chore: initial snapshot for gitea/github upload
This commit is contained in:
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@@ -0,0 +1,506 @@
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"""
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Unified deployment affinity (session stickiness) for the Router.
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Features (independently enable-able):
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1. Responses API continuity: when a `previous_response_id` is provided, route to the
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deployment that generated the original response (highest priority).
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2. API-key affinity: map an API key hash -> deployment id for a TTL and re-use that
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deployment for subsequent requests to the same router deployment model name
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(alias-safe, aligns to `model_map_information.model_map_key`).
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This is designed to support "implicit prompt caching" scenarios (no explicit cache_control),
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where routing to a consistent deployment is still beneficial.
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"""
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import hashlib
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from typing import Any, Dict, List, Optional, cast
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from typing_extensions import TypedDict
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from litellm._logging import verbose_router_logger
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from litellm.caching.dual_cache import DualCache
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from litellm.integrations.custom_logger import CustomLogger, Span
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from litellm.responses.utils import ResponsesAPIRequestUtils
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from litellm.types.llms.openai import AllMessageValues
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from litellm.types.utils import CallTypes
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class DeploymentAffinityCacheValue(TypedDict):
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model_id: str
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class DeploymentAffinityCheck(CustomLogger):
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"""
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Router deployment affinity callback.
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NOTE: This is a Router-only callback intended to be wired through
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`Router(optional_pre_call_checks=[...])`.
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"""
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CACHE_KEY_PREFIX = "deployment_affinity:v1"
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def __init__(
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self,
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cache: DualCache,
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ttl_seconds: int,
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enable_user_key_affinity: bool,
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enable_responses_api_affinity: bool,
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enable_session_id_affinity: bool = False,
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):
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super().__init__()
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self.cache = cache
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self.ttl_seconds = ttl_seconds
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self.enable_user_key_affinity = enable_user_key_affinity
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self.enable_responses_api_affinity = enable_responses_api_affinity
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self.enable_session_id_affinity = enable_session_id_affinity
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@staticmethod
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def _looks_like_sha256_hex(value: str) -> bool:
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if len(value) != 64:
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return False
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try:
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int(value, 16)
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except ValueError:
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return False
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return True
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@staticmethod
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def _hash_user_key(user_key: str) -> str:
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"""
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Hash user identifiers before storing them in cache keys.
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This avoids putting raw API keys / user identifiers into Redis keys (and therefore
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into logs/metrics), while keeping the cache key stable and a fixed length.
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"""
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# If the proxy already provides a stable SHA-256 (e.g. `metadata.user_api_key_hash`),
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# keep it as-is to avoid double-hashing and to make correlation/debugging possible.
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if DeploymentAffinityCheck._looks_like_sha256_hex(user_key):
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return user_key.lower()
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return hashlib.sha256(user_key.encode("utf-8")).hexdigest()
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@staticmethod
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def _get_model_map_key_from_litellm_model_name(
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litellm_model_name: str,
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) -> Optional[str]:
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"""
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Best-effort derivation of a stable "model map key" for affinity scoping.
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The intent is to align with `standard_logging_payload.model_map_information.model_map_key`,
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which is typically the base model identifier (stable across deployments/endpoints).
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Notes:
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- When the model name is in "provider/model" format, the provider prefix is stripped.
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- For Azure, the string after "azure/" is commonly an *Azure deployment name*, which may
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differ across instances. If `base_model` is not explicitly set, we skip deriving a
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model-map key from the model string to avoid generating unstable keys.
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"""
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if not litellm_model_name:
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return None
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if "/" not in litellm_model_name:
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return litellm_model_name
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provider_prefix, remainder = litellm_model_name.split("/", 1)
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if provider_prefix == "azure":
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return None
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return remainder
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@staticmethod
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def _get_model_map_key_from_deployment(deployment: dict) -> Optional[str]:
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"""
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Derive a stable model-map key from a router deployment dict.
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Primary source: `deployment.model_name` (Router's canonical group name after
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alias resolution). This is stable across provider-specific deployments (e.g.,
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Azure/Vertex/Bedrock for the same logical model) and aligns with
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`model_map_information.model_map_key` in standard logging.
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Prefer `base_model` when available (important for Azure), otherwise fall back to
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parsing `litellm_params.model`.
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"""
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model_name = deployment.get("model_name")
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if isinstance(model_name, str) and model_name:
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return model_name
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model_info = deployment.get("model_info")
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if isinstance(model_info, dict):
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base_model = model_info.get("base_model")
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if isinstance(base_model, str) and base_model:
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return base_model
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litellm_params = deployment.get("litellm_params")
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if isinstance(litellm_params, dict):
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base_model = litellm_params.get("base_model")
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if isinstance(base_model, str) and base_model:
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return base_model
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litellm_model_name = litellm_params.get("model")
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if isinstance(litellm_model_name, str) and litellm_model_name:
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return (
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DeploymentAffinityCheck._get_model_map_key_from_litellm_model_name(
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litellm_model_name
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)
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)
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return None
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@staticmethod
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def _get_stable_model_map_key_from_deployments(
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healthy_deployments: List[dict],
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) -> Optional[str]:
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"""
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Only use model-map key scoping when it is stable across the deployment set.
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This prevents accidentally keying on per-deployment identifiers like Azure deployment
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names (when `base_model` is not configured).
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"""
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if not healthy_deployments:
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return None
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keys: List[str] = []
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for deployment in healthy_deployments:
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key = DeploymentAffinityCheck._get_model_map_key_from_deployment(deployment)
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if key is None:
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return None
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keys.append(key)
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unique_keys = set(keys)
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if len(unique_keys) != 1:
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return None
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return keys[0]
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@staticmethod
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def _shorten_for_logs(value: str, keep: int = 8) -> str:
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if len(value) <= keep:
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return value
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return f"{value[:keep]}..."
