diff --git a/.github/workflows/test-unit-proxy-db.yml b/.github/workflows/test-unit-proxy-db.yml index 8ee3a1ed0cd..49a36aa23f0 100644 --- a/.github/workflows/test-unit-proxy-db.yml +++ b/.github/workflows/test-unit-proxy-db.yml @@ -215,6 +215,7 @@ jobs: tests/proxy_unit_tests/test_models_fallback_endpoint.py tests/proxy_unit_tests/test_google_endpoint_routing.py tests/proxy_unit_tests/test_google_gemini_proxy_request.py + tests/proxy_unit_tests/test_gemini_agents_endpoints.py tests/proxy_unit_tests/test_get_favicon.py tests/proxy_unit_tests/test_get_image.py tests/proxy_unit_tests/test_ui_path_detection.py diff --git a/.gitignore b/.gitignore index 20355a8e4ef..dff64e3c9e9 100644 --- a/.gitignore +++ b/.gitignore @@ -101,4 +101,23 @@ STABILIZATION_TODO.md **/*.storageState.json **/coverage test-config + +# ---------- Terraform ---------- +# Provider binaries + module cache — regenerated by `terraform init`. +**/.terraform/ +# State files often contain secrets (DB passwords, API keys snapshotted from +# data sources). Keep state in a remote backend, never in git. +*.tfstate +*.tfstate.* +*.tfstate.backup +# Plan files can also contain sensitive values (variables in plaintext). +*.tfplan +# User-specific variable inputs — example files (terraform.tfvars.example) are +# tracked because they end in .example, which doesn't match the glob below. +*.tfvars +*.auto.tfvars +crash.log +crash.*.log +# .terraform.lock.hcl is intentionally NOT ignored — it pins provider versions +# and should be committed. .vscode \ No newline at end of file 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-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index ee27cc3585e..0654f17ec68 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.72" +version = "0.4.73" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.4.72" +version = "0.4.73" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index e1b367fb234..1e8f8613fba 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 @@ -1287,6 +1288,18 @@ from .responses.main import * # Interactions API is available as litellm.interactions module # Usage: litellm.interactions.create(), litellm.interactions.get(), etc. from . import interactions +from .interactions.agents.main import ( + acreate as acreate_agent, + create as create_agent, + alist as alist_agents, + list as list_agents, + aget as aget_agent, + get as get_agent, + adelete as adelete_agent, + delete as delete_agent, + alist_versions as alist_agent_versions, + list_versions as list_agent_versions, +) from .skills.main import ( create_skill, acreate_skill, @@ -1880,6 +1893,12 @@ 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 29f9efba6d6..a70574952b8 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -673,6 +673,15 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): if parent_otel_span is not None: parent_otel_span.set_status(Status(StatusCode.ERROR)) + # Stamp team attributes onto the SERVER (root) span too, so the + # trace root is team-filterable on the failure path like the + # child exception span below. + self._set_team_attributes_on_span( + span=parent_otel_span, + team_id=user_api_key_dict.team_id, + team_alias=user_api_key_dict.team_alias, + ) + # Stamp structured error attrs on the SERVER span itself; the # failure path otherwise only sets its status (_handle_failure # records on the litellm_request child span). Inline import: @@ -709,6 +718,11 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): key="exception", value=str(original_exception), ) + self._set_team_attributes_on_span( + span=exception_logging_span, + team_id=user_api_key_dict.team_id, + team_alias=user_api_key_dict.team_alias, + ) exception_logging_span.set_status(Status(StatusCode.ERROR)) exception_logging_span.end(end_time=self._to_ns(datetime.now())) @@ -736,6 +750,11 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): # Pre-request latency on the SERVER span (success path). self.set_preprocessing_duration_attribute(parent_span, kwargs) + # http.response.status_code on the SERVER span (success path). + # A successful proxy response is HTTP 200; the failure path sets + # this from the error code in _record_exception_on_span. + self.set_response_status_code_attribute(parent_span, 200) + # 3. Guardrail span self._create_guardrail_span(kwargs=kwargs, context=ctx) @@ -1007,6 +1026,10 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): ): parent_span.end(end_time=self._to_ns(end_time)) + # Stamp team attributes onto the SERVER (root) span before it is + # closed, so the trace root carries them like every child span. + self._set_team_attributes_on_proxy_span_from_kwargs(kwargs) + # close the proxy span explicitly from kwargs metadata # after all child spans (litellm_request, guardrail, raw_request) # have been fully recorded and exported. @@ -1065,8 +1088,70 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): ) raw_span.set_status(Status(StatusCode.OK)) self.set_raw_request_attributes(raw_span, kwargs, response_obj) + self._set_team_attributes_from_kwargs(raw_span, kwargs) raw_span.end(end_time=self._to_ns(end_time)) + def _set_team_attributes_on_span( + self, + span: Span, + team_id: Optional[str], + team_alias: Optional[str], + ) -> None: + """Stamp team_id / team_alias onto a span so every child span of a + litellm_request trace carries them, not just the root span. + + Empty strings are treated as absent: a request made with the master + key or a team-less virtual key carries ``user_api_key_team_id=""`` + in ``standard_logging_object.metadata``; propagating that to every + span only adds noise that makes traces look mis-instrumented. + """ + if team_id: + self.safe_set_attribute( + span=span, + key="metadata.user_api_key_team_id", + value=team_id, + ) + if team_alias: + self.safe_set_attribute( + span=span, + key="metadata.user_api_key_team_alias", + value=team_alias, + ) + + def _set_team_attributes_from_kwargs(self, span: Span, kwargs: dict) -> None: + """Pull team_id / team_alias from the standard logging metadata in kwargs and stamp them onto span.""" + std_log = kwargs.get("standard_logging_object") + md: dict = {} + if isinstance(std_log, dict): + md = std_log.get("metadata") or {} + elif std_log is not None: + md = getattr(std_log, "metadata", None) or {} + self._set_team_attributes_on_span( + span=span, + team_id=md.get("user_api_key_team_id"), + team_alias=md.get("user_api_key_team_alias"), + ) + + def _set_team_attributes_on_proxy_span_from_kwargs(self, kwargs: dict) -> None: + """Stamp team attributes onto the proxy SERVER (root) span so the + trace root is filterable by team, not just its children. The root + span is created in auth before the team is resolved and is + otherwise only closed (never re-attributed) on the success path. + + Guarded to the LiteLLM-created proxy span (by name + recording) so + externally provided parent spans are never mutated. + """ + litellm_params = kwargs.get("litellm_params") or {} + metadata = litellm_params.get("metadata") or {} + proxy_span = metadata.get("litellm_parent_otel_span") + if ( + proxy_span is not None + and getattr(proxy_span, "name", None) == LITELLM_PROXY_REQUEST_SPAN_NAME + and hasattr(proxy_span, "is_recording") + and proxy_span.is_recording() + ): + self._set_team_attributes_from_kwargs(proxy_span, kwargs) + def _record_metrics(self, kwargs, response_obj, start_time, end_time): duration_s = (end_time - start_time).total_seconds() params = kwargs.get("litellm_params") or {} @@ -1102,8 +1187,13 @@ 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( @@ -1527,6 +1617,8 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): value=guardrail_information.get("guardrail_response"), ) + self._set_team_attributes_from_kwargs(guardrail_span, kwargs) + guardrail_span.end(end_time=self._to_ns(end_time_datetime)) def _handle_failure(self, kwargs, response_obj, start_time, end_time): @@ -2957,6 +3049,25 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger): span=span, key=HTTP_ROUTE_ATTRIBUTE, value=http_route ) + def set_response_status_code_attribute( + self, span: Optional[Span], status_code: Optional[int] + ) -> None: + """ + Set OTel-standard ``http.response.status_code`` (int) on the proxy + SERVER span. The failure path sets this from the error code in + ``_record_exception_on_span``; this is the success-path counterpart + so the attribute is present on every SERVER span regardless of + outcome (required by the HTTP semconv, and needed for error-ratio / + status-breakdown dashboards). No-op if span/value missing. + """ + if span is None or status_code is None: + return + self.safe_set_attribute( + span=span, + key=HTTP_RESPONSE_STATUS_CODE_ATTRIBUTE, + value=int(status_code), + ) + def set_preprocessing_duration_attribute( self, span: Optional[Span], container: Any ) -> None: 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/interactions/__init__.py b/litellm/interactions/__init__.py index e1125b649a6..ed01462cba6 100644 --- a/litellm/interactions/__init__.py +++ b/litellm/interactions/__init__.py @@ -5,31 +5,40 @@ This module provides SDK methods for Google's Interactions API. Usage: import litellm - + # Create an interaction with a model response = litellm.interactions.create( model="gemini-2.5-flash", input="Hello, how are you?" ) - + # Create an interaction with an agent response = litellm.interactions.create( agent="deep-research-pro-preview-12-2025", input="Research the current state of cancer research" ) - + # Async version response = await litellm.interactions.acreate(...) - + # Get an interaction response = litellm.interactions.get(interaction_id="...") - + # Delete an interaction result = litellm.interactions.delete(interaction_id="...") - + # Cancel an interaction result = litellm.interactions.cancel(interaction_id="...") + # Create a managed agent on the provider side + result = litellm.interactions.agents.create( + name="waverunner", + custom_llm_provider="gemini", + api_key="...", + base_agent="gemini-2.5-flash", + instructions="You are a helpful assistant.", + ) + Methods: - create(): Sync create interaction - acreate(): Async create interaction @@ -39,8 +48,12 @@ Methods: - adelete(): Async delete interaction - cancel(): Sync cancel interaction - acancel(): Async cancel interaction + +Sub-modules: +- agents: Provider-side agent creation (litellm.interactions.agents.create) """ +from litellm.interactions import agents from litellm.interactions.main import ( acancel, acreate, @@ -65,4 +78,6 @@ __all__ = [ # Cancel "cancel", "acancel", + # Sub-modules + "agents", ] diff --git a/litellm/interactions/agents/__init__.py b/litellm/interactions/agents/__init__.py new file mode 100644 index 00000000000..711a54fdcbb --- /dev/null +++ b/litellm/interactions/agents/__init__.py @@ -0,0 +1,39 @@ +""" +litellm.interactions.agents + +Full CRUD SDK for provider-side managed agents (e.g. Gemini v1beta/agents). + + litellm.interactions.agents.create(name=..., ...) + litellm.interactions.agents.list(api_key=...) + litellm.interactions.agents.get(name=..., ...) + litellm.interactions.agents.delete(name=..., ...) + litellm.interactions.agents.list_versions(name=..., ...) + +Async counterparts: acreate, alist, aget, adelete, alist_versions +""" + +from litellm.interactions.agents.main import ( + acreate, + adelete, + aget, + alist, + alist_versions, + create, + delete, + get, + list, + list_versions, +) + +__all__ = [ + "create", + "acreate", + "list", + "alist", + "get", + "aget", + "delete", + "adelete", + "list_versions", + "alist_versions", +] diff --git a/litellm/interactions/agents/http_handler.py b/litellm/interactions/agents/http_handler.py new file mode 100644 index 00000000000..d45ca6f4346 --- /dev/null +++ b/litellm/interactions/agents/http_handler.py @@ -0,0 +1,478 @@ +""" +HTTP handler for the Agents API. + +Extends InteractionsHTTPHandler so that the shared HTTP infrastructure +(_handle_error, _sync_client, _async_client) is reused rather than +duplicated. BaseAgentsAPIConfig stays as pure transform code. +""" + +from typing import Any, Coroutine, Dict, Optional, Union + +import httpx + +from litellm.constants import request_timeout +from litellm.interactions.http_handler import InteractionsHTTPHandler +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.types.agents import ( + AgentCreateResponse, + AgentDeleteResult, + AgentListResponse, + AgentVersionsResponse, +) +from litellm.types.router import GenericLiteLLMParams + + +class AgentsHTTPHandler(InteractionsHTTPHandler): + """HTTP handler for Agents API CRUD requests.""" + + # ------------------------------------------------------------------ # + # CREATE # + # ------------------------------------------------------------------ # + + def create_agent( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[HTTPHandler] = None, + _is_async: bool = False, + ) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]: + if _is_async: + return self.async_create_agent( + agents_api_config=agents_api_config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + extra_body=extra_body, + timeout=timeout, + ) + + sync_httpx_client = self._sync_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url = agents_api_config.get_complete_url( + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + data = agents_api_config.transform_create_request( + name=name, litellm_params=dict(litellm_params) + ) + if extra_body: + data.update(extra_body) + + logging_obj.pre_call( + input=name, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": url, + "headers": headers, + }, + ) + try: + response = sync_httpx_client.post( + url=url, headers=headers, json=data, timeout=timeout or request_timeout + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call( + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + return agents_api_config.transform_create_response( + raw_response=response, name=name + ) + + async def async_create_agent( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[AsyncHTTPHandler] = None, + ) -> AgentCreateResponse: + async_httpx_client = self._async_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url = agents_api_config.get_complete_url( + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + data = agents_api_config.transform_create_request( + name=name, litellm_params=dict(litellm_params) + ) + if extra_body: + data.update(extra_body) + + logging_obj.pre_call( + input=name, + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": url, + "headers": headers, + }, + ) + try: + response = await async_httpx_client.post( + url=url, headers=headers, json=data, timeout=timeout or request_timeout + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call( + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + return agents_api_config.transform_create_response( + raw_response=response, name=name + ) + + # ------------------------------------------------------------------ # + # LIST # + # ------------------------------------------------------------------ # + + def list_agents( + self, + agents_api_config: BaseAgentsAPIConfig, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[HTTPHandler] = None, + _is_async: bool = False, + ) -> Union[AgentListResponse, Coroutine[Any, Any, AgentListResponse]]: + if _is_async: + return self.async_list_agents( + agents_api_config=agents_api_config, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + ) + + sync_httpx_client = self._sync_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url, params = agents_api_config.transform_list_request( + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input="list_agents", + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = sync_httpx_client.get(url=url, headers=headers, params=params) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_list_response(raw_response=response) + + async def async_list_agents( + self, + agents_api_config: BaseAgentsAPIConfig, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[AsyncHTTPHandler] = None, + ) -> AgentListResponse: + async_httpx_client = self._async_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url, params = agents_api_config.transform_list_request( + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input="list_agents", + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = await async_httpx_client.get( + url=url, headers=headers, params=params + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_list_response(raw_response=response) + + # ------------------------------------------------------------------ # + # GET # + # ------------------------------------------------------------------ # + + def get_agent( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[HTTPHandler] = None, + _is_async: bool = False, + ) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]: + if _is_async: + return self.async_get_agent( + agents_api_config=agents_api_config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + ) + + sync_httpx_client = self._sync_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url, params = agents_api_config.transform_get_request( + name=name, + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input=name, + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = sync_httpx_client.get(url=url, headers=headers, params=params) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_get_response( + raw_response=response, name=name + ) + + async def async_get_agent( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[AsyncHTTPHandler] = None, + ) -> AgentCreateResponse: + async_httpx_client = self._async_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url, params = agents_api_config.transform_get_request( + name=name, + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input=name, + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = await async_httpx_client.get( + url=url, headers=headers, params=params + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_get_response( + raw_response=response, name=name + ) + + # ------------------------------------------------------------------ # + # DELETE # + # ------------------------------------------------------------------ # + + def delete_agent( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[HTTPHandler] = None, + _is_async: bool = False, + ) -> Union[AgentDeleteResult, Coroutine[Any, Any, AgentDeleteResult]]: + if _is_async: + return self.async_delete_agent( + agents_api_config=agents_api_config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + ) + + sync_httpx_client = self._sync_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url = agents_api_config.transform_delete_request( + name=name, + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input=name, + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = sync_httpx_client.delete( + url=url, headers=headers, timeout=timeout or request_timeout + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_delete_response( + raw_response=response, name=name + ) + + async def async_delete_agent( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[AsyncHTTPHandler] = None, + ) -> AgentDeleteResult: + async_httpx_client = self._async_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url = agents_api_config.transform_delete_request( + name=name, + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input=name, + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = await async_httpx_client.delete( + url=url, headers=headers, timeout=timeout or request_timeout + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_delete_response( + raw_response=response, name=name + ) + + # ------------------------------------------------------------------ # + # LIST VERSIONS # + # ------------------------------------------------------------------ # + + def list_agent_versions( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[HTTPHandler] = None, + _is_async: bool = False, + ) -> Union[AgentVersionsResponse, Coroutine[Any, Any, AgentVersionsResponse]]: + if _is_async: + return self.async_list_agent_versions( + agents_api_config=agents_api_config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + ) + + sync_httpx_client = self._sync_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url, params = agents_api_config.transform_list_versions_request( + name=name, + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input=name, + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = sync_httpx_client.get(url=url, headers=headers, params=params) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_list_versions_response( + raw_response=response, name=name + ) + + async def async_list_agent_versions( + self, + agents_api_config: BaseAgentsAPIConfig, + name: str, + litellm_params: GenericLiteLLMParams, + logging_obj: LiteLLMLoggingObj, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[AsyncHTTPHandler] = None, + ) -> AgentVersionsResponse: + async_httpx_client = self._async_client(litellm_params, client) + headers = agents_api_config.validate_environment( + headers=extra_headers or {}, litellm_params=dict(litellm_params) + ) + url, params = agents_api_config.transform_list_versions_request( + name=name, + api_base=litellm_params.get("api_base"), + litellm_params=dict(litellm_params), + ) + logging_obj.pre_call( + input=name, + api_key="", + additional_args={"api_base": url, "headers": headers}, + ) + try: + response = await async_httpx_client.get( + url=url, headers=headers, params=params + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=agents_api_config) + + logging_obj.post_call(original_response=response.text, additional_args={}) + return agents_api_config.transform_list_versions_response( + raw_response=response, name=name + ) + + +agents_http_handler = AgentsHTTPHandler() diff --git a/litellm/interactions/agents/main.py b/litellm/interactions/agents/main.py new file mode 100644 index 00000000000..7375fd6273f --- /dev/null +++ b/litellm/interactions/agents/main.py @@ -0,0 +1,523 @@ +""" +LiteLLM Agents API - Main Module + +Usage: + import litellm + + # Create + response = litellm.interactions.agents.create( + name="waverunner", + custom_llm_provider="gemini", + api_key="...", + base_agent="gemini-2.5-flash", + instructions="You are a helpful assistant.", + ) + + # List + response = litellm.interactions.agents.list(api_key="...", custom_llm_provider="gemini") + + # Get + response = litellm.interactions.agents.get(name="waverunner", api_key="...") + + # Delete + result = litellm.interactions.agents.delete(name="waverunner", api_key="...") + + # List versions + result = litellm.interactions.agents.list_versions(name="waverunner", api_key="...") + + # Async versions: acreate, alist, aget, adelete, alist_versions +""" + +import asyncio +import contextvars +from functools import partial +from typing import Any, Coroutine, Dict, Optional, Union + +import httpx + +import litellm +from litellm.interactions.agents.http_handler import agents_http_handler +from litellm.interactions.agents.utils import get_provider_agents_api_config +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.types.agents import ( + AgentCreateResponse, + AgentDeleteResult, + AgentListResponse, + AgentVersionsResponse, +) +from litellm.types.interactions import InteractionEnvironment +from litellm.types.router import GenericLiteLLMParams +from litellm.utils import client + + +# ------------------------------------------------------------------ # +# Shared helpers # +# ------------------------------------------------------------------ # + + +def _get_agents_api_config(custom_llm_provider: str): + config = get_provider_agents_api_config(custom_llm_provider) + if config is None: + raise litellm.BadRequestError( + message=( + f"Provider '{custom_llm_provider}' does not have a native " + "agents API. Use the proxy POST /v1/agents endpoint to store " + "agents locally." + ), + model="", + llm_provider=custom_llm_provider, + ) + return config + + +def _make_logging_obj( + kwargs: Dict[str, Any], + model: str, + custom_llm_provider: str, + call_type: str, + optional_params: Dict[str, Any], +) -> LiteLLMLoggingObj: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + litellm_logging_obj.update_from_kwargs( + kwargs=kwargs, + model=model, + optional_params=optional_params, + litellm_params={"litellm_call_id": litellm_call_id}, + custom_llm_provider=custom_llm_provider, + ) + return litellm_logging_obj + + +# ================================================================== # +# CREATE # +# ================================================================== # + + +@client +async def acreate( + name: str, + base_agent: Optional[str] = None, + instructions: Optional[str] = None, + base_environment: Optional[InteractionEnvironment] = None, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> AgentCreateResponse: + """Async: Create a managed agent on the provider side.""" + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["acreate_agent"] = True + func = partial( + create, + name=name, + base_agent=base_agent, + instructions=instructions, + base_environment=base_environment, + custom_llm_provider=custom_llm_provider or "gemini", + extra_headers=extra_headers, + extra_body=extra_body, + timeout=timeout, + **kwargs, + ) + ctx = contextvars.copy_context() + init_response = await loop.run_in_executor(None, partial(ctx.run, func)) + if asyncio.iscoroutine(init_response): + return await init_response + return init_response + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider or "gemini", + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def create( + name: str, + base_agent: Optional[str] = None, + instructions: Optional[str] = None, + base_environment: Optional[InteractionEnvironment] = None, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + extra_body: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]: + """ + Sync: Create a managed agent on the provider side. + + Args: + name: Name for the agent (required). + base_agent: Base agent to derive from (e.g. "waverunner"). + instructions: System instructions for the agent. + base_environment: Environment to fork from — an env_id string or a + dict like ``{"type": "remote", "sources": [...]}``. + custom_llm_provider: Provider to use, e.g. "gemini". + extra_headers: Additional HTTP headers. + extra_body: Additional request body fields. + timeout: Request timeout. + **kwargs: Forwarded to GenericLiteLLMParams (api_key, api_base, etc.). + """ + local_vars = locals() + custom_llm_provider = ( + custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini" + ) + try: + _is_async = kwargs.pop("acreate_agent", False) is True + if base_agent is not None: + kwargs["base_agent"] = base_agent + if instructions is not None: + kwargs["instructions"] = instructions + if base_environment is not None: + kwargs["base_environment"] = base_environment + kwargs.setdefault("custom_llm_provider", custom_llm_provider) + litellm_params = GenericLiteLLMParams(**kwargs) + logging_obj = _make_logging_obj( + kwargs, name, custom_llm_provider, "create_agent", {} + ) + config = _get_agents_api_config(custom_llm_provider) + return agents_http_handler.create_agent( + agents_api_config=config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + extra_body=extra_body, + timeout=timeout, + _is_async=_is_async, + ) + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +# ================================================================== # +# LIST # +# ================================================================== # + + +@client +async def alist( + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> AgentListResponse: + """Async: List all agents on the provider side.""" + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["alist_agents"] = True + func = partial( + list, + custom_llm_provider=custom_llm_provider or "gemini", + extra_headers=extra_headers, + timeout=timeout, + **kwargs, + ) + ctx = contextvars.copy_context() + init_response = await loop.run_in_executor(None, partial(ctx.run, func)) + if asyncio.iscoroutine(init_response): + return await init_response + return init_response + except Exception as e: + raise litellm.exception_type( + model="", + custom_llm_provider=custom_llm_provider or "gemini", + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def list( + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> Union[AgentListResponse, Coroutine[Any, Any, AgentListResponse]]: + """Sync: List all agents on the provider side.""" + local_vars = locals() + custom_llm_provider = ( + custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini" + ) + try: + _is_async = kwargs.pop("alist_agents", False) is True + kwargs.setdefault("custom_llm_provider", custom_llm_provider) + litellm_params = GenericLiteLLMParams(**kwargs) + logging_obj = _make_logging_obj( + kwargs, "", custom_llm_provider, "list_agents", {} + ) + config = _get_agents_api_config(custom_llm_provider) + return agents_http_handler.list_agents( + agents_api_config=config, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + _is_async=_is_async, + ) + except Exception as e: + raise litellm.exception_type( + model="", + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +# ================================================================== # +# GET # +# ================================================================== # + + +@client +async def aget( + name: str, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> AgentCreateResponse: + """Async: Get a specific agent by name.""" + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["aget_agent"] = True + func = partial( + get, + name=name, + custom_llm_provider=custom_llm_provider or "gemini", + extra_headers=extra_headers, + timeout=timeout, + **kwargs, + ) + ctx = contextvars.copy_context() + init_response = await loop.run_in_executor(None, partial(ctx.run, func)) + if asyncio.iscoroutine(init_response): + return await init_response + return init_response + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider or "gemini", + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def get( + name: str, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> Union[AgentCreateResponse, Coroutine[Any, Any, AgentCreateResponse]]: + """Sync: Get a specific agent by name.""" + local_vars = locals() + custom_llm_provider = ( + custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini" + ) + try: + _is_async = kwargs.pop("aget_agent", False) is True + kwargs.setdefault("custom_llm_provider", custom_llm_provider) + litellm_params = GenericLiteLLMParams(**kwargs) + logging_obj = _make_logging_obj( + kwargs, name, custom_llm_provider, "get_agent", {"name": name} + ) + config = _get_agents_api_config(custom_llm_provider) + return agents_http_handler.get_agent( + agents_api_config=config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + _is_async=_is_async, + ) + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +# ================================================================== # +# DELETE # +# ================================================================== # + + +@client +async def adelete( + name: str, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> AgentDeleteResult: + """Async: Delete a specific agent by name.""" + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["adelete_agent"] = True + func = partial( + delete, + name=name, + custom_llm_provider=custom_llm_provider or "gemini", + extra_headers=extra_headers, + timeout=timeout, + **kwargs, + ) + ctx = contextvars.copy_context() + init_response = await loop.run_in_executor(None, partial(ctx.run, func)) + if asyncio.iscoroutine(init_response): + return await init_response + return init_response + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider or "gemini", + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def delete( + name: str, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> Union[AgentDeleteResult, Coroutine[Any, Any, AgentDeleteResult]]: + """Sync: Delete a specific agent by name.""" + local_vars = locals() + custom_llm_provider = ( + custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini" + ) + try: + _is_async = kwargs.pop("adelete_agent", False) is True + kwargs.setdefault("custom_llm_provider", custom_llm_provider) + litellm_params = GenericLiteLLMParams(**kwargs) + logging_obj = _make_logging_obj( + kwargs, name, custom_llm_provider, "delete_agent", {"name": name} + ) + config = _get_agents_api_config(custom_llm_provider) + return agents_http_handler.delete_agent( + agents_api_config=config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + _is_async=_is_async, + ) + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +# ================================================================== # +# LIST VERSIONS # +# ================================================================== # + + +@client +async def alist_versions( + name: str, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> AgentVersionsResponse: + """Async: List versions of a specific agent.""" + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["alist_agent_versions"] = True + func = partial( + list_versions, + name=name, + custom_llm_provider=custom_llm_provider or "gemini", + extra_headers=extra_headers, + timeout=timeout, + **kwargs, + ) + ctx = contextvars.copy_context() + init_response = await loop.run_in_executor(None, partial(ctx.run, func)) + if asyncio.iscoroutine(init_response): + return await init_response + return init_response + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider or "gemini", + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def list_versions( + name: str, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + **kwargs, +) -> Union[AgentVersionsResponse, Coroutine[Any, Any, AgentVersionsResponse]]: + """Sync: List versions of a specific agent.""" + local_vars = locals() + custom_llm_provider = ( + custom_llm_provider or kwargs.get("custom_llm_provider") or "gemini" + ) + try: + _is_async = kwargs.pop("alist_agent_versions", False) is True + kwargs.setdefault("custom_llm_provider", custom_llm_provider) + litellm_params = GenericLiteLLMParams(**kwargs) + logging_obj = _make_logging_obj( + kwargs, name, custom_llm_provider, "list_agent_versions", {"name": name} + ) + config = _get_agents_api_config(custom_llm_provider) + return agents_http_handler.list_agent_versions( + agents_api_config=config, + name=name, + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers=extra_headers, + timeout=timeout, + _is_async=_is_async, + ) + except Exception as e: + raise litellm.exception_type( + model=name, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) diff --git a/litellm/interactions/agents/utils.py b/litellm/interactions/agents/utils.py new file mode 100644 index 00000000000..d16a9597f53 --- /dev/null +++ b/litellm/interactions/agents/utils.py @@ -0,0 +1,23 @@ +""" +Utility functions for the Agents API SDK. +""" + +from typing import Optional + +from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig + + +def get_provider_agents_api_config( + custom_llm_provider: Optional[str], +) -> Optional[BaseAgentsAPIConfig]: + """ + Return a provider-specific BaseAgentsAPIConfig if the provider has a + native agent-creation API, or None otherwise. + """ + from litellm.types.utils import LlmProviders + + if custom_llm_provider == LlmProviders.GEMINI.value: + from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig + + return GeminiAgentsConfig() + return None diff --git a/litellm/interactions/http_handler.py b/litellm/interactions/http_handler.py index 7fead07043f..695da2be89a 100644 --- a/litellm/interactions/http_handler.py +++ b/litellm/interactions/http_handler.py @@ -41,27 +41,55 @@ from litellm.types.interactions import ( from litellm.types.router import GenericLiteLLMParams -class InteractionsHTTPHandler: +class _BaseHTTPHandler: + """ + Shared HTTP infrastructure for LiteLLM handler classes. + + Provides common client resolution and error-mapping helpers so that + handler subclasses (InteractionsHTTPHandler, AgentsHTTPHandler, …) do + not duplicate this boilerplate. + """ + + def _handle_error(self, e: Exception, provider_config: Any) -> Exception: + if isinstance(e, httpx.HTTPStatusError): + return provider_config.get_error_class( + error_message=e.response.text, + status_code=e.response.status_code, + headers=dict(e.response.headers), + ) + return e + + def _sync_client( + self, + litellm_params: GenericLiteLLMParams, + client: Optional[HTTPHandler], + ) -> HTTPHandler: + return client or _get_httpx_client( + params={"ssl_verify": litellm_params.get("ssl_verify", None)} + ) + + def _async_client( + self, + litellm_params: GenericLiteLLMParams, + client: Optional[AsyncHTTPHandler], + ) -> AsyncHTTPHandler: + # GenericLiteLLMParams.get uses getattr; an unset field is None, not the default. + custom_llm_provider = litellm_params.get("custom_llm_provider") or "gemini" + return client or get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + params={"ssl_verify": litellm_params.get("ssl_verify", None)}, + ) + + +class InteractionsHTTPHandler(_BaseHTTPHandler): """ HTTP handler for Interactions API requests. """ - def _handle_error( - self, - e: Exception, - provider_config: BaseInteractionsAPIConfig, - ) -> Exception: - """Handle errors from HTTP requests.""" - if isinstance(e, httpx.HTTPStatusError): - error_message = e.response.text - status_code = e.response.status_code - headers = dict(e.response.headers) - return provider_config.get_error_class( - error_message=error_message, - status_code=status_code, - headers=headers, - ) - return e + # _handle_error is inherited from _BaseHTTPHandler (accepts Any provider_config). + # AgentsHTTPHandler also extends this class and passes BaseAgentsAPIConfig, which + # is structurally compatible but a different type — keeping the override here with + # BaseInteractionsAPIConfig would cause type errors in the subclass. # ========================================================= # CREATE INTERACTION diff --git a/litellm/interactions/litellm_responses_transformation/streaming_iterator.py b/litellm/interactions/litellm_responses_transformation/streaming_iterator.py index 72a3afbc3c5..567e9b523e8 100644 --- a/litellm/interactions/litellm_responses_transformation/streaming_iterator.py +++ b/litellm/interactions/litellm_responses_transformation/streaming_iterator.py @@ -2,7 +2,7 @@ Streaming iterator for transforming Responses API stream to Interactions API stream. """ -from typing import Any, AsyncIterator, Dict, Iterator, Optional, cast +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, cast from litellm.responses.streaming_iterator import ( BaseResponsesAPIStreamingIterator, @@ -15,6 +15,7 @@ from litellm.types.interactions import ( InteractionsAPIStreamingResponse, ) from litellm.types.llms.openai import ( + ContentPartAddedEvent, OutputTextDeltaEvent, ResponseCompletedEvent, ResponseCreatedEvent, @@ -51,6 +52,7 @@ class LiteLLMResponsesInteractionsStreamingIterator: self.collected_text = "" self.sent_interaction_start = False self.sent_content_start = False + self._pending_events: List[InteractionsAPIStreamingResponse] = [] def _transform_responses_chunk_to_interactions_chunk( self, @@ -80,7 +82,49 @@ class LiteLLMResponsesInteractionsStreamingIterator: ) self.collected_text += delta_text - # Send interaction.start if not sent + # Fallback: emit interaction.start, and queue content.start carrying this + # delta so the first token is preserved in the stream. + if not self.sent_interaction_start: + self.sent_interaction_start = True + self.sent_content_start = True + self._pending_events.append( + InteractionsAPIStreamingResponse( + event_type="content.start", + id=getattr(responses_chunk, "item_id", None), + object="content", + delta={"type": "text", "text": delta_text}, + ) + ) + return InteractionsAPIStreamingResponse( + event_type="interaction.start", + id=getattr(responses_chunk, "item_id", None) + or f"interaction_{id(self)}", + object="interaction", + status="in_progress", + model=self.model, + ) + + # Fallback: emit content.start if ContentPartAddedEvent never arrived + if not self.sent_content_start: + self.sent_content_start = True + return InteractionsAPIStreamingResponse( + event_type="content.start", + id=getattr(responses_chunk, "item_id", None), + object="content", + delta={"type": "text", "text": delta_text}, + ) + + # Normal path: emit content.delta with type field + return InteractionsAPIStreamingResponse( + event_type="content.delta", + id=getattr(responses_chunk, "item_id", None), + object="content", + delta={"type": "text", "text": delta_text}, + ) + + # Handle ContentPartAddedEvent -> content.start (arrives before text deltas) + if isinstance(responses_chunk, ContentPartAddedEvent): + # Fallback: emit interaction.start if ResponseCreatedEvent never arrived if not self.sent_interaction_start: self.sent_interaction_start = True return InteractionsAPIStreamingResponse( @@ -91,8 +135,6 @@ class LiteLLMResponsesInteractionsStreamingIterator: status="in_progress", model=self.model, ) - - # Send content.start if not sent if not self.sent_content_start: self.sent_content_start = True return InteractionsAPIStreamingResponse( @@ -101,14 +143,7 @@ class LiteLLMResponsesInteractionsStreamingIterator: object="content", delta={"type": "text", "text": ""}, ) - - # Send content.delta - return InteractionsAPIStreamingResponse( - event_type="content.delta", - id=getattr(responses_chunk, "item_id", None), - object="content", - delta={"text": delta_text}, - ) + return None # Handle ResponseCreatedEvent or ResponseInProgressEvent -> interaction.start if isinstance(responses_chunk, (ResponseCreatedEvent, ResponseInProgressEvent)): @@ -172,6 +207,10 @@ class LiteLLMResponsesInteractionsStreamingIterator: delattr(self, "_pending_interaction_complete") return pending + # Drain events queued from a prior chunk (e.g. content.start emitted alongside + # the interaction.start fallback for the first OutputTextDeltaEvent). + if self._pending_events: + return self._pending_events.pop(0) # Use a loop instead of recursion to avoid stack overflow sync_iterator = cast( SyncResponsesAPIStreamingIterator, self.responses_stream_iterator @@ -237,6 +276,10 @@ class LiteLLMResponsesInteractionsStreamingIterator: delattr(self, "_pending_interaction_complete") return pending + # Drain events queued from a prior chunk (e.g. content.start emitted alongside + # the interaction.start fallback for the first OutputTextDeltaEvent). + if self._pending_events: + return self._pending_events.pop(0) # Use a loop instead of recursion to avoid stack overflow async_iterator = cast( ResponsesAPIStreamingIterator, self.responses_stream_iterator diff --git a/litellm/interactions/main.py b/litellm/interactions/main.py index ab429ef6db5..c6eca410fa7 100644 --- a/litellm/interactions/main.py +++ b/litellm/interactions/main.py @@ -48,6 +48,7 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from litellm.types.interactions import ( CancelInteractionResult, DeleteInteractionResult, + InteractionEnvironment, InteractionInput, InteractionsAPIResponse, InteractionsAPIStreamingResponse, @@ -80,6 +81,8 @@ async def acreate( store: Optional[bool] = None, # Background execution background: Optional[bool] = None, + # Agent execution environment ("remote", env id, or remote config object) + environment: Optional[InteractionEnvironment] = None, # Response format response_modalities: Optional[List[str]] = None, response_format: Optional[Dict[str, Any]] = None, @@ -109,6 +112,10 @@ async def acreate( stream: Whether to stream the response store: Whether to store the response for later retrieval background: Whether to run in background + environment: Agent execution environment — ``"remote"``, an existing env id + string, or a config object such as + ``{"type": "remote", "sources": [...]}`` / + ``{"type": "remote", "network": {...}}`` response_modalities: Requested response modalities (TEXT, IMAGE, AUDIO) response_format: JSON schema for response format response_mime_type: MIME type of the response @@ -144,6 +151,7 @@ async def acreate( stream=stream, store=store, background=background, + environment=environment, response_modalities=response_modalities, response_format=response_format, response_mime_type=response_mime_type, @@ -194,6 +202,8 @@ def create( store: Optional[bool] = None, # Background execution background: Optional[bool] = None, + # Agent execution environment ("remote", env id, or remote config object) + environment: Optional[InteractionEnvironment] = None, # Response format response_modalities: Optional[List[str]] = None, response_format: Optional[Dict[str, Any]] = None, @@ -231,6 +241,10 @@ def create( stream: Whether to stream the response store: Whether to store the response for later retrieval background: Whether to run in background + environment: Agent execution environment — ``"remote"``, an existing env id + string, or a config object such as + ``{"type": "remote", "sources": [...]}`` / + ``{"type": "remote", "network": {...}}`` response_modalities: Requested response modalities (TEXT, IMAGE, AUDIO) response_format: JSON schema for response format response_mime_type: MIME type of the response @@ -252,7 +266,14 @@ def create( litellm_params = GenericLiteLLMParams(**kwargs) - if model: + # Routing logic: + # - agent provided (no model, or model accidentally set to agent name) → gemini + # - model provided → resolve provider via get_llm_provider (normal routing) + if agent and model == agent: + model = None + if agent and not model: + custom_llm_provider = custom_llm_provider or "gemini" + elif model: model, custom_llm_provider, _, _ = litellm.get_llm_provider( model=model, custom_llm_provider=custom_llm_provider, diff --git a/litellm/interactions/utils.py b/litellm/interactions/utils.py index 3a18ddf52fe..84437f4d3d8 100644 --- a/litellm/interactions/utils.py +++ b/litellm/interactions/utils.py @@ -15,6 +15,7 @@ INTERACTIONS_API_OPTIONAL_PARAMS = { "stream", "store", "background", + "environment", "response_modalities", "response_format", "response_mime_type", 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..d6c011990c1 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1233,6 +1233,7 @@ def infer_protocol_value( def _gemini_tool_call_invoke_helper( function_call_params: ChatCompletionToolCallFunctionChunk, + tool_call_id: Optional[str] = None, ) -> Optional[VertexFunctionCall]: name = function_call_params.get("name", "") or "" arguments = function_call_params.get("arguments", "") @@ -1248,6 +1249,10 @@ def _gemini_tool_call_invoke_helper( name=name, args=arguments_dict, ) + if tool_call_id: + clean_id = tool_call_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] + if clean_id: + function_call["id"] = clean_id return function_call @@ -1384,12 +1389,23 @@ def convert_to_gemini_tool_call_invoke( tool_calls = message.get("tool_calls", None) function_call = message.get("function_call", None) + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexGeminiConfig, + ) + + forward_tool_call_id = bool( + model and VertexGeminiConfig._is_gemini_3_or_newer(model) + ) + if tool_calls is not None: for idx, tool in enumerate(tool_calls): if "function" in tool: gemini_function_call: Optional[VertexFunctionCall] = ( _gemini_tool_call_invoke_helper( - function_call_params=tool["function"] + function_call_params=tool["function"], + tool_call_id=( + tool.get("id") if forward_tool_call_id else None + ), ) ) if gemini_function_call is not None: @@ -1429,10 +1445,6 @@ def convert_to_gemini_tool_call_invoke( thought_signature = provider_fields.get("thought_signature") # If no signature found and model is gemini-3, use dummy signature - from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( - VertexGeminiConfig, - ) - if ( not thought_signature and model @@ -1462,6 +1474,7 @@ def convert_to_gemini_tool_call_invoke( def convert_to_gemini_tool_call_result( # noqa: PLR0915 message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage], last_message_with_tool_calls: Optional[dict], + model: Optional[str] = None, ) -> Union[VertexPartType, List[VertexPartType]]: """ OpenAI message with a tool result looks like: @@ -1602,6 +1615,21 @@ def convert_to_gemini_tool_call_result( # noqa: PLR0915 ): name = tool.get("function", {}).get("name", "") + # Echo the OpenAI tool_call_id on functionResponse (strip thought-signature suffix). + # Only Gemini 3+ accepts (and returns) an `id` on function_response parts; + # older Gemini models reject the field with a 400. + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + VertexGeminiConfig, + ) + + gemini_call_id: Optional[str] = None + if model and VertexGeminiConfig._is_gemini_3_or_newer(model): + raw_tool_call_id = message.get("tool_call_id") + if raw_tool_call_id and isinstance(raw_tool_call_id, str): + stripped_id = raw_tool_call_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] + if stripped_id: + gemini_call_id = stripped_id + if not name: raise Exception( "Missing corresponding tool call for tool response message. Received - message={}, last_message_with_tool_calls={}".format( @@ -1632,6 +1660,8 @@ def convert_to_gemini_tool_call_result( # noqa: PLR0915 name=name, response=response_data, # type: ignore ) + if gemini_call_id: + _function_response["id"] = gemini_call_id # Create part with function_response, and optionally inline_data for images (Computer Use) _part: VertexPartType = {"function_response": _function_response} @@ -2057,9 +2087,16 @@ 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 +4916,13 @@ 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 +4943,13 @@ 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/base_llm/agents/__init__.py b/litellm/llms/base_llm/agents/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/base_llm/agents/transformation.py b/litellm/llms/base_llm/agents/transformation.py new file mode 100644 index 00000000000..508e54cb7ab --- /dev/null +++ b/litellm/llms/base_llm/agents/transformation.py @@ -0,0 +1,165 @@ +""" +Base transformation class for provider-side Agents API. + +Providers that have a native agents CRUD API (e.g. Gemini v1beta/agents) +subclass BaseAgentsAPIConfig and implement the abstract methods. + +The HTTP calls are handled by AgentsHTTPHandler — this class is pure +transform logic (same separation as BaseInteractionsAPIConfig / +InteractionsHTTPHandler). +""" + +from abc import ABC, abstractmethod +from typing import Any, Dict, Optional, Tuple, Union + +import httpx + +from litellm.types.agents import ( + AgentCreateResponse, + AgentDeleteResult, + AgentListResponse, + AgentVersionsResponse, +) + + +class BaseAgentsAPIConfig(ABC): + """ + Minimal interface for providers that expose a native agents CRUD API. + """ + + # ------------------------------------------------------------------ # + # CREATE # + # ------------------------------------------------------------------ # + + @abstractmethod + def get_complete_url( + self, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> str: + """Return the full URL for POST /agents (create).""" + + @abstractmethod + def validate_environment( + self, + headers: Dict[str, str], + litellm_params: Dict[str, Any], + ) -> Dict[str, str]: + """Validate credentials and return auth headers.""" + + @abstractmethod + def transform_create_request( + self, + name: str, + litellm_params: Dict[str, Any], + ) -> Dict[str, Any]: + """Map name + litellm_params to the provider's create-agent body.""" + + @abstractmethod + def transform_create_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentCreateResponse: + """Parse create response. Raise on non-2xx.""" + + # ------------------------------------------------------------------ # + # LIST # + # ------------------------------------------------------------------ # + + @abstractmethod + def transform_list_request( + self, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> Tuple[str, Dict[str, Any]]: + """Return (url, query_params) for GET /agents.""" + + @abstractmethod + def transform_list_response( + self, + raw_response: httpx.Response, + ) -> AgentListResponse: + """Parse list-agents response. Raise on non-2xx.""" + + # ------------------------------------------------------------------ # + # GET # + # ------------------------------------------------------------------ # + + @abstractmethod + def transform_get_request( + self, + name: str, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> Tuple[str, Dict[str, Any]]: + """Return (url, query_params) for GET /agents/{name}.""" + + @abstractmethod + def transform_get_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentCreateResponse: + """Parse get-agent response. Raise on non-2xx.""" + + # ------------------------------------------------------------------ # + # DELETE # + # ------------------------------------------------------------------ # + + @abstractmethod + def transform_delete_request( + self, + name: str, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> str: + """Return the URL for DELETE /agents/{name}.""" + + @abstractmethod + def transform_delete_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentDeleteResult: + """Parse delete-agent response. Raise on non-2xx.""" + + # ------------------------------------------------------------------ # + # LIST VERSIONS # + # ------------------------------------------------------------------ # + + @abstractmethod + def transform_list_versions_request( + self, + name: str, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> Tuple[str, Dict[str, Any]]: + """Return (url, query_params) for GET /agents/{name}/versions.""" + + @abstractmethod + def transform_list_versions_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentVersionsResponse: + """Parse list-versions response. Raise on non-2xx.""" + + # ------------------------------------------------------------------ # + # ERROR HANDLING # + # ------------------------------------------------------------------ # + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers], + ) -> Exception: + """Map HTTP error status codes to provider-specific exceptions.""" + from litellm.llms.base_llm.chat.transformation import BaseLLMException + + return BaseLLMException( + status_code=status_code, + message=error_message, + headers=headers, + ) 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/chat/invoke_agent/transformation.py b/litellm/llms/bedrock/chat/invoke_agent/transformation.py index e4072c24557..c88fa32b6a0 100644 --- a/litellm/llms/bedrock/chat/invoke_agent/transformation.py +++ b/litellm/llms/bedrock/chat/invoke_agent/transformation.py @@ -299,9 +299,9 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM): ) def _get_response_stream_shape(self): - from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - return BEDROCK_RESPONSE_STREAM_SHAPE + return get_bedrock_response_stream_shape() def _extract_response_content(self, events: InvokeAgentEventList) -> str: """Extract the final response content from parsed events.""" diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 92ca75db95b..7a9916f1f31 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -68,9 +68,9 @@ from litellm.utils import CustomStreamWrapper, get_secret from ..base_aws_llm import BaseAWSLLM from ..common_utils import ( - BEDROCK_RESPONSE_STREAM_SHAPE, BedrockError, ModelResponseIterator, + get_bedrock_response_stream_shape, get_bedrock_tool_name, ) @@ -1828,7 +1828,8 @@ class AWSEventStreamDecoder: yield self._chunk_parser(chunk_data=_data) def _parse_message_from_event(self, event) -> Optional[str]: - if BEDROCK_RESPONSE_STREAM_SHAPE is None: + response_stream_shape = get_bedrock_response_stream_shape() + if response_stream_shape is None: raise BedrockError( status_code=500, message=( @@ -1837,9 +1838,7 @@ class AWSEventStreamDecoder: ), ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse( - response_dict, BEDROCK_RESPONSE_STREAM_SHAPE - ) + parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: decoded_body = response_dict["body"].decode() diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 0256d5d4b95..4f4729e4019 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -4,6 +4,7 @@ from __future__ import annotations Common utilities used across bedrock chat/embedding/image generation """ +import functools import json import os from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union @@ -963,10 +964,8 @@ def _load_bedrock_response_stream_shape(): """ Load the ResponseStream shape from botocore's bundled bedrock-runtime schema. - Called once at module import time; the result is stored in - ``BEDROCK_RESPONSE_STREAM_SHAPE`` and reused for the process lifetime. Returns ``None`` if botocore is unavailable or the service model cannot be - loaded, so the module still imports cleanly. + loaded. """ try: from botocore.loaders import Loader @@ -977,15 +976,22 @@ def _load_bedrock_response_stream_shape(): return ServiceModel(service_dict).shape_for("ResponseStream") except Exception as e: verbose_logger.warning( - "litellm: could not pre-load bedrock-runtime response stream shape " + "litellm: could not load bedrock-runtime response stream shape " "— Bedrock event-stream decoding will be unavailable. Error: %s", e, ) return None -# Eagerly resolved once per process — avoids per-instance or per-request disk I/O. -BEDROCK_RESPONSE_STREAM_SHAPE = _load_bedrock_response_stream_shape() +@functools.lru_cache(maxsize=1) +def get_bedrock_response_stream_shape(): + """ + Lazily load and cache the bedrock-runtime ResponseStream shape for the process. + + Avoids importing botocore (and logging warnings) unless Bedrock event-stream + decoding is actually needed. + """ + return _load_bedrock_response_stream_shape() class BedrockEventStreamDecoderBase: @@ -999,7 +1005,8 @@ class BedrockEventStreamDecoderBase: self.parser = EventStreamJSONParser() def _parse_message_from_event(self, event) -> Optional[str]: - if BEDROCK_RESPONSE_STREAM_SHAPE is None: + response_stream_shape = get_bedrock_response_stream_shape() + if response_stream_shape is None: raise BedrockError( status_code=500, message=( @@ -1008,9 +1015,7 @@ class BedrockEventStreamDecoderBase: ), ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse( - response_dict, BEDROCK_RESPONSE_STREAM_SHAPE - ) + parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: decoded_body = response_dict["body"].decode() 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/agents/__init__.py b/litellm/llms/gemini/agents/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/gemini/agents/transformation.py b/litellm/llms/gemini/agents/transformation.py new file mode 100644 index 00000000000..150918c4737 --- /dev/null +++ b/litellm/llms/gemini/agents/transformation.py @@ -0,0 +1,299 @@ +""" +Google AI Studio Agents API configuration. + +Proxies the Gemini v1beta Agents API: + POST /v1beta/agents create + GET /v1beta/agents list + GET /v1beta/agents/{name} get + DELETE /v1beta/agents/{name} delete + GET /v1beta/agents/{name}/versions list versions +""" + +from typing import Any, Dict, Optional, Tuple, Union + +import httpx + +from litellm._logging import verbose_logger +from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig +from litellm.llms.gemini.common_utils import GeminiError, GeminiModelInfo +from litellm.types.agents import ( + AgentCreateResponse, + AgentDeleteResult, + AgentListResponse, + AgentVersionsResponse, +) + + +# Keys inside litellm_params that should be forwarded to the Gemini +# create-agent body verbatim. +_GEMINI_AGENT_BODY_KEYS = ("base_agent", "instructions", "base_environment") + +# LiteLLM-internal keys that must never be forwarded to Gemini. +_LITELLM_INTERNAL_KEYS = frozenset( + { + "custom_llm_provider", + "api_key", + "api_base", + "make_public", + "cost_per_query", + "input_cost_per_token", + "output_cost_per_token", + "require_trace_id_on_calls_to_agent", + "require_trace_id_on_calls_by_agent", + "max_iterations", + "max_budget_per_session", + "guardrails", + "is_public", + "agent_name", + "agent_id", + "agent_card_params", + "provider_agent_response", + } +) + + +class GeminiAgentsConfig(BaseAgentsAPIConfig): + """ + Configuration for the Google AI Studio (Gemini) native Agents API. + + Authentication uses x-goog-api-key, resolved from (in order): + 1. litellm_params["api_key"] + 2. GOOGLE_API_KEY env var + 3. GEMINI_API_KEY env var + """ + + @property + def api_version(self) -> str: + return "v1beta" + + def _base_url(self, api_base: Optional[str]) -> str: + return f"{GeminiModelInfo.get_api_base(api_base)}/{self.api_version}" + + # ------------------------------------------------------------------ # + # Shared helpers # + # ------------------------------------------------------------------ # + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, httpx.Headers], + ) -> Exception: + return GeminiError( + message=error_message, + status_code=status_code, + headers=dict(headers), + ) + + def get_complete_url( + self, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> str: + return f"{self._base_url(api_base)}/agents" + + def validate_environment( + self, + headers: Dict[str, str], + litellm_params: Dict[str, Any], + ) -> Dict[str, str]: + headers = dict(headers) + headers["Content-Type"] = "application/json" + explicit_api_key = litellm_params.get("api_key") + # SECURITY: when the caller overrides ``api_base``, refuse to fall back + # to the process-wide GOOGLE_API_KEY / GEMINI_API_KEY env vars. Otherwise + # an authenticated proxy user could set ``api_base`` to an attacker- + # controlled host and have the proxy ship its shared Gemini key in the + # ``x-goog-api-key`` header. + if litellm_params.get("api_base") and not explicit_api_key: + raise ValueError( + "When overriding api_base for Gemini agents, you must also " + "supply an explicit api_key. Falling back to GOOGLE_API_KEY / " + "GEMINI_API_KEY env vars with a custom api_base is refused " + "to prevent leaking the shared provider key to arbitrary hosts." + ) + api_key = GeminiModelInfo.get_api_key(explicit_api_key) + if not api_key: + raise ValueError( + "Google API key is required. " + "Set GOOGLE_API_KEY or GEMINI_API_KEY, or pass api_key." + ) + headers["x-goog-api-key"] = api_key + return headers + + def _raise_for_status(self, raw_response: httpx.Response) -> None: + if not (200 <= raw_response.status_code < 300): + raise GeminiError( + message=raw_response.text, + status_code=raw_response.status_code, + headers=dict(raw_response.headers), + ) + + # ------------------------------------------------------------------ # + # CREATE # + # ------------------------------------------------------------------ # + + def transform_create_request( + self, + name: str, + litellm_params: Dict[str, Any], + ) -> Dict[str, Any]: + body: Dict[str, Any] = {"name": name} + for key in _GEMINI_AGENT_BODY_KEYS: + value = litellm_params.get(key) + if value is not None: + body[key] = value + verbose_logger.debug("GeminiAgentsConfig create body: %s", body) + return body + + def transform_create_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentCreateResponse: + """ + Gemini returns: + {"id": "my-agent", "base_agent": "waverunner", + "system_instruction": "...", "base_environment": {...}} + """ + self._raise_for_status(raw_response) + try: + data: Dict[str, Any] = raw_response.json() + except Exception: + verbose_logger.warning( + "GeminiAgentsConfig: non-JSON create response (status=%d).", + raw_response.status_code, + ) + data = {"id": name} + # Gemini uses "id" as the identifier; normalise to both fields. + data.setdefault("id", name) + data.setdefault("name", data["id"]) + verbose_logger.debug("GeminiAgentsConfig create response: %s", data) + return AgentCreateResponse(**data) + + # ------------------------------------------------------------------ # + # LIST # + # ------------------------------------------------------------------ # + + def transform_list_request( + self, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> Tuple[str, Dict[str, Any]]: + url = f"{self._base_url(api_base)}/agents" + params: Dict[str, Any] = {} + if litellm_params.get("page_size"): + params["pageSize"] = litellm_params["page_size"] + if litellm_params.get("page_token"): + params["pageToken"] = litellm_params["page_token"] + return url, params + + def transform_list_response( + self, + raw_response: httpx.Response, + ) -> AgentListResponse: + self._raise_for_status(raw_response) + try: + data = raw_response.json() + except Exception: + data = {} + verbose_logger.debug("GeminiAgentsConfig list response: %s", data) + return AgentListResponse( + agents=data.get("agents", []), + next_page_token=data.get("nextPageToken"), + ) + + # ------------------------------------------------------------------ # + # GET # + # ------------------------------------------------------------------ # + + def transform_get_request( + self, + name: str, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> Tuple[str, Dict[str, Any]]: + url = f"{self._base_url(api_base)}/agents/{name}" + return url, {} + + def transform_get_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentCreateResponse: + """Same shape as create response — Gemini returns "id" as identifier.""" + self._raise_for_status(raw_response) + try: + data = raw_response.json() + except Exception: + data = {"id": name} + data.setdefault("id", name) + data.setdefault("name", data["id"]) + verbose_logger.debug("GeminiAgentsConfig get response: %s", data) + return AgentCreateResponse(**data) + + # ------------------------------------------------------------------ # + # DELETE # + # ------------------------------------------------------------------ # + + def transform_delete_request( + self, + name: str, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> str: + return f"{self._base_url(api_base)}/agents/{name}" + + def transform_delete_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentDeleteResult: + """Gemini returns an empty body ``{}`` with HTTP 200 on success.""" + self._raise_for_status(raw_response) + verbose_logger.debug( + "GeminiAgentsConfig delete (status=%d) agent '%s'", + raw_response.status_code, + name, + ) + return AgentDeleteResult(name=name, deleted=True) + + # ------------------------------------------------------------------ # + # LIST VERSIONS # + # ------------------------------------------------------------------ # + + def transform_list_versions_request( + self, + name: str, + api_base: Optional[str], + litellm_params: Dict[str, Any], + ) -> Tuple[str, Dict[str, Any]]: + url = f"{self._base_url(api_base)}/agents/{name}/versions" + params: Dict[str, Any] = {} + if litellm_params.get("page_size"): + params["pageSize"] = litellm_params["page_size"] + if litellm_params.get("page_token"): + params["pageToken"] = litellm_params["page_token"] + return url, params + + def transform_list_versions_response( + self, + raw_response: httpx.Response, + name: str, + ) -> AgentVersionsResponse: + """ + Gemini returns: + {"agentVersions": [{"agent": "waverunner", "name": "agents/.../versions/uuid", ...