try:
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## GET LLM PROVIDER ##
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model, custom_llm_provider, _, _ = get_llm_provider(
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model=model, custom_llm_provider=custom_llm_provider
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)
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if custom_llm_provider == "predibase": # predibase supports this globally
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return True
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## GET MODEL INFO
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model_info = litellm.get_model_info(
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model=model, custom_llm_provider=custom_llm_provider
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)
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if model_info.get("supports_response_schema", False) is True:
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return True
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return False
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except Exception:
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verbose_logger.error(
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f"Model not in model_prices_and_context_window.json. You passed model={model}, custom_llm_provider={custom_llm_provider}."
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)
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return False
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def supports_function_calling(model: str) -> bool:
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"""
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Check if the given model supports function calling and return a boolean value.
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@ -2324,7 +2406,9 @@ def get_optional_params(
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elif k == "hf_model_name" and custom_llm_provider != "sagemaker":
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continue
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elif (
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k.startswith("vertex_") and custom_llm_provider != "vertex_ai" and custom_llm_provider != "vertex_ai_beta"
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k.startswith("vertex_")
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and custom_llm_provider != "vertex_ai"
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and custom_llm_provider != "vertex_ai_beta"
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): # allow dynamically setting vertex ai init logic
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continue
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passed_params[k] = v
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|
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@ -2756,6 +2840,11 @@ def get_optional_params(
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non_default_params=non_default_params,
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optional_params=optional_params,
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model=model,
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drop_params=(
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drop_params
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if drop_params is not None and isinstance(drop_params, bool)
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else False
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),
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)
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elif (
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custom_llm_provider == "vertex_ai" and model in litellm.vertex_anthropic_models
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@ -2824,12 +2913,7 @@ def get_optional_params(
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optional_params=optional_params,
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)
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)
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else:
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optional_params = litellm.AmazonAnthropicConfig().map_openai_params(
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non_default_params=non_default_params,
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optional_params=optional_params,
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)
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else: # bedrock httpx route
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elif model in litellm.BEDROCK_CONVERSE_MODELS:
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optional_params = litellm.AmazonConverseConfig().map_openai_params(
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model=model,
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non_default_params=non_default_params,
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|
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@ -2840,6 +2924,11 @@ def get_optional_params(
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else False
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),
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)
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else:
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optional_params = litellm.AmazonAnthropicConfig().map_openai_params(
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non_default_params=non_default_params,
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optional_params=optional_params,
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)
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elif "amazon" in model: # amazon titan llms
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_check_valid_arg(supported_params=supported_params)
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# see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large
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|
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@ -3755,23 +3844,18 @@ def get_supported_openai_params(
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return litellm.AzureOpenAIConfig().get_supported_openai_params()
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elif custom_llm_provider == "openrouter":
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return [
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"functions",
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"function_call",
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"temperature",
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"top_p",
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"n",
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"stream",
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"stop",
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"max_tokens",
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"presence_penalty",
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"frequency_penalty",
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"logit_bias",
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"user",
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"response_format",
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"presence_penalty",
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"repetition_penalty",
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"seed",
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"tools",
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"tool_choice",
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"max_retries",
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"max_tokens",
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"logit_bias",
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"logprobs",
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"top_logprobs",
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"response_format",
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"stop",
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]
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elif custom_llm_provider == "mistral" or custom_llm_provider == "codestral":
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# mistal and codestral api have the exact same params
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|
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@ -3789,6 +3873,10 @@ def get_supported_openai_params(
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"top_p",
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"stop",
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"seed",
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"tools",
|
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"tool_choice",
|
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"functions",
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"function_call",
|
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]
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elif custom_llm_provider == "huggingface":
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return litellm.HuggingfaceConfig().get_supported_openai_params()
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|
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@ -4434,8 +4522,7 @@ def get_max_tokens(model: str) -> Optional[int]:
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def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> ModelInfo:
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"""
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Get a dict for the maximum tokens (context window),
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input_cost_per_token, output_cost_per_token for a given model.
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Get a dict for the maximum tokens (context window), input_cost_per_token, output_cost_per_token for a given model.
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Parameters:
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- model (str): The name of the model.