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@classmethod
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def get_affinity_cache_key(cls, model_group: str, user_key: str) -> str:
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hashed_user_key = cls._hash_user_key(user_key=user_key)
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return f"{cls.CACHE_KEY_PREFIX}:{model_group}:{hashed_user_key}"
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@classmethod
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def get_session_affinity_cache_key(cls, model_group: str, session_id: str) -> str:
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return f"{cls.CACHE_KEY_PREFIX}:session:{model_group}:{session_id}"
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@staticmethod
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def _get_user_key_from_metadata_dict(metadata: dict) -> Optional[str]:
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# NOTE: affinity is keyed on the *API key hash* provided by the proxy (not the
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# OpenAI `user` parameter, which is an end-user identifier).
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user_key = metadata.get("user_api_key_hash")
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if user_key is None:
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return None
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return str(user_key)
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@staticmethod
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def _get_session_id_from_metadata_dict(metadata: dict) -> Optional[str]:
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session_id = metadata.get("session_id")
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if session_id is None:
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return None
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return str(session_id)
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@staticmethod
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def _iter_metadata_dicts(request_kwargs: dict) -> List[dict]:
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"""
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Return all metadata dicts available on the request.
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Depending on the endpoint, Router may populate `metadata` or `litellm_metadata`.
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Users may also send one or both, so we check both (rather than using `or`).
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"""
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metadata_dicts: List[dict] = []
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for key in ("litellm_metadata", "metadata"):
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md = request_kwargs.get(key)
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if isinstance(md, dict):
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metadata_dicts.append(md)
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return metadata_dicts
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@staticmethod
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def _get_user_key_from_request_kwargs(request_kwargs: dict) -> Optional[str]:
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"""
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Extract a stable affinity key from request kwargs.
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Source (proxy): `metadata.user_api_key_hash`
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Note: the OpenAI `user` parameter is an end-user identifier and is intentionally
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not used for deployment affinity.
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"""
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# Check metadata dicts (Proxy usage)
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for metadata in DeploymentAffinityCheck._iter_metadata_dicts(request_kwargs):
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user_key = DeploymentAffinityCheck._get_user_key_from_metadata_dict(
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metadata=metadata
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)
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if user_key is not None:
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return user_key
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return None
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@staticmethod
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def _get_session_id_from_request_kwargs(request_kwargs: dict) -> Optional[str]:
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for metadata in DeploymentAffinityCheck._iter_metadata_dicts(request_kwargs):
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session_id = DeploymentAffinityCheck._get_session_id_from_metadata_dict(
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metadata=metadata
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)
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if session_id is not None:
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return session_id
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return None
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@staticmethod
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def _find_deployment_by_model_id(
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healthy_deployments: List[dict], model_id: str
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) -> Optional[dict]:
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for deployment in healthy_deployments:
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model_info = deployment.get("model_info")
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if not isinstance(model_info, dict):
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continue
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deployment_model_id = model_info.get("id")
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if deployment_model_id is not None and str(deployment_model_id) == str(
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model_id
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):
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return deployment
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return None
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async def async_filter_deployments(
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self,
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model: str,
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healthy_deployments: List,
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messages: Optional[List[AllMessageValues]],
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request_kwargs: Optional[dict] = None,
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parent_otel_span: Optional[Span] = None,
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) -> List[dict]:
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"""
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Optionally filter healthy deployments based on:
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1. `previous_response_id` (Responses API continuity) [highest priority]
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2. cached API-key deployment affinity
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"""
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request_kwargs = request_kwargs or {}
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typed_healthy_deployments = cast(List[dict], healthy_deployments)
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# 1) Responses API continuity (high priority)
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if self.enable_responses_api_affinity:
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previous_response_id = request_kwargs.get("previous_response_id")
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if previous_response_id is not None:
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responses_model_id = (
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ResponsesAPIRequestUtils.get_model_id_from_response_id(
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str(previous_response_id)
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)
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)
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if responses_model_id is not None:
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deployment = self._find_deployment_by_model_id(
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healthy_deployments=typed_healthy_deployments,
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model_id=responses_model_id,
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)
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if deployment is not None:
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verbose_router_logger.debug(
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"DeploymentAffinityCheck: previous_response_id pinning -> deployment=%s",
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responses_model_id,
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)
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return [deployment]
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stable_model_map_key = self._get_stable_model_map_key_from_deployments(
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healthy_deployments=typed_healthy_deployments
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)
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if stable_model_map_key is None:
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return typed_healthy_deployments
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# 2) Session-id -> deployment affinity
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if self.enable_session_id_affinity:
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session_id = self._get_session_id_from_request_kwargs(
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request_kwargs=request_kwargs
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)
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if session_id is not None:
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session_cache_key = self.get_session_affinity_cache_key(
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model_group=stable_model_map_key, session_id=session_id
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)
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session_cache_result = await self.cache.async_get_cache(
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key=session_cache_key
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)
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session_model_id: Optional[str] = None
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if isinstance(session_cache_result, dict):
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session_model_id = cast(
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Optional[str], session_cache_result.get("model_id")
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)
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elif isinstance(session_cache_result, str):
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session_model_id = session_cache_result
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if session_model_id:
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session_deployment = self._find_deployment_by_model_id(
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healthy_deployments=typed_healthy_deployments,
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model_id=session_model_id,
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)
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if session_deployment is not None:
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verbose_router_logger.debug(
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"DeploymentAffinityCheck: session-id affinity hit -> deployment=%s session_id=%s",
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session_model_id,
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session_id,
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)
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return [session_deployment]
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else:
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verbose_router_logger.debug(
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"DeploymentAffinityCheck: session-id pinned deployment=%s not found in healthy_deployments",
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session_model_id,
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)
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# 3) User key -> deployment affinity
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if not self.enable_user_key_affinity:
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return typed_healthy_deployments
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user_key = self._get_user_key_from_request_kwargs(request_kwargs=request_kwargs)
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if user_key is None:
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return typed_healthy_deployments
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cache_key = self.get_affinity_cache_key(
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model_group=stable_model_map_key, user_key=user_key
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)
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cache_result = await self.cache.async_get_cache(key=cache_key)
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model_id: Optional[str] = None
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if isinstance(cache_result, dict):
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model_id = cast(Optional[str], cache_result.get("model_id"))
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elif isinstance(cache_result, str):
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# Backwards / safety: allow raw string values.
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model_id = cache_result
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if not model_id:
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return typed_healthy_deployments
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deployment = self._find_deployment_by_model_id(
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healthy_deployments=typed_healthy_deployments,
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model_id=model_id,
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)
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if deployment is None:
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verbose_router_logger.debug(
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"DeploymentAffinityCheck: pinned deployment=%s not found in healthy_deployments",
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model_id,
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)
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return typed_healthy_deployments
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verbose_router_logger.debug(
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"DeploymentAffinityCheck: api-key affinity hit -> deployment=%s user_key=%s",
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model_id,
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self._shorten_for_logs(user_key),
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)
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return [deployment]
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async def async_pre_call_deployment_hook(
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self, kwargs: Dict[str, Any], call_type: Optional[CallTypes]
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) -> Optional[dict]:
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"""
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Persist/update the API-key -> deployment mapping for this request.
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Why pre-call?
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- LiteLLM runs async success callbacks via a background logging worker for performance.
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- We want affinity to be immediately available for subsequent requests.
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"""
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if not self.enable_user_key_affinity and not self.enable_session_id_affinity:
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return None
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user_key = None
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if self.enable_user_key_affinity:
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user_key = self._get_user_key_from_request_kwargs(request_kwargs=kwargs)
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||||
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session_id = None
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if self.enable_session_id_affinity:
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session_id = self._get_session_id_from_request_kwargs(request_kwargs=kwargs)
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||||
|
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if user_key is None and session_id is None:
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return None
|
||||
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metadata_dicts = self._iter_metadata_dicts(kwargs)
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model_info = kwargs.get("model_info")
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if not isinstance(model_info, dict):
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model_info = None
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||||
|
||||
if model_info is None:
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for metadata in metadata_dicts:
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maybe_model_info = metadata.get("model_info")
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if isinstance(maybe_model_info, dict):
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model_info = maybe_model_info
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break
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||||
|
||||
if model_info is None:
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# Router sets `model_info` after selecting a deployment. If it's missing, this is
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||||
# likely a non-router call or a call path that doesn't support affinity.
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return None
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||||
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||||
model_id = model_info.get("id")
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||||
if not model_id:
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verbose_router_logger.warning(
|
||||
"DeploymentAffinityCheck: model_id missing; skipping affinity cache update."
|
||||
)
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||||
return None
|
||||
|
||||
# Scope affinity by the Router deployment model name (alias-safe, consistent across
|
||||
# heterogeneous providers, and matches standard logging's `model_map_key`).
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||||
deployment_model_name: Optional[str] = None
|
||||
for metadata in metadata_dicts:
|
||||
maybe_deployment_model_name = metadata.get("deployment_model_name")
|
||||
if (
|
||||
isinstance(maybe_deployment_model_name, str)
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||||
and maybe_deployment_model_name
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||||
):
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||||
deployment_model_name = maybe_deployment_model_name
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||||
break
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||||
|
||||
if not deployment_model_name:
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||||
verbose_router_logger.warning(
|
||||
"DeploymentAffinityCheck: deployment_model_name missing; skipping affinity cache update. model_id=%s",
|
||||
model_id,
|
||||
)
|
||||
return None
|
||||
|
||||
if user_key is not None:
|
||||
try:
|
||||
cache_key = self.get_affinity_cache_key(
|
||||
model_group=deployment_model_name, user_key=user_key
|
||||
)
|
||||
await self.cache.async_set_cache(
|
||||
cache_key,
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||||
DeploymentAffinityCacheValue(model_id=str(model_id)),
|
||||
ttl=self.ttl_seconds,
|
||||
)
|
||||
|
||||
verbose_router_logger.debug(
|
||||
"DeploymentAffinityCheck: set affinity mapping model_map_key=%s deployment=%s ttl=%s user_key=%s",
|
||||
deployment_model_name,
|
||||
model_id,
|
||||
self.ttl_seconds,
|
||||
self._shorten_for_logs(user_key),
|
||||
)
|
||||
except Exception as e:
|
||||
# Non-blocking: affinity is a best-effort optimization.
|
||||
verbose_router_logger.debug(
|
||||
"DeploymentAffinityCheck: failed to set user key affinity cache. model_map_key=%s error=%s",
|
||||
deployment_model_name,
|
||||
e,
|
||||
)
|
||||
|
||||
# Also persist Session-ID affinity if enabled and session-id is provided
|
||||
if session_id is not None:
|
||||
try:
|
||||
session_cache_key = self.get_session_affinity_cache_key(
|
||||
model_group=deployment_model_name, session_id=session_id
|
||||
)
|
||||
await self.cache.async_set_cache(
|
||||
session_cache_key,
|
||||
DeploymentAffinityCacheValue(model_id=str(model_id)),
|
||||
ttl=self.ttl_seconds,
|
||||
)
|
||||
verbose_router_logger.debug(
|
||||
"DeploymentAffinityCheck: set session affinity mapping model_map_key=%s deployment=%s ttl=%s session_id=%s",
|
||||
deployment_model_name,
|
||||
model_id,
|
||||
self.ttl_seconds,
|
||||
session_id,
|
||||
)
|
||||
except Exception as e:
|
||||
verbose_router_logger.debug(
|
||||
"DeploymentAffinityCheck: failed to set session affinity cache. model_map_key=%s error=%s",
|
||||
deployment_model_name,
|
||||
e,
|
||||
)
|
||||
|
||||
return None
|
||||
@@ -0,0 +1,172 @@
|
||||
"""
|
||||
Encrypted-content-aware deployment affinity for the Router.
