diff --git a/README.md b/README.md index 68aaa09ec98..92757fcbbc1 100644 --- a/README.md +++ b/README.md @@ -354,6 +354,8 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [Petals (`petals`)](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | | | | | | | | | [Pinstripes (`pinstripes`)](https://docs.litellm.ai/docs/providers/pinstripes) | ✅ | ✅ | ✅ | | | | | | | | | [Predibase (`predibase`)](https://docs.litellm.ai/docs/providers/predibase) | ✅ | ✅ | ✅ | | | | | | | | +| [Qwen AI Platform (`qwen_ai_platform`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | +| [QwenCloud (`qwencloud`)](https://docs.litellm.ai/docs/providers/qwencloud) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | | [Recraft (`recraft`)](https://docs.litellm.ai/docs/providers/recraft) | | | | | ✅ | | | | | | | [Replicate (`replicate`)](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | | | | | | | | | [Sagemaker Chat (`sagemaker_chat`)](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | | | | | | | | diff --git a/basedpyright-code-budget.json b/basedpyright-code-budget.json index a07b9352659..df52069e71f 100644 --- a/basedpyright-code-budget.json +++ b/basedpyright-code-budget.json @@ -1,6 +1,6 @@ { "reportAny": { - "limit": 14765 + "limit": 14076 }, "reportArgumentType": { "limit": 2216 @@ -24,7 +24,7 @@ "limit": 19 }, "reportExplicitAny": { - "limit": 4493 + "limit": 4128 }, "reportFunctionMemberAccess": { "limit": 7 @@ -54,10 +54,10 @@ "limit": 0 }, "reportMissingParameterType": { - "limit": 5607 + "limit": 5601 }, "reportMissingTypeArgument": { - "limit": 15310 + "limit": 15306 }, "reportMissingTypeStubs": { "limit": 40 @@ -105,13 +105,13 @@ "limit": 109 }, "reportUnknownMemberType": { - "limit": 38368 + "limit": 38350 }, "reportUnknownParameterType": { - "limit": 19633 + "limit": 19626 }, "reportUnknownVariableType": { - "limit": 29908 + "limit": 29890 }, "reportUnnecessaryCast": { "limit": 111 @@ -123,7 +123,7 @@ "limit": 5 }, "reportUnnecessaryIsInstance": { - "limit": 828 + "limit": 826 }, "reportUntypedBaseClass": { "limit": 0 diff --git a/litellm/__init__.py b/litellm/__init__.py index 1447e05fdf7..4eeececdb7e 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -659,6 +659,8 @@ aiml_models: Set = set() deepgram_models: Set = set() elevenlabs_models: Set = set() dashscope_models: Set = set() +qwencloud_models: Set = set() +qwen_ai_platform_models: Set = set() moonshot_models: Set = set() publicai_models: Set = set() darkbloom_models: Set = set() @@ -909,6 +911,10 @@ def _populate_provider_model_sets(model_cost_map: Dict) -> None: heroku_models.add(key) elif value.get("litellm_provider") == "dashscope": dashscope_models.add(key) + elif value.get("litellm_provider") == "qwencloud": + qwencloud_models.add(key) + elif value.get("litellm_provider") == "qwen_ai_platform": + qwen_ai_platform_models.add(key) elif value.get("litellm_provider") == "modelscope": modelscope_models.add(key) elif value.get("litellm_provider") == "moonshot": @@ -1072,6 +1078,8 @@ model_list = list( | deepgram_models | elevenlabs_models | dashscope_models + | qwencloud_models + | qwen_ai_platform_models | moonshot_models | publicai_models | darkbloom_models @@ -1178,6 +1186,8 @@ def _build_models_by_provider() -> dict: "elevenlabs": elevenlabs_models, "heroku": heroku_models, "dashscope": dashscope_models, + "qwencloud": qwencloud_models, + "qwen_ai_platform": qwen_ai_platform_models, "modelscope": modelscope_models, "moonshot": moonshot_models, "publicai": publicai_models, @@ -2014,6 +2024,24 @@ if TYPE_CHECKING: from .llms.dashscope.rerank.transformation import ( DashScopeRerankConfig as DashScopeRerankConfig, ) + from .llms.dashscope.qwencloud import ( + QwenCloudChatConfig as QwenCloudChatConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudEmbeddingConfig as QwenCloudEmbeddingConfig, + ) + from .llms.dashscope.qwencloud import ( + QwenCloudRerankConfig as QwenCloudRerankConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformChatConfig as QwenAIPlatformChatConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig as QwenAIPlatformEmbeddingConfig, + ) + from .llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformRerankConfig as QwenAIPlatformRerankConfig, + ) from .llms.modelscope.chat.transformation import ( ModelScopeChatConfig as ModelScopeChatConfig, ) diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 1c833256598..e9199e1ec80 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -310,6 +310,8 @@ LLM_CONFIG_NAMES: Final = ( "GigaChatConfig", "GigaChatEmbeddingConfig", "DashScopeChatConfig", + "QwenCloudChatConfig", + "QwenAIPlatformChatConfig", "ModelScopeChatConfig", "MoonshotChatConfig", "DockerModelRunnerChatConfig", @@ -1172,6 +1174,14 @@ _LLM_CONFIGS_IMPORT_MAP: Final = { ".llms.dashscope.chat.transformation", "DashScopeChatConfig", ), + "QwenCloudChatConfig": ( + ".llms.dashscope.qwencloud", + "QwenCloudChatConfig", + ), + "QwenAIPlatformChatConfig": ( + ".llms.dashscope.qwen_ai_platform", + "QwenAIPlatformChatConfig", + ), "GDCGeminiConfig": ( ".llms.gdc.chat.transformation", "GDCGeminiConfig", diff --git a/litellm/caching/caching.py b/litellm/caching/caching.py index cefe6aae9ed..754815fce47 100644 --- a/litellm/caching/caching.py +++ b/litellm/caching/caching.py @@ -12,6 +12,7 @@ import hashlib import json import time import traceback +from collections.abc import Mapping from enum import Enum from typing import Any, Final @@ -506,7 +507,7 @@ class Cache: def _get_cache_logic( self, - cached_result: Any | None, + cached_result: object | None, max_age: float | None, ): """ @@ -538,8 +539,8 @@ class Cache: return cached_result @staticmethod - def _get_safe_cache_lookup_kwargs(kwargs: dict[str, Any]) -> dict[str, Any]: - cache_lookup_kwargs: Final[dict[str, Any]] = {} + def _get_safe_cache_lookup_kwargs(kwargs: Mapping[str, object]) -> dict[str, object]: + cache_lookup_kwargs: Final[dict[str, object]] = {} for prompt_kwarg in ("messages", "input"): if prompt_kwarg in kwargs: cache_lookup_kwargs[prompt_kwarg] = kwargs[prompt_kwarg] @@ -552,7 +553,7 @@ class Cache: @staticmethod def _update_metadata_from_cache_lookup_kwargs( - original_kwargs: dict[str, Any], cache_lookup_kwargs: dict[str, Any] + original_kwargs: Mapping[str, object], cache_lookup_kwargs: Mapping[str, object] ) -> None: original_metadata: Final = original_kwargs.get("metadata") cache_lookup_metadata: Final = cache_lookup_kwargs.get("metadata") diff --git a/litellm/caching/qdrant_semantic_cache.py b/litellm/caching/qdrant_semantic_cache.py index 4898700c403..c5876e993d3 100644 --- a/litellm/caching/qdrant_semantic_cache.py +++ b/litellm/caching/qdrant_semantic_cache.py @@ -12,7 +12,7 @@ import ast import asyncio import json import os -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Protocol, cast import litellm from litellm._logging import print_verbose @@ -39,6 +39,12 @@ if TYPE_CHECKING: from litellm.router import Router +class _QdrantCollectionDetailsResponse(Protocol): + """The qdrant `/collections/{name}` response, whose body is kept as an opaque JSON object.""" + + def json(self) -> dict[str, object]: ... + + class QdrantSemanticCache(BaseCache): CACHE_KEY_FIELD_NAME = "litellm_cache_key" embedding_max_input_tokens: int | None = None @@ -115,15 +121,15 @@ class QdrantSemanticCache(BaseCache): raise ValueError(f"Error from qdrant checking if /collections exist {collection_exists.text}") if collection_exists.json()["result"]["exists"]: - collection_details = self.sync_client.get( + collection_details: _QdrantCollectionDetailsResponse = self.sync_client.get( url=f"{self.qdrant_api_base}/collections/{self.collection_name}", headers=self.headers, ) - self.collection_info = collection_details.json() + self.collection_info: dict[str, object] = collection_details.json() print_verbose(f"Collection already exists.\nCollection details:{self.collection_info}") self._ensure_cache_key_payload_index() else: - quantization_params: dict[str, Any] + quantization_params: dict[str, dict[str, object]] if quantization_config is None or quantization_config == "binary": quantization_params = { "binary": { @@ -214,7 +220,7 @@ class QdrantSemanticCache(BaseCache): resolve_embedding_max_input_tokens(self.embedding_max_input_tokens, self.embedding_model, router), ) - def _get_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + def _get_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: """Embed via the proxy Router when it serves the model, else direct.""" try: from litellm.proxy.proxy_server import llm_model_list, llm_router @@ -241,7 +247,7 @@ class QdrantSemanticCache(BaseCache): num_retries=0, ) - async def _get_async_embedding(self, prompt: str, metadata: dict[str, Any] | None = None) -> EmbeddingResponse: + async def _get_async_embedding(self, prompt: str, metadata: dict[str, object] | None = None) -> EmbeddingResponse: try: from litellm.proxy.proxy_server import llm_model_list, llm_router except ImportError: diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index 727c39c16ec..f494d6610a1 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -45,14 +45,14 @@ class ResponsesToCompletionBridgeHandler: return bool(stream) @staticmethod - def _is_preformatted_cached_chat_stream(result: Any) -> bool: + def _is_preformatted_cached_chat_stream(result: object) -> bool: from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper return isinstance(result, CustomStreamWrapper) and result.custom_llm_provider == "cached_response" @staticmethod def _coerce_response_object( - response_obj: Any, + response_obj: object, hidden_params: dict | None, ) -> "ResponsesAPIResponse": if isinstance(response_obj, ResponsesAPIResponse): @@ -78,8 +78,8 @@ class ResponsesToCompletionBridgeHandler: for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -93,8 +93,8 @@ class ResponsesToCompletionBridgeHandler: async for _ in stream_iter: pass - completed: Final = getattr(stream_iter, "completed_response", None) - response_obj: Final = getattr(completed, "response", None) if completed else None + completed: Final[object] = getattr(stream_iter, "completed_response", None) + response_obj: Final[object] = getattr(completed, "response", None) if completed else None if response_obj is None: raise ValueError("Stream ended without a completed response") @@ -157,7 +157,7 @@ class ResponsesToCompletionBridgeHandler: def completion( self, *args, **kwargs ) -> Union[ - Coroutine[Any, Any, Union["ModelResponse", "CustomStreamWrapper"]], + Coroutine[None, None, Union["ModelResponse", "CustomStreamWrapper"]], "ModelResponse", "CustomStreamWrapper", ]: diff --git a/litellm/constants.py b/litellm/constants.py index c482ab0e39a..a5751a416a1 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -630,6 +630,8 @@ LITELLM_CHAT_PROVIDERS: Final = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -799,6 +801,7 @@ openai_compatible_endpoints: Final[list] = [ "inference.api.nscale.com/v1", "api.studio.nebius.ai/v1", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "https://dashscope.aliyuncs.com/compatible-mode/v1", "https://api-inference.modelscope.cn/v1", "https://api.moonshot.ai/v1", "https://api.publicai.co/v1", @@ -872,6 +875,8 @@ openai_compatible_providers: Final[list] = [ "nscale", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "v0", @@ -902,6 +907,8 @@ openai_text_completion_compatible_providers: Final[list] = [ # providers that s "featherless_ai", "nebius", "dashscope", + "qwencloud", + "qwen_ai_platform", "modelscope", "moonshot", "publicai", @@ -1109,7 +1116,7 @@ nebius_models: Final[set] = set( ] ) -dashscope_models: Final[set] = set( +dashscope_models: Final[frozenset] = frozenset( [ "qwen-turbo", "qwen-plus", @@ -1124,6 +1131,10 @@ dashscope_models: Final[set] = set( ] ) +qwencloud_models: Final[frozenset] = frozenset(dashscope_models) + +qwen_ai_platform_models: Final[frozenset] = frozenset(dashscope_models) + nebius_embedding_models: Final[set] = set( [ "BAAI/bge-en-icl", diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 13005d7975e..b83e9b395a8 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -641,12 +641,12 @@ def cost_per_token( return xai_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "lemonade": return lemonade_cost_per_token(model=model, usage=usage_block) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): from litellm.llms.dashscope.cost_calculator import ( cost_per_token as dashscope_cost_per_token, ) - return dashscope_cost_per_token(model=model, usage=usage_block) + return dashscope_cost_per_token(model=model, usage=usage_block, custom_llm_provider=custom_llm_provider) elif custom_llm_provider == "azure_ai": return azure_ai_cost_per_token( model=model, diff --git a/litellm/google_genai/main.py b/litellm/google_genai/main.py index b5815bd3f7c..c1822e4720d 100644 --- a/litellm/google_genai/main.py +++ b/litellm/google_genai/main.py @@ -52,10 +52,10 @@ class GenerateContentSetupResult(BaseModel): model_config: ClassVar[ConfigDict] = ConfigDict(arbitrary_types_allowed=True) model: str - request_body: dict[str, Any] + request_body: dict[str, object] custom_llm_provider: str generate_content_provider_config: BaseGoogleGenAIGenerateContentConfig | None - generate_content_config_dict: dict[str, Any] + generate_content_config_dict: dict[str, object] native_request_fields: dict[str, object] litellm_params: GenericLiteLLMParams litellm_logging_obj: LiteLLMLoggingObj @@ -68,7 +68,7 @@ class GenerateContentHelper: @staticmethod def mock_generate_content_response( mock_response: str = "This is a mock response from Google GenAI generate_content.", - ) -> dict[str, Any]: + ) -> dict[str, object]: """Mock response for generate_content for testing purposes""" return { "text": mock_response, @@ -239,9 +239,9 @@ async def agenerate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -307,9 +307,9 @@ def generate_content( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -397,9 +397,9 @@ async def agenerate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -492,9 +492,9 @@ def generate_content_stream( tools: ToolConfigDict | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, diff --git a/litellm/images/main.py b/litellm/images/main.py index 1688087c2da..6a94e7c8df2 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -3,7 +3,7 @@ import contextvars import importlib from collections.abc import Coroutine from functools import partial -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast, overload +from typing import TYPE_CHECKING, Final, Literal, Optional, cast, overload if TYPE_CHECKING: from litellm.images.utils import ImageEditRequestUtils @@ -151,7 +151,7 @@ def image_generation( *, aimg_generation: Literal[True], **kwargs, -) -> Coroutine[Any, Any, ImageResponse]: +) -> Coroutine[object, object, ImageResponse]: ... @@ -197,7 +197,7 @@ def image_generation( api_version: str | None = None, custom_llm_provider=None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the https://api.openai.com/v1/images/generations endpoint. @@ -386,6 +386,8 @@ def image_generation( litellm.LlmProviders.VERTEX_AI, litellm.LlmProviders.OPENROUTER, litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, ): if image_generation_config is None: raise ValueError(f"image generation config is not supported for {custom_llm_provider}") @@ -723,14 +725,14 @@ def image_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, **kwargs, -) -> ImageResponse | Coroutine[Any, Any, ImageResponse]: +) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Maps the image edit functionality, similar to OpenAI's images/edits endpoint. """ @@ -769,7 +771,7 @@ def image_edit( images: Final = image if isinstance(image, list) else ([image] if image is not None else []) headers_from_kwargs: Final = kwargs.get("headers") - merged_extra_headers: Final[dict[str, Any]] = {} + merged_extra_headers: Final[dict[str, object]] = {} if isinstance(headers_from_kwargs, dict): merged_extra_headers.update(headers_from_kwargs) if isinstance(extra_headers, dict): @@ -974,9 +976,9 @@ async def aimage_edit( user: str | None = None, # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs. # The extra values given here take precedence over values defined on the client or passed to this method. - extra_headers: dict[str, Any] | None = None, - extra_query: dict[str, Any] | None = None, - extra_body: dict[str, Any] | None = None, + extra_headers: dict[str, object] | None = None, + extra_query: dict[str, object] | None = None, + extra_body: dict[str, object] | None = None, timeout: float | httpx.Timeout | None = None, # LiteLLM specific params, custom_llm_provider: str | None = None, @@ -1044,7 +1046,7 @@ async def aimage_edit( ) -def __getattr__(name: str) -> Any: +def __getattr__(name: str) -> type["ImageEditRequestUtils"]: """Lazy import handler for images.main module""" if name == "ImageEditRequestUtils": # Lazy load ImageEditRequestUtils to avoid heavy import from images.utils at module load time diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 94d734546be..c137164ecdb 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -545,7 +545,6 @@ class SlackAlerting(CustomBatchLogger): # Get the appropriate budget alert type handler budget_alert_class: Final = get_budget_alert_type(type) _id: Final = budget_alert_class.get_id(user_info) - user_info_json: Final = user_info.model_dump(exclude_none=True) user_info_str: Final = self._get_user_info_str(user_info) event_message = budget_alert_class.get_event_message() @@ -575,7 +574,22 @@ class SlackAlerting(CustomBatchLogger): webhook_event = WebhookEvent( event=event, event_message=event_message, - **user_info_json, + spend=user_info.spend, + max_budget=user_info.max_budget, + soft_budget=user_info.soft_budget, + token=user_info.token, + customer_id=user_info.customer_id, + user_id=user_info.user_id, + team_id=user_info.team_id, + team_alias=user_info.team_alias, + organization_id=user_info.organization_id, + user_email=user_info.user_email, + key_alias=user_info.key_alias, + projected_exceeded_date=user_info.projected_exceeded_date, + projected_spend=user_info.projected_spend, + event_group=user_info.event_group, + alert_emails=user_info.alert_emails, + max_budget_alert_emails=user_info.max_budget_alert_emails, ) await self.send_alert( message=event_message + "\n\n" + user_info_str, @@ -657,7 +671,7 @@ class SlackAlerting(CustomBatchLogger): """ Create a standard message for a budget alert """ - _all_fields_as_dict: Final = user_info.model_dump(exclude_none=True) + _all_fields_as_dict: Final[dict[str, object]] = user_info.model_dump(exclude_none=True) _all_fields_as_dict.pop("token") msg = "" for k, v in _all_fields_as_dict.items(): @@ -1006,7 +1020,7 @@ class SlackAlerting(CustomBatchLogger): except Exception: pass - async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: Any): + async def model_added_alert(self, model_name: str, litellm_model_name: str, passed_model_info: object): base_model_from_user: Final = getattr(passed_model_info, "base_model", None) model_info = {} base_model = "" @@ -1973,7 +1987,7 @@ Model Info: try: message = f"`{event_name}`\n" - key_event_dict: Final = key_event.model_dump() + key_event_dict: Final[dict[str, object]] = key_event.model_dump() # Add Created by information first message += "*Action Done by:*\n" diff --git a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py index 6a03e3ee93c..ff34bd91e31 100644 --- a/litellm/integrations/bitbucket/bitbucket_prompt_manager.py +++ b/litellm/integrations/bitbucket/bitbucket_prompt_manager.py @@ -3,6 +3,7 @@ BitBucket prompt manager that integrates with LiteLLM's prompt management system Fetches .prompt files from BitBucket repositories and provides team-based access control. """ +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final from jinja2 import DictLoader, select_autoescape @@ -65,7 +66,7 @@ class BitBucketTemplateManager: def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -123,7 +124,7 @@ class BitBucketTemplateManager: template_content = content # Parse YAML frontmatter - metadata: dict[str, Any] = {} + metadata: dict[str, object] = {} if frontmatter_str: try: import yaml @@ -141,9 +142,9 @@ class BitBucketTemplateManager: metadata=metadata, ) - def _parse_yaml_basic(self, yaml_str: str) -> dict[str, Any]: + def _parse_yaml_basic(self, yaml_str: str) -> dict[str, object]: """Basic YAML parser for simple cases when PyYAML is not available.""" - result: Final[dict[str, Any]] = {} + result: Final[dict[str, object]] = {} for line in yaml_str.split("\n"): line = line.strip() if ":" in line and not line.startswith("#"): @@ -162,7 +163,7 @@ class BitBucketTemplateManager: result[key] = value.strip("\"'") return result - def render_template(self, template_id: str, variables: dict[str, Any] | None = None) -> str: + def render_template(self, template_id: str, variables: Mapping[str, object] | None = None) -> str: """Render a template with the given variables.""" if template_id not in self.prompts: raise ValueError(f"Template '{template_id}' not found") @@ -209,7 +210,7 @@ class BitBucketPromptManager(CustomPromptManagement): def __init__( self, - bitbucket_config: dict[str, Any], + bitbucket_config: Mapping[str, object], prompt_id: str | None = None, ): self.bitbucket_config = bitbucket_config @@ -234,7 +235,7 @@ class BitBucketPromptManager(CustomPromptManagement): def get_prompt_template( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, ) -> tuple[str, dict[str, Any]]: """ Get a prompt template and render it with variables. @@ -267,12 +268,12 @@ class BitBucketPromptManager(CustomPromptManagement): self, user_id: str | None, messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: dict[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> tuple[list[AllMessageValues], dict[str, Any] | None]: + ) -> tuple[list[AllMessageValues], dict[str, object] | None]: """ Pre-call hook that processes the prompt template before making the LLM call. """ @@ -316,9 +317,9 @@ class BitBucketPromptManager(CustomPromptManagement): except Exception as e: # Log error but don't fail the call - import litellm + from litellm._logging import verbose_proxy_logger - litellm._logging.verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) + verbose_proxy_logger.error("Error in BitBucket prompt pre_call_hook: %s", e) return messages, litellm_params def _parse_prompt_to_messages(self, prompt_content: str) -> list[AllMessageValues]: @@ -384,14 +385,14 @@ class BitBucketPromptManager(CustomPromptManagement): def post_call_hook( self, user_id: str | None, - response: Any, + response: object, input_messages: list[AllMessageValues], - function_call: dict[str, Any] | str | None = None, - litellm_params: dict[str, Any] | None = None, + function_call: Mapping[str, object] | str | None = None, + litellm_params: Mapping[str, object] | None = None, prompt_id: str | None = None, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, **kwargs, - ) -> Any: + ) -> object: """ Post-call hook for any post-processing after the LLM call. """ diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index f0d4d67fc22..ffc8fe1c1f5 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -19,14 +19,29 @@ """Transform LiteLLM data to CloudZero AnyCost CBF format.""" from datetime import datetime -from typing import Any, Final +from typing import Final, SupportsFloat, SupportsIndex, SupportsInt import polars as pl +from typing_extensions import Buffer from ...types.integrations.cloudzero import CBFRecord from .cz_resource_names import CZEntityType, CZRNGenerator +def _as_int(value: object) -> int: + """The integer form of a spend table cell, computed the way :func:`int` computes it.""" + if isinstance(value, (str, Buffer, SupportsInt, SupportsIndex)): + return int(value) + raise TypeError(f"int() argument must be a string or a number, not {type(value).__name__!r}") + + +def _as_float(value: object) -> float: + """The floating point form of a spend table cell, computed the way :func:`float` computes it.""" + if isinstance(value, (str, Buffer, SupportsFloat, SupportsIndex)): + return float(value) + raise TypeError(f"float() argument must be a string or a number, not {type(value).__name__!r}") + + class CBFTransformer: """Transform LiteLLM usage data to CloudZero Billing Format (CBF).""" @@ -82,15 +97,15 @@ class CBFTransformer: return pl.DataFrame(cbf_data) - def _create_cbf_record(self, row: dict[str, Any]) -> CBFRecord: + def _create_cbf_record(self, row: dict[str, object]) -> CBFRecord: """Create a single CBF record from LiteLLM daily spend row.""" # Parse date (daily spend tables use date strings like '2025-04-19') usage_date: Final = self._parse_date(row.get("date")) # Calculate total tokens - prompt_tokens: Final = int(row.get("prompt_tokens", 0)) - completion_tokens: Final = int(row.get("completion_tokens", 0)) + prompt_tokens: Final = _as_int(row.get("prompt_tokens", 0)) + completion_tokens: Final = _as_int(row.get("completion_tokens", 0)) total_tokens: Final = prompt_tokens + completion_tokens # Create CloudZero Resource Name (CZRN) as resource_id @@ -154,7 +169,7 @@ class CBFTransformer: "time/usage_start": ( usage_date.isoformat() if usage_date else None ), # Required: ISO-formatted UTC datetime - "cost/cost": float(row.get("spend", 0.0)), # Required: billed cost + "cost/cost": _as_float(row.get("spend", 0.0)), # Required: billed cost "resource/id": resource_id, # CZRN (CloudZero Resource Name) # Usage metrics for token consumption "usage/amount": total_tokens, # Numeric value of tokens consumed @@ -187,7 +202,7 @@ class CBFTransformer: return CBFRecord(cbf_record) - def _parse_date(self, date_str) -> datetime | None: + def _parse_date(self, date_str: object) -> datetime | None: """Parse date string from daily spend tables (e.g., '2025-04-19').""" if date_str is None: return None diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 8dc6881d23e..e87ac9521ae 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -2,6 +2,7 @@ import contextvars import hashlib import os import secrets +from collections.abc import Mapping from datetime import datetime from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, Optional, get_args @@ -227,13 +228,13 @@ class CustomGuardrail(CustomLogger): ) super().__init__(**kwargs) - def render_violation_message(self, default: str, context: dict[str, Any] | None = None) -> str: + def render_violation_message(self, default: str, context: Mapping[str, object] | None = None) -> str: """Return a custom violation message if template is configured.""" if not self.violation_message_template: return default - format_context: Final[dict[str, Any]] = {"default_message": default} + format_context: Final[dict[str, object]] = {"default_message": default} if context: format_context.update(context) try: @@ -661,7 +662,7 @@ class CustomGuardrail(CustomLogger): value: Final = self._get_admin_metadata(data).get("opted_out_global_guardrails") return value if isinstance(value, list) else [] - def _is_valid_response_type(self, result: Any) -> bool: + def _is_valid_response_type(self, result: object) -> bool: """ Check if result is a valid LLMResponseTypes instance. @@ -722,7 +723,7 @@ class CustomGuardrail(CustomLogger): return None return f"{_PRE_CALL_EXECUTED_TOKEN}:{name}" - def mark_pre_call_hook_ran(self, data: dict[str, Any]) -> None: + def mark_pre_call_hook_ran(self, data: dict[str, object]) -> None: """ Record that this guardrail's ``async_pre_call_hook`` already ran for this request, so the deployment-level hook does not run it a second time. @@ -747,7 +748,7 @@ class CustomGuardrail(CustomLogger): return data["metadata"] = {PRE_CALL_EXECUTED_GUARDRAILS_KEY: [marker]} - def _pre_call_hook_already_ran(self, data: dict[str, Any]) -> bool: + def _pre_call_hook_already_ran(self, data: dict[str, object]) -> bool: marker: Final = self._pre_call_marker() if marker is None: return False @@ -1170,7 +1171,7 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ # Convert None to empty dict to satisfy type requirements - guardrail_response: dict[str, Any] | str = {} if response is None else response + guardrail_response: dict[str, object] | str = {} if response is None else response # For apply_guardrail functions in custom_code_guardrail scenario, # simplify the logged response to "allow", "deny", or "mask" diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 04f1c6dff15..866076a3c49 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -20,10 +20,11 @@ import time import traceback from collections.abc import Sequence from datetime import datetime as datetimeObj -from typing import Any, Final +from typing import TYPE_CHECKING, Any, Final import httpx from httpx import Response +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -62,6 +63,18 @@ from litellm.types.utils import StandardLoggingPayload from ..additional_logging_utils import AdditionalLoggingUtils +if TYPE_CHECKING: + from fastapi import HTTPException + + from litellm.proxy._types import UserAPIKeyAuth + + +class _DatadogLoggingKwargs(TypedDict, total=False): + """The subset of logging ``kwargs`` that the Datadog payload builder reads.""" + + standard_logging_object: ReadOnly[StandardLoggingPayload | None] + + # max number of logs DD API can accept @@ -87,6 +100,11 @@ def _resolve_dd_batch_size() -> int: return max(1, min(value, DD_MAX_BATCH_SIZE)) +def _span_attribute(span: object, name: str) -> object: + """Read an optional attribute off whatever span object the active tracer hands back.""" + return getattr(span, name, None) + + class DataDogLogger( CustomBatchLogger, AdditionalLoggingUtils, @@ -271,9 +289,9 @@ class DataDogLogger( self, request_data: dict, original_exception: Exception, - user_api_key_dict: Any, + user_api_key_dict: "UserAPIKeyAuth", traceback_str: str | None = None, - ) -> Any | None: + ) -> "HTTPException | None": """ Log proxy-level failures (e.g. 401 auth, DB connection errors) to Datadog. @@ -297,7 +315,7 @@ class DataDogLogger( status_code = int(_code) # Use project-standard sanitized user context when running in proxy - user_context: dict[str, Any] = {} + user_context: dict[str, object] = {} try: from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, @@ -553,8 +571,8 @@ class DataDogLogger( def create_datadog_logging_payload( self, - kwargs: dict | Any, - response_obj: Any, + kwargs: _DatadogLoggingKwargs, + response_obj: object, start_time: datetime.datetime, end_time: datetime.datetime, ) -> DatadogPayload: @@ -562,8 +580,8 @@ class DataDogLogger( Helper function to create a datadog payload for logging Args: - kwargs (Union[dict, Any]): request kwargs - response_obj (Any): llm api response + kwargs: request kwargs, read for its standard logging object + response_obj: llm api response start_time (datetime.datetime): start time of request end_time (datetime.datetime): end time of request @@ -625,7 +643,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -659,7 +677,7 @@ class DataDogLogger( self, payload: ServiceLoggerPayload, error: str | None = "", - parent_otel_span: Any | None = None, + parent_otel_span: object = None, start_time: datetimeObj | float | None = None, end_time: float | datetimeObj | None = None, event_metadata: dict | None = None, @@ -696,7 +714,7 @@ class DataDogLogger( def _create_v0_logging_payload( self, - kwargs: dict | Any, + kwargs: dict, response_obj: Any, start_time: datetime.datetime, end_time: datetime.datetime, @@ -810,11 +828,11 @@ class DataDogLogger( if current_span is None: return None - trace_id: Final = getattr(current_span, "trace_id", None) + trace_id: Final = _span_attribute(current_span, "trace_id") if trace_id is None: return None - span_id: Final = getattr(current_span, "span_id", None) + span_id: Final = _span_attribute(current_span, "span_id") trace_context: Final[dict[str, str]] = {"trace_id": str(trace_id)} if span_id is not None: trace_context["span_id"] = str(span_id) diff --git a/litellm/integrations/datadog/datadog_llm_obs.py b/litellm/integrations/datadog/datadog_llm_obs.py index 704f0323e95..e5789965c6e 100644 --- a/litellm/integrations/datadog/datadog_llm_obs.py +++ b/litellm/integrations/datadog/datadog_llm_obs.py @@ -9,6 +9,7 @@ API Reference: https://docs.datadoghq.com/llm_observability/setup/api/?tab=examp import asyncio import json import os +from collections.abc import Mapping, Sequence from datetime import datetime from typing import Any, Final, Literal @@ -334,7 +335,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): def _get_response_messages( self, standard_logging_payload: StandardLoggingPayload, call_type: str | None - ) -> list[Any]: + ) -> list[object]: """ Get the messages from the response object @@ -484,7 +485,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): # Default fallback for unknown or passthrough operations return "llm" - def _ensure_string_content(self, messages: str | list[Any] | dict[Any, Any] | None) -> list[Any]: + def _ensure_string_content(self, messages: str | Sequence[object] | Mapping[object, object] | None) -> list[object]: if messages is None: return [] if isinstance(messages, str): @@ -495,11 +496,11 @@ class DataDogLLMObsLogger(CustomBatchLogger): return [str(messages.get("content", ""))] return [] - def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: + def _get_dd_llm_obs_payload_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, object]: """ Fields to track in DD LLM Observability metadata from litellm standard logging payload """ - _metadata: Final[dict[str, Any]] = { + _metadata: Final[dict[str, object]] = { "model_name": standard_logging_payload.get("model", "unknown"), "model_provider": standard_logging_payload.get("custom_llm_provider", "unknown"), "id": standard_logging_payload.get("id", "unknown"), @@ -647,7 +648,7 @@ class DataDogLLMObsLogger(CustomBatchLogger): return spend_metrics - def _process_input_messages_preserving_tool_calls(self, messages: list[Any]) -> list[dict[str, Any]]: + def _process_input_messages_preserving_tool_calls(self, messages: Sequence[object]) -> list[dict[str, object]]: """ Process input messages while preserving tool_calls and tool message types. @@ -671,13 +672,13 @@ class DataDogLLMObsLogger(CustomBatchLogger): return processed @staticmethod - def _tool_calls_kv_pair(tool_calls: list[dict[str, Any]]) -> dict[str, Any]: + def _tool_calls_kv_pair(tool_calls: list[dict[str, Any]]) -> dict[str, object]: """ Extract tool call information into key-value pairs for Datadog metadata. Similar to OpenTelemetry's implementation but adapted for Datadog's format. """ - kv_pairs: Final[dict[str, Any]] = {} + kv_pairs: Final[dict[str, object]] = {} for idx, tool_call in enumerate(tool_calls): try: # Extract tool call ID @@ -712,11 +713,11 @@ class DataDogLLMObsLogger(CustomBatchLogger): return kv_pairs - def _extract_tool_call_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, Any]: + def _extract_tool_call_metadata(self, standard_logging_payload: StandardLoggingPayload) -> dict[str, object]: """ Extract tool call information from both input messages and response for Datadog metadata. """ - tool_call_metadata: Final[dict[str, Any]] = {} + tool_call_metadata: Final[dict[str, object]] = {} try: # Extract tool calls from input messages diff --git a/litellm/integrations/dotprompt/prompt_manager.py b/litellm/integrations/dotprompt/prompt_manager.py index fd0b17ba746..9c82ff7c5ba 100644 --- a/litellm/integrations/dotprompt/prompt_manager.py +++ b/litellm/integrations/dotprompt/prompt_manager.py @@ -3,12 +3,21 @@ Based on Google's GenAI Kit dotprompt implementation: https://google.github.io/d """ import re +from collections.abc import Mapping from pathlib import Path from typing import Any, Final import yaml from jinja2 import DictLoader, select_autoescape from jinja2.sandbox import ImmutableSandboxedEnvironment +from typing_extensions import NotRequired, ReadOnly, TypedDict + + +class _PromptFileJson(TypedDict): + """JSON form of a .prompt file: rendered template text plus its frontmatter.""" + + content: ReadOnly[NotRequired[str]] + metadata: ReadOnly[NotRequired[dict[str, object]]] def strip_version_suffix(prompt_id: str) -> str | None: @@ -167,7 +176,7 @@ class PromptManager: template_id=prompt_id, ) - def _parse_frontmatter(self, content: str) -> tuple[dict[str, Any], str]: + def _parse_frontmatter(self, content: str) -> tuple[dict[str, object], str]: """Parse YAML frontmatter from prompt content.""" # Match YAML frontmatter between --- delimiters frontmatter_pattern: Final = r"^---\s*\n(.*?)\n---\s*\n(.