two version of R2R are hereHEADmaster

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diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/bedrock/chat/converse_transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/bedrock/chat/converse_transformation.py
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+++ b/.venv/lib/python3.12/site-packages/litellm/llms/bedrock/chat/converse_transformation.py
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+"""
+Translating between OpenAI's `/chat/completion` format and Amazon's `/converse` format
+"""
+
+import copy
+import time
+import types
+from typing import List, Literal, Optional, Tuple, Union, cast, overload
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    BedrockConverseMessagesProcessor,
+    _bedrock_converse_messages_pt,
+    _bedrock_tools_pt,
+)
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.types.llms.bedrock import *
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionResponseMessage,
+    ChatCompletionSystemMessage,
+    ChatCompletionThinkingBlock,
+    ChatCompletionToolCallChunk,
+    ChatCompletionToolCallFunctionChunk,
+    ChatCompletionToolParam,
+    ChatCompletionToolParamFunctionChunk,
+    ChatCompletionUserMessage,
+    OpenAIMessageContentListBlock,
+)
+from litellm.types.utils import ModelResponse, PromptTokensDetailsWrapper, Usage
+from litellm.utils import add_dummy_tool, has_tool_call_blocks
+
+from ..common_utils import BedrockError, BedrockModelInfo, get_bedrock_tool_name
+
+
+class AmazonConverseConfig(BaseConfig):
+    """
+    Reference - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html
+    #2 - https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features
+    """
+
+    maxTokens: Optional[int]
+    stopSequences: Optional[List[str]]
+    temperature: Optional[int]
+    topP: Optional[int]
+    topK: Optional[int]
+
+    def __init__(
+        self,
+        maxTokens: Optional[int] = None,
+        stopSequences: Optional[List[str]] = None,
+        temperature: Optional[int] = None,
+        topP: Optional[int] = None,
+        topK: Optional[int] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @property
+    def custom_llm_provider(self) -> Optional[str]:
+        return "bedrock_converse"
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        supported_params = [
+            "max_tokens",
+            "max_completion_tokens",
+            "stream",
+            "stream_options",
+            "stop",
+            "temperature",
+            "top_p",
+            "extra_headers",
+            "response_format",
+        ]
+
+        ## Filter out 'cross-region' from model name
+        base_model = BedrockModelInfo.get_base_model(model)
+
+        if (
+            base_model.startswith("anthropic")
+            or base_model.startswith("mistral")
+            or base_model.startswith("cohere")
+            or base_model.startswith("meta.llama3-1")
+            or base_model.startswith("meta.llama3-2")
+            or base_model.startswith("meta.llama3-3")
+            or base_model.startswith("amazon.nova")
+        ):
+            supported_params.append("tools")
+
+        if litellm.utils.supports_tool_choice(
+            model=model, custom_llm_provider=self.custom_llm_provider
+        ):
+            # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+            supported_params.append("tool_choice")
+
+        if (
+            "claude-3-7" in model
+        ):  # [TODO]: move to a 'supports_reasoning_content' param from model cost map
+            supported_params.append("thinking")
+        return supported_params
+
+    def map_tool_choice_values(
+        self, model: str, tool_choice: Union[str, dict], drop_params: bool
+    ) -> Optional[ToolChoiceValuesBlock]:
+        if tool_choice == "none":
+            if litellm.drop_params is True or drop_params is True:
+                return None
+            else:
+                raise litellm.utils.UnsupportedParamsError(
+                    message="Bedrock doesn't support tool_choice={}. To drop it from the call, set `litellm.drop_params = True.".format(
+                        tool_choice
+                    ),
+                    status_code=400,
+                )
+        elif tool_choice == "required":
+            return ToolChoiceValuesBlock(any={})
+        elif tool_choice == "auto":
+            return ToolChoiceValuesBlock(auto={})
+        elif isinstance(tool_choice, dict):
+            # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+            specific_tool = SpecificToolChoiceBlock(
+                name=tool_choice.get("function", {}).get("name", "")
+            )
+            return ToolChoiceValuesBlock(tool=specific_tool)
+        else:
+            raise litellm.utils.UnsupportedParamsError(
+                message="Bedrock doesn't support tool_choice={}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.".format(
+                    tool_choice
+                ),
+                status_code=400,
+            )
+
+    def get_supported_image_types(self) -> List[str]:
+        return ["png", "jpeg", "gif", "webp"]
+
+    def get_supported_document_types(self) -> List[str]:
+        return ["pdf", "csv", "doc", "docx", "xls", "xlsx", "html", "txt", "md"]
+
+    def get_all_supported_content_types(self) -> List[str]:
+        return self.get_supported_image_types() + self.get_supported_document_types()
+
+    def _create_json_tool_call_for_response_format(
+        self,
+        json_schema: Optional[dict] = None,
+        schema_name: str = "json_tool_call",
+        description: Optional[str] = None,
+    ) -> ChatCompletionToolParam:
+        """
+        Handles creating a tool call for getting responses in JSON format.