|
||||
|
||||
When Codex or other models use `store: false` with `include: ["reasoning.encrypted_content"]`,
|
||||
the response output items contain encrypted reasoning tokens tied to the originating
|
||||
organization's API key. If a follow-up request containing those items is routed to a
|
||||
different deployment (different org), OpenAI rejects it with an `invalid_encrypted_content`
|
||||
error because the organization_id doesn't match.
|
||||
|
||||
This callback solves the problem by encoding the originating deployment's ``model_id``
|
||||
into the response output items that carry ``encrypted_content``. Two encoding strategies:
|
||||
|
||||
1. **Items with IDs**: Encode model_id into the item ID itself (e.g., ``encitem_...``)
|
||||
2. **Items without IDs** (Codex): Wrap the encrypted_content with model_id metadata
|
||||
(e.g., ``litellm_enc:{base64_metadata};{original_encrypted_content}``)
|
||||
|
||||
The encoded model_id is decoded on the next request so the router can pin to the correct
|
||||
deployment without any cache lookup.
|
||||
|
||||
Response post-processing (encoding) is handled by
|
||||
``ResponsesAPIRequestUtils._update_encrypted_content_item_ids_in_response`` which is
|
||||
called inside ``_update_responses_api_response_id_with_model_id`` in ``responses/utils.py``.
|
||||
|
||||
Request pre-processing (ID/content restoration before forwarding to upstream) is handled by
|
||||
``ResponsesAPIRequestUtils._restore_encrypted_content_item_ids_in_input`` which is called
|
||||
in ``get_optional_params_responses_api``.
|
||||
|
||||
This pre-call check is responsible only for the routing decision: it reads the encoded
|
||||
``model_id`` from either item IDs or wrapped encrypted_content and pins the request to
|
||||
the matching deployment.
|
||||
|
||||
Safe to enable globally:
|
||||
- Only activates when encoded markers appear in the request ``input``.
|
||||
- No effect on embedding models, chat completions, or first-time requests.
|
||||
- No quota reduction -- first requests are fully load balanced.
|
||||
- No cache required.
|
||||
"""
|
||||
|
||||
from typing import Any, List, Optional, cast
|
||||
|
||||
from litellm._logging import verbose_router_logger
|
||||
from litellm.integrations.custom_logger import CustomLogger, Span
|
||||
from litellm.responses.utils import ResponsesAPIRequestUtils
|
||||
from litellm.types.llms.openai import AllMessageValues
|
||||
|
||||
|
||||
class EncryptedContentAffinityCheck(CustomLogger):
|
||||
"""
|
||||
Routes follow-up Responses API requests to the deployment that produced
|
||||
the encrypted output items they reference.
|
||||
|
||||
The ``model_id`` is decoded directly from the litellm-encoded item IDs –
|
||||
no caching or TTL management needed.
|
||||
|
||||
Wired via ``Router(optional_pre_call_checks=["encrypted_content_affinity"])``.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _extract_model_id_from_input(request_input: Any) -> Optional[str]:
|
||||
"""
|
||||
Scan ``input`` items for litellm-encoded encrypted-content markers and
|
||||
return the ``model_id`` embedded in the first one found.
|
||||
|
||||
Checks both:
|
||||
1. Encoded item IDs (encitem_...) - for clients that send IDs
|
||||
2. Wrapped encrypted_content (litellm_enc:...) - for clients like Codex that don't send IDs
|
||||
|
||||
``input`` can be:
|
||||
- a plain string -> no encoded markers
|
||||
- a list of items -> check each item's ``id`` and ``encrypted_content`` fields
|
||||
"""
|
||||
if not isinstance(request_input, list):
|
||||
return None
|
||||
|
||||
for item in request_input:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
|
||||
# First, try to decode from item ID (if present)
|
||||
item_id = item.get("id")
|
||||
if item_id and isinstance(item_id, str):
|
||||
decoded = ResponsesAPIRequestUtils._decode_encrypted_item_id(item_id)
|
||||
if decoded:
|
||||
return decoded.get("model_id")
|
||||
|
||||
# If no encoded ID, check if encrypted_content itself is wrapped
|
||||
encrypted_content = item.get("encrypted_content")
|
||||
if encrypted_content and isinstance(encrypted_content, str):
|
||||
(
|
||||
model_id,
|
||||
_,
|
||||
) = ResponsesAPIRequestUtils._unwrap_encrypted_content_with_model_id(
|
||||
encrypted_content
|
||||
)
|
||||
if model_id:
|
||||
return model_id
|
||||
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _find_deployment_by_model_id(
|
||||
healthy_deployments: List[dict], model_id: str
|
||||
) -> Optional[dict]:
|
||||
for deployment in healthy_deployments:
|
||||
model_info = deployment.get("model_info")
|
||||
if not isinstance(model_info, dict):
|
||||
continue
|
||||
deployment_model_id = model_info.get("id")
|
||||
if deployment_model_id is not None and str(deployment_model_id) == str(
|
||||
model_id
|
||||
):
|
||||
return deployment
|
||||
return None
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Request routing (pre-call filter)
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
async def async_filter_deployments(
|
||||
self,
|
||||
model: str,
|
||||
healthy_deployments: List,
|
||||
messages: Optional[List[AllMessageValues]],
|
||||
request_kwargs: Optional[dict] = None,
|
||||
parent_otel_span: Optional[Span] = None,
|
||||
) -> List[dict]:
|
||||
"""
|
||||
If the request ``input`` contains litellm-encoded item IDs, decode the
|
||||
embedded ``model_id`` and pin the request to that deployment.
