structure saas with tools

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/__init__.py

@@ -0,0 +1,2 @@
+from .converse_handler import BedrockConverseLLM
+from .invoke_handler import BedrockLLM

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/converse_handler.py

@@ -0,0 +1,466 @@
+import json
+import urllib
+from typing import Any, Optional, Union
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObject
+from litellm.llms.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    HTTPHandler,
+    _get_httpx_client,
+    get_async_httpx_client,
+)
+from litellm.types.utils import ModelResponse
+from litellm.utils import CustomStreamWrapper
+
+from ..base_aws_llm import BaseAWSLLM, Credentials
+from ..common_utils import BedrockError
+from .invoke_handler import AWSEventStreamDecoder, MockResponseIterator, make_call
+
+
+def make_sync_call(
+    client: Optional[HTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj: LiteLLMLoggingObject,
+    json_mode: Optional[bool] = False,
+    fake_stream: bool = False,
+):
+    if client is None:
+        client = _get_httpx_client()  # Create a new client if none provided
+
+    response = client.post(
+        api_base,
+        headers=headers,
+        data=data,
+        stream=not fake_stream,
+        logging_obj=logging_obj,
+    )
+
+    if response.status_code != 200:
+        raise BedrockError(
+            status_code=response.status_code, message=str(response.read())
+        )
+
+    if fake_stream:
+        model_response: (
+            ModelResponse
+        ) = litellm.AmazonConverseConfig()._transform_response(
+            model=model,
+            response=response,
+            model_response=litellm.ModelResponse(),
+            stream=True,
+            logging_obj=logging_obj,
+            optional_params={},
+            api_key="",
+            data=data,
+            messages=messages,
+            encoding=litellm.encoding,
+        )  # type: ignore
+        completion_stream: Any = MockResponseIterator(
+            model_response=model_response, json_mode=json_mode
+        )
+    else:
+        decoder = AWSEventStreamDecoder(model=model)
+        completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024))
+
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response="first stream response received",
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream
+
+
+class BedrockConverseLLM(BaseAWSLLM):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def encode_model_id(self, model_id: str) -> str:
+        """
+        Double encode the model ID to ensure it matches the expected double-encoded format.
+        Args:
+            model_id (str): The model ID to encode.
+        Returns:
+            str: The double-encoded model ID.
+        """
+        return urllib.parse.quote(model_id, safe="")  # type: ignore
+
+    async def async_streaming(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params: dict,
+        credentials: Credentials,
+        logger_fn=None,
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+        fake_stream: bool = False,
+        json_mode: Optional[bool] = False,
+    ) -> CustomStreamWrapper:
+        request_data = await litellm.AmazonConverseConfig()._async_transform_request(
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        data = json.dumps(request_data)
+
+        prepped = self.get_request_headers(
+            credentials=credentials,
+            aws_region_name=litellm_params.get("aws_region_name") or "us-west-2",
+            extra_headers=headers,
+            endpoint_url=api_base,
+            data=data,
+            headers=headers,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": api_base,
+                "headers": dict(prepped.headers),
+            },
+        )
+
+        completion_stream = await make_call(
+            client=client,
+            api_base=api_base,
+            headers=dict(prepped.headers),
+            data=data,
+            model=model,
+            messages=messages,
+            logging_obj=logging_obj,
+            fake_stream=fake_stream,
+            json_mode=json_mode,
+        )
+        streaming_response = CustomStreamWrapper(
+            completion_stream=completion_stream,
+            model=model,
+            custom_llm_provider="bedrock",
+            logging_obj=logging_obj,
+        )
+        return streaming_response
+
+    async def async_completion(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj: LiteLLMLoggingObject,
+        stream,
+        optional_params: dict,
+        litellm_params: dict,
+        credentials: Credentials,
+        logger_fn=None,
+        headers: dict = {},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+        request_data = await litellm.AmazonConverseConfig()._async_transform_request(
