add test_function_calling_with_tool_response to base llm tests

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -15,6 +15,12 @@ from litellm import verbose_logger
 from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client
 from litellm.types.llms.anthropic import *
 from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
+from litellm.types.llms.bedrock import ToolBlock as BedrockToolBlock
+from litellm.types.llms.bedrock import (
+    ToolInputSchemaBlock as BedrockToolInputSchemaBlock,
+)
+from litellm.types.llms.bedrock import ToolJsonSchemaBlock as BedrockToolJsonSchemaBlock
+from litellm.types.llms.bedrock import ToolSpecBlock as BedrockToolSpecBlock
 from litellm.types.llms.custom_http import httpxSpecialProvider
 from litellm.types.llms.ollama import OllamaVisionModelObject
 from litellm.types.llms.openai import (
@@ -1041,10 +1047,10 @@ def convert_to_gemini_tool_call_invoke(
         if tool_calls is not None:
             for tool in tool_calls:
                 if "function" in tool:
-                    gemini_function_call: Optional[
+                    gemini_function_call: Optional[VertexFunctionCall] = (
-                        VertexFunctionCall
+                        _gemini_tool_call_invoke_helper(
-                    ] = _gemini_tool_call_invoke_helper(
+                            function_call_params=tool["function"]
-                        function_call_params=tool["function"]
+                        )
                     )
                     if gemini_function_call is not None:
                         _parts_list.append(
@@ -1139,7 +1145,7 @@ def convert_to_gemini_tool_call_result(
 
 
 def convert_to_anthropic_tool_result(
-    message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage]
+    message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage],
 ) -> AnthropicMessagesToolResultParam:
     """
     OpenAI message with a tool result looks like:
@@ -1449,9 +1455,9 @@ def anthropic_messages_pt(  # noqa: PLR0915
                             )
 
                             if "cache_control" in _content_element:
-                                _anthropic_content_element[
+                                _anthropic_content_element["cache_control"] = (
-                                    "cache_control"
+                                    _content_element["cache_control"]
-                                ] = _content_element["cache_control"]
+                                )
                             user_content.append(_anthropic_content_element)
                         elif m.get("type", "") == "text":
                             m = cast(ChatCompletionTextObject, m)
@@ -1502,9 +1508,9 @@ def anthropic_messages_pt(  # noqa: PLR0915
                     )
 
                     if "cache_control" in _content_element:
-                        _anthropic_content_text_element[
+                        _anthropic_content_text_element["cache_control"] = (
-                            "cache_control"
+                            _content_element["cache_control"]
-                        ] = _content_element["cache_control"]
+                        )
 
                     user_content.append(_anthropic_content_text_element)
 
@@ -2491,7 +2497,7 @@ def _convert_to_bedrock_tool_call_invoke(
 
 
 def _convert_to_bedrock_tool_call_result(
-    message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage]
+    message: Union[ChatCompletionToolMessage, ChatCompletionFunctionMessage],
 ) -> BedrockContentBlock:
     """
     OpenAI message with a tool result looks like:
@@ -2664,7 +2670,7 @@ def get_user_message_block_or_continue_message(
 def return_assistant_continue_message(
     assistant_continue_message: Optional[
         Union[str, ChatCompletionAssistantMessage]
-    ] = None
+    ] = None,
 ) -> ChatCompletionAssistantMessage:
     if assistant_continue_message and isinstance(assistant_continue_message, str):
         return ChatCompletionAssistantMessage(
@@ -3462,7 +3468,13 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
         for _, value in defs_copy.items():
             unpack_defs(value, defs_copy)
         unpack_defs(parameters, defs_copy)
-        tool_input_schema = BedrockToolInputSchemaBlock(json=parameters)
+        tool_input_schema = BedrockToolInputSchemaBlock(
+            json=BedrockToolJsonSchemaBlock(
+                type=parameters.get("type", ""),
+                properties=parameters.get("properties", {}),
+                required=parameters.get("required", []),
+            )
+        )
         tool_spec = BedrockToolSpecBlock(
             inputSchema=tool_input_schema, name=name, description=description
         )

litellm/types/llms/bedrock.py

@@ -125,8 +125,19 @@ class ConverseResponseBlock(TypedDict):
     usage: ConverseTokenUsageBlock
 
 
+class ToolJsonArgsBlock(TypedDict, total=False):
+    type: str
+    description: str
+
+
+class ToolJsonSchemaBlock(TypedDict, total=False):
+    type: Literal["object"]
+    properties: dict
+    required: List[str]
+
+
 class ToolInputSchemaBlock(TypedDict):
-    json: Optional[dict]
+    json: Optional[ToolJsonSchemaBlock]
 
 
 class ToolSpecBlock(TypedDict, total=False):

tests/llm_translation/base_llm_unit_tests.py

@@ -1037,6 +1037,7 @@ class BaseLLMChatTest(ABC):
     def test_function_calling_with_tool_response(self):
         from litellm.utils import supports_function_calling
         from litellm import completion
+        litellm._turn_on_debug()
 
         os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
         litellm.model_cost = litellm.get_model_cost_map(url="")
@@ -1056,6 +1057,7 @@ class BaseLLMChatTest(ABC):
                     "name": "get_weather",
                     "description": "Get the weather in a city",
                     "parameters": {
+                        "$id": "https://some/internal/name",
                         "type": "object",
                         "properties": {
                             "city": {

tests/llm_translation/test_bedrock_completion.py

@@ -2226,6 +2226,28 @@ class TestBedrockConverseChatNormal(BaseLLMChatTest):
         """
         pass
 
+class TestBedrockConverseNovaTestSuite(BaseLLMChatTest):
+    def get_base_completion_call_args(self) -> dict:
+        os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+        litellm.model_cost = litellm.get_model_cost_map(url="")
+        litellm.add_known_models()
+        return {
+            "model": "bedrock/us.amazon.nova-lite-v1:0",
+            "aws_region_name": "us-east-1",
+        }
+
+    def test_tool_call_no_arguments(self, tool_call_no_arguments):
+        """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
+        pass
+
+    def test_multilingual_requests(self):
+        """
+        Bedrock API raises a 400 BadRequest error when the request contains invalid utf-8 sequences.
+
+        Todo: if litellm.modify_params is True ensure it's a valid utf-8 sequence
+        """
+        pass
+
 
 class TestBedrockRerank(BaseLLMRerankTest):
     def get_custom_llm_provider(self) -> litellm.LlmProviders: