Merge pull request #50849 from BerriAI/litellm_rc109_beta_header_test_bundled_config

test: repair four failing tests on rc/1.109.0

.circleci/scripts/run_integration.sh

@@ -255,6 +255,7 @@ elif [ "${INTEGRATION_COVERAGE:-0}" = 1 ]; then
 fi
 
 env -i PATH="$PATH" HOME="$HOME" PYTHONPATH="$PYTHONPATH" \
+  UV_OFFLINE=true UV_CACHE_DIR="$(uv cache dir)" \
   INTEGRATION_RUN_ID="$integration_identity" \
   DATABASE_URL="$DATABASE_URL" \
   "${shared_services[@]}" "${worker_coverage[@]}" \

enterprise/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "litellm-enterprise"
-version = "0.1.81"
+version = "0.1.82"
 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 = "0.1.81"
+version = "0.1.82"
 version_files = [
     "pyproject.toml:^version",
     "../pyproject.toml:litellm-enterprise==",

litellm-proxy-extras/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "litellm-proxy-extras"
-version = "0.4.112"
+version = "0.4.113"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 readme = "README.md"
 requires-python = ">=3.9"
@@ -30,7 +30,7 @@ required-version = ">=0.10.9"
 module-root = ""
 
 [tool.commitizen]
-version = "0.4.112"
+version = "0.4.113"
 version_files = [
     "pyproject.toml:^version",
     "../pyproject.toml:litellm-proxy-extras==",

litellm/cost_calculator.py

@@ -155,6 +155,8 @@ from litellm.utils import (
     TranscriptionResponse,
     cached_cost_map_key,
     cached_get_model_info_helper,
+    get_model_info_for_pricing,
+    get_model_info_helper_for_pricing,
     mapped_model_fill_missing,
     model_info_with_pricing_overrides,
     runtime_model_cost_registrations,
@@ -379,7 +381,7 @@ def _per_second_pricing_cost(
     audio_seconds: float = 0.0,
 ) -> tuple[float, float] | None:
     try:
-        model_info: Final = cached_get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider)
+        model_info: Final = get_model_info_helper_for_pricing(model=model, custom_llm_provider=custom_llm_provider)
     except Exception:  # noqa: BLE001  # the lookup raises plain Exception for an unmapped model
         return None
     if _has_token_or_tiered_pricing(model_info) or not _bills_wall_clock_seconds(model_info):
@@ -600,7 +602,9 @@ def cost_per_token(
         model = model_without_prefix
 
     if is_decisions_call_type(call_type):
-        decisions_model_info: Final = cached_get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider)
+        decisions_model_info: Final = get_model_info_helper_for_pricing(
+            model=model, custom_llm_provider=custom_llm_provider
+        )
         decisions_overrides: Final = decisions_pricing_fields(
             litellm.model_cost.get(decisions_model_info["key"], {})  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # model cost map is dynamically loaded
         )
@@ -808,7 +812,7 @@ def cost_per_token(
             service_tier=service_tier,
         )
     else:
-        model_info: Final = cached_get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider)
+        model_info: Final = get_model_info_helper_for_pricing(model=model, custom_llm_provider=custom_llm_provider)
         if _has_token_or_tiered_pricing(model_info):
             return generic_cost_per_token(
                 model=model,
@@ -2604,7 +2608,7 @@ def _layered_ocr_pricing(*sources: Mapping[str, object] | None) -> OCRPricing:
 
 def _cost_map_model_info(model: str, custom_llm_provider: str | None) -> ModelInfo | None:
     try:
-        return litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)
+        return get_model_info_for_pricing(model=model, custom_llm_provider=custom_llm_provider)
     except Exception:
         return None
 

litellm/integrations/prometheus.py

@@ -3036,7 +3036,7 @@ class PrometheusLogger(CustomLogger):
 
     async def async_post_call_failure_hook(
         self,
-        request_data: dict,
+        request_data: dict[str, object],
         original_exception: Exception,
         user_api_key_dict: UserAPIKeyAuth,
         traceback_str: str | None = None,
@@ -3059,10 +3059,14 @@ class PrometheusLogger(CustomLogger):
         try:
             _tags: Final = StandardLoggingPayloadSetup.get_request_tags(
                 litellm_params=request_data,
-                proxy_server_request=request_data.get("proxy_server_request", {}),
+                proxy_server_request=_logged_fields(request_data.get("proxy_server_request")),
+            )
+            _metadata: Final = _logged_fields(request_data.get("metadata"))
+            model_id: Final = (
+                _logged_fields(_metadata.get("model_info")).get("id")
+                or _logged_fields(request_data.get("model_info")).get("id")
+                or _logged_fields(request_data.get("standard_logging_object")).get("model_id")
             )
-            _metadata: Final = request_data.get("metadata", {}) or {}
-            model_id: Final = _metadata.get("model_info", {}).get("id") or request_data.get("model_info", {}).get("id")
             rate_limit_category, rate_limit_type = self._extract_rate_limit_labels(original_exception)
             api_provider: Final = self._extract_api_provider_from_request_data(
                 request_data

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -146,6 +146,10 @@ def handle_anthropic_text_model_custom_llm_provider(
     return model, custom_llm_provider
 
 
+def declared_provider(model: str | None, custom_llm_provider: str | None = None) -> str | None:
+    return custom_llm_provider or (model.split("/", 1)[0] if model and "/" in model else None)
+
+
 def declared_authenticating_provider(model: str | None, custom_llm_provider: str | None = None) -> str | None:
     """The authenticating provider this pair already names, or None.
 
@@ -154,7 +158,7 @@ def declared_authenticating_provider(model: str | None, custom_llm_provider: str
     and for a declared pair the resolver's answer is the declaration itself, so metadata callers
     adopt the declaration instead of resolving.
     """
-    declared: Final = custom_llm_provider or (model.split("/", 1)[0] if model and "/" in model else None)
+    declared: Final = declared_provider(model, custom_llm_provider)
     return declared if declared in PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO else None
 
 

litellm/litellm_core_utils/litellm_logging.py

@@ -211,7 +211,7 @@ from litellm.types.utils import (
     batch_chat_usage,
 )
 from litellm.types.videos.main import VideoObject
-from litellm.utils import get_base_model_from_metadata, print_verbose
+from litellm.utils import get_base_model_from_metadata, get_model_info_for_pricing, print_verbose
 
 from ..integrations.argilla import ArgillaLogger
 from ..integrations.arize.arize_phoenix import ArizePhoenixLogger
@@ -7113,7 +7113,7 @@ class StandardLoggingPayloadSetup:
             model_cost_information = StandardLoggingModelInformation(model_map_key="", model_map_value=None)
         else:
             try:
-                _model_cost_information: Final = litellm.get_model_info(
+                _model_cost_information: Final = get_model_info_for_pricing(
                     model=pricing_model_name or model_cost_name,
                     custom_llm_provider=custom_llm_provider,
                     api_base=api_base,

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -35,7 +35,7 @@ from litellm.types.utils import (
     Usage,
     text_tokens_without_nested_reasoning,
 )
-from litellm.utils import get_model_info
+from litellm.utils import get_model_info, get_model_info_for_pricing
 
 # Pre-resolved CallTypes enum values for fast membership checks
 _IMAGE_RESPONSE_CALL_TYPES: Final = frozenset(
@@ -1491,7 +1491,9 @@ def generic_cost_per_token(
     # per-deployment override: those are registered under the deployment id and kept off
     # the shared model-name key, so resolving from the name here reads the public rate.
     resolved_model_info: Final = apply_provider_cache_read_default(
-        get_model_info(model=model, custom_llm_provider=custom_llm_provider) if model_info is None else model_info,
+        get_model_info_for_pricing(model=model, custom_llm_provider=custom_llm_provider)
+        if model_info is None
+        else model_info,
         custom_llm_provider,
     )
 
@@ -1824,7 +1826,7 @@ def get_billed_token_rates(
         resolved_model_info: Final = apply_provider_cache_read_default(
             model_info
             if model_info is not None
-            else get_model_info(model=model, custom_llm_provider=custom_llm_provider),
+            else get_model_info_for_pricing(model=model, custom_llm_provider=custom_llm_provider),
             custom_llm_provider,
         )
     except Exception:  # noqa: BLE001  # get_model_info raises a bare Exception for an unmapped model: no rates

litellm/llms/base_llm/decisions/transformation.py

@@ -60,6 +60,7 @@ from litellm.types.decisions import (
     systemone_choice_key,
 )
 from litellm.types.utils import is_decisions_model_mode
+from litellm.utils import get_model_info_helper
 
 _PAYLOAD_ADAPTER: Final[TypeAdapter[object]] = TypeAdapter(object)
 _SYSTEMONE_RESPONSE_ADAPTER: Final[TypeAdapter[DecisionsResponse]] = TypeAdapter(DecisionsResponse)
@@ -500,7 +501,9 @@ def ir_to_systemone_response(response: DecisionsIRResponse, request: DecisionsIR
 
 def catalog_mode(model: str, custom_llm_provider: str) -> str | None:
     try:
-        key: Final = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)["key"]
+        key: Final = get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider, catalog_only=True)[
+            "key"
+        ]
     except Exception:  # noqa: BLE001  # get_model_info raises bare Exception for an unmapped model, which the catalog does not list
         return None
     entry: Final = GetModelCostMap.loaded_model_cost_map().get(key)
@@ -510,7 +513,9 @@ def catalog_mode(model: str, custom_llm_provider: str) -> str | None:
 
 def _is_decisions_model(model: str, custom_llm_provider: str) -> bool:
     try:
-        model_mode: Final = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider).get("mode")
+        model_mode: Final = get_model_info_helper(
+            model=model, custom_llm_provider=custom_llm_provider, catalog_only=True
+        ).get("mode")
     except Exception:  # noqa: BLE001  # get_model_info raises bare Exception for an unmapped model, which has no mode
         return False
     return is_decisions_model_mode(model_mode)

litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude_transformation.py

@@ -1,11 +1,13 @@
-from collections.abc import Mapping
+from collections.abc import Callable, Mapping
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
 from pydantic import TypeAdapter
 
+import litellm
 from litellm.anthropic_beta_gated_fields import strip_beta_gated_request_body
 from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers
+from litellm.constants import DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS
 from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import object_mapping
 from litellm.litellm_core_utils.prompt_templates.factory import (
     convert_to_anthropic_image_obj,
@@ -25,7 +27,9 @@ from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation
 )
 from litellm.llms.bedrock.common_utils import (
     BEDROCK_INVOKE_TRACE_FIELD,
+    BedrockModelInfo,
     apply_bedrock_invoke_structured_output,
+    bedrock_capability_model,
     bedrock_chat_uses_native_structured_output,
     bedrock_cleaned_output_schema,
     bedrock_supports_tool_search,
@@ -35,6 +39,7 @@ from litellm.llms.bedrock.common_utils import (
     normalize_custom_field_on_tools,
     normalize_tool_input_schema_types_for_bedrock_invoke,
     requests_opt_in_native_output,
+    strip_bedrock_routing_prefix,
     strip_unsupported_bedrock_invoke_output_config_keys,
     tools_without_eager_input_streaming,
 )
@@ -59,6 +64,19 @@ _NON_DEFAULT_PARAMS: Final = TypeAdapter(Mapping[str, object])
 _LITELLM_PARAMS: Final = TypeAdapter(Mapping[str, object])
 
 
+def _anthropic_config_max_tokens() -> int | None:
+    return litellm.AnthropicConfig.max_tokens
+
+
+def _mapped_max_tokens(model: str) -> int | None:
+    row: Final = object_mapping(litellm.model_cost.get(model)) or {}  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # cost map rows are untyped JSON
+    match row.get("max_output_tokens"):
+        case int() as max_tokens:
+            return max_tokens
+        case _:
+            return None
+
+
 class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
     """
     Reference:
@@ -79,9 +97,18 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
 
     anthropic_version: str = "bedrock-2023-05-31"
 
-    def __init__(self, model_info: Mapping[str, object] | None = None) -> None:
+    def __init__(
+        self,
+        model_info: Mapping[str, object] | None = None,
+        read_configured_max_tokens: Callable[[], int | None] = _anthropic_config_max_tokens,
+    ) -> None:
         super().__init__()
         self._deployment_model_info: Final = model_info
+        self._read_configured_max_tokens: Final = read_configured_max_tokens
+
+    @classmethod
+    def get_config(cls, *, model: str | None = None) -> dict[str, object]:  # mutable-ok: overrides BaseConfig's dict
+        return {key: value for key, value in super().get_config(model=model).items() if key != "max_tokens"}
 
     @property
     def custom_llm_provider(self) -> str | None:
@@ -241,6 +268,36 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
             headers=headers,
         )
 
+    def _default_max_tokens_models(self, model: str, litellm_params: Mapping[str, object]) -> tuple[str, ...]:
+        return (
+            BedrockModelInfo.get_base_model(
+                bedrock_capability_model(strip_bedrock_routing_prefix(model), litellm_params.get("base_model"))
+            ),
+        )
+
+    def _default_max_tokens(self, model: str, litellm_params: Mapping[str, object]) -> int:
+        configured_max_tokens: Final = self._read_configured_max_tokens()
+        match deployment_model_info(litellm_params).get("max_output_tokens"):
+            case int() as deployment_max_tokens:
+                return (
+                    deployment_max_tokens
+                    if configured_max_tokens is None
+                    else min(deployment_max_tokens, configured_max_tokens)
+                )
+            case _ if configured_max_tokens is not None:
+                return configured_max_tokens
+            case _:
+                return next(
+                    (
+                        max_tokens
+                        for max_tokens in map(
+                            _mapped_max_tokens, self._default_max_tokens_models(model, litellm_params)
+                        )
+                        if max_tokens is not None
+                    ),
+                    DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS,
+                )
+
     def _build_bedrock_anthropic_request_base(
         self,
         model: str,
@@ -257,13 +314,21 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
                 model=model,
                 output_config=filtered_params["output_config"],
             )
-        filtered_params = self._normalize_bedrock_tool_search_tools(filtered_params)
+        normalized_params: Final = self._normalize_bedrock_tool_search_tools(filtered_params)
+        default_max_tokens: Final = (
+            None
+            if "max_tokens" in normalized_params
+            else self._default_max_tokens(model, _LITELLM_PARAMS.validate_python(litellm_params))
+        )
+        request_params: Final = (
+            normalized_params if default_max_tokens is None else {**normalized_params, "max_tokens": default_max_tokens}
+        )
 
         anthropic_request: Final = AnthropicConfig.transform_request(
             self,
             model=model,
             messages=messages,
-            optional_params=filtered_params,
+            optional_params=request_params,
             litellm_params=litellm_params,
             headers=headers,
         )
@@ -377,7 +442,7 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
                     "data": image_chunk["data"],
                 }
 
-    def _normalize_bedrock_tool_search_tools(self, optional_params: dict) -> dict:
+    def _normalize_bedrock_tool_search_tools(self, optional_params: dict) -> dict[str, object]:
         """
         Convert tool search entries to the format supported by the Bedrock Invoke API.
         """

litellm/llms/bedrock/chat/mantle/transformation.py

@@ -54,6 +54,9 @@ class AmazonMantleConfig(AmazonAnthropicClaudeConfig):
             region=region,
         )
 
+    def _default_max_tokens_models(self, model: str, litellm_params: Mapping[str, object]) -> tuple[str, ...]:
+        return (f"bedrock_mantle/{model}", *super()._default_max_tokens_models(model, litellm_params))
+
     def validate_environment(
         self,
         headers: dict,

litellm/proxy/_experimental/out/__next.!KGRhc2hib2FyZCk.__PAGE__.txt

@@ -1,71 +1,76 @@
 1:"$Sreact.fragment"
 2:"/litellm-asset-prefix/_next/static/chunks/0a-rekt06v0sv.js"
-3:"/litellm-asset-prefix/_next/static/chunks/2_9r2jeem0dh8.js"
-4:"/litellm-asset-prefix/_next/static/chunks/0tx19jeq-wskb.js"
-5:I[363178,["$2","$3","$4"],"ThemeProvider"]
-6:I[12985,["$2","$3","$4"],"NuqsAdapter"]
-7:I[867271,["$2","$3","$4"],"default"]
-8:"TerminologyProvider"
-9:I[283634,["$2","$3","$4"],"$8"]
-a:I[656682,["$2","$3","$4"],"default"]
-b:I[557951,["$2","$3","$4"],"AuthProvider"]
-c:I[339756,["$2","$3","$4"],"default"]
-d:I[837457,["$2","$3","$4"],"default"]
-e:I[713354,["$2","$3","$4"],"Toaster"]
-18:"ViewportBoundary"
-19:I[897367,["$2","$3","$4"],"$18"]
-1a:"MetadataBoundary"
-1b:I[897367,["$2","$3","$4"],"$1a"]
-1c:"$Sreact.suspense"
-1d:I[27201,["$2","$3","$4"],"IconMark"]
+3:"/litellm-asset-prefix/_next/static/chunks/0whs1nv27w-4p.js"
+4:"/litellm-asset-prefix/_next/static/chunks/23z3yylt1gm8n.js"
+5:"/litellm-asset-prefix/_next/static/chunks/2xpjos3nb6yh4.js"
+6:I[419159,["$2","$3","$4","$5"],"default"]
+7:I[363178,["$2","$3","$4","$5"],"ThemeProvider"]
+8:I[12985,["$2","$3","$4","$5"],"NuqsAdapter"]
+9:I[867271,["$2","$3","$4","$5"],"default"]
+a:"TerminologyProvider"
+b:I[283634,["$2","$3","$4","$5"],"$a"]
+c:I[656682,["$2","$3","$4","$5"],"default"]
+d:I[557951,["$2","$3","$4","$5"],"AuthProvider"]
+e:I[339756,["$2","$3","$4","$5"],"default"]
+f:I[837457,["$2","$3","$4","$5"],"default"]
+10:I[713354,["$2","$3","$4","$5"],"Toaster"]
+1c:"ViewportBoundary"
+1d:I[897367,["$2","$3","$4","$5"],"$1c"]
+1e:"MetadataBoundary"
+1f:I[897367,["$2","$3","$4","$5"],"$1e"]
+20:"$Sreact.suspense"
+21:I[27201,["$2","$3","$4","$5"],"IconMark"]
 :HL["/litellm-asset-prefix/_next/static/chunks/1bnh8r0-prf7e.css","style"]
-:HL["/litellm-asset-prefix/_next/static/chunks/0598zsj78ni2h.css","style"]
-10:X
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