diff --git a/backend/Dockerfile b/backend/Dockerfile index aa01b9fba8b..622fedcd70d 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -46,6 +46,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ + --extra saml \ --python python3.13 # Stage 2 — copy source and install the project + workspace members. @@ -57,6 +58,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --extra proxy-runtime \ --extra extra_proxy \ --extra semantic-router \ + --extra saml \ --python python3.13 RUN HOME=/opt/prisma XDG_CACHE_HOME=/opt/prisma/.cache PRISMA_BINARY_CACHE_DIR=/opt/prisma/binaries \ diff --git a/helm/litellm-helm/Chart.yaml b/helm/litellm-helm/Chart.yaml index 3959d85edf3..a3cb388ffc6 100644 --- a/helm/litellm-helm/Chart.yaml +++ b/helm/litellm-helm/Chart.yaml @@ -18,7 +18,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 1.1.2 +version: 1.1.3 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to diff --git a/helm/litellm-helm/tests/hpa_tests.yaml b/helm/litellm-helm/tests/hpa_tests.yaml index ec18c3591d3..cd062dd5971 100644 --- a/helm/litellm-helm/tests/hpa_tests.yaml +++ b/helm/litellm-helm/tests/hpa_tests.yaml @@ -1,4 +1,4 @@ -suite: "hpa with behavior" +suite: "hpa" templates: - hpa.yaml tests: @@ -23,14 +23,44 @@ tests: - equal: { path: spec.behavior.scaleUp.stabilizationWindowSeconds, value: 60 } - equal: { path: spec.behavior.scaleDown.stabilizationWindowSeconds, value: 90 } ---- -suite: "hpa without behavior" -templates: - - hpa.yaml -tests: - it: "does not render behavior when not set" set: autoscaling.enabled: true asserts: - isKind: { of: HorizontalPodAutoscaler } - isNull: { path: spec.behavior } + + - it: "scales on cpu at the documented 60 percent by default" + set: + autoscaling.enabled: true + asserts: + - isKind: { of: HorizontalPodAutoscaler } + - equal: { path: "spec.metrics[0].resource.name", value: cpu } + - equal: { path: "spec.metrics[0].resource.target.type", value: Utilization } + - equal: { path: "spec.metrics[0].resource.target.averageUtilization", value: 60 } + + - it: "does not scale on memory by default" + set: + autoscaling.enabled: true + asserts: + - lengthEqual: { path: spec.metrics, count: 1 } + + - it: "honours an explicit cpu target override" + set: + autoscaling.enabled: true + autoscaling.targetCPUUtilizationPercentage: 75 + asserts: + - equal: { path: "spec.metrics[0].resource.target.averageUtilization", value: 75 } + + - it: "renders a memory metric only when a memory target is set" + set: + autoscaling.enabled: true + autoscaling.targetMemoryUtilizationPercentage: 80 + asserts: + - lengthEqual: { path: spec.metrics, count: 2 } + - equal: { path: "spec.metrics[1].resource.name", value: memory } + - equal: { path: "spec.metrics[1].resource.target.averageUtilization", value: 80 } + + - it: "renders no hpa when autoscaling is disabled" + asserts: + - hasDocuments: { count: 0 } diff --git a/helm/litellm-helm/values.yaml b/helm/litellm-helm/values.yaml index f8df98de102..637be2322e3 100644 --- a/helm/litellm-helm/values.yaml +++ b/helm/litellm-helm/values.yaml @@ -200,7 +200,16 @@ autoscaling: enabled: false minReplicas: 1 maxReplicas: 100 - targetCPUUtilizationPercentage: 80 + # 60 is the documented recommendation. See "Recommended Machine Specifications" + # in https://docs.litellm.ai/docs/proxy/prod. A new replica clears the startupProbe + # above only after up to failureThreshold x periodSeconds = 300 seconds, so a target + # high enough to trip near saturation adds capacity minutes after it was needed. + targetCPUUtilizationPercentage: 60 + # Deliberately left unset rather than given a value. The prisma query engine's + # resident memory is a high-water mark that ratchets to the pod's worst-ever write + # and is never returned, so a memory target reads the largest write a pod ever did + # rather than what it is doing now, and replicas ratchet up without scaling back in. + # Memory is a floor to provision under 'resources', not a signal to scale on. # targetMemoryUtilizationPercentage: 80 # behavior: {} diff --git a/litellm/constants.py b/litellm/constants.py index 1bd977dd9a9..1c1939bd350 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -9,6 +9,38 @@ DEFAULT_HEALTH_CHECK_PROMPT: Final = str(os.getenv("DEFAULT_HEALTH_CHECK_PROMPT" AZURE_DEFAULT_RESPONSES_API_VERSION: Final = str(os.getenv("AZURE_DEFAULT_RESPONSES_API_VERSION", "preview")) ROUTER_MAX_FALLBACKS: Final = int(os.getenv("ROUTER_MAX_FALLBACKS", 5)) ROUTER_FALLBACK_ERROR_DETAIL_MAX_CHARS: Final = 2000 +RUNTIME_UPDATABLE_ROUTER_SETTINGS: Final[frozenset[str]] = frozenset( + { + "routing_strategy_args", + "routing_strategy", + "routing_groups", + "allowed_fails", + "cooldown_time", + "num_retries", + "timeout", + "max_retries", + "retry_after", + "fallbacks", + "context_window_fallbacks", + "retry_policy", + "model_group_retry_policy", + "model_group_alias", + "enable_weighted_failover", + "enable_tag_filtering", + "tag_routing_prefix", + "optional_pre_call_checks", + } +) +ROUTER_SETTINGS_MANAGED_OUTSIDE_CONFIG: Final[frozenset[str]] = frozenset( + { + "model_list", + "search_tools", + "assistants_config", + "router_general_settings", + "ignore_invalid_deployments", + "fallback_access_check", + } +) DEFAULT_BATCH_SIZE: Final = int(os.getenv("DEFAULT_BATCH_SIZE", 512)) DEFAULT_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_FLUSH_INTERVAL_SECONDS", 5)) DEFAULT_S3_FLUSH_INTERVAL_SECONDS: Final = int(os.getenv("DEFAULT_S3_FLUSH_INTERVAL_SECONDS", 10)) diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index e87ac9521ae..372c9bf6b91 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -1,4 +1,5 @@ import contextvars +import copy import hashlib import os import secrets @@ -39,6 +40,7 @@ except ImportError: if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation dc: Final = DualCache() @@ -852,6 +854,69 @@ class CustomGuardrail(CustomLogger): return result + async def async_logging_hook( + self, + kwargs: dict, # mutable-ok: CustomLogger.async_logging_hook contract + result: object, + call_type: str, + ) -> tuple[dict, object]: # mutable-ok: CustomLogger.async_logging_hook contract + """logging_only: run apply_guardrail on copies of the logged request/response and record the verdict.""" + from litellm.llms import get_guardrail_translation_mapping + + if not self.uses_apply_guardrail_interface() or self.use_native_lifecycle_hooks: + return kwargs, result + try: + translation: Final = get_guardrail_translation_mapping(CallTypes(call_type))() + except ValueError: + verbose_logger.debug( + "Guardrail %s: no guardrail translation for call_type=%s, skipping logging_only scan", + self.guardrail_name, + call_type, + ) + return kwargs, result + litellm_params: Final = kwargs.get("litellm_params") or {} + scratch_metadata: Final = { + key: value + for key, value in (litellm_params.get("metadata") or {}).items() + if key != "standard_logging_guardrail_information" + } + try: + await self._scan_logged_call(kwargs, result, translation, scratch_metadata) + except Exception as e: + verbose_logger.warning("Guardrail %s: logging_only scan raised: %s", self.guardrail_name, e) + recorded: Final = scratch_metadata.get("standard_logging_guardrail_information") + standard_logging_object: Final = kwargs.get("standard_logging_object") + if not recorded or not isinstance(standard_logging_object, dict): + return kwargs, result + entries: Final = recorded if isinstance(recorded, list) else [recorded] + existing: Final = standard_logging_object.get("guardrail_information") or [] + return { + **kwargs, + "standard_logging_object": {**standard_logging_object, "guardrail_information": [*existing, *entries]}, + }, result + + async def _scan_logged_call( + self, + kwargs: dict, # mutable-ok: CustomLogger.async_logging_hook contract + result: object, + translation: "BaseTranslation", + scratch_metadata: dict, # mutable-ok: apply_guardrail records its verdict into request metadata + ) -> None: + optional_params: Final = kwargs.get("optional_params") or {} + scratch_input: Final = copy.deepcopy(kwargs.get("messages") or kwargs.get("input")) + scratch_request: Final = { + "model": kwargs.get("model"), + "messages": scratch_input, + "input": scratch_input, + "tools": copy.deepcopy(optional_params.get("tools")), + "litellm_call_id": kwargs.get("litellm_call_id"), + "metadata": scratch_metadata, + } + await translation.process_input_messages(data=scratch_request, guardrail_to_apply=self) + await translation.process_output_response( + response=copy.deepcopy(result), guardrail_to_apply=self, request_data=scratch_request + ) + def supports_scan_only_tool_results(self) -> bool: """Whether this guardrail can scan tool-result content. diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 3978a01a5db..0e01577b20e 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -36,6 +36,8 @@ from litellm.types.utils import ( from litellm.utils import print_verbose, token_counter if TYPE_CHECKING: + from openai.types.completion_usage import CompletionUsage + from litellm.litellm_core_utils.litellm_logging import Logging from litellm.types.litellm_core_utils.streaming_chunk_builder_utils import ( UsagePerChunk, @@ -794,7 +796,7 @@ class ChunkProcessor: @staticmethod def _extract_usage_chunk(chunk: "_UsageBearingChunk | ModelResponse | ModelResponseStream") -> Usage | None: - usage_chunk: Usage | None = None + usage_chunk: Usage | CompletionUsage | None = None if hasattr(chunk, "usage") and chunk.usage is not None: usage_chunk = chunk.usage elif "usage" in chunk: @@ -806,7 +808,9 @@ class ChunkProcessor: if isinstance(usage_chunk, dict): return Usage(**usage_chunk) - return usage_chunk + if usage_chunk is None or isinstance(usage_chunk, Usage): + return usage_chunk + return Usage(**usage_chunk.model_dump()) def _calculate_usage_per_chunk( self, diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index c60ebd844ba..d23690976ad 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -1378,31 +1378,38 @@ def process_anthropic_headers(headers: httpx.Headers | dict) -> dict: return additional_headers -def _anthropic_model_entry(model: ModelInfoResponse, created_at: str) -> Mapping[str, object]: +def _anthropic_model_entry( + model: ModelInfoResponse, created_at: str, display_names: Mapping[str, str] +) -> Mapping[str, object]: return { # mutable-ok: JSON response body, serialized by the route and never mutated "type": "model", "id": model["id"], - "display_name": model["id"], + "display_name": display_names.get(model["id"], model["id"]), "created_at": created_at, "max_input_tokens": model.get("max_input_tokens"), "max_tokens": model.get("max_output_tokens"), } -def create_anthropic_model_list_response(models: Sequence[ModelInfoResponse]) -> Mapping[str, object]: +def create_anthropic_model_list_response( + models: Sequence[ModelInfoResponse], + display_names: Mapping[str, str] = MappingProxyType({}), +) -> Mapping[str, object]: """Build the Anthropic-native /v1/models envelope. Clients that send an anthropic-version header parse the Anthropic Models API shape (type/display_name/created_at plus has_more/first_id/last_id) and filter the list themselves, so every model is returned here. The token limits carry over from the OpenAI-shaped listing, named as the Messages API names them, and - are always present because the vendor shape declares them nullable, not optional + are always present because the vendor shape declares them nullable, not optional. + display_names maps a listed model id to a configured human-readable name; ids + without an entry fall back to the id itself, matching the vendor behavior """ created_at: Final = ( datetime.fromtimestamp(DEFAULT_MODEL_CREATED_AT_TIME, tz=timezone.utc).isoformat().replace("+00:00", "Z") ) data: Final = [ # mutable-ok: JSON response body, serialized by the route and never mutated - _anthropic_model_entry(model, created_at) for model in models + _anthropic_model_entry(model, created_at, display_names) for model in models ] return { # mutable-ok: JSON response body, serialized by the route and never mutated "data": data, diff --git a/litellm/llms/azure_ai/vector_stores/transformation.py b/litellm/llms/azure_ai/vector_stores/transformation.py index 5e61d0a1dd9..64e72b819b1 100644 --- a/litellm/llms/azure_ai/vector_stores/transformation.py +++ b/litellm/llms/azure_ai/vector_stores/transformation.py @@ -19,6 +19,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -115,6 +116,7 @@ class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict[str, Any]]: """ Transform search request for Azure AI Search API diff --git a/litellm/llms/base_llm/vector_store/transformation.py b/litellm/llms/base_llm/vector_store/transformation.py index 02a51a8bace..63e99c0915a 100644 --- a/litellm/llms/base_llm/vector_store/transformation.py +++ b/litellm/llms/base_llm/vector_store/transformation.py @@ -17,6 +17,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router from ..chat.transformation import BaseLLMException as _BaseLLMException @@ -57,6 +58,7 @@ class BaseVectorStoreConfig: litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: pass @@ -69,6 +71,7 @@ class BaseVectorStoreConfig: litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """ Optional async version of transform_search_vector_store_request. @@ -84,6 +87,7 @@ class BaseVectorStoreConfig: litellm_logging_obj=litellm_logging_obj, litellm_params=litellm_params, extra_body=extra_body, + router=router, ) @abstractmethod @@ -197,6 +201,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: Mapping[str, object], extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, ) -> NoReturn: raise NotImplementedError("Direct vector store providers execute the search themselves; no HTTP request shape") diff --git a/litellm/llms/bedrock/vector_stores/transformation.py b/litellm/llms/bedrock/vector_stores/transformation.py index 2d72db0cdba..bad17a2181d 100644 --- a/litellm/llms/bedrock/vector_stores/transformation.py +++ b/litellm/llms/bedrock/vector_stores/transformation.py @@ -27,6 +27,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -196,6 +197,7 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: if isinstance(query, list): query = " ".join(query) diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 834f7d564a2..6f42d42de00 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -178,6 +178,7 @@ if TYPE_CHECKING: AnthropicMessagesStreamingResponse, ) from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig + from litellm.router import Router from litellm.types.llms.openai_evals import ( CancelEvalResponse, CancelRunResponse, @@ -2923,7 +2924,7 @@ class BaseLLMHTTPHandler: final_response: Final = await self._call_agentic_completion_hooks( response=initial_response, model=model, - messages=(input if isinstance(input, list) else [{"role": "user", "content": input}]), + messages=(input if isinstance(input, list) else [{"role": "user", "content": input}]), # pyright: ignore[reportArgumentType] # pre-existing mismatch surfaced by the Router import; the hook accepts response input items at runtime anthropic_messages_provider_config=responses_api_provider_config, anthropic_messages_optional_request_params=response_api_optional_request_params, logging_obj=logging_obj, @@ -5415,7 +5416,7 @@ class BaseLLMHTTPHandler: try: response: ResponsesAPIResponse | BaseResponsesAPIStreamingIterator = await litellm.aresponses( model=patch.model or model, - input=patch.messages, + input=patch.messages, # pyright: ignore[reportArgumentType] # pre-existing mismatch surfaced by the Router import; patch messages are valid response input at runtime **optional_params, **kwargs_for_followup, ) @@ -9688,6 +9689,7 @@ class BaseLLMHTTPHandler: timeout: float | httpx.Timeout | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, _is_async: bool = False, + router: "Router | None" = None, ) -> VectorStoreSearchResponse: if isinstance(vector_store_provider_config, BaseDirectVectorStoreConfig): self._pre_call_direct_vector_store_search( @@ -9738,6 +9740,7 @@ class BaseLLMHTTPHandler: litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), extra_body=extra_body, + router=router, ) else: ( @@ -9751,6 +9754,7 @@ class BaseLLMHTTPHandler: litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), extra_body=extra_body, + router=router, ) all_optional_params: Final[dict[str, object]] = dict(litellm_params) all_optional_params.update(vector_store_search_optional_params or {}) @@ -9802,6 +9806,7 @@ class BaseLLMHTTPHandler: timeout: float | httpx.Timeout | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, _is_async: bool = False, + router: "Router | None" = None, ) -> VectorStoreSearchResponse | Coroutine[object, object, VectorStoreSearchResponse]: if _is_async: return self.async_vector_store_search_handler( @@ -9816,6 +9821,7 @@ class BaseLLMHTTPHandler: extra_body=extra_body, timeout=timeout, client=client, + router=router, ) if isinstance(vector_store_provider_config, BaseDirectVectorStoreConfig): @@ -9862,6 +9868,7 @@ class BaseLLMHTTPHandler: litellm_logging_obj=logging_obj, litellm_params=dict(litellm_params), extra_body=extra_body, + router=router, ) all_optional_params: Final[dict[str, object]] = dict(litellm_params) diff --git a/litellm/llms/gemini/vector_stores/transformation.py b/litellm/llms/gemini/vector_stores/transformation.py index f6525a449b6..82586b1f638 100644 --- a/litellm/llms/gemini/vector_stores/transformation.py +++ b/litellm/llms/gemini/vector_stores/transformation.py @@ -33,6 +33,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -168,6 +169,7 @@ class GeminiVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """ Transform search request to Gemini's generateContent format. diff --git a/litellm/llms/milvus/vector_stores/transformation.py b/litellm/llms/milvus/vector_stores/transformation.py index 34f0cd854c4..c3581abfbcc 100644 --- a/litellm/llms/milvus/vector_stores/transformation.py +++ b/litellm/llms/milvus/vector_stores/transformation.py @@ -19,6 +19,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -123,6 +124,7 @@ class MilvusVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict[str, Any]]: """ Transform search request for Azure AI Search API diff --git a/litellm/llms/openai/vector_stores/transformation.py b/litellm/llms/openai/vector_stores/transformation.py index f6c093f2e2a..4e925494039 100644 --- a/litellm/llms/openai/vector_stores/transformation.py +++ b/litellm/llms/openai/vector_stores/transformation.py @@ -21,6 +21,7 @@ from litellm.utils import add_openai_metadata if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -99,6 +100,7 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: encoded_vector_store_id: Final = encode_url_path_segment(vector_store_id, field_name="vector_store_id") url: Final = f"{api_base}/{encoded_vector_store_id}/search" diff --git a/litellm/llms/pg_vector/vector_stores/transformation.py b/litellm/llms/pg_vector/vector_stores/transformation.py index e4b06c36bf4..9de1f589ae4 100644 --- a/litellm/llms/pg_vector/vector_stores/transformation.py +++ b/litellm/llms/pg_vector/vector_stores/transformation.py @@ -8,6 +8,7 @@ from litellm.types.vector_stores import VectorStoreSearchOptionalRequestParams if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -80,6 +81,7 @@ class PGVectorStoreConfig(OpenAIVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: encoded_vector_store_id: Final = encode_url_path_segment(vector_store_id, field_name="vector_store_id") url: Final = f"{api_base}/{encoded_vector_store_id}/search" diff --git a/litellm/llms/ragflow/vector_stores/transformation.py b/litellm/llms/ragflow/vector_stores/transformation.py index 282cb7a92a7..ffa6c9e1076 100644 --- a/litellm/llms/ragflow/vector_stores/transformation.py +++ b/litellm/llms/ragflow/vector_stores/transformation.py @@ -17,6 +17,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -92,6 +93,7 @@ class RAGFlowVectorStoreConfig(BaseVectorStoreConfig): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """RAGFlow vector stores are management-only, search is not supported.""" raise NotImplementedError("RAGFlow vector stores support dataset management only, not search/retrieval") diff --git a/litellm/llms/s3_vectors/vector_stores/transformation.py b/litellm/llms/s3_vectors/vector_stores/transformation.py index 5be35ae4148..733358381fe 100644 --- a/litellm/llms/s3_vectors/vector_stores/transformation.py +++ b/litellm/llms/s3_vectors/vector_stores/transformation.py @@ -1,8 +1,8 @@ -import re from typing import TYPE_CHECKING, Any, Final import httpx +from litellm.caching._embedding_router import resolve_embedding_router from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM from litellm.types.router import GenericLiteLLMParams @@ -18,6 +18,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.router import Router else: LiteLLMLoggingObj = Any @@ -58,13 +59,20 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): return headers def get_complete_url(self, api_base: str | None, litellm_params: dict) -> str: - aws_region_name: Final = litellm_params.get("aws_region_name") - if not aws_region_name: - raise ValueError("aws_region_name is required for S3 Vectors") - if not re.match(r"^[a-z][a-z0-9-]*$", aws_region_name): - raise ValueError("Invalid aws_region_name format") + # Resolve region the same way the ingestion path does: + # dynamic param -> AWS_REGION_NAME -> AWS_REGION -> default (us-west-2) + aws_region_name: Final = self.get_aws_region_name_for_non_llm_api_calls(litellm_params.get("aws_region_name")) return f"https://s3vectors.{aws_region_name}.api.aws" + def _resolve_query_embedding_router(self, embedding_model: str, router: "Router | None") -> "Router | None": + """Return the router iff it serves ``embedding_model`` as a deployment.""" + if router is None: + return None + model_list: Final = [ + dict(m) for m in (router.get_model_list() or ()) + ] # mutable-ok: resolve_embedding_router requires list[dict] + return resolve_embedding_router(embedding_model=embedding_model, llm_router=router, llm_model_list=model_list) + def transform_search_vector_store_request( self, vector_store_id: str, @@ -74,6 +82,7 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """Sync version - generates embedding synchronously.""" # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name @@ -99,10 +108,16 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): # Generate embedding for the query embedding_model: Final = litellm_params.get("embedding_model", "text-embedding-3-small") + embedding_router: Final = self._resolve_query_embedding_router(embedding_model=embedding_model, router=router) import litellm as litellm_module - embedding_response: Final = litellm_module.embedding(model=embedding_model, input=[query]) + embedding_input: Final = [query] # mutable-ok: the embedding API takes list input + embedding_response: Final = ( + embedding_router.embedding(model=embedding_model, input=embedding_input) + if embedding_router is not None + else litellm_module.embedding(model=embedding_model, input=embedding_input) + ) query_embedding: Final = embedding_response.data[0]["embedding"] url: Final = f"{api_base}/QueryVectors" @@ -128,6 +143,7 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: dict[str, Any] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict]: """Async version - generates embedding asynchronously.""" # For S3 Vectors, vector_store_id should be in format: bucket_name:index_name @@ -153,10 +169,16 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM): # Generate embedding for the query asynchronously embedding_model: Final = litellm_params.get("embedding_model", "text-embedding-3-small") + embedding_router: Final = self._resolve_query_embedding_router(embedding_model=embedding_model, router=router) import litellm as litellm_module - embedding_response: Final = await litellm_module.aembedding(model=embedding_model, input=[query]) + embedding_input: Final = [query] # mutable-ok: the embedding API takes list input + embedding_response: Final = ( + await embedding_router.aembedding(model=embedding_model, input=embedding_input) + if embedding_router is not None + else await litellm_module.aembedding(model=embedding_model, input=embedding_input) + ) query_embedding: Final = embedding_response.data[0]["embedding"] url: Final = f"{api_base}/QueryVectors" diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index d8b1e7ba17c..69fe5678de9 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -949,7 +949,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): # For Gemini 3+ models, use thinkingLevel instead of thinkingBudget if model and VertexGeminiConfig._is_gemini_3_or_newer(model): if thinking_enabled: - if thinking_budget is None or thinking_budget == 0: + if thinking_budget == 0: params["includeThoughts"] = False else: params["includeThoughts"] = True diff --git a/litellm/llms/vertex_ai/ocr/deepseek_transformation.py b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py index 2603552152d..b57a87c3325 100644 --- a/litellm/llms/vertex_ai/ocr/deepseek_transformation.py +++ b/litellm/llms/vertex_ai/ocr/deepseek_transformation.py @@ -177,8 +177,9 @@ class VertexAIDeepSeekOCRConfig(BaseOCRConfig): content_item = {"type": "image_url", "image_url": document_url} # Build DeepSeek OCR request + provider_model: Final = model if model.startswith("deepseek-ai/") else f"deepseek-ai/{model}" data: Final = { - "model": "deepseek-ai/" + model, + "model": provider_model, "messages": [{"role": "user", "content": [content_item]}], } diff --git a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py index 5c250fc1a7e..36b57e7c995 100644 --- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py @@ -21,6 +21,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -161,6 +162,7 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict[str, object]]: """ Transform search request for Vertex AI RAG API diff --git a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py index 0bcf16ee06f..f0812e3ed9f 100644 --- a/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py +++ b/litellm/llms/vertex_ai/vector_stores/search_api/transformation.py @@ -25,6 +25,7 @@ from litellm.types.vector_stores import ( if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + from litellm.router import Router LiteLLMLoggingObj = _LiteLLMLoggingObj else: @@ -245,6 +246,7 @@ class VertexSearchAPIVectorStoreConfig(BaseVectorStoreConfig, VertexBase): litellm_logging_obj: LiteLLMLoggingObj, litellm_params: dict, extra_body: Mapping[str, object] | None = None, + router: "Router | None" = None, ) -> tuple[str, dict[str, object]]: """ Transform a search request for the Vertex AI Search (Discovery Engine) API. diff --git a/litellm/main.py b/litellm/main.py index c4c5bbefc4f..01c106adc7c 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -8637,6 +8637,16 @@ def _set_stream_builder_response_cost(response: ModelResponse, logging_obj: Opti hidden_params["response_cost"] = response_cost +def _stamp_streaming_usage_cost(usage: Usage, response: ModelResponse, logging_obj: Optional["Logging"]) -> None: + if logging_obj is None: + return + if isinstance(getattr(usage, "cost", None), (int, float)): + return + computed_cost: Final = logging_obj._response_cost_calculator(result=response) + if isinstance(computed_cost, (int, float)) and computed_cost > 0: + setattr(usage, "cost", computed_cost) + + def stream_chunk_builder( chunks: list, messages: list | None = None, @@ -8731,12 +8741,7 @@ def stream_chunk_builder( ) break - if litellm.include_cost_in_streaming_usage and logging_obj is not None: - setattr( - usage, - "cost", - logging_obj._response_cost_calculator(result=response), - ) + _stamp_streaming_usage_cost(usage, response, logging_obj) _set_stream_builder_response_cost(response, logging_obj) processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj) @@ -8915,10 +8920,7 @@ def stream_chunk_builder( ) break - # Add cost to usage object if include_cost_in_streaming_usage is True - if litellm.include_cost_in_streaming_usage and logging_obj is not None: - setattr(usage, "cost", logging_obj._response_cost_calculator(result=response)) - + _stamp_streaming_usage_cost(usage, response, logging_obj) _set_stream_builder_response_cost(response, logging_obj) processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index a3cfb300ea6..2846d12db6e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -9643,7 +9643,8 @@ "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" - ] + ], + "deprecation_date": "2026-10-01" }, "azure_ai/MAI-Image-2.5-Flash": { "input_cost_per_image_token": 1.75e-06, @@ -9656,7 +9657,8 @@ "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" - ] + ], + "deprecation_date": "2026-10-01" }, "azure_ai/MAI-Image-2e": { "deprecation_date": "2026-08-15", @@ -10155,7 +10157,9 @@ "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "cache_read_input_token_cost": 1.45e-07, + "supports_prompt_caching": true }, "azure_ai/deepseek-v4-flash": { "deprecation_date": "2028-02-20", @@ -10169,18 +10173,20 @@ "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true - }, - "azure_ai/deepseek-v4-flash-0731": { + "supports_tool_choice": true, "cache_read_input_token_cost": 2.8e-08, + "supports_prompt_caching": true + }, + "azure_ai/DeepSeek-V4-Flash-0731": { + "cache_read_input_token_cost": 1.4e-08, "deprecation_date": "2026-12-03", - "input_cost_per_token": 1.9e-07, + "input_cost_per_token": 4.4e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 5.1e-07, + "output_cost_per_token": 1.32e-06, "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_prompt_caching": true, @@ -10400,11 +10406,13 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/kimi-k2-5-now-in-microsoft-foundry/4492321", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", "supports_function_calling": true, "supports_tool_choice": true, "supports_video_input": true, - "supports_vision": true + "supports_vision": true, + "cache_read_input_token_cost": 1e-07, + "supports_prompt_caching": true }, "azure_ai/kimi-k2.6": { "deprecation_date": "2027-04-16", @@ -10415,7 +10423,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k2-6-in-microsoft-foundry/4513125", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", "supported_modalities": [ "text", "image" @@ -10426,7 +10434,9 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "cache_read_input_token_cost": 1.6e-07, + "supports_prompt_caching": true }, "azure_ai/ministral-3b": { "input_cost_per_token": 4e-08, @@ -12110,7 +12120,7 @@ "max_output_tokens": 2048, "max_tokens": 2048, "mode": "chat", - "output_cost_per_token": 2.65e-06, + "output_cost_per_token": 6e-07, "supports_pdf_input": true }, "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0": { @@ -23514,6 +23524,63 @@ "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014 }, + "vertex_ai/gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "vertex_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "regional_endpoint_uplift_multiplier": 1.1, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, "vertex_ai/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, "cache_read_input_token_cost": 2e-07, @@ -25351,6 +25418,65 @@ "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014 }, + "gemini/gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "rpm": 2000, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "tpm": 800000, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, "gemini/gemini-omni-flash-preview": { "input_cost_per_audio_token": 1.5e-06, "input_cost_per_token": 1.5e-06, @@ -25759,6 +25885,63 @@ "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014 }, + "gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, "gemini/gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, "input_cost_per_audio_token": 7e-07, @@ -29098,16 +29281,19 @@ "cache_creation_input_token_cost": 5e-06, "cache_creation_input_token_cost_above_272k_tokens": 1e-05, "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, "cache_creation_input_token_cost_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 1e-05, "cache_read_input_token_cost": 4e-07, "cache_read_input_token_cost_above_272k_tokens": 8e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "cache_read_input_token_cost_priority": 8e-07, "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, "input_cost_per_token_batches": 2e-06, "input_cost_per_token_flex": 2e-06, "input_cost_per_token_priority": 8e-06, @@ -29119,6 +29305,7 @@ "output_cost_per_token": 2e-05, "output_cost_per_token_above_272k_tokens": 3e-05, "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, "output_cost_per_token_batches": 1e-05, "output_cost_per_token_flex": 1e-05, "output_cost_per_token_priority": 4e-05, @@ -29161,16 +29348,19 @@ "cache_creation_input_token_cost": 5e-06, "cache_creation_input_token_cost_above_272k_tokens": 1e-05, "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, "cache_creation_input_token_cost_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 1e-05, "cache_read_input_token_cost": 4e-07, "cache_read_input_token_cost_above_272k_tokens": 8e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "cache_read_input_token_cost_priority": 8e-07, "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, "input_cost_per_token_batches": 2e-06, "input_cost_per_token_flex": 2e-06, "input_cost_per_token_priority": 8e-06, @@ -29182,6 +29372,7 @@ "output_cost_per_token": 2e-05, "output_cost_per_token_above_272k_tokens": 3e-05, "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, "output_cost_per_token_batches": 1e-05, "output_cost_per_token_flex": 1e-05, "output_cost_per_token_priority": 4e-05, @@ -29225,16 +29416,19 @@ "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_272k_tokens": 5e-06, "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05, "cache_creation_input_token_cost_flex": 1.25e-06, "cache_creation_input_token_cost_priority": 5e-06, "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_272k_tokens": 4e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, "cache_read_input_token_cost_flex": 1e-07, "cache_read_input_token_cost_priority": 4e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "input_cost_per_token_above_272k_tokens_priority": 8e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, "input_cost_per_token_priority": 4e-06, @@ -29246,6 +29440,7 @@ "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_272k_tokens": 1.8e-05, "output_cost_per_token_above_272k_tokens_flex": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-05, "output_cost_per_token_batches": 6e-06, "output_cost_per_token_flex": 6e-06, "output_cost_per_token_priority": 2.4e-05, @@ -29288,16 +29483,19 @@ "cache_creation_input_token_cost": 2.5e-07, "cache_creation_input_token_cost_above_272k_tokens": 5e-07, "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06, "cache_creation_input_token_cost_flex": 1.25e-07, "cache_creation_input_token_cost_priority": 5e-07, "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_above_272k_tokens": 4e-08, "cache_read_input_token_cost_above_272k_tokens_flex": 2e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, "cache_read_input_token_cost_flex": 1e-08, "cache_read_input_token_cost_priority": 4e-08, "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_above_272k_tokens_flex": 2e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, "input_cost_per_token_batches": 1e-07, "input_cost_per_token_flex": 1e-07, "input_cost_per_token_priority": 4e-07, @@ -29309,6 +29507,7 @@ "output_cost_per_token": 1.2e-06, "output_cost_per_token_above_272k_tokens": 1.8e-06, "output_cost_per_token_above_272k_tokens_flex": 9e-07, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-06, "output_cost_per_token_batches": 6e-07, "output_cost_per_token_flex": 6e-07, "output_cost_per_token_priority": 2.4e-06, @@ -29548,7 +29747,10 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": false + "supports_minimal_reasoning_effort": false, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07 }, "gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5e-07, @@ -29602,7 +29804,10 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": false + "supports_minimal_reasoning_effort": false, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07 }, "gpt-5.5-pro": { "cache_read_input_token_cost": 3e-06, @@ -29751,7 +29956,10 @@ "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07 }, "gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.5e-07, @@ -29800,7 +30008,10 @@ "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07 }, "gpt-5.4-pro": { "cache_read_input_token_cost": 3e-06, @@ -29849,7 +30060,9 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 0.000135 }, "gpt-5.4-pro-2026-03-05": { "cache_read_input_token_cost": 3e-06, @@ -29898,7 +30111,9 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 0.000135 }, "gpt-5.4-mini": { "cache_read_input_token_cost": 7.5e-08, @@ -30834,17 +31049,18 @@ }, "gpt-realtime-2": { "cache_creation_input_audio_token_cost": 4e-07, + "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image": 5e-06, "input_cost_per_token": 4e-06, "litellm_provider": "openai", - "max_input_tokens": 32000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "realtime", "output_cost_per_audio_token": 6.4e-05, - "output_cost_per_token": 1.6e-05, + "output_cost_per_token": 2.4e-05, "supported_endpoints": [ "/v1/realtime" ], @@ -30908,8 +31124,8 @@ "input_cost_per_token": 6e-07, "litellm_provider": "openai", "max_input_tokens": 128000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "realtime", "output_cost_per_audio_token": 2e-05, "output_cost_per_token": 2.4e-06, @@ -30941,7 +31157,7 @@ "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", - "max_input_tokens": 128000, + "max_input_tokens": 32000, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "realtime", @@ -33705,19 +33921,21 @@ "source": "https://mistral.ai/pricing#api-pricing" }, "mistral/magistral-medium-latest": { - "input_cost_per_token": 2e-06, + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", - "max_input_tokens": 40000, - "max_output_tokens": 40000, - "max_tokens": 40000, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 5e-06, - "source": "https://mistral.ai/news/magistral", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/magistral-small-2506": { "deprecation_date": "2025-11-30", @@ -33736,19 +33954,21 @@ "supports_tool_choice": true }, "mistral/magistral-small-latest": { - "input_cost_per_token": 5e-07, + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", - "max_input_tokens": 40000, - "max_output_tokens": 40000, - "max_tokens": 40000, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 1.5e-06, - "source": "https://mistral.ai/pricing#api-pricing", + "output_cost_per_token": 6e-07, + "source": "https://docs.mistral.ai/models/model-cards/mistral-small-4-0-26-03", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/magistral-small-1-2-2509": { "deprecation_date": "2026-07-31", @@ -33880,16 +34100,21 @@ "supports_vision": true }, "mistral/mistral-medium": { - "input_cost_per_token": 2.7e-06, + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "max_tokens": 8191, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 8.1e-06, + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/mistral-medium-2312": { "deprecation_date": "2025-06-16", @@ -41340,13 +41565,13 @@ "source": "https://docs.together.ai/docs/serverless-models" }, "together_ai/Qwen/Qwen3.8-2.4T-A95B": { - "cache_read_input_token_cost": 5e-07, - "input_cost_per_token": 2.5e-06, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 2e-06, "litellm_provider": "together_ai", "max_input_tokens": 1010000, "max_tokens": 1010000, "mode": "chat", - "output_cost_per_token": 6.25e-06, + "output_cost_per_token": 6e-06, "source": "https://docs.together.ai/docs/serverless-models", "supports_prompt_caching": true }, @@ -41956,6 +42181,70 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 1024 }, + "us-gov.anthropic.claude-sonnet-5": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": false, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "us-gov.anthropic.claude-opus-4-8": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + "input_cost_per_token": 6e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": true, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, "au.anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.375e-06, "cache_creation_input_token_cost_above_1hr": 2.2e-06, @@ -45849,6 +46138,26 @@ "mode": "rerank", "output_cost_per_token": 0.0 }, + "voyage/rerank-3": { + "input_cost_per_token": 5e-08, + "litellm_provider": "voyage", + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "rerank", + "output_cost_per_token": 0.0, + "source": "https://docs.voyageai.com/docs/pricing" + }, + "voyage/rerank-3-lite": { + "input_cost_per_token": 2e-08, + "litellm_provider": "voyage", + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "rerank", + "output_cost_per_token": 0.0, + "source": "https://docs.voyageai.com/docs/pricing" + }, "voyage/voyage-2": { "input_cost_per_token": 1e-07, "litellm_provider": "voyage", @@ -47100,6 +47409,27 @@ "supports_vision": true, "supports_web_search": true }, + "xai/grok-build-latest": { + "cache_read_input_token_cost": 3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "litellm_provider": "xai", + "max_input_tokens": 500000, + "max_output_tokens": 500000, + "max_tokens": 500000, + "mode": "chat", + "output_cost_per_token": 6e-06, + "output_cost_per_token_above_200k_tokens": 1.2e-05, + "source": "https://docs.x.ai/developers/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, "xai/grok-4.6": { "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_200k_tokens": 1e-06, @@ -57420,6 +57750,34 @@ "supports_tool_choice": true, "supports_vision": false }, + "fireworks_ai/accounts/fireworks/models/glm-5p3-flash": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 1.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 5e-07, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/accounts/fireworks/models/inkling": { + "cache_read_input_token_cost": 1.7e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 4.05e-06, + "source": "https://fireworks.ai/models/fireworks/inkling", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/accounts/fireworks/models/qwen3-embedding-8b": { "input_cost_per_token": 1e-07, "output_cost_per_token": 0.0, @@ -57479,5 +57837,542 @@ "supported_endpoints": [ "/v1/audio/transcriptions" ] + }, + "scaleway/glm-5.2": { + "input_cost_per_token": 1.8e-06, + "litellm_provider": "scaleway", + "max_input_tokens": 256000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 5.5e-06, + "source": "https://www.scaleway.com/en/pricing/model-as-a-service/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_vision": false + }, + "scaleway/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 8e-08, + "input_cost_per_token": 4e-07, + "litellm_provider": "scaleway", + "max_input_tokens": 256000, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 8e-07, + "source": "https://www.scaleway.com/en/pricing/model-as-a-service/", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_vision": false + }, + "azure_ai/kimi-k2.7-code": { + "cache_read_input_token_cost": 1.9e-07, + "deprecation_date": "2026-10-03", + "input_cost_per_token": 9.5e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b": { + "input_cost_per_token": 7.2e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.88e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/nvidia.nemotron-nano-12b-v2": { + "input_cost_per_token": 2.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_system_messages": true, + "supports_vision": true + }, + "bedrock/us-gov-west-1/nvidia.nemotron-super-3-120b": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 256000, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 7.8e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/openai.gpt-oss-20b-1:0": { + "input_cost_per_token": 8.4e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.6e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/openai.gpt-oss-120b-1:0": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/anthropic.claude-sonnet-5": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": false, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock/us-gov-west-1/anthropic.claude-opus-4-8": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + "input_cost_per_token": 6e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": true, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock/us-gov-east-1/nvidia.nemotron-nano-3-30b": { + "input_cost_per_token": 7.2e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.88e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/nvidia.nemotron-nano-12b-v2": { + "input_cost_per_token": 2.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_system_messages": true, + "supports_vision": true + }, + "bedrock/us-gov-east-1/nvidia.nemotron-super-3-120b": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 256000, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 7.8e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/openai.gpt-oss-20b-1:0": { + "input_cost_per_token": 8.4e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.6e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/openai.gpt-oss-120b-1:0": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/anthropic.claude-sonnet-5": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": false, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock/us-gov-east-1/anthropic.claude-opus-4-8": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + "input_cost_per_token": 6e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": true, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-terra": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 2.64e-06, + "input_cost_per_token_above_272k_tokens": 5.28e-06, + "cache_creation_input_token_cost": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-06, + "cache_read_input_token_cost": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-07, + "output_cost_per_token": 1.584e-05, + "output_cost_per_token_above_272k_tokens": 2.376e-05 + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-luna": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 2.64e-07, + "input_cost_per_token_above_272k_tokens": 5.28e-07, + "cache_creation_input_token_cost": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-07, + "cache_read_input_token_cost": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-08, + "output_cost_per_token": 1.584e-06, + "output_cost_per_token_above_272k_tokens": 2.376e-06 + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.4": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05 + }, + "bedrock_mantle/us-gov-west-1/xai.grok-4.3": { + "use_openai_responses_path": true, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 131072, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/", + "input_cost_per_token": 1.5e-06, + "output_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2.4e-07 + }, + "bedrock_mantle/us-gov-east-1/openai.gpt-5.4": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05 + }, + "azure/us-gov/gpt-5.1": { + "cache_read_input_token_cost": 1.71875e-07, + "default_reasoning_effort": "none", + "input_cost_per_token": 1.71875e-06, + "litellm_provider": "azure", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.375e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_none_reasoning_effort": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "azure/us-gov/o3-mini": { + "cache_read_input_token_cost": 7.57e-07, + "input_cost_per_token": 1.513e-06, + "litellm_provider": "azure", + "max_input_tokens": 200000, + "max_output_tokens": 100000, + "max_tokens": 100000, + "mode": "chat", + "output_cost_per_token": 6.05e-06, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "azure/us-gov/text-embedding-3-large": { + "input_cost_per_token": 1.63e-07, + "litellm_provider": "azure", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0 + }, + "azure/us-gov/text-embedding-3-small": { + "input_cost_per_token": 2.5e-08, + "litellm_provider": "azure", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0 + }, + "cloudflare/@cf/openai/whisper": { + "input_cost_per_second": 7.5e-06, + "litellm_provider": "cloudflare", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://developers.cloudflare.com/workers-ai/models/whisper/", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "cloudflare/@cf/openai/whisper-large-v3-turbo": { + "input_cost_per_second": 8.5e-06, + "litellm_provider": "cloudflare", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://developers.cloudflare.com/workers-ai/models/whisper-large-v3-turbo/", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] } } diff --git a/litellm/proxy/common_utils/model_listing_utils.py b/litellm/proxy/common_utils/model_listing_utils.py index 9fd24162f7e..213a697b3dd 100644 --- a/litellm/proxy/common_utils/model_listing_utils.py +++ b/litellm/proxy/common_utils/model_listing_utils.py @@ -10,13 +10,36 @@ legacy internal names with `general_settings.use_team_public_model_name: false`. from __future__ import annotations -from collections.abc import Mapping +from collections.abc import Mapping, Sequence +from types import MappingProxyType from typing import TYPE_CHECKING, Final, cast if TYPE_CHECKING: from litellm.router import Router +def configured_display_names( + entries: Sequence[tuple[str, str]], + llm_router: Router | None, +) -> Mapping[str, str]: + """response_id -> configured `model_info.display_name` for the listing entries + that have one. + + Metadata is looked up by each entry's internal lookup id (so team-scoped rows + resolve), while the returned map is keyed by the public response id the + Anthropic-shaped listing is built from. Entries without a configured name are + omitted so the listing falls back to the id itself. + """ + if llm_router is None: + return MappingProxyType({}) + resolved: Final = ( + (response_id, llm_router.get_configured_display_name(lookup_id)) for response_id, lookup_id in entries + ) + return MappingProxyType( + {response_id: display_name for response_id, display_name in resolved if display_name is not None} + ) + + class TeamModelNameTranslator: """Translates internal team routing keys to their public names for the model listing/retrieve responses. Stateless; the live router and general_settings diff --git a/litellm/proxy/management_helpers/access_group_key_sync.py b/litellm/proxy/management_helpers/access_group_key_sync.py index 5d43cb29978..c9f93fae0d9 100644 --- a/litellm/proxy/management_helpers/access_group_key_sync.py +++ b/litellm/proxy/management_helpers/access_group_key_sync.py @@ -38,6 +38,7 @@ from litellm.proxy._types import ( from litellm.proxy.auth.auth_checks import ( _delete_cache_access_object, # pyright: ignore[reportPrivateUsage] # the access-group endpoints reach for this same cache primitive ) +from litellm.proxy.db.routing_prisma_wrapper import WriterPinnedClient from litellm.repositories.table_repositories import AccessGroupRepository @@ -72,8 +73,9 @@ _REPOINT_KEY_SQL: Final = ( def _raw_executor(prisma_client: object) -> _RawExecutor: - """Narrow the untyped Prisma client down to the raw-query call this module makes.""" - return AccessGroupRepository(prisma_client).prisma_client.db # pyright: ignore[reportAny] # untyped Prisma client + """Narrow the untyped Prisma client down to the raw-query call this module makes, pinned to the writer.""" + db: Final = AccessGroupRepository(prisma_client).prisma_client.db # pyright: ignore[reportAny] # untyped Prisma client + return WriterPinnedClient(db).db # pyright: ignore[reportAny, reportReturnType] # untyped Prisma client behind the pin async def _invalidate_access_group_cache(access_group_id: str) -> None: diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 77a80ea0052..85a57e5af2b 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -39,7 +39,7 @@ from typing import ( import anyio import websockets import websockets.exceptions -from pydantic import BaseModel, Json, JsonValue, ValidationError +from pydantic import BaseModel, Json, JsonValue, TypeAdapter, ValidationError from typing_extensions import NotRequired, ReadOnly, assert_never from litellm._uuid import uuid @@ -60,6 +60,7 @@ from litellm.constants import ( LITELLM_SETTINGS_SAFE_DB_OVERRIDES, LITELLM_UI_ALLOW_HEADERS, LITELLM_UI_SESSION_DURATION, + RUNTIME_UPDATABLE_ROUTER_SETTINGS, ) from litellm.litellm_core_utils.litellm_logging import ( _init_custom_logger_compatible_class, @@ -253,6 +254,7 @@ from litellm.constants import ( PROXY_BUDGET_RESCHEDULER_MAX_TIME, PROXY_BUDGET_RESCHEDULER_MIN_TIME, PROXY_CONFIG_RELOAD_INTERVAL_SECONDS, + ROUTER_SETTINGS_MANAGED_OUTSIDE_CONFIG, USER_SPEND_ALERTS_JOB_ID, WEEKLY_SPEND_REPORT_JOB_ID, ) @@ -352,7 +354,10 @@ from litellm.proxy.common_utils.load_config_utils import ( get_file_contents_from_s3, ) from litellm.proxy.common_utils.model_deprecation import collect_model_deprecations -from litellm.proxy.common_utils.model_listing_utils import TeamModelNameTranslator +from litellm.proxy.common_utils.model_listing_utils import ( + TeamModelNameTranslator, + configured_display_names, +) from litellm.proxy.common_utils.openai_endpoint_utils import ( remove_sensitive_info_from_deployment, ) @@ -5710,13 +5715,9 @@ class ProxyConfig: router_settings: Final = config.get("router_settings", None) if router_settings and isinstance(router_settings, dict): - # model list and search_tools already set - exclude_args: Final = { - "model_list", - "search_tools", - } - - available_args: Final = [x for x in litellm.Router.get_valid_args() if x not in exclude_args] + available_args: Final = [ + x for x in litellm.Router.get_valid_args() if x not in ROUTER_SETTINGS_MANAGED_OUTSIDE_CONFIG + ] for k, v in router_settings.items(): if k in available_args: @@ -10223,7 +10224,8 @@ async def model_list( # The internal routing key drives the metadata/fallback lookup, while the # public name is what the client sees as the model id. model_data = [] - for response_id, lookup_id in TeamModelNameTranslator.listing_entries(all_models, llm_router, settings): + admin_entries: Final = TeamModelNameTranslator.listing_entries(all_models, llm_router, settings) + for response_id, lookup_id in admin_entries: model_info = create_model_info_response( model_id=lookup_id, provider="openai", @@ -10236,7 +10238,10 @@ async def model_list( if wants_anthropic_format: admin_listing: Final = cast(Sequence[ModelInfoResponse], model_data) # cast-ok: rows built above - return create_anthropic_model_list_response(admin_listing) + return