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        data = json.dumps(request_data)
+
+        prepped = self.get_request_headers(
+            credentials=credentials,
+            aws_region_name=litellm_params.get("aws_region_name") or "us-west-2",
+            extra_headers=headers,
+            endpoint_url=api_base,
+            data=data,
+            headers=headers,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": api_base,
+                "headers": prepped.headers,
+            },
+        )
+
+        headers = dict(prepped.headers)
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            client = get_async_httpx_client(
+                params=_params, llm_provider=litellm.LlmProviders.BEDROCK
+            )
+        else:
+            client = client  # type: ignore
+
+        try:
+            response = await client.post(
+                url=api_base,
+                headers=headers,
+                data=data,
+                logging_obj=logging_obj,
+            )  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise BedrockError(status_code=error_code, message=err.response.text)
+        except httpx.TimeoutException:
+            raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+        return litellm.AmazonConverseConfig()._transform_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            stream=stream if isinstance(stream, bool) else False,
+            logging_obj=logging_obj,
+            api_key="",
+            data=data,
+            messages=messages,
+            optional_params=optional_params,
+            encoding=encoding,
+        )
+
+    def completion(  # noqa: PLR0915
+        self,
+        model: str,
+        messages: list,
+        api_base: Optional[str],
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        encoding,
+        logging_obj: LiteLLMLoggingObject,
+        optional_params: dict,
+        acompletion: bool,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        litellm_params: dict,
+        logger_fn=None,
+        extra_headers: Optional[dict] = None,
+        client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
+    ):
+        ## SETUP ##
+        stream = optional_params.pop("stream", None)
+        unencoded_model_id = optional_params.pop("model_id", None)
+        fake_stream = optional_params.pop("fake_stream", False)
+        json_mode = optional_params.get("json_mode", False)
+        if unencoded_model_id is not None:
+            modelId = self.encode_model_id(model_id=unencoded_model_id)
+        else:
+            modelId = self.encode_model_id(model_id=model)
+
+        if stream is True and "ai21" in modelId:
+            fake_stream = True
+
+        ### SET REGION NAME ###
+        aws_region_name = self._get_aws_region_name(
+            optional_params=optional_params,
+            model=model,
+            model_id=unencoded_model_id,
+        )
+
+        ## CREDENTIALS ##
+        # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
+        aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
+        aws_access_key_id = optional_params.pop("aws_access_key_id", None)
+        aws_session_token = optional_params.pop("aws_session_token", None)
+        aws_role_name = optional_params.pop("aws_role_name", None)
+        aws_session_name = optional_params.pop("aws_session_name", None)
+        aws_profile_name = optional_params.pop("aws_profile_name", None)
+        aws_bedrock_runtime_endpoint = optional_params.pop(
+            "aws_bedrock_runtime_endpoint", None
+        )  # https://bedrock-runtime.{region_name}.amazonaws.com
+        aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
+        aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None)
+        optional_params.pop("aws_region_name", None)
+
+        litellm_params[
+            "aws_region_name"
+        ] = aws_region_name  # [DO NOT DELETE] important for async calls
+
+        credentials: Credentials = self.get_credentials(
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            aws_session_token=aws_session_token,
+            aws_region_name=aws_region_name,
+            aws_session_name=aws_session_name,
+            aws_profile_name=aws_profile_name,
+            aws_role_name=aws_role_name,
+            aws_web_identity_token=aws_web_identity_token,
+            aws_sts_endpoint=aws_sts_endpoint,
+        )
+
+        ### SET RUNTIME ENDPOINT ###
+        endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint(
+            api_base=api_base,
+            aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint,
+            aws_region_name=aws_region_name,
+        )
