mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 10:44:24 +00:00
use helper for init client + check if we should init sync clients
This commit is contained in:
Ishaan Jaff
parent
80a31c5dab
commit
2609de43d0
2 changed files with 578 additions and 519 deletions
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@ -55,6 +55,10 @@ from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler
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from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler
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from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler
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from litellm.router_strategy.lowest_tpm_rpm_v2 import LowestTPMLoggingHandler_v2
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from litellm.router_utils.client_initalization_utils import (
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set_client,
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should_initialize_sync_client,
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)
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from litellm.router_utils.handle_error import send_llm_exception_alert
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from litellm.scheduler import FlowItem, Scheduler
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from litellm.types.llms.openai import (
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@ -79,6 +83,7 @@ from litellm.types.router import (
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ModelInfo,
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RetryPolicy,
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RouterErrors,
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RouterGeneralSettings,
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updateDeployment,
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updateLiteLLMParams,
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)
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@ -169,6 +174,7 @@ class Router:
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routing_strategy_args: dict = {}, # just for latency-based routing
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semaphore: Optional[asyncio.Semaphore] = None,
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alerting_config: Optional[AlertingConfig] = None,
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router_general_settings: Optional[RouterGeneralSettings] = None,
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) -> None:
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"""
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Initialize the Router class with the given parameters for caching, reliability, and routing strategy.
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@ -246,6 +252,9 @@ class Router:
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verbose_router_logger.setLevel(logging.INFO)
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elif debug_level == "DEBUG":
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verbose_router_logger.setLevel(logging.DEBUG)
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self.router_general_settings: Optional[RouterGeneralSettings] = (
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router_general_settings
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)
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self.assistants_config = assistants_config
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self.deployment_names: List = (
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@ -3247,520 +3256,6 @@ class Router:
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except Exception as e:
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raise e
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def set_client(self, model: dict):
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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 = self.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=self.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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self.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 = litellm.get_secret(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 = 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 = litellm.get_secret(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 = litellm.get_secret(api_version_env_name)
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litellm_params["api_version"] = api_version
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timeout = litellm_params.pop("timeout", None) or litellm.request_timeout
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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 = litellm.get_secret(timeout_env_name)
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litellm_params["timeout"] = timeout
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stream_timeout = 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 = litellm.get_secret(stream_timeout_env_name)
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litellm_params["stream_timeout"] = stream_timeout
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max_retries = 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 = litellm.get_secret(max_retries_env_name)
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litellm_params["max_retries"] = max_retries
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# proxy support
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import os
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import httpx
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# Check if the HTTP_PROXY and HTTPS_PROXY environment variables are set and use them accordingly.
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http_proxy = os.getenv("HTTP_PROXY", None)
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https_proxy = os.getenv("HTTPS_PROXY", None)
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no_proxy = os.getenv("NO_PROXY", None)
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# Create the proxies dictionary only if the environment variables are set.