*)$" @@ -178,7 +187,7 @@ class PromptManager: template_content = match.group(2) try: - frontmatter = yaml.safe_load(frontmatter_yaml) or {} + frontmatter: dict[str, object] = yaml.safe_load(frontmatter_yaml) or {} except yaml.YAMLError as e: raise ValueError(f"Invalid YAML frontmatter: {e}") else: @@ -191,7 +200,7 @@ class PromptManager: def render( self, prompt_id: str, - prompt_variables: dict[str, Any] | None = None, + prompt_variables: Mapping[str, object] | None = None, version: int | None = None, ) -> str: """ @@ -231,7 +240,7 @@ class PromptManager: except Exception as e: raise ValueError(f"Error rendering template '{prompt_id}': {e}") - def _validate_input(self, variables: dict[str, Any], schema: dict[str, Any]) -> None: + def _validate_input(self, variables: Mapping[str, object], schema: Mapping[str, str]) -> None: """Basic validation of input variables against schema.""" for field_name, field_type in schema.items(): if field_name in variables: @@ -291,7 +300,7 @@ class PromptManager: """Get a list of all available prompt IDs.""" return list(self.prompts.keys()) - def get_prompt_metadata(self, prompt_id: str) -> dict[str, Any] | None: + def get_prompt_metadata(self, prompt_id: str) -> dict[str, object] | None: """Get metadata for a specific prompt.""" template: Final = self.prompts.get(prompt_id) return template.metadata if template else None @@ -302,12 +311,12 @@ class PromptManager: if self.prompt_directory: self._load_prompts() - def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, Any] | None = None) -> None: + def add_prompt(self, prompt_id: str, content: str, metadata: dict[str, object] | None = None) -> None: """Add a prompt template programmatically.""" template: Final = PromptTemplate(content=content, metadata=metadata or {}, template_id=prompt_id) self.prompts[prompt_id] = template - def prompt_file_to_json(self, file_path: str | Path) -> dict[str, Any]: + def prompt_file_to_json(self, file_path: str | Path) -> _PromptFileJson: """Convert a .prompt file to JSON format. Args: @@ -324,7 +333,7 @@ class PromptManager: return {"content": template_content.strip(), "metadata": frontmatter} - def json_to_prompt_file(self, prompt_data: dict[str, Any]) -> str: + def json_to_prompt_file(self, prompt_data: _PromptFileJson) -> str: """Convert JSON prompt data to .prompt file format. Args: diff --git a/litellm/integrations/galileo.py b/litellm/integrations/galileo.py index 23727801a6f..b27618993a3 100644 --- a/litellm/integrations/galileo.py +++ b/litellm/integrations/galileo.py @@ -6,10 +6,11 @@ import re import uuid from collections.abc import Mapping, Sequence from datetime import datetime, timezone, tzinfo -from typing import Any, Final, TypedDict, cast +from typing import Any, Final, Protocol, cast import httpx from pydantic import BaseModel, Field +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -35,6 +36,34 @@ GALILEO_CLOUD_API_BASE_URL: Final = "https://api.galileo.ai" GALILEO_MAX_IN_MEMORY_RECORDS: Final = 1000 +class _GalileoLoginBody(TypedDict): + """Decoded body of the Galileo login response.""" + + access_token: ReadOnly[str] + + +class _GalileoLoginResponse(Protocol): + """The login call's HTTP response, read for the access token it carries.""" + + def json(self) -> _GalileoLoginBody: ... + + +class _JsonResponse(Protocol): + """An HTTP response read only for whatever JSON body it decodes to.""" + + def json(self) -> object: ... + + +def _login_access_token(response: _GalileoLoginResponse) -> str: + """Read the bearer token out of a Galileo login response body.""" + return response.json()["access_token"] + + +def _decoded_body(response: _JsonResponse) -> object: + """Decode a response body without asserting anything about its shape.""" + return response.json() + + class GalileoStandardLoggingFields(TypedDict, total=False): call_type: str model: str @@ -156,7 +185,7 @@ class GalileoObserve(CustomLogger): }, ) galileo_login_response.raise_for_status() - access_token: Final = galileo_login_response.json()["access_token"] + access_token: Final = _login_access_token(galileo_login_response) self.headers = { "accept": "application/json", "Content-Type": "application/json", @@ -421,7 +450,7 @@ class GalileoObserve(CustomLogger): try: verbose_logger.debug( "Galileo Logger HTTP error response json: %s", - response.json(), + _decoded_body(response), ) except Exception: pass diff --git a/litellm/integrations/gitlab/gitlab_client.py b/litellm/integrations/gitlab/gitlab_client.py index 0690ccc8c15..813a2ef2821 100644 --- a/litellm/integrations/gitlab/gitlab_client.py +++ b/litellm/integrations/gitlab/gitlab_client.py @@ -4,12 +4,80 @@ Now supports selecting a tag via `config["tag"]`; falls back to branch ("main"). """ import base64 -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, Protocol, TypedDict from urllib.parse import quote +from typing_extensions import ReadOnly + from litellm.llms.custom_httpx.http_handler import HTTPHandler +class GitLabFilePayload(TypedDict, total=False): + """A repository-files API entry.""" + + content: ReadOnly[str] + encoding: ReadOnly[str] + + +class GitLabTreeEntry(TypedDict, total=False): + """A repository-tree API entry.""" + + path: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabBranch(TypedDict, total=False): + """A repository-branches API entry.""" + + name: ReadOnly[str] + type: ReadOnly[str] + + +class GitLabFileMetadata(TypedDict): + """The response headers a raw file request exposes as metadata.""" + + content_type: ReadOnly[str | None] + content_length: ReadOnly[str | None] + last_modified: ReadOnly[str | None] + + +class _FileJsonResponse(Protocol): + def json(self) -> GitLabFilePayload: ... + + +class _TreeJsonResponse(Protocol): + def json(self) -> Sequence[GitLabTreeEntry] | None: ... + + +class _ProjectJsonResponse(Protocol): + def json(self) -> Mapping[str, object]: ... + + +class _BranchesJsonResponse(Protocol): + def json(self) -> Sequence[GitLabBranch] | None: ... + + +def _file_payload(resp: _FileJsonResponse) -> GitLabFilePayload: + """The JSON body of a repository-files response.""" + return resp.json() + + +def _tree_entries(resp: _TreeJsonResponse) -> Sequence[GitLabTreeEntry]: + """The entries of a repository-tree response.""" + return resp.json() or [] + + +def _project_info(resp: _ProjectJsonResponse) -> Mapping[str, object]: + """The JSON body of a project response.""" + return resp.json() + + +def _branch_entries(resp: _BranchesJsonResponse) -> Sequence[GitLabBranch] | None: + """The JSON body of a repository-branches response.""" + return resp.json() + + class GitLabClient: """ Client for interacting with the GitLab API to fetch files. @@ -42,12 +110,12 @@ class GitLabClient: self.project: str | int = project self.access_token: str = str(access_token) - self.auth_method = config.get("auth_method", "token") # 'token' or 'oauth' + self.auth_method: str = config.get("auth_method", "token") # 'token' or 'oauth' self.branch = config.get("branch", None) if not self.branch: self.branch = "main" self.tag = config.get("tag") - self.base_url = config.get("base_url", "https://gitlab.com/api/v4") + self.base_url: str = config.get("base_url", "https://gitlab.com/api/v4") if not all([self.project, self.access_token]): raise ValueError("project and access_token are required") @@ -159,7 +227,7 @@ class GitLabClient: if resp.status_code == 404: return None resp.raise_for_status() - data: Final = resp.json() + data: Final = _file_payload(resp) content: Final = data.get("content") encoding: Final = data.get("encoding", "") if content and encoding == "base64": @@ -208,7 +276,7 @@ class GitLabClient: return [] resp.raise_for_status() - data: Final = resp.json() or [] + data: Final = _tree_entries(resp) files: Final[list[str]] = [] for item in data: if item.get("type") == "blob": @@ -229,13 +297,13 @@ class GitLabClient: raise Exception("Authentication failed. Check your GitLab token and auth_method.") raise Exception(f"Failed to list files in '{directory_path}': {e}") - def get_repository_info(self) -> dict[str, Any]: + def get_repository_info(self) -> Mapping[str, object]: """Get information about the project/repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - return resp.json() + return _project_info(resp) except Exception as e: raise Exception(f"Failed to get repository info: {e}") @@ -247,18 +315,18 @@ class GitLabClient: except Exception: return False - def get_branches(self) -> list[dict[str, Any]]: + def get_branches(self) -> list[GitLabBranch]: """Get list of branches in the repository.""" url: Final = f"{self.base_url}/projects/{self._project_enc}/repository/branches" try: resp: Final = self.http_handler.get(url, headers=self.headers) resp.raise_for_status() - data: Final = resp.json() + data: Final = _branch_entries(resp) return data if isinstance(data, list) else [] except Exception as e: raise Exception(f"Failed to get branches: {e}") - def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> dict[str, Any] | None: + def get_file_metadata(self, file_path: str, *, ref: str | None = None) -> GitLabFileMetadata | None: """ Get minimal metadata about a file via RAW endpoint headers at a given ref. diff --git a/litellm/integrations/langfuse/langfuse.py b/litellm/integrations/langfuse/langfuse.py index 296c2b5714e..9576eabaa34 100644 --- a/litellm/integrations/langfuse/langfuse.py +++ b/litellm/integrations/langfuse/langfuse.py @@ -89,7 +89,7 @@ def _extract_cache_read_input_tokens(usage_obj) -> int: # Check prompt_tokens_details.cached_tokens (used by Gemini and other providers) if hasattr(usage_obj, "prompt_tokens_details"): - prompt_tokens_details: Final = getattr(usage_obj, "prompt_tokens_details", None) + prompt_tokens_details: Final[object] = getattr(usage_obj, "prompt_tokens_details", None) if prompt_tokens_details is not None and hasattr(prompt_tokens_details, "cached_tokens"): cached_tokens: Final = getattr(prompt_tokens_details, "cached_tokens", None) if cached_tokens is not None and isinstance(cached_tokens, (int, float)) and cached_tokens > 0: @@ -623,9 +623,16 @@ class LangFuseLogger: ) # Apply custom masking function if provided - if masking_function is not None and callable(masking_function): - input = self._apply_masking_function(input, masking_function) - output = self._apply_masking_function(output, masking_function) + masked_input: Final[object] = ( + self._apply_masking_function(input, masking_function) + if masking_function is not None and callable(masking_function) + else input + ) + masked_output: Final[object] = ( + self._apply_masking_function(output, masking_function) + if masking_function is not None and callable(masking_function) + else output + ) clean_metadata = redact_user_api_key_info(metadata=clean_metadata) @@ -651,15 +658,15 @@ class LangFuseLogger: # Special keys that are found in the function arguments and not the metadata if "input" in update_trace_keys: - trace_params["input"] = input if not mask_input else "redacted-by-litellm" + trace_params["input"] = masked_input if not mask_input else "redacted-by-litellm" if "output" in update_trace_keys: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" else: # don't overwrite an existing trace trace_params = { "id": trace_id, "name": trace_name, "session_id": session_id, - "input": input if not mask_input else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", "version": clean_metadata.pop( "trace_version", clean_metadata.get("version", None) ), # If provided just version, it will applied to the trace as well, if applied a trace version it will take precedence @@ -669,9 +676,9 @@ class LangFuseLogger: trace_params[key.replace("trace_", "")] = clean_metadata.pop(key, None) if level == "ERROR": - trace_params["status_message"] = output + trace_params["status_message"] = masked_output else: - trace_params["output"] = output if not mask_output else "redacted-by-litellm" + trace_params["output"] = masked_output if not mask_output else "redacted-by-litellm" if debug is True or (isinstance(debug, str) and debug.lower() == "true"): debug_metadata: Final = { @@ -708,7 +715,7 @@ class LangFuseLogger: ("aws_region_name", aws_region_name, bool(aws_region_name)), ("cache_hit", kwargs.get("cache_hit") or False, self._supports_tags() and "cache_hit" in kwargs), ) - enrichments: Final[Mapping[str, Any]] = { + enrichments: Final[Mapping[str, object]] = { key: value for key, value, include in candidate_enrichments if include } @@ -802,8 +809,8 @@ class LangFuseLogger: "end_time": end_time, "model": model_name, "model_parameters": optional_params, - "input": input if not mask_input else "redacted-by-litellm", - "output": output if not mask_output else "redacted-by-litellm", + "input": masked_input if not mask_input else "redacted-by-litellm", + "output": masked_output if not mask_output else "redacted-by-litellm", "usage": usage, "usage_details": usage_details, "metadata": { @@ -825,8 +832,8 @@ class LangFuseLogger: prompt_management_metadata=prompt_management_metadata, langfuse_client=self.Langfuse, ) - if output is not None and isinstance(output, str) and level == "ERROR": - generation_params["status_message"] = output + if masked_output is not None and isinstance(masked_output, str) and level == "ERROR": + generation_params["status_message"] = masked_output if self._supports_completion_start_time(): generation_params["completion_start_time"] = kwargs.get("completion_start_time", None) @@ -935,7 +942,7 @@ class LangFuseLogger: return Version(self.langfuse_sdk_version) >= Version("2.7.3") @staticmethod - def _apply_masking_function(data: Any, masking_function: Callable[[Any], Any]) -> Any: + def _apply_masking_function(data: object, masking_function: Callable[[object], object]) -> object: """ Apply a masking function to data, handling different data types. @@ -1049,7 +1056,7 @@ def _add_prompt_to_generation_params( generation_params: dict, clean_metadata: dict, prompt_management_metadata: StandardLoggingPromptManagementMetadata | None, - langfuse_client: Any, + langfuse_client: object, ) -> dict: from langfuse import Langfuse from langfuse.model import ( diff --git a/litellm/integrations/opik/opik.py b/litellm/integrations/opik/opik.py index fae93f03d1e..ce47d7fe27a 100644 --- a/litellm/integrations/opik/opik.py +++ b/litellm/integrations/opik/opik.py @@ -4,9 +4,12 @@ Opik Logger that logs LLM events to an Opik server import asyncio import traceback +from collections.abc import Mapping from datetime import datetime from typing import Any, Final +from typing_extensions import ReadOnly, TypedDict, Unpack + from litellm._logging import verbose_logger from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.llms.custom_httpx.http_handler import ( @@ -23,7 +26,7 @@ except Exception: opik_client = None -def _should_skip_event(kwargs: dict[str, Any]) -> bool: +def _should_skip_event(kwargs: Mapping[str, object]) -> bool: """Check if event should be skipped due to missing standard_logging_object.""" if kwargs.get("standard_logging_object") is None: verbose_logger.debug("OpikLogger skipping event; no standard_logging_object found") @@ -31,12 +34,24 @@ def _should_skip_event(kwargs: dict[str, Any]) -> bool: return False +class _OpikLoggerKwargs(TypedDict, total=False): + """Constructor options accepted by ``OpikLogger``.""" + + project_name: ReadOnly[str | None] + url: ReadOnly[str | None] + api_key: ReadOnly[str | None] + workspace: ReadOnly[str | None] + batch_size: ReadOnly[int | None] + flush_interval: ReadOnly[int | None] + max_queue_size: ReadOnly[int | None] + + class OpikLogger(CustomBatchLogger): """ Opik Logger for logging events to an Opik Server """ - def __init__(self, **kwargs: Any) -> None: + def __init__(self, **kwargs: Unpack[_OpikLoggerKwargs]) -> None: self.async_httpx_client = get_async_httpx_client(llm_provider=httpxSpecialProvider.LoggingCallback) self.sync_httpx_client = _get_httpx_client() @@ -95,7 +110,7 @@ class OpikLogger(CustomBatchLogger): async def async_log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -163,7 +178,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + def _sync_send(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = self.sync_httpx_client.post( url=url, @@ -178,7 +193,7 @@ class OpikLogger(CustomBatchLogger): def log_success_event( self, - kwargs: dict[str, Any], + kwargs: dict[str, object], response_obj: Any, start_time: datetime, end_time: datetime, @@ -247,7 +262,7 @@ class OpikLogger(CustomBatchLogger): except Exception as e: verbose_logger.exception("OpikLogger failed to log success event - %s\n%s", e, traceback.format_exc()) - async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, Any]) -> None: + async def _submit_batch(self, url: str, headers: dict[str, str], batch: dict[str, object]) -> None: try: response: Final = await self.async_httpx_client.post( url=url, diff --git a/litellm/integrations/opik/opik_payload_builder/extractors.py b/litellm/integrations/opik/opik_payload_builder/extractors.py index 92a7eca7f3e..4dd3d40fae3 100644 --- a/litellm/integrations/opik/opik_payload_builder/extractors.py +++ b/litellm/integrations/opik/opik_payload_builder/extractors.py @@ -1,6 +1,7 @@ """Data extraction functions for Opik payload building.""" import json +from collections.abc import Mapping from typing import Any, Final from litellm import _logging @@ -35,8 +36,8 @@ def normalize_provider_name(provider: str | None) -> str | None: def extract_opik_metadata( - litellm_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], + litellm_metadata: Mapping[str, Any], + standard_logging_metadata: Mapping[str, Any], ) -> dict[str, Any]: """ Merge Opik metadata from three sources in increasing priority order: @@ -97,7 +98,7 @@ def extract_span_identifiers( def extract_tags( - opik_metadata: dict[str, Any], + opik_metadata: Mapping[str, Any], custom_llm_provider: str | None, ) -> list[str]: """ @@ -122,7 +123,7 @@ def apply_proxy_header_overrides( project_name: str, tags: list[str], thread_id: str | None, - proxy_headers: dict[str, Any], + proxy_headers: Mapping[str, str], ) -> tuple[str, list[str], str | None]: """ Apply overrides from proxy request headers (opik_* prefix). @@ -148,7 +149,7 @@ def apply_proxy_header_overrides( thread_id = value elif param_key == "tags": try: - parsed_tags = json.loads(value) + parsed_tags: object = json.loads(value) if isinstance(parsed_tags, list): tags.extend(parsed_tags) except (json.JSONDecodeError, TypeError): @@ -158,11 +159,11 @@ def apply_proxy_header_overrides( def extract_and_build_metadata( - opik_metadata: dict[str, Any], - standard_logging_metadata: dict[str, Any], - standard_logging_object: dict[str, Any], - litellm_kwargs: dict[str, Any], -) -> dict[str, Any]: + opik_metadata: Mapping[str, object], + standard_logging_metadata: Mapping[str, object], + standard_logging_object: Mapping[str, object], + litellm_kwargs: Mapping[str, object], +) -> dict[str, object]: """ Build the complete metadata dictionary from all available sources. diff --git a/litellm/integrations/otel/plumbing/metrics.py b/litellm/integrations/otel/plumbing/metrics.py index c7e491c002a..e1623f4697f 100644 --- a/litellm/integrations/otel/plumbing/metrics.py +++ b/litellm/integrations/otel/plumbing/metrics.py @@ -11,9 +11,10 @@ identical metrics. The attribute cardinality filter is reused from v1 by import from collections.abc import Mapping from dataclasses import dataclass from datetime import datetime -from typing import Any, Final, TypeAlias +from typing import Any, Final, Literal, Protocol, TypeAlias from opentelemetry.metrics import Histogram, Meter +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -151,6 +152,29 @@ METRIC_ATTRIBUTE_CEILING: Final[frozenset[str]] = frozenset( BOUNDED_HIDDEN_PARAM_KEYS: Final[tuple[str, ...]] = ("model_id",) +class _TokenUsage(TypedDict, total=False): + """The token counts a response's ``usage`` carries, as the recorder reads them.""" + + prompt_tokens: ReadOnly[int] + completion_tokens: ReadOnly[int] + + +class _ResponseView(Protocol): + """The one read the recorder makes on a litellm response object.""" + + def get(self, key: Literal["usage"], /) -> _TokenUsage | None: ... + + +class _MetricKwargs(TypedDict, total=False): + """The logging kwargs the recorder reads directly.""" + + call_type: ReadOnly[str | None] + litellm_params: ReadOnly[Mapping[str, object] | None] + response_cost: ReadOnly[float | None] + completion_start_time: ReadOnly[datetime | float | str | None] + api_call_start_time: ReadOnly[datetime | float | str | None] + + def resolve_error_type(kwargs: Mapping[str, Any]) -> str: """The ``error.type`` value for a failed request. @@ -192,8 +216,8 @@ class GenAIMetricRecorder: def record( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, start_time: datetime, end_time: datetime, ) -> None: @@ -218,7 +242,7 @@ class GenAIMetricRecorder: def record_failure( self, - kwargs: Mapping[str, Any], + kwargs: _MetricKwargs, start_time: datetime, end_time: datetime, ) -> None: @@ -342,7 +366,7 @@ class GenAIMetricRecorder: # Per-metric recording # ------------------------------------------------------------------ # - def _record_token_usage(self, response_obj: Any, common_attrs: dict) -> None: + def _record_token_usage(self, response_obj: _ResponseView | None, common_attrs: dict) -> None: if not response_obj: return usage: Final = response_obj.get("usage") @@ -353,7 +377,7 @@ class GenAIMetricRecorder: self._metrics.token_usage.record(usage.get("prompt_tokens", 0), attributes=in_attrs) self._metrics.token_usage.record(usage.get("completion_tokens", 0), attributes=out_attrs) - def _record_time_to_first_token(self, kwargs: Mapping[str, Any], common_attrs: dict) -> None: + def _record_time_to_first_token(self, kwargs: _MetricKwargs, common_attrs: dict) -> None: time_to_first_chunk: Final = time_to_first_chunk_seconds(kwargs) if time_to_first_chunk is None: return @@ -361,15 +385,14 @@ class GenAIMetricRecorder: def _record_time_per_output_token( self, - kwargs: Mapping[str, Any], - response_obj: Any, + kwargs: _MetricKwargs, + response_obj: _ResponseView | None, end_time: datetime, duration_s: float, common_attrs: dict, ) -> None: - completion_tokens = None - if response_obj and (usage := response_obj.get("usage")): - completion_tokens = usage.get("completion_tokens") + usage: Final = response_obj.get("usage") if response_obj else None + completion_tokens: Final = usage.get("completion_tokens") if usage else None if completion_tokens is None or completion_tokens <= 0: return diff --git a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py index aa29162ba1f..07d4f959489 100644 --- a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py +++ b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py @@ -13,7 +13,7 @@ from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage from litellm.types.prompts.init_prompts import PromptSpec -from litellm.types.utils import StandardCallbackDynamicParams +from litellm.types.utils import CallTypes, StandardCallbackDynamicParams from litellm.types.vector_stores import ( LiteLLM_ManagedVectorStore, VectorStoreResultContent, @@ -226,7 +226,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the response after successful LLM call. @@ -283,7 +283,7 @@ class VectorStorePreCallHook(CustomLogger): self, request_data: dict, response_chunk: Any, - call_type: Any | None, + call_type: CallTypes | None, ) -> Any | None: """ Add search results to the final streaming chunk. diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 9b53b79bbe6..207e024ce0b 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -536,6 +536,14 @@ def get_llm_provider( ) +def _dashscope_family_chat_config(custom_llm_provider: str) -> "litellm.DashScopeChatConfig": + if custom_llm_provider == "qwencloud": + return litellm.QwenCloudChatConfig() + if custom_llm_provider == "qwen_ai_platform": + return litellm.QwenAIPlatformChatConfig() + return litellm.DashScopeChatConfig() + + def _get_openai_compatible_provider_info( model: str, api_base: str | None, @@ -785,11 +793,11 @@ def _get_openai_compatible_provider_info( api_base, dynamic_api_key, ) = litellm.HerokuChatConfig()._get_openai_compatible_provider_info(api_base, api_key) - elif custom_llm_provider == "dashscope": + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): ( api_base, dynamic_api_key, - ) = litellm.DashScopeChatConfig()._get_openai_compatible_provider_info(api_base, api_key) + ) = _dashscope_family_chat_config(custom_llm_provider)._get_openai_compatible_provider_info(api_base, api_key) elif custom_llm_provider == "modelscope": ( api_base, diff --git a/litellm/litellm_core_utils/realtime_streaming.py b/litellm/litellm_core_utils/realtime_streaming.py index 9125ed6e70a..8479e108d17 100644 --- a/litellm/litellm_core_utils/realtime_streaming.py +++ b/litellm/litellm_core_utils/realtime_streaming.py @@ -1500,6 +1500,6 @@ class RealTimeStreaming: pass -def client_sent_openai_beta_realtime_header(websocket: Any) -> bool: +def client_sent_openai_beta_realtime_header(websocket: _ScopedWebSocket) -> bool: """True when the client WebSocket includes ``OpenAI-Beta: realtime=v1``.""" return RealTimeStreaming._detect_beta_header(websocket) diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 0e2139d688b..3978a01a5db 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -73,6 +73,18 @@ class _ContentChunk(TypedDict): choices: Sequence[_ContentChoice] +class _FunctionCallDelta(TypedDict): + function_call: ReadOnly[FunctionCall] + + +class _FunctionCallChoice(TypedDict): + delta: ReadOnly[_FunctionCallDelta] + + +class _FunctionCallChunk(TypedDict): + choices: ReadOnly[Sequence[_FunctionCallChoice]] + + class _AudioDelta(TypedDict, total=False): audio: ChatCompletionAudioDelta | None @@ -588,7 +600,7 @@ class ChunkProcessor: return tool_calls_list - def get_combined_function_call_content(self, function_call_chunks: list[dict[str, Any]]) -> FunctionCall: + def get_combined_function_call_content(self, function_call_chunks: Sequence["_FunctionCallChunk"]) -> FunctionCall: argument_list: Final = [] delta = function_call_chunks[0]["choices"][0]["delta"] function_call = delta.get("function_call", "") diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index f5f671e7585..480b1921c18 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -862,6 +862,8 @@ class CustomStreamWrapper: model_response: Final = ModelResponseStream(**args) if self.response_id is not None: model_response.id = self.response_id + elif model_response.id: + self.response_id = model_response.id if self.system_fingerprint is not None: model_response.system_fingerprint = self.system_fingerprint diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py index 1c5ba951942..f1c7451796d 100644 --- a/litellm/llms/a2a/chat/guardrail_translation/handler.py +++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py @@ -11,8 +11,11 @@ A2A Protocol Format: """ import json +from collections.abc import Sequence from typing import TYPE_CHECKING, Any, Final, Optional +from typing_extensions import ReadOnly, TypedDict + from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.types.utils import GenericGuardrailAPIInputs @@ -23,6 +26,13 @@ if TYPE_CHECKING: from litellm.proxy._types import UserAPIKeyAuth +class _A2ATextPart(TypedDict, total=False): + """The subset of an A2A message part this handler reads text from.""" + + kind: ReadOnly[str] + text: ReadOnly[str] + + class A2AGuardrailHandler(BaseTranslation): """ Handler for processing A2A Protocol messages with guardrails. @@ -41,7 +51,7 @@ class A2AGuardrailHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> Any: + ) -> dict: """ Process A2A input messages by applying guardrails to text content. @@ -214,12 +224,12 @@ class A2AGuardrailHandler(BaseTranslation): async def process_output_streaming_response( self, - responses_so_far: list[Any], + responses_so_far: list[object], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, user_api_key_dict: Optional["UserAPIKeyAuth"] = None, request_data: dict | None = None, - ) -> list[Any]: + ) -> list[object]: """ Process A2A streaming output by applying guardrails to accumulated text. @@ -305,11 +315,12 @@ class A2AGuardrailHandler(BaseTranslation): def _parse_streaming_responses( self, - responses_so_far: list[Any], - ) -> tuple[list[dict[str, Any] | None], list[tuple[int, dict[str, Any]]]]: + responses_so_far: list[object], + ) -> tuple[list[dict[str, object] | None], list[tuple[int, dict[str, object]]]]: """Parse JSON-RPC items, returning aligned parsed list and valid entries.""" - parsed: Final[list[dict[str, Any] | None]] = [None] * len(responses_so_far) + parsed: Final[list[dict[str, object] | None]] = [None] * len(responses_so_far) for i, item in enumerate(responses_so_far): + obj: dict[str, object] if isinstance(item, dict): obj = item elif isinstance(item, str): @@ -326,7 +337,7 @@ class A2AGuardrailHandler(BaseTranslation): def _collect_text_from_parsed_chunks( self, - valid_parsed: list[tuple[int, dict[str, Any]]], + valid_parsed: list[tuple[int, dict[str, object]]], ) -> tuple[str, list[int]]: """Collect text from parsed chunks, returning combined text and indices.""" from litellm.llms.a2a.common_utils import extract_text_from_a2a_response @@ -411,7 +422,7 @@ class A2AGuardrailHandler(BaseTranslation): def _extract_texts_from_parts( self, - parts: list[dict[str, Any]], + parts: Sequence[_A2ATextPart], path: tuple[str, ...], texts_to_check: list[str], task_mappings: list[tuple[tuple[str, ...], int]], diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index e1387a9068c..3e6d96b9780 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -7,6 +7,7 @@ from typing import TYPE_CHECKING, Any, Final, NoReturn, cast import httpx from pydantic import ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm.constants import ( @@ -125,7 +126,25 @@ else: _ANTHROPIC_TOOL_NAME_INVALID_CHARS: Final = re.compile(r"[^a-zA-Z0-9_-]") _ANTHROPIC_TOOL_NAME_MAX_LEN: Final = 128 -_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[Any], bool]]] = MappingProxyType( + +class _AnthropicUsageIteration(TypedDict, total=False): + """One entry of the ``usage.iterations`` array on an Anthropic response.""" + + input_tokens: ReadOnly[int | None] + output_tokens: ReadOnly[int | None] + cache_creation_input_tokens: ReadOnly[int | None] + cache_read_input_tokens: ReadOnly[int | None] + + +class _AnthropicToolResultBlock(TypedDict, total=False): + """A ``*_tool_result`` content block on an Anthropic response.""" + + type: ReadOnly[str] + tool_use_id: ReadOnly[str] + content: ReadOnly[object] + + +_ENUM_TYPE_CHECKS: Final[Mapping[str, Callable[[object], bool]]] = MappingProxyType( { "null": lambda v: v is None, "boolean": lambda v: isinstance(v, bool), @@ -440,7 +459,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): optional_params.pop("speed", None) @staticmethod - def _raise_invalid_reasoning_effort(model: str, value: Any, llm_provider: str) -> NoReturn: + def _raise_invalid_reasoning_effort(model: str, value: object, llm_provider: str) -> NoReturn: """Raise a ``BadRequestError`` for an unrecognised ``reasoning_effort``. Args: @@ -1992,19 +2011,35 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return data def _apply_output_config(self, data: dict, model: str, optional_params: dict) -> None: - """Validate and apply output_config to the request data.""" + """Validate and apply output_config to the request data. + + The ``drop_params`` gate here is an effort gate: ``format`` is a + structured-output field, not an effort field, so it survives the drop + and is vetted where it is consumed (the map's + ``supports_native_structured_output`` flag on emission paths). + """ if "output_config" not in optional_params: return output_config: Final = optional_params.get("output_config") if not output_config or not isinstance(output_config, dict): return - if litellm.drop_params is True and not self._model_supports_effort_param(model, self._resolved_provider): + if ( + litellm.drop_params is True + and any(key != "format" for key in output_config) + and not self._model_supports_effort_param(model, self._resolved_provider) + ): litellm.verbose_logger.warning( DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING, model, ) - optional_params.pop("output_config", None) - data.pop("output_config", None) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + optional_params.pop("output_config", None) + data.pop("output_config", None) + return + format_only: Final = {"format": preserved_format} # mutable-ok: json body + optional_params["output_config"] = format_only # rebind-ok: out-param store + data["output_config"] = format_only # rebind-ok: out-param store return effort: Final = output_config.get("effort") valid_efforts: Final = ["high", "medium", "low", "xhigh", "max"] @@ -2059,22 +2094,22 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): self, completion_response: dict ) -> tuple[ str, - list[Any] | None, + list[object] | None, list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, str | None, list[ChatCompletionToolCallChunk], - list[Any] | None, - list[Any] | None, - list[Any] | None, + list[object] | None, + list[_AnthropicToolResultBlock] | None, + list[object] | None, ]: text_content = "" - citations: list[Any] | None = None + citations: list[object] | None = None thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None = None reasoning_content: str | None = None tool_calls: Final[list[ChatCompletionToolCallChunk]] = [] - web_search_results: list[Any] | None = None - tool_results: list[Any] | None = None - compaction_blocks: list[Any] | None = None + web_search_results: list[object] | None = None + tool_results: list[_AnthropicToolResultBlock] | None = None + compaction_blocks: list[object] | None = None for idx, content in enumerate(completion_response["content"]): if content["type"] == "text": text_content += content["text"] @@ -2284,7 +2319,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): raw_speed: Final = _usage.get("speed") resolved_speed: Final = raw_speed if isinstance(raw_speed, str) else speed - iterations: Final[list[Any] | None] = _usage.get("iterations") + iterations: Final[Sequence[_AnthropicUsageIteration] | None] = _usage.get("iterations") if iterations: prompt_tokens = sum(it.get("input_tokens", 0) or 0 for it in iterations) completion_tokens = sum(it.get("output_tokens", 0) or 0 for it in iterations) @@ -2377,7 +2412,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_code_interpreter_results( self, - tool_results: list[Any], + tool_results: Sequence[_AnthropicToolResultBlock], code_by_id: dict[str, str], container_id: str | None, ) -> list[OutputCodeInterpreterCall]: @@ -2403,11 +2438,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def _build_provider_specific_fields( self, completion_response: dict, - citations: list[Any] | None, + citations: Sequence[object] | None, thinking_blocks: list[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock] | None, - web_search_results: list[Any] | None, - tool_results: list[Any] | None, - compaction_blocks: list[Any] | None, + web_search_results: Sequence[object] | None, + tool_results: Sequence[_AnthropicToolResultBlock] | None, + compaction_blocks: Sequence[object] | None, tool_calls: list[ChatCompletionToolCallChunk], ) -> dict[str, Any]: provider_specific_fields: Final[dict[str, Any]] = { diff --git a/litellm/llms/anthropic/files/handler.py b/litellm/llms/anthropic/files/handler.py index 5fdf2ceff7f..dfd62ca575b 100644 --- a/litellm/llms/anthropic/files/handler.py +++ b/litellm/llms/anthropic/files/handler.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Coroutine -from typing import Any, Final +from typing import Final import httpx @@ -116,7 +116,7 @@ class AnthropicFilesHandler: api_key: str | None = None, timeout: float | httpx.Timeout = 600.0, max_retries: int | None = None, - ) -> HttpxBinaryResponseContent | Coroutine[Any, Any, HttpxBinaryResponseContent]: + ) -> HttpxBinaryResponseContent | Coroutine[object, object, HttpxBinaryResponseContent]: """ Retrieve file content from Anthropic. diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index 2bcc830851a..46a9dd1a531 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -2,7 +2,7 @@ import asyncio import json import time from collections.abc import Callable, Coroutine -from typing import Any, Final +from typing import Final import httpx from openai import ( @@ -374,7 +374,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: error_headers = getattr(error_response, "headers", None) @@ -392,7 +392,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): model: str, api_base: str, data: dict, - timeout: Any, + timeout: float | httpx.Timeout, dynamic_params: bool, model_response: ModelResponse, logging_obj: LiteLLMLoggingObj, @@ -502,7 +502,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict[str, object], model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -578,7 +578,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): dynamic_params: bool, data: dict, model: str, - timeout: Any, + timeout: float | httpx.Timeout, max_retries: int, azure_ad_token: str | None = None, azure_ad_token_provider: Callable | None = None, @@ -634,7 +634,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): except Exception as e: status_code: Final = getattr(e, "status_code", 500) error_headers = getattr(e, "headers", None) - error_response: Final = getattr(e, "response", None) + error_response: Final[object] = getattr(e, "response", None) message: Final = getattr(e, "message", str(e)) error_body: Final = getattr(e, "body", None) if error_headers is None and error_response: @@ -754,7 +754,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): aembedding=None, headers: dict | None = None, litellm_params: dict | None = None, - ) -> EmbeddingResponse | Coroutine[Any, Any, EmbeddingResponse]: + ) -> EmbeddingResponse | Coroutine[object, object, EmbeddingResponse]: if headers: optional_params["extra_headers"] = headers if self._client_session is None: @@ -1268,7 +1268,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): headers["Authorization"] = f"Bearer {azure_ad_token}" # init AzureOpenAI Client - azure_client_params: Final[dict[str, Any]] = self.initialize_azure_sdk_client( + azure_client_params: Final[dict[str, object]] = self.initialize_azure_sdk_client( litellm_params=litellm_params or {}, api_key=api_key, model_name=model or "", diff --git a/litellm/llms/azure_ai/agents/handler.py b/litellm/llms/azure_ai/agents/handler.py index a13b1300e55..f7382190fca 100644 --- a/litellm/llms/azure_ai/agents/handler.py +++ b/litellm/llms/azure_ai/agents/handler.py @@ -51,15 +51,13 @@ else: AsyncHTTPHandler = Any -class _AzureRawAnnotation(TypedDict, total=False): - type: ReadOnly[str] +class _AzureRawAnnotation(ChatCompletionAnnotation, total=False): text: ReadOnly[str] start_index: ReadOnly[int] end_index: ReadOnly[int] - url_citation: ReadOnly[ChatCompletionAnnotationURLCitation] -_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation | _AzureRawAnnotation +_TransformedAnnotation: TypeAlias = ChatCompletionAnnotation class _AzureText(TypedDict, total=False): @@ -223,18 +221,11 @@ class AzureAIAgentsHandler: """Build the ModelResponse from agent output.""" from litellm.types.utils import Choices, Message, Usage - message_kwargs: Final[dict[str, Any]] = { - "content": content, - "role": "assistant", - } - if annotations: - message_kwargs["annotations"] = annotations - model_response.choices = [ Choices( finish_reason="stop", index=0, - message=Message(**message_kwargs), + message=Message(content=content, role="assistant", annotations=annotations or None), ) ] model_response.model = model @@ -655,9 +646,6 @@ class AzureAIAgentsHandler: if data_str == "[DONE]": # Send final chunk with finish_reason - final_delta_kwargs: dict[str, Any] = {"content": None} - if collected_annotations: - final_delta_kwargs["annotations"] = collected_annotations final_chunk = ModelResponseStream( id=response_id, created=created, @@ -667,7 +655,7 @@ class AzureAIAgentsHandler: StreamingChoices( finish_reason="stop", index=0, - delta=Delta(**final_delta_kwargs), + delta=Delta(content=None, annotations=collected_annotations or None), ) ], ) diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index 40b90014f3b..8e709349400 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -3,7 +3,6 @@ from typing import TYPE_CHECKING, Any, Final import httpx from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers -from litellm.litellm_core_utils.litellm_logging import verbose_logger from litellm.litellm_core_utils.prompt_templates.factory import ( convert_to_anthropic_image_obj, ) @@ -16,17 +15,16 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, get_anthropic_beta_from_headers, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse -from litellm.utils import _supports_factory if TYPE_CHECKING: import tiktoken @@ -212,36 +210,14 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): anthropic_request.pop("model", None) anthropic_request.pop("stream", None) anthropic_request.pop("stream_chunk_size", None) - output_format: Final = anthropic_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_request, - ) - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_request, + ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_request, + ) if "anthropic_version" not in anthropic_request: anthropic_request["anthropic_version"] = self.anthropic_version diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 9cbceb4880c..30a77d57f24 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -177,6 +177,95 @@ def convert_bedrock_invoke_output_format_to_inline_schema( request_body["messages"] = new_messages +def _bedrock_model_supports(model: str, key: str) -> bool: + from litellm.utils import _supports_factory + + return _supports_factory(model=model, custom_llm_provider="bedrock", key=key) + + +def apply_bedrock_invoke_structured_output( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Route Anthropic structured-output params to what the Bedrock model supports. + + Consumes the legacy top-level ``output_format`` and the newer + ``output_config.format``, keeping the pre-existing precedence of the legacy + field when a request carries both. Models flagged + ``supports_native_structured_output`` in the model map get the schema + forwarded as ``output_config.format``, which Bedrock relays to the model for + enforced structured output. For every other model the schema is inlined into + the last user message as best-effort text, with a warning because nothing + enforces it. + """ + legacy_output_format: Final = request_body.pop("output_format", None) + output_config_format: Final = pop_bedrock_invoke_output_config_format(request_body) + schema_format: Final = legacy_output_format if isinstance(legacy_output_format, dict) else output_config_format + if schema_format is None: + return + + if _bedrock_model_supports(model, "supports_native_structured_output"): + existing_output_config: Final = request_body.get("output_config") + if isinstance(existing_output_config, dict): + existing_output_config["format"] = schema_format + else: + request_body["output_config"] = {"format": schema_format} # rebind-ok: out-param # mutable-ok: json + return + + verbose_logger.warning( + "Bedrock Invoke: model=%s does not advertise `supports_native_structured_output` " + "in model_prices_and_context_window.json, so the JSON schema was inlined into " + "the last user message and is NOT enforced by the model.", + model, + ) + convert_bedrock_invoke_output_format_to_inline_schema( + output_format=schema_format, + request_body=request_body, + ) + + +def strip_unsupported_bedrock_invoke_output_config_keys( + model: str, + request_body: dict[str, object], # mutable-ok: edited in place like siblings +) -> None: + """ + Drop ``output_config`` keys the Bedrock model does not accept. + + ``format`` survives unconditionally: it is only attached for models whose map + entry advertises ``supports_native_structured_output``. Effort-bearing keys + survive only when the map flags ``supports_output_config`` or a + ``supports_*_reasoning_effort`` tier; otherwise they are dropped with a + warning so Bedrock does not reject the request. + """ + from litellm.llms.anthropic.chat.transformation import AnthropicConfig + + output_config: Final = request_body.get("output_config") + if not isinstance(output_config, dict): + return + if all(key == "format" for key in output_config): + return + if _bedrock_model_supports(model, "supports_output_config") or AnthropicConfig._model_supports_effort_param( + model, "bedrock" + ): + return + + verbose_logger.warning( + "Bedrock Invoke: stripping unsupported `output_config` keys for " + "model=%s: neither `supports_output_config` nor any " + "`supports_*_reasoning_effort` flag is set in " + "model_prices_and_context_window.json. Add the capability " + "flag to the model JSON entry if this model accepts " + "`output_config`.", + model, + ) + preserved_format: Final = output_config.get("format") + if preserved_format is None: + request_body.pop("output_config", None) + else: + request_body["output_config"] = {"format": preserved_format} # rebind-ok: out-param # mutable-ok: json + + def normalize_custom_field_on_tools(request_body: dict) -> None: """ Drop the ``custom`` field from each tool, first hoisting a boolean diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index f74a290d773..6ff9f0155f9 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -29,14 +29,14 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation AmazonInvokeConfig, ) from litellm.llms.bedrock.common_utils import ( - convert_bedrock_invoke_output_format_to_inline_schema, + apply_bedrock_invoke_structured_output, ensure_bedrock_anthropic_messages_tool_names, get_anthropic_beta_from_headers, is_claude_4_5_on_bedrock, normalize_bedrock_opus_output_config_effort, normalize_custom_field_on_tools, normalize_tool_input_schema_types_for_bedrock_invoke, - pop_bedrock_invoke_output_config_format, + strip_unsupported_bedrock_invoke_output_config_keys, ) from litellm.llms.bedrock.request_metadata import ( bedrock_request_metadata_headers, @@ -51,7 +51,6 @@ from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import GenericStreamingChunk, ModelResponseStream from litellm.types.utils import GenericStreamingChunk as GChunk -from litellm.utils import _supports_factory if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -708,52 +707,25 @@ class AmazonAnthropicClaudeMessagesConfig( # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models) self._remove_ttl_from_cache_control(anthropic_messages_request=anthropic_messages_request, model=model) - # 5. Convert structured-output params to inline schema. - # Bedrock Invoke doesn't support top-level `output_format`; its - # accepted `output_config` subset is also narrower than Anthropic's, so - # consume the newer `output_config.format` shape here instead of - # forwarding it as an unknown nested key. + # 5. Route structured-output params (`output_format` / + # `output_config.format`) to native enforcement or the inline-schema + # fallback, then strip `output_config` keys the model does not accept. + # Ref: https://github.com/BerriAI/litellm/issues/22797 existing_output_config: Final = anthropic_messages_request.get("output_config") if isinstance(existing_output_config, dict): anthropic_messages_request["output_config"] = dict(existing_output_config) - output_format: Final = anthropic_messages_request.pop("output_format", None) - output_config_format: Final = pop_bedrock_invoke_output_config_format(anthropic_messages_request) - if output_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_format, - request_body=anthropic_messages_request, - ) - elif output_config_format: - convert_bedrock_invoke_output_format_to_inline_schema( - output_format=output_config_format, - request_body=anthropic_messages_request, - ) + apply_bedrock_invoke_structured_output( + model=model, + request_body=anthropic_messages_request, + ) normalize_bedrock_opus_output_config_effort( model=model, output_config=anthropic_messages_request.get("output_config"), ) - - # 5a. Bedrock Invoke supports output_config (effort) for Claude 4.6+ models, - # but older models do not — strip it to avoid request rejection. - # Ref: https://github.com/BerriAI/litellm/issues/22797 - if not ( - _supports_factory( - model=model, - custom_llm_provider="bedrock", - key="supports_output_config", - ) - or AnthropicConfig._model_supports_effort_param(model, "bedrock") - ): - if anthropic_messages_request.pop("output_config", None) is not None: - verbose_logger.warning( - "Bedrock Invoke: stripping unsupported `output_config` for " - "model=%s — neither `supports_output_config` nor any " - "`supports_*_reasoning_effort` flag is set in " - "model_prices_and_context_window.json. Add the capability " - "flag to the model JSON entry if this model accepts " - "`output_config`.", - model, - ) + strip_unsupported_bedrock_invoke_output_config_keys( + model=model, + request_body=anthropic_messages_request, + ) # 5b. Hoist `custom.defer_loading` then drop `custom` (Bedrock doesn't support it) # Ref: https://github.com/BerriAI/litellm/issues/22847 @@ -774,9 +746,11 @@ class AmazonAnthropicClaudeMessagesConfig( if filtered_betas: anthropic_messages_request["anthropic_beta"] = filtered_betas + remaining_output_config: Final = anthropic_messages_request.get("output_config") if ( litellm.drop_params is True - and "output_config" in anthropic_messages_request + and isinstance(remaining_output_config, dict) + and any(key != "format" for key in remaining_output_config) and not AnthropicConfig._model_supports_effort_param(model, "bedrock") ): verbose_logger.warning( diff --git a/litellm/llms/bedrock/realtime/transformation.py b/litellm/llms/bedrock/realtime/transformation.py index 28c2e446d10..1f4c81d6491 100644 --- a/litellm/llms/bedrock/realtime/transformation.py +++ b/litellm/llms/bedrock/realtime/transformation.py @@ -7,7 +7,7 @@ Transforms between OpenAI Realtime API format and Bedrock Nova Sonic format. import base64 import json import uuid as uuid_lib -from typing import Any, Final, cast +from typing import Final, cast from pydantic import BaseModel @@ -633,7 +633,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): List of Bedrock format messages (JSON strings) """ try: - json_message: Final = json.loads(message) + json_message: Final[dict[str, object]] = json.loads(message) except json.JSONDecodeError: verbose_logger.warning("Invalid JSON message: %s", message[:200]) return [] @@ -1182,7 +1182,7 @@ class BedrockRealtimeConfig(BaseRealtimeConfig): # Create a function call arguments done event # This is a custom event format that matches what clients expect - function_call_event: Final[dict[str, Any]] = { + function_call_event: Final[dict[str, object]] = { "type": "response.function_call_arguments.done", "event_id": f"event_{uuid.uuid4()}", "response_id": current_response_id, diff --git a/litellm/llms/black_forest_labs/image_edit/handler.py b/litellm/llms/black_forest_labs/image_edit/handler.py index 1ff02a6f8d9..178acb0de0d 100644 --- a/litellm/llms/black_forest_labs/image_edit/handler.py +++ b/litellm/llms/black_forest_labs/image_edit/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,6 +35,42 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageEditConfig +class _BFLSubmitBody(TypedDict, total=False): + """Decoded body of the BFL submit response, which hands back a polling URL.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + + +class _BFLPollBody(TypedDict, total=False): + """Decoded body of a BFL polling response.""" + + status: ReadOnly[str] + + +class _BFLSubmitResponse(Protocol): + """The submit call's HTTP response, read for its status, body text and decoded body.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _BFLSubmitBody: ... + + +class _BFLPollResponse(Protocol): + """A polling call's HTTP response, read only for the task status it carries.""" + + def json(self) -> _BFLPollBody: ... + + +def _poll_status(response: _BFLPollResponse) -> str | None: + """Read the task status out of a BFL polling response body.""" + return response.json().get("status") + + class BlackForestLabsImageEdit: """ Black Forest Labs Image Edit handler. @@ -53,10 +91,10 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimage_edit: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image edit requests. @@ -185,7 +223,7 @@ class BlackForestLabsImageEdit: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, object] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -281,7 +319,7 @@ class BlackForestLabsImageEdit: def _poll_for_result_sync( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, sync_client: HTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -356,8 +394,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) @@ -383,7 +420,7 @@ class BlackForestLabsImageEdit: async def _poll_for_result_async( self, - initial_response: httpx.Response, + initial_response: _BFLSubmitResponse, headers: dict, async_client: AsyncHTTPHandler, max_wait: float = DEFAULT_MAX_POLLING_TIME, @@ -447,8 +484,7 @@ class BlackForestLabsImageEdit: message=f"Polling failed: {response.text}", ) - data = response.json() - status = data.get("status") + status = _poll_status(response) verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/black_forest_labs/image_generation/handler.py b/litellm/llms/black_forest_labs/image_generation/handler.py index 03e4999c5aa..879bef37b58 100644 --- a/litellm/llms/black_forest_labs/image_generation/handler.py +++ b/litellm/llms/black_forest_labs/image_generation/handler.py @@ -8,9 +8,11 @@ then we poll until the result is ready. import asyncio import time -from typing import Any, Final +from collections.abc import Coroutine, Mapping +from typing import Final, Protocol, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm._logging import verbose_logger @@ -33,6 +35,23 @@ from ..common_utils import ( from .transformation import BlackForestLabsImageGenerationConfig +class _BFLTaskPayload(TypedDict, total=False): + """The body BFL returns for a submitted or polled generation task.""" + + errors: ReadOnly[object] + polling_url: ReadOnly[str] + status: ReadOnly[str] + + +class _TaskJsonResponse(Protocol): + def json(self) -> _BFLTaskPayload: ... + + +def _task_payload(response: _TaskJsonResponse) -> _BFLTaskPayload: + """The JSON body of a BFL task submission or poll response.""" + return response.json() + + class BlackForestLabsImageGeneration: """ Black Forest Labs Image Generation handler. @@ -53,10 +72,10 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, aimg_generation: bool = False, - ) -> ImageResponse | Any: + ) -> ImageResponse | Coroutine[object, object, ImageResponse]: """ Main entry point for image generation requests. @@ -187,7 +206,7 @@ class BlackForestLabsImageGeneration: litellm_params: GenericLiteLLMParams | dict, logging_obj: LiteLLMLoggingObj, timeout: float | httpx.Timeout | None, - extra_headers: dict[str, Any] | None = None, + extra_headers: Mapping[str, str] | None = None, client: AsyncHTTPHandler | None = None, ) -> ImageResponse: """ @@ -305,7 +324,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -350,7 +369,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) @@ -396,7 +415,7 @@ class BlackForestLabsImageGeneration: # Parse initial response to get polling URL try: - response_data: Final = initial_response.json() + response_data: Final = _task_payload(initial_response) except Exception as e: raise BlackForestLabsError( status_code=initial_response.status_code, @@ -441,7 +460,7 @@ class BlackForestLabsImageGeneration: message=f"Polling failed: {response.text}", ) - data = response.json() + data = _task_payload(response) status = data.get("status") verbose_logger.debug("BFL poll status: %s", status) diff --git a/litellm/llms/codestral/completion/handler.py b/litellm/llms/codestral/completion/handler.py index 8c08b2bc33c..f8486d3b274 100644 --- a/litellm/llms/codestral/completion/handler.py +++ b/litellm/llms/codestral/completion/handler.py @@ -4,9 +4,10 @@ import json from collections.abc import Callable from functools import partial -from typing import Final +from typing import Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging @@ -23,6 +24,53 @@ from litellm.types.utils import TextChoices from litellm.utils import CustomStreamWrapper, TextCompletionResponse +class _CodestralChoiceMessage(TypedDict): + """`choices[].message` of a Codestral FIM completion.""" + + role: ReadOnly[NotRequired[str]] + content: ReadOnly[NotRequired[str | None]] + + +class _CodestralChoice(TypedDict): + """One entry of `choices` in a Codestral FIM completion.""" + + index: ReadOnly[int] + message: ReadOnly[NotRequired[_CodestralChoiceMessage]] + finish_reason: ReadOnly[NotRequired[str | None]] + logprobs: ReadOnly[NotRequired[dict[str, object] | None]] + + +class _CodestralUsage(TypedDict): + """Token accounting returned alongside a Codestral FIM completion.""" + + prompt_tokens: ReadOnly[NotRequired[int]] + completion_tokens: ReadOnly[NotRequired[int]] + total_tokens: ReadOnly[NotRequired[int]] + + +class _CodestralCompletionResponse(TypedDict): + """Body returned by the Codestral `/v1/fim/completions` endpoint.""" + + id: ReadOnly[NotRequired[str]] + created: ReadOnly[NotRequired[int]] + model: ReadOnly[NotRequired[str]] + object: ReadOnly[NotRequired[str]] + usage: ReadOnly[NotRequired[_CodestralUsage]] + choices: ReadOnly[NotRequired[list[_CodestralChoice]]] + + +class _CodestralHTTPResponse(Protocol): + """The Codestral completion response as this handler reads it.""" + + @property + def status_code(self) -> int: ... + + @property + def text(self) -> str: ... + + def json(self) -> _CodestralCompletionResponse: ... + + class TextCompletionCodestralError(Exception): def __init__( self, @@ -115,7 +163,7 @@ class CodestralTextCompletion: def process_text_completion_response( self, model: str, - response: httpx.Response, + response: _CodestralHTTPResponse, model_response: TextCompletionResponse, stream: bool, logging_obj: LiteLLMLogging, diff --git a/litellm/llms/dashscope/chat/transformation.py b/litellm/llms/dashscope/chat/transformation.py index 5ab7fbf3658..26e60fa959d 100644 --- a/litellm/llms/dashscope/chat/transformation.py +++ b/litellm/llms/dashscope/chat/transformation.py @@ -54,6 +54,9 @@ class DashScopeChatConfig(OpenAIGPTConfig): dynamic_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") return api_base, dynamic_api_key + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or "https://dashscope.aliyuncs.com/compatible-mode/v1" + def get_complete_url( self, api_base: str | None, @@ -66,10 +69,7 @@ class DashScopeChatConfig(OpenAIGPTConfig): """ If api_base is not provided, use the default DashScope /chat/completions endpoint. """ - if not api_base: - api_base = "https://dashscope.aliyuncs.com/compatible-mode/v1" - - if not api_base.endswith("/chat/completions"): - api_base = f"{api_base}/chat/completions" - - return api_base + resolved_api_base: Final = self._resolve_chat_api_base(api_base) + if resolved_api_base.endswith("/chat/completions"): + return resolved_api_base + return f"{resolved_api_base}/chat/completions" diff --git a/litellm/llms/dashscope/common_utils.py b/litellm/llms/dashscope/common_utils.py index 9a7dd4da8d3..b7c97893a15 100644 --- a/litellm/llms/dashscope/common_utils.py +++ b/litellm/llms/dashscope/common_utils.py @@ -2,9 +2,89 @@ Common utilities for the DashScope LLM provider. """ +from typing import TYPE_CHECKING + import httpx from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.secret_managers.main import get_secret_str + +if TYPE_CHECKING: + from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig + from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, + ) + from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig + + +def get_dashscope_family_embedding_config(custom_llm_provider: str) -> "BaseEmbeddingConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudEmbeddingConfig + + return QwenCloudEmbeddingConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformEmbeddingConfig, + ) + + return QwenAIPlatformEmbeddingConfig() + from litellm.llms.dashscope.embed.transformation import DashScopeEmbeddingConfig + + return DashScopeEmbeddingConfig() + + +def get_dashscope_family_rerank_config(custom_llm_provider: str) -> "BaseRerankConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudRerankConfig + + return QwenCloudRerankConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import QwenAIPlatformRerankConfig + + return QwenAIPlatformRerankConfig() + from litellm.llms.dashscope.rerank.transformation import DashScopeRerankConfig + + return DashScopeRerankConfig() + + +def get_dashscope_family_image_generation_config( + custom_llm_provider: str, +) -> "BaseImageGenerationConfig": + if custom_llm_provider == "qwencloud": + from litellm.llms.dashscope.qwencloud import QwenCloudImageGenerationConfig + + return QwenCloudImageGenerationConfig() + if custom_llm_provider == "qwen_ai_platform": + from litellm.llms.dashscope.qwen_ai_platform import ( + QwenAIPlatformImageGenerationConfig, + ) + + return QwenAIPlatformImageGenerationConfig() + from litellm.llms.dashscope.image_generation.transformation import ( + DashScopeImageGenerationConfig, + ) + + return DashScopeImageGenerationConfig() + + +def resolve_dashscope_family_api_key(custom_llm_provider: str, api_key: str | None) -> str | None: + if custom_llm_provider == "dashscope": + return api_key or get_secret_str("DASHSCOPE_API_KEY") + return api_key or get_secret_str(f"{custom_llm_provider.upper()}_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def missing_dashscope_family_key_message(custom_llm_provider: str) -> str: + if custom_llm_provider == "qwencloud": + return ( + "Missing API key for QwenCloud. Set QWENCLOUD_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + if custom_llm_provider == "qwen_ai_platform": + return ( + "Missing API key for Qwen AI Platform. Set QWEN_AI_PLATFORM_API_KEY or " + "DASHSCOPE_API_KEY environment variable or pass api_key parameter." + ) + return "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." class DashScopeError(BaseLLMException): diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 771ce140f66..dd5bee1fe8b 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -110,7 +110,7 @@ def _calculate_completion_cost( return (breakdown.completion_tokens * output_cost) + (breakdown.reasoning_tokens * reasoning_cost) -def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: +def cost_per_token(model: str, usage: Usage, custom_llm_provider: str = "dashscope") -> tuple[float, float]: """ Calculate cost per token for Dashscope models. @@ -119,11 +119,12 @@ def cost_per_token(model: str, usage: Usage) -> tuple[float, float]: Args: model: Model name without provider prefix usage: LiteLLM Usage block + custom_llm_provider: The provider id the request resolved to; dashscope or one of its brand aliases Returns: Tuple[float, float] - (prompt_cost_in_usd, completion_cost_in_usd) """ - model_info: Final = get_model_info(model=model, custom_llm_provider="dashscope") + model_info: Final = get_model_info(model=model, custom_llm_provider=custom_llm_provider) breakdown: Final = _extract_token_breakdown(usage) raw_tiers: Final = model_info.get("tiered_pricing") tiered_pricing: Final = raw_tiers if isinstance(raw_tiers, list) else None diff --git a/litellm/llms/dashscope/embed/transformation.py b/litellm/llms/dashscope/embed/transformation.py index 6d13f1e53f7..63ee984a65c 100644 --- a/litellm/llms/dashscope/embed/transformation.py +++ b/litellm/llms/dashscope/embed/transformation.py @@ -62,6 +62,17 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): # for drop_params=False before this method is called. return optional_params + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE + def validate_environment( self, headers: dict, @@ -72,17 +83,11 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - default_headers: Final = { + return { "Content-Type": "application/json", - "Authorization": f"Bearer {api_key}", + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", + **headers, } - return {**default_headers, **headers} def get_complete_url( self, @@ -93,8 +98,7 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig): litellm_params: dict, stream: bool | None = None, ) -> str: - base = api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE - base = base.rstrip("/") + base: Final = self._resolve_embedding_api_base(api_base).rstrip("/") if base.endswith("/embeddings"): return base return f"{base}/embeddings" diff --git a/litellm/llms/dashscope/image_generation/transformation.py b/litellm/llms/dashscope/image_generation/transformation.py index a7f0e98865f..c0e278a96ef 100644 --- a/litellm/llms/dashscope/image_generation/transformation.py +++ b/litellm/llms/dashscope/image_generation/transformation.py @@ -91,6 +91,15 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): mapped[k] = v return mapped + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") + if not resolved_api_key: + raise ValueError("DASHSCOPE_API_KEY is not set") + return resolved_api_key + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + def get_complete_url( self, api_base: str | None, @@ -103,7 +112,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): image_api_base: Final = ( api_base if api_base and not api_base.rstrip("/").endswith(CHAT_COMPATIBLE_MODE_PATH) else None ) - return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE + return self._resolve_image_api_base(image_api_base) def validate_environment( self, @@ -115,10 +124,7 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig): api_key: str | None = None, api_base: str | None = None, ) -> dict: - final_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY") - if not final_api_key: - raise ValueError("DASHSCOPE_API_KEY is not set") - headers["Authorization"] = f"Bearer {final_api_key}" + headers["Authorization"] = f"Bearer {self._resolve_api_key(api_key)}" headers["Content-Type"] = "application/json" return headers diff --git a/litellm/llms/dashscope/qwen_ai_platform.py b/litellm/llms/dashscope/qwen_ai_platform.py new file mode 100644 index 00000000000..9a44eaf574a --- /dev/null +++ b/litellm/llms/dashscope/qwen_ai_platform.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWEN_AI_PLATFORM_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-mode/v1" +QWEN_AI_PLATFORM_RERANK_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" +QWEN_AI_PLATFORM_IMAGE_API_BASE: Final = ( + "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwen_ai_platform_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWEN_AI_PLATFORM_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwen_ai_platform_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwen_ai_platform_api_key(api_key) + if resolved is None: + raise ValueError( + "Qwen AI Platform API key is required. Set 'QWEN_AI_PLATFORM_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenAIPlatformChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwen_ai_platform_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE + + +class QwenAIPlatformRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_RERANK") or QWEN_AI_PLATFORM_RERANK_API_BASE + + +class QwenAIPlatformImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwen_ai_platform_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_IMAGE") or QWEN_AI_PLATFORM_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/qwencloud.py b/litellm/llms/dashscope/qwencloud.py new file mode 100644 index 00000000000..d8d53e340ef --- /dev/null +++ b/litellm/llms/dashscope/qwencloud.py @@ -0,0 +1,62 @@ +from typing import Final + +from litellm.secret_managers.main import get_secret_str + +from .chat.transformation import DashScopeChatConfig +from .embed.transformation import DashScopeEmbeddingConfig +from .image_generation.transformation import DashScopeImageGenerationConfig +from .rerank.transformation import DashScopeRerankConfig + +QWENCLOUD_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" +QWENCLOUD_RERANK_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/compatible-api/v1/reranks" +QWENCLOUD_IMAGE_API_BASE: Final = ( + "https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation" +) + + +def _resolve_qwencloud_api_key(api_key: str | None) -> str | None: + return api_key or get_secret_str("QWENCLOUD_API_KEY") or get_secret_str("DASHSCOPE_API_KEY") + + +def _require_qwencloud_api_key(api_key: str | None) -> str: + resolved: Final = _resolve_qwencloud_api_key(api_key) + if resolved is None: + raise ValueError( + "QwenCloud API key is required. Set 'QWENCLOUD_API_KEY' or 'DASHSCOPE_API_KEY' env var " + "or pass api_key explicitly." + ) + return resolved + + +class QwenCloudChatConfig(DashScopeChatConfig): + def _get_openai_compatible_provider_info( + self, api_base: str | None, api_key: str | None + ) -> tuple[str | None, str | None]: + return self._resolve_chat_api_base(api_base), _resolve_qwencloud_api_key(api_key) + + def _resolve_chat_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudEmbeddingConfig(DashScopeEmbeddingConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_embedding_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE + + +class QwenCloudRerankConfig(DashScopeRerankConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + return api_base or get_secret_str("QWENCLOUD_API_BASE_RERANK") or QWENCLOUD_RERANK_API_BASE + + +class QwenCloudImageGenerationConfig(DashScopeImageGenerationConfig): + def _resolve_api_key(self, api_key: str | None) -> str: + return _require_qwencloud_api_key(api_key) + + def _resolve_image_api_base(self, image_api_base: str | None) -> str: + return image_api_base or get_secret_str("QWENCLOUD_API_BASE_IMAGE") or QWENCLOUD_IMAGE_API_BASE diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py index 98be4e4f2e7..3dd3996b2ee 100644 --- a/litellm/llms/dashscope/rerank/transformation.py +++ b/litellm/llms/dashscope/rerank/transformation.py @@ -58,19 +58,30 @@ class DashScopeRerankConfig(BaseRerankConfig): def __init__(self) -> None: pass + def _resolve_api_key(self, api_key: str | None) -> str: + resolved_api_key: Final = api_key if api_key is not None else get_secret_str("DASHSCOPE_API_KEY") + if resolved_api_key is None: + raise ValueError( + "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." + ) + return resolved_api_key + + def _resolve_rerank_api_base(self, api_base: str | None) -> str: + if api_base is not None: + return api_base + return get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + def get_complete_url( self, api_base: str | None, model: str, optional_params: dict | None = None, ) -> str: - if api_base is None: - api_base = get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + resolved_api_base: Final = self._resolve_rerank_api_base(api_base) + if resolved_api_base == DEFAULT_RERANK_URL: + return resolved_api_base - if api_base == DEFAULT_RERANK_URL: - return DEFAULT_RERANK_URL - - cleaned: Final = api_base.rstrip("/") + cleaned: Final = resolved_api_base.rstrip("/") if cleaned.endswith("/reranks") or cleaned.endswith("/rerank"): return cleaned @@ -88,19 +99,12 @@ class DashScopeRerankConfig(BaseRerankConfig): optional_params: dict | None = None, litellm_params: Mapping[str, object] | None = None, ) -> dict: - if api_key is None: - api_key = get_secret_str("DASHSCOPE_API_KEY") - if api_key is None: - raise ValueError( - "DashScope API key is required. Set 'DASHSCOPE_API_KEY' env var or pass api_key explicitly." - ) - - default_headers: Final = { - "Authorization": f"Bearer {api_key}", + return { + "Authorization": f"Bearer {self._resolve_api_key(api_key)}", "accept": "application/json", "content-type": "application/json", + **headers, } - return {**default_headers, **headers} def get_supported_cohere_rerank_params(self, model: str) -> list: return ["query", "documents", "top_n", "return_documents"] diff --git a/litellm/llms/deepinfra/rerank/transformation.py b/litellm/llms/deepinfra/rerank/transformation.py index e52c56af82b..a3d0482af0a 100644 --- a/litellm/llms/deepinfra/rerank/transformation.py +++ b/litellm/llms/deepinfra/rerank/transformation.py @@ -2,10 +2,11 @@ Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format. """ -from collections.abc import Mapping -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._uuid import uuid from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj @@ -24,6 +25,36 @@ from litellm.types.rerank import ( ) +class _DeepinfraInferenceStatus(TypedDict, total=False): + """The ``inference_status`` block of a DeepInfra rerank response.""" + + status: ReadOnly[str] + runtime_ms: ReadOnly[float] + cost: ReadOnly[float] + tokens_generated: ReadOnly[int] + tokens_input: ReadOnly[int] + + +class _DeepinfraRerankResponse(TypedDict, total=False): + """Body of a DeepInfra ``/rerank`` response.""" + + scores: ReadOnly[Sequence[float]] + input_tokens: ReadOnly[int] + request_id: ReadOnly[str | None] + inference_status: ReadOnly[_DeepinfraInferenceStatus] + + +class _DeepinfraRerankResponseSource(Protocol): + """The DeepInfra ``/rerank`` HTTP response, read for the body it decodes to.""" + + def json(self) -> _DeepinfraRerankResponse: ... + + +def _deepinfra_rerank_body(response: _DeepinfraRerankResponseSource) -> _DeepinfraRerankResponse: + """Decode the body of a DeepInfra ``/rerank`` response.""" + return response.json() + + class DeepinfraRerankConfig(BaseRerankConfig): """ Deepinfra Rerank - Follows the same Spec as Cohere Rerank @@ -95,7 +126,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): model: str, drop_params: bool, query: str, - documents: list[str | dict[str, Any]], + documents: list[str | dict[str, object]], custom_llm_provider: str | None = None, top_n: int | None = None, rank_fields: list[str] | None = None, @@ -150,7 +181,7 @@ class DeepinfraRerankConfig(BaseRerankConfig): litellm_params: dict = {}, ) -> RerankResponse: try: - response_json: Final = raw_response.json() + response_json: Final = _deepinfra_rerank_body(raw_response) logging_obj.post_call(original_response=raw_response.text) # Extract the scores from the response diff --git a/litellm/llms/gemini/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py index dcd2e4e3471..6d0f211ed7b 100644 --- a/litellm/llms/gemini/interactions/transformation.py +++ b/litellm/llms/gemini/interactions/transformation.py @@ -12,9 +12,10 @@ Schema versioning: litellm.use_legacy_interactions_schema = True. Remove flag after June 8, 2026. """ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol, TypeAlias import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -41,6 +42,53 @@ else: LiteLLMLoggingObj = Any +_JsonObject: TypeAlias = dict[str, object] + + +class _InteractionPayload(TypedDict, total=False): + """JSON body of an Interactions API interaction, keyed as ``InteractionsAPIResponse`` fields.