diff --git a/README.md b/README.md index 72fd43925c9..8df351e9303 100644 --- a/README.md +++ b/README.md @@ -292,7 +292,7 @@ curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ | [CompactifAI (`compactifai`)](https://docs.litellm.ai/docs/providers/compactifai) | ✅ | ✅ | ✅ | | | | | | | | | [Custom (`custom`)](https://docs.litellm.ai/docs/providers/custom_llm_server) | ✅ | ✅ | ✅ | | | | | | | | | [Custom OpenAI (`custom_openai`)](https://docs.litellm.ai/docs/providers/openai_compatible) | ✅ | ✅ | ✅ | | | ✅ | ✅ | ✅ | ✅ | | -| [Dashscope (`dashscope`)](https://docs.litellm.ai/docs/providers/dashscope) | ✅ | ✅ | ✅ | | | | | | | | +| [Dashscope (`dashscope`)](https://docs.litellm.ai/docs/providers/dashscope) | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | | [Databricks (`databricks`)](https://docs.litellm.ai/docs/providers/databricks) | ✅ | ✅ | ✅ | | | | | | | | | [DataRobot (`datarobot`)](https://docs.litellm.ai/docs/providers/datarobot) | ✅ | ✅ | ✅ | | | | | | | | | [Deepgram (`deepgram`)](https://docs.litellm.ai/docs/providers/deepgram) | ✅ | ✅ | ✅ | | | ✅ | | | | | diff --git a/deploy/charts/litellm-helm/templates/hpa.yaml b/deploy/charts/litellm-helm/templates/hpa.yaml index 71e199c5aeb..fec4d1f5c5e 100644 --- a/deploy/charts/litellm-helm/templates/hpa.yaml +++ b/deploy/charts/litellm-helm/templates/hpa.yaml @@ -12,6 +12,10 @@ spec: name: {{ include "litellm.fullname" . }} minReplicas: {{ .Values.autoscaling.minReplicas }} maxReplicas: {{ .Values.autoscaling.maxReplicas }} + {{- if .Values.autoscaling.behavior }} + behavior: + {{- toYaml .Values.autoscaling.behavior | nindent 4 }} + {{- end }} metrics: {{- if .Values.autoscaling.targetCPUUtilizationPercentage }} - type: Resource diff --git a/deploy/charts/litellm-helm/tests/hpa_tests.yaml b/deploy/charts/litellm-helm/tests/hpa_tests.yaml new file mode 100644 index 00000000000..ec18c3591d3 --- /dev/null +++ b/deploy/charts/litellm-helm/tests/hpa_tests.yaml @@ -0,0 +1,36 @@ +suite: "hpa with behavior" +templates: + - hpa.yaml +tests: + - it: "renders behavior when set" + set: + autoscaling.enabled: true + autoscaling.behavior: + scaleUp: + stabilizationWindowSeconds: 60 + policies: + - type: Pods + value: 2 + periodSeconds: 60 + scaleDown: + stabilizationWindowSeconds: 90 + policies: + - type: Pods + value: 1 + periodSeconds: 60 + asserts: + - isKind: { of: HorizontalPodAutoscaler } + - equal: { path: spec.behavior.scaleUp.stabilizationWindowSeconds, value: 60 } + - equal: { path: spec.behavior.scaleDown.stabilizationWindowSeconds, value: 90 } + +--- +suite: "hpa without behavior" +templates: + - hpa.yaml +tests: + - it: "does not render behavior when not set" + set: + autoscaling.enabled: true + asserts: + - isKind: { of: HorizontalPodAutoscaler } + - isNull: { path: spec.behavior } diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index 9c7c013341b..81558ed5b29 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -184,6 +184,7 @@ autoscaling: maxReplicas: 100 targetCPUUtilizationPercentage: 80 # targetMemoryUtilizationPercentage: 80 + # behavior: {} # Autoscaling with keda is mutually exclusive with hpa keda: diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index 9698e7912d4..9f37b52d94c 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.40" +version = "0.1.41" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.40" +version = "0.1.41" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql new file mode 100644 index 00000000000..3253b63a884 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260514120000_add_blocked_to_proxy_model_table/migration.sql @@ -0,0 +1,4 @@ +-- AlterTable +-- Adds the admin-toggleable pause flag used by the router's blocked filter and the +-- credential lookup helpers; defaults to false so existing rows behave unchanged. +ALTER TABLE "LiteLLM_ProxyModelTable" ADD COLUMN IF NOT EXISTS "blocked" BOOLEAN NOT NULL DEFAULT false; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index b53507abe6a..78143fe0411 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable { // Models on proxy model LiteLLM_ProxyModelTable { model_id String @id @default(uuid()) - model_name String + model_name String litellm_params Json - model_info Json? + model_info Json? + blocked Boolean @default(false) created_at DateTime @default(now()) @map("created_at") created_by String updated_at DateTime @default(now()) @updatedAt @map("updated_at") diff --git a/litellm/__init__.py b/litellm/__init__.py index e1b367fb234..c868ae55b4f 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -416,6 +416,7 @@ custom_prometheus_metadata_labels: List[str] = [] custom_prometheus_tags: List[str] = [] prometheus_metrics_config: Optional[List] = None prometheus_emit_stream_label: bool = False +prometheus_user_budget_label_include_email_alias: bool = False prometheus_end_user_metrics_max_series_per_metric: Optional[int] = 10000 prometheus_end_user_metrics_ttl_seconds: Optional[float] = 3600.0 prometheus_end_user_metrics_cleanup_interval_seconds: Optional[float] = 60.0 @@ -1880,6 +1881,12 @@ if TYPE_CHECKING: from .llms.dashscope.chat.transformation import ( DashScopeChatConfig as DashScopeChatConfig, ) + from .llms.dashscope.embed.transformation import ( + DashScopeEmbeddingConfig as DashScopeEmbeddingConfig, + ) + from .llms.dashscope.rerank.transformation import ( + DashScopeRerankConfig as DashScopeRerankConfig, + ) from .llms.moonshot.chat.transformation import ( MoonshotChatConfig as MoonshotChatConfig, ) diff --git a/litellm/_redis.py b/litellm/_redis.py index 65284162663..5ab551453bb 100644 --- a/litellm/_redis.py +++ b/litellm/_redis.py @@ -100,6 +100,8 @@ def _get_redis_cluster_kwargs(client=None): "azure_tenant_id", "azure_client_secret", "max_connections", + "socket_timeout", + "socket_connect_timeout", } return available_args diff --git a/litellm/caching/caching_handler.py b/litellm/caching/caching_handler.py index 3cf1d911d7f..3f4e54382c9 100644 --- a/litellm/caching/caching_handler.py +++ b/litellm/caching/caching_handler.py @@ -87,6 +87,16 @@ class CachingHandlerResponse(BaseModel): in_memory_cache_obj = InMemoryCache() +def _is_chat_completion_cached_dict(cached_result: dict) -> bool: + cached_id = cached_result.get("id") + if isinstance(cached_id, str) and cached_id.startswith("chatcmpl"): + return True + obj = cached_result.get("object") + if isinstance(obj, str): + return obj.startswith("chat.completion") + return "choices" in cached_result + + def _should_defer_streaming_cache_hit_callbacks(*, kwargs: Dict[str, Any]) -> bool: """ When stream=True, do not run success callbacks at cache-hit time. @@ -861,27 +871,47 @@ class LLMCachingHandler: elif (call_type == "aresponses" or call_type == "responses") and isinstance( cached_result, dict ): - from litellm.responses.streaming_iterator import ( - CachedResponsesAPIStreamingIterator, - ) - - response_obj = ResponsesAPIResponse(**cached_result) - if ( - hasattr(response_obj, "_hidden_params") - and response_obj._hidden_params is not None - and isinstance(response_obj._hidden_params, dict) - ): - response_obj._hidden_params["cache_hit"] = True - - if kwargs.get("stream", False) is True: - cached_result = CachedResponsesAPIStreamingIterator( - response=response_obj, - logging_obj=logging_obj, - request_data=kwargs, - call_type=call_type, - ) + use_chat_completion_cache = _is_chat_completion_cached_dict(cached_result) + if use_chat_completion_cache: + if kwargs.get("stream", False) is True: + bridge_call_type = ( + CallTypes.acompletion.value + if call_type == "aresponses" + else CallTypes.completion.value + ) + cached_result = self._convert_cached_stream_response( + cached_result=cached_result, + call_type=bridge_call_type, + logging_obj=logging_obj, + model=model, + ) + else: + cached_result = convert_to_model_response_object( + response_object=cached_result, + model_response_object=ModelResponse(), + ) else: - cached_result = response_obj + from litellm.responses.streaming_iterator import ( + CachedResponsesAPIStreamingIterator, + ) + + response_obj = ResponsesAPIResponse(**cached_result) + if ( + hasattr(response_obj, "_hidden_params") + and response_obj._hidden_params is not None + and isinstance(response_obj._hidden_params, dict) + ): + response_obj._hidden_params["cache_hit"] = True + + if kwargs.get("stream", False) is True: + cached_result = CachedResponsesAPIStreamingIterator( + response=response_obj, + logging_obj=logging_obj, + request_data=kwargs, + call_type=call_type, + ) + else: + cached_result = response_obj if ( hasattr(cached_result, "_hidden_params") diff --git a/litellm/completion_extras/litellm_responses_transformation/handler.py b/litellm/completion_extras/litellm_responses_transformation/handler.py index ce398ee8288..2de7bda6467 100644 --- a/litellm/completion_extras/litellm_responses_transformation/handler.py +++ b/litellm/completion_extras/litellm_responses_transformation/handler.py @@ -37,6 +37,15 @@ class ResponsesToCompletionBridgeHandler: stream = litellm_params.get("stream", False) return bool(stream) + @staticmethod + def _is_preformatted_cached_chat_stream(result: Any) -> 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, @@ -177,6 +186,8 @@ class ResponsesToCompletionBridgeHandler: **request_data, ) + from litellm.types.utils import ModelResponse + stream = self._resolve_stream_flag(optional_params, litellm_params) if isinstance(result, ResponsesAPIResponse): return self.transformation_handler.transform_response( @@ -192,6 +203,8 @@ class ResponsesToCompletionBridgeHandler: api_key=kwargs.get("api_key"), json_mode=kwargs.get("json_mode"), ) + elif isinstance(result, ModelResponse): + return result elif not stream: responses_api_response = self._collect_response_from_stream(result) return self.transformation_handler.transform_response( @@ -208,6 +221,10 @@ class ResponsesToCompletionBridgeHandler: json_mode=kwargs.get("json_mode"), ) else: + if self._is_preformatted_cached_chat_stream(result): + return self._apply_post_stream_processing( + result, model, custom_llm_provider + ) completion_stream = self.transformation_handler.get_model_response_iterator( streaming_response=result, # type: ignore sync_stream=True, @@ -256,6 +273,8 @@ class ResponsesToCompletionBridgeHandler: aresponses=True, ) + from litellm.types.utils import ModelResponse + stream = self._resolve_stream_flag(optional_params, litellm_params) if isinstance(result, ResponsesAPIResponse): return self.transformation_handler.transform_response( @@ -271,6 +290,8 @@ class ResponsesToCompletionBridgeHandler: api_key=kwargs.get("api_key"), json_mode=kwargs.get("json_mode"), ) + elif isinstance(result, ModelResponse): + return result elif not stream: responses_api_response = await self._collect_response_from_stream_async( result @@ -289,6 +310,10 @@ class ResponsesToCompletionBridgeHandler: json_mode=kwargs.get("json_mode"), ) else: + if self._is_preformatted_cached_chat_stream(result): + return self._apply_post_stream_processing( + result, model, custom_llm_provider + ) completion_stream = self.transformation_handler.get_model_response_iterator( streaming_response=result, # type: ignore sync_stream=False, diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 32423f23314..e3cbf422e5d 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -1141,6 +1141,14 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator): event_type = parsed_chunk.get("type") if isinstance(event_type, ResponsesAPIStreamEvents): event_type = event_type.value + + if parsed_chunk.get("object") == "chat.completion.chunk" or ( + event_type is None + and isinstance(parsed_chunk.get("choices"), list) + and parsed_chunk.get("choices") + ): + return ModelResponseStream(**parsed_chunk) + verbose_logger.debug(f"Chat provider: Processing event type: {event_type}") if event_type == "response.created": diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index c0ec148d03a..2257861aff6 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -173,17 +173,45 @@ def _cost_per_token_custom_pricing_helper( prompt_tokens: float = 0, completion_tokens: float = 0, response_time_ms: Optional[float] = 0.0, + cached_tokens: float = 0, + cache_creation_tokens: float = 0, ### CUSTOM PRICING ### custom_cost_per_token: Optional[CostPerToken] = None, custom_cost_per_second: Optional[float] = None, ) -> Optional[Tuple[float, float]]: - """Internal helper function for calculating cost, if custom pricing given""" + """Internal helper function for calculating cost, if custom pricing given. + + prompt_tokens is assumed to include both cached_tokens and cache_creation_tokens + (OpenAI-compatible convention). Anthropic-style usage where prompt_tokens excludes + cache tokens is handled at the caller (cost_per_token) before invoking this helper. + """ if custom_cost_per_token is None and custom_cost_per_second is None: return None if custom_cost_per_token is not None: - input_cost = custom_cost_per_token["input_cost_per_token"] * prompt_tokens - output_cost = custom_cost_per_token["output_cost_per_token"] * completion_tokens + input_cost_per_token = custom_cost_per_token["input_cost_per_token"] + output_cost_per_token = custom_cost_per_token["output_cost_per_token"] + + cache_read_input_token_cost = custom_cost_per_token.get( + "cache_read_input_token_cost", + input_cost_per_token, + ) + cache_creation_input_token_cost = custom_cost_per_token.get( + "cache_creation_input_token_cost", + input_cost_per_token, + ) + + regular_prompt_tokens = max( + prompt_tokens - cached_tokens - cache_creation_tokens, + 0, + ) + + input_cost = ( + regular_prompt_tokens * input_cost_per_token + + cached_tokens * cache_read_input_token_cost + + cache_creation_tokens * cache_creation_input_token_cost + ) + output_cost = completion_tokens * output_cost_per_token return input_cost, output_cost elif custom_cost_per_second is not None: output_cost = custom_cost_per_second * response_time_ms / 1000 # type: ignore @@ -323,10 +351,56 @@ def cost_per_token( # noqa: PLR0915 ) ## CUSTOM PRICING ## + # Normalize cache token counts across providers: + # - OpenAI-compatible: usage.prompt_tokens_details.cached_tokens + # (prompt_tokens already INCLUDES cached_tokens) + # - Anthropic: usage.cache_read_input_tokens / cache_creation_input_tokens + # (prompt_tokens does NOT include these — adjust before calling helper) + _cache_read_tokens: float = 0 + _cache_creation_tokens: float = 0 + _is_anthropic_style = False + + if usage_object is not None: + _pt_details = getattr(usage_object, "prompt_tokens_details", None) + if _pt_details is not None: + _cache_read_tokens = float(getattr(_pt_details, "cached_tokens", 0) or 0) + # OpenAI-compatible providers report cache-write tokens under + # either `cache_write_tokens` (kimi-k2) or `cache_creation_tokens`. + # Mirror db_spend_update_writer to stay symmetric. + _cache_creation_tokens = float( + getattr(_pt_details, "cache_write_tokens", 0) + or getattr(_pt_details, "cache_creation_tokens", 0) + or 0 + ) + + _anthropic_read = getattr(usage_object, "cache_read_input_tokens", None) + _anthropic_create = getattr(usage_object, "cache_creation_input_tokens", None) + if _anthropic_read is not None or _anthropic_create is not None: + _is_anthropic_style = True + if _anthropic_read is not None: + _cache_read_tokens = float(_anthropic_read) + if _anthropic_create is not None: + _cache_creation_tokens = float(_anthropic_create) + + if not _cache_read_tokens and cache_read_input_tokens: + _cache_read_tokens = float(cache_read_input_tokens) + _is_anthropic_style = True + if not _cache_creation_tokens and cache_creation_input_tokens: + _cache_creation_tokens = float(cache_creation_input_tokens) + _is_anthropic_style = True + + # Anthropic reports prompt_tokens as input_tokens (excluding cache tokens). + # Adjust so the helper's "prompt_tokens includes cache tokens" invariant holds. + _normalized_prompt_tokens = float(prompt_tokens) + if _is_anthropic_style: + _normalized_prompt_tokens += _cache_read_tokens + _cache_creation_tokens + response_cost = _cost_per_token_custom_pricing_helper( - prompt_tokens=prompt_tokens, + prompt_tokens=_normalized_prompt_tokens, completion_tokens=completion_tokens, response_time_ms=response_time_ms, + cached_tokens=_cache_read_tokens, + cache_creation_tokens=_cache_creation_tokens, custom_cost_per_second=custom_cost_per_second, custom_cost_per_token=custom_cost_per_token, ) diff --git a/litellm/exceptions.py b/litellm/exceptions.py index 8b005291556..17f5b43c273 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -918,9 +918,11 @@ class GuardrailRaisedException(Exception): guardrail_name: Optional[str] = None, message: str = "", should_wrap_with_default_message: bool = True, + status_code: int = 400, ): default_message = f"Guardrail raised an exception, Guardrail: {guardrail_name}, Message: {message}" self.guardrail_name = guardrail_name + self.status_code = status_code self.message = default_message if should_wrap_with_default_message else message super().__init__(self.message) @@ -930,12 +932,14 @@ class BlockedPiiEntityError(Exception): self, entity_type: str, guardrail_name: Optional[str] = None, + status_code: int = 400, ): """ Raised when a blocked entity is detected by a guardrail. """ self.entity_type = entity_type self.guardrail_name = guardrail_name + self.status_code = status_code self.message = f"Blocked entity detected: {entity_type} by Guardrail: {guardrail_name}. This entity is not allowed to be used in this request." super().__init__(self.message) diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index c937ad0a7bf..82a35f2eedd 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -43,7 +43,11 @@ if TYPE_CHECKING: dc = DualCache() -from litellm.exceptions import ModifyResponseException as ModifyResponseException +from litellm.exceptions import ( + BlockedPiiEntityError, + GuardrailRaisedException, + ModifyResponseException, +) class CustomGuardrail(CustomLogger): @@ -737,12 +741,15 @@ class CustomGuardrail(CustomLogger): (this was logged previously as an API failure - guardrail_failed_to_respond). Guardrails signal intentional blocks by raising: + - GuardrailRaisedException (generic guardrail API, tool permission) + - BlockedPiiEntityError (Presidio PII detection) - HTTPException with status 400 (content policy violation) - ModifyResponseException (passthrough mode violation) """ - if isinstance(e, ModifyResponseException): return True + if isinstance(e, (GuardrailRaisedException, BlockedPiiEntityError)): + return True if ( HTTPException is not None and isinstance(e, HTTPException) diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 2de8e8bf7f1..ced15a01660 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -1107,8 +1107,13 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): "mcp_tool_call_metadata", "vector_store_request_metadata", ]: - if md.get(key) is not None: - common_attrs[f"metadata.{key}"] = str(md[key]) + value = md.get(key) + if value is None: + continue + if isinstance(value, (dict, list)): + common_attrs[f"metadata.{key}"] = safe_dumps(value) + else: + common_attrs[f"metadata.{key}"] = str(value) # get hidden params hidden_params = getattr(std_log, "hidden_params", None) or (std_log or {}).get( diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 30af0dcb8ed..2c63455565c 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -3540,6 +3540,10 @@ class PrometheusLogger(CustomLogger): user_object.budget_reset_at = user_info.budget_reset_at if user_object.max_budget is None and user_info.max_budget is not None: user_object.max_budget = user_info.max_budget + if user_info.user_email is not None: + user_object.user_email = user_info.user_email + if user_info.user_alias is not None: + user_object.user_alias = user_info.user_alias return user_object @@ -3556,6 +3560,8 @@ class PrometheusLogger(CustomLogger): """ enum_values = UserAPIKeyLabelValues( user=user.user_id, + user_email=user.user_email or "", + user_alias=user.user_alias or "", ) _labels = prometheus_label_factory( diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index f417b4a5f61..3ee56dfc5ca 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -20,6 +20,7 @@ from typing import ( cast, ) +import litellm from litellm import verbose_logger from litellm.router_utils.batch_utils import InMemoryFile from litellm.types.llms.openai import ( @@ -1170,9 +1171,16 @@ def migrate_file_to_image_url( ChatCompletionImageUrlObject, ) - file_id = message["file"].get("file_id") - file_data = message["file"].get("file_data") - format = message["file"].get("format") + file_sub = message.get("file") + if file_sub is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider=None, + ) + file_id = file_sub.get("file_id") + file_data = file_sub.get("file_data") + format = file_sub.get("format") if not file_id and not file_data: raise ValueError("file_id and file_data are both None") image_url_object = ChatCompletionImageObject( diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index d40ca4e3597..79d527d1eb8 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -2057,9 +2057,16 @@ def anthropic_process_openai_file_message( AnthropicMessagesContainerUploadParam, ]: file_message = cast(ChatCompletionFileObject, message) - file_data = file_message["file"].get("file_data") - file_id = file_message["file"].get("file_id") - format = file_message["file"].get("format") + file_sub = file_message.get("file") + if file_sub is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="anthropic", + ) + file_data = file_sub.get("file_data") + file_id = file_sub.get("file_id") + format = file_sub.get("format") if file_data: image_chunk = convert_to_anthropic_image_obj( openai_image_url=file_data, @@ -4879,7 +4886,13 @@ class BedrockConverseMessagesProcessor: @staticmethod def _process_file_message(message: ChatCompletionFileObject) -> BedrockContentBlock: - file_message = message["file"] + file_message = message.get("file") + if file_message is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="bedrock", + ) file_data = file_message.get("file_data") file_id = file_message.get("file_id") @@ -4900,7 +4913,13 @@ class BedrockConverseMessagesProcessor: async def _async_process_file_message( message: ChatCompletionFileObject, ) -> BedrockContentBlock: - file_message = message["file"] + file_message = message.get("file") + if file_message is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="bedrock", + ) file_data = file_message.get("file_data") file_id = file_message.get("file_id") format = file_message.get("format") diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index 0602b1c2f62..620bc91732d 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -5,6 +5,7 @@ from typing import Any, Dict, List, Literal, Optional, Union, cast from httpx import Headers, Response +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.secret_managers.main import get_secret_str @@ -263,9 +264,32 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): cancelling_at=None, cancelled_at=None, request_counts=None, - metadata=original_request.get("metadata", {}), + metadata=self._get_openai_compatible_batch_metadata( + original_request.get("metadata", {}) + ), ) + @staticmethod + def _get_openai_compatible_batch_metadata(metadata: Any) -> Dict[str, str]: + """ + OpenAI Batch metadata only accepts string values. + """ + if not isinstance(metadata, dict): + return {} + + sanitized_metadata: Dict[str, str] = {} + for key, value in metadata.items(): + if key == "standard_logging_guardrail_information" or value is None: + continue + + str_key = str(key) + if isinstance(value, str): + sanitized_metadata[str_key] = value + else: + sanitized_metadata[str_key] = safe_dumps(value) + + return sanitized_metadata + def transform_retrieve_batch_request( self, batch_id: str, diff --git a/litellm/llms/bedrock/embed/cohere_transformation.py b/litellm/llms/bedrock/embed/cohere_transformation.py index d00cb74aae0..2c0dc834144 100644 --- a/litellm/llms/bedrock/embed/cohere_transformation.py +++ b/litellm/llms/bedrock/embed/cohere_transformation.py @@ -22,7 +22,7 @@ class BedrockCohereEmbeddingConfig: ) -> dict: for k, v in non_default_params.items(): if k == "encoding_format": - optional_params["embedding_types"] = v + optional_params["embedding_types"] = v if isinstance(v, list) else [v] elif k == "dimensions": optional_params["output_dimension"] = v return optional_params diff --git a/litellm/llms/dashscope/common_utils.py b/litellm/llms/dashscope/common_utils.py new file mode 100644 index 00000000000..b3b89cbbebf --- /dev/null +++ b/litellm/llms/dashscope/common_utils.py @@ -0,0 +1,28 @@ +""" +Common utilities for the DashScope LLM provider. +""" + +from typing import Optional + +import httpx + +from litellm.llms.base_llm.chat.transformation import BaseLLMException + + +class DashScopeError(BaseLLMException): + """Exception class for DashScope provider errors.""" + + def __init__( + self, + status_code: int, + message: str, + headers: Optional[httpx.Headers] = None, + ): + self.status_code = status_code + self.message = message + self.headers = headers or httpx.Headers() + super().__init__( + status_code=status_code, + message=message, + headers=dict(self.headers), + ) diff --git a/litellm/llms/dashscope/embed/__init__.py b/litellm/llms/dashscope/embed/__init__.py new file mode 100644 index 00000000000..4962b1f3251 --- /dev/null +++ b/litellm/llms/dashscope/embed/__init__.py @@ -0,0 +1,7 @@ +""" +DashScope Embedding Module +""" + +from .transformation import DashScopeEmbeddingConfig + +__all__ = ["DashScopeEmbeddingConfig"] diff --git a/litellm/llms/dashscope/embed/transformation.py b/litellm/llms/dashscope/embed/transformation.py new file mode 100644 index 00000000000..5bc0e5ca817 --- /dev/null +++ b/litellm/llms/dashscope/embed/transformation.py @@ -0,0 +1,191 @@ +""" +Transformation logic from OpenAI /v1/embeddings format to DashScope's /v1/embeddings format. + +Supports +- text-embedding-v4 +- text-embedding-v3 + +Endpoint +- https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings + +Docs - https://help.aliyun.com/zh/model-studio/text-embedding-synchronous-api +""" + +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + +from ..common_utils import DashScopeError + +DEFAULT_API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1" + + +class DashScopeEmbeddingConfig(BaseEmbeddingConfig): + """ + Reference: https://help.aliyun.com/zh/model-studio/text-embedding-synchronous-api + + DashScope exposes an OpenAI-compatible /v1/embeddings endpoint, so the + request and response shapes are nearly identical to OpenAI's. + """ + + def __init__(self) -> None: + pass + + def get_supported_openai_params(self, model: str) -> List[str]: + # DashScope's compatible-mode embeddings API accepts the same params as OpenAI. + # `dimensions` / `encoding_format` are only honored by text-embedding-v3 / v4; + # earlier versions silently ignore them server-side. + return ["dimensions", "encoding_format", "user"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool = False, + ) -> dict: + supported = self.get_supported_openai_params(model) + for k, v in non_default_params.items(): + if v is None: + continue + if k in supported: + optional_params[k] = v + # unsupported params are dropped when drop_params=True; + # the upstream _check_valid_arg already raised UnsupportedParamsError + # for drop_params=False before this method is called. + return optional_params + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = 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 = { + "Content-Type": "application/json", + "Authorization": f"Bearer {api_key}", + } + return {**default_headers, **headers} + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + base = api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE + base = base.rstrip("/") + if base.endswith("/embeddings"): + return base + return f"{base}/embeddings" + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + data: dict = { + "model": model, + "input": input, + } + for key in ("dimensions", "encoding_format", "user"): + value = optional_params.get(key) + if value is not None: + data[key] = value + return data + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + try: + response_json = raw_response.json() + except Exception as e: + raise DashScopeError( + status_code=raw_response.status_code, + message=f"Failed to parse DashScope response as JSON: {str(e)}", + ) + + logging_obj.post_call( + input=request_data.get("input"), + api_key=api_key, + additional_args={"complete_input_dict": request_data}, + original_response=response_json, + ) + + if "error" in response_json: + error = response_json["error"] + message = ( + error.get("message", str(error)) + if isinstance(error, dict) + else str(error) + ) + raise DashScopeError( + status_code=raw_response.status_code, + message=message, + ) + + model_response.object = "list" + model_response.data = response_json.get("data", []) + model_response.model = response_json.get("model", model) + + usage = response_json.get("usage") or {} + prompt_tokens = usage.get("prompt_tokens", 0) + total_tokens = usage.get("total_tokens", prompt_tokens) + setattr( + model_response, + "usage", + Usage( + prompt_tokens=prompt_tokens, + completion_tokens=0, + total_tokens=total_tokens, + ), + ) + + if "id" in response_json: + setattr(model_response, "id", response_json["id"]) + + return model_response + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers], + ) -> BaseLLMException: + if isinstance(headers, dict): + headers = httpx.Headers(headers) + return DashScopeError( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/llms/dashscope/rerank/__init__.py b/litellm/llms/dashscope/rerank/__init__.py new file mode 100644 index 00000000000..2a1401f6dc0 --- /dev/null +++ b/litellm/llms/dashscope/rerank/__init__.py @@ -0,0 +1,7 @@ +""" +DashScope Rerank Module +""" + +from .transformation import DashScopeRerankConfig + +__all__ = ["DashScopeRerankConfig"] diff --git a/litellm/llms/dashscope/rerank/transformation.py b/litellm/llms/dashscope/rerank/transformation.py new file mode 100644 index 00000000000..629f3cf4af7 --- /dev/null +++ b/litellm/llms/dashscope/rerank/transformation.py @@ -0,0 +1,241 @@ +""" +Transformation logic for DashScope's OpenAI-compatible /v1/reranks API. + +Supports +- qwen3-rerank + +(Other DashScope rerankers — gte-rerank-v2 / qwen3-vl-rerank — share the same +endpoint but have not been validated against this transformer. Behavior with +those models is undefined.) + +Endpoint +- https://dashscope.aliyuncs.com/compatible-api/v1/reranks + +Note: chat/embed live under `/compatible-mode/v1/`, but DashScope's rerank +route is exposed under `/compatible-api/v1/reranks` per the docs. Override +with `DASHSCOPE_API_BASE_RERANK` to point at a different host or path. + +Empirically, qwen3-rerank accepts `return_documents=true` and echoes +`results[].document.text` back, even though the public docs list the flag +as supported only for gte-rerank-v2 / qwen3-vl-rerank. + +Docs - https://help.aliyun.com/zh/model-studio/text-rerank-api +""" + +from typing import Any, Dict, List, Optional, Union + +import httpx + +from litellm._uuid import uuid +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.rerank import ( + OptionalRerankParams, + RerankBilledUnits, + RerankResponse, + RerankResponseMeta, + RerankTokens, +) + +from ..common_utils import DashScopeError + +DEFAULT_RERANK_URL = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" + + +class DashScopeRerankConfig(BaseRerankConfig): + """ + Reference: https://help.aliyun.com/zh/model-studio/text-rerank-api + + Targets DashScope's qwen3-rerank model. Request fields: model, query, + documents, top_n, return_documents. Response: results[].index, + results[].relevance_score, optionally results[].document.text (when + return_documents=true), plus a top-level usage.total_tokens counter. + """ + + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: Optional[dict] = None, + ) -> str: + if api_base is None: + api_base = get_secret_str("DASHSCOPE_API_BASE_RERANK") or DEFAULT_RERANK_URL + + if api_base == DEFAULT_RERANK_URL: + return DEFAULT_RERANK_URL + + cleaned = api_base.rstrip("/") + if cleaned.endswith("/reranks") or cleaned.endswith("/rerank"): + return cleaned + + if cleaned.endswith("/v1"): + return f"{cleaned}/reranks" + + # Unknown base: append /reranks rather than silently ignoring the caller's api_base. + return f"{cleaned}/reranks" + + def validate_environment( + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + optional_params: Optional[dict] = 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 = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + "content-type": "application/json", + } + return {**default_headers, **headers} + + def get_supported_cohere_rerank_params(self, model: str) -> list: + return ["query", "documents", "top_n", "return_documents"] + + def map_cohere_rerank_params( + self, + non_default_params: Optional[dict], + model: str, + drop_params: bool, + query: str, + documents: List[Union[str, Dict[str, Any]]], + custom_llm_provider: Optional[str] = None, + top_n: Optional[int] = None, + rank_fields: Optional[List[str]] = None, + return_documents: Optional[bool] = True, + max_chunks_per_doc: Optional[int] = None, + max_tokens_per_doc: Optional[int] = None, + ) -> Dict: + # qwen3-rerank accepts query/documents/top_n/return_documents. The + # rest (rank_fields, max_*_per_doc) are silently dropped. + params: OptionalRerankParams = OptionalRerankParams( + query=query, + documents=documents, + ) + if top_n is not None: + params["top_n"] = top_n + if return_documents is not None: + params["return_documents"] = return_documents + return dict(params) + + def transform_rerank_request( + self, + model: str, + optional_rerank_params: Dict, + headers: dict, + litellm_params: Optional[dict] = None, + ) -> dict: + if "query" not in optional_rerank_params: + raise ValueError("query is required for DashScope rerank") + if "documents" not in optional_rerank_params: + raise ValueError("documents is required for DashScope rerank") + + request: Dict[str, Any] = { + "model": model, + "query": optional_rerank_params["query"], + "documents": optional_rerank_params["documents"], + } + if optional_rerank_params.get("top_n") is not None: + request["top_n"] = optional_rerank_params["top_n"] + if optional_rerank_params.get("return_documents") is not None: + request["return_documents"] = optional_rerank_params["return_documents"] + return request + + def transform_rerank_response( + self, + model: str, + raw_response: httpx.Response, + model_response: RerankResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str] = None, + request_data: Optional[dict] = None, + optional_params: Optional[dict] = None, + litellm_params: Optional[dict] = None, + ) -> RerankResponse: + request_data = request_data or {} + optional_params = optional_params or {} + litellm_params = litellm_params or {} + try: + response_json = raw_response.json() + except Exception: + raise DashScopeError( + status_code=raw_response.status_code, + message=raw_response.text, + ) + + logging_obj.post_call( + input=request_data.get("query"), + api_key=api_key, + additional_args={"complete_input_dict": request_data}, + original_response=response_json, + ) + + # DashScope error envelope: {"code": "...", "message": "...", "request_id": "..."} + if "code" in response_json and "results" not in response_json: + raise DashScopeError( + status_code=raw_response.status_code, + message=response_json.get("message", str(response_json)), + ) + + results = response_json.get("results") + if results is None: + raise DashScopeError( + status_code=raw_response.status_code, + message=f"No results in DashScope rerank response: {response_json}", + ) + + # qwen3-rerank returns: + # {"index": int, "relevance_score": float} + # plus, when return_documents=true was sent: + # "document": {"text": "..."} + # which already matches LiteLLM's RerankResponseDocument shape. + transformed_results: List[dict] = [] + for r in results: + item: Dict[str, Any] = { + "index": r["index"], + "relevance_score": r["relevance_score"], + } + doc = r.get("document") + if isinstance(doc, dict): + item["document"] = doc + elif isinstance(doc, str): + # Defensive: spec says dict, but normalize string-shaped echoes. + item["document"] = {"text": doc} + transformed_results.append(item) + + usage = response_json.get("usage") or {} + total_tokens = usage.get("total_tokens") + billed_units = RerankBilledUnits(total_tokens=total_tokens) + tokens = RerankTokens(input_tokens=total_tokens) + meta = RerankResponseMeta(billed_units=billed_units, tokens=tokens) + + return RerankResponse( + id=response_json.get("id") or str(uuid.uuid4()), + results=transformed_results, # type: ignore + meta=meta, + ) + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers], + ) -> BaseLLMException: + if isinstance(headers, dict): + headers = httpx.Headers(headers) + return DashScopeError( + status_code=status_code, + message=error_message, + headers=headers, + ) diff --git a/litellm/llms/deepseek/chat/transformation.py b/litellm/llms/deepseek/chat/transformation.py index 5cd8d119542..7ed3e484535 100644 --- a/litellm/llms/deepseek/chat/transformation.py +++ b/litellm/llms/deepseek/chat/transformation.py @@ -2,13 +2,15 @@ Translates from OpenAI's `/v1/chat/completions` to DeepSeek's `/v1/chat/completions` """ -from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, overload +from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, cast, overload +import litellm from litellm.litellm_core_utils.prompt_templates.common_utils import ( handle_messages_with_content_list_to_str_conversion, ) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues +from litellm.utils import supports_reasoning from ...openai.chat.gpt_transformation import OpenAIGPTConfig @@ -62,6 +64,48 @@ class DeepSeekChatConfig(OpenAIGPTConfig): return optional_params + def _fill_reasoning_content( + self, messages: List[AllMessageValues] + ) -> List[AllMessageValues]: + """ + DeepSeek thinking mode requires `reasoning_content` to be passed back on + every assistant message in multi-turn conversations. If it is missing, + the API returns: + "The reasoning_content in the thinking mode must be passed back to the API." + + For each assistant message that is missing `reasoning_content`: + 1. Promote it from `provider_specific_fields["reasoning_content"]` if present + (LiteLLM stores provider-specific response fields there). + 2. Otherwise inject a single space — the minimum value the API accepts. + """ + result: List[AllMessageValues] = [] + for msg in messages: + if msg.get("role") == "assistant" and not msg.get("reasoning_content"): + patched = dict(cast(dict, msg)) + provider_fields = patched.get("provider_specific_fields") or {} + stored = provider_fields.get("reasoning_content") + if stored: + patched["reasoning_content"] = stored + cleaned = dict(provider_fields) + cleaned.pop("reasoning_content", None) + patched["provider_specific_fields"] = cleaned + else: + litellm.verbose_logger.warning( + "DeepSeek thinking mode: assistant message is missing " + "`reasoning_content` and none was saved in " + "`provider_specific_fields`. A single-space placeholder " + "is being injected to satisfy API validation, but the " + "model will receive a blank reasoning chain for this turn, " + "which may silently degrade multi-turn response quality. " + "Preserve `reasoning_content` from the original assistant " + "response when building multi-turn conversation history." + ) + patched["reasoning_content"] = " " + result.append(cast(AllMessageValues, patched)) + else: + result.append(msg) + return result + @overload def _transform_messages( self, messages: List[AllMessageValues], model: str, is_async: Literal[True] @@ -91,6 +135,66 @@ class DeepSeekChatConfig(OpenAIGPTConfig): messages=messages, model=model, is_async=False ) + def _thinking_mode_active(self, model: str, optional_params: dict) -> bool: + """ + Returns True only when thinking mode is actually active for this request: + - model supports reasoning (capability check) + - user explicitly passed thinking={"type": "enabled"} (opt-in check) + """ + return ( + supports_reasoning(model=model, custom_llm_provider="deepseek") + and (optional_params.get("thinking") or {}).get("type") == "enabled" + ) + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Ensures `reasoning_content` is forwarded on assistant messages for + multi-turn thinking-mode conversations (issue #28045). + + Only runs when thinking mode is actually active - guarded by both + supports_reasoning() (model capability) and optional_params["thinking"] + (user explicitly enabled it), preventing spurious injection on models + like deepseek-v3.2 that support thinking as opt-in but not always-on. + """ + if self._thinking_mode_active(model=model, optional_params=optional_params): + messages = self._fill_reasoning_content(messages) + return super().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + + async def async_transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Async equivalent of transform_request — applies the same reasoning_content + fix for multi-turn thinking-mode conversations. + """ + if self._thinking_mode_active(model=model, optional_params=optional_params): + messages = self._fill_reasoning_content(messages) + return await super().async_transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + def _get_openai_compatible_provider_info( self, api_base: Optional[str], api_key: Optional[str] ) -> Tuple[Optional[str], Optional[str]]: diff --git a/litellm/llms/deepseek/messages/transformation.py b/litellm/llms/deepseek/messages/transformation.py new file mode 100644 index 00000000000..ad60478960e --- /dev/null +++ b/litellm/llms/deepseek/messages/transformation.py @@ -0,0 +1,133 @@ +""" +DeepSeek Anthropic-compatible messages transformation config. +""" + +from typing import Any, Dict, List, Optional, Tuple + +import litellm +from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( + AnthropicMessagesConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.router import GenericLiteLLMParams + + +class DeepSeekAnthropicMessagesConfig(AnthropicMessagesConfig): + """ + DeepSeek exposes an Anthropic-compatible Messages API at + https://api.deepseek.com/anthropic. + + It accepts the native Anthropic Messages conversation shape, including + thinking blocks in assistant history, but rejects Anthropic's explicit + custom-tool discriminator (`{"type": "custom"}`). + """ + + @property + def custom_llm_provider(self) -> Optional[str]: + return "deepseek" + + @staticmethod + def get_api_key(api_key: Optional[str] = None) -> Optional[str]: + return api_key or get_secret_str("DEEPSEEK_API_KEY") or litellm.api_key + + @staticmethod + def get_api_base(api_base: Optional[str] = None) -> str: + return ( + api_base + or get_secret_str("DEEPSEEK_ANTHROPIC_API_BASE") + or get_secret_str("DEEPSEEK_API_BASE") + or "https://api.deepseek.com/anthropic" + ) + + def validate_anthropic_messages_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> Tuple[dict, Optional[str]]: + dynamic_api_key = self.get_api_key(api_key=api_key) + + if ( + "x-api-key" not in headers + and "authorization" not in headers + and dynamic_api_key is not None + ): + headers["x-api-key"] = dynamic_api_key + + if "anthropic-version" not in headers: + headers["anthropic-version"] = "2023-06-01" + if "content-type" not in headers: + headers["content-type"] = "application/json" + + headers = self._update_headers_with_anthropic_beta( + headers=headers, + optional_params=optional_params, + custom_llm_provider=self.custom_llm_provider or "deepseek", + ) + + return headers, api_base + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + base_url = self.get_api_base(api_base=api_base).rstrip("/") + + if base_url.endswith("/v1/messages") and "/anthropic/" in base_url: + return base_url + if base_url.endswith("/v1/messages"): + base_url = base_url[: -len("/v1/messages")] + if base_url.endswith("/v1"): + base_url = base_url[: -len("/v1")] + if base_url.endswith("/beta"): + base_url = base_url[: -len("/beta")] + + if not base_url.endswith("/anthropic") and "/anthropic/" not in base_url: + base_url = f"{base_url}/anthropic" + + return f"{base_url}/v1/messages" + + @staticmethod + def _sanitize_tools_for_deepseek(tools: Any) -> Any: + if not isinstance(tools, list): + return tools + + sanitized_tools = [] + for tool in tools: + if isinstance(tool, dict) and tool.get("type") == "custom": + sanitized_tool = dict(tool) + sanitized_tool.pop("type", None) + sanitized_tools.append(sanitized_tool) + else: + sanitized_tools.append(tool) + return sanitized_tools + + def transform_anthropic_messages_request( + self, + model: str, + messages: List[Dict], + anthropic_messages_optional_request_params: Dict, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + anthropic_messages_request = super().transform_anthropic_messages_request( + model=model, + messages=messages, + anthropic_messages_optional_request_params=anthropic_messages_optional_request_params, + litellm_params=litellm_params, + headers=headers, + ) + if "tools" in anthropic_messages_request: + anthropic_messages_request["tools"] = self._sanitize_tools_for_deepseek( + anthropic_messages_request["tools"] + ) + return anthropic_messages_request diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py index 72569e5c6cd..16e17dcc876 100644 --- a/litellm/llms/gemini/chat/transformation.py +++ b/litellm/llms/gemini/chat/transformation.py @@ -1,5 +1,7 @@ from typing import List, Optional, cast +import litellm + from litellm.litellm_core_utils.prompt_templates.factory import ( convert_generic_image_chunk_to_openai_image_obj, convert_to_anthropic_image_obj, @@ -101,7 +103,10 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): return supported_params def _transform_messages( - self, messages: List[AllMessageValues], model: Optional[str] = None + self, + messages: List[AllMessageValues], + model: Optional[str] = None, + litellm_params: Optional[dict] = None, ) -> List[ContentType]: """ Google AI Studio Gemini does not support HTTP/HTTPS URLs for files. @@ -141,14 +146,23 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig): img_element["image_url"] = converted_image_url # type: ignore elif element.get("type") == "file": file_element = cast(ChatCompletionFileObject, element) - file_id = file_element["file"].get("file_id") + _file_field = file_element.get("file") + if _file_field is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=model, + llm_provider="gemini", + ) + file_id = _file_field.get("file_id") if file_id and ("http://" in file_id or "https://" in file_id): # Convert HTTP/HTTPS file URL to base64 data try: base64_data = convert_url_to_base64(file_id) - file_element["file"]["file_data"] = base64_data # type: ignore - file_element["file"].pop("file_id", None) # type: ignore + _file_field["file_data"] = base64_data # type: ignore + _file_field.pop("file_id", None) # type: ignore except Exception: # If conversion fails, leave as is and let the API handle it pass - return _gemini_convert_messages_with_history(messages=messages, model=model) + return _gemini_convert_messages_with_history( + messages=messages, model=model, litellm_params=litellm_params + ) diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 6b7ec4dfb1c..5464b5bb7ee 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -287,7 +287,13 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): content_item["image_url"] = new_image_url_obj elif content_item.get("type") == "file": content_item = cast(ChatCompletionFileObject, content_item) - file_obj = content_item["file"] + file_obj = content_item.get("file") + if file_obj is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=None, + llm_provider="openai", + ) new_file_obj = ChatCompletionFileObjectFile( **{ # type: ignore k: v diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 9afa5dec465..f56992a2502 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -6,13 +6,16 @@ Why separate file? Make it easy to see how transformation works import json import os -from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple, Union, cast +import re +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Union, cast +from urllib.parse import quote import httpx from pydantic import BaseModel import litellm from litellm._logging import verbose_logger +from litellm.litellm_core_utils.asyncify import asyncify from litellm.litellm_core_utils.prompt_templates.common_utils import ( _get_image_mime_type_from_url, ) @@ -57,6 +60,45 @@ from ..common_utils import ( get_supports_system_message, ) +# Typed as Any to avoid introducing a module-load-time cyclic import to +# vertex_llm_base. The instance is lazily constructed by _get_vertex_base() +# the first time GCS metadata needs to be fetched. +_GCS_METADATA_VERTEX_BASE: Optional[Any] = None +# Shared sync client for GCS JSON API metadata reads so proxy/SSL settings +# from litellm's HTTP stack apply (see Greptile review on PR #27278). +_GCS_METADATA_HTTP_HANDLER: Optional[HTTPHandler] = None +_GEMINI_MIME_TYPE_ALIASES: Dict[str, str] = { + "image/jpg": "image/jpeg", +} + + +def _apply_gemini_mime_type_aliases(mime_type: str) -> str: + """Normalize known MIME aliases only; does not consult the file-type registry. + + Also strips MIME parameters (e.g. ``; charset=utf-8``) so that values + sourced from GCS object metadata (``contentType``) validate correctly. + """ + normalized = mime_type.split(";", 1)[0].strip().lower() + return _GEMINI_MIME_TYPE_ALIASES.get(normalized, normalized) + + +def _get_vertex_base() -> Any: + """Lazily return the shared VertexBase instance to avoid a module-load-time cyclic import.""" + global _GCS_METADATA_VERTEX_BASE + if _GCS_METADATA_VERTEX_BASE is None: + from ..vertex_llm_base import VertexBase + + _GCS_METADATA_VERTEX_BASE = VertexBase() + return _GCS_METADATA_VERTEX_BASE + + +def _get_gcs_metadata_http_handler() -> HTTPHandler: + global _GCS_METADATA_HTTP_HANDLER + if _GCS_METADATA_HTTP_HANDLER is None: + _GCS_METADATA_HTTP_HANDLER = HTTPHandler(timeout=5.0) + return _GCS_METADATA_HTTP_HANDLER + + if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -171,12 +213,299 @@ def _apply_gemini_metadata( return cast(PartType, part_dict) +def _parse_gs_uri(gs_uri: str) -> Tuple[str, str]: + if not gs_uri.startswith("gs://"): + raise ValueError(f"Invalid gs URI: {gs_uri}") + uri_without_scheme = gs_uri[5:] # drop gs:// + uri_parts = uri_without_scheme.split("/", 1) + if len(uri_parts) != 2 or not uri_parts[0] or not uri_parts[1]: + raise ValueError(f"Invalid gs URI: {gs_uri}") + return uri_parts[0], uri_parts[1] + + +def _is_valid_gcs_bucket_name(bucket: str) -> bool: + """ + Validate bucket name against core GCS naming constraints. + """ + bucket_length = len(bucket) + max_bucket_length = 222 if "." in bucket else 63 + if bucket_length < 3 or bucket_length > max_bucket_length: + return False + if "." in bucket and any( + len(label) == 0 or len(label) > 63 for label in bucket.split(".") + ): + return False + if not re.fullmatch(r"[a-z0-9][a-z0-9._-]*[a-z0-9]", bucket): + return False + if ".." in bucket: + return False + if re.fullmatch(r"\d+\.\d+\.\d+\.\d+", bucket): + return False + return True + + +def _gs_uri_requires_content_type_metadata(url: str) -> bool: + """ + True when _process_gemini_media would call _get_gcs_object_content_type + (extension-less gs:// and no explicit format passed into that helper). + """ + if "gs://" not in url: + return False + extension_with_dot = os.path.splitext(url)[-1] + extension = extension_with_dot[1:] if extension_with_dot else "" + return len(extension) == 0 + + +def _image_url_payload_may_need_sync_gcs_metadata_fetch( + raw_image_url: Any, +) -> bool: + """ + True when this image_url value (content-part image_url or assistant ``images[]`` + entry) can trigger a blocking GCS metadata read for MIME resolution. + """ + fmt: Optional[str] = None + url: Optional[str] = None + if isinstance(raw_image_url, dict): + url = raw_image_url.get("url") # type: ignore[assignment] + if not isinstance(url, str): + return False + fmt = ( + raw_image_url.get("format") + or raw_image_url.get("mime_type") + or raw_image_url.get("content_type") + ) + elif isinstance(raw_image_url, str): + url = raw_image_url + else: + return False + if "gs://" not in url or fmt: + return False + return _gs_uri_requires_content_type_metadata(url) + + +def _openai_messages_may_need_sync_gcs_metadata_fetch( + messages: List[AllMessageValues], +) -> bool: + """ + Heuristic: True if any message part can trigger a blocking GCS JSON + metadata read inside _transform_request_body (extension-less gs:// without + explicit MIME hints). Covers user/system ``content`` parts and assistant + ``images`` (same paths as ``_gemini_convert_messages_with_history``). Used + to decide whether ``async_transform_request_body`` should offload the sync + transform via ``asyncify``. + """ + for raw in messages: + msg: Any = raw + if not isinstance(msg, dict) and hasattr(msg, "model_dump"): + msg = msg.model_dump(exclude_none=False) + if not isinstance(msg, dict): + continue + images_field = msg.get("images") + if isinstance(images_field, list): + for image_item in images_field: + if not isinstance(image_item, dict): + continue + if _image_url_payload_may_need_sync_gcs_metadata_fetch( + image_item.get("image_url") + ): + return True + + content = msg.get("content") + if not isinstance(content, list): + continue + for item in content: + if not isinstance(item, dict): + continue + itype = item.get("type") + if itype == "image_url": + if _image_url_payload_may_need_sync_gcs_metadata_fetch( + item.get("image_url") + ): + return True + elif itype == "file": + file_obj = item.get("file") + if not isinstance(file_obj, dict): + continue + fmt = ( + file_obj.get("format") + or file_obj.get("mime_type") + or file_obj.get("content_type") + ) + passed = file_obj.get("file_id") or file_obj.get("file_data") + if ( + isinstance(passed, str) + and "gs://" in passed + and not fmt + and _gs_uri_requires_content_type_metadata(passed) + ): + return True + return False + + +def _get_gcs_object_content_type( + image_url: str, + vertex_project: Optional[str] = None, + vertex_credentials: Optional[Any] = None, +) -> Optional[str]: + """ + Resolve content type from GCS object metadata. + + Only attaches a Bearer token when the caller explicitly supplies Vertex + credentials, to avoid using the server's default Google credentials on + the Gemini API-key (Google AI Studio) path and being used as an oracle + for private GCS object metadata. Without explicit credentials we only + issue an anonymous request, which only succeeds for publicly-readable + objects. + """ + try: + bucket, object_name = _parse_gs_uri(image_url) + except ValueError: + return None + if not _is_valid_gcs_bucket_name(bucket): + return None + + headers: Dict[str, str] = {} + explicit_vertex_auth_provided = ( + vertex_project is not None or vertex_credentials is not None + ) + if explicit_vertex_auth_provided: + try: + access_token, _ = _get_vertex_base().get_access_token( + credentials=vertex_credentials, + project_id=vertex_project, + ) + headers["Authorization"] = f"Bearer {access_token}" + except Exception as e: + raise litellm.BadRequestError( + message=( + "Unable to fetch GCS metadata with provided Vertex credentials/project. " + f"Original error: {str(e)}" + ), + model=None, + llm_provider="vertex_ai", + ) + + # Build the URL via httpx.URL with a fixed scheme/host and URL-encode both + # bucket and object so CodeQL does not flag the interpolation as a + # potential SSRF that could resolve to an arbitrary host. + encoded_bucket = quote(bucket, safe="") + encoded_object = quote(object_name, safe="") + metadata_url = httpx.URL( + scheme="https", + host="storage.googleapis.com", + path=f"/storage/v1/b/{encoded_bucket}/o/{encoded_object}", + params={"fields": "contentType"}, + ) + try: + response = _get_gcs_metadata_http_handler().get( + url=str(metadata_url), + headers=headers or None, + ) + except httpx.RequestError as e: + if explicit_vertex_auth_provided: + raise litellm.BadRequestError( + message=( + "Unable to reach GCS JSON API for object metadata with provided " + f"Vertex credentials. {type(e).__name__}: {e}" + ), + model=None, + llm_provider="vertex_ai", + ) from e + return None + + if response.is_error: + if explicit_vertex_auth_provided: + preview = (response.text or "")[:1024] + raise litellm.BadRequestError( + message=( + "Unable to read GCS object metadata with provided Vertex credentials. " + f"HTTP {response.status_code}. Response body (truncated): {preview!r}" + ), + model=None, + llm_provider="vertex_ai", + ) + return None + + try: + payload = response.json() + except ValueError as e: + if explicit_vertex_auth_provided: + raise litellm.BadRequestError( + message=( + "GCS metadata response was not valid JSON when using provided " + f"Vertex credentials (HTTP {response.status_code}). Error: {e}" + ), + model=None, + llm_provider="vertex_ai", + ) from e + return None + + if not isinstance(payload, dict): + if explicit_vertex_auth_provided: + raise litellm.BadRequestError( + message=( + "GCS metadata response was not a JSON object when using provided " + f"Vertex credentials (HTTP {response.status_code})." + ), + model=None, + llm_provider="vertex_ai", + ) + return None + + content_type = payload.get("contentType") + if isinstance(content_type, str) and len(content_type) > 0: + return content_type + + if explicit_vertex_auth_provided: + preview = (response.text or "")[:1024] + raise litellm.BadRequestError( + message=( + "GCS metadata JSON did not include a non-empty contentType field when " + f"using provided Vertex credentials (HTTP {response.status_code}). " + f"Body (truncated): {preview!r}" + ), + model=None, + llm_provider="vertex_ai", + ) + return None + + +def _normalize_and_validate_gemini_mime_type( + mime_type: str, model: Optional[str] +) -> str: + # Import lazily to avoid a module-level cyclic-import alert with + # litellm.types.files. + from litellm.types.files import get_file_extension_from_mime_type + + normalized_mime_type = _apply_gemini_mime_type_aliases(mime_type) + try: + file_extension = get_file_extension_from_mime_type(normalized_mime_type) + file_type = get_file_type_from_extension(file_extension) + except ValueError: + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {normalized_mime_type}", + model=model, + llm_provider="vertex_ai", + ) + + if not is_gemini_1_5_accepted_file_type(file_type): + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {file_type}", + model=model, + llm_provider="vertex_ai", + ) + + return get_file_mime_type_for_file_type(file_type) + + def _process_gemini_media( image_url: str, format: Optional[str] = None, media_resolution_enum: Optional[Dict[str, str]] = None, model: Optional[str] = None, video_metadata: Optional[Dict[str, Any]] = None, + vertex_project: Optional[str] = None, + vertex_credentials: Optional[Any] = None, ) -> PartType: """ Given a media URL (image, audio, or video), return the appropriate PartType for Gemini @@ -193,20 +522,63 @@ def _process_gemini_media( try: # GCS URIs if "gs://" in image_url: - # Figure out file type extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png" extension = extension_with_dot[1:] # Ex: "png" + explicit_gcs_format = False if not format: - file_type = get_file_type_from_extension(extension) + mime_type: Optional[str] = None + # For extension-less gs:// URIs, we cannot infer from path. + # If callers pass `format`/`mime_type`, this branch is skipped. + if extension: + file_type = get_file_type_from_extension(extension) - # Validate the file type is supported by Gemini - if not is_gemini_1_5_accepted_file_type(file_type): - raise Exception(f"File type not supported by gemini - {file_type}") + # Validate the file type is supported by Gemini + if not is_gemini_1_5_accepted_file_type(file_type): + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {file_type}", + model=model, + llm_provider="vertex_ai", + ) - mime_type = get_file_mime_type_for_file_type(file_type) + mime_type = get_file_mime_type_for_file_type(file_type) + else: + mime_type = _get_gcs_object_content_type( + image_url=image_url, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, + ) + if mime_type is None: + raise litellm.BadRequestError( + message=( + f"Unable to determine mime type for gs URI: {image_url}. " + "This gs:// URI has no file extension and GCS metadata " + "lookup failed. Set it explicitly using image_url.format " + "(or image_url.mime_type/content_type) or " + "message.content[].file.format." + ), + model=model, + llm_provider="vertex_ai", + ) else: mime_type = format + explicit_gcs_format = True + if mime_type is None: + raise litellm.BadRequestError( + message=f"File type not supported by gemini - {image_url}", + model=model, + llm_provider="vertex_ai", + ) + if explicit_gcs_format: + # Callers who pass format/mime_type explicitly for gs:// URIs + # rely on pass-through to Gemini (pre-PR behavior). Only apply + # known MIME aliases; skip litellm's file-type registry. + mime_type = _apply_gemini_mime_type_aliases(mime_type) + else: + mime_type = _normalize_and_validate_gemini_mime_type( + mime_type=mime_type, + model=model, + ) file_data = FileDataType(mime_type=mime_type, file_uri=image_url) part: PartType = {"file_data": file_data} return _apply_gemini_metadata( @@ -258,8 +630,6 @@ def _snake_to_camel(snake_str: str) -> str: def _camel_to_snake(camel_str: str) -> str: """Convert camelCase to snake_case""" - import re - return re.sub(r"(? List[ContentType]: """ Converts given messages from OpenAI format to Gemini format @@ -326,6 +697,16 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 msg_i = 0 tool_call_responses = [] + vertex_project = None + vertex_credentials = None + if litellm_params: + vertex_project = litellm_params.get("vertex_project") or litellm_params.get( + "vertex_ai_project" + ) + vertex_credentials = litellm_params.get( + "vertex_credentials" + ) or litellm_params.get("vertex_ai_credentials") + try: while msg_i < len(messages): user_content: List[PartType] = [] @@ -351,20 +732,42 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 img_element = element format: Optional[str] = None media_resolution_enum: Optional[Dict[str, str]] = None - if isinstance(img_element["image_url"], dict): - image_url = img_element["image_url"]["url"] - format = img_element["image_url"].get("format") - detail = img_element["image_url"].get("detail") + raw_image_url = img_element.get("image_url") + if raw_image_url is None: + raise litellm.BadRequestError( + message="Invalid message content: element type is 'image_url' but 'image_url' field is missing ", + model=model, + llm_provider="vertex_ai", + ) + if isinstance(raw_image_url, dict): + image_url = raw_image_url.get("url") + if image_url is None: + raise litellm.BadRequestError( + message="Invalid message content: element type is 'image_url' but 'url' field is missing inside 'image_url' ", + model=model, + llm_provider="vertex_ai", + ) + # TypedDict does not declare mime_type/content_type; + # read via Dict[str, Any] for caller-provided MIME fields. + image_url_dict = cast(Dict[str, Any], raw_image_url) + format = ( + image_url_dict.get("format") + or image_url_dict.get("mime_type") + or image_url_dict.get("content_type") + ) + detail = image_url_dict.get("detail") media_resolution_enum = ( _convert_detail_to_media_resolution_enum(detail) ) else: - image_url = img_element["image_url"] + image_url = raw_image_url _part = _process_gemini_media( image_url=image_url, format=format, media_resolution_enum=media_resolution_enum, model=model, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) _parts.append(_part) elif element["type"] == "input_audio": @@ -390,15 +793,31 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 image_url=openai_image_str, format=audio_format_modified, model=model, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) _parts.append(_part) elif element["type"] == "file": file_element = cast(ChatCompletionFileObject, element) - file_id = file_element["file"].get("file_id") - format = file_element["file"].get("format") - file_data = file_element["file"].get("file_data") - detail = file_element["file"].get("detail") - video_metadata = file_element["file"].get("video_metadata") + _file_field = file_element.get("file") + if _file_field is None: + raise litellm.BadRequestError( + message="Content block has type='file' but is missing the required 'file' field", + model=model, + llm_provider="vertex_ai", + ) + # TypedDict does not declare mime_type/content_type; + # read via Dict[str, Any] for caller-provided MIME fields. + file_dict = cast(Dict[str, Any], _file_field) + file_id = file_dict.get("file_id") + format = ( + file_dict.get("format") + or file_dict.get("mime_type") + or file_dict.get("content_type") + ) + file_data = file_dict.get("file_data") + detail = file_dict.get("detail") + video_metadata = file_dict.get("video_metadata") passed_file = file_id or file_data if passed_file is None: raise Exception( @@ -417,13 +836,23 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 model=model, media_resolution_enum=media_resolution_enum, video_metadata=video_metadata, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) _parts.append(_part) - except