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sync_proxy_mounts = None
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async_proxy_mounts = None
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if http_proxy is not None and https_proxy is not None:
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sync_proxy_mounts = {
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"http://": httpx.HTTPTransport(proxy=httpx.Proxy(url=http_proxy)),
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"https://": httpx.HTTPTransport(proxy=httpx.Proxy(url=https_proxy)),
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}
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async_proxy_mounts = {
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"http://": httpx.AsyncHTTPTransport(
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proxy=httpx.Proxy(url=http_proxy)
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),
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"https://": httpx.AsyncHTTPTransport(
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proxy=httpx.Proxy(url=https_proxy)
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),
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}
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# assume no_proxy is a list of comma separated urls
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if no_proxy is not None and isinstance(no_proxy, str):
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no_proxy_urls = no_proxy.split(",")
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for url in no_proxy_urls: # set no-proxy support for specific urls
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sync_proxy_mounts[url] = None # type: ignore
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async_proxy_mounts[url] = None # type: ignore
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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 = litellm.get_secret(organization_env_name)
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litellm_params["organization"] = organization
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if custom_llm_provider == "azure" or custom_llm_provider == "azure_text":
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if api_base is None or not isinstance(api_base, str):
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filtered_litellm_params = {
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k: v
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for k, v in model["litellm_params"].items()
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if k != "api_key"
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}
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_filtered_model = {
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"model_name": model["model_name"],
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"litellm_params": filtered_litellm_params,
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}
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raise ValueError(
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f"api_base is required for Azure OpenAI. Set it on your config. Model - {_filtered_model}"
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)
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azure_ad_token = litellm_params.get("azure_ad_token")
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if azure_ad_token is not None:
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if azure_ad_token.startswith("oidc/"):
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azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
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if api_version is None:
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api_version = litellm.AZURE_DEFAULT_API_VERSION
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if "gateway.ai.cloudflare.com" in api_base:
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if not api_base.endswith("/"):
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api_base += "/"
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azure_model = model_name.replace("azure/", "")
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api_base += f"{azure_model}"
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cache_key = f"{model_id}_async_client"
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_client = openai.AsyncAzureOpenAI(
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api_key=api_key,
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azure_ad_token=azure_ad_token,
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base_url=api_base,
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api_version=api_version,
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timeout=timeout,
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max_retries=max_retries,
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http_client=httpx.AsyncClient(
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transport=AsyncCustomHTTPTransport(
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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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),
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mounts=async_proxy_mounts,
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), # type: ignore
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)
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self.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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cache_key = f"{model_id}_client"
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_client = openai.AzureOpenAI( # type: ignore
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api_key=api_key,
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azure_ad_token=azure_ad_token,
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base_url=api_base,
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api_version=api_version,
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timeout=timeout,
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max_retries=max_retries,
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http_client=httpx.Client(
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transport=CustomHTTPTransport(
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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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),
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mounts=sync_proxy_mounts,