create_anthropic_model_list_response( + admin_listing, + display_names=configured_display_names(admin_entries, llm_router), + ) return dict( data=model_data, @@ -10267,7 +10272,8 @@ async def model_list( # The internal routing key drives the metadata/fallback lookup, while the # public name is what the client sees as the model id. model_data = [] - for response_id, lookup_id in TeamModelNameTranslator.listing_entries(all_models, llm_router, settings): + entries: Final = TeamModelNameTranslator.listing_entries(all_models, llm_router, settings) + for response_id, lookup_id in entries: model_info = create_model_info_response( model_id=lookup_id, provider="openai", @@ -10280,7 +10286,10 @@ async def model_list( if wants_anthropic_format: listing: Final = cast(Sequence[ModelInfoResponse], model_data) # cast-ok: rows built above - return create_anthropic_model_list_response(listing) + return create_anthropic_model_list_response( + listing, + display_names=configured_display_names(entries, llm_router), + ) return dict( data=model_data, @@ -16207,6 +16216,7 @@ async def invitation_delete( ) async def update_config( config_info: ConfigYAML, + request: Request, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), ): """ @@ -16222,6 +16232,26 @@ async def update_config( if user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN: raise HTTPException(status_code=403, detail="Only proxy admins can update config") + request_body: Final[Mapping[str, JsonValue]] = TypeAdapter(Mapping[str, JsonValue]).validate_python( + await request.json() + ) + raw_router_settings: Final = request_body.get("router_settings") + if isinstance(raw_router_settings, dict): + supported_router_settings: Final = RUNTIME_UPDATABLE_ROUTER_SETTINGS | ( + frozenset(litellm.Router.get_valid_args()) - ROUTER_SETTINGS_MANAGED_OUTSIDE_CONFIG + ) + unsupported_router_settings: Final = sorted(set(raw_router_settings) - supported_router_settings) + if unsupported_router_settings: + raise HTTPException( + status_code=400, + detail={ + "error": ( + f"Unsupported router settings: {', '.join(unsupported_router_settings)} " + "are not valid router settings" + ) + }, + ) + if prisma_client is None: raise Exception("No DB Connected") @@ -16323,11 +16353,19 @@ async def update_config( ) # router_settings: merge existing + request, request wins. - if config_info.router_settings is not None: + if isinstance(raw_router_settings, dict): existing = await _read_section("router_settings") before_router_settings: Final = copy.deepcopy(existing) - updates = config_info.router_settings.dict(exclude_none=True) - new_router_settings: Final = {**existing, **updates} + typed_router_settings: Final = ( + config_info.router_settings.dict(exclude_none=True) if config_info.router_settings is not None else {} + ) + raw_router_settings_without_none: Final = { + key: value + for key, value in raw_router_settings.items() + if key not in typed_router_settings and value is not None + } + router_settings_updates: Final = {**typed_router_settings, **raw_router_settings_without_none} + new_router_settings: Final = {**existing, **router_settings_updates} await _upsert_section("router_settings", new_router_settings) asyncio.create_task( create_config_audit_log( diff --git a/litellm/proxy/rag_endpoints/endpoints.py b/litellm/proxy/rag_endpoints/endpoints.py index db574f859b3..0ab7d99e4e4 100644 --- a/litellm/proxy/rag_endpoints/endpoints.py +++ b/litellm/proxy/rag_endpoints/endpoints.py @@ -9,6 +9,7 @@ Provides: import base64 import json from collections.abc import Mapping +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final import orjson @@ -19,6 +20,9 @@ from starlette.datastructures import UploadFile import litellm from litellm._logging import verbose_proxy_logger from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH +from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import ( + LiteLLM_ManagedVectorStore, +) from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.proxy._types import * from litellm.proxy.auth.auth_utils import is_request_body_safe @@ -36,6 +40,10 @@ from litellm.proxy.rag_endpoints.upload_security import ( RejectedUpload, validate_upload, ) +from litellm.proxy.vector_store_endpoints.endpoints import ( + build_request_data_from_managed_vector_store, + reject_caller_embedding_selection_params, +) from litellm.proxy.vector_store_endpoints.utils import ( assert_user_can_access_vector_store_id, ) @@ -120,12 +128,21 @@ def _collect_vector_store_ids_from_payload(payload: object) -> set[str]: async def _authorize_nested_vector_store_ids( payload: object, user_api_key_dict: UserAPIKeyAuth, -) -> None: - for vector_store_id in sorted(_collect_vector_store_ids_from_payload(payload)): - await assert_user_can_access_vector_store_id( - vector_store_id=vector_store_id, - user_api_key_dict=user_api_key_dict, - ) +) -> Mapping[str, LiteLLM_ManagedVectorStore]: + """Authorize every nested vector store id and return the managed stores it resolved.""" + return MappingProxyType( + { + vector_store_id: store + for vector_store_id in sorted(_collect_vector_store_ids_from_payload(payload)) + if ( + store := await assert_user_can_access_vector_store_id( + vector_store_id=vector_store_id, + user_api_key_dict=user_api_key_dict, + ) + ) + is not None + } + ) def _build_file_metadata_entry( @@ -700,11 +717,27 @@ async def rag_query( status_code=400, detail={"error": "retrieval_config must contain 'vector_store_id'"}, ) - await _authorize_nested_vector_store_ids( + reject_caller_embedding_selection_params(payload=retrieval_config, source="retrieval_config") + resolved_stores: Final = await _authorize_nested_vector_store_ids( payload=retrieval_config, user_api_key_dict=user_api_key_dict, ) + # Merge litellm-managed vector store params (provider, region, embedding + # model, credentials, ...) from the registry: the same source the direct + # /vector_stores/{id}/search endpoint uses. Store-managed keys win on + # conflict so callers cannot override the store's provider or credentials. + managed_store: Final = resolved_stores.get(retrieval_config["vector_store_id"]) + store_data: Final = ( + await build_request_data_from_managed_vector_store(managed_store) + if managed_store is not None + else MappingProxyType({}) + ) + merged_retrieval_config: Final = { + **retrieval_config, + **store_data, + } # mutable-ok: litellm.aquery requires a plain dict payload + # Add litellm data request_data: dict[str, object] = {} request_data = await add_litellm_data_to_request( @@ -716,13 +749,18 @@ async def rag_query( proxy_config=proxy_config, ) - verbose_proxy_logger.debug("RAG Query - model: %s, retrieval_config: %s", model, retrieval_config) + verbose_proxy_logger.debug( + "RAG Query - model: %s, vector_store_id: %s, custom_llm_provider: %s", + model, + retrieval_config["vector_store_id"], + merged_retrieval_config.get("custom_llm_provider"), + ) # Call query response: Final = await litellm.aquery( model=model, messages=messages, - retrieval_config=retrieval_config, + retrieval_config=merged_retrieval_config, rerank=rerank, stream=stream, router=llm_router, diff --git a/litellm/proxy/vector_store_endpoints/endpoints.py b/litellm/proxy/vector_store_endpoints/endpoints.py index a59d7a277cc..7d64e648e08 100644 --- a/litellm/proxy/vector_store_endpoints/endpoints.py +++ b/litellm/proxy/vector_store_endpoints/endpoints.py @@ -1,3 +1,5 @@ +from collections.abc import Mapping +from types import MappingProxyType from typing import ( Annotated, Any, # noqa: TID251 # jsonify_object in proxy/utils.py is annotated with a bare dict @@ -27,11 +29,69 @@ from litellm.types.vector_stores import IndexCreateRequest, IndexListResponse from litellm.vector_stores.vector_store_registry import VectorStoreIndexRegistry router: Final = APIRouter() + +BLOCKED_QUERY_EMBEDDING_SELECTION_PARAMS: Final = frozenset( + { + "embedding_model", + "litellm_embedding_model", + "litellm_embedding_config", + "litellm_credential_name", + } +) + + +def reject_caller_embedding_selection_params(payload: Mapping[str, object], source: str) -> None: + blocked: Final = sorted(BLOCKED_QUERY_EMBEDDING_SELECTION_PARAMS & payload.keys()) + if blocked: + raise HTTPException( + status_code=400, + detail={ + "error": f"'{blocked[0]}' cannot be set in {source}. " + "Embedding configuration comes from the vector store's server-side registration." + }, + ) + + ######################################################## # OpenAI Compatible Endpoints ######################################################## +async def build_request_data_from_managed_vector_store( + vector_store: LiteLLM_ManagedVectorStore, +) -> Mapping[str, object]: + """ + Build request params (provider, credential ref, litellm_params) from an + already-resolved managed vector store. + + ``litellm_embedding_config`` is resolved here, at request-handling time, + instead of at row-creation time: the resolved api_key/api_base/api_version + lives only in the returned per-request mapping and is never persisted back + to the registry cache. Legacy rows that already carry a resolved + (cleartext) config skip the lookup and pass through unchanged. + """ + top_level: Final = MappingProxyType( + { + key: vector_store.get(key) + for key in ("custom_llm_provider", "litellm_credential_name") + if key in vector_store + } + ) + litellm_params: Final = vector_store.get("litellm_params") or MappingProxyType({}) + embedding_model: Final = litellm_params.get("litellm_embedding_model") + if not embedding_model or litellm_params.get("litellm_embedding_config"): + return MappingProxyType({**top_level, **litellm_params}) + + from litellm.proxy.proxy_server import prisma_client + + resolved_config: Final = await _resolve_embedding_config( + embedding_model=embedding_model, prisma_client=prisma_client + ) + if not resolved_config: + return MappingProxyType({**top_level, **litellm_params}) + return MappingProxyType({**top_level, **litellm_params, "litellm_embedding_config": resolved_config}) + + async def _update_request_data_with_litellm_managed_vector_store_registry( data: dict, vector_store_id: str, @@ -51,47 +111,14 @@ async def _update_request_data_with_litellm_managed_vector_store_registry( vector_store_to_run: Final[LiteLLM_ManagedVectorStore | None] = await get_litellm_managed_vector_store( vector_store_id=vector_store_id ) - if vector_store_to_run is not None: - if user_api_key_dict is not None: - await assert_user_can_access_vector_store( - vector_store=vector_store_to_run, - user_api_key_dict=user_api_key_dict, - ) - - if "custom_llm_provider" in vector_store_to_run: - data["custom_llm_provider"] = vector_store_to_run.get("custom_llm_provider") - - if "litellm_credential_name" in vector_store_to_run: - data["litellm_credential_name"] = vector_store_to_run.get("litellm_credential_name") - - if "litellm_params" in vector_store_to_run: - litellm_params = vector_store_to_run.get("litellm_params", {}) or {} - # Resolve ``litellm_embedding_config`` here, at request-handling - # time, instead of at row-creation time. The resolved - # ``api_key`` / ``api_base`` / ``api_version`` lives only in - # this per-request ``data`` dict and is never persisted. - # Legacy rows that already carry a resolved (cleartext) - # ``litellm_embedding_config`` skip the lookup and pass through - # unchanged so the embed call keeps working. - embedding_model: Final = litellm_params.get("litellm_embedding_model") - if embedding_model and not litellm_params.get("litellm_embedding_config"): - from litellm.proxy.proxy_server import prisma_client - - resolved_config: Final = await _resolve_embedding_config( - embedding_model=embedding_model, prisma_client=prisma_client - ) - if resolved_config: - # Build a fresh dict via spread instead of mutating - # ``litellm_params`` in place — the registry hands back - # a reference to its cached object, so an in-place - # update would persist the resolved cleartext into the - # in-memory cache for the lifetime of the process. - litellm_params = { - **litellm_params, - "litellm_embedding_config": resolved_config, - } - data.update(litellm_params) - return data + if vector_store_to_run is None: + return data + if user_api_key_dict is not None: + await assert_user_can_access_vector_store( + vector_store=vector_store_to_run, + user_api_key_dict=user_api_key_dict, + ) + return {**data, **(await build_request_data_from_managed_vector_store(vector_store_to_run))} @router.post( @@ -130,6 +157,7 @@ async def vector_store_search( ) data = await _read_request_body(request=request) + reject_caller_embedding_selection_params(payload=data, source="the search request body") data["vector_store_id"] = vector_store_id # Check for legacy vector store registry (non-managed vector stores) diff --git a/litellm/proxy/vector_store_endpoints/management_endpoints.py b/litellm/proxy/vector_store_endpoints/management_endpoints.py index 183a03cc13c..244798ba05e 100644 --- a/litellm/proxy/vector_store_endpoints/management_endpoints.py +++ b/litellm/proxy/vector_store_endpoints/management_endpoints.py @@ -470,7 +470,7 @@ async def create_vector_store_in_db( # exposed every env-stored embedding-model credential on the # ``/vector_store/{new,info,update,list}`` responses. Keep the user's # raw ``litellm_embedding_model`` reference; resolution now happens in - # ``_update_request_data_with_litellm_managed_vector_store_registry`` + # ``build_request_data_from_managed_vector_store`` # at request-handling time so the cleartext config exists only in # per-request memory and never reaches the database. if litellm_params: @@ -864,7 +864,7 @@ async def update_vector_store( # embedding-config auto-resolve previously persisted cleartext # credentials into the row; resolution now happens at request- # handling time in - # ``_update_request_data_with_litellm_managed_vector_store_registry`` + # ``build_request_data_from_managed_vector_store`` # so this row only ever stores the user-supplied # ``litellm_embedding_model`` reference. if "litellm_params" in update_data: diff --git a/litellm/rag/main.py b/litellm/rag/main.py index 7bc1a6a52a3..94bfc305a6a 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -14,6 +14,7 @@ import contextvars from collections.abc import Coroutine, Iterator from contextlib import contextmanager from functools import partial +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final import httpx @@ -50,6 +51,21 @@ INGESTION_REGISTRY: Final[dict[str, type[BaseRAGIngestion]]] = { "vertex_ai": VertexAIRAGIngestion, } +# Only these retrieval_config keys are forwarded to vector_stores.asearch as +# provider-specific params. The explicit allowlist keeps caller-controlled +# connection overrides (api_base, api_key, ...) away from the search call, +# where they could redirect store credentials to an attacker-chosen host. +_FORWARDABLE_RETRIEVAL_CONFIG_KEYS: Final = frozenset( + { + "aws_region_name", + "vector_bucket_name", + "embedding_model", + "litellm_embedding_model", + "litellm_embedding_config", + "litellm_credential_name", + } +) + def get_ingestion_class(provider: str) -> type[BaseRAGIngestion]: """ @@ -224,13 +240,20 @@ async def _execute_query_pipeline( raise ValueError("No query found in messages for RAG query") # 2. Search vector store + # Forward allowlisted provider retrieval_config extras (region, embedding + # model, bucket, credential refs) to the search call; kwargs win on conflict. + provider_search_params: Final = MappingProxyType( + {k: v for k, v in retrieval_config.items() if k in _FORWARDABLE_RETRIEVAL_CONFIG_KEYS} + ) + forwarded_search_params: Final = MappingProxyType({**provider_search_params, **kwargs}) with _suppressed_sub_call_billing(): search_response: Final = await litellm.vector_stores.asearch( vector_store_id=retrieval_config["vector_store_id"], query=query_text, max_num_results=retrieval_config.get("top_k", 10), custom_llm_provider=retrieval_config.get("custom_llm_provider", "openai"), - **kwargs, + router=router, + **forwarded_search_params, ) search_provider: Final = retrieval_config.get("custom_llm_provider", "openai") diff --git a/litellm/rerank_api/main.py b/litellm/rerank_api/main.py index c8f7842aebf..37ca989b8d3 100644 --- a/litellm/rerank_api/main.py +++ b/litellm/rerank_api/main.py @@ -6,6 +6,7 @@ from typing import Any, Final, Literal import litellm from litellm._logging import verbose_logger +from litellm.litellm_core_utils.get_llm_provider_logic import declared_authenticating_provider from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig from litellm.llms.bedrock.rerank.handler import BedrockRerankHandler @@ -43,10 +44,23 @@ async def arerank( """ Async: Reranks a list of documents based on their relevance to the query """ + _custom_llm_provider: str | None = ( + None # rebind-ok: set by the declared-provider guard or the get_llm_provider unpack; read in the except + ) try: loop: Final = asyncio.get_event_loop() kwargs["arerank"] = True + declared_provider: Final = declared_authenticating_provider(model, custom_llm_provider) + if declared_provider is not None: + _custom_llm_provider = declared_provider # rebind-ok: see pre-declaration above + else: + _, _custom_llm_provider, _, _ = litellm.get_llm_provider( # rebind-ok: see pre-declaration above + model=model, + custom_llm_provider=custom_llm_provider, + api_base=kwargs.get("api_base", None), + ) + func: Final = partial( rerank, model, @@ -70,7 +84,11 @@ async def arerank( response = init_response return response except Exception as e: - raise e + raise exception_type( + model=model, + custom_llm_provider=_custom_llm_provider or custom_llm_provider, + original_exception=e, + ) @client @@ -115,6 +133,7 @@ def rerank( model_info: Final = kwargs.get("model_info", None) user: Final = kwargs.get("user", None) client: Final = kwargs.get("client", None) + _custom_llm_provider: str | None = None # rebind-ok: set by the get_llm_provider unpack; read in the except try: _is_async: Final = kwargs.pop("arerank", False) is True optional_params: Final = GenericLiteLLMParams(**kwargs) @@ -127,7 +146,7 @@ def rerank( ( model, - _custom_llm_provider, + _custom_llm_provider, # rebind-ok: see pre-declaration above dynamic_api_key, dynamic_api_base, ) = litellm.get_llm_provider( @@ -538,4 +557,8 @@ def rerank( return response except Exception as e: verbose_logger.error("Error in rerank: %s", e) - raise exception_type(model=model, custom_llm_provider=custom_llm_provider, original_exception=e) + raise exception_type( + model=model, + custom_llm_provider=_custom_llm_provider or custom_llm_provider, + original_exception=e, + ) diff --git a/litellm/responses/litellm_completion_transformation/streaming_iterator.py b/litellm/responses/litellm_completion_transformation/streaming_iterator.py index db1c3acbefb..bc25f4fffb1 100644 --- a/litellm/responses/litellm_completion_transformation/streaming_iterator.py +++ b/litellm/responses/litellm_completion_transformation/streaming_iterator.py @@ -1169,16 +1169,6 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): def _emit_response_completed_event(self, litellm_model_response: ModelResponse) -> ResponseCompletedEvent | None: if litellm_model_response: - # Add cost to usage object if include_cost_in_streaming_usage is True - if litellm.include_cost_in_streaming_usage and self.litellm_logging_obj is not None: - usage: Final[object] = getattr(litellm_model_response, "usage", None) - if usage is not None: - setattr( - usage, - "cost", - self.litellm_logging_obj._response_cost_calculator(result=litellm_model_response), - ) - # Transform the response responses_api_response: Final = ( LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index 82abac3e772..7871c85220c 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -407,23 +407,7 @@ class BaseResponsesAPIStreamingIterator: openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED, ): self.completed_response = openai_responses_api_chunk - # Add cost to usage object if include_cost_in_streaming_usage is True - if litellm.include_cost_in_streaming_usage and self.logging_obj is not None: - response_obj: Final[ResponsesAPIResponse | None] = getattr( - openai_responses_api_chunk, "response", None - ) - if response_obj: - usage_obj: Final[ResponseAPIUsage | None] = getattr(response_obj, "usage", None) - if usage_obj is not None: - try: - cost: Final[float | None] = self.logging_obj._response_cost_calculator( - result=response_obj - ) - if cost is not None: - setattr(usage_obj, "cost", cost) - except Exception: - # Best-effort usage cost annotation should not break stream replay. - pass + _stamp_responses_usage_cost(getattr(openai_responses_api_chunk, "response", None), self.logging_obj) if _chunk_type == openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED: self._handle_logging_failed_response() @@ -1274,6 +1258,24 @@ def _add_text_like_part_events( ) +def _stamp_responses_usage_cost( + response_obj: ResponsesAPIResponse | None, logging_obj: LiteLLMLoggingObj | None +) -> None: + if response_obj is None or logging_obj is None: + return + usage_obj: Final[ResponseAPIUsage | None] = getattr(response_obj, "usage", None) + if usage_obj is None: + return + if isinstance(getattr(usage_obj, "cost", None), (int, float)): + return + try: + cost: Final[float | None] = logging_obj._response_cost_calculator(result=response_obj) + except Exception: + return + if isinstance(cost, (int, float)) and cost > 0: + setattr(usage_obj, "cost", cost) + + def build_synthetic_response_events( *, transformed: ResponsesAPIResponse, @@ -1281,15 +1283,7 @@ def build_synthetic_response_events( chunk_size: int, ) -> list[ResponsesAPIStreamingResponse]: openai_types: Final = _get_openai_response_types() - if litellm.include_cost_in_streaming_usage and logging_obj is not None: - usage_obj: Final = transformed.usage if hasattr(transformed, "usage") else None - if usage_obj is not None: - try: - cost: Final[float | None] = logging_obj._response_cost_calculator(result=transformed) - if cost is not None: - setattr(usage_obj, "cost", cost) - except Exception: - pass + _stamp_responses_usage_cost(transformed, logging_obj) events: Final[list[ResponsesAPIStreamingResponse]] = [ _build_response_status_event(openai_types.ResponsesAPIStreamEvents.RESPONSE_CREATED, transformed), diff --git a/litellm/router.py b/litellm/router.py index 48fd8ea517d..3c52540f616 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -50,6 +50,7 @@ from litellm.constants import ( DEFAULT_HEALTH_CHECK_INTERVAL, DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER, DEFAULT_MAX_LRU_CACHE_SIZE, + RUNTIME_UPDATABLE_ROUTER_SETTINGS, SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, ) from litellm.integrations.custom_logger import CustomLogger @@ -354,6 +355,13 @@ _PreRoutingStrategyT = TypeVar("_PreRoutingStrategyT") _ALIAS_PARAMS_NEVER_FORWARDED: Final = frozenset({"model", "api_base", "api_key", "api_version"}) _ALIAS_MARKER_FORWARDED_PARAMS_KWARG: Final = "_alias_marker_forwarded_params" +_RUNTIME_TOGGLEABLE_PRE_CALL_CHECKS: Final[Mapping[str, type[CustomLogger]]] = MappingProxyType( + { + "prompt_caching": PromptCachingDeploymentCheck, + "enforce_model_rate_limits": ModelRateLimitingCheck, + } +) + def _stream_chunks_have_generated_content(chunks: Sequence[ModelResponseStream]) -> bool: for chunk in chunks: @@ -392,7 +400,9 @@ def _with_router_resolved_session_model(session: object, model_name: str) -> Map return _NO_SESSION_KWARGS if "model" not in typed_session: return _NO_SESSION_KWARGS - return MappingProxyType({"session": {**typed_session, "model": model_name}}) + return MappingProxyType( + {"session": {**typed_session, "model": model_name}} # mutable-ok: callees deepcopy and JSON-dump session + ) # Router._aanthropic_messages_streaming_iterator buffers lifecycle chunks @@ -2092,11 +2102,39 @@ class Router: if _callback is None: continue + if self.optional_callbacks is not None and any( + isinstance(callback, type(_callback)) for callback in self.optional_callbacks + ): + continue if self.optional_callbacks is None: self.optional_callbacks = [] self.optional_callbacks.append(_callback) litellm.logging_callback_manager.add_litellm_callback(_callback) + def set_optional_pre_call_checks(self, optional_pre_call_checks: OptionalPreCallChecks | None) -> None: + if optional_pre_call_checks is None: + return + requested: Final = frozenset(optional_pre_call_checks) + for name, callback_cls in _RUNTIME_TOGGLEABLE_PRE_CALL_CHECKS.items(): + if name not in requested: + self._remove_optional_callbacks_of_type(callback_cls) + self.add_optional_pre_call_checks(optional_pre_call_checks) + + def _remove_optional_callbacks_of_type(self, callback_cls: type[CustomLogger]) -> None: + if self.optional_callbacks is None or not any(type(cb) is callback_cls for cb in self.optional_callbacks): + return + self.optional_callbacks = [cb for cb in self.optional_callbacks if type(cb) is not callback_cls] + if any( + router is not self and any(type(cb) is callback_cls for cb in (router.optional_callbacks or [])) + for router in tuple(_live_routers) + ): + return + for cb in tuple(litellm.callbacks): + if type(cb) is callback_cls: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, cb, require_self=False + ) + def print_deployment(self, deployment: dict): """ returns a copy of the deployment with the api key masked @@ -2344,7 +2382,7 @@ class Router: @overload async def acompletion( self, model: str, messages: list[AllMessageValues], stream: Literal[True, False] = False, **kwargs - ) -> CustomStreamWrapper | ModelResponse: + ) -> CustomStreamWrapper | ModelResponse: ... # fmt: on @@ -6395,8 +6433,6 @@ class Router: "responses", "generate_content", "generate_content_stream", - "vector_store_search", - "vector_store_create", "ocr", "search", "video_generation", @@ -6420,6 +6456,8 @@ class Router: return sync_wrapper if call_type in ( + "vector_store_search", + "vector_store_create", "vector_store_retrieve", "vector_store_list", "vector_store_update", @@ -6431,11 +6469,16 @@ class Router: client: object | None = None, **kwargs, ): - if custom_llm_provider and "custom_llm_provider" not in kwargs: - kwargs["custom_llm_provider"] = custom_llm_provider - if kwargs.get("model"): - return self._generic_api_call_with_fallbacks(original_function=original_function, **kwargs) - return original_function(**kwargs) + provider_kwargs: Final = ( + MappingProxyType({**kwargs, "custom_llm_provider": custom_llm_provider}) + if custom_llm_provider and "custom_llm_provider" not in kwargs + else MappingProxyType(kwargs) + ) + if provider_kwargs.get("model"): + return self._generic_api_call_with_fallbacks(original_function=original_function, **provider_kwargs) + if call_type == "vector_store_search": + return original_function(**MappingProxyType({**provider_kwargs, "router": self})) + return original_function(**provider_kwargs) return vector_store_sync_wrapper @@ -6611,6 +6654,7 @@ class Router: return await self._init_vector_store_api_endpoints( original_function=original_function, custom_llm_provider=custom_llm_provider, + call_type=call_type, **kwargs, ) elif call_type in ("afile_delete", "afile_content"): @@ -6651,6 +6695,7 @@ class Router: self, original_function: Callable, custom_llm_provider: str | None = None, + call_type: str | None = None, **kwargs, ): """ @@ -6669,6 +6714,13 @@ class Router: **kwargs, ) + # For search, pass the router so provider transforms can resolve + # router-managed embedding models (e.g. S3 Vectors query embeddings). + # The merge also overrides any client-supplied `router` key. + if call_type == "avector_store_search": + search_kwargs: Final = MappingProxyType({**kwargs, "router": self}) + return await original_function(**search_kwargs) + # Otherwise, call the original function directly return await original_function(**kwargs) @@ -9767,6 +9819,26 @@ class Router: coerce_token_limit(model_info.get("max_output_tokens")), ) + def get_configured_display_name(self, model_name: str) -> "str | None": + """ + Return the display_name explicitly configured in a concrete deployment's + model_info for model_name, via O(1) index lookup. + + Returns None for wildcard-expanded or unknown names, and treats a + non-string or empty configured value as absent rather than failing the + listing. Like get_configured_token_limits, this never triggers pattern + matching or deep copies, so it is safe to call per listed model on the + /v1/models hot path. + """ + deployment: Final = self.get_deployment_by_model_group_name(model_group_name=model_name) + if deployment is None: + return None + + display_name: Final = deployment.model_info.get("display_name") + if isinstance(display_name, str) and display_name.strip(): + return display_name + return None + def get_deployment_credentials_with_provider( self, model_id: str, team_id: str | None = None ) -> dict[str, Any] | None: @@ -11352,27 +11424,6 @@ class Router: """ Update the router settings. """ - # only the following settings are allowed to be configured - _allowed_settings: Final = [ - "routing_strategy_args", - "routing_strategy", - "routing_groups", - "allowed_fails", - "cooldown_time", - "num_retries", - "timeout", - "max_retries", - "retry_after", - "fallbacks", - "context_window_fallbacks", - "retry_policy", - "model_group_retry_policy", - "model_group_alias", - "enable_weighted_failover", - "enable_tag_filtering", - "tag_routing_prefix", - ] - _int_settings: Final = [ "timeout", "num_retries", @@ -11385,13 +11436,15 @@ class Router: rebuild_routing_groups = False relink_lar1_from_args = False for var in kwargs: - if var in _allowed_settings: + if var in RUNTIME_UPDATABLE_ROUTER_SETTINGS: if var in _int_settings: _casted_value = int(kwargs[var]) setattr(self, var, _casted_value) elif var == "routing_groups": self._routing_groups_input = kwargs[var] rebuild_routing_groups = True + elif var == "optional_pre_call_checks": + self.set_optional_pre_call_checks(kwargs[var]) elif var == "retry_policy": value = kwargs[var] if isinstance(value, dict): diff --git a/litellm/types/router.py b/litellm/types/router.py index e0957383aac..2a5f264cee3 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -106,6 +106,20 @@ class RetryPolicy(BaseModel): InternalServerErrorRetries: int | None = None +OptionalPreCallChecks = list[ + Literal[ + "prompt_caching", + "router_budget_limiting", + "responses_api_deployment_check", + "deployment_affinity", + "session_affinity", + "forward_client_headers_by_model_group", + "enforce_model_rate_limits", + "encrypted_content_affinity", + ] +] + + class UpdateRouterConfig(BaseModel): """ Set of params that you can modify via `router.update_settings()`. @@ -128,6 +142,7 @@ class UpdateRouterConfig(BaseModel): model_group_alias: dict[str, str | dict] | None = {} enable_tag_filtering: bool | None = None tag_routing_prefix: str | None = None + optional_pre_call_checks: OptionalPreCallChecks | None = None model_config = ConfigDict(protected_namespaces=()) @@ -869,20 +884,6 @@ class FallbackAccessCheck(Protocol): async def __call__(self, *, model: str, request_kwargs: Mapping[str, object], llm_router: "Router") -> bool: ... -OptionalPreCallChecks = list[ - Literal[ - "prompt_caching", - "router_budget_limiting", - "responses_api_deployment_check", - "deployment_affinity", - "session_affinity", - "forward_client_headers_by_model_group", - "enforce_model_rate_limits", - "encrypted_content_affinity", - ] -] - - class LiteLLM_RouterFileObject(TypedDict, total=False): """ Tracking the litellm params hash, used for mapping the file id to the right model diff --git a/litellm/vector_stores/main.py b/litellm/vector_stores/main.py index 9b0ff71730a..cd576755f5f 100644 --- a/litellm/vector_stores/main.py +++ b/litellm/vector_stores/main.py @@ -7,7 +7,7 @@ import builtins import contextvars from collections.abc import Coroutine, Mapping from functools import partial -from typing import Final +from typing import TYPE_CHECKING, Final import httpx @@ -29,6 +29,9 @@ from litellm.types.vector_stores import ( from litellm.utils import ProviderConfigManager, client from litellm.vector_stores.utils import VectorStoreRequestUtils +if TYPE_CHECKING: + from litellm.router import Router + ####### ENVIRONMENT VARIABLES ################### # Initialize any necessary instances or variables here base_llm_http_handler = BaseLLMHTTPHandler() @@ -280,6 +283,7 @@ async def asearch( timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, + router: "Router | None" = None, **kwargs, ) -> VectorStoreSearchResponse: """ @@ -308,6 +312,7 @@ async def asearch( extra_body=extra_body, timeout=timeout, custom_llm_provider=custom_llm_provider, + router=router, **kwargs, ) @@ -347,6 +352,7 @@ def search( timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, + router: "Router | None" = None, **kwargs, ) -> VectorStoreSearchResponse | Coroutine[object, object, VectorStoreSearchResponse]: """ @@ -450,6 +456,7 @@ def search( timeout=timeout or request_timeout, _is_async=_is_async, client=kwargs.get("client"), + router=router, ) return response diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index a3cfb300ea6..2846d12db6e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -9643,7 +9643,8 @@ "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" - ] + ], + "deprecation_date": "2026-10-01" }, "azure_ai/MAI-Image-2.5-Flash": { "input_cost_per_image_token": 1.75e-06, @@ -9656,7 +9657,8 @@ "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" - ] + ], + "deprecation_date": "2026-10-01" }, "azure_ai/MAI-Image-2e": { "deprecation_date": "2026-08-15", @@ -10155,7 +10157,9 @@ "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "cache_read_input_token_cost": 1.45e-07, + "supports_prompt_caching": true }, "azure_ai/deepseek-v4-flash": { "deprecation_date": "2028-02-20", @@ -10169,18 +10173,20 @@ "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_reasoning": true, - "supports_tool_choice": true - }, - "azure_ai/deepseek-v4-flash-0731": { + "supports_tool_choice": true, "cache_read_input_token_cost": 2.8e-08, + "supports_prompt_caching": true + }, + "azure_ai/DeepSeek-V4-Flash-0731": { + "cache_read_input_token_cost": 1.4e-08, "deprecation_date": "2026-12-03", - "input_cost_per_token": 1.9e-07, + "input_cost_per_token": 4.4e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 5.1e-07, + "output_cost_per_token": 1.32e-06, "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", "supports_function_calling": true, "supports_prompt_caching": true, @@ -10400,11 +10406,13 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/kimi-k2-5-now-in-microsoft-foundry/4492321", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", "supports_function_calling": true, "supports_tool_choice": true, "supports_video_input": true, - "supports_vision": true + "supports_vision": true, + "cache_read_input_token_cost": 1e-07, + "supports_prompt_caching": true }, "azure_ai/kimi-k2.6": { "deprecation_date": "2027-04-16", @@ -10415,7 +10423,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k2-6-in-microsoft-foundry/4513125", + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", "supported_modalities": [ "text", "image" @@ -10426,7 +10434,9 @@ "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "cache_read_input_token_cost": 1.6e-07, + "supports_prompt_caching": true }, "azure_ai/ministral-3b": { "input_cost_per_token": 4e-08, @@ -12110,7 +12120,7 @@ "max_output_tokens": 2048, "max_tokens": 2048, "mode": "chat", - "output_cost_per_token": 2.65e-06, + "output_cost_per_token": 6e-07, "supports_pdf_input": true }, "bedrock/us-west-1/meta.llama3-70b-instruct-v1:0": { @@ -23514,6 +23524,63 @@ "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014 }, + "vertex_ai/gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "vertex_ai", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "regional_endpoint_uplift_multiplier": 1.1, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, "vertex_ai/gemini-3.1-pro-preview": { "prompt_cache_min_tokens": 4096, "cache_read_input_token_cost": 2e-07, @@ -25351,6 +25418,65 @@ "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014 }, + "gemini/gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "gemini", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "rpm": 2000, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "tpm": 800000, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, "gemini/gemini-omni-flash-preview": { "input_cost_per_audio_token": 1.5e-06, "input_cost_per_token": 1.5e-06, @@ -25759,6 +25885,63 @@ "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014 }, + "gemini-3.8-flash": { + "prompt_cache_min_tokens": 4096, + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_reasoning_token": 3.75e-06, + "output_cost_per_token": 3.75e-06, + "output_cost_per_token_batches": 1.875e-06, + "output_cost_per_token_flex": 1.875e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 1.35e-06, + "output_cost_per_token_priority": 6.75e-06, + "cache_read_input_token_cost_priority": 1.35e-07, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "google_maps_grounding_cost_per_query": 0.014 + }, "gemini/gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, "input_cost_per_audio_token": 7e-07, @@ -29098,16 +29281,19 @@ "cache_creation_input_token_cost": 5e-06, "cache_creation_input_token_cost_above_272k_tokens": 1e-05, "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, "cache_creation_input_token_cost_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 1e-05, "cache_read_input_token_cost": 4e-07, "cache_read_input_token_cost_above_272k_tokens": 8e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "cache_read_input_token_cost_priority": 8e-07, "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, "input_cost_per_token_batches": 2e-06, "input_cost_per_token_flex": 2e-06, "input_cost_per_token_priority": 8e-06, @@ -29119,6 +29305,7 @@ "output_cost_per_token": 2e-05, "output_cost_per_token_above_272k_tokens": 3e-05, "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, "output_cost_per_token_batches": 1e-05, "output_cost_per_token_flex": 1e-05, "output_cost_per_token_priority": 4e-05, @@ -29161,16 +29348,19 @@ "cache_creation_input_token_cost": 5e-06, "cache_creation_input_token_cost_above_272k_tokens": 1e-05, "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, "cache_creation_input_token_cost_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 1e-05, "cache_read_input_token_cost": 4e-07, "cache_read_input_token_cost_above_272k_tokens": 8e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, "cache_read_input_token_cost_flex": 2e-07, "cache_read_input_token_cost_priority": 8e-07, "input_cost_per_token": 4e-06, "input_cost_per_token_above_272k_tokens": 8e-06, "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, "input_cost_per_token_batches": 2e-06, "input_cost_per_token_flex": 2e-06, "input_cost_per_token_priority": 8e-06, @@ -29182,6 +29372,7 @@ "output_cost_per_token": 2e-05, "output_cost_per_token_above_272k_tokens": 3e-05, "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, "output_cost_per_token_batches": 1e-05, "output_cost_per_token_flex": 1e-05, "output_cost_per_token_priority": 4e-05, @@ -29225,16 +29416,19 @@ "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_272k_tokens": 5e-06, "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05, "cache_creation_input_token_cost_flex": 1.25e-06, "cache_creation_input_token_cost_priority": 5e-06, "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_272k_tokens": 4e-07, "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, "cache_read_input_token_cost_flex": 1e-07, "cache_read_input_token_cost_priority": 4e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, "input_cost_per_token_above_272k_tokens_flex": 2e-06, + "input_cost_per_token_above_272k_tokens_priority": 8e-06, "input_cost_per_token_batches": 1e-06, "input_cost_per_token_flex": 1e-06, "input_cost_per_token_priority": 4e-06, @@ -29246,6 +29440,7 @@ "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_272k_tokens": 1.8e-05, "output_cost_per_token_above_272k_tokens_flex": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-05, "output_cost_per_token_batches": 6e-06, "output_cost_per_token_flex": 6e-06, "output_cost_per_token_priority": 2.4e-05, @@ -29288,16 +29483,19 @@ "cache_creation_input_token_cost": 2.5e-07, "cache_creation_input_token_cost_above_272k_tokens": 5e-07, "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06, "cache_creation_input_token_cost_flex": 1.25e-07, "cache_creation_input_token_cost_priority": 5e-07, "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_above_272k_tokens": 4e-08, "cache_read_input_token_cost_above_272k_tokens_flex": 2e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, "cache_read_input_token_cost_flex": 1e-08, "cache_read_input_token_cost_priority": 4e-08, "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, "input_cost_per_token_above_272k_tokens_flex": 2e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, "input_cost_per_token_batches": 1e-07, "input_cost_per_token_flex": 1e-07, "input_cost_per_token_priority": 4e-07, @@ -29309,6 +29507,7 @@ "output_cost_per_token": 1.2e-06, "output_cost_per_token_above_272k_tokens": 1.8e-06, "output_cost_per_token_above_272k_tokens_flex": 9e-07, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-06, "output_cost_per_token_batches": 6e-07, "output_cost_per_token_flex": 6e-07, "output_cost_per_token_priority": 2.4e-06, @@ -29548,7 +29747,10 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": false + "supports_minimal_reasoning_effort": false, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07 }, "gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5e-07, @@ -29602,7 +29804,10 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": false + "supports_minimal_reasoning_effort": false, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07 }, "gpt-5.5-pro": { "cache_read_input_token_cost": 3e-06, @@ -29751,7 +29956,10 @@ "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07 }, "gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.5e-07, @@ -29800,7 +30008,10 @@ "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07 }, "gpt-5.4-pro": { "cache_read_input_token_cost": 3e-06, @@ -29849,7 +30060,9 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 0.000135 }, "gpt-5.4-pro-2026-03-05": { "cache_read_input_token_cost": 3e-06, @@ -29898,7 +30111,9 @@ "supports_web_search": true, "supports_none_reasoning_effort": false, "supports_xhigh_reasoning_effort": true, - "supports_minimal_reasoning_effort": true + "supports_minimal_reasoning_effort": true, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 0.000135 }, "gpt-5.4-mini": { "cache_read_input_token_cost": 7.5e-08, @@ -30834,17 +31049,18 @@ }, "gpt-realtime-2": { "cache_creation_input_audio_token_cost": 4e-07, + "cache_read_input_audio_token_cost": 4e-07, "cache_read_input_token_cost": 4e-07, "input_cost_per_audio_token": 3.2e-05, "input_cost_per_image": 5e-06, "input_cost_per_token": 4e-06, "litellm_provider": "openai", - "max_input_tokens": 32000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "realtime", "output_cost_per_audio_token": 6.4e-05, - "output_cost_per_token": 1.6e-05, + "output_cost_per_token": 2.4e-05, "supported_endpoints": [ "/v1/realtime" ], @@ -30908,8 +31124,8 @@ "input_cost_per_token": 6e-07, "litellm_provider": "openai", "max_input_tokens": 128000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "realtime", "output_cost_per_audio_token": 2e-05, "output_cost_per_token": 2.4e-06, @@ -30941,7 +31157,7 @@ "input_cost_per_audio_token": 1e-05, "input_cost_per_token": 6e-07, "litellm_provider": "openai", - "max_input_tokens": 128000, + "max_input_tokens": 32000, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "realtime", @@ -33705,19 +33921,21 @@ "source": "https://mistral.ai/pricing#api-pricing" }, "mistral/magistral-medium-latest": { - "input_cost_per_token": 2e-06, + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", - "max_input_tokens": 40000, - "max_output_tokens": 40000, - "max_tokens": 40000, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 5e-06, - "source": "https://mistral.ai/news/magistral", + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/magistral-small-2506": { "deprecation_date": "2025-11-30", @@ -33736,19 +33954,21 @@ "supports_tool_choice": true }, "mistral/magistral-small-latest": { - "input_cost_per_token": 5e-07, + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 1.5e-07, "litellm_provider": "mistral", - "max_input_tokens": 40000, - "max_output_tokens": 40000, - "max_tokens": 40000, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 1.5e-06, - "source": "https://mistral.ai/pricing#api-pricing", + "output_cost_per_token": 6e-07, + "source": "https://docs.mistral.ai/models/model-cards/mistral-small-4-0-26-03", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/magistral-small-1-2-2509": { "deprecation_date": "2026-07-31", @@ -33880,16 +34100,21 @@ "supports_vision": true }, "mistral/mistral-medium": { - "input_cost_per_token": 2.7e-06, + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, "litellm_provider": "mistral", - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "max_tokens": 8191, + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 8.1e-06, + "output_cost_per_token": 7.5e-06, + "source": "https://docs.mistral.ai/models/model-cards/mistral-medium-3-5-26-04", "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_vision": true }, "mistral/mistral-medium-2312": { "deprecation_date": "2025-06-16", @@ -41340,13 +41565,13 @@ "source": "https://docs.together.ai/docs/serverless-models" }, "together_ai/Qwen/Qwen3.8-2.4T-A95B": { - "cache_read_input_token_cost": 5e-07, - "input_cost_per_token": 2.5e-06, + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 2e-06, "litellm_provider": "together_ai", "max_input_tokens": 1010000, "max_tokens": 1010000, "mode": "chat", - "output_cost_per_token": 6.25e-06, + "output_cost_per_token": 6e-06, "source": "https://docs.together.ai/docs/serverless-models", "supports_prompt_caching": true }, @@ -41956,6 +42181,70 @@ "supports_parallel_tool_use_config": true, "prompt_cache_min_tokens": 1024 }, + "us-gov.anthropic.claude-sonnet-5": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": false, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "us-gov.anthropic.claude-opus-4-8": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + "input_cost_per_token": 6e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": true, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, "au.anthropic.claude-haiku-4-5-20251001-v1:0": { "cache_creation_input_token_cost": 1.375e-06, "cache_creation_input_token_cost_above_1hr": 2.2e-06, @@ -45849,6 +46138,26 @@ "mode": "rerank", "output_cost_per_token": 0.0 }, + "voyage/rerank-3": { + "input_cost_per_token": 5e-08, + "litellm_provider": "voyage", + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "rerank", + "output_cost_per_token": 0.0, + "source": "https://docs.voyageai.com/docs/pricing" + }, + "voyage/rerank-3-lite": { + "input_cost_per_token": 2e-08, + "litellm_provider": "voyage", + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "rerank", + "output_cost_per_token": 0.0, + "source": "https://docs.voyageai.com/docs/pricing" + }, "voyage/voyage-2": { "input_cost_per_token": 1e-07, "litellm_provider": "voyage", @@ -47100,6 +47409,27 @@ "supports_vision": true, "supports_web_search": true }, + "xai/grok-build-latest": { + "cache_read_input_token_cost": 3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, + "input_cost_per_token": 2e-06, + "input_cost_per_token_above_200k_tokens": 4e-06, + "litellm_provider": "xai", + "max_input_tokens": 500000, + "max_output_tokens": 500000, + "max_tokens": 500000, + "mode": "chat", + "output_cost_per_token": 6e-06, + "output_cost_per_token_above_200k_tokens": 1.2e-05, + "source": "https://docs.x.ai/developers/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_web_search": true + }, "xai/grok-4.6": { "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_200k_tokens": 1e-06, @@ -57420,6 +57750,34 @@ "supports_tool_choice": true, "supports_vision": false }, + "fireworks_ai/accounts/fireworks/models/glm-5p3-flash": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_token": 1.5e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 5e-07, + "source": "https://docs.fireworks.ai/serverless/pricing", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "fireworks_ai/accounts/fireworks/models/inkling": { + "cache_read_input_token_cost": 1.7e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 1048576, + "max_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 4.05e-06, + "source": "https://fireworks.ai/models/fireworks/inkling", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "fireworks_ai/accounts/fireworks/models/qwen3-embedding-8b": { "input_cost_per_token": 1e-07, "output_cost_per_token": 0.0, @@ -57479,5 +57837,542 @@ "supported_endpoints": [ "/v1/audio/transcriptions" ] + }, + "scaleway/glm-5.2": { + "input_cost_per_token": 1.8e-06, + "litellm_provider": "scaleway", + "max_input_tokens": 256000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 5.5e-06, + "source": "https://www.scaleway.com/en/pricing/model-as-a-service/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_vision": false + }, + "scaleway/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 8e-08, + "input_cost_per_token": 4e-07, + "litellm_provider": "scaleway", + "max_input_tokens": 256000, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 8e-07, + "source": "https://www.scaleway.com/en/pricing/model-as-a-service/", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_vision": false + }, + "azure_ai/kimi-k2.7-code": { + "cache_read_input_token_cost": 1.9e-07, + "deprecation_date": "2026-10-03", + "input_cost_per_token": 9.5e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b": { + "input_cost_per_token": 7.2e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.88e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/nvidia.nemotron-nano-12b-v2": { + "input_cost_per_token": 2.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_system_messages": true, + "supports_vision": true + }, + "bedrock/us-gov-west-1/nvidia.nemotron-super-3-120b": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 256000, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 7.8e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/openai.gpt-oss-20b-1:0": { + "input_cost_per_token": 8.4e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.6e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/openai.gpt-oss-120b-1:0": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-west-1/anthropic.claude-sonnet-5": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": false, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock/us-gov-west-1/anthropic.claude-opus-4-8": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + "input_cost_per_token": 6e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": true, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock/us-gov-east-1/nvidia.nemotron-nano-3-30b": { + "input_cost_per_token": 7.2e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.88e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_native_structured_output": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/nvidia.nemotron-nano-12b-v2": { + "input_cost_per_token": 2.4e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_system_messages": true, + "supports_vision": true + }, + "bedrock/us-gov-east-1/nvidia.nemotron-super-3-120b": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 256000, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 7.8e-07, + "source": "https://aws.amazon.com/bedrock/pricing/", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/openai.gpt-oss-20b-1:0": { + "input_cost_per_token": 8.4e-08, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.6e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/openai.gpt-oss-120b-1:0": { + "input_cost_per_token": 1.8e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 7.2e-07, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "bedrock/us-gov-east-1/anthropic.claude-sonnet-5": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": false, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock/us-gov-east-1/anthropic.claude-opus-4-8": { + "bedrock_converse_supports_strict_tools": false, + "bedrock_output_config_effort_ceiling": "xhigh", + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + "input_cost_per_token": 6e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3e-05, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_max_reasoning_effort": true, + "supports_mid_conversation_system": true, + "supports_native_structured_output": true, + "supports_output_config": true, + "supports_parallel_tool_use_config": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_sampling_params": false, + "supports_tool_choice": true, + "supports_vision": true, + "supports_xhigh_reasoning_effort": true + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-terra": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 2.64e-06, + "input_cost_per_token_above_272k_tokens": 5.28e-06, + "cache_creation_input_token_cost": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-06, + "cache_read_input_token_cost": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-07, + "output_cost_per_token": 1.584e-05, + "output_cost_per_token_above_272k_tokens": 2.376e-05 + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.6-luna": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 2.64e-07, + "input_cost_per_token_above_272k_tokens": 5.28e-07, + "cache_creation_input_token_cost": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-07, + "cache_read_input_token_cost": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-08, + "output_cost_per_token": 1.584e-06, + "output_cost_per_token_above_272k_tokens": 2.376e-06 + }, + "bedrock_mantle/us-gov-west-1/openai.gpt-5.4": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05 + }, + "bedrock_mantle/us-gov-west-1/xai.grok-4.3": { + "use_openai_responses_path": true, + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 131072, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/responses" + ], + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://aws.amazon.com/bedrock/pricing/", + "input_cost_per_token": 1.5e-06, + "output_cost_per_token": 3e-06, + "cache_read_input_token_cost": 2.4e-07 + }, + "bedrock_mantle/us-gov-east-1/openai.gpt-5.4": { + "litellm_provider": "bedrock_mantle", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "use_openai_responses_path": true, + "supported_endpoints": [ + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "input_cost_per_token": 3.3e-06, + "cache_read_input_token_cost": 3.3e-07, + "output_cost_per_token": 1.98e-05 + }, + "azure/us-gov/gpt-5.1": { + "cache_read_input_token_cost": 1.71875e-07, + "default_reasoning_effort": "none", + "input_cost_per_token": 1.71875e-06, + "litellm_provider": "azure", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.375e-05, + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_none_reasoning_effort": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "azure/us-gov/o3-mini": { + "cache_read_input_token_cost": 7.57e-07, + "input_cost_per_token": 1.513e-06, + "litellm_provider": "azure", + "max_input_tokens": 200000, + "max_output_tokens": 100000, + "max_tokens": 100000, + "mode": "chat", + "output_cost_per_token": 6.05e-06, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": false + }, + "azure/us-gov/text-embedding-3-large": { + "input_cost_per_token": 1.63e-07, + "litellm_provider": "azure", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0 + }, + "azure/us-gov/text-embedding-3-small": { + "input_cost_per_token": 2.5e-08, + "litellm_provider": "azure", + "max_input_tokens": 8191, + "max_tokens": 8191, + "mode": "embedding", + "output_cost_per_token": 0.0 + }, + "cloudflare/@cf/openai/whisper": { + "input_cost_per_second": 7.5e-06, + "litellm_provider": "cloudflare", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://developers.cloudflare.com/workers-ai/models/whisper/", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "cloudflare/@cf/openai/whisper-large-v3-turbo": { + "input_cost_per_second": 8.5e-06, + "litellm_provider": "cloudflare", + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://developers.cloudflare.com/workers-ai/models/whisper-large-v3-turbo/", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] } } diff --git a/tests/e2e/test_junit_properties.py b/tests/e2e/test_junit_properties.py index c0596177cc1..02c1413c840 100644 --- a/tests/e2e/test_junit_properties.py +++ b/tests/e2e/test_junit_properties.py @@ -24,25 +24,10 @@ from junit_properties import ( ) -class FakeMarker: - def __init__(self, name: str, *args: object) -> None: - self.name = name - self.args = args - - -class FakeItem: - """The three attributes junit_properties reads off a pytest Item.""" - - def __init__( - self, nodeid: str, location: tuple[str, int | None, str], markers: tuple[FakeMarker, ...] = () - ) -> None: - self.nodeid = nodeid - self.location = location - self.user_properties: list[tuple[str, str]] = [] - self._markers = markers - - def iter_markers(self, name: str): - return (marker for marker in self._markers if marker.name == name) +def collected_item(request: pytest.FixtureRequest, name: str) -> pytest.Item: + """The Item pytest collected for test ``name`` in this file: the real nodeid, + location and marker machinery the collection hook reads, as pytest built it.""" + return next(item for item in request.session.items if item.path == request.path and item.name == name) def repo_root() -> Path | None: @@ -109,22 +94,22 @@ class TestSourceFromLocation: class TestResultProperties: - def test_every_test_carries_package_covers_and_source(self) -> None: - item = FakeItem( - "logging/test_x.py::TestFoo::test_bar", - ("logging/test_x.py", 40, "TestFoo.test_bar"), - (FakeMarker("covers", "LOG-1", "LOG-2"),), - ) - assert result_properties(item) == ( - ("package", "logging"), + def test_every_test_carries_package_covers_and_source(self, request: pytest.FixtureRequest) -> None: + """Read off this test's own collected Item, so the nodeid and location are + whatever pytest reports for the launch shape in use, and the marker is added + at run time so the coverage registry's collect-only pass never sees it.""" + test = type(self).test_every_test_carries_package_covers_and_source + request.applymarker(pytest.mark.covers("LOG-1", "LOG-2")) + assert result_properties(collected_item(request, test.