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + model: ReadOnly[str | None] + agent: ReadOnly[str | None] + status: ReadOnly[str | None] + created: ReadOnly[str | None] + updated: ReadOnly[str | None] + outputs: ReadOnly[list[_JsonObject] | None] + steps: ReadOnly[list[_JsonObject] | None] + usage: ReadOnly[_JsonObject | None] + + +class _CancelPayload(TypedDict, total=False): + """JSON body of an Interactions API cancel response.""" + + id: ReadOnly[str | None] + status: ReadOnly[str | None] + + +class _InteractionPayloadSource(Protocol): + """An Interactions API HTTP response, read for the interaction body it decodes to.""" + + def json(self) -> _InteractionPayload: ... + + +class _CancelPayloadSource(Protocol): + """An Interactions API cancel HTTP response, read for the body it decodes to.""" + + def json(self) -> _CancelPayload: ... + + +def _interaction_body(response: _InteractionPayloadSource) -> _InteractionPayload: + """Decode the body of an Interactions API interaction response.""" + return response.json() + + +def _cancel_body(response: _CancelPayloadSource) -> _CancelPayload: + """Decode the body of an Interactions API cancel response.""" + return response.json() + + class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ Configuration for Google AI Studio Interactions API. @@ -143,7 +191,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): """ use_legacy: Final[bool] = litellm.use_legacy_interactions_schema - request_body: Final[dict[str, Any]] = {} + request_body: Final[dict[str, object]] = {} # Model or Agent (one required) if model: @@ -189,7 +237,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): and (not isinstance(response_format, dict) or "mime_type" not in response_format) ): # Wrap the legacy schema into the new polymorphic format. - new_rf: Final[dict[str, Any]] = { + new_rf: Final[dict[str, object]] = { "type": "text", "mime_type": response_mime_type, } @@ -215,7 +263,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): if image_config is not None: # Move image_config to response_format with type=image. - image_rf: Final[dict[str, Any]] = {"type": "image", **image_config} + image_rf: Final[_JsonObject] = {"type": "image", **image_config} existing_rf: Final = request_body.get("response_format") if existing_rf is None: request_body["response_format"] = image_rf @@ -239,7 +287,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): original_response=raw_response.text, additional_args={"complete_input_dict": {}}, ) - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -290,7 +338,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> InteractionsAPIResponse: try: - raw_json: Final = raw_response.json() + raw_json: Final = _interaction_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, @@ -355,7 +403,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> CancelInteractionResult: try: - raw_json: Final = raw_response.json() + raw_json: Final = _cancel_body(raw_response) except Exception: raise GeminiError( message=raw_response.text, diff --git a/litellm/llms/gemini/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py index 6a1fc144c42..ff4c675b02f 100644 --- a/litellm/llms/gemini/videos/transformation.py +++ b/litellm/llms/gemini/videos/transformation.py @@ -1,4 +1,5 @@ import base64 +from collections.abc import Mapping from typing import TYPE_CHECKING, Any, Final import httpx @@ -54,8 +55,13 @@ def _convert_image_to_gemini_format(image_file) -> dict[str, str]: return {"bytesBase64Encoded": base64_encoded, "mimeType": mime_type} +def _json_payload(raw_response: httpx.Response) -> object: + """Read an HTTP response body as an opaque JSON payload.""" + return raw_response.json() + + def _usage_video_resolution_from_parameters( - parameters: dict[str, Any], + parameters: Mapping[str, object], ) -> str | None: """Normalize Veo ``parameters.resolution`` for usage and cost tracking.""" res: Final = parameters.get("resolution") @@ -97,7 +103,7 @@ class GeminiVideoConfig(BaseVideoConfig): video_create_optional_params: VideoCreateOptionalRequestParams, model: str, drop_params: bool, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Map OpenAI-style parameters to Veo format. @@ -111,7 +117,7 @@ class GeminiVideoConfig(BaseVideoConfig): All other params are passed through as-is to support Gemini-specific parameters. """ - mapped_params: Final[dict[str, Any]] = {} + mapped_params: Final[dict[str, object]] = {} # Get supported OpenAI params (exclude "model" and "prompt" which are handled separately) supported_openai_params: Final = self.get_supported_openai_params(model) @@ -312,11 +318,11 @@ class GeminiVideoConfig(BaseVideoConfig): - status: "processing" - usage: includes duration_seconds and optional video_resolution for cost calculation """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety try: - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) except Exception as e: raise ValueError(f"Failed to parse operation response: {e}") @@ -336,7 +342,7 @@ class GeminiVideoConfig(BaseVideoConfig): model=model, ) - usage_data: Final[dict[str, Any]] = {} + usage_data: Final[dict[str, float | str]] = {} if request_data: parameters: Final = request_data.get("parameters", {}) duration: Final = parameters.get("durationSeconds") or DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS @@ -367,7 +373,7 @@ class GeminiVideoConfig(BaseVideoConfig): """ operation_name: Final = extract_original_video_id(video_id) url: Final = f"{api_base.rstrip('/')}/v1beta/{operation_name}" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return url, params @@ -403,9 +409,9 @@ class GeminiVideoConfig(BaseVideoConfig): } } """ - response_data: Final = raw_response.json() + response_data: Final = _json_payload(raw_response) # Parse response using Pydantic model for type safety - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) operation_name: Final = operation_response.name is_done: Final = operation_response.done @@ -443,9 +449,9 @@ class GeminiVideoConfig(BaseVideoConfig): client: Final = litellm.module_level_client status_response: Final = client.get(url=status_url, headers=headers) status_response.raise_for_status() - response_data: Final = status_response.json() + response_data: Final = _json_payload(status_response) - operation_response: Final = GeminiLongRunningOperationResponse(**response_data) + operation_response: Final = GeminiLongRunningOperationResponse.model_validate(response_data) if not operation_response.done: raise ValueError( @@ -458,7 +464,7 @@ class GeminiVideoConfig(BaseVideoConfig): generated_samples: Final = operation_response.response.generateVideoResponse.generatedSamples download_url: Final = generated_samples[0].video.uri - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} return download_url, params @@ -480,7 +486,7 @@ class GeminiVideoConfig(BaseVideoConfig): api_base: str, litellm_params: GenericLiteLLMParams, headers: dict, - extra_body: dict[str, Any] | None = None, + extra_body: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video remix is not supported by Veo API. @@ -506,7 +512,7 @@ class GeminiVideoConfig(BaseVideoConfig): after: str | None = None, limit: int | None = None, order: str | None = None, - extra_query: dict[str, Any] | None = None, + extra_query: Mapping[str, object] | None = None, ) -> tuple[str, dict]: """ Video list is not supported by Veo API. @@ -547,7 +553,7 @@ class GeminiVideoConfig(BaseVideoConfig): """Video delete is not supported.""" raise NotImplementedError("Video delete is not supported by Google Veo.") - def transform_video_create_character_request(self, name, video, api_base, litellm_params, headers): + def transform_video_create_character_request(self, name, video: object, api_base, litellm_params, headers): raise NotImplementedError("video create character is not supported for Gemini") def transform_video_create_character_response(self, raw_response, logging_obj): diff --git a/litellm/llms/huggingface/embedding/transformation.py b/litellm/llms/huggingface/embedding/transformation.py index d3db3530109..f6fe7f2fa10 100644 --- a/litellm/llms/huggingface/embedding/transformation.py +++ b/litellm/llms/huggingface/embedding/transformation.py @@ -1,8 +1,9 @@ import json import os import time +from collections.abc import Sequence from copy import deepcopy -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol import httpx @@ -24,6 +25,8 @@ from litellm.utils import token_counter from ..common_utils import HuggingFaceError, hf_task_list, hf_tasks, output_parser if TYPE_CHECKING: + import tiktoken + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj LoggingClass = LiteLLMLoggingObj @@ -31,6 +34,12 @@ else: LoggingClass = Any +class _TokenEncoding(Protocol): + """Tokenizer handle the caller passes in; only `encode` is used, to count completion tokens.""" + + def encode(self, text: str, /) -> Sequence[object]: ... + + tgi_models_cache = None conv_models_cache = None @@ -369,7 +378,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): model_response: ModelResponse, task: hf_tasks | None, optional_params: dict, - encoding: Any, + encoding: "_TokenEncoding | None", messages: list[AllMessageValues], model: str, ): @@ -439,9 +448,10 @@ class HuggingFaceEmbeddingConfig(BaseConfig): if output_text is not None and len(output_text) > 0: completion_tokens = 0 try: - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) ##[TODO] use the llama2 tokenizer here + if encoding is not None: + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) ##[TODO] use the llama2 tokenizer here except Exception: # this should remain non blocking we should not block a response returning if calculating usage fails pass @@ -469,7 +479,7 @@ class HuggingFaceEmbeddingConfig(BaseConfig): messages: list[AllMessageValues], optional_params: dict, litellm_params: dict, - encoding: Any, + encoding: "tiktoken.Encoding | None", api_key: str | None = None, json_mode: bool | None = None, ) -> ModelResponse: diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index d4747b2fb06..3a7f78fd5ba 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -325,7 +325,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): @overload def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: Literal[True] - ) -> Coroutine[Any, Any, list[AllMessageValues]]: + ) -> Coroutine[object, object, list[AllMessageValues]]: ... @overload @@ -341,7 +341,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): def _transform_messages( self, messages: list[AllMessageValues], model: str, is_async: bool = False - ) -> list[AllMessageValues] | Coroutine[Any, Any, list[AllMessageValues]]: + ) -> list[AllMessageValues] | Coroutine[object, object, list[AllMessageValues]]: """OpenAI no longer supports image_url as a string, so we need to convert it to a dict""" stripped_messages: Final = drop_tool_reference_parts_from_tool_messages(messages) hoisted_messages: Final = hoist_images_from_tool_messages(stripped_messages) @@ -497,8 +497,12 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): return None tool_call_names: Final = get_tool_call_names(optional_params.get("tools", [])) try: - json_content: Final = json.loads(content) - if json_content.get("type") == "function" and json_content.get("name") in tool_call_names: + json_content: Final[object] = json.loads(content) + if ( + isinstance(json_content, dict) + and json_content.get("type") == "function" + and json_content.get("name") in tool_call_names + ): return ChatCompletionMessageToolCall( function=Function( name=json_content.get("name"), @@ -622,7 +626,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): ## RESPONSE OBJECT try: - completion_response: Final = raw_response.json() + completion_response: Final[dict[str, object]] = raw_response.json() except Exception as e: response_headers: Final = getattr(raw_response, "headers", None) raise OpenAIError( diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index de15fefe943..ed628f55350 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -51,6 +51,7 @@ if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth class OpenAIChatCompletionsHandler(BaseTranslation): @@ -80,7 +81,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): data: dict, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - ) -> Any: + ) -> dict: """ Process input messages by applying guardrails to text content. """ @@ -329,9 +330,9 @@ class OpenAIChatCompletionsHandler(BaseTranslation): response: "ModelResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - user_api_key_dict: Any | None = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, - ) -> Any: + ) -> ModelResponse: """ Process output response by applying guardrails to text content. @@ -436,7 +437,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - user_api_key_dict: Any | None = None, + user_api_key_dict: "UserAPIKeyAuth | None" = None, request_data: dict | None = None, stream_transform_sink: StreamTransformSink | None = None, ) -> list["ModelResponseStream"]: @@ -486,7 +487,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None", - user_api_key_dict: Any | None, + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, ) -> list["ModelResponseStream"]: """Block-only streaming path: run the guardrail so an in-flight BLOCK can @@ -589,8 +590,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation): def build_stream_error_items( self, exc: "HTTPException", - responses_so_far: Sequence[Any] | None = None, - ) -> Sequence[Any] | None: + responses_so_far: Sequence[object] | None = None, + ) -> Sequence[bytes] | None: import json from litellm.proxy.common_request_processing import sse_error_payload @@ -630,7 +631,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): responses_so_far: list["ModelResponseStream"], guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: "LiteLLMLoggingObj | None", - user_api_key_dict: Any | None, + user_api_key_dict: "UserAPIKeyAuth | None", request_data: dict | None, sink: StreamTransformSink, ) -> None: @@ -794,7 +795,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): # Determine content source and tool calls based on choice type content = None - tool_calls: list[Any] | None = None + tool_calls: Sequence[object] | None = None if isinstance(choice, litellm.Choices): content = choice.message.content tool_calls = choice.message.tool_calls diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index eadc087383a..09028b6dc5f 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -1,10 +1,11 @@ from collections.abc import Mapping, Sequence from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final, cast, get_type_hints +from typing import TYPE_CHECKING, Any, Final, Protocol, cast, get_type_hints import httpx from openai.types.responses import ResponseReasoningItem from pydantic import BaseModel, ValidationError +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -37,6 +38,36 @@ _MODEL_FAMILIES_REJECTING_TOP_LEVEL_SCHEMA_COMBINATORS: Final = ("gpt-4", "gpt-3 _PROVIDERS_WITH_COMBINATOR_REJECTING_VALIDATOR: Final = frozenset({LlmProviders.AZURE, LlmProviders.OPENAI}) +class _DeleteResponseBody(TypedDict): + """Decoded body of the Responses API delete call.""" + + id: ReadOnly[str | None] + object: ReadOnly[str | None] + deleted: ReadOnly[bool | None] + + +class _DeleteResponse(Protocol): + """The delete call's HTTP response, read for the decoded body it carries.""" + + def json(self) -> _DeleteResponseBody: ... + + +class _JsonObjectResponse(Protocol): + """A Responses API HTTP response, read for the JSON object it decodes to.""" + + def json(self) -> dict[str, object]: ... + + +def _delete_response_body(response: _DeleteResponse) -> _DeleteResponseBody: + """Decode a delete response body into the id, object and deleted fields it carries.""" + return response.json() + + +def _json_object_body(response: _JsonObjectResponse) -> dict[str, object]: + """Decode a Responses API response body into its JSON object form.""" + return response.json() + + class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): @property def custom_llm_provider(self) -> LlmProviders: @@ -469,7 +500,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): return None @staticmethod - def get_event_model_class(event_type: str) -> Any: + def get_event_model_class(event_type: str) -> type[BaseLiteLLMOpenAIResponseObject]: """ Returns the appropriate event model class based on the event type. @@ -583,7 +614,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the delete response API response into a DeleteResponseResult """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _delete_response_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) return DeleteResponseResult(**raw_response_json) @@ -618,7 +649,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the get response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) @@ -646,7 +677,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ) -> tuple[str, dict]: encoded_response_id: Final = encode_url_path_segment(response_id, field_name="response_id") url: Final = f"{api_base}/{encoded_response_id}/input_items" - params: Final[dict[str, Any]] = {} + params: Final[dict[str, object]] = {} if after is not None: params["after"] = after if before is not None: @@ -665,7 +696,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): logging_obj: LiteLLMLoggingObj, ) -> dict: try: - return raw_response.json() + return _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) @@ -699,7 +730,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): Transform the cancel response API response into a ResponsesAPIResponse """ try: - raw_response_json: Final = raw_response.json() + raw_response_json: Final = _json_object_body(raw_response) except Exception: raise OpenAIError(message=raw_response.text, status_code=raw_response.status_code) raw_response_headers: Final = dict(raw_response.headers) diff --git a/litellm/llms/openai_like/chat/handler.py b/litellm/llms/openai_like/chat/handler.py index 8c548b6b0d6..855c49c320b 100644 --- a/litellm/llms/openai_like/chat/handler.py +++ b/litellm/llms/openai_like/chat/handler.py @@ -5,10 +5,11 @@ For handling OpenAI-like chat completions, like IBM WatsonX, etc. """ import json -from collections.abc import Callable -from typing import Any, Final +from collections.abc import Callable, Mapping, Sequence +from typing import Final, TypedDict import httpx +from typing_extensions import ReadOnly import litellm from litellm import LlmProviders @@ -25,6 +26,23 @@ from ..common_utils import OpenAILikeBase, OpenAILikeError from .transformation import OpenAILikeChatConfig +class _OpenAILikeChatCompletion(TypedDict, total=False): + """The chat-completion JSON body an OpenAI-like provider returns for a non-streamed call.""" + + id: ReadOnly[str] + choices: ReadOnly[Sequence[Mapping[str, object]]] + created: ReadOnly[int] + model: ReadOnly[str] + system_fingerprint: ReadOnly[str] + usage: ReadOnly[Mapping[str, object]] + object: ReadOnly[str] + + +def _fake_streamed_model_response(payload: _OpenAILikeChatCompletion) -> ModelResponse: + """Build the single response a fake-streamed provider call replays as one chunk.""" + return ModelResponse(**payload) + + async def make_call( client: AsyncHTTPHandler | None, api_base: str, @@ -42,9 +60,9 @@ async def make_call( response: Final = await client.post(api_base, headers=headers, data=data, stream=not fake_stream) if streaming_decoder is not None: - completion_stream: Any = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) + completion_stream = streaming_decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.aiter_lines(), sync_stream=False) @@ -82,7 +100,7 @@ def make_sync_call( if streaming_decoder is not None: completion_stream = streaming_decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) elif fake_stream: - model_response: Final = ModelResponse(**response.json()) + model_response: Final = _fake_streamed_model_response(response.json()) completion_stream = MockResponseIterator(model_response=model_response) else: completion_stream = ModelResponseIterator(streaming_response=response.iter_lines(), sync_stream=True) diff --git a/litellm/llms/runwayml/image_generation/transformation.py b/litellm/llms/runwayml/image_generation/transformation.py index cde65addb65..5913709c8a0 100644 --- a/litellm/llms/runwayml/image_generation/transformation.py +++ b/litellm/llms/runwayml/image_generation/transformation.py @@ -1,8 +1,10 @@ import asyncio import time +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final import httpx +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_logger from litellm.constants import ( @@ -29,6 +31,16 @@ else: LiteLLMLoggingObj = Any +class _RunwayMLTask(TypedDict, total=False): + """The RunwayML task payload returned by POST /v1/text_to_image and GET /v1/tasks/{id}.""" + + id: ReadOnly[str] + status: ReadOnly[str] + output: ReadOnly[Sequence[str | Mapping[str, str]]] + failure: ReadOnly[str] + failureCode: ReadOnly[str] + + class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): """ Configuration for RunwayML image generation models. @@ -80,7 +92,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): @staticmethod def _transform_runwayml_response_to_openai( - response_data: dict[str, Any], + response_data: _RunwayMLTask, model_response: ImageResponse, ) -> ImageResponse: """ @@ -155,7 +167,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): raise TimeoutError(f"RunwayML task polling timed out after {timeout_secs} seconds") @staticmethod - def _check_task_status(response_data: dict[str, Any]) -> str: + def _check_task_status(response_data: _RunwayMLTask) -> str: """ Check RunwayML task status from response. @@ -227,7 +239,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -276,7 +288,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): response = await client.get(url=task_url, headers=headers) response.raise_for_status() - response_data = response.json() + response_data: _RunwayMLTask = response.json() # Check task status status = self._check_task_status(response_data=response_data) @@ -322,7 +334,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): } """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", @@ -382,7 +394,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig): We need to poll the task until it completes (status SUCCEEDED) using async polling. """ try: - response_data = raw_response.json() + response_data: _RunwayMLTask = raw_response.json() except Exception as e: raise self.get_error_class( error_message=f"Error transforming image generation response: {e}", diff --git a/litellm/llms/sap/credentials.py b/litellm/llms/sap/credentials.py index d7743d4d337..a2a93b6114a 100644 --- a/litellm/llms/sap/credentials.py +++ b/litellm/llms/sap/credentials.py @@ -8,9 +8,10 @@ from dataclasses import dataclass from datetime import datetime, timedelta, timezone from pathlib import Path from threading import Lock -from typing import Any, Final +from typing import Any, Final, Protocol import httpx +from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -33,8 +34,8 @@ def _get_home() -> str: return os.getenv(HOME_PATH_ENV_VAR, DEFAULT_HOME_PATH) -def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: - cur: Any = d +def _get_nested(d: object, path: Sequence[str]) -> object: + cur: object = d if isinstance(cur, str): # This shouldn't happen if service keys are pre-parsed correctly try: @@ -54,7 +55,7 @@ def _get_nested(d: dict[str, Any] | str, path: Sequence[str]) -> Any: return cur -def _load_json_env(var_name: str) -> dict[str, Any] | None: +def _load_json_env(var_name: str) -> dict[str, object] | None: raw: Final = os.environ.get(var_name) if not raw: return None @@ -64,7 +65,7 @@ def _load_json_env(var_name: str) -> dict[str, Any] | None: return None -def _str_or_none(value) -> str | None: +def _str_or_none(value: object) -> str | None: try: return str(value) if value is not None else None except Exception: @@ -124,7 +125,7 @@ CREDENTIAL_VALUES: Final[list[CredentialsValue]] = [ ] -def init_conf(profile: str | None = None) -> dict[str, Any]: +def init_conf(profile: str | None = None) -> dict[str, object]: """ Loads config JSON from: 1) $AICORE_CONFIG if set, otherwise @@ -191,7 +192,7 @@ def resolve_resource_group(sources: list[Source]) -> str | None: def _parse_service_key_once( service_key: str | dict | None, -) -> dict[str, Any] | None: +) -> dict[str, object] | None: """ Pre-parse service_key if it's a string to avoid repeated JSON parsing. @@ -348,8 +349,33 @@ def validate_credentials( ) +class _TokenBody(TypedDict): + """Decoded body of the SAP AI Core OAuth2 token response.""" + + access_token: ReadOnly[str] + expires_in: ReadOnly[NotRequired[int]] + + +class _TokenResponse(Protocol): + """The token endpoint's HTTP response, read for the decoded token body it carries.""" + + def json(self) -> _TokenBody: ... + + +def _bearer_token_and_expiry(response: _TokenResponse) -> tuple[str, datetime]: + """Read a token response into the Authorization header value and the token's absolute expiry.""" + payload: Final = response.json() + expires_in: Final = int(payload.get("expires_in", 3600)) + access_token: Final = payload["access_token"] + return f"Bearer {access_token}", datetime.now(timezone.utc) + timedelta(seconds=expires_in) + + def _request_token( - client_id: str, auth_url: str, timeout: float, cert_pair=None, client_secret=None + client_id: str, + auth_url: str, + timeout: float, + cert_pair: tuple[str, str] | None = None, + client_secret: str | None = None, ) -> tuple[str, datetime]: data: Final = {"grant_type": "client_credentials", "client_id": client_id} if client_secret: @@ -361,15 +387,10 @@ def _request_token( with httpx.Client(cert=cert_pair) as raw_client: handler = HTTPHandler(client=raw_client) resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - else: - handler = _get_httpx_client() - resp = handler.post(auth_url, data=data, timeout=timeout) - payload = resp.json() - access_token: Final = payload["access_token"] - expires_in: Final = int(payload.get("expires_in", 3600)) - expiry_date: Final = datetime.now(timezone.utc) + timedelta(seconds=expires_in) - return f"Bearer {access_token}", expiry_date + return _bearer_token_and_expiry(resp) + handler = _get_httpx_client() + resp = handler.post(auth_url, data=data, timeout=timeout) + return _bearer_token_and_expiry(resp) except Exception as e: msg: Final = resp.text if resp is not None else getattr(e, "text", str(e)) raise RuntimeError(f"Token request failed: {msg}") from e diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index b7f91bfba0d..b6ad9fbcc04 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -12,7 +12,7 @@ from urllib.parse import quote, unquote import httpx from httpx import Headers, Response from openai.types.file_deleted import FileDeleted -from typing_extensions import ReadOnly +from typing_extensions import ReadOnly, Required import litellm from litellm._uuid import uuid @@ -104,6 +104,27 @@ class _VertexBatchRow(TypedDict, total=False): processed_time: ReadOnly[str] +class _VertexEmbeddingVector(TypedDict): + values: ReadOnly[list[float]] + + +class _VertexEmbeddingUsageMetadata(TypedDict, total=False): + promptTokenCount: ReadOnly[int] + + +class _VertexEmbeddingResponse(TypedDict, total=False): + embedding: ReadOnly[Required[_VertexEmbeddingVector]] + usageMetadata: ReadOnly[_VertexEmbeddingUsageMetadata] + tokenCount: ReadOnly[int] + + +class _VertexEmbeddingBatchRow(TypedDict, total=False): + key: ReadOnly[str] + request: ReadOnly[Mapping[str, object]] + status: ReadOnly[Required[str]] + response: ReadOnly[Required[_VertexEmbeddingResponse]] + + class _OpenAIBatchOutputError(TypedDict): code: ReadOnly[str] message: ReadOnly[str] @@ -111,7 +132,7 @@ class _OpenAIBatchOutputError(TypedDict): class _OpenAIBatchOutputResponse(TypedDict): status_code: ReadOnly[int] - request_id: ReadOnly[str] + request_id: ReadOnly[object] body: ReadOnly[Mapping[str, object]] @@ -218,7 +239,7 @@ def _get_litellm_batch_custom_id_from_labels(labels: Mapping[str, object] | None return str(labels.get("litellm_custom_id", "unknown")) -def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) -> bool: +def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, object]) -> bool: """ Whether a Vertex batch output row came from an `EmbedContentRequest`. @@ -237,7 +258,7 @@ def _is_vertex_embeddings_batch_output_row(vertex_output_row: Mapping[str, Any]) def _openai_batch_output_row( custom_id: str, - body: Mapping[str, Any] | None = None, + body: Mapping[str, object] | None = None, error_code: str | None = None, error_message: str = "", ) -> _OpenAIBatchOutputRow: @@ -259,7 +280,7 @@ def _openai_batch_output_row( } -def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, int, int]: +def _split_vertex_batch_key(vertex_output_row: Mapping[str, object]) -> tuple[str, int, int]: """ Resolve `(custom_id, index within that custom_id, group size)` for a Vertex batch output row. @@ -278,7 +299,7 @@ def _split_vertex_batch_key(vertex_output_row: Mapping[str, Any]) -> tuple[str, return unquote(match["custom_id"]), int(match["index"]), int(match["total"]) -def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: +def _embedding_prompt_token_count(vertex_response: _VertexEmbeddingResponse) -> int: """ Prompt tokens billed for one Vertex Gemini Embedding batch row. @@ -293,7 +314,7 @@ def _embedding_prompt_token_count(vertex_response: Mapping[str, Any]) -> int: def _vertex_embeddings_rows_to_openai_batch_output_row( custom_id: str, - vertex_output_rows: tuple[Mapping[str, Any], ...], + vertex_output_rows: tuple[_VertexEmbeddingBatchRow, ...], element_indices: tuple[int, ...], element_count: int, model: str | None, @@ -348,7 +369,7 @@ def _vertex_embeddings_rows_to_openai_batch_output_row( def _transform_vertex_embeddings_batch_output_to_openai( - vertex_output_rows: Iterable[Mapping[str, Any]], + vertex_output_rows: Iterable[_VertexEmbeddingBatchRow], model: str | None, ) -> tuple[_OpenAIBatchOutputRow, ...]: """ @@ -388,7 +409,7 @@ def _model_from_managed_gcs_url(url: str) -> str | None: return match.group(1) if match else None -def _is_embeddings_batch_entry(openai_entry: Mapping[str, Any]) -> bool: +def _is_embeddings_batch_entry(openai_entry: Mapping[str, object]) -> bool: """ Whether an OpenAI batch JSONL line targets the embeddings endpoint. @@ -431,7 +452,7 @@ def _vertex_batch_embeddings_key(custom_id: str, index: int, total: int) -> str: return encoded_custom_id if total < 2 else f"{encoded_custom_id}#{index}/{total}" -def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, Any]) -> Mapping[str, Any]: +def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, object]) -> Mapping[str, object]: """ One Vertex Gemini Embedding batch input row. @@ -453,8 +474,8 @@ def _vertex_embeddings_row(key: str | None, embed_content_request: Mapping[str, def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( - openai_entry: Mapping[str, Any], -) -> tuple[Mapping[str, Any], ...]: + openai_entry: Mapping[str, object], +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI `/v1/embeddings` batch entry into Vertex Gemini Embedding batch rows, one per requested embedding. @@ -512,7 +533,7 @@ def _openai_batch_jsonl_entry_to_vertex_embeddings_rows( def _openai_batch_jsonl_entry_to_vertex_rows( openai_entry: dict[str, Any], map_openai_to_vertex_params: Callable[[dict[str, Any]], dict[str, Any]], -) -> tuple[Mapping[str, Any], ...]: +) -> tuple[Mapping[str, object], ...]: """ Transforms a single OpenAI JSONL batch entry into the Vertex rows it maps to. @@ -533,7 +554,7 @@ def _openai_batch_jsonl_entry_to_vertex_rows( cached_content=None, ) - custom_id: Final = openai_entry.get("custom_id") + custom_id: Final[object] = openai_entry.get("custom_id") if custom_id is not None: if "labels" not in vertex_request_body: vertex_request_body["labels"] = {} diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 11c026010ee..e2d62be6a69 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -250,7 +250,7 @@ def _gs_uri_requires_content_type_metadata(url: str) -> bool: def _image_url_payload_may_need_sync_gcs_metadata_fetch( - raw_image_url: Any, + raw_image_url: object, ) -> bool: """ True when this image_url value (content-part image_url or assistant ``images[]`` @@ -326,7 +326,7 @@ def _openai_messages_may_need_sync_gcs_metadata_fetch( def _get_gcs_object_content_type( image_url: str, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> str | None: """ Resolve content type from GCS object metadata. @@ -479,7 +479,7 @@ def _process_gemini_media( model: str | None = None, video_metadata: dict[str, Any] | None = None, vertex_project: str | None = None, - vertex_credentials: Any | None = None, + vertex_credentials: object = None, ) -> PartType: """ Given a media URL (image, audio, or video), return the appropriate PartType for Gemini @@ -1002,7 +1002,7 @@ def _gemini_convert_messages_with_history( if isinstance(_ss_invocations, list): for invocation in _ss_invocations: # Re-inject toolCall part - tc_part: dict[str, Any] = { + tc_part: dict[str, object] = { "toolCall": { "toolType": invocation.get("tool_type"), "id": invocation.get("id"), @@ -1015,13 +1015,13 @@ def _gemini_convert_messages_with_history( # Re-inject toolResponse part if response is present if "response" in invocation: - tr_dict: dict[str, Any] = { + tr_dict: dict[str, object] = { "id": invocation.get("id"), "response": invocation.get("response"), } if invocation.get("tool_type"): tr_dict["toolType"] = invocation["tool_type"] - tr_part: dict[str, Any] = {"toolResponse": tr_dict} + tr_part: dict[str, object] = {"toolResponse": tr_dict} if "response_thought_signature" in invocation: tr_part["thoughtSignature"] = invocation["response_thought_signature"] assistant_content.append(tr_part) @@ -1090,7 +1090,7 @@ def _pop_and_merge_extra_body(data: RequestBody, optional_params: dict) -> None: data_dict[k] = v -def _has_google_maps_tool(tools: Any | None) -> bool: +def _has_google_maps_tool(tools: object) -> bool: """Return True if any tool object in the list has a 'googleMaps' key.""" if not isinstance(tools, list): return False @@ -1127,7 +1127,7 @@ def _rewrite_mime_type_to_response_format(generation_config: GenerationConfig) - schema = generation_config.pop("response_schema", None) generation_config.pop("response_mime_type", None) - response_format: Final[dict[str, Any]] = {"text": {"mimeType": "APPLICATION_JSON"}} + response_format: Final[dict[str, dict[str, object]]] = {"text": {"mimeType": "APPLICATION_JSON"}} if schema is not None: response_format["text"]["schema"] = schema generation_config["responseFormat"] = response_format @@ -1316,7 +1316,7 @@ async def async_transform_request_body( timeout: float | httpx.Timeout | None, extra_headers: dict | None, optional_params: dict, - logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, + logging_obj: LiteLLMLoggingObj, custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, vertex_project: str | None, diff --git a/litellm/llms/vertex_ai/vertex_llm_base.py b/litellm/llms/vertex_ai/vertex_llm_base.py index aca257dc095..1942bc850f1 100644 --- a/litellm/llms/vertex_ai/vertex_llm_base.py +++ b/litellm/llms/vertex_ai/vertex_llm_base.py @@ -9,7 +9,7 @@ import json import os import threading from collections.abc import Mapping -from typing import TYPE_CHECKING, Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol from urllib.parse import urlparse import litellm @@ -47,6 +47,21 @@ else: GoogleCredentialsObject = Any +class _VertexCredentialsObject(Protocol): + """Structural view of the google-auth credentials handle that this class caches and refreshes.""" + + @property + def token(self) -> object: ... + + @property + def quota_project_id(self) -> str | None: ... + + @property + def expired(self) -> object: ... + + def refresh(self, request: object) -> None: ... + + class VertexBase: def __init__(self) -> None: super().