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|
|
@ -4520,6 +4607,7 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
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mode="chat",
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supported_openai_params=supported_openai_params,
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supports_system_messages=None,
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supports_response_schema=None,
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)
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else:
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"""
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|
|
@ -4541,36 +4629,6 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
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pass
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else:
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raise Exception
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return ModelInfo(
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max_tokens=_model_info.get("max_tokens", None),
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max_input_tokens=_model_info.get("max_input_tokens", None),
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max_output_tokens=_model_info.get("max_output_tokens", None),
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input_cost_per_token=_model_info.get("input_cost_per_token", 0),
|
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input_cost_per_character=_model_info.get(
|
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"input_cost_per_character", None
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),
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input_cost_per_token_above_128k_tokens=_model_info.get(
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"input_cost_per_token_above_128k_tokens", None
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||||
),
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output_cost_per_token=_model_info.get("output_cost_per_token", 0),
|
||||
output_cost_per_character=_model_info.get(
|
||||
"output_cost_per_character", None
|
||||
),
|
||||
output_cost_per_token_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_token_above_128k_tokens", None
|
||||
),
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||||
output_cost_per_character_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_character_above_128k_tokens", None
|
||||
),
|
||||
litellm_provider=_model_info.get(
|
||||
"litellm_provider", custom_llm_provider
|
||||
),
|
||||
mode=_model_info.get("mode"),
|
||||
supported_openai_params=supported_openai_params,
|
||||
supports_system_messages=_model_info.get(
|
||||
"supports_system_messages", None
|
||||
),
|
||||
)
|
||||
elif model in litellm.model_cost:
|
||||
_model_info = litellm.model_cost[model]
|
||||
_model_info["supported_openai_params"] = supported_openai_params
|
||||
|
|
@ -4584,36 +4642,6 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
|
|||
pass
|
||||
else:
|
||||
raise Exception
|
||||
return ModelInfo(
|
||||
max_tokens=_model_info.get("max_tokens", None),
|
||||
max_input_tokens=_model_info.get("max_input_tokens", None),
|
||||
max_output_tokens=_model_info.get("max_output_tokens", None),
|
||||
input_cost_per_token=_model_info.get("input_cost_per_token", 0),
|
||||
input_cost_per_character=_model_info.get(
|
||||
"input_cost_per_character", None
|
||||
),
|
||||
input_cost_per_token_above_128k_tokens=_model_info.get(
|
||||
"input_cost_per_token_above_128k_tokens", None
|
||||
),
|
||||
output_cost_per_token=_model_info.get("output_cost_per_token", 0),
|
||||
output_cost_per_character=_model_info.get(
|
||||
"output_cost_per_character", None
|
||||
),
|
||||
output_cost_per_token_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_token_above_128k_tokens", None
|
||||
),
|
||||
output_cost_per_character_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_character_above_128k_tokens", None
|
||||
),
|
||||
litellm_provider=_model_info.get(
|
||||
"litellm_provider", custom_llm_provider
|
||||
),
|
||||
mode=_model_info.get("mode"),
|
||||
supported_openai_params=supported_openai_params,
|
||||
supports_system_messages=_model_info.get(
|
||||
"supports_system_messages", None
|
||||
),
|
||||
)
|
||||
elif split_model in litellm.model_cost:
|
||||
_model_info = litellm.model_cost[split_model]
|
||||
_model_info["supported_openai_params"] = supported_openai_params
|
||||
|
|
@ -4627,40 +4655,48 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
|
|||
pass
|
||||
else:
|
||||
raise Exception
|
||||
return ModelInfo(
|
||||
max_tokens=_model_info.get("max_tokens", None),
|
||||
max_input_tokens=_model_info.get("max_input_tokens", None),
|
||||
max_output_tokens=_model_info.get("max_output_tokens", None),
|
||||
input_cost_per_token=_model_info.get("input_cost_per_token", 0),
|
||||
input_cost_per_character=_model_info.get(
|
||||
"input_cost_per_character", None
|
||||
),
|
||||
input_cost_per_token_above_128k_tokens=_model_info.get(
|
||||
"input_cost_per_token_above_128k_tokens", None
|
||||
),
|
||||
output_cost_per_token=_model_info.get("output_cost_per_token", 0),
|
||||
output_cost_per_character=_model_info.get(
|
||||
"output_cost_per_character", None
|
||||
),
|
||||
output_cost_per_token_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_token_above_128k_tokens", None
|
||||
),
|
||||
output_cost_per_character_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_character_above_128k_tokens", None
|
||||
),
|
||||
litellm_provider=_model_info.get(
|
||||
"litellm_provider", custom_llm_provider
|
||||
),
|
||||
mode=_model_info.get("mode"),
|
||||
supported_openai_params=supported_openai_params,
|
||||
supports_system_messages=_model_info.get(
|
||||
"supports_system_messages", None
|
||||
),
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
|
||||
)
|
||||
|
||||
## PROVIDER-SPECIFIC INFORMATION
|
||||
if custom_llm_provider == "predibase":
|
||||
_model_info["supports_response_schema"] = True
|
||||
|
||||
return ModelInfo(
|
||||
max_tokens=_model_info.get("max_tokens", None),
|
||||
max_input_tokens=_model_info.get("max_input_tokens", None),
|
||||
max_output_tokens=_model_info.get("max_output_tokens", None),
|
||||
input_cost_per_token=_model_info.get("input_cost_per_token", 0),
|
||||
input_cost_per_character=_model_info.get(
|
||||
"input_cost_per_character", None
|
||||
),
|
||||
input_cost_per_token_above_128k_tokens=_model_info.get(
|
||||
"input_cost_per_token_above_128k_tokens", None
|
||||
),
|
||||
output_cost_per_token=_model_info.get("output_cost_per_token", 0),
|
||||
output_cost_per_character=_model_info.get(
|
||||
"output_cost_per_character", None
|
||||
),
|
||||
output_cost_per_token_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_token_above_128k_tokens", None
|
||||
),
|
||||
output_cost_per_character_above_128k_tokens=_model_info.get(
|
||||
"output_cost_per_character_above_128k_tokens", None
|
||||
),
|
||||
litellm_provider=_model_info.get(
|
||||
"litellm_provider", custom_llm_provider
|
||||
),
|
||||
mode=_model_info.get("mode"),
|
||||
supported_openai_params=supported_openai_params,
|
||||
supports_system_messages=_model_info.get(
|
||||
"supports_system_messages", None
|
||||
),
|
||||
supports_response_schema=_model_info.get(
|
||||
"supports_response_schema", None
|
||||
),
|
||||
)
|
||||
except Exception:
|
||||
raise Exception(
|
||||
"This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
|
||||
|
|
@ -5278,6 +5314,27 @@ def convert_to_model_response_object(
|
|||
hidden_params: Optional[dict] = None,
|
||||
):
|
||||
received_args = locals()
|
||||
### CHECK IF ERROR IN RESPONSE ### - openrouter returns these in the dictionary
|
||||
if (
|
||||
response_object is not None
|
||||
and "error" in response_object
|
||||
and response_object["error"] is not None
|
||||
):
|
||||
error_args = {"status_code": 422, "message": "Error in response object"}
|
||||
if isinstance(response_object["error"], dict):
|
||||
if "code" in response_object["error"]:
|
||||
error_args["status_code"] = response_object["error"]["code"]
|
||||
if "message" in response_object["error"]:
|
||||
if isinstance(response_object["error"]["message"], dict):
|
||||
message_str = json.dumps(response_object["error"]["message"])
|
||||
else:
|
||||
message_str = str(response_object["error"]["message"])
|
||||
error_args["message"] = message_str
|
||||
raised_exception = Exception()
|
||||
setattr(raised_exception, "status_code", error_args["status_code"])
|
||||
setattr(raised_exception, "message", error_args["message"])