+
+        Args:
+            json_schema (Optional[dict]): The JSON schema the response should be in
+
+        Returns:
+            AnthropicMessagesTool: The tool call to send to Anthropic API to get responses in JSON format
+        """
+
+        if json_schema is None:
+            # Anthropic raises a 400 BadRequest error if properties is passed as None
+            # see usage with additionalProperties (Example 5) https://github.com/anthropics/anthropic-cookbook/blob/main/tool_use/extracting_structured_json.ipynb
+            _input_schema = {
+                "type": "object",
+                "additionalProperties": True,
+                "properties": {},
+            }
+        else:
+            _input_schema = json_schema
+
+        tool_param_function_chunk = ChatCompletionToolParamFunctionChunk(
+            name=schema_name, parameters=_input_schema
+        )
+        if description:
+            tool_param_function_chunk["description"] = description
+
+        _tool = ChatCompletionToolParam(
+            type="function",
+            function=tool_param_function_chunk,
+        )
+        return _tool
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+        messages: Optional[List[AllMessageValues]] = None,
+    ) -> dict:
+        for param, value in non_default_params.items():
+            if param == "response_format" and isinstance(value, dict):
+
+                ignore_response_format_types = ["text"]
+                if value["type"] in ignore_response_format_types:  # value is a no-op
+                    continue
+
+                json_schema: Optional[dict] = None
+                schema_name: str = ""
+                description: Optional[str] = None
+                if "response_schema" in value:
+                    json_schema = value["response_schema"]
+                    schema_name = "json_tool_call"
+                elif "json_schema" in value:
+                    json_schema = value["json_schema"]["schema"]
+                    schema_name = value["json_schema"]["name"]
+                    description = value["json_schema"].get("description")
+
+                if "type" in value and value["type"] == "text":
+                    continue
+
+                """
+                Follow similar approach to anthropic - translate to a single tool call. 
+
+                When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode
+                - You usually want to provide a single tool
+                - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool
+                - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective.
