mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-12 23:01:41 +00:00
refactor(utils): lazy load additional heavy imports to improve import time
- Move get_llm_provider, _is_non_openai_azure_model to lazy loading - Move get_supported_openai_params to lazy loading - Move convert_dict_to_response functions (LiteLLMResponseObjectHandler, convert_to_model_response_object, etc.) to lazy loading - Move get_api_base and ResponseMetadata to lazy loading - Move _parse_content_for_reasoning to lazy loading - Update all internal usages to access via getattr(sys.modules[__name__], ...) This reduces import time and memory usage by only loading these modules when they're actually accessed, not during module import.
This commit is contained in:
Alexsander Hamir
parent
7523bef6b8
commit
35f65c9901
1 changed files with 115 additions and 21 deletions
136
litellm/utils.py
136
litellm/utils.py
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@ -91,34 +91,13 @@ from litellm.litellm_core_utils.get_litellm_params import (
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_get_base_model_from_litellm_call_metadata,
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get_litellm_params,
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)
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from litellm.litellm_core_utils.get_llm_provider_logic import (
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_is_non_openai_azure_model,
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get_llm_provider,
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)
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from litellm.litellm_core_utils.get_supported_openai_params import (
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get_supported_openai_params,
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)
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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.llm_response_utils.convert_dict_to_response import (
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LiteLLMResponseObjectHandler,
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_handle_invalid_parallel_tool_calls,
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convert_to_model_response_object,
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convert_to_streaming_response,
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convert_to_streaming_response_async,
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)
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from litellm.litellm_core_utils.llm_response_utils.get_api_base import get_api_base
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from litellm.litellm_core_utils.llm_response_utils.get_formatted_prompt import (
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get_formatted_prompt,
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)
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from litellm.litellm_core_utils.llm_response_utils.get_headers import (
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get_response_headers,
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)
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from litellm.litellm_core_utils.llm_response_utils.response_metadata import (
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ResponseMetadata,
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)
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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_parse_content_for_reasoning,
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)
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from litellm.litellm_core_utils.redact_messages import (
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LiteLLMLoggingObject,
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redact_message_input_output_from_logging,
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@ -2262,6 +2241,7 @@ def supports_response_schema(
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"""
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## GET LLM PROVIDER ##
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try:
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get_llm_provider = getattr(sys.modules[__name__], '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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@ -2959,6 +2939,9 @@ def get_optional_params_embeddings( # noqa: PLR0915
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additional_drop_params: Optional[List[str]] = None,
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**kwargs,
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):
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# Lazy load get_supported_openai_params
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get_supported_openai_params = getattr(sys.modules[__name__], 'get_supported_openai_params')
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# retrieve all parameters passed to the function
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passed_params = locals()
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custom_llm_provider = passed_params.pop("custom_llm_provider", None)
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@ -3761,6 +3744,7 @@ def get_optional_params( # noqa: PLR0915
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message=f"{custom_llm_provider} does not support parameters: {list(unsupported_params.keys())}, for model={model}. To drop these, set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\n. \n If you want to use these params dynamically send allowed_openai_params={list(unsupported_params.keys())} in your request.",
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)
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get_supported_openai_params = getattr(sys.modules[__name__], 'get_supported_openai_params')
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supported_params = get_supported_openai_params(
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model=model, custom_llm_provider=custom_llm_provider
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)
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@ -4898,6 +4882,7 @@ def get_max_tokens(model: str) -> Optional[int]:
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return litellm.model_cost[model]["max_output_tokens"]
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elif "max_tokens" in litellm.model_cost[model]:
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return litellm.model_cost[model]["max_tokens"]
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get_llm_provider = getattr(sys.modules[__name__], 'get_llm_provider')
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model, custom_llm_provider, _, _ = get_llm_provider(model=model)
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if custom_llm_provider == "huggingface":
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max_tokens = _get_max_position_embeddings(model_name=model)
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@ -5018,6 +5003,7 @@ def _get_potential_model_names(
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if custom_llm_provider is None:
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# Get custom_llm_provider
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try:
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get_llm_provider = getattr(sys.modules[__name__], 'get_llm_provider')
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split_model, custom_llm_provider, _, _ = get_llm_provider(model=model)
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except Exception:
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split_model = model
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@ -5740,6 +5726,7 @@ def validate_environment( # noqa: PLR0915
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}
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## EXTRACT LLM PROVIDER - if model name provided
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try:
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get_llm_provider = getattr(sys.modules[__name__], 'get_llm_provider')
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_, custom_llm_provider, _, _ = get_llm_provider(model=model)
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except Exception:
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custom_llm_provider = None
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@ -6302,6 +6289,7 @@ def register_prompt_template(
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complete_model = model
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potential_models = [complete_model]
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try:
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get_llm_provider = getattr(sys.modules[__name__], 'get_llm_provider')
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model = get_llm_provider(model=model)[0]
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potential_models.append(model)
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except Exception:
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@ -6387,6 +6375,7 @@ class TextCompletionStreamWrapper:
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except StopIteration:
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raise StopIteration
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except Exception as e:
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exception_type = getattr(sys.modules[__name__], 'exception_type')
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raise exception_type(
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model=self.model,
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custom_llm_provider=self.custom_llm_provider or "",
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@ -8768,4 +8757,109 @@ def __getattr__(name: str) -> Any:
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_globals["_get_response_headers"] = __get_response_headers
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return _globals["_get_response_headers"]
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# Lazy load get_llm_provider_logic functions to avoid loading at module import time
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if name == "get_llm_provider":
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# Check if already cached
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if "get_llm_provider" not in _globals:
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from litellm.litellm_core_utils.get_llm_provider_logic import (
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get_llm_provider as _get_llm_provider,
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)
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_globals["get_llm_provider"] = _get_llm_provider
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return _globals["get_llm_provider"]
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if name == "_is_non_openai_azure_model":
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# Check if already cached
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if "_is_non_openai_azure_model" not in _globals:
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from litellm.litellm_core_utils.get_llm_provider_logic import (
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_is_non_openai_azure_model as __is_non_openai_azure_model,
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)
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_globals["_is_non_openai_azure_model"] = __is_non_openai_azure_model
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return _globals["_is_non_openai_azure_model"]
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# Lazy load get_supported_openai_params to avoid loading at module import time
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if name == "get_supported_openai_params":
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# Check if already cached
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if "get_supported_openai_params" not in _globals:
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from litellm.litellm_core_utils.get_supported_openai_params import (
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get_supported_openai_params as _get_supported_openai_params,
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)
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_globals["get_supported_openai_params"] = _get_supported_openai_params
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return _globals["get_supported_openai_params"]
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# Lazy load convert_dict_to_response functions to avoid loading at module import time
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if name == "LiteLLMResponseObjectHandler":
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# Check if already cached
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if "LiteLLMResponseObjectHandler" not in _globals:
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from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
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LiteLLMResponseObjectHandler as _LiteLLMResponseObjectHandler,
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)
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_globals["LiteLLMResponseObjectHandler"] = _LiteLLMResponseObjectHandler
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return _globals["LiteLLMResponseObjectHandler"]
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if name == "_handle_invalid_parallel_tool_calls":
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# Check if already cached
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if "_handle_invalid_parallel_tool_calls" not in _globals:
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from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
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_handle_invalid_parallel_tool_calls as __handle_invalid_parallel_tool_calls,
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)
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_globals["_handle_invalid_parallel_tool_calls"] = __handle_invalid_parallel_tool_calls
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return _globals["_handle_invalid_parallel_tool_calls"]
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if name == "convert_to_model_response_object":
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# Check if already cached
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if "convert_to_model_response_object" not in _globals:
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from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
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convert_to_model_response_object as _convert_to_model_response_object,
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)
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_globals["convert_to_model_response_object"] = _convert_to_model_response_object
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return _globals["convert_to_model_response_object"]
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if name == "convert_to_streaming_response":
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# Check if already cached
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if "convert_to_streaming_response" not in _globals:
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from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
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convert_to_streaming_response as _convert_to_streaming_response,
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)
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_globals["convert_to_streaming_response"] = _convert_to_streaming_response
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return _globals["convert_to_streaming_response"]
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if name == "convert_to_streaming_response_async":
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# Check if already cached
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if "convert_to_streaming_response_async" not in _globals:
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from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
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convert_to_streaming_response_async as _convert_to_streaming_response_async,
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)
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_globals["convert_to_streaming_response_async"] = _convert_to_streaming_response_async
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return _globals["convert_to_streaming_response_async"]
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# Lazy load get_api_base to avoid loading at module import time
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if name == "get_api_base":
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# Check if already cached
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if "get_api_base" not in _globals:
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from litellm.litellm_core_utils.llm_response_utils.get_api_base import (
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get_api_base as _get_api_base,
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)
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_globals["get_api_base"] = _get_api_base
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return _globals["get_api_base"]
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# Lazy load ResponseMetadata to avoid loading at module import time
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if name == "ResponseMetadata":
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# Check if already cached
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if "ResponseMetadata" not in _globals:
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from litellm.litellm_core_utils.llm_response_utils.response_metadata import (
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ResponseMetadata as _ResponseMetadata,
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)
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_globals["ResponseMetadata"] = _ResponseMetadata
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return _globals["ResponseMetadata"]
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# Lazy load _parse_content_for_reasoning to avoid loading at module import time
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if name == "_parse_content_for_reasoning":
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# Check if already cached
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if "_parse_content_for_reasoning" not in _globals:
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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_parse_content_for_reasoning as __parse_content_for_reasoning,
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)
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_globals["_parse_content_for_reasoning"] = __parse_content_for_reasoning
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return _globals["_parse_content_for_reasoning"]
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raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
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