|
||||
raise raised_exception
|
||||
|
||||
try:
|
||||
if response_type == "completion" and (
|
||||
model_response_object is None
|
||||
|
|
@ -5733,7 +5790,10 @@ def exception_type(
|
|||
print() # noqa
|
||||
try:
|
||||
if model:
|
||||
error_str = str(original_exception)
|
||||
if hasattr(original_exception, "message"):
|
||||
error_str = str(original_exception.message)
|
||||
else:
|
||||
error_str = str(original_exception)
|
||||
if isinstance(original_exception, BaseException):
|
||||
exception_type = type(original_exception).__name__
|
||||
else:
|
||||
|
|
@ -5755,6 +5815,18 @@ def exception_type(
|
|||
_model_group = _metadata.get("model_group")
|
||||
_deployment = _metadata.get("deployment")
|
||||
extra_information = f"\nModel: {model}"
|
||||
|
||||
exception_provider = "Unknown"
|
||||
if (
|
||||
isinstance(custom_llm_provider, str)
|
||||
and len(custom_llm_provider) > 0
|
||||
):
|
||||
exception_provider = (
|
||||
custom_llm_provider[0].upper()
|
||||
+ custom_llm_provider[1:]
|
||||
+ "Exception"
|
||||
)
|
||||
|
||||
if _api_base:
|
||||
extra_information += f"\nAPI Base: `{_api_base}`"
|
||||
if (
|
||||
|
|
@ -5805,10 +5877,13 @@ def exception_type(
|
|||
or custom_llm_provider in litellm.openai_compatible_providers
|
||||
):
|
||||
# custom_llm_provider is openai, make it OpenAI
|
||||
if hasattr(original_exception, "message"):
|
||||
message = original_exception.message
|
||||
else:
|
||||
message = str(original_exception)
|
||||
message = get_error_message(error_obj=original_exception)
|
||||
if message is None:
|
||||
if hasattr(original_exception, "message"):
|
||||
message = original_exception.message
|
||||
else:
|
||||
message = str(original_exception)
|
||||
|
||||
if message is not None and isinstance(message, str):
|
||||
message = message.replace("OPENAI", custom_llm_provider.upper())
|
||||
message = message.replace("openai", custom_llm_provider)
|
||||
|
|
@ -6141,7 +6216,6 @@ def exception_type(
|
|||
)
|
||||
elif (
|
||||
original_exception.status_code == 400
|
||||
or original_exception.status_code == 422
|
||||
or original_exception.status_code == 413
|
||||
):
|
||||
exception_mapping_worked = True
|
||||
|
|
@ -6151,6 +6225,14 @@ def exception_type(
|
|||
llm_provider="replicate",
|
||||
response=original_exception.response,
|
||||
)
|
||||
elif original_exception.status_code == 422:
|
||||
exception_mapping_worked = True
|
||||
raise UnprocessableEntityError(
|
||||
message=f"ReplicateException - {original_exception.message}",
|
||||
model=model,
|
||||
llm_provider="replicate",
|
||||
response=original_exception.response,
|
||||
)
|
||||
elif original_exception.status_code == 408:
|
||||
exception_mapping_worked = True
|
||||
raise Timeout(
|
||||
|
|
@ -7254,10 +7336,17 @@ def exception_type(
|
|||
request=original_exception.request,
|
||||
)
|
||||
elif custom_llm_provider == "azure":
|
||||
message = get_error_message(error_obj=original_exception)
|
||||
if message is None:
|
||||
if hasattr(original_exception, "message"):
|
||||
message = original_exception.message
|
||||
else:
|
||||
message = str(original_exception)
|
||||
|
||||
if "Internal server error" in error_str:
|
||||
exception_mapping_worked = True
|
||||
raise litellm.InternalServerError(
|
||||
message=f"AzureException Internal server error - {original_exception.message}",
|
||||
message=f"AzureException Internal server error - {message}",
|
||||
llm_provider="azure",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7270,7 +7359,7 @@ def exception_type(
|
|||
elif "This model's maximum context length is" in error_str:
|
||||
exception_mapping_worked = True
|
||||
raise ContextWindowExceededError(
|
||||
message=f"AzureException ContextWindowExceededError - {original_exception.message}",
|
||||
message=f"AzureException ContextWindowExceededError - {message}",
|
||||
llm_provider="azure",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7279,7 +7368,7 @@ def exception_type(
|
|||
elif "DeploymentNotFound" in error_str:
|
||||
exception_mapping_worked = True
|
||||
raise NotFoundError(
|
||||
message=f"AzureException NotFoundError - {original_exception.message}",
|
||||
message=f"AzureException NotFoundError - {message}",
|
||||
llm_provider="azure",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7299,7 +7388,7 @@ def exception_type(