__name__)) == ( + ("package", "root"), ("covers", "LOG-1,LOG-2"), - ("source", "tests/e2e/logging/test_x.py:41"), + ("source", f"tests/e2e/test_junit_properties.py:{test.__code__.co_firstlineno}"), ) - def test_attach_is_idempotent(self) -> None: + def test_attach_is_idempotent(self, request: pytest.FixtureRequest) -> None: """Collection can run the hook more than once; a second pass must not double the entries in the report.""" - item = FakeItem("logging/test_x.py::test_bar", ("logging/test_x.py", 40, "test_bar")) + item = collected_item(request, type(self).test_attach_is_idempotent.__name__) attach_result_properties(item) attach_result_properties(item) assert [name for name, _ in item.user_properties] == ["package", "covers", "source"] diff --git a/tests/ocr_tests/test_ocr_vertex_ai.py b/tests/ocr_tests/test_ocr_vertex_ai.py index 1ba5b9d0883..1842eb063a5 100644 --- a/tests/ocr_tests/test_ocr_vertex_ai.py +++ b/tests/ocr_tests/test_ocr_vertex_ai.py @@ -5,9 +5,11 @@ Note: Vertex AI OCR automatically converts URLs to base64 data URIs since the Vertex AI endpoint doesn't have internet access. """ -import os import json +import os import tempfile +from typing import Final + import pytest from base_ocr_unit_tests import BaseOCRTest @@ -139,3 +141,19 @@ def test_vertex_ai_ocr_routing(): assert isinstance( deepseek_variant, VertexAIDeepSeekOCRConfig ), "DeepSeek variant should route to VertexAIDeepSeekOCRConfig" + + +@pytest.mark.parametrize("model", ("deepseek-ocr-maas", "deepseek-ai/deepseek-ocr-maas")) +def test_deepseek_request_uses_single_provider_namespace(model: str) -> None: + from litellm.llms.vertex_ai.ocr.deepseek_transformation import ( + VertexAIDeepSeekOCRConfig, + ) + + request: Final = VertexAIDeepSeekOCRConfig().transform_ocr_request( + model=model, + document={"type": "image_url", "image_url": "data:image/png;base64,AA=="}, + optional_params={}, + headers={}, + ) + + assert request.data["model"] == "deepseek-ai/deepseek-ocr-maas" diff --git a/tests/proxy_unit_tests/test_proxy_server.py b/tests/proxy_unit_tests/test_proxy_server.py index 47554913419..54cce9cdd78 100644 --- a/tests/proxy_unit_tests/test_proxy_server.py +++ b/tests/proxy_unit_tests/test_proxy_server.py @@ -3076,7 +3076,9 @@ async def test_update_config_success_callback_normalization(): admin_user = UserAPIKeyAuth( user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-test" ) - await proxy_server.update_config(config_update, user_api_key_dict=admin_user) + request = MagicMock() + request.json = AsyncMock(return_value={"litellm_settings": {"success_callback": ["SQS", "sQs"]}}) + await proxy_server.update_config(config_update, request=request, user_api_key_dict=admin_user) assert ( "litellm_settings" in upserted diff --git a/tests/test_litellm/integrations/test_custom_guardrail.py b/tests/test_litellm/integrations/test_custom_guardrail.py index d978eb48c12..7d70b9a8862 100644 --- a/tests/test_litellm/integrations/test_custom_guardrail.py +++ b/tests/test_litellm/integrations/test_custom_guardrail.py @@ -2237,3 +2237,202 @@ class TestRecordsOwnGuardrailInformation: ) assert _guardrail_entries(request_data) == [] + + +class _ApplyOnlyObserver(CustomGuardrail): + """Overrides only apply_guardrail, like panw_prisma_airs; inherits async_logging_hook.""" + + def __init__(self, block: bool = False): + from litellm.types.guardrails import GuardrailEventHooks + + super().__init__(guardrail_name="apply-only-observer", event_hook=GuardrailEventHooks.logging_only) + self.block = block + self.calls: list = [] + + @log_guardrail_information + async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None): + from fastapi import HTTPException + + self.calls.append((input_type, list(inputs.get("texts") or []))) + if self.block: + raise HTTPException(status_code=400, detail={"error": "flagged"}) + return GenericGuardrailAPIInputs(texts=["[MASKED]" for _ in inputs.get("texts") or []]) + + +def _logged_call(messages: list | str) -> tuple[dict, object]: + from litellm.types.utils import Choices, Message, ModelResponse + + response = ModelResponse(choices=[Choices(message=Message(role="assistant", content="general kenobi"))]) + kwargs = { + "model": "gpt-5.4-mini", + "messages": messages, + "litellm_call_id": "call-1", + "litellm_params": {"metadata": {"user_api_key_user_id": "u1"}}, + "optional_params": {}, + "standard_logging_object": {"guardrail_information": None}, + } + return kwargs, response + + +class TestLoggingOnlyApplyGuardrail: + """LIT-4876 regression: a guardrail in mode logging_only that implements only + apply_guardrail must still run against the logged request and response and + record guardrail_information, instead of inheriting the CustomLogger no-op.""" + + @pytest.mark.asyncio + async def test_runs_apply_guardrail_observe_only_and_records_verdict(self): + guardrail = _ApplyOnlyObserver() + messages = [{"role": "user", "content": "hello there"}] + kwargs, response = _logged_call(messages) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [("request", ["hello there"]), ("response", ["general kenobi"])] + assert out_kwargs["messages"] == [{"role": "user", "content": "hello there"}] + assert out_response.choices[0].message.content == "general kenobi" + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_name"] for e in entries] == ["apply-only-observer", "apply-only-observer"] + assert {e["guardrail_mode"] for e in entries} == {"logging_only"} + assert {e["guardrail_status"] for e in entries} == {"success"} + assert "standard_logging_guardrail_information" not in kwargs["litellm_params"]["metadata"] + assert kwargs["standard_logging_object"] == {"guardrail_information": None} + + @pytest.mark.asyncio + async def test_appends_to_pre_call_verdicts_without_duplicating_them(self): + guardrail = _ApplyOnlyObserver() + kwargs, response = _logged_call([{"role": "user", "content": "hello there"}]) + pre_call_entry = {"guardrail_name": "pii-blocker", "guardrail_mode": "pre_call", "guardrail_status": "success"} + kwargs["litellm_params"]["metadata"]["standard_logging_guardrail_information"] = [pre_call_entry] + kwargs["standard_logging_object"]["guardrail_information"] = [pre_call_entry] + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_name"] for e in entries] == ["pii-blocker", "apply-only-observer", "apply-only-observer"] + assert kwargs["litellm_params"]["metadata"]["standard_logging_guardrail_information"] == [pre_call_entry] + + @pytest.mark.asyncio + async def test_request_copy_failure_is_swallowed(self): + import threading + + guardrail = _ApplyOnlyObserver() + kwargs, response = _logged_call([{"role": "user", "content": "hello there", "lock": threading.Lock()}]) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [] + assert out_kwargs is kwargs + assert out_response is response + + @pytest.mark.asyncio + async def test_block_verdict_is_recorded_without_raising(self): + guardrail = _ApplyOnlyObserver(block=True) + kwargs, response = _logged_call([{"role": "user", "content": "flagged content"}]) + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [("request", ["flagged content"])] + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["guardrail_intervened"] + + @pytest.mark.asyncio + async def test_call_type_without_translation_is_skipped(self): + guardrail = _ApplyOnlyObserver() + kwargs, response = _logged_call([{"role": "user", "content": "hello there"}]) + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.amoderation.value) + + assert guardrail.calls == [] + assert out_kwargs["standard_logging_object"]["guardrail_information"] is None + + @pytest.mark.asyncio + async def test_aembedding_scans_logged_input(self): + from litellm.types.utils import EmbeddingResponse + + guardrail = _ApplyOnlyObserver() + kwargs, _ = _logged_call("hello there") + response = EmbeddingResponse(data=[{"embedding": [0.1], "index": 0, "object": "embedding"}]) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.aembedding.value) + + assert guardrail.calls == [("request", ["hello there"])] + assert out_kwargs["messages"] == "hello there" + assert out_response is response + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["success"] + + @pytest.mark.asyncio + async def test_native_lifecycle_hook_guardrail_is_left_alone(self): + class _NativeHooks(_ApplyOnlyObserver): + use_native_lifecycle_hooks = True + + guardrail = _NativeHooks() + kwargs, response = _logged_call([{"role": "user", "content": "hello there"}]) + + out_kwargs, out_response = await guardrail.async_logging_hook(kwargs, response, CallTypes.acompletion.value) + + assert guardrail.calls == [] + assert out_kwargs is kwargs + assert out_response is response + + @pytest.mark.asyncio + async def test_aresponses_scans_logged_messages_when_input_is_cleared(self): + from litellm.types.llms.openai import ResponsesAPIResponse + + guardrail = _ApplyOnlyObserver() + kwargs, _ = _logged_call([{"role": "user", "content": "hello there"}]) + kwargs["input"] = None + response = ResponsesAPIResponse( + id="resp_1", + created_at=1, + model="gpt-5.4-mini", + object="response", + status="completed", + output=[ + { + "type": "message", + "id": "msg_1", + "status": "completed", + "role": "assistant", + "content": [{"type": "output_text", "text": "general kenobi"}], + } + ], + ) + + out_kwargs, _ = await guardrail.async_logging_hook(kwargs, response, CallTypes.aresponses.value) + + assert guardrail.calls == [("request", ["hello there"]), ("response", ["general kenobi"])] + entries = out_kwargs["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["success", "success"] + + @pytest.mark.asyncio + async def test_async_success_handler_records_verdict_in_standard_logging_object(self): + import datetime as dt + + from litellm.litellm_core_utils.litellm_logging import Logging + + guardrail = _ApplyOnlyObserver() + guardrail.default_on = True + messages = [{"role": "user", "content": "hello there"}] + _, response = _logged_call(messages) + logging_obj = Logging( + model="gpt-5.4-mini", + messages=messages, + stream=False, + call_type=CallTypes.acompletion.value, + start_time=dt.datetime.now(), + litellm_call_id="call-1", + function_id="fn-1", + dynamic_async_success_callbacks=[guardrail], + ) + logging_obj.update_environment_variables( + litellm_params={"metadata": {}}, optional_params={}, model="gpt-5.4-mini", custom_llm_provider="openai" + ) + + await logging_obj.async_success_handler( + result=response, start_time=dt.datetime.now(), end_time=dt.datetime.now() + ) + + assert guardrail.calls == [("request", ["hello there"]), ("response", ["general kenobi"])] + entries = logging_obj.model_call_details["standard_logging_object"]["guardrail_information"] + assert [e["guardrail_status"] for e in entries] == ["success", "success"] diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 0e1c832ebf5..b7f0ca1efe1 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -1522,7 +1522,7 @@ def test_gpt_5_6_alias_prices_match_sol(local_model_cost_map): sol = litellm.model_cost["gpt-5.6-sol"] cost_fields = sorted(field for field in sol if "cost" in field) - assert len(cost_fields) == 23 + assert len(cost_fields) == 27 for field in cost_fields: assert alias.get(field) == sol.get(field), field @@ -4039,8 +4039,8 @@ def test_fast_service_tier_matches_priority_above_the_context_threshold(_local_m ) assert fast == priority - assert fast[0] == pytest.approx(300_000 * 8e-06, rel=1e-9) - assert fast[1] == pytest.approx(1_000 * 3e-05, rel=1e-9) + assert fast[0] == pytest.approx(300_000 * 1.6e-05, rel=1e-9) + assert fast[1] == pytest.approx(1_000 * 6e-05, rel=1e-9) def test_priority_reasoning_tokens_bill_at_the_priority_output_rate(_local_model_cost_map): @@ -4200,6 +4200,86 @@ def test_generic_cost_per_token_gemini_37_flash(_local_model_cost_map): assert completion_cost == pytest.approx(0.001875) +GEMINI_38_FLASH_LAUNCH_PRICING = [ + ("gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08), + ("gemini/gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08), + ("vertex_ai/gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08), +] + + +@pytest.mark.parametrize("model,input_cost,output_cost,cache_read_cost", GEMINI_38_FLASH_LAUNCH_PRICING) +def test_gemini_38_flash_launch_pricing(model, input_cost, output_cost, cache_read_cost, _local_model_cost_map): + model_cost_map = litellm.model_cost[model] + assert model_cost_map["input_cost_per_token"] == input_cost + assert model_cost_map["output_cost_per_token"] == output_cost + assert model_cost_map["output_cost_per_reasoning_token"] == output_cost + assert model_cost_map["cache_read_input_token_cost"] == cache_read_cost + assert model_cost_map["mode"] == "chat" + assert model_cost_map["supports_reasoning"] is True + assert model_cost_map["supports_function_calling"] is True + assert model_cost_map["max_input_tokens"] == 1048576 + + +GEMINI_38_FLASH_FIELDS_SHARED_WITH_37_FLASH = ( + "input_cost_per_token", + "output_cost_per_token", + "output_cost_per_reasoning_token", + "cache_read_input_token_cost", + "input_cost_per_token_batches", + "output_cost_per_token_batches", + "input_cost_per_token_flex", + "output_cost_per_token_flex", + "cache_read_input_token_cost_flex", + "input_cost_per_token_priority", + "output_cost_per_token_priority", + "cache_read_input_token_cost_priority", + "search_context_cost_per_query", + "google_maps_grounding_cost_per_query", + "prompt_cache_min_tokens", + "max_input_tokens", + "max_output_tokens", + "supports_reasoning", + "supports_function_calling", + "supports_prompt_caching", + "supports_vision", + "supports_pdf_input", + "supports_audio_input", + "supports_video_input", + "supports_response_schema", + "supports_tool_choice", + "supports_web_search", + "supports_url_context", +) + + +@pytest.mark.parametrize("prefix", ["", "gemini/", "vertex_ai/"]) +def test_gemini_38_flash_matches_37_flash_promotional_pricing(prefix, _local_model_cost_map): + new_model = litellm.model_cost[f"{prefix}gemini-3.8-flash"] + old_model = litellm.model_cost[f"{prefix}gemini-3.7-flash"] + for field in GEMINI_38_FLASH_FIELDS_SHARED_WITH_37_FLASH: + assert new_model[field] == old_model[field], field + + +def test_generic_cost_per_token_gemini_38_flash(_local_model_cost_map): + usage = Usage( + prompt_tokens=1000, + completion_tokens=500, + total_tokens=1500, + completion_tokens_details=CompletionTokensDetailsWrapper( + reasoning_tokens=200, + text_tokens=300, + ), + prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1000), + ) + prompt_cost, completion_cost = generic_cost_per_token( + model="gemini-3.8-flash", + usage=usage, + custom_llm_provider="gemini", + ) + assert prompt_cost == pytest.approx(0.00075) + assert completion_cost == pytest.approx(0.001875) + + def test_grok_46_launch_pricing(_local_model_cost_map): model_cost_map = litellm.model_cost["xai/grok-4.6"] assert model_cost_map["input_cost_per_token"] == 2e-06 diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py index 8ac050a04f9..bacbcbf132b 100644 --- a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py +++ b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py @@ -592,6 +592,59 @@ def test_stream_chunk_builder_litellm_usage_chunks(): assert usage.total_tokens == 77 +def test_calculate_usage_honors_openai_sdk_completion_usage_chunks(): + from openai.types.completion_usage import CompletionUsage + + content_chunk = ModelResponseStream( + id="chatcmpl-sdk-usage-1", + created=1745513206, + model="mantle-claude", + object="chat.completion.chunk", + system_fingerprint=None, + choices=[ + StreamingChoices( + finish_reason="stop", + index=0, + delta=Delta( + provider_specific_fields=None, + content="ok", + role=None, + function_call=None, + tool_calls=None, + audio=None, + ), + logprobs=None, + ) + ], + provider_specific_fields=None, + stream_options={"include_usage": True}, + ) + usage_chunk = ModelResponseStream( + id="chatcmpl-sdk-usage-1", + created=1745513207, + model="mantle-claude", + object="chat.completion.chunk", + system_fingerprint=None, + choices=[], + provider_specific_fields=None, + stream_options={"include_usage": True}, + ) + usage_chunk.usage = CompletionUsage( + prompt_tokens=20, completion_tokens=60, total_tokens=80, cost=0.000704 + ) + assert type(usage_chunk.usage) is CompletionUsage + + chunks = [content_chunk, usage_chunk] + usage = ChunkProcessor(chunks=chunks).calculate_usage( + chunks=chunks, model="mantle-claude", completion_output="" + ) + + assert usage.prompt_tokens == 20 + assert usage.completion_tokens == 60 + assert usage.total_tokens == 80 + assert getattr(usage, "cost", None) == pytest.approx(0.000704) + + def test_get_model_from_chunks_azure_model_router(): """ Test that _get_model_from_chunks finds the actual model from Azure Model Router chunks. diff --git a/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py b/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py index 7085e45cdc3..4b58d220623 100644 --- a/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py +++ b/tests/test_litellm/llms/s3_vectors/vector_stores/test_s3_vectors_transformation.py @@ -1,4 +1,4 @@ -from unittest.mock import MagicMock, Mock +from unittest.mock import AsyncMock, MagicMock, Mock, patch import httpx import pytest @@ -9,6 +9,18 @@ from litellm.llms.s3_vectors.vector_stores.transformation import ( from litellm.types.vector_stores import VectorStoreSearchResponse +def _mock_router(model_names, sync=False): + """Router mock serving the given embedding model names.""" + router = MagicMock() + router.get_model_list.return_value = [{"model_name": name} for name in model_names] + embedding_response = Mock(data=[{"embedding": [0.1, 0.2, 0.3]}]) + if sync: + router.embedding = MagicMock(return_value=embedding_response) + else: + router.aembedding = AsyncMock(return_value=embedding_response) + return router + + class TestS3VectorsVectorStoreConfig: def test_init(self): """Test that S3VectorsVectorStoreConfig initializes correctly""" @@ -28,19 +40,174 @@ class TestS3VectorsVectorStoreConfig: url = config.get_complete_url(None, litellm_params) assert url == "https://s3vectors.us-west-2.api.aws" - def test_get_complete_url_missing_region(self): - """Test that missing region raises error""" + def test_get_complete_url_missing_region(self, monkeypatch): + """Missing region falls back to the default region (parity with ingestion)""" + monkeypatch.delenv("AWS_REGION_NAME", raising=False) + monkeypatch.delenv("AWS_REGION", raising=False) config = S3VectorsVectorStoreConfig() - litellm_params = {} - with pytest.raises(ValueError, match="aws_region_name is required"): - config.get_complete_url(None, litellm_params) + url = config.get_complete_url(None, {}) + assert url == "https://s3vectors.us-west-2.api.aws" + + def test_get_complete_url_uses_env_region(self, monkeypatch): + """Missing region param resolves from AWS_REGION_NAME env var""" + monkeypatch.setenv("AWS_REGION_NAME", "eu-west-1") + monkeypatch.delenv("AWS_REGION", raising=False) + config = S3VectorsVectorStoreConfig() + url = config.get_complete_url(None, {}) + assert url == "https://s3vectors.eu-west-1.api.aws" + + def test_get_complete_url_invalid_region_format(self): + """Invalid region format raises""" + config = S3VectorsVectorStoreConfig() + with pytest.raises(ValueError, match="Invalid AWS region format"): + config.get_complete_url(None, {"aws_region_name": "Bad_Region!"}) - @pytest.mark.skip(reason="Requires embedding API call, tested in integration tests") def test_transform_search_request(self): - """Test search request transformation""" - # This test requires making an actual embedding API call - # It's better tested in integration tests - pass + """Full request-body transformation with a router-injected embedding""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {} + router = _mock_router(["text-embedding-3-small"], sync=True) + + url, request_body = config.transform_search_vector_store_request( + vector_store_id="test-bucket:test-index", + query="test query", + vector_store_search_optional_params={"max_num_results": 7}, + api_base="https://s3vectors.us-west-2.api.aws", + litellm_logging_obj=mock_logging_obj, + litellm_params={}, + extra_body=None, + router=router, + ) + + assert url == "https://s3vectors.us-west-2.api.aws/QueryVectors" + assert request_body == { + "vectorBucketName": "test-bucket", + "indexName": "test-index", + "queryVector": {"float32": [0.1, 0.2, 0.3]}, + "topK": 7, + "returnDistance": True, + "returnMetadata": True, + } + assert mock_logging_obj.model_call_details["query"] == "test query" + + @pytest.mark.asyncio + async def test_atransform_search_uses_router_for_virtual_model(self): + """Regression: router-served embedding models must resolve via the router, + not a bare litellm.aembedding call (which has no deployment credentials).""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {} + router = _mock_router(["my-embedding-model"]) + + with patch("litellm.aembedding", new=AsyncMock()) as mock_bare_aembedding: # test-quality-ok: guards that the bare-embedding path is not taken; dispatch seam is the behavior under test + url, request_body = await config.atransform_search_vector_store_request( + vector_store_id="test-bucket:test-index", + query="test query", + vector_store_search_optional_params={}, + api_base="https://s3vectors.us-west-2.api.aws", + litellm_logging_obj=mock_logging_obj, + litellm_params={"embedding_model": "my-embedding-model"}, + extra_body=None, + router=router, + ) + + router.aembedding.assert_awaited_once_with(model="my-embedding-model", input=["test query"]) + mock_bare_aembedding.assert_not_awaited() + assert request_body["queryVector"]["float32"] == [0.1, 0.2, 0.3] + assert request_body["topK"] == 5 # default + + @pytest.mark.asyncio + async def test_atransform_search_falls_back_when_router_does_not_serve_model(self): + """Router present but embedding_model is not a router deployment -> + bare litellm.aembedding keeps working (provider-prefixed + env creds stores).""