}]} + """ + self._raise_for_status(raw_response) + try: + data = raw_response.json() + except Exception: + data = {} + verbose_logger.debug( + "GeminiAgentsConfig list_versions response for '%s': %s", name, data + ) + return AgentVersionsResponse( + agent_versions=data.get("agentVersions", []), + next_page_token=data.get("nextPageToken"), + ) 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/gemini/interactions/transformation.py b/litellm/llms/gemini/interactions/transformation.py index 593cbf7c2cf..73435c8db6a 100644 --- a/litellm/llms/gemini/interactions/transformation.py +++ b/litellm/llms/gemini/interactions/transformation.py @@ -64,6 +64,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): "stream", "store", "background", + "environment", "response_modalities", "response_format", "response_mime_type", @@ -142,6 +143,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig): "stream", "store", "background", + "environment", "response_modalities", "response_format", "response_mime_type", 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/sagemaker/common_utils.py b/litellm/llms/sagemaker/common_utils.py index 50c8ee4220e..6c15d642f8c 100644 --- a/litellm/llms/sagemaker/common_utils.py +++ b/litellm/llms/sagemaker/common_utils.py @@ -1,3 +1,4 @@ +import functools import json from typing import AsyncIterator, Iterator, List, Optional, Union @@ -22,14 +23,22 @@ def _load_sagemaker_response_stream_shape(): ) except Exception as e: verbose_logger.warning( - "litellm: could not pre-load sagemaker-runtime response stream shape " + "litellm: could not load sagemaker-runtime response stream shape " "— SageMaker event-stream decoding will be unavailable. Error: %s", e, ) return None -SAGEMAKER_RESPONSE_STREAM_SHAPE = _load_sagemaker_response_stream_shape() +@functools.lru_cache(maxsize=1) +def get_sagemaker_response_stream_shape(): + """ + Lazily load and cache the sagemaker-runtime stream shape for the process. + + Avoids importing botocore (and logging warnings) unless SageMaker event-stream + decoding is actually needed. + """ + return _load_sagemaker_response_stream_shape() class SagemakerError(BaseLLMException): @@ -207,7 +216,8 @@ class AWSEventStreamDecoder: verbose_logger.error(f"Final error parsing accumulated JSON: {e}") def _parse_message_from_event(self, event) -> Optional[str]: - if SAGEMAKER_RESPONSE_STREAM_SHAPE is None: + response_stream_shape = get_sagemaker_response_stream_shape() + if response_stream_shape is None: raise SagemakerError( status_code=500, message=( @@ -216,9 +226,7 @@ class AWSEventStreamDecoder: ), ) response_dict = event.to_response_dict() - parsed_response = self.parser.parse( - response_dict, SAGEMAKER_RESPONSE_STREAM_SHAPE - ) + parsed_response = self.parser.parse(response_dict, response_stream_shape) if response_dict["status_code"] != 200: raise ValueError(f"Bad response code, expected 200: {response_dict}") diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 9afa5dec465..ea57339e35f 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) @@ -607,7 +1042,9 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 and messages[msg_i]["role"] in tool_call_message_roles ): _part = convert_to_gemini_tool_call_result( - messages[msg_i], last_message_with_tool_calls # type: ignore + messages[msg_i], # type: ignore + last_message_with_tool_calls, # type: ignore + model=model, ) msg_i += 1 # Handle both single part and list of parts (for Computer Use with images) @@ -713,11 +1150,11 @@ 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 +1330,20 @@ 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..e99f69fcd3e 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 @@ -280,6 +280,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): - gemini-3-pro-preview - gemini-3-flash - gemini-3-flash-preview (Gemini 3 Flash) + - gemini-3.1-pro-preview, gemini-3.1-flash, gemini-3.1-flash-lite-preview + - gemini-3.5-flash - Any future Gemini 3.x models """ # Check for Gemini 3 models @@ -300,6 +302,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): supported_params = [ "temperature", "top_p", + "top_k", "max_tokens", "max_completion_tokens", "stream", @@ -363,6 +366,66 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): """ return Tools(googleSearch={}) + @staticmethod + def _search_tool_keys() -> set: + return { + VertexToolName.GOOGLE_SEARCH.value, + VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value, + VertexToolName.ENTERPRISE_WEB_SEARCH.value, + VertexToolName.URL_CONTEXT.value, + "google_search", + "google_search_retrieval", + "enterprise_web_search", + "urlContext", + } + + @classmethod + def _drop_search_tools_mixed_with_functions(cls, optional_params: dict) -> None: + """ + Drop search tools from optional_params when mixed with function declarations + and include_server_side_tool_invocations is not enabled. + + Runs after map_openai_params merges tools and web_search_options so both + code paths (single _map_function call vs split tools + web_search_options) + get the same conflict resolution. + """ + if optional_params.get("include_server_side_tool_invocations"): + return + + tools = optional_params.get("tools") + if not isinstance(tools, list) or not tools: + return + + search_tool_keys = cls._search_tool_keys() + has_function_declarations = any( + isinstance(tool, dict) and tool.get("function_declarations") + for tool in tools + ) + if not has_function_declarations: + return + + has_search_tools = any( + isinstance(tool, dict) and any(key in tool for key in search_tool_keys) + for tool in tools + ) + if not has_search_tools: + return + + verbose_logger.warning( + "Vertex AI does not support mixing function declarations with " + "search tools (googleSearch, enterpriseWebSearch, urlContext, " + "googleSearchRetrieval) in the same request. Dropping search " + "tools and keeping function declarations. To use search tools, " + "send a request without function calling tools." + ) + optional_params["tools"] = [ + tool + for tool in tools + if not ( + isinstance(tool, dict) and any(key in tool for key in search_tool_keys) + ) + ] + def _map_service_tier_param(self, value: str, optional_params: dict) -> None: """ Map OpenAI service_tier (string) to Gemini serviceTier. @@ -884,9 +947,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): GeminiThinkingConfig with thinkingLevel and includeThoughts """ # Check if this is gemini-3-flash which supports MINIMAL thinking level - # Covers gemini-3-flash, gemini-3-flash-preview, gemini-3.1-flash, gemini-3.1-flash-lite-preview, etc. + # Covers gemini-3-flash, gemini-3-flash-preview, gemini-3.1-flash, gemini-3.1-flash-lite-preview, + # gemini-3.5-flash, and any future 3.x-flash variants. is_gemini3flash = model and ( - "gemini-3-flash" in model.lower() or "gemini-3.1-flash" in model.lower() + "flash" in model.lower() and "gemini-3" in model.lower() ) is_gemini31pro = model and ("gemini-3.1-pro-preview" in model.lower()) if reasoning_effort == "minimal": @@ -982,8 +1046,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): # Follow provider defaults unless explicitly opted into legacy behavior. if litellm.enable_gemini_default_thinking_level_low is True: is_gemini3flash = ( - "gemini-3-flash-preview" in model.lower() - or "gemini-3-flash" in model.lower() + "gemini-3" in model.lower() and "flash" in model.lower() ) params["thinkingLevel"] = ( "minimal" if is_gemini3flash else "low" @@ -1077,6 +1140,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): model: str, drop_params: bool, ) -> Dict: + gemini_sampling_params_warned: bool = False for param, value in non_default_params.items(): if param == "temperature": if VertexGeminiConfig._is_gemini_3_or_newer(model): @@ -1086,9 +1150,41 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): "can cause infinite loops, degraded reasoning performance, and failure on complex tasks. " "Strongly recommended to use temperature = 1.0 (default)." ) + if not gemini_sampling_params_warned: + verbose_logger.warning( + "DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to " + f"function for Gemini 3+ ({model}) but are planned for removal in a " + "future release. Move sampling guidance into the `system` " + "instructions instead." + ) + gemini_sampling_params_warned = True optional_params["temperature"] = value elif param == "top_p": + if ( + VertexGeminiConfig._is_gemini_3_or_newer(model) + and not gemini_sampling_params_warned + ): + verbose_logger.warning( + "DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to " + f"function for Gemini 3+ ({model}) but are planned for removal in a " + "future release. Move sampling guidance into the `system` " + "instructions instead." + ) + gemini_sampling_params_warned = True optional_params["top_p"] = value + elif param == "top_k": + if ( + VertexGeminiConfig._is_gemini_3_or_newer(model) + and not gemini_sampling_params_warned + ): + verbose_logger.warning( + "DeprecationWarning: `temperature`, `top_p`, and `top_k` continue to " + f"function for Gemini 3+ ({model}) but are planned for removal in a " + "future release. Move sampling guidance into the `system` " + "instructions instead." + ) + gemini_sampling_params_warned = True + optional_params["top_k"] = value elif ( param == "stream" and value is True ): # sending stream = False, can cause it to get passed unchecked and raise issues @@ -1139,11 +1235,14 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if _tool_choice_value is not None: optional_params["tool_choice"] = _tool_choice_value elif param == "parallel_tool_calls": - if value is False and not ( - drop_params or litellm.drop_params - ): # if drop params is True, then we should just ignore this - self.validate_parallel_tool_calls(value, non_default_params) - else: + tools_list = non_default_params.get( + "tools", non_default_params.get("functions") + ) + num_tools = len(tools_list) if isinstance(tools_list, list) else 0 + # Gemini does not support parallel_tool_calls=False with multiple + # tools. Drop the param instead of failing — Responses API clients + # often send parallel_tool_calls=false by default. + if not (value is False and num_tools > 1): optional_params["parallel_tool_calls"] = value elif param == "seed": optional_params["seed"] = value @@ -1216,6 +1315,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if "temperature" not in optional_params: optional_params["temperature"] = 1.0 + self._drop_search_tools_mixed_with_functions(optional_params) + return optional_params def get_mapped_special_auth_params(self) -> dict: @@ -1588,6 +1689,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): } # Extract thought signature if present thought_signature = part.get("thoughtSignature") + # Gemini 3.5+ returns a stable `id` per function call to enable + # strict response matching. Preserve it as the OpenAI + # tool_call_id so it can be echoed back unchanged. + gemini_call_id = part["functionCall"].get("id") if is_function_call is True: function_dict: Dict[str, Any] = dict(_function_chunk) @@ -1605,6 +1710,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): "function": _function_chunk, "index": cumulative_tool_call_idx, } + # Gemini 3.5+ returns a stable native `id`; prefer it over + # the synthetic call_ so the same value can be echoed + # back on the matching `functionResponse`. + if gemini_call_id: + _tool_response_chunk["id"] = gemini_call_id # Embed thought signature in ID for OpenAI client compatibility if thought_signature: _tool_response_chunk["provider_specific_fields"] = { # type: ignore @@ -2533,9 +2643,14 @@ 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 +3254,31 @@ 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 +3396,11 @@ 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/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 543172b8381..9ba337da0a5 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1448,6 +1448,35 @@ "supports_native_structured_output": true, "supports_minimal_reasoning_effort": true }, + "jp.anthropic.claude-sonnet-4-6": { + "cache_creation_input_token_cost": 4.125e-06, + "cache_read_input_token_cost": 3.3e-07, + "input_cost_per_token": 3.3e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.65e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_max_reasoning_effort": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346, + "supports_native_structured_output": true, + "supports_minimal_reasoning_effort": true + }, "anthropic.claude-sonnet-4-20250514-v1:0": { "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, @@ -2112,6 +2141,380 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "azure_ai/gpt-5.4": { + "cache_read_input_token_cost": 2.5e-07, + "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_priority": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-2026-03-05": { + "cache_read_input_token_cost": 2.5e-07, + "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_priority": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro": { + "cache_read_input_token_cost": 3e-06, + "cache_read_input_token_cost_above_272k_tokens": 6e-06, + "cache_read_input_token_cost_priority": 6e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro-2026-03-05": { + "cache_read_input_token_cost": 3e-06, + "cache_read_input_token_cost_above_272k_tokens": 6e-06, + "cache_read_input_token_cost_priority": 6e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-mini": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_above_272k_tokens": 1.5e-07, + "cache_read_input_token_cost_priority": 1.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 3e-07, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_above_272k_tokens": 1.5e-06, + "input_cost_per_token_priority": 1.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 3e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "output_cost_per_token_above_272k_tokens": 6.75e-06, + "output_cost_per_token_priority": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 1.35e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-mini", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-mini-2026-03-17": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_above_272k_tokens": 1.5e-07, + "cache_read_input_token_cost_priority": 1.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 3e-07, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_above_272k_tokens": 1.5e-06, + "input_cost_per_token_priority": 1.5e-06, + "input_cost_per_token_above_272k_tokens_priority": 3e-06, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 4.5e-06, + "output_cost_per_token_above_272k_tokens": 6.75e-06, + "output_cost_per_token_priority": 9e-06, + "output_cost_per_token_above_272k_tokens_priority": 1.35e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-mini", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano-2026-03-17": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, "azure_ai/model_router": { "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, @@ -9228,6 +9631,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "supports_adaptive_thinking": true, "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, @@ -9421,6 +9825,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -9454,6 +9859,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -9487,6 +9893,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -9521,6 +9928,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, "supports_function_calling": true, @@ -14509,7 +14917,65 @@ "mode": "chat", "output_cost_per_reasoning_token": 1.5e-06, "output_cost_per_token": 1.5e-06, - "source": "https://ai.google.dev/gemini-api/docs/models", + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_audio_output": false, + "supports_code_execution": true, + "supports_file_search": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "supports_service_tier": true + }, + "gemini-3.1-flash-lite": { + "cache_read_input_token_cost": 4.5e-08, + "cache_read_input_token_cost_per_audio_token": 9e-08, + "input_cost_per_audio_token": 9e-07, + "input_cost_per_token": 4.5e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 2.7e-06, + "output_cost_per_token": 2.7e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -15237,6 +15703,64 @@ }, "web_search_billing_unit": "per_query" }, + "vertex_ai/gemini-3.5-flash": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "input_cost_per_audio_token": 1e-06, + "litellm_provider": "vertex_ai", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 9e-06, + "output_cost_per_token": 9e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 2.7e-06, + "input_cost_per_audio_token_priority": 1.8e-06, + "output_cost_per_token_priority": 1.62e-05, + "cache_read_input_token_cost_priority": 2.7e-07, + "supports_service_tier": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "vertex_ai/gemini-3.1-pro-preview": { "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_200k_tokens": 4e-07, @@ -16555,6 +17079,66 @@ "web_search_billing_unit": "per_query", "supports_service_tier": true }, + "gemini/gemini-3.1-flash-lite": { + "cache_read_input_token_cost": 4.5e-08, + "cache_read_input_token_cost_per_audio_token": 9e-08, + "input_cost_per_audio_token": 9e-07, + "input_cost_per_token": 4.5e-07, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 2.7e-06, + "output_cost_per_token": 2.7e-06, + "rpm": 15, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_audio_output": false, + "supports_code_execution": true, + "supports_file_search": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "tpm": 250000, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "supports_service_tier": true + }, "gemini/gemini-3-flash-preview": { "cache_read_input_token_cost": 5e-08, "input_cost_per_audio_token": 1e-06, @@ -16614,6 +17198,67 @@ }, "web_search_billing_unit": "per_query" }, + "gemini/gemini-3.5-flash": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 9e-06, + "output_cost_per_token": 9e-06, + "rpm": 2000, + "source": "https://ai.google.dev/pricing/gemini-3", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "tpm": 800000, + "input_cost_per_token_priority": 2.7e-06, + "input_cost_per_audio_token_priority": 1.8e-06, + "output_cost_per_token_priority": 1.62e-05, + "cache_read_input_token_cost_priority": 2.7e-07, + "supports_service_tier": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini/gemini-3.1-pro-preview": { "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_200k_tokens": 4e-07, @@ -16799,6 +17444,65 @@ }, "web_search_billing_unit": "per_query" }, + "gemini-3.5-flash": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 9e-06, + "output_cost_per_token": 9e-06, + "source": "https://ai.google.dev/pricing/gemini-3", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 2.7e-06, + "input_cost_per_audio_token_priority": 1.8e-06, + "output_cost_per_token_priority": 1.62e-05, + "cache_read_input_token_cost_priority": 2.7e-07, + "supports_service_tier": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini/gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, @@ -23733,6 +24437,21 @@ "supports_tool_choice": true, "supports_vision": true }, + "mistral/ministral-8b-2512": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1.5e-07, + "source": "https://mistral.ai/pricing", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "mistral/mistral-tiny": { "input_cost_per_token": 2.5e-07, "litellm_provider": "mistral", @@ -33053,6 +33772,64 @@ }, "web_search_billing_unit": "per_query" }, + "vertex_ai/gemini-3.1-flash-lite": { + "cache_read_input_token_cost": 4.5e-08, + "cache_read_input_token_cost_per_audio_token": 9e-08, + "input_cost_per_audio_token": 9e-07, + "input_cost_per_token": 4.5e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 2.7e-06, + "output_cost_per_token": 2.7e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_audio_output": false, + "supports_code_execution": true, + "supports_file_search": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "supports_service_tier": true + }, "vertex_ai/deep-research-pro-preview-12-2025": { "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, diff --git a/litellm/proxy/_experimental/mcp_server/oauth_utils.py b/litellm/proxy/_experimental/mcp_server/oauth_utils.py index e6691329709..8541a691e88 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth_utils.py +++ b/litellm/proxy/_experimental/mcp_server/oauth_utils.py @@ -30,23 +30,43 @@ _DEFAULT_PORTS = {"http": 80, "https": 443} _TRUSTED_REDIRECT_ORIGINS_ENV = "MCP_TRUSTED_REDIRECT_ORIGINS" +_warned_invalid_proxy_base_url: Optional[str] = None + + +def _resolve_proxy_base_url_env() -> Optional[str]: + global _warned_invalid_proxy_base_url + configured = os.environ.get("PROXY_BASE_URL", "").strip() + if not configured: + return None + parsed = urlparse(configured) + if parsed.scheme in ("http", "https") and parsed.netloc: + normalized = urlunparse((parsed.scheme, parsed.netloc, parsed.path, "", "", "")) + return normalized.rstrip("/") + if _warned_invalid_proxy_base_url != configured: + verbose_logger.warning( + "PROXY_BASE_URL=%r is not a valid http(s) URL (missing scheme " + "or host) and will be ignored for MCP OAuth origin resolution. " + "Set it to a full URL like https://litellm.example.com.", + configured, + ) + _warned_invalid_proxy_base_url = configured + return None + + def get_request_base_url(request: Request) -> str: """ Get the base URL for the request, considering X-Forwarded-* headers. - X-Forwarded-Proto / X-Forwarded-Host / X-Forwarded-Port are only honoured - when the request comes from a configured trusted proxy - (``use_x_forwarded_for`` enabled AND caller in ``mcp_trusted_proxy_ranges``). - Otherwise the request's literal ``base_url`` is returned, so an - untrusted caller cannot poison OAuth-discovery / redirect_uri values - by injecting headers. - - Args: - request: FastAPI Request object - - Returns: - The reconstructed base URL (e.g., "https://proxy.example.com") + Resolution order: ``PROXY_BASE_URL`` env var, then X-Forwarded-* when + the caller is a trusted proxy (``use_x_forwarded_for`` enabled AND + caller in ``mcp_trusted_proxy_ranges``), otherwise the request's + literal ``base_url``. Untrusted callers cannot poison OAuth-discovery + / redirect_uri values by injecting headers. """ + configured = _resolve_proxy_base_url_env() + if configured: + return configured + base_url = str(request.base_url).rstrip("/") parsed = urlparse(base_url) @@ -284,4 +304,22 @@ def validate_trusted_redirect_uri(request: Request, redirect_uri: str) -> None: if _matches_trusted_origin_entry(redirect_netloc, entry): return + verbose_logger.warning( + "MCP OAuth: rejecting redirect_uri %r as invalid_request. " + "Computed proxy base=%r (PROXY_BASE_URL=%r). " + "Inbound headers: X-Forwarded-Proto=%r X-Forwarded-Host=%r " + "X-Forwarded-Port=%r Host=%r. " + "Trusted-redirect-origins env=%r. " + "If this should be accepted, either align ingress X-Forwarded-* " + "with the browser URL, set PROXY_BASE_URL to your public origin, " + "or add the redirect_uri host to MCP_TRUSTED_REDIRECT_ORIGINS.", + redirect_uri, + proxy_base, + os.environ.get("PROXY_BASE_URL"), + request.headers.get("X-Forwarded-Proto"), + request.headers.get("X-Forwarded-Host"), + request.headers.get("X-Forwarded-Port"), + request.headers.get("Host"), + os.environ.get(_TRUSTED_REDIRECT_ORIGINS_ENV), + ) raise HTTPException(status_code=400, detail="invalid_request") diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 3e20cf89e47..46e13ca9931 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

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diff --git a/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt b/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt index 4c5c61d3d94..c41a5828d28 100644 --- a/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt +++ b/litellm/proxy/_experimental/out/virtual-keys/__next._tree.txt @@ -1,4 +1,4 @@ :HL["/litellm-asset-prefix/_next/static/chunks/4e20891f2fd03463.css","style"] :HL["/litellm-asset-prefix/_next/static/chunks/91037395c95e366d.css","style"] :HL["/litellm-asset-prefix/_next/static/media/83afe278b6a6bb3c-s.p.3a6ba036.woff2","font",{"crossOrigin":"","type":"font/woff2"}] 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+0:{"buildId":"LpD6ruZoEpvYpT5IvMEoa","tree":{"name":"","paramType":null,"paramKey":"","hasRuntimePrefetch":false,"slots":{"children":{"name":"(dashboard)","paramType":null,"paramKey":"(dashboard)","hasRuntimePrefetch":false,"slots":{"children":{"name":"virtual-keys","paramType":null,"paramKey":"virtual-keys","hasRuntimePrefetch":false,"slots":{"children":{"name":"__PAGE__","paramType":null,"paramKey":"__PAGE__","hasRuntimePrefetch":false,"slots":null,"isRootLayout":false}},"isRootLayout":false}},"isRootLayout":false}},"isRootLayout":true},"staleTime":300} diff --git a/litellm/proxy/_lazy_features.py b/litellm/proxy/_lazy_features.py index 9f034575222..a70c5b3a920 100644 --- a/litellm/proxy/_lazy_features.py +++ b/litellm/proxy/_lazy_features.py @@ -84,6 +84,11 @@ LAZY_FEATURES: Tuple[LazyFeature, ...] = ( module_path="litellm.proxy.agent_endpoints.endpoints", path_prefixes=("/v1/agents", "/agents", "/agent/"), ), + LazyFeature( + name="gemini_agents", + module_path="litellm.proxy.google_endpoints.agents_endpoints", + path_prefixes=("/v1beta/agents",), + ), LazyFeature( name="a2a", module_path="litellm.proxy.agent_endpoints.a2a_endpoints", diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index d4fa497698a..9337aa7c8ea 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -481,6 +481,10 @@ class LiteLLMRoutes(enum.Enum): "/v1beta/interactions/{interaction_id}", "/interactions/{interaction_id}/cancel", "/v1beta/interactions/{interaction_id}/cancel", + # Google Managed Agents API + "/v1beta/agents", + "/v1beta/agents/{name}", + "/v1beta/agents/{name}/versions", ] apply_guardrail_routes = [ @@ -4549,6 +4553,7 @@ 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/agent_endpoints/utils.py b/litellm/proxy/agent_endpoints/utils.py index 2b968de54be..393f5934fd9 100644 --- a/litellm/proxy/agent_endpoints/utils.py +++ b/litellm/proxy/agent_endpoints/utils.py @@ -2,6 +2,12 @@ from typing import Dict, Mapping, Optional +# Re-export from the canonical SDK location so the proxy and SDK always +# share the same provider-config lookup logic. +from litellm.interactions.agents.utils import ( # noqa: F401 + get_provider_agents_api_config, +) + def merge_agent_headers( *, diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index 038d2d81277..7d2954fd2dd 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -807,6 +807,11 @@ class ProxyBaseLLMRequestProcessing: "aget_interaction", "adelete_interaction", "acancel_interaction", + "acreate_agent", + "alist_agents", + "aget_agent", + "adelete_agent", + "alist_agent_versions", "asend_message", "call_mcp_tool", "acreate_eval", @@ -1074,6 +1079,11 @@ class ProxyBaseLLMRequestProcessing: "aget_interaction", "adelete_interaction", "acancel_interaction", + "acreate_agent", + "alist_agents", + "aget_agent", + "adelete_agent", + "alist_agent_versions", "asend_message", "call_mcp_tool", "acreate_eval", 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/db/spend_counter_reseed.py b/litellm/proxy/db/spend_counter_reseed.py index 19ec6699390..e7c5fa3f72c 100644 --- a/litellm/proxy/db/spend_counter_reseed.py +++ b/litellm/proxy/db/spend_counter_reseed.py @@ -178,15 +178,28 @@ class SpendCounterReseed: if db_spend is None: return None # Warm even when 0 so subsequent reads hit cache, not DB. + # + # Seed via SET NX (cross-pod safe): only one pod initializes the + # Redis key with db_spend; concurrent seeders read the winner's + # value. INCRBYFLOAT-of-db_spend from N pods would multiply the + # counter (N x db_spend) and trigger spurious budget alerts. + current_value: float = float(db_spend) try: if spend_counter_cache.redis_cache is not None: - current_value = ( - await spend_counter_cache.redis_cache.async_increment( - key=counter_key, - value=db_spend, - refresh_ttl=True, - ) + seeded = await spend_counter_cache.redis_cache.async_set_cache( + key=counter_key, + value=db_spend, + nx=True, ) + if seeded: + current_value = float(db_spend) + else: + cached = await spend_counter_cache.redis_cache.async_get_cache( + key=counter_key + ) + current_value = ( + float(cached) if cached is not None else float(db_spend) + ) spend_counter_cache.in_memory_cache.set_cache( key=counter_key, value=current_value, @@ -202,7 +215,7 @@ class SpendCounterReseed: ) if require_cache_warm: raise - return db_spend + return current_value @staticmethod async def window_from_spend_logs( diff --git a/litellm/proxy/google_endpoints/agents_endpoints.py b/litellm/proxy/google_endpoints/agents_endpoints.py new file mode 100644 index 00000000000..779284023a0 --- /dev/null +++ b/litellm/proxy/google_endpoints/agents_endpoints.py @@ -0,0 +1,445 @@ +""" +Google AI Studio Managed Agents API Proxy Endpoints. + +Exposes Gemini's /v1beta/agents surface through the LiteLLM proxy so that +user curl commands transfer 1-to-1 by swapping the host + auth header. + +Routes: + POST /v1beta/agents -> acreate_agent + GET /v1beta/agents -> alist_agents + GET /v1beta/agents/{name} -> aget_agent + DELETE /v1beta/agents/{name} -> adelete_agent + GET /v1beta/agents/{name}/versions -> alist_agent_versions + +These are distinct from the A2A agent registry at /v1/agents. +""" + +import json + +from fastapi import APIRouter, Depends, HTTPException, Request, Response, status +from fastapi.responses import ORJSONResponse + +from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth +from litellm.proxy.auth.user_api_key_auth import user_api_key_auth +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing +from litellm.proxy.common_utils.http_parsing_utils import ( + _read_request_body, + _safe_get_request_query_params, +) + +router = APIRouter(tags=["gemini managed agents"]) + + +def _is_proxy_admin(user_api_key_dict: UserAPIKeyAuth) -> bool: + return ( + user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN + or user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value + ) + + +def _enforce_caller_supplied_provider_key( + data: dict, + user_api_key_dict: UserAPIKeyAuth, +) -> None: + """ + SECURITY: refuse to use the proxy's shared GOOGLE_API_KEY / GEMINI_API_KEY + env fallback for non-admin callers on Gemini managed-agent CRUD endpoints. + + These endpoints are part of ``llm_api_routes`` so any authenticated LLM key + can reach them, but unlike ``/v1beta/models/...:generateContent`` they are + *not* routed through ``model_list`` — the only credential source is either + the per-request ``litellm_params_template`` or the env var fallback. Without + this guard, any ordinary proxy user could list, create, or delete managed + agents inside the operator's Gemini project using the operator's key. + + Proxy admins (master key) keep the env-fallback convenience for ops use. + """ + if _is_proxy_admin(user_api_key_dict): + return + if data.get("api_key"): + return + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=( + "Gemini managed-agent endpoints require a caller-supplied " + "Gemini api_key (via 'litellm_params_template'). Falling back to " + "the proxy's GOOGLE_API_KEY / GEMINI_API_KEY env vars is only " + "permitted for proxy admins." + ), + ) + + +def _merge_query_params_into_data(data: dict, request: Request) -> dict: + """ + For GET/DELETE endpoints that cannot carry a JSON body, read a + JSON-encoded ``litellm_params_template`` query parameter and merge its + contents into *data*, without overwriting keys that are already present + (e.g. path params like ``name`` or the fixed ``custom_llm_provider``). + + This mirrors the ``litellm_params_template`` handling in + ``create_gemini_agent`` and is the supported way for multi-tenant + callers to supply per-request credentials on non-POST endpoints: + + .. code-block:: bash + + curl "http://localhost:4000/v1beta/agents?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\ + -H "Authorization: Bearer sk-..." + + Credentials MUST NOT be passed as plain flat query parameters (e.g. + ``?api_key=AIza...``) because URL query strings appear verbatim in + web-server access logs, CDN edge logs, browser history, and Referer + headers. Use the ``litellm_params_template`` JSON body field on POST + requests, or the JSON-encoded query parameter above for GET/DELETE. + """ + query_params = _safe_get_request_query_params(request) + if not query_params: + return data + + raw_template = query_params.get("litellm_params_template") + if raw_template: + try: + template = ( + json.loads(raw_template) + if isinstance(raw_template, str) + else raw_template + ) + except (json.JSONDecodeError, ValueError): + template = {} + if isinstance(template, dict): + for key, value in template.items(): + data.setdefault(key, value) + + return data + + +def _proxy_server_imports(): + from litellm.proxy.proxy_server import ( # noqa: PLC0415 + general_settings, + llm_router, + proxy_config, + proxy_logging_obj, + select_data_generator, + user_api_base, + user_max_tokens, + user_model, + user_request_timeout, + user_temperature, + version, + ) + + return dict( + general_settings=general_settings, + llm_router=llm_router, + proxy_config=proxy_config, + proxy_logging_obj=proxy_logging_obj, + select_data_generator=select_data_generator, + user_api_base=user_api_base, + user_max_tokens=user_max_tokens, + user_model=user_model, + user_request_timeout=user_request_timeout, + user_temperature=user_temperature, + version=version, + ) + + +@router.post( + "/v1beta/agents", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, +) +async def create_gemini_agent( + request: Request, + fastapi_response: Response, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + Create a named custom agent on the Gemini side. + + Example: + ```bash + curl -X POST "http://localhost:4000/v1beta/agents" \\ + -H "Authorization: Bearer sk-..." \\ + -H "Content-Type: application/json" \\ + -d '{ + "name": "my-custom-slides-agent", + "base_agent": "waverunner", + "instructions": "You are a helpful assistant that creates slides.", + "base_environment": { + "type": "remote", + "sources": [ + {"type": "gcs", "source": "gs://eap-templates/slides-skill", + "target": "/.agents/skills/slides-skill"} + ] + } + }' + ``` + """ + srv = _proxy_server_imports() + data = await _read_request_body(request=request) + # Merge litellm_params_template (e.g. custom_llm_provider, api_key) into the request + litellm_params_template = data.pop("litellm_params_template", None) or {} + if isinstance(litellm_params_template, dict): + for key, value in litellm_params_template.items(): + if key not in data: + data[key] = value + data.setdefault("custom_llm_provider", "gemini") + _enforce_caller_supplied_provider_key(data, user_api_key_dict) + + processor = ProxyBaseLLMRequestProcessing(data=data) + try: + return await processor.base_process_llm_request( + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + route_type="acreate_agent", + proxy_logging_obj=srv["proxy_logging_obj"], + llm_router=srv["llm_router"], + general_settings=srv["general_settings"], + proxy_config=srv["proxy_config"], + select_data_generator=srv["select_data_generator"], + model=None, + user_model=srv["user_model"], + user_temperature=srv["user_temperature"], + user_request_timeout=srv["user_request_timeout"], + user_max_tokens=srv["user_max_tokens"], + user_api_base=srv["user_api_base"], + version=srv["version"], + ) + except Exception as e: + raise await processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=srv["proxy_logging_obj"], + version=srv["version"], + ) + + +@router.get( + "/v1beta/agents", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, +) +async def list_gemini_agents( + request: Request, + fastapi_response: Response, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + List all custom agents on the Gemini side. + + Pass per-request Gemini credentials via the JSON-encoded + ``litellm_params_template`` query parameter. Flat query parameters + (e.g. ``?api_key=AIza...``) are intentionally ignored — see + ``_merge_query_params_into_data`` for the rationale. + + ```bash + curl "http://localhost:4000/v1beta/agents?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\ + -H "Authorization: Bearer sk-..." + ``` + """ + srv = _proxy_server_imports() + data: dict = {"custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + _enforce_caller_supplied_provider_key(data, user_api_key_dict) + + processor = ProxyBaseLLMRequestProcessing(data=data) + try: + return await processor.base_process_llm_request( + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + route_type="alist_agents", + proxy_logging_obj=srv["proxy_logging_obj"], + llm_router=srv["llm_router"], + general_settings=srv["general_settings"], + proxy_config=srv["proxy_config"], + select_data_generator=srv["select_data_generator"], + model=None, + user_model=srv["user_model"], + user_temperature=srv["user_temperature"], + user_request_timeout=srv["user_request_timeout"], + user_max_tokens=srv["user_max_tokens"], + user_api_base=srv["user_api_base"], + version=srv["version"], + ) + except Exception as e: + raise await processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=srv["proxy_logging_obj"], + version=srv["version"], + ) + + +@router.get( + "/v1beta/agents/{name}", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, +) +async def get_gemini_agent( + request: Request, + name: str, + fastapi_response: Response, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + Get a specific custom agent by name. + + Pass per-request Gemini credentials via the JSON-encoded + ``litellm_params_template`` query parameter. Flat query parameters + (e.g. ``?api_key=AIza...``) are intentionally ignored — see + ``_merge_query_params_into_data`` for the rationale. + + ```bash + curl "http://localhost:4000/v1beta/agents/my-custom-slides-agent?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\ + -H "Authorization: Bearer sk-..." + ``` + """ + srv = _proxy_server_imports() + data = {"name": name, "custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + _enforce_caller_supplied_provider_key(data, user_api_key_dict) + + processor = ProxyBaseLLMRequestProcessing(data=data) + try: + return await processor.base_process_llm_request( + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + route_type="aget_agent", + proxy_logging_obj=srv["proxy_logging_obj"], + llm_router=srv["llm_router"], + general_settings=srv["general_settings"], + proxy_config=srv["proxy_config"], + select_data_generator=srv["select_data_generator"], + model=None, + user_model=srv["user_model"], + user_temperature=srv["user_temperature"], + user_request_timeout=srv["user_request_timeout"], + user_max_tokens=srv["user_max_tokens"], + user_api_base=srv["user_api_base"], + version=srv["version"], + ) + except Exception as e: + raise await processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=srv["proxy_logging_obj"], + version=srv["version"], + ) + + +@router.delete( + "/v1beta/agents/{name}", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, +) +async def delete_gemini_agent( + request: Request, + name: str, + fastapi_response: Response, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + Delete a custom agent by name. + + Pass per-request Gemini credentials via the JSON-encoded + ``litellm_params_template`` query parameter. Flat query parameters + (e.g. ``?api_key=AIza...``) are intentionally ignored — see + ``_merge_query_params_into_data`` for the rationale. + + ```bash + curl -X DELETE "http://localhost:4000/v1beta/agents/my-custom-slides-agent?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\ + -H "Authorization: Bearer sk-..." + ``` + """ + srv = _proxy_server_imports() + data = {"name": name, "custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + _enforce_caller_supplied_provider_key(data, user_api_key_dict) + + processor = ProxyBaseLLMRequestProcessing(data=data) + try: + return await processor.base_process_llm_request( + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + route_type="adelete_agent", + proxy_logging_obj=srv["proxy_logging_obj"], + llm_router=srv["llm_router"], + general_settings=srv["general_settings"], + proxy_config=srv["proxy_config"], + select_data_generator=srv["select_data_generator"], + model=None, + user_model=srv["user_model"], + user_temperature=srv["user_temperature"], + user_request_timeout=srv["user_request_timeout"], + user_max_tokens=srv["user_max_tokens"], + user_api_base=srv["user_api_base"], + version=srv["version"], + ) + except Exception as e: + raise await processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=srv["proxy_logging_obj"], + version=srv["version"], + ) + + +@router.get( + "/v1beta/agents/{name}/versions", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, +) +async def list_gemini_agent_versions( + request: Request, + name: str, + fastapi_response: Response, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + List versions of a custom agent. + + Pass per-request Gemini credentials via the JSON-encoded + ``litellm_params_template`` query parameter. Flat query parameters + (e.g. ``?api_key=AIza...``) are intentionally ignored — see + ``_merge_query_params_into_data`` for the rationale. + + ```bash + curl "http://localhost:4000/v1beta/agents/my-custom-slides-agent/versions?litellm_params_template=%7B%22api_key%22%3A%22AIza...%22%7D" \\ + -H "Authorization: Bearer sk-..." + ``` + """ + srv = _proxy_server_imports() + data = {"name": name, "custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + _enforce_caller_supplied_provider_key(data, user_api_key_dict) + + processor = ProxyBaseLLMRequestProcessing(data=data) + try: + return await processor.base_process_llm_request( + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + route_type="alist_agent_versions", + proxy_logging_obj=srv["proxy_logging_obj"], + llm_router=srv["llm_router"], + general_settings=srv["general_settings"], + proxy_config=srv["proxy_config"], + select_data_generator=srv["select_data_generator"], + model=None, + user_model=srv["user_model"], + user_temperature=srv["user_temperature"], + user_request_timeout=srv["user_request_timeout"], + user_max_tokens=srv["user_max_tokens"], + user_api_base=srv["user_api_base"], + version=srv["version"], + ) + except Exception as e: + raise await processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=srv["proxy_logging_obj"], + version=srv["version"], + ) diff --git a/litellm/proxy/google_endpoints/endpoints.py b/litellm/proxy/google_endpoints/endpoints.py index 967ac9f0ac4..1f503247bf4 100644 --- a/litellm/proxy/google_endpoints/endpoints.py +++ b/litellm/proxy/google_endpoints/endpoints.py @@ -285,7 +285,7 @@ async def create_interaction( general_settings=general_settings, proxy_config=proxy_config, select_data_generator=select_data_generator, - model=data.get("model") or data.get("agent"), + model=data.get("model"), user_model=user_model, user_temperature=user_temperature, user_request_timeout=user_request_timeout, 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 268b244c86d..879914e5ac6 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -20,6 +20,7 @@ from typing import ( TYPE_CHECKING, Any, AsyncGenerator, + Callable, Dict, List, Literal, @@ -4726,6 +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 @@ -6915,6 +6917,15 @@ async def async_data_generator( # noqa: PLR0915 if isinstance(chunk, BaseModel): chunk = _serialize_streaming_chunk(chunk) + elif isinstance(chunk, bytes): + # Some upstream streaming iterators (e.g. AsyncGoogleGenAIGenerateContentStreamingIterator + # for /v1beta/.../streamGenerateContent) yield raw SSE bytes from Gemini. + # Decode to str so the f-string below does not emit a Python b'...' literal, + # and pass already-formatted SSE through unchanged to avoid double "data:" prefix. + chunk = chunk.decode("utf-8", errors="replace") + if chunk.startswith(("data:", "event:", ":")): + yield chunk if chunk.endswith("\n\n") else chunk + "\n\n" + continue elif isinstance(chunk, str) and chunk.startswith("data: "): error_message = chunk break @@ -8088,6 +8099,11 @@ 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 @@ -8120,6 +8136,10 @@ 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: @@ -8153,6 +8173,10 @@ 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: @@ -10889,13 +10913,130 @@ def _enrich_model_info_with_litellm_data( return model +async def _get_caller_byok_team_scope( + user_api_key_dict: Optional[UserAPIKeyAuth], + prisma_client: Optional[Any], +) -> Optional[Set[str]]: + """ + Return the team IDs whose BYOK rows the caller is allowed to see via + `/v2/model/info` search results. + + `None` means "no scoping" — used for admins and for callers/paths that + have already been scoped upstream (or in tests that supply their own + pre-filtered input set). A returned set (possibly empty) means BYOK rows + must have `model_info.team_id` ∈ that set, otherwise they belong to a + team the caller is not a member of and must be dropped. + """ + if user_api_key_dict is None or prisma_client is None: + return None + if user_api_key_dict.user_role in ( + LitellmUserRoles.PROXY_ADMIN, + LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + ): + return None + user_id = user_api_key_dict.user_id + if user_id is None: + return set() + try: + user_row = await prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_id} + ) + except Exception: + verbose_proxy_logger.exception( + "Failed to look up caller teams while scoping BYOK search; " + "defaulting to no team access." + ) + return set() + if user_row is None: + return set() + return set(user_row.teams or []) + + +# Hard cap on rows the DB-side BYOK search may pull when results need to be +# sorted across the full match set. Without this, an authenticated caller +# can hit `/v2/model/info?search=&sortBy=` and force the +# proxy to materialize and decrypt every matching BYOK row on each request. +_SORTED_SEARCH_DB_FETCH_CAP = 500 + + +async def _fetch_db_models_for_search( + prisma_client: Any, + proxy_config: Any, + search_lower: str, + db_model_ids_in_router: Set[str], + router_models_count: int, + page: int, + size: int, + sort_by: Optional[str], + is_byok_outside_caller_teams: Callable[[Dict[str, Any]], bool], +) -> Tuple[List[Dict[str, Any]], int]: + """ + Run the bounded DB query that backs `/v2/model/info?search=`. Returns + `(decrypted_models, total_count)` where `total_count` is the cheap + `count(...)` of rows matching `search` (not yet team-scoped) so the + UI's pagination stays accurate without materializing every row. + + Earlier iterations also OR'd a JSON-path match on + `model_info.team_public_model_name` to surface BYOK rows that live + only in the DB. That branch fell back to `string_contains: ""` + because Prisma's JSON `string_contains` is case-sensitive on + Postgres, which let any authenticated caller force a full BYOK-table + read via `/v2/model/info?search=x`. We rely on the router-side + filter for `team_public_model_name` instead and keep the DB cost + bounded by `search`. + """ + db_where_condition: Dict[str, Any] = { + "model_name": {"contains": search_lower, "mode": "insensitive"} + } + if db_model_ids_in_router: + db_where_condition["model_id"] = {"not": {"in": list(db_model_ids_in_router)}} + + # Unsorted searches only need enough DB rows to fill the current + # page after counting router-side matches. Sorted searches need + # ordering across the full match set, so fall back to a hard cap. + if sort_by: + take_limit = _SORTED_SEARCH_DB_FETCH_CAP + else: + take_limit = max(0, page * size - router_models_count) + + db_models_total_count = await prisma_client.db.litellm_proxymodeltable.count( + where=db_where_condition + ) + + db_models_raw: list = [] + if take_limit > 0: + db_models_raw = await prisma_client.db.litellm_proxymodeltable.find_many( + where=db_where_condition, + take=take_limit, + ) + + # Scope BYOK rows to the caller's allowed teams so non-admin callers + # can't enumerate other teams' BYOK metadata via `?search=...`. + matching_db_rows = [ + m + for m in db_models_raw + if not is_byok_outside_caller_teams( + m.model_info if isinstance(m.model_info, dict) else {} + ) + ] + + decrypted: List[Dict[str, Any]] = [] + for db_model in matching_db_rows: + decrypted_models = proxy_config.decrypt_model_list_from_db([db_model]) + if decrypted_models: + decrypted.extend(decrypted_models) + + return decrypted, db_models_total_count + + async def _apply_search_filter_to_models( all_models: List[Dict[str, Any]], search: str, - page: int, - size: int, prisma_client: Optional[Any], proxy_config: Any, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, + page: int = 1, + size: int = 50, sort_by: Optional[str] = None, ) -> Tuple[List[Dict[str, Any]], Optional[int]]: """ @@ -10904,11 +11045,19 @@ async def _apply_search_filter_to_models( Args: all_models: List of models to filter search: Search term (case-insensitive) - page: Current page number - size: Page size prisma_client: Prisma client for database queries proxy_config: Proxy config for decrypting models - sort_by: Optional sort field - if provided, fetch all matching models instead of paginating at DB level + user_api_key_dict: Caller identity used to scope BYOK matches to + teams the caller belongs to. When omitted (None), no team + scoping is applied — pass it from request handlers that expose + this function to non-admin callers. + page: Current page number (1-indexed). Used with ``size`` to bound + the DB ``find_many(take=...)`` so a broad search term can't + force a full table read + decrypt on every request. + size: Page size. See ``page``. + sort_by: Sort field. When set, results must be sorted across the + full match set, so the DB fetch is capped at + ``_SORTED_SEARCH_DB_FETCH_CAP`` instead of one page. Returns: Tuple of (filtered_models, total_count). total_count is None if not searching. @@ -10918,9 +11067,43 @@ async def _apply_search_filter_to_models( search_lower = search.lower().strip() - # Filter models in router by search term + allowed_team_ids = await _get_caller_byok_team_scope( + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + ) + + def _is_byok_outside_caller_teams(model_info_dict: Dict[str, Any]) -> bool: + # `team_id` is only set on team BYOK rows. Non-team rows fall + # through unaffected — they are gated by other paths (router + # membership, direct_access, include_team_models). + if allowed_team_ids is None: + return False + team_id = model_info_dict.get("team_id") + if team_id is None: + return False + return team_id not in allowed_team_ids + + def _model_matches_search(m: Dict[str, Any]) -> bool: + # Team BYOK models persist an internal `model_name` + # (e.g. `model_name_{team_id}_{uuid}`) and expose the user-facing + # name via `model_info.team_public_model_name`. Match both so the + # name shown in the UI is searchable. + if search_lower in (m.get("model_name") or "").lower(): + return True + team_public_model_name = (m.get("model_info") or {}).get( + "team_public_model_name" + ) or "" + return search_lower in team_public_model_name.lower() + + # Filter models in router by search term, dropping BYOK rows that + # belong to teams the caller is not a member of so search can't leak + # other teams' models when the request omits `include_team_models` / + # `teamId`. filtered_router_models = [ - m for m in all_models if search_lower in m.get("model_name", "").lower() + m + for m in all_models + if _model_matches_search(m) + and not _is_byok_outside_caller_teams(m.get("model_info") or {}) ] # Separate filtered models into config vs db models, and track db model IDs @@ -10942,91 +11125,30 @@ async def _apply_search_filter_to_models( router_models_count = config_models_count + db_models_in_router_count # Query database for additional models with search term - db_models = [] - db_models_total_count = 0 - models_needed_for_page = size * page - - # Only query database if prisma_client is available + db_models: List[Dict[str, Any]] = [] if prisma_client is not None: try: - # Build where condition for database query - db_where_condition: Dict[str, Any] = { - "model_name": { - "contains": search_lower, - "mode": "insensitive", - } - } - # Exclude models already in router if we have any - if db_model_ids_in_router: - db_where_condition["model_id"] = { - "not": {"in": list(db_model_ids_in_router)} - } - - # Get total count of matching database models - db_models_total_count = ( - await prisma_client.db.litellm_proxymodeltable.count( - where=db_where_condition - ) + db_models, db_models_total_count = await _fetch_db_models_for_search( + prisma_client=prisma_client, + proxy_config=proxy_config, + search_lower=search_lower, + db_model_ids_in_router=db_model_ids_in_router, + router_models_count=router_models_count, + page=page, + size=size, + sort_by=sort_by, + is_byok_outside_caller_teams=_is_byok_outside_caller_teams, ) - - # Calculate total count for search results search_total_count = router_models_count + db_models_total_count - - # If sorting is requested, we need to fetch ALL matching models to sort correctly - # Otherwise, we can optimize by only fetching what's needed for the current page - if sort_by: - # Fetch all matching database models for sorting - if db_models_total_count > 0: - db_models_raw = ( - await prisma_client.db.litellm_proxymodeltable.find_many( - where=db_where_condition, - take=db_models_total_count, # Fetch all matching models - ) - ) - - # Convert database models to router format - for db_model in db_models_raw: - decrypted_models = proxy_config.decrypt_model_list_from_db( - [db_model] - ) - if decrypted_models: - db_models.extend(decrypted_models) - else: - # Fetch database models if we need more for the current page - if router_models_count < models_needed_for_page: - models_to_fetch = min( - models_needed_for_page - router_models_count, - db_models_total_count, - ) - - if models_to_fetch > 0: - db_models_raw = ( - await prisma_client.db.litellm_proxymodeltable.find_many( - where=db_where_condition, - take=models_to_fetch, - ) - ) - - # Convert database models to router format - for db_model in db_models_raw: - decrypted_models = proxy_config.decrypt_model_list_from_db( - [db_model] - ) - if decrypted_models: - db_models.extend(decrypted_models) except Exception as e: verbose_proxy_logger.exception( f"Error querying database models with search: {str(e)}" ) - # If error, use router models count as fallback search_total_count = router_models_count else: - # If no prisma_client, only use router models search_total_count = router_models_count - # Combine all models - filtered_models = filtered_router_models + db_models - return filtered_models, search_total_count + return filtered_router_models + db_models, search_total_count def _normalize_datetime_for_sorting(dt: Any) -> Optional[datetime]: @@ -11102,6 +11224,15 @@ def _sort_models( model_info = model.get("model_info", {}) if sort_by == "model_name": + # Team BYOK models persist an internal `model_name` (e.g. + # `model_name_{team_id}_{uuid}`) and expose the user-facing + # name via `model_info.team_public_model_name` — same as the + # UI's getDisplayModelName. Sort by the displayed name so + # BYOK rows interleave alphabetically with non-BYOK rows + # instead of clumping at the end on their opaque IDs. + team_public_model_name = model_info.get("team_public_model_name") + if team_public_model_name: + return str(team_public_model_name).lower() return model.get("model_name", "").lower() elif sort_by == "created_at": @@ -11286,28 +11417,81 @@ async def _gather_team_accessible_model_ids( return team_accessible_model_ids +async def _authorize_team_id_query( + team_id: str, + user_api_key_dict: UserAPIKeyAuth, + prisma_client: PrismaClient, +) -> None: + """ + `teamId` arrives untrusted via the /v2/model/info query string and the + filter below includes BYOK rows solely on `model_info.team_id == team_id`. + Without this guard, any authenticated user who knows (or guesses) another + team's id could enumerate that team's BYOK model metadata. Allow only + proxy admins or members of the requested team. + """ + if user_api_key_dict.user_role in ( + LitellmUserRoles.PROXY_ADMIN, + LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + ): + return + + user_id = user_api_key_dict.user_id + if user_id is None: + raise HTTPException( + status_code=403, + detail={"error": "Not authorized to view this team's models"}, + ) + try: + user_row = await prisma_client.db.litellm_usertable.find_unique( + where={"user_id": user_id} + ) + except Exception: + verbose_proxy_logger.exception( + "Failed to look up caller teams while authorizing teamId filter" + ) + raise HTTPException( + status_code=403, + detail={"error": "Not authorized to view this team's models"}, + ) + + if user_row is None or team_id not in (user_row.teams or []): + raise HTTPException( + status_code=403, + detail={"error": "Not authorized to view this team's models"}, + ) + + async def _filter_models_by_team_id( all_models: List[Dict[str, Any]], team_id: str, prisma_client: PrismaClient, llm_router: Router, + user_api_key_dict: Optional[UserAPIKeyAuth] = None, ) -> List[Dict[str, Any]]: """ Filter models by team ID. Returns models where: - - direct_access is True, OR - - team_id is in access_via_team_ids - - Also searches config and database for models accessible to the team. + - team_id matches the model's BYOK team_id, OR + - team_id is in access_via_team_ids, OR + - model_id is reachable via team.models / access groups Args: all_models: List of models to filter team_id: Team ID to filter by prisma_client: Prisma client for database queries llm_router: Router instance for config queries + user_api_key_dict: Caller auth context. When provided, the caller must + be a proxy admin or a member of `team_id`; otherwise raises 403. Returns: Filtered list of models """ + if user_api_key_dict is not None: + await _authorize_team_id_query( + team_id=team_id, + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + ) + team_object = await _load_team_object_for_model_filter(team_id, prisma_client) if team_object is None: return [] @@ -11316,26 +11500,30 @@ async def _filter_models_by_team_id( team_object, team_id, prisma_client, llm_router ) - # Filter models based on direct_access or access_via_team_ids - # Models are already enriched with these fields before this function is called + # When filtering by a specific team we want exactly the models that team + # can use: its BYOK rows and the deployments resolved from team.models / + # access groups. `direct_access` describes the viewer's own permissions + # (the admin path sets it on every non-team model) and must NOT widen the + # team's visible set, otherwise selecting a team in the UI still shows + # every public model the admin can call. filtered_models = [] for _model in all_models: model_info = _model.get("model_info", {}) model_id = model_info.get("id", None) - # Include if direct_access is True - if model_info.get("direct_access", False): + # BYOK rows owned by this team are always accessible to it, even if + # they haven't been re-added to team.models for some reason. + if model_info.get("team_id") == team_id: filtered_models.append(_model) continue - # Include if team_id is in access_via_team_ids access_via_team_ids = model_info.get("access_via_team_ids", []) if isinstance(access_via_team_ids, list) and team_id in access_via_team_ids: filtered_models.append(_model) continue - # Also include if model_id is in team_accessible_model_ids (from config/db search) - # This catches models that might not have been enriched with access_via_team_ids yet + # Catches models resolved from team.models / access groups that + # weren't enriched with access_via_team_ids upstream. if model_id and model_id in team_accessible_model_ids: filtered_models.append(_model) @@ -11477,10 +11665,11 @@ async def model_info_v2( all_models, search_total_count = await _apply_search_filter_to_models( all_models=all_models, search=search or "", - page=page, - size=size, prisma_client=prisma_client, proxy_config=proxy_config, + user_api_key_dict=user_api_key_dict, + page=page, + size=size, sort_by=sortBy, ) @@ -11516,6 +11705,7 @@ async def model_info_v2( team_id=teamId.strip(), prisma_client=prisma_client, llm_router=llm_router, + user_api_key_dict=user_api_key_dict, ) # Update search_total_count after teamId filter is applied search_total_count = len(all_models) diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index f86cde87401..8f6f7084a0c 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -94,6 +94,12 @@ ROUTE_ENDPOINT_MAPPING = { "aget_interaction": "/interactions/{interaction_id}", "adelete_interaction": "/interactions/{interaction_id}", "acancel_interaction": "/interactions/{interaction_id}/cancel", + # Google Managed Agents API routes + "acreate_agent": "/v1beta/agents", + "alist_agents": "/v1beta/agents", + "aget_agent": "/v1beta/agents/{name}", + "adelete_agent": "/v1beta/agents/{name}", + "alist_agent_versions": "/v1beta/agents/{name}/versions", # OpenAI Evals API routes "acreate_eval": "/evals", "alist_evals": "/evals", @@ -311,6 +317,11 @@ async def route_request( # noqa: PLR0915 - Complex routing function, refactorin "aget_interaction", "adelete_interaction", "acancel_interaction", + "acreate_agent", + "alist_agents", + "aget_agent", + "adelete_agent", + "alist_agent_versions", "asend_message", "call_mcp_tool", "acancel_batch", @@ -430,7 +441,11 @@ 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 ) @@ -464,6 +479,15 @@ async def route_request( # noqa: PLR0915 - Complex routing function, refactorin "acancel_interaction", ]: return getattr(llm_router, f"{route_type}")(**data) + # Managed Agents API: these don't need model routing + if route_type in [ + "acreate_agent", + "alist_agents", + "aget_agent", + "adelete_agent", + "alist_agent_versions", + ]: + return getattr(llm_router, f"{route_type}")(**data) if route_type in [ "avideo_list", "avideo_status", 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..019f565e5c4 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -30,6 +30,7 @@ from typing import ( List, Literal, Optional, + Set, Tuple, Union, cast, @@ -1578,6 +1579,44 @@ class Router: cancel_interaction, call_type="cancel_interaction" ) + def _initialize_managed_agents_endpoints(self): + """Initialize Google Managed Agents API endpoints (v1beta/agents).""" + from litellm.interactions.agents import acreate as acreate_agent + from litellm.interactions.agents import adelete as adelete_agent + from litellm.interactions.agents import aget as aget_agent + from litellm.interactions.agents import alist as alist_agents + from litellm.interactions.agents import alist_versions as alist_agent_versions + from litellm.interactions.agents import create as create_agent + from litellm.interactions.agents import delete as delete_agent + from litellm.interactions.agents import get as get_agent + from litellm.interactions.agents import list as list_agents + from litellm.interactions.agents import list_versions as list_agent_versions + + self.acreate_agent = self.factory_function( + acreate_agent, call_type="acreate_agent" + ) + self.create_agent = self.factory_function( + create_agent, call_type="create_agent" + ) + self.alist_agents = self.factory_function( + alist_agents, call_type="alist_agents" + ) + self.list_agents = self.factory_function(list_agents, call_type="list_agents") + self.aget_agent = self.factory_function(aget_agent, call_type="aget_agent") + self.get_agent = self.factory_function(get_agent, call_type="get_agent") + self.adelete_agent = self.factory_function( + adelete_agent, call_type="adelete_agent" + ) + self.delete_agent = self.factory_function( + delete_agent, call_type="delete_agent" + ) + self.alist_agent_versions = self.factory_function( + alist_agent_versions, call_type="alist_agent_versions" + ) + self.list_agent_versions = self.factory_function( + list_agent_versions, call_type="list_agent_versions" + ) + def _initialize_specialized_endpoints(self): """Helper to initialize specialized router endpoints (vector store, OCR, search, video, container, skills, interactions).""" self._initialize_vector_store_endpoints() @@ -1590,6 +1629,7 @@ class Router: self._initialize_container_endpoints() self._initialize_skills_endpoints() self._initialize_interactions_endpoints() + self._initialize_managed_agents_endpoints() def initialize_router_endpoints(self): self._initialize_core_endpoints() @@ -5321,6 +5361,16 @@ class Router: "delete_interaction", "acancel_interaction", "cancel_interaction", + "acreate_agent", + "create_agent", + "alist_agents", + "list_agents", + "aget_agent", + "get_agent", + "adelete_agent", + "delete_agent", + "alist_agent_versions", + "list_agent_versions", ] = "assistants", ): """ @@ -5405,6 +5455,27 @@ class Router: return vector_store_file_sync_wrapper + if call_type in ( + "create_agent", + "list_agents", + "get_agent", + "delete_agent", + "list_agent_versions", + ): + + def managed_agents_sync_wrapper( + custom_llm_provider: Optional[str] = None, + client: Optional[Any] = None, + **kwargs, + ): + if custom_llm_provider and "custom_llm_provider" not in kwargs: + kwargs["custom_llm_provider"] = custom_llm_provider + if "custom_llm_provider" not in kwargs: + kwargs["custom_llm_provider"] = "gemini" + return original_function(**kwargs) + + return managed_agents_sync_wrapper + # Handle asynchronous call types async def async_wrapper( custom_llm_provider: Optional[str] = None, @@ -5468,8 +5539,6 @@ class Router: "alist_skills", "aget_skill", "adelete_skill", - "acreate_interaction", - "create_interaction", ): return await self._ageneric_api_call_with_fallbacks( original_function=original_function, @@ -5529,6 +5598,8 @@ class Router: **kwargs, ) elif call_type in ( + "acreate_interaction", + "create_interaction", "aget_interaction", "adelete_interaction", "acancel_interaction", @@ -5538,6 +5609,18 @@ class Router: custom_llm_provider=custom_llm_provider, **kwargs, ) + elif call_type in ( + "acreate_agent", + "alist_agents", + "aget_agent", + "adelete_agent", + "alist_agent_versions", + ): + return await self._init_managed_agents_api_endpoints( + original_function=original_function, + custom_llm_provider=custom_llm_provider, + **kwargs, + ) return async_wrapper @@ -5642,6 +5725,34 @@ class Router: if custom_llm_provider and "custom_llm_provider" not in kwargs: kwargs["custom_llm_provider"] = custom_llm_provider # Default to gemini for interactions API + if "custom_llm_provider" not in kwargs: + kwargs["custom_llm_provider"] = "gemini" + # If the proxy accidentally passed agent name as model, clear it + if kwargs.get("agent") and kwargs.get("model") == kwargs.get("agent"): + kwargs["model"] = None + # Model-based interactions use deployment routing + fallbacks; agent-only calls + # must not enter model-group lookup (agent name is not a LiteLLM deployment). + if kwargs.get("model"): + return await self._ageneric_api_call_with_fallbacks( + original_function=original_function, + **kwargs, + ) + return await original_function(**kwargs) + + async def _init_managed_agents_api_endpoints( + self, + original_function: Callable, + custom_llm_provider: Optional[str] = None, + **kwargs, + ): + """ + Initialize the Managed Agents API endpoints on the router (v1beta/agents). + + CRUD operations for Gemini managed agents don't need model-based routing, + so we call the original function directly with the custom_llm_provider. + """ + if custom_llm_provider and "custom_llm_provider" not in kwargs: + kwargs["custom_llm_provider"] = custom_llm_provider if "custom_llm_provider" not in kwargs: kwargs["custom_llm_provider"] = "gemini" return await original_function(**kwargs) @@ -6957,12 +7068,11 @@ 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 +7100,12 @@ 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 +8254,14 @@ 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 +8306,9 @@ 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 +8334,7 @@ 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 +9361,29 @@ 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 +10272,12 @@ 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 +10311,8 @@ 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 +10536,12 @@ 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 +10710,12 @@ 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 +10746,8 @@ 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 +10867,12 @@ 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 +10912,9 @@ 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 +11004,36 @@ 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/agents.py b/litellm/types/agents.py index efb2e73bfb5..8556b6bac93 100644 --- a/litellm/types/agents.py +++ b/litellm/types/agents.py @@ -228,6 +228,66 @@ class ListAgentsResponse(BaseModel): agents: List[AgentResponse] +class AgentCreateResponse(LiteLLMPydanticObjectBase): + """ + Response from a provider-side agent creation or get call (e.g. Gemini v1beta/agents). + + Gemini returns ``"id"`` as the agent identifier; we surface both ``id`` + (Gemini's value) and ``name`` (the user-supplied name, equal to ``id`` for + Gemini) so callers can use either. All extra fields returned by the + provider (e.g. ``base_agent``, ``system_instruction``, ``base_environment``) + are preserved via extra="allow". + """ + + id: Optional[str] = None + name: Optional[str] = None + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + +class AgentDeleteResult(LiteLLMPydanticObjectBase): + """Result of a provider-side agent deletion (e.g. Gemini DELETE /v1beta/agents/{name}). + + Gemini returns an empty body ``{}`` on success; we synthesise ``name`` and + ``deleted`` so callers always get a consistent response object. + """ + + name: str + deleted: bool = True + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + +class AgentListResponse(LiteLLMPydanticObjectBase): + """Response from listing agents on the provider side (e.g. Gemini GET /v1beta/agents). + + Gemini returns ``{"agents": [{"id": "..."}, ...]}``; each item is kept as + a plain dict so no fields are silently dropped. + """ + + agents: List[Dict[str, Any]] = [] + next_page_token: Optional[str] = None + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + +class AgentVersionsResponse(LiteLLMPydanticObjectBase): + """Response from listing versions of an agent (e.g. Gemini GET /v1beta/agents/{name}/versions). + + Gemini returns ``{"agentVersions": [...]