|
||||
"""
|
||||
request_kwargs = request_kwargs or {}
|
||||
typed_healthy_deployments = cast(List[dict], healthy_deployments)
|
||||
|
||||
# Signal to the response post-processor that encrypted item IDs should be
|
||||
# encoded in the output of this request.
|
||||
litellm_metadata = request_kwargs.setdefault("litellm_metadata", {})
|
||||
litellm_metadata["encrypted_content_affinity_enabled"] = True
|
||||
|
||||
request_input = request_kwargs.get("input")
|
||||
model_id = self._extract_model_id_from_input(request_input)
|
||||
if not model_id:
|
||||
return typed_healthy_deployments
|
||||
|
||||
verbose_router_logger.debug(
|
||||
"EncryptedContentAffinityCheck: decoded model_id=%s from input item IDs",
|
||||
model_id,
|
||||
)
|
||||
|
||||
deployment = self._find_deployment_by_model_id(
|
||||
healthy_deployments=typed_healthy_deployments,
|
||||
model_id=model_id,
|
||||
)
|
||||
if deployment is not None:
|
||||
verbose_router_logger.debug(
|
||||
"EncryptedContentAffinityCheck: pinning -> deployment=%s",
|
||||
model_id,
|
||||
)
|
||||
request_kwargs["_encrypted_content_affinity_pinned"] = True
|
||||
return [deployment]
|
||||
|
||||
verbose_router_logger.error(
|
||||
"EncryptedContentAffinityCheck: decoded deployment=%s not found in healthy_deployments",
|
||||
model_id,
|
||||
)
|
||||
return typed_healthy_deployments
|
||||
@@ -0,0 +1,332 @@
|
||||
"""
|
||||
Enforce TPM/RPM rate limits set on model deployments.
|
||||
|
||||
This pre-call check ensures that model-level TPM/RPM limits are enforced
|
||||
across all requests, regardless of routing strategy.
|
||||
|
||||
When enabled via `enforce_model_rate_limits: true` in litellm_settings,
|
||||
requests that exceed the configured TPM/RPM limits will receive a 429 error.
|
||||
"""
|
||||
|
||||
from typing import TYPE_CHECKING, Any, Dict, Optional, Union
|
||||
|
||||
import httpx
|
||||
|
||||
import litellm
|
||||
from litellm._logging import verbose_router_logger
|
||||
from litellm.caching.dual_cache import DualCache
|
||||
from litellm.integrations.custom_logger import CustomLogger
|
||||
from litellm.types.router import RouterErrors
|
||||
from litellm.types.utils import StandardLoggingPayload
|
||||
from litellm.utils import get_utc_datetime
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from opentelemetry.trace import Span as _Span
|
||||
|
||||
Span = Union[_Span, Any]
|
||||
else:
|
||||
Span = Any
|
||||
|
||||
|
||||
class RoutingArgs:
|
||||
ttl: int = 60 # 1min (RPM/TPM expire key)
|
||||
|
||||
|
||||
class ModelRateLimitingCheck(CustomLogger):
|
||||
"""
|
||||
Pre-call check that enforces TPM/RPM limits on model deployments.
|
||||
|
||||
This check runs before each request and raises a RateLimitError
|
||||
if the deployment has exceeded its configured TPM or RPM limits.
|
||||
|
||||
Unlike the usage-based-routing strategy which uses limits for routing decisions,
|
||||
this check actively enforces those limits across ALL routing strategies.
|
||||
"""
|
||||
|
||||
def __init__(self, dual_cache: DualCache):
|
||||
self.dual_cache = dual_cache
|
||||
|
||||
def _get_deployment_limits(
|
||||
self, deployment: Dict
|
||||
) -> tuple[Optional[int], Optional[int]]:
|
||||
"""
|
||||
Extract TPM and RPM limits from a deployment configuration.
|
||||
|
||||
Checks in order:
|
||||
1. Top-level 'tpm'/'rpm' fields
|
||||
2. litellm_params.tpm/rpm
|
||||
3. model_info.tpm/rpm
|
||||
|
||||
Returns:
|
||||
Tuple of (tpm_limit, rpm_limit)
|
||||
"""
|
||||
# Check top-level
|
||||
tpm = deployment.get("tpm")
|
||||
rpm = deployment.get("rpm")
|
||||
|
||||
# Check litellm_params
|
||||
if tpm is None:
|
||||
tpm = deployment.get("litellm_params", {}).get("tpm")
|
||||
if rpm is None:
|
||||
rpm = deployment.get("litellm_params", {}).get("rpm")
|
||||
|
||||
# Check model_info
|
||||
if tpm is None:
|
||||
tpm = deployment.get("model_info", {}).get("tpm")
|
||||
if rpm is None:
|
||||
rpm = deployment.get("model_info", {}).get("rpm")
|
||||
|
||||
return tpm, rpm
|
||||
|
||||
def _get_cache_keys(self, deployment: Dict, current_minute: str) -> tuple[str, str]:
|
||||
"""Get the cache keys for TPM and RPM tracking."""