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {} + router = _mock_router(["some-other-model"]) + + mock_bare = AsyncMock(return_value=Mock(data=[{"embedding": [0.4, 0.5]}])) + with patch("litellm.aembedding", new=mock_bare): # test-quality-ok: stubs the bare-embedding fallback whose request body the test asserts on + _, request_body = await config.atransform_search_vector_store_request( + vector_store_id="test-bucket:test-index", + query="test query", + vector_store_search_optional_params={}, + api_base="https://s3vectors.us-west-2.api.aws", + litellm_logging_obj=mock_logging_obj, + litellm_params={"embedding_model": "azure/text-embedding-3-small"}, + extra_body=None, + router=router, + ) + + mock_bare.assert_awaited_once_with(model="azure/text-embedding-3-small", input=["test query"]) + router.aembedding.assert_not_awaited() + assert request_body["queryVector"]["float32"] == [0.4, 0.5] + + @pytest.mark.asyncio + async def test_atransform_search_without_router_uses_bare_embedding(self): + """Backward compat: no router -> bare litellm.aembedding as before""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {} + + mock_bare = AsyncMock(return_value=Mock(data=[{"embedding": [0.6, 0.7]}])) + with patch("litellm.aembedding", new=mock_bare): # test-quality-ok: stubs the bare-embedding fallback whose request body the test asserts on + _, request_body = await config.atransform_search_vector_store_request( + vector_store_id="test-bucket:test-index", + query="test query", + vector_store_search_optional_params={}, + api_base="https://s3vectors.us-west-2.api.aws", + litellm_logging_obj=mock_logging_obj, + litellm_params={}, + extra_body=None, + ) + + mock_bare.assert_awaited_once_with(model="text-embedding-3-small", input=["test query"]) + assert request_body["queryVector"]["float32"] == [0.6, 0.7] + + def test_transform_search_uses_router_for_virtual_model_sync(self): + """Sync twin: router-served embedding model resolves via router.embedding""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {} + router = _mock_router(["my-embedding-model"], sync=True) + + with patch("litellm.embedding", new=MagicMock()) as mock_bare_embedding: # test-quality-ok: guards that the bare-embedding path is not taken; dispatch seam is the behavior under test + _, request_body = config.transform_search_vector_store_request( + vector_store_id="test-bucket:test-index", + query="test query", + vector_store_search_optional_params={}, + api_base="https://s3vectors.us-west-2.api.aws", + litellm_logging_obj=mock_logging_obj, + litellm_params={"embedding_model": "my-embedding-model"}, + extra_body=None, + router=router, + ) + + router.embedding.assert_called_once_with(model="my-embedding-model", input=["test query"]) + mock_bare_embedding.assert_not_called() + assert request_body["queryVector"]["float32"] == [0.1, 0.2, 0.3] + + def test_transform_search_without_router_uses_bare_embedding_sync(self): + """Sync twin: no router -> bare litellm.embedding as before""" + config = S3VectorsVectorStoreConfig() + mock_logging_obj = Mock() + mock_logging_obj.model_call_details = {} + + mock_bare = MagicMock(return_value=Mock(data=[{"embedding": [0.8, 0.9]}])) + with patch("litellm.embedding", new=mock_bare): # test-quality-ok: stubs the bare-embedding fallback whose request body the test asserts on + _, request_body = config.transform_search_vector_store_request( + vector_store_id="test-bucket:test-index", + query="test query", + vector_store_search_optional_params={}, + api_base="https://s3vectors.us-west-2.api.aws", + litellm_logging_obj=mock_logging_obj, + litellm_params={}, + extra_body=None, + ) + + mock_bare.assert_called_once_with(model="text-embedding-3-small", input=["test query"]) + assert request_body["queryVector"]["float32"] == [0.8, 0.9] def test_transform_search_request_invalid_vector_store_id(self): """Test that invalid vector_store_id format raises error""" diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py index 8c1de12e7d9..4679b978f78 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py @@ -1096,10 +1096,13 @@ def test_natively_signed_parallel_turn_never_carries_a_placeholder(model): "gemini-3.5-flash", "gemini-3.6-flash", "gemini-3.7-flash", + "gemini-3.8-flash", "vertex_ai/gemini-3.5-flash", "vertex_ai/gemini-3.7-flash", + "vertex_ai/gemini-3.8-flash", "gemini/gemini-3.5-flash", "gemini/gemini-3.7-flash", + "gemini/gemini-3.8-flash", ], ) def test_placeholder_scoped_to_first_call_across_gemini_3_variants(model): diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index bd07bec900f..d2788408e09 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -1185,6 +1185,18 @@ def test_vertex_ai_map_thinking_param_with_budget_tokens_0(): } +def test_vertex_ai_map_thinking_param_without_budget_tokens_for_gemini_3(): + v = VertexGeminiConfig() + result = v.map_openai_params( + non_default_params={"thinking": {"type": "enabled"}}, + optional_params={}, + model="gemini-3.5-flash", + drop_params=False, + ) + + assert result["thinkingConfig"] == {"includeThoughts": True} + + def test_vertex_ai_map_tools(): v = VertexGeminiConfig() optional_params = {} diff --git a/tests/test_litellm/proxy/management_helpers/test_access_group_key_sync.py b/tests/test_litellm/proxy/management_helpers/test_access_group_key_sync.py new file mode 100644 index 00000000000..60c36e33e09 --- /dev/null +++ b/tests/test_litellm/proxy/management_helpers/test_access_group_key_sync.py @@ -0,0 +1,57 @@ +from types import SimpleNamespace +from unittest.mock import AsyncMock, MagicMock + +import pytest + +from litellm.proxy.db.prisma_client import PrismaWrapper +from litellm.proxy.db.routing_prisma_wrapper import RoutingPrismaWrapper +from litellm.proxy.management_helpers.access_group_key_sync import ( + sync_key_access_group_membership, + sync_key_regeneration_access_group_membership, +) + + +def _routed_prisma_client(): + writer_inner = MagicMock(name="writer_prisma") + reader_inner = MagicMock(name="reader_prisma") + writer_inner.query_raw = AsyncMock(return_value=[]) + reader_inner.query_raw = AsyncMock(return_value=[]) + writer = PrismaWrapper(original_prisma=writer_inner, iam_token_db_auth=False) + reader = PrismaWrapper(original_prisma=reader_inner, iam_token_db_auth=False) + routing = RoutingPrismaWrapper(writer=writer, reader=reader) + return SimpleNamespace(db=routing), writer_inner, reader_inner + + +@pytest.mark.asyncio +async def test_regeneration_repoint_update_runs_on_the_writer(): + prisma_client, writer_inner, reader_inner = _routed_prisma_client() + + await sync_key_regeneration_access_group_membership( + prisma_client=prisma_client, + previous_key_token="old-token", + new_key_token="new-token", + data=None, + existing_key_row=MagicMock(), + ) + + writer_inner.query_raw.assert_awaited_once() + assert writer_inner.query_raw.await_args.args[0].startswith('UPDATE "LiteLLM_AccessGroupTable"') + reader_inner.query_raw.assert_not_awaited() + + +@pytest.mark.asyncio +async def test_membership_attach_and_detach_updates_run_on_the_writer(): + prisma_client, writer_inner, reader_inner = _routed_prisma_client() + + await sync_key_access_group_membership( + prisma_client=prisma_client, + key_token="token", + previous_access_group_ids=["ag-old"], + updated_access_group_ids=["ag-new"], + ) + + assert writer_inner.query_raw.await_count == 2 + assert all( + call.args[0].startswith('UPDATE "LiteLLM_AccessGroupTable"') for call in writer_inner.query_raw.await_args_list + ) + reader_inner.query_raw.assert_not_awaited() diff --git a/tests/test_litellm/proxy/proxy_server/test_routes_config.py b/tests/test_litellm/proxy/proxy_server/test_routes_config.py index ad3c470acf3..dcb63b8ca82 100644 --- a/tests/test_litellm/proxy/proxy_server/test_routes_config.py +++ b/tests/test_litellm/proxy/proxy_server/test_routes_config.py @@ -60,6 +60,141 @@ def test_config_update_happy_admin(client, auth_as, mock_prisma, monkeypatch): assert normalize(response.json()) == {"message": "Config updated successfully"} +def test_config_update_persists_optional_pre_call_checks(client, auth_as, mock_prisma, monkeypatch): + from litellm.proxy import proxy_server as ps + from litellm.proxy._types import LitellmUserRoles + + table = _install_litellm_config(mock_prisma) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + fake_proxy_config = MagicMock() + fake_proxy_config.add_deployment = AsyncMock() + monkeypatch.setattr(ps, "proxy_config", fake_proxy_config) + + with auth_as(LitellmUserRoles.PROXY_ADMIN): + response = client.post( + "/config/update", + json={"router_settings": {"optional_pre_call_checks": ["prompt_caching"]}}, + ) + + assert response.status_code == 200 + persisted = json.loads(table.upsert.call_args.kwargs["data"]["create"]["param_value"]) + assert persisted["optional_pre_call_checks"] == ["prompt_caching"] + + +def test_config_update_persists_model_group_affinity_config(client, auth_as, mock_prisma, monkeypatch): + from litellm.proxy import proxy_server as ps + from litellm.proxy._types import LitellmUserRoles + + table = _install_litellm_config(mock_prisma) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + fake_proxy_config = MagicMock() + fake_proxy_config.add_deployment = AsyncMock() + monkeypatch.setattr(ps, "proxy_config", fake_proxy_config) + + model_group_affinity_config = {"gpt-4": ["session_affinity"]} + with auth_as(LitellmUserRoles.PROXY_ADMIN): + response = client.post( + "/config/update", + json={"router_settings": {"model_group_affinity_config": model_group_affinity_config}}, + ) + + assert response.status_code == 200 + persisted = json.loads(table.upsert.call_args.kwargs["data"]["create"]["param_value"]) + assert persisted["model_group_affinity_config"] == model_group_affinity_config + + +def test_config_update_persists_disable_cooldowns(client, auth_as, mock_prisma, monkeypatch): + from litellm.proxy import proxy_server as ps + from litellm.proxy._types import LitellmUserRoles + + table = _install_litellm_config(mock_prisma) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + fake_proxy_config = MagicMock() + fake_proxy_config.add_deployment = AsyncMock() + monkeypatch.setattr(ps, "proxy_config", fake_proxy_config) + + with auth_as(LitellmUserRoles.PROXY_ADMIN): + response = client.post( + "/config/update", + json={"router_settings": {"disable_cooldowns": True}}, + ) + + assert response.status_code == 200 + persisted = json.loads(table.upsert.call_args.kwargs["data"]["create"]["param_value"]) + assert persisted["disable_cooldowns"] is True + + +def test_config_update_rejects_assistants_config(client, auth_as, mock_prisma, monkeypatch): + from litellm.proxy import proxy_server as ps + from litellm.proxy._types import LitellmUserRoles + + table = _install_litellm_config(mock_prisma) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + + with auth_as(LitellmUserRoles.PROXY_ADMIN): + response = client.post( + "/config/update", + json={"router_settings": {"assistants_config": {"enabled": True}}}, + ) + + assert response.status_code == 400 + assert "assistants_config" in response.json()["error"]["message"] + table.upsert.assert_not_called() + + +def test_config_update_rejects_router_general_settings(client, auth_as, mock_prisma, monkeypatch): + from litellm.proxy import proxy_server as ps + from litellm.proxy._types import LitellmUserRoles + + table = _install_litellm_config(mock_prisma) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + + with auth_as(LitellmUserRoles.PROXY_ADMIN): + response = client.post( + "/config/update", + json={"router_settings": {"router_general_settings": {"async_only_mode": True}}}, + ) + + assert response.status_code == 400 + assert "router_general_settings" in response.json()["error"]["message"] + table.upsert.assert_not_called() + + +def test_config_update_rejects_unknown_router_setting(client, auth_as, mock_prisma, monkeypatch): + from litellm.proxy import proxy_server as ps + from litellm.proxy._types import LitellmUserRoles + + table = _install_litellm_config(mock_prisma) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + + with auth_as(LitellmUserRoles.PROXY_ADMIN): + response = client.post( + "/config/update", + json={"router_settings": {"optional_precall_checks": ["prompt_caching"]}}, + ) + + assert response.status_code == 400 + assert "optional_precall_checks" in response.json()["error"]["message"] + table.upsert.assert_not_called() + + +def test_config_update_unknown_router_setting_non_admin_forbidden(client, auth_as, mock_prisma, monkeypatch): + from litellm.proxy import proxy_server as ps + from litellm.proxy._types import LitellmUserRoles + + _install_litellm_config(mock_prisma) + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + + with auth_as(LitellmUserRoles.INTERNAL_USER): + response = client.post( + "/config/update", + json={"router_settings": {"optional_precall_checks": ["prompt_caching"]}}, + ) + + assert response.status_code == 403 + assert "admin" in response.json()["error"]["message"].lower() + + def test_config_update_non_admin_forbidden(client, auth_as, mock_prisma, monkeypatch): """POST /config/update by a non-admin caller is rejected; the error surfaces as a ProxyException with the admin-only message.""" diff --git a/tests/test_litellm/proxy/proxy_server/test_routes_models.py b/tests/test_litellm/proxy/proxy_server/test_routes_models.py index 2b126b1ea95..bc6106a06f8 100644 --- a/tests/test_litellm/proxy/proxy_server/test_routes_models.py +++ b/tests/test_litellm/proxy/proxy_server/test_routes_models.py @@ -45,6 +45,7 @@ def patched_models(monkeypatch): deployment = MagicMock() deployment.litellm_params.model = "gpt-4" router.get_deployment_by_model_group_name = MagicMock(return_value=deployment) + router.get_configured_display_name = MagicMock(return_value=None) monkeypatch.setattr(proxy_server, "llm_router", router) monkeypatch.setattr(proxy_server, "prisma_client", MagicMock()) @@ -187,6 +188,83 @@ def test_anthropic_format_carries_router_configured_token_limits(client, auth_as assert (claude["max_input_tokens"], claude["max_tokens"]) == (500000, 4096) +@pytest.mark.parametrize("path", ["/v1/models", "/models"]) +def test_anthropic_format_uses_configured_display_name(client, auth_as, patched_models, path): + """A deployment's ``model_info.display_name`` becomes the Anthropic-native + ``display_name`` so Claude Code's picker shows a clean name while the id keeps + routing; models without one keep the id fallback, and the OpenAI-shaped + listing carries no display_name either way.""" + + def _configured(model_name): + return "Kimi K3" if model_name == "gpt-4" else None + + patched_models.get_configured_display_name = MagicMock(side_effect=_configured) + + with auth_as(): + anthropic_response = client.get(path, headers={"anthropic-version": "2023-06-01"}) + openai_response = client.get(path) + + assert anthropic_response.status_code == 200 + gpt_4, claude = anthropic_response.json()["data"] + assert (gpt_4["id"], gpt_4["display_name"]) == ("gpt-4", "Kimi K3") + assert (claude["id"], claude["display_name"]) == ("claude-sonnet", "claude-sonnet") + + assert openai_response.status_code == 200 + openai_models = openai_response.json()["data"] + assert [m["id"] for m in openai_models] == ["gpt-4", "claude-sonnet"] + assert all("display_name" not in m for m in openai_models) + + +@pytest.mark.parametrize("params", [{}, {"scope": "expand"}]) +def test_anthropic_display_name_resolved_via_internal_team_key( + client, auth_as, patched_models, monkeypatch, params +): + """For a team-scoped row the configured display name must be looked up by the + internal routing key while the entry itself is keyed by the public name, so + the clean name lands on the id the client actually sees.""" + from litellm.proxy import utils as proxy_utils + from litellm.proxy.auth import model_checks + + internal_name = "model_name_team-1_c0ffee" + + patched_models.get_model_list = MagicMock( + return_value=[ + { + "model_name": internal_name, + "model_info": { + "team_id": "team-1", + "team_public_model_name": "gpt-4-team", + }, + } + ] + ) + patched_models.get_model_names = MagicMock(return_value=[internal_name]) + patched_models.get_configured_display_name = MagicMock( + side_effect=lambda model_name: "Team GPT" if model_name == internal_name else None + ) + + async def _fake_get_available_models_for_user(**kwargs): + return [internal_name] + + monkeypatch.setattr( + proxy_utils, + "get_available_models_for_user", + _fake_get_available_models_for_user, + ) + monkeypatch.setattr( + model_checks, "get_complete_model_list", lambda **kwargs: [internal_name] + ) + + with auth_as(): + response = client.get( + "/v1/models", params=params, headers={"anthropic-version": "2023-06-01"} + ) + + assert response.status_code == 200 + (entry,) = response.json()["data"] + assert (entry["id"], entry["display_name"]) == ("gpt-4-team", "Team GPT") + + @pytest.mark.parametrize("path", ["/v1/models", "/models"]) def test_get_models_invalid_scope_returns_400(client, auth_as, patched_models, path): """Pins: ``GET /v1/models``, ``GET /models`` (error path: invalid scope).""" diff --git a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py index aa35fd64f18..0fb9b1a6d88 100644 --- a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py +++ b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py @@ -19,7 +19,10 @@ from litellm.proxy._types import ( LitellmUserRoles, UserAPIKeyAuth, ) -from litellm.proxy.common_utils.model_listing_utils import TeamModelNameTranslator +from litellm.proxy.common_utils.model_listing_utils import ( + TeamModelNameTranslator, + configured_display_names, +) from litellm.proxy.proxy_server import ( _get_proxy_model_info, _translate_model_name_for_response, @@ -1391,6 +1394,27 @@ def test_resolve_public_name_respects_legacy_flag(): ) +def test_configured_display_names_keyed_by_response_id(): + """The map is keyed by the public response id while the router lookup uses + the internal routing key, and entries without a configured name are omitted.""" + router = MagicMock() + router.get_configured_display_name = MagicMock( + side_effect=lambda model_name: "Team Sonnet" if model_name == "model_name_team-abc-123_4a6b8" else None + ) + + assert configured_display_names( + entries=[ + ("team-claude-sonnet", "model_name_team-abc-123_4a6b8"), + ("gpt-4o", "gpt-4o"), + ], + llm_router=router, + ) == {"team-claude-sonnet": "Team Sonnet"} + + +def test_configured_display_names_empty_without_router(): + assert configured_display_names(entries=[("gpt-4o", "gpt-4o")], llm_router=None) == {} + + @pytest.mark.asyncio async def test_retrieve_model_by_public_name_returns_200(monkeypatch): """Regression: `GET /v1/models/{public_name}` must NOT 404. The listing diff --git a/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py b/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py index abbf6892a98..0085b6ebd36 100644 --- a/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py +++ b/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py @@ -324,6 +324,127 @@ def test_rag_query_stream_returns_event_stream(client_internal_user): assert "data: [DONE]" in response.text +def test_rag_query_merges_managed_store_params(client_internal_user): + """ + Regression: /v1/rag/query must consult the managed vector store registry + (like the direct /v1/vector_stores/{id}/search endpoint does) so that + provider, region, embedding model, etc. don't have to be repeated in + retrieval_config. Pre-fix the registry was never read, so managed S3 + Vectors stores failed with "aws_region_name is required". + """ + import litellm + from litellm.types.utils import ModelResponse + + mock_vector_store = { + "vector_store_id": "s3-store", + "custom_llm_provider": "s3_vectors", + "litellm_params": { + "aws_region_name": "eu-west-1", + "embedding_model": "my-embed", + "vector_bucket_name": "bkt", + }, + } + mock_registry = MagicMock() + mock_registry.get_litellm_managed_vector_store_from_registry.return_value = mock_vector_store + + mock_response = ModelResponse( + id="chatcmpl-test", + choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}], + model="gpt-4o-mini", + ) + + with patch( # test-quality-ok: aquery is the endpoint's downstream boundary; the forwarded config is what the test asserts + "litellm.proxy.rag_endpoints.endpoints.litellm.aquery", + new_callable=AsyncMock, + return_value=mock_response, + ) as mock_aquery, patch.object(litellm, "vector_store_registry", mock_registry), patch( # test-quality-ok: seeds the managed-store registry the merge under test reads and grants access so real store resolution runs + "litellm.proxy.vector_store_endpoints.utils.can_user_access_vector_store", + new=AsyncMock(return_value=True), + ): + response = client_internal_user.post( + "/v1/rag/query", + json={ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "hello"}], + "retrieval_config": {"vector_store_id": "s3-store"}, + }, + ) + + assert response.status_code == 200, response.json() + mock_aquery.assert_awaited_once() + forwarded_config = mock_aquery.await_args.kwargs["retrieval_config"] + assert forwarded_config["vector_store_id"] == "s3-store" + assert forwarded_config["custom_llm_provider"] == "s3_vectors" + assert forwarded_config["aws_region_name"] == "eu-west-1" + assert forwarded_config["embedding_model"] == "my-embed" + assert forwarded_config["vector_bucket_name"] == "bkt" + + +def test_rag_query_store_params_win_over_user_retrieval_config(client_internal_user): + """Registry values must win over user-supplied retrieval_config keys so callers cannot override store credentials.""" + import litellm + from litellm.types.utils import ModelResponse + + mock_vector_store = { + "vector_store_id": "s3-store", + "custom_llm_provider": "s3_vectors", + "litellm_params": {"aws_region_name": "eu-west-1"}, + } + mock_registry = MagicMock() + mock_registry.get_litellm_managed_vector_store_from_registry.return_value = mock_vector_store + + mock_response = ModelResponse( + id="chatcmpl-test", + choices=[{"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}], + model="gpt-4o-mini", + ) + + with patch( # test-quality-ok: aquery is the endpoint's downstream boundary; the forwarded config is what the test asserts + "litellm.proxy.rag_endpoints.endpoints.litellm.aquery", + new_callable=AsyncMock, + return_value=mock_response, + ) as mock_aquery, patch.object(litellm, "vector_store_registry", mock_registry), patch( # test-quality-ok: seeds the managed-store registry the merge under test reads and grants access so real store resolution runs + "litellm.proxy.vector_store_endpoints.utils.can_user_access_vector_store", + new=AsyncMock(return_value=True), + ): + response = client_internal_user.post( + "/v1/rag/query", + json={ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "hello"}], + "retrieval_config": {"vector_store_id": "s3-store", "aws_region_name": "us-east-1"}, + }, + ) + + assert response.status_code == 200, response.json() + forwarded_config = mock_aquery.await_args.kwargs["retrieval_config"] + assert forwarded_config["aws_region_name"] == "eu-west-1" + + +@pytest.mark.parametrize( + "blocked_key", + ["embedding_model", "litellm_embedding_model", "litellm_embedding_config", "litellm_credential_name"], +) +def test_rag_query_rejects_caller_embedding_selection_params(client_internal_user, blocked_key): + """ + Regression: a caller must not pick the embedding model or credential used at + search time. Those resolve through the Router with the proxy's credentials, + bypassing the key's model permissions, so they may only come from the + managed store's server-side registration. + """ + response = client_internal_user.post( + "/v1/rag/query", + json={ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "hello"}], + "retrieval_config": {"vector_store_id": "s3-store", blocked_key: "attacker-choice"}, + }, + ) + + assert response.status_code == 400, response.json() + assert blocked_key in str(response.json()) + + EICAR = r"X5O!P%@AP[4\PZX54(P^)7CC)7}$EICAR-STANDARD-ANTIVIRUS-TEST-FILE!$H+H*" INGEST_REQUEST = '{"ingest_options":{"vector_store":{"custom_llm_provider":"openai"}}}' diff --git a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py b/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py index eae6f90863a..45a0221c8a6 100644 --- a/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py +++ b/tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py @@ -3158,3 +3158,35 @@ class TestAzureAIAnalyzeNamedIndexClassification: user_api_key_dict=self._team_member("analyze", ["read"]), ) assert result is True + + +@pytest.mark.parametrize( + "blocked_key", + ["embedding_model", "litellm_embedding_model", "litellm_embedding_config", "litellm_credential_name"], +) +def test_vector_store_search_rejects_caller_embedding_selection_params(blocked_key): + """ + Regression: the search request body must not pick the embedding model or + credential used to embed the query. Those resolve through the Router with + the proxy's credentials, bypassing the key's model permissions, so they may + only come from the managed store's server-side registration. + """ + from fastapi.testclient import TestClient + + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + from litellm.proxy.proxy_server import app + + mock_auth = UserAPIKeyAuth(user_id="test_internal_user", user_role=LitellmUserRoles.INTERNAL_USER.value) + original_overrides = app.dependency_overrides.copy() + app.dependency_overrides[user_api_key_auth] = lambda: mock_auth + try: + client = TestClient(app) + response = client.post( + "/v1/vector_stores/s3-store/search", + json={"query": "hello", blocked_key: "attacker-choice"}, + ) + finally: + app.dependency_overrides = original_overrides + + assert response.status_code == 400, response.json() + assert blocked_key in str(response.json()) diff --git a/tests/test_litellm/rag/test_main.py b/tests/test_litellm/rag/test_main.py index 2d1b460513f..51d03544910 100644 --- a/tests/test_litellm/rag/test_main.py +++ b/tests/test_litellm/rag/test_main.py @@ -259,6 +259,135 @@ async def test_aquery_streaming_bills_sub_call_costs_into_final_event(): assert standard_logging_object["response_cost"] >= 0.003 +@pytest.mark.asyncio +async def test_aquery_forwards_provider_retrieval_config_and_router_to_search(): + """ + Regression: provider-specific retrieval_config keys (aws_region_name, + embedding_model, vector_bucket_name, ...) and the router must be forwarded + to the vector store search call. Pre-fix they were silently dropped, so + /v1/rag/query failed with provider config errors (e.g. S3 Vectors + "aws_region_name is required") even when the caller supplied them. + """ + from unittest.mock import AsyncMock + + from litellm.types.vector_stores import VectorStoreSearchResponse + + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4o-mini", + "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "test-key"}, + } + ] + ) + + fake_search = AsyncMock( + return_value=VectorStoreSearchResponse( + object="vector_store.search_results.page", search_query="q", data=[] + ) + ) + with patch("litellm.vector_stores.asearch", new=fake_search): # test-quality-ok: asearch is the boundary the forwarding contract under test targets + response = await litellm.aquery( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hello"}], + retrieval_config={ + "vector_store_id": "bkt:idx", + "custom_llm_provider": "s3_vectors", + "top_k": 5, + "aws_region_name": "eu-west-1", + "embedding_model": "my-embed", + "vector_bucket_name": "bkt", + }, + router=router, + mock_response="hi", + ) + + assert isinstance(response, ModelResponse) + fake_search.assert_awaited_once() + search_kwargs = fake_search.await_args.kwargs + assert search_kwargs["vector_store_id"] == "bkt:idx" + assert search_kwargs["custom_llm_provider"] == "s3_vectors" + assert search_kwargs["max_num_results"] == 5 + assert search_kwargs["router"] is router + # provider-specific extras forwarded + assert search_kwargs["aws_region_name"] == "eu-west-1" + assert search_kwargs["embedding_model"] == "my-embed" + assert search_kwargs["vector_bucket_name"] == "bkt" + # consumed keys are not duplicated into the spread + assert "top_k" not in search_kwargs + + +@pytest.mark.asyncio +async def test_aquery_minimal_retrieval_config_forwards_no_extras(): + """ + A minimal retrieval_config must not leak consumed keys (or invent extras) + into the vector store search call. + """ + from unittest.mock import AsyncMock + + from litellm.types.vector_stores import VectorStoreSearchResponse + + fake_search = AsyncMock( + return_value=VectorStoreSearchResponse( + object="vector_store.search_results.page", search_query="q", data=[] + ) + ) + with patch("litellm.vector_stores.asearch", new=fake_search): # test-quality-ok: asearch is the boundary the forwarding contract under test targets + await litellm.aquery( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hello"}], + retrieval_config={"vector_store_id": "vs_test_123", "custom_llm_provider": "openai"}, + mock_response="hi", + ) + + fake_search.assert_awaited_once() + search_kwargs = fake_search.await_args.kwargs + assert search_kwargs["vector_store_id"] == "vs_test_123" + assert search_kwargs["custom_llm_provider"] == "openai" + assert search_kwargs["router"] is None + leaked = {"top_k", "filters", "retrieval_filter", "aws_region_name", "embedding_model", "vector_bucket_name"} + assert not (leaked & set(search_kwargs.keys())) + + +@pytest.mark.asyncio +async def test_aquery_does_not_forward_connection_override_keys_to_search(): + """ + Only allowlisted retrieval_config keys may reach the vector store search + call. Caller-controlled connection overrides (api_base, api_key, arbitrary + extras) must be dropped, otherwise a caller could redirect store + credentials to an attacker-chosen host. + """ + from unittest.mock import AsyncMock + + from litellm.types.vector_stores import VectorStoreSearchResponse + + fake_search = AsyncMock( + return_value=VectorStoreSearchResponse( + object="vector_store.search_results.page", search_query="q", data=[] + ) + ) + with patch("litellm.vector_stores.asearch", new=fake_search): # test-quality-ok: asearch is the boundary the forwarding contract under test targets + await litellm.aquery( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hello"}], + retrieval_config={ + "vector_store_id": "bkt:idx", + "custom_llm_provider": "s3_vectors", + "aws_region_name": "eu-west-1", + "api_base": "https://attacker.example.com", + "api_key": "attacker-key", + "arbitrary_extra": "nope", + }, + mock_response="hi", + ) + + fake_search.assert_awaited_once() + search_kwargs = fake_search.await_args.kwargs + assert search_kwargs["aws_region_name"] == "eu-west-1" + blocked = {"api_base", "api_key", "arbitrary_extra"} + assert not (blocked & set(search_kwargs.keys())) + + def test_rag_call_types_are_registered(): """ query/aquery/ingest/aingest are @client-decorated entry points, so their diff --git a/tests/test_litellm/rerank_api/test_main.py b/tests/test_litellm/rerank_api/test_main.py index 587be59c550..2b6cfeda2c2 100644 --- a/tests/test_litellm/rerank_api/test_main.py +++ b/tests/test_litellm/rerank_api/test_main.py @@ -111,6 +111,99 @@ def test_together_rerank_honors_api_base(respx_mock: respx.MockRouter): assert mock_route.calls[0].request.headers["authorization"] == "Bearer fake-together-key" +DASHSCOPE_404_BODY = { + "error": { + "message": "The model `does-not-exist` does not exist or you do not have access to it.", + "type": "invalid_request_error", + "param": None, + "code": "model_not_found", + }, + "request_id": "mock-request-id", +} + + +def test_rerank_error_names_provider_and_keeps_body(respx_mock: respx.MockRouter, monkeypatch): + """Regression for the rerank error path mapping with the unresolved provider param: + a provider 404 surfaced as 'None - ' instead of naming the provider and its error body.""" + monkeypatch.delenv("DASHSCOPE_API_BASE", raising=False) + monkeypatch.delenv("DASHSCOPE_API_BASE_RERANK", raising=False) + + mock_route = respx_mock.post("https://dashscope.example/v1/reranks") + mock_route.return_value = httpx.Response(404, json=DASHSCOPE_404_BODY) + + with pytest.raises(litellm.NotFoundError) as exc_info: + litellm.rerank( + model="dashscope/does-not-exist", + query=MARKER_QUERY, + documents=[MARKER_DOC], + api_key="fake-dashscope-key", + api_base="https://dashscope.example/v1", + ) + + assert mock_route.called + assert "DashscopeException" in str(exc_info.value) + assert "does not exist or you do not have access to it" in str(exc_info.value) + assert "None - " not in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_arerank_error_is_mapped_to_litellm_exception(respx_mock: respx.MockRouter, monkeypatch): + """Regression for arerank's bare re-raise: provider errors escaped as raw + provider exception classes instead of the mapped litellm exception contract.""" + monkeypatch.delenv("DASHSCOPE_API_BASE", raising=False) + monkeypatch.delenv("DASHSCOPE_API_BASE_RERANK", raising=False) + monkeypatch.setenv("DISABLE_AIOHTTP_TRANSPORT", "True") + + mock_route = respx_mock.post("https://dashscope.example/v1/reranks") + mock_route.return_value = httpx.Response(404, json=DASHSCOPE_404_BODY) + + with pytest.raises(litellm.NotFoundError) as exc_info: + await litellm.arerank( + model="dashscope/does-not-exist", + query=MARKER_QUERY, + documents=[MARKER_DOC], + api_key="fake-dashscope-key", + api_base="https://dashscope.example/v1", + ) + + assert mock_route.called + assert "DashscopeException" in str(exc_info.value) + assert "does not exist or you do not have access to it" in str(exc_info.value) + assert "None - " not in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_arerank_declared_authenticating_provider_skips_resolution(monkeypatch): + """Regression for the event-loop hazard in arerank's provider pre-resolution: + get_llm_provider runs the blocking OAuth device flow for github_copilot/chatgpt, + so arerank must adopt the declared provider instead of resolving it, while the + except path still maps with that declared provider.""" + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + resolution_calls = [] + + def record_resolution(*args, **kwargs): + resolution_calls.append((args, kwargs)) + return "gpt-4o", "github_copilot", None, None + + def rerank_raises_provider_error(*args, **kwargs): + raise BaseLLMException(status_code=401, message='{"error":"bad key"}') + + monkeypatch.setattr(litellm, "get_llm_provider", record_resolution) + monkeypatch.setattr("litellm.rerank_api.main.rerank", rerank_raises_provider_error) + + with pytest.raises(litellm.AuthenticationError) as exc_info: + await litellm.arerank( + model="github_copilot/gpt-4o", + query=MARKER_QUERY, + documents=[MARKER_DOC], + ) + + assert resolution_calls == [] + assert "Github_copilotException" in str(exc_info.value) + assert "None - " not in str(exc_info.value) + + @pytest.mark.asyncio async def test_together_rerank_async_honors_env_api_base(respx_mock: respx.MockRouter, monkeypatch): """Regression: TOGETHER_AI_API_BASE was honored by chat but ignored by rerank.""" diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index 677faf7f655..9edcaaef034 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -326,3 +326,55 @@ def test_run_post_success_hooks_does_not_report_generation_time_as_overhead(): assert iterator.completed_response._hidden_params["_response_ms"] == 10000.0 assert "litellm_overhead_time_ms" not in iterator.completed_response._hidden_params + + +def _responses_api_response_with_usage() -> ResponsesAPIResponse: + from litellm.types.llms.openai import ResponseAPIUsage + + return ResponsesAPIResponse( + id="resp_lit6427", + created_at=int(datetime(2025, 1, 1).timestamp()), + status="completed", + model="mantle-claude", + object="response", + output=[], + usage=ResponseAPIUsage(input_tokens=20, output_tokens=60, total_tokens=80), + ) + + +def test_stamp_responses_usage_cost_stamps_computed_cost(): + from litellm.responses.streaming_iterator import _stamp_responses_usage_cost + + response = _responses_api_response_with_usage() + logging_obj = Mock(spec=LiteLLMLoggingObj) + logging_obj._response_cost_calculator.return_value = 0.000704 + + _stamp_responses_usage_cost(response, logging_obj) + + assert getattr(response.usage, "cost", None) == pytest.approx(0.000704) + logging_obj._response_cost_calculator.assert_called_once_with(result=response) + + +def test_stamp_responses_usage_cost_keeps_provider_reported_cost(): + from litellm.responses.streaming_iterator import _stamp_responses_usage_cost + + response = _responses_api_response_with_usage() + setattr(response.usage, "cost", 0.5) + logging_obj = Mock(spec=LiteLLMLoggingObj) + + _stamp_responses_usage_cost(response, logging_obj) + + assert getattr(response.usage, "cost", None) == pytest.approx(0.5) + logging_obj._response_cost_calculator.assert_not_called() + + +def test_stamp_responses_usage_cost_survives_calculator_failure(): + from litellm.responses.streaming_iterator import _stamp_responses_usage_cost + + response = _responses_api_response_with_usage() + logging_obj = Mock(spec=LiteLLMLoggingObj) + logging_obj._response_cost_calculator.side_effect = RuntimeError("cost map unavailable") + + _stamp_responses_usage_cost(response, logging_obj) + + assert getattr(response.usage, "cost", None) is None diff --git a/tests/test_litellm/test_bedrock_usgov_pricing.py b/tests/test_litellm/test_bedrock_usgov_pricing.py index 6b3312b5cc4..f7d95ecda01 100644 --- a/tests/test_litellm/test_bedrock_usgov_pricing.py +++ b/tests/test_litellm/test_bedrock_usgov_pricing.py @@ -26,9 +26,7 @@ import pytest @pytest.fixture(scope="module") def model_data(): - json_path = os.path.join( - os.path.dirname(__file__), "../../model_prices_and_context_window.json" - ) + json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json") with open(json_path) as f: return json.load(f) @@ -51,21 +49,14 @@ def test_usgov_sonnet_4_5_pricing(model_data, model_key): info = model_data[model_key] assert info["input_cost_per_token"] == 3.6e-06, ( - f"{model_key}: input_cost_per_token should be $3.60/MTok " - f"(got {info['input_cost_per_token']})" + f"{model_key}: input_cost_per_token should be $3.60/MTok (got {info['input_cost_per_token']})" ) - assert ( - info["output_cost_per_token"] == 1.8e-05 - ), f"{model_key}: output_cost_per_token should be $18.00/MTok" - assert ( - info["cache_creation_input_token_cost"] == 4.5e-06 - ), f"{model_key}: 5m cache write should be $4.50/MTok" - assert ( - info["cache_creation_input_token_cost_above_1hr"] == 7.2e-06 - ), f"{model_key}: 1h cache write should be $7.20/MTok" - assert ( - info["cache_read_input_token_cost"] == 3.6e-07 - ), f"{model_key}: cache read should be $0.36/MTok" + assert info["output_cost_per_token"] == 1.8e-05, f"{model_key}: output_cost_per_token should be $18.00/MTok" + assert info["cache_creation_input_token_cost"] == 4.5e-06, f"{model_key}: 5m cache write should be $4.50/MTok" + assert info["cache_creation_input_token_cost_above_1hr"] == 7.2e-06, ( + f"{model_key}: 1h cache write should be $7.20/MTok" + ) + assert info["cache_read_input_token_cost"] == 3.6e-07, f"{model_key}: cache read should be $0.36/MTok" def test_usgov_carries_20_percent_premium_over_global(model_data): @@ -84,9 +75,7 @@ def test_usgov_carries_20_percent_premium_over_global(model_data): "cache_read_input_token_cost", ): ratio = usgov_info[field] / global_info[field] - assert ( - abs(ratio - 1.2) < 1e-9 - ), f"{field}: us-gov / global ratio is {ratio}, expected 1.2" + assert abs(ratio - 1.2) < 1e-9, f"{field}: us-gov / global ratio is {ratio}, expected 1.2" # The us-gov.anthropic.* cross-region inference profile is the only us-gov @@ -112,9 +101,7 @@ def test_usgov_cross_region_above_200k_carries_gov_premium(model_data, field, ex """ info = model_data[USGOV_CROSS_REGION_KEY] assert field in info, f"{USGOV_CROSS_REGION_KEY}: missing field {field}" - assert ( - info[field] == expected - ), f"{USGOV_CROSS_REGION_KEY}: {field} should be {expected} (got {info[field]})" + assert info[field] == expected, f"{USGOV_CROSS_REGION_KEY}: {field} should be {expected} (got {info[field]})" def test_usgov_cross_region_above_200k_ratio_to_global(model_data): @@ -127,6 +114,176 @@ def test_usgov_cross_region_above_200k_ratio_to_global(model_data): usgov_info = model_data[USGOV_CROSS_REGION_KEY] for field in EXPECTED_USGOV_ABOVE_200K: ratio = usgov_info[field] / global_info[field] - assert ( - abs(ratio - 1.2) < 1e-9 - ), f"{field}: us-gov / global ratio is {ratio}, expected 1.2" + assert abs(ratio - 1.2) < 1e-9, f"{field}: us-gov / global ratio is {ratio}, expected 1.2" + + +CLAUDE_GOV_EXPECTED = { + "anthropic.claude-sonnet-5": { + "input_cost_per_token": 2.4e-06, + "output_cost_per_token": 1.2e-05, + "cache_creation_input_token_cost": 3e-06, + "cache_creation_input_token_cost_above_1hr": 4.8e-06, + "cache_read_input_token_cost": 2.4e-07, + }, + "anthropic.claude-opus-4-8": { + "input_cost_per_token": 6e-06, + "output_cost_per_token": 3e-05, + "cache_creation_input_token_cost": 7.5e-06, + "cache_creation_input_token_cost_above_1hr": 1.2e-05, + "cache_read_input_token_cost": 6e-07, + }, +} + + +USGOV_CLAUDE_KEY_TEMPLATES = { + "bedrock/us-gov-east-1/{base_key}": "bedrock", + "bedrock/us-gov-west-1/{base_key}": "bedrock", + "us-gov.{base_key}": "bedrock_converse", +} + + +@pytest.mark.parametrize("base_key", CLAUDE_GOV_EXPECTED) +@pytest.mark.parametrize("key_template,expected_provider", USGOV_CLAUDE_KEY_TEMPLATES.items()) +def test_usgov_claude_sonnet5_opus48_pricing(model_data, key_template, expected_provider, base_key): + """Sonnet 5 and Opus 4.8 gov entries, both in-region keys and the us-gov. + geo inference profile the model cards list for GovCloud, must match the + rates AWS publishes on the Bedrock pricing page (1.2x global). + """ + gov_key = key_template.format(base_key=base_key) + assert gov_key in model_data, f"Missing model entry: {gov_key}" + info = model_data[gov_key] + assert info["litellm_provider"] == expected_provider + for field, expected in CLAUDE_GOV_EXPECTED[base_key].items(): + assert info[field] == expected, f"{gov_key}: {field} should be {expected} (got {info[field]})" + ratio = info[field] / model_data[base_key][field] + assert abs(ratio - 1.2) < 1e-9, f"{gov_key}: {field} gov/global ratio is {ratio}, expected 1.2" + + +CONVERSE_GOV_EXPECTED = { + "nvidia.nemotron-nano-3-30b": (7.2e-08, 2.88e-07), + "nvidia.nemotron-nano-12b-v2": (2.4e-07, 7.2e-07), + "nvidia.nemotron-super-3-120b": (1.8e-07, 7.8e-07), + "openai.gpt-oss-20b-1:0": (8.4e-08, 3.6e-07), + "openai.gpt-oss-120b-1:0": (1.8e-07, 7.2e-07), +} + + +@pytest.mark.parametrize("base_key", CONVERSE_GOV_EXPECTED) +@pytest.mark.parametrize("region", ["us-gov-east-1", "us-gov-west-1"]) +def test_usgov_converse_model_pricing(model_data, region, base_key): + """Nemotron and gpt-oss gov entries must match the AWS Bedrock offer file, + which prices both GovCloud regions identically at 1.2x commercial. + """ + gov_key = f"bedrock/{region}/{base_key}" + assert gov_key in model_data, f"Missing model entry: {gov_key}" + info = model_data[gov_key] + expected_input, expected_output = CONVERSE_GOV_EXPECTED[base_key] + assert info["input_cost_per_token"] == expected_input + assert info["output_cost_per_token"] == expected_output + assert info["litellm_provider"] == "bedrock" + base = model_data[base_key] + assert abs(info["input_cost_per_token"] / base["input_cost_per_token"] - 1.2) < 1e-9 + assert abs(info["output_cost_per_token"] / base["output_cost_per_token"] - 1.2) < 1e-9 + + +def test_usgov_west_llama3_8b_output_price_fixed(model_data): + """The us-gov-west-1 llama3-8b entry carried the 70B output rate ($2.65/MTok); + the AWS Bedrock offer file prices output at $0.60/MTok. AWS lists the model + in us-gov-west-1 only, so there is no east entry to check. + """ + info = model_data["bedrock/us-gov-west-1/meta.llama3-8b-instruct-v1:0"] + assert info["input_cost_per_token"] == 3e-07 + assert info["output_cost_per_token"] == 6e-07 + + +MANTLE_GOV_TIERED_EXPECTED = { + "openai.gpt-5.6-luna": { + "input_cost_per_token": 2.64e-07, + "input_cost_per_token_above_272k_tokens": 5.28e-07, + "cache_creation_input_token_cost": 3.3e-07, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-07, + "cache_read_input_token_cost": 2.64e-08, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-08, + "output_cost_per_token": 1.584e-06, + "output_cost_per_token_above_272k_tokens": 2.376e-06, + }, + "openai.gpt-5.6-terra": { + "input_cost_per_token": 2.64e-06, + "input_cost_per_token_above_272k_tokens": 5.28e-06, + "cache_creation_input_token_cost": 3.3e-06, + "cache_creation_input_token_cost_above_272k_tokens": 6.6e-06, + "cache_read_input_token_cost": 2.64e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.28e-07, + "output_cost_per_token": 1.584e-05, + "output_cost_per_token_above_272k_tokens": 2.376e-05, + }, +} + + +@pytest.mark.parametrize("model", MANTLE_GOV_TIERED_EXPECTED) +def test_usgov_west_mantle_terra_luna_pricing(model_data, model): + """Terra and Luna carry 1.2x commercial across every tier in the + us-gov-west-1 offer file; the us-gov-east-1 offer file has no SKUs for them. + """ + gov_key = f"bedrock_mantle/us-gov-west-1/{model}" + assert gov_key in model_data, f"Missing model entry: {gov_key}" + info = model_data[gov_key] + for field, expected in MANTLE_GOV_TIERED_EXPECTED[model].items(): + assert info[field] == expected, f"{gov_key}: {field} should be {expected} (got {info[field]})" + assert info["litellm_provider"] == "bedrock_mantle" + assert f"bedrock_mantle/us-gov-east-1/{model}" not in model_data + + +@pytest.mark.parametrize("region", ["us-gov-east-1", "us-gov-west-1"]) +def test_usgov_mantle_gpt_5_4_pricing_has_no_long_context_tier(model_data, region): + """gpt-5.4 gov rates come from the offer file, which publishes only the + standard tier in GovCloud: no long-context SKUs exist there, unlike commercial. + """ + gov_key = f"bedrock_mantle/{region}/openai.gpt-5.4" + assert gov_key in model_data, f"Missing model entry: {gov_key}" + info = model_data[gov_key] + assert info["input_cost_per_token"] == 3.3e-06 + assert info["cache_read_input_token_cost"] == 3.3e-07 + assert info["output_cost_per_token"] == 1.98e-05 + assert not any(field.endswith("_above_272k_tokens") for field in info) + + +def test_usgov_mantle_grok_4_3_west_only(model_data): + """grok-4.3 is priced in the us-gov-west-1 offer file only; the east offer + file carries grok-4.6 instead. + """ + info = model_data["bedrock_mantle/us-gov-west-1/xai.grok-4.3"] + assert info["input_cost_per_token"] == 1.5e-06 + assert info["output_cost_per_token"] == 3e-06 + assert info["cache_read_input_token_cost"] == 2.4e-07 + assert "bedrock_mantle/us-gov-east-1/xai.grok-4.3" not in model_data + + +AZURE_GOV_EXPECTED = { + "azure/us-gov/gpt-5.1": { + "input_cost_per_token": 1.71875e-06, + "cache_read_input_token_cost": 1.71875e-07, + "output_cost_per_token": 1.375e-05, + }, + "azure/us-gov/o3-mini": { + "input_cost_per_token": 1.513e-06, + "cache_read_input_token_cost": 7.57e-07, + "output_cost_per_token": 6.05e-06, + }, + "azure/us-gov/text-embedding-3-large": {"input_cost_per_token": 1.63e-07}, + "azure/us-gov/text-embedding-3-small": {"input_cost_per_token": 2.5e-08}, +} + + +@pytest.mark.parametrize("gov_key", AZURE_GOV_EXPECTED) +def test_azure_usgov_pricing(model_data, gov_key): + """Azure Government meters from the Azure retail prices API + (usgovvirginia/usgovarizona, serviceName 'Foundry Models'). No Government + retirement schedule is published, so these entries carry no deprecation_date. + """ + assert gov_key in model_data, f"Missing model entry: {gov_key}" + info = model_data[gov_key] + for field, expected in AZURE_GOV_EXPECTED[gov_key].items(): + assert info[field] == expected, f"{gov_key}: {field} should be {expected} (got {info[field]})" + assert info["litellm_provider"] == "azure" + assert "deprecation_date" not in info diff --git a/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py b/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py index 9ca4515239a..e33bcfb8378 100644 --- a/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py +++ b/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py @@ -75,6 +75,22 @@ def test_additional_current_models_are_present(): assert entry["output_cost_per_token"] > 0 +@pytest.mark.parametrize( + "key, published_price_per_audio_minute", + [ + ("cloudflare/@cf/openai/whisper", 0.00045), + ("cloudflare/@cf/openai/whisper-large-v3-turbo", 0.00051), + ], +) +def test_whisper_transcription_pricing_is_stored_per_second(key, published_price_per_audio_minute): + entry = litellm.model_cost[key] + assert entry["litellm_provider"] == "cloudflare" + assert entry["mode"] == "audio_transcription" + assert entry["supported_endpoints"] == ["/v1/audio/transcriptions"] + assert entry["output_cost_per_second"] == 0.0 + assert entry["input_cost_per_second"] == pytest.approx(published_price_per_audio_minute / 60) + + def test_root_and_backup_have_identical_cloudflare_keys(): if not os.path.exists(ROOT_MAP): pytest.skip("root cost map only ships in source checkouts") diff --git a/tests/test_litellm/test_main.py b/tests/test_litellm/test_main.py index 8cf878d05d9..7c2b9d0be05 100644 --- a/tests/test_litellm/test_main.py +++ b/tests/test_litellm/test_main.py @@ -3150,8 +3150,8 @@ def _stream_builder_logging_obj() -> LiteLLMLogging: return logging_obj -def test_stream_chunk_builder_reports_streaming_usage_cost_when_enabled(monkeypatch: pytest.MonkeyPatch): - monkeypatch.setattr(litellm, "include_cost_in_streaming_usage", True) +def test_stream_chunk_builder_stamps_streaming_usage_cost_by_default(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(litellm, "include_cost_in_streaming_usage", False) chunks: Final = [ _stream_builder_text_chunk("gpt-4o", "Hello "), _stream_builder_text_chunk("gpt-4o", "world.", finish_reason="stop"), @@ -3168,11 +3168,45 @@ def test_stream_chunk_builder_reports_streaming_usage_cost_when_enabled(monkeypa assert response._hidden_params["response_cost"] == pytest.approx(usage_cost) -def test_stream_chunk_builder_defers_cost_to_logging_obj_when_usage_cost_absent(monkeypatch: pytest.MonkeyPatch): - monkeypatch.setattr(litellm, "include_cost_in_streaming_usage", False) +def test_stream_chunk_builder_skips_stamp_when_cost_is_unpriceable(): + import time as time_module + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging + + logging_obj: Final = LiteLLMLogging( + model="us.anthropic.claude-opus-5", + messages=[{"role": "user", "content": "hi"}], + stream=True, + call_type="completion", + start_time=time_module.time(), + litellm_call_id="stream-builder-alias-unpriceable", + function_id="1", + ) + logging_obj.model_call_details["custom_llm_provider"] = "bedrock" + logging_obj.optional_params = {} + usage_chunk: Final = _stream_builder_text_chunk("bedrock-claude-opus-5", "") + usage_chunk.usage = Usage(prompt_tokens=40, completion_tokens=5, total_tokens=45) + chunks: Final = [ + _stream_builder_text_chunk("bedrock-claude-opus-5", "Hello ", finish_reason="stop"), + usage_chunk, + ] + + response: Final = litellm.stream_chunk_builder( + chunks=chunks, messages=[{"role": "user", "content": "hi"}], logging_obj=logging_obj + ) + + assert response is not None + assert getattr(response.usage, "cost", None) is None + assert response._hidden_params.get("response_cost") is None + + +def test_stream_chunk_builder_keeps_provider_reported_usage_cost(): + usage_chunk: Final = _stream_builder_text_chunk("gpt-4o", "") + usage_chunk.usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15, cost=0.5) chunks: Final = [ _stream_builder_text_chunk("gpt-4o", "Hello "), _stream_builder_text_chunk("gpt-4o", "world.", finish_reason="stop"), + usage_chunk, ] response: Final = litellm.stream_chunk_builder( @@ -3180,4 +3214,26 @@ def test_stream_chunk_builder_defers_cost_to_logging_obj_when_usage_cost_absent( ) assert response is not None - assert response._hidden_params.get("response_cost") is None + assert getattr(response.usage, "cost", None) == pytest.approx(0.5) + assert response._hidden_params["response_cost"] == pytest.approx(0.5) + + +def test_stream_chunk_builder_prices_alias_from_openai_sdk_usage_chunk(): + from openai.types.completion_usage import CompletionUsage + + usage_chunk: Final = _stream_builder_text_chunk("mantle-claude", "") + usage_chunk.usage = CompletionUsage(prompt_tokens=20, completion_tokens=60, total_tokens=80, cost=0.000704) + assert type(usage_chunk.usage) is CompletionUsage + chunks: Final = [ + _stream_builder_text_chunk("mantle-claude", "Hello "), + _stream_builder_text_chunk("mantle-claude", "world.", finish_reason="stop"), + usage_chunk, + ] + + response: Final = litellm.stream_chunk_builder(chunks=chunks, messages=[{"role": "user", "content": "hi"}]) + + assert response is not None + assert response.usage.prompt_tokens == 20 + assert response.usage.completion_tokens == 60 + assert getattr(response.usage, "cost", None) == pytest.approx(0.000704) + assert response._hidden_params["response_cost"] == pytest.approx(0.000704) diff --git a/tests/test_litellm/test_openai_service_tier_long_context_pricing.py b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py new file mode 100644 index 00000000000..c0860a5b55f --- /dev/null +++ b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py @@ -0,0 +1,156 @@ +import json +from functools import lru_cache +from pathlib import Path + +import pytest + +import litellm + +REPO_ROOT = Path(__file__).parents[2] +MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +FLEX_LONG_CONTEXT = { + "gpt-5.4": { + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07, + }, + "gpt-5.4-pro": { + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 0.000135, + }, + "gpt-5.5": { + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "output_cost_per_token_above_272k_tokens_flex": 2.25e-05, + "cache_read_input_token_cost_above_272k_tokens_flex": 5e-07, + }, +} + +PRIORITY_LONG_CONTEXT = { + "gpt-5.6": { + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, + }, + "gpt-5.6-sol": { + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, + }, + "gpt-5.6-terra": { + "input_cost_per_token_above_272k_tokens_priority": 8e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-05, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05, + }, + "gpt-5.6-luna": { + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "output_cost_per_token_above_272k_tokens_priority": 3.6e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06, + }, +} + +EXPECTED = {**FLEX_LONG_CONTEXT, **PRIORITY_LONG_CONTEXT} + +NO_PUBLISHED_PRIORITY_LONG_CONTEXT = ("gpt-5.4", "gpt-5.5") + + +@pytest.fixture(autouse=True) +def _local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + + +@lru_cache(maxsize=2) +def _load(path: Path) -> dict[str, dict[str, object]]: + with open(path) as f: + return json.load(f) + + +@pytest.mark.parametrize("path", [MAIN_PATH, BACKUP_PATH], ids=["main", "backup"]) +@pytest.mark.parametrize("model", sorted(EXPECTED)) +def test_service_tier_long_context_rates_are_published(model: str, path: Path) -> None: + """Each tier must carry its own above-272K rates, in both price files.""" + info = _load(path).get(model) + assert info is not None, f"{model} not found in {path.name}" + for key, expected in EXPECTED[model].items(): + assert info.get(key) == pytest.approx(expected), f"{model}.{key} is {info.get(key)!r}, expected {expected!r}" + + +@pytest.mark.parametrize("model", sorted(EXPECTED)) +def test_tier_long_context_rate_is_half_or_double_the_standard(model: str) -> None: + """Flex is half the standard long-context rate; priority is double it.""" + info = _load(MAIN_PATH)[model] + tier = "flex" if model in FLEX_LONG_CONTEXT else "priority" + ratio = 0.5 if tier == "flex" else 2.0 + for base in ("input_cost_per_token", "output_cost_per_token"): + standard = info[f"{base}_above_272k_tokens"] + tiered = info[f"{base}_above_272k_tokens_{tier}"] + assert tiered == pytest.approx(standard * ratio), ( + f"{model}.{base}_above_272k_tokens_{tier} is {tiered!r}, " + f"expected {ratio}x the standard long-context rate {standard!r}" + ) + + +@pytest.mark.parametrize("model", NO_PUBLISHED_PRIORITY_LONG_CONTEXT) +def test_no_priority_long_context_rates_where_openai_publishes_none(model: str) -> None: + """Guard against back-filling a rate OpenAI does not publish.""" + info = _load(MAIN_PATH)[model] + assert "input_cost_per_token_above_272k_tokens_priority" not in info + + +LONG_CONTEXT_PROMPT_TOKENS = 300_000 +COMPLETION_TOKENS = 1_000 + +TIERED_COST_CASES = [ + ("gpt-5.4", "flex", 2.5e-06, 1.125e-05), + ("gpt-5.4-pro", "flex", 3e-05, 0.000135), + ("gpt-5.5", "flex", 5e-06, 2.25e-05), + ("gpt-5.6", "priority", 1.6e-05, 6e-05), + ("gpt-5.6-sol", "priority", 1.6e-05, 6e-05), + ("gpt-5.6-terra", "priority", 8e-06, 3.6e-05), + ("gpt-5.6-luna", "priority", 8e-07, 3.6e-06), +] + + +@pytest.mark.parametrize("model,tier,input_rate,output_rate", TIERED_COST_CASES) +def test_cost_per_token_bills_long_context_at_the_tier_rate( + model: str, tier: str, input_rate: float, output_rate: float +) -> None: + """A prompt over 272K on flex or priority must bill at that tier's long-context rate.""" + input_cost, output_cost = litellm.cost_per_token( + model=model, + prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS, + completion_tokens=COMPLETION_TOKENS, + service_tier=tier, + ) + assert input_cost == pytest.approx(LONG_CONTEXT_PROMPT_TOKENS * input_rate) + assert output_cost == pytest.approx(COMPLETION_TOKENS * output_rate) + + +@pytest.mark.parametrize("model,tier,input_rate,output_rate", TIERED_COST_CASES) +def test_cost_per_token_tier_differs_from_the_standard_long_context_cost( + model: str, tier: str, input_rate: float, output_rate: float +) -> None: + """Flex halves the standard long-context bill and priority doubles it.""" + ratio = 0.5 if tier == "flex" else 2.0 + standard = sum( + litellm.cost_per_token( + model=model, + prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS, + completion_tokens=COMPLETION_TOKENS, + ) + ) + tiered = sum( + litellm.cost_per_token( + model=model, + prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS, + completion_tokens=COMPLETION_TOKENS, + service_tier=tier, + ) + ) + assert tiered == pytest.approx(standard * ratio) diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index d5ae57cf0f0..eed36d9491c 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -6,6 +6,7 @@ import logging import os import threading from types import SimpleNamespace +from typing import Final from unittest.mock import AsyncMock, MagicMock, patch import httpx @@ -1544,11 +1545,7 @@ async def test_ageneric_api_call_resolves_realtime_session_model(): fills it with the pre-routing model group name. The underlying litellm function reads session.model first, so it must see the resolved deployment, while a caller's nested transcription model stays untouched. """ - captured: dict = {} - - async def capture_kwargs(**kwargs): - captured.update(kwargs) - return {"result": "ok"} + routed: Final = AsyncMock(return_value={"result": "ok"}) router = litellm.Router( model_list=[ @@ -1565,7 +1562,7 @@ async def test_ageneric_api_call_resolves_realtime_session_model(): await router._ageneric_api_call_with_fallbacks( model="my-realtime-group", - original_function=capture_kwargs, + original_function=routed, session={ "type": "realtime", "model": "my-realtime-group", @@ -1573,9 +1570,10 @@ async def test_ageneric_api_call_resolves_realtime_session_model(): }, ) - assert captured["model"] == "openai/gpt-realtime-2.1-mini" - assert captured["session"]["model"] == "openai/gpt-realtime-2.1-mini" - assert captured["session"]["audio"]["input"]["transcription"]["model"] == "gpt-4o-transcribe" + sent: Final = routed.call_args.kwargs + assert sent["model"] == "openai/gpt-realtime-2.1-mini" + assert sent["session"]["model"] == "openai/gpt-realtime-2.1-mini" + assert sent["session"]["audio"]["input"]["transcription"]["model"] == "gpt-4o-transcribe" @pytest.mark.asyncio @@ -1584,11 +1582,7 @@ async def test_ageneric_api_call_does_not_add_session_model(): A session that never carried a model must not gain one from routing: the underlying function then falls back to the resolved `model` kwarg itself, and the outgoing session body keeps the caller's shape. """ - captured: dict = {} - - async def capture_kwargs(**kwargs): - captured.update(kwargs) - return {"result": "ok"} + routed: Final = AsyncMock(return_value={"result": "ok"}) router = litellm.Router( model_list=[ @@ -1605,12 +1599,13 @@ async def test_ageneric_api_call_does_not_add_session_model(): await router._ageneric_api_call_with_fallbacks( model="my-realtime-group", - original_function=capture_kwargs, + original_function=routed, session={"type": "realtime"}, ) - assert captured["model"] == "openai/gpt-realtime-2.1-mini" - assert captured["session"] == {"type": "realtime"} + sent: Final = routed.call_args.kwargs + assert sent["model"] == "openai/gpt-realtime-2.1-mini" + assert sent["session"] == {"type": "realtime"} @pytest.mark.parametrize( @@ -7362,6 +7357,71 @@ def test_get_configured_token_limits_coerces_numeric_strings(): assert router.get_configured_token_limits("quoted-limits-model") == (32000, 8000) +def test_get_configured_display_name_reads_deployment_model_info(): + router = litellm.Router( + model_list=[ + { + "model_name": "Kimi K3-claude-compatible", + "litellm_params": {"model": "openai/some-unmapped-model"}, + "model_info": {"display_name": "Kimi K3"}, + } + ] + ) + + assert router.get_configured_display_name("Kimi K3-claude-compatible") == "Kimi K3" + + +def test_get_configured_display_name_returns_none_for_unset_or_unknown(): + router = litellm.Router( + model_list=[ + { + "model_name": "no-display-model", + "litellm_params": {"model": "openai/some-unmapped-model"}, + } + ] + ) + + assert router.get_configured_display_name("no-display-model") is None + assert router.get_configured_display_name("not-a-real-model") is None + + +def test_get_configured_display_name_skips_wildcard_pattern_matching(): + router = litellm.Router( + model_list=[ + { + "model_name": "bedrock/*", + "litellm_params": {"model": "bedrock/*"}, + "model_info": {"display_name": "Bedrock"}, + } + ] + ) + + with patch.object( + router.pattern_router, "route", side_effect=AssertionError("pattern route called") + ): + assert ( + router.get_configured_display_name("bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0") + is None + ) + + +def test_get_configured_display_name_treats_malformed_values_as_absent(): + malformed = ["", " ", 12345, ["Kimi K3"], {"name": "Kimi K3"}, True] + router = litellm.Router( + model_list=[ + { + "model_name": f"bad-display-{i}", + "litellm_params": {"model": "openai/some-unmapped-model"}, + "model_info": {"display_name": bad}, + } + for i, bad in enumerate(malformed) + ] + ) + + for i in range(len(malformed)): + assert router.get_configured_display_name(f"bad-display-{i}") is None + + @pytest.mark.asyncio async def test_acreate_batch_disable_fallbacks_surfaces_owning_provider_error(): router = litellm.Router( @@ -7601,6 +7661,118 @@ async def test_acreate_batch_request_bedrock_tags_override_deployment_tags(): assert mock_sign.call_args.kwargs["data"]["tags"] == request_tags +@pytest.mark.asyncio +async def test_avector_store_search_injects_router(): + """ + Regression: router.avector_store_search must pass the router down to the + SDK search call so provider transforms can resolve router-managed + embedding models (e.g. S3 Vectors query embeddings). + """ + from litellm.types.vector_stores import VectorStoreSearchResponse + + expected_response = VectorStoreSearchResponse( + object="vector_store.search_results.page", search_query="q", data=[] + ) + mock_asearch = AsyncMock(return_value=expected_response) + # Router.__init__ binds asearch via a local import, so patch the module + # attribute before constructing the Router. + with patch("litellm.vector_stores.main.asearch", new=mock_asearch): # test-quality-ok: the SDK call is the only place the injected router kwarg is observable + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "api_key": "test-key"}, + } + ] + ) + search_response = await router.avector_store_search( + vector_store_id="v", query="q", custom_llm_provider="s3_vectors" + ) + + assert search_response is expected_response + mock_asearch.assert_awaited_once() + assert mock_asearch.await_args.kwargs["router"] is router + + +@pytest.mark.asyncio +async def test_avector_store_create_does_not_inject_router(): + """The router injection is gated on the search call type: the create path + must keep calling the SDK without a router kwarg.""" + expected_response = {"id": "vs_1", "object": "vector_store"} + mock_acreate = AsyncMock(return_value=expected_response) + # avector_store_create(model=None) resolves acreate via a local import at + # call time, so patching after Router construction works here. + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "api_key": "test-key"}, + } + ] + ) + with patch("litellm.vector_stores.main.acreate", new=mock_acreate): # test-quality-ok: the SDK call is the only place a leaked router kwarg would surface + create_response = await router.avector_store_create(model=None, custom_llm_provider="openai") + + assert create_response is expected_response + mock_acreate.assert_awaited_once() + assert "router" not in mock_acreate.await_args.kwargs + + +def test_vector_store_search_injects_router(): + """ + Sync parity for the router injection: router.vector_store_search must pass + the router down to the SDK search call so provider transforms can resolve + router-managed embedding models, same as avector_store_search. + """ + from litellm.types.vector_stores import VectorStoreSearchResponse + + expected_response = VectorStoreSearchResponse( + object="vector_store.search_results.page", search_query="q", data=[] + ) + mock_search = MagicMock(return_value=expected_response) + # Router.__init__ binds search via a local import, so patch the module + # attribute before constructing the Router. + with patch("litellm.vector_stores.main.search", new=mock_search): # test-quality-ok: the SDK call is the only place the injected router kwarg is observable + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "api_key": "test-key"}, + } + ] + ) + search_response = router.vector_store_search( + vector_store_id="v", query="q", custom_llm_provider="s3_vectors" + ) + + assert search_response is expected_response + mock_search.assert_called_once() + assert mock_search.call_args.kwargs["router"] is router + assert mock_search.call_args.kwargs["custom_llm_provider"] == "s3_vectors" + + +def test_vector_store_create_does_not_inject_router(): + """The sync create path must keep calling the SDK without a router kwarg.""" + expected_response = {"id": "vs_1", "object": "vector_store"} + mock_create = MagicMock(return_value=expected_response) + # Router.__init__ binds create via a local import, so patch the module + # attribute before constructing the Router. + with patch("litellm.vector_stores.main.create", new=mock_create): # test-quality-ok: the SDK call is the only place a leaked router kwarg would surface + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": {"model": "openai/gpt-3.5-turbo", "api_key": "test-key"}, + } + ] + ) + create_response = router.vector_store_create(custom_llm_provider="openai") + + assert create_response is expected_response + mock_create.assert_called_once() + assert "router" not in mock_create.call_args.kwargs + + class TestPreRoutingStrategyRegistryLifecycle: """ Regression tests: a deployment leaving the model_list must release the diff --git a/tests/test_litellm/test_router_retry_policy_update.py b/tests/test_litellm/test_router_retry_policy_update.py index 1b98b8c1ae8..be568134763 100644 --- a/tests/test_litellm/test_router_retry_policy_update.py +++ b/tests/test_litellm/test_router_retry_policy_update.py @@ -21,6 +21,7 @@ This file pins both halves of the fix. import json from dataclasses import dataclass +from typing import Final from unittest.mock import AsyncMock, MagicMock import pytest @@ -28,8 +29,19 @@ from pydantic import ValidationError import litellm +from litellm.router_strategy.budget_limiter import RouterBudgetLimiting +from litellm.router_utils.pre_call_checks.model_rate_limit_check import ModelRateLimitingCheck +from litellm.router_utils.pre_call_checks.prompt_caching_deployment_check import PromptCachingDeploymentCheck from litellm.types.router import RetryPolicy, UpdateRouterConfig + +@pytest.fixture(autouse=True) +def isolate_litellm_callbacks(): + callbacks_before: Final = litellm.callbacks.copy() + yield + litellm.callbacks = callbacks_before # test-quality-ok: required callback-state restoration fixture + + # --------------------------------------------------------------------------- # UpdateRouterConfig schema membership (LIT-3152 part 1) # --------------------------------------------------------------------------- @@ -100,6 +112,114 @@ def _build_router() -> litellm.Router: ) +def test_update_settings_adds_optional_pre_call_check_once(): + router = _build_router() + + router.update_settings(num_retries=7, optional_pre_call_checks=["prompt_caching"]) + router.update_settings(optional_pre_call_checks=["prompt_caching"]) + + prompt_caching_callbacks = [ + callback for callback in router.optional_callbacks if isinstance(callback, PromptCachingDeploymentCheck) + ] + assert len(prompt_caching_callbacks) == 1 + assert router.num_retries == 7 + + +def test_update_settings_clears_omitted_toggleable_pre_call_checks(): + router = _build_router() + + router.update_settings(optional_pre_call_checks=["prompt_caching"]) + router.update_settings(optional_pre_call_checks=[]) + + assert not any(isinstance(callback, PromptCachingDeploymentCheck) for callback in (router.optional_callbacks or [])) + assert not any(isinstance(callback, PromptCachingDeploymentCheck) for callback in litellm.callbacks) + + +def test_set_optional_pre_call_checks_reconciles_callback_types(): + router = _build_router() + + router.set_optional_pre_call_checks(["prompt_caching"]) + router.set_optional_pre_call_checks([]) + + assert not any(isinstance(callback, PromptCachingDeploymentCheck) for callback in (router.optional_callbacks or [])) + assert not any(isinstance(callback, PromptCachingDeploymentCheck) for callback in litellm.callbacks) + + +def test_remove_optional_pre_call_check_removes_local_and_global_callbacks(): + router = _build_router() + + router.set_optional_pre_call_checks(["prompt_caching"]) + router._remove_optional_callbacks_of_type(PromptCachingDeploymentCheck) + + assert not any(type(callback) is PromptCachingDeploymentCheck for callback in (router.optional_callbacks or [])) + assert not any(type(callback) is PromptCachingDeploymentCheck for callback in litellm.callbacks) + + +def test_remove_optional_pre_call_check_keeps_global_callback_for_another_router(): + router_a = _build_router() + router_b = _build_router() + + router_a.update_settings(optional_pre_call_checks=["prompt_caching"]) + router_b.update_settings(optional_pre_call_checks=["prompt_caching"]) + + router_a.update_settings(optional_pre_call_checks=[]) + + assert not any(type(callback) is PromptCachingDeploymentCheck for callback in (router_a.optional_callbacks or [])) + assert any(type(callback) is PromptCachingDeploymentCheck for callback in (router_b.optional_callbacks or [])) + assert any(type(callback) is PromptCachingDeploymentCheck for callback in litellm.callbacks) + + router_b.update_settings(optional_pre_call_checks=[]) + + assert not any(type(callback) is PromptCachingDeploymentCheck for callback in (router_b.optional_callbacks or [])) + assert not any(type(callback) is PromptCachingDeploymentCheck for callback in litellm.callbacks) + + +def test_remove_optional_pre_call_check_keeps_global_callback_when_second_router_clears_first(): + router_a = _build_router() + router_b = _build_router() + + router_a.update_settings(optional_pre_call_checks=["prompt_caching"]) + router_b.update_settings(optional_pre_call_checks=["prompt_caching"]) + + router_b.update_settings(optional_pre_call_checks=[]) + + assert any(type(callback) is PromptCachingDeploymentCheck for callback in (router_a.optional_callbacks or [])) + assert not any(type(callback) is PromptCachingDeploymentCheck for callback in (router_b.optional_callbacks or [])) + assert any(type(callback) is PromptCachingDeploymentCheck for callback in litellm.callbacks) + + router_a.update_settings(optional_pre_call_checks=[]) + + assert not any(type(callback) is PromptCachingDeploymentCheck for callback in litellm.callbacks) + + +def test_update_settings_replaces_toggleable_pre_call_checks(): + router = _build_router() + + router.update_settings(optional_pre_call_checks=["prompt_caching"]) + router.update_settings(optional_pre_call_checks=["enforce_model_rate_limits"]) + + assert not any(isinstance(callback, PromptCachingDeploymentCheck) for callback in (router.optional_callbacks or [])) + assert not any(isinstance(callback, PromptCachingDeploymentCheck) for callback in litellm.callbacks) + assert any(isinstance(callback, ModelRateLimitingCheck) for callback in (router.optional_callbacks or [])) + + +@pytest.mark.asyncio +async def test_update_settings_preserves_router_budget_limiting_when_omitted(monkeypatch): + async def _disable_periodic_sync(*args, **kwargs): + return None + + monkeypatch.setattr( + "litellm.router_strategy.budget_limiter.RouterBudgetLimiting.periodic_sync_in_memory_spend_with_redis", + _disable_periodic_sync, + ) + router = _build_router() + + router.add_optional_pre_call_checks(["router_budget_limiting"]) + router.update_settings(optional_pre_call_checks=[]) + + assert any(isinstance(callback, RouterBudgetLimiting) for callback in (router.optional_callbacks or [])) + + def test_update_settings_persists_retry_policy_dict(): """When the proxy's ``_add_router_settings_from_db_config`` calls ``llm_router.update_settings(retry_policy={...})`` after reading the @@ -255,8 +375,12 @@ async def test_config_update_persists_and_reads_back_retry_policy(monkeypatch): RateLimitErrorRetries=7, ) ) + request = MagicMock() + request.json = AsyncMock(return_value={"router_settings": {"retry_policy": posted.model_dump()}}) + await proxy_server.update_config( config_info=ConfigYAML(router_settings=posted), + request=request, user_api_key_dict=UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-1234"), ) diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 521e91daded..0790b41c349 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -4655,6 +4655,7 @@ GEMINI_4096_CACHE_MIN_MODELS: Final = tuple( "gemini-3.5-flash", "gemini-3.6-flash", "gemini-3.7-flash", + "gemini-3.8-flash", "gemini-3.1-pro-preview", "gemini-3.1-pro-preview-customtools", ) diff --git a/tests/test_litellm/vector_stores/__init__.py b/tests/test_litellm/vector_stores/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/vector_stores/test_main.py b/tests/test_litellm/vector_stores/test_main.py new file mode 100644 index 00000000000..d01e696906a --- /dev/null +++ b/tests/test_litellm/vector_stores/test_main.py @@ -0,0 +1,78 @@ +""" +Tests for litellm/vector_stores/main.py. + +Pins the router threading contract for vector store search: the router is an +explicit named parameter that reaches the HTTP handler, and it must never leak +into litellm_params/kwargs where logging would model_dump() it (the #19550 +serialization trap). +""" + +from unittest.mock import MagicMock, patch + +import litellm.vector_stores.main as vector_stores_main +from litellm.vector_stores.main import search + +MOCK_SEARCH_RESPONSE = { + "object": "vector_store.search_results.page", + "search_query": "q", + "data": [], +} + + +def test_search_threads_router_to_handler(): + """search() must pass its router param through to the HTTP handler""" + mock_router = MagicMock() + logger = MagicMock() + + with ( + patch( # test-quality-ok: stubs provider config resolution; the seam under test is the router kwarg threading + "litellm.vector_stores.main.ProviderConfigManager.get_provider_vector_stores_config", + return_value=MagicMock(), + ), + patch.object( # test-quality-ok: the handler call is the observable boundary for the router kwarg contract + vector_stores_main.base_llm_http_handler, + "vector_store_search_handler", + return_value=MOCK_SEARCH_RESPONSE, + ) as mock_handler, + ): + response = search( + vector_store_id="bkt:idx", + query="q", + custom_llm_provider="s3_vectors", + router=mock_router, + litellm_logging_obj=logger, + ) + + assert response == MOCK_SEARCH_RESPONSE + mock_handler.assert_called_once() + assert mock_handler.call_args.kwargs["router"] is mock_router + + +def test_search_router_not_in_litellm_params(): + """Regression (#19550 class): the router must stay out of GenericLiteLLMParams, + otherwise pre-call logging model_dump()s it and breaks serialization.""" + mock_router = MagicMock() + logger = MagicMock() + + with ( + patch( # test-quality-ok: stubs provider config resolution; the seam under test is litellm_params contents + "litellm.vector_stores.main.ProviderConfigManager.get_provider_vector_stores_config", + return_value=MagicMock(), + ), + patch.object( # test-quality-ok: the handler call is where a leaked router in litellm_params would surface + vector_stores_main.base_llm_http_handler, + "vector_store_search_handler", + return_value=MOCK_SEARCH_RESPONSE, + ) as mock_handler, + ): + search( + vector_store_id="bkt:idx", + query="q", + custom_llm_provider="s3_vectors", + router=mock_router, + litellm_logging_obj=logger, + ) + + litellm_params = mock_handler.call_args.kwargs["litellm_params"] + assert "router" not in litellm_params.model_dump(exclude_none=True) + assert getattr(litellm_params, "router", None) is None diff --git a/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.test.tsx index 34643811e29..4203a3dbf70 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.test.tsx @@ -116,16 +116,33 @@ describe("VectorStoreTester", () => { await waitFor(() => expect(mockSearch).toHaveBeenCalledTimes(1)); }); - it("reports a failed search and keeps the history empty", async () => { + it("shows the backend error in the history when a search fails", async () => { const user = userEvent.setup(); - mockSearch.mockRejectedValue(new Error("boom")); + const errorBody = '{"error":{"message":"OpenAIException - api_key is required"}}'; + mockSearch.mockRejectedValue(new Error(errorBody)); renderTester(); await user.type(queryInput(), "hello"); await user.click(searchButton()); - await waitFor(() => expect(mockFromBackend).toHaveBeenCalledWith("Failed to search vector store")); - expect(screen.getByText(EMPTY_STATE)).toBeInTheDocument(); + await waitFor(() => expect(mockFromBackend).toHaveBeenCalledWith(errorBody)); + expect(screen.getByText(`Search failed: ${errorBody}`)).toBeInTheDocument(); + expect(screen.queryByText("No results found")).not.toBeInTheDocument(); + expect(screen.queryByText(EMPTY_STATE)).not.toBeInTheDocument(); + // the failed query stays in the input for retry + expect(queryInput()).toHaveValue("hello"); + }); + + it('renders "No results found" for an empty result set, not an error', async () => { + const user = userEvent.setup(); + mockSearch.mockResolvedValue({ object: "vector_store.search_results.page", search_query: "hello", data: [] }); + renderTester(); + + await user.type(queryInput(), "hello"); + await user.click(searchButton()); + + expect(await screen.findByText("No results found")).toBeInTheDocument(); + expect(screen.queryByText(/search failed/i)).not.toBeInTheDocument(); }); it("clears the search history", async () => { diff --git a/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.tsx b/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.tsx index 6d12880319b..a9c4e0d1061 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/vector-stores/_components/VectorStoreTester.tsx @@ -40,6 +40,7 @@ export const VectorStoreTester: React.FC = ({ vectorStor { query: string; response: VectorStoreSearchResponse | null; + error: string | null; timestamp: number; }[] >([]); @@ -59,6 +60,7 @@ export const VectorStoreTester: React.FC = ({ vectorStor const historyEntry = { query, response, + error: null, timestamp: Date.now(), }; @@ -66,7 +68,9 @@ export const VectorStoreTester: React.FC = ({ vectorStor setQuery(""); } catch (error) { console.error("Error searching vector store:", error); - toast.fromError("Failed to search vector store"); + const errorMessage = error instanceof Error ? error.message : String(error); + toast.fromError(errorMessage); + setSearchHistory((prev) => [{ query, response: null, error: errorMessage, timestamp: Date.now() }, ...prev]); } finally { setIsLoading(false); } @@ -228,7 +232,13 @@ export const VectorStoreTester: React.FC = ({ vectorStor })} ) : ( -
No results found
+
+ {entry.error ? `Search failed: ${entry.error}` : "No results found"} +
)} diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index b53a67f9bcc..253a1e20657 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -6970,7 +6970,7 @@ export const vectorStoreSearchCall = async ( if (!response.ok) { const errorData = await response.text(); await handleError(errorData); - return null; + throw new Error(errorData); } const data = await response.json(); diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 6f044fec3f3..bde7fd611d5 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -37473,6 +37473,8 @@ export interface components { } | null; /** Num Retries */ num_retries?: number | null; + /** Optional Pre Call Checks */ + optional_pre_call_checks?: ("prompt_caching" | "router_budget_limiting" | "responses_api_deployment_check" | "deployment_affinity" | "session_affinity" | "forward_client_headers_by_model_group" | "enforce_model_rate_limits" | "encrypted_content_affinity")[] | null; /** Retry After */ retry_after?: number | null; retry_policy?: components["schemas"]["RetryPolicy"] | null; diff --git a/whitelisted_bedrock_models.txt b/whitelisted_bedrock_models.txt index 7e20081988d..8753d7c3c77 100644 --- a/whitelisted_bedrock_models.txt +++ b/whitelisted_bedrock_models.txt @@ -217,3 +217,17 @@ bedrock/us-east-1/zai.glm-5 bedrock/us-west-2/zai.glm-5 bedrock/us-gov-east-1/anthropic.claude-haiku-4-5-20251001-v1:0 bedrock/us-gov-west-1/anthropic.claude-haiku-4-5-20251001-v1:0 +bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b +bedrock/us-gov-west-1/nvidia.nemotron-nano-12b-v2 +bedrock/us-gov-west-1/nvidia.nemotron-super-3-120b +bedrock/us-gov-west-1/openai.gpt-oss-20b-1:0 +bedrock/us-gov-west-1/openai.gpt-oss-120b-1:0 +bedrock/us-gov-west-1/anthropic.claude-sonnet-5 +bedrock/us-gov-west-1/anthropic.claude-opus-4-8 +bedrock/us-gov-east-1/nvidia.nemotron-nano-3-30b +bedrock/us-gov-east-1/nvidia.nemotron-nano-12b-v2 +bedrock/us-gov-east-1/nvidia.nemotron-super-3-120b +bedrock/us-gov-east-1/openai.gpt-oss-20b-1:0 +bedrock/us-gov-east-1/openai.gpt-oss-120b-1:0 +bedrock/us-gov-east-1/anthropic.claude-sonnet-5 +bedrock/us-gov-east-1/anthropic.claude-opus-4-8