+        if (stream is not None and stream is True) and not fake_stream:
+            endpoint_url = f"{endpoint_url}/model/{modelId}/converse-stream"
+            proxy_endpoint_url = f"{proxy_endpoint_url}/model/{modelId}/converse-stream"
+        else:
+            endpoint_url = f"{endpoint_url}/model/{modelId}/converse"
+            proxy_endpoint_url = f"{proxy_endpoint_url}/model/{modelId}/converse"
+
+        ## COMPLETION CALL
+        headers = {"Content-Type": "application/json"}
+        if extra_headers is not None:
+            headers = {"Content-Type": "application/json", **extra_headers}
+
+        ### ROUTING (ASYNC, STREAMING, SYNC)
+        if acompletion:
+            if isinstance(client, HTTPHandler):
+                client = None
+            if stream is True:
+                return self.async_streaming(
+                    model=model,
+                    messages=messages,
+                    api_base=proxy_endpoint_url,
+                    model_response=model_response,
+                    encoding=encoding,
+                    logging_obj=logging_obj,
+                    optional_params=optional_params,
+                    stream=True,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    headers=headers,
+                    timeout=timeout,
+                    client=client,
+                    json_mode=json_mode,
+                    fake_stream=fake_stream,
+                    credentials=credentials,
+                )  # type: ignore
+            ### ASYNC COMPLETION
+            return self.async_completion(
+                model=model,
+                messages=messages,
+                api_base=proxy_endpoint_url,
+                model_response=model_response,
+                encoding=encoding,
+                logging_obj=logging_obj,
+                optional_params=optional_params,
+                stream=stream,  # type: ignore
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                headers=headers,
+                timeout=timeout,
+                client=client,
+                credentials=credentials,
+            )  # type: ignore
+
+        ## TRANSFORMATION ##
+
+        _data = litellm.AmazonConverseConfig()._transform_request(
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+        )
+        data = json.dumps(_data)
+
+        prepped = self.get_request_headers(
+            credentials=credentials,
+            aws_region_name=aws_region_name,
+            extra_headers=extra_headers,
+            endpoint_url=proxy_endpoint_url,
+            data=data,
+            headers=headers,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": proxy_endpoint_url,
+                "headers": prepped.headers,
+            },
+        )
+        if client is None or isinstance(client, AsyncHTTPHandler):
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            client = _get_httpx_client(_params)  # type: ignore
+        else:
+            client = client
+
+        if stream is not None and stream is True:
+            completion_stream = make_sync_call(
+                client=(
+                    client
+                    if client is not None and isinstance(client, HTTPHandler)
+                    else None
+                ),
+                api_base=proxy_endpoint_url,
+                headers=prepped.headers,  # type: ignore
+                data=data,
+                model=model,
+                messages=messages,
+                logging_obj=logging_obj,
+                json_mode=json_mode,
+                fake_stream=fake_stream,
+            )
+            streaming_response = CustomStreamWrapper(
+                completion_stream=completion_stream,
+                model=model,
+                custom_llm_provider="bedrock",
+                logging_obj=logging_obj,
+            )
+
+            return streaming_response
+
+        ### COMPLETION
+
+        try:
+            response = client.post(
+                url=proxy_endpoint_url,
+                headers=prepped.headers,
+                data=data,
+                logging_obj=logging_obj,
+            )  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise BedrockError(status_code=error_code, message=err.response.text)
+        except httpx.TimeoutException:
+            raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+        return litellm.AmazonConverseConfig()._transform_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            stream=stream if isinstance(stream, bool) else False,
+            logging_obj=logging_obj,
+            api_key="",
+            data=data,
+            messages=messages,
+            optional_params=optional_params,
+            encoding=encoding,
+        )