}``; each version has a ``name`` + field of the form ``agents/{agent_id}/versions/{uuid}``. + """ + + agent_versions: List[Dict[str, Any]] = [] + next_page_token: Optional[str] = None + model_config = {"extra": "allow"} + + _hidden_params: dict = PrivateAttr(default_factory=dict) + + class AgentMakePublicResponse(BaseModel): message: str public_agent_groups: List[str] 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/interactions/__init__.py b/litellm/types/interactions/__init__.py index a3acdc4cb1f..0f934fa0152 100644 --- a/litellm/types/interactions/__init__.py +++ b/litellm/types/interactions/__init__.py @@ -37,6 +37,7 @@ from litellm.types.interactions.generated import ( ImageContent, Interaction, InteractionEvent, + InteractionEnvironment, InteractionInput, InteractionsAPIOptionalRequestParams, InteractionsAPIResponse, @@ -115,6 +116,7 @@ __all__ = [ "ResponseModality", "Annotation", # LiteLLM types + "InteractionEnvironment", "InteractionInput", "InteractionsAPIResponse", "InteractionsAPIStreamingResponse", diff --git a/litellm/types/interactions/generated.py b/litellm/types/interactions/generated.py index ed626b0b7c8..2ce6331b448 100644 --- a/litellm/types/interactions/generated.py +++ b/litellm/types/interactions/generated.py @@ -1257,3 +1257,6 @@ class CancelInteractionResult(BaseLiteLLMOpenAIResponseObject): InteractionTool = Tool InteractionToolChoiceConfig = ToolChoiceConfig InteractionsAPIOptionalRequestParams = Dict[str, Any] + +# Agent interaction execution environment +InteractionEnvironment = Union[str, Dict[str, Any]] diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py index 87bf11a9026..b357b64156d 100644 --- a/litellm/types/llms/vertex_ai.py +++ b/litellm/types/llms/vertex_ai.py @@ -14,13 +14,19 @@ from litellm.types.llms.openai import EmbeddingInput GeminiEmbeddingInput = Union[EmbeddingInput, List[List[str]]] -class FunctionResponse(TypedDict): - name: str +class FunctionResponse(TypedDict, total=False): + # `id` correlates this response with the originating `functionCall` part. + # Required by Gemini 3.5+ for strict function-calling response matching. + id: str + name: Required[str] response: Optional[dict] -class FunctionCall(TypedDict): - name: str +class FunctionCall(TypedDict, total=False): + # `id` is returned by Gemini 3.5+ to correlate the corresponding + # `functionResponse`. Older Gemini models omit this field. + id: str + name: Required[str] args: Optional[dict] @@ -45,8 +51,11 @@ class PartType(TypedDict, total=False): media_resolution: Literal["low", "medium", "high"] -class HttpxFunctionCall(TypedDict): - name: str +class HttpxFunctionCall(TypedDict, total=False): + # `id` is returned by Gemini 3.5+ to correlate the corresponding + # `functionResponse`. Older Gemini models omit this field. + id: str + name: Required[str] args: dict 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/model_prices_and_context_window.json b/model_prices_and_context_window.json index 3eabb82093b..27d6a59740f 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -2141,6 +2141,380 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "azure_ai/gpt-5.4": { + "cache_read_input_token_cost": 2.5e-07, + "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_priority": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-2026-03-05": { + "cache_read_input_token_cost": 2.5e-07, + "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_priority": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_above_272k_tokens": 5e-06, + "input_cost_per_token_priority": 5e-06, + "input_cost_per_token_above_272k_tokens_priority": 1e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_272k_tokens": 2.25e-05, + "output_cost_per_token_priority": 3e-05, + "output_cost_per_token_above_272k_tokens_priority": 4.5e-05, + "source": "https://ai.azure.com/catalog/models/gpt-5.4", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro": { + "cache_read_input_token_cost": 3e-06, + "cache_read_input_token_cost_above_272k_tokens": 6e-06, + "cache_read_input_token_cost_priority": 6e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-pro-2026-03-05": { + "cache_read_input_token_cost": 3e-06, + "cache_read_input_token_cost_above_272k_tokens": 6e-06, + "cache_read_input_token_cost_priority": 6e-06, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.2e-05, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_priority": 6e-05, + "input_cost_per_token_above_272k_tokens_priority": 0.00012, + "litellm_provider": "azure_ai", + "max_input_tokens": 1050000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "responses", + "output_cost_per_token": 0.00018, + "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_priority": 0.00036, + "output_cost_per_token_above_272k_tokens_priority": 0.00054, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-pro", + "supported_endpoints": [ + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": false, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": false, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": true + }, + "azure_ai/gpt-5.4-mini": { + "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_above_272k_tokens": 1.5e-07, + "cache_read_input_token_cost_priority": 1.5e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 3e-07, + "input_cost_per_token": 7.5e-07, + "input_cost_per_token_above_272k_tokens": 1.5e-06, + "input_cost_per_token_priority": 1.5e-06, + 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"https://ai.azure.com/catalog/models/gpt-5.4-mini", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, + "azure_ai/gpt-5.4-nano-2026-03-17": { + "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_priority": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_priority": 4e-07, + "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1.25e-06, + "output_cost_per_token_above_272k_tokens": 1.875e-06, + "output_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_above_272k_tokens_priority": 3.75e-06, + "source": "https://ai.azure.com/catalog/models/gpt-5.4-nano", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/batch", + "/v1/responses" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text" + ], + "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_service_tier": true, + "supports_vision": true, + "supports_web_search": true, + "supports_none_reasoning_effort": true, + "supports_xhigh_reasoning_effort": true, + "supports_minimal_reasoning_effort": false + }, "azure_ai/model_router": { "input_cost_per_token": 1.4e-07, "output_cost_per_token": 0, @@ -14583,6 +14957,64 @@ "web_search_billing_unit": "per_query", "supports_service_tier": true }, + "gemini-3.1-flash-lite": { + "cache_read_input_token_cost": 4.5e-08, + "cache_read_input_token_cost_per_audio_token": 9e-08, + "input_cost_per_audio_token": 9e-07, + "input_cost_per_token": 4.5e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 2.7e-06, + "output_cost_per_token": 2.7e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_audio_output": false, + "supports_code_execution": true, + "supports_file_search": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "supports_service_tier": true + }, "deep-research-pro-preview-12-2025": { "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, @@ -15271,6 +15703,64 @@ }, "web_search_billing_unit": "per_query" }, + "vertex_ai/gemini-3.5-flash": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_token": 1.5e-06, + "input_cost_per_audio_token": 1e-06, + "litellm_provider": "vertex_ai", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 9e-06, + "output_cost_per_token": 9e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 2.7e-06, + "input_cost_per_audio_token_priority": 1.8e-06, + "output_cost_per_token_priority": 1.62e-05, + "cache_read_input_token_cost_priority": 2.7e-07, + "supports_service_tier": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "vertex_ai/gemini-3.1-pro-preview": { "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_200k_tokens": 4e-07, @@ -16589,6 +17079,66 @@ "web_search_billing_unit": "per_query", "supports_service_tier": true }, + "gemini/gemini-3.1-flash-lite": { + "cache_read_input_token_cost": 4.5e-08, + "cache_read_input_token_cost_per_audio_token": 9e-08, + "input_cost_per_audio_token": 9e-07, + "input_cost_per_token": 4.5e-07, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 2.7e-06, + "output_cost_per_token": 2.7e-06, + "rpm": 15, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_audio_output": false, + "supports_code_execution": true, + "supports_file_search": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "tpm": 250000, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "supports_service_tier": true + }, "gemini/gemini-3-flash-preview": { "cache_read_input_token_cost": 5e-08, "input_cost_per_audio_token": 1e-06, @@ -16648,6 +17198,67 @@ }, "web_search_billing_unit": "per_query" }, + "gemini/gemini-3.5-flash": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 9e-06, + "output_cost_per_token": 9e-06, + "rpm": 2000, + "source": "https://ai.google.dev/pricing/gemini-3", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "tpm": 800000, + "input_cost_per_token_priority": 2.7e-06, + "input_cost_per_audio_token_priority": 1.8e-06, + "output_cost_per_token_priority": 1.62e-05, + "cache_read_input_token_cost_priority": 2.7e-07, + "supports_service_tier": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini/gemini-3.1-pro-preview": { "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_200k_tokens": 4e-07, @@ -16833,6 +17444,65 @@ }, "web_search_billing_unit": "per_query" }, + "gemini-3.5-flash": { + "cache_read_input_token_cost": 1.5e-07, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 1.5e-06, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 9e-06, + "output_cost_per_token": 9e-06, + "source": "https://ai.google.dev/pricing/gemini-3", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_audio_input": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "input_cost_per_token_priority": 2.7e-06, + "input_cost_per_audio_token_priority": 1.8e-06, + "output_cost_per_token_priority": 1.62e-05, + "cache_read_input_token_cost_priority": 2.7e-07, + "supports_service_tier": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query" + }, "gemini/gemini-2.5-pro-preview-tts": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, @@ -23767,6 +24437,21 @@ "supports_tool_choice": true, "supports_vision": true }, + "mistral/ministral-8b-2512": { + "input_cost_per_token": 1.5e-07, + "litellm_provider": "mistral", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1.5e-07, + "source": "https://mistral.ai/pricing", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "mistral/mistral-tiny": { "input_cost_per_token": 2.5e-07, "litellm_provider": "mistral", @@ -33087,6 +33772,64 @@ }, "web_search_billing_unit": "per_query" }, + "vertex_ai/gemini-3.1-flash-lite": { + "cache_read_input_token_cost": 4.5e-08, + "cache_read_input_token_cost_per_audio_token": 9e-08, + "input_cost_per_audio_token": 9e-07, + "input_cost_per_token": 4.5e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_pdf_size_mb": 30, + "max_tokens": 65536, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 2.7e-06, + "output_cost_per_token": 2.7e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#gemini-models", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_input": true, + "supports_audio_output": false, + "supports_code_execution": true, + "supports_file_search": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_video_input": true, + "supports_vision": true, + "supports_web_search": true, + "supports_native_streaming": true, + "search_context_cost_per_query": { + "search_context_size_low": 0.014, + "search_context_size_medium": 0.014, + "search_context_size_high": 0.014 + }, + "web_search_billing_unit": "per_query", + "supports_service_tier": true + }, "vertex_ai/deep-research-pro-preview-12-2025": { "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, diff --git a/pyproject.toml b/pyproject.toml index a397c10ce66..70681c4ed6c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm" -version = "1.85.0" +version = "1.87.0" description = "Library to easily interface with LLM API providers" readme = "README.md" requires-python = ">=3.10, <3.14" @@ -56,8 +56,8 @@ proxy = [ "azure-identity==1.25.2", "azure-storage-blob==12.28.0", "mcp==1.26.0", - "litellm-proxy-extras==0.4.72", - "litellm-enterprise==0.1.40", + "litellm-proxy-extras==0.4.73", + "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 = "1.87.0" 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/terraform/litellm/README.md b/terraform/litellm/README.md new file mode 100644 index 00000000000..5ca704b96dd --- /dev/null +++ b/terraform/litellm/README.md @@ -0,0 +1,159 @@ +# LiteLLM Terraform stacks + +Two self-contained Terraform root modules that deploy the **componentized** +LiteLLM proxy — the gateway, backend, and UI as three independent containers +(see `helm/litellm/` for the canonical chart with the same split). + +| Stack | Compute | Database (writer + reader) | Cache | Object store | Public entrypoint | +| ------ | ----------- | ---------------------------------- | ----------- | ------------ | ------------------ | +| `aws/` | ECS Fargate | Aurora Postgres (IAM auth) | ElastiCache | S3 | Application LB | +| `gcp/` | Cloud Run | Cloud SQL Postgres (password auth) | Memorystore | GCS | External HTTPS LB | + +Each stack creates its own VPC and managed data stores — drop in a tfvars +file and run `terraform apply`. Both stacks support a typed `proxy_config` +input (mirrors `helm/litellm`'s `gateway.config.proxy_config`) and per-component +extra env vars / secret-manager refs. + +## Components + +The proxy is split into three deployables: + +| Component | Default image | Port | Role | +| --------- | ---------------------------------------- | ---- | -------------------------------------------------------------------- | +| `gateway` | `ghcr.io/berriai/litellm-gateway:main-stable` | 4000 | LLM data plane (`/v1/chat/completions`, `/v1/embeddings`, …) | +| `backend` | `ghcr.io/berriai/litellm-backend:main-stable` | 4001 | Management API (`/key/*`, `/user/*`, `/team/*`, `/model/*`, …) | +| `ui` | `ghcr.io/berriai/litellm-ui:main-stable` | 3000 | Static Next.js dashboard served by nginx | + +The load balancer routes gateway path prefixes (mirrored verbatim from +`gateway/routes/allowlist.py`) to the gateway, UI asset paths (`/`, +`/litellm-asset-prefix/*`, `/_next/*`, `/favicon.ico`) to the UI, and +everything else to the backend. + +## Architecture + +### AWS (`terraform/litellm/aws/`) + +``` + ┌───────────────────────────────────────┐ + │ Public Internet │ + └─────────────────┬─────────────────────┘ + │ HTTP/80 + ┌───────────────▼───────────────┐ + │ Application Load Balancer │ + │ (path-routing listener) │ + └─┬─────────────┬─────────────┬─┘ + │ │ │ + UI assets, / │ /v1/chat, │ /key/* │ + /_next/*, … │ /v1/embed, │ /user/* │ + │ … │ … │ + ┌─────────────▼───┐ ┌──────▼──────┐ ┌───▼──────────────┐ + │ ECS Service │ │ ECS Service │ │ ECS Service │ + │ (ui) │ │ (gateway) │ │ (backend) │ + │ Fargate :3000 │ │ Fargate:4000│ │ Fargate :4001 │ + └─────────────────┘ └──────┬──────┘ └────────┬─────────┘ + │ │ + ┌─── private subnets (one per AZ) ──────────────────────┐ + │ │ + │ ┌────────────────────────┐ ┌────────────────┐ │ + │ │ Aurora Postgres │ │ ElastiCache │ │ + │ │ cluster (IAM auth) │ │ Redis (1 node)│ │ + │ │ ┌───────┐ ┌───────┐ │ └────────────────┘ │ + │ │ │writer │ │reader │ │ │ + │ │ └───────┘ └───────┘ │ ┌────────────────┐ │ + │ └────────────────────────┘ │ S3 bucket │ │ + │ │ (versioned) │ │ + │ ┌────────────────────────┐ └────────────────┘ │ + │ │ Secrets Manager │ │ + │ │ • LITELLM_MASTER_KEY │ ┌────────────────┐ │ + │ │ • DB master password │ │ One-off ECS │ │ + │ │ • user-supplied API │ │ task: prisma │ │ + │ │ keys (referenced) │ │ migrate deploy │ │ + │ └────────────────────────┘ └────────────────┘ │ + │ │ + └─── VPC ───────────────────────────────────────────────┘ + │ NAT gateway in one public subnet + ▼ + egress to LLM providers +``` + +### GCP (`terraform/litellm/gcp/`) + +``` + ┌───────────────────────────────────────┐ + │ Public Internet │ + └─────────────────┬─────────────────────┘ + │ HTTP/80 + ┌───────────────▼───────────────┐ + │ External HTTPS Load Balancer │ + │ (global, URL map routing) │ + └─┬─────────────┬─────────────┬─┘ + │ │ │ + │ Serverless NEGs (one per service) + │ │ │ + ┌─────────────▼───┐ ┌──────▼──────┐ ┌───▼──────────────┐ + │ Cloud Run │ │ Cloud Run │ │ Cloud Run │ + │ (ui) │ │ (gateway) │ │ (backend) │ + │ :3000 │ │ :4000 │ │ :4001 │ + └─────────────────┘ └──────┬──────┘ └────────┬─────────┘ + │ │ + │ Serverless VPC Access connector + ┌─── VPC (private services access range) ──────────────────┐ + │ │ + │ ┌────────────────────────┐ ┌──────────────────┐ │ + │ │ Cloud SQL Postgres │ │ Memorystore │ │ + │ │ ┌───────┐ ┌───────┐ │ │ Redis │ │ + │ │ │writer │ │reader │ │ └──────────────────┘ │ + │ │ └───────┘ └───────┘ │ │ + │ └────────────────────────┘ ┌──────────────────┐ │ + │ │ GCS bucket │ │ + │ ┌────────────────────────┐ │ (versioned) │ │ + │ │ Secret Manager │ └──────────────────┘ │ + │ │ • LITELLM_MASTER_KEY │ │ + │ │ • DB password │ ┌──────────────────┐ │ + │ │ • user-supplied API │ │ Cloud Run Job: │ │ + │ │ keys (referenced) │ │ prisma migrate │ │ + │ └────────────────────────┘ │ deploy │ │ + │ └──────────────────┘ │ + └──────────────────────────────────────────────────────────┘ +``` + +## Images + +Both stacks take per-component image references as variables. The defaults +point at the public `ghcr.io/berriai/litellm-:main-stable` +images, so the stack is runnable end-to-end without pre-flight setup — +pin to a specific tag for production: + +- **AWS** can pull from any registry the task execution role can reach. + The role gets `AmazonECSTaskExecutionRolePolicy` attached, which grants + ECR pull permissions for repositories in the same account. + +- **GCP Cloud Run** can only pull from Artifact Registry or + `gcr.io`-style registries. To use images hosted elsewhere, mirror them + into Artifact Registry first. + +## Migrations + +LiteLLM's proxy runs `prisma migrate deploy` at startup, but on first apply +the gateway/backend can race the empty database. Both stacks expose a +one-off migration task that runs `python litellm/proxy/prisma_migration.py` +against the backend image: + +- AWS: an `aws_ecs_task_definition` (`litellm-migrations`). Run with + `aws ecs run-task` — the command is printed in `terraform output`. +- GCP: a `google_cloud_run_v2_job` (`litellm-migrations`). Run with + `gcloud run jobs execute` — the command is printed in `terraform output`. + +Run the migration job once after the first `terraform apply` and before the +gateway/backend services start serving traffic. + +## What's not included + +- TLS certificates / custom domains. Both stacks expose plain-HTTP load + balancers; bring your own ACM cert (AWS) or managed cert (GCP) and wire + it into the LB resource. +- Remote state backends. Default local state — add an `s3` or `gcs` + backend block to `versions.tf` when graduating to a team environment. +- Observability beyond the cloud provider's defaults (CloudWatch logs on + AWS, Cloud Logging on GCP). Wire your own Prometheus / Datadog / Langfuse + via the `*_extra_env` variables. diff --git a/terraform/litellm/aws/.terraform.lock.hcl b/terraform/litellm/aws/.terraform.lock.hcl new file mode 100644 index 00000000000..30b2a194c4c --- /dev/null +++ b/terraform/litellm/aws/.terraform.lock.hcl @@ -0,0 +1,45 @@ +# This file is maintained automatically by "terraform init". +# Manual edits may be lost in future updates. + +provider "registry.terraform.io/hashicorp/aws" { + version = "5.100.0" + constraints = "~> 5.60" + hashes = [ + "h1:Ijt7pOlB7Tr7maGQIqtsLFbl7pSMIj06TVdkoSBcYOw=", + "zh:054b8dd49f0549c9a7cc27d159e45327b7b65cf404da5e5a20da154b90b8a644", + "zh:0b97bf8d5e03d15d83cc40b0530a1f84b459354939ba6f135a0086c20ebbe6b2", + "zh:1589a2266af699cbd5d80737a0fe02e54ec9cf2ca54e7e00ac51c7359056f274", + "zh:6330766f1d85f01ae6ea90d1b214b8b74cc8c1badc4696b165b36ddd4cc15f7b", + "zh:7c8c2e30d8e55291b86fcb64bdf6c25489d538688545eb48fd74ad622e5d3862", + "zh:99b1003bd9bd32ee323544da897148f46a527f622dc3971af63ea3e251596342", + "zh:9b12af85486a96aedd8d7984b0ff811a4b42e3d88dad1a3fb4c0b580d04fa425", + "zh:9f8b909d3ec50ade83c8062290378b1ec553edef6a447c56dadc01a99f4eaa93", + "zh:aaef921ff9aabaf8b1869a86d692ebd24fbd4e12c21205034bb679b9caf883a2", + "zh:ac882313207aba00dd5a76dbd572a0ddc818bb9cbf5c9d61b28fe30efaec951e", + "zh:bb64e8aff37becab373a1a0cc1080990785304141af42ed6aa3dd4913b000421", + "zh:dfe495f6621df5540d9c92ad40b8067376350b005c637ea6efac5dc15028add4", + "zh:f0ddf0eaf052766cfe09dea8200a946519f653c384ab4336e2a4a64fdd6310e9", + "zh:f1b7e684f4c7ae1eed272b6de7d2049bb87a0275cb04dbb7cda6636f600699c9", + "zh:ff461571e3f233699bf690db319dfe46aec75e58726636a0d97dd9ac6e32fb70", + ] +} + +provider "registry.terraform.io/hashicorp/random" { + version = "3.8.1" + constraints = "~> 3.6" + hashes = [ + "h1:u8AKlWVDTH5r9YLSeswoVEjiY72Rt4/ch7U+61ZDkiQ=", + "zh:08dd03b918c7b55713026037c5400c48af5b9f468f483463321bd18e17b907b4", + "zh:0eee654a5542dc1d41920bbf2419032d6f0d5625b03bd81339e5b33394a3e0ae", + "zh:229665ddf060aa0ed315597908483eee5b818a17d09b6417a0f52fd9405c4f57", + "zh:2469d2e48f28076254a2a3fc327f184914566d9e40c5780b8d96ebf7205f8bc0", + "zh:37d7eb334d9561f335e748280f5535a384a88675af9a9eac439d4cfd663bcb66", + "zh:741101426a2f2c52dee37122f0f4a2f2d6af6d852cb1db634480a86398fa3511", + "zh:78d5eefdd9e494defcb3c68d282b8f96630502cac21d1ea161f53cfe9bb483b3", + "zh:a902473f08ef8df62cfe6116bd6c157070a93f66622384300de235a533e9d4a9", + "zh:b85c511a23e57a2147355932b3b6dce2a11e856b941165793a0c3d7578d94d05", + "zh:c5172226d18eaac95b1daac80172287b69d4ce32750c82ad77fa0768be4ea4b8", + "zh:dab4434dba34aad569b0bc243c2d3f3ff86dd7740def373f2a49816bd2ff819b", + "zh:f49fd62aa8c5525a5c17abd51e27ca5e213881d58882fd42fec4a545b53c9699", + ] +} diff --git a/terraform/litellm/aws/README.md b/terraform/litellm/aws/README.md new file mode 100644 index 00000000000..8638ea800ec --- /dev/null +++ b/terraform/litellm/aws/README.md @@ -0,0 +1,254 @@ +# LiteLLM on AWS (ECS Fargate) + +Deploys the componentized LiteLLM proxy on AWS: + +- **VPC** with public + private subnets across the AZs you pass in, one NAT gateway +- **Aurora Postgres** cluster — one writer instance + one reader instance, **IAM database authentication enabled** +- **ElastiCache Redis** (private, replication group with multi-AZ failover and at-rest + in-transit encryption) for caching + rate limiting +- **S3 bucket** (private, versioned, SSE-S3) — exposed to gateway + backend as `S3_BUCKET_NAME` / `S3_REGION_NAME` for cache backend, request log archival, and `/v1/files` storage +- **Secrets Manager** entries for `LITELLM_MASTER_KEY` (auto-generated, `sk-…`) and the Aurora master password (bootstrap-only) +- **ECS Fargate cluster** running three services — `gateway`, `backend`, `ui` +- **Application Load Balancer** (public, HTTP/80) with path-based routing: + - LLM data-plane prefixes (`/v1/chat/*`, `/v1/embeddings`, …) → `gateway` + - UI assets (`/`, `/_next/*`, `/litellm-asset-prefix/*`, …) → `ui` + - Everything else (management API: `/key/*`, `/user/*`, …) → `backend` +- **One-off migration task** (`litellm-migrations`) that runs `prisma migrate deploy` from the dedicated `ghcr.io/berriai/litellm-migrations` image + +## Aurora + IAM auth + +The cluster runs with `iam_database_authentication_enabled = true`. Enabling +that on the cluster doesn't by itself let any Postgres user log in with an IAM +token — you also need to `CREATE USER ... GRANT rds_iam` once. `bootstrap.tf` +does this automatically during `terraform apply` via a one-shot Fargate task +(`postgres:16-alpine` running the bootstrap SQL with the master password from +Secrets Manager). The SQL is idempotent, so re-applies are safe. + +The same apply also runs the prisma schema migration via the existing +`litellm-migrations` task definition, and the gateway/backend services +`depends_on` the migration so they don't start until the schema is in place. + +At runtime, the proxy assembles `DATABASE_URL` from `DATABASE_HOST/PORT/USER/NAME` +plus a short-lived IAM token — see `litellm/proxy/auth/rds_iam_token.py`. The +task role has `rds-db:connect` scoped to the IAM-authed user on the cluster. + +**Break-glass.** If you need to run the bootstrap or migration by hand (e.g., +to re-apply against an externally provisioned cluster), `db_bootstrap_sql` and +`migration_run_command` are still exposed as outputs. + +**Prerequisite.** `terraform apply` shells out to `aws ecs run-task` / +`aws ecs wait` in `local-exec` provisioners, so the machine running terraform +needs the `aws` CLI installed and authenticated. + +## Configuring the proxy + +### `proxy_config` (preferred) + +Mirrors the helm chart's `gateway.config.proxy_config`. The map is YAML-encoded +and base64-passed to gateway, backend, and the migration task; each container +decodes it to `/tmp/litellm-config.yaml` at startup and sets `CONFIG_FILE_PATH` +to match. + +```hcl +proxy_config = { + model_list = [ + { + model_name = "gpt-4o" + litellm_params = { + model = "openai/gpt-4o" + api_key = "os.environ/OPENAI_API_KEY" + } + }, + ] + general_settings = { + master_key = "os.environ/LITELLM_MASTER_KEY" + database_url = "os.environ/DATABASE_URL" + } +} +``` + +LiteLLM resolves `os.environ/` references in the YAML against the +container's environment. That means provider API keys belong in +`*_extra_secrets` (next section), and your YAML just references them by name. + +### Extra env vars + +Non-sensitive plaintext (feature flags, observability hosts, etc.): + +```hcl +gateway_extra_env = { + LANGFUSE_HOST = "https://us.cloud.langfuse.com" +} +backend_extra_env = { + STORE_MODEL_IN_DB = "True" +} +``` + +### Extra secrets (API keys) + +Sensitive values — provider API keys, third-party tokens — live in **existing +Secrets Manager secrets**. Reference them by ARN: + +```hcl +gateway_extra_secrets = { + OPENAI_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:openai-api-key-AbCdEf" + ANTHROPIC_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:anthropic-api-key-GhIjKl" +} +``` + +What happens under the hood: +- The execution role auto-gains `secretsmanager:GetSecretValue` on every ARN + listed here. +- ECS resolves each secret at task launch and injects its value into the + container as the env var named on the left. +- The `proxy_config` YAML references the resulting env var via + `os.environ/OPENAI_API_KEY`. + +To pluck a single field out of a JSON secret, use ECS's `:fieldName::` suffix: + +```hcl +gateway_extra_secrets = { + OPENAI_API_KEY = "arn:…:secret:provider-keys-AbCdEf:openai_api_key::" +} +``` + +To create the secret beforehand: + +```bash +aws secretsmanager create-secret \ + --name openai-api-key \ + --secret-string "sk-proj-..." +``` + +## Tenant deployment + +Every resource the stack creates is named `${tenant}-litellm-${env}` (or +that plus a per-resource suffix), so multiple tenants and multiple +environments coexist in the same account as long as the `(tenant, env)` +pair differs: + +| `tenant` | `env` | Example resource name | +| -------- | ------- | ---------------------------------- | +| `acme` | `stage` | `acme-litellm-stage-gateway` | +| `acme` | `prod` | `acme-litellm-prod-master-key` | +| `globex` | `dev` | `globex-litellm-dev-license` | + +For a per-tenant instance, the only inputs that change are the tenant +slug, env, and the two pre-issued secrets: + +```bash +export TF_VAR_litellm_master_key="sk-..." # the tenant's master key +export TF_VAR_litellm_license="lic-..." # their LITELLM_LICENSE + +terraform apply \ + -var "region=us-west-2" \ + -var 'azs=["us-west-2a","us-west-2b"]' \ + -var "tenant=acme" \ + -var "env=stage" +``` + +Both `litellm_master_key` and `litellm_license` are optional: +- Omit `litellm_master_key` → the stack auto-generates a random `sk-…` + value (trial/dev path). +- Omit `litellm_license` → no license secret is created and gateway/ + backend run without `LITELLM_LICENSE` (OSS-only). + +Use `TF_VAR_*` env vars rather than tfvars files for these — values +written to a tfvars file end up in `terraform.tfstate` and any committed +example files. + +## Quick start + +```bash +cd terraform/litellm/aws +cp terraform.tfvars.example terraform.tfvars +# Edit: region, tenant, env, azs, *_image, proxy_config, gateway_extra_secrets. + +terraform init +terraform apply +``` + +That single apply provisions everything, runs the DB user bootstrap, runs the +schema migration, and only then starts the gateway/backend services. When it +returns, the stack is serving traffic. + +```bash +terraform output alb_url +# UI login: admin / +aws secretsmanager get-secret-value \ + --secret-id "$(terraform output -raw master_key_secret_arn)" \ + --query SecretString --output text +``` + +## Image pulls + +The defaults pull from `ghcr.io/berriai/litellm-:v1.86.0-dev`, +which is anonymous-readable. There are four images: `litellm-gateway`, +`litellm-backend`, `litellm-ui`, and `litellm-migrations` (slim image used +only by the one-off migration task — runs `prisma migrate deploy` against +the writer DB and exits). Bump them together when bumping LiteLLM. To pull +from a private registry: + +- **ECR (same account)**: the execution role already has + `AmazonECSTaskExecutionRolePolicy`, which grants ECR pull for repos in + the same account. No extra config needed. +- **ECR (cross-account)**: attach a policy to the execution role allowing + `ecr:GetAuthorizationToken` + `ecr:BatchGetImage` on the foreign repo + ARNs. +- **Other private registries** (GHCR with a PAT, Docker Hub, …): create a + secret holding `{"auths":{"":{"auth":""}}}` + in Secrets Manager and set `repositoryCredentials.credentialsParameter` + on the task def container — extend `ecs.tf` accordingly. + +## TLS + +`terraform plan` refuses to provision an HTTP-only ALB by default — TLS +is the supported posture. Two paths: + +**Production / staging — provide an ACM certificate:** + +1. Create or import an ACM cert in `var.region` covering the DNS name you + plan to point at the ALB. +2. Set `acm_certificate_arn = "arn:aws:acm:..."` in tfvars and apply. + +Result: a 443 listener carries the path-routing rules; the 80 listener +serves a permanent 301 redirect to HTTPS, so HTTP clients are +automatically upgraded. + +**Trial / dev — explicitly opt into HTTP-only:** + +Set `allow_plaintext_alb = true` in tfvars. Without this flag, plan fails +with a clear error pointing at the precondition. Intended for short-lived +trial / dev stacks only. + +## Storage and database retention + +Three opt-in tripwires guard against accidental data loss on +`terraform destroy`: + +- **`skip_final_snapshot`** (Aurora; default `false`) — destroying the + cluster takes a `-final-` snapshot first. +- **`s3_force_destroy`** (S3 bucket holding request log archives, + `/v1/files` content, and the S3 cache backend; default `false`) — + `terraform destroy` against a non-empty bucket fails. + +Flip either to `true` only for ephemeral / CI stacks where you accept +losing the contents. + +## Files + +| File | What's in it | +| ----------------- | --------------------------------------------------------------------- | +| `versions.tf` | Terraform + provider version constraints | +| `providers.tf` | AWS provider (region + default tags) | +| `variables.tf` | All input variables | +| `locals.tf` | Path-prefix lists for ALB routing (mirror of `helm/.../ingress.yaml`) | +| `network.tf` | VPC, subnets, IGW, NAT, route tables, security groups | +| `secrets.tf` | Secrets Manager entries + random passwords | +| `rds.tf` | Aurora Postgres cluster + writer / reader instances | +| `redis.tf` | ElastiCache Redis | +| `s3.tf` | S3 bucket + task-role policy scoped to it | +| `iam.tf` | Task execution + task roles, including `rds-db:connect` | +| `ecs.tf` | ECS cluster, task definitions, services for the three components | +| `alb.tf` | ALB, listener, target groups, path-routing rules | +| `migrations.tf` | One-off migration task definition | +| `outputs.tf` | DNS name, secret ARN, bootstrap SQL, migration `run-task` command | diff --git a/terraform/litellm/aws/alb.tf b/terraform/litellm/aws/alb.tf new file mode 100644 index 00000000000..de0d9c2310f --- /dev/null +++ b/terraform/litellm/aws/alb.tf @@ -0,0 +1,179 @@ +resource "aws_lb" "this" { + name = local.name + load_balancer_type = "application" + internal = false + security_groups = [aws_security_group.alb.id] + subnets = aws_subnet.public[*].id + + idle_timeout = 120 +} + +locals { + # When an ACM cert ARN is provided we provision a 443 listener carrying + # the path-routing rules and downgrade the 80 listener to a redirect. + tls_enabled = var.acm_certificate_arn != "" + rules_listener_arn = local.tls_enabled ? aws_lb_listener.https[0].arn : aws_lb_listener.http.arn +} + +# Target groups — one per component. IP target type because Fargate tasks +# are addressed by ENI IP, not instance. + +resource "aws_lb_target_group" "gateway" { + name = "${local.name}-gateway" + port = 4000 + protocol = "HTTP" + target_type = "ip" + vpc_id = aws_vpc.this.id + + health_check { + path = "/health/readiness" + matcher = "200-299" + interval = 30 + timeout = 10 + healthy_threshold = 2 + unhealthy_threshold = 3 + } + + deregistration_delay = 30 +} + +resource "aws_lb_target_group" "backend" { + name = "${local.name}-backend" + port = 4001 + protocol = "HTTP" + target_type = "ip" + vpc_id = aws_vpc.this.id + + health_check { + path = "/health/readiness" + matcher = "200-299" + interval = 30 + timeout = 10 + healthy_threshold = 2 + unhealthy_threshold = 3 + } + + deregistration_delay = 30 +} + +resource "aws_lb_target_group" "ui" { + name = "${local.name}-ui" + port = 3000 + protocol = "HTTP" + target_type = "ip" + vpc_id = aws_vpc.this.id + + health_check { + path = "/healthz" + matcher = "200-299" + interval = 30 + timeout = 5 + healthy_threshold = 2 + unhealthy_threshold = 3 + } + + deregistration_delay = 30 +} + +# HTTP listener. When TLS is enabled this only serves a permanent +# 301 redirect to HTTPS; otherwise it carries the path-routing rules +# (default → backend). +resource "aws_lb_listener" "http" { + load_balancer_arn = aws_lb.this.arn + port = 80 + protocol = "HTTP" + + default_action { + type = local.tls_enabled ? "redirect" : "forward" + + dynamic "redirect" { + for_each = local.tls_enabled ? [1] : [] + content { + port = "443" + protocol = "HTTPS" + status_code = "HTTP_301" + } + } + + target_group_arn = local.tls_enabled ? null : aws_lb_target_group.backend.arn + } + + # Default-deny on the HTTP-only path: TLS is the supported posture. + # Operators must either supply an ACM cert or explicitly opt in. + lifecycle { + precondition { + condition = local.tls_enabled || var.allow_plaintext_alb + error_message = "ALB has no HTTPS listener. Either set `acm_certificate_arn` to enable TLS, or set `allow_plaintext_alb = true` to opt into HTTP-only (trial / dev only)." + } + } +} + +# HTTPS listener. Only created when an ACM cert ARN is supplied — terminates +# TLS and carries the same default + path-routing rules. +resource "aws_lb_listener" "https" { + count = local.tls_enabled ? 1 : 0 + load_balancer_arn = aws_lb.this.arn + port = 443 + protocol = "HTTPS" + ssl_policy = "ELBSecurityPolicy-TLS13-1-2-2021-06" + certificate_arn = var.acm_certificate_arn + + default_action { + type = "forward" + target_group_arn = aws_lb_target_group.backend.arn + } +} + +# UI exact paths (/, /favicon.ico, /ui) — priority 10. +resource "aws_lb_listener_rule" "ui_exact" { + listener_arn = local.rules_listener_arn + priority = 10 + + action { + type = "forward" + target_group_arn = aws_lb_target_group.ui.arn + } + + condition { + path_pattern { + values = local.ui_exact_paths + } + } +} + +# UI prefix paths (/_next/*, /litellm-asset-prefix/*, /assets/*, /ui/*) — priority 20. +resource "aws_lb_listener_rule" "ui_prefix" { + listener_arn = local.rules_listener_arn + priority = 20 + + action { + type = "forward" + target_group_arn = aws_lb_target_group.ui.arn + } + + condition { + path_pattern { + values = local.ui_path_prefixes + } + } +} + +# Gateway prefix rules — one per chunk-of-5 because ALB caps a path-pattern +# condition at 5 values. Priorities 100..(100 + N). +resource "aws_lb_listener_rule" "gateway" { + for_each = { for idx, chunk in local.gateway_path_chunks : idx => chunk } + + listener_arn = local.rules_listener_arn + priority = 100 + tonumber(each.key) + + action { + type = "forward" + target_group_arn = aws_lb_target_group.gateway.arn + } + + condition { + path_pattern { + values = each.value + } + } +} diff --git a/terraform/litellm/aws/autoscaling.tf b/terraform/litellm/aws/autoscaling.tf new file mode 100644 index 00000000000..71b6c24fac7 --- /dev/null +++ b/terraform/litellm/aws/autoscaling.tf @@ -0,0 +1,105 @@ +# Application Auto Scaling for the three ECS services. Mirrors the HPA values +# baked into the helm chart at helm/litellm/values.yaml: +# +# gateway: 1-10 replicas, target 70% CPU + 80% memory +# backend: 1-4 replicas, target 70% CPU +# ui: 1-3 replicas, target 80% CPU (off by default; nginx static export) +# +# Each service gets a scalable target plus one target-tracking policy per metric. +# When autoscaling is disabled (count=0) the resources collapse cleanly out of +# the plan; the service's desired_count from ecs.tf stays in effect. + +# ---------- Gateway ---------- +resource "aws_appautoscaling_target" "gateway" { + count = var.gateway_autoscaling_enabled ? 1 : 0 + service_namespace = "ecs" + resource_id = "service/${aws_ecs_cluster.this.name}/${aws_ecs_service.gateway.name}" + scalable_dimension = "ecs:service:DesiredCount" + min_capacity = var.gateway_min_capacity + max_capacity = var.gateway_max_capacity +} + +resource "aws_appautoscaling_policy" "gateway_cpu" { + count = var.gateway_autoscaling_enabled ? 1 : 0 + name = "${local.name}-gateway-cpu" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.gateway[0].service_namespace + resource_id = aws_appautoscaling_target.gateway[0].resource_id + scalable_dimension = aws_appautoscaling_target.gateway[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageCPUUtilization" + } + target_value = var.gateway_cpu_target + } +} + +resource "aws_appautoscaling_policy" "gateway_memory" { + # Memory policy is optional; set gateway_memory_target = 0 to omit it. + count = var.gateway_autoscaling_enabled && var.gateway_memory_target > 0 ? 1 : 0 + name = "${local.name}-gateway-memory" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.gateway[0].service_namespace + resource_id = aws_appautoscaling_target.gateway[0].resource_id + scalable_dimension = aws_appautoscaling_target.gateway[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageMemoryUtilization" + } + target_value = var.gateway_memory_target + } +} + +# ---------- Backend ---------- +resource "aws_appautoscaling_target" "backend" { + count = var.backend_autoscaling_enabled ? 1 : 0 + service_namespace = "ecs" + resource_id = "service/${aws_ecs_cluster.this.name}/${aws_ecs_service.backend.name}" + scalable_dimension = "ecs:service:DesiredCount" + min_capacity = var.backend_min_capacity + max_capacity = var.backend_max_capacity +} + +resource "aws_appautoscaling_policy" "backend_cpu" { + count = var.backend_autoscaling_enabled ? 1 : 0 + name = "${local.name}-backend-cpu" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.backend[0].service_namespace + resource_id = aws_appautoscaling_target.backend[0].resource_id + scalable_dimension = aws_appautoscaling_target.backend[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageCPUUtilization" + } + target_value = var.backend_cpu_target + } +} + +# ---------- UI ---------- +resource "aws_appautoscaling_target" "ui" { + count = var.ui_autoscaling_enabled ? 1 : 0 + service_namespace = "ecs" + resource_id = "service/${aws_ecs_cluster.this.name}/${aws_ecs_service.ui.name}" + scalable_dimension = "ecs:service:DesiredCount" + min_capacity = var.ui_min_capacity + max_capacity = var.ui_max_capacity +} + +resource "aws_appautoscaling_policy" "ui_cpu" { + count = var.ui_autoscaling_enabled ? 1 : 0 + name = "${local.name}-ui-cpu" + policy_type = "TargetTrackingScaling" + service_namespace = aws_appautoscaling_target.ui[0].service_namespace + resource_id = aws_appautoscaling_target.ui[0].resource_id + scalable_dimension = aws_appautoscaling_target.ui[0].scalable_dimension + + target_tracking_scaling_policy_configuration { + predefined_metric_specification { + predefined_metric_type = "ECSServiceAverageCPUUtilization" + } + target_value = var.ui_cpu_target + } +} diff --git a/terraform/litellm/aws/bootstrap.tf b/terraform/litellm/aws/bootstrap.tf new file mode 100644 index 00000000000..e9a56dedbb5 --- /dev/null +++ b/terraform/litellm/aws/bootstrap.tf @@ -0,0 +1,185 @@ +# Auto-runs the two manual steps that used to follow `terraform apply`: +# +# 1. Create the IAM-authed Postgres user (litellm_app) — uses the postgres:16 +# image with the master password from Secrets Manager. +# 2. Run prisma migrate deploy — reuses the existing aws_ecs_task_definition +# .migrations task def from migrations.tf. +# +# Both are invoked via `terraform_data` provisioners. Gateway/backend services +# in ecs.tf depend on `terraform_data.migration`, so on a fresh apply they +# don't start until the schema is in place — no crash-loop window. +# +# Triggers: +# - bootstrap_db re-runs if the Aurora cluster is recreated, or if the +# bootstrap task definition (image/SQL) changes. +# - migration re-runs if the migration task def revision changes (e.g., new +# backend image with new prisma migration files) or if bootstrap re-ran. +# +# Requires `aws` CLI on the machine running terraform. For laptop usage that's +# fine; for CI/CD the runner image needs `aws`. + +# ---------- IAM ---------- +# Execution role can already read the runtime secrets (master_key, user-provided +# extras — see iam.tf). The DB master password lives in a separate secret used +# only here, so we grant access in an additive policy. +resource "aws_iam_policy" "bootstrap_secrets" { + name = "${local.name}-bootstrap-secrets-access" + policy = jsonencode({ + Version = "2012-10-17" + Statement = [{ + Effect = "Allow" + Action = ["secretsmanager:GetSecretValue"] + Resource = [aws_secretsmanager_secret.db_master_password.arn] + }] + }) +} + +resource "aws_iam_role_policy_attachment" "task_execution_bootstrap_secrets" { + role = aws_iam_role.task_execution.name + policy_arn = aws_iam_policy.bootstrap_secrets.arn +} + +# ---------- Bootstrap task def ---------- +resource "aws_cloudwatch_log_group" "bootstrap_db" { + name = "/ecs/${local.name}/bootstrap-db" + retention_in_days = var.log_retention_days +} + +locals { + # Idempotent: CREATE USER is wrapped in DO/EXCEPTION; GRANTs are + # idempotent by definition (re-granting is a no-op). Safe to re-run on + # any subsequent apply. + bootstrap_sql = <<-SQL + DO $$ + BEGIN + CREATE USER ${var.db_username}; + EXCEPTION WHEN duplicate_object THEN NULL; + END $$; + GRANT rds_iam TO ${var.db_username}; + GRANT ALL PRIVILEGES ON DATABASE ${var.db_name} TO ${var.db_username}; + GRANT ALL ON SCHEMA public TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON TABLES TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON SEQUENCES TO ${var.db_username}; + SQL +} + +resource "aws_ecs_task_definition" "bootstrap_db" { + family = "${local.name}-bootstrap-db" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = 256 + memory = 512 + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([{ + name = "psql" + image = "postgres:16-alpine" + essential = true + + environment = [ + { name = "PGHOST", value = aws_rds_cluster.this.endpoint }, + { name = "PGPORT", value = tostring(aws_rds_cluster.this.port) }, + { name = "PGUSER", value = var.db_master_username }, + { name = "PGDATABASE", value = var.db_name }, + { name = "BOOTSTRAP_SQL", value = local.bootstrap_sql }, + ] + secrets = [ + # `:password::` extracts the password field out of the JSON secret. + { name = "PGPASSWORD", valueFrom = "${aws_secretsmanager_secret.db_master_password.arn}:password::" }, + ] + + entryPoint = ["sh", "-c"] + command = ["echo \"$BOOTSTRAP_SQL\" | psql -v ON_ERROR_STOP=1"] + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.bootstrap_db.name + awslogs-region = var.region + awslogs-stream-prefix = "bootstrap" + } + } + }]) +} + +# ---------- Bootstrap trigger ---------- +resource "terraform_data" "bootstrap_db" { + triggers_replace = { + cluster_resource_id = aws_rds_cluster.this.cluster_resource_id + task_def_revision = aws_ecs_task_definition.bootstrap_db.revision + } + + provisioner "local-exec" { + interpreter = ["bash", "-c"] + environment = { + CLUSTER = aws_ecs_cluster.this.name + TASK_DEF = aws_ecs_task_definition.bootstrap_db.arn + SUBNETS = join(",", aws_subnet.private[*].id) + SG = aws_security_group.tasks.id + REGION = var.region + LOG_GRP = aws_cloudwatch_log_group.bootstrap_db.name + } + command = <<-EOT + set -euo pipefail + task_arn=$(aws ecs run-task --region "$REGION" --cluster "$CLUSTER" \ + --launch-type FARGATE --task-definition "$TASK_DEF" \ + --network-configuration "awsvpcConfiguration={subnets=[$SUBNETS],securityGroups=[$SG],assignPublicIp=DISABLED}" \ + --query 'tasks[0].taskArn' --output text) + echo "bootstrap task: $task_arn" + aws ecs wait tasks-stopped --region "$REGION" --cluster "$CLUSTER" --tasks "$task_arn" + task_id=$(echo "$task_arn" | awk -F/ '{print $NF}') + exit_code=$(aws ecs describe-tasks --region "$REGION" --cluster "$CLUSTER" --tasks "$task_id" \ + --query 'tasks[0].containers[0].exitCode' --output text) + if [ "$exit_code" != "0" ]; then + echo "Bootstrap failed (exit=$exit_code). Logs: $LOG_GRP" >&2 + exit 1 + fi + EOT + } + + depends_on = [ + aws_rds_cluster_instance.writer, + aws_iam_role_policy_attachment.task_execution_bootstrap_secrets, + ] +} + +# ---------- Migration trigger ---------- +# Reuses the task definition from migrations.tf — this resource just invokes +# it and waits. +resource "terraform_data" "migration" { + triggers_replace = { + task_def_revision = aws_ecs_task_definition.migrations.revision + bootstrap_id = terraform_data.bootstrap_db.id + } + + provisioner "local-exec" { + interpreter = ["bash", "-c"] + environment = { + CLUSTER = aws_ecs_cluster.this.name + TASK_DEF = aws_ecs_task_definition.migrations.arn + SUBNETS = join(",", aws_subnet.private[*].id) + SG = aws_security_group.tasks.id + REGION = var.region + LOG_GRP = aws_cloudwatch_log_group.migrations.name + } + command = <<-EOT + set -euo pipefail + task_arn=$(aws ecs run-task --region "$REGION" --cluster "$CLUSTER" \ + --launch-type FARGATE --task-definition "$TASK_DEF" \ + --network-configuration "awsvpcConfiguration={subnets=[$SUBNETS],securityGroups=[$SG],assignPublicIp=DISABLED}" \ + --query 'tasks[0].taskArn' --output text) + echo "migration task: $task_arn" + aws ecs wait tasks-stopped --region "$REGION" --cluster "$CLUSTER" --tasks "$task_arn" + task_id=$(echo "$task_arn" | awk -F/ '{print $NF}') + exit_code=$(aws ecs describe-tasks --region "$REGION" --cluster "$CLUSTER" --tasks "$task_id" \ + --query 'tasks[0].containers[0].exitCode' --output text) + if [ "$exit_code" != "0" ]; then + echo "Migration failed (exit=$exit_code). Logs: $LOG_GRP" >&2 + exit 1 + fi + EOT + } + + depends_on = [terraform_data.bootstrap_db] +} diff --git a/terraform/litellm/aws/ecs.tf b/terraform/litellm/aws/ecs.tf new file mode 100644 index 00000000000..a6d2350c681 --- /dev/null +++ b/terraform/litellm/aws/ecs.tf @@ -0,0 +1,347 @@ +resource "aws_ecs_cluster" "this" { + name = local.name + + setting { + name = "containerInsights" + value = "enabled" + } +} + +resource "aws_cloudwatch_log_group" "gateway" { + name = "/ecs/${local.name}/gateway" + retention_in_days = var.log_retention_days +} + +resource "aws_cloudwatch_log_group" "backend" { + name = "/ecs/${local.name}/backend" + retention_in_days = var.log_retention_days +} + +resource "aws_cloudwatch_log_group" "ui" { + name = "/ecs/${local.name}/ui" + retention_in_days = var.log_retention_days +} + +resource "aws_cloudwatch_log_group" "migrations" { + name = "/ecs/${local.name}/migrations" + retention_in_days = var.log_retention_days +} + +# Shared env block fed to gateway, backend, and the migration task. Mirrors +# the helm chart's `litellm.serverEnv` helper on the IAM-auth branch: +# DATABASE_URL is assembled at runtime by +# litellm/proxy/auth/rds_iam_token.py::init_iam_db_url_from_env from +# HOST/PORT/USER/NAME plus an IAM-signed token, so no DB password is needed +# in the task definition. +locals { + shared_env = [ + { name = "IAM_TOKEN_DB_AUTH", value = "true" }, + { name = "DATABASE_HOST", value = aws_rds_cluster.this.endpoint }, + { name = "DATABASE_PORT", value = tostring(aws_rds_cluster.this.port) }, + { name = "DATABASE_USER", value = var.db_username }, + { name = "DATABASE_NAME", value = var.db_name }, + { name = "DATABASE_HOST_READ_REPLICA", value = aws_rds_cluster.this.reader_endpoint }, + { name = "DATABASE_PORT_READ_REPLICA", value = tostring(aws_rds_cluster.this.port) }, + { name = "REDIS_HOST", value = aws_elasticache_replication_group.this.primary_endpoint_address }, + { name = "REDIS_PORT", value = tostring(aws_elasticache_replication_group.this.port) }, + # transit_encryption_enabled = true on the replication group means the + # proxy must connect via rediss://. _redis.get_redis_url_from_environment + # honors REDIS_SSL to flip the scheme. + { name = "REDIS_SSL", value = "true" }, + # S3 bucket — referenced from proxy_config via os.environ/S3_BUCKET_NAME + # (e.g. cache backend, request log archival, /files passthrough). + { name = "S3_BUCKET_NAME", value = aws_s3_bucket.this.bucket }, + { name = "S3_REGION_NAME", value = var.region }, + # boto3 inside generate_iam_auth_token reads AWS_REGION_NAME first, then + # AWS_REGION. Set both for compatibility. + { name = "AWS_REGION", value = var.region }, + { name = "AWS_REGION_NAME", value = var.region }, + ] + + shared_secrets = concat( + [ + { name = "LITELLM_MASTER_KEY", valueFrom = aws_secretsmanager_secret.master_key.arn }, + ], + var.litellm_license == "" ? [] : [ + { name = "LITELLM_LICENSE", valueFrom = aws_secretsmanager_secret.license[0].arn }, + ], + ) + + # Backend-only managed secrets. UI_PASSWORD is consumed by the management + # API (UI login flow) and has no use on the gateway data plane. + backend_managed_secrets = var.ui_password == "" ? [] : [ + { name = "UI_PASSWORD", valueFrom = aws_secretsmanager_secret.ui_password[0].arn }, + ] + + gateway_extra_env_list = [ + for k, v in var.gateway_extra_env : { name = k, value = v } + ] + backend_extra_env_list = [ + for k, v in var.backend_extra_env : { name = k, value = v } + ] + + backend_default_env = [ + { name = "STORE_MODEL_IN_DB", value = "true" }, + ] + gateway_extra_secrets_list = [ + for k, v in var.gateway_extra_secrets : { name = k, valueFrom = v } + ] + backend_extra_secrets_list = [ + for k, v in var.backend_extra_secrets : { name = k, valueFrom = v } + ] + + # Mirrors the helm chart's gateway.config.create / configmap pattern. + # ECS Fargate has no ConfigMap analogue, so we pass the YAML as a + # base64-encoded env var and decode it at container start via a tiny + # python shim that prepends the image's normal uvicorn entrypoint. + proxy_config_enabled = length(keys(var.proxy_config)) > 0 + proxy_config_b64 = local.proxy_config_enabled ? base64encode(yamlencode(var.proxy_config)) : "" + + proxy_config_env = local.proxy_config_enabled ? [ + { name = "LITELLM_PROXY_CONFIG_B64", value = local.proxy_config_b64 }, + { name = "CONFIG_FILE_PATH", value = "/tmp/litellm-config.yaml" }, + ] : [] + + # Gateway always needs --workers wired in (no NUM_WORKERS env var support + # in the image entrypoint). When proxy_config is enabled we also have to + # decode the base64 config first, so the command goes through `sh -c`; + # otherwise we keep the image's ENTRYPOINT and only override `command`. + gateway_uvicorn_args = "--host 0.0.0.0 --port 4000 --workers ${var.gateway_num_workers}" + backend_uvicorn_args = "--host 0.0.0.0 --port 4001" + + gateway_proxy_overrides = local.proxy_config_enabled ? { + entryPoint = ["sh", "-c"] + command = [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['CONFIG_FILE_PATH']).write_bytes(base64.b64decode(os.environ['LITELLM_PROXY_CONFIG_B64']))\" && exec uvicorn gateway.main:app ${local.gateway_uvicorn_args}" + ] + } : { + # Mirror the image's ENTRYPOINT so we can append --workers via command. + entryPoint = ["uvicorn", "gateway.main:app"] + command = split(" ", local.gateway_uvicorn_args) + } + + backend_proxy_overrides = local.proxy_config_enabled ? { + entryPoint = ["sh", "-c"] + command = [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['CONFIG_FILE_PATH']).write_bytes(base64.b64decode(os.environ['LITELLM_PROXY_CONFIG_B64']))\" && exec uvicorn backend.main:app ${local.backend_uvicorn_args}" + ] + } : {} +} + +# ---------- Gateway ---------- +resource "aws_ecs_task_definition" "gateway" { + family = "${local.name}-gateway" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = var.gateway_cpu + memory = var.gateway_memory + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([ + merge( + { + name = "gateway" + image = var.gateway_image + essential = true + + portMappings = [{ containerPort = 4000, protocol = "tcp" }] + environment = concat( + local.shared_env, + local.gateway_extra_env_list, + local.proxy_config_env, + ) + secrets = concat(local.shared_secrets, local.gateway_extra_secrets_list) + + # Container-level healthCheck intentionally omitted — the wolfi + # runtime image doesn't ship curl/wget. The ALB target group polls + # /health/readiness. + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.gateway.name + awslogs-region = var.region + awslogs-stream-prefix = "gateway" + } + } + }, + local.gateway_proxy_overrides, + ) + ]) +} + +resource "aws_ecs_service" "gateway" { + name = "${local.name}-gateway" + cluster = aws_ecs_cluster.this.id + task_definition = aws_ecs_task_definition.gateway.arn + desired_count = var.gateway_desired_count + launch_type = "FARGATE" + + network_configuration { + subnets = aws_subnet.private[*].id + security_groups = [aws_security_group.tasks.id] + assign_public_ip = false + } + + load_balancer { + target_group_arn = aws_lb_target_group.gateway.arn + container_name = "gateway" + container_port = 4000 + } + + deployment_minimum_healthy_percent = 50 + deployment_maximum_percent = 200 + + # desired_count is owned by Application Auto Scaling once enabled (autoscaling.tf). + # Terraform sets the initial value from var.gateway_desired_count, then steps aside. + lifecycle { + ignore_changes = [desired_count] + } + + # Don't start until the schema migration has run. Otherwise the proxy + # boots, Prisma fails on the missing tables, and ECS thrashes the task. + depends_on = [ + aws_lb_listener.http, + aws_lb_listener.https, + terraform_data.migration, + ] +} + +# ---------- Backend ---------- +resource "aws_ecs_task_definition" "backend" { + family = "${local.name}-backend" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = var.backend_cpu + memory = var.backend_memory + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([ + merge( + { + name = "backend" + image = var.backend_image + essential = true + + portMappings = [{ containerPort = 4001, protocol = "tcp" }] + environment = concat( + local.shared_env, + local.backend_default_env, + local.backend_extra_env_list, + local.proxy_config_env, + ) + secrets = concat(local.shared_secrets, local.backend_managed_secrets, local.backend_extra_secrets_list) + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.backend.name + awslogs-region = var.region + awslogs-stream-prefix = "backend" + } + } + }, + local.backend_proxy_overrides, + ) + ]) +} + +resource "aws_ecs_service" "backend" { + name = "${local.name}-backend" + cluster = aws_ecs_cluster.this.id + task_definition = aws_ecs_task_definition.backend.arn + desired_count = var.backend_desired_count + launch_type = "FARGATE" + + network_configuration { + subnets = aws_subnet.private[*].id + security_groups = [aws_security_group.tasks.id] + assign_public_ip = false + } + + load_balancer { + target_group_arn = aws_lb_target_group.backend.arn + container_name = "backend" + container_port = 4001 + } + + deployment_minimum_healthy_percent = 50 + deployment_maximum_percent = 200 + + lifecycle { + ignore_changes = [desired_count] + } + + depends_on = [ + aws_lb_listener.http, + aws_lb_listener.https, + terraform_data.migration, + ] +} + +# ---------- UI ---------- +# task_role is deliberately the unprivileged ui_task — the UI has no DB, +# S3, or Secrets Manager dependency, and inheriting the shared `task` +# role would expose every data-plane secret to a compromised UI +# container via the task metadata endpoint. +resource "aws_ecs_task_definition" "ui" { + family = "${local.name}-ui" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + cpu = var.ui_cpu + memory = var.ui_memory + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.ui_task.arn + + container_definitions = jsonencode([ + { + name = "ui" + image = var.ui_image + essential = true + portMappings = [{ containerPort = 3000, protocol = "tcp" }] + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.ui.name + awslogs-region = var.region + awslogs-stream-prefix = "ui" + } + } + } + ]) +} + +resource "aws_ecs_service" "ui" { + name = "${local.name}-ui" + cluster = aws_ecs_cluster.this.id + task_definition = aws_ecs_task_definition.ui.arn + desired_count = var.ui_desired_count + launch_type = "FARGATE" + + network_configuration { + subnets = aws_subnet.private[*].id + security_groups = [aws_security_group.tasks.id] + assign_public_ip = false + } + + load_balancer { + target_group_arn = aws_lb_target_group.ui.arn + container_name = "ui" + container_port = 3000 + } + + deployment_minimum_healthy_percent = 50 + deployment_maximum_percent = 200 + + lifecycle { + ignore_changes = [desired_count] + } + + depends_on = [ + aws_lb_listener.http, + aws_lb_listener.https, + ] +} diff --git a/terraform/litellm/aws/iam.tf b/terraform/litellm/aws/iam.tf new file mode 100644 index 00000000000..504e0fe1d63 --- /dev/null +++ b/terraform/litellm/aws/iam.tf @@ -0,0 +1,114 @@ +# ECS task execution role — used by the agent to pull images, write logs, +# and resolve secrets at task start. +data "aws_iam_policy_document" "task_assume" { + statement { + actions = ["sts:AssumeRole"] + principals { + type = "Service" + identifiers = ["ecs-tasks.amazonaws.com"] + } + } +} + +resource "aws_iam_role" "task_execution" { + name = "${local.name}-task-execution" + assume_role_policy = data.aws_iam_policy_document.task_assume.json +} + +resource "aws_iam_role_policy_attachment" "task_execution" { + role = aws_iam_role.task_execution.name + policy_arn = "arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy" +} + +# User-provided extra secrets may be passed as the bare secret ARN +# ("arn:aws:secretsmanager:...:secret:name-AbCdEf") or the JSON-key form +# ECS supports — fully spelled out as +# "arn:...:secret:name-AbCdEf:jsonKey:versionStage:versionId" with any of +# the trailing parts blank ("...:jsonKey::" being the most common). The IAM +# policy resource must always be the bare ARN, so we split on ':' and keep +# the first 7 components — robust to any combination of empty/non-empty +# version-stage/version-id suffixes that a regex would otherwise have to +# enumerate. +locals { + extra_secret_value_froms = concat( + values(var.gateway_extra_secrets), + values(var.backend_extra_secrets), + ) + + extra_secret_arns = distinct([ + for v in local.extra_secret_value_froms : + join(":", slice(split(":", v), 0, 7)) + ]) +} + +# Execution role can read the managed secrets + any caller-provided extras +# so ECS can resolve them when launching tasks. Image pulls inherit the +# managed AmazonECSTaskExecutionRolePolicy. +data "aws_iam_policy_document" "secrets_access" { + statement { + actions = ["secretsmanager:GetSecretValue"] + resources = concat( + [aws_secretsmanager_secret.master_key.arn], + aws_secretsmanager_secret.license[*].arn, + aws_secretsmanager_secret.ui_password[*].arn, + local.extra_secret_arns, + ) + } +} + +resource "aws_iam_policy" "secrets_access" { + name = "${local.name}-secrets-access" + policy = data.aws_iam_policy_document.secrets_access.json +} + +resource "aws_iam_role_policy_attachment" "task_execution_secrets" { + role = aws_iam_role.task_execution.name + policy_arn = aws_iam_policy.secrets_access.arn +} + +# ---------- Task role ---------- +# +# Assumed by the running container. Gets `rds-db:connect` so the proxy can +# mint IAM-signed Postgres tokens for the app user. Layer additional +# policies here (e.g. Bedrock invoke, S3 read) when the proxy needs them. + +resource "aws_iam_role" "task" { + name = "${local.name}-task" + assume_role_policy = data.aws_iam_policy_document.task_assume.json +} + +data "aws_caller_identity" "current" {} + +data "aws_iam_policy_document" "rds_iam_connect" { + statement { + actions = ["rds-db:connect"] + resources = [ + "arn:aws:rds-db:${var.region}:${data.aws_caller_identity.current.account_id}:dbuser:${aws_rds_cluster.this.cluster_resource_id}/${var.db_username}", + ] + } +} + +resource "aws_iam_policy" "rds_iam_connect" { + name = "${local.name}-rds-iam-connect" + policy = data.aws_iam_policy_document.rds_iam_connect.json +} + +resource "aws_iam_role_policy_attachment" "task_rds_iam_connect" { + role = aws_iam_role.task.name + policy_arn = aws_iam_policy.rds_iam_connect.arn +} + +# ---------- UI task role ---------- +# +# The UI is static nginx with no DB, S3, or Secrets Manager dependencies, +# so it deliberately does NOT inherit the shared `task` role's +# rds-db:connect / S3 / extra-secrets policies. Empty policy set — the +# only thing exposed via the task metadata endpoint is an identity that +# can't reach any LiteLLM data-plane resource. The shared +# `task_execution` role still pulls the image and writes logs (its +# credentials aren't surfaced to the container). + +resource "aws_iam_role" "ui_task" { + name = "${local.name}-ui-task" + assume_role_policy = data.aws_iam_policy_document.task_assume.json +} diff --git a/terraform/litellm/aws/locals.tf b/terraform/litellm/aws/locals.tf new file mode 100644 index 00000000000..85c3b6eaaad --- /dev/null +++ b/terraform/litellm/aws/locals.tf @@ -0,0 +1,75 @@ +# Gateway path prefixes — mirrored verbatim from gateway/routes/allowlist.py +# and the helm ingress in helm/litellm/templates/ingress.yaml. Anything not in +# this list and not a UI asset path falls through to the backend (management +# API) catch-all rule on the ALB. +# +# ALB listener rules cap path-pattern conditions at 5 values per rule, so we +# chunk this list and emit one rule per chunk. +locals { + # Every resource the stack creates is named `-litellm-` + # (or that with a per-resource suffix). Computed once here so the rest of + # the stack can reference local.name. + name = "${var.tenant}-litellm-${var.env}" + + gateway_path_prefixes = [ + "/v1/chat/*", "/chat/*", + "/v1/completions*", "/completions*", + "/v1/embeddings*", "/embeddings*", + "/v1/moderations*", "/moderations*", + "/v1/audio/*", "/audio/*", + "/v1/images/*", "/images/*", + "/v1/files*", "/files*", + "/v1/batches*", "/batches*", + "/v1/fine_tuning/*", "/fine_tuning/*", + "/v1/fine-tuning/*", "/fine-tuning/*", + "/v1/responses*", "/responses*", + "/v1/threads*", "/threads*", + "/v1/assistants*", "/assistants*", + "/v1/vector_stores*", "/vector_stores*", + "/v1/indexes*", + "/v1/models*", "/models*", + "/openai/*", "/engines/*", + "/v1/messages*", "/messages*", + "/v1/skills/*", "/v1/a2a/*", + "/v1/rerank*", "/v2/rerank*", "/rerank*", + "/v1/ocr*", "/ocr*", + "/v1/rag/*", "/rag/*", + "/v1/video/*", "/v1/videos/*", "/video/*", "/videos/*", + "/v1/search*", "/search*", + "/v1/containers/*", "/containers/*", + "/v1/evals/*", + "/v1/memory/*", + "/queue/chat/*", + "/v1beta/*", + "/interactions/*", + "/anthropic/*", "/azure/*", "/azure_ai/*", "/aws/*", "/bedrock/*", + "/cohere/*", "/gemini/*", "/google/*", + "/vertex_ai/*", "/vertex-ai/*", + "/assemblyai/*", "/eu.assemblyai/*", + "/langfuse/*", "/vllm/*", + "/mistral/*", "/groq/*", "/voyage/*", "/cursor/*", "/milvus/*", + "/openai_passthrough/*", + "/toolset/*", + "/v1/realtime*", "/realtime*", + "/health*", "/metrics", "/test*", + ] + + # Static UI asset prefixes — handled by the UI service, not the backend + # catch-all. /favicon.ico and / are also UI but added as exact rules. + ui_path_prefixes = [ + "/litellm-asset-prefix/*", + "/_next/*", + "/assets/*", + "/ui/*", + ] + + ui_exact_paths = [ + "/", + "/favicon.ico", + "/ui", + ] + + # ALB rules accept ≤ 5 path-pattern values per condition. Chunk the prefix + # list so each chunk becomes one rule. + gateway_path_chunks = chunklist(local.gateway_path_prefixes, 5) +} diff --git a/terraform/litellm/aws/migrations.tf b/terraform/litellm/aws/migrations.tf new file mode 100644 index 00000000000..fc4e2ce0cab --- /dev/null +++ b/terraform/litellm/aws/migrations.tf @@ -0,0 +1,45 @@ +# Task definition for the dedicated litellm-migrations image. Mirrors the +# pre-install/pre-upgrade Helm hook in helm/litellm/templates/migrations-job.yaml. +# +# The image (built from migrations/Dockerfile) ships with +# `ENTRYPOINT ["python3", "/app/run.py"]`. run.py assembles DATABASE_URL from +# the discrete DATABASE_* env vars (IAM auth here) via DatabaseURLSettings, +# then calls ProxyExtrasDBManager.setup_database() — i.e. `prisma migrate +# deploy` with the v2 resolver and P3005/P3009/P3018 recovery. It does NOT +# read CONFIG_FILE_PATH, the master key, or DISABLE_SCHEMA_UPDATE, so we +# don't pass them. +# +# Invoked automatically by `terraform_data.migration` in bootstrap.tf during +# every apply (after the IAM-authed user has been created). The +# `migration_run_command` output is preserved for break-glass manual re-runs. +resource "aws_ecs_task_definition" "migrations" { + family = "${local.name}-migrations" + network_mode = "awsvpc" + requires_compatibilities = ["FARGATE"] + # Prisma's Node + Rust engine plus the v2 migration resolver routinely + # peaks well above 1 GiB while applying the schema. 4 GiB gives plenty + # of headroom; CPU stays low because `prisma migrate deploy` is + # single-threaded. + cpu = 512 + memory = 4096 + execution_role_arn = aws_iam_role.task_execution.arn + task_role_arn = aws_iam_role.task.arn + + container_definitions = jsonencode([{ + name = "migrations" + image = var.migrations_image + essential = true + + # No entryPoint/command override — the image's ENTRYPOINT runs run.py. + environment = local.shared_env + + logConfiguration = { + logDriver = "awslogs" + options = { + awslogs-group = aws_cloudwatch_log_group.migrations.name + awslogs-region = var.region + awslogs-stream-prefix = "migrations" + } + } + }]) +} diff --git a/terraform/litellm/aws/network.tf b/terraform/litellm/aws/network.tf new file mode 100644 index 00000000000..d5ed49c1b8a --- /dev/null +++ b/terraform/litellm/aws/network.tf @@ -0,0 +1,172 @@ +data "aws_availability_zones" "available" { + state = "available" +} + +resource "aws_vpc" "this" { + cidr_block = var.vpc_cidr + enable_dns_hostnames = true + enable_dns_support = true + + tags = { Name = local.name } +} + +resource "aws_internet_gateway" "this" { + vpc_id = aws_vpc.this.id + tags = { Name = local.name } +} + +# Public subnets (ALB + NAT). One per AZ. +resource "aws_subnet" "public" { + count = length(var.azs) + vpc_id = aws_vpc.this.id + cidr_block = cidrsubnet(var.vpc_cidr, 8, count.index) + availability_zone = var.azs[count.index] + map_public_ip_on_launch = true + + tags = { Name = "${local.name}-public-${var.azs[count.index]}" } +} + +# Private subnets (ECS tasks, RDS, ElastiCache). One per AZ, separate from +# public range. +resource "aws_subnet" "private" { + count = length(var.azs) + vpc_id = aws_vpc.this.id + cidr_block = cidrsubnet(var.vpc_cidr, 8, count.index + 10) + availability_zone = var.azs[count.index] + + tags = { Name = "${local.name}-private-${var.azs[count.index]}" } +} + +resource "aws_eip" "nat" { + domain = "vpc" + tags = { Name = "${local.name}-nat" } + + depends_on = [aws_internet_gateway.this] +} + +# Single NAT gateway in the first public subnet. For HA, replicate per AZ — +# adds ~$30/mo per gateway, so off by default for a baseline deployment. +resource "aws_nat_gateway" "this" { + allocation_id = aws_eip.nat.id + subnet_id = aws_subnet.public[0].id + + tags = { Name = local.name } + + depends_on = [aws_internet_gateway.this] +} + +resource "aws_route_table" "public" { + vpc_id = aws_vpc.this.id + + route { + cidr_block = "0.0.0.0/0" + gateway_id = aws_internet_gateway.this.id + } + + tags = { Name = "${local.name}-public" } +} + +resource "aws_route_table_association" "public" { + count = length(var.azs) + subnet_id = aws_subnet.public[count.index].id + route_table_id = aws_route_table.public.id +} + +resource "aws_route_table" "private" { + vpc_id = aws_vpc.this.id + + route { + cidr_block = "0.0.0.0/0" + nat_gateway_id = aws_nat_gateway.this.id + } + + tags = { Name = "${local.name}-private" } +} + +resource "aws_route_table_association" "private" { + count = length(var.azs) + subnet_id = aws_subnet.private[count.index].id + route_table_id = aws_route_table.private.id +} + +# ---------- Security groups ---------- + +resource "aws_security_group" "alb" { + name = "${local.name}-alb" + description = "Inbound HTTP/HTTPS to the LiteLLM ALB." + vpc_id = aws_vpc.this.id + + ingress { + description = "HTTP from anywhere" + from_port = 80 + to_port = 80 + protocol = "tcp" + cidr_blocks = ["0.0.0.0/0"] + } + + ingress { + description = "HTTPS from anywhere" + from_port = 443 + to_port = 443 + protocol = "tcp" + cidr_blocks = ["0.0.0.0/0"] + } + + egress { + description = "All egress" + from_port = 0 + to_port = 0 + protocol = "-1" + cidr_blocks = ["0.0.0.0/0"] + } +} + +resource "aws_security_group" "tasks" { + name = "${local.name}-tasks" + description = "ECS tasks (gateway/backend/ui)." + vpc_id = aws_vpc.this.id + + ingress { + description = "ALB to tasks" + from_port = 0 + to_port = 65535 + protocol = "tcp" + security_groups = [aws_security_group.alb.id] + } + + egress { + description = "All egress (LLM providers, RDS, Redis)" + from_port = 0 + to_port = 0 + protocol = "-1" + cidr_blocks = ["0.0.0.0/0"] + } +} + +resource "aws_security_group" "rds" { + name = "${local.name}-rds" + description = "RDS Postgres - tasks only." + vpc_id = aws_vpc.this.id + + ingress { + description = "Postgres from ECS tasks" + from_port = 5432 + to_port = 5432 + protocol = "tcp" + security_groups = [aws_security_group.tasks.id] + } +} + +resource "aws_security_group" "redis" { + name = "${local.name}-redis" + description = "ElastiCache Redis - tasks only." + vpc_id = aws_vpc.this.id + + ingress { + description = "Redis from ECS tasks" + from_port = 6379 + to_port = 6379 + protocol = "tcp" + security_groups = [aws_security_group.tasks.id] + } +} diff --git a/terraform/litellm/aws/outputs.tf b/terraform/litellm/aws/outputs.tf new file mode 100644 index 00000000000..9c36b1a7e0f --- /dev/null +++ b/terraform/litellm/aws/outputs.tf @@ -0,0 +1,72 @@ +output "alb_dns_name" { + description = "Public DNS name of the LiteLLM ALB." + value = aws_lb.this.dns_name +} + +output "alb_url" { + description = "Proxy URL. Switches scheme based on whether acm_certificate_arn is set; the underlying DNS name is the ALB. The dashboard is served at /, the API at /v1/*." + value = "${local.tls_enabled ? "https" : "http"}://${aws_lb.this.dns_name}" +} + +output "ecs_cluster" { + description = "ECS cluster name." + value = aws_ecs_cluster.this.name +} + +output "aurora_writer_endpoint" { + description = "Aurora writer endpoint (cluster endpoint). Used by gateway/backend as DATABASE_HOST." + value = aws_rds_cluster.this.endpoint +} + +output "aurora_reader_endpoint" { + description = "Aurora reader endpoint. Used by gateway/backend as DATABASE_HOST_READ_REPLICA." + value = aws_rds_cluster.this.reader_endpoint +} + +output "redis_endpoint" { + description = "ElastiCache Redis primary endpoint (TLS, transit_encryption_enabled = true)." + value = "${aws_elasticache_replication_group.this.primary_endpoint_address}:${aws_elasticache_replication_group.this.port}" +} + +output "s3_bucket" { + description = "S3 bucket name. Exposed to gateway + backend as S3_BUCKET_NAME / S3_REGION_NAME. Reference from proxy_config via `os.environ/S3_BUCKET_NAME`." + value = aws_s3_bucket.this.bucket +} + +output "master_key_secret_arn" { + description = "Secrets Manager ARN holding LITELLM_MASTER_KEY. Fetch with `aws secretsmanager get-secret-value --secret-id `." + value = aws_secretsmanager_secret.master_key.arn +} + +output "db_master_password_secret_arn" { + description = "Secrets Manager ARN holding the Aurora master credentials (bootstrap-only). Used to create the IAM-authed application user." + value = aws_secretsmanager_secret.db_master_password.arn +} + +# Pre-baked SQL to run once as the master user, creating the IAM-authed +# application user that gateway/backend/migration tasks will authenticate as. +output "db_bootstrap_sql" { + description = "Run this once as the master DB user (after the first apply) to create the IAM-authed app user." + value = <<-SQL + CREATE USER ${var.db_username}; + GRANT rds_iam TO ${var.db_username}; + GRANT ALL PRIVILEGES ON DATABASE ${var.db_name} TO ${var.db_username}; + GRANT ALL ON SCHEMA public TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON TABLES TO ${var.db_username}; + ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT ALL ON SEQUENCES TO ${var.db_username}; + SQL +} + +# Pre-baked command for running the one-off migration task. ECS run-task +# needs the subnet + SG IDs at call time, so we render the full command. +output "migration_run_command" { + description = "Shell command that runs the one-off prisma migration task against Aurora. Run this once, after the bootstrap SQL above, before sending traffic." + value = format( + "aws ecs run-task --cluster %s --launch-type FARGATE --task-definition %s --network-configuration 'awsvpcConfiguration={subnets=[%s],securityGroups=[%s],assignPublicIp=DISABLED}' --region %s", + aws_ecs_cluster.this.name, + aws_ecs_task_definition.migrations.arn, + join(",", aws_subnet.private[*].id), + aws_security_group.tasks.id, + var.region, + ) +} diff --git a/terraform/litellm/aws/providers.tf b/terraform/litellm/aws/providers.tf new file mode 100644 index 00000000000..5e7d506c23f --- /dev/null +++ b/terraform/litellm/aws/providers.tf @@ -0,0 +1,13 @@ +provider "aws" { + region = var.region + + default_tags { + tags = merge( + { + "litellm:stack" = local.name + "managed-by" = "terraform" + }, + var.tags, + ) + } +} diff --git a/terraform/litellm/aws/rds.tf b/terraform/litellm/aws/rds.tf new file mode 100644 index 00000000000..8e3b70a8d62 --- /dev/null +++ b/terraform/litellm/aws/rds.tf @@ -0,0 +1,85 @@ +# Aurora Postgres cluster with one writer + one reader instance, IAM +# database authentication enabled. +# +# Important: enabling IAM auth on the cluster does not by itself grant any +# Postgres user the ability to log in with an IAM token. After the first +# apply, connect as the master user (password lives in Secrets Manager — +# see `master_user_secret_arn` in outputs) and run, once: +# +# CREATE USER {var.db_username}; +# GRANT rds_iam TO {var.db_username}; +# GRANT ALL PRIVILEGES ON DATABASE {var.db_name} TO {var.db_username}; +# GRANT ALL ON SCHEMA public TO {var.db_username}; +# +# After that, the gateway/backend/migration tasks (which authenticate as +# `{var.db_username}` via IAM-signed tokens) can connect. The master user +# itself is a superuser and Postgres refuses to grant `rds_iam` to +# superusers — keep it for break-glass only. + +resource "aws_db_subnet_group" "this" { + name = "${local.name}-db" + subnet_ids = aws_subnet.private[*].id +} + +resource "aws_rds_cluster_parameter_group" "this" { + name = "${local.name}-cluster-pg" + family = "aurora-postgresql${split(".", var.db_engine_version)[0]}" + description = "LiteLLM Aurora Postgres cluster parameters." +} + +resource "aws_rds_cluster" "this" { + cluster_identifier = local.name + engine = "aurora-postgresql" + engine_mode = "provisioned" + engine_version = var.db_engine_version + database_name = var.db_name + master_username = var.db_master_username + master_password = random_password.db_master_password.result + db_subnet_group_name = aws_db_subnet_group.this.name + vpc_security_group_ids = [aws_security_group.rds.id] + db_cluster_parameter_group_name = aws_rds_cluster_parameter_group.this.name + + iam_database_authentication_enabled = true + storage_encrypted = true + apply_immediately = true + + # Final-snapshot guard. With the safe default (skip_final_snapshot = false), + # `terraform destroy` takes a snapshot named `-final-` + # before dropping the cluster. The short SHA disambiguates repeated + # destroy/recreate cycles so each snapshot has a unique name. + skip_final_snapshot = var.skip_final_snapshot + final_snapshot_identifier = var.skip_final_snapshot ? null : "${local.name}-final-${substr(md5(local.name), 0, 8)}" + + backup_retention_period = 7 + preferred_backup_window = "07:00-09:00" +} + +resource "aws_rds_cluster_instance" "writer" { + identifier = "${local.name}-writer" + cluster_identifier = aws_rds_cluster.this.id + instance_class = var.db_instance_class + engine = aws_rds_cluster.this.engine + engine_version = aws_rds_cluster.this.engine_version + + publicly_accessible = false + performance_insights_enabled = true + + # Promotion tier 0 — first in line during failover, so this instance stays + # the writer unless it goes unhealthy. + promotion_tier = 0 +} + +resource "aws_rds_cluster_instance" "reader" { + identifier = "${local.name}-reader" + cluster_identifier = aws_rds_cluster.this.id + instance_class = var.db_instance_class + engine = aws_rds_cluster.this.engine + engine_version = aws_rds_cluster.this.engine_version + + publicly_accessible = false + performance_insights_enabled = true + + # Higher promotion tier — won't be picked as writer during a failover + # unless the writer instance itself is gone. + promotion_tier = 15 +} diff --git a/terraform/litellm/aws/redis.tf b/terraform/litellm/aws/redis.tf new file mode 100644 index 00000000000..2a6fab2d89f --- /dev/null +++ b/terraform/litellm/aws/redis.tf @@ -0,0 +1,33 @@ +resource "aws_elasticache_subnet_group" "this" { + name = "${local.name}-redis" + subnet_ids = aws_subnet.private[*].id +} + +# Replication group (not aws_elasticache_cluster, which is the +# Memcached / single-node Redis resource and can't be upgraded in-place +# to HA). With redis_num_replicas >= 1 we get automatic_failover_enabled +# + multi_az_enabled; at_rest_encryption_enabled and +# transit_encryption_enabled are on unconditionally so Redis traffic is +# TLS-protected — the proxy connects via the rediss:// scheme thanks to +# REDIS_SSL=true in the shared task env (see ecs.tf). +resource "aws_elasticache_replication_group" "this" { + replication_group_id = "${local.name}-redis" + description = "LiteLLM ElastiCache Redis" + + engine = "redis" + engine_version = "7.1" + node_type = var.redis_node_type + num_cache_clusters = 1 + var.redis_num_replicas + parameter_group_name = "default.redis7" + port = 6379 + + subnet_group_name = aws_elasticache_subnet_group.this.name + security_group_ids = [aws_security_group.redis.id] + + automatic_failover_enabled = var.redis_num_replicas >= 1 + multi_az_enabled = var.redis_num_replicas >= 1 + at_rest_encryption_enabled = true + transit_encryption_enabled = true + + apply_immediately = true +} diff --git a/terraform/litellm/aws/s3.tf b/terraform/litellm/aws/s3.tf new file mode 100644 index 00000000000..375bc73bb71 --- /dev/null +++ b/terraform/litellm/aws/s3.tf @@ -0,0 +1,80 @@ +# General-purpose S3 bucket for the proxy. LiteLLM uses S3 for: +# - Cache backend (cache_params.s3_bucket_name in proxy_config) +# - Request log archival (S3_REQUEST_LOGS_BUCKET_NAME) +# - /v1/files endpoint passthrough storage +# +# The bucket name + region are exposed to gateway + backend as S3_BUCKET_NAME +# / S3_REGION_NAME so proxy_config can reference them via +# `os.environ/S3_BUCKET_NAME`. The task role is scoped to this bucket only. + +resource "random_id" "s3_suffix" { + byte_length = 4 +} + +resource "aws_s3_bucket" "this" { + bucket = "${local.name}-${random_id.s3_suffix.hex}" + + # Default false → `terraform destroy` refuses on a non-empty bucket so + # cached responses, archived request logs, and /v1/files storage stay put. + # Flip to true only for ephemeral / CI stacks (`var.s3_force_destroy`). + force_destroy = var.s3_force_destroy +} + +resource "aws_s3_bucket_versioning" "this" { + bucket = aws_s3_bucket.this.id + versioning_configuration { + status = "Enabled" + } +} + +resource "aws_s3_bucket_server_side_encryption_configuration" "this" { + bucket = aws_s3_bucket.this.id + + rule { + apply_server_side_encryption_by_default { + sse_algorithm = "AES256" + } + } +} + +resource "aws_s3_bucket_public_access_block" "this" { + bucket = aws_s3_bucket.this.id + + block_public_acls = true + block_public_policy = true + ignore_public_acls = true + restrict_public_buckets = true +} + +# Task role gains object-level read/write on this bucket. Bucket-level perms +# (list/location) are also scoped to this bucket only. +data "aws_iam_policy_document" "s3_access" { + statement { + actions = [ + "s3:ListBucket", + "s3:GetBucketLocation", + ] + resources = [aws_s3_bucket.this.arn] + } + + statement { + actions = [ + "s3:GetObject", + "s3:PutObject", + "s3:DeleteObject", + "s3:AbortMultipartUpload", + "s3:ListMultipartUploadParts", + ] + resources = ["${aws_s3_bucket.this.arn}/*"] + } +} + +resource "aws_iam_policy" "s3_access" { + name = "${local.name}-s3-access" + policy = data.aws_iam_policy_document.s3_access.json +} + +resource "aws_iam_role_policy_attachment" "task_s3_access" { + role = aws_iam_role.task.name + policy_arn = aws_iam_policy.s3_access.arn +} diff --git a/terraform/litellm/aws/secrets.tf b/terraform/litellm/aws/secrets.tf new file mode 100644 index 00000000000..dd13fdc1239 --- /dev/null +++ b/terraform/litellm/aws/secrets.tf @@ -0,0 +1,86 @@ +resource "random_password" "master_key" { + length = 48 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +# Master DB password — used once to bootstrap the IAM-authed application +# user (see rds.tf header). Runtime services authenticate via IAM tokens +# and never read this secret. +resource "random_password" "db_master_password" { + length = 32 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +# LITELLM_MASTER_KEY — must begin with `sk-` per the proxy's validator. +resource "aws_secretsmanager_secret" "master_key" { + name = "${local.name}-master-key" + description = "LITELLM_MASTER_KEY for gateway + backend." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "master_key" { + secret_id = aws_secretsmanager_secret.master_key.id + # When the operator passes litellm_master_key, use it verbatim. Otherwise + # fall back to the auto-generated `sk-…` value (trial / OSS path). + secret_string = coalesce(var.litellm_master_key, "sk-${random_password.master_key.result}") +} + +# LITELLM_LICENSE — only created when the operator supplies one. The +# task-execution role gets GetSecretValue via iam.tf, and gateway + backend +# pick the env var up through shared_secrets in ecs.tf. +resource "aws_secretsmanager_secret" "license" { + count = var.litellm_license == "" ? 0 : 1 + + name = "${local.name}-license" + description = "LITELLM_LICENSE for gateway + backend." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret_id = aws_secretsmanager_secret.license[0].id + secret_string = var.litellm_license +} + +# UI_PASSWORD — backend-only. Same pattern as license: only created when +# the operator supplies one. The execution role gets GetSecretValue via +# iam.tf, and the backend task picks the env var up through +# backend_managed_secrets in ecs.tf. +resource "aws_secretsmanager_secret" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + name = "${local.name}-ui-password" + description = "UI_PASSWORD for the backend (UI admin login)." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret_id = aws_secretsmanager_secret.ui_password[0].id + secret_string = var.ui_password +} + +resource "aws_secretsmanager_secret" "db_master_password" { + name = "${local.name}-db-master-password" + description = "Aurora master-user password - bootstrap only. Runtime auth is IAM-token." + recovery_window_in_days = 0 +} + +resource "aws_secretsmanager_secret_version" "db_master_password" { + secret_id = aws_secretsmanager_secret.db_master_password.id + secret_string = jsonencode({ + username = var.db_master_username + password = random_password.db_master_password.result + host = aws_rds_cluster.this.endpoint + port = aws_rds_cluster.this.port + dbname = var.db_name + }) +} diff --git a/terraform/litellm/aws/terraform.tfvars.example b/terraform/litellm/aws/terraform.tfvars.example new file mode 100644 index 00000000000..2be573949ef --- /dev/null +++ b/terraform/litellm/aws/terraform.tfvars.example @@ -0,0 +1,88 @@ +region = "us-west-2" +azs = ["us-west-2a", "us-west-2b"] + +# Resource naming: every AWS resource the stack creates is named +# `${tenant}-litellm-${env}` (or that plus a per-resource suffix). E.g. +# tenant="acme" + env="stage" → ALB `acme-litellm-stage`, ECS service +# `acme-litellm-stage-gateway`, etc. +tenant = "acme" +env = "stage" + +# Tenant-supplied secrets. Prefer TF_VAR_litellm_master_key / +# TF_VAR_litellm_license / TF_VAR_ui_password env vars so the values don't +# end up in a committed tfvars file. All three are optional — when +# omitted the stack auto-generates a master key, runs without a license, +# and falls back to LITELLM_MASTER_KEY for UI login. +# litellm_master_key = "sk-..." +# litellm_license = "lic-..." +# ui_password = "..." + +# TLS: provide an ACM cert for production. Without one, plan fails unless +# allow_plaintext_alb = true is set explicitly (trial/dev only). +# acm_certificate_arn = "arn:aws:acm:us-west-2:111122223333:certificate/..." +# allow_plaintext_alb = true + +# Storage retention: false (default) makes `terraform destroy` refuse on a +# non-empty bucket. Flip to true only for ephemeral / CI stacks. +# s3_force_destroy = false + +# Component images. Defaults pin all four to the same GHCR release tag — +# bump them together when bumping LiteLLM. Override here to pull from a +# private registry or to mix-and-match versions. +# gateway_image = "ghcr.io/berriai/litellm-gateway:1.86.0-dev" +# backend_image = "ghcr.io/berriai/litellm-backend:1.86.0-dev" +# ui_image = "ghcr.io/berriai/litellm-ui:1.86.0-dev" +# migrations_image = "ghcr.io/berriai/litellm-migrations:1.86.0-dev" + +# Per-task sizing for the gateway. Defaults are 1 vCPU / 4 GiB / 1 worker. +# uvicorn rule of thumb for CPU-bound work is (2 * vCPU) + 1 workers. +# gateway_cpu = 1024 # 1024 = 1 vCPU +# gateway_memory = 4096 # MiB +# gateway_num_workers = 1 + +# ---------- proxy_config (mirrors helm gateway.config.proxy_config) ---------- +# proxy_config = { +# model_list = [ +# { +# model_name = "gpt-4o" +# litellm_params = { +# model = "openai/gpt-4o" +# api_key = "os.environ/OPENAI_API_KEY" +# } +# }, +# { +# model_name = "claude-sonnet-4-6" +# litellm_params = { +# model = "anthropic/claude-sonnet-4-6" +# api_key = "os.environ/ANTHROPIC_API_KEY" +# } +# }, +# ] +# general_settings = { +# master_key = "os.environ/LITELLM_MASTER_KEY" +# database_url = "os.environ/DATABASE_URL" +# } +# } + +# ---------- Extra env / secrets ---------- +# Plain-text env vars (non-sensitive). Land directly in the ECS task def. +# gateway_extra_env = { +# LANGFUSE_HOST = "https://us.cloud.langfuse.com" +# } + +# Backend env vars commonly tuned in prod: SSO redirect, docs branding, +# UI admin username. UI_PASSWORD is its own first-class var (see top). +# backend_extra_env = { +# AUTO_REDIRECT_UI_LOGIN_TO_SSO = "true" +# DOCS_TITLE = "Acme LiteLLM" +# UI_USERNAME = "admin" +# } + +# Provider API keys, sourced from existing Secrets Manager entries. The +# execution role auto-gains GetSecretValue on each ARN listed here. The +# values you reference above as `os.environ/OPENAI_API_KEY` must appear +# here. Same shape works for backend_extra_secrets. +# gateway_extra_secrets = { +# OPENAI_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:openai-api-key-AbCdEf" +# ANTHROPIC_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:anthropic-api-key-GhIjKl" +# } diff --git a/terraform/litellm/aws/variables.tf b/terraform/litellm/aws/variables.tf new file mode 100644 index 00000000000..946cd7ebbf3 --- /dev/null +++ b/terraform/litellm/aws/variables.tf @@ -0,0 +1,458 @@ +variable "region" { + description = "AWS region to deploy into." + type = string +} + +variable "tenant" { + description = "Tenant slug — used as the prefix for every AWS resource the stack creates. Combined with var.env to form `-litellm-` (e.g. `acme-litellm-stage`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,20}$", var.tenant)) + error_message = "tenant must be 1-21 chars, lower-kebab-case, starting with a letter." + } +} + +variable "env" { + description = "Environment suffix appended to every resource name (e.g. `stage`, `prod`, `dev`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,8}$", var.env)) + error_message = "env must be 1-9 chars, lower-kebab-case, starting with a letter." + } +} + +variable "tags" { + description = "Additional tags merged into the provider default_tags." + type = map(string) + default = {} +} + +# ---------- Tenant-supplied secrets ---------- +# +# Both default to "" so the stack stays usable for trial / OSS deploys. +# Set via TF_VAR_litellm_master_key / TF_VAR_litellm_license to keep the +# values out of state files committed to a VCS. + +variable "litellm_master_key" { + description = <<-EOT + Pre-existing LITELLM_MASTER_KEY (must begin with `sk-`). When set, this + value is written to the master-key Secrets Manager entry. When empty, + the stack auto-generates a random `sk-…` key (preserving today's + trial-deploy behavior). + EOT + type = string + default = "" + sensitive = true +} + +variable "litellm_license" { + description = <<-EOT + LiteLLM enterprise license string. When set, the stack creates a + `-litellm--license` Secrets Manager entry, grants the + task-execution role GetSecretValue on it, and exposes its value to + gateway + backend as `LITELLM_LICENSE`. Leave empty for OSS-only deploys. + EOT + type = string + default = "" + sensitive = true +} + +variable "ui_password" { + description = <<-EOT + UI admin password. When set, the stack creates a + `-litellm--ui-password` Secrets Manager entry, grants the + task-execution role GetSecretValue on it, and exposes its value to the + backend as `UI_PASSWORD`. Pair with `backend_extra_env.UI_USERNAME` to + set the matching username. Leave empty to skip — the proxy then falls + back to the LITELLM_MASTER_KEY for UI login. + EOT + type = string + default = "" + sensitive = true +} + +# ---------- Networking ---------- + +variable "vpc_cidr" { + description = "CIDR block for the VPC." + type = string + default = "10.40.0.0/16" +} + +variable "azs" { + description = "Availability zones to spread subnets across. At least 2 required for RDS and ALB." + type = list(string) + validation { + condition = length(var.azs) >= 2 + error_message = "Provide at least 2 availability zones." + } +} + +# ---------- Component images ---------- +# +# Defaults pin the four componentized images at the same release tag on +# GHCR. Override on a per-component basis in tfvars when bumping; bump them +# together when bumping the LiteLLM release. + +variable "gateway_image" { + description = "Container image for the gateway (data plane, port 4000). Tag must match a tag actually published to GHCR — the split images use the `v`-prefixed semver convention." + type = string + default = "ghcr.io/berriai/litellm-gateway:v1.86.0-dev" +} + +variable "backend_image" { + description = "Container image for the backend (management API, port 4001)." + type = string + default = "ghcr.io/berriai/litellm-backend:v1.86.0-dev" +} + +variable "ui_image" { + description = "Container image for the UI (nginx static export, port 3000)." + type = string + default = "ghcr.io/berriai/litellm-ui:v1.86.0-dev" +} + +variable "migrations_image" { + description = <<-EOT + Container image for the one-off prisma migration task. Built from + `migrations/Dockerfile` — slim image whose ENTRYPOINT runs + `python3 /app/run.py` (assembles DATABASE_URL from DATABASE_* env vars + via DatabaseURLSettings, then runs `prisma migrate deploy`). Should track + the same release tag as gateway/backend/ui. + EOT + type = string + default = "ghcr.io/berriai/litellm-migrations:v1.86.0-dev" +} + +# ---------- Service sizing ---------- + +variable "gateway_cpu" { + description = "Fargate CPU units for the gateway task (1024 = 1 vCPU)." + type = number + default = 1024 +} + +variable "gateway_memory" { + description = "Fargate memory (MiB) for the gateway task." + type = number + default = 4096 +} + +variable "gateway_desired_count" { + description = "Desired number of gateway tasks." + type = number + default = 2 +} + +variable "gateway_num_workers" { + description = "uvicorn worker processes per gateway task (passed as --workers). Size relative to gateway_cpu — uvicorn recommends ~(2 × vCPU) + 1 for CPU-bound work." + type = number + default = 1 + + validation { + condition = var.gateway_num_workers >= 1 + error_message = "gateway_num_workers must be >= 1." + } +} + +variable "backend_cpu" { + description = "Fargate CPU units for the backend task (1024 = 1 vCPU)." + type = number + default = 1024 +} + +variable "backend_memory" { + description = "Fargate memory (MiB) for the backend task. The proxy_server import chain alone needs >1 GiB; 4 GiB matches gateway." + type = number + default = 4096 +} + +variable "backend_desired_count" { + description = "Desired number of backend tasks." + type = number + default = 1 +} + +variable "ui_cpu" { + description = "Fargate CPU units for the UI task." + type = number + default = 256 +} + +variable "ui_memory" { + description = "Fargate memory (MiB) for the UI task." + type = number + default = 512 +} + +variable "ui_desired_count" { + description = "Desired number of UI tasks." + type = number + default = 1 +} + +# ---------- Autoscaling ---------- +# Defaults mirror helm/litellm/values.yaml HPAs. The "*_desired_count" vars +# above seed the initial task count; once autoscaling is enabled, the service's +# desired_count is left to Application Auto Scaling (ecs.tf ignores future +# changes to it). + +variable "gateway_autoscaling_enabled" { + description = "Toggle Application Auto Scaling target-tracking on the gateway service." + type = bool + default = true +} + +variable "gateway_min_capacity" { + description = "Minimum gateway task count under autoscaling." + type = number + default = 1 +} + +variable "gateway_max_capacity" { + description = "Maximum gateway task count under autoscaling." + type = number + default = 10 +} + +variable "gateway_cpu_target" { + description = "Target average CPU utilization (%) for the gateway autoscaling policy." + type = number + default = 70 +} + +variable "gateway_memory_target" { + description = "Target average memory utilization (%) for the gateway autoscaling policy. Set 0 to skip the memory policy and scale on CPU only." + type = number + default = 80 +} + +variable "backend_autoscaling_enabled" { + description = "Toggle Application Auto Scaling target-tracking on the backend service." + type = bool + default = true +} + +variable "backend_min_capacity" { + description = "Minimum backend task count under autoscaling." + type = number + default = 1 +} + +variable "backend_max_capacity" { + description = "Maximum backend task count under autoscaling." + type = number + default = 4 +} + +variable "backend_cpu_target" { + description = "Target average CPU utilization (%) for the backend autoscaling policy." + type = number + default = 70 +} + +variable "ui_autoscaling_enabled" { + description = "Toggle Application Auto Scaling target-tracking on the UI service. Off by default — UI is a static nginx export and one task is usually enough." + type = bool + default = false +} + +variable "ui_min_capacity" { + description = "Minimum UI task count under autoscaling." + type = number + default = 1 +} + +variable "ui_max_capacity" { + description = "Maximum UI task count under autoscaling." + type = number + default = 3 +} + +variable "ui_cpu_target" { + description = "Target average CPU utilization (%) for the UI autoscaling policy." + type = number + default = 80 +} + +# ---------- RDS ---------- + +variable "db_instance_class" { + description = "Aurora instance class for both writer and reader." + type = string + default = "db.r6g.large" +} + +variable "db_engine_version" { + description = "Aurora Postgres engine version. Major version drives the parameter-group family (aurora-postgresql)." + type = string + default = "16.4" +} + +variable "db_name" { + description = "Initial database name created on the Aurora cluster." + type = string + default = "litellm" +} + +variable "db_master_username" { + description = "Aurora master (superuser) username — used only to bootstrap the IAM-authed application user." + type = string + default = "postgres" +} + +variable "db_username" { + description = "IAM-authed Postgres user the proxy connects as. Must be CREATEd in the cluster and granted the rds_iam role — see terraform/litellm/aws/README.md." + type = string + default = "litellm_app" +} + +# ---------- Redis ---------- + +variable "redis_node_type" { + description = "ElastiCache node type." + type = string + default = "cache.t4g.small" +} + +variable "redis_num_replicas" { + description = "Number of read replicas in the Redis replication group. The primary plus this many replicas form the cluster — set to 0 for a single-node dev deployment, 1+ for HA. multi_az_enabled and automatic_failover_enabled require >= 1." + type = number + default = 1 + + validation { + condition = var.redis_num_replicas >= 0 + error_message = "redis_num_replicas must be >= 0." + } +} + +# ---------- TLS ---------- + +variable "acm_certificate_arn" { + description = <<-EOT + ACM certificate ARN for the ALB's HTTPS listener. When set, the stack + provisions a 443 listener carrying the same path-routing rules as the 80 + listener, and the 80 listener is rewritten to redirect HTTP→HTTPS. Leave + empty ("") to disable TLS (must combine with `allow_plaintext_alb = true` + for the plan to succeed — see README.md "TLS"). + EOT + type = string + default = "" +} + +variable "allow_plaintext_alb" { + description = <<-EOT + Opt into HTTP-only mode on the ALB (port 80, no TLS). Default false: + `terraform plan` fails when `acm_certificate_arn = ""` so the operator + must either provide an ACM cert or consciously opt out. Intended for + short-lived trial / dev stacks only. + EOT + type = bool + default = false +} + +# ---------- RDS ---------- + +variable "skip_final_snapshot" { + description = "Skip the Aurora final snapshot on `terraform destroy`. Default false — destroying the cluster takes a snapshot first so data is recoverable. Set true only for ephemeral / CI environments where you accept permanent data loss on destroy." + type = bool + default = false +} + +variable "s3_force_destroy" { + description = <<-EOT + Allow `terraform destroy` to delete the S3 bucket even when it still + contains objects (request log archives, /v1/files storage, S3 cache + backend). Default false — destroying a non-empty bucket fails, acting + as a tripwire against accidental data loss. Set true only for + ephemeral / CI environments. Mirrors the safety posture of + `skip_final_snapshot` on Aurora. + EOT + type = bool + default = false +} + +# ---------- Extra env ---------- + +variable "gateway_extra_env" { + description = <<-EOT + Additional plain-text env vars for the gateway container. Use this for + non-sensitive config (LANGFUSE_HOST, custom feature flags, …). For API + keys, use gateway_extra_secrets instead. + EOT + type = map(string) + default = {} +} + +variable "backend_extra_env" { + description = "Additional plain-text env vars for the backend container." + type = map(string) + default = {} +} + +variable "gateway_extra_secrets" { + description = <<-EOT + Extra env vars sourced from AWS Secrets Manager. Map of env-var name to + Secrets Manager ARN. Pass the bare secret ARN to inject the whole secret + string as the env var value, or append ":::" to extract a single + JSON field (ECS docs). + + Example for OPENAI_API_KEY: + gateway_extra_secrets = { + OPENAI_API_KEY = "arn:aws:secretsmanager:us-west-2:111122223333:secret:openai-api-key-AbCdEf" + } + + The stack's task execution role automatically gains GetSecretValue on every + ARN referenced here (suffix-stripped). + EOT + type = map(string) + default = {} +} + +variable "backend_extra_secrets" { + description = "Same shape as gateway_extra_secrets, but layered onto the backend container." + type = map(string) + default = {} +} + +variable "proxy_config" { + description = <<-EOT + LiteLLM proxy config (the contents of config.yaml). Mirrors the helm + chart's `gateway.config.proxy_config` value. Passed to gateway, backend, + and the migration task as a base64-encoded env var and decoded to + /tmp/litellm-config.yaml at container start; CONFIG_FILE_PATH is set + automatically. + + Example: + proxy_config = { + model_list = [ + { + model_name = "gpt-4o" + litellm_params = { + model = "openai/gpt-4o" + api_key = "os.environ/OPENAI_API_KEY" + } + }, + ] + general_settings = { + master_key = "os.environ/LITELLM_MASTER_KEY" + database_url = "os.environ/DATABASE_URL" + ui_username = "admin" + } + } + + Leave empty ({}) to skip mounting a config — the proxy then runs with + defaults. Use the "os.environ/" syntax in the YAML to reference + env vars provided by *_extra_env or *_extra_secrets. + EOT + type = any + default = {} +} + +variable "log_retention_days" { + description = "CloudWatch log retention for the three services." + type = number + default = 30 +} diff --git a/terraform/litellm/aws/versions.tf b/terraform/litellm/aws/versions.tf new file mode 100644 index 00000000000..73b88e91dce --- /dev/null +++ b/terraform/litellm/aws/versions.tf @@ -0,0 +1,14 @@ +terraform { + required_version = ">= 1.6.0" + + required_providers { + aws = { + source = "hashicorp/aws" + version = "~> 5.60" + } + random = { + source = "hashicorp/random" + version = "~> 3.6" + } + } +} diff --git a/terraform/litellm/gcp/.terraform.lock.hcl b/terraform/litellm/gcp/.terraform.lock.hcl new file mode 100644 index 00000000000..013391b53d4 --- /dev/null +++ b/terraform/litellm/gcp/.terraform.lock.hcl @@ -0,0 +1,62 @@ +# This file is maintained automatically by "terraform init". +# Manual edits may be lost in future updates. + +provider "registry.terraform.io/hashicorp/google" { + version = "6.50.0" + constraints = "~> 6.10" + hashes = [ + "h1:79CwMTsp3Ud1nOl5hFS5mxQHyT0fGVye7pqpU0PPlHI=", + "zh:1f3513fcfcbf7ca53d667a168c5067a4dd91a4d4cccd19743e248ff31065503c", + "zh:3da7db8fc2c51a77dd958ea8baaa05c29cd7f829bd8941c26e2ea9cb3aadc1e5", + "zh:3e09ac3f6ca8111cbb659d38c251771829f4347ab159a12db195e211c76068bb", + "zh:7bb9e41c568df15ccf1a8946037355eefb4dfb4e35e3b190808bb7c4abae547d", + "zh:81e5d78bdec7778e6d67b5c3544777505db40a826b6eb5abe9b86d4ba396866b", + "zh:8d309d020fb321525883f5c4ea864df3d5942b6087f6656d6d8b3a1377f340fc", + "zh:93e112559655ab95a523193158f4a4ac0f2bfed7eeaa712010b85ebb551d5071", + "zh:d3efe589ffd625b300cef5917c4629513f77e3a7b111c9df65075f76a46a63c7", + "zh:d4a4d672bbef756a870d8f32b35925f8ce2ef4f6bbd5b71a3cb764f1b6c85421", + "zh:e13a86bca299ba8a118e80d5f84fbdd708fe600ecdceea1a13d4919c068379fe", + "zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c", + "zh:fec30c095647b583a246c39d557704947195a1b7d41f81e369ba377d997faef6", + ] +} + +provider "registry.terraform.io/hashicorp/google-beta" { + version = "6.50.0" + constraints = "~> 6.10" + hashes = [ + "h1:P2GiUJM1frlPtBViwKn1A9V2dVBdGuWcX80w9TdH8ZE=", + "zh:18b442bd0a05321d39dda1e9e3f1bdede4e61bc2ac62cc7a67037a3864f75101", + "zh:2e387c51455862828bec923a3ec81abf63a4d998da470cf00e09003bda53d668", + "zh:3942e708fa84ebe54996086f4b1398cb747fe19cbcd0be07ace528291fb35dee", + "zh:496287dd48b34ae6197cb1f887abeafd07c33f389dbe431bb01e24846754cfdd", + "zh:6eca885419969ce5c2a706f34dce1f10bde9774757675f2d8a92d12e5a1be390", + "zh:710dbef826c3fe7f76f844dae47937e8e4c1279dd9205ec4610be04cf3327244", + "zh:777ebf44b24bfc7bdbf770dc089f1a72f143b4718fdedb8c6bd75983115a1ec2", + "zh:9c8703bba37b8c7ad857efc3513392c5a096c519397c1cb822d7612f38e4262f", + "zh:c4f1d3a73de2702277c99d5348ad6d374705bcfdd367ad964ff4cfd2cf06c281", + "zh:eca8df11af3f5a948492d5b8b5d01b4ec705aad10bc30ec1524205508ae28393", + "zh:f41e7fd5f2628e8fd6b8ea136366923858f54428d1729898925469b862c275c2", + "zh:f569b65999264a9416862bca5cd2a6177d94ccb0424f3a4ef424428912b9cb3c", + ] +} + +provider "registry.terraform.io/hashicorp/random" { + version = "3.8.1" + constraints = "~> 3.6" + hashes = [ + "h1:u8AKlWVDTH5r9YLSeswoVEjiY72Rt4/ch7U+61ZDkiQ=", + "zh:08dd03b918c7b55713026037c5400c48af5b9f468f483463321bd18e17b907b4", + "zh:0eee654a5542dc1d41920bbf2419032d6f0d5625b03bd81339e5b33394a3e0ae", + "zh:229665ddf060aa0ed315597908483eee5b818a17d09b6417a0f52fd9405c4f57", + "zh:2469d2e48f28076254a2a3fc327f184914566d9e40c5780b8d96ebf7205f8bc0", + "zh:37d7eb334d9561f335e748280f5535a384a88675af9a9eac439d4cfd663bcb66", + "zh:741101426a2f2c52dee37122f0f4a2f2d6af6d852cb1db634480a86398fa3511", + "zh:78d5eefdd9e494defcb3c68d282b8f96630502cac21d1ea161f53cfe9bb483b3", + "zh:a902473f08ef8df62cfe6116bd6c157070a93f66622384300de235a533e9d4a9", + "zh:b85c511a23e57a2147355932b3b6dce2a11e856b941165793a0c3d7578d94d05", + "zh:c5172226d18eaac95b1daac80172287b69d4ce32750c82ad77fa0768be4ea4b8", + "zh:dab4434dba34aad569b0bc243c2d3f3ff86dd7740def373f2a49816bd2ff819b", + "zh:f49fd62aa8c5525a5c17abd51e27ca5e213881d58882fd42fec4a545b53c9699", + ] +} diff --git a/terraform/litellm/gcp/README.md b/terraform/litellm/gcp/README.md new file mode 100644 index 00000000000..504cfa066e4 --- /dev/null +++ b/terraform/litellm/gcp/README.md @@ -0,0 +1,296 @@ +# LiteLLM on GCP (Cloud Run) + +Deploys the componentized LiteLLM proxy on GCP: + +- **VPC** + Private Services Access range + a Serverless VPC Access connector + so Cloud Run can reach private IPs +- **Cloud SQL for PostgreSQL** — primary instance + cross-zone read replica, + password auth via Secret Manager +- **Memorystore (Redis)** for caching + rate limiting, private IP only +- **GCS bucket** — private, versioned, uniform IAM; exposed as `GCS_BUCKET_NAME` +- **Secret Manager** entries for `LITELLM_MASTER_KEY` and `DATABASE_PASSWORD` +- **Cloud Run v2** services for `gateway` (port 4000), `backend` (port 4001), + and `ui` (port 3000), all using a shared runtime service account +- **Cloud Run Job** (`litellm-migrations`) that runs `prisma migrate deploy` from the dedicated `ghcr.io/berriai/litellm-migrations` image +- **External global HTTP(S) load balancer** with serverless NEGs and a URL + map mirroring the helm-chart ingress path routing: + - LLM data-plane prefixes → `gateway` + - UI asset paths → `ui` + - Everything else → `backend` + +## Image pulls + +There are four images: `litellm-gateway`, `litellm-backend`, `litellm-ui`, +and `litellm-migrations` (slim image used only by the one-off Cloud Run +Job — runs `prisma migrate deploy` against the writer DB and exits). +Bump them together when bumping LiteLLM. + +Cloud Run only accepts images from Artifact Registry, `[region.]gcr.io`, +or `docker.io` — `ghcr.io` URIs are rejected at apply time. The four +images are published to GHCR upstream, so any real deploy needs an +Artifact Registry remote repository pointed at GHCR. + +**One-time setup (per project):** create a remote repo and let Cloud Run +pull through it. + +```bash +gcloud artifacts repositories create litellm \ + --repository-format=docker \ + --location=us-central1 \ + --mode=remote-repository \ + --remote-repo-config-desc="GitHub Container Registry passthrough" \ + --remote-docker-repo=https://ghcr.io +``` + +Then point the stack at it via `image_registry`: + +```hcl +image_registry = "us-central1-docker.pkg.dev/my-gcp-project/litellm/berriai" +image_tag = "v1.86.0-dev" +``` + +The four `litellm-:${image_tag}` URIs are composed from those +two vars. Set `gateway_image` / `backend_image` / `ui_image` / +`migrations_image` only if you need a per-component override (custom +build, different tag). + +Two further notes: + +- The runtime SAs the stack creates do **not** need + `roles/artifactregistry.reader` — Cloud Run pulls images using the + per-project serverless agent + (`service-@serverless-robot-prod.iam.gserviceaccount.com`), + not the runtime SA. +- For a fully air-gapped option, mirror the images into a regular AR + repository instead of a remote repo: + + ```bash + for c in gateway backend ui migrations; do + docker pull ghcr.io/berriai/litellm-$c: + docker tag ghcr.io/berriai/litellm-$c: \ + us-central1-docker.pkg.dev/$PROJECT/litellm/$c: + docker push us-central1-docker.pkg.dev/$PROJECT/litellm/$c: + done + ``` + + then set `image_registry = "us-central1-docker.pkg.dev/$PROJECT/litellm"` + (drop the `/berriai` suffix — the mirrored layout has no org segment). + +## Database authentication + +LiteLLM's `init_iam_db_url_from_env()` mints **AWS RDS** tokens via boto3 — +it doesn't speak GCP IAM. To IAM-auth against Cloud SQL from Cloud Run you'd +need the Cloud SQL Auth Proxy as a sidecar, which complicates the service +spec. This stack therefore uses **password authentication**: + +- A random password is generated and stored in Secret Manager + (`-db-password`). +- Each Cloud Run service receives the password as `DATABASE_PASSWORD` via + `value_source.secret_key_ref`. +- The container's entrypoint shim assembles `DATABASE_URL` (and + `DATABASE_URL_READ_REPLICA`) from `DATABASE_HOST` / `DATABASE_PASSWORD` + before exec'ing uvicorn — so the password never appears in the service + spec or in logs. + +If you need GCP-native IAM auth later, add `cloud-sql-proxy` as a sidecar +container under `template.template.containers` (Cloud Run v2 supports +multiple containers) and replace the password-based URL with the proxy's +Unix socket. + +## Configuring the proxy + +### `proxy_config` + +Mirrors the helm chart's `gateway.config.proxy_config`. The map is +YAML-encoded and base64-passed to gateway, backend, and the migration job; +each container decodes it to `/tmp/litellm-config.yaml` at startup and sets +`CONFIG_FILE_PATH`. + +```hcl +proxy_config = { + model_list = [ + { + model_name = "gpt-4o" + litellm_params = { + model = "openai/gpt-4o" + api_key = "os.environ/OPENAI_API_KEY" + } + }, + ] + general_settings = { + master_key = "os.environ/LITELLM_MASTER_KEY" + database_url = "os.environ/DATABASE_URL" + } +} +``` + +LiteLLM resolves `os.environ/` references against the container +environment. Provider API keys belong in `*_extra_secrets` and are +referenced from the YAML by env-var name. + +### Extra env / secrets + +Non-sensitive env vars: + +```hcl +gateway_extra_env = { + LANGFUSE_HOST = "https://us.cloud.langfuse.com" +} +``` + +Sensitive values — create the secret in Secret Manager first, then reference +its resource ID: + +```bash +echo -n "sk-proj-..." | gcloud secrets create openai-api-key --data-file=- +``` + +```hcl +gateway_extra_secrets = { + OPENAI_API_KEY = "projects/my-gcp-project/secrets/openai-api-key" +} +``` + +The Cloud Run runtime SA auto-gains `roles/secretmanager.secretAccessor` on +every secret referenced. **Pass the bare secret resource ID only** — +`projects/.../secrets/openai-api-key`, never the version-suffixed form +`projects/.../secrets/openai-api-key/versions/3`. The Cloud Run +`secret_key_ref` binding and the stack's IAM `secret_id` grant both +reject the version suffix; version is always resolved as `latest`. If +you need a pinned version, edit `local.gateway_extra_secret_kv` in +`cloudrun.tf` directly to set `version = "3"` for the entry in question. + +## Tenant deployment + +Every resource the stack creates is named `${tenant}-litellm-${env}` (or +that plus a per-resource suffix), so multiple tenants and multiple +environments coexist in the same project as long as the `(tenant, env)` +pair differs: + +| `tenant` | `env` | Example resource name | +| -------- | ------- | ---------------------------------- | +| `acme` | `stage` | `acme-litellm-stage-gateway` | +| `acme` | `prod` | `acme-litellm-prod-master-key` | +| `globex` | `dev` | `globex-litellm-dev-license` | + +For a per-tenant instance, the only inputs that change are the tenant +slug, env, and the two pre-issued secrets: + +```bash +export TF_VAR_litellm_master_key="sk-..." # the tenant's master key +export TF_VAR_litellm_license="lic-..." # their LITELLM_LICENSE + +terraform apply \ + -var "project=my-gcp-project" \ + -var "region=us-central1" \ + -var "tenant=acme" \ + -var "env=stage" +``` + +Both `litellm_master_key` and `litellm_license` are optional: +- Omit `litellm_master_key` → the stack auto-generates a random `sk-…` + value (trial/dev path). +- Omit `litellm_license` → no license secret is created and gateway/ + backend run without `LITELLM_LICENSE` (OSS-only). + +Use `TF_VAR_*` env vars rather than tfvars files for these — values +written to a tfvars file end up in `terraform.tfstate` and any committed +example files. + +## Quick start + +```bash +cd terraform/litellm/gcp +cp terraform.tfvars.example terraform.tfvars +# Edit: project, region, tenant, env, *_image, proxy_config, gateway_extra_secrets. + +terraform init +terraform apply +``` + +That single apply provisions everything, runs the prisma schema migration via +the Cloud Run job (auto-triggered by `bootstrap.tf`), and only then starts the +gateway/backend services. When it returns, the stack is serving traffic. + +```bash +terraform output lb_url +# UI login: admin / +gcloud secrets versions access latest --secret="$(terraform output -raw master_key_secret_id)" +``` + +The `migration_run_command` output is preserved for break-glass manual re-runs. + +**Prerequisite**: `gcloud` must be authenticated (`gcloud auth login`) and the +required APIs must be enabled (run, sqladmin, redis, secretmanager, +vpcaccess, compute, servicenetworking, storage, artifactregistry). + +## TLS + +`terraform plan` refuses to provision an HTTP-only LB by default — TLS +is the supported posture. Two paths: + +**Production / staging — set `lb_domains`:** + +1. `terraform apply` once with `allow_plaintext_lb = true` (intentional + chicken-and-egg escape hatch) to provision the LB and read the anycast + IP from `terraform output -raw lb_ip`. +2. Point each DNS name you want to serve from at that IP. +3. Set `lb_domains = ["proxy.example.com"]` and remove + `allow_plaintext_lb`; re-apply. + +Result: a 443 forwarding rule with a Google-managed cert covering each +listed domain; the 80 forwarding rule is rewritten to serve a permanent +301 redirect to HTTPS, so HTTP clients are automatically upgraded. The +managed cert sits in `PROVISIONING` for ~15-60 min on first apply until +DNS propagation completes — `gcloud compute ssl-certificates describe +-litellm--cert` shows the state. + +**Trial / dev — explicitly opt into HTTP-only:** + +Set `allow_plaintext_lb = true` and leave `lb_domains = []`. Without the +flag, plan fails with a clear error pointing at the precondition. +Intended for short-lived trial / dev stacks only. + +## Storage and database retention + +Two opt-in tripwires guard against accidental data loss on +`terraform destroy`: + +- **`cloudsql_deletion_protection`** (Cloud SQL writer + reader; + default `true`) — destroy fails with a clear error rather than + dropping the database. +- **`gcs_force_destroy`** (GCS bucket holding request log archives, + `/v1/files` content, and the GCS cache backend; default `false`) — + `terraform destroy` against a non-empty bucket fails. + +Flip `cloudsql_deletion_protection` to `false` or `gcs_force_destroy` to +`true` only for ephemeral / CI stacks where you accept losing the data. + +## Redis encryption + +Memorystore runs with `transit_encryption_mode = "SERVER_AUTHENTICATION"`, +so the proxy connects via `rediss://`. The instance's self-signed CA cert +(`server_ca_certs[0].cert`) is shipped to gateway + backend as +`REDIS_CA_PEM_B64`; their entrypoint shell decodes it to `/tmp/redis-ca.pem` +before uvicorn starts and points `REDIS_SSL_CA_CERTS` at that path. No +extra config needed — but if you ever swap Memorystore for an external +Redis, override `REDIS_HOST`/`REDIS_PORT` and either drop these env vars +or point them at your own CA. + +## Files + +| File | What's in it | +| ----------------- | -------------------------------------------------------------------- | +| `versions.tf` | Terraform + provider version constraints | +| `providers.tf` | Google + Google-Beta providers | +| `variables.tf` | All input variables | +| `locals.tf` | Path-prefix lists (mirror of `helm/.../ingress.yaml`) + proxy_config helpers | +| `network.tf` | VPC, subnet, PSA range, Serverless VPC connector | +| `secrets.tf` | Secret Manager entries + random master_key | +| `cloudsql.tf` | Cloud SQL writer + read replica + app user + password secret | +| `redis.tf` | Memorystore Redis (private IP) | +| `gcs.tf` | GCS bucket + objectAdmin binding | +| `iam.tf` | Runtime SA + Cloud SQL client + Secret Manager accessor | +| `cloudrun.tf` | 3 Cloud Run services + Cloud Run Job for migrations | +| `load_balancer.tf`| External HTTPS LB, serverless NEGs, URL map for path routing | +| `outputs.tf` | LB IP, service URLs, secret IDs, migration `execute` command | diff --git a/terraform/litellm/gcp/bootstrap.tf b/terraform/litellm/gcp/bootstrap.tf new file mode 100644 index 00000000000..47ad885ff12 --- /dev/null +++ b/terraform/litellm/gcp/bootstrap.tf @@ -0,0 +1,43 @@ +# Auto-runs the prisma schema migration as part of `terraform apply`. Mirrors +# the AWS stack's terraform_data.migration in spirit. Cloud SQL doesn't need a +# separate user-bootstrap step because google_sql_user.app already creates the +# application user — so the only post-cluster work is the migration. +# +# Gateway/backend Cloud Run services depend on this resource (in cloudrun.tf) +# so they don't go live until the schema is in place. +# +# Triggers: +# - re-runs if the migrations image changes (new release ships new prisma +# migration files). +# - re-runs if the migration job is recreated. +# +# Requires `gcloud` on the machine running terraform, with user creds live +# enough to invoke Cloud Run admin APIs (`gcloud auth login`). + +resource "terraform_data" "migration" { + triggers_replace = { + job_id = google_cloud_run_v2_job.migrations.id + job_image = local.migrations_image + } + + provisioner "local-exec" { + interpreter = ["bash", "-c"] + environment = { + JOB = google_cloud_run_v2_job.migrations.name + REGION = var.region + PROJECT = var.project + } + command = <<-EOT + set -euo pipefail + gcloud run jobs execute "$JOB" \ + --region "$REGION" \ + --project "$PROJECT" \ + --wait + EOT + } + + depends_on = [ + google_cloud_run_v2_job.migrations, + google_sql_user.app, + ] +} diff --git a/terraform/litellm/gcp/cloudrun.tf b/terraform/litellm/gcp/cloudrun.tf new file mode 100644 index 00000000000..28e1145b081 --- /dev/null +++ b/terraform/litellm/gcp/cloudrun.tf @@ -0,0 +1,430 @@ +# Three Cloud Run v2 services + one Cloud Run v2 job for migrations. +# All four use the same service account and the same VPC connector for +# private egress to Cloud SQL + Memorystore. + +locals { + # Memorystore exposes a self-signed CA cert per instance; we ship it as + # a base64 env var and decode it to a file at container startup so the + # rediss:// connection can validate. Public cert, not sensitive. + redis_ca_pem_b64 = base64encode(google_redis_instance.this.server_ca_certs[0].cert) + + shared_env_kv = [ + { name = "DATABASE_HOST", value = google_sql_database_instance.writer.private_ip_address }, + { name = "DATABASE_PORT", value = "5432" }, + { name = "DATABASE_USER", value = var.db_username }, + { name = "DATABASE_NAME", value = var.db_name }, + { name = "DATABASE_HOST_READ_REPLICA", value = google_sql_database_instance.reader.private_ip_address }, + { name = "DATABASE_PORT_READ_REPLICA", value = "5432" }, + { name = "REDIS_HOST", value = google_redis_instance.this.host }, + { name = "REDIS_PORT", value = tostring(google_redis_instance.this.port) }, + # _redis.get_redis_url_from_environment honors REDIS_SSL to flip the + # scheme to rediss://; REDIS_SSL_CA_CERTS is mapped via + # _get_redis_env_kwarg_mapping → ssl_ca_certs on the redis-py client. + { name = "REDIS_SSL", value = "true" }, + { name = "REDIS_SSL_CA_CERTS", value = "/tmp/redis-ca.pem" }, + { name = "REDIS_CA_PEM_B64", value = local.redis_ca_pem_b64 }, + { name = "GCS_BUCKET_NAME", value = google_storage_bucket.this.name }, + ] + + # Cloud Run v2 secret env vars use value_source.secret_key_ref pointing at a + # secret resource ID. Shared between gateway and backend (the migrations + # job has its own narrower env list — see migrations_env_secrets below). + shared_env_secrets = concat( + [ + { name = "LITELLM_MASTER_KEY", secret = google_secret_manager_secret.master_key.id, version = "latest" }, + { name = "DATABASE_PASSWORD", secret = google_secret_manager_secret.db_password.id, version = "latest" }, + ], + var.litellm_license == "" ? [] : [ + { name = "LITELLM_LICENSE", secret = google_secret_manager_secret.license[0].id, version = "latest" }, + ], + ) + + # Backend-only managed secrets. UI_PASSWORD is consumed by the management + # API (UI login flow) and has no use on the gateway data plane. + backend_managed_env_secrets = var.ui_password == "" ? [] : [ + { name = "UI_PASSWORD", secret = google_secret_manager_secret.ui_password[0].id, version = "latest" }, + ] + + # Per-component extras (from variables). + gateway_extra_env_kv = [ + for k, v in var.gateway_extra_env : { name = k, value = v } + ] + backend_extra_env_kv = [ + for k, v in var.backend_extra_env : { name = k, value = v } + ] + + backend_default_env_kv = [ + { name = "STORE_MODEL_IN_DB", value = "true" }, + ] + gateway_extra_secret_kv = [ + for k, v in var.gateway_extra_secrets : { name = k, secret = v, version = "latest" } + ] + backend_extra_secret_kv = [ + for k, v in var.backend_extra_secrets : { name = k, secret = v, version = "latest" } + ] + + # Shell fragments composed with && so any failure short-circuits the + # whole startup instead of falling through to `exec uvicorn`. The + # python step is only included when the caller provided a proxy_config. + proxy_config_fragment = local.proxy_config_enabled ? [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['CONFIG_FILE_PATH']).write_bytes(base64.b64decode(os.environ['LITELLM_PROXY_CONFIG_B64']))\"" + ] : [] + + # Decode the Memorystore CA cert (passed as REDIS_CA_PEM_B64) to the + # path REDIS_SSL_CA_CERTS points at, so the redis-py client can validate + # the rediss:// handshake. + redis_ca_fragment = [ + "python -c \"import os, base64, pathlib; pathlib.Path(os.environ['REDIS_SSL_CA_CERTS']).write_bytes(base64.b64decode(os.environ['REDIS_CA_PEM_B64']))\"" + ] + + database_url_fragment = [ + "export DATABASE_URL=\"postgresql://$${DATABASE_USER}:$${DATABASE_PASSWORD}@$${DATABASE_HOST}:$${DATABASE_PORT}/$${DATABASE_NAME}\"", + "export DATABASE_URL_READ_REPLICA=\"postgresql://$${DATABASE_USER}:$${DATABASE_PASSWORD}@$${DATABASE_HOST_READ_REPLICA}:$${DATABASE_PORT_READ_REPLICA}/$${DATABASE_NAME}\"", + ] + + gateway_args = join(" && ", concat( + local.proxy_config_fragment, + local.redis_ca_fragment, + local.database_url_fragment, + ["exec uvicorn gateway.main:app --host 0.0.0.0 --port 4000"], + )) + + backend_args = join(" && ", concat( + local.proxy_config_fragment, + local.redis_ca_fragment, + local.database_url_fragment, + ["exec uvicorn backend.main:app --host 0.0.0.0 --port 4001"], + )) + + # Env shipped to the migrations Job. The migrations image runs run.py + # which assembles DATABASE_URL from these discrete vars itself, so we + # only need writer-side DB env (no read replica, no proxy_config, no + # master key). + migrations_env_kv = [ + { name = "DATABASE_HOST", value = google_sql_database_instance.writer.private_ip_address }, + { name = "DATABASE_PORT", value = "5432" }, + { name = "DATABASE_USER", value = var.db_username }, + { name = "DATABASE_NAME", value = var.db_name }, + ] + + migrations_env_secrets = [ + { name = "DATABASE_PASSWORD", secret = google_secret_manager_secret.db_password.id, version = "latest" }, + ] +} + +# ---------- Gateway ---------- +resource "google_cloud_run_v2_service" "gateway" { + name = "${local.name}-gateway" + location = var.region + ingress = "INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER" + + template { + service_account = google_service_account.runtime.email + max_instance_request_concurrency = var.gateway_max_instance_request_concurrency + + vpc_access { + connector = google_vpc_access_connector.this.id + egress = "PRIVATE_RANGES_ONLY" + } + + scaling { + min_instance_count = var.gateway_min_instances + max_instance_count = var.gateway_max_instances + } + + containers { + image = local.gateway_image + command = ["sh", "-c"] + args = [local.gateway_args] + + ports { + container_port = 4000 + } + + resources { + limits = { + cpu = var.gateway_cpu + memory = var.gateway_memory + } + } + + dynamic "env" { + for_each = concat(local.shared_env_kv, local.gateway_extra_env_kv, local.proxy_config_env) + content { + name = env.value.name + value = env.value.value + } + } + + dynamic "env" { + for_each = concat(local.shared_env_secrets, local.gateway_extra_secret_kv) + content { + name = env.value.name + value_source { + secret_key_ref { + secret = env.value.secret + version = env.value.version + } + } + } + } + + startup_probe { + http_get { + path = "/health/readiness" + port = 4000 + } + initial_delay_seconds = 10 + period_seconds = 10 + timeout_seconds = 5 + failure_threshold = 12 + } + + liveness_probe { + http_get { + path = "/health/liveliness" + port = 4000 + } + period_seconds = 30 + timeout_seconds = 5 + } + } + } + + depends_on = [ + google_secret_manager_secret_iam_member.master_key, + google_secret_manager_secret_iam_member.db_password, + google_secret_manager_secret_iam_member.license, + google_secret_manager_secret_iam_member.extras, + google_sql_user.app, + # Don't go live until the schema is migrated; otherwise the proxy boots, + # fails on missing tables, and Cloud Run keeps cold-restarting. + terraform_data.migration, + ] +} + +# ---------- Backend ---------- +resource "google_cloud_run_v2_service" "backend" { + name = "${local.name}-backend" + location = var.region + ingress = "INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER" + + template { + service_account = google_service_account.runtime.email + max_instance_request_concurrency = var.backend_max_instance_request_concurrency + + vpc_access { + connector = google_vpc_access_connector.this.id + egress = "PRIVATE_RANGES_ONLY" + } + + scaling { + min_instance_count = var.backend_min_instances + max_instance_count = var.backend_max_instances + } + + containers { + image = local.backend_image + command = ["sh", "-c"] + args = [local.backend_args] + + ports { + container_port = 4001 + } + + resources { + limits = { + cpu = var.backend_cpu + memory = var.backend_memory + } + } + + dynamic "env" { + for_each = concat(local.shared_env_kv, local.backend_default_env_kv, local.backend_extra_env_kv, local.proxy_config_env) + content { + name = env.value.name + value = env.value.value + } + } + + dynamic "env" { + for_each = concat(local.shared_env_secrets, local.backend_managed_env_secrets, local.backend_extra_secret_kv) + content { + name = env.value.name + value_source { + secret_key_ref { + secret = env.value.secret + version = env.value.version + } + } + } + } + + startup_probe { + http_get { + path = "/health/readiness" + port = 4001 + } + initial_delay_seconds = 10 + period_seconds = 10 + timeout_seconds = 5 + failure_threshold = 12 + } + + liveness_probe { + http_get { + path = "/health/liveliness" + port = 4001 + } + period_seconds = 30 + timeout_seconds = 5 + } + } + } + + depends_on = [ + google_secret_manager_secret_iam_member.master_key, + google_secret_manager_secret_iam_member.db_password, + google_secret_manager_secret_iam_member.license, + google_secret_manager_secret_iam_member.ui_password, + google_secret_manager_secret_iam_member.extras, + google_sql_user.app, + terraform_data.migration, + ] +} + +# ---------- UI ---------- +# Static nginx — no DB, no Redis, no secrets. Runs as ui_runtime, a SA +# with zero IAM bindings, so a compromised UI container can't pivot to +# Secret Manager / Cloud SQL via the metadata service. +resource "google_cloud_run_v2_service" "ui" { + name = "${local.name}-ui" + location = var.region + ingress = "INGRESS_TRAFFIC_INTERNAL_LOAD_BALANCER" + + template { + service_account = google_service_account.ui_runtime.email + max_instance_request_concurrency = var.ui_max_instance_request_concurrency + + scaling { + min_instance_count = var.ui_min_instances + max_instance_count = var.ui_max_instances + } + + containers { + image = local.ui_image + + ports { + container_port = 3000 + } + + resources { + limits = { + cpu = var.ui_cpu + memory = var.ui_memory + } + } + + startup_probe { + http_get { + path = "/healthz" + port = 3000 + } + initial_delay_seconds = 5 + period_seconds = 10 + timeout_seconds = 3 + failure_threshold = 6 + } + } + } +} + +# Allow the LB (any unauthenticated traffic from the configured serverless +# NEG) to invoke the Cloud Run services. The actual auth is in the proxy +# (LITELLM_MASTER_KEY); these IAM bindings just open up Cloud Run's invoker +# gate so the LB request makes it to the container. +resource "google_cloud_run_v2_service_iam_member" "gateway_allusers" { + project = var.project + location = google_cloud_run_v2_service.gateway.location + name = google_cloud_run_v2_service.gateway.name + role = "roles/run.invoker" + member = "allUsers" +} + +resource "google_cloud_run_v2_service_iam_member" "backend_allusers" { + project = var.project + location = google_cloud_run_v2_service.backend.location + name = google_cloud_run_v2_service.backend.name + role = "roles/run.invoker" + member = "allUsers" +} + +resource "google_cloud_run_v2_service_iam_member" "ui_allusers" { + project = var.project + location = google_cloud_run_v2_service.ui.location + name = google_cloud_run_v2_service.ui.name + role = "roles/run.invoker" + member = "allUsers" +} + +# ---------- Migrations job ---------- +# Dedicated litellm-migrations image — slim, ENTRYPOINT runs run.py which +# assembles DATABASE_URL from the DATABASE_* env vars and runs `prisma +# migrate deploy`. No proxy_config, no master key, no shell wrapper. +resource "google_cloud_run_v2_job" "migrations" { + name = "${local.name}-migrations" + location = var.region + + template { + template { + service_account = google_service_account.runtime.email + + vpc_access { + connector = google_vpc_access_connector.this.id + egress = "PRIVATE_RANGES_ONLY" + } + + containers { + image = local.migrations_image + + # Prisma's Node + Rust engine plus the v2 migration resolver + # routinely peaks above 1 GiB while applying the schema, so 2 GiB + # is the floor — 1 GiB OOM-kills mid-migrate. CPU stays at 1 vCPU + # (Cloud Run requires >= 1 with concurrency > 1, and `prisma + # migrate deploy` is single-threaded so more buys nothing). + resources { + limits = { + cpu = "1000m" + memory = "4Gi" + } + } + + dynamic "env" { + for_each = local.migrations_env_kv + content { + name = env.value.name + value = env.value.value + } + } + + dynamic "env" { + for_each = local.migrations_env_secrets + content { + name = env.value.name + value_source { + secret_key_ref { + secret = env.value.secret + version = env.value.version + } + } + } + } + } + } + } + + depends_on = [ + google_secret_manager_secret_iam_member.db_password, + google_sql_user.app, + ] +} diff --git a/terraform/litellm/gcp/cloudsql.tf b/terraform/litellm/gcp/cloudsql.tf new file mode 100644 index 00000000000..70939c049c3 --- /dev/null +++ b/terraform/litellm/gcp/cloudsql.tf @@ -0,0 +1,102 @@ +# Cloud SQL for PostgreSQL — one primary + one read replica. +# +# Note on auth: LiteLLM's IAM-auth helper (rds_iam_token.py) mints AWS RDS +# tokens via boto3 and doesn't speak GCP IAM. Cloud SQL IAM auth from Cloud +# Run requires the Cloud SQL Auth Proxy as a sidecar, which complicates the +# Cloud Run service spec. We instead use password auth: a random password +# lives in Secret Manager and is injected into the Cloud Run services as +# DATABASE_PASSWORD. The writer's DATABASE_URL is assembled inside the +# container at startup; the reader URL is built from the replica's IP. + +resource "google_sql_database_instance" "writer" { + name = local.name + region = var.region + database_version = var.db_version + + depends_on = [google_service_networking_connection.psa] + + settings { + # ENTERPRISE accepts the db-custom-* and db-n1-* tiers we default to. + # ENTERPRISE_PLUS only accepts db-perf-optimized-* and is ~3x cost — set + # var.db_edition = "ENTERPRISE_PLUS" + change var.db_tier together if you + # want it. + edition = var.db_edition + tier = var.db_tier + availability_type = "REGIONAL" + disk_size = 20 + disk_autoresize = true + + backup_configuration { + enabled = true + point_in_time_recovery_enabled = true + start_time = "07:00" + } + + ip_configuration { + ipv4_enabled = false + private_network = google_compute_network.this.id + } + + insights_config { + query_insights_enabled = true + record_application_tags = true + record_client_address = true + } + } + + deletion_protection = var.cloudsql_deletion_protection +} + +resource "google_sql_database_instance" "reader" { + name = "${local.name}-reader" + region = var.region + database_version = var.db_version + master_instance_name = google_sql_database_instance.writer.name + + depends_on = [google_service_networking_connection.psa] + + settings { + edition = var.db_edition + tier = var.db_tier + availability_type = "ZONAL" + disk_autoresize = true + + ip_configuration { + ipv4_enabled = false + private_network = google_compute_network.this.id + } + } + + deletion_protection = var.cloudsql_deletion_protection +} + +resource "google_sql_database" "this" { + name = var.db_name + instance = google_sql_database_instance.writer.name +} + +resource "random_password" "db_password" { + length = 32 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +resource "google_sql_user" "app" { + name = var.db_username + instance = google_sql_database_instance.writer.name + password = random_password.db_password.result +} + +resource "google_secret_manager_secret" "db_password" { + secret_id = "${local.name}-db-password" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "db_password" { + secret = google_secret_manager_secret.db_password.id + secret_data = random_password.db_password.result +} diff --git a/terraform/litellm/gcp/gcs.tf b/terraform/litellm/gcp/gcs.tf new file mode 100644 index 00000000000..86511d38a31 --- /dev/null +++ b/terraform/litellm/gcp/gcs.tf @@ -0,0 +1,29 @@ +# General-purpose GCS bucket — same role as the AWS S3 bucket. The bucket +# name is exposed to gateway + backend as GCS_BUCKET_NAME; reference it +# from proxy_config via `os.environ/GCS_BUCKET_NAME`. + +resource "random_id" "bucket_suffix" { + byte_length = 4 +} + +resource "google_storage_bucket" "this" { + name = "${var.project}-${local.name}-${random_id.bucket_suffix.hex}" + location = var.region + uniform_bucket_level_access = true + force_destroy = var.gcs_force_destroy + + versioning { + enabled = true + } + + public_access_prevention = "enforced" + + labels = var.labels +} + +# Cloud Run runtime SA gains object admin on this bucket only. +resource "google_storage_bucket_iam_member" "runtime" { + bucket = google_storage_bucket.this.name + role = "roles/storage.objectAdmin" + member = "serviceAccount:${google_service_account.runtime.email}" +} diff --git a/terraform/litellm/gcp/iam.tf b/terraform/litellm/gcp/iam.tf new file mode 100644 index 00000000000..93a9997ed7a --- /dev/null +++ b/terraform/litellm/gcp/iam.tf @@ -0,0 +1,71 @@ +# Runtime SA used by the gateway, backend, and migration job — has Cloud +# SQL client + Secret Manager accessor on every managed/extra secret. The +# UI deliberately uses a *different* SA (below) so a compromised UI +# container can't read master_key / db_password / license / ui_password / +# provider creds via the metadata service. +resource "google_service_account" "runtime" { + account_id = "${local.name}-runtime" + display_name = "LiteLLM Cloud Run runtime" +} + +# UI runtime SA — no role bindings. The UI is static nginx with no DB, +# Redis, or Secret Manager dependencies, so its task identity should not +# be able to read any of those. Cloud Run pulls the UI image via the +# project's serverless service agent (not this SA), so it doesn't need +# artifactregistry.reader either. +resource "google_service_account" "ui_runtime" { + account_id = "${local.name}-ui-runtime" + display_name = "LiteLLM Cloud Run UI runtime (no data-plane access)" +} + +# Cloud SQL client — lets the Cloud Run services connect to the instance +# over private IP via the VPC connector. +resource "google_project_iam_member" "runtime_cloudsql" { + project = var.project + role = "roles/cloudsql.client" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# Secret Manager accessor — managed secrets first (split out as separate +# resources because their IDs are computed-at-apply and can't drive a +# for_each). +resource "google_secret_manager_secret_iam_member" "master_key" { + secret_id = google_secret_manager_secret.master_key.id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +resource "google_secret_manager_secret_iam_member" "db_password" { + secret_id = google_secret_manager_secret.db_password.id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# License secret accessor — only created when var.litellm_license is set. +resource "google_secret_manager_secret_iam_member" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret_id = google_secret_manager_secret.license[0].id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# UI password secret accessor — only created when var.ui_password is set. +resource "google_secret_manager_secret_iam_member" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret_id = google_secret_manager_secret.ui_password[0].id + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} + +# User-supplied extras. Dedupe on the secret resource ID — two different +# env-var names could reference the same secret, and we want exactly one +# IAM binding per (secret, role, member) tuple in state. +resource "google_secret_manager_secret_iam_member" "extras" { + for_each = toset(values(merge(var.gateway_extra_secrets, var.backend_extra_secrets))) + + secret_id = each.value + role = "roles/secretmanager.secretAccessor" + member = "serviceAccount:${google_service_account.runtime.email}" +} diff --git a/terraform/litellm/gcp/load_balancer.tf b/terraform/litellm/gcp/load_balancer.tf new file mode 100644 index 00000000000..b0081786f13 --- /dev/null +++ b/terraform/litellm/gcp/load_balancer.tf @@ -0,0 +1,185 @@ +# External global HTTP(S) load balancer fronting all three Cloud Run +# services. URL map mirrors the helm-chart ingress path routing: +# - LLM data-plane paths → gateway +# - UI asset paths → ui +# - Everything else → backend (management API: /key/*, /user/*, …) +# +# By default the LB serves plain HTTP on port 80. Set var.lb_domains to a +# list of DNS names already pointing at lb_ip and the stack provisions a +# Google-managed SSL cert + 443 forwarding rule, and the 80 forwarding rule +# is rewritten to redirect HTTP→HTTPS via a redirect-only URL map. + +locals { + tls_enabled = length(var.lb_domains) > 0 +} + +resource "google_compute_global_address" "lb" { + name = "${local.name}-lb-ip" +} + +# Serverless NEGs — one per Cloud Run service. +resource "google_compute_region_network_endpoint_group" "gateway" { + name = "${local.name}-gateway-neg" + region = var.region + network_endpoint_type = "SERVERLESS" + + cloud_run { + service = google_cloud_run_v2_service.gateway.name + } +} + +resource "google_compute_region_network_endpoint_group" "backend" { + name = "${local.name}-backend-neg" + region = var.region + network_endpoint_type = "SERVERLESS" + + cloud_run { + service = google_cloud_run_v2_service.backend.name + } +} + +resource "google_compute_region_network_endpoint_group" "ui" { + name = "${local.name}-ui-neg" + region = var.region + network_endpoint_type = "SERVERLESS" + + cloud_run { + service = google_cloud_run_v2_service.ui.name + } +} + +# Backend services wrap each NEG. +resource "google_compute_backend_service" "gateway" { + name = "${local.name}-gateway-bs" + protocol = "HTTP" + load_balancing_scheme = "EXTERNAL_MANAGED" + + backend { + group = google_compute_region_network_endpoint_group.gateway.id + } +} + +resource "google_compute_backend_service" "backend" { + name = "${local.name}-backend-bs" + protocol = "HTTP" + load_balancing_scheme = "EXTERNAL_MANAGED" + + backend { + group = google_compute_region_network_endpoint_group.backend.id + } +} + +resource "google_compute_backend_service" "ui" { + name = "${local.name}-ui-bs" + protocol = "HTTP" + load_balancing_scheme = "EXTERNAL_MANAGED" + + backend { + group = google_compute_region_network_endpoint_group.ui.id + } +} + +# URL map. Default → backend (management API). Path matchers route the +# gateway and UI prefixes elsewhere. +resource "google_compute_url_map" "this" { + name = local.name + default_service = google_compute_backend_service.backend.id + + host_rule { + hosts = ["*"] + path_matcher = "main" + } + + path_matcher { + name = "main" + default_service = google_compute_backend_service.backend.id + + # UI paths (catch them before any /v1/* gateway rules so /favicon.ico + # and / take precedence). + path_rule { + paths = local.ui_path_prefixes + service = google_compute_backend_service.ui.id + } + + # Gateway path prefixes. GCP URL maps cap a path_rule at 10 path globs, + # so chunk into rules of 10. + dynamic "path_rule" { + for_each = { for idx, chunk in chunklist(local.gateway_path_prefixes, 10) : idx => chunk } + content { + paths = path_rule.value + service = google_compute_backend_service.gateway.id + } + } + } +} + +# Permanent HTTP→HTTPS redirect URL map. Only attached to the port-80 +# target proxy when TLS is enabled; otherwise the regular path-routing +# URL map is attached to the HTTP proxy and everything stays plaintext. +resource "google_compute_url_map" "https_redirect" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-redirect" + + default_url_redirect { + https_redirect = true + redirect_response_code = "MOVED_PERMANENTLY_DEFAULT" + strip_query = false + } +} + +resource "google_compute_target_http_proxy" "this" { + name = "${local.name}-http" + url_map = local.tls_enabled ? google_compute_url_map.https_redirect[0].id : google_compute_url_map.this.id + + # Default-deny on the HTTP-only path: TLS is the supported posture. + # Operators must either supply DNS names or explicitly opt in. + lifecycle { + precondition { + condition = local.tls_enabled || var.allow_plaintext_lb + error_message = "LB has no HTTPS forwarding rule. Either set `lb_domains` to a list of DNS names you want a Google-managed cert for, or set `allow_plaintext_lb = true` to opt into HTTP-only (trial / dev only)." + } + } +} + +resource "google_compute_global_forwarding_rule" "http" { + name = "${local.name}-http" + ip_protocol = "TCP" + port_range = "80" + load_balancing_scheme = "EXTERNAL_MANAGED" + ip_address = google_compute_global_address.lb.address + target = google_compute_target_http_proxy.this.id +} + +# ---------- HTTPS (gated on var.lb_domains) ---------- +# +# Google-managed certs require each listed domain to resolve to lb_ip +# *before* the cert provisions; on first apply the cert sits in +# PROVISIONING for ~15-60 min until DNS propagates. The LB starts serving +# 443 immediately, but cert handshakes fail until the managed cert +# transitions to ACTIVE. + +resource "google_compute_managed_ssl_certificate" "this" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-cert" + + managed { + domains = var.lb_domains + } +} + +resource "google_compute_target_https_proxy" "this" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-https" + url_map = google_compute_url_map.this.id + ssl_certificates = [google_compute_managed_ssl_certificate.this[0].id] +} + +resource "google_compute_global_forwarding_rule" "https" { + count = local.tls_enabled ? 1 : 0 + name = "${local.name}-https" + ip_protocol = "TCP" + port_range = "443" + load_balancing_scheme = "EXTERNAL_MANAGED" + ip_address = google_compute_global_address.lb.address + target = google_compute_target_https_proxy.this[0].id +} diff --git a/terraform/litellm/gcp/locals.tf b/terraform/litellm/gcp/locals.tf new file mode 100644 index 00000000000..2d1231fb197 --- /dev/null +++ b/terraform/litellm/gcp/locals.tf @@ -0,0 +1,79 @@ +# Gateway path prefixes — mirrored verbatim from gateway/routes/allowlist.py +# and helm/litellm/templates/ingress.yaml. URL maps use the "path matcher" +# rule with `paths` lists; up to 10 path globs per rule, up to 50 rules +# per matcher. Easily fits the gateway list in one rule per chunk-of-10. +locals { + # Every resource the stack creates is named `${tenant}-litellm-${env}` + # (or that with a per-resource suffix). Computed once here so the rest of + # the stack can reference local.name. + name = "${var.tenant}-litellm-${var.env}" + + gateway_path_prefixes = [ + "/v1/chat/*", "/chat/*", + "/v1/completions*", "/completions*", + "/v1/embeddings*", "/embeddings*", + "/v1/moderations*", "/moderations*", + "/v1/audio/*", "/audio/*", + "/v1/images/*", "/images/*", + "/v1/files*", "/files*", + "/v1/batches*", "/batches*", + "/v1/fine_tuning/*", "/fine_tuning/*", + "/v1/fine-tuning/*", "/fine-tuning/*", + "/v1/responses*", "/responses*", + "/v1/threads*", "/threads*", + "/v1/assistants*", "/assistants*", + "/v1/vector_stores*", "/vector_stores*", + "/v1/indexes*", + "/v1/models*", "/models*", + "/openai/*", "/engines/*", + "/v1/messages*", "/messages*", + "/v1/skills/*", "/v1/a2a/*", + "/v1/rerank*", "/v2/rerank*", "/rerank*", + "/v1/ocr*", "/ocr*", + "/v1/rag/*", "/rag/*", + "/v1/video/*", "/v1/videos/*", "/video/*", "/videos/*", + "/v1/search*", "/search*", + "/v1/containers/*", "/containers/*", + "/v1/evals/*", + "/v1/memory/*", + "/queue/chat/*", + "/v1beta/*", + "/interactions/*", + "/anthropic/*", "/azure/*", "/azure_ai/*", "/aws/*", "/bedrock/*", + "/cohere/*", "/gemini/*", "/google/*", + "/vertex_ai/*", "/vertex-ai/*", + "/assemblyai/*", "/eu.assemblyai/*", + "/langfuse/*", "/vllm/*", + "/mistral/*", "/groq/*", "/voyage/*", "/cursor/*", "/milvus/*", + "/openai_passthrough/*", + "/toolset/*", + "/v1/realtime*", "/realtime*", + "/health*", "/metrics", "/test*", + ] + + ui_path_prefixes = [ + "/", + "/favicon.ico", + "/litellm-asset-prefix/*", + "/_next/*", + "/assets/*", + "/ui", + "/ui/*", + ] + + proxy_config_enabled = length(keys(var.proxy_config)) > 0 + proxy_config_b64 = local.proxy_config_enabled ? base64encode(yamlencode(var.proxy_config)) : "" + + proxy_config_env = local.proxy_config_enabled ? [ + { name = "LITELLM_PROXY_CONFIG_B64", value = local.proxy_config_b64 }, + { name = "CONFIG_FILE_PATH", value = "/tmp/litellm-config.yaml" }, + ] : [] + + # Resolved image URIs: per-component override wins, otherwise compose + # from image_registry + image_tag. Cloud Run only accepts AR / gcr.io / + # docker.io paths — see variables.tf for the full constraint list. + gateway_image = var.gateway_image != "" ? var.gateway_image : "${var.image_registry}/litellm-gateway:${var.image_tag}" + backend_image = var.backend_image != "" ? var.backend_image : "${var.image_registry}/litellm-backend:${var.image_tag}" + ui_image = var.ui_image != "" ? var.ui_image : "${var.image_registry}/litellm-ui:${var.image_tag}" + migrations_image = var.migrations_image != "" ? var.migrations_image : "${var.image_registry}/litellm-migrations:${var.image_tag}" +} diff --git a/terraform/litellm/gcp/network.tf b/terraform/litellm/gcp/network.tf new file mode 100644 index 00000000000..a1ccaed02f9 --- /dev/null +++ b/terraform/litellm/gcp/network.tf @@ -0,0 +1,46 @@ +resource "google_compute_network" "this" { + name = local.name + auto_create_subnetworks = false + routing_mode = "REGIONAL" +} + +resource "google_compute_subnetwork" "this" { + name = "${local.name}-${var.region}" + region = var.region + network = google_compute_network.this.id + ip_cidr_range = var.subnet_cidr + private_ip_google_access = true +} + +# Private Services Access (PSA) range for Cloud SQL + Memorystore. Both +# managed services peer with the VPC over the connection below using +# addresses from this range. +resource "google_compute_global_address" "psa" { + name = "${local.name}-psa" + purpose = "VPC_PEERING" + address_type = "INTERNAL" + prefix_length = 16 + network = google_compute_network.this.id +} + +resource "google_service_networking_connection" "psa" { + network = google_compute_network.this.id + service = "servicenetworking.googleapis.com" + reserved_peering_ranges = [google_compute_global_address.psa.name] +} + +# Serverless VPC Access connector — required so Cloud Run can reach +# Cloud SQL / Memorystore private IPs via the PSA range. +# +# min/max instances are required by the API now (you can't just set +# machine_type alone). Defaults: 2 e2-micro instances scale up to 3 — fine +# for low-to-moderate Cloud Run egress; bump max if your services push +# heavy private-network traffic. +resource "google_vpc_access_connector" "this" { + name = "${local.name}-conn" + region = var.region + network = google_compute_network.this.name + ip_cidr_range = var.vpc_connector_cidr + min_instances = 2 + max_instances = 3 +} diff --git a/terraform/litellm/gcp/outputs.tf b/terraform/litellm/gcp/outputs.tf new file mode 100644 index 00000000000..df25215adcc --- /dev/null +++ b/terraform/litellm/gcp/outputs.tf @@ -0,0 +1,64 @@ +output "lb_ip" { + description = "Global anycast IP of the external HTTPS load balancer." + value = google_compute_global_address.lb.address +} + +output "lb_url" { + description = "Proxy URL. Switches scheme based on whether lb_domains is set; when TLS is enabled the URL points at the first listed domain (since managed certs are tied to the hostname, not the anycast IP). The dashboard is served at /, the API at /v1/*." + value = local.tls_enabled ? "https://${var.lb_domains[0]}" : "http://${google_compute_global_address.lb.address}" +} + +output "gateway_service_url" { + description = "Default Cloud Run URL for the gateway (bypasses the LB)." + value = google_cloud_run_v2_service.gateway.uri +} + +output "backend_service_url" { + description = "Default Cloud Run URL for the backend (bypasses the LB)." + value = google_cloud_run_v2_service.backend.uri +} + +output "ui_service_url" { + description = "Default Cloud Run URL for the UI (bypasses the LB)." + value = google_cloud_run_v2_service.ui.uri +} + +output "cloudsql_writer_ip" { + description = "Private IP of the Cloud SQL writer." + value = google_sql_database_instance.writer.private_ip_address +} + +output "cloudsql_reader_ip" { + description = "Private IP of the Cloud SQL read replica." + value = google_sql_database_instance.reader.private_ip_address +} + +output "redis_endpoint" { + description = "Memorystore Redis endpoint." + value = "${google_redis_instance.this.host}:${google_redis_instance.this.port}" +} + +output "gcs_bucket" { + description = "GCS bucket name. Exposed to gateway + backend as GCS_BUCKET_NAME. Reference from proxy_config via `os.environ/GCS_BUCKET_NAME`." + value = google_storage_bucket.this.name +} + +output "master_key_secret_id" { + description = "Secret Manager resource ID holding LITELLM_MASTER_KEY. Fetch with `gcloud secrets versions access latest --secret=`." + value = google_secret_manager_secret.master_key.secret_id +} + +output "db_password_secret_id" { + description = "Secret Manager resource ID holding the Cloud SQL app-user password." + value = google_secret_manager_secret.db_password.secret_id +} + +output "migration_run_command" { + description = "Shell command that executes the one-off migration job against Cloud SQL. Run this once after the first apply." + value = format( + "gcloud run jobs execute %s --region %s --project %s --wait", + google_cloud_run_v2_job.migrations.name, + var.region, + var.project, + ) +} diff --git a/terraform/litellm/gcp/providers.tf b/terraform/litellm/gcp/providers.tf new file mode 100644 index 00000000000..fd1584463f8 --- /dev/null +++ b/terraform/litellm/gcp/providers.tf @@ -0,0 +1,9 @@ +provider "google" { + project = var.project + region = var.region +} + +provider "google-beta" { + project = var.project + region = var.region +} diff --git a/terraform/litellm/gcp/redis.tf b/terraform/litellm/gcp/redis.tf new file mode 100644 index 00000000000..f7e174ecbae --- /dev/null +++ b/terraform/litellm/gcp/redis.tf @@ -0,0 +1,20 @@ +resource "google_redis_instance" "this" { + name = local.name + tier = var.redis_tier + memory_size_gb = var.redis_memory_size_gb + region = var.region + + authorized_network = google_compute_network.this.id + connect_mode = "PRIVATE_SERVICE_ACCESS" + + redis_version = "REDIS_7_0" + + # In-transit encryption between Cloud Run and Memorystore. The instance + # exposes its self-signed CA via `server_ca_certs` (read in cloudrun.tf + # and passed to the proxy as REDIS_CA_PEM_B64); the proxy decodes it to + # /tmp/redis-ca.pem at startup and uses it to validate the rediss:// + # handshake. Mirrors `transit_encryption_enabled = true` on AWS. + transit_encryption_mode = "SERVER_AUTHENTICATION" + + depends_on = [google_service_networking_connection.psa] +} diff --git a/terraform/litellm/gcp/secrets.tf b/terraform/litellm/gcp/secrets.tf new file mode 100644 index 00000000000..80312e06a91 --- /dev/null +++ b/terraform/litellm/gcp/secrets.tf @@ -0,0 +1,62 @@ +resource "random_password" "master_key" { + length = 48 + special = false + min_lower = 4 + min_upper = 4 + min_numeric = 4 +} + +# LITELLM_MASTER_KEY (sk-…) lives in Secret Manager. The Cloud Run service +# account gets accessor permission on it (see iam.tf). +resource "google_secret_manager_secret" "master_key" { + secret_id = "${local.name}-master-key" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "master_key" { + secret = google_secret_manager_secret.master_key.id + # When the operator passes litellm_master_key, use it verbatim. Otherwise + # fall back to the auto-generated `sk-…` value (trial / OSS path). + secret_data = coalesce(var.litellm_master_key, "sk-${random_password.master_key.result}") +} + +# LITELLM_LICENSE — only created when the operator supplies one. The runtime +# SA gets accessor permission via iam.tf, and gateway + backend pick it up +# through shared_env_secrets in cloudrun.tf. +resource "google_secret_manager_secret" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret_id = "${local.name}-license" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "license" { + count = var.litellm_license == "" ? 0 : 1 + + secret = google_secret_manager_secret.license[0].id + secret_data = var.litellm_license +} + +# UI_PASSWORD — backend-only. Same pattern as license: only created when +# the operator supplies one. The runtime SA gets accessor permission via +# iam.tf, and the backend service picks the env var up through +# backend_managed_env_secrets in cloudrun.tf. +resource "google_secret_manager_secret" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret_id = "${local.name}-ui-password" + replication { + auto {} + } +} + +resource "google_secret_manager_secret_version" "ui_password" { + count = var.ui_password == "" ? 0 : 1 + + secret = google_secret_manager_secret.ui_password[0].id + secret_data = var.ui_password +} diff --git a/terraform/litellm/gcp/terraform.tfvars.example b/terraform/litellm/gcp/terraform.tfvars.example new file mode 100644 index 00000000000..5c22a14c6d6 --- /dev/null +++ b/terraform/litellm/gcp/terraform.tfvars.example @@ -0,0 +1,77 @@ +project = "my-gcp-project" +region = "us-central1" + +# Resource naming: every GCP resource the stack creates is named +# `${tenant}-litellm-${env}` (or that plus a per-resource suffix). E.g. +# tenant="acme" + env="stage" → Cloud Run service `acme-litellm-stage-gateway`, +# Cloud SQL instance `acme-litellm-stage`, etc. +tenant = "acme" +env = "stage" + +# Tenant-supplied secrets. Prefer TF_VAR_litellm_master_key / +# TF_VAR_litellm_license / TF_VAR_ui_password env vars so the values don't +# end up in a committed tfvars file. All three are optional — when +# omitted the stack auto-generates a master key, runs without a license, +# and falls back to LITELLM_MASTER_KEY for UI login. +# litellm_master_key = "sk-..." +# litellm_license = "lic-..." +# ui_password = "..." + +# TLS: provide DNS names already pointing at the LB IP for a Google-managed +# cert. Without one, plan fails unless allow_plaintext_lb = true is set +# explicitly (trial/dev only). +# lb_domains = ["proxy.example.com"] +# allow_plaintext_lb = true + +# Storage and database retention. Defaults are safe — destroy preserves +# data. Flip these only for ephemeral / CI stacks. +# cloudsql_deletion_protection = true # default: refuse destroy on the DB +# gcs_force_destroy = false # default: refuse destroy on a non-empty bucket + +# Component images. Defaults pin all four to the same GHCR release tag — +# bump them together when bumping LiteLLM. To use private images, mirror +# them into Artifact Registry first — Cloud Run only authenticates against +# AR / gcr.io. +# gateway_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/gateway:1.86.0-dev" +# backend_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/backend:1.86.0-dev" +# ui_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/ui:1.86.0-dev" +# migrations_image = "us-central1-docker.pkg.dev/my-gcp-project/litellm/migrations:1.86.0-dev" + +# ---------- proxy_config (mirrors helm gateway.config.proxy_config) ---------- +# proxy_config = { +# model_list = [ +# { +# model_name = "gpt-4o" +# litellm_params = { +# model = "openai/gpt-4o" +# api_key = "os.environ/OPENAI_API_KEY" +# } +# }, +# ] +# general_settings = { +# master_key = "os.environ/LITELLM_MASTER_KEY" +# database_url = "os.environ/DATABASE_URL" +# } +# } + +# ---------- Extra env / secrets ---------- +# Plain-text env vars (non-sensitive). Land directly in the Cloud Run service spec. +# gateway_extra_env = { +# LANGFUSE_HOST = "https://us.cloud.langfuse.com" +# } + +# Backend env vars commonly tuned in prod: SSO redirect, docs branding, +# UI admin username. UI_PASSWORD is its own first-class var (see top). +# backend_extra_env = { +# AUTO_REDIRECT_UI_LOGIN_TO_SSO = "true" +# DOCS_TITLE = "Acme LiteLLM" +# UI_USERNAME = "admin" +# } + +# Provider API keys — Secret Manager resource IDs (NOT secret values). The +# Cloud Run SA auto-gains roles/secretmanager.secretAccessor on every +# secret listed here. Same shape works for backend_extra_secrets. +# gateway_extra_secrets = { +# OPENAI_API_KEY = "projects/my-gcp-project/secrets/openai-api-key" +# ANTHROPIC_API_KEY = "projects/my-gcp-project/secrets/anthropic-api-key" +# } diff --git a/terraform/litellm/gcp/variables.tf b/terraform/litellm/gcp/variables.tf new file mode 100644 index 00000000000..fe726b0317a --- /dev/null +++ b/terraform/litellm/gcp/variables.tf @@ -0,0 +1,405 @@ +variable "project" { + description = "GCP project ID." + type = string +} + +variable "region" { + description = "GCP region for VPC, Cloud SQL, Memorystore, Cloud Run, and the LB IP." + type = string + default = "us-central1" +} + +variable "tenant" { + description = "Tenant slug — used as the prefix for every GCP resource the stack creates. Combined with var.env to form `-litellm-` (e.g. `acme-litellm-stage`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,20}$", var.tenant)) + error_message = "tenant must be 1-21 chars, lower-kebab-case, starting with a letter." + } +} + +variable "env" { + description = "Environment suffix appended to every resource name (e.g. `stage`, `prod`, `dev`)." + type = string + + validation { + condition = can(regex("^[a-z][a-z0-9-]{0,8}$", var.env)) + error_message = "env must be 1-9 chars, lower-kebab-case, starting with a letter." + } +} + +variable "labels" { + description = "Resource labels merged into every label-supporting resource." + type = map(string) + default = { + "managed-by" = "terraform" + } +} + +# ---------- Tenant-supplied secrets ---------- +# +# Both default to "" so the stack stays usable for trial / OSS deploys. +# Set via TF_VAR_litellm_master_key / TF_VAR_litellm_license to keep the +# values out of state files committed to a VCS. + +variable "litellm_master_key" { + description = <<-EOT + Pre-existing LITELLM_MASTER_KEY (must begin with `sk-`). When set, this + value is written to the master-key Secret Manager entry. When empty, + the stack auto-generates a random `sk-…` key (preserving today's + trial-deploy behavior). + EOT + type = string + default = "" + sensitive = true +} + +variable "litellm_license" { + description = <<-EOT + LiteLLM enterprise license string. When set, the stack creates a + `-litellm--license` Secret Manager entry, grants the + runtime SA accessor on it, and exposes its value to gateway + backend + as `LITELLM_LICENSE`. Leave empty for OSS-only deploys. + EOT + type = string + default = "" + sensitive = true +} + +variable "ui_password" { + description = <<-EOT + UI admin password. When set, the stack creates a + `-litellm--ui-password` Secret Manager entry, grants the + runtime SA accessor on it, and exposes its value to the backend as + `UI_PASSWORD`. Pair with `backend_extra_env.UI_USERNAME` to set the + matching username. Leave empty to skip — the proxy then falls back to + the LITELLM_MASTER_KEY for UI login. + EOT + type = string + default = "" + sensitive = true +} + +# ---------- Networking ---------- + +variable "subnet_cidr" { + description = "Primary CIDR block for the LiteLLM subnet." + type = string + default = "10.40.0.0/16" +} + +variable "vpc_connector_cidr" { + description = "CIDR for the Serverless VPC Access connector. /28 required." + type = string + default = "10.41.0.0/28" +} + +# ---------- Component images ---------- +# +# Cloud Run only pulls from Artifact Registry, [region.]gcr.io, or +# docker.io — it rejects arbitrary registries (notably ghcr.io) at apply +# time. The four images live on GHCR upstream, so any real deploy must +# either set `image_registry` to an Artifact Registry remote repository +# pointed at ghcr.io (e.g. `us-central1-docker.pkg.dev/my-proj/litellm/berriai`) +# or override the per-component `*_image` vars individually with full URIs. + +variable "image_registry" { + description = <<-EOT + Registry path prefix used to compose the four LiteLLM image URIs as + `/litellm-:`. The default + (`ghcr.io/berriai`) only works on registries Cloud Run accepts — for + GHCR-backed deploys, create an Artifact Registry remote repository + pointed at `https://ghcr.io` and set this to that repo's path + (e.g. `us-central1-docker.pkg.dev///berriai`). + Per-component overrides (`gateway_image`, `backend_image`, `ui_image`, + `migrations_image`) bypass this entirely when set. + EOT + type = string + default = "ghcr.io/berriai" +} + +variable "image_tag" { + description = "Tag applied to all four litellm-* images when composed from `image_registry`. Bump in lockstep when bumping LiteLLM. Must match a tag actually published to GHCR — the split images use the `v`-prefixed semver convention (e.g. `v1.86.0-dev`)." + type = string + default = "v1.86.0-dev" +} + +variable "gateway_image" { + description = "Full image URI for the gateway. Empty (default) composes from `image_registry` + `image_tag`. Public images or Artifact Registry only — Cloud Run won't authenticate against arbitrary private registries." + type = string + default = "" +} + +variable "backend_image" { + description = "Full image URI for the backend. Empty (default) composes from `image_registry` + `image_tag`." + type = string + default = "" +} + +variable "ui_image" { + description = "Full image URI for the UI. Empty (default) composes from `image_registry` + `image_tag`." + type = string + default = "" +} + +variable "migrations_image" { + description = <<-EOT + Full image URI for the one-off prisma migration Cloud Run Job. Empty + (default) composes from `image_registry` + `image_tag` as + `litellm-migrations`. Built from `migrations/Dockerfile` — slim image + whose ENTRYPOINT runs `python3 /app/run.py` (assembles DATABASE_URL + from DATABASE_* env vars via DatabaseURLSettings, then runs + `prisma migrate deploy`). Should track the same release tag as + gateway/backend/ui. + EOT + type = string + default = "" +} + +# ---------- Service sizing ---------- + +variable "gateway_cpu" { + description = "Cloud Run CPU per gateway instance." + type = string + default = "1000m" +} + +variable "gateway_memory" { + description = "Cloud Run memory per gateway instance." + type = string + default = "4Gi" +} + +# Cloud Run autoscales out of the box (request-rate driven). The min/max +# bounds mirror the HPA replica bounds in helm/litellm/values.yaml so each +# stack scales over the same range. Cloud Run has no direct CPU-utilization +# target; the request-concurrency knob below is the closest analog. + +variable "gateway_min_instances" { + description = "Lower bound on gateway Cloud Run instances. Matches helm HPA minReplicas." + type = number + default = 1 +} + +variable "gateway_max_instances" { + description = "Upper bound on gateway Cloud Run instances. Matches helm HPA maxReplicas." + type = number + default = 10 +} + +variable "gateway_max_instance_request_concurrency" { + description = "Concurrent requests one gateway instance handles before Cloud Run scales out. Cloud Run v2 default is 80; lower it for LLM streams that pin a worker for tens of seconds." + type = number + default = 80 +} + +variable "backend_cpu" { + description = "Cloud Run CPU per backend instance. Cloud Run rejects sub-1 CPU when `backend_max_instance_request_concurrency > 1`, so the default is 1000m. Lower this only if you also drop concurrency to 1." + type = string + default = "1000m" +} + +variable "backend_memory" { + description = "Cloud Run memory per backend instance." + type = string + default = "4Gi" +} + +variable "backend_min_instances" { + description = "Lower bound on backend Cloud Run instances. Matches helm HPA minReplicas." + type = number + default = 1 +} + +variable "backend_max_instances" { + description = "Upper bound on backend Cloud Run instances. Matches helm HPA maxReplicas." + type = number + default = 4 +} + +variable "backend_max_instance_request_concurrency" { + description = "Concurrent requests one backend instance handles before Cloud Run scales out." + type = number + default = 80 +} + +variable "ui_cpu" { + description = "Cloud Run CPU per UI instance. Cloud Run rejects sub-1 CPU when `ui_max_instance_request_concurrency > 1`, so the default is 1000m. Lower this only if you also drop concurrency to 1 (which makes nginx scale 1:1 with traffic — almost never what you want)." + type = string + default = "1000m" +} + +variable "ui_memory" { + description = "Cloud Run memory per UI instance. Cloud Run rejects `< 512Mi` when CPU is always-allocated (the default whenever `ui_min_instances > 0`), so the default is 512Mi." + type = string + default = "512Mi" +} + +variable "ui_min_instances" { + description = "Lower bound on UI Cloud Run instances. Matches helm HPA minReplicas." + type = number + default = 1 +} + +variable "ui_max_instances" { + description = "Upper bound on UI Cloud Run instances. Matches helm HPA maxReplicas." + type = number + default = 3 +} + +variable "ui_max_instance_request_concurrency" { + description = "Concurrent requests one UI instance handles before Cloud Run scales out. The UI is static nginx, so this can be high." + type = number + default = 200 +} + +# ---------- Cloud SQL ---------- + +variable "db_tier" { + description = "Cloud SQL tier (machine type) for the writer instance." + type = string + default = "db-custom-2-7680" +} + +variable "db_edition" { + description = "Cloud SQL edition. ENTERPRISE accepts the db-custom-* and db-n1-* tiers. ENTERPRISE_PLUS only accepts db-perf-optimized-* tiers and is ~3x cost — change db_tier in lockstep when switching." + type = string + default = "ENTERPRISE" + + validation { + condition = contains(["ENTERPRISE", "ENTERPRISE_PLUS"], var.db_edition) + error_message = "db_edition must be ENTERPRISE or ENTERPRISE_PLUS." + } +} + +variable "db_version" { + description = "Cloud SQL Postgres version." + type = string + default = "POSTGRES_16" +} + +variable "db_name" { + description = "Initial database created on the Cloud SQL instance." + type = string + default = "litellm" +} + +variable "db_username" { + description = "Application Postgres user (password-auth). Password is auto-generated and stored in Secret Manager." + type = string + default = "litellm_app" +} + +variable "lb_domains" { + description = <<-EOT + DNS names for a Google-managed SSL certificate fronting the LB. When + non-empty, the stack provisions a 443 forwarding rule + HTTPS target + proxy + managed cert covering these domains, and the existing 80 + forwarding rule serves a permanent 301 redirect to HTTPS. Leave empty + ([]) to disable TLS (must combine with `allow_plaintext_lb = true` for + the plan to succeed — see README.md "TLS"). Each domain must already + resolve to the LB's anycast IP (`lb_ip` output) for managed-cert + provisioning to succeed. + EOT + type = list(string) + default = [] +} + +variable "allow_plaintext_lb" { + description = <<-EOT + Opt into HTTP-only mode on the load balancer (port 80, no TLS). + Default false: `terraform plan` fails when `lb_domains = []` so the + operator must either provide DNS names for a managed cert or + consciously opt out. Intended for short-lived trial / dev stacks only. + EOT + type = bool + default = false +} + +variable "cloudsql_deletion_protection" { + description = "Cloud SQL instance-level deletion protection (writer + reader). Default true — `terraform destroy` (and `terraform apply` operations that replace the instance) will fail with a clear error rather than silently dropping the database. Set false only for ephemeral / CI environments." + type = bool + default = true +} + +variable "gcs_force_destroy" { + description = <<-EOT + Allow `terraform destroy` to delete the GCS bucket even when it still + contains objects (request log archives, /v1/files storage, GCS cache + backend). Default false — destroying a non-empty bucket fails, acting + as a tripwire against accidental data loss. Set true only for + ephemeral / CI environments. Mirrors `s3_force_destroy` on AWS and + `cloudsql_deletion_protection` on the database side. + EOT + type = bool + default = false +} + +# ---------- Memorystore (Redis) ---------- + +variable "redis_tier" { + description = "Memorystore tier — STANDARD_HA for production, BASIC for dev." + type = string + default = "STANDARD_HA" +} + +variable "redis_memory_size_gb" { + type = number + default = 1 +} + +# ---------- Extras / proxy_config ---------- + +variable "gateway_extra_env" { + description = "Plain-text env vars layered onto the gateway." + type = map(string) + default = {} +} + +variable "backend_extra_env" { + description = "Plain-text env vars layered onto the backend." + type = map(string) + default = {} +} + +variable "gateway_extra_secrets" { + description = <<-EOT + Extra env vars sourced from Google Secret Manager, applied to the + gateway. Map of env-var name to the Secret Manager **secret resource + ID** (`projects//secrets/` — *not* a version resource + ID; the Cloud Run secret_key_ref binding and the stack's IAM grant + both reject `/versions/` suffixes). Versions are always resolved + as `latest`; if you need a pinned version, edit + `local.gateway_extra_secret_kv` in `cloudrun.tf` directly. + + Example: + gateway_extra_secrets = { + OPENAI_API_KEY = "projects/my-proj/secrets/openai-api-key" + } + + The Cloud Run service account auto-gains roles/secretmanager.secretAccessor + on each secret listed here. + EOT + type = map(string) + default = {} +} + +variable "backend_extra_secrets" { + description = "Same shape as gateway_extra_secrets (secret resource ID, version always `latest`), layered onto the backend." + type = map(string) + default = {} +} + +variable "proxy_config" { + description = <<-EOT + LiteLLM proxy config (contents of config.yaml). Mirrors the helm chart's + `gateway.config.proxy_config`. Passed to gateway, backend, and the + migration job as a base64-encoded env var and decoded to + /tmp/litellm-config.yaml at container start; CONFIG_FILE_PATH is set + automatically. Reference env-injected secrets from the YAML via + `os.environ/`. Leave empty ({}) to skip. + EOT + type = any + default = {} +} diff --git a/terraform/litellm/gcp/versions.tf b/terraform/litellm/gcp/versions.tf new file mode 100644 index 00000000000..a630c59afd0 --- /dev/null +++ b/terraform/litellm/gcp/versions.tf @@ -0,0 +1,18 @@ +terraform { + required_version = ">= 1.6.0" + + required_providers { + google = { + source = "hashicorp/google" + version = "~> 6.10" + } + google-beta = { + source = "hashicorp/google-beta" + version = "~> 6.10" + } + random = { + source = "hashicorp/random" + version = "~> 3.6" + } + } +} 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_gpt4o_audio.py b/tests/llm_translation/test_gpt4o_audio.py index 169fe855163..a50d07406d4 100644 --- a/tests/llm_translation/test_gpt4o_audio.py +++ b/tests/llm_translation/test_gpt4o_audio.py @@ -59,7 +59,7 @@ async def test_audio_output_from_model(stream): litellm.set_verbose = False try: completion = await litellm.acompletion( - model="gpt-4o-audio-preview", + model="gpt-audio-1.5", modalities=["text", "audio"], audio={"voice": "alloy", "format": "pcm16"}, messages=[{"role": "user", "content": "response in 1 word - yes or no"}], @@ -69,8 +69,14 @@ async def test_audio_output_from_model(stream): print(e) pytest.skip("Skipping test due to timeout") except Exception as e: - if "openai-internal" in str(e): - pytest.skip("Skipping test due to openai-internal error") + err = str(e).lower() + if ( + "model_not_found" in err + or "does not exist" in err + or "openai-internal" in err + ): + pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}") + raise if stream is True: await check_streaming_response(completion) @@ -85,7 +91,7 @@ async def test_audio_output_from_model(stream): @pytest.mark.asyncio @pytest.mark.parametrize("stream", [True, False]) -@pytest.mark.parametrize("model", ["gpt-4o-audio-preview"]) # "gpt-4o-audio-preview", +@pytest.mark.parametrize("model", ["gpt-audio-1.5"]) async def test_audio_input_to_model(stream, model): # Fetch the audio file and convert it to a base64 encoded string audio_format = "pcm16" @@ -121,9 +127,14 @@ async def test_audio_input_to_model(stream, model): print(e) pytest.skip("Skipping test due to timeout") except Exception as e: - if "openai-internal" in str(e): - pytest.skip("Skipping test due to openai-internal error") - raise e + err = str(e).lower() + if ( + "model_not_found" in err + or "does not exist" in err + or "openai-internal" in err + ): + pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}") + raise if stream is True: await check_streaming_response(completion) else: 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..06637b844b1 100644 --- a/tests/local_testing/conftest.py +++ b/tests/local_testing/conftest.py @@ -22,14 +22,31 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from tests._vcr_conftest_common import ( # noqa: E402 +# ``litellm.model_cost`` is loaded at import time from the URL pinned to +# ``main`` (``LITELLM_MODEL_COST_MAP_URL``). The in-tree backup ships with +# this branch and can include pricing entries that main has not yet picked +# up (e.g. an upstream provider rotates a model id and the test cassette +# records the new name). Backfill any entries that are missing from the +# remote-fetched map so cost-calculator lookups in tests succeed against +# the cassette state the branch is being tested with. +from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap + +_local_cost_map = GetModelCostMap.load_local_model_cost_map() +for _k, _v in _local_cost_map.items(): + litellm.model_cost.setdefault(_k, _v) +del _local_cost_map + +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 +101,7 @@ 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 +111,7 @@ 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/local_testing/test_custom_callback_input.py b/tests/local_testing/test_custom_callback_input.py index 545039e60ba..6a4ec9206f7 100644 --- a/tests/local_testing/test_custom_callback_input.py +++ b/tests/local_testing/test_custom_callback_input.py @@ -1125,7 +1125,7 @@ def test_standard_logging_payload_audio(turn_off_message_logging, stream): ) as mock_client: try: response = litellm.completion( - model="gpt-4o-audio-preview", + model="gpt-audio-1.5", modalities=["text", "audio"], audio={"voice": "alloy", "format": "pcm16"}, messages=[ @@ -1134,8 +1134,14 @@ def test_standard_logging_payload_audio(turn_off_message_logging, stream): stream=stream, ) except Exception as e: - if "openai-internal" in str(e): - pytest.skip("Skipping test due to openai-internal error") + err = str(e).lower() + if ( + "model_not_found" in err + or "does not exist" in err + or "openai-internal" in err + ): + pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}") + raise if stream: for chunk in response: diff --git a/tests/local_testing/test_stream_chunk_builder.py b/tests/local_testing/test_stream_chunk_builder.py index 24fdf49c16c..38e04b93f18 100644 --- a/tests/local_testing/test_stream_chunk_builder.py +++ b/tests/local_testing/test_stream_chunk_builder.py @@ -649,7 +649,7 @@ def test_stream_chunk_builder_openai_audio_output_usage(): try: completion = client.chat.completions.create( - model="gpt-4o-audio-preview", + model="gpt-audio-1.5", modalities=["text", "audio"], audio={"voice": "alloy", "format": "pcm16"}, messages=[{"role": "user", "content": "response in 1 word - yes or no"}], @@ -657,8 +657,14 @@ def test_stream_chunk_builder_openai_audio_output_usage(): stream_options={"include_usage": True}, ) except Exception as e: - if "openai-internal" in str(e): - pytest.skip("Skipping test due to openai-internal error") + err = str(e).lower() + if ( + "model_not_found" in err + or "does not exist" in err + or "openai-internal" in err + ): + pytest.skip(f"Skipping - upstream gpt-audio-1.5 unavailable: {e}") + raise chunks = [] for chunk in completion: 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/proxy_unit_tests/test_gemini_agents_endpoints.py b/tests/proxy_unit_tests/test_gemini_agents_endpoints.py new file mode 100644 index 00000000000..bdac9348f71 --- /dev/null +++ b/tests/proxy_unit_tests/test_gemini_agents_endpoints.py @@ -0,0 +1,519 @@ +""" +Unit tests for litellm/proxy/google_endpoints/agents_endpoints.py + +Focus: verify that list_gemini_agents, get_gemini_agent, delete_gemini_agent, +and list_gemini_agent_versions correctly forward per-request credentials +(api_key, api_base, …) supplied via the JSON-encoded litellm_params_template +query parameter. Flat credential query params (e.g. ?api_key=…) are no +longer accepted — they would appear in server logs. +""" + +import json +import os +import sys +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +from fastapi import Request +from fastapi.datastructures import Headers, QueryParams + +sys.path.insert(0, os.path.abspath("../..")) + +from litellm.proxy.google_endpoints.agents_endpoints import ( + _merge_query_params_into_data, +) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _make_request(query_string: str = "") -> MagicMock: + """Build a minimal mock Request whose query_params match *query_string*.""" + req = MagicMock(spec=Request) + req.query_params = QueryParams(query_string) + req.headers = Headers({}) + return req + + +# --------------------------------------------------------------------------- +# _merge_query_params_into_data – unit tests for the helper +# --------------------------------------------------------------------------- + + +class TestMergeQueryParamsIntoData: + def test_no_query_params_leaves_data_unchanged(self): + data = {"custom_llm_provider": "gemini"} + request = _make_request("") + result = _merge_query_params_into_data(data, request) + assert result == {"custom_llm_provider": "gemini"} + + def test_flat_api_key_is_ignored(self): + """Flat credential params must NOT be merged (they leak into server logs).""" + data = {"custom_llm_provider": "gemini"} + request = _make_request("api_key=AIzaSyTest123") + _merge_query_params_into_data(data, request) + assert "api_key" not in data + assert data["custom_llm_provider"] == "gemini" + + def test_flat_params_are_silently_dropped(self): + """Flat params (including name injection attempts) are ignored entirely.""" + data = {"name": "my-agent", "custom_llm_provider": "gemini"} + request = _make_request("name=INJECTED&api_key=AIzaSyTest") + _merge_query_params_into_data(data, request) + assert data["name"] == "my-agent" + assert "api_key" not in data + + def test_litellm_params_template_json_is_expanded(self): + template = json.dumps( + {"api_key": "AIzaFromTemplate", "api_base": "https://example.com"} + ) + from urllib.parse import quote + + request = _make_request(f"litellm_params_template={quote(template)}") + data = {"custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + assert data["api_key"] == "AIzaFromTemplate" + assert data["api_base"] == "https://example.com" + # The raw template key itself must NOT appear in data + assert "litellm_params_template" not in data + + def test_litellm_params_template_does_not_overwrite_existing(self): + template = json.dumps( + {"api_key": "FromTemplate", "custom_llm_provider": "openai"} + ) + from urllib.parse import quote + + request = _make_request(f"litellm_params_template={quote(template)}") + data = {"custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + # custom_llm_provider was already set; template must not override it + assert data["custom_llm_provider"] == "gemini" + assert data["api_key"] == "FromTemplate" + + def test_invalid_litellm_params_template_json_is_ignored(self): + request = _make_request("litellm_params_template=NOT_VALID_JSON") + data = {"custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + # Bad JSON is silently skipped; other data stays intact + assert data == {"custom_llm_provider": "gemini"} + + def test_template_only_no_flat_params_merged(self): + """Only litellm_params_template is expanded; unknown flat params are dropped.""" + template = json.dumps({"api_key": "FromTemplate"}) + from urllib.parse import quote + + qs = f"litellm_params_template={quote(template)}&vertex_project=my-project" + request = _make_request(qs) + data = {"custom_llm_provider": "gemini"} + _merge_query_params_into_data(data, request) + assert data["api_key"] == "FromTemplate" + # flat vertex_project is ignored since it wasn't in litellm_params_template + assert "vertex_project" not in data + assert "litellm_params_template" not in data + + +# --------------------------------------------------------------------------- +# Endpoint-level smoke tests: data dict is populated before the processor call +# --------------------------------------------------------------------------- + + +@pytest.fixture +def mock_srv(): + """Patch _proxy_server_imports to return lightweight fakes.""" + srv = { + "general_settings": {}, + "llm_router": MagicMock(), + "proxy_config": MagicMock(), + "proxy_logging_obj": MagicMock(), + "select_data_generator": MagicMock(), + "user_api_base": None, + "user_max_tokens": None, + "user_model": None, + "user_request_timeout": None, + "user_temperature": None, + "version": "0.0.0", + } + with patch( + "litellm.proxy.google_endpoints.agents_endpoints._proxy_server_imports", + return_value=srv, + ): + yield srv + + +@pytest.fixture +def user_api_key_dict(): + from litellm.proxy._types import UserAPIKeyAuth + + return UserAPIKeyAuth(api_key="test-key") + + +def _make_endpoint_request(query_string: str = "") -> MagicMock: + req = MagicMock(spec=Request) + req.query_params = QueryParams(query_string) + req.headers = Headers({}) + req.scope = {} + + async def _body(): + return b"" + + req.body = _body + return req + + +@pytest.mark.asyncio +async def test_list_gemini_agents_passes_api_key_to_processor( + mock_srv, user_api_key_dict +): + from urllib.parse import quote + + from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents + + template = json.dumps({"api_key": "AIzaListTest"}) + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request(f"litellm_params_template={quote(template)}") + await list_gemini_agents( + request=request, + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + + init_data = MockProcessor.call_args[1]["data"] + assert init_data.get("api_key") == "AIzaListTest" + assert init_data.get("custom_llm_provider") == "gemini" + + +@pytest.mark.asyncio +async def test_get_gemini_agent_passes_api_key_to_processor( + mock_srv, user_api_key_dict +): + from urllib.parse import quote + + from litellm.proxy.google_endpoints.agents_endpoints import get_gemini_agent + + template = json.dumps({"api_key": "AIzaGetTest"}) + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request(f"litellm_params_template={quote(template)}") + await get_gemini_agent( + request=request, + name="my-agent", + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + + init_data = MockProcessor.call_args[1]["data"] + assert init_data.get("api_key") == "AIzaGetTest" + assert init_data.get("name") == "my-agent" + assert init_data.get("custom_llm_provider") == "gemini" + + +@pytest.mark.asyncio +async def test_delete_gemini_agent_passes_api_key_to_processor( + mock_srv, user_api_key_dict +): + from urllib.parse import quote + + from litellm.proxy.google_endpoints.agents_endpoints import delete_gemini_agent + + template = json.dumps({"api_key": "AIzaDeleteTest"}) + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request(f"litellm_params_template={quote(template)}") + await delete_gemini_agent( + request=request, + name="my-agent", + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + + init_data = MockProcessor.call_args[1]["data"] + assert init_data.get("api_key") == "AIzaDeleteTest" + assert init_data.get("name") == "my-agent" + assert init_data.get("custom_llm_provider") == "gemini" + + +@pytest.mark.asyncio +async def test_list_gemini_agent_versions_passes_api_key_to_processor( + mock_srv, user_api_key_dict +): + from urllib.parse import quote + + from litellm.proxy.google_endpoints.agents_endpoints import ( + list_gemini_agent_versions, + ) + + template = json.dumps({"api_key": "AIzaVersionsTest"}) + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request(f"litellm_params_template={quote(template)}") + await list_gemini_agent_versions( + request=request, + name="my-agent", + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + + init_data = MockProcessor.call_args[1]["data"] + assert init_data.get("api_key") == "AIzaVersionsTest" + assert init_data.get("name") == "my-agent" + assert init_data.get("custom_llm_provider") == "gemini" + + +@pytest.mark.asyncio +async def test_get_gemini_agent_name_not_overwritten_by_query_param( + mock_srv, user_api_key_dict +): + """Path-param ``name`` must not be replaced by an attacker-controlled query param.""" + from urllib.parse import quote + + from litellm.proxy.google_endpoints.agents_endpoints import get_gemini_agent + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + # Even if a caller tries to inject "name" via flat query param, it is + # ignored (flat params are not merged). The path-param name wins. + # ``api_key`` is supplied via the JSON template (required for non-admin + # callers — see test_*_non_admin_without_api_key_is_rejected below). + template = json.dumps({"api_key": "AIzaTest"}) + request = _make_endpoint_request( + f"name=INJECTED&litellm_params_template={quote(template)}" + ) + await get_gemini_agent( + request=request, + name="real-agent", + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + + init_data = MockProcessor.call_args[1]["data"] + assert init_data["name"] == "real-agent" + + +@pytest.mark.asyncio +async def test_list_agents_template_via_query_param(mock_srv, user_api_key_dict): + """litellm_params_template in query string is expanded.""" + from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents + from urllib.parse import quote + + template = json.dumps({"api_key": "TemplateKey", "vertex_project": "proj-x"}) + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request(f"litellm_params_template={quote(template)}") + await list_gemini_agents( + request=request, + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + + init_data = MockProcessor.call_args[1]["data"] + assert init_data["api_key"] == "TemplateKey" + assert init_data["vertex_project"] == "proj-x" + assert "litellm_params_template" not in init_data + + +# --------------------------------------------------------------------------- +# Security guards (veria-flagged findings) +# --------------------------------------------------------------------------- + + +@pytest.fixture +def proxy_admin_user_api_key_dict(): + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + + return UserAPIKeyAuth( + api_key="sk-admin", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + + +@pytest.mark.asyncio +async def test_list_agents_non_admin_without_api_key_is_rejected( + mock_srv, user_api_key_dict +): + """Non-admin callers must supply an explicit api_key — the proxy must not + silently fall back to the operator's shared GOOGLE_API_KEY/GEMINI_API_KEY. + """ + from fastapi import HTTPException + + from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request("") + with pytest.raises(HTTPException) as excinfo: + await list_gemini_agents( + request=request, + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + assert excinfo.value.status_code == 401 + # Processor must never be invoked + instance.base_process_llm_request.assert_not_called() + + +@pytest.mark.asyncio +async def test_delete_agent_non_admin_without_api_key_is_rejected( + mock_srv, user_api_key_dict +): + from fastapi import HTTPException + + from litellm.proxy.google_endpoints.agents_endpoints import delete_gemini_agent + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request("") + with pytest.raises(HTTPException) as excinfo: + await delete_gemini_agent( + request=request, + name="my-agent", + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + assert excinfo.value.status_code == 401 + instance.base_process_llm_request.assert_not_called() + + +@pytest.mark.asyncio +async def test_create_agent_non_admin_without_api_key_is_rejected( + mock_srv, user_api_key_dict +): + from fastapi import HTTPException + + from litellm.proxy.google_endpoints.agents_endpoints import create_gemini_agent + + with ( + patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor, + patch( + "litellm.proxy.google_endpoints.agents_endpoints._read_request_body", + new=AsyncMock(return_value={"name": "agent-1", "base_agent": "waverunner"}), + ), + ): + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request("") + with pytest.raises(HTTPException) as excinfo: + await create_gemini_agent( + request=request, + fastapi_response=MagicMock(), + user_api_key_dict=user_api_key_dict, + ) + assert excinfo.value.status_code == 401 + instance.base_process_llm_request.assert_not_called() + + +@pytest.mark.asyncio +async def test_list_agents_proxy_admin_may_use_env_fallback( + mock_srv, proxy_admin_user_api_key_dict +): + """Proxy admins (master key) keep the env-fallback convenience.""" + from litellm.proxy.google_endpoints.agents_endpoints import list_gemini_agents + + with patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing" + ) as MockProcessor: + instance = MockProcessor.return_value + instance.base_process_llm_request = AsyncMock(return_value=MagicMock()) + + request = _make_endpoint_request("") + await list_gemini_agents( + request=request, + fastapi_response=MagicMock(), + user_api_key_dict=proxy_admin_user_api_key_dict, + ) + + init_data = MockProcessor.call_args[1]["data"] + assert "api_key" not in init_data + instance.base_process_llm_request.assert_awaited_once() + + +def test_validate_environment_rejects_api_base_override_without_explicit_key( + monkeypatch, +): + """SECURITY: caller-supplied api_base must be paired with an explicit + api_key — otherwise the proxy's shared GOOGLE_API_KEY leaks to the + attacker-controlled host via the x-goog-api-key header. + """ + from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig + + # Even if env-fallback is available, api_base override must require api_key. + monkeypatch.setenv("GOOGLE_API_KEY", "AIzaSharedSecret") + + cfg = GeminiAgentsConfig() + with pytest.raises(ValueError, match="api_base"): + cfg.validate_environment( + headers={}, + litellm_params={"api_base": "https://attacker.example"}, + ) + + +def test_validate_environment_allows_api_base_with_explicit_key(monkeypatch): + """api_base override is OK when paired with an explicit api_key.""" + from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig + + monkeypatch.delenv("GOOGLE_API_KEY", raising=False) + monkeypatch.delenv("GEMINI_API_KEY", raising=False) + + cfg = GeminiAgentsConfig() + headers = cfg.validate_environment( + headers={}, + litellm_params={ + "api_base": "https://my-gemini-proxy.example", + "api_key": "AIzaCallerOwned", + }, + ) + assert headers["x-goog-api-key"] == "AIzaCallerOwned" + + +def test_validate_environment_env_fallback_when_no_api_base_override(monkeypatch): + """Without api_base override, env fallback continues to work for SDK use.""" + from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig + + monkeypatch.setenv("GOOGLE_API_KEY", "AIzaFromEnv") + monkeypatch.delenv("GEMINI_API_KEY", raising=False) + + cfg = GeminiAgentsConfig() + headers = cfg.validate_environment(headers={}, litellm_params={}) + assert headers["x-goog-api-key"] == "AIzaFromEnv" 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_opentelemetry.py b/tests/test_litellm/integrations/test_opentelemetry.py index a98ad2c95af..6de855262bd 100644 --- a/tests/test_litellm/integrations/test_opentelemetry.py +++ b/tests/test_litellm/integrations/test_opentelemetry.py @@ -88,6 +88,149 @@ class TestOpenTelemetryGuardrails(unittest.TestCase): otel.tracer.start_span.assert_not_called() +class TestOpenTelemetryTeamAttributesOnChildSpans(unittest.TestCase): + """team_id / team_alias must land on every child span of a + litellm_request trace, not only the root litellm_request span.""" + + def _slo_metadata(self): + return { + "user_api_key_team_id": "team-123", + "user_api_key_team_alias": "my-team", + } + + @patch("litellm.integrations.opentelemetry.datetime") + def test_guardrail_span_has_team_attributes(self, mock_datetime): + otel = OpenTelemetry() + otel.tracer = MagicMock() + mock_span = MagicMock() + otel.tracer.start_span.return_value = mock_span + + guardrail_info = { + "guardrail_name": "test_guardrail", + "guardrail_mode": "input", + "guardrail_response": "filtered_content", + "start_time": 1609459200.0, + "end_time": 1609459201.0, + } + kwargs = { + "standard_logging_object": { + "guardrail_information": [guardrail_info], + "metadata": self._slo_metadata(), + } + } + + otel._create_guardrail_span(kwargs=kwargs, context=None) + + mock_span.set_attribute.assert_any_call( + "metadata.user_api_key_team_id", "team-123" + ) + mock_span.set_attribute.assert_any_call( + "metadata.user_api_key_team_alias", "my-team" + ) + + @patch.dict(os.environ, {"OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT": ""}) + @patch("litellm.turn_off_message_logging", False) + def test_raw_request_span_has_team_attributes(self): + otel = OpenTelemetry() + otel.message_logging = True + + mock_tracer = MagicMock() + mock_span = MagicMock() + mock_tracer.start_span.return_value = mock_span + otel.get_tracer_to_use_for_request = MagicMock(return_value=mock_tracer) + otel.set_raw_request_attributes = MagicMock() + otel._to_ns = MagicMock(return_value=1234567890) + + kwargs = { + "litellm_params": {"metadata": {}}, + "standard_logging_object": {"metadata": self._slo_metadata()}, + } + otel._maybe_log_raw_request( + kwargs, {}, datetime.now(), datetime.now(), MagicMock() + ) + + mock_span.set_attribute.assert_any_call( + "metadata.user_api_key_team_id", "team-123" + ) + mock_span.set_attribute.assert_any_call( + "metadata.user_api_key_team_alias", "my-team" + ) + + def test_helper_skips_when_team_values_missing(self): + otel = OpenTelemetry() + mock_span = MagicMock() + + otel._set_team_attributes_on_span(span=mock_span, team_id=None, team_alias=None) + + mock_span.set_attribute.assert_not_called() + + def test_helper_skips_when_team_values_are_empty_strings(self): + """A master-key / team-less request carries user_api_key_team_id='' + in metadata. Propagating '' to every span is noise that makes + traces look mis-instrumented; treat empty as absent.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + otel._set_team_attributes_on_span(span=mock_span, team_id="", team_alias="") + + mock_span.set_attribute.assert_not_called() + + def test_helper_reads_metadata_from_kwargs(self): + otel = OpenTelemetry() + mock_span = MagicMock() + + otel._set_team_attributes_from_kwargs( + mock_span, + {"standard_logging_object": {"metadata": self._slo_metadata()}}, + ) + + mock_span.set_attribute.assert_any_call( + "metadata.user_api_key_team_id", "team-123" + ) + mock_span.set_attribute.assert_any_call( + "metadata.user_api_key_team_alias", "my-team" + ) + + def test_helper_handles_missing_standard_logging_object(self): + otel = OpenTelemetry() + mock_span = MagicMock() + + otel._set_team_attributes_from_kwargs(mock_span, {}) + + mock_span.set_attribute.assert_not_called() + + def test_failure_hook_exception_span_has_team_attributes(self): + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + tracer = provider.get_tracer(__name__) + + otel = OpenTelemetry() + otel.tracer = tracer + server_span = tracer.start_span("Received Proxy Server Request") + + user_api_key_dict = MagicMock() + user_api_key_dict.parent_otel_span = server_span + user_api_key_dict.team_id = "team-123" + user_api_key_dict.team_alias = "my-team" + + asyncio.run( + otel.async_post_call_failure_hook( + request_data={}, + original_exception=ValueError("boom"), + user_api_key_dict=user_api_key_dict, + traceback_str="trace", + ) + ) + + finished = {s.name: s for s in exporter.get_finished_spans()} + exception_span = finished["Failed Proxy Server Request"] + assert exception_span.attributes["metadata.user_api_key_team_id"] == "team-123" + assert ( + exception_span.attributes["metadata.user_api_key_team_alias"] == "my-team" + ) + + class TestOpenTelemetryCostBreakdown(unittest.TestCase): def test_cost_breakdown_emitted_to_otel_span(self): """ @@ -4808,6 +4951,41 @@ class TestOpenTelemetrySetProxyRequestRouteAttributes(unittest.TestCase): otel.set_proxy_request_route_attributes(None, url_path="/x", http_route="/x") +class TestOpenTelemetrySetResponseStatusCodeAttribute(unittest.TestCase): + """http.response.status_code must land on the SERVER span on the + success path too (failure path sets it in _record_exception_on_span). + Without this the attribute is failure-only, so error-ratio / + status-breakdown dashboards have no 2xx bucket. + """ + + def _set(self, status_code): + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + tracer = provider.get_tracer(__name__) + + otel = OpenTelemetry() + span = tracer.start_span("Received Proxy Server Request") + otel.set_response_status_code_attribute(span, status_code) + span.end() + return exporter.get_finished_spans()[0] + + def test_success_sets_int_200(self): + span = self._set(200) + # Exact OTel-standard name, stored as int (regression guard). + assert span.attributes["http.response.status_code"] == 200 + assert isinstance(span.attributes["http.response.status_code"], int) + + def test_none_status_code_omits_attribute(self): + span = self._set(None) + assert "http.response.status_code" not in span.attributes + + def test_none_span_is_noop(self): + otel = OpenTelemetry() + # Mirrors the Langfuse-override path (create span returns None). + otel.set_response_status_code_attribute(None, 200) + + class TestOpenTelemetryPreprocessingDuration(unittest.TestCase): """litellm.preprocessing.duration_ms (proxy-receive -> first provider handoff) on the SERVER span. Read from container metadata so the diff --git a/tests/test_litellm/integrations/test_otel_team_attributes_matrix.py b/tests/test_litellm/integrations/test_otel_team_attributes_matrix.py new file mode 100644 index 00000000000..1ce55fa7a58 --- /dev/null +++ b/tests/test_litellm/integrations/test_otel_team_attributes_matrix.py @@ -0,0 +1,285 @@ +""" +Matrix test: team_id / team_alias must land on EVERY span of a proxy +request trace, for a representative set of endpoints x HTTP outcomes. + +Endpoints + - /v1/chat/completions (OpenAI-format LLM path) + - /v1/messages (Anthropic-format LLM path) + - /team/info (management/admin path) + +Outcomes + - 2xx success + - 3xx redirect (LLM endpoints never 3xx -> N/A; admin too) + - 4xx client error (auth / validation failure) + - 5xx server error (upstream / DB failure) + +Strategy + These assertions exercise the real OpenTelemetry callback the proxy + invokes for each path, with a SERVER parent span (as + ``user_api_key_auth`` creates) and an in-memory exporter. Each cell + drives the path, then asserts team attributes on every span that path + actually emits. + + - success path -> ``log_success_event`` -> litellm_request + + raw_gen_ai_request + guardrail child spans. + - failure path -> ``async_post_call_failure_hook`` -> Failed Proxy + Server Request exception child span. + + Admin endpoints do not run the LLM success callback, so their only + trace surface is the SERVER span (success) or the exception child span + (failure) -- the cells below assert exactly that. +""" + +import asyncio +import os +import sys +import unittest +from datetime import datetime +from unittest.mock import MagicMock + +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, +) + +sys.path.insert(0, os.path.abspath("../..")) + +from litellm.integrations.opentelemetry import ( + LITELLM_PROXY_REQUEST_SPAN_NAME, + OpenTelemetry, +) + +TEAM_ID = "team-123" +TEAM_ALIAS = "my-team" +TEAM_ID_ATTR = "metadata.user_api_key_team_id" +TEAM_ALIAS_ATTR = "metadata.user_api_key_team_alias" + + +def _make_otel(): + """OTel callback whose every span lands in an in-memory exporter.""" + exporter = InMemorySpanExporter() + provider = TracerProvider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + + otel = OpenTelemetry() + otel.tracer = provider.get_tracer(__name__) + # raw_gen_ai_request sub-span is gated on message logging. + otel.message_logging = True + return otel, exporter + + +def _server_span(otel): + """Mirror the SERVER span user_api_key_auth opens per request.""" + return otel.create_litellm_proxy_request_started_span( + start_time=datetime.now(), headers={} + ) + + +def _slo(call_type, with_guardrail=False): + """standard_logging_object the proxy attaches, carrying team metadata.""" + md = { + "user_api_key_team_id": TEAM_ID, + "user_api_key_team_alias": TEAM_ALIAS, + } + slo = {"metadata": md, "call_type": call_type} + if with_guardrail: + slo["guardrail_information"] = [ + { + "guardrail_name": "test_guardrail", + "guardrail_mode": "input", + "guardrail_response": "ok", + "start_time": 1609459200.0, + "end_time": 1609459201.0, + } + ] + return slo + + +def _success_kwargs(call_type, server_span, with_guardrail=True): + """kwargs the success callback receives for an LLM proxy request.""" + return { + "model": "gpt-4.1-mini", + "litellm_call_id": "call-abc", + "call_type": call_type, + "litellm_params": { + "metadata": { + "litellm_parent_otel_span": server_span, + "user_api_key_team_id": TEAM_ID, + "user_api_key_team_alias": TEAM_ALIAS, + } + }, + "standard_logging_object": _slo(call_type, with_guardrail=with_guardrail), + "messages": [{"role": "user", "content": "hi"}], + } + + +def _team_user_api_key_dict(server_span): + d = MagicMock() + d.parent_otel_span = server_span + d.team_id = TEAM_ID + d.team_alias = TEAM_ALIAS + return d + + +def _spans_by_name(exporter): + return {s.name: s for s in exporter.get_finished_spans()} + + +def _assert_team_attrs(span, where): + assert span.attributes.get(TEAM_ID_ATTR) == TEAM_ID, ( + f"{where}: missing/blank {TEAM_ID_ATTR} " + f"(got {span.attributes.get(TEAM_ID_ATTR)!r})" + ) + assert span.attributes.get(TEAM_ALIAS_ATTR) == TEAM_ALIAS, ( + f"{where}: missing/blank {TEAM_ALIAS_ATTR} " + f"(got {span.attributes.get(TEAM_ALIAS_ATTR)!r})" + ) + + +class _Boom(Exception): + """Upstream/DB style 5xx.""" + + status_code = 500 + + +class _ClientErr(Exception): + """Auth/validation style 4xx.""" + + status_code = 401 + + +# --------------------------------------------------------------------------- +# LLM success cells: litellm_request + raw_gen_ai_request + guardrail spans +# --------------------------------------------------------------------------- +class TestLLMSuccessCells(unittest.TestCase): + def _run_success(self, call_type): + otel, exporter = _make_otel() + server_span = _server_span(otel) + kwargs = _success_kwargs(call_type, server_span) + now = datetime.now() + otel.log_success_event(kwargs, {"id": "resp-1"}, now, now) + return _spans_by_name(exporter) + + def test_chat_completions_2xx(self): + spans = self._run_success("completion") + for name in ( + LITELLM_PROXY_REQUEST_SPAN_NAME, + "litellm_request", + "raw_gen_ai_request", + "guardrail", + ): + assert name in spans, f"chat/completions 2xx: missing span {name}" + _assert_team_attrs(spans[name], f"chat/completions 2xx [{name}]") + + def test_v1_messages_2xx(self): + spans = self._run_success("anthropic_messages") + for name in ( + LITELLM_PROXY_REQUEST_SPAN_NAME, + "litellm_request", + "raw_gen_ai_request", + "guardrail", + ): + assert name in spans, f"v1/messages 2xx: missing span {name}" + _assert_team_attrs(spans[name], f"v1/messages 2xx [{name}]") + + +# --------------------------------------------------------------------------- +# LLM failure cells: Failed Proxy Server Request exception child span +# --------------------------------------------------------------------------- +class TestLLMFailureCells(unittest.TestCase): + def _run_failure(self, exc): + """Drive the failure hook, then close the SERVER span (the proxy + closes it after the hook in real flow) so both the exception child + span and the SERVER root span are asserted.""" + otel, exporter = _make_otel() + server_span = _server_span(otel) + asyncio.run( + otel.async_post_call_failure_hook( + request_data={}, + original_exception=exc, + user_api_key_dict=_team_user_api_key_dict(server_span), + traceback_str="tb", + ) + ) + server_span.end() + return _spans_by_name(exporter) + + def _assert_all(self, spans, where): + for name in ("Failed Proxy Server Request", LITELLM_PROXY_REQUEST_SPAN_NAME): + assert name in spans, f"{where}: missing span {name}" + _assert_team_attrs(spans[name], f"{where} [{name}]") + + def test_chat_completions_4xx(self): + self._assert_all( + self._run_failure(_ClientErr("bad key")), "chat/completions 4xx" + ) + + def test_chat_completions_5xx(self): + self._assert_all( + self._run_failure(_Boom("upstream blew up")), "chat/completions 5xx" + ) + + def test_v1_messages_4xx(self): + self._assert_all( + self._run_failure(_ClientErr("bad anthropic key")), "v1/messages 4xx" + ) + + def test_v1_messages_5xx(self): + self._assert_all( + self._run_failure(_Boom("anthropic upstream timeout")), "v1/messages 5xx" + ) + + +# --------------------------------------------------------------------------- +# Admin /team/info cells. +# 2xx: admin path never runs the LLM success callback -> its only trace +# surface is the SERVER span; no child spans are emitted. +# 3xx: management endpoints do not redirect -> N/A (documented, no run). +# 4xx/5xx: proxy_logging post_call_failure_hook -> exception child span. +# --------------------------------------------------------------------------- +class TestAdminTeamInfoCells(unittest.TestCase): + def _run_admin_failure(self, exc): + otel, exporter = _make_otel() + server_span = _server_span(otel) + asyncio.run( + otel.async_post_call_failure_hook( + request_data={}, + original_exception=exc, + user_api_key_dict=_team_user_api_key_dict(server_span), + traceback_str="tb", + ) + ) + server_span.end() + return _spans_by_name(exporter) + + def test_team_info_4xx(self): + spans = self._run_admin_failure(_ClientErr("team not found")) + for name in ("Failed Proxy Server Request", LITELLM_PROXY_REQUEST_SPAN_NAME): + _assert_team_attrs(spans[name], f"/team/info 4xx [{name}]") + + def test_team_info_5xx(self): + spans = self._run_admin_failure(_Boom("db connection lost")) + for name in ("Failed Proxy Server Request", LITELLM_PROXY_REQUEST_SPAN_NAME): + _assert_team_attrs(spans[name], f"/team/info 5xx [{name}]") + + def test_team_info_2xx_only_server_span_no_orphan_children(self): + """Admin success path emits no LLM child spans; nothing to stamp + beyond the SERVER span. This pins that contract so a future + regression that starts emitting child spans here without team + attrs is caught.""" + otel, exporter = _make_otel() + server_span = _server_span(otel) + server_span.end() + spans = _spans_by_name(exporter) + assert set(spans) == { + LITELLM_PROXY_REQUEST_SPAN_NAME + }, f"/team/info 2xx: unexpected child spans {set(spans)}" + + def test_team_info_3xx_not_applicable(self): + """Management endpoints return JSON, never a 3xx redirect.""" + self.skipTest("/team/info has no 3xx redirect path (N/A)") + + +if __name__ == "__main__": + unittest.main() 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/interactions/test_agents_http_handler.py b/tests/test_litellm/interactions/test_agents_http_handler.py new file mode 100644 index 00000000000..6947503e0bb --- /dev/null +++ b/tests/test_litellm/interactions/test_agents_http_handler.py @@ -0,0 +1,587 @@ +""" +Unit tests for litellm/interactions/agents/http_handler.py + +These tests exercise both the sync and async branches of every CRUD method +on AgentsHTTPHandler using stub httpx clients, plus the _is_async dispatch +branches, error mapping, and pre/post logging hooks. + +No real HTTP traffic is made. +""" + +import os +import sys +from unittest.mock import AsyncMock, MagicMock + +import httpx +import pytest + +sys.path.insert(0, os.path.abspath("../../..")) + +from litellm.interactions.agents.http_handler import ( + AgentsHTTPHandler, + agents_http_handler, +) +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig +from litellm.llms.gemini.common_utils import GeminiError +from litellm.types.agents import ( + AgentCreateResponse, + AgentDeleteResult, + AgentListResponse, + AgentVersionsResponse, +) +from litellm.types.router import GenericLiteLLMParams + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _make_response(status_code: int = 200, json_data=None, text: str = "") -> MagicMock: + """Build a stub httpx-like response.""" + response = MagicMock() + response.status_code = status_code + response.text = text or (str(json_data) if json_data is not None else "") + response.headers = {} + if json_data is not None: + response.json.return_value = json_data + else: + response.json.return_value = {} + return response + + +def _make_sync_client() -> MagicMock: + client = MagicMock(spec=HTTPHandler) + return client + + +def _make_async_client() -> MagicMock: + client = MagicMock(spec=AsyncHTTPHandler) + client.post = AsyncMock() + client.get = AsyncMock() + client.delete = AsyncMock() + return client + + +def _make_logging_obj() -> MagicMock: + return MagicMock() + + +@pytest.fixture +def handler() -> AgentsHTTPHandler: + return AgentsHTTPHandler() + + +@pytest.fixture +def config() -> GeminiAgentsConfig: + return GeminiAgentsConfig() + + +@pytest.fixture +def litellm_params() -> GenericLiteLLMParams: + return GenericLiteLLMParams(api_key="AIza-test") + + +# --------------------------------------------------------------------------- +# Module-level singleton sanity check +# --------------------------------------------------------------------------- + + +def test_module_singleton_is_agents_http_handler_instance(): + assert isinstance(agents_http_handler, AgentsHTTPHandler) + + +# --------------------------------------------------------------------------- +# CREATE +# --------------------------------------------------------------------------- + + +class TestCreateAgent: + def test_sync_returns_parsed_create_response(self, handler, config, litellm_params): + client = _make_sync_client() + client.post.return_value = _make_response( + 200, json_data={"id": "agent-x", "base_agent": "gemini-2.5-flash"} + ) + logging_obj = _make_logging_obj() + + result = handler.create_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=logging_obj, + extra_headers={"X-Test": "1"}, + extra_body={"foo": "bar"}, + client=client, + ) + + assert isinstance(result, AgentCreateResponse) + assert result.id == "agent-x" + client.post.assert_called_once() + kwargs = client.post.call_args.kwargs + assert kwargs["url"].endswith("/v1beta/agents") + assert kwargs["json"]["name"] == "agent-x" + assert kwargs["json"]["foo"] == "bar" + assert kwargs["headers"]["X-Test"] == "1" + logging_obj.pre_call.assert_called_once() + logging_obj.post_call.assert_called_once() + + def test_sync_dispatches_to_async_when_is_async( + self, handler, config, litellm_params + ): + client = _make_sync_client() + + result = handler.create_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + _is_async=True, + ) + + import asyncio + + assert asyncio.iscoroutine(result) + result.close() + + def test_sync_maps_http_error_via_config(self, handler, config, litellm_params): + client = _make_sync_client() + bad = _make_response(404, text="not found") + client.post.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + handler.create_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + @pytest.mark.asyncio + async def test_async_returns_parsed_create_response( + self, handler, config, litellm_params + ): + client = _make_async_client() + client.post.return_value = _make_response( + 200, json_data={"id": "agent-y", "base_agent": "gemini-2.5-flash"} + ) + + result = await handler.async_create_agent( + agents_api_config=config, + name="agent-y", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + extra_body={"baz": "qux"}, + client=client, + ) + + assert isinstance(result, AgentCreateResponse) + assert result.id == "agent-y" + client.post.assert_awaited_once() + + @pytest.mark.asyncio + async def test_async_maps_http_error_via_config( + self, handler, config, litellm_params + ): + client = _make_async_client() + bad = _make_response(500, text="server error") + client.post.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + await handler.async_create_agent( + agents_api_config=config, + name="agent-y", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + +# --------------------------------------------------------------------------- +# LIST +# --------------------------------------------------------------------------- + + +class TestListAgents: + def test_sync_returns_list_response(self, handler, config, litellm_params): + client = _make_sync_client() + client.get.return_value = _make_response( + 200, + json_data={ + "agents": [{"id": "a-1"}, {"id": "a-2"}], + "nextPageToken": "tok", + }, + ) + + result = handler.list_agents( + agents_api_config=config, + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentListResponse) + assert len(result.agents) == 2 + assert result.next_page_token == "tok" + client.get.assert_called_once() + + def test_sync_dispatches_to_async_when_is_async( + self, handler, config, litellm_params + ): + client = _make_sync_client() + + result = handler.list_agents( + agents_api_config=config, + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + _is_async=True, + ) + + import asyncio + + assert asyncio.iscoroutine(result) + result.close() + + def test_sync_maps_http_error_via_config(self, handler, config, litellm_params): + client = _make_sync_client() + bad = _make_response(403, text="forbidden") + client.get.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + handler.list_agents( + agents_api_config=config, + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + @pytest.mark.asyncio + async def test_async_returns_list_response(self, handler, config, litellm_params): + client = _make_async_client() + client.get.return_value = _make_response( + 200, json_data={"agents": [{"id": "a-1"}]} + ) + + result = await handler.async_list_agents( + agents_api_config=config, + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentListResponse) + assert len(result.agents) == 1 + client.get.assert_awaited_once() + + @pytest.mark.asyncio + async def test_async_maps_http_error_via_config( + self, handler, config, litellm_params + ): + client = _make_async_client() + bad = _make_response(429, text="rate limited") + client.get.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + await handler.async_list_agents( + agents_api_config=config, + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + +# --------------------------------------------------------------------------- +# GET +# --------------------------------------------------------------------------- + + +class TestGetAgent: + def test_sync_returns_get_response(self, handler, config, litellm_params): + client = _make_sync_client() + client.get.return_value = _make_response(200, json_data={"id": "agent-x"}) + + result = handler.get_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentCreateResponse) + assert result.id == "agent-x" + kwargs = client.get.call_args.kwargs + assert kwargs["url"].endswith("/v1beta/agents/agent-x") + + def test_sync_dispatches_to_async_when_is_async( + self, handler, config, litellm_params + ): + result = handler.get_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=_make_sync_client(), + _is_async=True, + ) + import asyncio + + assert asyncio.iscoroutine(result) + result.close() + + def test_sync_maps_http_error_via_config(self, handler, config, litellm_params): + client = _make_sync_client() + bad = _make_response(404, text="not found") + client.get.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + handler.get_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + @pytest.mark.asyncio + async def test_async_returns_get_response(self, handler, config, litellm_params): + client = _make_async_client() + client.get.return_value = _make_response(200, json_data={"id": "agent-y"}) + + result = await handler.async_get_agent( + agents_api_config=config, + name="agent-y", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentCreateResponse) + assert result.id == "agent-y" + + @pytest.mark.asyncio + async def test_async_maps_http_error_via_config( + self, handler, config, litellm_params + ): + client = _make_async_client() + bad = _make_response(404, text="not found") + client.get.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + await handler.async_get_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + +# --------------------------------------------------------------------------- +# DELETE +# --------------------------------------------------------------------------- + + +class TestDeleteAgent: + def test_sync_returns_delete_result(self, handler, config, litellm_params): + client = _make_sync_client() + client.delete.return_value = _make_response(200, json_data={}) + + result = handler.delete_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentDeleteResult) + assert result.name == "agent-x" + assert result.deleted is True + kwargs = client.delete.call_args.kwargs + assert kwargs["url"].endswith("/v1beta/agents/agent-x") + + def test_sync_dispatches_to_async_when_is_async( + self, handler, config, litellm_params + ): + result = handler.delete_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=_make_sync_client(), + _is_async=True, + ) + import asyncio + + assert asyncio.iscoroutine(result) + result.close() + + def test_sync_maps_http_error_via_config(self, handler, config, litellm_params): + client = _make_sync_client() + bad = _make_response(403, text="forbidden") + client.delete.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + handler.delete_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + @pytest.mark.asyncio + async def test_async_returns_delete_result(self, handler, config, litellm_params): + client = _make_async_client() + client.delete.return_value = _make_response(200, json_data={}) + + result = await handler.async_delete_agent( + agents_api_config=config, + name="agent-y", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentDeleteResult) + assert result.name == "agent-y" + assert result.deleted is True + + @pytest.mark.asyncio + async def test_async_maps_http_error_via_config( + self, handler, config, litellm_params + ): + client = _make_async_client() + bad = _make_response(500, text="server error") + client.delete.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + await handler.async_delete_agent( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + +# --------------------------------------------------------------------------- +# LIST VERSIONS +# --------------------------------------------------------------------------- + + +class TestListAgentVersions: + def test_sync_returns_versions_response(self, handler, config, litellm_params): + client = _make_sync_client() + client.get.return_value = _make_response( + 200, + json_data={ + "agentVersions": [ + {"agent": "agent-x", "name": "agents/agent-x/versions/v1"} + ], + "nextPageToken": "tok", + }, + ) + + result = handler.list_agent_versions( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentVersionsResponse) + assert len(result.agent_versions) == 1 + assert result.next_page_token == "tok" + kwargs = client.get.call_args.kwargs + assert kwargs["url"].endswith("/v1beta/agents/agent-x/versions") + + def test_sync_dispatches_to_async_when_is_async( + self, handler, config, litellm_params + ): + result = handler.list_agent_versions( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=_make_sync_client(), + _is_async=True, + ) + import asyncio + + assert asyncio.iscoroutine(result) + result.close() + + def test_sync_maps_http_error_via_config(self, handler, config, litellm_params): + client = _make_sync_client() + bad = _make_response(404, text="not found") + client.get.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + handler.list_agent_versions( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + @pytest.mark.asyncio + async def test_async_returns_versions_response( + self, handler, config, litellm_params + ): + client = _make_async_client() + client.get.return_value = _make_response(200, json_data={"agentVersions": []}) + + result = await handler.async_list_agent_versions( + agents_api_config=config, + name="agent-y", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) + + assert isinstance(result, AgentVersionsResponse) + assert result.agent_versions == [] + + @pytest.mark.asyncio + async def test_async_maps_http_error_via_config( + self, handler, config, litellm_params + ): + client = _make_async_client() + bad = _make_response(500, text="server error") + client.get.side_effect = httpx.HTTPStatusError( + "boom", request=MagicMock(), response=bad + ) + + with pytest.raises(GeminiError): + await handler.async_list_agent_versions( + agents_api_config=config, + name="agent-x", + litellm_params=litellm_params, + logging_obj=_make_logging_obj(), + client=client, + ) diff --git a/tests/test_litellm/interactions/test_agents_main_and_utils.py b/tests/test_litellm/interactions/test_agents_main_and_utils.py new file mode 100644 index 00000000000..7c0183d20c6 --- /dev/null +++ b/tests/test_litellm/interactions/test_agents_main_and_utils.py @@ -0,0 +1,354 @@ +""" +Unit tests for litellm/interactions/agents/utils.py and main.py +focused on the managed agents SDK surface added in the +"Gemini managed agents support" PR. + +The tests mock the underlying HTTP handler so they cover the public +sync + async create/list/get/delete/list_versions entry points and the +small helper utilities without touching the network. +""" + +import asyncio +import os +import sys +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +sys.path.insert(0, os.path.abspath("../../..")) + +import litellm +from litellm.interactions.agents import ( + acreate, + adelete, + aget, + alist, + alist_versions, + create, + delete, + get, + list as list_agents, + list_versions, +) +from litellm.interactions.agents.main import ( + _get_agents_api_config, + _make_logging_obj, +) +from litellm.interactions.agents.utils import get_provider_agents_api_config +from litellm.llms.base_llm.agents.transformation import BaseAgentsAPIConfig +from litellm.llms.gemini.agents.transformation import GeminiAgentsConfig + + +_HANDLER_PATH = "litellm.interactions.agents.main.agents_http_handler" + + +# --------------------------------------------------------------------------- +# utils.get_provider_agents_api_config +# --------------------------------------------------------------------------- + + +class TestGetProviderAgentsApiConfig: + def test_returns_gemini_config_for_gemini(self): + cfg = get_provider_agents_api_config("gemini") + assert isinstance(cfg, GeminiAgentsConfig) + assert isinstance(cfg, BaseAgentsAPIConfig) + + @pytest.mark.parametrize( + "provider", ["openai", "anthropic", "bedrock", "vertex_ai", "unknown"] + ) + def test_returns_none_for_non_gemini(self, provider): + assert get_provider_agents_api_config(provider) is None + + def test_returns_none_for_none(self): + assert get_provider_agents_api_config(None) is None + + +# --------------------------------------------------------------------------- +# main._get_agents_api_config +# --------------------------------------------------------------------------- + + +class TestGetAgentsApiConfig: + def test_returns_config_for_gemini(self): + cfg = _get_agents_api_config("gemini") + assert isinstance(cfg, GeminiAgentsConfig) + + def test_raises_bad_request_for_unsupported_provider(self): + with pytest.raises(litellm.BadRequestError) as excinfo: + _get_agents_api_config("openai") + assert "does not have a native" in str(excinfo.value) + + +# --------------------------------------------------------------------------- +# main._make_logging_obj +# --------------------------------------------------------------------------- + + +class TestMakeLoggingObj: + def test_calls_update_from_kwargs_and_returns_same_obj(self): + logging_obj = MagicMock() + kwargs = {"litellm_logging_obj": logging_obj, "litellm_call_id": "abc-123"} + + returned = _make_logging_obj( + kwargs=kwargs, + model="my-agent", + custom_llm_provider="gemini", + call_type="create_agent", + optional_params={"foo": "bar"}, + ) + + assert returned is logging_obj + logging_obj.update_from_kwargs.assert_called_once() + kwargs_call = logging_obj.update_from_kwargs.call_args.kwargs + assert kwargs_call["model"] == "my-agent" + assert kwargs_call["optional_params"] == {"foo": "bar"} + assert kwargs_call["custom_llm_provider"] == "gemini" + assert kwargs_call["litellm_params"]["litellm_call_id"] == "abc-123" + + +# --------------------------------------------------------------------------- +# Sync entry points: create / list / get / delete / list_versions +# --------------------------------------------------------------------------- + + +def _stub_handler(return_value): + """Build a stub AgentsHTTPHandler whose CRUD methods return *return_value*.""" + handler = MagicMock() + handler.create_agent.return_value = return_value + handler.list_agents.return_value = return_value + handler.get_agent.return_value = return_value + handler.delete_agent.return_value = return_value + handler.list_agent_versions.return_value = return_value + return handler + + +class TestSyncEntryPoints: + def test_create_passes_args_to_handler(self): + sentinel = MagicMock(name="create_response") + with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler: + response = create( + name="waverunner", + base_agent="gemini-2.5-flash", + instructions="be helpful", + base_environment={"type": "remote"}, + custom_llm_provider="gemini", + api_key="AIza-test", + extra_headers={"X-Test": "1"}, + extra_body={"foo": "bar"}, + ) + + assert response is sentinel + handler.create_agent.assert_called_once() + kw = handler.create_agent.call_args.kwargs + assert kw["name"] == "waverunner" + assert kw["_is_async"] is False + assert kw["extra_headers"] == {"X-Test": "1"} + assert kw["extra_body"] == {"foo": "bar"} + assert isinstance(kw["agents_api_config"], GeminiAgentsConfig) + + def test_create_defaults_custom_llm_provider_to_gemini(self): + sentinel = MagicMock(name="create_response") + with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler: + create(name="agent-x", api_key="AIza") + assert handler.create_agent.call_args.kwargs["_is_async"] is False + cfg = handler.create_agent.call_args.kwargs["agents_api_config"] + assert isinstance(cfg, GeminiAgentsConfig) + + def test_create_raises_for_unsupported_provider(self): + with pytest.raises(litellm.exceptions.BadRequestError): + create(name="agent-x", custom_llm_provider="openai", api_key="sk-x") + + def test_list_passes_args_to_handler(self): + sentinel = MagicMock(name="list_response") + with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler: + response = list_agents(custom_llm_provider="gemini", api_key="AIza") + assert response is sentinel + handler.list_agents.assert_called_once() + assert handler.list_agents.call_args.kwargs["_is_async"] is False + + def test_get_passes_args_to_handler(self): + sentinel = MagicMock(name="get_response") + with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler: + response = get(name="waverunner", api_key="AIza") + assert response is sentinel + kw = handler.get_agent.call_args.kwargs + assert kw["name"] == "waverunner" + assert kw["_is_async"] is False + + def test_delete_passes_args_to_handler(self): + sentinel = MagicMock(name="delete_response") + with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler: + response = delete(name="waverunner", api_key="AIza") + assert response is sentinel + kw = handler.delete_agent.call_args.kwargs + assert kw["name"] == "waverunner" + assert kw["_is_async"] is False + + def test_list_versions_passes_args_to_handler(self): + sentinel = MagicMock(name="versions_response") + with patch(_HANDLER_PATH, _stub_handler(sentinel)) as handler: + response = list_versions(name="waverunner", api_key="AIza") + assert response is sentinel + kw = handler.list_agent_versions.call_args.kwargs + assert kw["name"] == "waverunner" + assert kw["_is_async"] is False + + +# --------------------------------------------------------------------------- +# Async entry points +# --------------------------------------------------------------------------- + + +class TestAsyncEntryPoints: + """Async entry points delegate to their sync counterparts via run_in_executor.""" + + @pytest.mark.asyncio + async def test_acreate_dispatches_with_async_flag(self): + sentinel = MagicMock(name="acreate_response") + + def fake_create_agent(**kwargs): + assert kwargs["_is_async"] is True + assert kwargs["name"] == "waverunner" + return sentinel + + handler = MagicMock() + handler.create_agent.side_effect = fake_create_agent + + with patch(_HANDLER_PATH, handler): + response = await acreate( + name="waverunner", + base_agent="gemini-2.5-flash", + api_key="AIza", + ) + assert response is sentinel + + @pytest.mark.asyncio + async def test_acreate_awaits_coroutine_result(self): + async def _coro(): + return "async-value" + + handler = MagicMock() + handler.create_agent.return_value = _coro() + + with patch(_HANDLER_PATH, handler): + response = await acreate(name="waverunner", api_key="AIza") + + assert response == "async-value" + + @pytest.mark.asyncio + async def test_alist_dispatches_with_async_flag(self): + sentinel = MagicMock(name="alist_response") + + def fake_list_agents(**kwargs): + assert kwargs["_is_async"] is True + return sentinel + + handler = MagicMock() + handler.list_agents.side_effect = fake_list_agents + + with patch(_HANDLER_PATH, handler): + response = await alist(api_key="AIza") + assert response is sentinel + + @pytest.mark.asyncio + async def test_aget_dispatches_with_async_flag(self): + sentinel = MagicMock(name="aget_response") + + def fake_get_agent(**kwargs): + assert kwargs["_is_async"] is True + assert kwargs["name"] == "waverunner" + return sentinel + + handler = MagicMock() + handler.get_agent.side_effect = fake_get_agent + + with patch(_HANDLER_PATH, handler): + response = await aget(name="waverunner", api_key="AIza") + assert response is sentinel + + @pytest.mark.asyncio + async def test_adelete_dispatches_with_async_flag(self): + sentinel = MagicMock(name="adelete_response") + + def fake_delete_agent(**kwargs): + assert kwargs["_is_async"] is True + assert kwargs["name"] == "waverunner" + return sentinel + + handler = MagicMock() + handler.delete_agent.side_effect = fake_delete_agent + + with patch(_HANDLER_PATH, handler): + response = await adelete(name="waverunner", api_key="AIza") + assert response is sentinel + + @pytest.mark.asyncio + async def test_alist_versions_dispatches_with_async_flag(self): + sentinel = MagicMock(name="alist_versions_response") + + def fake_versions(**kwargs): + assert kwargs["_is_async"] is True + assert kwargs["name"] == "waverunner" + return sentinel + + handler = MagicMock() + handler.list_agent_versions.side_effect = fake_versions + + with patch(_HANDLER_PATH, handler): + response = await alist_versions(name="waverunner", api_key="AIza") + assert response is sentinel + + +# --------------------------------------------------------------------------- +# Async error wrapping: exception_type must be invoked +# --------------------------------------------------------------------------- + + +class TestAsyncErrorWrapping: + """If the underlying handler raises, async entry points re-raise via + litellm.exception_type so users get a normalised provider error.""" + + @pytest.mark.asyncio + async def test_acreate_wraps_exception(self): + handler = MagicMock() + handler.create_agent.side_effect = RuntimeError("kaboom") + + with patch(_HANDLER_PATH, handler): + with pytest.raises(Exception): + await acreate(name="waverunner", api_key="AIza") + + @pytest.mark.asyncio + async def test_aget_wraps_exception(self): + handler = MagicMock() + handler.get_agent.side_effect = RuntimeError("kaboom") + + with patch(_HANDLER_PATH, handler): + with pytest.raises(Exception): + await aget(name="waverunner", api_key="AIza") + + @pytest.mark.asyncio + async def test_alist_wraps_exception(self): + handler = MagicMock() + handler.list_agents.side_effect = RuntimeError("kaboom") + + with patch(_HANDLER_PATH, handler): + with pytest.raises(Exception): + await alist(api_key="AIza") + + @pytest.mark.asyncio + async def test_adelete_wraps_exception(self): + handler = MagicMock() + handler.delete_agent.side_effect = RuntimeError("kaboom") + + with patch(_HANDLER_PATH, handler): + with pytest.raises(Exception): + await adelete(name="waverunner", api_key="AIza") + + @pytest.mark.asyncio + async def test_alist_versions_wraps_exception(self): + handler = MagicMock() + handler.list_agent_versions.side_effect = RuntimeError("kaboom") + + with patch(_HANDLER_PATH, handler): + with pytest.raises(Exception): + await alist_versions(name="waverunner", api_key="AIza") diff --git a/tests/test_litellm/interactions/test_gemini_interactions_transformation.py b/tests/test_litellm/interactions/test_gemini_interactions_transformation.py index 758ff3ea38e..2e596a72158 100644 --- a/tests/test_litellm/interactions/test_gemini_interactions_transformation.py +++ b/tests/test_litellm/interactions/test_gemini_interactions_transformation.py @@ -9,15 +9,24 @@ Covers credential leak prevention changes: import os import sys -from unittest.mock import patch +from unittest.mock import MagicMock, patch import pytest sys.path.insert(0, os.path.abspath("../../..")) +from litellm.interactions.litellm_responses_transformation.streaming_iterator import ( + LiteLLMResponsesInteractionsStreamingIterator, +) from litellm.llms.gemini.interactions.transformation import ( GoogleAIStudioInteractionsConfig, ) +from litellm.types.llms.openai import ( + ContentPartAddedEvent, + OutputTextDeltaEvent, + ResponseCompletedEvent, + ResponseCreatedEvent, +) from litellm.types.router import GenericLiteLLMParams _PATCH_GET_API_KEY = "litellm.llms.gemini.common_utils.GeminiModelInfo.get_api_key" @@ -113,6 +122,236 @@ class TestGetCompleteUrl: ) +class TestTransformRequest: + def test_passes_environment_to_request_body(self, config): + request_body = config.transform_request( + model=None, + agent="my-custom-slides-agent", + input=[{"type": "text", "text": "Create a 5-slide presentation about AI trends."}], + optional_params={ + "environment": "remote", + "stream": False, + }, + litellm_params=GenericLiteLLMParams(api_key="test-api-key"), + headers={}, + ) + + assert request_body["agent"] == "my-custom-slides-agent" + assert request_body["environment"] == "remote" + assert request_body["stream"] is False + assert request_body["input"] == [ + {"type": "text", "text": "Create a 5-slide presentation about AI trends."} + ] + + def test_passes_environment_object_to_request_body(self, config): + environment_config = { + "type": "remote", + "sources": [{"type": "gcs", "uri": "gs://bucket/skills.zip"}], + "network": {"egress": "allow_all"}, + } + request_body = config.transform_request( + model=None, + agent="waverunner", + input="What is 2 + 2?", + optional_params={"environment": environment_config}, + litellm_params=GenericLiteLLMParams(api_key="test-api-key"), + headers={}, + ) + + assert request_body["environment"] == environment_config + + def test_passes_existing_environment_id_to_request_body(self, config): + env_id = "env-abc123" + request_body = config.transform_request( + model=None, + agent="my-custom-slides-agent", + input="Continue the presentation.", + optional_params={"environment": env_id}, + litellm_params=GenericLiteLLMParams(api_key="test-api-key"), + headers={}, + ) + + assert request_body["environment"] == env_id +class TestStreamingIterator: + def _make_iterator(self) -> LiteLLMResponsesInteractionsStreamingIterator: + return LiteLLMResponsesInteractionsStreamingIterator( + model="gpt-5.4", + litellm_custom_stream_wrapper=MagicMock(), + request_input="hi", + optional_params={}, + ) + + def _make_text_delta( + self, text: str, item_id: str = "item_1" + ) -> OutputTextDeltaEvent: + event = MagicMock(spec=OutputTextDeltaEvent) + event.delta = text + event.item_id = item_id + return event + + def _make_part_added(self, item_id: str = "item_1") -> ContentPartAddedEvent: + event = MagicMock(spec=ContentPartAddedEvent) + event.item_id = item_id + return event + + def _make_response_created(self) -> ResponseCreatedEvent: + event = MagicMock(spec=ResponseCreatedEvent) + event.response = MagicMock(id="resp_123") + return event + + def test_content_delta_includes_type_field(self): + """content.delta events must carry delta.type='text' so the UI can display them.""" + it = self._make_iterator() + it.sent_interaction_start = True + it.sent_content_start = True + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_text_delta("Hello") + ) + + assert chunk is not None + assert chunk.event_type == "content.delta" + assert chunk.delta == {"type": "text", "text": "Hello"} + + def test_response_part_added_emits_content_start(self): + """ContentPartAddedEvent (arrives before text deltas) should emit content.start + so the first OutputTextDeltaEvent immediately emits content.delta without dropping text. + """ + it = self._make_iterator() + it.sent_interaction_start = True + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_part_added() + ) + + assert chunk is not None + assert chunk.event_type == "content.start" + assert it.sent_content_start is True + + def test_first_text_delta_not_dropped_when_part_added_seen(self): + """After ContentPartAddedEvent, the first text delta must yield content.delta + (not content.start), preserving the token text.""" + it = self._make_iterator() + it.sent_interaction_start = True + it._transform_responses_chunk_to_interactions_chunk(self._make_part_added()) + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_text_delta("Hello") + ) + + assert chunk is not None + assert chunk.event_type == "content.delta" + assert chunk.delta is not None + assert chunk.delta.get("text") == "Hello" + + def test_part_added_emits_interaction_start_fallback_when_not_sent(self): + """If ContentPartAddedEvent arrives before any ResponseCreatedEvent, + the iterator must emit interaction.start before content.start to honor + the documented event ordering contract.""" + it = self._make_iterator() + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_part_added(item_id="item_42") + ) + + assert chunk is not None + assert chunk.event_type == "interaction.start" + assert chunk.id == "item_42" + assert chunk.status == "in_progress" + assert chunk.model == "gpt-5.4" + assert it.sent_interaction_start is True + assert it.sent_content_start is False + + def test_part_added_returns_none_when_already_started(self): + """A second ContentPartAddedEvent (after content.start was already emitted) + should be a no-op so we don't re-emit content.start.""" + it = self._make_iterator() + it.sent_interaction_start = True + it.sent_content_start = True + + chunk = it._transform_responses_chunk_to_interactions_chunk( + self._make_part_added() + ) + + assert chunk is None + + def test_part_added_without_item_id_falls_back_to_self_id(self): + """When ContentPartAddedEvent has no item_id and we emit the interaction.start + fallback, the id must default to an interaction_ string.""" + it = self._make_iterator() + event = MagicMock(spec=ContentPartAddedEvent) + event.item_id = None + + chunk = it._transform_responses_chunk_to_interactions_chunk(event) + + assert chunk is not None + assert chunk.event_type == "interaction.start" + assert chunk.id == f"interaction_{id(it)}" + + def test_first_text_delta_not_dropped_when_no_prior_start_events(self): + """When OutputTextDeltaEvent arrives before any ResponseCreatedEvent or + ContentPartAddedEvent, the iterator must emit interaction.start *and* + immediately follow with a content.start that carries this delta's text, + so the first token is never silently dropped from the stream.""" + events = [ + self._make_text_delta("Hello"), + self._make_text_delta(" World"), + ] + wrapper = MagicMock() + wrapper.__iter__ = lambda self: iter(events) + wrapper.__next__ = lambda self, _it=iter(events): next(_it) + it = LiteLLMResponsesInteractionsStreamingIterator( + model="gpt-5.4", + litellm_custom_stream_wrapper=wrapper, + request_input="hi", + optional_params={}, + ) + + first = it._transform_responses_chunk_to_interactions_chunk(events[0]) + assert first is not None + assert first.event_type == "interaction.start" + assert it.sent_interaction_start is True + assert it.sent_content_start is True + assert len(it._pending_events) == 1 + pending = it._pending_events[0] + assert pending.event_type == "content.start" + assert pending.delta == {"type": "text", "text": "Hello"} + + second = it._transform_responses_chunk_to_interactions_chunk(events[1]) + assert second is not None + assert second.event_type == "content.delta" + assert second.delta == {"type": "text", "text": " World"} + + +class TestTransformRequest: + def test_stream_param_included_in_request_body(self, config): + """When stream=True is in optional_params, the request body must include it + so the proxy forwards the SSE streaming flag to Google's backend.""" + body = config.transform_request( + model="gemini-2.5-flash", + agent=None, + input="Hello", + optional_params={"stream": True}, + litellm_params=GenericLiteLLMParams(api_key="test-key"), + headers={}, + ) + + assert body.get("stream") is True + assert body.get("input") == "Hello" + + def test_stream_false_not_included_when_absent(self, config): + body = config.transform_request( + model="gemini-2.5-flash", + agent=None, + input="Hello", + optional_params={}, + litellm_params=GenericLiteLLMParams(api_key="test-key"), + headers={}, + ) + + assert "stream" not in body + + class TestInteractionOperationUrls: """Test that get/delete/cancel interaction URLs exclude API key.""" diff --git a/tests/test_litellm/interactions/test_openapi_compliance.py b/tests/test_litellm/interactions/test_openapi_compliance.py index 1d3b6b8ae1e..aededaaca77 100644 --- a/tests/test_litellm/interactions/test_openapi_compliance.py +++ b/tests/test_litellm/interactions/test_openapi_compliance.py @@ -179,7 +179,10 @@ class TestResponseCompliance: # `status` is an output-only field; validate against the response schema. schema = spec_dict["components"]["schemas"]["Interaction"] status_prop = schema["properties"]["status"] - # Google Interactions API uses lowercase status values (updated Feb 2026) + # Google Interactions API uses lowercase status values (updated Feb 2026). + # Keep this an exact match: this test intentionally breaks CI when + # Google changes the live spec — that breakage is how we get notified + # to review the change. expected_statuses = [ "in_progress", "requires_action", @@ -187,6 +190,7 @@ class TestResponseCompliance: "failed", "cancelled", "incomplete", + "budget_exceeded", ] assert status_prop["enum"] == expected_statuses print(f"✓ Status enum values: {expected_statuses}") diff --git a/tests/test_litellm/litellm_core_utils/test_token_counter.py b/tests/test_litellm/litellm_core_utils/test_token_counter.py index 3aa5f012467..324bace0e96 100644 --- a/tests/test_litellm/litellm_core_utils/test_token_counter.py +++ b/tests/test_litellm/litellm_core_utils/test_token_counter.py @@ -437,13 +437,38 @@ def test_gpt_4o_token_counter(): @pytest.mark.parametrize( "img_url", [ - "https://blog.purpureus.net/assets/blog/personal_key_rotation/simplified-asset-graph.jpg", + "https://example.com/test-image.png", "data:image/png;base64,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", ], ) -def test_img_url_token_counter(img_url): +def test_img_url_token_counter(img_url, monkeypatch): + """ + Verify get_image_dimensions returns valid (width, height) for both an + HTTPS URL and a base64 data URI. The HTTPS branch is exercised with a + mocked HTTP fetch so the test is hermetic - it can't break when a + third-party image URL goes away. + """ + import base64 from litellm.litellm_core_utils.token_counter import get_image_dimensions + # Minimal valid 1x1 PNG, served by the mocked safe_get for the URL case. + _tiny_png = base64.b64decode( + "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=" + ) + + if img_url.startswith(("http://", "https://")): + + class _FakeResponse: + headers = {"Content-Length": str(len(_tiny_png))} + + def read(self): + return _tiny_png + + monkeypatch.setattr( + "litellm.litellm_core_utils.token_counter.safe_get", + lambda client, url, **kw: _FakeResponse(), + ) + width, height = get_image_dimensions(data=img_url) print(width, height) 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/bedrock/test_bedrock_common_utils.py b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py index 8fa9290d3de..c39fb427a01 100644 --- a/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py +++ b/tests/test_litellm/llms/bedrock/test_bedrock_common_utils.py @@ -14,18 +14,46 @@ from litellm.llms.bedrock.common_utils import BedrockModelInfo # --------------------------------------------------------------------------- # -# BEDROCK_RESPONSE_STREAM_SHAPE eager-load tests # +# get_bedrock_response_stream_shape lazy-load tests # # --------------------------------------------------------------------------- # -def test_bedrock_response_stream_shape_loaded_at_import(): +@pytest.fixture(autouse=True) +def _reset_bedrock_response_stream_shape_cache(): + """Prevent lru_cache leakage between tests in this module.""" + import litellm.llms.bedrock.common_utils as mod + + mod.get_bedrock_response_stream_shape.cache_clear() + yield + mod.get_bedrock_response_stream_shape.cache_clear() + + +def test_bedrock_response_stream_shape_lazy_loads_once(): """ - BEDROCK_RESPONSE_STREAM_SHAPE is resolved at module import time. + get_bedrock_response_stream_shape() loads from botocore at most once per process. + """ + from unittest.mock import MagicMock, patch + + import litellm.llms.bedrock.common_utils as mod + + sentinel = MagicMock() + with patch.object( + mod, "_load_bedrock_response_stream_shape", return_value=sentinel + ) as mock_load: + assert mod.get_bedrock_response_stream_shape() is sentinel + assert mod.get_bedrock_response_stream_shape() is sentinel + mock_load.assert_called_once() + + +def test_bedrock_response_stream_shape_loaded_on_first_access(): + """ + get_bedrock_response_stream_shape() loads once on first use. In a standard environment with botocore installed it must be non-None. """ - from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE + pytest.importorskip("botocore") + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - assert BEDROCK_RESPONSE_STREAM_SHAPE is not None + assert get_bedrock_response_stream_shape() is not None def test_bedrock_response_stream_shape_load_failure_returns_none(): @@ -38,6 +66,7 @@ def test_bedrock_response_stream_shape_load_failure_returns_none(): import litellm.llms.bedrock.common_utils as mod + pytest.importorskip("botocore") with patch( "botocore.loaders.Loader.load_service_model", side_effect=Exception("no data"), @@ -51,31 +80,29 @@ def test_bedrock_response_stream_shape_is_structure_shape(): The loaded shape should be the botocore StructureShape for ResponseStream, not a plain dict or any other type. """ + pytest.importorskip("botocore") from botocore.model import StructureShape - from litellm.llms.bedrock.common_utils import BEDROCK_RESPONSE_STREAM_SHAPE + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - assert BEDROCK_RESPONSE_STREAM_SHAPE is not None, ( - "BEDROCK_RESPONSE_STREAM_SHAPE is None — botocore may not be installed" - ) - shape: StructureShape = BEDROCK_RESPONSE_STREAM_SHAPE # remove Optional + loaded_shape = get_bedrock_response_stream_shape() + assert ( + loaded_shape is not None + ), "get_bedrock_response_stream_shape() is None — botocore may not be installed" + shape: StructureShape = loaded_shape assert isinstance(shape, StructureShape) assert shape.name == "ResponseStream" -def test_bedrock_response_stream_shape_same_object_across_imports(): +def test_bedrock_response_stream_shape_same_object_across_calls(): """ - Both bedrock modules that use the shape must reference the identical object — - confirming the constant is not re-loaded per import. + Repeated calls must return the identical cached object. """ - from litellm.llms.bedrock.chat.invoke_handler import ( - BEDROCK_RESPONSE_STREAM_SHAPE as invoke_shape, - ) - from litellm.llms.bedrock.common_utils import ( - BEDROCK_RESPONSE_STREAM_SHAPE as common_shape, - ) + from litellm.llms.bedrock.common_utils import get_bedrock_response_stream_shape - assert common_shape is invoke_shape + first = get_bedrock_response_stream_shape() + second = get_bedrock_response_stream_shape() + assert first is second def test_bedrock_event_stream_decoder_base_uses_module_shape(): @@ -95,19 +122,23 @@ def test_bedrock_event_stream_decoder_base_uses_module_shape(): def test_bedrock_parse_message_from_event_raises_on_none_shape(): """ - When BEDROCK_RESPONSE_STREAM_SHAPE is None (botocore unavailable), + When get_bedrock_response_stream_shape() returns None (botocore unavailable), _parse_message_from_event must raise BedrockError before touching the botocore parser — not an opaque AttributeError from inside botocore. """ from unittest.mock import MagicMock, patch import litellm.llms.bedrock.common_utils as mod - from litellm.llms.bedrock.common_utils import BedrockError, BedrockEventStreamDecoderBase + from litellm.llms.bedrock.common_utils import ( + BedrockError, + BedrockEventStreamDecoderBase, + ) - decoder = BedrockEventStreamDecoderBase() + decoder = BedrockEventStreamDecoderBase.__new__(BedrockEventStreamDecoderBase) + decoder.parser = MagicMock() mock_event = MagicMock() - with patch.object(mod, "BEDROCK_RESPONSE_STREAM_SHAPE", None): + with patch.object(mod, "get_bedrock_response_stream_shape", return_value=None): with pytest.raises(BedrockError) as exc_info: decoder._parse_message_from_event(mock_event) 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/sagemaker/test_sagemaker_common_utils.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py index 9d7706557b5..7e13459bca1 100644 --- a/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py +++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_common_utils.py @@ -12,18 +12,46 @@ from litellm.llms.sagemaker.completion.transformation import SagemakerConfig # --------------------------------------------------------------------------- # -# SAGEMAKER_RESPONSE_STREAM_SHAPE eager-load tests # +# get_sagemaker_response_stream_shape lazy-load tests # # --------------------------------------------------------------------------- # -def test_sagemaker_response_stream_shape_loaded_at_import(): +@pytest.fixture(autouse=True) +def _reset_sagemaker_response_stream_shape_cache(): + """Prevent lru_cache leakage between tests in this module.""" + import litellm.llms.sagemaker.common_utils as mod + + mod.get_sagemaker_response_stream_shape.cache_clear() + yield + mod.get_sagemaker_response_stream_shape.cache_clear() + + +def test_sagemaker_response_stream_shape_lazy_loads_once(): """ - SAGEMAKER_RESPONSE_STREAM_SHAPE is resolved at module import time. + get_sagemaker_response_stream_shape() loads from botocore at most once per process. + """ + from unittest.mock import MagicMock, patch + + import litellm.llms.sagemaker.common_utils as mod + + sentinel = MagicMock() + with patch.object( + mod, "_load_sagemaker_response_stream_shape", return_value=sentinel + ) as mock_load: + assert mod.get_sagemaker_response_stream_shape() is sentinel + assert mod.get_sagemaker_response_stream_shape() is sentinel + mock_load.assert_called_once() + + +def test_sagemaker_response_stream_shape_loaded_on_first_access(): + """ + get_sagemaker_response_stream_shape() loads once on first use. In a standard environment with botocore installed it must be non-None. """ - from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE + pytest.importorskip("botocore") + from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape - assert SAGEMAKER_RESPONSE_STREAM_SHAPE is not None + assert get_sagemaker_response_stream_shape() is not None def test_sagemaker_response_stream_shape_load_failure_returns_none(): @@ -36,6 +64,7 @@ def test_sagemaker_response_stream_shape_load_failure_returns_none(): import litellm.llms.sagemaker.common_utils as mod + pytest.importorskip("botocore") with patch( "botocore.loaders.Loader.load_service_model", side_effect=Exception("no data"), @@ -49,14 +78,16 @@ def test_sagemaker_response_stream_shape_is_structure_shape(): The loaded shape should be the botocore StructureShape for InvokeEndpointWithResponseStreamOutput, not a plain dict or any other type. """ + pytest.importorskip("botocore") from botocore.model import StructureShape - from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE + from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape - assert SAGEMAKER_RESPONSE_STREAM_SHAPE is not None, ( - "SAGEMAKER_RESPONSE_STREAM_SHAPE is None — botocore may not be installed" - ) - shape: StructureShape = SAGEMAKER_RESPONSE_STREAM_SHAPE # remove Optional + shape = get_sagemaker_response_stream_shape() + assert ( + shape is not None + ), "get_sagemaker_response_stream_shape() is None — botocore may not be installed" + shape: StructureShape = shape # remove Optional assert isinstance(shape, StructureShape) assert shape.name == "InvokeEndpointWithResponseStreamOutput" @@ -64,29 +95,25 @@ def test_sagemaker_response_stream_shape_is_structure_shape(): def test_sagemaker_response_stream_shape_not_reloaded_on_new_decoder(): """ Creating multiple AWSEventStreamDecoder instances must not trigger - additional botocore Loader calls — the shape is resolved once at import - time and reused. + additional botocore Loader calls — the shape is cached after first access. """ - from litellm.llms.sagemaker.common_utils import SAGEMAKER_RESPONSE_STREAM_SHAPE + from litellm.llms.sagemaker.common_utils import get_sagemaker_response_stream_shape - decoder_a = AWSEventStreamDecoder(model="test-model-a") - decoder_b = AWSEventStreamDecoder(model="test-model-b") + decoder_a = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder) + decoder_b = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder) - # Both decoders should use the same pre-loaded shape object (identity check) assert "_response_stream_shape_cache" not in decoder_a.__dict__ assert "_response_stream_shape_cache" not in decoder_b.__dict__ - # The module constant is still the same object - from litellm.llms.sagemaker.common_utils import ( - SAGEMAKER_RESPONSE_STREAM_SHAPE as shape_after, - ) - assert SAGEMAKER_RESPONSE_STREAM_SHAPE is shape_after + first = get_sagemaker_response_stream_shape() + second = get_sagemaker_response_stream_shape() + assert first is second def test_sagemaker_parse_message_from_event_raises_on_none_shape(): """ - When SAGEMAKER_RESPONSE_STREAM_SHAPE is None (botocore unavailable), - _parse_message_from_event must raise ValueError before touching the + When get_sagemaker_response_stream_shape() returns None (botocore unavailable), + _parse_message_from_event must raise SagemakerError before touching the botocore parser — not an opaque AttributeError from inside botocore. """ from unittest.mock import MagicMock, patch @@ -94,10 +121,14 @@ def test_sagemaker_parse_message_from_event_raises_on_none_shape(): import litellm.llms.sagemaker.common_utils as mod from litellm.llms.sagemaker.common_utils import SagemakerError - decoder = AWSEventStreamDecoder(model="test-model") + decoder = AWSEventStreamDecoder.__new__(AWSEventStreamDecoder) + decoder.model = "test-model" + decoder.parser = MagicMock() + decoder.content_blocks = [] + decoder.is_messages_api = None mock_event = MagicMock() - with patch.object(mod, "SAGEMAKER_RESPONSE_STREAM_SHAPE", None): + with patch.object(mod, "get_sagemaker_response_stream_shape", return_value=None): with pytest.raises(SagemakerError) as exc_info: decoder._parse_message_from_event(mock_event) 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..db993245426 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 @@ -2959,6 +2959,38 @@ def test_vertex_ai_gemini3_tool_combination_no_drop(): assert len(tools) == 3 +def test_vertex_ai_mixed_tools_and_web_search_options_drops_search(): + """ + When function tools and web_search_options are sent separately (Codex-style), + search tools are dropped unless include_server_side_tool_invocations is set. + """ + v = VertexGeminiConfig() + optional_params: dict = {} + non_default_params = { + "tools": [ + { + "type": "function", + "function": {"name": "exec_command", "description": "Run a command"}, + } + ], + "web_search_options": {}, + } + + result = v.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model="gemini-3.5-flash", + drop_params=True, + ) + + assert not result.get("include_server_side_tool_invocations") + tool_keys = set() + for tool in result.get("tools", []): + tool_keys.update(tool.keys()) + assert "function_declarations" in tool_keys + assert "googleSearch" not in tool_keys + + def test_vertex_ai_openai_web_search_tool_transformation(): """ Test that OpenAI-style web_search and web_search_preview tools are transformed to googleSearch. @@ -4290,3 +4322,444 @@ 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..ec73e5e42be 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 ( @@ -1490,39 +1517,31 @@ def test_vertex_parallel_tool_calls_true(): assert "tools" in optional_params -def test_vertex_parallel_tool_calls_false_multiple_tools_error(): +def test_vertex_parallel_tool_calls_false_multiple_tools_dropped(): """ - Test that parallel_tool_calls = False with multiple tools raises UnsupportedParamsError - when drop_params is False. + parallel_tool_calls=False with multiple tools is dropped for Gemini + (unsupported upstream). Request should succeed without the param. """ tools = [ {"type": "function", "function": {"name": "get_weather"}}, {"type": "function", "function": {"name": "get_time"}}, ] - with pytest.raises(litellm.utils.UnsupportedParamsError) as excinfo: - get_optional_params( - model="gemini-1.5-pro", - custom_llm_provider="vertex_ai", - tools=tools, - parallel_tool_calls=False, - ) - assert ( - "`parallel_tool_calls=False` is not supported by Gemini when multiple tools are" - in str(excinfo.value) + optional_params = get_optional_params( + model="gemini-1.5-pro", + custom_llm_provider="vertex_ai", + tools=tools, + parallel_tool_calls=False, ) + assert "parallel_tool_calls" not in optional_params + assert "tools" in optional_params - # works when specified as "functions" - with pytest.raises(litellm.utils.UnsupportedParamsError) as excinfo: - get_optional_params( - model="gemini-1.5-pro", - custom_llm_provider="vertex_ai", - functions=tools, - parallel_tool_calls=False, - ) - assert ( - "`parallel_tool_calls=False` is not supported by Gemini when multiple tools are" - in str(excinfo.value) + optional_params = get_optional_params( + model="gemini-1.5-pro", + custom_llm_provider="vertex_ai", + functions=tools, + parallel_tool_calls=False, ) + assert "parallel_tool_calls" not in optional_params def test_vertex_parallel_tool_calls_false_single_tool(): 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/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 581324d47d2..b06cc7f0f12 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -1278,6 +1278,188 @@ def test_xff_misconfig_warning_emitted_once(caplog): ), f"expected exactly one warning, got {len(matching)}: {[r.getMessage() for r in matching]}" +def test_get_request_base_url_honors_proxy_base_url_env(monkeypatch): + try: + from fastapi import Request + + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + get_request_base_url, + ) + except ImportError: + pytest.skip("MCP discoverable endpoints not available") + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "10.0.0.7" + headers = { + "X-Forwarded-Proto": "https", + "X-Forwarded-Host": "litellm-internal:4000", + "X-Forwarded-Port": "9999", + } + mock_request.headers.get = lambda name, default=None: headers.get(name, default) + + monkeypatch.setenv("PROXY_BASE_URL", "https://litellm.example.com") + assert get_request_base_url(mock_request) == "https://litellm.example.com" + + monkeypatch.setenv("PROXY_BASE_URL", "https://litellm.example.com/") + assert get_request_base_url(mock_request) == "https://litellm.example.com" + + +def test_validate_trusted_redirect_uri_logs_diagnostic_on_rejection( + caplog, monkeypatch +): + try: + from fastapi import HTTPException, Request + + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + validate_trusted_redirect_uri, + ) + except ImportError: + pytest.skip("MCP oauth_utils not available") + + monkeypatch.delenv("PROXY_BASE_URL", raising=False) + monkeypatch.delenv("MCP_TRUSTED_REDIRECT_ORIGINS", raising=False) + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "203.0.113.5" + headers = { + "X-Forwarded-Proto": "https", + "X-Forwarded-Host": "litellm.example.com", + "X-Forwarded-Port": "443", + "Host": "litellm-internal:4000", + } + mock_request.headers.get = lambda name, default=None: headers.get(name, default) + + import logging + + with ( + caplog.at_level(logging.WARNING, logger="LiteLLM"), + patch("litellm.proxy.proxy_server.general_settings", {}, create=True), + ): + with pytest.raises(HTTPException) as exc_info: + validate_trusted_redirect_uri( + mock_request, + "https://litellm.example.com/ui/mcp/oauth/callback", + ) + assert exc_info.value.status_code == 400 + assert exc_info.value.detail == "invalid_request" + + matching = [r for r in caplog.records if "rejecting redirect_uri" in r.getMessage()] + assert len(matching) == 1, ( + "expected exactly one diagnostic warning, got " + f"{[r.getMessage() for r in caplog.records]}" + ) + msg = matching[0].getMessage() + assert "https://litellm.example.com/ui/mcp/oauth/callback" in msg + assert "litellm-internal:4000" in msg + assert "X-Forwarded-Host" in msg + + +@pytest.mark.parametrize( + "bad_value", + [ + "litellm.example.com", + "litellm.example.com/", + "://litellm.example.com", + "ftp://litellm.example.com", + "https://", + "not a url at all", + ], +) +def test_get_request_base_url_rejects_malformed_proxy_base_url( + bad_value, monkeypatch, caplog +): + try: + from fastapi import Request + + from litellm.proxy._experimental.mcp_server import oauth_utils + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + get_request_base_url, + ) + except ImportError: + pytest.skip("MCP oauth_utils not available") + + oauth_utils._warned_invalid_proxy_base_url = None + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "127.0.0.1" + mock_request.headers.get = lambda name, default=None: default + + monkeypatch.setenv("PROXY_BASE_URL", bad_value) + + import logging + + with ( + caplog.at_level(logging.WARNING, logger="LiteLLM"), + patch("litellm.proxy.proxy_server.general_settings", {}, create=True), + ): + result = get_request_base_url(mock_request) + + assert result == "http://litellm-internal:4000", ( + f"malformed PROXY_BASE_URL={bad_value!r} should be ignored, " f"got {result!r}" + ) + matching = [ + r + for r in caplog.records + if "PROXY_BASE_URL" in r.getMessage() and "ignored" in r.getMessage() + ] + assert len(matching) == 1, ( + "expected one diagnostic for malformed PROXY_BASE_URL, got " + f"{[r.getMessage() for r in caplog.records]}" + ) + assert ( + repr(bad_value) in matching[0].getMessage() + or bad_value in matching[0].getMessage() + ) + + +def test_get_request_base_url_malformed_proxy_base_url_warning_is_one_shot( + monkeypatch, caplog +): + try: + from fastapi import Request + + from litellm.proxy._experimental.mcp_server import oauth_utils + from litellm.proxy._experimental.mcp_server.oauth_utils import ( + get_request_base_url, + ) + except ImportError: + pytest.skip("MCP oauth_utils not available") + + oauth_utils._warned_invalid_proxy_base_url = None + + mock_request = MagicMock(spec=Request) + mock_request.base_url = "http://litellm-internal:4000/" + mock_request.client = MagicMock() + mock_request.client.host = "127.0.0.1" + mock_request.headers.get = lambda name, default=None: default + + monkeypatch.setenv("PROXY_BASE_URL", "litellm.example.com") + + import logging + + with ( + caplog.at_level(logging.WARNING, logger="LiteLLM"), + patch("litellm.proxy.proxy_server.general_settings", {}, create=True), + ): + for _ in range(5): + get_request_base_url(mock_request) + + matching = [ + r + for r in caplog.records + if "PROXY_BASE_URL" in r.getMessage() and "ignored" in r.getMessage() + ] + assert ( + len(matching) == 1 + ), f"expected exactly one warning across 5 calls, got {len(matching)}" + + # ------------------------------------------------------------------- # Tests for scopes_supported when mcp_server.scopes is None # ------------------------------------------------------------------- diff --git a/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py b/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py index 1063f59afb6..f3cec320532 100644 --- a/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py +++ b/tests/test_litellm/proxy/google_endpoints/test_interactions_agent_param.py @@ -1,75 +1,103 @@ """ -Test for interactions endpoint agent parameter handling. +Tests for managed-agent interaction routing. -Tests that the /v1beta/interactions endpoint correctly extracts -the `agent` parameter as a fallback when `model` is not provided. +Custom Gemini agents are identified by ``agent`` (name/id), not ``model``. +The proxy must not pass the agent name as ``model`` or LiteLLM may route to +openai/* wildcards instead of Gemini interactions. """ +from unittest.mock import MagicMock, patch + import pytest class TestInteractionsAgentParameter: - """Test agent parameter handling in interactions endpoint.""" + """Proxy endpoint must keep agent and model separate.""" - def test_agent_parameter_fallback_logic(self): - """ - Test the core logic: model or agent extraction. - - This tests the fix in endpoints.py line ~267: - model=data.get("model") or data.get("agent") - """ - # Case 1: Only agent provided (Deep Research use case) + def test_create_interaction_uses_model_only_from_body(self): + """POST /v1beta/interactions: model kwarg is only the request's model field.""" data = { - "agent": "deep-research-pro-preview-12-2025", - "input": "Research quantum computing", - "background": True, + "agent": "mqy-custom-slides-agent", + "input": "hello", } - model = data.get("model") or data.get("agent") - assert model == "deep-research-pro-preview-12-2025" + # Fixed behavior: do NOT fall back agent → model + model_for_routing = data.get("model") + assert model_for_routing is None + assert data.get("agent") == "mqy-custom-slides-agent" - # Case 2: Only model provided (normal use case) + def test_model_field_still_used_when_present(self): data = { "model": "gemini-2.5-flash", - "input": "Hello world", + "input": "hello", } - model = data.get("model") or data.get("agent") - assert model == "gemini-2.5-flash" + model_for_routing = data.get("model") + assert model_for_routing == "gemini-2.5-flash" - # Case 3: Both provided (model takes precedence) - data = { - "model": "gemini-2.5-flash", - "agent": "deep-research-pro-preview-12-2025", - "input": "Test", - } - model = data.get("model") or data.get("agent") - assert model == "gemini-2.5-flash" - # Case 4: Neither provided - data = { - "input": "Test", - } - model = data.get("model") or data.get("agent") - assert model is None +class TestInteractionsAgentOnlyProviderRouting: + """SDK: agent-only create must not call get_llm_provider on the agent name.""" - def test_route_type_in_skip_model_routing_list(self): - """ - Test that acreate_interaction is in the list of routes - that skip model-based routing. + @patch("litellm.interactions.main.interactions_http_handler") + @patch("litellm.interactions.main.get_provider_interactions_api_config") + @patch("litellm.get_llm_provider") + def test_agent_only_skips_get_llm_provider( + self, + mock_get_llm_provider, + mock_get_config, + mock_handler, + ): + from litellm.interactions.main import create + from litellm.types.interactions import InteractionsAPIResponse - This tests the fix in route_llm_request.py. - """ - # The list of routes that skip model routing for interactions - skip_model_routing_routes = [ - "acreate_interaction", - "aget_interaction", - "adelete_interaction", - "acancel_interaction", - ] + mock_get_config.return_value = MagicMock() + mock_handler.create_interaction.return_value = InteractionsAPIResponse( + id="int-1", + status="completed", + object="interaction", + ) - # acreate_interaction should be in the list (this is the fix) - assert "acreate_interaction" in skip_model_routing_routes + logging_obj = MagicMock() + create( + agent="mqy-custom-slides-agent", + input="test", + custom_llm_provider="gemini", + litellm_logging_obj=logging_obj, + ) - # All interaction routes should be covered - assert "aget_interaction" in skip_model_routing_routes - assert "adelete_interaction" in skip_model_routing_routes - assert "acancel_interaction" in skip_model_routing_routes + mock_get_llm_provider.assert_not_called() + call_kwargs = mock_handler.create_interaction.call_args.kwargs + assert call_kwargs["agent"] == "mqy-custom-slides-agent" + assert call_kwargs["model"] is None + assert call_kwargs["custom_llm_provider"] == "gemini" + + @patch("litellm.interactions.main.interactions_http_handler") + @patch("litellm.interactions.main.get_provider_interactions_api_config") + @patch("litellm.get_llm_provider") + def test_proxy_mistake_model_equals_agent_is_corrected( + self, + mock_get_llm_provider, + mock_get_config, + mock_handler, + ): + """If model was wrongly set to the agent name, clear it before the HTTP call.""" + from litellm.interactions.main import create + from litellm.types.interactions import InteractionsAPIResponse + + mock_get_config.return_value = MagicMock() + mock_handler.create_interaction.return_value = InteractionsAPIResponse( + id="int-1", + status="completed", + object="interaction", + ) + + logging_obj = MagicMock() + create( + model="mqy-custom-slides-agent", + agent="mqy-custom-slides-agent", + input="test", + custom_llm_provider="gemini", + litellm_logging_obj=logging_obj, + ) + + mock_get_llm_provider.assert_not_called() + assert mock_handler.create_interaction.call_args.kwargs["model"] is None diff --git a/tests/test_litellm/proxy/google_endpoints/test_managed_agents_model_param.py b/tests/test_litellm/proxy/google_endpoints/test_managed_agents_model_param.py new file mode 100644 index 00000000000..5485d0f2929 --- /dev/null +++ b/tests/test_litellm/proxy/google_endpoints/test_managed_agents_model_param.py @@ -0,0 +1,199 @@ +""" +Tests verifying that managed-agent proxy endpoints never pass the agent name +as the ``model`` parameter to ``base_process_llm_request``. + +Passing ``model=`` would cause ``common_processing_pre_call_logic`` +to write the agent name into ``self.data["model"]``, which triggers spurious +model-alias mapping, rate-limiting lookups, and logging tied to a +non-existent model deployment. The agent name is already carried in +``data["name"]`` and must not pollute the ``model`` slot. +""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + + +def _build_agents_client(): + """Build a TestClient whose auth dependency is overridden to a PROXY_ADMIN + user. Using ``dependency_overrides`` is the only reliable way to bypass the + real ``user_api_key_auth`` for FastAPI route tests — patching the module- + level name does not affect the function reference captured by ``Depends``. + The PROXY_ADMIN role also bypasses the caller-supplied-api_key guard so + these tests can focus on the ``model=None`` invariant. + """ + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + from litellm.proxy.google_endpoints.agents_endpoints import router as agents_router + + app = FastAPI() + app.include_router(agents_router) + + async def _fake_user_api_key_auth(): + return UserAPIKeyAuth( + api_key="sk-test", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + + app.dependency_overrides[user_api_key_auth] = _fake_user_api_key_auth + return TestClient(app) + + +def _patch_proxy_server_imports(client=None): + """Return a context-manager that stubs _proxy_server_imports so tests + don't need a running proxy.""" + mock_srv = { + "general_settings": {}, + "llm_router": MagicMock(), + "proxy_config": MagicMock(), + "proxy_logging_obj": MagicMock(), + "select_data_generator": None, + "user_api_base": None, + "user_max_tokens": None, + "user_model": None, + "user_request_timeout": None, + "user_temperature": None, + "version": "0.0.0", + } + return patch( + "litellm.proxy.google_endpoints.agents_endpoints._proxy_server_imports", + return_value=mock_srv, + ) + + +def _patch_base_process(return_value=None): + if return_value is None: + return_value = {"name": "agents/my-agent", "displayName": "My Agent"} + return patch( + "litellm.proxy.google_endpoints.agents_endpoints.ProxyBaseLLMRequestProcessing.base_process_llm_request", + new_callable=AsyncMock, + return_value=return_value, + ) + + +def _patch_auth(): + """Deprecated no-op kept for call-site compatibility. + + ``_build_agents_client`` now installs a FastAPI ``dependency_overrides`` + entry that injects a PROXY_ADMIN ``UserAPIKeyAuth``, so individual tests + no longer need to patch the module-level ``user_api_key_auth`` name. + """ + return patch("os.getpid") + + +class TestManagedAgentsModelParam: + """Endpoints must pass model=None, not the agent name, to base_process_llm_request.""" + + def test_create_agent_passes_model_none(self): + """POST /v1beta/agents: model kwarg must be None, not the name field.""" + try: + client = _build_agents_client() + except ImportError as exc: + pytest.skip(f"Skipping: missing dependency {exc}") + + with ( + _patch_proxy_server_imports(), + _patch_base_process() as mock_process, + _patch_auth(), + ): + client.post( + "/v1beta/agents", + json={ + "name": "my-custom-slides-agent", + "base_agent": "waverunner", + "instructions": "Be helpful.", + }, + ) + + mock_process.assert_called_once() + kwargs = mock_process.call_args.kwargs + assert kwargs["model"] is None, ( + f"create_gemini_agent must not pass model={kwargs['model']!r}; " + "the agent name must stay in data['name'], not pollute data['model']" + ) + assert kwargs["route_type"] == "acreate_agent" + + def test_get_agent_passes_model_none(self): + """GET /v1beta/agents/{name}: model kwarg must be None.""" + try: + client = _build_agents_client() + except ImportError as exc: + pytest.skip(f"Skipping: missing dependency {exc}") + + with ( + _patch_proxy_server_imports(), + _patch_base_process() as mock_process, + _patch_auth(), + ): + client.get("/v1beta/agents/my-custom-slides-agent") + + mock_process.assert_called_once() + kwargs = mock_process.call_args.kwargs + assert ( + kwargs["model"] is None + ), f"get_gemini_agent must not pass model={kwargs['model']!r}" + assert kwargs["route_type"] == "aget_agent" + + def test_delete_agent_passes_model_none(self): + """DELETE /v1beta/agents/{name}: model kwarg must be None.""" + try: + client = _build_agents_client() + except ImportError as exc: + pytest.skip(f"Skipping: missing dependency {exc}") + + with ( + _patch_proxy_server_imports(), + _patch_base_process() as mock_process, + _patch_auth(), + ): + client.delete("/v1beta/agents/my-custom-slides-agent") + + mock_process.assert_called_once() + kwargs = mock_process.call_args.kwargs + assert ( + kwargs["model"] is None + ), f"delete_gemini_agent must not pass model={kwargs['model']!r}" + assert kwargs["route_type"] == "adelete_agent" + + def test_list_agent_versions_passes_model_none(self): + """GET /v1beta/agents/{name}/versions: model kwarg must be None.""" + try: + client = _build_agents_client() + except ImportError as exc: + pytest.skip(f"Skipping: missing dependency {exc}") + + with ( + _patch_proxy_server_imports(), + _patch_base_process() as mock_process, + _patch_auth(), + ): + client.get("/v1beta/agents/my-custom-slides-agent/versions") + + mock_process.assert_called_once() + kwargs = mock_process.call_args.kwargs + assert ( + kwargs["model"] is None + ), f"list_gemini_agent_versions must not pass model={kwargs['model']!r}" + assert kwargs["route_type"] == "alist_agent_versions" + + def test_list_agents_already_passes_model_none(self): + """GET /v1beta/agents: existing list endpoint already passes model=None — keep it so.""" + try: + client = _build_agents_client() + except ImportError as exc: + pytest.skip(f"Skipping: missing dependency {exc}") + + with ( + _patch_proxy_server_imports(), + _patch_base_process(return_value={"agents": []}) as mock_process, + _patch_auth(), + ): + client.get("/v1beta/agents") + + mock_process.assert_called_once() + kwargs = mock_process.call_args.kwargs + assert kwargs["model"] is None + assert kwargs["route_type"] == "alist_agents" 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/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index bedc9c1f6e8..ae0996d16d5 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -1169,6 +1169,434 @@ def test_add_team_models_to_all_models(): assert result == {"gpt-4-model-2": {"team1"}} +@pytest.mark.asyncio +async def test_apply_search_filter_matches_team_public_model_name(): + """ + Regression test: team BYOK models persist an internal model_name + (e.g. `model_name_{team_id}_{uuid}`) and surface the user-facing name + via `model_info.team_public_model_name`. The /v2/model/info search + filter must match that public name so BYOK rows appear in results. + """ + from litellm.proxy.proxy_server import _apply_search_filter_to_models + + byok_model = { + "model_name": "model_name_team-abc-123_4a6b8", + "litellm_params": {"model": "claude-sonnet-4-5"}, + "model_info": { + "id": "byok-id-1", + "team_id": "team-abc-123", + "team_public_model_name": "team-claude-sonnet", + "db_model": True, + }, + } + unrelated_model = { + "model_name": "gpt-4", + "litellm_params": {"model": "gpt-4"}, + "model_info": {"id": "normal-id-1", "db_model": False}, + } + + # Search matching only team_public_model_name should still include BYOK + filtered, _ = await _apply_search_filter_to_models( + all_models=[byok_model, unrelated_model], + search="claude", + prisma_client=None, + proxy_config=MagicMock(), + ) + filtered_ids = {m["model_info"]["id"] for m in filtered} + assert "byok-id-1" in filtered_ids + assert "normal-id-1" not in filtered_ids + + # Search by internal model_name still matches as before + filtered, _ = await _apply_search_filter_to_models( + all_models=[byok_model, unrelated_model], + search="model_name_team-abc-123", + prisma_client=None, + proxy_config=MagicMock(), + ) + assert [m["model_info"]["id"] for m in filtered] == ["byok-id-1"] + + # Non-matching search returns nothing + filtered, _ = await _apply_search_filter_to_models( + all_models=[byok_model, unrelated_model], + search="gemini", + prisma_client=None, + proxy_config=MagicMock(), + ) + assert filtered == [] + + +@pytest.mark.asyncio +async def test_apply_search_filter_scopes_byok_to_caller_teams(): + """ + Regression test: `/v2/model/info?search=...` must not leak BYOK rows + from teams the caller is not a member of. Even with a bounded + `model_name`-contains DB query, a non-admin caller could otherwise + see other teams' BYOK rows that happen to match by internal name. + The post-fetch team scope drops those. + """ + from litellm.proxy.proxy_server import _apply_search_filter_to_models + + # In-router BYOK rows: one in the caller's team, one in someone else's. + caller_team_byok = { + "model_name": "model_name_team-mine_internal", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": { + "id": "byok-mine", + "team_id": "team-mine", + "team_public_model_name": "claude-sonnet-prod", + "db_model": True, + }, + } + other_team_byok = { + "model_name": "model_name_team-other_internal", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": { + "id": "byok-other", + "team_id": "team-other", + "team_public_model_name": "claude-sonnet-staging", + "db_model": True, + }, + } + # Non-team row stays in the router-side result regardless of teams. + public_model = { + "model_name": "claude-public", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": {"id": "public-id", "db_model": False}, + } + + # DB-only BYOK rows fetched by the over-broad JSON branch. + db_caller_row = MagicMock() + db_caller_row.model_id = "byok-db-mine" + db_caller_row.model_name = "model_name_team-mine_db" + db_caller_row.model_info = { + "id": "byok-db-mine", + "team_id": "team-mine", + "team_public_model_name": "Claude DB Mine", + "db_model": True, + } + db_other_row = MagicMock() + db_other_row.model_id = "byok-db-other" + db_other_row.model_name = "model_name_team-other_db" + db_other_row.model_info = { + "id": "byok-db-other", + "team_id": "team-other", + "team_public_model_name": "Claude DB Other", + "db_model": True, + } + + prisma_client = MagicMock() + prisma_client.db.litellm_proxymodeltable.count = AsyncMock(return_value=2) + prisma_client.db.litellm_proxymodeltable.find_many = AsyncMock( + return_value=[db_caller_row, db_other_row] + ) + caller_user_row = MagicMock() + caller_user_row.teams = ["team-mine"] + prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=caller_user_row + ) + + proxy_config = MagicMock() + proxy_config.decrypt_model_list_from_db = lambda rows: [ + { + "model_name": r.model_name, + "model_info": r.model_info, + "litellm_params": {"model": "claude-sonnet"}, + } + for r in rows + ] + + non_admin = MagicMock(spec=UserAPIKeyAuth) + non_admin.user_role = LitellmUserRoles.INTERNAL_USER + non_admin.user_id = "user-mine" + + filtered, total_count = await _apply_search_filter_to_models( + all_models=[caller_team_byok, other_team_byok, public_model], + search="claude", + prisma_client=prisma_client, + proxy_config=proxy_config, + user_api_key_dict=non_admin, + ) + + filtered_ids = {m["model_info"]["id"] for m in filtered} + assert "byok-mine" in filtered_ids + assert "byok-db-mine" in filtered_ids + assert "public-id" in filtered_ids + assert "byok-other" not in filtered_ids, ( + "router-side BYOK from another team must be dropped from search " + "when caller doesn't belong to that team" + ) + assert "byok-db-other" not in filtered_ids, ( + "DB-only BYOK from another team must be dropped from search when " + "caller doesn't belong to that team" + ) + # total_count is router_models_count (2: caller_team_byok + public_model, + # other_team_byok dropped router-side) + DB count (2 from the mocked + # `count()`). The DB count is the *unscoped* match count; non-admin + # team scoping applies only to the returned page so the count can be + # over-reported, but it must never under-report (callers can paginate + # within the bound). + assert total_count == 4 + + # Admins keep the un-scoped view across teams. + admin = MagicMock(spec=UserAPIKeyAuth) + admin.user_role = LitellmUserRoles.PROXY_ADMIN + admin.user_id = "admin-1" + + filtered_admin, _ = await _apply_search_filter_to_models( + all_models=[caller_team_byok, other_team_byok, public_model], + search="claude", + prisma_client=prisma_client, + proxy_config=proxy_config, + user_api_key_dict=admin, + ) + admin_ids = {m["model_info"]["id"] for m in filtered_admin} + assert "byok-other" in admin_ids + assert "byok-db-other" in admin_ids + + +@pytest.mark.asyncio +async def test_apply_search_filter_bounds_db_fetch_by_page_and_cap(): + """ + Regression test: a broad search term must not force a full BYOK-table + read + decrypt on each request. + + * Unsorted searches: `find_many(take=N)` where N is just enough to + fill the current page after counting router-side matches. + * Sorted searches: `find_many(take=cap)` falls back to + `_SORTED_SEARCH_DB_FETCH_CAP` so ordering still works across a + large match set without scanning the whole table. + """ + from litellm.proxy.proxy_server import ( + _SORTED_SEARCH_DB_FETCH_CAP, + _apply_search_filter_to_models, + ) + + prisma_client = MagicMock() + prisma_client.db.litellm_proxymodeltable.count = AsyncMock(return_value=10_000) + prisma_client.db.litellm_proxymodeltable.find_many = AsyncMock(return_value=[]) + + proxy_config = MagicMock() + proxy_config.decrypt_model_list_from_db = lambda rows: [] + + # Unsorted: page=1, size=50, no router-side matches -> take must be 50. + await _apply_search_filter_to_models( + all_models=[], + search="model", + prisma_client=prisma_client, + proxy_config=proxy_config, + page=1, + size=50, + sort_by=None, + ) + take = prisma_client.db.litellm_proxymodeltable.find_many.call_args.kwargs["take"] + assert take == 50, "unsorted search must take just one page's worth of rows" + + # Sorted: still bounded, but by the hard cap rather than the page. + prisma_client.db.litellm_proxymodeltable.find_many.reset_mock() + await _apply_search_filter_to_models( + all_models=[], + search="model", + prisma_client=prisma_client, + proxy_config=proxy_config, + page=1, + size=50, + sort_by="model_name", + ) + take = prisma_client.db.litellm_proxymodeltable.find_many.call_args.kwargs["take"] + assert take == _SORTED_SEARCH_DB_FETCH_CAP + assert take < 10_000, "sorted search must cap below the full match set" + + +@pytest.mark.asyncio +async def test_filter_models_by_team_id_excludes_viewer_direct_access(): + """ + Regression test: when the UI picks a specific team in the Current Team + selector, the model list must show only that team's BYOK rows + the + models assigned to the team. The admin viewer's `direct_access` flag + (set on every non-team model upstream) must NOT widen the team's + visible set, or selecting team-111 still shows every public model. + """ + from litellm.proxy.proxy_server import _filter_models_by_team_id + + public_model = { + "model_name": "gpt-4", + "litellm_params": {"model": "gpt-4"}, + "model_info": { + "id": "public-id", + # admin viewer has direct_access on this public model + "direct_access": True, + # team-111 is NOT in access_via_team_ids -> shouldn't show for team-111 + "access_via_team_ids": ["team-222"], + }, + } + team111_byok = { + "model_name": "model_name_team-111_uuid", + "litellm_params": {"model": "claude-sonnet"}, + "model_info": { + "id": "byok-team-111", + "team_id": "team-111", + "team_public_model_name": "team-claude", + "access_via_team_ids": ["team-111"], + }, + } + team222_byok = { + "model_name": "model_name_team-222_uuid", + "litellm_params": {"model": "claude-haiku"}, + "model_info": { + "id": "byok-team-222", + "team_id": "team-222", + "team_public_model_name": "team-haiku", + "access_via_team_ids": ["team-222"], + }, + } + + prisma = MagicMock() + team_db = MagicMock() + team_db.model_dump.return_value = { + "team_id": "team-111", + "team_alias": "Team 111", + # specific models list that doesn't include the BYOK's internal name + "models": ["some-other-model"], + "access_group_ids": None, + } + prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=team_db) + prisma.db.litellm_proxymodeltable.find_many = AsyncMock(return_value=[]) + + router = MagicMock() + router.get_model_access_groups = MagicMock(return_value={}) + # team-111 only resolves "some-other-model", which has no deployments + router.get_model_list = MagicMock(return_value=[]) + + filtered = await _filter_models_by_team_id( + all_models=[public_model, team111_byok, team222_byok], + team_id="team-111", + prisma_client=prisma, + llm_router=router, + ) + visible_ids = sorted(m["model_info"]["id"] for m in filtered) + + assert "byok-team-111" in visible_ids, "team-111's own BYOK must always be visible" + assert "byok-team-222" not in visible_ids, "must not leak other teams' BYOK" + assert ( + "public-id" not in visible_ids + ), "viewer's direct_access must not widen the team's visible set" + + +@pytest.mark.asyncio +async def test_filter_models_by_team_id_rejects_non_member(): + """ + Regression test: /v2/model/info?teamId=X includes BYOK rows solely on + `model_info.team_id == X`. Without an auth check, any authenticated user + could enumerate another team's BYOK metadata by guessing its id. Callers + that are neither proxy admins nor members of `team_id` must get 403. + """ + from fastapi import HTTPException + + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.proxy_server import _filter_models_by_team_id + + byok = { + "model_name": "model_name_team-111_uuid", + "litellm_params": {"model": "claude"}, + "model_info": {"id": "byok-team-111", "team_id": "team-111"}, + } + + prisma = MagicMock() + # Caller is in team-222 only + user_row = MagicMock() + user_row.teams = ["team-222"] + prisma.db.litellm_usertable.find_unique = AsyncMock(return_value=user_row) + + caller = UserAPIKeyAuth( + user_id="alice", + user_role=LitellmUserRoles.INTERNAL_USER, + api_key="sk-test", + ) + + with pytest.raises(HTTPException) as excinfo: + await _filter_models_by_team_id( + all_models=[byok], + team_id="team-111", + prisma_client=prisma, + llm_router=MagicMock(), + user_api_key_dict=caller, + ) + assert excinfo.value.status_code == 403 + + +@pytest.mark.asyncio +async def test_filter_models_by_team_id_allows_team_member(): + """ + A caller who IS a member of `team_id` must be allowed to filter, and + should see that team's BYOK rows. + """ + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.proxy_server import _filter_models_by_team_id + + byok = { + "model_name": "model_name_team-111_uuid", + "litellm_params": {"model": "claude"}, + "model_info": {"id": "byok-team-111", "team_id": "team-111"}, + } + + prisma = MagicMock() + user_row = MagicMock() + user_row.teams = ["team-111", "team-999"] + prisma.db.litellm_usertable.find_unique = AsyncMock(return_value=user_row) + team_db = MagicMock() + team_db.model_dump.return_value = { + "team_id": "team-111", + "team_alias": "Team 111", + "models": [], + "access_group_ids": None, + } + prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=team_db) + prisma.db.litellm_proxymodeltable.find_many = AsyncMock(return_value=[]) + + router = MagicMock() + router.get_model_access_groups = MagicMock(return_value={}) + router.get_model_list = MagicMock(return_value=[byok]) + + caller = UserAPIKeyAuth( + user_id="bob", + user_role=LitellmUserRoles.INTERNAL_USER, + api_key="sk-test", + ) + + result = await _filter_models_by_team_id( + all_models=[byok], + team_id="team-111", + prisma_client=prisma, + llm_router=router, + user_api_key_dict=caller, + ) + assert [m["model_info"]["id"] for m in result] == ["byok-team-111"] + + +@pytest.mark.asyncio +async def test_caller_byok_team_scope_treats_view_only_admin_as_unscoped(): + """ + Regression test: `PROXY_ADMIN_VIEW_ONLY` is an admin role + ("can login, view all own keys, view all spend"). Search results for + this role must show BYOK rows across all teams, not be silently scoped + to the user-id's `teams` field — that path narrows results to whatever + teams the admin happens to be a member of, regressing pre-PR behavior. + """ + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.proxy_server import _get_caller_byok_team_scope + + caller = UserAPIKeyAuth( + user_id="view-admin", + user_role=LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + api_key="sk-test", + ) + scope = await _get_caller_byok_team_scope( + user_api_key_dict=caller, + prisma_client=MagicMock(), + ) + assert scope is None, "PROXY_ADMIN_VIEW_ONLY must be unscoped, like PROXY_ADMIN" + + @pytest.mark.asyncio async def test_add_access_group_models_to_team_models(): """ @@ -4637,6 +5065,66 @@ async def test_async_data_generator_uses_direct_stream_fast_path_without_callbac mock_response.aclose.assert_awaited_once() +@pytest.mark.asyncio +async def test_async_data_generator_passes_through_google_native_sse_bytes(): + """ + Google-native streamGenerateContent yields raw SSE bytes; they must not be + re-wrapped as data: b'data: {...}'. + """ + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.proxy_server import async_data_generator + from litellm.proxy.utils import ProxyLogging + + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_request_data = { + "model": "gemini-2.0-flash", + "messages": [{"role": "user", "content": "test"}], + } + gemini_event = b'data: {"candidates": [{"content": "hi"}]}\n\n' + gemini_event_without_terminator = b'data: {"candidates": [{"content": "there"}]}' + raw_payload = b'{"partial": true}' + + class MockStream: + def __aiter__(self): + return self._stream() + + async def _stream(self): + yield gemini_event + yield gemini_event_without_terminator + yield raw_payload + + async def aclose(self): + pass + + mock_response = MockStream() + mock_response.aclose = AsyncMock() + mock_proxy_logging_obj = MagicMock(spec=ProxyLogging) + mock_proxy_logging_obj.has_streaming_callbacks.return_value = False + mock_proxy_logging_obj.needs_iterator_wrap.return_value = False + mock_proxy_logging_obj.needs_per_chunk_streaming_hook.return_value = False + mock_proxy_logging_obj.async_post_call_streaming_iterator_hook = MagicMock() + mock_proxy_logging_obj.async_post_call_streaming_hook = AsyncMock() + mock_proxy_logging_obj.post_call_failure_hook = AsyncMock() + + with patch("litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging_obj): + with patch.object(ProxyLogging, "_fire_deferred_stream_logging"): + yielded_data = [] + async for data in async_data_generator( + mock_response, mock_user_api_key_dict, mock_request_data + ): + yielded_data.append(data) + + yielded_text = [ + chunk.decode("utf-8") if isinstance(chunk, bytes) else chunk + for chunk in yielded_data + ] + assert yielded_text[0] == gemini_event.decode("utf-8") + assert yielded_text[1] == gemini_event_without_terminator.decode("utf-8") + "\n\n" + assert yielded_text[2] == f'data: {raw_payload.decode("utf-8")}\n\n' + assert "b'data:" not in "".join(yielded_text) + assert yielded_text[-1] == "data: [DONE]\n\n" + + @pytest.mark.asyncio async def test_async_data_generator_cleanup_on_normal_completion(): """ @@ -5220,6 +5708,7 @@ async def test_init_and_increment_spend_counter_reseeds_from_db_on_counter_miss( fake_redis = AsyncMock() fake_redis.async_increment = AsyncMock(side_effect=record_increment) fake_redis.async_get_cache = AsyncMock(return_value=None) # counter missing + fake_redis.async_set_cache = AsyncMock(return_value=True) # SET NX wins counter_cache.redis_cache = fake_redis # Prisma returns spend=42.0 (authoritative) while the stale cached @@ -5256,16 +5745,131 @@ async def test_init_and_increment_spend_counter_reseeds_from_db_on_counter_miss( fake_prisma.db.litellm_teamtable.find_unique.assert_awaited_once_with( where={"team_id": "team-9"} ) - # Two increments keyed on the counter: seed ($42) then request ($1.50). + # Seed uses SET NX with db_spend (42) — cross-pod safe, no INCR of 42. + # Only the per-request delta (1.5) goes through INCRBYFLOAT. + fake_redis.async_set_cache.assert_awaited_once_with( + key="spend:team:team-9", value=42.0, nx=True + ) writes = [(c["key"], c["value"]) for c in recorded_increments] - assert ("spend:team:team-9", 42.0) in writes - assert ("spend:team:team-9", 1.5) in writes + assert writes == [("spend:team:team-9", 1.5)] finally: ps.user_api_key_cache = orig_user ps.spend_counter_cache = orig_counter ps.prisma_client = orig_prisma +@pytest.mark.asyncio +async def test_primary_spend_counter_redis_concurrent_seed_does_not_double_seed(): + """Two pods both observing a missing Redis counter must not both + INCRBYFLOAT the full DB spend. SpendCounterReseed.coalesced uses SET NX + so the loser reads the winner's value; final Redis = db_spend, not + 2 * db_spend. + + The per-counter asyncio.Lock is per-process, so it does NOT coordinate + across pods. We simulate two pods by patching _get_lock to return a + fresh lock per call (each "pod" has its own lock registry in real life). + """ + from litellm.caching.dual_cache import DualCache + from litellm.proxy.db.spend_counter_reseed import SpendCounterReseed + + counter_key = "spend:team:team-concurrent-seed" + redis_store: dict = {} + db_read_count = 0 + set_results: list = [] + get_after_set_count = 0 + set_completed_count = 0 + + async def redis_set_cache(key, value, nx=False, **_): + # Yield BEFORE the membership check so two concurrent callers + # interleave the way real atomic Redis SET NX does: the first + # to resume runs check + write atomically and wins; the second + # resumes after the key exists and loses. Yielding *after* the + # check would let both callers pass the empty-store check before + # either writes, so neither would ever lose. + await asyncio.sleep(0) + if nx and key in redis_store: + set_results.append(False) + return False + redis_store[key] = float(value) + set_results.append(True) + nonlocal set_completed_count + set_completed_count += 1 + return True + + async def redis_get_cache(key): + # Track reads that happen after at least one SET NX has completed + # — those are the loser-path fallback reads we want to verify. + if set_completed_count > 0: + nonlocal get_after_set_count + get_after_set_count += 1 + return redis_store.get(key) + + fake_redis = AsyncMock() + fake_redis.async_get_cache = AsyncMock(side_effect=redis_get_cache) + fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache) + + async def slow_find_unique(**_): + nonlocal db_read_count + db_read_count += 1 + # Both pods read DB before either's SET NX lands. + await asyncio.sleep(0) + row = MagicMock() + row.spend = 506.0 + return row + + fake_prisma = MagicMock() + fake_prisma.db.litellm_teamtable.find_unique = AsyncMock( + side_effect=slow_find_unique + ) + + pod_a = DualCache() + pod_a.redis_cache = fake_redis + pod_b = DualCache() + pod_b.redis_cache = fake_redis + + # Each "pod" has its own per-process lock registry. Patch _get_lock to + # always return a fresh lock so the two coalesced calls do not serialize + # via one in-process lock (which is what would happen across pods). + async def fresh_lock(_counter_key): + return asyncio.Lock() + + with patch.object(SpendCounterReseed, "_get_lock", side_effect=fresh_lock): + results = await asyncio.gather( + SpendCounterReseed.coalesced( + prisma_client=fake_prisma, + spend_counter_cache=pod_a, + counter_key=counter_key, + ), + SpendCounterReseed.coalesced( + prisma_client=fake_prisma, + spend_counter_cache=pod_b, + counter_key=counter_key, + ), + ) + + assert all(r == 506.0 for r in results), results + assert redis_store[counter_key] == pytest.approx(506.0), redis_store + # Both pods read the DB and both attempted SET NX; exactly one wrote + # (winner) and one was rejected (loser). + assert db_read_count == 2 + assert fake_redis.async_set_cache.await_count == 2 + nx_writes = [ + call + for call in fake_redis.async_set_cache.await_args_list + if call.kwargs.get("nx") is True + ] + assert len(nx_writes) == 2 + assert sorted(set_results) == [False, True], ( + f"expected exactly one SET NX winner and one loser, got {set_results}" + ) + # Loser path executed: after the winner's SET NX returned True, the + # losing coalesced() call falls back to async_get_cache to read the + # winner's value rather than re-seeding. + assert get_after_set_count >= 1, ( + "loser branch (else: read back winner's value) was never exercised" + ) + + @pytest.mark.asyncio async def test_reseed_spend_from_db_user_and_org_prefixes(): """User and org counters reseed from their own DB tables. @@ -5389,9 +5993,16 @@ async def test_init_spend_counter_redis_clean_miss_skips_stale_in_memory(): redis_store[key] = (redis_store.get(key) or 0.0) + value return redis_store[key] + async def redis_set_cache(key, value, nx=False, **_): + if nx and key in redis_store: + return False + redis_store[key] = float(value) + return True + fake_redis = AsyncMock() fake_redis.async_get_cache = AsyncMock(return_value=None) fake_redis.async_increment = AsyncMock(side_effect=redis_increment) + fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache) counter_cache.redis_cache = fake_redis db_row = MagicMock() @@ -5419,6 +6030,7 @@ async def test_init_spend_counter_redis_clean_miss_skips_stale_in_memory(): fake_prisma.db.litellm_teamtable.find_unique.assert_awaited_once_with( where={"team_id": "team-stale-local"} ) + # Seed via SET NX (42) + delta via INCRBYFLOAT (1.5) = 43.5. assert redis_store[counter_key] == pytest.approx(43.5) assert counter_cache.in_memory_cache.get_cache( key=counter_key @@ -5809,14 +6421,14 @@ async def test_get_current_spend_reseeds_from_db_when_counter_missing(): from litellm.proxy.proxy_server import get_current_spend counter_cache = DualCache() - recorded_warms: list = [] + recorded_seeds: list = [] - async def record_increment(key, value, ttl=None, **kwargs): - recorded_warms.append({"key": key, "value": value}) - return value + async def record_set_cache(key, value, nx=False, **kwargs): + recorded_seeds.append({"key": key, "value": value, "nx": nx}) + return True fake_redis = AsyncMock() - fake_redis.async_increment = AsyncMock(side_effect=record_increment) + fake_redis.async_set_cache = AsyncMock(side_effect=record_set_cache) fake_redis.async_get_cache = AsyncMock(return_value=None) counter_cache.redis_cache = fake_redis @@ -5841,9 +6453,9 @@ async def test_get_current_spend_reseeds_from_db_when_counter_missing(): f"expected DB reseed to return 362.0, got {spend} " f"(fallback would have returned 30.0 and caused bypass)" ) - # Counter warmed so subsequent reads are fast - assert ("spend:team_member:user-1:team-1", 362.0) in [ - (w["key"], w["value"]) for w in recorded_warms + # Counter warmed via SET NX so subsequent reads are fast. + assert ("spend:team_member:user-1:team-1", 362.0, True) in [ + (s["key"], s["value"], s["nx"]) for s in recorded_seeds ] assert counter_cache.in_memory_cache.get_cache( key="spend:team_member:user-1:team-1" @@ -5920,8 +6532,15 @@ async def test_get_current_spend_coalesces_concurrent_reseeds(): redis_store[key] = (redis_store.get(key) or 0.0) + value return redis_store[key] + async def redis_set_cache(key, value, nx=False, **_): + if nx and key in redis_store: + return False + redis_store[key] = float(value) + return True + fake_redis.async_get_cache = AsyncMock(side_effect=redis_get) fake_redis.async_increment = AsyncMock(side_effect=redis_increment) + fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache) counter_cache.redis_cache = fake_redis fake_prisma = MagicMock() @@ -6028,9 +6647,16 @@ async def test_concurrent_read_and_write_paths_share_one_db_query(): redis_store[key] = (redis_store.get(key) or 0.0) + value return redis_store[key] + async def redis_set_cache(key, value, nx=False, **_): + if nx and key in redis_store: + return False + redis_store[key] = float(value) + return True + fake_redis = AsyncMock() fake_redis.async_get_cache = AsyncMock(side_effect=redis_get) fake_redis.async_increment = AsyncMock(side_effect=redis_increment) + fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache) counter_cache.redis_cache = fake_redis fake_prisma = MagicMock() @@ -6133,9 +6759,16 @@ async def test_reseed_warms_cache_even_on_zero_db_spend(): redis_store[key] = (redis_store.get(key) or 0.0) + value return redis_store[key] + async def redis_set_cache(key, value, nx=False, **_): + if nx and key in redis_store: + return False + redis_store[key] = float(value) + return True + fake_redis = AsyncMock() fake_redis.async_get_cache = AsyncMock(side_effect=redis_get) fake_redis.async_increment = AsyncMock(side_effect=redis_increment) + fake_redis.async_set_cache = AsyncMock(side_effect=redis_set_cache) counter_cache.redis_cache = fake_redis db_call_count = 0 @@ -6922,3 +7555,80 @@ class TestTransformRequestBannedParams: f"Expected 400 for banned param '{banned}', " f"got {response.status_code}: {response.json()}" ) + + +class TestSortModelsByDisplayName: + """Regression: team BYOK rows persist an internal `model_name` like + `model_name_{team_id}_{uuid}` and expose the user-facing name via + `model_info.team_public_model_name`. Sorting must use the displayed + name so BYOK rows interleave with non-BYOK rows alphabetically — + otherwise they clump at the end on their opaque IDs even though the + UI shows them under a normal-looking name. + """ + + def test_byok_models_sort_by_team_public_model_name(self): + from litellm.proxy.proxy_server import _sort_models + + models = [ + {"model_name": "claude-haiku-4-5", "model_info": {}}, + { + # Opaque internal name; UI displays team_public_model_name. + "model_name": "model_name_team-1_abc123", + "model_info": {"team_public_model_name": "anthropic/claude"}, + }, + {"model_name": "gpt-4o", "model_info": {}}, + ] + + sorted_models = _sort_models( + all_models=models, sort_by="model_name", sort_order="asc" + ) + displayed_order = [ + m["model_info"].get("team_public_model_name") or m["model_name"] + for m in sorted_models + ] + assert displayed_order == [ + "anthropic/claude", + "claude-haiku-4-5", + "gpt-4o", + ] + + def test_byok_models_sort_descending_by_display_name(self): + from litellm.proxy.proxy_server import _sort_models + + models = [ + {"model_name": "claude-haiku-4-5", "model_info": {}}, + { + "model_name": "model_name_team-1_zzz", + "model_info": {"team_public_model_name": "zeta/model"}, + }, + {"model_name": "gpt-4o", "model_info": {}}, + ] + + sorted_models = _sort_models( + all_models=models, sort_by="model_name", sort_order="desc" + ) + displayed_order = [ + m["model_info"].get("team_public_model_name") or m["model_name"] + for m in sorted_models + ] + assert displayed_order == [ + "zeta/model", + "gpt-4o", + "claude-haiku-4-5", + ] + + def test_empty_team_public_model_name_falls_back_to_model_name(self): + # Empty string for team_public_model_name (not None) must still + # fall back to model_name — otherwise BYOK rows with a blank + # display name would sort to the top. + from litellm.proxy.proxy_server import _sort_models + + models = [ + {"model_name": "alpha", "model_info": {"team_public_model_name": ""}}, + {"model_name": "beta", "model_info": {}}, + ] + + sorted_models = _sort_models( + all_models=models, sort_by="model_name", sort_order="asc" + ) + assert [m["model_name"] for m in sorted_models] == ["alpha", "beta"] diff --git a/tests/test_litellm/router_utils/test_router_interactions_endpoints.py b/tests/test_litellm/router_utils/test_router_interactions_endpoints.py index c5468d73810..91bea170458 100644 --- a/tests/test_litellm/router_utils/test_router_interactions_endpoints.py +++ b/tests/test_litellm/router_utils/test_router_interactions_endpoints.py @@ -140,3 +140,159 @@ class TestInitInteractionsApiEndpoints: custom_llm_provider="vertex_ai", ) assert result == {"result": "success"} + + @pytest.mark.asyncio + async def test_init_interactions_api_endpoints_clears_model_when_equals_agent( + self, + ): + """Managed agent interactions must not pass agent name as model to the SDK.""" + router = Router(model_list=[]) + + mock_function = AsyncMock(return_value={"result": "success"}) + + await router._init_interactions_api_endpoints( + original_function=mock_function, + agent="mqy-custom-slides-agent", + model="mqy-custom-slides-agent", + input="hello", + ) + + mock_function.assert_called_once_with( + custom_llm_provider="gemini", + agent="mqy-custom-slides-agent", + model=None, + input="hello", + ) + + +class TestRouterCreateInteractionRouting: + """acreate_interaction routing: agent-only vs model + fallbacks.""" + + @pytest.mark.asyncio + async def test_acreate_interaction_agent_only_uses_init_interactions(self): + """Agent-only create must not use model-group fallback lookup.""" + router = Router( + model_list=[ + { + "model_name": "openai/*", + "litellm_params": {"model": "gpt-4"}, + } + ] + ) + + with ( + patch.object( + router, + "_init_interactions_api_endpoints", + new_callable=AsyncMock, + return_value={"id": "int-1"}, + ) as mock_init, + patch.object( + router, + "_ageneric_api_call_with_fallbacks", + new_callable=AsyncMock, + ) as mock_generic, + ): + result = await router.acreate_interaction( + agent="mqy-custom-slides-agent", + input="hello", + custom_llm_provider="gemini", + ) + + mock_init.assert_called_once() + mock_generic.assert_not_called() + assert result == {"id": "int-1"} + + @pytest.mark.asyncio + async def test_init_interactions_model_uses_generic_fallbacks(self): + """Model-based create uses _ageneric_api_call_with_fallbacks inside _init_interactions.""" + router = Router(model_list=[]) + + with patch.object( + router, + "_ageneric_api_call_with_fallbacks", + new_callable=AsyncMock, + return_value={"id": "int-1"}, + ) as mock_generic: + result = await router._init_interactions_api_endpoints( + original_function=AsyncMock(), + model="gemini-2.5-flash", + input="hello", + custom_llm_provider="gemini", + ) + + mock_generic.assert_called_once() + assert result == {"id": "int-1"} + + +class TestInitializeManagedAgentsEndpoints: + """Tests for _initialize_managed_agents_endpoints.""" + + def test_initialize_managed_agents_endpoints_creates_methods(self): + router = Router( + model_list=[ + { + "model_name": "gpt-4", + "litellm_params": {"model": "gpt-4"}, + } + ] + ) + + for method_name in ( + "acreate_agent", + "alist_agents", + "aget_agent", + "adelete_agent", + "alist_agent_versions", + ): + assert hasattr(router, method_name), f"missing {method_name}" + assert callable(getattr(router, method_name)), f"{method_name} not callable" + + def test_initialize_managed_agents_endpoints_can_be_called_directly(self): + router = Router(model_list=[]) + router._initialize_managed_agents_endpoints() + assert callable(router.acreate_agent) + assert callable(router.alist_agents) + + +class TestInitManagedAgentsApiEndpoints: + """Tests for _init_managed_agents_api_endpoints.""" + + @pytest.mark.asyncio + async def test_init_managed_agents_api_endpoints_defaults_to_gemini(self): + router = Router(model_list=[]) + mock_fn = AsyncMock(return_value={"agents": []}) + + await router._init_managed_agents_api_endpoints( + original_function=mock_fn, + ) + + call_kwargs = mock_fn.call_args.kwargs + assert call_kwargs["custom_llm_provider"] == "gemini" + + @pytest.mark.asyncio + async def test_init_managed_agents_api_endpoints_passes_custom_provider(self): + router = Router(model_list=[]) + mock_fn = AsyncMock(return_value={"agents": []}) + + await router._init_managed_agents_api_endpoints( + original_function=mock_fn, + custom_llm_provider="vertex_ai", + ) + + call_kwargs = mock_fn.call_args.kwargs + assert call_kwargs["custom_llm_provider"] == "vertex_ai" + + @pytest.mark.asyncio + async def test_init_managed_agents_api_endpoints_does_not_override_existing_provider( + self, + ): + router = Router(model_list=[]) + mock_fn = AsyncMock(return_value={"agents": []}) + + await router._init_managed_agents_api_endpoints( + original_function=mock_fn, + custom_llm_provider="vertex_ai", + ) + + mock_fn.assert_called_once_with(custom_llm_provider="vertex_ai") diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index d5c1132c5fd..18ab8a2a07a 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -2057,3 +2057,336 @@ 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_gemini_3_1_flash_lite_pricing(): + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + litellm.model_cost = litellm.get_model_cost_map(url="") + + for model_name in ( + "gemini-3.1-flash-lite", + "gemini/gemini-3.1-flash-lite", + "vertex_ai/gemini-3.1-flash-lite", + ): + model_info = litellm.model_cost.get(model_name) + assert model_info is not None, f"Missing model pricing entry: {model_name}" + assert model_info["input_cost_per_token"] == 4.5e-07 + assert model_info["input_cost_per_audio_token"] == 9e-07 + assert model_info["output_cost_per_token"] == 2.7e-06 + assert model_info["output_cost_per_reasoning_token"] == 2.7e-06 + assert model_info["cache_read_input_token_cost"] == 4.5e-08 + assert model_info["max_input_tokens"] == 1048576 + + +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 ad859f7da66..62a065a894d 100644 --- a/ui/litellm-dashboard/src/components/model_info_view.tsx +++ b/ui/litellm-dashboard/src/components/model_info_view.tsx @@ -300,6 +300,34 @@ 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 { @@ -683,6 +711,22 @@ 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) @@ -770,6 +814,52 @@ 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/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx index 06a09ca2c37..a80cf935c4b 100644 --- a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx +++ b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx @@ -49,6 +49,7 @@ import { fetchAvailableModels, ModelGroup } from "../llm_calls/fetch_models"; import { makeOpenAIImageEditsRequest } from "../llm_calls/image_edits"; import { makeOpenAIImageGenerationRequest } from "../llm_calls/image_generation"; import { makeOpenAIResponsesRequest } from "../llm_calls/responses_api"; +import { makeInteractionsRequest } from "../llm_calls/interactions_api"; import A2AMetrics from "./A2AMetrics"; import AdditionalModelSettings from "./AdditionalModelSettings"; import AudioRenderer from "./AudioRenderer"; @@ -649,6 +650,7 @@ const ChatUI: React.FC = ({ EndpointType.ANTHROPIC_MESSAGES, EndpointType.EMBEDDINGS, EndpointType.TRANSCRIPTION, + EndpointType.INTERACTIONS, ]; if (modelRequiredEndpoints.includes(endpointType as EndpointType) && !selectedModel) { @@ -914,6 +916,16 @@ const ChatUI: React.FC = ({ customProxyBaseUrl || undefined, ); } + } else if (endpointType === EndpointType.INTERACTIONS) { + await makeInteractionsRequest( + inputMessage, + (text, model) => updateTextUI("assistant", text, model), + selectedModel, + effectiveApiKey, + selectedTags, + signal, + customProxyBaseUrl || undefined, + ); } } @@ -1241,10 +1253,11 @@ const ChatUI: React.FC = ({ return true; } const optionEndpoint = getEndpointType(option.mode); - // Show chat models for responses/anthropic_messages endpoints as they are compatible + // Show chat models for responses/anthropic_messages/interactions endpoints as they are compatible if ( endpointType === EndpointType.RESPONSES || - endpointType === EndpointType.ANTHROPIC_MESSAGES + endpointType === EndpointType.ANTHROPIC_MESSAGES || + endpointType === EndpointType.INTERACTIONS ) { return optionEndpoint === endpointType || optionEndpoint === EndpointType.CHAT; } @@ -2089,7 +2102,8 @@ const ChatUI: React.FC = ({ endpointType === EndpointType.CHAT || endpointType === EndpointType.EMBEDDINGS || endpointType === EndpointType.RESPONSES || - endpointType === EndpointType.ANTHROPIC_MESSAGES + endpointType === EndpointType.ANTHROPIC_MESSAGES || + endpointType === EndpointType.INTERACTIONS ? "Type your message... (Shift+Enter for new line)" : endpointType === EndpointType.A2A_AGENTS ? "Send a message to the A2A agent..." diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts b/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts index 919e3bc1c65..9592a521250 100644 --- a/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts +++ b/ui/litellm-dashboard/src/components/playground/chat_ui/chatConstants.ts @@ -45,4 +45,5 @@ export const ENDPOINT_OPTIONS = [ { value: EndpointType.A2A_AGENTS, label: "/v1/a2a/message/send" }, { value: EndpointType.MCP, label: "/mcp-rest/tools/call" }, { value: EndpointType.REALTIME, label: "/v1/realtime" }, + { value: EndpointType.INTERACTIONS, label: "/v1beta/interactions" }, ]; diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx index f354efe641e..3b2a5e0fd60 100644 --- a/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx +++ b/ui/litellm-dashboard/src/components/playground/chat_ui/mode_endpoint_mapping.tsx @@ -28,6 +28,7 @@ export enum EndpointType { A2A_AGENTS = "a2a_agents", MCP = "mcp", REALTIME = "realtime", + INTERACTIONS = "interactions", } // Create a mapping between the model mode and the corresponding endpoint type diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx new file mode 100644 index 00000000000..6a6e4a2cfa0 --- /dev/null +++ b/ui/litellm-dashboard/src/components/playground/llm_calls/interactions_api.tsx @@ -0,0 +1,124 @@ +import NotificationManager from "@/components/molecules/notifications_manager"; +import { getGlobalLitellmHeaderName, getProxyBaseUrl } from "@/components/networking"; + +export async function makeInteractionsRequest( + input: string, + updateUI: (text: string, model?: string) => void, + selectedModel: string, + accessToken: string, + tags?: string[], + signal?: AbortSignal, + customBaseUrl?: string, + previousInteractionId?: string, +): Promise { + if (!accessToken) { + throw new Error("Virtual Key is required"); + } + + const isLocal = process.env.NODE_ENV === "development"; + if (isLocal !== true) { + console.log = function () {}; + } + + const proxyBaseUrl = customBaseUrl || getProxyBaseUrl(); + const normalizedBaseUrl = proxyBaseUrl.endsWith("/") ? proxyBaseUrl.slice(0, -1) : proxyBaseUrl; + const requestUrl = `${normalizedBaseUrl}/v1beta/interactions`; + + const headers: Record = { + "Content-Type": "application/json", + [getGlobalLitellmHeaderName()]: `Bearer ${accessToken}`, + }; + if (tags && tags.length > 0) { + headers["x-litellm-tags"] = tags.join(","); + } + + const body: Record = { + model: selectedModel, + input, + stream: true, + }; + if (previousInteractionId) { + body.previous_interaction_id = previousInteractionId; + } + + try { + const response = await fetch(requestUrl, { + method: "POST", + headers, + body: JSON.stringify(body), + signal, + }); + + if (!response.ok) { + const errorText = await response.text(); + throw new Error(errorText || `Request failed with status ${response.status}`); + } + + if (!response.body) { + throw new Error("No response body received"); + } + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + let responseModel: string | undefined; + let buffer = ""; + + while (true) { + const { done, value } = await reader.read(); + if (done) break; + + buffer += decoder.decode(value, { stream: true }); + + // SSE lines are separated by double newlines; split on single newlines and + // look for "data: " prefixed lines. + const lines = buffer.split("\n"); + // Keep the last (potentially incomplete) line in the buffer + buffer = lines.pop() ?? ""; + + for (const line of lines) { + const trimmed = line.trim(); + if (!trimmed.startsWith("data:")) continue; + + const jsonStr = trimmed.slice("data:".length).trim(); + if (!jsonStr || jsonStr === "[DONE]") continue; + + let event: Record; + try { + event = JSON.parse(jsonStr); + } catch { + continue; + } + + const eventType = event.event_type as string | undefined; + + if (eventType === "interaction.start" || eventType === "interaction.complete") { + // Capture model from either the native Gemini shape (nested under + // `interaction`) or the bridge shape (top-level `model` field). + const interaction = event.interaction as Record | undefined; + if (typeof interaction?.model === "string" && interaction.model) { + responseModel = interaction.model; + } else if (typeof event.model === "string" && event.model) { + responseModel = event.model; + } + } else if (eventType === "content.delta" || eventType === "content.start") { + const delta = event.delta as Record | undefined; + // Accept both native Gemini format {"type":"text","text":"..."} and bridge + // format {"text":"..."} (no type discriminator) + if (typeof delta?.text === "string" && delta.text) { + updateUI(delta.text, responseModel ?? selectedModel); + } + } + // content.start, content.stop, interaction.status_update — no UI action needed + } + } + } catch (error: unknown) { + if (signal?.aborted) { + console.log("Interactions request was cancelled"); + throw error; + } + NotificationManager.fromBackend( + `Error occurred while making Interactions API request. Error: ${error}`, + ); + throw error; + } +} diff --git a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx index a2da5136755..85a38e26977 100644 --- a/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/LogDetailsDrawer/LogDetailContent.test.tsx @@ -172,12 +172,7 @@ describe("LogDetailContent", () => { }); it("should display loading state when isLoadingDetails is true", () => { - render( - , - ); + render(); expect(screen.getByText("Loading request & response data...")).toBeInTheDocument(); }); @@ -298,6 +293,37 @@ describe("LogDetailContent", () => { expect(screen.getByText("42.50 ms")).toBeInTheDocument(); }); + it("should not display LiteLLM Overhead when litellm_overhead_time_ms is absent from metadata", () => { + render(); + + expect(screen.queryByText("LiteLLM Overhead")).not.toBeInTheDocument(); + }); + + const retriesItem = () => screen.getByText("Retries").closest(".ant-descriptions-item") as HTMLElement; + + it("should display attempted_retries / max_retries for Retries when attempted_retries > 0", () => { + render( + , + ); + + expect(within(retriesItem()).getByText("2 / 3")).toBeInTheDocument(); + }); + + it("should display a green 'None' tag for Retries when attempted_retries is 0", () => { + render(); + + const noneTag = within(retriesItem()).getByText("None"); + expect(noneTag.closest(".ant-tag")).toHaveClass("ant-tag-green"); + }); + + it("should display '-' for Retries when attempted_retries is absent from metadata", () => { + render(); + + expect(within(retriesItem()).getByText("-")).toBeInTheDocument(); + }); + it("should display start and end time in ISO format", () => { render( void; + startTime: string; + onStartTimeChange: (value: string) => void; + endTime: string; + onEndTimeChange: (value: string) => void; + isCustomDate: boolean; + onIsCustomDateChange: (value: boolean) => void; + selectedTimeInterval: { value: number; unit: string }; + onSelectedTimeIntervalChange: (value: { value: number; unit: string }) => void; + isLiveTail: boolean; + onIsLiveTailChange: (value: boolean) => void; + currentPage: number; + onCurrentPageChange: (updater: number | ((prev: number) => number)) => void; + pageSize: number; + isLoading: boolean; + isButtonLoading: boolean; + onRefetch: () => void; + filteredLogs: PaginatedResponse; +} + +export function LogsTableToolbar({ + searchTerm, + onSearchChange, + startTime, + onStartTimeChange, + endTime, + onEndTimeChange, + isCustomDate, + onIsCustomDateChange, + selectedTimeInterval, + onSelectedTimeIntervalChange, + isLiveTail, + onIsLiveTailChange, + currentPage, + onCurrentPageChange, + pageSize, + isLoading, + isButtonLoading, + onRefetch, + filteredLogs, +}: LogsTableToolbarProps) { + const [quickSelectOpen, setQuickSelectOpen] = useState(false); + const quickSelectRef = useRef(null); + + useEffect(() => { + function handleClickOutside(event: MouseEvent) { + if (quickSelectRef.current && !quickSelectRef.current.contains(event.target as Node)) { + setQuickSelectOpen(false); + } + } + document.addEventListener("mousedown", handleClickOutside); + return () => document.removeEventListener("mousedown", handleClickOutside); + }, []); + + const selectedOption = QUICK_SELECT_OPTIONS.find( + (option) => option.value === selectedTimeInterval.value && option.unit === selectedTimeInterval.unit, + ); + const displayLabel = isCustomDate ? getTimeRangeDisplay(isCustomDate, startTime, endTime) : selectedOption?.label; + + return ( + <> +
+
+
+
+ onSearchChange(e.target.value)} + /> + + + +
+ +
+
+ + + {quickSelectOpen && ( +
+
+ {QUICK_SELECT_OPTIONS.map((option) => ( + + ))} +
+ +
+
+ )} +
+ +
+ Live Tail + +
+ + +
+ + {isCustomDate && ( +
+
+ { + onStartTimeChange(e.target.value); + onCurrentPageChange(1); + }} + className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500" + /> +
+ to +
+ { + onEndTimeChange(e.target.value); + onCurrentPageChange(1); + }} + className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500" + /> +
+
+ )} +
+ +
+ + Showing {isLoading ? "..." : filteredLogs ? (currentPage - 1) * pageSize + 1 : 0} -{" "} + {isLoading + ? "..." + : filteredLogs + ? Math.min(currentPage * pageSize, filteredLogs.total) + : 0}{" "} + of {isLoading ? "..." : filteredLogs ? filteredLogs.total : 0} results + +
+ + Page {isLoading ? "..." : currentPage} of{" "} + {isLoading ? "..." : filteredLogs ? filteredLogs.total_pages : 1} + + + +
+
+
+
+ {isLiveTail && currentPage === 1 && ( +
+
+ Auto-refreshing every 15 seconds +
+ +
+ )} + + ); +} diff --git a/ui/litellm-dashboard/src/components/view_logs/filter_options.ts b/ui/litellm-dashboard/src/components/view_logs/filter_options.ts new file mode 100644 index 00000000000..59ac58b6745 --- /dev/null +++ b/ui/litellm-dashboard/src/components/view_logs/filter_options.ts @@ -0,0 +1,77 @@ +import FilterTeamDropdown from "../common_components/FilterTeamDropdown"; +import { PaginatedKeyAliasSelect } from "../KeyAliasSelect/PaginatedKeyAliasSelect/PaginatedKeyAliasSelect"; +import { PaginatedModelSelect } from "../ModelSelect/PaginatedModelSelect/PaginatedModelSelect"; +import { FilterOption } from "../molecules/filter"; +import { allEndUsersCall } from "../networking"; +import { ERROR_CODE_OPTIONS } from "./constants"; +import { FILTER_KEYS } from "./log_filter_logic"; + +export function getLogFilterOptions(accessToken: string): FilterOption[] { + return [ + { + name: "Team ID", + label: "Team ID", + customComponent: FilterTeamDropdown, + }, + { + name: "Status", + label: "Status", + isSearchable: false, + options: [ + { label: "Success", value: "success" }, + { label: "Failure", value: "failure" }, + ], + }, + { + name: "Model", + label: "Model", + customComponent: PaginatedModelSelect, + }, + { + name: FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL, + label: "Public model / search tool", + isSearchable: false, + }, + { + name: "Key Alias", + label: "Key Alias", + customComponent: PaginatedKeyAliasSelect, + }, + { + name: "End User", + label: "End User", + isSearchable: true, + searchFn: async (searchText: string) => { + const data = await allEndUsersCall(accessToken); + const users = data?.map((u: any) => u.user_id) || []; + const filtered = users.filter((u: string) => u.toLowerCase().includes(searchText.toLowerCase())); + return filtered.map((u: string) => ({ label: u, value: u })); + }, + }, + { + name: "Error Code", + label: "Error Code", + isSearchable: true, + searchFn: async (searchText: string) => { + if (!searchText) return ERROR_CODE_OPTIONS; + const lower = searchText.toLowerCase(); + const filtered = ERROR_CODE_OPTIONS.filter((opt) => opt.label.toLowerCase().includes(lower)); + const isExactValue = ERROR_CODE_OPTIONS.some((opt) => opt.value === searchText.trim()); + if (!isExactValue && searchText.trim()) { + filtered.push({ label: `Use custom code: ${searchText.trim()}`, value: searchText.trim() }); + } + return filtered; + }, + }, + { + name: "Key Hash", + label: "Key Hash", + isSearchable: false, + }, + { + name: "Error Message", + label: "Error Message", + isSearchable: false, + }, + ]; +} diff --git a/ui/litellm-dashboard/src/components/view_logs/index.test.tsx b/ui/litellm-dashboard/src/components/view_logs/index.test.tsx index 7a9a541d3e0..aed194a2972 100644 --- a/ui/litellm-dashboard/src/components/view_logs/index.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/index.test.tsx @@ -1,12 +1,8 @@ -import { render, screen, waitFor } from "@testing-library/react"; +import { screen, waitFor } from "@testing-library/react"; import userEvent from "@testing-library/user-event"; -import moment from "moment"; import { beforeEach, describe, expect, it, vi } from "vitest"; -import SpendLogsTable, { RequestViewer } from "./index"; -import type { LogEntry } from "./columns"; -import type { Row } from "@tanstack/react-table"; +import SpendLogsTable from "./index"; import { renderWithProviders } from "../../../tests/test-utils"; -import { uiSpendLogsCall } from "../networking"; const mockHandleFilterResetFromHook = vi.fn(); vi.mock("./log_filter_logic", async (importOriginal) => { @@ -14,14 +10,8 @@ vi.mock("./log_filter_logic", async (importOriginal) => { return { ...actual, useLogFilterLogic: vi.fn(() => ({ - filters: {}, - filteredLogs: { - data: [], - total: 0, - page: 1, - page_size: 50, - total_pages: 1, - }, + logsQuery: { isLoading: false, isFetching: false, refetch: vi.fn() }, + filteredLogs: { data: [], total: 0, page: 1, page_size: 50, total_pages: 1 }, allTeams: [], handleFilterChange: vi.fn(), handleFilterReset: mockHandleFilterResetFromHook, @@ -50,139 +40,6 @@ vi.mock("../key_team_helpers/filter_helpers", () => ({ fetchAllTeams: vi.fn().mockResolvedValue([]), })); -const baseLogEntry: LogEntry = { - request_id: "chatcmpl-test-id", - api_key: "api-key", - team_id: "team-id", - model: "gpt-4", - model_id: "gpt-4", - call_type: "chat", - spend: 0, - total_tokens: 0, - prompt_tokens: 0, - completion_tokens: 0, - startTime: "2025-11-14T00:00:00Z", - endTime: "2025-11-14T00:00:00Z", - cache_hit: "miss", - request_duration_ms: 1000, - messages: [{ role: "user", content: "hello" }], - response: { status: "ok" }, - metadata: { - status: "success", - additional_usage_values: { - cache_read_input_tokens: 0, - cache_creation_input_tokens: 0, - }, - }, - request_tags: {}, - custom_llm_provider: "openai", - api_base: "https://api.example.com", -}; - -const createRow = (overrides: Partial = {}): Row => - ({ - original: { - ...baseLogEntry, - ...overrides, - }, - }) as unknown as Row; - -describe("Request Viewer", () => { - it("renders the request details heading", () => { - render(); - expect(screen.getByText("Request Details")).toBeInTheDocument(); - }); - - it("should truncate the request id if it is longer than 64 characters", () => { - const LONG_REQUEST_ID = "a".repeat(128); - const TRUNCATED_REQUEST_ID = `${"a".repeat(64)}...`; - render( - , - ); - - expect(screen.getByText(TRUNCATED_REQUEST_ID)).toBeInTheDocument(); - }); - - it("should display LiteLLM Overhead when litellm_overhead_time_ms is present in metadata", () => { - render( - , - ); - - expect(screen.getByText("LiteLLM Overhead:")).toBeInTheDocument(); - expect(screen.getByText("150 ms")).toBeInTheDocument(); - }); - - it("should not display LiteLLM Overhead when litellm_overhead_time_ms is not present in metadata", () => { - render(); - - expect(screen.queryByText("LiteLLM Overhead:")).not.toBeInTheDocument(); - }); - - it("should display retry count when attempted_retries > 0 in metadata", () => { - render( - , - ); - - expect(screen.getByText("Retries:")).toBeInTheDocument(); - expect(screen.getByText("2 / 3")).toBeInTheDocument(); - }); - - it("should display green 'None' tag when attempted_retries is 0", () => { - render( - , - ); - - expect(screen.getByText("Retries:")).toBeInTheDocument(); - expect(screen.getByText("None")).toBeInTheDocument(); - }); - - it("should display '-' for Retries when attempted_retries is not present in metadata", () => { - render(); - - expect(screen.getByText("Retries:")).toBeInTheDocument(); - expect(screen.getByText("-")).toBeInTheDocument(); - }); -}); - describe("SpendLogsTable", () => { const defaultProps = { accessToken: "test-token", @@ -215,7 +72,9 @@ describe("SpendLogsTable", () => { renderWithProviders(); // Open the time range quick select dropdown (button shows current range like "Last 24 Hours") - const quickSelectButton = screen.getByRole("button", { name: /Last 24 Hours|Last 15 Minutes|Last Hour|Last 4 Hours|Last 7 Days/i }); + const quickSelectButton = screen.getByRole("button", { + name: /Last 24 Hours|Last 15 Minutes|Last Hour|Last 4 Hours|Last 7 Days/i, + }); await user.click(quickSelectButton); // Click "Custom Range" to enable custom date selection @@ -241,51 +100,19 @@ describe("SpendLogsTable", () => { }); }); - describe("Quick Select time range", () => { - const waitForWindowSeconds = async (minMinutes: number) => { - let diff = -1; - await waitFor(() => { - const lastCall = vi.mocked(uiSpendLogsCall).mock.calls.at(-1)?.[0]; - if (!lastCall) throw new Error("uiSpendLogsCall was not called"); - diff = moment - .utc(lastCall.end_date, "YYYY-MM-DD HH:mm:ss") - .diff(moment.utc(lastCall.start_date, "YYYY-MM-DD HH:mm:ss"), "seconds"); - // start_date is rounded down to the minute boundary; end_date is current time - expect(diff).toBeGreaterThanOrEqual(minMinutes * 60); - expect(diff).toBeLessThan((minMinutes + 1) * 60); - }); - return diff; - }; + describe("auth-not-ready guard", () => { + it("shows a loading spinner when credentials are not yet resolved", () => { + renderWithProviders(); - it("should pass a ~1-minute window to uiSpendLogsCall when 'Last Minute' is selected", async () => { - const user = userEvent.setup(); - renderWithProviders(); - - await user.click(screen.getByRole("button", { name: /Last 24 Hours/i })); - await user.click(await screen.findByRole("button", { name: "Last Minute" })); - - await waitForWindowSeconds(1); + expect(document.querySelector(".ant-spin")).toBeInTheDocument(); + expect(screen.queryByRole("button", { name: "Reset Filters" })).not.toBeInTheDocument(); }); - it("should pass a ~15-minute window to uiSpendLogsCall when 'Last 15 Minutes' is selected", async () => { - const user = userEvent.setup(); + it("renders the table (no spinner) once all credentials are present", () => { renderWithProviders(); - await user.click(screen.getByRole("button", { name: /Last 24 Hours/i })); - await user.click(await screen.findByRole("button", { name: "Last 15 Minutes" })); - - await waitForWindowSeconds(15); - }); - - it("should update the time-range button label to 'Last Minute' after selecting it", async () => { - const user = userEvent.setup(); - renderWithProviders(); - - await user.click(screen.getByRole("button", { name: /Last 24 Hours/i })); - await user.click(await screen.findByRole("button", { name: "Last Minute" })); - - expect(screen.getByRole("button", { name: "Last Minute" })).toBeInTheDocument(); - expect(screen.queryByRole("button", { name: /Last 24 Hours/i })).not.toBeInTheDocument(); + expect(document.querySelector(".ant-spin")).not.toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Reset Filters" })).toBeInTheDocument(); }); }); }); diff --git a/ui/litellm-dashboard/src/components/view_logs/index.tsx b/ui/litellm-dashboard/src/components/view_logs/index.tsx index 2f9e8fe8780..03d917cd923 100644 --- a/ui/litellm-dashboard/src/components/view_logs/index.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/index.tsx @@ -1,35 +1,22 @@ -import { keepPreviousData, useQuery, useQueryClient } from "@tanstack/react-query"; import moment from "moment"; -import { useCallback, useDeferredValue, useEffect, useRef, useState } from "react"; -import GuardrailViewer from "@/components/view_logs/GuardrailViewer/GuardrailViewer"; -import { formatNumberWithCommas } from "@/utils/dataUtils"; -import { truncateString } from "@/utils/textUtils"; -import { SyncOutlined } from "@ant-design/icons"; -import { Row } from "@tanstack/react-table"; -import { Switch, Tab, TabGroup, TabList, TabPanel, TabPanels } from "@tremor/react"; -import { Button, Tag, Tooltip } from "antd"; +import { useCallback, useDeferredValue, useEffect, useMemo, useState } from "react"; +import { Tab, TabGroup, TabList, TabPanel, TabPanels } from "@tremor/react"; import { internalUserRoles } from "../../utils/roles"; import DeletedKeysPage from "../DeletedKeysPage/DeletedKeysPage"; import DeletedTeamsPage from "../DeletedTeamsPage/DeletedTeamsPage"; -import FilterTeamDropdown from "../common_components/FilterTeamDropdown"; import { KeyResponse } from "../key_team_helpers/key_list"; -import { PaginatedKeyAliasSelect } from "../KeyAliasSelect/PaginatedKeyAliasSelect/PaginatedKeyAliasSelect"; -import { PaginatedModelSelect } from "../ModelSelect/PaginatedModelSelect/PaginatedModelSelect"; -import FilterComponent, { FilterOption } from "../molecules/filter"; -import { allEndUsersCall, keyInfoV1Call, uiSpendLogsCall } from "../networking"; +import FilterComponent from "../molecules/filter"; +import { keyInfoV1Call } from "../networking"; import KeyInfoView from "../templates/key_info_view"; import AuditLogs from "./audit_logs"; import { createColumns, LogEntry, type LogsSortField } from "./columns"; -import { ConfigInfoMessage } from "./ConfigInfoMessage"; -import { AGENT_CALL_TYPES, ERROR_CODE_OPTIONS, MCP_CALL_TYPES, QUICK_SELECT_OPTIONS } from "./constants"; -import { CostBreakdownViewer } from "./CostBreakdownViewer"; -import { ErrorViewer } from "./ErrorViewer"; -import { FILTER_KEYS, useLogFilterLogic } from "./log_filter_logic"; +import { AGENT_CALL_TYPES, MCP_CALL_TYPES } from "./constants"; +import { getLogFilterOptions } from "./filter_options"; +import { useLogFilterLogic, defaultFilters, type LogFilterState } from "./log_filter_logic"; import { LogDetailsDrawer } from "./LogDetailsDrawer"; -import { getTimeRangeDisplay } from "./logs_utils"; -import { RequestResponsePanel } from "./RequestResponsePanel"; +import { LogsTableToolbar } from "./LogsTableToolbar"; import { DataTable } from "./table"; -import { VectorStoreViewer } from "./VectorStoreViewer"; +import { AntDLoadingSpinner } from "../ui/AntDLoadingSpinner"; interface SpendLogsTableProps { accessToken: string | null; @@ -39,45 +26,19 @@ interface SpendLogsTableProps { premiumUser: boolean; } -export interface PaginatedResponse { - data: LogEntry[]; - total: number; - page: number; - page_size: number; - total_pages: number; -} - -export default function SpendLogsTable({ - accessToken, - token, - userRole, - userID, - premiumUser, -}: SpendLogsTableProps) { +export default function SpendLogsTable({ accessToken, token, userRole, userID, premiumUser }: SpendLogsTableProps) { const [searchTerm, setSearchTerm] = useState(""); - const [showFilters, setShowFilters] = useState(false); - const [showColumnDropdown, setShowColumnDropdown] = useState(false); const [currentPage, setCurrentPage] = useState(1); const [pageSize] = useState(50); - const dropdownRef = useRef(null); - const filtersRef = useRef(null); - const quickSelectRef = useRef(null); // New state variables for Start and End Time const [startTime, setStartTime] = useState(moment().subtract(24, "hours").format("YYYY-MM-DDTHH:mm")); const [endTime, setEndTime] = useState(moment().format("YYYY-MM-DDTHH:mm")); const [isCustomDate, setIsCustomDate] = useState(false); - const [quickSelectOpen, setQuickSelectOpen] = useState(false); - const [tempTeamId, setTempTeamId] = useState(""); - const [tempKeyHash, setTempKeyHash] = useState(""); - const [selectedTeamId, setSelectedTeamId] = useState(""); - const [selectedKeyHash, setSelectedKeyHash] = useState(""); - const [selectedModelId, setSelectedModelId] = useState(""); + const [filters, setFilters] = useState(defaultFilters); const [selectedKeyInfo, setSelectedKeyInfo] = useState(null); const [selectedKeyIdInfoView, setSelectedKeyIdInfoView] = useState(null); - const [selectedStatus, setSelectedStatus] = useState(""); - const [selectedEndUser, setSelectedEndUser] = useState(""); const [filterByCurrentUser, setFilterByCurrentUser] = useState(userRole && internalUserRoles.includes(userRole)); const [activeTab, setActiveTab] = useState("request logs"); @@ -88,12 +49,10 @@ export default function SpendLogsTable({ const [sortBy, setSortBy] = useState("startTime"); const [sortOrder, setSortOrder] = useState<"asc" | "desc">("desc"); - // Tracks whether any filter that uses performSearch (backend) is active. - // Used to disable the main query so it doesn't fire redundant unfiltered requests - // when time range / sort / page changes while a backend filter is in effect. - const [isMainQueryEnabled, setIsMainQueryEnabled] = useState(true); - - const queryClient = useQueryClient(); + const [selectedTimeInterval, setSelectedTimeInterval] = useState<{ value: number; unit: string }>({ + value: 24, + unit: "hours", + }); const [isLiveTail, setIsLiveTail] = useState(() => { const storedValue = sessionStorage.getItem("isLiveTail"); @@ -105,11 +64,6 @@ export default function SpendLogsTable({ sessionStorage.setItem("isLiveTail", JSON.stringify(isLiveTail)); }, [isLiveTail]); - const [selectedTimeInterval, setSelectedTimeInterval] = useState<{ value: number; unit: string }>({ - value: 24, - unit: "hours", - }); - useEffect(() => { const fetchKeyInfo = async () => { if (selectedKeyIdInfoView && accessToken) { @@ -126,132 +80,33 @@ export default function SpendLogsTable({ fetchKeyInfo(); }, [selectedKeyIdInfoView, accessToken]); - // Close dropdown when clicking outside - useEffect(() => { - function handleClickOutside(event: MouseEvent) { - if (dropdownRef.current && !dropdownRef.current.contains(event.target as Node)) { - setShowColumnDropdown(false); - } - if (filtersRef.current && !filtersRef.current.contains(event.target as Node)) { - setShowFilters(false); - } - if (quickSelectRef.current && !quickSelectRef.current.contains(event.target as Node)) { - setQuickSelectOpen(false); - } - } - - document.addEventListener("mousedown", handleClickOutside); - return () => document.removeEventListener("mousedown", handleClickOutside); - }, []); - useEffect(() => { if (userRole && internalUserRoles.includes(userRole)) { setFilterByCurrentUser(true); } }, [userRole]); - const LiveTailControls = () => { - return ( -
- Live Tail - -
- ); - }; - - const logs = useQuery({ - queryKey: [ - "logs", - "table", - currentPage, - pageSize, - startTime, - endTime, - selectedTeamId, - selectedKeyHash, - filterByCurrentUser ? userID : null, - selectedStatus, - selectedModelId, - sortBy, - sortOrder, - ], - queryFn: async () => { - if (!accessToken || !token || !userRole || !userID) { - return { - data: [], - total: 0, - page: 1, - page_size: pageSize, - total_pages: 0, - }; - } - - const formattedStartTime = moment(startTime).utc().format("YYYY-MM-DD HH:mm:ss"); - const formattedEndTime = isCustomDate - ? moment(endTime).utc().format("YYYY-MM-DD HH:mm:ss") - : moment().utc().format("YYYY-MM-DD HH:mm:ss"); - - // Get base response from API - // NOTE: We only fetch the list of logs here (lightweight). - // Log details (messages/response) are fetched on-demand when user clicks a row. - const response = await uiSpendLogsCall({ - accessToken, - start_date: formattedStartTime, - end_date: formattedEndTime, - page: currentPage, - page_size: pageSize, - params: { - api_key: selectedKeyHash || undefined, - team_id: selectedTeamId || undefined, - user_id: filterByCurrentUser ? userID ?? undefined : undefined, - end_user: selectedEndUser || undefined, - status_filter: selectedStatus || undefined, - model_id: selectedModelId || undefined, - sort_by: sortBy, - sort_order: sortOrder, - }, - }); - - return response; - }, - enabled: !!accessToken && !!token && !!userRole && !!userID && activeTab === "request logs" && isMainQueryEnabled, - refetchInterval: isLiveTail && currentPage === 1 ? 15000 : false, - placeholderData: keepPreviousData, - refetchIntervalInBackground: true, - }); - - // Defer the transition from "Fetching" to "Fetch" so the button stays loading until - // the table has rendered with the new data (avoids the visual gap where the button - // exits loading state before the table updates) - const isFetchingDeferred = useDeferredValue(logs.isFetching); - const isButtonLoading = logs.isFetching || isFetchingDeferred; - - const logsData = logs.data || { - data: [], - total: 0, - page: 1, - page_size: pageSize || 10, - total_pages: 1, - }; - const { - filters, + logsQuery, filteredLogs, - hasBackendFilters, allTeams, handleFilterChange, handleFilterReset: handleFilterResetFromHook, - refetchWithFilters, } = useLogFilterLogic({ - logs: logsData, accessToken, + token, + userRole, + userID, + filters, + setFilters, + filterByCurrentUser: !!filterByCurrentUser, + activeTab, + isLiveTail, startTime, endTime, pageSize, isCustomDate, setCurrentPage, - userID, - userRole, sortBy, sortOrder, currentPage, @@ -259,7 +114,6 @@ export default function SpendLogsTable({ const handleFilterReset = useCallback(() => { handleFilterResetFromHook(); - // Reset custom time range to default (last 24 hours) setStartTime(moment().subtract(24, "hours").format("YYYY-MM-DDTHH:mm")); setEndTime(moment().format("YYYY-MM-DDTHH:mm")); setIsCustomDate(false); @@ -267,111 +121,102 @@ export default function SpendLogsTable({ setCurrentPage(1); }, [handleFilterResetFromHook]); - // Disable the main query whenever backend filters are active so it doesn't fire - // redundant unfiltered requests when time range / sort / page changes. - useEffect(() => { - setIsMainQueryEnabled(!hasBackendFilters); - }, [hasBackendFilters]); + const handleSortChange = useCallback((newSortBy: LogsSortField, newSortOrder: "asc" | "desc") => { + setSortBy(newSortBy); + setSortOrder(newSortOrder); + setCurrentPage(1); + }, []); - // Sync filter state into the individual selectedX state variables used by the main query - useEffect(() => { - if (!accessToken) return; + const columns = useMemo( + () => createColumns({ sortBy, sortOrder, onSortChange: handleSortChange }), + [sortBy, sortOrder, handleSortChange], + ); - if (filters["Team ID"]) { - setSelectedTeamId(filters["Team ID"]); - } else { - setSelectedTeamId(""); + const filteredData = useMemo(() => { + const searchedLogs = filteredLogs.data.filter((log) => { + const matchesSearch = + !searchTerm || + log.request_id.includes(searchTerm) || + log.model.includes(searchTerm) || + (log.user && log.user.includes(searchTerm)); + + // No need for additional filtering since we're now handling this in the API call + return matchesSearch; + }); + + const sessionCompositionById = searchedLogs.reduce>( + (acc, log) => { + if (!log.session_id) return acc; + if (!acc[log.session_id]) { + acc[log.session_id] = { llm: 0, agent: 0, mcp: 0 }; + } + if (MCP_CALL_TYPES.includes(log.call_type)) { + acc[log.session_id].mcp += 1; + } else if (AGENT_CALL_TYPES.includes(log.call_type)) { + acc[log.session_id].agent += 1; + } else { + acc[log.session_id].llm += 1; + } + return acc; + }, + {}, + ); + + // Build a single-pass map of session_id → representative request_id. + // Prefers an LLM row over an MCP row as the representative. + const sessionRepresentativeMap = new Map(); + for (const log of searchedLogs) { + if (!log.session_id || (log.session_total_count || 1) <= 1) continue; + const isMcp = MCP_CALL_TYPES.includes(log.call_type); + const existing = sessionRepresentativeMap.get(log.session_id); + if (!existing || (existing.isMcp && !isMcp)) { + sessionRepresentativeMap.set(log.session_id, { requestId: log.request_id, isMcp }); + } } - setSelectedStatus(filters["Status"] || ""); - setSelectedModelId(filters["Model"] || ""); - setSelectedEndUser(filters["End User"] || ""); - // Key Alias filtering is handled server-side by performSearch via the key_alias param. - // We intentionally do not translate the alias to a hash here to avoid firing a - // redundant main-query request (api_key=hash) alongside performSearch's key_alias request. - setSelectedKeyHash(filters["Key Hash"] || ""); - }, [filters, accessToken]); + return ( + searchedLogs + .map((log) => { + const sessionComposition = log.session_id ? sessionCompositionById[log.session_id] : undefined; + return { + ...log, + request_duration_ms: log.request_duration_ms, + session_llm_count: sessionComposition?.llm ?? undefined, + session_mcp_count: sessionComposition?.mcp ?? undefined, + session_agent_count: sessionComposition?.agent ?? undefined, + onKeyHashClick: (keyHash: string) => setSelectedKeyIdInfoView(keyHash), + onSessionClick: (sessionId: string) => { + if (sessionId) { + setSelectedSessionId(sessionId); + setSelectedLog(log); + setIsDrawerOpen(true); + } + }, + }; + }) + // Deduplicate multi-call sessions using the pre-built map (O(1) per row). + .filter((log) => { + if (!log.session_id || (log.session_total_count || 1) <= 1) return true; + return sessionRepresentativeMap.get(log.session_id)?.requestId === log.request_id; + }) + ); + }, [filteredLogs.data, searchTerm]); + + // Keep the Fetch button busy until the table has actually committed the new + // rows. `keepPreviousData` leaves logsQuery.isLoading false on refetch, so + // without this the button clears while stale rows are still on screen. + const deferredData = useDeferredValue(filteredData); + const isStale = deferredData !== filteredData; + const isButtonLoading = logsQuery.isFetching || isStale; if (!accessToken || !token || !userRole || !userID) { - return null; + return ( +
+ +
+ ); } - const searchedLogs = filteredLogs.data.filter((log) => { - const matchesSearch = - !searchTerm || - log.request_id.includes(searchTerm) || - log.model.includes(searchTerm) || - (log.user && log.user.includes(searchTerm)); - - // No need for additional filtering since we're now handling this in the API call - return matchesSearch; - }); - - const sessionCompositionById = searchedLogs.reduce>((acc, log) => { - if (!log.session_id) return acc; - if (!acc[log.session_id]) { - acc[log.session_id] = { llm: 0, agent: 0, mcp: 0 }; - } - if (MCP_CALL_TYPES.includes(log.call_type)) { - acc[log.session_id].mcp += 1; - } else if (AGENT_CALL_TYPES.includes(log.call_type)) { - acc[log.session_id].agent += 1; - } else { - acc[log.session_id].llm += 1; - } - return acc; - }, {}); - - // Build a single-pass map of session_id → representative request_id. - // Prefers an LLM row over an MCP row as the representative. - const sessionRepresentativeMap = new Map(); - for (const log of searchedLogs) { - if (!log.session_id || (log.session_total_count || 1) <= 1) continue; - const isMcp = MCP_CALL_TYPES.includes(log.call_type); - const existing = sessionRepresentativeMap.get(log.session_id); - if (!existing || (existing.isMcp && !isMcp)) { - sessionRepresentativeMap.set(log.session_id, { requestId: log.request_id, isMcp }); - } - } - - const filteredData = - searchedLogs - .map((log) => { - const sessionComposition = log.session_id ? sessionCompositionById[log.session_id] : undefined; - return { - ...log, - request_duration_ms: log.request_duration_ms, - session_llm_count: sessionComposition?.llm ?? undefined, - session_mcp_count: sessionComposition?.mcp ?? undefined, - session_agent_count: sessionComposition?.agent ?? undefined, - onKeyHashClick: (keyHash: string) => setSelectedKeyIdInfoView(keyHash), - onSessionClick: (sessionId: string) => { - if (sessionId) { - setSelectedSessionId(sessionId); - setSelectedLog(log); - setIsDrawerOpen(true); - } - }, - }; - }) - // Deduplicate multi-call sessions using the pre-built map (O(1) per row). - .filter((log) => { - if (!log.session_id || (log.session_total_count || 1) <= 1) return true; - return sessionRepresentativeMap.get(log.session_id)?.requestId === log.request_id; - }) || []; - - // Add this function to handle manual refresh - const handleRefresh = () => { - if (hasBackendFilters) { - // When backend filters (e.g. Key Alias) are active the main TanStack Query - // is disabled and its params do not include filter values like key_alias. - // Route through the filter-aware refetch so all active filters are preserved. - refetchWithFilters(); - } else { - logs.refetch(); - } - }; - const handleRowClick = (log: LogEntry) => { // Multi-call session row: open in the same right-side drawer (session mode) if (log.session_id && (log.session_total_count || 1) > 1) { @@ -386,100 +231,6 @@ export default function SpendLogsTable({ setIsDrawerOpen(true); }; - const handleCloseDrawer = () => { - setIsDrawerOpen(false); - setSelectedSessionId(null); - }; - - const handleSelectLog = (log: LogEntry) => { - setSelectedLog(log); - }; - - const logFilterOptions: FilterOption[] = [ - { - name: "Team ID", - label: "Team ID", - customComponent: FilterTeamDropdown, - }, - { - name: "Status", - label: "Status", - isSearchable: false, - options: [ - { label: "Success", value: "success" }, - { label: "Failure", value: "failure" }, - ], - }, - { - name: "Model", - label: "Model", - customComponent: PaginatedModelSelect, - }, - { - name: FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL, - label: "Public model / search tool", - isSearchable: false, - }, - { - name: "Key Alias", - label: "Key Alias", - customComponent: PaginatedKeyAliasSelect, - }, - { - name: "End User", - label: "End User", - isSearchable: true, - searchFn: async (searchText: string) => { - if (!accessToken) return []; - const data = await allEndUsersCall(accessToken); - // data if set, is a list of objects, with key = user_id - const users = data?.map((u: any) => u.user_id) || []; - const filtered = users.filter((u: string) => u.toLowerCase().includes(searchText.toLowerCase())); - return filtered.map((u: string) => ({ label: u, value: u })); - }, - }, - { - name: "Error Code", - label: "Error Code", - isSearchable: true, - searchFn: async (searchText: string) => { - if (!searchText) return ERROR_CODE_OPTIONS; - const lower = searchText.toLowerCase(); - const filtered = ERROR_CODE_OPTIONS.filter((opt) => opt.label.toLowerCase().includes(lower)); - const isExactValue = ERROR_CODE_OPTIONS.some((opt) => opt.value === searchText.trim()); - if (!isExactValue && searchText.trim()) { - filtered.push({ label: `Use custom code: ${searchText.trim()}`, value: searchText.trim() }); - } - return filtered; - }, - }, - { - name: "Key Hash", - label: "Key Hash", - isSearchable: false, - }, - { - name: "Error Message", - label: "Error Message", - isSearchable: false, - }, - ]; - - const formatTimeUnit = (value: number, unit: string) => { - if (value === 1) { - if (unit === "minutes") return "minute"; - if (unit === "hours") return "hour"; - if (unit === "days") return "day"; - } - return unit; - }; - - const selectedOption = QUICK_SELECT_OPTIONS.find( - (option) => option.value === selectedTimeInterval.value && option.unit === selectedTimeInterval.unit, - ); - - const displayLabel = isCustomDate ? getTimeRangeDisplay(isCustomDate, startTime, endTime) : selectedOption?.label; - return (
setActiveTab(index === 0 ? "request logs" : "audit logs")}> @@ -505,192 +256,37 @@ export default function SpendLogsTable({ ) : ( <>
-
-
-
-
- setSearchTerm(e.target.value)} - /> - - - -
- -
-
- - - {quickSelectOpen && ( -
-
- {QUICK_SELECT_OPTIONS.map((option) => ( - - ))} -
- -
-
- )} -
- - - - -
- - {isCustomDate && ( -
-
- { - setStartTime(e.target.value); - setCurrentPage(1); - }} - className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500" - /> -
- to -
- { - setEndTime(e.target.value); - setCurrentPage(1); - }} - className="px-3 py-2 border rounded-md text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500" - /> -
-
- )} -
- -
- - Showing {logs.isLoading ? "..." : filteredLogs ? (currentPage - 1) * pageSize + 1 : 0} -{" "} - {logs.isLoading - ? "..." - : filteredLogs - ? Math.min(currentPage * pageSize, filteredLogs.total) - : 0}{" "} - of {logs.isLoading ? "..." : filteredLogs ? filteredLogs.total : 0} results - -
- - Page {logs.isLoading ? "..." : currentPage} of{" "} - {logs.isLoading ? "..." : filteredLogs ? filteredLogs.total_pages : 1} - - - -
-
-
-
- {isLiveTail && currentPage === 1 && isMainQueryEnabled && ( -
-
- Auto-refreshing every 15 seconds -
- -
- )} + logsQuery.refetch()} + filteredLogs={filteredLogs} + /> { - setSortBy(newSortBy); - setSortOrder(newSortOrder); - setCurrentPage(1); - }, - })} - data={filteredData} + columns={columns} + data={deferredData} onRowClick={handleRowClick} - isLoading={logs.isLoading} + isLoading={logsQuery.isLoading} />
@@ -706,330 +302,29 @@ export default function SpendLogsTable({ premiumUser={premiumUser} /> - - + + + + + + {/* Log Details Drawer */} { + setIsDrawerOpen(false); + setSelectedSessionId(null); + }} logEntry={selectedLog} sessionId={selectedSessionId} accessToken={accessToken} allLogs={filteredData} - onSelectLog={handleSelectLog} + onSelectLog={setSelectedLog} startTime={moment(startTime).utc().format("YYYY-MM-DD HH:mm:ss")} />
); } - -export function RequestViewer({ row }: { row: Row }) { - // Helper function to clean metadata by removing specific fields - const formatData = (input: any) => { - if (typeof input === "string") { - try { - return JSON.parse(input); - } catch { - return input; - } - } - return input; - }; - - // New helper function to get raw request - const getRawRequest = () => { - // First check if proxy_server_request exists in metadata - if (row.original?.proxy_server_request) { - return formatData(row.original.proxy_server_request); - } - // Fall back to messages if proxy_server_request is empty - return formatData(row.original.messages); - }; - - // Extract error information from metadata if available - const metadata = row.original.metadata || {}; - const hasError = metadata.status === "failure"; - const errorInfo = hasError ? metadata.error_information : null; - - // Check if request/response data is missing - const hasMessages = - row.original.messages && - (Array.isArray(row.original.messages) - ? row.original.messages.length > 0 - : Object.keys(row.original.messages).length > 0); - const hasResponse = row.original.response && Object.keys(formatData(row.original.response)).length > 0; - const missingData = !hasMessages && !hasResponse && !hasError; - - // Format the response with error details if present - const formattedResponse = () => { - if (hasError && errorInfo) { - return { - error: { - message: errorInfo.error_message || "An error occurred", - type: errorInfo.error_class || "error", - code: errorInfo.error_code || "unknown", - param: null, - }, - }; - } - return formatData(row.original.response); - }; - - // Extract vector store request metadata if available - const hasVectorStoreData = - metadata.vector_store_request_metadata && - Array.isArray(metadata.vector_store_request_metadata) && - metadata.vector_store_request_metadata.length > 0; - - // Extract guardrail information from metadata if available - const guardrailInfo = row.original.metadata?.guardrail_information; - const guardrailEntries = Array.isArray(guardrailInfo) ? guardrailInfo : guardrailInfo ? [guardrailInfo] : []; - const hasGuardrailData = guardrailEntries.length > 0; - - // Calculate total masked entities if guardrail data exists - const totalMaskedEntities = guardrailEntries.reduce((sum, entry) => { - const maskedCounts = entry?.masked_entity_count; - if (!maskedCounts) { - return sum; - } - return ( - sum + - Object.values(maskedCounts).reduce((acc, count) => (typeof count === "number" ? acc + count : acc), 0) - ); - }, 0); - - const primaryGuardrailLabel = - guardrailEntries.length === 1 - ? guardrailEntries[0]?.guardrail_name ?? "-" - : guardrailEntries.length > 1 - ? `${guardrailEntries.length} guardrails` - : "-"; - - const truncatedRequestId = truncateString(row.original.request_id, 64); - - return ( -
- {/* Combined Info Card */} -
-
-

Request Details

-
-
-
-
- Request ID: - {row.original.request_id.length > 64 ? ( - - {truncatedRequestId} - - ) : ( - {row.original.request_id} - )} -
-
- Model: - {row.original.model} -
-
- Model ID: - {row.original.model_id} -
-
- Call Type: - {row.original.call_type} -
-
- Provider: - {row.original.custom_llm_provider || "-"} -
-
- API Base: - - {row.original.api_base || "-"} - -
- {row?.original?.requester_ip_address && ( -
- IP Address: - {row?.original?.requester_ip_address} -
- )} - {hasGuardrailData && ( -
- Guardrail: -
- {primaryGuardrailLabel} - {totalMaskedEntities > 0 && ( - - {totalMaskedEntities} masked - - )} -
-
- )} -
-
-
- Tokens: - - {row.original.total_tokens} ({row.original.prompt_tokens} prompt tokens +{" "} - {row.original.completion_tokens} completion tokens) - -
-
- Cache Read Tokens: - - {formatNumberWithCommas(row.original.metadata?.additional_usage_values?.cache_read_input_tokens || 0)} - -
-
- Cache Creation Tokens: - - {formatNumberWithCommas(row.original.metadata?.additional_usage_values.cache_creation_input_tokens)} - -
-
- Cost: - ${formatNumberWithCommas(row.original.spend || 0, 6)} -
-
- Cache Hit: - {row.original.cache_hit} -
- -
- Status: - - {(row.original.metadata?.status || "Success").toLowerCase() !== "failure" ? "Success" : "Failure"} - -
-
- Start Time: - {row.original.startTime} -
-
- End Time: - {row.original.endTime} -
-
- Duration: - {row.original.request_duration_ms != null ? (row.original.request_duration_ms / 1000).toFixed(3) : "-"} s. -
- {row.original.metadata?.litellm_overhead_time_ms !== undefined && ( -
- LiteLLM Overhead: - {row.original.metadata.litellm_overhead_time_ms} ms -
- )} -
- Retries: - - {row.original.metadata?.attempted_retries !== undefined && row.original.metadata?.attempted_retries !== null - ? row.original.metadata.attempted_retries > 0 - ? `${row.original.metadata.attempted_retries}${row.original.metadata.max_retries !== undefined && row.original.metadata.max_retries !== null ? ` / ${row.original.metadata.max_retries}` : ''}` - : None - : '-'} - -
-
-
-
- - {/* Cost Breakdown - Show if cost breakdown data is available */} - - - {/* Configuration Info Message - Show when data is missing */} - - - {/* Request/Response Panel */} -
- -
- - {/* Guardrail Data - Show only if present */} - {hasGuardrailData && } - - {/* Vector Store Request Data - Show only if present */} - {hasVectorStoreData && } - - {/* Error Card - Only show for failures */} - {hasError && errorInfo && } - - {/* Tags Card - Only show if there are tags */} - {row.original.request_tags && Object.keys(row.original.request_tags).length > 0 && ( -
-
-

Request Tags

-
-
-
- {Object.entries(row.original.request_tags).map(([key, value]) => ( - - {key}: {String(value)} - - ))} -
-
-
- )} - - {/* Metadata Card - Only show if there's metadata */} - {row.original.metadata && Object.keys(row.original.metadata).length > 0 && ( -
-
-

Metadata

- -
-
- 
-              {JSON.stringify(row.original.metadata, null, 2)}
-
-
-
- )} -
- ); -} diff --git a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx index 17c50771526..cbe37e0b70f 100644 --- a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.test.tsx @@ -1,10 +1,16 @@ import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; import { act, renderHook, waitFor } from "@testing-library/react"; -import React, { ReactNode } from "react"; +import React, { ReactNode, useState } from "react"; import { beforeEach, describe, expect, it, vi } from "vitest"; -import type { PaginatedResponse } from "."; -import type { LogEntry, LogsSortField } from "./columns"; -import { useLogFilterLogic } from "./log_filter_logic"; +import type { LogsSortField } from "./columns"; +import { + defaultFilters, + getLiveTailRefetchInterval, + LIVE_TAIL_INTERVAL_MS, + useLogFilterLogic, + type LogFilterState, + type PaginatedResponse, +} from "./log_filter_logic"; vi.mock("../networking", () => ({ uiSpendLogsCall: vi.fn(), @@ -16,767 +22,642 @@ vi.mock("@/components/key_team_helpers/filter_helpers", () => ({ import { uiSpendLogsCall } from "../networking"; -const createLogEntry = (overrides: Partial = {}): LogEntry => -({ - request_id: "req-1", - api_key: "key-1", - team_id: "team-1", - model: "gpt-4", - model_id: "gpt-4", - call_type: "chat", - spend: 0, - total_tokens: 0, - prompt_tokens: 0, - completion_tokens: 0, - startTime: "2025-01-01T00:00:00Z", - endTime: "2025-01-01T00:01:00Z", - cache_hit: "miss", - messages: [], - response: {}, - metadata: {}, - request_tags: {}, - ...overrides, -} as LogEntry); - -const createPaginatedResponse = (data: LogEntry[]): PaginatedResponse => ({ - data, - total: data.length, +const emptyResponse: PaginatedResponse = { + data: [], + total: 0, page: 1, page_size: 50, - total_pages: 1, -}); + total_pages: 0, +}; const defaultProps = { - logs: createPaginatedResponse([]), - accessToken: "test-token", + accessToken: "test-token" as string | null, + token: "test-token" as string | null, + userRole: "Admin" as string | null, + userID: "user-1" as string | null, + filterByCurrentUser: false, + activeTab: "request logs", + isLiveTail: false, startTime: "2025-01-01T00:00:00", endTime: "2025-01-01T23:59:59", isCustomDate: true, - setCurrentPage: vi.fn(), - userID: "user-1", - userRole: "Admin", + sortBy: "startTime" as LogsSortField, + sortOrder: "desc" as "asc" | "desc", + currentPage: 1, }; +type HookOverrides = Partial[0], "filters" | "setFilters">>; + describe("useLogFilterLogic", () => { let queryClient: QueryClient; beforeEach(() => { queryClient = new QueryClient({ - defaultOptions: { - queries: { - retry: false, - }, - }, + defaultOptions: { queries: { retry: false } }, }); vi.clearAllMocks(); - vi.mocked(uiSpendLogsCall).mockResolvedValue({ - data: [], - total: 0, - page: 1, - page_size: 50, - total_pages: 0, - }); + vi.mocked(uiSpendLogsCall).mockResolvedValue(emptyResponse); }); const wrapper = ({ children }: { children: ReactNode }) => React.createElement(QueryClientProvider, { client: queryClient }, children); - it("should return filters, filteredLogs, allTeams, handleFilterChange, and handleFilterReset", () => { - const { result } = renderHook( - () => - useLogFilterLogic({ + function renderFilterHook(overrides: HookOverrides = {}) { + const setCurrentPage = overrides.setCurrentPage ?? vi.fn(); + const rendered = renderHook( + () => { + const [filters, setFilters] = useState(defaultFilters); + const hook = useLogFilterLogic({ ...defaultProps, - logs: createPaginatedResponse([createLogEntry()]), - }), + ...overrides, + filters, + setFilters, + setCurrentPage, + }); + return { ...hook, filters, setFilters }; + }, { wrapper }, ); + return { ...rendered, setCurrentPage }; + } - expect(result.current.filters).toBeDefined(); - expect(result.current.filteredLogs).toBeDefined(); - expect(result.current).toHaveProperty("allTeams"); - expect(result.current.handleFilterChange).toBeDefined(); - expect(result.current.handleFilterReset).toBeDefined(); - }); + describe("return shape", () => { + it("exposes filteredLogs, allTeams, handleFilterChange, handleFilterReset", () => { + const { result } = renderFilterHook(); - it("should initialize filters with all keys empty", () => { - const { result } = renderHook(() => useLogFilterLogic(defaultProps), { wrapper }); - - const filters = result.current.filters; - expect(filters["Team ID"]).toBe(""); - expect(filters["Key Hash"]).toBe(""); - expect(filters["Request ID"]).toBe(""); - expect(filters["Model"]).toBe(""); - expect(filters["User ID"]).toBe(""); - expect(filters["End User"]).toBe(""); - expect(filters["Status"]).toBe(""); - expect(filters["Key Alias"]).toBe(""); - expect(filters["Error Code"]).toBe(""); - expect(filters["Error Message"]).toBe(""); - expect(filters["Public model / search tool"]).toBe(""); - }); - - it("should return all logs when no filters are applied", () => { - const logs = createPaginatedResponse([ - createLogEntry({ request_id: "req-1" }), - createLogEntry({ request_id: "req-2" }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - expect(result.current.filteredLogs.data).toHaveLength(2); - expect(result.current.filteredLogs.data).toEqual(logs.data); - }); - - it("should filter logs by team_id when Team ID filter is set", () => { - const logs = createPaginatedResponse([ - createLogEntry({ request_id: "req-1", team_id: "team-a" }), - createLogEntry({ request_id: "req-2", team_id: "team-b" }), - createLogEntry({ request_id: "req-3", team_id: "team-a" }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Team ID": "team-a" }); - }); - - expect(result.current.filteredLogs.data).toHaveLength(2); - expect(result.current.filteredLogs.data.every((log) => log.team_id === "team-a")).toBe(true); - }); - - it("should filter logs by status when Status filter is set to success", () => { - const logs = createPaginatedResponse([ - createLogEntry({ request_id: "req-1", status: "success" }), - createLogEntry({ request_id: "req-2" }), - createLogEntry({ request_id: "req-3", status: "error" }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ Status: "success" }); - }); - - expect(result.current.filteredLogs.data).toHaveLength(2); - expect(result.current.filteredLogs.data.every((log) => !log.status || log.status === "success")).toBe(true); - }); - - it("should filter logs by status when Status filter is set to error", () => { - const logs = createPaginatedResponse([ - createLogEntry({ request_id: "req-1", status: "success" }), - createLogEntry({ request_id: "req-2", status: "error" }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ Status: "error" }); - }); - - expect(result.current.filteredLogs.data).toHaveLength(1); - expect(result.current.filteredLogs.data[0].status).toBe("error"); - }); - - it("should filter logs by model_id when Model filter is set", async () => { - const filteredLogs = [ - createLogEntry({ request_id: "req-1", model_id: "gpt-4" }), - createLogEntry({ request_id: "req-3", model_id: "gpt-4" }), - ]; - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse(filteredLogs), - ); - const logs = createPaginatedResponse([ - createLogEntry({ request_id: "req-1", model_id: "gpt-4" }), - createLogEntry({ request_id: "req-2", model_id: "gpt-3.5" }), - createLogEntry({ request_id: "req-3", model_id: "gpt-4" }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ Model: "gpt-4" }); - }); - - await waitFor( - () => { - expect(result.current.filteredLogs.data).toHaveLength(2); - expect(result.current.filteredLogs.data.every((log) => log.model_id === "gpt-4")).toBe(true); - }, - { timeout: 500 }, - ); - }); - - it("should pass model param and filter search-tool rows by spend log model column", async () => { - const searchRows = [ - createLogEntry({ - request_id: "s1", - call_type: "asearch", - model: "tavily-marketing", - model_id: "", - team_id: "team-x", - }), - ]; - vi.mocked(uiSpendLogsCall).mockResolvedValue(createPaginatedResponse(searchRows)); - const logs = createPaginatedResponse([ - ...searchRows, - createLogEntry({ - request_id: "c1", - call_type: "chat", - model: "gpt-4o", - model_id: "mid-1", - team_id: "team-x", - }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Public model / search tool": "tavily-marketing" }); - }); - - await waitFor( - () => { - expect(result.current.filteredLogs.data).toHaveLength(1); - expect(result.current.filteredLogs.data[0].model).toBe("tavily-marketing"); - expect(result.current.filteredLogs.data[0].call_type).toBe("asearch"); - }, - { timeout: 500 }, - ); - - expect(vi.mocked(uiSpendLogsCall)).toHaveBeenCalledWith( - expect.objectContaining({ - params: expect.objectContaining({ - model: "tavily-marketing", - }), - }), - ); - }); - - it("should filter logs by api_key when Key Hash filter is set", async () => { - const filteredLog = createLogEntry({ request_id: "req-1", api_key: "key-x" }); - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([filteredLog]), - ); - const logs = createPaginatedResponse([ - createLogEntry({ request_id: "req-1", api_key: "key-x" }), - createLogEntry({ request_id: "req-2", api_key: "key-y" }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Key Hash": "key-x" }); - }); - - await waitFor( - () => { - expect(result.current.filteredLogs.data).toHaveLength(1); - expect(result.current.filteredLogs.data[0].api_key).toBe("key-x"); - }, - { timeout: 500 }, - ); - }); - - it("should filter logs by end_user when End User filter is set", async () => { - const filteredLog = createLogEntry({ request_id: "req-1", end_user: "user-a" }); - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([filteredLog]), - ); - const logs = createPaginatedResponse([ - createLogEntry({ request_id: "req-1", end_user: "user-a" }), - createLogEntry({ request_id: "req-2", end_user: "user-b" }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "End User": "user-a" }); - }); - - await waitFor( - () => { - expect(result.current.filteredLogs.data).toHaveLength(1); - expect(result.current.filteredLogs.data[0].end_user).toBe("user-a"); - }, - { timeout: 500 }, - ); - }); - - it("should filter logs by error_code when Error Code filter is set", async () => { - const filteredLog = createLogEntry({ - request_id: "req-1", - metadata: { error_information: { error_code: "429" } }, - }); - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([filteredLog]), - ); - const logs = createPaginatedResponse([ - createLogEntry({ - request_id: "req-1", - metadata: { error_information: { error_code: "429" } }, - }), - createLogEntry({ - request_id: "req-2", - metadata: { error_information: { error_code: "500" } }, - }), - ]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Error Code": "429" }); - }); - - await waitFor( - () => { - expect(result.current.filteredLogs.data).toHaveLength(1); - expect(result.current.filteredLogs.data[0].metadata?.error_information?.error_code).toBe("429"); - }, - { timeout: 500 }, - ); - }); - - it("should return empty data when logs is null or has no data", () => { - const { result } = renderHook( - () => - useLogFilterLogic({ - ...defaultProps, - logs: { data: [], total: 0, page: 1, page_size: 50, total_pages: 0 }, - }), - { wrapper }, - ); - - expect(result.current.filteredLogs.data).toEqual([]); - expect(result.current.filteredLogs.total).toBe(0); - }); - - it("should reset filters when handleFilterReset is called", () => { - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Team ID": "team-1", Status: "success" }); - }); - - expect(result.current.filters["Team ID"]).toBe("team-1"); - expect(result.current.filters["Status"]).toBe("success"); - - act(() => { - result.current.handleFilterReset(); - }); - - expect(result.current.filters["Team ID"]).toBe(""); - expect(result.current.filters["Status"]).toBe(""); - }); - - it("should call setCurrentPage with 1 when handleFilterChange is invoked", () => { - const setCurrentPage = vi.fn(); - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook( - () => useLogFilterLogic({ ...defaultProps, logs, setCurrentPage }), - { wrapper }, - ); - - act(() => { - result.current.handleFilterChange({ "Team ID": "team-1" }); - }); - - expect(setCurrentPage).toHaveBeenCalledWith(1); - }); - - it("should call uiSpendLogsCall when backend filter is set and debounce elapses", async () => { - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor( - () => { - expect(uiSpendLogsCall).toHaveBeenCalled(); - }, - { timeout: 500 }, - ); - }); - - it("should not call uiSpendLogsCall when accessToken is null", async () => { - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook( - () => useLogFilterLogic({ ...defaultProps, logs, accessToken: null }), - { wrapper }, - ); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await new Promise((resolve) => setTimeout(resolve, 350)); - - expect(uiSpendLogsCall).not.toHaveBeenCalled(); - }); - - it("should use backend filtered logs when backend filters are active and API returns data", async () => { - const backendLog = createLogEntry({ request_id: "backend-req" }); - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([backendLog]), - ); - const logs = createPaginatedResponse([createLogEntry({ request_id: "client-req" })]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor( - () => { - expect(result.current.filteredLogs.data).toHaveLength(1); - expect(result.current.filteredLogs.data[0].request_id).toBe("backend-req"); - }, - { timeout: 500 }, - ); - }); - - it("should call uiSpendLogsCall with request_id when Request ID filter is set", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry({ request_id: "req-xyz" })]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Request ID": "req-xyz" }); - }); - - await waitFor( - () => { - expect(uiSpendLogsCall).toHaveBeenCalledWith( - expect.objectContaining({ - params: expect.objectContaining({ request_id: "req-xyz" }), - }), - ); - }, - { timeout: 500 }, - ); - }); - - it("should call uiSpendLogsCall with user_id when User ID filter is set", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "User ID": "user-123" }); - }); - - await waitFor( - () => { - expect(uiSpendLogsCall).toHaveBeenCalledWith( - expect.objectContaining({ - params: expect.objectContaining({ user_id: "user-123" }), - }), - ); - }, - { timeout: 500 }, - ); - }); - - it("should call uiSpendLogsCall with error_message when Error Message filter is set", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Error Message": "rate limit exceeded" }); - }); - - await waitFor( - () => { - expect(uiSpendLogsCall).toHaveBeenCalledWith( - expect.objectContaining({ - params: expect.objectContaining({ error_message: "rate limit exceeded" }), - }), - ); - }, - { timeout: 500 }, - ); - }); - - it("should return empty results when backend filters are active but API returns empty", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue({ - data: [], - total: 0, - page: 1, - page_size: 50, - total_pages: 0, - }); - const clientLog = createLogEntry({ request_id: "client-req" }); - const logs = createPaginatedResponse([clientLog]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor( - () => { - expect(uiSpendLogsCall).toHaveBeenCalled(); - }, - { timeout: 500 }, - ); - - expect(result.current.filteredLogs.data).toHaveLength(0); - }); - - it("should refetch when sortBy changes and backend filters are active", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result, rerender } = renderHook( - (props: { sortBy?: LogsSortField }) => - useLogFilterLogic({ ...defaultProps, logs, ...props }), - { wrapper, initialProps: { sortBy: "startTime" as LogsSortField } }, - ); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { - timeout: 500, - }); - - rerender({ sortBy: "spend" as LogsSortField }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { - timeout: 500, - }); - expect(uiSpendLogsCall).toHaveBeenLastCalledWith( - expect.objectContaining({ - params: expect.objectContaining({ sort_by: "spend" }), - }), - ); - }); - - it("should refetch when sortOrder changes and backend filters are active", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result, rerender } = renderHook( - (props: { sortOrder?: "asc" | "desc" }) => - useLogFilterLogic({ ...defaultProps, logs, ...props }), - { wrapper, initialProps: { sortOrder: "desc" } }, - ); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { - timeout: 500, - }); - - rerender({ sortOrder: "asc" }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { - timeout: 500, - }); - expect(uiSpendLogsCall).toHaveBeenLastCalledWith( - expect.objectContaining({ - params: expect.objectContaining({ sort_order: "asc" }), - }), - ); - }); - - it("should refetch when currentPage changes and backend filters are active", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result, rerender } = renderHook( - (props) => useLogFilterLogic({ ...defaultProps, logs, ...props }), - { wrapper, initialProps: { currentPage: 1 } }, - ); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { - timeout: 500, - }); - - rerender({ currentPage: 2 }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { - timeout: 500, - }); - expect(uiSpendLogsCall).toHaveBeenLastCalledWith( - expect.objectContaining({ page: 2 }), - ); - }); - - it("should refetch when startTime changes and backend filters are active", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result, rerender } = renderHook( - (props: { startTime?: string }) => - useLogFilterLogic({ ...defaultProps, logs, ...props }), - { wrapper, initialProps: { startTime: "2025-01-01T00:00:00Z" } }, - ); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { - timeout: 500, - }); - - rerender({ startTime: "2025-01-02T00:00:00Z" }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { - timeout: 500, - }); - expect(uiSpendLogsCall).toHaveBeenLastCalledWith( - expect.objectContaining({ - start_date: "2025-01-02 00:00:00", - }), - ); - }); - - it("should refetch when isCustomDate changes and backend filters are active", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result, rerender } = renderHook( - (props: { isCustomDate?: boolean }) => - useLogFilterLogic({ ...defaultProps, logs, ...props }), - { wrapper, initialProps: { isCustomDate: false } }, - ); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { - timeout: 500, - }); - - rerender({ isCustomDate: true }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { - timeout: 500, + expect(result.current.filteredLogs).toBeDefined(); + expect(result.current).toHaveProperty("allTeams"); + expect(result.current.handleFilterChange).toBeInstanceOf(Function); + expect(result.current.handleFilterReset).toBeInstanceOf(Function); }); }); - it("should not call setCurrentPage when handleFilterChange receives identical filters", async () => { - const setCurrentPage = vi.fn(); - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook( - () => useLogFilterLogic({ ...defaultProps, logs, setCurrentPage }), - { wrapper }, - ); + describe("handleFilterReset", () => { + it("restores filters to defaults after changes", () => { + const { result } = renderFilterHook(); - act(() => { - result.current.handleFilterChange({ "Team ID": "team-1" }); + act(() => { + result.current.handleFilterChange({ "Team ID": "team-1", Status: "success" }); + }); + + expect(result.current.filters["Team ID"]).toBe("team-1"); + expect(result.current.filters["Status"]).toBe("success"); + + act(() => { + result.current.handleFilterReset(); + }); + + expect(result.current.filters["Team ID"]).toBe(""); + expect(result.current.filters["Status"]).toBe(""); }); - await waitFor(() => expect(setCurrentPage).toHaveBeenCalledTimes(1), { - timeout: 500, + it("calls setCurrentPage(1)", () => { + const setCurrentPage = vi.fn(); + const { result } = renderFilterHook({ setCurrentPage }); + + act(() => { + result.current.handleFilterReset(); + }); + + expect(setCurrentPage).toHaveBeenCalledWith(1); }); - setCurrentPage.mockClear(); + it("triggers a fetch with all filter params undefined", async () => { + vi.mocked(uiSpendLogsCall).mockResolvedValue(emptyResponse); + const { result } = renderFilterHook(); - await act(async () => { - result.current.handleFilterChange({ "Team ID": "team-1" }); - await new Promise((resolve) => setTimeout(resolve, 350)); - }); + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); - expect(setCurrentPage).not.toHaveBeenCalled(); - }); + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 }); - it("should not crash when uiSpendLogsCall throws", async () => { - vi.mocked(uiSpendLogsCall).mockRejectedValue(new Error("Network error")); - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); + act(() => { + result.current.handleFilterReset(); + }); - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { - timeout: 500, - }); - - expect(result.current.filteredLogs).toBeDefined(); - expect(result.current.filters).toBeDefined(); - }); - - it("should clear backendFilteredLogs when handleFilterReset is called", async () => { - const backendLog = createLogEntry({ request_id: "backend-req" }); - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([backendLog]), - ); - const logs = createPaginatedResponse([createLogEntry({ request_id: "client-req" })]); - const { result } = renderHook(() => useLogFilterLogic({ ...defaultProps, logs }), { wrapper }); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor( - () => { - expect(result.current.filteredLogs.data[0].request_id).toBe("backend-req"); - }, - { timeout: 500 }, - ); - - act(() => { - result.current.handleFilterReset(); - }); - - expect(result.current.filteredLogs.data).toEqual(logs.data); - expect(result.current.filteredLogs.data[0].request_id).toBe("client-req"); - }); - - it("should pass correct start_date, end_date, sort_by, and sort_order to uiSpendLogsCall", async () => { - vi.mocked(uiSpendLogsCall).mockResolvedValue( - createPaginatedResponse([createLogEntry()]), - ); - const logs = createPaginatedResponse([createLogEntry()]); - const { result } = renderHook( - () => - useLogFilterLogic({ - ...defaultProps, - logs, - startTime: "2025-01-15T00:00:00Z", - endTime: "2025-01-15T23:59:59Z", - isCustomDate: true, - sortBy: "spend", - sortOrder: "asc", - }), - { wrapper }, - ); - - act(() => { - result.current.handleFilterChange({ "Key Alias": "alias-1" }); - }); - - await waitFor( - () => { - expect(uiSpendLogsCall).toHaveBeenCalledWith( - expect.objectContaining({ - start_date: "2025-01-15 00:00:00", - end_date: "2025-01-15 23:59:59", - params: expect.objectContaining({ - sort_by: "spend", - sort_order: "asc", + await waitFor( + () => { + expect(uiSpendLogsCall).toHaveBeenLastCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ + team_id: undefined, + api_key: undefined, + request_id: undefined, + user_id: undefined, + end_user: undefined, + status_filter: undefined, + model_id: undefined, + key_alias: undefined, + error_code: undefined, + error_message: undefined, + }), }), - }), + ); + }, + { timeout: 500 }, + ); + }); + }); + + describe("handleFilterChange", () => { + it("calls setCurrentPage(1) when filters change", () => { + const setCurrentPage = vi.fn(); + const { result } = renderFilterHook({ setCurrentPage }); + + act(() => { + result.current.handleFilterChange({ "Team ID": "team-1" }); + }); + + expect(setCurrentPage).toHaveBeenCalledWith(1); + }); + + it("merges partial updates without clobbering other filter keys", () => { + const { result } = renderFilterHook(); + + act(() => { + result.current.handleFilterChange({ "Team ID": "team-a" }); + }); + expect(result.current.filters["Team ID"]).toBe("team-a"); + + act(() => { + result.current.handleFilterChange({ Model: "gpt-4" }); + }); + + expect(result.current.filters["Team ID"]).toBe("team-a"); + expect(result.current.filters["Model"]).toBe("gpt-4"); + }); + + it("does not call setCurrentPage when filters are identical", async () => { + const setCurrentPage = vi.fn(); + const { result } = renderFilterHook({ setCurrentPage }); + + act(() => { + result.current.handleFilterChange({ "Team ID": "team-1" }); + }); + + await waitFor(() => expect(setCurrentPage).toHaveBeenCalledTimes(1), { timeout: 500 }); + + setCurrentPage.mockClear(); + + await act(async () => { + result.current.handleFilterChange({ "Team ID": "team-1" }); + await new Promise((resolve) => setTimeout(resolve, 350)); + }); + + expect(setCurrentPage).not.toHaveBeenCalled(); + }); + }); + + describe("query params — filter keys", () => { + const filterCases: Array<{ + filterKey: keyof LogFilterState; + paramName: string; + value: string; + }> = [ + { filterKey: "Team ID", paramName: "team_id", value: "team-a" }, + { filterKey: "Key Hash", paramName: "api_key", value: "key-x" }, + { filterKey: "Request ID", paramName: "request_id", value: "req-xyz" }, + { filterKey: "User ID", paramName: "user_id", value: "user-123" }, + { filterKey: "End User", paramName: "end_user", value: "user-a" }, + { filterKey: "Status", paramName: "status_filter", value: "error" }, + { filterKey: "Model", paramName: "model_id", value: "gpt-4" }, + { filterKey: "Public model / search tool", paramName: "model", value: "tavily-marketing" }, + { filterKey: "Error Code", paramName: "error_code", value: "429" }, + { filterKey: "Error Message", paramName: "error_message", value: "rate limit exceeded" }, + ]; + + it.each(filterCases)( + "forwards $filterKey as params.$paramName to uiSpendLogsCall", + async ({ filterKey, paramName, value }) => { + const { result } = renderFilterHook(); + + act(() => { + result.current.handleFilterChange({ [filterKey]: value } as Partial); + }); + + await waitFor( + () => { + expect(uiSpendLogsCall).toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ [paramName]: value }), + }), + ); + }, + { timeout: 500 }, ); }, - { timeout: 500 }, ); }); + + describe("query params — date & sort", () => { + it("passes start_date, end_date, sort_by, and sort_order to uiSpendLogsCall", async () => { + const { result } = renderFilterHook({ + startTime: "2025-01-15T00:00:00Z", + endTime: "2025-01-15T23:59:59Z", + isCustomDate: true, + sortBy: "spend" as LogsSortField, + sortOrder: "asc", + }); + + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); + + await waitFor( + () => { + expect(uiSpendLogsCall).toHaveBeenCalledWith( + expect.objectContaining({ + start_date: "2025-01-15 00:00:00", + end_date: "2025-01-15 23:59:59", + params: expect.objectContaining({ + sort_by: "spend", + sort_order: "asc", + }), + }), + ); + }, + { timeout: 500 }, + ); + }); + }); + + describe("debounce", () => { + it("calls uiSpendLogsCall after the debounce elapses for text filters", async () => { + const { result } = renderFilterHook(); + + act(() => { + result.current.handleFilterChange({ "Key Hash": "hash-1" }); + }); + + await waitFor( + () => + expect(uiSpendLogsCall).toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ api_key: "hash-1" }), + }), + ), + { timeout: 500 }, + ); + }); + + it("does not call uiSpendLogsCall with a text filter before the debounce elapses", async () => { + const { result } = renderFilterHook(); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 }); + vi.mocked(uiSpendLogsCall).mockClear(); + + act(() => { + result.current.handleFilterChange({ "Key Hash": "hash-1" }); + }); + + await new Promise((resolve) => setTimeout(resolve, 100)); + expect(uiSpendLogsCall).not.toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ api_key: "hash-1" }), + }), + ); + + await waitFor( + () => + expect(uiSpendLogsCall).toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ api_key: "hash-1" }), + }), + ), + { timeout: 500 }, + ); + }); + + it("applies dropdown filter changes without waiting for the debounce", async () => { + const { result } = renderFilterHook(); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 }); + vi.mocked(uiSpendLogsCall).mockClear(); + + act(() => { + result.current.handleFilterChange({ "Team ID": "team-instant" }); + }); + + await waitFor( + () => + expect(uiSpendLogsCall).toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ team_id: "team-instant" }), + }), + ), + { timeout: 100 }, + ); + }); + + // Guards the TEXT_FILTER_KEYS fix: this free-text filter must debounce, not fire per keystroke. + it("debounces the 'Public model / search tool' text filter", async () => { + const { result } = renderFilterHook(); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 }); + vi.mocked(uiSpendLogsCall).mockClear(); + + act(() => { + result.current.handleFilterChange({ "Public model / search tool": "tavily-marketing" }); + }); + + await new Promise((resolve) => setTimeout(resolve, 100)); + expect(uiSpendLogsCall).not.toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ model: "tavily-marketing" }), + }), + ); + + await waitFor( + () => + expect(uiSpendLogsCall).toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ model: "tavily-marketing" }), + }), + ), + { timeout: 500 }, + ); + }); + }); + + describe("handleFilterReset", () => { + it("flushes the text-filter debounce so a pending typed value is not sent", async () => { + const { result } = renderFilterHook(); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 }); + vi.mocked(uiSpendLogsCall).mockClear(); + + act(() => { + result.current.handleFilterChange({ "Key Hash": "pending-hash" }); + }); + + act(() => { + result.current.handleFilterReset(); + }); + + await new Promise((resolve) => setTimeout(resolve, 400)); + + for (const call of vi.mocked(uiSpendLogsCall).mock.calls) { + expect(call[0].params?.api_key).toBeUndefined(); + } + }); + }); + + describe("backend filtered logs", () => { + it("returns the query payload as filteredLogs when backend filters are active", async () => { + const backendLog = { request_id: "backend-req" }; + vi.mocked(uiSpendLogsCall).mockResolvedValue({ + data: [backendLog], + total: 1, + page: 1, + page_size: 50, + total_pages: 1, + } as PaginatedResponse); + + const { result } = renderFilterHook(); + + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); + + await waitFor( + () => { + expect(result.current.filteredLogs.data).toHaveLength(1); + expect(result.current.filteredLogs.data[0].request_id).toBe("backend-req"); + }, + { timeout: 500 }, + ); + }); + + it("returns empty data when the API returns an empty payload", async () => { + vi.mocked(uiSpendLogsCall).mockResolvedValue(emptyResponse); + const { result } = renderFilterHook(); + + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 }); + + expect(result.current.filteredLogs.data).toHaveLength(0); + }); + }); + + describe("refetch triggers", () => { + it("refetches when sortBy changes", async () => { + const { rerender } = renderHook( + (props: { sortBy: LogsSortField }) => { + const [filters, setFilters] = useState(defaultFilters); + return useLogFilterLogic({ + ...defaultProps, + filters, + setFilters, + setCurrentPage: vi.fn(), + sortBy: props.sortBy, + }); + }, + { wrapper, initialProps: { sortBy: "startTime" } }, + ); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 }); + + rerender({ sortBy: "spend" }); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 }); + expect(uiSpendLogsCall).toHaveBeenLastCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ sort_by: "spend" }), + }), + ); + }); + + it("refetches when sortOrder changes", async () => { + const { rerender } = renderHook( + (props: { sortOrder: "asc" | "desc" }) => { + const [filters, setFilters] = useState(defaultFilters); + return useLogFilterLogic({ + ...defaultProps, + filters, + setFilters, + setCurrentPage: vi.fn(), + sortOrder: props.sortOrder, + }); + }, + { wrapper, initialProps: { sortOrder: "desc" } }, + ); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 }); + + rerender({ sortOrder: "asc" }); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 }); + expect(uiSpendLogsCall).toHaveBeenLastCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ sort_order: "asc" }), + }), + ); + }); + + it("refetches when currentPage changes", async () => { + const { rerender } = renderHook( + (props: { currentPage: number }) => { + const [filters, setFilters] = useState(defaultFilters); + return useLogFilterLogic({ + ...defaultProps, + filters, + setFilters, + setCurrentPage: vi.fn(), + currentPage: props.currentPage, + }); + }, + { wrapper, initialProps: { currentPage: 1 } }, + ); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 }); + + rerender({ currentPage: 2 }); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 }); + expect(uiSpendLogsCall).toHaveBeenLastCalledWith(expect.objectContaining({ page: 2 })); + }); + + it("refetches when startTime changes", async () => { + const { rerender } = renderHook( + (props: { startTime: string }) => { + const [filters, setFilters] = useState(defaultFilters); + return useLogFilterLogic({ + ...defaultProps, + filters, + setFilters, + setCurrentPage: vi.fn(), + startTime: props.startTime, + }); + }, + { wrapper, initialProps: { startTime: "2025-01-01T00:00:00Z" } }, + ); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 }); + + rerender({ startTime: "2025-01-02T00:00:00Z" }); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 }); + expect(uiSpendLogsCall).toHaveBeenLastCalledWith(expect.objectContaining({ start_date: "2025-01-02 00:00:00" })); + }); + + it("refetches with a different end_date when isCustomDate toggles", async () => { + const customEndTime = "2025-01-15T23:59:59Z"; + const customEndFormatted = "2025-01-15 23:59:59"; + + const { rerender } = renderHook( + (props: { isCustomDate: boolean }) => { + const [filters, setFilters] = useState(defaultFilters); + return useLogFilterLogic({ + ...defaultProps, + endTime: customEndTime, + filters, + setFilters, + setCurrentPage: vi.fn(), + isCustomDate: props.isCustomDate, + }); + }, + { wrapper, initialProps: { isCustomDate: false } }, + ); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(1), { timeout: 500 }); + const firstEndDate = vi.mocked(uiSpendLogsCall).mock.calls[0][0].end_date; + expect(firstEndDate).not.toBe(customEndFormatted); + + rerender({ isCustomDate: true }); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalledTimes(2), { timeout: 500 }); + expect(vi.mocked(uiSpendLogsCall).mock.calls[1][0].end_date).toBe(customEndFormatted); + }); + }); + + describe("query enablement", () => { + const nullCredentialCases: Array<{ name: string; override: HookOverrides }> = [ + { name: "accessToken", override: { accessToken: null } }, + { name: "token", override: { token: null } }, + { name: "userRole", override: { userRole: null } }, + { name: "userID", override: { userID: null } }, + ]; + + it.each(nullCredentialCases)("does not call uiSpendLogsCall when $name is null", async ({ override }) => { + const { result } = renderFilterHook(override); + + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); + + await new Promise((resolve) => setTimeout(resolve, 350)); + + expect(uiSpendLogsCall).not.toHaveBeenCalled(); + }); + + it("does not call uiSpendLogsCall when activeTab is not 'request logs'", async () => { + const { result } = renderFilterHook({ activeTab: "audit logs" }); + + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); + + await new Promise((resolve) => setTimeout(resolve, 350)); + + expect(uiSpendLogsCall).not.toHaveBeenCalled(); + }); + }); + + describe("filterByCurrentUser", () => { + it("sends user_id: userID when the User ID filter is blank", async () => { + const { result } = renderFilterHook({ + filterByCurrentUser: true, + userID: "me-123", + }); + + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); + + await waitFor( + () => { + expect(uiSpendLogsCall).toHaveBeenCalledWith( + expect.objectContaining({ + params: expect.objectContaining({ user_id: "me-123" }), + }), + ); + }, + { timeout: 500 }, + ); + }); + }); + + describe("error handling", () => { + it("does not crash when uiSpendLogsCall throws", async () => { + vi.mocked(uiSpendLogsCall).mockRejectedValue(new Error("Network error")); + const { result } = renderFilterHook(); + + act(() => { + result.current.handleFilterChange({ "Key Alias": "alias-1" }); + }); + + await waitFor(() => expect(uiSpendLogsCall).toHaveBeenCalled(), { timeout: 500 }); + + expect(result.current.filteredLogs).toBeDefined(); + expect(result.current.filteredLogs.data).toEqual([]); + }); + }); +}); + +describe("getLiveTailRefetchInterval", () => { + it("polls every 15s when live tail is on and on page 1", () => { + expect(getLiveTailRefetchInterval(true, 1)).toBe(LIVE_TAIL_INTERVAL_MS); + }); + + it("does not poll when live tail is off", () => { + expect(getLiveTailRefetchInterval(false, 1)).toBe(false); + }); + + it("does not poll when not on page 1, even with live tail on", () => { + expect(getLiveTailRefetchInterval(true, 2)).toBe(false); + }); }); diff --git a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx index 8f916999c16..d9830a83f6e 100644 --- a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx @@ -1,13 +1,28 @@ import moment from "moment"; -import { useCallback, useEffect, useState, useRef, useMemo } from "react"; +import { useEffect, useMemo, useState } from "react"; import { uiSpendLogsCall } from "../networking"; import { Team } from "../key_team_helpers/key_list"; -import { useQuery } from "@tanstack/react-query"; +import { keepPreviousData, useQuery } from "@tanstack/react-query"; import { fetchAllTeams } from "../../components/key_team_helpers/filter_helpers"; -import { debounce } from "lodash"; import { defaultPageSize } from "../constants"; -import { PaginatedResponse } from "."; -import type { LogsSortField } from "./columns"; +import type { LogEntry, LogsSortField } from "./columns"; + +export interface PaginatedResponse { + data: LogEntry[]; + total: number; + page: number; + page_size: number; + total_pages: number; +} + +function useDebouncedValue(value: T, delayMs: number): [T, React.Dispatch>] { + const [debounced, setDebounced] = useState(value); + useEffect(() => { + const timer = setTimeout(() => setDebounced(value), delayMs); + return () => clearTimeout(timer); + }, [value, delayMs]); + return [debounced, setDebounced]; +} /** Spend log `model` column (LLM public model name or `search_tool_name` for /search). */ export const FILTER_KEYS = { @@ -28,324 +43,188 @@ export const FILTER_KEYS = { export type FilterKey = keyof typeof FILTER_KEYS; export type LogFilterState = Record<(typeof FILTER_KEYS)[FilterKey], string>; +// Keys whose UI is a free-form text input; only these need debouncing. +const TEXT_FILTER_KEYS: readonly (keyof LogFilterState)[] = [ + FILTER_KEYS.KEY_HASH, + FILTER_KEYS.ERROR_MESSAGE, + FILTER_KEYS.REQUEST_ID, + FILTER_KEYS.USER_ID, + FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL, +]; + +// Live-tail polls every 15s, but only on page 1 (newest) while live tail is on. +export const LIVE_TAIL_INTERVAL_MS = 15000; +export const getLiveTailRefetchInterval = (isLiveTail: boolean, currentPage: number): number | false => + isLiveTail && currentPage === 1 ? LIVE_TAIL_INTERVAL_MS : false; + +export const defaultFilters: LogFilterState = { + [FILTER_KEYS.TEAM_ID]: "", + [FILTER_KEYS.KEY_HASH]: "", + [FILTER_KEYS.REQUEST_ID]: "", + [FILTER_KEYS.MODEL]: "", + [FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]: "", + [FILTER_KEYS.USER_ID]: "", + [FILTER_KEYS.END_USER]: "", + [FILTER_KEYS.STATUS]: "", + [FILTER_KEYS.KEY_ALIAS]: "", + [FILTER_KEYS.ERROR_CODE]: "", + [FILTER_KEYS.ERROR_MESSAGE]: "", +}; + export function useLogFilterLogic({ - logs, accessToken, - startTime, // Receive from SpendLogsTable - endTime, // Receive from SpendLogsTable + token, + userRole, + userID, + filters, + setFilters, + filterByCurrentUser, + activeTab, + isLiveTail, + startTime, + endTime, pageSize = defaultPageSize, isCustomDate, setCurrentPage, - userID, - userRole, sortBy = "startTime", sortOrder = "desc", currentPage = 1, }: { - logs: PaginatedResponse; accessToken: string | null; + token: string | null; + userRole: string | null; + userID: string | null; + filters: LogFilterState; + setFilters: React.Dispatch>; + filterByCurrentUser: boolean | null; + activeTab: string; + isLiveTail: boolean; startTime: string; endTime: string; pageSize?: number; isCustomDate: boolean; setCurrentPage: (page: number) => void; - userID: string | null; - userRole: string | null; sortBy?: LogsSortField; sortOrder?: "asc" | "desc"; currentPage?: number; }) { - const defaultFilters = useMemo( - () => ({ - [FILTER_KEYS.TEAM_ID]: "", - [FILTER_KEYS.KEY_HASH]: "", - [FILTER_KEYS.REQUEST_ID]: "", - [FILTER_KEYS.MODEL]: "", - [FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]: "", - [FILTER_KEYS.USER_ID]: "", - [FILTER_KEYS.END_USER]: "", - [FILTER_KEYS.STATUS]: "", - [FILTER_KEYS.KEY_ALIAS]: "", - [FILTER_KEYS.ERROR_CODE]: "", - [FILTER_KEYS.ERROR_MESSAGE]: "", - }), - [], - ); + const [debouncedFilters, setDebouncedFilters] = useDebouncedValue(filters, 300); - const [filters, setFilters] = useState(defaultFilters); - const [backendFilteredLogs, setBackendFilteredLogs] = useState(null); - const lastSearchTimestamp = useRef(0); + // Live values for dropdown keys, debounced for text keys. + const effectiveFilters = useMemo(() => { + const merged = { ...filters }; + for (const k of TEXT_FILTER_KEYS) { + merged[k] = debouncedFilters[k]; + } + return merged; + }, [filters, debouncedFilters]); - // Refs that always hold the latest filters and hasBackendFilters values. - // The sort/page/time effect below intentionally omits these from its dep array - // to avoid double-fetches when a filter changes; reading from refs instead of - // the closure prevents stale-closure bugs (e.g. the effect using a snapshot of - // filters taken before the user selected Key Alias). - const filtersRef = useRef(filters); - const hasBackendFiltersRef = useRef(false); - const performSearch = useCallback( - async (filters: LogFilterState, page = 1) => { - if (!accessToken) return; - - console.log("Filters being sent to API:", filters); - const currentTimestamp = Date.now(); - lastSearchTimestamp.current = currentTimestamp; + const logsQuery = useQuery({ + queryKey: [ + "logs", + "table", + currentPage, + pageSize, + startTime, + endTime, + isCustomDate, + effectiveFilters, + filterByCurrentUser ? userID : null, + sortBy, + sortOrder, + ], + queryFn: async () => { + if (!accessToken || !token || !userRole || !userID) { + return { + data: [], + total: 0, + page: 1, + page_size: pageSize, + total_pages: 0, + }; + } const formattedStartTime = moment(startTime).utc().format("YYYY-MM-DD HH:mm:ss"); const formattedEndTime = isCustomDate ? moment(endTime).utc().format("YYYY-MM-DD HH:mm:ss") : moment().utc().format("YYYY-MM-DD HH:mm:ss"); - try { - const response = await uiSpendLogsCall({ - accessToken, - start_date: formattedStartTime, - end_date: formattedEndTime, - page, - page_size: pageSize, - params: { - api_key: filters[FILTER_KEYS.KEY_HASH] || undefined, - team_id: filters[FILTER_KEYS.TEAM_ID] || undefined, - request_id: filters[FILTER_KEYS.REQUEST_ID] || undefined, - user_id: filters[FILTER_KEYS.USER_ID] || undefined, - end_user: filters[FILTER_KEYS.END_USER] || undefined, - status_filter: filters[FILTER_KEYS.STATUS] || undefined, - model_id: filters[FILTER_KEYS.MODEL] || undefined, - model: filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL] || undefined, - key_alias: filters[FILTER_KEYS.KEY_ALIAS] || undefined, - error_code: filters[FILTER_KEYS.ERROR_CODE] || undefined, - error_message: filters[FILTER_KEYS.ERROR_MESSAGE] || undefined, - sort_by: sortBy, - sort_order: sortOrder, - }, - }); + const response = await uiSpendLogsCall({ + accessToken, + start_date: formattedStartTime, + end_date: formattedEndTime, + page: currentPage, + page_size: pageSize, + params: { + api_key: effectiveFilters[FILTER_KEYS.KEY_HASH] || undefined, + team_id: effectiveFilters[FILTER_KEYS.TEAM_ID] || undefined, + request_id: effectiveFilters[FILTER_KEYS.REQUEST_ID] || undefined, + user_id: effectiveFilters[FILTER_KEYS.USER_ID] || (filterByCurrentUser ? userID ?? undefined : undefined), + end_user: effectiveFilters[FILTER_KEYS.END_USER] || undefined, + status_filter: effectiveFilters[FILTER_KEYS.STATUS] || undefined, + model_id: effectiveFilters[FILTER_KEYS.MODEL] || undefined, + model: effectiveFilters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL] || undefined, + key_alias: effectiveFilters[FILTER_KEYS.KEY_ALIAS] || undefined, + error_code: effectiveFilters[FILTER_KEYS.ERROR_CODE] || undefined, + error_message: effectiveFilters[FILTER_KEYS.ERROR_MESSAGE] || undefined, + sort_by: sortBy, + sort_order: sortOrder, + }, + }); - if (currentTimestamp === lastSearchTimestamp.current) { - setBackendFilteredLogs({ - ...response, - data: response.data ?? [], - }); - } - } catch (error) { - console.error("Error searching users:", error); - setBackendFilteredLogs({ - data: [], - total: 0, - page: 1, - page_size: pageSize, - total_pages: 0, - }); - } + return response; }, - [accessToken, startTime, endTime, isCustomDate, pageSize, sortBy, sortOrder], - ); + enabled: !!accessToken && !!token && !!userRole && !!userID && activeTab === "request logs", + refetchInterval: getLiveTailRefetchInterval(isLiveTail, currentPage), + placeholderData: keepPreviousData, + // Only live-tail-poll while the tab is visible. + refetchIntervalInBackground: false, + }); - const debouncedSearch = useMemo( - () => debounce((filters: LogFilterState, page: number) => performSearch(filters, page), 300), - [performSearch], - ); + const filteredLogs: PaginatedResponse = logsQuery.data ?? { + data: [], + total: 0, + page: 1, + page_size: pageSize, + total_pages: 0, + }; - useEffect(() => { - return () => debouncedSearch.cancel(); - }, [debouncedSearch]); - - // Determine when backend filters are active (server-side filtering) - const hasBackendFilters = useMemo( - () => - !!( - filters[FILTER_KEYS.KEY_ALIAS] || - filters[FILTER_KEYS.KEY_HASH] || - filters[FILTER_KEYS.REQUEST_ID] || - filters[FILTER_KEYS.USER_ID] || - filters[FILTER_KEYS.END_USER] || - filters[FILTER_KEYS.ERROR_CODE] || - filters[FILTER_KEYS.ERROR_MESSAGE] || - filters[FILTER_KEYS.MODEL] || - filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL] - ), - [filters], - ); - - // Keep refs in sync on every render so the sort/page/time effect always reads - // the latest values without those values being in its dep array. - useEffect(() => { - filtersRef.current = filters; - hasBackendFiltersRef.current = hasBackendFilters; - }, [filters, hasBackendFilters]); - - // Refetch when sort, page, or time range changes (backend filters use their own fetch, not the main query) - useEffect(() => { - if (hasBackendFiltersRef.current && accessToken) { - // Cancel any pending debounced search to prevent it from overwriting this page's results - debouncedSearch.cancel(); - performSearch(filtersRef.current, currentPage); - } - // filters / hasBackendFilters are read via refs — avoids stale-closure bugs - // when sort/page/time changes after a filter (e.g. Key Alias) was set. - // debouncedSearch / performSearch: filter changes go through handleFilterChange - // → debouncedSearch; adding them here would cause double-fetches on filter apply. - // accessToken: stable across sort/page/time changes. - // eslint-disable-next-line react-hooks/exhaustive-deps - }, [sortBy, sortOrder, currentPage, startTime, endTime, isCustomDate]); - - // Compute client-side filtered logs directly from incoming logs and filters - const clientDerivedFilteredLogs: PaginatedResponse = useMemo(() => { - if (!logs || !logs.data) { - return { - data: [], - total: 0, - page: 1, - page_size: pageSize, - total_pages: 0, - }; - } - - // If backend filters are on, don't perform client-side filtering here - if (hasBackendFilters) { - return logs; - } - - let filteredData = [...logs.data]; - - if (filters[FILTER_KEYS.TEAM_ID]) { - filteredData = filteredData.filter((log) => log.team_id === filters[FILTER_KEYS.TEAM_ID]); - } - - if (filters[FILTER_KEYS.STATUS]) { - filteredData = filteredData.filter((log) => { - if (filters[FILTER_KEYS.STATUS] === "success") { - return !log.status || log.status === "success"; - } - return log.status === filters[FILTER_KEYS.STATUS]; - }); - } - - if (filters[FILTER_KEYS.MODEL]) { - filteredData = filteredData.filter((log) => log.model_id === filters[FILTER_KEYS.MODEL]); - } - - if (filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]) { - const m = filters[FILTER_KEYS.PUBLIC_MODEL_OR_SEARCH_TOOL]; - filteredData = filteredData.filter((log) => log.model === m); - } - - if (filters[FILTER_KEYS.KEY_HASH]) { - filteredData = filteredData.filter((log) => log.api_key === filters[FILTER_KEYS.KEY_HASH]); - } - - if (filters[FILTER_KEYS.END_USER]) { - filteredData = filteredData.filter((log) => log.end_user === filters[FILTER_KEYS.END_USER]); - } - - if (filters[FILTER_KEYS.ERROR_CODE]) { - filteredData = filteredData.filter((log) => { - const metadata = log.metadata || {}; - const errorInfo = metadata.error_information; - return errorInfo && errorInfo.error_code === filters[FILTER_KEYS.ERROR_CODE]; - }); - } - - return { - data: filteredData, - total: logs.total, - page: logs.page, - page_size: logs.page_size, - total_pages: logs.total_pages, - }; - }, [logs, filters, hasBackendFilters]); - - // Choose which filtered logs to expose: backend result when active, otherwise client-derived - const filteredLogs: PaginatedResponse = useMemo(() => { - if (hasBackendFilters) { - // When backend filters are active, only show backend results. - // If search hasn't completed yet (null), show empty state rather than - // falling back to unfiltered logs — that caused filtered views to - // display mismatched data when the filter matched zero rows. - if (backendFilteredLogs !== null) { - return backendFilteredLogs; - } - return { - data: [], - total: 0, - page: 1, - page_size: pageSize, - total_pages: 0, - }; - } - return clientDerivedFilteredLogs; - }, [hasBackendFilters, backendFilteredLogs, clientDerivedFilteredLogs]); - - // Fetch all teams and users for potential filter dropdowns (optional, can be adapted) const { data: allTeams } = useQuery({ queryKey: ["allTeamsForLogFilters", accessToken], queryFn: async () => { if (!accessToken) return []; - // Use fetchAllTeams helper function for consistency and abstraction - // Assuming fetchAllTeams returns Team[] directly const teamsData = await fetchAllTeams(accessToken); - return teamsData || []; // Ensure it returns an array + return teamsData || []; }, enabled: !!accessToken, }); - // Update filters state const handleFilterChange = (newFilters: Partial) => { setFilters((prev) => { const updatedFilters = { ...prev, ...newFilters }; - - // Ensure all keys in LogFilterState are present, defaulting to '' if not in newFilters for (const key of Object.keys(defaultFilters) as Array) { if (!(key in updatedFilters)) { updatedFilters[key] = defaultFilters[key]; } } - - // Only call debouncedSearch if filters have actually changed if (JSON.stringify(updatedFilters) !== JSON.stringify(prev)) { setCurrentPage(1); - setBackendFilteredLogs(null); - debouncedSearch(updatedFilters, 1); } - return updatedFilters as LogFilterState; }); }; const handleFilterReset = () => { - // Reset filters state setFilters(defaultFilters); - - // Clear backend filtered logs to ensure fresh render - setBackendFilteredLogs(null); - - // Cancel any in-flight debounced search - debouncedSearch.cancel(); - - // Reset to first page so the unfiltered view starts at page 1 + setDebouncedFilters(defaultFilters); setCurrentPage(1); }; - // Expose a filter-aware refetch so callers (e.g. the manual Fetch button) can - // refresh results while keeping all active backend filters intact. The plain - // `logs.refetch()` in the parent only re-runs the main TanStack Query, which - // does not carry key_alias or other backend-only filter params. - const refetchWithFilters = useCallback( - (page = currentPage) => { - if (hasBackendFilters && accessToken) { - debouncedSearch.cancel(); - performSearch(filters, page); - } - }, - [hasBackendFilters, accessToken, filters, currentPage, performSearch, debouncedSearch], - ); - return { - filters, + logsQuery, filteredLogs, - hasBackendFilters, allTeams, handleFilterChange, handleFilterReset, - refetchWithFilters, }; } diff --git a/ui/litellm-dashboard/src/components/view_logs/logs_utils.test.tsx b/ui/litellm-dashboard/src/components/view_logs/logs_utils.test.tsx new file mode 100644 index 00000000000..b0b74df4c24 --- /dev/null +++ b/ui/litellm-dashboard/src/components/view_logs/logs_utils.test.tsx @@ -0,0 +1,45 @@ +import moment from "moment"; +import { describe, expect, it } from "vitest"; +import { getTimeRangeDisplay } from "./logs_utils"; + +// startTime built relative to "now"; getTimeRangeDisplay computes now() internally. +const ago = (amount: number, unit: moment.unitOfTime.DurationConstructor) => + moment().subtract(amount, unit).toISOString(); + +describe("getTimeRangeDisplay", () => { + it("labels a ~1-minute window as 'Last 1 Minute'", () => { + expect(getTimeRangeDisplay(false, ago(1, "minutes"), "")).toBe("Last 1 Minute"); + }); + + it("labels a ~10-minute window as 'Last 15 Minutes'", () => { + expect(getTimeRangeDisplay(false, ago(10, "minutes"), "")).toBe("Last 15 Minutes"); + }); + + it("labels a ~30-minute window as 'Last Hour'", () => { + expect(getTimeRangeDisplay(false, ago(30, "minutes"), "")).toBe("Last Hour"); + }); + + it("labels a ~2-hour window as 'Last 4 Hours'", () => { + expect(getTimeRangeDisplay(false, ago(2, "hours"), "")).toBe("Last 4 Hours"); + }); + + it("labels a ~10-hour window as 'Last 24 Hours'", () => { + expect(getTimeRangeDisplay(false, ago(10, "hours"), "")).toBe("Last 24 Hours"); + }); + + it("labels a ~3-day window as 'Last 7 Days'", () => { + expect(getTimeRangeDisplay(false, ago(3, "days"), "")).toBe("Last 7 Days"); + }); + + it("falls back to a 'MMM D - MMM D' range beyond 7 days", () => { + const label = getTimeRangeDisplay(false, ago(30, "days"), ""); + expect(label).toMatch(/^[A-Z][a-z]{2} \d{1,2} - [A-Z][a-z]{2} \d{1,2}$/); + }); + + it("renders an explicit start - end range when isCustomDate is true", () => { + const start = "2025-01-02T03:04:00Z"; + const end = "2025-01-05T06:07:00Z"; + const expected = `${moment(start).format("MMM D, h:mm A")} - ${moment(end).format("MMM D, h:mm A")}`; + expect(getTimeRangeDisplay(true, start, end)).toBe(expected); + }); +}); diff --git a/ui/litellm-dashboard/tests/view_logs/useLogFilterLogic.min.test.tsx b/ui/litellm-dashboard/tests/view_logs/useLogFilterLogic.min.test.tsx deleted file mode 100644 index faede84f4c2..00000000000 --- a/ui/litellm-dashboard/tests/view_logs/useLogFilterLogic.min.test.tsx +++ /dev/null @@ -1,62 +0,0 @@ -import React from "react"; -import { describe, it, expect } from "vitest"; -import { render, screen } from "@testing-library/react"; -import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; -import { useLogFilterLogic } from "../../src/components/view_logs/log_filter_logic"; - -// Minimal mocks to avoid real network during hook init -vi.mock("../../src/components/key_team_helpers/filter_helpers", () => ({ - fetchAllKeyAliases: vi.fn().mockResolvedValue([]), - fetchAllTeams: vi.fn().mockResolvedValue([]), -})); - -const createQueryClient = () => - new QueryClient({ - defaultOptions: { queries: { retry: false, gcTime: 0 } }, - }); - -function Harness({ logs }: { logs: any }) { - const { filteredLogs } = useLogFilterLogic({ - logs, - accessToken: "token", - startTime: "2025-01-01 00:00:00", - endTime: "2025-01-02 00:00:00", - pageSize: 50, - isCustomDate: true, - setCurrentPage: () => {}, - userID: "user-1", - userRole: "admin", - }); - - return
{filteredLogs.data.length}
; -} - -describe("useLogFilterLogic (minimal)", () => { - it("useLogFilterLogic minimal: updates filteredLogs when logs change", async () => { - const qc = createQueryClient(); - const logsA = { data: [{ request_id: "a" }], total: 1, page: 1, page_size: 50, total_pages: 1 }; - const logsB = { - data: [{ request_id: "a" }, { request_id: "b" }], - total: 2, - page: 1, - page_size: 50, - total_pages: 1, - }; - - const { rerender } = render( - - - , - ); - - expect(await screen.findByTestId("count")).toHaveTextContent("1"); - - rerender( - - - , - ); - - expect(await screen.findByTestId("count")).toHaveTextContent("2"); - }); -}); diff --git a/uv.lock b/uv.lock index 5e2311a9b1f..cafb6664958 100644 --- a/uv.lock +++ b/uv.lock @@ -3189,7 +3189,7 @@ wheels = [ [[package]] name = "litellm" -version = "1.85.0" +version = "1.87.0" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -3534,12 +3534,12 @@ proxy-dev = [ [[package]] name = "litellm-enterprise" -version = "0.1.40" +version = "0.1.41" source = { editable = "enterprise" } [[package]] name = "litellm-proxy-extras" -version = "0.4.72" +version = "0.4.73" source = { editable = "litellm-proxy-extras" } [[package]] @@ -4298,7 +4298,7 @@ wheels = [ [[package]] name = "openai" -version = "2.36.0" +version = "2.33.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -4310,9 +4310,9 @@ dependencies = [ { name = "tqdm" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/f4/a1/4d5e84cf51720fc1526cc49e10ac1961abcccb55b0efb3d970db1e9a2728/openai-2.36.0.tar.gz", hash = "sha256:139dea0edd2f1b30c33d46ae1a6929e03906254140318e4608e98fe8c566f2e7", size = 753003, upload-time = "2026-05-07T17:33:17.075Z" } +sdist = { url = "https://files.pythonhosted.org/packages/f0/ee/d056c82f63c05f06baac0cffb4a90952d8274f90c49dfe244f20497b9bbd/openai-2.33.0.tar.gz", hash = "sha256:f850c435e2a4685bba3295bd54912dd26315d9c1b7733068186134d6e0599f9a", size = 693254, upload-time = "2026-04-28T14:04:42.428Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/9d/1c/5d43735b2553baae2a5e899dcbcd0670a86930d993184d72ca909bf11c9b/openai-2.36.0-py3-none-any.whl", hash = "sha256:143f6194b548dbc2c921af1f1b03b9f14c85fed8a75b5b516f5bcc11a2a50c63", size = 1302361, upload-time = "2026-05-07T17:33:15.063Z" }, + { url = "https://files.pythonhosted.org/packages/7d/32/37734d769bc8b42e4938785313cc05aade6cb0fa72479d3220a0d61a4e78/openai-2.33.0-py3-none-any.whl", hash = "sha256:03ac37d70e8c9e3a8124214e3afa785e2cbc12e627fbd98177a086ef2fd87ad5", size = 1162695, upload-time = "2026-04-28T14:04:40.482Z" }, ] [[package]]