Exception: - raise Exception( - "Unable to determine mime type for file_id: {}, set this explicitly using message[{}].content[{}].file.format".format( - file_id, msg_i, element_idx - ) + except litellm.BadRequestError: + raise + except Exception as e: + raise litellm.BadRequestError( + message=( + f"Unable to determine mime type for file: " + f"{file_id or 'provided data'}, set this explicitly " + f"using message[{msg_i}].content[{element_idx}].file.format " + f"(or file.mime_type/content_type). " + f"Original error: {str(e)}" + ), + model=model, + llm_provider="vertex_ai", ) user_content.extend(_parts) elif _message_content is not None and isinstance(_message_content, str): @@ -528,7 +957,11 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 image_url_obj = image_item.get("image_url") if isinstance(image_url_obj, dict): assistant_image_url = image_url_obj.get("url") - format = image_url_obj.get("format") + format = ( + image_url_obj.get("format") + or image_url_obj.get("mime_type") + or image_url_obj.get("content_type") + ) detail = image_url_obj.get("detail") media_resolution_enum = ( _convert_detail_to_media_resolution_enum(detail) @@ -539,6 +972,8 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 format=format, media_resolution_enum=media_resolution_enum, model=model, + vertex_project=vertex_project, + vertex_credentials=vertex_credentials, ) assistant_content.append(_part) @@ -713,11 +1148,11 @@ def _transform_request_body( # noqa: PLR0915 try: if custom_llm_provider == "gemini": content = litellm.GoogleAIStudioGeminiConfig()._transform_messages( - messages=messages, model=model + messages=messages, model=model, litellm_params=litellm_params ) else: content = litellm.VertexGeminiConfig()._transform_messages( - messages=messages, model=model + messages=messages, model=model, litellm_params=litellm_params ) tools: Optional[Tools] = optional_params.pop("tools", None) tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None) @@ -893,6 +1328,20 @@ async def async_transform_request_body( vertex_auth_header=vertex_auth_header, ) + if _openai_messages_may_need_sync_gcs_metadata_fetch(messages): + # _transform_request_body may issue a sync httpx.get (up to 5s timeout) + # via _get_gcs_object_content_type to fetch GCS object metadata. Run the + # whole sync transformation on a worker thread so it does not block the + # async event loop. + return await asyncify(_transform_request_body)( + messages=messages, + model=model, + custom_llm_provider=custom_llm_provider, + litellm_params=litellm_params, + cached_content=cached_content, + optional_params=optional_params, + ) + return _transform_request_body( messages=messages, model=model, diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index 6278de662f8..49c1c335467 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -2533,9 +2533,14 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return model_response def _transform_messages( - self, messages: List[AllMessageValues], model: Optional[str] = None + self, + messages: List[AllMessageValues], + model: Optional[str] = None, + litellm_params: Optional[dict] = None, ) -> List[ContentType]: - return _gemini_convert_messages_with_history(messages=messages, model=model) + return _gemini_convert_messages_with_history( + messages=messages, model=model, litellm_params=litellm_params + ) def get_error_class( self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers] @@ -3139,6 +3144,31 @@ class ModelResponseIterator: self.cumulative_tool_call_index: int = 0 self.has_seen_tool_calls: bool = False + @staticmethod + def _check_streaming_error(chunk: dict) -> None: + """Detect embedded errors (e.g. 429 RESOURCE_EXHAUSTED) in streaming chunks and raise VertexAIError.""" + if "error" not in chunk: + return + error_data = chunk["error"] + if not isinstance(error_data, dict): + raise VertexAIError( + status_code=500, + message=f"Unexpected error format in mid-stream chunk: {error_data}", + ) + raw_code = error_data.get("code", 500) + if raw_code is None: + raw_code = 500 + try: + error_code = int(raw_code) + except (TypeError, ValueError): + error_code = 500 + error_message = error_data.get("message", "Unknown error") + error_status = error_data.get("status", "UNKNOWN") + raise VertexAIError( + status_code=error_code, + message=f"{error_status} - {error_message}", + ) + def _apply_stream_candidates( self, _candidates: List[Candidates], @@ -3256,6 +3286,11 @@ class ModelResponseIterator: def chunk_parser(self, chunk: dict) -> Optional["ModelResponseStream"]: try: verbose_logger.debug(f"RAW GEMINI CHUNK: {chunk}") + + # Detect mid-stream error chunks (e.g. 429 RESOURCE_EXHAUSTED). + # Vertex AI can return errors as HTTP 200 but with an "error" field in the SSE body. + self._check_streaming_error(chunk) + from litellm.types.utils import ModelResponseStream processed_chunk = GenerateContentResponseBody(**chunk) # type: ignore diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py index 123d925f7c1..eb67e3aa828 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py @@ -292,22 +292,15 @@ class VertexAIPartnerModels(VertexBase): Returns: Dict containing token count information """ - try: - import vertexai - except Exception as e: - raise VertexAIError( - status_code=400, - message=f"""vertexai import failed please run `pip install -U "google-cloud-aiplatform>=1.38"`. Got error: {e}""", - ) - - if not ( - hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models") - ): - raise VertexAIError( - status_code=400, - message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""", - ) - + # Note: we intentionally do not import `vertexai` (the Gemini SDK shipped + # by `google-cloud-aiplatform`) on this path. Partner models such as + # Claude on Vertex use the Anthropic Messages API protocol directly via + # `:rawPredict`, and `VertexAIPartnerModelsTokenCounter` reaches that + # endpoint with an authenticated httpx client — it never touches the + # Gemini SDK. Requiring `google-cloud-aiplatform>=1.38` here turned a + # SDK-free Anthropic-protocol call into a hard dependency on the Gemini + # SDK (see #28084), breaking `/v1/messages/count_tokens` for Claude-on- + # Vertex on any LiteLLM install without that extra. Stay SDK-free. try: from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import ( VertexAIPartnerModelsTokenCounter, diff --git a/litellm/main.py b/litellm/main.py index c3d1c2e05b0..b5364f8ba17 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -5720,6 +5720,33 @@ def embedding( # noqa: PLR0915 aembedding=aembedding, headers=headers, ) + elif custom_llm_provider == "dashscope": + dashscope_key = ( + api_key or litellm.api_key or get_secret_str("DASHSCOPE_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." + ) + if extra_headers is not None and isinstance(extra_headers, dict): + headers = extra_headers + else: + headers = {} + response = base_llm_http_handler.embedding( + model=model, + input=input, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + logging_obj=logging, + api_base=api_base, + optional_params=optional_params, + litellm_params={}, + model_response=EmbeddingResponse(), + api_key=dashscope_key, + client=client, + aembedding=aembedding, + headers=headers, + ) elif custom_llm_provider == "ovhcloud": api_key = api_key or litellm.api_key or get_secret_str("OVHCLOUD_API_KEY") api_base = ( diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index d4fa497698a..c989f5dff13 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -4549,6 +4549,7 @@ class PrismaCompatibleUpdateDBModel(TypedDict, total=False): model_name: str litellm_params: str model_info: str + blocked: bool updated_at: str updated_by: str diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 7611c9c9692..e7f14df5294 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -69,6 +69,35 @@ else: ProxyLogging = Any +def _extract_cache_read_tokens(usage_obj: dict) -> int: + """ + Anthropic: top-level cache_read_input_tokens field. + OpenAI-compatible (moonshotai, openai, deepseek, etc.): prompt_tokens_details.cached_tokens. + """ + explicit = usage_obj.get("cache_read_input_tokens", 0) or 0 + if explicit: + return int(explicit) + details = usage_obj.get("prompt_tokens_details") or {} + return int(details.get("cached_tokens", 0) or 0) + + +def _extract_cache_creation_tokens(usage_obj: dict) -> int: + """ + Anthropic: top-level cache_creation_input_tokens field. + OpenAI-compatible (kimi-k2 etc.): prompt_tokens_details.cache_write_tokens + or prompt_tokens_details.cache_creation_tokens. + """ + explicit = usage_obj.get("cache_creation_input_tokens", 0) or 0 + if explicit: + return int(explicit) + details = usage_obj.get("prompt_tokens_details") or {} + return int( + details.get("cache_write_tokens", 0) + or details.get("cache_creation_tokens", 0) + or 0 + ) + + class DBSpendUpdateWriter: """ Module responsible for @@ -1992,12 +2021,8 @@ class DBSpendUpdateWriter: api_requests=1, successful_requests=1 if request_status == "success" else 0, failed_requests=1 if request_status != "success" else 0, - cache_read_input_tokens=usage_obj.get("cache_read_input_tokens", 0) - or 0, - cache_creation_input_tokens=usage_obj.get( - "cache_creation_input_tokens", 0 - ) - or 0, + cache_read_input_tokens=_extract_cache_read_tokens(usage_obj), + cache_creation_input_tokens=_extract_cache_creation_tokens(usage_obj), ) return daily_transaction except Exception as e: diff --git a/litellm/proxy/management_endpoints/common_utils.py b/litellm/proxy/management_endpoints/common_utils.py index a43d15a580f..dc27e87726a 100644 --- a/litellm/proxy/management_endpoints/common_utils.py +++ b/litellm/proxy/management_endpoints/common_utils.py @@ -1,6 +1,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from fastapi import HTTPException, status +from pydantic import BaseModel from litellm._logging import verbose_proxy_logger from litellm.caching import DualCache @@ -53,6 +54,37 @@ def require_caller_user_id_for_non_admin( return user_api_key_dict.user_id +def _check_passthrough_routes_caller_permission( + data: BaseModel, + user_api_key_dict: UserAPIKeyAuth, + *, + entity: str = "key", +) -> None: + """ + Only proxy admins may set `allowed_passthrough_routes` (top-level or under + `metadata`) — it short-circuits the role-based route gate, so keys and teams + must be gated identically. + """ + # view-only admins excluded by design; blocked upstream from writes anyway + if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value: + return + if getattr(data, "allowed_passthrough_routes", None): + raise HTTPException( + status_code=403, + detail={ + "error": f"Only proxy admins can set `allowed_passthrough_routes` on a {entity}." + }, + ) + metadata = getattr(data, "metadata", None) + if isinstance(metadata, dict) and metadata.get("allowed_passthrough_routes"): + raise HTTPException( + status_code=403, + detail={ + "error": f"Only proxy admins can set `metadata.allowed_passthrough_routes` on a {entity}." + }, + ) + + def _is_user_team_admin( user_api_key_dict: UserAPIKeyAuth, team_obj: LiteLLM_TeamTable ) -> bool: diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 7ff706eb91e..2ab147043d9 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -55,6 +55,7 @@ from litellm.proxy.common_utils.rbac_utils import check_org_admin_can_generate_k from litellm.proxy.common_utils.timezone_utils import get_budget_reset_time from litellm.proxy.hooks.key_management_event_hooks import KeyManagementEventHooks from litellm.proxy.management_endpoints.common_utils import ( + _check_passthrough_routes_caller_permission, _is_user_org_admin_for_team, _is_user_team_admin, _set_object_metadata_field, @@ -548,36 +549,6 @@ def _check_allowed_routes_caller_permission( ) -def _check_passthrough_routes_caller_permission( - data: BaseModel, - user_api_key_dict: UserAPIKeyAuth, -) -> None: - """ - Only proxy admins may set `allowed_passthrough_routes` on a key, either at - the top level of the request or nested under `metadata`. - - The route gate evaluates passthrough access ahead of the standard role - gate, so the field is restricted to admins to keep that ordering safe. - """ - if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value: - return - if getattr(data, "allowed_passthrough_routes", None): - raise HTTPException( - status_code=403, - detail={ - "error": "Only proxy admins can set `allowed_passthrough_routes` on a key." - }, - ) - metadata = getattr(data, "metadata", None) - if isinstance(metadata, dict) and metadata.get("allowed_passthrough_routes"): - raise HTTPException( - status_code=403, - detail={ - "error": "Only proxy admins can set `metadata.allowed_passthrough_routes` on a key." - }, - ) - - async def validate_team_id_used_in_service_account_request( team_id: Optional[str], prisma_client: Optional[PrismaClient], diff --git a/litellm/proxy/management_endpoints/model_management_endpoints.py b/litellm/proxy/management_endpoints/model_management_endpoints.py index af84bc123ff..472306eb818 100644 --- a/litellm/proxy/management_endpoints/model_management_endpoints.py +++ b/litellm/proxy/management_endpoints/model_management_endpoints.py @@ -150,6 +150,9 @@ def update_db_model( model_info[key] = value.isoformat() prisma_compatible_model_dict["model_info"] = json.dumps(model_info) + if updated_patch.blocked is not None: + prisma_compatible_model_dict["blocked"] = updated_patch.blocked + return prisma_compatible_model_dict @@ -230,6 +233,20 @@ async def patch_model( premium_user=premium_user, ) + # Pause/resume (`blocked`) is a proxy-admin-only privilege. Team admins + # passed the auth check above for team-scoped models, but they must not + # be able to unblock (or block) a model their proxy admin has paused. + if ( + patch_data.blocked is not None + and user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN + ): + raise ProxyException( + message="Only proxy admins can change a model's blocked flag.", + type=ProxyErrorTypes.auth_error.value, + code=status.HTTP_403_FORBIDDEN, + param="blocked", + ) + # Handle team model updates with proper alias management update_data = await _update_team_model_in_db( db_model=db_model, diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index 35e3d196e9e..86c4d6dcd9a 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -73,6 +73,7 @@ from litellm.proxy.auth.auth_checks import ( ) from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.management_endpoints.common_utils import ( + _check_passthrough_routes_caller_permission, _is_user_org_admin_for_team, _is_user_team_admin, _set_object_metadata_field, @@ -1049,6 +1050,10 @@ async def new_team( # noqa: PLR0915 Member(role="admin", user_id=user_api_key_dict.user_id) ) + _check_passthrough_routes_caller_permission( + data, user_api_key_dict, entity="team" + ) + ## ADD TO MODEL TABLE _model_id = None if data.model_aliases is not None and isinstance(data.model_aliases, dict): @@ -1646,6 +1651,10 @@ async def update_team( # noqa: PLR0915 user_api_key_dict=user_api_key_dict, ) + _check_passthrough_routes_caller_permission( + data, user_api_key_dict, entity="team" + ) + if data.soft_budget is not None: max_budget_to_check = ( data.max_budget diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 97252b4c977..5d89d3fa9c5 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -4727,6 +4727,7 @@ class ProxyConfig: if _id is not None: model.model_info["id"] = _id model.model_info["db_model"] = True + model.model_info["blocked"] = bool(getattr(model, "blocked", False)) if premium_user is True: # seeing "created_at", "updated_at", "created_by", "updated_by" is a LiteLLM Enterprise Feature @@ -8089,6 +8090,11 @@ async def model_list( proxy_logging_obj=proxy_logging_obj, ) + # Compute once — used in both branches below to hide paused models from the listing. + blocked_names = ( + llm_router.get_fully_blocked_model_names() if llm_router is not None else set() + ) + # If scope=expand and user has admin privileges, return all proxy models if should_expand_scope: # Get all proxy models as if user is a proxy admin @@ -8121,6 +8127,10 @@ async def model_list( only_model_access_groups=only_model_access_groups or False, ) + # Hide paused models from the public listing (admins manage them via /model/info) + if blocked_names: + all_models = [m for m in all_models if m not in blocked_names] + # Build response data with all proxy models model_data = [] for model in all_models: @@ -8154,6 +8164,10 @@ async def model_list( user_api_key_cache=user_api_key_cache, ) + # Hide paused models from the public listing (admins manage them via /model/info) + if blocked_names: + all_models = [m for m in all_models if m not in blocked_names] + # Build response data model_data = [] for model in all_models: diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index f86cde87401..06fd35448f1 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -430,7 +430,11 @@ async def route_request( # noqa: PLR0915 - Complex routing function, refactorin deployment = llm_router.get_deployment_by_model_group_name( model_group_name=model ) - if deployment and deployment.litellm_params: + if ( + deployment + and deployment.litellm_params + and not llm_router._is_deployment_blocked(deployment) + ): deployment_creds = deployment.litellm_params.model_dump( exclude_none=True ) diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index b53507abe6a..78143fe0411 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable { // Models on proxy model LiteLLM_ProxyModelTable { model_id String @id @default(uuid()) - model_name String + model_name String litellm_params Json - model_info Json? + model_info Json? + blocked Boolean @default(false) created_at DateTime @default(now()) @map("created_at") created_by String updated_at DateTime @default(now()) @updatedAt @map("updated_at") diff --git a/litellm/router.py b/litellm/router.py index fac48b45fb3..420c9b8a816 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -30,6 +30,7 @@ from typing import ( List, Literal, Optional, + Set, Tuple, Union, cast, @@ -6957,12 +6958,11 @@ class Router: unhealthy_deployments = _get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) - healthy_deployments: list = [] - for deployment in _all_deployments: - if deployment["model_info"]["id"] in unhealthy_deployments: - continue - else: - healthy_deployments.append(deployment) + unhealthy_set = set(unhealthy_deployments) + healthy_deployments: list = [ + d for d in _all_deployments if d["model_info"]["id"] not in unhealthy_set + ] + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) return healthy_deployments, _all_deployments @@ -6990,10 +6990,12 @@ class Router: ) # Convert to set for O(1) lookup instead of O(n) unhealthy_deployments_set = set(unhealthy_deployments) - healthy_deployments: list = [] - for deployment in _all_deployments: - if deployment["model_info"]["id"] not in unhealthy_deployments_set: - healthy_deployments.append(deployment) + healthy_deployments: list = [ + d + for d in _all_deployments + if d["model_info"]["id"] not in unhealthy_deployments_set + ] + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) return healthy_deployments, _all_deployments def routing_strategy_pre_call_checks(self, deployment: dict): @@ -8142,10 +8144,14 @@ class Router: def get_deployment_credentials(self, model_id: str) -> Optional[dict]: """ - Returns -> dict of credentials for a given model id + Returns -> dict of credentials for a given model id. + + Returns None if the deployment is paused via `LiteLLM_ProxyModelTable.blocked`, + so file/batch/passthrough callers that resolve credentials directly cannot keep + using a paused deployment. """ deployment = self.get_deployment(model_id=model_id) - if deployment is None: + if deployment is None or self._is_deployment_blocked(deployment): return None return CredentialLiteLLMParams( **deployment.litellm_params.model_dump(exclude_none=True) @@ -8190,7 +8196,9 @@ class Router: Returns: Dictionary containing api_key, api_base, custom_llm_provider, etc. - Returns None if model not found. + Returns None if model not found, or if the resolved deployment is + paused via `LiteLLM_ProxyModelTable.blocked` (so passthrough callers + cannot bypass an admin pause by resolving credentials directly). Example: credentials = router.get_deployment_credentials_with_provider("gpt-4o-litellm") @@ -8216,7 +8224,7 @@ class Router: elif isinstance(deployment_dict, Deployment): deployment = deployment_dict - if deployment is None: + if deployment is None or self._is_deployment_blocked(deployment): return None # Get basic credentials @@ -9243,6 +9251,29 @@ class Router: return model_names + def get_fully_blocked_model_names(self) -> Set[str]: + """ + Returns the set of model_names where every backing deployment has `blocked=True`. + + Used by `/v1/models` to hide paused models from client listings while still + surfacing them on admin endpoints (e.g. `/model/info`). A model with at least + one non-blocked deployment is still serviceable and remains visible. + """ + deployments = self.get_model_list() or [] + blocked_by_name: Dict[str, bool] = {} + for deployment in deployments: + name = deployment.get("model_name") or "" + if not name: + continue + is_blocked = (deployment.get("model_info") or {}).get("blocked") is True + if name in blocked_by_name: + blocked_by_name[name] = blocked_by_name[name] and is_blocked + else: + blocked_by_name[name] = is_blocked + return { + name for name, fully_blocked in blocked_by_name.items() if fully_blocked + } + def _get_team_specific_model( self, deployment: DeploymentTypedDict, team_id: Optional[str] = None ) -> Optional[str]: @@ -10131,6 +10162,12 @@ class Router: ) if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) return healthy_deployments # Health-check-based filtering (before cooldown) @@ -10164,6 +10201,8 @@ class Router: ) healthy_deployments = _pre_cooldown_deployments + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) + healthy_deployments = await self.async_callback_filter_deployments( model=model, healthy_deployments=healthy_deployments, @@ -10387,6 +10426,12 @@ class Router: # 3. If specific deployment returned, verify if it supports pass-through if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) litellm_params = healthy_deployments.get("litellm_params", {}) if litellm_params.get("use_in_pass_through"): return healthy_deployments @@ -10555,6 +10600,12 @@ class Router: ) if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) return healthy_deployments parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs( @@ -10585,6 +10636,8 @@ class Router: ) healthy_deployments = _pre_cooldown_deployments + healthy_deployments = self._filter_blocked_deployments(healthy_deployments) + # filter pre-call checks if self.enable_pre_call_checks and messages is not None: healthy_deployments = self._pre_call_checks( @@ -10704,6 +10757,12 @@ class Router: # 2. If the returned is a specific deployment (Dict), verify and return directly if isinstance(healthy_deployments, dict): + if (healthy_deployments.get("model_info") or {}).get("blocked") is True: + raise litellm.ServiceUnavailableError( + message=f"Model '{model}' is administratively paused. Contact your proxy admin to unblock it.", + model=model, + llm_provider="", + ) litellm_params = healthy_deployments.get("litellm_params", {}) if litellm_params.get("use_in_pass_through"): return healthy_deployments @@ -10743,6 +10802,9 @@ class Router: healthy_deployments=pass_through_deployments, cooldown_deployments=cooldown_deployments, ) + pass_through_deployments = self._filter_blocked_deployments( + pass_through_deployments + ) # 5. Apply pre-call checks (if enabled) if self.enable_pre_call_checks and messages is not None: @@ -10832,6 +10894,36 @@ class Router: if deployment["model_info"]["id"] not in cooldown_set ] + def _filter_blocked_deployments( + self, healthy_deployments: List[Dict] + ) -> List[Dict]: + """ + Filters out deployments that an admin has paused via `LiteLLM_ProxyModelTable.blocked`. + + Applied alongside the cooldown filter on every routing entry point that calls + `_common_checks_available_deployment` directly — the primary sync/async path, + the sync pass-through path, and the retry / health-check helpers — so paused + deployments never serve a request. The async pass-through path inherits this + filter through its delegation to `async_get_healthy_deployments`. + """ + return [ + deployment + for deployment in healthy_deployments + if (deployment.get("model_info") or {}).get("blocked") is not True + ] + + @staticmethod + def _is_deployment_blocked(deployment: "Deployment") -> bool: + """ + Returns True when a `Deployment` Pydantic instance carries the admin-paused + flag. Used by credential-lookup helpers so passthrough file / batch endpoints + cannot bypass the pause by resolving credentials directly. + """ + model_info = getattr(deployment, "model_info", None) + if model_info is None: + return False + return getattr(model_info, "blocked", None) is True + async def _async_filter_health_check_unhealthy_deployments( self, healthy_deployments: List[Dict], diff --git a/litellm/types/integrations/prometheus.py b/litellm/types/integrations/prometheus.py index 43a287f29bc..7b5c5ab2969 100644 --- a/litellm/types/integrations/prometheus.py +++ b/litellm/types/integrations/prometheus.py @@ -160,6 +160,7 @@ class UserAPIKeyLabelNames(Enum): END_USER = "end_user" USER = "user" USER_EMAIL = "user_email" + USER_ALIAS = "user_alias" API_KEY_HASH = "hashed_api_key" API_KEY_ALIAS = "api_key_alias" TEAM = "team" @@ -533,17 +534,9 @@ class PrometheusMetricLabels: UserAPIKeyLabelNames.USER.value, ] - litellm_user_max_budget_metric = [ - UserAPIKeyLabelNames.USER.value, - ] + litellm_user_max_budget_metric = litellm_remaining_user_budget_metric - litellm_user_budget_remaining_hours_metric = [ - UserAPIKeyLabelNames.USER.value, - ] - - litellm_user_budget_remaining_hours_metric = [ - UserAPIKeyLabelNames.USER.value, - ] + litellm_user_budget_remaining_hours_metric = litellm_remaining_user_budget_metric litellm_remaining_api_key_requests_for_model = [ UserAPIKeyLabelNames.API_KEY_HASH.value, @@ -730,6 +723,22 @@ class PrometheusMetricLabels: ): custom_labels.append(UserAPIKeyLabelNames.STREAM.value) + _user_budget_metrics = { + "litellm_remaining_user_budget_metric", + "litellm_user_max_budget_metric", + "litellm_user_budget_remaining_hours_metric", + } + if ( + label_name in _user_budget_metrics + and litellm.prometheus_user_budget_label_include_email_alias is True + ): + for label in [ + UserAPIKeyLabelNames.USER_EMAIL.value, + UserAPIKeyLabelNames.USER_ALIAS.value, + ]: + if label not in default_labels and label not in custom_labels: + custom_labels.append(label) + if label_name in PrometheusMetricLabels._org_label_metrics: for label in [ UserAPIKeyLabelNames.ORG_ID.value, @@ -759,6 +768,7 @@ class UserAPIKeyLabelValues: end_user: Optional[str] = None user: Optional[str] = None user_email: Optional[str] = None + user_alias: Optional[str] = None hashed_api_key: Optional[str] = None api_key_alias: Optional[str] = None team: Optional[str] = None diff --git a/litellm/types/router.py b/litellm/types/router.py index 926815ba317..6601f552b52 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -133,6 +133,9 @@ class ModelInfo(BaseModel): # the model_name that can be used by the team when making LLM calls team_public_model_name: Optional[str] = None + # admin-toggled pause flag; mirrors LiteLLM_ProxyModelTable.blocked + blocked: Optional[bool] = None + def __init__(self, id: Optional[Union[str, int]] = None, **params): if id is None: id = str(uuid.uuid4()) # Generate a UUID if id is None or not provided @@ -323,6 +326,7 @@ class updateDeployment(BaseModel): model_name: Optional[str] = None litellm_params: Optional[updateLiteLLMParams] = None model_info: Optional[ModelInfo] = None + blocked: Optional[bool] = None model_config = ConfigDict(protected_namespaces=()) diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 400edcac889..6084f14e2df 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -107,9 +107,13 @@ class LiteLLMCommonStrings(Enum): SupportedCacheControls = ["ttl", "s-maxage", "no-cache", "no-store"] -class CostPerToken(TypedDict): - input_cost_per_token: float - output_cost_per_token: float +class CostPerToken(TypedDict, total=False): + # Required base rates — kept under total=False so we can mark them + # Required individually while leaving the cache rates NotRequired. + input_cost_per_token: Required[float] + output_cost_per_token: Required[float] + cache_read_input_token_cost: float + cache_creation_input_token_cost: float class ProviderField(TypedDict): diff --git a/litellm/utils.py b/litellm/utils.py index cefd348078b..001c89fee4c 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -8402,6 +8402,12 @@ class ProviderConfigManager: ) return VolcEngineEmbeddingConfig() + elif litellm.LlmProviders.DASHSCOPE == provider: + from litellm.llms.dashscope.embed.transformation import ( + DashScopeEmbeddingConfig, + ) + + return DashScopeEmbeddingConfig() elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() elif litellm.LlmProviders.SNOWFLAKE == provider: @@ -8481,6 +8487,12 @@ 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, + ) + + return DashScopeRerankConfig() return litellm.CohereRerankConfig() @staticmethod @@ -8527,6 +8539,12 @@ class ProviderConfigManager: ) return MinimaxMessagesConfig() + elif litellm.LlmProviders.DEEPSEEK == provider: + from litellm.llms.deepseek.messages.transformation import ( + DeepSeekAnthropicMessagesConfig, + ) + + return DeepSeekAnthropicMessagesConfig() return None @staticmethod diff --git a/pyproject.toml b/pyproject.toml index a397c10ce66..f63770105dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm" -version = "1.85.0" +version = "1.86.0" description = "Library to easily interface with LLM API providers" readme = "README.md" requires-python = ">=3.10, <3.14" @@ -57,7 +57,7 @@ proxy = [ "azure-storage-blob==12.28.0", "mcp==1.26.0", "litellm-proxy-extras==0.4.72", - "litellm-enterprise==0.1.40", + "litellm-enterprise==0.1.41", "RestrictedPython==8.1", "rich==13.9.4", "polars==1.38.1", @@ -251,7 +251,7 @@ source-exclude = [ profile = "black" [tool.commitizen] -version = "1.85.0" +version = "1.86.0" version_files = [ "pyproject.toml:^version", ] diff --git a/schema.prisma b/schema.prisma index b53507abe6a..78143fe0411 100644 --- a/schema.prisma +++ b/schema.prisma @@ -48,9 +48,10 @@ model LiteLLM_CredentialsTable { // Models on proxy model LiteLLM_ProxyModelTable { model_id String @id @default(uuid()) - model_name String + model_name String litellm_params Json - model_info Json? + model_info Json? + blocked Boolean @default(false) created_at DateTime @default(now()) @map("created_at") created_by String updated_at DateTime @default(now()) @updatedAt @map("updated_at") diff --git a/tests/_vcr_conftest_common.py b/tests/_vcr_conftest_common.py index a179a21ba69..cb43f1abbdd 100644 --- a/tests/_vcr_conftest_common.py +++ b/tests/_vcr_conftest_common.py @@ -36,6 +36,75 @@ SAFE_BODY_MATCHER_NAME = "safe_body" KEY_FINGERPRINT_MATCHER_NAME = "key_fingerprint" KEY_FINGERPRINT_HEADER = "x-litellm-key-fp" +VCR_DIAG_DIR_ENV = "LITELLM_VCR_DIAG_DIR" +VCR_DIAG_DIR_DEFAULT = "test-results/vcr-diagnostics" + + +def _vcr_diag_dir() -> str: + return os.environ.get(VCR_DIAG_DIR_ENV) or VCR_DIAG_DIR_DEFAULT + + +def vcr_diag_write_line(msg: str) -> None: + try: + directory = _vcr_diag_dir() + os.makedirs(directory, exist_ok=True) + path = os.path.join(directory, f"{os.getpid()}.log") + with open(path, "a", encoding="utf-8") as fh: + fh.write(msg.rstrip("\n") + "\n") + except OSError: + pass + + +def reset_vcr_diag_dir() -> None: + if os.environ.get("PYTEST_XDIST_WORKER"): + return + directory = _vcr_diag_dir() + if not os.path.isdir(directory): + return + try: + names = os.listdir(directory) + except OSError: + return + for name in names: + if name.endswith(".log"): + try: + os.remove(os.path.join(directory, name)) + except OSError: + pass + + +def emit_vcr_diagnostic_log(terminalreporter) -> None: + directory = _vcr_diag_dir() + if not os.path.isdir(directory): + return + try: + files = sorted(f for f in os.listdir(directory) if f.endswith(".log")) + except OSError: + return + if not files: + return + terminalreporter.write_sep("=", "VCR DIAGNOSTIC LOG", bold=True) + terminalreporter.write_line( + f" source dir: {directory} (also archived as a CI artifact)" + ) + for name in files: + path = os.path.join(directory, name) + try: + with open(path, "r", encoding="utf-8") as fh: + content = fh.read() + except OSError as exc: + terminalreporter.write_line( + f" [failed to read {name}: {type(exc).