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/converse_like/handler.py

@@ -0,0 +1,5 @@
+"""
+Uses base_llm_http_handler to call the 'converse like' endpoint.
+
+Relevant issue: https://github.com/BerriAI/litellm/issues/8085
+"""

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/converse_like/transformation.py

@@ -0,0 +1,3 @@
+"""
+Uses `converse_transformation.py` to transform the messages to the format required by Bedrock Converse.
+"""

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/converse_transformation.py

@@ -0,0 +1,852 @@
+"""
+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.anthropic.chat.transformation import AnthropicConfig
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.types.llms.bedrock import *
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionRedactedThinkingBlock,
+    ChatCompletionResponseMessage,
+    ChatCompletionSystemMessage,
+    ChatCompletionThinkingBlock,
+    ChatCompletionToolCallChunk,
+    ChatCompletionToolCallFunctionChunk,
+    ChatCompletionToolParam,
+    ChatCompletionToolParamFunctionChunk,
+    ChatCompletionUserMessage,
+    OpenAIChatCompletionToolParam,
+    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_blocks(cls) -> dict:
+        return {
+            "guardrailConfig": GuardrailConfigBlock,
+            "performanceConfig": PerformanceConfigBlock,
+        }
+
+    @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")
+            supported_params.append("reasoning_effort")
+        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 _apply_tool_call_transformation(
+        self,
+        tools: List[OpenAIChatCompletionToolParam],
+        model: str,
+        non_default_params: dict,
+        optional_params: dict,
+    ):
+        optional_params = self._add_tools_to_optional_params(
+            optional_params=optional_params, tools=tools
+        )
+
+        if (
+            "meta.llama3-3-70b-instruct-v1:0" in model
+            and non_default_params.get("stream", False) is True
+        ):
+            optional_params["fake_stream"] = True
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        is_thinking_enabled = self.is_thinking_enabled(non_default_params)
+
+        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
+                    )
+                    and not is_thinking_enabled
+                ):
+                    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):
+                self._apply_tool_call_transformation(
+                    tools=cast(List[OpenAIChatCompletionToolParam], value),
+                    model=model,
+                    non_default_params=non_default_params,
+                    optional_params=optional_params,
+                )
+            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
+            elif param == "reasoning_effort" and isinstance(value, str):
+                optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
+                    value
+                )
+
+        self.update_optional_params_with_thinking_tokens(
+            non_default_params=non_default_params, optional_params=optional_params
+        )
+
+        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_prompt_indices.append(idx)
+                if isinstance(message["content"], str) and message["content"]:
+                    system_content_blocks.append(
+                        SystemContentBlock(text=message["content"])
+                    )
+                    cache_block = self._get_cache_point_block(
+                        message, block_type="system"
+                    )
+                    if cache_block:
+                        system_content_blocks.append(cache_block)
+                elif isinstance(message["content"], list):
+                    for m in message["content"]:
+                        if m.get("type") == "text" and m.get("text"):
+                            system_content_blocks.append(
+                                SystemContentBlock(text=m["text"])
+                            )
+                            cache_block = self._get_cache_point_block(
+                                m, block_type="system"
+                            )
+                            if cache_block:
+                                system_content_blocks.append(cache_block)
+        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_config_params = list(self.get_config_blocks().keys())
+        total_supported_params = (
+            supported_converse_params
+            + supported_tool_call_params
+            + supported_config_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
+            ),
+        }
+
+        # Handle all config blocks
+        for config_name, config_class in self.get_config_blocks().items():
+            config_value = inference_params.pop(config_name, None)
+            if config_value is not None:
+                data[config_name] = config_class(**config_value)  # type: ignore
+
+        # 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[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]:
+        """Return a consistent format for thinking blocks between Anthropic and Bedrock."""
+        thinking_blocks_list: List[
+            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
+        ] = []
+        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)
+            elif "redactedContent" in block:
+                _redacted_block = ChatCompletionRedactedThinkingBlock(
+                    type="redacted_thinking", data=block["redactedContent"]
+                )
+                thinking_blocks_list.append(_redacted_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:
+            """
+            Do not increment prompt_tokens with cacheWriteInputTokens
+            """
+            cache_creation_input_tokens = usage["cacheWriteInputTokens"]
+
+        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,
+        litellm_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key:
+            headers["Authorization"] = f"Bearer {api_key}"
+        return headers

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/amazon_ai21_transformation.py