+                """
+                _tool = self._create_json_tool_call_for_response_format(
+                    json_schema=json_schema,
+                    schema_name=schema_name if schema_name != "" else "json_tool_call",
+                    description=description,
+                )
+                optional_params = self._add_tools_to_optional_params(
+                    optional_params=optional_params, tools=[_tool]
+                )
+                if litellm.utils.supports_tool_choice(
+                    model=model, custom_llm_provider=self.custom_llm_provider
+                ):
+                    optional_params["tool_choice"] = ToolChoiceValuesBlock(
+                        tool=SpecificToolChoiceBlock(
+                            name=schema_name if schema_name != "" else "json_tool_call"
+                        )
+                    )
+                optional_params["json_mode"] = True
+                if non_default_params.get("stream", False) is True:
+                    optional_params["fake_stream"] = True
+            if param == "max_tokens" or param == "max_completion_tokens":
+                optional_params["maxTokens"] = value
+            if param == "stream":
+                optional_params["stream"] = value
+            if param == "stop":
+                if isinstance(value, str):
+                    if len(value) == 0:  # converse raises error for empty strings
+                        continue
+                    value = [value]
+                optional_params["stopSequences"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["topP"] = value
+            if param == "tools" and isinstance(value, list):
+                optional_params = self._add_tools_to_optional_params(
+                    optional_params=optional_params, tools=value
+                )
+            if param == "tool_choice":
+                _tool_choice_value = self.map_tool_choice_values(
+                    model=model, tool_choice=value, drop_params=drop_params  # type: ignore
+                )
+                if _tool_choice_value is not None:
+                    optional_params["tool_choice"] = _tool_choice_value
+            if param == "thinking":
+                optional_params["thinking"] = value
+        return optional_params
+
+    @overload
+    def _get_cache_point_block(
+        self,
+        message_block: Union[
+            OpenAIMessageContentListBlock,
+            ChatCompletionUserMessage,
+            ChatCompletionSystemMessage,
+        ],
+        block_type: Literal["system"],
+    ) -> Optional[SystemContentBlock]:
+        pass
+
+    @overload
+    def _get_cache_point_block(
+        self,
+        message_block: Union[
+            OpenAIMessageContentListBlock,
+            ChatCompletionUserMessage,
+            ChatCompletionSystemMessage,
+        ],
+        block_type: Literal["content_block"],
+    ) -> Optional[ContentBlock]:
+        pass
+
+    def _get_cache_point_block(
+        self,
+        message_block: Union[
+            OpenAIMessageContentListBlock,
+            ChatCompletionUserMessage,
+            ChatCompletionSystemMessage,
+        ],
+        block_type: Literal["system", "content_block"],
+    ) -> Optional[Union[SystemContentBlock, ContentBlock]]:
+        if message_block.get("cache_control", None) is None:
+            return None
+        if block_type == "system":
+            return SystemContentBlock(cachePoint=CachePointBlock(type="default"))
+        else:
+            return ContentBlock(cachePoint=CachePointBlock(type="default"))
+
+    def _transform_system_message(
+        self, messages: List[AllMessageValues]
+    ) -> Tuple[List[AllMessageValues], List[SystemContentBlock]]:
+        system_prompt_indices = []
+        system_content_blocks: List[SystemContentBlock] = []
+        for idx, message in enumerate(messages):
+            if message["role"] == "system":
+                _system_content_block: Optional[SystemContentBlock] = None
+                _cache_point_block: Optional[SystemContentBlock] = None
+                if isinstance(message["content"], str) and len(message["content"]) > 0:
+                    _system_content_block = SystemContentBlock(text=message["content"])
+                    _cache_point_block = self._get_cache_point_block(
+                        message, block_type="system"
+                    )
+                elif isinstance(message["content"], list):
+                    for m in message["content"]:
+                        if m.get("type", "") == "text" and len(m["text"]) > 0:
+                            _system_content_block = SystemContentBlock(text=m["text"])
+                            _cache_point_block = self._get_cache_point_block(
+                                m, block_type="system"
+                            )
+                if _system_content_block is not None:
+                    system_content_blocks.append(_system_content_block)
+                if _cache_point_block is not None:
+                    system_content_blocks.append(_cache_point_block)
+                system_prompt_indices.append(idx)
+        if len(system_prompt_indices) > 0:
+            for idx in reversed(system_prompt_indices):
+                messages.pop(idx)
+        return messages, system_content_blocks
+
+    def _transform_inference_params(self, inference_params: dict) -> InferenceConfig:
+        if "top_k" in inference_params:
+            inference_params["topK"] = inference_params.pop("top_k")
+        return InferenceConfig(**inference_params)
+
+    def _handle_top_k_value(self, model: str, inference_params: dict) -> dict:
+        base_model = BedrockModelInfo.get_base_model(model)
+
+        val_top_k = None
+        if "topK" in inference_params:
+            val_top_k = inference_params.pop("topK")
+        elif "top_k" in inference_params:
+            val_top_k = inference_params.pop("top_k")
+
+        if val_top_k:
+            if base_model.startswith("anthropic"):
+                return {"top_k": val_top_k}
+            if base_model.startswith("amazon.nova"):
+                return {"inferenceConfig": {"topK": val_top_k}}
+
+        return {}
+
+    def _transform_request_helper(
+        self,
+        model: str,
+        system_content_blocks: List[SystemContentBlock],
+        optional_params: dict,
+        messages: Optional[List[AllMessageValues]] = None,
+    ) -> CommonRequestObject:
+
+        ## VALIDATE REQUEST
+        """
+        Bedrock doesn't support tool calling without `tools=` param specified.
+        """
+        if (
+            "tools" not in optional_params
+            and messages is not None
+            and has_tool_call_blocks(messages)
+        ):
+            if litellm.modify_params:
+                optional_params["tools"] = add_dummy_tool(
+                    custom_llm_provider="bedrock_converse"
+                )
+            else:
+                raise litellm.UnsupportedParamsError(
+                    message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.",
+                    model="",
+                    llm_provider="bedrock",
+                )
+
+        inference_params = copy.deepcopy(optional_params)
+        supported_converse_params = list(
+            AmazonConverseConfig.__annotations__.keys()
+        ) + ["top_k"]
+        supported_tool_call_params = ["tools", "tool_choice"]
+        supported_guardrail_params = ["guardrailConfig"]
+        total_supported_params = (
+            supported_converse_params
+            + supported_tool_call_params
+            + supported_guardrail_params
+        )
+        inference_params.pop("json_mode", None)  # used for handling json_schema
+
+        # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params'
+        additional_request_params = {
+            k: v for k, v in inference_params.items() if k not in total_supported_params
+        }
+        inference_params = {
+            k: v for k, v in inference_params.items() if k in total_supported_params
+        }
+
+        # Only set the topK value in for models that support it
+        additional_request_params.update(
+            self._handle_top_k_value(model, inference_params)
+        )
+
+        bedrock_tools: List[ToolBlock] = _bedrock_tools_pt(
+            inference_params.pop("tools", [])
+        )
+        bedrock_tool_config: Optional[ToolConfigBlock] = None
+        if len(bedrock_tools) > 0:
+            tool_choice_values: ToolChoiceValuesBlock = inference_params.pop(
+                "tool_choice", None
+            )
+            bedrock_tool_config = ToolConfigBlock(
+                tools=bedrock_tools,
+            )
+            if tool_choice_values is not None:
+                bedrock_tool_config["toolChoice"] = tool_choice_values
+
+        data: CommonRequestObject = {
+            "additionalModelRequestFields": additional_request_params,
+            "system": system_content_blocks,
+            "inferenceConfig": self._transform_inference_params(
+                inference_params=inference_params
+            ),
+        }
+
+        # Guardrail Config
+        guardrail_config: Optional[GuardrailConfigBlock] = None
+        request_guardrails_config = inference_params.pop("guardrailConfig", None)
+        if request_guardrails_config is not None:
+            guardrail_config = GuardrailConfigBlock(**request_guardrails_config)
+            data["guardrailConfig"] = guardrail_config
+
+        # Tool Config
+        if bedrock_tool_config is not None:
+            data["toolConfig"] = bedrock_tool_config
+
+        return data
+
+    async def _async_transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> RequestObject:
+        messages, system_content_blocks = self._transform_system_message(messages)
+        ## TRANSFORMATION ##
+
+        _data: CommonRequestObject = self._transform_request_helper(
+            model=model,
+            system_content_blocks=system_content_blocks,
+            optional_params=optional_params,
+            messages=messages,
+        )
+
+        bedrock_messages = (
+            await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+                messages=messages,
+                model=model,
+                llm_provider="bedrock_converse",
+                user_continue_message=litellm_params.pop("user_continue_message", None),
+            )
+        )
+
+        data: RequestObject = {"messages": bedrock_messages, **_data}
+
+        return data
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        return cast(
+            dict,
+            self._transform_request(
+                model=model,
+                messages=messages,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+            ),
+        )
+
+    def _transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+    ) -> RequestObject:
+        messages, system_content_blocks = self._transform_system_message(messages)
+
+        _data: CommonRequestObject = self._transform_request_helper(
+            model=model,
+            system_content_blocks=system_content_blocks,
+            optional_params=optional_params,
+            messages=messages,
+        )
+
+        ## TRANSFORMATION ##
+        bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt(
+            messages=messages,
+            model=model,
+            llm_provider="bedrock_converse",
+            user_continue_message=litellm_params.pop("user_continue_message", None),
+        )
+
+        data: RequestObject = {"messages": bedrock_messages, **_data}
+
+        return data
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: Logging,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        return self._transform_response(
+            model=model,
+            response=raw_response,
+            model_response=model_response,
+            stream=optional_params.get("stream", False),
+            logging_obj=logging_obj,
+            optional_params=optional_params,
+            api_key=api_key,
+            data=request_data,
+            messages=messages,
+            encoding=encoding,
+        )
+
+    def _transform_reasoning_content(
+        self, reasoning_content_blocks: List[BedrockConverseReasoningContentBlock]
+    ) -> str:
+        """
+        Extract the reasoning text from the reasoning content blocks
+
+        Ensures deepseek reasoning content compatible output.