__init__() @@ -55,7 +70,7 @@ class VertexBase: self._credentials: GoogleCredentialsObject | None = None self._credentials_project_mapping: dict[ tuple[VERTEX_CREDENTIALS_TYPES | None, str | None], - tuple[GoogleCredentialsObject, str | None], + tuple[_VertexCredentialsObject, str | None], ] = {} self.project_id: str | None = None self.async_handler: AsyncHTTPHandler | None = None @@ -109,7 +124,7 @@ class VertexBase: self, credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, - ) -> tuple[Any, str]: + ) -> tuple[_VertexCredentialsObject | None, str]: if credentials is not None: if isinstance(credentials, str): _is_path: Final = os.path.exists( @@ -209,7 +224,7 @@ class VertexBase: return creds, project_id # Google Auth Helpers -- extracted for mocking purposes in tests - def _credentials_from_identity_pool(self, json_obj, scopes): + def _credentials_from_identity_pool(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import identity_pool except ImportError: @@ -220,7 +235,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_pluggable(self, json_obj, scopes): + def _credentials_from_pluggable(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import pluggable except ImportError: @@ -231,7 +246,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_identity_pool_with_aws(self, json_obj, scopes): + def _credentials_from_identity_pool_with_aws(self, json_obj, scopes) -> _VertexCredentialsObject: try: from google.auth import aws except ImportError: @@ -242,7 +257,7 @@ class VertexBase: creds = creds.with_scopes(scopes) return creds - def _credentials_from_authorized_user(self, json_obj, scopes): + def _credentials_from_authorized_user(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.credentials except ImportError: @@ -250,7 +265,7 @@ class VertexBase: return google.oauth2.credentials.Credentials.from_authorized_user_info(json_obj, scopes=scopes) - def _credentials_from_service_account(self, json_obj, scopes): + def _credentials_from_service_account(self, json_obj, scopes) -> _VertexCredentialsObject: try: import google.oauth2.service_account except ImportError: @@ -258,7 +273,7 @@ class VertexBase: return google.oauth2.service_account.Credentials.from_service_account_info(json_obj, scopes=scopes) - def _credentials_from_default_auth(self, scopes): + def _credentials_from_default_auth(self, scopes) -> tuple[_VertexCredentialsObject, str | None]: try: import google.auth as google_auth except ImportError: @@ -350,7 +365,7 @@ class VertexBase: ) return api_base - def refresh_auth(self, credentials: Any) -> None: + def refresh_auth(self, credentials: _VertexCredentialsObject) -> None: try: from google.auth.transport.requests import ( Request, @@ -426,7 +441,7 @@ class VertexBase: self, credential_cache_key: tuple, project_id: str | None, - ) -> tuple[str, str, "TokenState", Any, str | None] | None: + ) -> tuple[str, str, "TokenState", _VertexCredentialsObject, str | None] | None: """ Look up cached credentials and return usable token info for FRESH or STALE tokens (both are still valid for outbound requests). STALE @@ -449,7 +464,9 @@ class VertexBase: return None return creds.token, resolved_project, token_state, creds, cached_project_id - def _unpack_cached_credentials(self, credential_cache_key: tuple) -> tuple[Any, str | None]: + def _unpack_cached_credentials( + self, credential_cache_key: tuple + ) -> tuple[_VertexCredentialsObject | None, str | None]: """ Return (credentials, project_id) from the cache, or (None, None) if not cached. Handles both tuple and legacy cache formats. @@ -461,7 +478,7 @@ class VertexBase: return cached_entry return cached_entry, cached_entry.quota_project_id or getattr(cached_entry, "project_id", None) - def _get_token_state(self, credentials: Any) -> "TokenState": + def _get_token_state(self, credentials: _VertexCredentialsObject) -> "TokenState": """ Return the token state using google-auth's TokenState enum. @@ -485,7 +502,7 @@ class VertexBase: credentials: VERTEX_CREDENTIALS_TYPES | None, project_id: str | None, credential_cache_key: tuple, - ) -> tuple[Any, str | None]: + ) -> tuple[_VertexCredentialsObject, str | None]: """Load credentials via load_auth (in thread) and cache the result.""" try: _credentials, credential_project_id = await asyncify(self.load_auth)( @@ -505,7 +522,7 @@ class VertexBase: async def _background_refresh_credentials( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -557,7 +574,7 @@ class VertexBase: def _schedule_background_refresh( self, - credentials: Any, + credentials: _VertexCredentialsObject, credential_cache_key: tuple, credential_project_id: str | None, ) -> None: @@ -575,7 +592,7 @@ class VertexBase: self._background_refresh_credentials(credentials, credential_cache_key, credential_project_id) ) - def _drop_background_refresh_task(_fut: asyncio.Future[Any]) -> None: + def _drop_background_refresh_task(_fut: asyncio.Future[None]) -> None: if self._background_refresh_tasks.get(credential_cache_key) is _fut: self._background_refresh_tasks.pop(credential_cache_key, None) @@ -888,7 +905,7 @@ class VertexBase: # Convert dict credentials to string for caching cache_credentials: Final = json.dumps(credentials) if isinstance(credentials, dict) else credentials credential_cache_key: Final = (cache_credentials, project_id) - _credentials: GoogleCredentialsObject | None = None + _credentials: _VertexCredentialsObject | None = None verbose_logger.debug("Checking cached credentials for project_id: %s", project_id) diff --git a/litellm/main.py b/litellm/main.py index c7d44e32719..c4c5bbefc4f 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -6949,12 +6949,18 @@ def embedding( aembedding=aembedding, headers=headers, ) - elif custom_llm_provider == "dashscope": - dashscope_key: Final = api_key or litellm.api_key or get_secret_str("DASHSCOPE_API_KEY") + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): + from litellm.llms.dashscope.common_utils import ( + missing_dashscope_family_key_message, + resolve_dashscope_family_api_key, + ) + + dashscope_key: Final = resolve_dashscope_family_api_key( + custom_llm_provider=custom_llm_provider, + api_key=api_key or litellm.api_key, + ) if dashscope_key is None: - raise ValueError( - "Missing API key for DashScope. Set DASHSCOPE_API_KEY environment variable or pass api_key parameter." - ) + raise ValueError(missing_dashscope_family_key_message(custom_llm_provider)) if extra_headers is not None and isinstance(extra_headers, dict): headers = extra_headers else: diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 5ca956c766d..27ff525c15e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1592,7 +1592,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1628,7 +1628,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1664,7 +1664,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1700,7 +1700,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1736,7 +1736,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1772,7 +1772,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -2065,7 +2065,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2102,7 +2102,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2139,7 +2139,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2176,7 +2176,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2213,7 +2213,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2250,7 +2250,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -14740,6 +14740,1910 @@ "/v1/images/generations" ] }, + "qwencloud/deepseek-v4-flash": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + 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131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 6e-06, + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "qwen_ai_platform/qwq-plus": { + "input_cost_per_token": 8e-07, + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 98304, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.4e-06, + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "qwen_ai_platform/qwen-image-2.0": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-2.0-pro": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-3.0": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-3.0-pro": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, "databricks/databricks-bge-large-en": { "cache_creation_input_token_cost": 1.0003e-07, "cache_read_input_token_cost": 1.0003e-07, diff --git a/litellm/passthrough/main.py b/litellm/passthrough/main.py index 9095cee15a9..c4bd03fb1c3 100644 --- a/litellm/passthrough/main.py +++ b/litellm/passthrough/main.py @@ -6,7 +6,7 @@ from __future__ import annotations import asyncio import contextvars -from collections.abc import AsyncGenerator, AsyncIterator, Coroutine, Generator, Iterator +from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Coroutine, Generator, Iterator from functools import partial from types import TracebackType from typing import Any, Final, cast @@ -27,19 +27,19 @@ base_llm_http_handler = BaseLLMHTTPHandler() from .utils import BasePassthroughUtils -async def _as_async_generator(iterable: AsyncIterator[bytes]) -> AsyncGenerator[bytes, Any]: +async def _as_async_generator(iterable: AsyncIterator[bytes]) -> AsyncGenerator[bytes, bytes]: async for chunk in iterable: yield chunk -def _as_generator(iterable: Iterator[bytes]) -> Generator[bytes, Any, Any]: +def _as_generator(iterable: Iterator[bytes]) -> Generator[bytes, bytes, None]: yield from iterable -class AsyncPassthroughStreamingResponse(AsyncGenerator[Any, Any]): +class AsyncPassthroughStreamingResponse(AsyncGenerator[bytes, bytes]): def __init__( self, - response: Coroutine[Any, Any, httpx.Response], + response: Awaitable[httpx.Response], litellm_logging_obj: LiteLLMLoggingObj, provider_config: BasePassthroughConfig, ) -> None: @@ -48,7 +48,7 @@ class AsyncPassthroughStreamingResponse(AsyncGenerator[Any, Any]): self._headers = httpx.Headers() self._response_coro = response self._response: httpx.Response - self._iterator: AsyncGenerator[bytes, Any] + self._iterator: AsyncGenerator[bytes, bytes] self._litellm_logging_obj = litellm_logging_obj self._provider_config = provider_config self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks @@ -172,7 +172,7 @@ class AsyncPassthroughStreamingResponse(AsyncGenerator[Any, Any]): pass -class PassthroughStreamingResponse(Generator[Any, Any, Any]): +class PassthroughStreamingResponse(Generator[bytes, bytes, None]): def __init__( self, response: httpx.Response, @@ -184,7 +184,7 @@ class PassthroughStreamingResponse(Generator[Any, Any, Any]): self.status_code = response.status_code self._litellm_logging_obj = litellm_logging_obj self._provider_config = provider_config - self._iterator: Generator[bytes, Any, Any] = _as_generator(response.iter_bytes()) + self._iterator: Generator[bytes, bytes, None] = _as_generator(response.iter_bytes()) self._raw_bytes: list[bytes] = [] # mutable-ok: instance buffer for streaming chunks self._flush_scheduled = False @@ -263,7 +263,7 @@ async def allm_passthrough_route( cookies: CookieTypes | None = None, client: HTTPHandler | AsyncHTTPHandler | None = None, **kwargs, -) -> httpx.Response | AsyncGenerator[Any, Any]: +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Async: Reranks a list of documents based on their relevance to the query """ @@ -390,10 +390,10 @@ def llm_passthrough_route( **kwargs, ) -> ( httpx.Response - | Coroutine[Any, Any, httpx.Response] - | Coroutine[Any, Any, httpx.Response | AsyncGenerator[Any, Any]] - | Generator[Any, Any, Any] - | AsyncGenerator[Any, Any] + | Coroutine[object, object, httpx.Response] + | Coroutine[object, object, httpx.Response | AsyncGenerator[bytes, bytes]] + | Generator[bytes, bytes, None] + | AsyncGenerator[bytes, bytes] ): """ Pass through requests to the LLM APIs. @@ -592,7 +592,7 @@ async def _async_passthrough_request( is_streaming_request: bool, litellm_logging_obj: LiteLLMLoggingObj, provider_config: BasePassthroughConfig, -) -> httpx.Response | AsyncGenerator[Any, Any]: +) -> httpx.Response | AsyncGenerator[bytes, bytes]: """ Handle async passthrough requests. Uses async client to send request and properly handles streaming. diff --git a/litellm/provider_endpoints_support_backup.json b/litellm/provider_endpoints_support_backup.json index ead26ab65c5..9d6b1e18f59 100644 --- a/litellm/provider_endpoints_support_backup.json +++ b/litellm/provider_endpoints_support_backup.json @@ -671,6 +671,42 @@ "interactions": true } }, + "qwencloud": { + "display_name": "QwenCloud (`qwencloud`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, + "qwen_ai_platform": { + "display_name": "Qwen AI Platform (`qwen_ai_platform`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, "databricks": { "display_name": "Databricks (`databricks`)", "url": "https://docs.litellm.ai/docs/providers/databricks", diff --git a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py index 7ec0f4b5192..dcf1b01bc25 100644 --- a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py +++ b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py @@ -5,6 +5,7 @@ Filters MCP tools semantically for /chat/completions and /responses endpoints. """ import asyncio +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final from litellm._logging import verbose_logger @@ -74,7 +75,7 @@ class SemanticMCPToolFilter: self.router_instance = litellm_router_instance self.tool_router: SemanticRouter | None = None self.context_window_error: str | None = None - self._tool_map: dict[str, Any] = {} # MCPTool objects or OpenAI function dicts + self._tool_map: dict[str, object] = {} # MCPTool objects or OpenAI function dicts self._index_sync_lock = asyncio.Lock() async def build_router_from_mcp_registry(self) -> None: @@ -182,11 +183,11 @@ class SemanticMCPToolFilter: return raise - def _has_tools_missing_from_index(self, tools: list[Any]) -> bool: + def _has_tools_missing_from_index(self, tools: Sequence[object]) -> bool: """Allocation-free check for any named tool not yet in the semantic index.""" return any(name and name not in self._tool_map for name in (self._extract_tool_info(t)[0] for t in tools)) - def _tools_missing_from_index(self, tools: list[Any]) -> dict[str, Any]: + def _tools_missing_from_index(self, tools: Sequence[object]) -> Mapping[str, object]: """Map name -> tool for every named tool not yet in the semantic index.""" return { name: tool @@ -194,7 +195,7 @@ class SemanticMCPToolFilter: if name and name not in self._tool_map } - async def _ensure_tools_indexed(self, available_tools: list[Any]) -> None: + async def _ensure_tools_indexed(self, available_tools: Sequence[object]) -> None: """ Index request-time tools the startup build never saw. @@ -385,7 +386,7 @@ class SemanticMCPToolFilter: separator: Final = client_name[-len(canonical) - 1] return separator in ("_", "-") - def _get_tools_by_names(self, tool_names: list[str], available_tools: list[Any]) -> list[Any]: + def _get_tools_by_names(self, tool_names: Sequence[str], available_tools: Sequence[object]) -> list[object]: """ Get tools from available_tools by their names, preserving the semantic router's ordering. @@ -401,14 +402,14 @@ class SemanticMCPToolFilter: # Exact matches win over suffix matches when both are present, and # each incoming tool is returned at most once even if two canonical # names happen to be tail-compatible with the same incoming name. - available_by_name: Final[dict[str, Any]] = {} + available_by_name: Final[dict[str, object]] = {} for tool in available_tools: client_name, _ = self._extract_tool_info(tool) if client_name and client_name not in available_by_name: available_by_name[client_name] = tool - matched: Final[list[Any]] = [] - used_ids: Final[set] = set() + matched: Final[list[object]] = [] + used_ids: Final[set[int]] = set() for canonical in tool_names: tool = available_by_name.get(canonical) if tool is None: @@ -430,7 +431,7 @@ class SemanticMCPToolFilter: used_ids.add(id(tool)) return matched - def extract_user_query(self, messages: list[dict[str, Any]]) -> str: + def extract_user_query(self, messages: Sequence[Mapping[str, object]]) -> str: """ Extract user query from messages for /chat/completions or /responses. diff --git a/litellm/proxy/agent_endpoints/a2a_endpoints.py b/litellm/proxy/agent_endpoints/a2a_endpoints.py index bd02cfdf907..31b05320cd3 100644 --- a/litellm/proxy/agent_endpoints/a2a_endpoints.py +++ b/litellm/proxy/agent_endpoints/a2a_endpoints.py @@ -14,7 +14,7 @@ import json from collections.abc import AsyncGenerator, Mapping from copy import deepcopy from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol from urllib.parse import urlparse from fastapi import APIRouter, Depends, HTTPException, Request, Response @@ -215,11 +215,20 @@ def _enforce_inbound_trace_id(agent: "AgentResponse", request: Request) -> None: ) +class _JsonRpcResponse(Protocol): + def json(self) -> dict[str, object]: ... + + +def _jsonrpc_body(response: _JsonRpcResponse) -> dict[str, object]: + """The decoded JSON-RPC body of ``response``.""" + return response.json() + + async def _forward_jsonrpc( agent_url: str, body: dict[str, object], extra_headers: Mapping[str, str] | None = None, -) -> dict[str, Any]: +) -> dict[str, object]: from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.types.llms.custom_http import httpxSpecialProvider @@ -230,7 +239,7 @@ async def _forward_jsonrpc( ) resp: Final = await handler.post(agent_url, json=body, headers=headers) try: - result: Final = resp.json() + result: Final = _jsonrpc_body(resp) except Exception: resp.raise_for_status() raise @@ -940,8 +949,8 @@ async def invoke_agent_a2a( ) result = await _forward_jsonrpc(agent_url, forward_body, extra_headers=caller_headers) if method == "agent/getAuthenticatedExtendedCard": - if isinstance(result.get("result"), dict): - card: Final = result["result"] + card: Final = result.get("result") + if isinstance(card, dict): proxy_url: Final = get_custom_url(str(request.base_url), route=f"a2a/{agent_id}") # Rewrite the upstream agent URL in both 0.3 (top-level `url`) # and 1.0 (`supportedInterfaces[0].url`) wire formats so that diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 39e6ca9a369..0795cee7409 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -14,8 +14,8 @@ import hashlib import os import re import time -from collections.abc import Awaitable, Callable -from typing import Any, Final, Literal, NoReturn, TypeVar, cast +from collections.abc import Awaitable, Callable, Sequence +from typing import Any, Final, Literal, NoReturn, Protocol, TypeVar, cast import httpx import jwt @@ -24,6 +24,7 @@ from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import serialization from fastapi import HTTPException, status from jwt.api_jwk import PyJWK +from typing_extensions import ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.dot_notation_indexing import get_nested_value @@ -93,6 +94,47 @@ UNREACHABLE_CACHE_KEY_PREFIX: Final = "litellm_jwks_unreachable_" _CachedValueT = TypeVar("_CachedValueT", bound=JWKKeyValue | str) +class _JWTAuthSettings(Protocol): + """The JWT auth settings block this handler reads back through ``getattr``, when one is configured.""" + + @property + def issuers(self) -> Sequence[JWTIssuerConfig] | None: ... + + @property + def public_key_ttl(self) -> float: ... + + @property + def public_key_stale_ttl(self) -> float: ... + + +class _OIDCDiscoveryBody(TypedDict, total=False): + """Decoded OIDC discovery document, read for the JWKS endpoint it advertises.""" + + jwks_uri: ReadOnly[str] + + +class _OIDCDiscoveryResponse(Protocol): + """The discovery endpoint's HTTP response, read for the decoded document it carries.""" + + def json(self) -> _OIDCDiscoveryBody: ... + + +class _UserInfoResponse(Protocol): + """The OIDC UserInfo endpoint's HTTP response, read for the identity document it carries.""" + + def json(self) -> dict[str, object]: ... + + +def _discovery_document(response: _OIDCDiscoveryResponse) -> _OIDCDiscoveryBody: + """Decode an OIDC discovery response body.""" + return response.json() + + +def _userinfo_document(response: _UserInfoResponse) -> dict[str, object]: + """Decode an OIDC UserInfo response body into its JSON object form.""" + return response.json() + + def jwks_unavailable_exception(error: JWKSUnreachableError) -> ProxyException: return ProxyException( message=( @@ -794,7 +836,7 @@ class JWTHandler: f"JWT Auth: OIDC discovery endpoint {url} returned status {response.status_code}: {response.text}" ) try: - discovery: Final = response.json() + discovery: Final = _discovery_document(response) except Exception as e: raise Exception(f"JWT Auth: Failed to parse OIDC discovery document at {url}: {e}") @@ -806,13 +848,13 @@ class JWTHandler: return jwks_uri def _get_public_key_cache_ttl(self) -> float: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return 600 return litellm_jwtauth.public_key_ttl def _get_public_key_stale_ttl(self) -> float: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return DEFAULT_JWKS_STALE_TTL return litellm_jwtauth.public_key_stale_ttl @@ -938,7 +980,7 @@ class JWTHandler: if response.status_code != 200: raise Exception(f"OIDC UserInfo endpoint returned status {response.status_code}: {response.text}") - userinfo: Final = response.json() + userinfo: Final = _userinfo_document(response) verbose_proxy_logger.debug("Received OIDC UserInfo: %s", userinfo) # Cache the userinfo response @@ -996,7 +1038,7 @@ class JWTHandler: } def _get_configured_issuer(self, token: str) -> JWTIssuerConfig | None: - litellm_jwtauth: Final = getattr(self, "litellm_jwtauth", None) + litellm_jwtauth: Final[_JWTAuthSettings | None] = getattr(self, "litellm_jwtauth", None) if litellm_jwtauth is None: return None diff --git a/litellm/proxy/common_utils/debug_utils.py b/litellm/proxy/common_utils/debug_utils.py index 3a1d18b48cc..554a6ae8d1a 100644 --- a/litellm/proxy/common_utils/debug_utils.py +++ b/litellm/proxy/common_utils/debug_utils.py @@ -6,9 +6,11 @@ import os import sys import tracemalloc from collections import Counter -from typing import Any, Final +from collections.abc import Mapping, Sequence +from typing import Any, Final, NamedTuple, Protocol, TypedDict from fastapi import APIRouter, Depends, HTTPException, Query +from typing_extensions import ReadOnly from litellm import get_secret_str from litellm._logging import verbose_proxy_logger @@ -194,6 +196,42 @@ async def memory_usage_in_mem_cache_items( } +class _ProcessMemoryInfo(Protocol): + """The resident and virtual sizes psutil reports for a process.""" + + @property + def rss(self) -> int: ... + + @property + def vms(self) -> int: ... + + +class _ProcessHandle(Protocol): + """The psutil process handle members this module reads.""" + + def memory_info(self) -> _ProcessMemoryInfo: ... + + def memory_percent(self) -> float: ... + + +class _ProcessMemoryUsage(NamedTuple): + """Memory usage of a single worker process.""" + + resident_megabytes: float + virtual_megabytes: float + percent: float + + +def _process_memory_usage(process: _ProcessHandle) -> _ProcessMemoryUsage: + """Read resident/virtual megabytes and system memory share for ``process``.""" + memory_info: Final = process.memory_info() + return _ProcessMemoryUsage( + resident_megabytes=memory_info.rss / (1024 * 1024), + virtual_megabytes=memory_info.vms / (1024 * 1024), + percent=process.memory_percent(), + ) + + @router.get("/debug/memory/summary", include_in_schema=False) async def get_memory_summary( _: UserAPIKeyAuth = Depends(user_api_key_auth), @@ -227,10 +265,9 @@ async def get_memory_summary( try: import psutil - process: Final = psutil.Process() - memory_info: Final = process.memory_info() - memory_mb: Final = memory_info.rss / (1024 * 1024) - memory_percent: Final = process.memory_percent() + usage: Final = _process_memory_usage(psutil.Process()) + memory_mb: Final = usage.resident_megabytes + memory_percent: Final = usage.percent process_memory = { "summary": f"{memory_mb:.1f} MB ({memory_percent:.1f}% of system memory)", @@ -252,7 +289,7 @@ async def get_memory_summary( process_memory["error"] = str(e) # Get cache information - caches: Final[dict[str, Any]] = {} + caches: Final[dict[str, object]] = {} total_cache_items = 0 try: @@ -313,7 +350,7 @@ async def get_memory_summary( } -def _get_gc_statistics() -> dict[str, Any]: +def _get_gc_statistics() -> Mapping[str, object]: """Get garbage collector statistics.""" return { "enabled": gc.isenabled(), @@ -341,30 +378,42 @@ def _get_gc_statistics() -> dict[str, Any]: } -def _get_object_type_counts(top_n: int) -> tuple[int, list[dict[str, Any]]]: +class _ObjectTypeCount(TypedDict): + """One row of the tracked-object histogram.""" + + type: ReadOnly[str] + count: ReadOnly[int] + count_readable: ReadOnly[str] + + +def _type_name_counts(objects: Sequence[object]) -> Counter[str]: + """Count ``objects`` by the name of their type.""" + return Counter(type(obj).__name__ for obj in objects) + + +def _get_object_type_counts(top_n: int) -> tuple[int, list[_ObjectTypeCount]]: """Count objects by type and return total count and top N types.""" - type_counts: Final[Counter] = Counter() - total_objects = 0 + type_counts: Final = _type_name_counts(gc.get_objects()) - for obj in gc.get_objects(): - total_objects += 1 - obj_type = type(obj).__name__ - type_counts[obj_type] += 1 - - top_object_types: Final = [ + top_object_types: Final[list[_ObjectTypeCount]] = [ {"type": obj_type, "count": count, "count_readable": f"{count:,}"} for obj_type, count in type_counts.most_common(top_n) ] - return total_objects, top_object_types + return sum(type_counts.values()), top_object_types -def _get_uncollectable_objects_info() -> dict[str, Any]: +def _type_names(objects: Sequence[object]) -> Sequence[str]: + """The type name of each object in ``objects``.""" + return [type(obj).__name__ for obj in objects] + + +def _get_uncollectable_objects_info() -> Mapping[str, object]: """Get information about uncollectable objects (potential memory leaks).""" uncollectable: Final = gc.garbage return { "count": len(uncollectable), - "sample_types": [type(obj).__name__ for obj in uncollectable[:10]], + "sample_types": _type_names(uncollectable[:10]), "warning": ( "If count > 0, you may have reference cycles preventing garbage collection" if len(uncollectable) > 0 @@ -373,9 +422,11 @@ def _get_uncollectable_objects_info() -> dict[str, Any]: } -def _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache) -> dict[str, Any]: +def _get_cache_memory_stats( + user_api_key_cache, llm_router, proxy_logging_obj, redis_usage_cache +) -> Mapping[str, object]: """Calculate memory usage for all caches.""" - cache_stats: Final[dict[str, Any]] = {} + cache_stats: Final[dict[str, object]] = {} try: # User API key cache user_cache_size: Final = sys.getsizeof(user_api_key_cache.in_memory_cache.cache_dict) @@ -439,9 +490,9 @@ def _get_cache_memory_stats(user_api_key_cache, llm_router, proxy_logging_obj, r return cache_stats -def _get_router_memory_stats(llm_router) -> dict[str, Any]: +def _get_router_memory_stats(llm_router) -> Mapping[str, object]: """Get memory usage statistics for LiteLLM router.""" - litellm_router_memory: dict[str, Any] = {} + litellm_router_memory: dict[str, object] = {} try: if llm_router is not None: # Model list memory size @@ -505,7 +556,7 @@ def _get_router_memory_stats(llm_router) -> dict[str, Any]: return litellm_router_memory -def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> dict[str, Any] | None: +def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> Mapping[str, object] | None: """Get process-level memory information using psutil.""" if not include_process_info: return None @@ -514,10 +565,10 @@ def _get_process_memory_info(worker_pid: int, include_process_info: bool) -> dic import psutil process: Final = psutil.Process() - memory_info: Final = process.memory_info() - ram_usage_mb: Final = round(memory_info.rss / (1024 * 1024), 2) - virtual_memory_mb: Final = round(memory_info.vms / (1024 * 1024), 2) - memory_percent: Final = round(process.memory_percent(), 2) + usage: Final = _process_memory_usage(process) + ram_usage_mb: Final = round(usage.resident_megabytes, 2) + virtual_memory_mb: Final = round(usage.virtual_megabytes, 2) + memory_percent: Final = round(usage.percent, 2) return { "pid": worker_pid, diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 202a95ba29b..e6880d521f1 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -211,7 +211,7 @@ class DBSpendUpdateWriter: org_id: str | None, # Completion object fields kwargs: dict | None, - completion_response: litellm.ModelResponse | Any | Exception | None, + completion_response: object, start_time: datetime | None, end_time: datetime | None, response_cost: float | None, @@ -323,7 +323,7 @@ class DBSpendUpdateWriter: async def _enqueue_tool_usage_transaction( self, payload: SpendLogsPayload, - completion_response: "litellm.ModelResponse | Any | Exception | None", + completion_response: object, prisma_client: "PrismaClient | None", kwargs: "dict | None" = None, ) -> None: @@ -396,7 +396,7 @@ class DBSpendUpdateWriter: def _enqueue_tool_registry_upsert( self, kwargs: dict | None, - completion_response: Any | None, + completion_response: object, hashed_token: str | None = None, team_id: str | None = None, ) -> None: @@ -849,7 +849,7 @@ class DBSpendUpdateWriter: return # Parse tags from JSON string - tags = [] + tags: Sequence[object] = [] if isinstance(request_tags, str): tags = safe_json_loads(request_tags, default=[]) if not tags: @@ -2260,7 +2260,7 @@ class DBSpendUpdateWriter: verbose_proxy_logger.debug("request_tags is None for request. Skipping incrementing tag spend.") return - request_tags = [] + request_tags: Sequence[str] = [] if isinstance(payload["request_tags"], str): request_tags = json.loads(payload["request_tags"]) elif isinstance(payload["request_tags"], list): diff --git a/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py index 3716d00774f..2c27531cea1 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py +++ b/litellm/proxy/guardrails/guardrail_hooks/akto/akto.py @@ -162,10 +162,10 @@ class AktoGuardrail(CustomGuardrail): def build_request_body( inputs: GenericGuardrailAPIInputs, request_data: dict | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the LLM request body from guardrail inputs (messages, model, tools).""" model: Final = inputs.get("model", "") or "" - body: Final[dict[str, Any]] = {"model": model} + body: Final[dict[str, object]] = {"model": model} structured: Final = inputs.get("structured_messages") if structured: @@ -194,7 +194,7 @@ class AktoGuardrail(CustomGuardrail): def build_response_body( inputs: GenericGuardrailAPIInputs, request_data: dict | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the LLM response body, preferring the actual model response if available.""" model_response: Final = request_data.get("response") if request_data else None if model_response is not None and hasattr(model_response, "model_dump"): @@ -224,7 +224,7 @@ class AktoGuardrail(CustomGuardrail): *, status_code: int = 200, include_response: bool = False, - ) -> dict[str, Any]: + ) -> dict[str, object]: """Build the flat MIRRORING payload sent to Akto's HTTP proxy endpoint. All body fields use double-encoding: json.dumps({"body": json.dumps(actual_body)}) diff --git a/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py b/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py index 955a868a0d6..48832f8ed5e 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py +++ b/litellm/proxy/guardrails/guardrail_hooks/grayswan/grayswan.py @@ -2,9 +2,10 @@ import os import time -from typing import TYPE_CHECKING, Any, Final, Literal, Optional +from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol from fastapi import HTTPException +from typing_extensions import NotRequired, ReadOnly, TypedDict from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import ( @@ -27,6 +28,33 @@ if TYPE_CHECKING: GRAYSWAN_BLOCK_ERROR_MSG: Final = "Blocked by Gray Swan Guardrail" +class _GraySwanMonitorResponse(TypedDict): + """Body returned by Gray Swan's `/cygnal/monitor` endpoint.""" + + violation: ReadOnly[NotRequired[float | None]] + violated_rules: ReadOnly[NotRequired[list[object]]] + violated_rule_descriptions: ReadOnly[NotRequired[list[object]]] + mutation: ReadOnly[NotRequired[bool | None]] + ipi: ReadOnly[NotRequired[bool | None]] + + +class _GraySwanMonitorHTTPResponse(Protocol): + def raise_for_status(self) -> object: ... + + def json(self) -> _GraySwanMonitorResponse: ... + + +class _GraySwanMonitorHTTPClient(Protocol): + async def post( + self, + *, + url: str, + headers: dict[str, str], + json: dict[str, object], + timeout: float, + ) -> _GraySwanMonitorHTTPResponse: ... + + class GraySwanGuardrailMissingSecrets(Exception): """Raised when the Gray Swan API key is missing.""" @@ -77,7 +105,9 @@ class GraySwanGuardrail(CustomGuardrail): guardrail_timeout: float | None = 30.0, **kwargs: Any, ) -> None: - self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + self.async_handler: _GraySwanMonitorHTTPClient = get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) api_key_value: Final = api_key or os.getenv("GRAYSWAN_API_KEY") if not api_key_value: @@ -266,7 +296,7 @@ class GraySwanGuardrail(CustomGuardrail): # Legacy Test Interface (for backward compatibility) # ------------------------------------------------------------------ - async def run_grayswan_guardrail(self, payload: dict) -> dict[str, Any]: + async def run_grayswan_guardrail(self, payload: dict[str, object]) -> _GraySwanMonitorResponse: """ Run the GraySwan guardrail on a payload. @@ -285,7 +315,7 @@ class GraySwanGuardrail(CustomGuardrail): def _process_grayswan_response( self, - response_json: dict, + response_json: _GraySwanMonitorResponse, data: dict | None = None, hook_type: GuardrailEventHooks | None = None, ) -> None: @@ -385,7 +415,7 @@ class GraySwanGuardrail(CustomGuardrail): # Core GraySwan API interaction # ------------------------------------------------------------------ - async def _call_grayswan_api(self, payload: dict) -> dict[str, Any]: + async def _call_grayswan_api(self, payload: dict[str, object]) -> _GraySwanMonitorResponse: """Call the GraySwan monitoring API.""" headers: Final = self._prepare_headers() @@ -406,7 +436,7 @@ class GraySwanGuardrail(CustomGuardrail): def _process_response_internal( self, - response_json: dict[str, Any], + response_json: _GraySwanMonitorResponse, request_data: dict, inputs: GenericGuardrailAPIInputs, is_output: bool, @@ -534,8 +564,8 @@ class GraySwanGuardrail(CustomGuardrail): dynamic_body: dict, request_data: dict, logging_obj: Optional["LiteLLMLoggingObj"] = None, - ) -> dict[str, Any] | None: - payload: Final[dict[str, Any]] = {"messages": messages} + ) -> dict[str, object] | None: + payload: Final[dict[str, object]] = {"messages": messages} categories: Final = dynamic_body.get("categories") or self.categories if categories: @@ -563,13 +593,13 @@ class GraySwanGuardrail(CustomGuardrail): {**existing_headers, **inbound_headers} if isinstance(existing_headers, dict) else inbound_headers ) if cleaned_litellm_metadata: - sanitized: Final = safe_json_loads(safe_dumps(cleaned_litellm_metadata), default={}) + sanitized: Final[object] = safe_json_loads(safe_dumps(cleaned_litellm_metadata), default={}) if isinstance(sanitized, dict) and sanitized: payload["litellm_metadata"] = sanitized return payload - def _format_violation_message(self, detection_info: Any, is_output: bool = False) -> str: + def _format_violation_message(self, detection_info: object, is_output: bool = False) -> str: """ Format detection info into a user-friendly violation message. diff --git a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py index ea022510309..cf5da27e9ca 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py +++ b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py @@ -8,6 +8,7 @@ import json import os import uuid +from collections.abc import Mapping, Sequence from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict try: @@ -128,7 +129,7 @@ class LassoGuardrail(CustomGuardrail): @staticmethod def _extract_tool_call_fields( - call: Any, + call: object, ) -> tuple[str | None, str | None, dict[str, object] | None]: """Extract (call_id, name, parsed_input) from a tool call. @@ -476,7 +477,7 @@ class LassoGuardrail(CustomGuardrail): def _map_masked_messages_back( self, original_messages: list[dict[str, Any]], - masked_messages: list[dict[str, Any]], + masked_messages: Sequence[Mapping[str, object]], ) -> list[dict[str, object]]: """Map Lasso-format masked messages back onto the original OpenAI-format messages. @@ -638,7 +639,7 @@ class LassoGuardrail(CustomGuardrail): }, ) - def _expand_messages_for_classification(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]: + def _expand_messages_for_classification(self, messages: list[dict[str, Any]]) -> list[dict[str, object]]: """ Convert raw OpenAI-format messages to Lasso API format with content blocks. @@ -646,7 +647,7 @@ class LassoGuardrail(CustomGuardrail): - role=tool messages → developer role + tool_result block - plain text messages pass through unchanged """ - expanded: Final[list[dict[str, Any]]] = [] + expanded: Final[list[dict[str, object]]] = [] for msg in messages: role = msg.get("role", "") content = msg.get("content") @@ -917,7 +918,7 @@ class LassoGuardrail(CustomGuardrail): def _apply_masking_to_model_response( self, model_response: litellm.ModelResponse, - masked_messages: list[dict[str, Any]], + masked_messages: Sequence[Mapping[str, object]], ) -> None: """Apply masking to the actual model response when mask=True and masked content is available.""" # Index masked tool_use blocks by id for O(1) lookup. diff --git a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py index 78639ce4fd0..7021d41475b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py +++ b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py @@ -8,11 +8,12 @@ # Standard library imports import json import os -from typing import TYPE_CHECKING, Any, Final, Literal +from typing import TYPE_CHECKING, Any, Final, Literal, Protocol from urllib.parse import quote # Third-party imports from fastapi import HTTPException +from typing_extensions import NotRequired, ReadOnly, TypedDict # LiteLLM imports from litellm import DualCache @@ -42,7 +43,34 @@ if TYPE_CHECKING: MAX_PILLAR_HEADER_VALUE_BYTES: Final = 8 * 1024 -def _encode_json_for_header(data: Any) -> str: +class _PillarProtectResponse(TypedDict): + """Body returned by Pillar's `/api/v1/protect` endpoint.""" + + flagged: ReadOnly[NotRequired[bool]] + session_id: ReadOnly[NotRequired[str]] + scanners: ReadOnly[NotRequired[dict[str, object]]] + evidence: ReadOnly[NotRequired[list[object]]] + masked_session_messages: ReadOnly[NotRequired[list[object]]] + + +class _PillarProtectHTTPResponse(Protocol): + def raise_for_status(self) -> object: ... + + def json(self) -> _PillarProtectResponse: ... + + +class _PillarProtectHTTPClient(Protocol): + async def post( + self, + *, + url: str, + headers: dict[str, str], + json: dict[str, object], + timeout: float, + ) -> _PillarProtectHTTPResponse: ... + + +def _encode_json_for_header(data: object) -> str: """ JSON-serialize and URL-encode data for safe header transmission. """ @@ -50,7 +78,9 @@ def _encode_json_for_header(data: Any) -> str: return quote(json_payload, safe="") -def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER_VALUE_BYTES) -> tuple[Any, str, bool]: +def _truncate_evidence_payload( + evidence: object, max_bytes: int = MAX_PILLAR_HEADER_VALUE_BYTES +) -> tuple[object, str, bool]: """ Truncate evidence payload so the encoded header value stays within max_bytes. @@ -66,12 +96,12 @@ def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER truncated_value: Final = "[truncated]" return truncated_value, _encode_json_for_header(truncated_value), True - truncated: Final[list[Any]] = [] + truncated: Final[list[object]] = [] encoded = _encode_json_for_header(truncated) truncated_flag = False for entry in evidence: - working_entry: Any + working_entry: object if isinstance(entry, dict): working_entry = dict(entry) else: @@ -105,7 +135,7 @@ def _truncate_evidence_payload(evidence: Any, max_bytes: int = MAX_PILLAR_HEADER return truncated, encoded, truncated_flag -def build_pillar_response_headers(metadata_store: dict[str, Any]) -> dict[str, str]: +def build_pillar_response_headers(metadata_store: dict[str, object]) -> dict[str, str]: """ Create URL-safe Pillar response headers and apply truncation metadata. """ @@ -191,7 +221,9 @@ class PillarGuardrail(CustomGuardrail): LiteLLM virtual key context (user_id, team_id, key_alias, etc.) is always automatically passed as X-LiteLLM-* headers to enable application/user tracking. """ - self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback) + self.async_handler: _PillarProtectHTTPClient = get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) self.api_key = api_key or os.environ.get("PILLAR_API_KEY") if self.api_key is None: @@ -686,7 +718,7 @@ class PillarGuardrail(CustomGuardrail): ) return payload - async def _call_pillar_api(self, headers: dict[str, str], payload: dict[str, Any]) -> dict[str, Any]: + async def _call_pillar_api(self, headers: dict[str, str], payload: dict[str, Any]) -> _PillarProtectResponse: """ Call the Pillar API and return the response. @@ -714,7 +746,7 @@ class PillarGuardrail(CustomGuardrail): verbose_proxy_logger.debug("Pillar Guardrail: Analysis complete - flagged=%s, session=%s", flagged, session_id) return res - def _process_pillar_response(self, pillar_response: dict[str, Any], original_data: dict) -> None: + def _process_pillar_response(self, pillar_response: _PillarProtectResponse, original_data: dict) -> None: """ Process the Pillar API response and handle detections based on configuration. @@ -774,7 +806,7 @@ class PillarGuardrail(CustomGuardrail): build_pillar_response_headers(metadata_store) - def _raise_pillar_detection_exception(self, pillar_response: dict[str, Any]) -> None: + def _raise_pillar_detection_exception(self, pillar_response: _PillarProtectResponse) -> None: """ Raise an HTTPException for Pillar security detections. @@ -784,7 +816,7 @@ class PillarGuardrail(CustomGuardrail): Raises: HTTPException: Always raises with security detection details """ - pillar_response_dict: Final = { + pillar_response_dict: Final[dict[str, object]] = { "session_id": pillar_response.get("session_id"), } diff --git a/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py b/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py index e34beec4d3e..2fbd50b5863 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/semantic_guard/semantic_guard.py @@ -6,7 +6,7 @@ via embedding similarity. Smarter than regex (understands intent), lighter than an LLM call (~20-50ms per request for embedding). """ -from typing import TYPE_CHECKING, Any, Final +from typing import TYPE_CHECKING, Any, Final, Protocol from litellm._logging import verbose_logger from litellm.integrations.custom_guardrail import ( @@ -50,7 +50,7 @@ class SemanticGuardrail(CustomGuardrail): similarity_threshold: float, route_templates: list[str] | None = None, custom_routes_file: str | None = None, - custom_routes: list[dict[str, Any]] | None = None, + custom_routes: list[dict[str, object]] | None = None, on_flagged_action: str = "block", event_hook: GuardrailEventHooks | list[GuardrailEventHooks] | Mode | None = None, default_on: bool = False, @@ -157,7 +157,14 @@ class SemanticGuardrail(CustomGuardrail): return response -def _get_top_route_choice(result: Any) -> Any: +class _RouteChoice(Protocol): + """The semantic-router match this guardrail reads: the route that fired, if any.""" + + @property + def name(self) -> str | None: ... + + +def _get_top_route_choice(result: _RouteChoice | list[_RouteChoice] | None) -> _RouteChoice | None: """Extract the top RouteChoice from SemanticRouter result. SemanticRouter.__call__ can return RouteChoice or List[RouteChoice]. @@ -194,7 +201,7 @@ def _extract_response_text(response: Any) -> str: return "" -def _content_to_text(content: Any) -> str: +def _content_to_text(content: object) -> str: if isinstance(content, str): return content if isinstance(content, list): diff --git a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py index 3c5625bc272..a8b33109900 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py +++ b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py @@ -1,9 +1,10 @@ import json import re from collections.abc import AsyncGenerator, AsyncIterable, Mapping, Sequence -from typing import Any, Final, Literal +from typing import Any, Final, Literal, TypedDict from fastapi import HTTPException +from typing_extensions import ReadOnly, Required from litellm import ChatCompletionToolParam from litellm._logging import verbose_proxy_logger @@ -51,6 +52,27 @@ def _object_list(value: object) -> Sequence[object] | None: return value if isinstance(value, list) else None +class _ToolPermissionRuleFields(TypedDict, total=False): + """The config-file shape a :class:`ToolPermissionRule` is built from.""" + + id: ReadOnly[Required[str]] + tool_name: ReadOnly[str | None] + tool_type: ReadOnly[str | None] + decision: ReadOnly[Required[Literal["allow", "deny"]]] + allowed_param_patterns: ReadOnly[dict[str, str] | None] + + +def _rule_from_fields(fields: _ToolPermissionRuleFields) -> ToolPermissionRule: + """Validate one config-file rule entry into a :class:`ToolPermissionRule`.""" + return ToolPermissionRule(**fields) + + +def _is_tool_use_block(block: object) -> bool: + """Whether ``block`` is an Anthropic ``tool_use`` content block.""" + fields: Final = _object_mapping(block) + return fields is not None and fields.get("type") == "tool_use" + + class ToolPermissionGuardrail(CustomGuardrail): def __init__( self, @@ -101,7 +123,7 @@ class ToolPermissionGuardrail(CustomGuardrail): compiled_patterns: Final[dict[str, dict[str, re.Pattern]]] = {} for rule_item in rules or []: - rule = rule_item if isinstance(rule_item, ToolPermissionRule) else ToolPermissionRule(**rule_item) + rule = rule_item if isinstance(rule_item, ToolPermissionRule) else _rule_from_fields(rule_item) target_patterns: dict[str, re.Pattern | None] = { "tool_name": None, @@ -440,7 +462,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return is_allowed, None, message @staticmethod - def _get_mapping_value(item: Any, key: str) -> Any: + def _get_mapping_value(item: object, key: str) -> Any: if isinstance(item, dict): return item.get(key) return getattr(item, key, None) @@ -450,7 +472,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return f"legacy_function_call_{choice_index}" def _legacy_function_call_to_tool_call( - self, function_call: Any, choice_index: int + self, function_call: object, choice_index: int ) -> ChatCompletionMessageToolCall | None: if function_call is None: return None @@ -549,7 +571,7 @@ class ToolPermissionGuardrail(CustomGuardrail): def _modify_anthropic_content_with_permission_errors( self, response: object, - content: tuple[Any, ...], + content: tuple[object, ...], denied_tools: tuple[tuple[ChatCompletionMessageToolCall, PermissionError], ...], ) -> None: if not denied_tools or not isinstance(response, dict): @@ -557,27 +579,33 @@ class ToolPermissionGuardrail(CustomGuardrail): verbose_proxy_logger.info("Blocking %s unauthorized tool uses", len(denied_tools)) - error_by_tool_use_id: Final = { # mutable-ok: read-only lookup, never mutated after construction + error_by_tool_use_id: Final[ + Mapping[object, str] + ] = { # mutable-ok: read-only lookup, never mutated after construction tool_call.id: self._create_permission_error_result(tool_call, error).content for tool_call, error in denied_tools } - denied_block_ids: Final = frozenset(error_by_tool_use_id) - def _is_denied(block: object) -> bool: - return isinstance(block, dict) and block.get("type") == "tool_use" and block.get("id") in denied_block_ids + def _denied_message(block: object) -> str | None: + fields: Final = _object_mapping(block) + if fields is None or fields.get("type") != "tool_use": + return None + return error_by_tool_use_id.get(fields.get("id")) - error_messages: Final = tuple(error_by_tool_use_id[block["id"]] for block in content if _is_denied(block)) - kept_blocks: Final = tuple(block for block in content if not _is_denied(block)) + error_messages: Final = tuple( + message for message in (_denied_message(block) for block in content) if message is not None + ) + kept_blocks: Final = tuple(block for block in content if _denied_message(block) is None) new_content: Final = [ # mutable-ok: response content is a JSON array on the wire *kept_blocks, {"type": "text", "text": "\n".join(error_messages)}, # mutable-ok: content block is a JSON object ] response["content"] = new_content # rebind-ok: the guardrail rewrites the provider response in place - if not any(isinstance(block, dict) and block.get("type") == "tool_use" for block in kept_blocks): + if not any(_is_tool_use_block(block) for block in kept_blocks): response["stop_reason"] = "end_turn" # rebind-ok: dropping every tool_use ends the turn - def _get_request_tool_name(self, tool: Any) -> tuple[str | None, str | None]: + def _get_request_tool_name(self, tool: object) -> tuple[str | None, str | None]: tool_type: Final = self._get_mapping_value(tool, "type") if tool_type != "function": return None, tool_type @@ -586,7 +614,7 @@ class ToolPermissionGuardrail(CustomGuardrail): tool_name: Final = self._get_mapping_value(function, "name") return tool_name, tool_type - def _get_legacy_function_name(self, function: Any) -> str | None: + def _get_legacy_function_name(self, function: object) -> str | None: return self._get_mapping_value(function, "name") def _get_named_tool_choice(self, data: dict) -> str | None: diff --git a/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py b/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py index 6b8148645aa..a5945a39589 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/vigil_guard/vigil_guard.py @@ -433,7 +433,7 @@ class VigilGuardGuardrail(CustomGuardrail): return collected @staticmethod - def _clamp_metadata_value(value: Any) -> _MetadataValue | None: + def _clamp_metadata_value(value: object) -> _MetadataValue | None: if isinstance(value, bool): return None if isinstance(value, str): diff --git a/litellm/proxy/hooks/litellm_skills/main.py b/litellm/proxy/hooks/litellm_skills/main.py index 569ec32c1a0..9edbc6dbf1c 100644 --- a/litellm/proxy/hooks/litellm_skills/main.py +++ b/litellm/proxy/hooks/litellm_skills/main.py @@ -67,6 +67,24 @@ class _ChatMessage(Protocol): def tool_calls(self) -> Sequence[_ChatToolCall] | None: ... +class _ChatChoice(Protocol): + @property + def message(self) -> _ChatMessage: ... + + @property + def finish_reason(self) -> str | None: ... + + +class _ChatCompletion(Protocol): + @property + def choices(self) -> Sequence[_ChatChoice]: ... + + +def _first_choice(response: _ChatCompletion) -> _ChatChoice: + """The first choice of an OpenAI shaped completion response.""" + return response.choices[0] + + class SkillsInjectionHook(CustomLogger): """ Pre/Post-call hook that processes skills from container.skills parameter. @@ -738,8 +756,9 @@ print('No executable skill module found') for iteration in range(self.max_iterations): # OpenAI format response has choices[0].message - assistant_message: _ChatMessage = current_response.choices[0].message - stop_reason: str | None = current_response.choices[0].finish_reason + choice: _ChatChoice = _first_choice(current_response) + assistant_message: _ChatMessage = choice.message + stop_reason: str | None = choice.finish_reason # Build assistant message for conversation history assistant_msg_dict: dict[str, object] = { diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index 1e65da5b867..63129602082 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -8,7 +8,7 @@ import asyncio import binascii import os import uuid -from collections.abc import Callable, Mapping, Sequence, Set +from collections.abc import Awaitable, Callable, Mapping, Sequence, Set from contextvars import ContextVar from dataclasses import dataclass, field from datetime import datetime @@ -386,6 +386,12 @@ CacheCounterValues: TypeAlias = Sequence[CacheCounterValue | None] ParallelGaugeCacheValue: TypeAlias = dict[str, object] | int | float | str | bytes +class _AsyncLuaScript(Protocol): + """A Lua script registered against the async Redis client, called with KEYS and ARGV.""" + + def __call__(self, *, keys: Sequence[str], args: Sequence[object]) -> Awaitable[list[CacheCounterValue]]: ... + + class RateLimitDescriptorRateLimitObject(TypedDict, total=False): requests_per_unit: int | None tokens_per_unit: int | None @@ -577,6 +583,14 @@ def _parse_output_cap_value(raw_value: object) -> int | None: class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): + batch_rate_limiter_script: _AsyncLuaScript | None + token_increment_script: _AsyncLuaScript | None + check_and_increment_by_n_script: _AsyncLuaScript | None + window_guarded_token_increment_script: _AsyncLuaScript | None + parallel_acquire_script: _AsyncLuaScript | None + parallel_release_script: _AsyncLuaScript | None + parallel_count_script: _AsyncLuaScript | None + def __init__( self, internal_usage_cache: InternalUsageCache, @@ -3855,7 +3869,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): expected_window_start = operation.get("expected_window_start") if window_key is None or expected_window_start is None: continue - active_window_start = await self.internal_usage_cache.async_get_cache( + active_window_start: CacheCounterValue | None = await self.internal_usage_cache.async_get_cache( key=window_key, litellm_parent_otel_span=parent_otel_span, local_only=True, @@ -4144,7 +4158,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): def _collect_tpm_scope_targets( self, standard_logging_metadata: dict[str, Any], - kwargs: Any, + kwargs: object, model_group: str | None, ) -> list[tuple[str, str]]: """ @@ -4301,8 +4315,8 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): def _build_success_event_pipeline_operations( self, - kwargs: Any, - response_obj: Any, + kwargs: dict[str, Any], + response_obj: object, rate_limit_type: Literal["output", "input", "total"], ) -> list[RedisPipelineIncrementOperation]: """Build Redis pipeline increment ops for TPM / parallel-request counters.""" diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py index 012aec38458..14d2332a7eb 100644 --- a/litellm/proxy/management_endpoints/model_management_endpoints.py +++ b/litellm/proxy/management_endpoints/model_management_endpoints.py @@ -543,7 +543,7 @@ def update_db_model(db_model: Deployment, updated_patch: updateDeployment) -> Pr _raise_if_ptu_cost_attribution_disabled(updated_patch.model_info.model_dump(exclude_none=True)) merged_model_name: Final = updated_patch.model_name or db_model.model_name merged_litellm_params: Final = db_model.litellm_params.model_dump(exclude_none=True) - merged_model_info: Final = db_model.model_info.model_dump(exclude_none=True) + merged_model_info: Final[dict[str, object]] = db_model.model_info.model_dump(exclude_none=True) # update litellm params if updated_patch.litellm_params: @@ -1982,7 +1982,7 @@ async def update_model( ### MERGE WITH EXISTING DATA ### merged_dictionary: Final = {} - _mp: Final = model_params.litellm_params.dict() + _mp: Final[dict[str, object]] = model_params.litellm_params.dict() for key, value in _mp.items(): if value is not None: diff --git a/litellm/proxy/management_endpoints/organization_endpoints.py b/litellm/proxy/management_endpoints/organization_endpoints.py index 9198aa35f3f..5e38a016099 100644 --- a/litellm/proxy/management_endpoints/organization_endpoints.py +++ b/litellm/proxy/management_endpoints/organization_endpoints.py @@ -487,12 +487,11 @@ async def new_organization( for m in data.models: await can_user_call_model(m, llm_router=llm_router, user_object=user_object_correct_type) - organization_row: Final = LiteLLM_OrganizationTable( - **data.json(exclude_none=True), - object_permission_id=object_permission_id, - created_by=user_api_key_dict.user_id or litellm_proxy_admin_name, - updated_by=user_api_key_dict.user_id or litellm_proxy_admin_name, - ) + organization_payload: Final = _STR_OBJECT_DICT_ADAPTER.validate_python(data.json(exclude_none=True)) + organization_payload["object_permission_id"] = object_permission_id + organization_payload["created_by"] = user_api_key_dict.user_id or litellm_proxy_admin_name + organization_payload["updated_by"] = user_api_key_dict.user_id or litellm_proxy_admin_name + organization_row: Final = LiteLLM_OrganizationTable.model_validate(organization_payload) for field in LiteLLM_ManagementEndpoint_MetadataFields: if getattr(data, field, None) is not None: @@ -644,7 +643,7 @@ async def update_organization( ) # Transform UI payload to expected format - raw_data: Final = await request.json() + raw_data: Final[dict[str, object]] = await request.json() raw_data_with_flat_budget_fields: Final = handle_nested_budget_structure_in_organization_update_request(raw_data) # Create validated data model @@ -691,7 +690,7 @@ async def update_organization( # Merge metadata from existing organization with updated metadata if updated_organization_row_json.get("metadata") is not None: existing_metadata: Final = existing_organization_row.metadata or {} - updated_metadata: Final = updated_organization_row_json.get("metadata", {}) + updated_metadata: Final[dict[str, object]] = updated_organization_row_json.get("metadata", {}) merged_metadata: Final[Mapping[str, object]] = _update_dictionary( existing_dict=cast( # cast-ok: prisma de-serializes a Json column to the plain python dict it stores "dict[str, object]", existing_metadata diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index 613508da22b..606569c5b8b 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -502,7 +502,7 @@ def _set_nested_metadata_value(metadata: dict[str, object], key_path: str, value placeholder: Final = "\x00" parts = key_path.replace("\\.", placeholder).split(".") parts = [p.replace(placeholder, ".") for p in parts] - current: Any = metadata + current: dict[str, object] = metadata for part in parts[:-1]: existing = current.get(part) if not isinstance(existing, dict): @@ -4076,7 +4076,7 @@ class SSOAuthenticationHandler: ) if resp.status_code == 200: try: - userinfo_raw: Final = resp.json() + userinfo_raw: Final[dict[str, object] | None] = resp.json() if not userinfo_raw: # JSON null (None) or empty dict ({}) — no identity claims. # Treat as failure so id_token fallback can be attempted. @@ -4406,7 +4406,7 @@ class MicrosoftSSOHandler: ) -> tuple[list[str], str | None]: """Helper function to fetch and parse group data from a URL""" response: Final = await async_client.get(url, headers=headers) - response_json: Final = response.json() + response_json: Final[dict[str, object]] = response.json() response_typed: Final = await MicrosoftSSOHandler._cast_graph_api_response_dict(response=response_json) group_ids: Final = MicrosoftSSOHandler._get_group_ids_from_graph_api_response(response=response_typed) return group_ids, response_typed.get("odata_nextLink") diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index ee9a5d94440..49ec18013b5 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -267,7 +267,7 @@ class VertexPassthroughLoggingHandler: model: Final = VertexPassthroughLoggingHandler.extract_model_from_url(url_route) - _json_response: Final = httpx_response.json() + _json_response: Final[dict[str, object]] = httpx_response.json() litellm_prediction_response: ModelResponse | EmbeddingResponse | ImageResponse = ModelResponse() if vertex_image_generation_class.is_image_generation_response(_json_response): @@ -422,7 +422,7 @@ class VertexPassthroughLoggingHandler: - Creates standard logging object - Logs in litellm callbacks """ - kwargs: dict[str, Any] = {} + kwargs: dict[str, object] = {} vertex_location: Final = get_vertex_location_from_url(url_route) if vertex_location is not None: litellm_logging_obj.optional_params["vertex_location"] = vertex_location diff --git a/litellm/proxy/public_endpoints/provider_create_fields.json b/litellm/proxy/public_endpoints/provider_create_fields.json index a746e9af326..66f8c2ea36f 100644 --- a/litellm/proxy/public_endpoints/provider_create_fields.json +++ b/litellm/proxy/public_endpoints/provider_create_fields.json @@ -986,6 +986,62 @@ ], "default_model_placeholder": "gpt-3.5-turbo" }, + { + "provider": "QwenCloud", + "provider_display_name": "QwenCloud", + "litellm_provider": "qwencloud", + "credential_fields": [ + { + "key": "api_key", + "label": "QwenCloud API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + "tooltip": "The base URL for QwenCloud. Defaults to https://dashscope-intl.aliyuncs.com/compatible-mode/v1 if not specified.", + "required": true, + "field_type": "text", + "options": null, + "default_value": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" + } + ], + "default_model_placeholder": "gpt-3.5-turbo" + }, + { + "provider": "Qwen_AI_Platform", + "provider_display_name": "Qwen AI Platform", + "litellm_provider": "qwen_ai_platform", + "credential_fields": [ + { + "key": "api_key", + "label": "Qwen AI Platform API Key", + "placeholder": null, + "tooltip": null, + "required": true, + "field_type": "password", + "options": null, + "default_value": null + }, + { + "key": "api_base", + "label": "API Base", + "placeholder": "https://dashscope.aliyuncs.com/compatible-mode/v1", + "tooltip": "The base URL for Qwen AI Platform. Defaults to https://dashscope.aliyuncs.com/compatible-mode/v1 if not specified.", + "required": true, + "field_type": "text", + "options": null, + "default_value": "https://dashscope.aliyuncs.com/compatible-mode/v1" + } + ], + "default_model_placeholder": "gpt-3.5-turbo" + }, { "provider": "Databricks", "provider_display_name": "Databricks", diff --git a/litellm/proxy/response_api_endpoints/endpoints.py b/litellm/proxy/response_api_endpoints/endpoints.py index aa7595ed13d..5907ffc64eb 100644 --- a/litellm/proxy/response_api_endpoints/endpoints.py +++ b/litellm/proxy/response_api_endpoints/endpoints.py @@ -52,7 +52,7 @@ _TOOL_PAYLOAD_KEYS: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType( "function": ("name", "description", "parameters", "strict"), } ) -_EMPTY_TOOL_PAYLOAD: Final[Mapping[str, Any]] = MappingProxyType({}) +_EMPTY_TOOL_PAYLOAD: Final[Mapping[str, object]] = MappingProxyType({}) def _convert_tool_payload_value(key: str, value: object, *, to_chat: bool) -> object: @@ -105,7 +105,7 @@ def _normalize_tool_dialect( return {**data, **{key: value for key, value in replaceable if key in data}} # mutable-ok: plain body dict -def _is_chat_completions_body(data: Mapping[str, Any]) -> bool: +def _is_chat_completions_body(data: Mapping[str, object]) -> bool: messages: Final = data.get("messages") if isinstance(messages, list) and messages: return True @@ -1373,7 +1373,7 @@ async def _enforce_responses_ws_first_frame_model_auth( request: Request, model: str, user_api_key_dict: UserAPIKeyAuth, - llm_router: Any | None, + llm_router: "Router | None", ) -> None: from litellm.proxy.auth.user_api_key_auth import ( _enforce_key_and_fallback_model_access, @@ -1417,7 +1417,7 @@ async def _enforce_responses_ws_first_frame_model_auth( async def responses_websocket_endpoint( websocket: WebSocket, model: str | None = fastapi.Query(None, description="The model to use for the responses WebSocket session."), - user_api_key_dict=Depends(user_api_key_auth_websocket), + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth_websocket), ): """ Responses API WebSocket mode endpoint. @@ -1462,7 +1462,7 @@ async def responses_websocket_endpoint( return model, first_message = result - data: dict[str, Any] = { + data: dict[str, object] = { "model": model, "websocket": websocket, } @@ -1471,7 +1471,7 @@ async def responses_websocket_endpoint( # Construct a synthetic Request for pre-call processing headers_list: Final = list(websocket.scope.get("headers") or []) - scope: Final[dict[str, Any]] = { + scope: Final[dict[str, object]] = { "type": "http", "method": "POST", "path": "/v1/responses", diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index 91a0c68fd58..3d0bd5e61c9 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -50,12 +50,12 @@ def _route_user_config_request(data: dict, route_type: str): return ret_val -def _is_a2a_agent_model(model_name: Any) -> bool: +def _is_a2a_agent_model(model_name: object) -> bool: """Check if the model name is for an A2A agent (a2a/ prefix).""" return isinstance(model_name, str) and model_name.startswith("a2a/") -def _raise_if_model_fully_blocked(llm_router: LitellmRouter, model_name: Any, team_id: str | None) -> None: +def _raise_if_model_fully_blocked(llm_router: LitellmRouter, model_name: object, team_id: str | None) -> None: if not isinstance(model_name, str) or not model_name: return if not isinstance(llm_router, litellm.Router): diff --git a/litellm/proxy/video_endpoints/endpoints.py b/litellm/proxy/video_endpoints/endpoints.py index d985a546fa7..66071c05b4f 100644 --- a/litellm/proxy/video_endpoints/endpoints.py +++ b/litellm/proxy/video_endpoints/endpoints.py @@ -1,6 +1,6 @@ #### Video Endpoints ##### -from typing import Any, Final +from typing import Final from fastapi import APIRouter, Depends, File, Form, Request, Response, UploadFile from fastapi.responses import ORJSONResponse @@ -161,7 +161,7 @@ async def video_list( # Read query parameters query_params: Final = dict(request.query_params) - data: Final[dict[str, Any]] = {"query_params": query_params} + data: Final[dict[str, object]] = {"query_params": query_params} # Extract custom_llm_provider from headers, query params, or body custom_llm_provider: Final = ( @@ -246,7 +246,7 @@ async def video_status( ) # Create data with video_id - data: Final[dict[str, Any]] = {"video_id": video_id} + data: Final[dict[str, object]] = {"video_id": video_id} decoded: Final = decode_video_id_with_provider(video_id) provider_from_id: Final = decoded.get("custom_llm_provider") @@ -345,7 +345,7 @@ async def video_content( ) # Create data with video_id - data: Final[dict[str, Any]] = {"video_id": video_id} + data: Final[dict[str, object]] = {"video_id": video_id} decoded: Final = decode_video_id_with_provider(video_id) provider_from_id: Final = decoded.get("custom_llm_provider") @@ -653,7 +653,7 @@ async def video_get_character( ) original_requested_character_id: Final = character_id - data: Final[dict[str, Any]] = {"character_id": character_id} + data: Final[dict[str, object]] = {"character_id": character_id} decoded: Final = decode_character_id_with_provider(character_id) provider_from_id: Final = decoded.get("custom_llm_provider") diff --git a/litellm/rag/main.py b/litellm/rag/main.py index 2e940a1613a..bd6788b3a1b 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -30,6 +30,7 @@ from litellm.rag.ingestion.openai_ingestion import OpenAIRAGIngestion from litellm.rag.ingestion.s3_vectors_ingestion import S3VectorsRAGIngestion from litellm.rag.ingestion.vertex_ai_ingestion import VertexAIRAGIngestion from litellm.rag.rag_query import RAGQuery +from litellm.types.llms.openai import AllMessageValues from litellm.types.rag import ( RAGIngestOptions, RAGIngestResponse, @@ -213,7 +214,7 @@ def _suppressed_sub_call_billing() -> Iterator[None]: async def _execute_query_pipeline( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, @@ -327,7 +328,7 @@ async def _execute_query_pipeline( @client async def aquery( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, @@ -374,12 +375,12 @@ async def aquery( @client def query( model: str, - messages: list[Any], + messages: list[AllMessageValues], retrieval_config: dict[str, Any], rerank: dict[str, Any] | None = None, stream: bool = False, **kwargs, -) -> ModelResponse | Coroutine[Any, Any, ModelResponse]: +) -> ModelResponse | Coroutine[None, None, ModelResponse]: """ Query a RAG pipeline. """ @@ -426,7 +427,7 @@ def ingest( file_id: str | None = None, timeout: float | httpx.Timeout | None = None, **kwargs, -) -> RAGIngestResponse | Coroutine[Any, Any, RAGIngestResponse]: +) -> RAGIngestResponse | Coroutine[None, None, RAGIngestResponse]: """ Ingest a document into a vector store. diff --git a/litellm/responses/mcp/litellm_proxy_mcp_handler.py b/litellm/responses/mcp/litellm_proxy_mcp_handler.py index 197d0c02ba8..367915156d1 100644 --- a/litellm/responses/mcp/litellm_proxy_mcp_handler.py +++ b/litellm/responses/mcp/litellm_proxy_mcp_handler.py @@ -399,7 +399,7 @@ class LiteLLM_Proxy_MCP_Handler: @staticmethod async def _process_mcp_tools_without_openai_transform( - user_api_key_auth: Any, + user_api_key_auth: "UserAPIKeyAuth | None", mcp_tools_with_litellm_proxy: Sequence[Mapping[str, object]], litellm_trace_id: str | None = None, mcp_auth_header: str | None = None, @@ -636,7 +636,7 @@ class LiteLLM_Proxy_MCP_Handler: async def _execute_tool_calls( tool_server_map: dict[str, str], tool_calls: Sequence[object], - user_api_key_auth: Any, + user_api_key_auth: "UserAPIKeyAuth | None", mcp_auth_header: str | None = None, mcp_server_auth_headers: dict[str, dict[str, str]] | None = None, oauth2_headers: dict[str, str] | None = None, diff --git a/litellm/router_strategy/budget_limiter.py b/litellm/router_strategy/budget_limiter.py index d57d7da0410..a8d51f95e45 100644 --- a/litellm/router_strategy/budget_limiter.py +++ b/litellm/router_strategy/budget_limiter.py @@ -20,6 +20,7 @@ anthropic: import asyncio import builtins +from collections.abc import Mapping from datetime import datetime, timedelta, timezone from typing import Any, Final @@ -54,19 +55,19 @@ class _LiteLLMParamsDictView: __slots__ = ("_params",) - def __init__(self, params: dict[str, Any]): + def __init__(self, params: Mapping[str, object]): self._params = params - def __getattr__(self, key: str) -> Any: + def __getattr__(self, key: str) -> object: return self._params.get(key) - def __getitem__(self, key: str) -> Any: + def __getitem__(self, key: str) -> object: return self._params.get(key) def __contains__(self, key: str) -> bool: return key in self._params - def get(self, key: str, default: Any = None) -> Any: + def get(self, key: str, default: object = None) -> object: return self._params.get(key, default) def keys(self): @@ -84,10 +85,10 @@ class _LiteLLMParamsDictView: def __len__(self) -> int: return len(self._params) - def dict(self) -> dict[str, Any]: + def dict(self) -> builtins.dict[str, object]: return dict(self._params) - def model_dump(self) -> builtins.dict[str, Any]: + def model_dump(self) -> builtins.dict[str, object]: return dict(self._params) diff --git a/litellm/router_strategy/complexity_router/complexity_router.py b/litellm/router_strategy/complexity_router/complexity_router.py index be7653902a7..577cee0920d 100644 --- a/litellm/router_strategy/complexity_router/complexity_router.py +++ b/litellm/router_strategy/complexity_router/complexity_router.py @@ -282,7 +282,7 @@ def _response_cost_or_none(response: ModelResponse) -> float | None: return float(cost) -def _effective_turn_off_message_logging(request_kwargs: Mapping[str, Any] | None) -> bool | None: +def _effective_turn_off_message_logging(request_kwargs: Mapping[str, object] | None) -> bool | None: from litellm.litellm_core_utils.initialize_dynamic_callback_params import ( initialize_standard_callback_dynamic_params, ) @@ -1925,7 +1925,7 @@ class ComplexityRouter(CustomLogger): ceiling_severity: Final = self._active_tier_severity(hard_ceiling) if hard_ceiling is not None else None best_model: str | None = None best_score = float("-inf") - candidate_scores: Final[list[dict[str, Any]]] = [] + candidate_scores: Final[list[dict[str, object]]] = [] for model in candidates: if floor_severity is not None and all( self._active_tier_severity(model_tier) < floor_severity diff --git a/litellm/secret_managers/hashicorp_secret_manager.py b/litellm/secret_managers/hashicorp_secret_manager.py index e2662d96b52..8f677b54700 100644 --- a/litellm/secret_managers/hashicorp_secret_manager.py +++ b/litellm/secret_managers/hashicorp_secret_manager.py @@ -1,7 +1,9 @@ import os -from typing import Any, Final +from collections.abc import Mapping +from typing import Final, Protocol import httpx +from typing_extensions import ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger @@ -17,6 +19,72 @@ from litellm.proxy._types import KeyManagementSystem from .base_secret_manager import BaseSecretManager, raise_if_unsafe_secret_name +class _VaultAuthData(TypedDict): + """The ``auth`` block Vault returns from a login endpoint.""" + + client_token: ReadOnly[str] + lease_duration: ReadOnly[int] + + +class _VaultLoginResponse(TypedDict): + """Body of a Vault ``/v1/auth/.../login`` response.""" + + auth: ReadOnly[_VaultAuthData] + + +class _VaultSecretTarget(TypedDict): + """Resolved coordinates of one Vault KV v2 secret.""" + + url: ReadOnly[str] + data_key: ReadOnly[str] + secret_name: ReadOnly[str] + + +class _VaultSecretDataBlock(TypedDict, total=False): + """The inner ``data`` block of a Vault KV v2 read body.""" + + data: ReadOnly[Mapping[str, object]] + + +class _VaultSecretReadResponse(TypedDict, total=False): + """Body of a Vault KV v2 secret read, narrowed to the nesting this module walks.""" + + data: ReadOnly[_VaultSecretDataBlock] + + +class _VaultLoginResponseSource(Protocol): + """A Vault login call's HTTP response, read for the auth block it carries.""" + + def json(self) -> _VaultLoginResponse: ... + + +class _VaultSecretReadSource(Protocol): + """A Vault KV v2 read response, read for the nested secret data it carries.""" + + def json(self) -> _VaultSecretReadResponse: ... + + +class _JsonObjectSource(Protocol): + """A Vault response whose body is a JSON object nothing further is assumed about.""" + + def json(self) -> dict[str, object]: ... + + +def _vault_login_body(response: _VaultLoginResponseSource) -> _VaultLoginResponse: + """Decode the body of a Vault login response.""" + return response.json() + + +def _vault_secret_read_body(response: _VaultSecretReadSource) -> _VaultSecretReadResponse: + """Decode the body of a Vault KV v2 secret read response.""" + return response.json() + + +def _json_object_body(response: _JsonObjectSource) -> dict[str, object]: + """Decode a Vault response body as a plain JSON object.""" + return response.json() + + class HashicorpSecretManager(BaseSecretManager): def __init__(self): from litellm.proxy.proxy_server import CommonProxyErrors, premium_user @@ -130,7 +198,8 @@ class HashicorpSecretManager(BaseSecretManager): ) resp.raise_for_status() - auth_data: Final = resp.json()["auth"] + login_response: Final = _vault_login_body(resp) + auth_data: Final = login_response["auth"] token: Final = auth_data["client_token"] _lease_duration: Final = auth_data["lease_duration"] @@ -191,8 +260,10 @@ class HashicorpSecretManager(BaseSecretManager): json=self._get_tls_cert_auth_body(), ) resp.raise_for_status() - token: Final = resp.json()["auth"]["client_token"] - _lease_duration: Final = resp.json()["auth"]["lease_duration"] + token_response: Final = _vault_login_body(resp) + token: Final = token_response["auth"]["client_token"] + lease_response: Final = _vault_login_body(resp) + _lease_duration: Final = lease_response["auth"]["lease_duration"] verbose_logger.debug("Successfully obtained Vault token via TLS cert auth.") self.cache.set_cache(key="hcp_vault_token", value=token, ttl=_lease_duration) return token @@ -205,9 +276,9 @@ class HashicorpSecretManager(BaseSecretManager): def get_url( self, secret_name: str, - namespace: str | None = None, - mount_name: str | None = None, - path_prefix: str | None = None, + namespace: object = None, + mount_name: object = None, + path_prefix: object = None, ) -> str: """ Constructs the Vault URL for KV v2 secrets. @@ -238,7 +309,7 @@ class HashicorpSecretManager(BaseSecretManager): _url += secret_name return _url - def _sanitize_plain_value(self, value: str | int | None) -> str | None: + def _sanitize_plain_value(self, value: object) -> str | None: if value is None: return None value_str: Final = str(value).strip() @@ -246,23 +317,23 @@ class HashicorpSecretManager(BaseSecretManager): return None return value_str - def _sanitize_path_component(self, value: str | int | None) -> str | None: + def _sanitize_path_component(self, value: object) -> str | None: sanitized_value = self._sanitize_plain_value(value) if sanitized_value is None: return None sanitized_value = sanitized_value.strip("/") return sanitized_value or None - def _extract_secret_manager_settings(self, optional_params: dict | None) -> dict[str, Any]: + def _extract_secret_manager_settings(self, optional_params: dict | None) -> dict[str, object]: if not isinstance(optional_params, dict): return {} candidate: Final = optional_params.get("secret_manager_settings") - source: Final = candidate if isinstance(candidate, dict) else optional_params + source: Final[Mapping[str, object]] = candidate if isinstance(candidate, dict) else optional_params allowed_keys: Final = {"namespace", "mount", "path_prefix", "data"} return {k: source[k] for k in allowed_keys if k in source} - def _build_secret_target(self, secret_name: str, optional_params: dict | None) -> dict[str, Any]: + def _build_secret_target(self, secret_name: str, optional_params: dict | None) -> _VaultSecretTarget: settings: Final = self._extract_secret_manager_settings(optional_params) namespace: Final = settings.get("namespace", self.vault_namespace) @@ -331,7 +402,7 @@ class HashicorpSecretManager(BaseSecretManager): response.raise_for_status() # For KV v2, the secret is in response.json()["data"]["data"] - json_resp: Final = response.json() + json_resp: Final = _json_object_body(response) _value: Final = self._get_secret_value_from_json_response(json_resp) self.cache.set_cache(secret_name, _value) return _value @@ -362,7 +433,7 @@ class HashicorpSecretManager(BaseSecretManager): response.raise_for_status() # For KV v2, the secret is in response.json()["data"]["data"] - json_resp: Final = response.json() + json_resp: Final = _json_object_body(response) _value: Final = self._get_secret_value_from_json_response(json_resp) self.cache.set_cache(secret_name, _value) return _value @@ -379,7 +450,7 @@ class HashicorpSecretManager(BaseSecretManager): optional_params: dict | None = None, timeout: float | httpx.Timeout | None = None, tags: dict | list | None = None, - ) -> dict[str, Any]: + ) -> dict[str, object]: """ Writes a secret to Vault KV v2 using an async HTTPX client. @@ -413,7 +484,7 @@ class HashicorpSecretManager(BaseSecretManager): json=data, ) response.raise_for_status() - return response.json() + return _json_object_body(response) except Exception as e: verbose_logger.exception("Error writing secret to Hashicorp Vault: %s", e) return {"status": "error", "message": str(e)} @@ -500,7 +571,7 @@ class HashicorpSecretManager(BaseSecretManager): headers=self._get_request_headers(), ) response.raise_for_status() - json_resp: Final = response.json() + json_resp: Final = _vault_secret_read_body(response) # Use data_key from target to get the correct value data_key: Final = new_target["data_key"] new_secret_value_from_vault: Final = json_resp.get("data", {}).get("data", {}).get(data_key, None) diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index fcade835cce..32d88da0085 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -327,6 +327,10 @@ class BatchGuardrailReport(BaseModel): """Every record that was redacted or dropped, in file order.""" +_JsonValue: TypeAlias = object +"""Alias for ``object``, usable inside model bodies that declare a field named ``object``.""" + + BATCH_GUARDRAIL_RESPONSE_FIELD: Final = "litellm_batch_guardrail" @@ -1191,7 +1195,7 @@ class ShellToolParam(TypedDict, total=False): type: Required[Literal["shell"] | str] """The type of tool. Use ``\"shell\"``.""" - environment: Required[dict[str, Any]] + environment: Required[dict[str, object]] """Environment config: ``type`` (e.g. ``\"container_auto\"``, ``\"container_reference\"``, ``\"local\"``), optional ``container_id``, ``network_policy``, ``domain_secrets``, ``skills``.""" @@ -1308,7 +1312,7 @@ class ResponseAPIUsage(BaseLiteLLMOpenAIResponseObject): @field_validator("cost", mode="before") @classmethod - def parse_cost(cls, v: Any) -> float | None: + def parse_cost(cls, v: object) -> object: """Normalise cost: accept either a float or a dict with a ``total_cost`` key.""" if isinstance(v, dict): return v.get("total_cost") @@ -1805,7 +1809,7 @@ class ErrorEventError(BaseLiteLLMOpenAIResponseObject): type: str # e.g., 'invalid_request_error' code: str # e.g., 'context_length_exceeded' message: str - param: str | dict[str, Any] | None = None + param: str | dict[str, object] | None = None class ErrorEvent(BaseLiteLLMOpenAIResponseObject): @@ -2418,7 +2422,7 @@ class OpenAIVideoObject(BaseModel): expires_at: int | None = None """Unix timestamp (seconds) for when the downloadable assets expire, if set.""" - error: dict[str, Any] | None = None + error: dict[str, _JsonValue] | None = None """Error payload that explains why generation failed, if applicable.""" progress: int | None = None @@ -2436,15 +2440,15 @@ class OpenAIVideoObject(BaseModel): model: str | None = None """The video generation model that produced the job.""" - _hidden_params: dict[str, Any] = {} + _hidden_params: dict[str, _JsonValue] = {} def __contains__(self, key) -> bool: return hasattr(self, key) - def get(self, key, default=None): + def get(self, key, default=None) -> _JsonValue: return getattr(self, key, default) - def __getitem__(self, key): + def __getitem__(self, key) -> _JsonValue: return getattr(self, key) def json(self, **kwargs): diff --git a/litellm/types/router.py b/litellm/types/router.py index ab6c807ba20..e0957383aac 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -369,7 +369,7 @@ class GenericLiteLLMParams(CredentialLiteLLMParams, CustomPricingLiteLLMParams): @model_validator(mode="before") @classmethod - def preprocess_input_data(cls, data: Any) -> Any: + def preprocess_input_data(cls, data: object) -> object: """ Pre-process input data before validation: 1. Filter out reserved Python keywords ('self', 'params', '__class__') to prevent @@ -627,6 +627,11 @@ class AlertingConfig(BaseModel): alerting_threshold: float | None = 300 +def _resolved_annotations(model_class: type[object]) -> Mapping[str, object]: + """Resolve a class's annotations, keeping each resolved annotation opaque.""" + return get_type_hints(model_class) + + class ModelGroupInfo(BaseModel): model_group: str providers: list[str] @@ -655,7 +660,7 @@ class ModelGroupInfo(BaseModel): configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None def __init__(self, **data) -> None: - for field_name, field_type in get_type_hints(self.__class__).items(): + for field_name, field_type in _resolved_annotations(self.__class__).items(): if field_type is bool and data.get(field_name) is None: data[field_name] = False super().__init__(**data) diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 55a32989b1c..addb7b730de 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -3771,6 +3771,8 @@ class LlmProviders(str, Enum): CODESTRAL = "codestral" TEXT_COMPLETION_CODESTRAL = "text-completion-codestral" DASHSCOPE = "dashscope" + QWENCLOUD = "qwencloud" + QWEN_AI_PLATFORM = "qwen_ai_platform" MODELSCOPE = "modelscope" MOONSHOT = "moonshot" PUBLICAI = "publicai" diff --git a/litellm/utils.py b/litellm/utils.py index b130dd45e89..028682661f7 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -6586,11 +6586,11 @@ def validate_environment( keys_in_environment = True else: missing_keys.append("WANDB_API_KEY") - elif custom_llm_provider == "dashscope": - if "DASHSCOPE_API_KEY" in os.environ: + elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"): + if f"{custom_llm_provider.upper()}_API_KEY" in os.environ or "DASHSCOPE_API_KEY" in os.environ: keys_in_environment = True else: - missing_keys.append("DASHSCOPE_API_KEY") + missing_keys.append(f"{custom_llm_provider.upper()}_API_KEY") elif custom_llm_provider == "modelscope": if "MODELSCOPE_API_KEY" in os.environ: keys_in_environment = True @@ -8152,6 +8152,11 @@ class ProviderConfigManager: LlmProviders.NEBIUS: (lambda: litellm.NebiusConfig(), False), LlmProviders.WANDB: (lambda: litellm.WandbConfig(), False), LlmProviders.DASHSCOPE: (lambda: litellm.DashScopeChatConfig(), False), + LlmProviders.QWENCLOUD: (lambda: litellm.QwenCloudChatConfig(), False), + LlmProviders.QWEN_AI_PLATFORM: ( + lambda: litellm.QwenAIPlatformChatConfig(), + False, + ), LlmProviders.MODELSCOPE: (lambda: litellm.ModelScopeChatConfig(), False), LlmProviders.MOONSHOT: (lambda: litellm.MoonshotChatConfig(), False), LlmProviders.DOCKER_MODEL_RUNNER: ( @@ -8366,12 +8371,16 @@ class ProviderConfigManager: ) return VolcEngineEmbeddingConfig() - elif litellm.LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.embed.transformation import ( - DashScopeEmbeddingConfig, + elif provider in ( + litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_embedding_config, ) - return DashScopeEmbeddingConfig() + return get_dashscope_family_embedding_config(provider.value) elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() elif litellm.LlmProviders.SNOWFLAKE == provider: @@ -8444,12 +8453,16 @@ class ProviderConfigManager: return litellm.VoyageRerankConfig() elif litellm.LlmProviders.WATSONX == provider: return litellm.IBMWatsonXRerankConfig() - elif litellm.LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.rerank.transformation import ( - DashScopeRerankConfig, + elif provider in ( + litellm.LlmProviders.DASHSCOPE, + litellm.LlmProviders.QWENCLOUD, + litellm.LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_rerank_config, ) - return DashScopeRerankConfig() + return get_dashscope_family_rerank_config(provider.value) return litellm.CohereRerankConfig() @staticmethod @@ -9122,12 +9135,16 @@ class ProviderConfigManager: ) return get_openrouter_image_generation_config(model) - elif LlmProviders.DASHSCOPE == provider: - from litellm.llms.dashscope.image_generation import ( - get_dashscope_image_generation_config, + elif provider in ( + LlmProviders.DASHSCOPE, + LlmProviders.QWENCLOUD, + LlmProviders.QWEN_AI_PLATFORM, + ): + from litellm.llms.dashscope.common_utils import ( + get_dashscope_family_image_generation_config, ) - return get_dashscope_image_generation_config(model) + return get_dashscope_family_image_generation_config(provider.value) elif LlmProviders.MODELSCOPE == provider: from litellm.llms.modelscope.image_generation import ( get_modelscope_image_generation_config, diff --git a/litellm/vector_stores/vector_store_registry.py b/litellm/vector_stores/vector_store_registry.py index 22d27bc3266..b71d6784873 100644 --- a/litellm/vector_stores/vector_store_registry.py +++ b/litellm/vector_stores/vector_store_registry.py @@ -112,7 +112,9 @@ class VectorStoreRegistry: Dynamically extracts all parameters defined in VECTOR_STORE_OPENAI_PARAMS. """ # Get the list of supported param names from the Literal type - supported_params: Final = get_args(VECTOR_STORE_OPENAI_PARAMS) + supported_params: Final = tuple( + param for param in get_args(VECTOR_STORE_OPENAI_PARAMS) if isinstance(param, str) + ) # Extract only the params that exist in the tool kwargs: Final = {param: tool.get(param) for param in supported_params if param in tool} @@ -503,7 +505,7 @@ class VectorStoreRegistry: vector_stores_from_db.append(_litellm_managed_vector_store) return vector_stores_from_db - def get_credentials_for_vector_store(self, vector_store_id: str) -> dict[str, Any]: + def get_credentials_for_vector_store(self, vector_store_id: str) -> dict[str, object]: """ Get the credentials for a vector store diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 5ca956c766d..27ff525c15e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -1592,7 +1592,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1628,7 +1628,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1664,7 +1664,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1700,7 +1700,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1736,7 +1736,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -1772,7 +1772,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, @@ -2065,7 +2065,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2102,7 +2102,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2139,7 +2139,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2176,7 +2176,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2213,7 +2213,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -2250,7 +2250,7 @@ "supports_tool_choice": true, "supports_vision": true, "supports_xhigh_reasoning_effort": true, - "supports_native_structured_output": true, + "supports_native_structured_output": false, "supports_max_reasoning_effort": true, "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", @@ -14740,6 +14740,1910 @@ "/v1/images/generations" ] }, + "qwencloud/deepseek-v4-flash": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-flash-0731": { + "cache_read_input_token_cost": 4e-08, + "input_cost_per_token": 2e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/deepseek-v4-pro": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 2.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 393216, + "max_tokens": 393216, + "mode": "chat", + "output_cost_per_token": 4.8e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/glm-5.1": { + "cache_read_input_token_cost": 2.6e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 202745, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/glm-5.2": { + "cache_read_input_token_cost": 2.8e-07, + "input_cost_per_token": 1.4e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4.4e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwencloud/kimi-k2.7-code": { + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 9.5e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 229376, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 4e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "qwencloud/qwen-coder": { + "input_cost_per_token": 3e-07, + "litellm_provider": "qwencloud", + "max_input_tokens": 1000000, + "max_output_tokens": 16384, + "max_tokens": 16384, + "mode": "chat", + "output_cost_per_token": 1.5e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "qwencloud/qwen-flash": { + "litellm_provider": "qwencloud", + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "tiered_pricing": [ + { + "input_cost_per_token": 5e-08, + "output_cost_per_token": 4e-07, + "range": [ + 0, + 256000.0 + ] + }, + { + "input_cost_per_token": 2.5e-07, + "output_cost_per_token": 2e-06, + "range": [ + 256000.0, + 1000000.0 + ] + } + ] + }, + "qwencloud/qwen-flash-2025-07-28": { + "litellm_provider": "qwencloud", + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "tiered_pricing": [ + { + "input_cost_per_token": 5e-08, + "output_cost_per_token": 4e-07, + "range": [ + 0, + 256000.0 + ] + }, + { + "input_cost_per_token": 2.5e-07, + "output_cost_per_token": 2e-06, + "range": [ + 256000.0, + 1000000.0 + ] + } + ] + }, + "qwencloud/qwen-max": { + "input_cost_per_token": 1.6e-06, + "litellm_provider": "qwencloud", + "max_input_tokens": 30720, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 6.4e-06, + "source": "https://www.qwencloud.com/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "qwencloud/qwen-plus": { + 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"qwen_ai_platform/qwen3-vl-32b-thinking": { + "input_cost_per_token": 1.6e-07, + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 2.87e-06, + "source": "https://www.alibabacloud.com/help/en/model-studio/model-pricing", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "qwen_ai_platform/qwen3-vl-plus": { + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 260096, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true, + "tiered_pricing": [ + { + "input_cost_per_token": 2e-07, + "output_cost_per_token": 1.6e-06, + "range": [ + 0, + 32000.0 + ] + }, + { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 2.4e-06, + "range": [ + 32000.0, + 128000.0 + ] + }, + { + "input_cost_per_token": 6e-07, + "output_cost_per_token": 4.8e-06, + "range": [ + 128000.0, + 256000.0 + ] + } + ] + }, + "qwen_ai_platform/qwen3.5-plus": { + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 991808, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true, + "tiered_pricing": [ + { + "input_cost_per_token": 4e-07, + "output_cost_per_token": 2.4e-06, + "range": [ + 0, + 256000.0 + ] + }, + { + "input_cost_per_token": 5e-07, + "output_cost_per_token": 3e-06, + "range": [ + 256000.0, + 1000000.0 + ] + } + ] + }, + "qwen_ai_platform/qwen3.7-max": { + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 2.5e-06, + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 991808, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 7.5e-06, + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "qwen_ai_platform/qwen3.7-plus": { + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 991808, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tiered_pricing": [ + { + "cache_read_input_token_cost": 8e-08, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.6e-06, + "range": [ + 0, + 256000.0 + ] + }, + { + "cache_read_input_token_cost": 2.4e-07, + "input_cost_per_token": 1.2e-06, + "output_cost_per_token": 4.8e-06, + "range": [ + 256000.0, + 1000000.0 + ] + } + ] + }, + "qwen_ai_platform/qwen3.8-max": { + "cache_read_input_token_cost": 2.5e-07, + "input_cost_per_token": 2e-06, + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 991808, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 6e-06, + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "qwen_ai_platform/qwq-plus": { + "input_cost_per_token": 8e-07, + "litellm_provider": "qwen_ai_platform", + "max_input_tokens": 98304, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.4e-06, + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "qwen_ai_platform/qwen-image-2.0": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-2.0-pro": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-3.0": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "qwen_ai_platform/qwen-image-3.0-pro": { + "litellm_provider": "qwen_ai_platform", + "mode": "image_generation", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, "databricks/databricks-bge-large-en": { "cache_creation_input_token_cost": 1.0003e-07, "cache_read_input_token_cost": 1.0003e-07, diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json index 7c7d508856f..ebc220b3496 100644 --- a/provider_endpoints_support.json +++ b/provider_endpoints_support.json @@ -724,6 +724,42 @@ "interactions": true } }, + "qwencloud": { + "display_name": "QwenCloud (`qwencloud`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, + "qwen_ai_platform": { + "display_name": "Qwen AI Platform (`qwen_ai_platform`)", + "url": "https://docs.litellm.ai/docs/providers/qwencloud", + "endpoints": { + "chat_completions": true, + "messages": true, + "responses": true, + "embeddings": true, + "image_generations": true, + "audio_transcriptions": false, + "audio_speech": false, + "moderations": false, + "batches": false, + "rerank": true, + "a2a": true, + "interactions": true + } + }, "databricks": { "display_name": "Databricks (`databricks`)", "url": "https://docs.litellm.ai/docs/providers/databricks", diff --git a/ruff-strict-budget.json b/ruff-strict-budget.json index 0b64051f9d2..9b1cc977a64 100644 --- a/ruff-strict-budget.json +++ b/ruff-strict-budget.json @@ -1,6 +1,6 @@ { "ANN001": { - "limit": 2991 + "limit": 2985 }, "ANN002": { "limit": 71 @@ -12,10 +12,10 @@ "limit": 2001 }, "ANN202": { - "limit": 841 + "limit": 835 }, "ANN204": { - "limit": 694 + "limit": 693 }, "ANN205": { "limit": 112 @@ -24,7 +24,7 @@ "limit": 133 }, "ANN401": { - "limit": 387 + "limit": 307 }, "ASYNC230": { "limit": 11 @@ -231,7 +231,7 @@ "limit": 5 }, "TID251": { - "limit": 1084 + "limit": 1073 }, "TRY002": { "limit": 524 @@ -246,7 +246,7 @@ "limit": 111 }, "TRY300": { - "limit": 855 + "limit": 854 }, "UP028": { "limit": 2 diff --git a/tests/e2e/ui/tests/budgets/budgets.spec.ts b/tests/e2e/ui/tests/budgets/budgets.spec.ts new file mode 100644 index 00000000000..1ad1e488d25 --- /dev/null +++ b/tests/e2e/ui/tests/budgets/budgets.spec.ts @@ -0,0 +1,133 @@ +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; +import { ADMIN_STORAGE_PATH } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation"; +import { masterKey } from "../../helpers/traffic"; + +interface StoredBudget { + budget_id: string; + max_budget: number | null; + tpm_limit: number | null; + rpm_limit: number | null; + budget_duration: string | null; +} + +/** A different route from the one the table renders from, so a row that only lives in its cache fails here. */ +async function findBudget(page: PlaywrightPage, budgetId: string): Promise { + const res = await page.request.get("/budget/list", { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(res.ok(), `GET /budget/list (${res.status()})`).toBe(true); + return ((await res.json()) as StoredBudget[]).find((row) => row.budget_id === budgetId); +} + +async function createBudgetViaApi(page: PlaywrightPage, budget: Partial): Promise { + const res = await page.request.post("/budget/new", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: budget, + }); + expect(res.ok(), `POST /budget/new failed (${res.status()}): ${await res.text()}`).toBe(true); +} + +async function searchForBudget(page: PlaywrightPage, budgetId: string): Promise { + await page.getByPlaceholder("Search by budget ID").fill(budgetId); +} + +test.describe("Budgets", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Create a budget with rate limits and a spend cap", async ({ page }) => { + const budgetId = `e2e-budget-create-${Date.now()}`; + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await page.getByRole("button", { name: "Create Budget" }).click(); + + const modal = page.getByRole("dialog", { name: "Create Budget" }); + await expect(modal).toBeVisible({ timeout: 10_000 }); + + await modal.getByRole("textbox", { name: "Budget ID" }).fill(budgetId); + await modal.getByRole("spinbutton", { name: "Max Tokens per minute" }).fill("5000"); + await modal.getByRole("spinbutton", { name: "Max Requests per minute" }).fill("60"); + + await modal.getByRole("button", { name: "Optional Settings" }).click(); + await modal.getByRole("spinbutton", { name: "Max Budget (USD)" }).fill("25.5"); + await modal.getByRole("combobox", { name: "Reset Budget" }).click(); + await page.getByRole("option", { name: "weekly" }).click(); + + await modal.getByRole("button", { name: "Create Budget" }).click(); + await expect(modal).not.toBeVisible({ timeout: 10_000 }); + + await searchForBudget(page, budgetId); + const row = page.getByRole("row").filter({ hasText: budgetId }); + await expect(row).toBeVisible({ timeout: 10_000 }); + await expect(row).toContainText("$25.50"); + + const stored = await findBudget(page, budgetId); + expect(stored, `budget ${budgetId} readable from /budget/list`).toBeTruthy(); + expect(stored?.max_budget, "spend cap persisted").toBe(25.5); + expect(stored?.tpm_limit, "TPM limit persisted").toBe(5000); + expect(stored?.rpm_limit, "RPM limit persisted").toBe(60); + expect(stored?.budget_duration, "reset window persisted").toBe("7d"); + }); + + test("Raising a budget's spend cap leaves its rate limits alone", async ({ page }) => { + const budgetId = `e2e-budget-edit-${Date.now()}`; + await createBudgetViaApi(page, { budget_id: budgetId, max_budget: 10, tpm_limit: 1000, rpm_limit: 20 }); + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await searchForBudget(page, budgetId); + await expect(page.getByRole("row").filter({ hasText: budgetId })).toBeVisible({ timeout: 10_000 }); + + await page.getByTestId(`budget-actions-${budgetId}`).click(); + await page.getByTestId("budget-action-edit").click(); + + const modal = page.getByRole("dialog", { name: "Edit Budget" }); + await expect(modal).toBeVisible({ timeout: 10_000 }); + + await modal.getByRole("button", { name: "Optional Settings" }).click(); + await modal.getByRole("spinbutton", { name: "Max Budget (USD)" }).fill("99"); + await modal.getByRole("button", { name: "Save", exact: true }).click(); + await expect(modal).not.toBeVisible({ timeout: 10_000 }); + + await expect(page.getByRole("row").filter({ hasText: budgetId })).toContainText("$99.00", { timeout: 10_000 }); + + // Not hypothetical: the edit form posts the whole budget, so a field it fails to + // seed from the existing row goes to the server as null and silently clears. + const stored = await findBudget(page, budgetId); + expect(stored?.max_budget, "spend cap raised").toBe(99); + expect(stored?.tpm_limit, "TPM limit untouched by a spend-cap edit").toBe(1000); + expect(stored?.rpm_limit, "RPM limit untouched by a spend-cap edit").toBe(20); + }); + + test("Delete a budget", async ({ page }) => { + const budgetId = `e2e-budget-delete-${Date.now()}`; + await createBudgetViaApi(page, { budget_id: budgetId, max_budget: 5 }); + + await navigateToPage(page, Page.Budgets); + await dismissFeedbackPopup(page); + + await searchForBudget(page, budgetId); + await expect(page.getByRole("row").filter({ hasText: budgetId })).toBeVisible({ timeout: 10_000 }); + + await page.getByTestId(`budget-actions-${budgetId}`).click(); + await page.getByTestId("budget-action-delete").click(); + + const modal = page.getByRole("dialog", { name: "Delete Budget?" }); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByRole("row").filter({ hasText: budgetId })).toHaveCount(0, { timeout: 10_000 }); + + // The row disappearing is a cache invalidation; the budget is gone when the route stops serving it. + await expect + .poll(async () => await findBudget(page, budgetId), { + message: `budget ${budgetId} still readable from /budget/list after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + }); +}); diff --git a/tests/e2e/ui/tests/guardrails/guardrails.spec.ts b/tests/e2e/ui/tests/guardrails/guardrails.spec.ts index 77ff020510b..1e43c7a2b22 100644 --- a/tests/e2e/ui/tests/guardrails/guardrails.spec.ts +++ b/tests/e2e/ui/tests/guardrails/guardrails.spec.ts @@ -1,11 +1,213 @@ -import { test, expect } from "@playwright/test"; +import { test, expect, type Page as PlaywrightPage } from "@playwright/test"; import { ADMIN_STORAGE_PATH, E2E_TEAM_NO_ADMIN_ID } from "../../constants"; import { Page } from "../../fixtures/pages"; import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; +import { CHAT_MODEL_A, MOCK_RESPONSE_TEXT, masterKey } from "../../helpers/traffic"; + +interface StoredGuardrail { + guardrail_id: string; + guardrail_name: string | null; +} + +async function listGuardrails(page: PlaywrightPage): Promise { + const res = await page.request.get("/v2/guardrails/list", { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(res.ok(), `GET /v2/guardrails/list (${res.status()})`).toBe(true); + return ((await res.json()) as { guardrails: StoredGuardrail[] }).guardrails; +} + +async function findGuardrail(page: PlaywrightPage, name: string): Promise { + return (await listGuardrails(page)).find((row) => row.guardrail_name === name); +} + +const createdGuardrails: string[] = []; + +async function createKeywordGuardrailViaApi(page: PlaywrightPage, name: string, keyword: string): Promise { + const res = await page.request.post("/guardrails", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + guardrail: { + guardrail_name: name, + litellm_params: { + guardrail: "litellm_content_filter", + mode: "pre_call", + default_on: false, + blocked_words: [{ keyword, action: "BLOCK" }], + }, + }, + }, + }); + expect(res.ok(), `POST /guardrails failed (${res.status()}): ${await res.text()}`).toBe(true); + createdGuardrails.push(name); + const guardrail = await findGuardrail(page, name); + expect(guardrail?.guardrail_id, `guardrail ${name} has an id`).toBeTruthy(); + return guardrail!.guardrail_id; +} + +async function openKeywordsStep(page: PlaywrightPage, name: string) { + await page.getByRole("button", { name: "Add New Guardrail" }).click(); + await page.getByRole("menuitem", { name: "Add Provider Guardrail" }).click(); + + const wizard = page.getByRole("dialog", { name: "Create guardrail" }); + await expect(wizard).toBeVisible({ timeout: 10_000 }); + + await wizard.getByRole("textbox", { name: "Guardrail Name" }).fill(name); + await wizard.getByRole("combobox", { name: "Guardrail Provider" }).click(); + // The content filter runs inside the proxy, so this is the one provider a test can + // configure end to end without standing up a third-party moderation service. + await page.getByRole("option", { name: /LiteLLM Content Filter/ }).click(); + + for (const step of ["Topics", "Patterns", "Keywords"]) { + await wizard.getByRole("button", { name: "Next" }).click(); + await expect(wizard).toContainText(step, { timeout: 10_000 }); + } + return wizard; +} test.describe("Guardrails", () => { test.use({ storageState: ADMIN_STORAGE_PATH }); + test.afterEach(async ({ page }) => { + // Guardrails live in the database and show up in the table and the playground list, so a run + // that leaves them behind changes what the next run sees. + for (const name of createdGuardrails.splice(0)) { + const guardrail = await findGuardrail(page, name); + if (guardrail) { + const deleted = await page.request.delete(`/guardrails/${guardrail.guardrail_id}`, { + headers: { Authorization: `Bearer ${masterKey()}` }, + }); + expect(deleted.ok(), `DELETE /guardrails/${guardrail.guardrail_id} (${deleted.status()})`).toBe(true); + } + } + }); + + test("A guardrail created through the wizard blocks the keyword it was given", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-create-${stamp}`; + // Unique per run so a concurrent test's prompt can never trip this guardrail, or vice versa. + const bannedKeyword = `e2ebanned${stamp}`; + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + createdGuardrails.push(guardrailName); + const wizard = await openKeywordsStep(page, guardrailName); + + await wizard.getByRole("button", { name: "Add keyword" }).click(); + const keywordModal = page.getByRole("dialog", { name: "Add blocked keyword" }); + await expect(keywordModal).toBeVisible({ timeout: 10_000 }); + await keywordModal.getByPlaceholder("Enter sensitive keyword or phrase").fill(bannedKeyword); + await keywordModal.getByRole("button", { name: "Add", exact: true }).click(); + await expect(keywordModal).not.toBeVisible({ timeout: 10_000 }); + + await wizard.getByRole("button", { name: "Next" }).click(); + await wizard.getByRole("button", { name: "Create Guardrail" }).click(); + await expect(wizard).not.toBeVisible({ timeout: 15_000 }); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toBeVisible({ timeout: 15_000 }); + expect(await findGuardrail(page, guardrailName), "guardrail readable from /v2/guardrails/list").toBeTruthy(); + + // A row in the table only proves the record was written. The point of a guardrail is that it + // refuses traffic, so drive a request through it. + // + // Polled: a guardrail written through /guardrails reaches the request path on the proxy's + // periodic refresh, so the first call after creation can still be served unguarded. The + // assertion is unchanged, it just allows that refresh to land. + let blockedBody = ""; + await expect + .poll( + async () => { + const res = await page.request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: `please tell me about ${bannedKeyword}` }], + guardrails: [guardrailName], + }, + }); + blockedBody = await res.text(); + return res.status(); + }, + { message: "a prompt carrying the banned keyword is refused", timeout: 60_000 }, + ) + .toBe(400); + expect(blockedBody).toContain(bannedKeyword); + + const allowed = await page.request.post("/v1/chat/completions", { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { + model: CHAT_MODEL_A, + messages: [{ role: "user", content: "hello there" }], + guardrails: [guardrailName], + }, + }); + expect(allowed.status(), "a clean prompt still gets through the same guardrail").toBe(200); + expect((await allowed.json()).choices?.