__name__}: {exc}]" + ) + continue + if not content.strip(): + continue + terminalreporter.write_sep("-", name, bold=False) + for line in content.splitlines(): + terminalreporter.write_line(line) + terminalreporter.write_sep("=", bold=True) + + # Intentionally narrower than ``FILTERED_REQUEST_HEADERS``: AWS SigV4 headers # carry secrets but their values rotate on every call, so fingerprinting them # would defeat caching. @@ -91,6 +160,32 @@ VCR_IMAGE_B64_PLACEHOLDER = "dGVzdA==" VCR_FIXED_MULTIPART_BOUNDARY = "vcr-static-boundary" +def pin_httpx_multipart_boundary(monkeypatch) -> None: + try: + import httpx._multipart as _httpx_multipart + except ImportError: + return + + _original_init = _httpx_multipart.MultipartStream.__init__ + + def _init_with_fixed_boundary(self, data, files, boundary=None, **kwargs): + if boundary is None: + boundary = VCR_FIXED_MULTIPART_BOUNDARY.encode("ascii") + return _original_init(self, data=data, files=files, boundary=boundary, **kwargs) + + monkeypatch.setattr( + _httpx_multipart.MultipartStream, "__init__", _init_with_fixed_boundary + ) + + +@pytest.fixture(scope="session", autouse=True) +def _pin_multipart_boundary(): + monkeypatch = pytest.MonkeyPatch() + pin_httpx_multipart_boundary(monkeypatch) + yield + monkeypatch.undo() + + def _scrub_response(response): if not isinstance(response, dict): return response @@ -139,9 +234,17 @@ def _strip_image_b64_payloads(response): preserves all those checks while shrinking cassettes by ~99%. """ if not isinstance(response, dict): + vcr_diag_write_line( + f"[vcr-strip-b64] response is {type(response).__name__!r}, not " + "dict; skipping b64 scrub" + ) return response body = response.get("body") if not isinstance(body, dict): + vcr_diag_write_line( + f"[vcr-strip-b64] response['body'] is {type(body).__name__!r}, " + "not dict; skipping b64 scrub" + ) return response raw = body.get("string") if raw is None: @@ -151,12 +254,20 @@ def _strip_image_b64_payloads(response): try: text = bytes(raw).decode("utf-8") except UnicodeDecodeError: + vcr_diag_write_line( + "[vcr-strip-b64] response body bytes are not valid UTF-8; " + "skipping b64 scrub" + ) return response was_bytes = True elif isinstance(raw, str): text = raw was_bytes = False else: + vcr_diag_write_line( + f"[vcr-strip-b64] response['body']['string'] is " + f"{type(raw).__name__!r}, not bytes/str; skipping b64 scrub" + ) return response try: @@ -186,6 +297,35 @@ def _before_record_response(response): return filter_non_2xx_response(_scrub_response(_strip_image_b64_payloads(response))) +def _canonical_body(request) -> tuple[bytes, str]: + pre_type = type(getattr(request, "body", None)).__name__ + _materialize_iterable_body(request) + body = getattr(request, "body", None) + if body is None: + return b"", pre_type + if isinstance(body, bytes): + return body, pre_type + if isinstance(body, bytearray): + return bytes(body), pre_type + if isinstance(body, str): + return body.encode("utf-8"), pre_type + if isinstance(body, (dict, list)): + try: + return ( + json.dumps(body, sort_keys=True, separators=(",", ":")).encode("utf-8"), + pre_type, + ) + except (TypeError, ValueError): + pass + method = getattr(request, "method", "?") + uri = getattr(request, "uri", getattr(request, "url", "?")) + vcr_diag_write_line( + f"[vcr-canonical-body] FALLBACK: {method} {uri} body type " + f"{type(body).__name__!r} not coerced to bytes; comparing as b''" + ) + return b"", pre_type + + def _safe_body_matcher(r1, r2) -> None: """Compare request bodies as bytes; never invokes ``json.loads``. @@ -195,27 +335,47 @@ def _safe_body_matcher(r1, r2) -> None: This matcher is strictly more conservative — the only equivalence it gives up vs. the default is "JSON key order doesn't matter". """ - body1 = getattr(r1, "body", None) - body2 = getattr(r2, "body", None) + body1, pre1 = _canonical_body(r1) + body2, pre2 = _canonical_body(r2) if body1 == body2: return - - def _to_bytes(b): - if b is None: - return b"" - if isinstance(b, bytes): - return b - if isinstance(b, str): - return b.encode("utf-8") - return None - - n1 = _to_bytes(body1) - n2 = _to_bytes(body2) - if n1 is not None and n2 is not None and n1 == n2: - return + _emit_body_mismatch_diagnostic(r1, r2, body1, body2, pre1, pre2) raise AssertionError("request bodies differ") +def _emit_body_mismatch_diagnostic(r1, r2, body1, body2, pre1, pre2) -> None: + def _describe(label, asbytes, pre_type): + return ( + f" {label}: pre_canonical_type={pre_type!r} length={len(asbytes)} " + f"sha256={hashlib.sha256(asbytes).hexdigest()} " + f"preview={asbytes[:120]!r}" + ) + + method_a = getattr(r1, "method", "?") + method_b = getattr(r2, "method", "?") + url_a = getattr(r1, "uri", getattr(r1, "url", "?")) + url_b = getattr(r2, "uri", getattr(r2, "url", "?")) + lines = [ + "[vcr-safe-body-matcher] request body mismatch", + f" request[a]: {method_a} {url_a}", + f" request[b]: {method_b} {url_b}", + _describe("body[a]", body1, pre1), + _describe("body[b]", body2, pre2), + ] + if body1 != body2: + offset = next( + (i for i in range(min(len(body1), len(body2))) if body1[i] != body2[i]), + min(len(body1), len(body2)), + ) + start = max(0, offset - 100) + end_a = min(len(body1), offset + 100) + end_b = min(len(body2), offset + 100) + lines.append(f" first divergent byte offset: {offset}") + lines.append(f" window[a] @ {start}..{end_a}: {body1[start:end_a]!r}") + lines.append(f" window[b] @ {start}..{end_b}: {body2[start:end_b]!r}") + vcr_diag_write_line("\n".join(lines)) + + def _iter_header_values(headers, name: str): if headers is None: return @@ -271,6 +431,13 @@ def _compute_key_fingerprint(request) -> str: stable = _stable_key_value(header_name, text) parts.append(f"{header_name}={stable}") if not parts: + method = getattr(request, "method", "?") + uri = getattr(request, "uri", getattr(request, "url", "?")) + vcr_diag_write_line( + f"[vcr-key-fingerprint] no API key header found on {method} " + f"{uri}; falling back to 'no-key'. If this request should have " + "carried auth, something earlier in the pipeline stripped it." + ) return "no-key" digest = hashlib.sha256("\n".join(parts).encode("utf-8")).hexdigest() return digest[:16] @@ -360,6 +527,13 @@ def _normalize_multipart_boundary(request) -> None: elif isinstance(body, str): new_body = body.replace(current_boundary, VCR_FIXED_MULTIPART_BOUNDARY) else: + vcr_diag_write_line( + f"[vcr-multipart-normalize] body normalization SKIPPED: " + f"body type {type(body).__name__!r} is not bytes/bytearray/str. " + f"content-type={content_type_value!r}. " + f"Recorded body will retain the random boundary substring " + f"and the safe_body matcher will miss on the next run." + ) return try: @@ -389,6 +563,7 @@ def _before_record_request(request): headers = getattr(request, "headers", None) if headers is None: return request + _materialize_iterable_body(request) if not any(_iter_header_values(headers, KEY_FINGERPRINT_HEADER)): fingerprint = _compute_key_fingerprint(request) try: @@ -400,6 +575,56 @@ def _before_record_request(request): return request +def _materialize_iterable_body(request) -> None: + body = getattr(request, "body", None) + if body is None or isinstance(body, (bytes, bytearray, str)): + return + if not hasattr(body, "__next__"): + return + try: + chunks = list(body) + except TypeError: + return + + out = _coalesce_chunks_to_bytes(chunks) + if out is None: + method = getattr(request, "method", "?") + uri = getattr(request, "uri", getattr(request, "url", "?")) + first_type = type(chunks[0]).__name__ if chunks else "empty" + vcr_diag_write_line( + f"[vcr-materialize] FALLBACK: {method} {uri} chunk type " + f"{first_type!r} not coerced to bytes; storing b''" + ) + out = b"" + + try: + request.body = out + except (AttributeError, TypeError): + pass + + for attr in ("_was_iter", "_was_file"): + try: + setattr(request, attr, False) + except (AttributeError, TypeError): + pass + + +def _coalesce_chunks_to_bytes(chunks): + if not chunks: + return b"" + first = chunks[0] + try: + if isinstance(first, int): + return bytes(chunks) + if isinstance(first, (bytes, bytearray)): + return b"".join(c if isinstance(c, bytes) else bytes(c) for c in chunks) + if isinstance(first, str): + return "".join(chunks).encode("utf-8") + except (TypeError, ValueError): + return None + return None + + def _key_fingerprint_matcher(r1, r2) -> None: def _fp(req): for value in _iter_header_values( @@ -410,7 +635,17 @@ def _key_fingerprint_matcher(r1, r2) -> None: return value if isinstance(value, str) else str(value) return "no-key" - if _fp(r1) != _fp(r2): + fp1, fp2 = _fp(r1), _fp(r2) + if fp1 != fp2: + method_a = getattr(r1, "method", "?") + method_b = getattr(r2, "method", "?") + url_a = getattr(r1, "uri", getattr(r1, "url", "?")) + url_b = getattr(r2, "uri", getattr(r2, "url", "?")) + vcr_diag_write_line( + "[vcr-key-fingerprint-matcher] API key fingerprints differ\n" + f" request[a]: {method_a} {url_a} fingerprint={fp1!r}\n" + f" request[b]: {method_b} {url_b} fingerprint={fp2!r}" + ) raise AssertionError("API key fingerprints differ") diff --git a/tests/_vcr_redis_persister.py b/tests/_vcr_redis_persister.py index 7fdb7267a38..373cb66696a 100644 --- a/tests/_vcr_redis_persister.py +++ b/tests/_vcr_redis_persister.py @@ -159,9 +159,20 @@ def make_redis_persister( raise CassetteNotFoundError() from exc if data is None: raise CassetteNotFoundError() - if isinstance(data, bytes): - data = data.decode("utf-8") - return deserialize(data, serializer) + try: + if isinstance(data, bytes): + data = data.decode("utf-8") + return deserialize(data, serializer) + except Exception as exc: + _record_cache_failure("load", exc) + msg = ( + f"VCR redis load failed for {cassette_path}; cached " + f"payload is corrupt, treating as cache miss: " + f"{type(exc).__name__}: {exc}" + ) + _log.warning(msg) + warnings.warn(msg, VCRCassetteCacheWarning, stacklevel=2) + raise CassetteNotFoundError() from exc @staticmethod def save_cassette(cassette_path, cassette_dict, serializer): diff --git a/tests/audio_tests/conftest.py b/tests/audio_tests/conftest.py index ff47853d494..c4ff576e5bd 100644 --- a/tests/audio_tests/conftest.py +++ b/tests/audio_tests/conftest.py @@ -5,14 +5,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -44,6 +47,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -57,3 +61,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/audio_tests/test_whisper.py b/tests/audio_tests/test_whisper.py index cdf079f8cb4..243d27614b1 100644 --- a/tests/audio_tests/test_whisper.py +++ b/tests/audio_tests/test_whisper.py @@ -23,12 +23,21 @@ pwd = os.path.dirname(os.path.realpath(__file__)) print(pwd) file_path = os.path.join(pwd, "gettysburg.wav") - -audio_file = open(file_path, "rb") - - file2_path = os.path.join(pwd, "eagle.wav") -audio_file2 = open(file2_path, "rb") + +with open(file_path, "rb") as _f: + _GETTYSBURG_BYTES = _f.read() +with open(file2_path, "rb") as _f: + _EAGLE_BYTES = _f.read() + + +def _audio_file(): + return ("gettysburg.wav", _GETTYSBURG_BYTES, "audio/wav") + + +def _audio_file2(): + return ("eagle.wav", _EAGLE_BYTES, "audio/wav") + load_dotenv() @@ -44,7 +53,7 @@ async def _run_transcription( ): transcript = await litellm.atranscription( model=model, - file=audio_file, + file=_audio_file(), api_key=api_key, api_base=api_base, response_format=response_format, @@ -101,7 +110,7 @@ async def test_transcription_caching(): response_1 = await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) await asyncio.sleep(5) @@ -110,7 +119,7 @@ async def test_transcription_caching(): response_2 = await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) print("response_1", response_1) @@ -122,7 +131,7 @@ async def test_transcription_caching(): response_3 = await litellm.atranscription( model="whisper-1", - file=audio_file2, + file=_audio_file2(), ) print("response_3", response_3) print("response3 hidden params", response_3._hidden_params) @@ -146,7 +155,7 @@ async def test_whisper_log_pre_call(): with patch.object(custom_logger, "log_pre_api_call") as mock_log_pre_call: await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) mock_log_pre_call.assert_called_once() @@ -165,7 +174,7 @@ async def test_whisper_log_pre_call(): with patch.object(custom_logger, "log_pre_api_call") as mock_log_pre_call: await litellm.atranscription( model="whisper-1", - file=audio_file, + file=_audio_file(), ) mock_log_pre_call.assert_called_once() @@ -177,7 +186,7 @@ async def test_gpt_4o_transcribe(): from unittest.mock import patch, MagicMock await litellm.atranscription( - model="openai/gpt-4o-transcribe", file=audio_file, response_format="json" + model="openai/gpt-4o-transcribe", file=_audio_file(), response_format="json" ) @@ -187,7 +196,9 @@ async def test_gpt_4o_transcribe_model_mapping(): # Test GPT-4o mini transcribe response = await litellm.atranscription( - model="openai/gpt-4o-mini-transcribe", file=audio_file, response_format="json" + model="openai/gpt-4o-mini-transcribe", + file=_audio_file(), + response_format="json", ) # Check that the response contains the correct model in hidden params @@ -198,7 +209,7 @@ async def test_gpt_4o_transcribe_model_mapping(): # Test GPT-4o transcribe response2 = await litellm.atranscription( - model="openai/gpt-4o-transcribe", file=audio_file, response_format="json" + model="openai/gpt-4o-transcribe", file=_audio_file(), response_format="json" ) # Check that the response contains the correct model in hidden params @@ -209,7 +220,7 @@ async def test_gpt_4o_transcribe_model_mapping(): # Test traditional whisper-1 still works response3 = await litellm.atranscription( - model="openai/whisper-1", file=audio_file, response_format="json" + model="openai/whisper-1", file=_audio_file(), response_format="json" ) # Check that the response contains the correct model in hidden params @@ -262,7 +273,7 @@ async def test_azure_transcribe_model_mapping(): # Make the transcription call response = await litellm.atranscription( model="azure/whisper-1", - file=audio_file, + file=_audio_file(), response_format="json", api_key="test-api-key", api_base="https://my-endpoint-europe-berri-992.openai.azure.com/", diff --git a/tests/guardrails_tests/conftest.py b/tests/guardrails_tests/conftest.py index eb563699b2b..f2f65645c3d 100644 --- a/tests/guardrails_tests/conftest.py +++ b/tests/guardrails_tests/conftest.py @@ -16,14 +16,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -55,6 +58,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -160,3 +164,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/image_gen_tests/conftest.py b/tests/image_gen_tests/conftest.py index 93dec98e708..9f808c11161 100644 --- a/tests/image_gen_tests/conftest.py +++ b/tests/image_gen_tests/conftest.py @@ -9,14 +9,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -58,6 +61,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -71,3 +75,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/image_gen_tests/test_image_edits.py b/tests/image_gen_tests/test_image_edits.py index 656b8a69117..ca8ec3bbe32 100644 --- a/tests/image_gen_tests/test_image_edits.py +++ b/tests/image_gen_tests/test_image_edits.py @@ -103,12 +103,6 @@ class BaseLLMImageEditTest(ABC): pwd = os.path.dirname(os.path.realpath(__file__)) -# Image fixtures must be regenerated per access — module-level -# ``open(...)`` handles get consumed after a single multipart upload, leaving -# subsequent tests in the same process to send empty bodies. That non-determinism -# (a) blows the recorded cassette past ``MAX_EPISODES_PER_CASSETTE`` so the -# persister refuses to save (see ``tests/_vcr_redis_persister.py``), and -# (b) re-bills the live image edit endpoint on every CI run. def _read_image_bytes(filename: str) -> bytes: with open(os.path.join(pwd, filename), "rb") as f: return f.read() @@ -119,32 +113,20 @@ _LITELLM_SITE_BYTES = _read_image_bytes("litellm_site.png") def _make_test_images() -> list: - """Return a fresh pair of image streams seeded with the fixture bytes. + return [_ISHAAN_GITHUB_BYTES, _LITELLM_SITE_BYTES] - Use this everywhere you'd previously have used the module-level - ``TEST_IMAGES``. Each call returns brand new ``BytesIO`` objects whose - file pointers start at 0, so multipart uploads encode the full image - bytes on every test invocation. Parametrized and ``flaky``-retried - test methods call ``get_base_image_edit_call_args`` once per - invocation, so a fresh stream per call is sufficient — the factory - must not auto-rewind on EOF or the SDK's multipart writer will read - the same bytes forever (worker OOM). - """ + +def _make_single_test_image() -> bytes: + return _ISHAAN_GITHUB_BYTES + + +def get_test_images_as_bytesio(): return [ BytesIO(_ISHAAN_GITHUB_BYTES), BytesIO(_LITELLM_SITE_BYTES), ] -def _make_single_test_image() -> BytesIO: - return BytesIO(_ISHAAN_GITHUB_BYTES) - - -def get_test_images_as_bytesio(): - """Helper function to get test images as BytesIO objects""" - return _make_test_images() - - class TestOpenAIImageEditGPTImage1(BaseLLMImageEditTest): """ Concrete implementation of BaseLLMImageEditTest for OpenAI image edits. @@ -710,10 +692,9 @@ async def test_multiple_image_edit_with_different_formats(): try: prompt = "Create a cohesive artistic style across all images" - # Test with mixed BytesIO and file objects mixed_images = [ - _make_single_test_image(), # File object - get_test_images_as_bytesio()[1], # BytesIO object + _make_single_test_image(), + get_test_images_as_bytesio()[1], ] result = await aimage_edit( diff --git a/tests/litellm_utils_tests/conftest.py b/tests/litellm_utils_tests/conftest.py index 08745c99c07..418ee76a399 100644 --- a/tests/litellm_utils_tests/conftest.py +++ b/tests/litellm_utils_tests/conftest.py @@ -12,14 +12,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -86,6 +89,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -116,3 +120,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/llm_responses_api_testing/conftest.py b/tests/llm_responses_api_testing/conftest.py index 2a08db57149..1928b540dad 100644 --- a/tests/llm_responses_api_testing/conftest.py +++ b/tests/llm_responses_api_testing/conftest.py @@ -13,14 +13,17 @@ sys.path.insert( import litellm # noqa: E402 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -52,6 +55,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -116,3 +120,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/llm_translation/conftest.py b/tests/llm_translation/conftest.py index 5fcd31aa32d..d346dae4308 100644 --- a/tests/llm_translation/conftest.py +++ b/tests/llm_translation/conftest.py @@ -18,14 +18,17 @@ sys.path.insert( import litellm # noqa: E402 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -73,6 +76,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -82,6 +86,7 @@ def pytest_runtest_logreport(report): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) # --------------------------------------------------------------------------- diff --git a/tests/llm_translation/realtime/test_openai_realtime.py b/tests/llm_translation/realtime/test_openai_realtime.py index c5f77de6beb..fc9f938b4cd 100644 --- a/tests/llm_translation/realtime/test_openai_realtime.py +++ b/tests/llm_translation/realtime/test_openai_realtime.py @@ -101,7 +101,9 @@ async def test_openai_realtime_direct_call_no_intent(): try: await litellm._arealtime( - model="openai/gpt-4o-realtime-preview", + # OpenAI shut down the gpt-4o-realtime-preview family (incl. the + # undated alias) on 2026-05-07; gpt-realtime is the GA successor. + model="openai/gpt-realtime", websocket=websocket_client, api_key=os.environ.get("OPENAI_API_KEY"), timeout=60, @@ -249,14 +251,16 @@ async def test_openai_realtime_direct_call_with_intent(): websocket_client = RealTimeWebSocketClient() caught_exception = None + # OpenAI shut down the gpt-4o-realtime-preview family (incl. the undated + # alias) on 2026-05-07; gpt-realtime is the GA successor. query_params: RealtimeQueryParams = { - "model": "openai/gpt-4o-realtime-preview", + "model": "openai/gpt-realtime", "intent": "chat", } try: await litellm._arealtime( - model="openai/gpt-4o-realtime-preview", + model="openai/gpt-realtime", websocket=websocket_client, api_key=os.environ.get("OPENAI_API_KEY"), query_params=query_params, diff --git a/tests/llm_translation/realtime/test_openai_realtime_simple.py b/tests/llm_translation/realtime/test_openai_realtime_simple.py index 5522d843e42..073c1ce11af 100644 --- a/tests/llm_translation/realtime/test_openai_realtime_simple.py +++ b/tests/llm_translation/realtime/test_openai_realtime_simple.py @@ -21,7 +21,10 @@ class TestOpenAIRealtime(BaseRealtimeTest): """ def get_model(self) -> str: - return "gpt-4o-realtime-preview" + # OpenAI shut down the entire gpt-4o-realtime-preview family + # (including the undated alias) on 2026-05-07. gpt-realtime is the + # current GA realtime model. + return "gpt-realtime" def get_api_key_env_var(self) -> str: return "OPENAI_API_KEY" diff --git a/tests/llm_translation/realtime/test_realtime_guardrails_openai.py b/tests/llm_translation/realtime/test_realtime_guardrails_openai.py index 170440f6b9f..50cedba2ac0 100644 --- a/tests/llm_translation/realtime/test_realtime_guardrails_openai.py +++ b/tests/llm_translation/realtime/test_realtime_guardrails_openai.py @@ -26,9 +26,7 @@ from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming from litellm.types.guardrails import GuardrailEventHooks OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") -OPENAI_REALTIME_URL = ( - "wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview-2024-12-17" -) +OPENAI_REALTIME_URL = "wss://api.openai.com/v1/realtime?model=gpt-realtime" pytestmark = pytest.mark.skipif( not OPENAI_API_KEY, @@ -192,10 +190,35 @@ async def test_text_message_blocked_by_guardrail_no_ai_response(): len(transcript_deltas) >= 1 ), f"Expected guardrail message in transcript delta, got: {event_types}" - # 3. No *real* AI response should have been generated. - # The guardrail may produce its own response (e.g. "Content blocked: ...") - # via response.cancel + conversation.item.create + response.create. - # We allow the guardrail's own block message but NOT original AI content. + # 3. No *real* AI response to the blocked content should have been + # generated. The original user message is blocked BEFORE it is + # forwarded to OpenAI, so the only thing the model ever sees is the + # guardrail's "say exactly: " prompt + # (see realtime_streaming.py). Two safe outcomes are possible: + # - the model voices the block message verbatim (older realtime + # snapshots did this -> text contains "blocked"), or + # - the model declines to repeat it (gpt-realtime tends to refuse + # verbatim-repeat instructions, e.g. "I'm sorry, but I can't + # repeat that message."). + # Both mean the blocked prompt itself was never answered, so we + # accept either. The hard invariant is that the blocked phrase must + # never leak into AI output, and the model must not have produced a + # normal answer to the user (which would have neither a block nor a + # refusal marker). + safe_markers = ( + "block", + "guardrail", + "content filter", + "policy", + "can't repeat", + "cannot repeat", + "won't repeat", + "can't assist", + "can't help", + "unable to", + "i'm sorry", + "i am sorry", + ) done_events = [e for e in client_events if e.get("type") == "response.done"] for done in done_events: output = done.get("response", {}).get("output", []) @@ -205,11 +228,19 @@ async def test_text_message_blocked_by_guardrail_no_ai_response(): for c in item.get("content", []) ] real_ai_text = " ".join(ai_texts).strip() - # Allow guardrail-generated block messages (contain "Content blocked" or "blocked") if real_ai_text: assert ( - "blocked" in real_ai_text.lower() - or "guardrail" in real_ai_text.lower() + BLOCKED_PHRASE not in real_ai_text + ), f"Blocked phrase leaked into AI response: {real_ai_text!r}" + normalized_ai_text = ( + real_ai_text.lower() + .replace("\u2019", "'") + .replace("\u2018", "'") + .replace("\u201c", '"') + .replace("\u201d", '"') + ) + assert any( + marker in normalized_ai_text for marker in safe_markers ), f"AI responded with non-guardrail content even though message was blocked: {real_ai_text!r}" finally: diff --git a/tests/llm_translation/test_deepseek_completion.py b/tests/llm_translation/test_deepseek_completion.py index da402a51b68..2ede5d3f3f8 100644 --- a/tests/llm_translation/test_deepseek_completion.py +++ b/tests/llm_translation/test_deepseek_completion.py @@ -176,3 +176,113 @@ def test_completion_cost_deepseek(): pass except Exception as e: pytest.fail(f"Error occurred: {e}") + + +def test_deepseek_fill_reasoning_content_multiturn(): + """ + Unit test for _fill_reasoning_content. + Reproduces issue #28045: DeepSeek thinking mode fails in multi-turn conversations + because reasoning_content is not passed back to the API. + """ + from litellm.llms.deepseek.chat.transformation import DeepSeekChatConfig + + config = DeepSeekChatConfig() + + # Case 1: assistant message already has reasoning_content — should be left as-is + messages_with_rc = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi", "reasoning_content": "I thought about it"}, + {"role": "user", "content": "Follow up"}, + ] + result = config._fill_reasoning_content(messages_with_rc) + assert result[1]["reasoning_content"] == "I thought about it" + + # Case 2: assistant message has reasoning_content in provider_specific_fields — should be promoted + messages_with_psf = [ + {"role": "user", "content": "Hello"}, + { + "role": "assistant", + "content": "Hi", + "provider_specific_fields": {"reasoning_content": "stored thinking"}, + }, + {"role": "user", "content": "Follow up"}, + ] + result = config._fill_reasoning_content(messages_with_psf) + assert result[1]["reasoning_content"] == "stored thinking" + # Should be removed from provider_specific_fields to avoid duplication + assert "reasoning_content" not in result[1].get("provider_specific_fields", {}) + + # Case 3: assistant message has no reasoning_content anywhere — should inject placeholder + messages_no_rc = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi"}, + {"role": "user", "content": "Follow up"}, + ] + result = config._fill_reasoning_content(messages_no_rc) + assert result[1]["reasoning_content"] == " " + + # Case 4: non-assistant messages should never be touched + messages_user_only = [ + {"role": "user", "content": "Hello"}, + {"role": "system", "content": "You are helpful"}, + ] + result = config._fill_reasoning_content(messages_user_only) + assert "reasoning_content" not in result[0] + assert "reasoning_content" not in result[1] + + +def test_deepseek_fill_reasoning_content_guard_in_transform_request(): + """ + _fill_reasoning_content must only run when BOTH conditions are true: + 1. supports_reasoning() is True for the model + 2. thinking mode is explicitly enabled in optional_params ({"type": "enabled"}) + + This prevents spurious injection on models like deepseek-v3.2 that support + thinking as opt-in but not always-on. Addresses oss-pr-review-agent feedback + on PR #28057. + """ + from litellm.llms.deepseek.chat.transformation import DeepSeekChatConfig + + config = DeepSeekChatConfig() + + messages = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi"}, + {"role": "user", "content": "Follow up"}, + ] + + # Case 1: reasoning model + thinking enabled -> injection should happen + result = config.transform_request( + model="deepseek-reasoner", + messages=messages, + optional_params={"thinking": {"type": "enabled"}}, + litellm_params={}, + headers={}, + ) + assert result["messages"][1].get("reasoning_content") == " ", ( + "reasoning_content should be injected when thinking is enabled" + ) + + # Case 2: reasoning model + thinking NOT in optional_params -> no injection + result = config.transform_request( + model="deepseek-reasoner", + messages=messages, + optional_params={}, + litellm_params={}, + headers={}, + ) + assert "reasoning_content" not in result["messages"][1], ( + "reasoning_content should not be injected when thinking is not enabled" + ) + + # Case 3: non-reasoning model + thinking enabled -> no injection + result = config.transform_request( + model="deepseek-chat", + messages=messages, + optional_params={"thinking": {"type": "enabled"}}, + litellm_params={}, + headers={}, + ) + assert "reasoning_content" not in result["messages"][1], ( + "reasoning_content should not be injected for non-reasoning models" + ) diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py index 469516407c8..80e764147bb 100644 --- a/tests/llm_translation/test_nvidia_nim.py +++ b/tests/llm_translation/test_nvidia_nim.py @@ -262,3 +262,44 @@ class TestNvidiaNim(BaseLLMRerankTest): def get_expected_cost(self) -> float: """Nvidia NIM rerank models are free (cost = 0.0)""" return 0.0 + + @pytest.mark.asyncio() + @pytest.mark.parametrize("sync_mode", [True, False]) + async def test_basic_rerank(self, sync_mode, monkeypatch): + """ + Override the base live rerank test with a mocked HTTP layer. + + NVIDIA reached end-of-life for the hosted + nvidia/llama-3.2-nv-rerankqa-1b-v2 rerank API on 2026-05-18 and + published no replacement model, so a live call now returns HTTP 410 + ("Gone"). NVIDIA's hosted catalog rotates on a schedule, so pointing + at another live model would only defer the same failure. Mock the + transport instead (same pattern as + test_nvidia_nim_rerank_ranking_endpoint above) so the request/response + transformation and cost calculation stay covered offline. + """ + monkeypatch.setenv("NVIDIA_NIM_API_KEY", "fake-api-key") + + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.headers = {} + mock_response.text = "" + mock_response.json.return_value = { + "rankings": [ + {"index": 0, "logit": 0.95}, + {"index": 1, "logit": 0.75}, + ], + "usage": {"total_tokens": 7}, + } + + with ( + patch( + "litellm.llms.custom_httpx.http_handler.HTTPHandler.post", + return_value=mock_response, + ), + patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + return_value=mock_response, + ), + ): + await super().test_basic_rerank(sync_mode=sync_mode) diff --git a/tests/local_testing/conftest.py b/tests/local_testing/conftest.py index 0ff7dff668a..6a746041f15 100644 --- a/tests/local_testing/conftest.py +++ b/tests/local_testing/conftest.py @@ -22,14 +22,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -84,6 +87,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -93,6 +97,7 @@ def pytest_runtest_logreport(report): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) # --------------------------------------------------------------------------- diff --git a/tests/local_testing/test_caching_handler.py b/tests/local_testing/test_caching_handler.py index 2b6712cbaa3..0f4539162a2 100644 --- a/tests/local_testing/test_caching_handler.py +++ b/tests/local_testing/test_caching_handler.py @@ -25,6 +25,7 @@ from unittest.mock import AsyncMock, patch, MagicMock from litellm.caching.caching_handler import ( LLMCachingHandler, CachingHandlerResponse, + _is_chat_completion_cached_dict, _should_defer_streaming_cache_hit_callbacks, ) from litellm.caching.caching import LiteLLMCacheType @@ -40,6 +41,7 @@ from litellm.types.utils import ( from litellm.types.llms.openai import ResponsesAPIResponse from datetime import timedelta, datetime from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging +from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm._logging import verbose_logger import logging @@ -1072,6 +1074,70 @@ def test_convert_cached_streaming_responses_result_to_iterator(): ) +def test_is_chat_completion_cached_dict(): + assert _is_chat_completion_cached_dict( + {"id": "chatcmpl-abc", "object": "chat.completion", "choices": []} + ) + assert _is_chat_completion_cached_dict( + {"id": "other", "object": "chat.completion.chunk", "choices": []} + ) + assert not _is_chat_completion_cached_dict( + {"id": "resp_abc", "object": "response", "output": []} + ) + + +def test_convert_cached_aresponses_bridge_chat_completion_stream(): + """ + openai/responses chat-completions bridge caches ModelResponse JSON on aresponses + cache keys; replay must not call ResponsesAPIResponse(**chatcmpl_dict). + """ + caching_handler = LLMCachingHandler( + original_function=aresponses, request_kwargs={}, start_time=datetime.now() + ) + logging_obj = LiteLLMLogging( + litellm_call_id=str(datetime.now()), + call_type=CallTypes.aresponses.value, + model="gpt-5.4", + messages=[], + function_id=str(uuid.uuid4()), + stream=True, + start_time=datetime.now(), + ) + cached_result = { + "id": "chatcmpl-bridge-cache-test", + "object": "chat.completion", + "created": int(time.time()), + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hi!"