+        """
+        reasoning_content_str = ""
+        for block in reasoning_content_blocks:
+            if "reasoningText" in block:
+                reasoning_content_str += block["reasoningText"]["text"]
+        return reasoning_content_str
+
+    def _transform_thinking_blocks(
+        self, thinking_blocks: List[BedrockConverseReasoningContentBlock]
+    ) -> List[ChatCompletionThinkingBlock]:
+        """Return a consistent format for thinking blocks between Anthropic and Bedrock."""
+        thinking_blocks_list: List[ChatCompletionThinkingBlock] = []
+        for block in thinking_blocks:
+            if "reasoningText" in block:
+                _thinking_block = ChatCompletionThinkingBlock(type="thinking")
+                _text = block["reasoningText"].get("text")
+                _signature = block["reasoningText"].get("signature")
+                if _text is not None:
+                    _thinking_block["thinking"] = _text
+                if _signature is not None:
+                    _thinking_block["signature"] = _signature
+                thinking_blocks_list.append(_thinking_block)
+        return thinking_blocks_list
+
+    def _transform_usage(self, usage: ConverseTokenUsageBlock) -> Usage:
+        input_tokens = usage["inputTokens"]
+        output_tokens = usage["outputTokens"]
+        total_tokens = usage["totalTokens"]
+        cache_creation_input_tokens: int = 0
+        cache_read_input_tokens: int = 0
+
+        if "cacheReadInputTokens" in usage:
+            cache_read_input_tokens = usage["cacheReadInputTokens"]
+            input_tokens += cache_read_input_tokens
+        if "cacheWriteInputTokens" in usage:
+            cache_creation_input_tokens = usage["cacheWriteInputTokens"]
+            input_tokens += cache_creation_input_tokens
+
+        prompt_tokens_details = PromptTokensDetailsWrapper(
+            cached_tokens=cache_read_input_tokens
+        )
+        openai_usage = Usage(
+            prompt_tokens=input_tokens,
+            completion_tokens=output_tokens,
+            total_tokens=total_tokens,
+            prompt_tokens_details=prompt_tokens_details,
+            cache_creation_input_tokens=cache_creation_input_tokens,
+            cache_read_input_tokens=cache_read_input_tokens,
+        )
+        return openai_usage
+
+    def _transform_response(
+        self,
+        model: str,
+        response: httpx.Response,
+        model_response: ModelResponse,
+        stream: bool,
+        logging_obj: Optional[Logging],
+        optional_params: dict,
+        api_key: Optional[str],
+        data: Union[dict, str],
+        messages: List,
+        encoding,
+    ) -> ModelResponse:
+        ## LOGGING
+        if logging_obj is not None:
+            logging_obj.post_call(
+                input=messages,
+                api_key=api_key,
+                original_response=response.text,
+                additional_args={"complete_input_dict": data},
+            )
+
+        json_mode: Optional[bool] = optional_params.pop("json_mode", None)
+        ## RESPONSE OBJECT
+        try:
+            completion_response = ConverseResponseBlock(**response.json())  # type: ignore
+        except Exception as e:
+            raise BedrockError(
+                message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
+                    response.text, str(e)
+                ),
+                status_code=422,
+            )
+
+        """
+        Bedrock Response Object has optional message block 
+
+        completion_response["output"].get("message", None)
+
+        A message block looks like this (Example 1): 
+        "output": {
+            "message": {
+                "role": "assistant",
+                "content": [
+                    {
+                        "text": "Is there anything else you'd like to talk about? Perhaps I can help with some economic questions or provide some information about economic concepts?"