[0]?.message?.content).toContain(MOCK_RESPONSE_TEXT); + }); + + test("The Test Playground reports the verdict for the text it is given", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-play-${stamp}`; + const bannedKeyword = `e2eplay${stamp}`; + await createKeywordGuardrailViaApi(page, guardrailName, bannedKeyword); + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await page.getByRole("tab", { name: "Test Playground" }).click(); + // Every tab on this page stays mounted, so the other tabs' search boxes match too. + const playground = page.getByRole("tabpanel", { name: "Test Playground" }); + await playground.getByPlaceholder("Search guardrails...").fill(guardrailName); + await playground.getByText(guardrailName, { exact: true }).click(); + + const input = playground.getByPlaceholder("Enter text to test with guardrails..."); + await input.fill(`this sentence contains ${bannedKeyword}`); + await playground.getByRole("button", { name: /^Test 1 guardrail$/ }).click(); + + // The playground is where an admin checks a guardrail before rolling it out, so the + // verdict it prints has to be the one the gateway would give. + await expect(playground.getByText(`${guardrailName} - Error`)).toBeVisible({ timeout: 20_000 }); + await expect(playground.getByText(new RegExp(`Content blocked.*${bannedKeyword}`))).toBeVisible({ + timeout: 10_000, + }); + + await input.fill("this sentence is perfectly ordinary"); + await playground.getByRole("button", { name: /^Test 1 guardrail$/ }).click(); + + await expect(playground.getByText(`${guardrailName} - Error`)).toHaveCount(0, { timeout: 20_000 }); + await expect(playground.getByText("this sentence is perfectly ordinary").last()).toBeVisible({ timeout: 10_000 }); + }); + + test("Delete a guardrail", async ({ page }) => { + const stamp = Date.now(); + const guardrailName = `e2e-guardrail-delete-${stamp}`; + const guardrailId = await createKeywordGuardrailViaApi(page, guardrailName, `e2edelete${stamp}`); + + await navigateToPage(page, Page.Guardrails); + await dismissFeedbackPopup(page); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toBeVisible({ timeout: 15_000 }); + + await page.getByTestId(`guardrail-actions-${guardrailId}`).click(); + await page.getByTestId("guardrail-action-delete").click(); + + const modal = page.getByRole("dialog"); + await expect(modal).toBeVisible({ timeout: 5_000 }); + await modal.getByRole("button", { name: "Delete", exact: true }).click(); + + await expect(page.getByRole("row").filter({ hasText: guardrailName })).toHaveCount(0, { timeout: 15_000 }); + + // The RC checklist deletes then reloads, because a row vanishing from the table has + // fooled us before; assert against the route the reload would read. + await expect + .poll(async () => await findGuardrail(page, guardrailName), { + message: `guardrail ${guardrailName} still listed after delete`, + timeout: 15_000, + }) + .toBeUndefined(); + }); + test("Create a Presidio guardrail, see it in team settings, and delete it", async ({ page }) => { const guardrailName = `e2e-presidio-${Date.now()}`; diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py index 7f54fbfb4c2..4c99bce2b0f 100644 --- a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py +++ b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py @@ -4692,3 +4692,82 @@ async def test_async_stream_assembled_response_keeps_vertex_traffic_type(logging assembled = litellm.stream_chunk_builder(chunks=received, messages=[{"role": "user", "content": "hi"}]) assert assembled is not None assert assembled._hidden_params["provider_specific_fields"]["traffic_type"] == "ON_DEMAND_FLEX" + + +class TestStableStreamingResponseId: + """ + All chunks of one streamed response must share the same top-level id + (OpenAI streaming contract). Providers streaming via GenericStreamingChunk + (e.g. GigaChat) do not propagate an upstream response id, so + CustomStreamWrapper must pin the id from the first chunk it creates, + mirroring the existing `created` pinning (issue #11437). + + Clients such as goose merge streamed deltas into one assistant message by + chunk id; per-chunk ids split a single reply into many messages. + """ + + def test_generic_chunks_share_one_id(self): + def _generic_chunks(): + return iter( + [ + { + "text": "Hello", + "tool_use": None, + "is_finished": False, + "finish_reason": "", + "usage": None, + "index": 0, + }, + { + "text": " world", + "tool_use": None, + "is_finished": False, + "finish_reason": "", + "usage": None, + "index": 0, + }, + { + "text": "", + "tool_use": None, + "is_finished": True, + "finish_reason": "stop", + "usage": { + "prompt_tokens": 1, + "completion_tokens": 2, + "total_tokens": 3, + }, + "index": 0, + }, + ] + ) + + wrapper = CustomStreamWrapper( + completion_stream=_generic_chunks(), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + ids = [chunk.id for chunk in wrapper if chunk.id] + assert ids, "no chunks emitted" + assert len(set(ids)) == 1, f"chunk ids differ across one stream: {ids}" + + def test_creator_pins_id_from_first_chunk(self): + wrapper = CustomStreamWrapper( + completion_stream=iter([]), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + first = wrapper.model_response_creator() + assert wrapper.response_id == first.id + assert wrapper.model_response_creator().id == first.id + + def test_provider_supplied_id_still_wins(self): + wrapper = CustomStreamWrapper( + completion_stream=iter([]), + model="gigachat/GigaChat-2-Max", + logging_obj=MagicMock(), + custom_llm_provider="gigachat", + ) + wrapper.response_id = "chatcmpl-from-provider" + assert wrapper.model_response_creator().id == "chatcmpl-from-provider" diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py index 25e2c3cda80..c4df46dea83 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py @@ -6207,3 +6207,45 @@ def test_disabled_thinking_omitted_only_for_always_on_models( assert "thinking" not in request else: assert request["thinking"] == {"type": "disabled"} + + +def test_anthropic_drop_params_keeps_format_only_output_config(monkeypatch): + """``drop_params=True`` must not consume ``output_config.format``: the drop + gate is an effort gate and ``format`` is a structured-output field.""" + monkeypatch.setattr(litellm, "drop_params", True) + config = AnthropicConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"z": {"type": "integer"}}}, + } + + result = config.transform_request( + model="claude-3-haiku-20240307", + messages=[{"role": "user", "content": "Hello"}], + optional_params={"output_config": {"format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_anthropic_drop_params_reduces_mixed_output_config_to_format(monkeypatch): + """``drop_params=True`` drops the effort key on unsupported models but keeps + ``format`` so structured outputs still reach the provider.""" + monkeypatch.setattr(litellm, "drop_params", True) + config = AnthropicConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"z": {"type": "integer"}}}, + } + + result = config.transform_request( + model="claude-3-haiku-20240307", + messages=[{"role": "user", "content": "Hello"}], + optional_params={"output_config": {"effort": "low", "format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} diff --git a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py index cea299280f8..a122d97a0f0 100644 --- a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py @@ -428,30 +428,58 @@ def test_output_config_forwarded_for_bedrock_chat_invoke_request(): def test_output_config_format_converted_for_bedrock_chat_invoke_request(): - """Bedrock Invoke chat path consumes ``output_config.format`` before forwarding.""" + """Bedrock Invoke chat path inlines ``output_config.format`` for models + without native structured-output support and keeps the effort key.""" config = AmazonAnthropicClaudeConfig() schema = { "type": "object", "properties": {"answer": {"type": "string"}}, } - result = config.transform_request( + with patch( # test-quality-ok: pin non-native path + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", + ): + result = config.transform_request( + model="anthropic.claude-opus-4-7", + messages=[{"role": "user", "content": "test"}], + optional_params={ + "max_tokens": 100, + "output_config": { + "effort": "xhigh", + "format": {"type": "json_schema", "schema": schema}, + }, + }, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"effort": "xhigh"} + last_content = result["messages"][0]["content"] + assert json.loads(last_content[-1]["text"]) == schema + + +def test_output_config_format_forwarded_for_bedrock_chat_invoke_request(): + """Bedrock Invoke chat path forwards ``output_config.format`` alongside effort + for models with native structured-output support (Claude Opus 4.7).""" + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"answer": {"type": "string"}}}, + } + + result = AmazonAnthropicClaudeConfig().transform_request( model="anthropic.claude-opus-4-7", messages=[{"role": "user", "content": "test"}], optional_params={ "max_tokens": 100, - "output_config": { - "effort": "xhigh", - "format": {"type": "json_schema", "schema": schema}, - }, + "output_config": {"effort": "xhigh", "format": schema_format}, }, litellm_params={}, headers={}, ) - assert result.get("output_config") == {"effort": "xhigh"} - last_content = result["messages"][0]["content"] - assert json.loads(last_content[-1]["text"]) == schema + assert result.get("output_config") == {"effort": "xhigh", "format": schema_format} + assert "answer" not in json.dumps(result["messages"]) @pytest.mark.parametrize( @@ -488,7 +516,7 @@ def test_bedrock_chat_invoke_checks_output_config_support_with_bedrock_provider( optional_params = {"max_tokens": 100, "output_config": {"effort": "high"}} with patch( - "litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ) as mock_supports_factory: result = config.transform_request( @@ -499,11 +527,7 @@ def test_bedrock_chat_invoke_checks_output_config_support_with_bedrock_provider( headers={}, ) - mock_supports_factory.assert_called_once_with( - model="us.anthropic.claude-opus-4-7", - custom_llm_provider="bedrock", - key="supports_output_config", - ) + mock_supports_factory.assert_called_once_with("us.anthropic.claude-opus-4-7", "supports_output_config") assert result["output_config"] == {"effort": "high"} @@ -542,3 +566,80 @@ def test_output_format_removed_from_bedrock_invoke_request(): assert ( "output_format" not in result ), f"output_format should be removed for Bedrock Invoke, got keys: {result.keys()}" + + +def test_bedrock_chat_invoke_forwards_output_config_format_natively(local_model_cost_map): + """Regression: ``output_config.format`` is forwarded verbatim on models Bedrock + enforces structured outputs for, instead of being inlined as prompt text.""" + import json + + config = AmazonAnthropicClaudeConfig() + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": {"zebra_count": {"type": "integer"}}, + "required": ["zebra_count"], + "additionalProperties": False, + }, + } + + result = config.transform_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + assert "zebra_count" not in json.dumps(result["messages"]) + + +def test_bedrock_chat_invoke_drop_params_keeps_native_output_config_format(local_model_cost_map, monkeypatch): + """``drop_params=True`` must not eat ``output_config.format`` before the + native-forwarding router runs (Sonnet 4.5 has no effort flags).""" + import litellm + + monkeypatch.setattr(litellm, "drop_params", True) + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = AmazonAnthropicClaudeConfig().transform_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={"max_tokens": 100, "output_config": {"format": schema_format}}, + litellm_params={}, + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_bedrock_chat_invoke_drop_params_still_inlines_for_non_native(local_model_cost_map, monkeypatch): + """``drop_params=True`` on a model without native structured-output support + still reaches the inline-schema fallback instead of losing the schema.""" + import litellm + + monkeypatch.setattr(litellm, "drop_params", True) + schema = {"type": "object", "properties": {"zebra_count": {"type": "integer"}}} + + result = AmazonAnthropicClaudeConfig().transform_request( + model="anthropic.claude-3-haiku-20240307-v1:0", + messages=[{"role": "user", "content": "say hello"}], + optional_params={ + "max_tokens": 100, + "output_config": {"format": {"type": "json_schema", "schema": schema}}, + }, + litellm_params={}, + headers={}, + ) + + assert "output_config" not in result + last_content = result["messages"][-1]["content"] + assert json.loads(last_content[-1]["text"]) == schema diff --git a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py index 8d07d38b1b6..09ebc1a3c95 100644 --- a/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py +++ b/tests/test_litellm/llms/bedrock/messages/invoke_transformations/test_anthropic_claude3_transformation.py @@ -935,7 +935,7 @@ def test_bedrock_messages_strips_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=False, ): result = cfg.transform_anthropic_messages_request( @@ -970,7 +970,7 @@ def test_bedrock_messages_preserves_output_config_for_claude_4_6(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1003,7 +1003,7 @@ def test_bedrock_messages_checks_output_config_support_with_bedrock_provider(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ) as mock_supports_factory: result = cfg.transform_anthropic_messages_request( @@ -1014,11 +1014,7 @@ def test_bedrock_messages_checks_output_config_support_with_bedrock_provider(): headers={}, ) - mock_supports_factory.assert_called_with( - model="us.anthropic.claude-opus-4-7", - custom_llm_provider="bedrock", - key="supports_output_config", - ) + mock_supports_factory.assert_called_with("us.anthropic.claude-opus-4-7", "supports_output_config") assert result["output_config"] == {"effort": "high"} @@ -1038,7 +1034,7 @@ def test_bedrock_messages_forwards_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1054,27 +1050,29 @@ def test_bedrock_messages_forwards_output_config(): def test_bedrock_messages_forwards_output_config_with_output_format(): - """``output_config`` is forwarded; ``output_format`` is converted to inline schema.""" + """Legacy ``output_format`` is forwarded as ``output_config.format`` on models + that support native structured outputs, alongside the effort key.""" from unittest.mock import patch from litellm.types.router import GenericLiteLLMParams cfg = AmazonAnthropicClaudeMessagesConfig() messages = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}] + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": {"answer": {"type": "string"}}, + }, + } optional_params = { "max_tokens": 4096, "output_config": {"effort": "low"}, - "output_format": { - "type": "json_schema", - "schema": { - "type": "object", - "properties": {"answer": {"type": "string"}}, - }, - }, + "output_format": schema_format, } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1085,12 +1083,14 @@ def test_bedrock_messages_forwards_output_config_with_output_format(): headers={}, ) - assert result.get("output_config") == {"effort": "low"} + assert result.get("output_config") == {"effort": "low", "format": schema_format} assert "output_format" not in result + assert "answer" not in json.dumps(result["messages"]) def test_bedrock_messages_converts_output_config_format_to_inline_schema(): - """``output_config.format`` is consumed so Bedrock does not see an unknown nested key.""" + """Without native structured-output support, ``output_config.format`` falls back + to the inline schema so Bedrock does not see an unknown nested key.""" from unittest.mock import patch from litellm.types.router import GenericLiteLLMParams @@ -1110,8 +1110,8 @@ def test_bedrock_messages_converts_output_config_format_to_inline_schema(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", - return_value=True, + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", ): result = cfg.transform_anthropic_messages_request( model="anthropic.claude-opus-4-7", @@ -1146,7 +1146,7 @@ def test_bedrock_messages_normalizes_output_config_effort_for_opus( cfg = AmazonAnthropicClaudeMessagesConfig() with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1184,8 +1184,8 @@ def test_bedrock_messages_does_not_mutate_callers_messages_when_embedding_schema } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", - return_value=True, + "litellm.llms.bedrock.common_utils._bedrock_model_supports", + side_effect=lambda _model, key: key == "supports_output_config", ): result = cfg.transform_anthropic_messages_request( model="anthropic.claude-opus-4-7", @@ -1229,7 +1229,7 @@ def test_bedrock_messages_does_not_mutate_callers_output_config(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): cfg.transform_anthropic_messages_request( @@ -1271,7 +1271,7 @@ def test_bedrock_messages_strips_output_config_with_output_format(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=False, ): result = cfg.transform_anthropic_messages_request( @@ -1332,7 +1332,7 @@ def test_bedrock_messages_drop_params_keeps_output_config_for_4_7(): litellm.drop_params = True try: with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1375,7 +1375,7 @@ def test_bedrock_messages_maps_reasoning_effort_for_adaptive_model( } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -1482,7 +1482,7 @@ def test_bedrock_messages_explicit_output_config_wins_over_reasoning_effort(): } with patch( - "litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation._supports_factory", + "litellm.llms.bedrock.common_utils._bedrock_model_supports", return_value=True, ): result = cfg.transform_anthropic_messages_request( @@ -3104,3 +3104,149 @@ async def test_bedrock_sse_wrapper_dispatches_logging_on_client_disconnect(): break await asyncio.sleep(0.01) assert logging_obj.completion_start_time is not None + + +def test_bedrock_messages_forwards_output_config_format_natively(local_model_cost_map): + """Regression: on a model Bedrock enforces structured outputs for (Claude + Sonnet 4.5), ``output_config.format`` must be forwarded verbatim, not + silently rewritten into inline prompt text.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": { + "type": "object", + "properties": { + "zebra_count": {"type": "integer"}, + "is_tuesday": {"type": "boolean"}, + }, + "required": ["zebra_count", "is_tuesday"], + "additionalProperties": False, + }, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + assert "zebra_count" not in json.dumps(result["messages"]) + + +def test_bedrock_messages_inlines_schema_for_claude_5(local_model_cost_map): + """Bedrock rejects ``output_config.format`` for the Claude 5 family, so the + schema falls back to the inline-text path instead of a deterministic 400.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema = { + "type": "object", + "properties": {"zebra_count": {"type": "integer"}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-5", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": {"type": "json_schema", "schema": schema}}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert "output_config" not in result + last_content = result["messages"][-1]["content"] + assert json.loads(last_content[-1]["text"]) == schema + + +def test_bedrock_messages_legacy_output_format_wins_over_output_config_format(local_model_cost_map): + """When a request carries both schema forms, the legacy top-level + ``output_format`` keeps winning, matching the pre-existing precedence.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + legacy_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"legacy_field": {"type": "string"}}}, + } + newer_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"newer_field": {"type": "string"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_format": legacy_format, + "output_config": {"format": newer_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": legacy_format} + assert "output_format" not in result + assert "newer_field" not in json.dumps(result) + + +def test_bedrock_messages_drop_params_keeps_native_output_config_format(local_model_cost_map, monkeypatch): + """``drop_params=True`` must not strip a natively forwarded + ``output_config.format`` on models without effort support (Sonnet 4.5).""" + import litellm + from litellm.types.router import GenericLiteLLMParams + + monkeypatch.setattr(litellm, "drop_params", True) + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "output_config": {"format": schema_format}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} + + +def test_bedrock_messages_strips_effort_but_keeps_format_for_sonnet_4_5(local_model_cost_map): + """Sonnet 4.5 has native structured-output support but no effort support, so + a mixed ``output_config`` keeps ``format`` and drops ``effort``.""" + from litellm.types.router import GenericLiteLLMParams + + cfg = AmazonAnthropicClaudeMessagesConfig() + schema_format = { + "type": "json_schema", + "schema": {"type": "object", "properties": {"zebra_count": {"type": "integer"}}}, + } + + result = cfg.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=[{"role": "user", "content": [{"type": "text", "text": "say hello"}]}], + anthropic_messages_optional_request_params={ + "max_tokens": 4096, + "output_config": {"format": schema_format, "effort": "high"}, + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert result.get("output_config") == {"format": schema_format} diff --git a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py index afd5e83ca52..9302dc01abe 100644 --- a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py +++ b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py @@ -522,6 +522,47 @@ def test_merge_bedrock_aws_request_params_keeps_caller_credentials_without_stati assert merged["aws_region_name"] == "us-west-2" +def test_strip_unsupported_output_config_keeps_format_drops_effort(local_model_cost_map): + """On a model with neither effort flag, only the ``format`` key survives.""" + from litellm.llms.bedrock.common_utils import ( + strip_unsupported_bedrock_invoke_output_config_keys, + ) + + schema_format = {"type": "json_schema", "schema": {"type": "object"}} + body = {"output_config": {"effort": "high", "format": schema_format}} + + strip_unsupported_bedrock_invoke_output_config_keys( + model="anthropic.claude-3-haiku-20240307-v1:0", + request_body=body, + ) + + assert body["output_config"] == {"format": schema_format} + + +def test_apply_structured_output_prefers_legacy_output_format(local_model_cost_map): + """The legacy ``output_format`` wins over ``output_config.format`` when a + request carries both, matching the pre-existing precedence.""" + from litellm.llms.bedrock.common_utils import ( + apply_bedrock_invoke_structured_output, + ) + + legacy = {"type": "json_schema", "schema": {"type": "object", "properties": {"a": {"type": "string"}}}} + newer = {"type": "json_schema", "schema": {"type": "object", "properties": {"b": {"type": "string"}}}} + body = { + "messages": [{"role": "user", "content": "hi"}], + "output_format": legacy, + "output_config": {"format": newer}, + } + + apply_bedrock_invoke_structured_output( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + request_body=body, + ) + + assert body["output_config"] == {"format": legacy} + assert "output_format" not in body + + def test_sign_aws_request_assumes_role_with_external_id(monkeypatch): """A trust policy requiring sts:ExternalId must be satisfied when signing batch API requests.""" import datetime diff --git a/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py b/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py new file mode 100644 index 00000000000..064d9d58f0c --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_qwen_brand_aliases.py @@ -0,0 +1,331 @@ +import math + +import pytest + +import litellm +from litellm import completion, get_llm_provider +from litellm.llms.dashscope.chat.transformation import DashScopeChatConfig +from litellm.llms.dashscope.cost_calculator import ( + cost_per_token as dashscope_cost_per_token, +) +from litellm.llms.dashscope.embed.transformation import DashScopeEmbeddingConfig +from litellm.llms.dashscope.image_generation.transformation import ( + DashScopeImageGenerationConfig, +) +from litellm.llms.dashscope.qwen_ai_platform import ( + QWEN_AI_PLATFORM_API_BASE, + QWEN_AI_PLATFORM_IMAGE_API_BASE, + QWEN_AI_PLATFORM_RERANK_API_BASE, + QwenAIPlatformChatConfig, + QwenAIPlatformEmbeddingConfig, + QwenAIPlatformImageGenerationConfig, + QwenAIPlatformRerankConfig, +) +from litellm.llms.dashscope.qwencloud import ( + QWENCLOUD_API_BASE, + QWENCLOUD_IMAGE_API_BASE, + QWENCLOUD_RERANK_API_BASE, + QwenCloudChatConfig, + QwenCloudEmbeddingConfig, + QwenCloudImageGenerationConfig, + QwenCloudRerankConfig, +) +from litellm.llms.dashscope.rerank.transformation import DashScopeRerankConfig +from litellm.types.utils import LlmProviders, Usage +from litellm.utils import ProviderConfigManager + +DASHSCOPE_FAMILY_ENV_VARS = [ + "DASHSCOPE_API_KEY", + "DASHSCOPE_API_BASE", + "DASHSCOPE_API_BASE_RERANK", + "DASHSCOPE_API_BASE_IMAGE", + "QWENCLOUD_API_KEY", + "QWENCLOUD_API_BASE", + "QWENCLOUD_API_BASE_RERANK", + "QWENCLOUD_API_BASE_IMAGE", + "QWEN_AI_PLATFORM_API_KEY", + "QWEN_AI_PLATFORM_API_BASE", + "QWEN_AI_PLATFORM_API_BASE_RERANK", + "QWEN_AI_PLATFORM_API_BASE_IMAGE", +] + +BRAND_CASES = [ + pytest.param( + { + "provider": "qwencloud", + "enum": LlmProviders.QWENCLOUD, + "key_env": "QWENCLOUD_API_KEY", + "base_env": "QWENCLOUD_API_BASE", + "default_base": QWENCLOUD_API_BASE, + "default_rerank_base": QWENCLOUD_RERANK_API_BASE, + "default_image_base": QWENCLOUD_IMAGE_API_BASE, + "chat_config": QwenCloudChatConfig, + "embedding_config": QwenCloudEmbeddingConfig, + "rerank_config": QwenCloudRerankConfig, + "image_config": QwenCloudImageGenerationConfig, + }, + id="qwencloud", + ), + pytest.param( + { + "provider": "qwen_ai_platform", + "enum": LlmProviders.QWEN_AI_PLATFORM, + "key_env": "QWEN_AI_PLATFORM_API_KEY", + "base_env": "QWEN_AI_PLATFORM_API_BASE", + "default_base": QWEN_AI_PLATFORM_API_BASE, + "default_rerank_base": QWEN_AI_PLATFORM_RERANK_API_BASE, + "default_image_base": QWEN_AI_PLATFORM_IMAGE_API_BASE, + "chat_config": QwenAIPlatformChatConfig, + "embedding_config": QwenAIPlatformEmbeddingConfig, + "rerank_config": QwenAIPlatformRerankConfig, + "image_config": QwenAIPlatformImageGenerationConfig, + }, + id="qwen_ai_platform", + ), +] + + +@pytest.fixture(autouse=True) +def clear_dashscope_family_env(monkeypatch): + for env_var in DASHSCOPE_FAMILY_ENV_VARS: + monkeypatch.delenv(env_var, raising=False) + + +class TestQwenBrandProviderResolution: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_get_llm_provider_resolves_brand_default_base(self, brand): + model, provider, api_key, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert model == "qwen-max" + assert provider == brand["provider"] + assert api_key == "sk-explicit" + assert api_base == brand["default_base"] + + def test_dashscope_resolution_unchanged(self): + model, provider, api_key, api_base = get_llm_provider("dashscope/qwen-max", api_key="sk-explicit") + assert model == "qwen-max" + assert provider == "dashscope" + assert api_base == "https://dashscope.aliyuncs.com/compatible-mode/v1" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_brand_env_key_wins_over_dashscope_key(self, monkeypatch, brand): + monkeypatch.setenv(brand["key_env"], "sk-brand") + monkeypatch.setenv("DASHSCOPE_API_KEY", "sk-dashscope") + _, _, api_key, _ = get_llm_provider(f"{brand['provider']}/qwen-max") + assert api_key == "sk-brand" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_dashscope_key_is_fallback(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_KEY", "sk-dashscope") + _, _, api_key, _ = get_llm_provider(f"{brand['provider']}/qwen-max") + assert api_key == "sk-dashscope" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_dashscope_api_base_does_not_leak_into_brand(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_BASE", "https://legacy.example.com/v1") + _, _, _, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert api_base == brand["default_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_brand_api_base_env_wins(self, monkeypatch, brand): + monkeypatch.setenv(brand["base_env"], "https://brand.example.com/v1") + _, _, _, api_base = get_llm_provider(f"{brand['provider']}/qwen-max", api_key="sk-explicit") + assert api_base == "https://brand.example.com/v1" + + +class TestQwenBrandConfigDispatch: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_chat_config(self, brand): + config = ProviderConfigManager.get_provider_chat_config("qwen-max", brand["enum"]) + assert isinstance(config, brand["chat_config"]) + assert isinstance(config, DashScopeChatConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_config(self, brand): + config = ProviderConfigManager.get_provider_embedding_config(model="text-embedding-v3", provider=brand["enum"]) + assert isinstance(config, brand["embedding_config"]) + assert isinstance(config, DashScopeEmbeddingConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_config(self, brand): + config = ProviderConfigManager.get_provider_rerank_config( + model="gte-rerank-v2", + provider=brand["enum"], + api_base=None, + present_version_params=[], + ) + assert isinstance(config, brand["rerank_config"]) + assert isinstance(config, DashScopeRerankConfig) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_config(self, brand): + config = ProviderConfigManager.get_provider_image_generation_config(model="qwen-image", provider=brand["enum"]) + assert isinstance(config, brand["image_config"]) + assert isinstance(config, DashScopeImageGenerationConfig) + + +class TestQwenBrandDefaultUrls: + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_chat_complete_url(self, brand): + url = brand["chat_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="qwen-max", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/chat/completions" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_complete_url(self, brand): + url = brand["embedding_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v3", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/embeddings" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_embedding_ignores_dashscope_api_base(self, monkeypatch, brand): + monkeypatch.setenv("DASHSCOPE_API_BASE", "https://legacy.example.com/v1") + url = brand["embedding_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v3", + optional_params={}, + litellm_params={}, + ) + assert url == f"{brand['default_base']}/embeddings" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_complete_url(self, brand): + url = brand["rerank_config"]().get_complete_url(api_base=None, model="gte-rerank-v2") + assert url == brand["default_rerank_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_rerank_env_override(self, monkeypatch, brand): + monkeypatch.setenv(f"{brand['base_env']}_RERANK", "https://rerank.example.com/v1/reranks") + url = brand["rerank_config"]().get_complete_url(api_base=None, model="gte-rerank-v2") + assert url == "https://rerank.example.com/v1/reranks" + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_complete_url(self, brand): + url = brand["image_config"]().get_complete_url( + api_base=None, + api_key="sk-test", + model="qwen-image", + optional_params={}, + litellm_params={}, + ) + assert url == brand["default_image_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_image_generation_ignores_chat_compatible_api_base(self, brand): + url = brand["image_config"]().get_complete_url( + api_base=brand["default_base"], + api_key="sk-test", + model="qwen-image", + optional_params={}, + litellm_params={}, + ) + assert url == brand["default_image_base"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_validate_environment_requires_key(self, brand): + with pytest.raises(ValueError, match="DASHSCOPE_API_KEY"): + brand["embedding_config"]().validate_environment( + headers={}, + model="text-embedding-v3", + messages=[], + optional_params={}, + litellm_params={}, + api_key=None, + api_base=None, + ) + + +class TestQwenBrandCostParity: + @pytest.fixture(autouse=True) + def setup_model_cost_map(self, monkeypatch): + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_get_model_info(self, brand): + model_info = litellm.get_model_info(f"{brand['provider']}/qwen-max") + dashscope_info = litellm.get_model_info("dashscope/qwen-max") + assert model_info["litellm_provider"] == brand["provider"] + assert model_info["input_cost_per_token"] == dashscope_info["input_cost_per_token"] + assert model_info["output_cost_per_token"] == dashscope_info["output_cost_per_token"] + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_flat_pricing_matches_dashscope(self, brand): + usage = Usage(prompt_tokens=1000, completion_tokens=500) + brand_costs = dashscope_cost_per_token(model="qwen-max", usage=usage, custom_llm_provider=brand["provider"]) + dashscope_costs = dashscope_cost_per_token(model="qwen-max", usage=usage) + assert brand_costs == dashscope_costs + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_tiered_pricing_matches_dashscope(self, brand): + usage = Usage(prompt_tokens=300000, completion_tokens=300000) + brand_costs = dashscope_cost_per_token(model="qwen-flash", usage=usage, custom_llm_provider=brand["provider"]) + dashscope_costs = dashscope_cost_per_token(model="qwen-flash", usage=usage) + assert brand_costs == dashscope_costs + tier_2 = litellm.get_model_info(f"{brand['provider']}/qwen-flash")["tiered_pricing"][1] + assert math.isclose(brand_costs[0], 300000 * tier_2["input_cost_per_token"], rel_tol=1e-10) + + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_public_cost_per_token_routes_to_dashscope_calculator(self, brand): + brand_costs = litellm.cost_per_token( + model=f"{brand['provider']}/qwen-max", + prompt_tokens=1000, + completion_tokens=500, + custom_llm_provider=brand["provider"], + ) + dashscope_costs = litellm.cost_per_token( + model="dashscope/qwen-max", + prompt_tokens=1000, + completion_tokens=500, + custom_llm_provider="dashscope", + ) + assert brand_costs == dashscope_costs + + +class TestQwenBrandCompletionMock: + @pytest.mark.respx() + @pytest.mark.parametrize("brand", BRAND_CASES) + def test_completion_hits_brand_default_host(self, respx_mock, brand, monkeypatch): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + respx_mock.post(f"{brand['default_base']}/chat/completions").respond( + json={ + "id": "chatcmpl-123", + "object": "chat.completion", + "created": 1677652288, + "model": "qwen-turbo", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hey from LiteLLM!"}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 9, + "completion_tokens": 12, + "total_tokens": 21, + }, + }, + status_code=200, + ) + + response = completion( + model=f"{brand['provider']}/qwen-turbo", + messages=[{"role": "user", "content": "say hey from LiteLLM"}], + api_key="fake-brand-key", + ) + + assert response.choices[0].message.content == "Hey from LiteLLM!" + request = respx_mock.calls[0].request + assert request.url == f"{brand['default_base']}/chat/completions" + assert request.headers["Authorization"] == "Bearer fake-brand-key" diff --git a/type-discipline-budget.json b/type-discipline-budget.json index 83c49afb538..3d2e97d55a5 100644 --- a/type-discipline-budget.json +++ b/type-discipline-budget.json @@ -1,9 +1,9 @@ { "LIT001": { - "limit": 22403 + "limit": 22367 }, "LIT002": { - "limit": 26780 + "limit": 26777 }, "LIT003": { "limit": 269 @@ -27,10 +27,10 @@ "limit": 0 }, "LIT010": { - "limit": 16512 + "limit": 16507 }, "LIT011": { - "limit": 5537 + "limit": 5535 }, "LIT012": { "limit": 4495 diff --git a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx index 5baa8138960..d01a6a34cbe 100644 --- a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx +++ b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx @@ -150,6 +150,8 @@ export enum Providers { PETALS = "Petals", PG_VECTOR = "Pg Vector", PREDIBASE = "Predibase", + Qwen_AI_Platform = "Qwen AI Platform", + QwenCloud = "QwenCloud", RECRAFT = "Recraft", REPLICATE = "Replicate", RunwayML = "RunwayML", @@ -262,6 +264,8 @@ export const provider_map: Record = { PETALS: "petals", PG_VECTOR: "pg_vector", PREDIBASE: "predibase", + Qwen_AI_Platform: "qwen_ai_platform", + QwenCloud: "qwencloud", RECRAFT: "recraft", REPLICATE: "replicate", RunwayML: "runwayml", @@ -357,6 +361,8 @@ export const providerLogoMap: Partial> = { [Providers.Openrouter]: openrouterLogo.src, [Providers.Oracle]: oracleLogo.src, [Providers.Perplexity]: perplexityAiLogo.src, + [Providers.Qwen_AI_Platform]: qwenLogo.src, + [Providers.QwenCloud]: qwenLogo.src, [Providers.RECRAFT]: recraftLogo.src, [Providers.REPLICATE]: replicateLogo.src, [Providers.RunwayML]: runwayLogo.src,