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), # type: ignore
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)
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self.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 can have diff timeouts
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cache_key = f"{model_id}_stream_async_client"
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_client = openai.AsyncAzureOpenAI( # type: ignore
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api_key=api_key,
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azure_ad_token=azure_ad_token,
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base_url=api_base,
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api_version=api_version,
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timeout=stream_timeout,
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max_retries=max_retries,
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http_client=httpx.AsyncClient(
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transport=AsyncCustomHTTPTransport(
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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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),
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mounts=async_proxy_mounts,
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), # type: ignore
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)
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self.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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cache_key = f"{model_id}_stream_client"
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_client = openai.AzureOpenAI( # type: ignore
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api_key=api_key,
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azure_ad_token=azure_ad_token,
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base_url=api_base,
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api_version=api_version,
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timeout=stream_timeout,
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max_retries=max_retries,
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http_client=httpx.Client(
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transport=CustomHTTPTransport(
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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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),
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mounts=sync_proxy_mounts,
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), # type: ignore
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)
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self.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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else:
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_api_key = api_key
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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 Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{_api_key}"
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)
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azure_client_params = {
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"api_key": api_key,
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"azure_endpoint": api_base,
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"api_version": api_version,
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"azure_ad_token": azure_ad_token,
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}
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from litellm.llms.azure import select_azure_base_url_or_endpoint
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# this decides if we should set azure_endpoint or base_url on Azure OpenAI Client
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# required to support GPT-4 vision enhancements, since base_url needs to be set on Azure OpenAI Client
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azure_client_params = select_azure_base_url_or_endpoint(
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azure_client_params
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)
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cache_key = f"{model_id}_async_client"
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_client = openai.AsyncAzureOpenAI( # type: ignore
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**azure_client_params,
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timeout=timeout,
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max_retries=max_retries,
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http_client=httpx.AsyncClient(
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transport=AsyncCustomHTTPTransport(
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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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),
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mounts=async_proxy_mounts,
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), # type: ignore
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)
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self.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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cache_key = f"{model_id}_client"
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_client = openai.AzureOpenAI( # type: ignore
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**azure_client_params,
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timeout=timeout,
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max_retries=max_retries,
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http_client=httpx.Client(
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transport=CustomHTTPTransport(
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verify=litellm.ssl_verify,
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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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),
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mounts=sync_proxy_mounts,
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), # type: ignore
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)
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self.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.AsyncAzureOpenAI( # type: ignore
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**azure_client_params,
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timeout=stream_timeout,
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max_retries=max_retries,
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http_client=httpx.AsyncClient(
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transport=AsyncCustomHTTPTransport(
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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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),
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mounts=async_proxy_mounts,
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),
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)
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self.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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cache_key = f"{model_id}_stream_client"
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_client = openai.AzureOpenAI( # type: ignore
|
||||
**azure_client_params,
|
||||
timeout=stream_timeout,
|
||||
max_retries=max_retries,
|
||||
http_client=httpx.Client(
|
||||
transport=CustomHTTPTransport(
|
||||
limits=httpx.Limits(
|
||||
max_connections=1000, max_keepalive_connections=100
|
||||
),
|
||||
verify=litellm.ssl_verify,
|
||||
),
|
||||
mounts=sync_proxy_mounts,
|
||||
),
|
||||
)
|
||||
self.cache.set_cache(
|
||||
key=cache_key,
|
||||
value=_client,
|
||||
ttl=client_ttl,
|
||||
local_only=True,
|
||||
) # cache for 1 hr
|
||||
|
||||
else:
|
||||
_api_key = api_key # type: ignore
|
||||
if _api_key is not None and isinstance(_api_key, str):
|
||||
# only show first 5 chars of api_key
|
||||
_api_key = _api_key[:8] + "*" * 15
|
||||
verbose_router_logger.debug(
|
||||
f"Initializing OpenAI Client for {model_name}, Api Base:{str(api_base)}, Api Key:{_api_key}"
|
||||
)
|
||||
cache_key = f"{model_id}_async_client"
|
||||
_client = openai.AsyncOpenAI( # type: ignore
|
||||
api_key=api_key,
|
||||
base_url=api_base,
|
||||
timeout=timeout,
|
||||
max_retries=max_retries,
|
||||
organization=organization,
|
||||
http_client=httpx.AsyncClient(
|
||||
transport=AsyncCustomHTTPTransport(
|
||||
limits=httpx.Limits(
|
||||
max_connections=1000, max_keepalive_connections=100
|
||||
),
|
||||
verify=litellm.ssl_verify,
|
||||
),
|
||||
mounts=async_proxy_mounts,
|
||||
), # type: ignore
|
||||
)
|
||||
self.cache.set_cache(
|
||||
key=cache_key,
|
||||
value=_client,
|
||||
ttl=client_ttl,
|
||||
local_only=True,
|
||||
) # cache for 1 hr
|
||||
|
||||
cache_key = f"{model_id}_client"
|
||||
_client = openai.OpenAI( # type: ignore
|
||||
api_key=api_key,
|
||||
base_url=api_base,
|
||||
timeout=timeout,
|
||||
max_retries=max_retries,
|
||||
organization=organization,
|
||||
http_client=httpx.Client(
|
||||
transport=CustomHTTPTransport(
|
||||
limits=httpx.Limits(
|
||||
max_connections=1000, max_keepalive_connections=100
|
||||
),
|
||||
verify=litellm.ssl_verify,
|
||||
),
|
||||
mounts=sync_proxy_mounts,
|
||||
), # type: ignore
|
||||
)
|
||||
self.cache.set_cache(
|
||||
key=cache_key,
|
||||
value=_client,
|
||||
ttl=client_ttl,
|
||||
local_only=True,
|
||||
) # cache for 1 hr
|
||||
|
||||
# streaming clients should have diff timeouts
|
||||
cache_key = f"{model_id}_stream_async_client"
|
||||
_client = openai.AsyncOpenAI( # type: ignore
|
||||
api_key=api_key,
|
||||
base_url=api_base,
|
||||
timeout=stream_timeout,
|
||||
max_retries=max_retries,
|
||||
organization=organization,
|
||||
http_client=httpx.AsyncClient(
|
||||
transport=AsyncCustomHTTPTransport(
|
||||
limits=httpx.Limits(
|
||||
max_connections=1000, max_keepalive_connections=100
|
||||
),
|
||||
verify=litellm.ssl_verify,
|
||||
),
|
||||
mounts=async_proxy_mounts,
|
||||
), # type: ignore
|
||||
)
|
||||
self.cache.set_cache(
|
||||
key=cache_key,
|
||||
value=_client,
|
||||
ttl=client_ttl,
|
||||
local_only=True,
|
||||
) # cache for 1 hr
|
||||
|
||||
# streaming clients should have diff timeouts
|
||||
cache_key = f"{model_id}_stream_client"
|
||||
_client = openai.OpenAI( # type: ignore
|
||||
api_key=api_key,
|
||||
base_url=api_base,
|
||||
timeout=stream_timeout,
|
||||
max_retries=max_retries,
|
||||
organization=organization,
|
||||
http_client=httpx.Client(
|
||||
transport=CustomHTTPTransport(
|
||||
limits=httpx.Limits(
|
||||
max_connections=1000, max_keepalive_connections=100
|
||||
),
|
||||
verify=litellm.ssl_verify,
|
||||
),
|
||||
mounts=sync_proxy_mounts,
|
||||
), # type: ignore
|
||||
)
|
||||
self.cache.set_cache(
|
||||
key=cache_key,
|
||||
value=_client,
|
||||
ttl=client_ttl,
|
||||
local_only=True,
|
||||
) # cache for 1 hr
|
||||
|
||||
def _generate_model_id(self, model_group: str, litellm_params: dict):
|
||||
"""
|
||||
Helper function to consistently generate the same id for a deployment
|
||||
|
|
@ -3904,7 +3399,9 @@ class Router:
|
|||
raise Exception(f"Unsupported provider - {custom_llm_provider}")
|
||||
|
||||
# init OpenAI, Azure clients
|
||||
self.set_client(model=deployment.to_json(exclude_none=True))
|
||||
set_client(
|
||||
litellm_router_instance=self, model=deployment.to_json(exclude_none=True)
|
||||
)
|
||||
|
||||
# set region (if azure model) ## PREVIEW FEATURE ##
|
||||
if litellm.enable_preview_features == True:
|
||||
|
|
@ -4432,7 +3929,7 @@ class Router:
|
|||
"""
|
||||
Re-initialize the client
|
||||
"""
|
||||
self.set_client(model=deployment)
|
||||
set_client(litellm_router_instance=self, model=deployment)
|
||||
client = self.cache.get_cache(key=cache_key, local_only=True)
|
||||
return client
|
||||
else:
|
||||
|
|
@ -4442,7 +3939,7 @@ class Router:
|
|||
"""
|
||||
Re-initialize the client
|
||||
"""
|
||||
self.set_client(model=deployment)
|
||||
set_client(litellm_router_instance=self, model=deployment)
|
||||
client = self.cache.get_cache(key=cache_key, local_only=True)
|
||||
return client
|
||||
else:
|
||||
|
|
@ -4453,7 +3950,7 @@ class Router:
|
|||
"""
|
||||
Re-initialize the client
|
||||
"""
|
||||
self.set_client(model=deployment)
|
||||
set_client(litellm_router_instance=self, model=deployment)
|
||||
client = self.cache.get_cache(key=cache_key)
|
||||
return client
|
||||
else:
|
||||
|
|
@ -4463,7 +3960,7 @@ class Router:
|
|||
"""
|
||||
Re-initialize the client
|
||||
"""
|
||||
self.set_client(model=deployment)
|
||||
set_client(litellm_router_instance=self, model=deployment)
|
||||
client = self.cache.get_cache(key=cache_key)
|
||||
return client
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue