QA: ensure all bedrock regional models have same supported_ as base + Anthropic nested pydantic object support (#7844)

* build: ensure all regional bedrock models have same supported values as base bedrock model

prevents drift

* test(base_llm_unit_tests.py): add testing for nested pydantic objects

* fix(test_utils.py): add test_get_potential_model_names

* fix(anthropic/chat/transformation.py): support nested pydantic objects

Fixes https://github.com/BerriAI/litellm/issues/7755

litellm/llms/base_llm/base_utils.py

@@ -1,5 +1,8 @@
 from abc import ABC, abstractmethod
-from typing import List, Optional
+from typing import List, Optional, Type, Union
+
+from openai.lib import _parsing, _pydantic
+from pydantic import BaseModel
 
 from litellm.types.utils import ModelInfoBase
 
@@ -26,3 +29,39 @@ class BaseLLMModelInfo(ABC):
     @abstractmethod
     def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
         pass
+
+
+def type_to_response_format_param(
+    response_format: Optional[Union[Type[BaseModel], dict]],
+    ref_template: Optional[str] = None,
+) -> Optional[dict]:
+    """
+    Re-implementation of openai's 'type_to_response_format_param' function
+
+    Used for converting pydantic object to api schema.
+    """
+    if response_format is None:
+        return None
+
+    if isinstance(response_format, dict):
+        return response_format
+
+    # type checkers don't narrow the negation of a `TypeGuard` as it isn't
+    # a safe default behaviour but we know that at this point the `response_format`
+    # can only be a `type`
+    if not _parsing._completions.is_basemodel_type(response_format):
+        raise TypeError(f"Unsupported response_format type - {response_format}")
+
+    if ref_template is not None:
+        schema = response_format.model_json_schema(ref_template=ref_template)
+    else:
+        schema = _pydantic.to_strict_json_schema(response_format)
+
+    return {
+        "type": "json_schema",
+        "json_schema": {
+            "schema": schema,
+            "name": response_format.__name__,
+            "strict": True,
+        },
+    }

litellm/llms/base_llm/chat/transformation.py

@@ -4,13 +4,25 @@ Common base config for all LLM providers
 
 import types
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterator,
+    Iterator,
+    List,
+    Optional,
+    Type,
+    Union,
+)
 
 import httpx
+from pydantic import BaseModel
 
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import ModelResponse
 
+from ..base_utils import type_to_response_format_param
+
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
 
@@ -71,6 +83,11 @@ class BaseConfig(ABC):
             and v is not None
         }
 
+    def get_json_schema_from_pydantic_object(
+        self, response_format: Optional[Union[Type[BaseModel], dict]]
+    ) -> Optional[dict]:
+        return type_to_response_format_param(response_format=response_format)
+
     def should_fake_stream(
         self,
         model: Optional[str],