}, + "finish_reason": "stop", + } + ], + "usage": { + "prompt_tokens": 7, + "completion_tokens": 11, + "total_tokens": 18, + }, + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.aresponses.value, + kwargs={ + "model": "gpt-5.4", + "stream": True, + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=logging_obj, + model="gpt-5.4", + args=(), + ) + + assert isinstance(result, CustomStreamWrapper) + + def test_convert_cached_streaming_reasoning_result_to_iterator(): caching_handler = LLMCachingHandler( original_function=responses, request_kwargs={}, start_time=datetime.now() diff --git a/tests/logging_callback_tests/conftest.py b/tests/logging_callback_tests/conftest.py index cdb9200bc83..6dde85f2ca7 100644 --- a/tests/logging_callback_tests/conftest.py +++ b/tests/logging_callback_tests/conftest.py @@ -19,14 +19,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -79,6 +82,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -229,3 +233,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/ocr_tests/conftest.py b/tests/ocr_tests/conftest.py index 66970b8579f..94790bd7aa3 100644 --- a/tests/ocr_tests/conftest.py +++ b/tests/ocr_tests/conftest.py @@ -12,14 +12,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -51,6 +54,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -64,3 +68,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/otel_tests/test_prometheus.py b/tests/otel_tests/test_prometheus.py index c9490af07cb..90c71037609 100644 --- a/tests/otel_tests/test_prometheus.py +++ b/tests/otel_tests/test_prometheus.py @@ -610,22 +610,18 @@ def extract_user_budget_metrics(metrics_text: str, user_id: str) -> Dict[str, fl # Escape user_id for regex pattern matching escaped_user_id = re.escape(user_id) - # Get remaining budget - remaining_pattern = ( - f'litellm_remaining_user_budget_metric{{user="{escaped_user_id}"}} ([0-9.]+)' - ) + # Get remaining budget (user_email and user_alias may also be present as labels) + remaining_pattern = rf'litellm_remaining_user_budget_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)' remaining_match = re.search(remaining_pattern, metrics_text) metrics["remaining"] = float(remaining_match.group(1)) if remaining_match else None # Get total budget - total_pattern = ( - f'litellm_user_max_budget_metric{{user="{escaped_user_id}"}} ([0-9.]+)' - ) + total_pattern = rf'litellm_user_max_budget_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)' total_match = re.search(total_pattern, metrics_text) metrics["total"] = float(total_match.group(1)) if total_match else None # Get remaining hours - hours_pattern = f'litellm_user_budget_remaining_hours_metric{{user="{escaped_user_id}"}} ([0-9.]+)' + hours_pattern = rf'litellm_user_budget_remaining_hours_metric{{[^}}]*user="{escaped_user_id}"[^}}]*}} ([0-9.]+)' hours_match = re.search(hours_pattern, metrics_text) metrics["remaining_hours"] = float(hours_match.group(1)) if hours_match else None diff --git a/tests/pass_through_unit_tests/conftest.py b/tests/pass_through_unit_tests/conftest.py index 42a95343eb7..390e14b7f11 100644 --- a/tests/pass_through_unit_tests/conftest.py +++ b/tests/pass_through_unit_tests/conftest.py @@ -5,14 +5,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -56,6 +59,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -71,3 +75,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/router_unit_tests/conftest.py b/tests/router_unit_tests/conftest.py index fe976515c92..6a8f3e589f4 100644 --- a/tests/router_unit_tests/conftest.py +++ b/tests/router_unit_tests/conftest.py @@ -12,14 +12,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -97,6 +100,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -123,3 +127,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/search_tests/conftest.py b/tests/search_tests/conftest.py index e06d3e95eee..78ba19a7724 100644 --- a/tests/search_tests/conftest.py +++ b/tests/search_tests/conftest.py @@ -13,14 +13,17 @@ import pytest sys.path.insert(0, os.path.abspath("../..")) -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -52,6 +55,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -65,3 +69,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/tests/test_callbacks_on_proxy.py b/tests/test_callbacks_on_proxy.py index 0b55d820532..17c0db9260f 100644 --- a/tests/test_callbacks_on_proxy.py +++ b/tests/test_callbacks_on_proxy.py @@ -9,12 +9,155 @@ import pytest import asyncio import aiohttp import os +import re import dotenv +from collections import Counter from dotenv import load_dotenv import pytest load_dotenv() +# A *leak* is sustained, monotonic growth of one callback TYPE across the whole +# sampling window. A one-time bump that then plateaus is benign pollution from +# other tests sharing this proxy (this suite runs `pytest -n 4` against a single +# proxy container, so other workers legitimately add team/key-scoped callbacks +# while this test sleeps). We therefore sample N times and only flag a type +# whose normalized count never decreases, grows in >=2 distinct intervals, and +# nets >= LEAK_MIN_NET_GROWTH overall. +NUM_SAMPLES = 4 +SAMPLE_INTERVAL_SECONDS = 20 +LEAK_MIN_NET_GROWTH = 5 +LEAK_MIN_GROWING_INTERVALS = 2 +# A routing-strategy switch / alerting config is a *known, bounded, one-time* +# registration (CCI diagnostic 2026-05-16: total 85->95 on the first interval +# after switching to latency-based-routing, then flat at 95 for 2.5 min under +# load). We absorb that step by settling before the baseline sample, so only +# growth *after* the deliberate perturbation can count as a leak. +SETTLE_SECONDS = 30 + +# Strip instance-identity noise so N leaked instances of one class collapse to +# one rising counter instead of N opaque, unrelated-looking strings. +_ADDR_RE = re.compile(r" at 0x[0-9a-fA-F]+") +_OBJ_RE = re.compile(r"<([\w.]+) object") + + +def _normalize_callback(cb_str: str) -> str: + """Reduce a callback's str() to a stable type key (drops 0x… addresses).""" + s = _ADDR_RE.sub("", cb_str) + m = _OBJ_RE.search(s) + if m: + return m.group(1).split(".")[-1] + # bound methods: ">" -> "Cls.m" + bm = re.search(r"bound method ([\w.]+)", s) + if bm: + return bm.group(1) + return s.strip() + + +def _summarize(all_litellm_callbacks) -> Counter: + return Counter(_normalize_callback(str(c)) for c in all_litellm_callbacks) + + +def _detect_leaks(samples): + """ + samples: list[Counter] taken in time order. + + Returns {callback_type: [counts across samples]} for types that grew + monotonically (never decreased), in >=LEAK_MIN_GROWING_INTERVALS intervals, + and netted >=LEAK_MIN_NET_GROWTH overall — i.e. a real leak, not a one-shot + step from a parallel test. + """ + leaks = {} + all_types = set().union(*[set(s) for s in samples]) if samples else set() + for t in all_types: + series = [s.get(t, 0) for s in samples] + deltas = [b - a for a, b in zip(series, series[1:])] + net = series[-1] - series[0] + non_decreasing = all(d >= 0 for d in deltas) + growing_intervals = sum(1 for d in deltas if d > 0) + if ( + non_decreasing + and net >= LEAK_MIN_NET_GROWTH + and growing_intervals >= LEAK_MIN_GROWING_INTERVALS + ): + leaks[t] = series + return leaks + + +def _terminal_suspects(samples): + """ + Types whose net growth clears the threshold monotonically but is confined + to the *final* interval — `growing_intervals == 1` with that one growing + interval being the last. `_detect_leaks`' `>= 2` guard silently passes + these, so a real leak that accumulates entirely in the last sampled window + is indistinguishable from a one-time terminal step *without one more + sample*. Returns the set of such types so the caller can re-confirm. + """ + suspects = set() + all_types = set().union(*[set(s) for s in samples]) if samples else set() + for t in all_types: + series = [s.get(t, 0) for s in samples] + deltas = [b - a for a, b in zip(series, series[1:])] + if not deltas: + continue + net = series[-1] - series[0] + non_decreasing = all(d >= 0 for d in deltas) + growing = [i for i, d in enumerate(deltas) if d > 0] + if ( + non_decreasing + and net >= LEAK_MIN_NET_GROWTH + and growing == [len(deltas) - 1] + ): + suspects.add(t) + return suspects + + +async def _detect_leaks_confirmed(session, samples): + """ + `_detect_leaks`, plus a single confirmation sample when growth is confined + to the final interval (see `_terminal_suspects`). A genuine ongoing leak + keeps climbing -> now grows in >= 2 intervals -> flagged; a one-time + terminal registration plateaus -> still 1 growing interval -> ignored. + Returns `(leaks, samples)` (samples may have one extra entry appended). + """ + leaks = _detect_leaks(samples) + if not leaks and _terminal_suspects(samples): + await asyncio.sleep(SAMPLE_INTERVAL_SECONDS) + _, _, all_cb = await get_active_callbacks(session=session) + samples = samples + [_summarize(all_cb)] + leaks = _detect_leaks(samples) + return leaks, samples + + +def _format_report(samples, leaks) -> str: + lines = ["Callback count per type across samples (time order):"] + all_types = sorted(set().union(*[set(s) for s in samples])) + for t in all_types: + series = [s.get(t, 0) for s in samples] + marker = " <-- LEAK" if t in leaks else "" + lines.append(f" {t}: {series}{marker}") + totals = [sum(s.values()) for s in samples] + lines.append(f"TOTAL callbacks per sample: {totals}") + if leaks: + lines.append( + "Leaking callback types (sustained monotonic growth): " + + ", ".join(sorted(leaks)) + ) + return "\n".join(lines) + + +async def _sample_callbacks(session, num_samples, interval): + """Take `num_samples` callback snapshots `interval`s apart.""" + samples = [] + alerts = [] + for i in range(num_samples): + if i > 0: + await asyncio.sleep(interval) + num_cb, num_alert, all_cb = await get_active_callbacks(session=session) + samples.append(_summarize(all_cb)) + alerts.append(num_alert) + return samples, alerts + async def config_update(session, routing_strategy=None): url = "http://0.0.0.0:4000/config/update" @@ -97,105 +240,65 @@ async def get_current_routing_strategy(session): @pytest.mark.asyncio @pytest.mark.order1 +@pytest.mark.flaky(reruns=2, reruns_delay=5) async def test_check_num_callbacks(): """ - Test 1: num callbacks should NOT increase over time - -> check current callbacks - -> sleep for 30 seconds - -> check current callbacks - -> sleep for 30 seconds - -> check current callbacks + PROD invariant: no callback TYPE should grow without bound over time. + + This suite runs `pytest -n 4` against one shared proxy, so the raw count is + noisy — other workers legitimately add team/key-scoped callbacks that then + plateau. We settle first, then sample several times, and only fail on + *sustained, monotonic* per-type growth (a genuine leak), naming the type. """ - from litellm._uuid import uuid - async with aiohttp.ClientSession() as session: - await asyncio.sleep(30) - num_callbacks_1, _, all_litellm_callbacks_1 = await get_active_callbacks( - session=session - ) - assert num_callbacks_1 > 0 - await asyncio.sleep(30) + # Absorb proxy warmup / in-flight parallel registration before baseline. + await asyncio.sleep(SETTLE_SECONDS) - num_callbacks_2, _, all_litellm_callbacks_2 = await get_active_callbacks( - session=session + samples, _ = await _sample_callbacks( + session, NUM_SAMPLES, SAMPLE_INTERVAL_SECONDS ) - print("all_litellm_callbacks_1", all_litellm_callbacks_1) + assert sum(samples[0].values()) > 0, "expected some callbacks registered" - print( - "diff in callbacks=", - set(all_litellm_callbacks_1) - set(all_litellm_callbacks_2), - ) - - assert abs(num_callbacks_1 - num_callbacks_2) <= 4 - - await asyncio.sleep(30) - - num_callbacks_3, _, all_litellm_callbacks_3 = await get_active_callbacks( - session=session - ) - - print( - "diff in callbacks = all_litellm_callbacks3 - all_litellm_callbacks2 ", - set(all_litellm_callbacks_3) - set(all_litellm_callbacks_2), - ) - - assert abs(num_callbacks_3 - num_callbacks_2) <= 4 + leaks, samples = await _detect_leaks_confirmed(session, samples) + report = _format_report(samples, leaks) + print(report) + assert not leaks, f"Callback leak detected.\n{report}" @pytest.mark.asyncio @pytest.mark.order2 +@pytest.mark.flaky(reruns=2, reruns_delay=5) async def test_check_num_callbacks_on_lowest_latency(): """ - Test 1: num callbacks should NOT increase over time - -> Update to lowest latency - -> check current callbacks - -> sleep for 30s - -> check current callbacks - -> sleep for 30s - -> check current callbacks - -> update back to original routing-strategy + Same PROD invariant as test_check_num_callbacks, but after switching the + router to latency-based-routing. That switch is a *known, bounded* one-time + registration (it adds the latency strategy handler + Slack alerting); we + settle past it before baselining so only post-switch growth counts as a + leak. Also asserts the alerting count is stable. """ - from litellm._uuid import uuid - async with aiohttp.ClientSession() as session: await asyncio.sleep(30) original_routing_strategy = await get_current_routing_strategy(session=session) await config_update(session=session, routing_strategy="latency-based-routing") - await asyncio.sleep(30) + try: + # Absorb the deliberate one-time config/update registration step. + await asyncio.sleep(SETTLE_SECONDS) - num_callbacks_1, num_alerts_1, all_litellm_callbacks_1 = ( - await get_active_callbacks(session=session) - ) + samples, alerts = await _sample_callbacks( + session, NUM_SAMPLES, SAMPLE_INTERVAL_SECONDS + ) - await asyncio.sleep(30) - - num_callbacks_2, num_alerts_2, all_litellm_callbacks_2 = ( - await get_active_callbacks(session=session) - ) - - print( - "diff in callbacks all_litellm_callbacks_2 - all_litellm_callbacks_1 =", - set(all_litellm_callbacks_2) - set(all_litellm_callbacks_1), - ) - - assert abs(num_callbacks_1 - num_callbacks_2) <= 4 - - await asyncio.sleep(30) - - num_callbacks_3, num_alerts_3, all_litellm_callbacks_3 = ( - await get_active_callbacks(session=session) - ) - - print( - "diff in callbacks all_litellm_callbacks_3 - all_litellm_callbacks_2 =", - set(all_litellm_callbacks_3) - set(all_litellm_callbacks_2), - ) - - assert abs(num_callbacks_2 - num_callbacks_3) <= 4 - - assert num_alerts_1 == num_alerts_2 == num_alerts_3 - - await config_update(session=session, routing_strategy=original_routing_strategy) + leaks, samples = await _detect_leaks_confirmed(session, samples) + report = _format_report(samples, leaks) + print(report) + assert not leaks, f"Callback leak detected.\n{report}" + assert ( + len(set(alerts)) == 1 + ), f"alerting count changed across samples: {alerts}" + finally: + await config_update( + session=session, routing_strategy=original_routing_strategy + ) diff --git a/tests/test_litellm/caching/test_caching_handler.py b/tests/test_litellm/caching/test_caching_handler.py index 742a4f410d4..3eb949d7f29 100644 --- a/tests/test_litellm/caching/test_caching_handler.py +++ b/tests/test_litellm/caching/test_caching_handler.py @@ -232,3 +232,207 @@ def test_combine_usage_handles_none_details(): combined = llm_caching_handler.combine_usage(usage_a, usage_c) assert combined.prompt_tokens_details is not None assert combined.prompt_tokens_details.image_count == 1 + + +def test_is_chat_completion_cached_dict(): + from litellm.caching.caching_handler import _is_chat_completion_cached_dict + + assert _is_chat_completion_cached_dict( + {"id": "chatcmpl-abc", "object": "chat.completion", "choices": []} + ) + assert _is_chat_completion_cached_dict( + {"id": "other", "object": "chat.completion.chunk", "choices": []} + ) + assert _is_chat_completion_cached_dict( + {"id": "no-object", "choices": [{"index": 0}]} + ) + assert not _is_chat_completion_cached_dict( + {"id": "resp_abc", "object": "response", "output": []} + ) + + +def _build_logging_obj(call_type: str, stream: bool): + import uuid as _uuid + + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging + + return LiteLLMLogging( + litellm_call_id=str(datetime.now()), + call_type=call_type, + model="gpt-5.4", + messages=[], + function_id=str(_uuid.uuid4()), + stream=stream, + start_time=datetime.now(), + ) + + +def test_convert_cached_aresponses_bridge_chat_completion_stream(): + """openai/responses chat-completions bridge: streaming cache hit replays as chat stream.""" + from litellm import aresponses + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper + from litellm.types.utils import CallTypes + + caching_handler = LLMCachingHandler( + original_function=aresponses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "chatcmpl-bridge-cache-test", + "object": "chat.completion", + "created": int(time.time()), + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hi!"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18}, + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.aresponses.value, + kwargs={ + "model": "gpt-5.4", + "stream": True, + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=_build_logging_obj(CallTypes.aresponses.value, stream=True), + model="gpt-5.4", + args=(), + ) + + assert isinstance(result, CustomStreamWrapper) + + +def test_convert_cached_responses_bridge_chat_completion_nonstream(): + """openai/responses chat-completions bridge: non-streaming cache hit replays as ModelResponse.""" + from litellm import responses + from litellm.types.utils import CallTypes, ModelResponse + + caching_handler = LLMCachingHandler( + original_function=responses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "chatcmpl-bridge-nonstream", + "object": "chat.completion", + "created": int(time.time()), + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hi!"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18}, + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.responses.value, + kwargs={ + "model": "gpt-5.4", + "stream": False, + "messages": [{"role": "user", "content": "hi"}], + }, + logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False), + model="gpt-5.4", + args=(), + ) + + assert isinstance(result, ModelResponse) + assert result.choices[0].message.content == "Hi!" + + +def test_convert_cached_responses_legacy_nonstream_path(): + """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) falls through legacy path.""" + from litellm import responses + from litellm.types.llms.openai import ResponsesAPIResponse + from litellm.types.utils import CallTypes + + caching_handler = LLMCachingHandler( + original_function=responses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "resp_legacy_nonstream", + "created_at": int(time.time()), + "status": "completed", + "model": "gpt-4o", + "object": "response", + "output": [ + { + "type": "message", + "id": "msg_legacy", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "legacy response", + "annotations": [], + } + ], + } + ], + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.responses.value, + kwargs={"model": "gpt-4o", "input": "hi", "stream": False}, + logging_obj=_build_logging_obj(CallTypes.responses.value, stream=False), + model="gpt-4o", + args=(), + ) + + assert isinstance(result, ResponsesAPIResponse) + assert result.id == "resp_legacy_nonstream" + + +def test_convert_cached_responses_legacy_stream_path(): + """Genuine ResponsesAPIResponse dict (no chatcmpl/choices) on stream falls through legacy path.""" + from litellm import responses + from litellm.responses.streaming_iterator import ( + CachedResponsesAPIStreamingIterator, + ) + from litellm.types.utils import CallTypes + + caching_handler = LLMCachingHandler( + original_function=responses, request_kwargs={}, start_time=datetime.now() + ) + cached_result = { + "id": "resp_legacy_stream", + "created_at": int(time.time()), + "status": "completed", + "model": "gpt-4o", + "object": "response", + "output": [ + { + "type": "message", + "id": "msg_legacy_stream", + "status": "completed", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": "legacy stream", + "annotations": [], + } + ], + } + ], + } + + result = caching_handler._convert_cached_result_to_model_response( + cached_result=cached_result, + call_type=CallTypes.responses.value, + kwargs={"model": "gpt-4o", "input": "hi", "stream": True}, + logging_obj=_build_logging_obj(CallTypes.responses.value, stream=True), + model="gpt-4o", + args=(), + ) + + assert isinstance(result, CachedResponsesAPIStreamingIterator) diff --git a/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py new file mode 100644 index 00000000000..734033ed6be --- /dev/null +++ b/tests/test_litellm/completion_extras/litellm_responses_transformation/test_completion_extras_litellm_responses_transformation_handler.py @@ -0,0 +1,150 @@ +import os +import sys +from datetime import datetime +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +sys.path.insert(0, os.path.abspath("../../..")) + +from litellm.completion_extras.litellm_responses_transformation.handler import ( + ResponsesToCompletionBridgeHandler, +) +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging +from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper +from litellm.types.utils import ModelResponse + + +def test_is_preformatted_cached_chat_stream_true(): + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "cached_response" + assert ( + ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream(stream) + is True + ) + + +def test_is_preformatted_cached_chat_stream_false_wrong_provider(): + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "openai" + assert ( + ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream(stream) + is False + ) + + +def test_is_preformatted_cached_chat_stream_false_wrong_type(): + assert ( + ResponsesToCompletionBridgeHandler._is_preformatted_cached_chat_stream( + {"object": "chat.completion.chunk"} + ) + is False + ) + + +def _bridge_kwargs(stream: bool): + logging_obj = LiteLLMLogging( + litellm_call_id="test-call", + call_type="completion", + model="gpt-5.4", + messages=[{"role": "user", "content": "hi"}], + function_id="fn-id", + stream=stream, + start_time=datetime.now(), + ) + return { + "model": "gpt-5.4", + "custom_llm_provider": "openai", + "messages": [{"role": "user", "content": "hi"}], + "optional_params": {"stream": stream}, + "litellm_params": {}, + "headers": {}, + "model_response": ModelResponse(), + "logging_obj": logging_obj, + } + + +def test_completion_returns_cached_model_response_directly(): + """Non-streaming bridge cache hit: responses() returns a ModelResponse -> bridge returns it as-is.""" + cached = ModelResponse(id="chatcmpl-cached-nonstream", model="gpt-5.4") + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.responses", return_value=cached), + ): + result = bridge.completion(**_bridge_kwargs(stream=False)) + + assert result is cached + + +@pytest.mark.asyncio +async def test_acompletion_returns_cached_model_response_directly(): + cached = ModelResponse(id="chatcmpl-cached-nonstream-async", model="gpt-5.4") + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.aresponses", new=AsyncMock(return_value=cached)), + ): + result = await bridge.acompletion(**_bridge_kwargs(stream=False)) + + assert result is cached + + +def test_completion_skips_rewrapping_preformatted_cached_chat_stream(): + """Streaming bridge cache hit returning CustomStreamWrapper(cached_response) -> bridge skips re-wrapping.""" + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "cached_response" + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.responses", return_value=stream), + patch.object( + bridge, + "_apply_post_stream_processing", + side_effect=lambda s, *a, **kw: s, + ) as post, + ): + result = bridge.completion(**_bridge_kwargs(stream=True)) + + post.assert_called_once() + assert result is stream + + +@pytest.mark.asyncio +async def test_acompletion_skips_rewrapping_preformatted_cached_chat_stream(): + stream = MagicMock(spec=CustomStreamWrapper) + stream.custom_llm_provider = "cached_response" + bridge = ResponsesToCompletionBridgeHandler() + + with ( + patch.object( + bridge.transformation_handler, + "transform_request", + return_value={"model": "gpt-5.4", "input": "hi"}, + ), + patch("litellm.aresponses", new=AsyncMock(return_value=stream)), + patch.object( + bridge, + "_apply_post_stream_processing", + side_effect=lambda s, *a, **kw: s, + ) as post, + ): + result = await bridge.acompletion(**_bridge_kwargs(stream=True)) + + post.assert_called_once() + assert result is stream diff --git a/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py b/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py index 009f432fca1..05bdc40112c 100644 --- a/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py +++ b/tests/test_litellm/completion_extras/test_litellm_responses_transformation_transformation.py @@ -230,3 +230,30 @@ def test_transform_request_drops_user_metadata_with_additional_drop_params(): assert "metadata" not in result assert result["litellm_metadata"]["internal_key"] == "secret" + + +def test_translate_responses_chunk_passthrough_chat_completion_chunk(): + from litellm.completion_extras.litellm_responses_transformation.transformation import ( + OpenAiResponsesToChatCompletionStreamIterator, + ) + + chat_chunk = { + "id": "chatcmpl-cache-passthrough", + "object": "chat.completion.chunk", + "created": 1779104834, + "model": "gpt-5.4", + "choices": [ + { + "index": 0, + "delta": {"role": "assistant", "content": "Hi! How can I help?"}, + "finish_reason": None, + } + ], + } + + result = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream( + chat_chunk + ) + + assert result.choices[0].delta.content == "Hi! How can I help?" + assert result.choices[0].finish_reason is None diff --git a/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py b/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py index 19ae819c85a..12f30ab6024 100644 --- a/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py +++ b/tests/test_litellm/integrations/test_prometheus_user_team_metrics.py @@ -460,6 +460,104 @@ async def test_assemble_user_object_does_not_override_metadata_max_budget( ), "max_budget from metadata must not be replaced by the DB value" +async def test_assemble_user_object_populates_user_email_and_alias_from_db( + prometheus_logger, +): + db_user = MagicMock() + db_user.max_budget = None + db_user.budget_reset_at = None + db_user.user_email = "alice@example.com" + db_user.user_alias = "Alice" + + with patch("litellm.proxy.auth.auth_checks.get_user_object") as mock_get_user: + mock_get_user.return_value = db_user + user_object = await prometheus_logger._assemble_user_object( + user_id="user-abc-123", + spend=10.0, + max_budget=None, + response_cost=0.5, + ) + + assert user_object.user_email == "alice@example.com" + assert user_object.user_alias == "Alice" + + +def test_set_user_budget_metrics_default_no_email_alias_labels( + prometheus_logger, +): + """By default (flag off), only user label is emitted.""" + import litellm + from litellm.proxy._types import LiteLLM_UserTable + + litellm.prometheus_user_budget_label_include_email_alias = False + + user = LiteLLM_UserTable( + user_id="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + spend=25.0, + max_budget=100.0, + budget_reset_at=datetime(2026, 3, 1, tzinfo=timezone.utc), + ) + + prometheus_logger.litellm_remaining_user_budget_metric = MagicMock() + prometheus_logger.litellm_user_max_budget_metric = MagicMock() + prometheus_logger.litellm_user_budget_remaining_hours_metric = MagicMock() + + prometheus_logger._set_user_budget_metrics(user) + + prometheus_logger.litellm_remaining_user_budget_metric.labels.assert_called_once_with( + user="user-abc-123", + ) + + +def test_set_user_budget_metrics_includes_user_email_and_alias_labels_when_opted_in( + prometheus_logger, +): + """When prometheus_user_budget_label_include_email_alias=True, email+alias labels appear.""" + import litellm + from litellm.proxy._types import LiteLLM_UserTable + + litellm.prometheus_user_budget_label_include_email_alias = True + + user = LiteLLM_UserTable( + user_id="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + spend=25.0, + max_budget=100.0, + budget_reset_at=datetime(2026, 3, 1, tzinfo=timezone.utc), + ) + + prometheus_logger.litellm_remaining_user_budget_metric = MagicMock() + prometheus_logger.litellm_user_max_budget_metric = MagicMock() + prometheus_logger.litellm_user_budget_remaining_hours_metric = MagicMock() + + try: + prometheus_logger._set_user_budget_metrics(user) + + prometheus_logger.litellm_remaining_user_budget_metric.labels.assert_called_once_with( + user="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + ) + prometheus_logger.litellm_remaining_user_budget_metric.labels().set.assert_called_once_with( + 75.0 + ) + prometheus_logger.litellm_user_max_budget_metric.labels.assert_called_once_with( + user="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + ) + prometheus_logger.litellm_user_budget_remaining_hours_metric.labels.assert_called_once_with( + user="user-abc-123", + user_email="alice@example.com", + user_alias="Alice", + ) + finally: + litellm.prometheus_user_budget_label_include_email_alias = False + + async def test_set_user_budget_metrics_after_api_request_no_inf_when_metadata_budget_none( prometheus_logger, ): diff --git a/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py b/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py new file mode 100644 index 00000000000..8de47331614 --- /dev/null +++ b/tests/test_litellm/llms/bedrock/batches/test_batch_metadata_sanitization.py @@ -0,0 +1,119 @@ +""" +Test that BedrockBatchesConfig._get_openai_compatible_batch_metadata +sanitizes non-string metadata values injected by proxy guardrail hooks. + +The OpenAI Batch Pydantic model requires metadata: Dict[str, str]. +Proxy hooks (Model Armor, OpenAI Moderations, queue time tracking) inject +dicts, floats, and other non-string values that cause a ValidationError +when constructing LiteLLMBatch. This test suite verifies the sanitization +layer prevents that. +""" + +import os +import sys + +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + + +class TestGetOpenaiCompatibleBatchMetadata: + """Tests for _get_openai_compatible_batch_metadata.""" + + def test_string_values_pass_through_unchanged(self): + metadata = {"user_key": "user_value", "run_id": "abc123"} + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert result == {"user_key": "user_value", "run_id": "abc123"} + + def test_dict_values_serialized_to_json_string(self): + metadata = { + "_model_armor_response": { + "sanitizationResult": {"filterMatchState": "MATCH_FOUND"} + } + } + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert "_model_armor_response" in result + assert isinstance(result["_model_armor_response"], str) + assert "MATCH_FOUND" in result["_model_armor_response"] + + def test_float_values_serialized_to_string(self): + metadata = {"queue_time_seconds": 0.5} + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert result == {"queue_time_seconds": "0.5"} + + def test_none_values_excluded(self): + metadata = {"key": "value", "empty": None} + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert "empty" not in result + assert result == {"key": "value"} + + def test_standard_logging_guardrail_information_excluded(self): + metadata = { + "standard_logging_guardrail_information": {"some": "logging_data"}, + "user_key": "keep_me", + } + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + assert "standard_logging_guardrail_information" not in result + assert result == {"user_key": "keep_me"} + + def test_non_dict_input_returns_empty_dict(self): + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata(None) == {} + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata("string") == {} + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata(123) == {} + + def test_empty_dict_returns_empty_dict(self): + assert BedrockBatchesConfig._get_openai_compatible_batch_metadata({}) == {} + + def test_mixed_metadata_from_guardrails(self): + """Simulate real metadata contaminated by proxy guardrails.""" + metadata = { + "_model_armor_response": {"sanitizationResult": {"key": "val"}}, + "_model_armor_status": "success", + "_openai_moderation_response": {"id": "mod-123", "flagged": False}, + "queue_time_seconds": 1.23, + "headers": {"Authorization": "Bearer sk-xxx"}, + "standard_logging_guardrail_information": {"internal": True}, + "user_metadata_key": "user_value", + "none_field": None, + } + result = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + + # All values must be strings + for key, value in result.items(): + assert isinstance(value, str), f"metadata[{key!r}] is {type(value)}, not str" + + # Excluded keys + assert "standard_logging_guardrail_information" not in result + assert "none_field" not in result + + # Preserved keys + assert result["_model_armor_status"] == "success" + assert result["user_metadata_key"] == "user_value" + + def test_result_compatible_with_litellm_batch(self): + """Verify sanitized metadata can construct a LiteLLMBatch without error.""" + import time + + from litellm.types.utils import LiteLLMBatch + + metadata = { + "_model_armor_response": {"blocked": True}, + "queue_time_seconds": 0.05, + "user_key": "value", + } + sanitized = BedrockBatchesConfig._get_openai_compatible_batch_metadata(metadata) + + # This would raise ValidationError before the fix + batch = LiteLLMBatch( + id="arn:aws:bedrock:us-east-1:123:model-invocation-job/test", + object="batch", + endpoint="/v1/chat/completions", + input_file_id="file-123", + completion_window="24h", + status="validating", + created_at=int(time.time()), + metadata=sanitized, + ) + assert batch.metadata == sanitized diff --git a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py index c67a8712340..9955851132c 100644 --- a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py +++ b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py @@ -957,3 +957,50 @@ def test_titan_image_embedding_cost_uses_per_image_rate(): assert response.usage is not None assert response.usage.prompt_tokens_details is not None assert response.usage.prompt_tokens_details.image_count == 1 + + +@pytest.mark.parametrize( + "encoding_format,expected_embedding_types", + [ + ("float", ["float"]), + ("base64", ["base64"]), + (["float", "int8"], ["float", "int8"]), + ], +) +def test_bedrock_cohere_embedding_types_wrapped_as_list( + encoding_format, expected_embedding_types +): + """ + Bedrock Cohere expects `embedding_types` as a JSON array, not a raw string. + + Regression test for: Bedrock returns + Malformed input request: #/embedding_types: expected type: JSONArray, found: String + when `encoding_format` is passed as a string. + """ + litellm.set_verbose = True + client = HTTPHandler() + model = "bedrock/cohere.embed-multilingual-v3" + + with patch.object(client, "post") as mock_post: + mock_response = Mock() + mock_response.status_code = 200 + mock_response.text = json.dumps(cohere_embedding_response) + mock_response.json = lambda: json.loads(mock_response.text) + mock_post.return_value = mock_response + + response = litellm.embedding( + model=model, + input=test_input, + encoding_format=encoding_format, + client=client, + aws_region_name="us-east-1", + aws_bedrock_runtime_endpoint="https://bedrock-runtime.us-east-1.amazonaws.com", + api_key="test-bearer-token-12345", + ) + + assert isinstance(response, litellm.EmbeddingResponse) + + request_body = json.loads(mock_post.call_args.kwargs.get("data", "{}")) + assert "embedding_types" in request_body + assert request_body["embedding_types"] == expected_embedding_types + assert isinstance(request_body["embedding_types"], list) diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py b/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py new file mode 100644 index 00000000000..5e4d0177e8d --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_dashscope_embedding_transformation.py @@ -0,0 +1,141 @@ +""" +Unit tests for DashScope embedding transformation. +""" + +import json +import os +import sys +from unittest.mock import MagicMock + +import httpx +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.dashscope.common_utils import DashScopeError +from litellm.llms.dashscope.embed.transformation import ( + DEFAULT_API_BASE, + DashScopeEmbeddingConfig, +) +from litellm.types.utils import EmbeddingResponse + + +def test_validate_environment_and_url(): + config = DashScopeEmbeddingConfig() + headers = config.validate_environment( + headers={}, + model="text-embedding-v4", + messages=[], + optional_params={}, + litellm_params={}, + api_key="sk-test", + ) + assert headers["Authorization"] == "Bearer sk-test" + + url = config.get_complete_url( + api_base=None, + api_key="sk-test", + model="text-embedding-v4", + optional_params={}, + litellm_params={}, + ) + assert url == f"{DEFAULT_API_BASE}/embeddings" + + +def test_transform_embedding_request(): + config = DashScopeEmbeddingConfig() + data = config.transform_embedding_request( + model="text-embedding-v4", + input=["风急天高猿啸哀"], + optional_params={"dimensions": 1024, "encoding_format": "float"}, + headers={}, + ) + assert data == { + "model": "text-embedding-v4", + "input": ["风急天高猿啸哀"], + "dimensions": 1024, + "encoding_format": "float", + } + + +def test_transform_embedding_response_success(): + config = DashScopeEmbeddingConfig() + payload = { + "data": [ + {"embedding": [0.1, 0.2], "index": 0, "object": "embedding"}, + ], + "model": "text-embedding-v4", + "object": "list", + "usage": {"prompt_tokens": 5, "total_tokens": 5}, + "id": "73591b79-xxxx", + } + raw = httpx.Response( + status_code=200, + content=json.dumps(payload).encode("utf-8"), + request=httpx.Request("POST", "https://example.com"), + ) + result = config.transform_embedding_response( + model="text-embedding-v4", + raw_response=raw, + model_response=EmbeddingResponse(), + logging_obj=MagicMock(), + api_key="sk-x", + request_data={"input": ["a"]}, + optional_params={}, + litellm_params={}, + ) + assert result.model == "text-embedding-v4" + assert len(result.data) == 1 + assert result.usage.prompt_tokens == 5 + + +def test_transform_embedding_request_user_param(): + config = DashScopeEmbeddingConfig() + data = config.transform_embedding_request( + model="text-embedding-v4", + input=["hello"], + optional_params={"user": "user-123"}, + headers={}, + ) + assert data["user"] == "user-123" + + +def test_map_openai_params_drops_unsupported_with_drop_params(): + config = DashScopeEmbeddingConfig() + result = config.map_openai_params( + non_default_params={"dimensions": 512, "unknown_param": "value"}, + optional_params={}, + model="text-embedding-v4", + drop_params=True, + ) + assert result == {"dimensions": 512} + assert "unknown_param" not in result + + +def test_transform_embedding_response_error(): + config = DashScopeEmbeddingConfig() + payload = { + "error": { + "message": "Incorrect API key provided.", + "type": "invalid_request_error", + "code": "invalid_api_key", + } + } + raw = httpx.Response( + status_code=401, + content=json.dumps(payload).encode("utf-8"), + request=httpx.Request("POST", "https://example.com"), + ) + with pytest.raises(DashScopeError) as exc: + config.transform_embedding_response( + model="text-embedding-v4", + raw_response=raw, + model_response=EmbeddingResponse(), + logging_obj=MagicMock(), + api_key="sk-bad", + request_data={"input": ["a"]}, + optional_params={}, + litellm_params={}, + ) + assert exc.value.status_code == 401 + assert "Incorrect API key" in exc.value.message diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py b/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py new file mode 100644 index 00000000000..0e8d58b6530 --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_dashscope_rerank_transformation.py @@ -0,0 +1,328 @@ +""" +Unit tests for DashScope rerank transformation. +""" + +import json +import os +import sys +from unittest.mock import MagicMock + +import httpx +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.dashscope.common_utils import DashScopeError +from litellm.llms.dashscope.rerank.transformation import ( + DEFAULT_RERANK_URL, + DashScopeRerankConfig, +) +from litellm.types.rerank import RerankResponse + + +class TestDashScopeRerankURL: + def setup_method(self): + self.config = DashScopeRerankConfig() + + def test_default_url(self): + url = self.config.get_complete_url(api_base=None, model="qwen3-rerank") + assert url == DEFAULT_RERANK_URL + + def test_explicit_v1_base_appends_reranks(self): + url = self.config.get_complete_url( + api_base="https://dashscope.aliyuncs.com/compatible-mode/v1", + model="qwen3-rerank", + ) + assert url == "https://dashscope.aliyuncs.com/compatible-mode/v1/reranks" + + def test_intl_v1_base_appends_reranks(self): + url = self.config.get_complete_url( + api_base="https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + model="qwen3-rerank", + ) + assert url == "https://dashscope-intl.aliyuncs.com/compatible-mode/v1/reranks" + + def test_already_complete_url_passthrough(self): + full = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks" + assert self.config.get_complete_url(api_base=full, model="qwen3-rerank") == full + + def test_trailing_slash_stripped(self): + full = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks/" + assert self.config.get_complete_url( + api_base=full, model="qwen3-rerank" + ) == full.rstrip("/") + + def test_custom_v1_base_appends_reranks(self): + url = self.config.get_complete_url( + api_base="https://my-proxy.example.com/v1", model="qwen3-rerank" + ) + assert url == "https://my-proxy.example.com/v1/reranks" + + +class TestDashScopeRerankRequest: + def setup_method(self): + self.config = DashScopeRerankConfig() + + def test_validate_environment_with_explicit_key(self): + headers = self.config.validate_environment( + headers={}, model="qwen3-rerank", api_key="sk-test" + ) + assert headers["Authorization"] == "Bearer sk-test" + assert headers["content-type"] == "application/json" + + def test_validate_environment_missing_key(self, monkeypatch): + monkeypatch.delenv("DASHSCOPE_API_KEY", raising=False) + with pytest.raises(ValueError, match="DASHSCOPE_API_KEY"): + self.config.validate_environment( + headers={}, model="qwen3-rerank", api_key=None + ) + + def test_validate_environment_falls_back_to_env(self, monkeypatch): + monkeypatch.setenv("DASHSCOPE_API_KEY", "env-key") + headers = self.config.validate_environment( + headers={}, model="qwen3-rerank", api_key=None + ) + assert headers["Authorization"] == "Bearer env-key" + + def test_supported_params(self): + assert self.config.get_supported_cohere_rerank_params("qwen3-rerank") == [ + "query", + "documents", + "top_n", + "return_documents", + ] + + def test_map_params_drops_unsupported(self): + # qwen3-rerank accepts query/documents/top_n/return_documents. + # rank_fields and max_*_per_doc are silently dropped. + params = self.config.map_cohere_rerank_params( + non_default_params={}, + model="qwen3-rerank", + drop_params=False, + query="什么是文本排序模型", + documents=["d1", "d2"], + top_n=2, + rank_fields=["title"], + return_documents=True, + max_chunks_per_doc=5, + max_tokens_per_doc=100, + ) + assert params == { + "query": "什么是文本排序模型", + "documents": ["d1", "d2"], + "top_n": 2, + "return_documents": True, + } + + def test_transform_request_full(self): + body = self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={ + "query": "如何制作美味的苹果派?", + "documents": ["a", "b"], + "top_n": 5, + "return_documents": True, + }, + headers={}, + ) + assert body == { + "model": "qwen3-rerank", + "query": "如何制作美味的苹果派?", + "documents": ["a", "b"], + "top_n": 5, + "return_documents": True, + } + + def test_transform_request_omits_unset_optional(self): + body = self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={"query": "q", "documents": ["a"]}, + headers={}, + ) + assert "top_n" not in body + assert "return_documents" not in body + + def test_transform_request_requires_query(self): + with pytest.raises(ValueError, match="query"): + self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={"documents": ["a"]}, + headers={}, + ) + + def test_transform_request_requires_documents(self): + with pytest.raises(ValueError, match="documents"): + self.config.transform_rerank_request( + model="qwen3-rerank", + optional_rerank_params={"query": "q"}, + headers={}, + ) + + +class TestDashScopeRerankResponse: + def setup_method(self): + self.config = DashScopeRerankConfig() + self.logging = MagicMock() + + def _resp(self, body, status_code=200): + return httpx.Response( + status_code=status_code, + content=json.dumps(body).encode(), + request=httpx.Request("POST", "https://example.com"), + ) + + def test_success_response(self): + body = { + "object": "list", + "results": [ + {"index": 0, "relevance_score": 0.93}, + {"index": 2, "relevance_score": 0.34}, + ], + "model": "qwen3-rerank", + "id": "85ba5752", + "usage": {"total_tokens": 79}, + } + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + api_key="sk", + request_data={"query": "q"}, + ) + assert out.id == "85ba5752" + assert out.results == [ + {"index": 0, "relevance_score": 0.93}, + {"index": 2, "relevance_score": 0.34}, + ] + assert out.meta == { + "billed_units": {"total_tokens": 79}, + "tokens": {"input_tokens": 79}, + } + + def test_response_with_return_documents_real_payload(self): + # Verbatim sample from a real qwen3-rerank call with return_documents=true. + body = { + "object": "list", + "results": [ + { + "document": { + "text": "苹果派的制作步骤包括准备面团、切苹果、调制馅料、组装和烘烤。" + }, + "index": 1, + "relevance_score": 0.8304247466067356, + }, + { + "document": { + "text": "制作苹果派时，预先煮软苹果可以缩短烘烤时间。" + }, + "index": 3, + "relevance_score": 0.7142660211908354, + }, + ], + "model": "qwen3-rerank", + "id": "e191b077-97c4-9929-b121-c2fbd2c7b0af", + "usage": {"total_tokens": 192}, + } + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + request_data={"query": "如何制作美味的苹果派?"}, + ) + assert out.id == "e191b077-97c4-9929-b121-c2fbd2c7b0af" + assert out.results == [ + { + "index": 1, + "relevance_score": 0.8304247466067356, + "document": { + "text": "苹果派的制作步骤包括准备面团、切苹果、调制馅料、组装和烘烤。" + }, + }, + { + "index": 3, + "relevance_score": 0.7142660211908354, + "document": {"text": "制作苹果派时，预先煮软苹果可以缩短烘烤时间。"}, + }, + ] + assert out.meta == { + "billed_units": {"total_tokens": 192}, + "tokens": {"input_tokens": 192}, + } + + def test_response_string_document_normalized(self): + # Defensive path: if a future API revision returns a bare string, + # normalize to {"text": ...} so downstream code stays consistent. + body = { + "results": [{"index": 0, "relevance_score": 0.9, "document": "hello"}], + "model": "qwen3-rerank", + "usage": {"total_tokens": 5}, + } + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + ) + assert out.results[0]["document"] == {"text": "hello"} + + def test_missing_id_generates_uuid(self): + body = {"results": [{"index": 0, "relevance_score": 0.5}], "usage": {}} + out = self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body), + model_response=RerankResponse(), + logging_obj=self.logging, + ) + assert out.id is not None and len(out.id) > 0 + + def test_error_envelope_raises(self): + body = { + "code": "InvalidApiKey", + "message": "Invalid API-key provided.", + "request_id": "fb53", + } + with pytest.raises(DashScopeError) as exc_info: + self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=self._resp(body, status_code=401), + model_response=RerankResponse(), + logging_obj=self.logging, + ) + assert "Invalid API-key provided." in str(exc_info.value) + + def test_non_json_response_raises(self): + bad = httpx.Response( + status_code=500, + content=b"bad gateway", + request=httpx.Request("POST", "https://example.com"), + ) + with pytest.raises(DashScopeError): + self.config.transform_rerank_response( + model="qwen3-rerank", + raw_response=bad, + model_response=RerankResponse(), + logging_obj=self.logging, + ) + + def test_get_error_class(self): + err = self.config.get_error_class( + error_message="boom", status_code=500, headers={} + ) + assert isinstance(err, DashScopeError) + assert err.status_code == 500 + + +class TestProviderConfigManagerDispatch: + def test_dashscope_returns_rerank_config(self): + import litellm + from litellm.utils import ProviderConfigManager + + cfg = ProviderConfigManager.get_provider_rerank_config( + model="qwen3-rerank", + provider=litellm.LlmProviders.DASHSCOPE, + api_base=None, + present_version_params=[], + ) + assert isinstance(cfg, DashScopeRerankConfig) diff --git a/tests/test_litellm/llms/deepseek/__init__.py b/tests/test_litellm/llms/deepseek/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/deepseek/messages/__init__.py b/tests/test_litellm/llms/deepseek/messages/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py b/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py new file mode 100644 index 00000000000..7c5f0483ded --- /dev/null +++ b/tests/test_litellm/llms/deepseek/messages/test_deepseek_anthropic_messages_transformation.py @@ -0,0 +1,189 @@ +import litellm +from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( + AnthropicMessagesConfig, +) +from litellm.llms.deepseek.messages.transformation import ( + DeepSeekAnthropicMessagesConfig, +) +from litellm.types.router import GenericLiteLLMParams +from litellm.utils import ProviderConfigManager + + +def test_deepseek_provider_uses_anthropic_messages_config(): + config = ProviderConfigManager.get_provider_anthropic_messages_config( + model="deepseek-v4-pro", + provider=litellm.LlmProviders.DEEPSEEK, + ) + + assert isinstance(config, DeepSeekAnthropicMessagesConfig) + assert config.custom_llm_provider == "deepseek" + + +def test_deepseek_anthropic_messages_config_defaults(): + config = DeepSeekAnthropicMessagesConfig() + + assert config.custom_llm_provider == "deepseek" + assert config.get_api_base() == "https://api.deepseek.com/anthropic" + + +def test_anthropic_provider_keeps_default_config_for_deepseek_named_model(): + config = ProviderConfigManager.get_provider_anthropic_messages_config( + model="deepseek-v4-pro", + provider=litellm.LlmProviders.ANTHROPIC, + ) + + assert isinstance(config, AnthropicMessagesConfig) + assert not isinstance(config, DeepSeekAnthropicMessagesConfig) + + +def test_deepseek_anthropic_messages_url_defaults_to_anthropic_endpoint(): + config = DeepSeekAnthropicMessagesConfig() + + assert ( + config.get_complete_url( + api_base=None, + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/anthropic/v1", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/anthropic", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/v1", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + assert ( + config.get_complete_url( + api_base="https://api.deepseek.com/v1/messages", + api_key=None, + model="deepseek-v4-pro", + optional_params={}, + litellm_params={}, + ) + == "https://api.deepseek.com/anthropic/v1/messages" + ) + + +def test_deepseek_anthropic_messages_headers_use_deepseek_key(): + config = DeepSeekAnthropicMessagesConfig() + + headers, api_base = config.validate_anthropic_messages_environment( + headers={}, + model="deepseek-v4-pro", + messages=[], + optional_params={}, + litellm_params={}, + api_key="sk-deepseek", + api_base="https://example.test/anthropic", + ) + + assert api_base == "https://example.test/anthropic" + assert headers["x-api-key"] == "sk-deepseek" + assert headers["anthropic-version"] == "2023-06-01" + assert headers["content-type"] == "application/json" + + +def test_deepseek_anthropic_messages_preserves_thinking_and_sanitizes_custom_tools(): + config = DeepSeekAnthropicMessagesConfig() + messages = [ + { + "role": "user", + "content": "Use the tool.", + }, + { + "role": "assistant", + "content": [ + { + "type": "thinking", + "thinking": "I should call the tool.", + "signature": "sig", + }, + { + "type": "tool_use", + "id": "toolu_123", + "name": "get_weather", + "input": {"city": "Sao Paulo"}, + }, + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_123", + "content": "Sunny", + } + ], + }, + ] + + request = config.transform_anthropic_messages_request( + model="deepseek-v4-pro", + messages=messages, + anthropic_messages_optional_request_params={ + "max_tokens": 100, + "thinking": {"type": "enabled", "budget_tokens": 1024}, + "tools": [ + { + "type": "custom", + "name": "get_weather", + "description": "Get weather", + "input_schema": {"type": "object"}, + }, + { + "type": "web_search_20260209", + "name": "web_search", + "max_uses": 1, + }, + ], + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + assert request["messages"] == messages + assert request["thinking"] == {"type": "enabled", "budget_tokens": 1024} + assert request["tools"][0] == { + "name": "get_weather", + "description": "Get weather", + "input_schema": {"type": "object"}, + } + assert request["tools"][1]["type"] == "web_search_20260209" diff --git a/tests/test_litellm/llms/test_file_content_block.py b/tests/test_litellm/llms/test_file_content_block.py new file mode 100644 index 00000000000..5552c1a4d68 --- /dev/null +++ b/tests/test_litellm/llms/test_file_content_block.py @@ -0,0 +1,433 @@ +""" +Tests for handling malformed or invalid 'file' content blocks (missing or null +`file` sub-field, HTTP file_id URLs for Google AI Studio). + +Regression tests for: +- litellm/llms/vertex_ai/gemini/transformation.py +- litellm/llms/gemini/chat/transformation.py +- litellm/litellm_core_utils/prompt_templates/common_utils.py + (migrate_file_to_image_url raises on missing `file`; file-id helpers skip non-OpenAI shapes) +- litellm/litellm_core_utils/prompt_templates/factory.py (Bedrock + Anthropic) +- litellm/llms/openai/chat/gpt_transformation.py +""" + +import asyncio +import copy +from typing import List, cast + +import pytest + +import litellm +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + get_file_ids_from_messages, + migrate_file_to_image_url, + update_messages_with_model_file_ids, +) +from litellm.litellm_core_utils.prompt_templates.factory import ( + BedrockConverseMessagesProcessor, + anthropic_process_openai_file_message, +) +from litellm.llms.gemini.chat.transformation import GoogleAIStudioGeminiConfig +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, +) +from litellm.types.llms.openai import ( + AllMessageValues, + ChatCompletionFileObject, + OpenAIMessageContentListBlock, +) + +_MALFORMED_MESSAGES_RAW = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + {"type": "file"}, # Missing required "file" sub-field + ], + } +] + +_WELL_FORMED_MESSAGES_RAW = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + { + "type": "file", + "file": {"file_id": "file-abc123", "format": "pdf"}, + }, + ], + } +] + +MALFORMED_FILE_OBJECT: ChatCompletionFileObject = cast( + ChatCompletionFileObject, {"type": "file"} +) + +EXPLICIT_NULL_FILE_OBJECT: ChatCompletionFileObject = cast( + ChatCompletionFileObject, + {"type": "file", "file": None}, +) + + +def _malformed() -> List[AllMessageValues]: + return copy.deepcopy(cast(List[AllMessageValues], _MALFORMED_MESSAGES_RAW)) + + +def _well_formed() -> List[AllMessageValues]: + return copy.deepcopy(cast(List[AllMessageValues], _WELL_FORMED_MESSAGES_RAW)) + + +def _explicit_null_file_in_content() -> List[AllMessageValues]: + return copy.deepcopy( + cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + {"type": "file", "file": None}, + ], + } + ], + ) + ) + + +# --------------------------------------------------------------------------- +# vertex_ai/gemini/transformation.py +# --------------------------------------------------------------------------- + + +def test_gemini_convert_messages_malformed_file_raises_bad_request(): + """_gemini_convert_messages_with_history should raise BadRequestError (not KeyError) + when a content block has type='file' but no 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + _gemini_convert_messages_with_history( + messages=_malformed(), + model="gemini-2.0-flash", + ) + + +def test_gemini_convert_messages_explicit_null_file_field_raises_bad_request(): + """Explicit JSON null for `file` must be rejected like a missing `file` key.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + _gemini_convert_messages_with_history( + messages=_explicit_null_file_in_content(), + model="gemini-2.0-flash", + ) + + +# --------------------------------------------------------------------------- +# gemini/chat/transformation.py - GoogleAIStudioGeminiConfig +# --------------------------------------------------------------------------- + + +def test_google_ai_studio_transform_messages_malformed_file_raises_bad_request(): + """GoogleAIStudioGeminiConfig._transform_messages should raise BadRequestError + when a content block has type='file' but no 'file' sub-field.""" + config = GoogleAIStudioGeminiConfig() + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._transform_messages(messages=_malformed(), model="gemini-2.0-flash") + + +def test_google_ai_studio_transform_messages_explicit_null_file_field_raises_bad_request(): + """Explicit JSON null for `file` must be rejected like a missing `file` key.""" + config = GoogleAIStudioGeminiConfig() + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._transform_messages( + messages=_explicit_null_file_in_content(), model="gemini-2.0-flash" + ) + + +def test_google_ai_studio_transform_messages_http_file_id_converts_to_base64(monkeypatch): + """Google AI Studio rejects raw HTTP(S) file URLs; _transform_messages should + fetch and replace them with base64 `file_data` before conversion.""" + # Data URL shape so downstream Gemini media parsing accepts the inlined bytes + # (mirrors real `convert_url_to_base64` output from `_process_image_response`). + fake_file_data = "data:application/pdf;base64,aGVsbG8=" + + def _fake_convert_url_to_base64(url: str) -> str: + assert url == "https://example.com/doc.pdf" + return fake_file_data + + monkeypatch.setattr( + "litellm.llms.gemini.chat.transformation.convert_url_to_base64", + _fake_convert_url_to_base64, + ) + messages = cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + { + "type": "file", + "file": { + "file_id": "https://example.com/doc.pdf", + "format": "pdf", + }, + }, + ], + } + ], + ) + config = GoogleAIStudioGeminiConfig() + config._transform_messages(messages=messages, model="gemini-2.0-flash") + content = messages[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file") + file_field = file_block.get("file") + assert isinstance(file_field, dict) + assert file_field.get("file_data") == fake_file_data + assert "file_id" not in file_field + + +def test_google_ai_studio_transform_messages_http_file_id_convert_failure_leaves_file_unchanged( + monkeypatch, +): + """If convert_url_to_base64 fails, the Studio prep step must not mutate the block + (see try/except in GoogleAIStudioGeminiConfig._transform_messages).""" + https_id = "https://example.com/missing.pdf" + + def _raise(_url: str) -> str: + raise litellm.ImageFetchError("simulated fetch failure") + + monkeypatch.setattr( + "litellm.llms.gemini.chat.transformation.convert_url_to_base64", + _raise, + ) + messages = cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + { + "type": "file", + "file": { + "file_id": https_id, + "format": "application/pdf", + }, + }, + ], + } + ], + ) + config = GoogleAIStudioGeminiConfig() + config._transform_messages(messages=messages, model="gemini-2.0-flash") + content = messages[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file") + file_field = file_block.get("file") + assert isinstance(file_field, dict) + assert file_field.get("file_id") == https_id + assert file_field.get("format") == "application/pdf" + assert "file_data" not in file_field + + +# --------------------------------------------------------------------------- +# common_utils.py - update_messages_with_model_file_ids +# --------------------------------------------------------------------------- + + +def test_update_messages_with_model_file_ids_malformed_skips_non_openai_file_block(): + """Non-OpenAI file blocks (e.g. missing nested `file` dict) are skipped so callers + relying on LangChain v1 / provider-native shapes are not rejected here.""" + messages = _malformed() + result = update_messages_with_model_file_ids( + messages=messages, + model_id="some-model", + model_file_id_mapping={}, + ) + assert result == messages + content = result[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if isinstance(c, dict) and c.get("type") == "file") + assert "file" not in file_block + + +def test_update_messages_with_model_file_ids_well_formed_updates(): + """update_messages_with_model_file_ids should update file_id for well-formed blocks.""" + mapping = {"file-abc123": {"some-model": "provider-file-xyz"}} + result = update_messages_with_model_file_ids( + messages=_well_formed(), + model_id="some-model", + model_file_id_mapping=mapping, + ) + content = result[0].get("content") + assert isinstance(content, list) + file_block = next(c for c in content if c.get("type") == "file") + assert file_block.get("file", {}).get("file_id") == "provider-file-xyz" + + +# --------------------------------------------------------------------------- +# common_utils.py - get_file_ids_from_messages +# --------------------------------------------------------------------------- + + +def test_get_file_ids_from_messages_malformed_skips_non_openai_file_block(): + """Blocks with type='file' but no OpenAI `file` sub-dict yield no extracted ids.""" + assert get_file_ids_from_messages(messages=_malformed()) == [] + + +def test_get_file_ids_from_messages_well_formed_returns_ids(): + """get_file_ids_from_messages should extract file_id from well-formed blocks.""" + messages: List[AllMessageValues] = cast( + List[AllMessageValues], + [ + { + "role": "user", + "content": [ + {"type": "text", "text": "hello"}, + {"type": "file", "file": {"file_id": "file-abc123", "format": "pdf"}}, + ], + } + ], + ) + result = get_file_ids_from_messages(messages=messages) + assert result == ["file-abc123"] + + +# --------------------------------------------------------------------------- +# factory.py - BedrockConverseMessagesProcessor (sync + async) +# --------------------------------------------------------------------------- + + +def test_bedrock_process_file_message_malformed_raises_bad_request(): + """_process_file_message should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + BedrockConverseMessagesProcessor._process_file_message(MALFORMED_FILE_OBJECT) + + +def test_bedrock_process_file_message_explicit_null_file_field_raises_bad_request(): + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + BedrockConverseMessagesProcessor._process_file_message(EXPLICIT_NULL_FILE_OBJECT) + + +def test_bedrock_async_process_file_message_malformed_raises_bad_request(): + """_async_process_file_message should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + + async def _run() -> None: + with pytest.raises( + litellm.BadRequestError, match="missing the required 'file' field" + ): + await BedrockConverseMessagesProcessor._async_process_file_message( + MALFORMED_FILE_OBJECT + ) + + asyncio.run(_run()) + + +def test_bedrock_async_process_file_message_explicit_null_file_field_raises_bad_request(): + async def _run() -> None: + with pytest.raises( + litellm.BadRequestError, match="missing the required 'file' field" + ): + await BedrockConverseMessagesProcessor._async_process_file_message( + EXPLICIT_NULL_FILE_OBJECT + ) + + asyncio.run(_run()) + + +# --------------------------------------------------------------------------- +# openai/chat/gpt_transformation.py +# --------------------------------------------------------------------------- + + +def test_openai_apply_common_transform_malformed_file_raises_bad_request(): + """_apply_common_transform_content_item should raise BadRequestError (not KeyError) + when a content block has type='file' but no 'file' sub-field.""" + config = OpenAIGPTConfig() + malformed_block: OpenAIMessageContentListBlock = cast( + OpenAIMessageContentListBlock, {"type": "file"} + ) + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._apply_common_transform_content_item(malformed_block) + + +def test_openai_apply_common_transform_explicit_null_file_field_raises_bad_request(): + config = OpenAIGPTConfig() + explicit_null_block: OpenAIMessageContentListBlock = cast( + OpenAIMessageContentListBlock, + {"type": "file", "file": None}, + ) + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + config._apply_common_transform_content_item(explicit_null_block) + + +def test_openai_apply_common_transform_well_formed_file_does_not_raise(): + """_apply_common_transform_content_item should not raise for well-formed file blocks.""" + config = OpenAIGPTConfig() + well_formed_block: OpenAIMessageContentListBlock = cast( + OpenAIMessageContentListBlock, + {"type": "file", "file": {"file_id": "file-abc123"}}, + ) + result = config._apply_common_transform_content_item(well_formed_block) + assert result.get("type") == "file" + file_field = cast(ChatCompletionFileObject, result).get("file", {}) + assert file_field.get("file_id") == "file-abc123" + + +# --------------------------------------------------------------------------- +# factory.py - anthropic_process_openai_file_message +# --------------------------------------------------------------------------- + + +def test_anthropic_process_openai_file_message_malformed_raises_bad_request(): + """anthropic_process_openai_file_message should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + anthropic_process_openai_file_message(MALFORMED_FILE_OBJECT) + + +def test_anthropic_process_openai_file_message_explicit_null_file_field_raises_bad_request(): + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + anthropic_process_openai_file_message(EXPLICIT_NULL_FILE_OBJECT) + + +def test_anthropic_process_openai_file_message_well_formed_file_id_does_not_raise(): + """anthropic_process_openai_file_message should not raise for a well-formed file_id block.""" + well_formed: ChatCompletionFileObject = cast( + ChatCompletionFileObject, + {"type": "file", "file": {"file_id": "file-abc123"}}, + ) + result = anthropic_process_openai_file_message(well_formed) + assert result.get("type") in ("document", "image", "container_upload") + + +# --------------------------------------------------------------------------- +# common_utils.py - migrate_file_to_image_url +# --------------------------------------------------------------------------- + + +def test_migrate_file_to_image_url_malformed_raises_bad_request(): + """migrate_file_to_image_url should raise BadRequestError (not KeyError) + when the file object is missing the 'file' sub-field.""" + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + migrate_file_to_image_url(MALFORMED_FILE_OBJECT) + + +def test_migrate_file_to_image_url_explicit_null_file_field_raises_bad_request(): + with pytest.raises(litellm.BadRequestError, match="missing the required 'file' field"): + migrate_file_to_image_url(EXPLICIT_NULL_FILE_OBJECT) + + +def test_migrate_file_to_image_url_well_formed_returns_image_url(): + """migrate_file_to_image_url should return an image_url block for a well-formed file.""" + well_formed: ChatCompletionFileObject = cast( + ChatCompletionFileObject, + {"type": "file", "file": {"file_id": "file-abc123", "format": "png"}}, + ) + result = migrate_file_to_image_url(well_formed) + assert result.get("type") == "image_url" + image_url = result.get("image_url", {}) + assert isinstance(image_url, dict) + assert image_url.get("url") == "file-abc123" diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py new file mode 100644 index 00000000000..10fc68ecaad --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_gemini_image_url_missing_field.py @@ -0,0 +1,52 @@ +import pytest +from typing import List, cast + +import litellm +from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, +) +from litellm.types.llms.openai import AllMessageValues + + +def test_missing_image_url_field_raises_bad_request_error(): + """When element type is 'image_url' but 'image_url' field is missing, a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url"}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'image_url' field is missing" in str(exc_info.value) + + +def test_missing_url_inside_image_url_dict_raises_bad_request_error(): + """When image_url is a dict but 'url' key is absent, a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url", "image_url": {"detail": "high"}}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'url' field is missing inside" in str(exc_info.value) + + +def test_explicit_null_image_url_raises_bad_request_error(): + """When image_url key is present but explicitly null, a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url", "image_url": None}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'image_url' field is missing" in str(exc_info.value) + + +def test_empty_dict_image_url_raises_bad_request_error(): + """When image_url is an empty dict (no url), a BadRequestError is raised.""" + messages = cast( + List[AllMessageValues], + [{"role": "user", "content": [{"type": "image_url", "image_url": {}}]}], + ) + with pytest.raises(litellm.BadRequestError) as exc_info: + _gemini_convert_messages_with_history(messages, model="gemini-1.5-pro") + assert "'url' field is missing inside" in str(exc_info.value) diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 353d19b0198..1e0ad04c3c2 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -4290,3 +4290,444 @@ def test_transform_response_does_not_leak_body_on_parse_failure(): msg = str(exc_info.value) assert "secret content" not in msg assert "Error converting to valid response block" in msg + + +def test_chunk_parser_raises_on_429_error_chunk(): + """Test chunk_parser raises VertexAIError on 429 RESOURCE_EXHAUSTED error chunk""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": 429, + "message": "Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/error-code-429 for more details.", + "status": "RESOURCE_EXHAUSTED", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 429 + assert "RESOURCE_EXHAUSTED" in exc_info.value.message + assert "Resource exhausted" in exc_info.value.message + + +def test_chunk_parser_raises_on_500_error_chunk(): + """Test chunk_parser raises VertexAIError on 500 INTERNAL error chunk""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": 500, + "message": "Internal error encountered.", + "status": "INTERNAL", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "INTERNAL" in exc_info.value.message + + +def test_chunk_parser_raises_on_error_chunk_with_minimal_fields(): + """Test chunk_parser handles error chunks with missing optional fields""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": 429, + "message": "Resource exhausted.", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 429 + + +def test_chunk_parser_normal_chunk_unaffected_by_error_check(): + """Test that normal streaming chunks still work correctly after error check addition""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + normal_chunk = { + "candidates": [ + { + "content": { + "role": "model", + "parts": [{"text": "Hello"}], + }, + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 5, + "candidatesTokenCount": 1, + "totalTokenCount": 6, + }, + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + result = streaming_obj.chunk_parser(normal_chunk) + assert result is not None + assert len(result.choices) > 0 + assert result.choices[0].delta.content == "Hello" + + +def test_chunk_parser_raises_on_non_dict_error(): + """Test chunk_parser raises VertexAIError when chunk['error'] is not a dict""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": "something went wrong"} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Unexpected error format" in exc_info.value.message + + +def test_chunk_parser_raises_on_string_error_code(): + """Test chunk_parser correctly converts string error code to int""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + # code field is a string "429" rather than an int + error_chunk = { + "error": { + "code": "429", + "message": "Resource exhausted.", + "status": "RESOURCE_EXHAUSTED", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 429 + assert isinstance(exc_info.value.status_code, int) + + +def test_chunk_parser_error_chunk_explicit_null_code_uses_500(): + """JSON null for code must not call int(None); status defaults to 500.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": None, + "message": "Something went wrong.", + "status": "UNKNOWN", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Something went wrong" in exc_info.value.message + + +def test_chunk_parser_error_chunk_non_numeric_code_defaults_to_500(): + """Non-numeric code must not become ValueError -> RuntimeError in __next__.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = { + "error": { + "code": "NOT_A_NUMBER", + "message": "Malformed.", + "status": "INVALID", + } + } + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Malformed" in exc_info.value.message + + +def test_chunk_parser_error_chunk_empty_dict_defaults_to_500(): + """Empty error object {} uses default code 500 and default message/status strings.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": {}} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "UNKNOWN" in exc_info.value.message + assert "Unknown error" in exc_info.value.message + + +def test_chunk_parser_error_chunk_non_dict_int_value(): + """Non-dict error payloads (e.g. bare JSON number) must raise with status 500, not TypeError.""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": 503} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Unexpected error format" in exc_info.value.message + assert "503" in exc_info.value.message + + +def test_chunk_parser_error_chunk_non_dict_null_value(): + """JSON null for error must hit the non-dict branch (same as int/string).""" + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + error_chunk = {"error": None} + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=iter([]), + sync_stream=True, + logging_obj=logging_obj, + ) + + with pytest.raises(VertexAIError) as exc_info: + streaming_obj.chunk_parser(error_chunk) + + assert exc_info.value.status_code == 500 + assert "Unexpected error format" in exc_info.value.message + + +def test_mid_stream_429_error_raises_during_iteration(): + """ + Simulate a full streaming scenario: normal thinking chunks arrive first, + then a 429 RESOURCE_EXHAUSTED error chunk arrives mid-stream. + Verify that ModelResponseIterator raises VertexAIError during iteration. + """ + import json + from unittest.mock import Mock + + from litellm.llms.vertex_ai.common_utils import VertexAIError + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + # Simulate Vertex AI SSE stream: normal chunks followed by a 429 error chunk + normal_chunk_1 = json.dumps( + { + "candidates": [ + { + "content": { + "role": "model", + "parts": [{"text": "Let me think about this...", "thought": True}], + }, + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15, + }, + "modelVersion": "gemini-3.1-flash-image-preview", + } + ) + + normal_chunk_2 = json.dumps( + { + "candidates": [ + { + "content": { + "role": "model", + "parts": [{"text": "I'll generate the image now.", "thought": True}], + }, + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 12, + "totalTokenCount": 22, + }, + } + ) + + error_chunk = json.dumps( + { + "error": { + "code": 429, + "message": "Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/error-code-429 for more details.", + "status": "RESOURCE_EXHAUSTED", + } + } + ) + + # Build a mock SSE stream (lines returned by iter_lines) + sse_lines = iter([normal_chunk_1, normal_chunk_2, error_chunk]) + + logging_obj = Mock() + logging_obj.optional_params = {} + + streaming_obj = ModelResponseIterator( + streaming_response=sse_lines, + sync_stream=True, + logging_obj=logging_obj, + ) + + # Iterate the stream: first chunks should succeed, then 429 error should be raised + results = [] + with pytest.raises(VertexAIError) as exc_info: + for chunk in streaming_obj: + if chunk is not None: + results.append(chunk) + + # Verify: received normal chunks before the error + assert ( + len(results) >= 1 + ), "Should have received at least 1 normal chunk before the error" + + # Verify: 429 error is properly raised + assert exc_info.value.status_code == 429 + assert "RESOURCE_EXHAUSTED" in str(exc_info.value.message) diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex.py b/tests/test_litellm/llms/vertex_ai/test_vertex.py index 2e9629f95de..be0e59e8b7d 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex.py @@ -1219,6 +1219,32 @@ def test_process_gemini_media(): mime_type="image/jpeg", file_uri="gs://bucket/image" ) + # Test gs url without extension using mime_type from image_url object + image_message = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "gs://bucket/image-without-extension", + "mime_type": "image/png", + }, + } + ], + } + ] + from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, + ) + + converted = _gemini_convert_messages_with_history( + messages=image_message, model="gemini-2.5-flash" + ) + assert converted[0]["parts"][0]["file_data"] == FileDataType( + mime_type="image/png", file_uri="gs://bucket/image-without-extension" + ) + # Test HTTPS JPG URL https_result = _process_gemini_media("https://example.com/image.jpg") print("https_result JPG", https_result) @@ -1256,6 +1282,7 @@ def test_process_gemini_media(): assert base64_result["inline_data"]["data"] == "/9j/4AAQSkZJRg..." + def test_get_image_mime_type_from_url(): """Test the _get_image_mime_type_from_url function for different image URLs""" from litellm.llms.vertex_ai.gemini.transformation import ( diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py b/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py new file mode 100644 index 00000000000..e0eccad80e2 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_gemini_gcs_uri_mime.py @@ -0,0 +1,466 @@ +"""Vertex Gemini: extensionless gs:// MIME + GCS metadata tests. + +Split from test_vertex.py to satisfy CI per-file size limits. +""" +import asyncio +import os +import sys +import time + +from dotenv import load_dotenv + +load_dotenv() + +import pytest + +import litellm +from unittest.mock import MagicMock, patch + +sys.path.insert(0, os.path.abspath("../..")) + +from litellm.llms.vertex_ai.gemini.transformation import _process_gemini_media + + +def test_process_gemini_media_gcs_explicit_format_octet_stream_and_alias(): + """Explicit format bypasses registry; image/jpg alias still applies.""" + from litellm.types.llms.vertex_ai import FileDataType + + r1 = _process_gemini_media( + "gs://bucket/object-no-ext", + format="application/octet-stream", + ) + assert r1["file_data"] == FileDataType( + mime_type="application/octet-stream", + file_uri="gs://bucket/object-no-ext", + ) + r2 = _process_gemini_media("gs://bucket/object-no-ext", format="image/jpg") + assert r2["file_data"] == FileDataType( + mime_type="image/jpeg", + file_uri="gs://bucket/object-no-ext", + ) + + +def test_process_gemini_media_gcs_without_extension_errors_and_metadata_mock(): + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value=None, + ): + with pytest.raises(litellm.BadRequestError) as exc: + _process_gemini_media("gs://bucket/image-without-extension") + assert "Unable to determine mime type for gs URI" in str(exc.value) + + from litellm.types.llms.vertex_ai import FileDataType + + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value="image/jpeg", + ) as m: + r = _process_gemini_media("gs://bucket/image-without-extension") + assert r["file_data"] == FileDataType( + mime_type="image/jpeg", file_uri="gs://bucket/image-without-extension" + ) + m.assert_called() + + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value="image/jpg", + ): + r_alias = _process_gemini_media("gs://bucket/image-without-extension") + assert r_alias["file_data"]["mime_type"] == "image/jpeg" + + +def test_process_gemini_media_rejects_gcs_metadata_mime_not_supported_by_gemini(): + """Non-empty GCS contentType that fails _normalize_and_validate_gemini_mime_type.""" + with patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_object_content_type", + return_value="application/x-litellm-unit-test-unknown-mime", + ): + with pytest.raises( + litellm.BadRequestError, + match="File type not supported by gemini", + ): + _process_gemini_media("gs://bucket/object-without-extension") + + +def test_file_block_uses_mime_type_alias_for_extensionless_gcs(): + from litellm.llms.vertex_ai.gemini.transformation import ( + _gemini_convert_messages_with_history, + ) + from litellm.types.llms.vertex_ai import FileDataType + + messages = [ + { + "role": "user", + "content": [ + { + "type": "file", + "file": { + "file_id": "gs://bucket/no-extension-object", + "mime_type": "application/pdf", + }, + } + ], + } + ] + converted = _gemini_convert_messages_with_history( + messages=messages, model="gemini-2.5-flash" + ) + assert converted[0]["parts"][0]["file_data"] == FileDataType( + mime_type="application/pdf", file_uri="gs://bucket/no-extension-object" + ) + + +@pytest.mark.parametrize( + "bucket,expected", + [ + (("a." * 110) + "aa", True), + ("ab", False), + ("a" * 64, False), + ("ab..cd", False), + ("1.2.3.4", False), + ("192.168.0.1", False), + ("Bucket-Upper", False), + ("bucket@name", False), + ("bucket name", False), + ("-mybucket", False), + ("mybucket-", False), + (".mybucket", False), + ("mybucket.", False), + ], +) +def test_is_valid_gcs_bucket_name_matrix(bucket, expected): + from litellm.llms.vertex_ai.gemini.transformation import _is_valid_gcs_bucket_name + + assert _is_valid_gcs_bucket_name(bucket) is expected + + +def test_get_gcs_object_content_type_explicit_vertex_success_and_token_failure(): + from litellm.llms.vertex_ai.gemini import transformation as gt + + mock_v = MagicMock() + mock_v.get_access_token.return_value = ("test-token", "test-project") + resp = MagicMock() + resp.is_error = False + resp.status_code = 200 + resp.json.return_value = {"contentType": "image/png"} + http = MagicMock() + http.get.return_value = resp + + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http, + ), + ): + assert ( + gt._get_gcs_object_content_type( + image_url="gs://my-bucket/path/to/image-without-extension", + vertex_project="project-123", + vertex_credentials="credential-json", + ) + == "image/png" + ) + mock_v.get_access_token.assert_called_once_with( + credentials="credential-json", + project_id="project-123", + ) + + mock_v2 = MagicMock() + mock_v2.get_access_token.side_effect = Exception("token failure") + with patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v2): + with pytest.raises( + litellm.BadRequestError, + match="Unable to fetch GCS metadata with provided Vertex credentials/project", + ): + gt._get_gcs_object_content_type( + image_url="gs://my-bucket/path/to/image-without-extension", + vertex_project="project-123", + vertex_credentials="credential-json", + ) + + +def test_get_gcs_object_content_type_http_error_explicit_vs_anonymous(): + from litellm.llms.vertex_ai.gemini import transformation as gt + + mock_v = MagicMock() + mock_v.get_access_token.return_value = ("t", "p") + err_resp = MagicMock() + err_resp.is_error = True + err_resp.status_code = 403 + err_resp.text = '{"error":{"message":"Permission denied"}}' + http = MagicMock() + http.get.return_value = err_resp + + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http, + ), + ): + with pytest.raises(litellm.BadRequestError, match="HTTP 403") as ei: + gt._get_gcs_object_content_type( + image_url="gs://my-bucket/path/to/obj", + vertex_project="project-123", + vertex_credentials="credential-json", + ) + assert "Permission denied" in str(ei.value) + + mock_v2 = MagicMock() + anon_err = MagicMock() + anon_err.is_error = True + anon_err.status_code = 403 + anon_err.text = "Forbidden" + http2 = MagicMock() + http2.get.return_value = anon_err + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v2), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http2, + ), + ): + assert ( + gt._get_gcs_object_content_type(image_url="gs://public-bucket/public-object") + is None + ) + mock_v2.get_access_token.assert_not_called() + + +def test_get_gcs_object_content_type_anonymous_success_no_auth_header(): + from litellm.llms.vertex_ai.gemini import transformation as gt + + mock_v = MagicMock() + ok = MagicMock() + ok.is_error = False + ok.status_code = 200 + ok.json.return_value = {"contentType": "image/jpeg"} + http = MagicMock() + http.get.return_value = ok + + with ( + patch.object(gt, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=http, + ), + ): + assert ( + gt._get_gcs_object_content_type(image_url="gs://public-bucket/public-object") + == "image/jpeg" + ) + mock_v.get_access_token.assert_not_called() + hdrs = http.get.call_args.kwargs.get("headers") + assert hdrs is None or "Authorization" not in hdrs + + +def test_async_transform_request_body_offloads_extensionless_gs_not_plain_text(): + from litellm.llms.vertex_ai.gemini import transformation as gemini_transformation + + messages = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "gs://bucket/image-without-extension"}, + } + ], + } + ] + + def slow_http_get(*args, **kwargs): + time.sleep(0.5) + response = MagicMock() + response.is_error = False + response.status_code = 200 + response.raise_for_status.return_value = None + response.json.return_value = {"contentType": "image/png"} + return response + + async def fake_check_and_create_cache(self, **kwargs): + return kwargs["messages"], kwargs["optional_params"], None + + mock_v = MagicMock() + mock_v.get_access_token.return_value = ("token", "project") + mock_http = MagicMock() + mock_http.get.side_effect = slow_http_get + + async def run_scenario() -> float: + async def concurrent_sleep() -> float: + start = time.monotonic() + await asyncio.sleep(0.05) + return time.monotonic() - start + + task = asyncio.create_task( + gemini_transformation.async_transform_request_body( + gemini_api_key=None, + messages=messages, + api_base=None, + model="gemini-2.5-flash", + client=None, + timeout=None, + extra_headers=None, + optional_params={}, + logging_obj=MagicMock(), + custom_llm_provider="vertex_ai", + litellm_params={}, + vertex_project=None, + vertex_location=None, + vertex_auth_header=None, + ) + ) + elapsed = await concurrent_sleep() + await task + return elapsed + + with ( + patch.object(gemini_transformation, "_GCS_METADATA_VERTEX_BASE", mock_v), + patch( + "litellm.llms.vertex_ai.gemini.transformation._get_gcs_metadata_http_handler", + return_value=mock_http, + ), + patch( + "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching." + "ContextCachingEndpoints.async_check_and_create_cache", + new=fake_check_and_create_cache, + ), + ): + sleep_elapsed = asyncio.run(run_scenario()) + + assert sleep_elapsed < 0.4, ( + f"Event loop blocked for {sleep_elapsed:.3f}s; " + "async_transform_request_body did not offload sync GCS metadata" + ) + + async def fake_cache2(self, **kwargs): + return kwargs["messages"], kwargs["optional_params"], None + + async def run_plain(): + with patch( + "litellm.llms.vertex_ai.gemini.transformation.asyncify", + side_effect=AssertionError("asyncify must not run without extensionless gs://"), + ): + return await gemini_transformation.async_transform_request_body( + gemini_api_key=None, + messages=[{"role": "user", "content": "hello"}], + api_base=None, + model="gemini-2.5-flash", + client=None, + timeout=None, + extra_headers=None, + optional_params={}, + logging_obj=MagicMock(), + custom_llm_provider="vertex_ai", + litellm_params={}, + vertex_project=None, + vertex_location=None, + vertex_auth_header=None, + ) + + with patch( + "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching." + "ContextCachingEndpoints.async_check_and_create_cache", + new=fake_cache2, + ): + body = asyncio.run(run_plain()) + assert body is not None and "contents" in body + + +@pytest.mark.parametrize( + "messages,expected", + [ + ([{"role": "user", "content": "hello"}], False), + ( + [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "gs://bucket/image-without-extension"}, + } + ], + } + ], + True, + ), + ( + [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "gs://bucket/image.png"}, + } + ], + } + ], + False, + ), + ( + [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "gs://bucket/image-without-extension", + "mime_type": "image/png", + }, + } + ], + } + ], + False, + ), + ( + [ + { + "role": "assistant", + "content": [], + "images": [ + {"image_url": {"url": "gs://bucket/gen-without-extension"}}, + ], + } + ], + True, + ), + ( + [ + { + "role": "assistant", + "content": [], + "images": [{"image_url": {"url": "gs://bucket/gen.png"}}], + } + ], + False, + ), + ( + [ + { + "role": "assistant", + "content": [], + "images": [ + { + "image_url": { + "url": "gs://bucket/gen-no-ext", + "mime_type": "image/png", + }, + } + ], + } + ], + False, + ), + ], +) +def test_openai_messages_may_need_sync_gcs_metadata_fetch_matrix(messages, expected): + from litellm.llms.vertex_ai.gemini.transformation import ( + _openai_messages_may_need_sync_gcs_metadata_fetch, + ) + + assert _openai_messages_may_need_sync_gcs_metadata_fetch(messages) is expected diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py new file mode 100644 index 00000000000..b483a75a939 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/test_count_tokens_no_vertexai_sdk.py @@ -0,0 +1,121 @@ +""" +Regression tests for #28084: + +`VertexAIPartnerModels.count_tokens` (for Claude / Mistral / Llama on Vertex) +used to gate on `import vertexai` even though the actual count-tokens path goes +through `VertexAIPartnerModelsTokenCounter.handle_count_tokens_request`, which +talks to the publisher's `:rawPredict` endpoint over plain httpx and never +touches the Gemini SDK. The unused gate broke `/v1/messages/count_tokens` for +any LiteLLM install that did not pull in `google-cloud-aiplatform` (which is +not in the default `proxy` / `proxy-dev` extras). + +These tests pin the absence of that gate by: + +1. simulating `vertexai` being unimportable and verifying the partner-model + path does not raise the historical "vertexai import failed" error before + reaching the network/auth layer, and +2. asserting that import of the partner-model count-tokens handler module by + itself does not pull `vertexai` into `sys.modules`. +""" + +import sys + +import pytest + +from litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler import ( + VertexAIPartnerModelsTokenCounter, +) +from litellm.llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels + + +@pytest.mark.asyncio +async def test_count_tokens_does_not_require_vertexai_sdk(monkeypatch): + """Even when `import vertexai` would fail, count_tokens must not raise the + historical "vertexai import failed" gate. The downstream handler talks to + `:rawPredict` over httpx with an access token — no Gemini SDK needed.""" + + # Simulate `vertexai` being unimportable, regardless of what is actually on + # the test environment's sys.path. + monkeypatch.setitem(sys.modules, "vertexai", None) + monkeypatch.setitem(sys.modules, "vertexai.preview", None) + + captured = {} + + async def fake_ensure_access_token( + self, credentials, project_id, custom_llm_provider + ): + return "fake-token", "fake-project" + + def fake_build_endpoint(self, model, project_id, vertex_location, api_base=None): + captured["model_to_endpoint"] = model + return "https://fake-endpoint" + + monkeypatch.setattr( + VertexAIPartnerModelsTokenCounter, + "_ensure_access_token_async", + fake_ensure_access_token, + ) + monkeypatch.setattr( + VertexAIPartnerModelsTokenCounter, + "_build_count_tokens_endpoint", + fake_build_endpoint, + ) + + class FakeResponse: + status_code = 200 + + def json(self): + return {"input_tokens": 9} + + class FakeClient: + async def post(self, url, headers=None, json=None, **kwargs): + captured["url"] = url + captured["headers"] = headers + captured["json"] = json + return FakeResponse() + + import litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler as handler_mod + + monkeypatch.setattr( + handler_mod, "get_async_httpx_client", lambda **kwargs: FakeClient() + ) + + result = await VertexAIPartnerModels().count_tokens( + model="claude-sonnet-4-6", + messages=[{"role": "user", "content": "hello"}], + litellm_params={"vertex_location": "us-east5"}, + vertex_project="test-project", + vertex_location="us-east5", + vertex_credentials=None, + ) + + # We should reach the publisher endpoint and parse its response, not raise + # the vertexai-import gate. + assert result == { + "input_tokens": 9, + "tokenizer_used": "vertex_ai_partner_models", + } + assert captured["headers"] == {"Authorization": "Bearer fake-token"} + assert captured["model_to_endpoint"] == "claude-sonnet-4-6" + + +def test_handler_module_does_not_import_vertexai_sdk(): + """Importing the partner-model count-tokens handler must not load the + Gemini SDK into sys.modules. Operators who only need Claude-on-Vertex + token counting should not pay for `google-cloud-aiplatform`.""" + + # Force-evict any prior load so this assertion measures what THIS module + # pulls in, not what an unrelated earlier test did. + for mod in list(sys.modules): + if mod == "vertexai" or mod.startswith("vertexai."): + sys.modules.pop(mod, None) + + # Re-import the handler module to verify it stays SDK-free. + import importlib + + import litellm.llms.vertex_ai.vertex_ai_partner_models.count_tokens.handler as handler_mod + + importlib.reload(handler_mod) + + leaked = [m for m in sys.modules if m == "vertexai" or m.startswith("vertexai.")] + assert leaked == [], f"unexpected vertexai SDK imports: {leaked}" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py index e01038cd35f..6ec793a1bb0 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py @@ -553,6 +553,7 @@ class TestGuardrailActions: # Verify the exception has the clean error message (no wrapper) assert str(exc_info.value) == "Content contains harmful instructions" assert exc_info.value.guardrail_name == "generic_guardrail_api" + assert exc_info.value.status_code == 400 @pytest.mark.asyncio async def test_action_intervened_modifies_content( diff --git a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py index f0bf4578636..b65f6305b77 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py @@ -1446,3 +1446,175 @@ class TestGetTeamDeployments: result = await _get_team_deployments(team_id, prisma_client) assert len(result) == 1 assert result[0] is dep1 + + +def _build_db_model_for_blocked_test(): + from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo + + return Deployment( + model_name="gpt-4o", + litellm_params=LiteLLM_Params(model="openai/gpt-4o"), + model_info=ModelInfo(id="dep-0"), + ) + + +class TestUpdateDBModelBlocked: + """`update_db_model` must thread `blocked` through to the Prisma payload only + when the caller explicitly set it — PATCH semantics: an absent field means + "leave the stored value untouched".""" + + def test_update_db_model_passes_blocked_true_to_db(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + update_db_model, + ) + + result = update_db_model( + db_model=_build_db_model_for_blocked_test(), + updated_patch=updateDeployment(blocked=True), + ) + assert result["blocked"] is True + + def test_update_db_model_passes_blocked_false_to_db(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + update_db_model, + ) + + result = update_db_model( + db_model=_build_db_model_for_blocked_test(), + updated_patch=updateDeployment(blocked=False), + ) + assert result["blocked"] is False + + def test_update_db_model_omits_blocked_when_patch_is_none(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + update_db_model, + ) + + result = update_db_model( + db_model=_build_db_model_for_blocked_test(), + updated_patch=updateDeployment(), + ) + assert "blocked" not in result + + +class TestGetModelInfoWithIdBlocked: + """`ProxyConfig.get_model_info_with_id` must propagate the DB-level `blocked` + column into the in-memory `model_info` dict so the router filter can read it.""" + + def test_get_model_info_with_id_propagates_blocked_true(self): + from litellm.proxy.proxy_server import ProxyConfig + + model = MagicMock() + model.model_id = "dep-1" + model.model_info = {} + model.blocked = True + info = ProxyConfig().get_model_info_with_id(model=model, db_model=True) + assert info.id == "dep-1" + assert getattr(info, "blocked") is True + + def test_get_model_info_with_id_defaults_blocked_to_false_when_missing(self): + from litellm.proxy.proxy_server import ProxyConfig + + model = MagicMock(spec=["model_id", "model_info"]) + model.model_id = "dep-2" + model.model_info = {} + info = ProxyConfig().get_model_info_with_id(model=model, db_model=True) + assert getattr(info, "blocked") is False + + +class TestPatchModelBlockedAuthGate: + """Only proxy admins may flip `blocked` — team admins authorized for + team-scoped models via `can_user_make_model_call` must still be rejected + when they attempt to toggle the pause flag.""" + + @pytest.mark.asyncio + async def test_team_admin_cannot_toggle_blocked(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + patch_model, + ) + + non_admin = UserAPIKeyAuth( + user_id="team_admin", + user_role=LitellmUserRoles.INTERNAL_USER, + ) + existing_row = MagicMock() + existing_row.litellm_params = {"model": "openai/gpt-4o-mini"} + existing_row.model_dump.return_value = { + "model_name": "gpt-4o-mini", + "litellm_params": existing_row.litellm_params, + "model_info": {"id": "m1"}, + } + existing_row.model_dump_json.return_value = "{}" + + mock_prisma = MagicMock() + mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock( + return_value=existing_row + ) + + with ( + patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), + patch("litellm.proxy.proxy_server.llm_router", MagicMock()), + patch("litellm.proxy.proxy_server.store_model_in_db", True), + patch("litellm.proxy.proxy_server.premium_user", True), + patch( + "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call", + new=AsyncMock(return_value=None), + ), + ): + with pytest.raises(Exception) as exc_info: + await patch_model( + model_id="m1", + patch_data=updateDeployment(blocked=True), + user_api_key_dict=non_admin, + ) + err = exc_info.value + assert getattr(err, "param", "") == "blocked" + assert "proxy admin" in getattr(err, "message", "").lower() + + @pytest.mark.asyncio + async def test_proxy_admin_can_toggle_blocked(self): + from litellm.proxy.management_endpoints.model_management_endpoints import ( + patch_model, + ) + + admin = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) + existing_row = MagicMock() + existing_row.litellm_params = {"model": "openai/gpt-4o-mini"} + existing_row.model_dump.return_value = { + "model_name": "gpt-4o-mini", + "litellm_params": existing_row.litellm_params, + "model_info": {"id": "m1"}, + } + existing_row.model_dump_json.return_value = "{}" + updated_row = MagicMock() + updated_row.model_dump_json.return_value = "{}" + + mock_prisma = MagicMock() + mock_prisma.db.litellm_proxymodeltable.find_unique = AsyncMock( + return_value=existing_row + ) + mock_prisma.db.litellm_proxymodeltable.update = AsyncMock( + return_value=updated_row + ) + + with ( + patch("litellm.proxy.proxy_server.prisma_client", mock_prisma), + patch("litellm.proxy.proxy_server.llm_router", MagicMock()), + patch("litellm.proxy.proxy_server.store_model_in_db", True), + patch("litellm.proxy.proxy_server.premium_user", True), + patch( + "litellm.proxy.management_endpoints.model_management_endpoints.ModelManagementAuthChecks.can_user_make_model_call", + new=AsyncMock(return_value=None), + ), + patch( + "litellm.proxy.management_endpoints.model_management_endpoints.clear_cache", + new=AsyncMock(return_value=None), + ), + ): + result = await patch_model( + model_id="m1", + patch_data=updateDeployment(blocked=True), + user_api_key_dict=admin, + ) + assert result is updated_row + mock_prisma.db.litellm_proxymodeltable.update.assert_awaited_once() diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index 5c7bbc46c95..b450a262907 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -7943,3 +7943,103 @@ async def test_team_member_me_returns_404_for_unknown_team(mock_db_client): user_api_key_dict=caller_auth, ) assert exc_info.value.status_code == 404 + + +def _non_admin_auth(): + return UserAPIKeyAuth( + user_id="u-team-admin", user_role=LitellmUserRoles.INTERNAL_USER + ) + + +def test_check_passthrough_routes_caller_permission_team(): + from litellm.proxy._types import NewTeamRequest + from litellm.proxy.management_endpoints.common_utils import ( + _check_passthrough_routes_caller_permission, + ) + + admin = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN) + non_admin = _non_admin_auth() + + _check_passthrough_routes_caller_permission( + NewTeamRequest(allowed_passthrough_routes=["/foo/*"]), admin, entity="team" + ) + + _check_passthrough_routes_caller_permission( + NewTeamRequest(), non_admin, entity="team" + ) + _check_passthrough_routes_caller_permission( + NewTeamRequest(allowed_passthrough_routes=[]), non_admin, entity="team" + ) + + with pytest.raises(HTTPException) as exc: + _check_passthrough_routes_caller_permission( + NewTeamRequest(allowed_passthrough_routes=["/admin/*"]), + non_admin, + entity="team", + ) + assert exc.value.status_code == 403 + assert "allowed_passthrough_routes" in str(exc.value.detail) + assert "team" in str(exc.value.detail) + + with pytest.raises(HTTPException) as exc: + _check_passthrough_routes_caller_permission( + NewTeamRequest(metadata={"allowed_passthrough_routes": ["/admin/*"]}), + non_admin, + entity="team", + ) + assert exc.value.status_code == 403 + assert "metadata.allowed_passthrough_routes" in str(exc.value.detail) + + +@pytest.mark.asyncio +async def test_new_team_blocks_non_admin_passthrough_routes(mock_db_client): + """A non-proxy-admin cannot self-grant pass-through routes via /team/new.""" + mock_db_client.db.litellm_teamtable.count = AsyncMock(return_value=0) + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException + from litellm.proxy.management_endpoints.team_endpoints import new_team + + with patch( + "litellm.proxy.management_endpoints.team_endpoints._check_user_team_limits", + AsyncMock(return_value=None), + ): + with pytest.raises(ProxyException) as exc: + await new_team( + data=NewTeamRequest( + team_alias="t", allowed_passthrough_routes=["/admin/*"] + ), + http_request=MagicMock(spec=Request), + user_api_key_dict=_non_admin_auth(), + ) + assert str(exc.value.code) == "403" + assert "allowed_passthrough_routes" in str(exc.value.message) + + +@pytest.mark.asyncio +async def test_update_team_blocks_non_admin_passthrough_routes(mock_db_client): + """Even a team manager (non-proxy-admin) cannot set pass-through routes via + /team/update — the gate runs after _verify_team_access.""" + from fastapi import Request + + from litellm.proxy._types import ProxyException, UpdateTeamRequest + from litellm.proxy.management_endpoints.team_endpoints import update_team + + existing = MagicMock() + existing.model_dump.return_value = {"team_id": "t1"} + mock_db_client.db.litellm_teamtable.find_unique = AsyncMock(return_value=existing) + + with patch( + "litellm.proxy.management_endpoints.team_endpoints._verify_team_access", + AsyncMock(return_value=None), + ): + with pytest.raises(ProxyException) as exc: + await update_team( + data=UpdateTeamRequest( + team_id="t1", allowed_passthrough_routes=["/admin/*"] + ), + http_request=MagicMock(spec=Request), + user_api_key_dict=_non_admin_auth(), + ) + assert str(exc.value.code) == "403" + assert "allowed_passthrough_routes" in str(exc.value.message) diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index d5c1132c5fd..1be4abbec6e 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -2057,3 +2057,317 @@ def test_openrouter_gemini_3_1_flash_lite_preview_pricing(): assert model_info["output_cost_per_token"] == 1.5e-06 assert model_info["max_input_tokens"] == 1048576 assert model_info["max_output_tokens"] == 65536 + + +def test_custom_pricing_applies_cache_read_input_cost(): + """ + Bug 1 reproduction: custom_cost_per_token with cache_read_input_token_cost + should bill cached prompt tokens at the cache rate, not the full input rate. + """ + usage = Usage( + prompt_tokens=6074, + completion_tokens=285, + total_tokens=6359, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=3456, + audio_tokens=0, + ), + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="openai/gpt-5.4", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="openai/gpt-5.4", + custom_llm_provider="openai", + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.00000025, + }, + ) + + expected = (6074 - 3456) * 0.0000025 + 3456 * 0.00000025 + 285 * 0.000015 + + assert cost == pytest.approx(expected) + + +def test_custom_pricing_applies_cache_creation_input_cost_via_prompt_details(): + """ + OpenAI-compatible providers report cache-write tokens under + prompt_tokens_details.cache_creation_tokens. The custom-pricing helper must + bill those at cache_creation_input_token_cost, not the full input rate. + """ + pt_details = PromptTokensDetailsWrapper(cached_tokens=1000, audio_tokens=0) + pt_details.cache_creation_tokens = 500 + + usage = Usage( + prompt_tokens=4000, + completion_tokens=100, + total_tokens=4100, + prompt_tokens_details=pt_details, + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="openai/gpt-5.4", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="openai/gpt-5.4", + custom_llm_provider="openai", + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.00000025, + "cache_creation_input_token_cost": 0.000003125, + }, + ) + + expected = ( + (4000 - 1000 - 500) * 0.0000025 + + 1000 * 0.00000025 + + 500 * 0.000003125 + + 100 * 0.000015 + ) + + assert cost == pytest.approx(expected) + + +def test_custom_pricing_applies_cache_creation_input_cost_via_cache_write_tokens_alias(): + """ + Some OpenAI-compatible providers (e.g. kimi-k2) emit cache-write tokens as + `cache_write_tokens` rather than `cache_creation_tokens`. The cost + calculator must mirror db_spend_update_writer and accept either name — + otherwise daily aggregation counts the tokens but the per-request cost + bills them at the full input rate. + + Drives `cost_per_token` directly with a SimpleNamespace usage stub so the + `cache_write_tokens` alias survives the call (Pydantic's Usage init + rebuilds prompt_tokens_details and drops dynamic attributes). + """ + from types import SimpleNamespace + + from litellm.cost_calculator import cost_per_token + + pt_details = SimpleNamespace(cached_tokens=1000, cache_write_tokens=500) + usage_stub = SimpleNamespace( + prompt_tokens=4000, + completion_tokens=100, + total_tokens=4100, + prompt_tokens_details=pt_details, + cache_read_input_tokens=None, + cache_creation_input_tokens=None, + ) + + prompt_cost, completion_cost = cost_per_token( + model="moonshotai/kimi-k2", + prompt_tokens=4000, + completion_tokens=100, + custom_llm_provider="openai", + usage_object=usage_stub, + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.00000025, + "cache_creation_input_token_cost": 0.000003125, + }, + ) + + expected_prompt = ( + (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125 + ) + expected_completion = 100 * 0.000015 + + assert prompt_cost == pytest.approx(expected_prompt) + assert completion_cost == pytest.approx(expected_completion) + + +# --------------------------------------------------------------------------- +# Bug 2 — db_spend_update_writer cache token extraction helpers. +# --------------------------------------------------------------------------- + + +def test_extract_cache_read_tokens_anthropic_top_level(): + from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens + + usage_obj = { + "prompt_tokens": 100, + "cache_read_input_tokens": 80, + "prompt_tokens_details": {"cached_tokens": 80}, + } + # Anthropic top-level value should win over prompt_tokens_details fallback. + assert _extract_cache_read_tokens(usage_obj) == 80 + + +def test_extract_cache_read_tokens_openai_compatible_fallback(): + from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens + + # Anthropic field absent — fall back to prompt_tokens_details.cached_tokens. + usage_obj = { + "prompt_tokens": 22583, + "prompt_tokens_details": {"cached_tokens": 22016}, + } + assert _extract_cache_read_tokens(usage_obj) == 22016 + + +def test_extract_cache_read_tokens_zero_when_missing(): + from litellm.proxy.db.db_spend_update_writer import _extract_cache_read_tokens + + assert _extract_cache_read_tokens({}) == 0 + assert _extract_cache_read_tokens({"cache_read_input_tokens": None}) == 0 + assert ( + _extract_cache_read_tokens({"prompt_tokens_details": {"cached_tokens": None}}) + == 0 + ) + + +def test_extract_cache_creation_tokens_anthropic_top_level(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + usage_obj = { + "prompt_tokens": 100, + "cache_creation_input_tokens": 50, + "prompt_tokens_details": {"cache_write_tokens": 50}, + } + # Anthropic top-level should short-circuit the fallback. + assert _extract_cache_creation_tokens(usage_obj) == 50 + + +def test_extract_cache_creation_tokens_openai_cache_write_alias(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + # kimi-k2 emits cache_write_tokens. + usage_obj = { + "prompt_tokens": 1000, + "prompt_tokens_details": {"cache_write_tokens": 200}, + } + assert _extract_cache_creation_tokens(usage_obj) == 200 + + +def test_extract_cache_creation_tokens_openai_cache_creation_alias(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + # Other OpenAI-compatible providers emit cache_creation_tokens. + usage_obj = { + "prompt_tokens": 1000, + "prompt_tokens_details": {"cache_creation_tokens": 300}, + } + assert _extract_cache_creation_tokens(usage_obj) == 300 + + +def test_extract_cache_creation_tokens_zero_when_missing(): + from litellm.proxy.db.db_spend_update_writer import ( + _extract_cache_creation_tokens, + ) + + assert _extract_cache_creation_tokens({}) == 0 + assert _extract_cache_creation_tokens({"cache_creation_input_tokens": None}) == 0 + assert ( + _extract_cache_creation_tokens( + {"prompt_tokens_details": {"cache_write_tokens": None}} + ) + == 0 + ) + + +def test_custom_pricing_anthropic_style_cache_tokens_not_double_counted(): + """ + Anthropic providers report cache tokens at the top level of Usage, and + `prompt_tokens` EXCLUDES them. The helper expects `prompt_tokens` to + include cache tokens, so cost_per_token must adjust before invoking it — + otherwise regular_prompt_tokens goes negative and clamps to 0. + """ + usage = Usage( + prompt_tokens=2000, + completion_tokens=100, + total_tokens=2100, + cache_read_input_tokens=1500, + cache_creation_input_tokens=300, + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="anthropic/claude-3-5-sonnet", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="anthropic/claude-3-5-sonnet", + custom_llm_provider="anthropic", + custom_cost_per_token={ + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000015, + "cache_read_input_token_cost": 0.0000003, + "cache_creation_input_token_cost": 0.00000375, + }, + ) + + # Anthropic prompt_tokens=2000 excludes cache. After normalization the + # helper sees 2000 + 1500 + 300 = 3800, of which 2000 are uncached. + expected = 2000 * 0.000003 + 1500 * 0.0000003 + 300 * 0.00000375 + 100 * 0.000015 + + assert cost == pytest.approx(expected) + + +def test_custom_pricing_without_cache_keys_preserves_legacy_behavior(): + """ + Backward compatibility: when custom_cost_per_token omits both cache rates, + cached tokens must be billed at input_cost_per_token (matching the pre-fix + behavior) so existing callers see no change. + """ + usage = Usage( + prompt_tokens=1000, + completion_tokens=100, + total_tokens=1100, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=400, + audio_tokens=0, + ), + ) + + response = ModelResponse( + id="test-id", + created=1234567890, + model="openai/gpt-5.4", + object="chat.completion", + choices=[], + usage=usage, + ) + + cost = litellm.completion_cost( + completion_response=response, + model="openai/gpt-5.4", + custom_llm_provider="openai", + custom_cost_per_token={ + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000015, + }, + ) + + # All 1000 prompt tokens billed at input rate, regardless of cached_tokens. + expected = 1000 * 0.0000025 + 100 * 0.000015 + + assert cost == pytest.approx(expected) diff --git a/tests/test_litellm/test_guardrail_exception_status_codes.py b/tests/test_litellm/test_guardrail_exception_status_codes.py new file mode 100644 index 00000000000..c4df1295580 --- /dev/null +++ b/tests/test_litellm/test_guardrail_exception_status_codes.py @@ -0,0 +1,66 @@ +""" +Tests for guardrail exception status codes. + +GuardrailRaisedException and BlockedPiiEntityError must carry +``status_code = 400`` so the proxy exception handler +(``getattr(e, "status_code", 500)``) returns HTTP 400 instead of 500 +for intentional guardrail blocks. +""" + +from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException + + +class TestGuardrailRaisedExceptionStatusCode: + """GuardrailRaisedException should default to status_code=400.""" + + def test_default_status_code(self): + exc = GuardrailRaisedException( + guardrail_name="test_guardrail", + message="blocked", + ) + assert exc.status_code == 400 + + def test_custom_status_code(self): + exc = GuardrailRaisedException( + guardrail_name="test_guardrail", + message="rate limited", + status_code=429, + ) + assert exc.status_code == 429 + + def test_getattr_fallback_resolves_to_400(self): + """The proxy uses ``getattr(e, 'status_code', 500)`` — verify it + resolves to 400, not the 500 default.""" + exc = GuardrailRaisedException( + guardrail_name="test_guardrail", + message="blocked", + ) + assert getattr(exc, "status_code", 500) == 400 + + +class TestBlockedPiiEntityErrorStatusCode: + """BlockedPiiEntityError should default to status_code=400.""" + + def test_default_status_code(self): + exc = BlockedPiiEntityError( + entity_type="CREDIT_CARD", + guardrail_name="presidio", + ) + assert exc.status_code == 400 + + def test_custom_status_code(self): + exc = BlockedPiiEntityError( + entity_type="SSN", + guardrail_name="presidio", + status_code=403, + ) + assert exc.status_code == 403 + + def test_getattr_fallback_resolves_to_400(self): + """The proxy uses ``getattr(e, 'status_code', 500)`` — verify it + resolves to 400, not the 500 default.""" + exc = BlockedPiiEntityError( + entity_type="PHONE_NUMBER", + guardrail_name="presidio", + ) + assert getattr(exc, "status_code", 500) == 400 diff --git a/tests/test_litellm/test_redis.py b/tests/test_litellm/test_redis.py index 282c9d72d51..a89e30a0e06 100644 --- a/tests/test_litellm/test_redis.py +++ b/tests/test_litellm/test_redis.py @@ -1,5 +1,4 @@ import json -import os from unittest.mock import MagicMock, patch import pytest @@ -165,6 +164,13 @@ def test_max_connections_in_cluster_kwargs(): ), "max_connections should be in available Redis cluster kwargs" +def test_socket_timeouts_in_cluster_kwargs(): + """Test that Redis cluster clients can receive socket timeout configuration""" + kwargs = _get_redis_cluster_kwargs() + assert "socket_timeout" in kwargs + assert "socket_connect_timeout" in kwargs + + def test_get_redis_async_client_with_connection_pool(): """Test that connection_pool parameter is properly passed to Redis client""" # Create a mock connection pool diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index 48facace528..d8be527689e 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -3697,3 +3697,173 @@ def test_try_early_resolve_deployments_for_model_not_in_names(): default_router.default_deployment["litellm_params"]["model"] == "openai/will-be-overridden" ) + + +def _router_with_two_deployments(blocked_flags): + import litellm + + model_list = [] + for idx, blocked in enumerate(blocked_flags): + model_list.append( + { + "model_name": "gpt-4o", + "litellm_params": {"model": f"openai/gpt-4o-{idx}"}, + "model_info": {"id": f"dep-{idx}", "blocked": blocked}, + } + ) + return litellm.Router(model_list=model_list) + + +def test_get_fully_blocked_model_names_marks_name_when_all_deployments_blocked(): + router = _router_with_two_deployments([True, True]) + assert router.get_fully_blocked_model_names() == {"gpt-4o"} + + +def test_get_fully_blocked_model_names_keeps_name_when_partial_blocked(): + router = _router_with_two_deployments([True, False]) + assert router.get_fully_blocked_model_names() == set() + + +def test_get_fully_blocked_model_names_treats_missing_key_as_unblocked(): + import litellm + + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4o", + "litellm_params": {"model": "openai/gpt-4o"}, + "model_info": {"id": "dep-0"}, + } + ] + ) + assert router.get_fully_blocked_model_names() == set() + + +@pytest.mark.asyncio +async def test_async_get_healthy_deployments_skips_blocked_deployment(): + router = _router_with_two_deployments([True, False]) + healthy, all_dep = await router._async_get_healthy_deployments( + model="gpt-4o", parent_otel_span=None + ) + healthy_ids = [d["model_info"]["id"] for d in healthy] + assert "dep-0" not in healthy_ids + assert "dep-1" in healthy_ids + assert len(all_dep) == 2 + + +def test_get_healthy_deployments_sync_skips_blocked_deployment(): + router = _router_with_two_deployments([False, True]) + healthy, all_dep = router._get_healthy_deployments( + model="gpt-4o", parent_otel_span=None + ) + healthy_ids = [d["model_info"]["id"] for d in healthy] + assert "dep-0" in healthy_ids + assert "dep-1" not in healthy_ids + assert len(all_dep) == 2 + + +def test_filter_blocked_deployments_drops_blocked_keeps_unblocked(): + router = _router_with_two_deployments([True, False]) + filtered = router._filter_blocked_deployments(router.get_model_list() or []) + ids = [d["model_info"]["id"] for d in filtered] + assert ids == ["dep-1"] + + +@pytest.mark.asyncio +async def test_public_async_get_healthy_deployments_skips_blocked_on_primary_path(): + router = _router_with_two_deployments([True, False]) + deployments = await router.async_get_healthy_deployments( + model="gpt-4o", request_kwargs={} + ) + assert isinstance(deployments, list) + ids = [d["model_info"]["id"] for d in deployments] + assert "dep-0" not in ids + assert "dep-1" in ids + + +def test_public_get_available_deployment_skips_blocked_on_primary_path(): + router = _router_with_two_deployments([True, False]) + deployment = router.get_available_deployment(model="gpt-4o", request_kwargs={}) + assert deployment["model_info"]["id"] == "dep-1" + + +def test_get_available_deployment_raises_when_addressed_dict_is_blocked(): + import litellm + + router = _router_with_two_deployments([True, True]) + with pytest.raises(litellm.ServiceUnavailableError): + router.get_available_deployment(model="dep-0", request_kwargs={}) + + +def _router_with_two_pass_through_deployments(blocked_flags): + import litellm + + model_list = [] + for idx, blocked in enumerate(blocked_flags): + model_list.append( + { + "model_name": "gpt-4o", + "litellm_params": { + "model": f"openai/gpt-4o-{idx}", + "api_key": "sk-fake-for-tests", + "use_in_pass_through": True, + }, + "model_info": {"id": f"pt-{idx}", "blocked": blocked}, + } + ) + return litellm.Router(model_list=model_list) + + +def test_get_available_deployment_for_pass_through_skips_blocked(): + router = _router_with_two_pass_through_deployments([True, False]) + deployment = router.get_available_deployment_for_pass_through( + model="gpt-4o", request_kwargs={} + ) + assert deployment["model_info"]["id"] == "pt-1" + + +def test_get_available_deployment_for_pass_through_raises_when_dict_blocked(): + import litellm + + router = _router_with_two_pass_through_deployments([True, True]) + with pytest.raises(litellm.ServiceUnavailableError): + router.get_available_deployment_for_pass_through( + model="pt-0", request_kwargs={} + ) + + +def test_get_deployment_credentials_returns_none_for_blocked_deployment(): + router = _router_with_two_deployments([True, False]) + assert router.get_deployment_credentials(model_id="dep-0") is None + assert router.get_deployment_credentials(model_id="dep-1") is not None + + +def test_get_deployment_credentials_with_provider_returns_none_for_blocked_deployment(): + router = _router_with_two_deployments([True, False]) + assert router.get_deployment_credentials_with_provider(model_id="dep-0") is None + assert router.get_deployment_credentials_with_provider(model_id="dep-1") is not None + + +def test_is_deployment_blocked_static_helper_reflects_blocked_flag(): + """ + Exercises Router._is_deployment_blocked so router_code_coverage.py (AST call graph) + marks the helper as covered by router-named tests. + """ + import types + + import litellm + + router = _router_with_two_deployments([True, False]) + blocked_dep = router.get_deployment("dep-0") + unblocked_dep = router.get_deployment("dep-1") + assert blocked_dep is not None and unblocked_dep is not None + assert litellm.Router._is_deployment_blocked(blocked_dep) is True + assert litellm.Router._is_deployment_blocked(unblocked_dep) is False + + # No model_info on deployment object → treated as not blocked + assert litellm.Router._is_deployment_blocked(object()) is False + missing_blocked = types.SimpleNamespace() + assert litellm.Router._is_deployment_blocked(types.SimpleNamespace(model_info=missing_blocked)) is False + assert litellm.Router._is_deployment_blocked( + types.SimpleNamespace(model_info=types.SimpleNamespace(blocked=True)) + ) is True diff --git a/tests/unified_google_tests/conftest.py b/tests/unified_google_tests/conftest.py index d28f89a77b0..5b4f57b8036 100644 --- a/tests/unified_google_tests/conftest.py +++ b/tests/unified_google_tests/conftest.py @@ -12,14 +12,17 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm # noqa: E402,F401 -from tests._vcr_conftest_common import ( # noqa: E402 +from tests._vcr_conftest_common import ( # noqa: E402,F401 VerboseReporterState, + _pin_multipart_boundary, apply_vcr_auto_marker_to_items, emit_cassette_cache_session_banner, emit_vcr_classification_summary, + emit_vcr_diagnostic_log, install_live_call_probe, record_vcr_outcome, register_persister_if_enabled, + reset_vcr_diag_dir, vcr_config_dict, ) @@ -84,6 +87,7 @@ def _vcr_outcome_gate(request, vcr): def pytest_configure(config): _verbose_state.remember_pluginmanager(config) + reset_vcr_diag_dir() def pytest_runtest_logreport(report): @@ -110,3 +114,4 @@ def pytest_collection_modifyitems(config, items): def pytest_terminal_summary(terminalreporter, exitstatus, config): emit_cassette_cache_session_banner(terminalreporter) emit_vcr_classification_summary(terminalreporter) + emit_vcr_diagnostic_log(terminalreporter) diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx index 50dd5e78f37..41dfdb21d1b 100644 --- a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx +++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx @@ -654,12 +654,7 @@ const UsagePage: React.FC = ({ teams, organizations }) => { Input Tokens - {Math.max( - 0, - (userSpendData.metadata?.total_prompt_tokens || 0) - - (userSpendData.metadata?.total_cache_read_input_tokens || 0) - - (userSpendData.metadata?.total_cache_creation_input_tokens || 0) - ).toLocaleString()} + {(userSpendData.metadata?.total_prompt_tokens || 0).toLocaleString()} diff --git a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx index 8ae90c1cbbc..554fe86bf66 100644 --- a/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx +++ b/ui/litellm-dashboard/src/components/add_model/advanced_settings.tsx @@ -52,6 +52,8 @@ const AdvancedSettings: React.FC = ({ form.setFieldsValue({ input_cost_per_token: undefined, output_cost_per_token: undefined, + cache_read_input_token_cost: undefined, + cache_creation_input_token_cost: undefined, input_cost_per_second: undefined, }); } @@ -211,6 +213,24 @@ const AdvancedSettings: React.FC = ({ > + + + + + + ) : ( , ac if (formValues.output_cost_per_token !== undefined && formValues.output_cost_per_token !== null && formValues.output_cost_per_token !== "") { formValues.output_cost_per_token = Number(formValues.output_cost_per_token) / 1000000; } + + // Cache Read Cost: if blank, default to Input Cost (already token-unit converted above) + if ( + formValues.cache_read_input_token_cost !== undefined && + formValues.cache_read_input_token_cost !== null && + formValues.cache_read_input_token_cost !== "" + ) { + formValues.cache_read_input_token_cost = + Number(formValues.cache_read_input_token_cost) / 1000000; + } else if ( + formValues.input_cost_per_token !== undefined && + formValues.input_cost_per_token !== null && + formValues.input_cost_per_token !== "" + ) { + formValues.cache_read_input_token_cost = Number(formValues.input_cost_per_token); + } else { + delete formValues.cache_read_input_token_cost; + } + + // Cache Write Cost: explicit value if provided, else leave unset so the + // backend keeps the model-level default (per-second pricing, model_prices + // entries, etc.). Sending 0 here would overwrite that default. + // The backend falls back to input_cost_per_token when this key is absent. + if ( + formValues.cache_creation_input_token_cost !== undefined && + formValues.cache_creation_input_token_cost !== null && + formValues.cache_creation_input_token_cost !== "" + ) { + formValues.cache_creation_input_token_cost = + Number(formValues.cache_creation_input_token_cost) / 1000000; + } else { + delete formValues.cache_creation_input_token_cost; + } // Keep input_cost_per_second as is, no conversion needed // Iterate through the key-value pairs in formValues @@ -119,7 +152,13 @@ export const prepareModelAddRequest = async (formValues: Record, ac } // Handle the pricing fields - else if (key === "input_cost_per_token" || key === "output_cost_per_token" || key === "input_cost_per_second") { + else if ( + key === "input_cost_per_token" || + key === "output_cost_per_token" || + key === "input_cost_per_second" || + key === "cache_read_input_token_cost" || + key === "cache_creation_input_token_cost" + ) { if (value !== undefined && value !== null && value !== "") { litellmParamsObj[key] = Number(value); } diff --git a/ui/litellm-dashboard/src/components/model_info_view.tsx b/ui/litellm-dashboard/src/components/model_info_view.tsx index 95a43862de5..5ed4c0468b8 100644 --- a/ui/litellm-dashboard/src/components/model_info_view.tsx +++ b/ui/litellm-dashboard/src/components/model_info_view.tsx @@ -263,6 +263,34 @@ export default function ModelInfoView({ updatedLitellmParams.output_cost_per_token = Number(values.output_cost) / 1_000_000; } + // Cache Read Cost: explicit value if provided, else fall back to input cost (when input cost touched). + if (form.isFieldTouched("cache_read_cost") || form.isFieldTouched("input_cost")) { + if ( + values.cache_read_cost !== undefined && + values.cache_read_cost !== null && + values.cache_read_cost !== "" + ) { + updatedLitellmParams.cache_read_input_token_cost = Number(values.cache_read_cost) / 1_000_000; + } else if (updatedLitellmParams.input_cost_per_token !== undefined) { + updatedLitellmParams.cache_read_input_token_cost = updatedLitellmParams.input_cost_per_token; + } + } + + // Cache Write Cost: explicit value if provided, else clear the override + // so the backend falls back to the model-level default. Sending 0 here + // would persist a zero rate even when the user intended to unset it. + if (form.isFieldTouched("cache_write_cost")) { + if ( + values.cache_write_cost !== undefined && + values.cache_write_cost !== null && + values.cache_write_cost !== "" + ) { + updatedLitellmParams.cache_creation_input_token_cost = Number(values.cache_write_cost) / 1_000_000; + } else { + delete updatedLitellmParams.cache_creation_input_token_cost; + } + } + if (values.litellm_credential_name) { updatedLitellmParams.litellm_credential_name = values.litellm_credential_name; } else { @@ -638,6 +666,22 @@ export default function ModelInfoView({ output_cost: localModelData.litellm_params?.output_cost_per_token ? localModelData.litellm_params.output_cost_per_token * 1_000_000 : localModelData.model_info?.output_cost_per_token * 1_000_000 || null, + cache_read_cost: + localModelData.litellm_params?.cache_read_input_token_cost !== undefined && + localModelData.litellm_params?.cache_read_input_token_cost !== null + ? localModelData.litellm_params.cache_read_input_token_cost * 1_000_000 + : localModelData.model_info?.cache_read_input_token_cost !== undefined && + localModelData.model_info?.cache_read_input_token_cost !== null + ? localModelData.model_info.cache_read_input_token_cost * 1_000_000 + : null, + cache_write_cost: + localModelData.litellm_params?.cache_creation_input_token_cost !== undefined && + localModelData.litellm_params?.cache_creation_input_token_cost !== null + ? localModelData.litellm_params.cache_creation_input_token_cost * 1_000_000 + : localModelData.model_info?.cache_creation_input_token_cost !== undefined && + localModelData.model_info?.cache_creation_input_token_cost !== null + ? localModelData.model_info.cache_creation_input_token_cost * 1_000_000 + : null, cache_control: localModelData.litellm_params?.cache_control_injection_points ? true : false, cache_control_injection_points: localModelData.litellm_params?.cache_control_injection_points || [], model_access_group: Array.isArray(localModelData.model_info?.access_groups) @@ -725,6 +769,52 @@ export default function ModelInfoView({ )} +
+ Cache Read Cost (per 1M tokens) + {isEditing ? ( + + + + ) : ( +
+ {localModelData?.litellm_params?.cache_read_input_token_cost !== undefined && + localModelData?.litellm_params?.cache_read_input_token_cost !== null + ? (localModelData.litellm_params.cache_read_input_token_cost * 1_000_000).toFixed(4) + : localModelData?.model_info?.cache_read_input_token_cost !== undefined && + localModelData?.model_info?.cache_read_input_token_cost !== null + ? (localModelData.model_info.cache_read_input_token_cost * 1_000_000).toFixed(4) + : "Not Set"} +
+ )} +
+ +
+ Cache Write Cost (per 1M tokens) + {isEditing ? ( + + + + ) : ( +
+ {localModelData?.litellm_params?.cache_creation_input_token_cost !== undefined && + localModelData?.litellm_params?.cache_creation_input_token_cost !== null + ? (localModelData.litellm_params.cache_creation_input_token_cost * 1_000_000).toFixed(4) + : localModelData?.model_info?.cache_creation_input_token_cost !== undefined && + localModelData?.model_info?.cache_creation_input_token_cost !== null + ? (localModelData.model_info.cache_creation_input_token_cost * 1_000_000).toFixed(4) + : "Not Set"} +
+ )} +
+
API Base {isEditing ? ( diff --git a/uv.lock b/uv.lock index dbf4d85f536..f3eaf6ca88c 100644 --- a/uv.lock +++ b/uv.lock @@ -3189,7 +3189,7 @@ wheels = [ [[package]] name = "litellm" -version = "1.85.0" +version = "1.86.0" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -3534,7 +3534,7 @@ proxy-dev = [ [[package]] name = "litellm-enterprise" -version = "0.1.40" +version = "0.1.41" source = { editable = "enterprise" } [[package]]