|
|||
):
|
||||
exception_mapping_worked = True
|
||||
raise ContentPolicyViolationError(
|
||||
message=f"litellm.ContentPolicyViolationError: AzureException - {original_exception.message}",
|
||||
message=f"litellm.ContentPolicyViolationError: AzureException - {message}",
|
||||
llm_provider="azure",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7308,7 +7397,7 @@ def exception_type(
|
|||
elif "invalid_request_error" in error_str:
|
||||
exception_mapping_worked = True
|
||||
raise BadRequestError(
|
||||
message=f"AzureException BadRequestError - {original_exception.message}",
|
||||
message=f"AzureException BadRequestError - {message}",
|
||||
llm_provider="azure",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7320,7 +7409,7 @@ def exception_type(
|
|||
):
|
||||
exception_mapping_worked = True
|
||||
raise AuthenticationError(
|
||||
message=f"{exception_provider} AuthenticationError - {original_exception.message}",
|
||||
message=f"{exception_provider} AuthenticationError - {message}",
|
||||
llm_provider=custom_llm_provider,
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7331,7 +7420,7 @@ def exception_type(
|
|||
if original_exception.status_code == 400:
|
||||
exception_mapping_worked = True
|
||||
raise BadRequestError(
|
||||
message=f"AzureException - {original_exception.message}",
|
||||
message=f"AzureException - {message}",
|
||||
llm_provider="azure",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7340,7 +7429,7 @@ def exception_type(
|
|||
elif original_exception.status_code == 401:
|
||||
exception_mapping_worked = True
|
||||
raise AuthenticationError(
|
||||
message=f"AzureException AuthenticationError - {original_exception.message}",
|
||||
message=f"AzureException AuthenticationError - {message}",
|
||||
llm_provider="azure",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7349,7 +7438,7 @@ def exception_type(
|
|||
elif original_exception.status_code == 408:
|
||||
exception_mapping_worked = True
|
||||
raise Timeout(
|
||||
message=f"AzureException Timeout - {original_exception.message}",
|
||||
message=f"AzureException Timeout - {message}",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
llm_provider="azure",
|
||||
|
|
@ -7357,7 +7446,7 @@ def exception_type(
|
|||
elif original_exception.status_code == 422:
|
||||
exception_mapping_worked = True
|
||||
raise BadRequestError(
|
||||
message=f"AzureException BadRequestError - {original_exception.message}",
|
||||
message=f"AzureException BadRequestError - {message}",
|
||||
model=model,
|
||||
llm_provider="azure",
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7366,7 +7455,7 @@ def exception_type(
|
|||
elif original_exception.status_code == 429:
|
||||
exception_mapping_worked = True
|
||||
raise RateLimitError(
|
||||
message=f"AzureException RateLimitError - {original_exception.message}",
|
||||
message=f"AzureException RateLimitError - {message}",
|
||||
model=model,
|
||||
llm_provider="azure",
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7375,7 +7464,7 @@ def exception_type(
|
|||
elif original_exception.status_code == 503:
|
||||
exception_mapping_worked = True
|
||||
raise ServiceUnavailableError(
|
||||
message=f"AzureException ServiceUnavailableError - {original_exception.message}",
|
||||
message=f"AzureException ServiceUnavailableError - {message}",
|
||||
model=model,
|
||||
llm_provider="azure",
|
||||
litellm_debug_info=extra_information,
|
||||
|
|
@ -7384,7 +7473,7 @@ def exception_type(
|
|||
elif original_exception.status_code == 504: # gateway timeout error
|
||||
exception_mapping_worked = True
|
||||
raise Timeout(
|
||||
message=f"AzureException Timeout - {original_exception.message}",
|
||||
message=f"AzureException Timeout - {message}",
|
||||
model=model,
|
||||
litellm_debug_info=extra_information,
|
||||
llm_provider="azure",
|
||||
|
|
@ -7393,7 +7482,7 @@ def exception_type(
|
|||
exception_mapping_worked = True
|
||||
raise APIError(
|
||||
status_code=original_exception.status_code,
|
||||
message=f"AzureException APIError - {original_exception.message}",
|
||||
message=f"AzureException APIError - {message}",
|
||||
llm_provider="azure",
|
||||
litellm_debug_info=extra_information,
|
||||
model=model,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue