mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 10:44:24 +00:00
288 lines
13 KiB
Python
288 lines
13 KiB
Python
import asyncio
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import os
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from typing import TYPE_CHECKING, Any, Callable, Optional
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import httpx
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import openai
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import litellm
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from litellm import get_secret, get_secret_str
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from litellm._logging import verbose_router_logger
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from litellm.utils import calculate_max_parallel_requests
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if TYPE_CHECKING:
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from litellm.router import Router as _Router
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LitellmRouter = _Router
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else:
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LitellmRouter = Any
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class InitalizeOpenAISDKClient:
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@staticmethod
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def should_initialize_sync_client(
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litellm_router_instance: LitellmRouter,
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) -> bool:
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"""
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Returns if Sync OpenAI, Azure Clients should be initialized.
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Do not init sync clients when router.router_general_settings.async_only_mode is True
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"""
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if litellm_router_instance is None:
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return False
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if litellm_router_instance.router_general_settings is not None:
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if (
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hasattr(litellm_router_instance, "router_general_settings")
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and hasattr(
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litellm_router_instance.router_general_settings, "async_only_mode"
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)
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and litellm_router_instance.router_general_settings.async_only_mode
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is True
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):
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return False
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return True
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@staticmethod
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def set_client( # noqa: PLR0915
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litellm_router_instance: LitellmRouter, model: dict
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):
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"""
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- Initializes Azure/OpenAI clients. Stores them in cache, b/c of this - https://github.com/BerriAI/litellm/issues/1278
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- Initializes Semaphore for client w/ rpm. Stores them in cache. b/c of this - https://github.com/BerriAI/litellm/issues/2994
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"""
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client_ttl = litellm_router_instance.client_ttl
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litellm_params = model.get("litellm_params", {})
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model_name = litellm_params.get("model")
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model_id = model["model_info"]["id"]
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# ### IF RPM SET - initialize a semaphore ###
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rpm = litellm_params.get("rpm", None)
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tpm = litellm_params.get("tpm", None)
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max_parallel_requests = litellm_params.get("max_parallel_requests", None)
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calculated_max_parallel_requests = calculate_max_parallel_requests(
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rpm=rpm,
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max_parallel_requests=max_parallel_requests,
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tpm=tpm,
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default_max_parallel_requests=litellm_router_instance.default_max_parallel_requests,
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)
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if calculated_max_parallel_requests:
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semaphore = asyncio.Semaphore(calculated_max_parallel_requests)
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cache_key = f"{model_id}_max_parallel_requests_client"
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litellm_router_instance.cache.set_cache(
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key=cache_key,
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value=semaphore,
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local_only=True,
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)
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#### for OpenAI / Azure we need to initalize the Client for High Traffic ########
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custom_llm_provider = litellm_params.get("custom_llm_provider")
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custom_llm_provider = custom_llm_provider or model_name.split("/", 1)[0] or ""
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default_api_base = None
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default_api_key = None
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if custom_llm_provider in litellm.openai_compatible_providers:
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_, custom_llm_provider, api_key, api_base = litellm.get_llm_provider(
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model=model_name
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)
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default_api_base = api_base
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default_api_key = api_key
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if (
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model_name in litellm.open_ai_chat_completion_models
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or custom_llm_provider in litellm.openai_compatible_providers
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or custom_llm_provider == "azure"
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or custom_llm_provider == "azure_text"
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or custom_llm_provider == "custom_openai"
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or custom_llm_provider == "openai"
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or custom_llm_provider == "text-completion-openai"
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or "ft:gpt-3.5-turbo" in model_name
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or model_name in litellm.open_ai_embedding_models
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):
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is_azure_ai_studio_model: bool = False
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if custom_llm_provider == "azure":
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if litellm.utils._is_non_openai_azure_model(model_name):
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is_azure_ai_studio_model = True
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custom_llm_provider = "openai"
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# remove azure prefx from model_name
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model_name = model_name.replace("azure/", "")
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# glorified / complicated reading of configs
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# user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env
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# we do this here because we init clients for Azure, OpenAI and we need to set the right key
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api_key = litellm_params.get("api_key") or default_api_key
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if (
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api_key
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and isinstance(api_key, str)
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and api_key.startswith("os.environ/")
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):
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api_key_env_name = api_key.replace("os.environ/", "")
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api_key = get_secret_str(api_key_env_name)
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litellm_params["api_key"] = api_key
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api_base = litellm_params.get("api_base")
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base_url: Optional[str] = litellm_params.get("base_url")
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api_base = (
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api_base or base_url or default_api_base
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) # allow users to pass in `api_base` or `base_url` for azure
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if api_base and api_base.startswith("os.environ/"):
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api_base_env_name = api_base.replace("os.environ/", "")
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api_base = get_secret_str(api_base_env_name)
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litellm_params["api_base"] = api_base
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## AZURE AI STUDIO MISTRAL CHECK ##
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"""
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Make sure api base ends in /v1/
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if not, add it - https://github.com/BerriAI/litellm/issues/2279
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"""
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if (
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is_azure_ai_studio_model is True
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and api_base is not None
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and isinstance(api_base, str)
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and not api_base.endswith("/v1/")
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):
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# check if it ends with a trailing slash
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if api_base.endswith("/"):
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api_base += "v1/"
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elif api_base.endswith("/v1"):
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api_base += "/"
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else:
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api_base += "/v1/"
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api_version = litellm_params.get("api_version")
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if api_version and api_version.startswith("os.environ/"):
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api_version_env_name = api_version.replace("os.environ/", "")
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api_version = get_secret_str(api_version_env_name)
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litellm_params["api_version"] = api_version
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timeout: Optional[float] = (
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litellm_params.pop("timeout", None) or litellm.request_timeout
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)
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if isinstance(timeout, str) and timeout.startswith("os.environ/"):
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timeout_env_name = timeout.replace("os.environ/", "")
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timeout = get_secret(timeout_env_name) # type: ignore
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litellm_params["timeout"] = timeout
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stream_timeout: Optional[float] = litellm_params.pop(
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"stream_timeout", timeout
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) # if no stream_timeout is set, default to timeout
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if isinstance(stream_timeout, str) and stream_timeout.startswith(
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"os.environ/"
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):
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stream_timeout_env_name = stream_timeout.replace("os.environ/", "")
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stream_timeout = get_secret(stream_timeout_env_name) # type: ignore
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litellm_params["stream_timeout"] = stream_timeout
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max_retries: Optional[int] = litellm_params.pop(
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"max_retries", 0
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) # router handles retry logic
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if isinstance(max_retries, str) and max_retries.startswith("os.environ/"):
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max_retries_env_name = max_retries.replace("os.environ/", "")
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max_retries = get_secret(max_retries_env_name) # type: ignore
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litellm_params["max_retries"] = max_retries
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organization = litellm_params.get("organization", None)
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if isinstance(organization, str) and organization.startswith("os.environ/"):
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organization_env_name = organization.replace("os.environ/", "")
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organization = get_secret_str(organization_env_name)
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litellm_params["organization"] = organization
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else:
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_api_key = api_key # type: ignore
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if _api_key is not None and isinstance(_api_key, str):
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# only show first 5 chars of api_key
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_api_key = _api_key[:8] + "*" * 15
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verbose_router_logger.debug(
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f"Initializing OpenAI Client for {model_name}, Api Base:{str(api_base)}, Api Key:{_api_key}"
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)
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cache_key = f"{model_id}_async_client"
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_client = openai.AsyncOpenAI( # type: ignore
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api_key=api_key,
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base_url=api_base,
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timeout=timeout, # type: ignore
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max_retries=max_retries, # type: ignore
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organization=organization,
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http_client=httpx.AsyncClient(
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limits=httpx.Limits(
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max_connections=1000, max_keepalive_connections=100
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),
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verify=litellm.ssl_verify,
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), # type: ignore
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)
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litellm_router_instance.cache.set_cache(
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key=cache_key,
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value=_client,
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ttl=client_ttl,
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local_only=True,
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) # cache for 1 hr
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if InitalizeOpenAISDKClient.should_initialize_sync_client(
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litellm_router_instance=litellm_router_instance
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):
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cache_key = f"{model_id}_client"
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_client = openai.OpenAI( # type: ignore
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api_key=api_key,
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base_url=api_base,
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timeout=timeout, # type: ignore
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max_retries=max_retries, # type: ignore
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organization=organization,
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http_client=httpx.Client(
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limits=httpx.Limits(
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max_connections=1000, max_keepalive_connections=100
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),
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verify=litellm.ssl_verify,
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), # type: ignore
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)
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litellm_router_instance.cache.set_cache(
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key=cache_key,
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value=_client,
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ttl=client_ttl,
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local_only=True,
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) # cache for 1 hr
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# streaming clients should have diff timeouts
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cache_key = f"{model_id}_stream_async_client"
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_client = openai.AsyncOpenAI( # type: ignore
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api_key=api_key,
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base_url=api_base,
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timeout=stream_timeout, # type: ignore
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max_retries=max_retries, # type: ignore
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organization=organization,
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http_client=httpx.AsyncClient(
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limits=httpx.Limits(
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max_connections=1000, max_keepalive_connections=100
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),
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verify=litellm.ssl_verify,
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), # type: ignore
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)
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litellm_router_instance.cache.set_cache(
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key=cache_key,
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value=_client,
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ttl=client_ttl,
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local_only=True,
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) # cache for 1 hr
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if InitalizeOpenAISDKClient.should_initialize_sync_client(
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litellm_router_instance=litellm_router_instance
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):
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# streaming clients should have diff timeouts
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cache_key = f"{model_id}_stream_client"
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_client = openai.OpenAI( # type: ignore
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api_key=api_key,
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base_url=api_base,
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timeout=stream_timeout, # type: ignore
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max_retries=max_retries, # type: ignore
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organization=organization,
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http_client=httpx.Client(
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limits=httpx.Limits(
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max_connections=1000, max_keepalive_connections=100
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),
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verify=litellm.ssl_verify,
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), # type: ignore
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)
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litellm_router_instance.cache.set_cache(
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key=cache_key,
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value=_client,
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ttl=client_ttl,
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local_only=True,
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) # cache for 1 hr
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