+                    }
+                ]
+            }
+        },
+        (Example 2):
+        "output": {
+            "message": {
+                "role": "assistant",
+                "content": [
+                    {
+                        "toolUse": {
+                            "toolUseId": "tooluse_hbTgdi0CSLq_hM4P8csZJA",
+                            "name": "top_song",
+                            "input": {
+                                "sign": "WZPZ"
+                            }
+                        }
+                    }
+                ]
+            }
+        }
+
+        """
+        message: Optional[MessageBlock] = completion_response["output"]["message"]
+        chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
+        content_str = ""
+        tools: List[ChatCompletionToolCallChunk] = []
+        reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = (
+            None
+        )
+
+        if message is not None:
+            for idx, content in enumerate(message["content"]):
+                """
+                - Content is either a tool response or text
+                """
+                if "text" in content:
+                    content_str += content["text"]
+                if "toolUse" in content:
+
+                    ## check tool name was formatted by litellm
+                    _response_tool_name = content["toolUse"]["name"]
+                    response_tool_name = get_bedrock_tool_name(
+                        response_tool_name=_response_tool_name
+                    )
+                    _function_chunk = ChatCompletionToolCallFunctionChunk(
+                        name=response_tool_name,
+                        arguments=json.dumps(content["toolUse"]["input"]),
+                    )
+
+                    _tool_response_chunk = ChatCompletionToolCallChunk(
+                        id=content["toolUse"]["toolUseId"],
+                        type="function",
+                        function=_function_chunk,
+                        index=idx,
+                    )
+                    tools.append(_tool_response_chunk)
+                if "reasoningContent" in content:
+                    if reasoningContentBlocks is None:
+                        reasoningContentBlocks = []
+                    reasoningContentBlocks.append(content["reasoningContent"])
+
+        if reasoningContentBlocks is not None:
+            chat_completion_message["provider_specific_fields"] = {
+                "reasoningContentBlocks": reasoningContentBlocks,
+            }
+            chat_completion_message["reasoning_content"] = (
+                self._transform_reasoning_content(reasoningContentBlocks)
+            )
+            chat_completion_message["thinking_blocks"] = (
+                self._transform_thinking_blocks(reasoningContentBlocks)
+            )
+        chat_completion_message["content"] = content_str
+        if json_mode is True and tools is not None and len(tools) == 1:
+            # to support 'json_schema' logic on bedrock models
+            json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments")
+            if json_mode_content_str is not None:
+                chat_completion_message["content"] = json_mode_content_str
+        else:
+            chat_completion_message["tool_calls"] = tools
+
+        ## CALCULATING USAGE - bedrock returns usage in the headers
+        usage = self._transform_usage(completion_response["usage"])
+
+        model_response.choices = [
+            litellm.Choices(
+                finish_reason=map_finish_reason(completion_response["stopReason"]),
+                index=0,
+                message=litellm.Message(**chat_completion_message),
+            )
+        ]
+        model_response.created = int(time.time())
+        model_response.model = model
+
+        setattr(model_response, "usage", usage)
+
+        # Add "trace" from Bedrock guardrails - if user has opted in to returning it
+        if "trace" in completion_response:
+            setattr(model_response, "trace", completion_response["trace"])
+
+        return model_response
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return BedrockError(
+            message=error_message,
+            status_code=status_code,
+            headers=headers,
+        )
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key:
+            headers["Authorization"] = f"Bearer {api_key}"
+        return headers