|
||||
model_id = deployment.get("model_info", {}).get("id")
|
||||
deployment_name = deployment.get("litellm_params", {}).get("model")
|
||||
|
||||
tpm_key = f"{model_id}:{deployment_name}:tpm:{current_minute}"
|
||||
rpm_key = f"{model_id}:{deployment_name}:rpm:{current_minute}"
|
||||
|
||||
return tpm_key, rpm_key
|
||||
|
||||
def pre_call_check(self, deployment: Dict) -> Optional[Dict]:
|
||||
"""
|
||||
Synchronous pre-call check for model rate limits.
|
||||
|
||||
Raises RateLimitError if deployment exceeds TPM/RPM limits.
|
||||
"""
|
||||
try:
|
||||
tpm_limit, rpm_limit = self._get_deployment_limits(deployment)
|
||||
|
||||
# If no limits are set, allow the request
|
||||
if tpm_limit is None and rpm_limit is None:
|
||||
return deployment
|
||||
|
||||
dt = get_utc_datetime()
|
||||
current_minute = dt.strftime("%H-%M")
|
||||
tpm_key, rpm_key = self._get_cache_keys(deployment, current_minute)
|
||||
|
||||
model_id = deployment.get("model_info", {}).get("id")
|
||||
model_name = deployment.get("litellm_params", {}).get("model")
|
||||
model_group = deployment.get("model_name", "")
|
||||
|
||||
# Check TPM limit
|
||||
if tpm_limit is not None:
|
||||
# First check local cache
|
||||
current_tpm = self.dual_cache.get_cache(key=tpm_key, local_only=True)
|
||||
if current_tpm is not None and current_tpm >= tpm_limit:
|
||||
raise litellm.RateLimitError(
|
||||
message=f"Model rate limit exceeded. TPM limit={tpm_limit}, current usage={current_tpm}",
|
||||
llm_provider="",
|
||||
model=model_name,
|
||||
response=httpx.Response(
|
||||
status_code=429,
|
||||
content=f"{RouterErrors.user_defined_ratelimit_error.value} tpm limit={tpm_limit}. current usage={current_tpm}. id={model_id}, model_group={model_group}",
|
||||
headers={"retry-after": str(60)},
|
||||
request=httpx.Request(
|
||||
method="model_rate_limit_check",
|
||||
url="https://github.com/BerriAI/litellm",
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
# Check RPM limit (atomic increment-first to avoid race conditions)
|
||||
if rpm_limit is not None:
|
||||
current_rpm = self.dual_cache.increment_cache(
|
||||
key=rpm_key, value=1, ttl=RoutingArgs.ttl
|
||||
)
|
||||
if current_rpm is not None and current_rpm > rpm_limit:
|
||||
raise litellm.RateLimitError(
|
||||
message=f"Model rate limit exceeded. RPM limit={rpm_limit}, current usage={current_rpm}",
|
||||
llm_provider="",
|
||||
model=model_name,
|
||||
response=httpx.Response(
|
||||
status_code=429,
|
||||
content=f"{RouterErrors.user_defined_ratelimit_error.value} rpm limit={rpm_limit}. current usage={current_rpm}. id={model_id}, model_group={model_group}",
|
||||
headers={"retry-after": str(60)},
|
||||
request=httpx.Request(
|
||||
method="model_rate_limit_check",
|
||||
url="https://github.com/BerriAI/litellm",
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
return deployment
|
||||
|
||||
except litellm.RateLimitError:
|
||||
raise
|
||||
except Exception as e:
|
||||
verbose_router_logger.debug(
|
||||
f"Error in ModelRateLimitingCheck.pre_call_check: {str(e)}"
|
||||
)
|
||||
# Don't fail the request if rate limit check fails
|
||||
return deployment
|
||||
|
||||
async def async_pre_call_check(
|
||||
self, deployment: Dict, parent_otel_span: Optional[Span] = None
|
||||
) -> Optional[Dict]:
|
||||
"""
|
||||
Async pre-call check for model rate limits.
|
||||
|
||||
Raises RateLimitError if deployment exceeds TPM/RPM limits.
|
||||
"""
|
||||
try:
|
||||
tpm_limit, rpm_limit = self._get_deployment_limits(deployment)
|
||||
|
||||
# If no limits are set, allow the request
|
||||
if tpm_limit is None and rpm_limit is None:
|
||||
return deployment
|
||||
|
||||
dt = get_utc_datetime()
|
||||
current_minute = dt.strftime("%H-%M")
|
||||
tpm_key, rpm_key = self._get_cache_keys(deployment, current_minute)
|
||||
|
||||
model_id = deployment.get("model_info", {}).get("id")
|
||||
model_name = deployment.get("litellm_params", {}).get("model")
|
||||
model_group = deployment.get("model_name", "")
|
||||
|
||||
# Check TPM limit
|
||||
if tpm_limit is not None:
|
||||
# First check local cache
|
||||
current_tpm = await self.dual_cache.async_get_cache(
|
||||
key=tpm_key, local_only=True
|
||||
)
|
||||
if current_tpm is not None and current_tpm >= tpm_limit:
|
||||
raise litellm.RateLimitError(
|
||||
message=f"Model rate limit exceeded. TPM limit={tpm_limit}, current usage={current_tpm}",
|
||||
llm_provider="",
|
||||
model=model_name,
|
||||
response=httpx.Response(
|
||||
status_code=429,
|
||||
content=f"{RouterErrors.user_defined_ratelimit_error.value} tpm limit={tpm_limit}. current usage={current_tpm}. id={model_id}, model_group={model_group}",
|
||||
headers={"retry-after": str(60)},
|
||||
request=httpx.Request(
|
||||
method="model_rate_limit_check",
|
||||
url="https://github.com/BerriAI/litellm",
|
||||
),
|
||||
),
|
||||
num_retries=0, # Don't retry - return 429 immediately
|
||||
)
|
||||
|
||||
# Check RPM limit (atomic increment-first to avoid race conditions)
|
||||
if rpm_limit is not None:
|
||||
current_rpm = await self.dual_cache.async_increment_cache(
|
||||
key=rpm_key,
|
||||
value=1,
|
||||
ttl=RoutingArgs.ttl,
|
||||
parent_otel_span=parent_otel_span,
|
||||
)
|
||||
if current_rpm is not None and current_rpm > rpm_limit:
|
||||
raise litellm.RateLimitError(
|
||||
message=f"Model rate limit exceeded. RPM limit={rpm_limit}, current usage={current_rpm}",
|
||||
llm_provider="",
|
||||
model=model_name,
|
||||
response=httpx.Response(
|
||||
status_code=429,
|
||||
content=f"{RouterErrors.user_defined_ratelimit_error.value} rpm limit={rpm_limit}. current usage={current_rpm}. id={model_id}, model_group={model_group}",
|
||||
headers={"retry-after": str(60)},
|
||||
request=httpx.Request(
|
||||
method="model_rate_limit_check",
|
||||
url="https://github.com/BerriAI/litellm",
|
||||
),
|
||||
),
|
||||
num_retries=0, # Don't retry - return 429 immediately
|
||||
)
|
||||
|
||||
return deployment
|
||||
|
||||
except litellm.RateLimitError:
|
||||
raise
|
||||
except Exception as e:
|
||||
verbose_router_logger.debug(
|
||||
f"Error in ModelRateLimitingCheck.async_pre_call_check: {str(e)}"
|
||||
)
|
||||
# Don't fail the request if rate limit check fails
|
||||
return deployment
|
||||
|
||||
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
|
||||
"""
|
||||
Track TPM usage after successful request.
|
||||
|
||||
This updates the TPM counter with the actual tokens used.
|
||||
Always tracks tokens - the pre-call check handles enforcement.
|
||||
"""
|
||||
try:
|
||||
standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
|
||||
"standard_logging_object"
|
||||
)
|
||||
if standard_logging_object is None:
|
||||
return
|
||||
|
||||
model_id = standard_logging_object.get("model_id")
|
||||
if model_id is None:
|
||||
return
|
||||
|
||||
total_tokens = standard_logging_object.get("total_tokens", 0)
|
||||
model = standard_logging_object.get("hidden_params", {}).get(
|
||||
"litellm_model_name"
|
||||
)
|
||||
|
||||
verbose_router_logger.debug(
|
||||
f"[TPM TRACKING] model_id={model_id}, total_tokens={total_tokens}, model={model}"
|
||||
)
|
||||
|
||||
if not model or not total_tokens:
|
||||
return
|
||||
|
||||
dt = get_utc_datetime()
|
||||
current_minute = dt.strftime("%H-%M")
|
||||
tpm_key = f"{model_id}:{model}:tpm:{current_minute}"
|
||||
|
||||
verbose_router_logger.debug(
|
||||
f"[TPM TRACKING] Incrementing {tpm_key} by {total_tokens}"
|
||||
)
|
||||
|
||||
await self.dual_cache.async_increment_cache(
|
||||
key=tpm_key,
|
||||
value=total_tokens,
|
||||
ttl=RoutingArgs.ttl,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
verbose_router_logger.debug(
|
||||
f"Error in ModelRateLimitingCheck.async_log_success_event: {str(e)}"
|
||||
)
|
||||
|
||||
def log_success_event(self, kwargs, response_obj, start_time, end_time):
|
||||
"""
|
||||
Sync version of tracking TPM usage after successful request.
|
||||
Always tracks tokens - the pre-call check handles enforcement.
|
||||
"""
|
||||
try:
|
||||
standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
|
||||
"standard_logging_object"
|
||||
)
|
||||
if standard_logging_object is None:
|
||||
return
|
||||
|
||||
model_id = standard_logging_object.get("model_id")
|
||||
if model_id is None:
|
||||
return
|
||||
|
||||
total_tokens = standard_logging_object.get("total_tokens", 0)
|
||||
model = standard_logging_object.get("hidden_params", {}).get(
|
||||
"litellm_model_name"
|
||||
)
|
||||
|
||||
if not model or not total_tokens:
|
||||
return
|
||||
|
||||
dt = get_utc_datetime()
|
||||
current_minute = dt.strftime("%H-%M")
|
||||
tpm_key = f"{model_id}:{model}:tpm:{current_minute}"
|
||||
|
||||
self.dual_cache.increment_cache(
|
||||
key=tpm_key,
|
||||
value=total_tokens,
|
||||
ttl=RoutingArgs.ttl,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
verbose_router_logger.debug(
|
||||
f"Error in ModelRateLimitingCheck.log_success_event: {str(e)}"
|
||||
)
|
||||
@@ -0,0 +1,100 @@
|
||||
"""
|
||||
Check if prompt caching is valid for a given deployment
|
||||
|
||||
Route to previously cached model id, if valid
|
||||
"""
|
||||
|
||||
from typing import List, Optional, cast
|
||||
|
||||
from litellm import verbose_logger
|
||||
from litellm.caching.dual_cache import DualCache
|
||||
from litellm.integrations.custom_logger import CustomLogger, Span
|
||||
from litellm.types.llms.openai import AllMessageValues
|
||||
from litellm.types.utils import CallTypes, StandardLoggingPayload
|
||||
from litellm.utils import is_prompt_caching_valid_prompt
|
||||
|
||||
from ..prompt_caching_cache import PromptCachingCache
|
||||
|
||||
|
||||
class PromptCachingDeploymentCheck(CustomLogger):
|
||||
def __init__(self, cache: DualCache):
|
||||
self.cache = cache
|
||||
|
||||
async def async_filter_deployments(
|
||||
self,
|
||||
model: str,
|
||||
healthy_deployments: List,
|
||||
messages: Optional[List[AllMessageValues]],
|
||||
request_kwargs: Optional[dict] = None,
|
||||
parent_otel_span: Optional[Span] = None,
|
||||
) -> List[dict]:
|
||||
if messages is not None and is_prompt_caching_valid_prompt(
|
||||
messages=messages,
|
||||
model=model,
|
||||
): # prompt > 1024 tokens
|
||||
prompt_cache = PromptCachingCache(
|
||||
cache=self.cache,
|
||||
)
|
||||
|
||||
model_id_dict = await prompt_cache.async_get_model_id(
|
||||
messages=cast(List[AllMessageValues], messages),
|
||||
tools=None,
|
||||
)
|
||||
if model_id_dict is not None:
|
||||
model_id = model_id_dict["model_id"]
|
||||
for deployment in healthy_deployments:
|
||||
if deployment["model_info"]["id"] == model_id:
|
||||
return [deployment]
|
||||
|
||||
return healthy_deployments
|
||||
|
||||
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
|
||||
standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
|
||||
"standard_logging_object", None
|
||||
)
|
||||
|
||||
if standard_logging_object is None:
|
||||
return
|
||||
|
||||
call_type = standard_logging_object["call_type"]
|
||||
|
||||
if (
|
||||
call_type != CallTypes.completion.value
|
||||
and call_type != CallTypes.acompletion.value
|
||||
and call_type != CallTypes.anthropic_messages.value
|
||||
): # only use prompt caching for completion calls
|
||||
verbose_logger.debug(
|
||||
"litellm.router_utils.pre_call_checks.prompt_caching_deployment_check: skipping adding model id to prompt caching cache, CALL TYPE IS NOT COMPLETION or ANTHROPIC MESSAGE"
|
||||
)
|
||||
return
|
||||
|
||||
model = standard_logging_object["model"]
|
||||
messages = standard_logging_object["messages"]
|
||||
model_id = standard_logging_object["model_id"]
|
||||
|
||||
if messages is None or not isinstance(messages, list):
|
||||
verbose_logger.debug(
|
||||
"litellm.router_utils.pre_call_checks.prompt_caching_deployment_check: skipping adding model id to prompt caching cache, MESSAGES IS NOT A LIST"
|
||||
)
|
||||
return
|
||||
if model_id is None:
|
||||
verbose_logger.debug(
|
||||
"litellm.router_utils.pre_call_checks.prompt_caching_deployment_check: skipping adding model id to prompt caching cache, MODEL ID IS NONE"
|
||||
)
|
||||
return
|
||||
|
||||
## PROMPT CACHING - cache model id, if prompt caching valid prompt + provider
|
||||
if is_prompt_caching_valid_prompt(
|
||||
model=model,
|
||||
messages=cast(List[AllMessageValues], messages),
|
||||
):
|
||||
cache = PromptCachingCache(
|
||||
cache=self.cache,
|
||||
)
|
||||
await cache.async_add_model_id(
|
||||
model_id=model_id,
|
||||
messages=messages,
|
||||
tools=None, # [TODO]: add tools once standard_logging_object supports it
|
||||
)
|
||||
|
||||
return
|
||||
@@ -0,0 +1,57 @@
|
||||
"""
|
||||
For Responses API, we need routing affinity when a user sends a previous_response_id.
|
||||
|
||||
eg. If proxy admins are load balancing between N gpt-4.1-turbo deployments, and a user sends a previous_response_id,
|
||||
we want to route to the same gpt-4.1-turbo deployment.
|
||||
|
||||
This is different from the normal behavior of the router, which does not have routing affinity for previous_response_id.
|
||||
|
||||
|
||||
If previous_response_id is provided, route to the deployment that returned the previous response
|
||||
"""
|
||||
|
||||
import warnings
|
||||
from typing import List, Optional
|
||||
|
||||
from litellm.integrations.custom_logger import CustomLogger, Span
|
||||
from litellm.responses.utils import ResponsesAPIRequestUtils
|
||||
from litellm.types.llms.openai import AllMessageValues
|
||||
|
||||
|
||||
class ResponsesApiDeploymentCheck(CustomLogger):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
warnings.warn(
|
||||
(
|
||||
"ResponsesApiDeploymentCheck is deprecated. "
|
||||
"Use DeploymentAffinityCheck(enable_responses_api_affinity=True) instead."
|
||||
),
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
async def async_filter_deployments(
|
||||
self,
|
||||
model: str,
|
||||
healthy_deployments: List,
|
||||
messages: Optional[List[AllMessageValues]],
|
||||
request_kwargs: Optional[dict] = None,
|
||||
parent_otel_span: Optional[Span] = None,
|
||||
) -> List[dict]:
|
||||
request_kwargs = request_kwargs or {}
|
||||
previous_response_id = request_kwargs.get("previous_response_id", None)
|
||||
if previous_response_id is None:
|
||||
return healthy_deployments
|
||||
|
||||
decoded_response = ResponsesAPIRequestUtils._decode_responses_api_response_id(
|
||||
response_id=previous_response_id,
|
||||
)
|
||||
model_id = decoded_response.get("model_id")
|
||||
if model_id is None:
|
||||
return healthy_deployments
|
||||
|
||||
for deployment in healthy_deployments:
|
||||
if deployment["model_info"]["id"] == model_id:
|
||||
return [deployment]
|
||||
|
||||
return healthy_deployments
|
||||
Reference in New Issue
Block a user