@@ -0,0 +1,99 @@
+import types
+from typing import List, Optional
+
+from litellm.llms.base_llm.chat.transformation import BaseConfig
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    AmazonInvokeConfig,
+)
+
+
+class AmazonAI21Config(AmazonInvokeConfig, BaseConfig):
+    """
+    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra
+
+    Supported Params for the Amazon / AI21 models:
+
+    - `maxTokens` (int32): The maximum number of tokens to generate per result. Optional, default is 16. If no `stopSequences` are given, generation stops after producing `maxTokens`.
+
+    - `temperature` (float): Modifies the distribution from which tokens are sampled. Optional, default is 0.7. A value of 0 essentially disables sampling and results in greedy decoding.
+
+    - `topP` (float): Used for sampling tokens from the corresponding top percentile of probability mass. Optional, default is 1. For instance, a value of 0.9 considers only tokens comprising the top 90% probability mass.
+
+    - `stopSequences` (array of strings): Stops decoding if any of the input strings is generated. Optional.
+
+    - `frequencyPenalty` (object): Placeholder for frequency penalty object.
+
+    - `presencePenalty` (object): Placeholder for presence penalty object.
+
+    - `countPenalty` (object): Placeholder for count penalty object.
+    """
+
+    maxTokens: Optional[int] = None
+    temperature: Optional[float] = None
+    topP: Optional[float] = None
+    stopSequences: Optional[list] = None
+    frequencePenalty: Optional[dict] = None
+    presencePenalty: Optional[dict] = None
+    countPenalty: Optional[dict] = None
+
+    def __init__(
+        self,
+        maxTokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        topP: Optional[float] = None,
+        stopSequences: Optional[list] = None,
+        frequencePenalty: Optional[dict] = None,
+        presencePenalty: Optional[dict] = None,
+        countPenalty: Optional[dict] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+        AmazonInvokeConfig.__init__(self)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not k.startswith("_abc")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self, model: str) -> List:
+        return [
+            "max_tokens",
+            "temperature",
+            "top_p",
+            "stream",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for k, v in non_default_params.items():
+            if k == "max_tokens":
+                optional_params["maxTokens"] = v
+            if k == "temperature":
+                optional_params["temperature"] = v
+            if k == "top_p":
+                optional_params["topP"] = v
+            if k == "stream":
+                optional_params["stream"] = v
+        return optional_params

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/amazon_cohere_transformation.py

@@ -0,0 +1,75 @@
+import types
+from typing import List, Optional
+
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    AmazonInvokeConfig,
+)
+from litellm.llms.cohere.chat.transformation import CohereChatConfig
+
+
+class AmazonCohereConfig(AmazonInvokeConfig, CohereChatConfig):
+    """
+    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=command
+
+    Supported Params for the Amazon / Cohere models:
+
+    - `max_tokens` (integer) max tokens,
+    - `temperature` (float) model temperature,
+    - `return_likelihood` (string) n/a
+    """
+
+    max_tokens: Optional[int] = None
+    return_likelihood: Optional[str] = None
+
+    def __init__(
+        self,
+        max_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        return_likelihood: Optional[str] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+        AmazonInvokeConfig.__init__(self)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not k.startswith("_abc")
+            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 = CohereChatConfig.get_supported_openai_params(
+            self, model=model
+        )
+        return supported_params
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        return CohereChatConfig.map_openai_params(
+            self,
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+            drop_params=drop_params,
+        )

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/amazon_deepseek_transformation.py

@@ -0,0 +1,135 @@
+from typing import Any, List, Optional, cast
+
+from httpx import Response
+
+from litellm import verbose_logger
+from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
+    _parse_content_for_reasoning,
+)
+from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    LiteLLMLoggingObj,
+)
+from litellm.types.llms.bedrock import AmazonDeepSeekR1StreamingResponse
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import (
+    ChatCompletionUsageBlock,
+    Choices,
+    Delta,
+    Message,
+    ModelResponse,
+    ModelResponseStream,
+    StreamingChoices,
+)
+
+from .amazon_llama_transformation import AmazonLlamaConfig
+
+
+class AmazonDeepSeekR1Config(AmazonLlamaConfig):
+    def transform_response(
+        self,
+        model: str,
+        raw_response: Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        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:
+        """
+        Extract the reasoning content, and return it as a separate field in the response.
+        """
+        response = super().transform_response(
+            model,
+            raw_response,
+            model_response,
+            logging_obj,
+            request_data,
+            messages,
+            optional_params,
+            litellm_params,
+            encoding,
+            api_key,
+            json_mode,
+        )
+        prompt = cast(Optional[str], request_data.get("prompt"))
+        message_content = cast(
+            Optional[str], cast(Choices, response.choices[0]).message.get("content")
+        )
+        if prompt and prompt.strip().endswith("<think>") and message_content:
+            message_content_with_reasoning_token = "<think>" + message_content
+            reasoning, content = _parse_content_for_reasoning(
+                message_content_with_reasoning_token
+            )
+            provider_specific_fields = (
+                cast(Choices, response.choices[0]).message.provider_specific_fields
+                or {}
+            )
+            if reasoning:
+                provider_specific_fields["reasoning_content"] = reasoning
+
+            message = Message(
+                **{
+                    **cast(Choices, response.choices[0]).message.model_dump(),
+                    "content": content,
+                    "provider_specific_fields": provider_specific_fields,
+                }
+            )
+            cast(Choices, response.choices[0]).message = message
+        return response
+
+
+class AmazonDeepseekR1ResponseIterator(BaseModelResponseIterator):
+    def __init__(self, streaming_response: Any, sync_stream: bool) -> None:
+        super().__init__(streaming_response=streaming_response, sync_stream=sync_stream)
+        self.has_finished_thinking = False
+
+    def chunk_parser(self, chunk: dict) -> ModelResponseStream:
+        """
+        Deepseek r1 starts by thinking, then it generates the response.
+        """
+        try:
+            typed_chunk = AmazonDeepSeekR1StreamingResponse(**chunk)  # type: ignore
+            generated_content = typed_chunk["generation"]
+            if generated_content == "</think>" and not self.has_finished_thinking:
+                verbose_logger.debug(
+                    "Deepseek r1: </think> received, setting has_finished_thinking to True"
+                )
+                generated_content = ""
+                self.has_finished_thinking = True
+
+            prompt_token_count = typed_chunk.get("prompt_token_count") or 0
+            generation_token_count = typed_chunk.get("generation_token_count") or 0
+            usage = ChatCompletionUsageBlock(
+                prompt_tokens=prompt_token_count,
+                completion_tokens=generation_token_count,
+                total_tokens=prompt_token_count + generation_token_count,
+            )
+
+            return ModelResponseStream(
+                choices=[
+                    StreamingChoices(
+                        finish_reason=typed_chunk["stop_reason"],
+                        delta=Delta(
+                            content=(
+                                generated_content
+                                if self.has_finished_thinking
+                                else None
+                            ),
+                            reasoning_content=(
+                                generated_content
+                                if not self.has_finished_thinking
+                                else None
+                            ),
+                        ),
+                    )
+                ],
+                usage=usage,
+            )
+
+        except Exception as e:
+            raise e

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/amazon_llama_transformation.py

@@ -0,0 +1,80 @@
+import types
+from typing import List, Optional
+
+from litellm.llms.base_llm.chat.transformation import BaseConfig
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    AmazonInvokeConfig,
+)
+
+
+class AmazonLlamaConfig(AmazonInvokeConfig, BaseConfig):
+    """
+    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=meta.llama2-13b-chat-v1
+
+    Supported Params for the Amazon / Meta Llama models:
+
+    - `max_gen_len` (integer) max tokens,
+    - `temperature` (float) temperature for model,
+    - `top_p` (float) top p for model
+    """
+
+    max_gen_len: Optional[int] = None
+    temperature: Optional[float] = None
+    topP: Optional[float] = None
+
+    def __init__(
+        self,
+        maxTokenCount: Optional[int] = None,
+        temperature: Optional[float] = None,
+        topP: 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)
+        AmazonInvokeConfig.__init__(self)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not k.startswith("_abc")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self, model: str) -> List:
+        return [
+            "max_tokens",
+            "temperature",
+            "top_p",
+            "stream",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for k, v in non_default_params.items():
+            if k == "max_tokens":
+                optional_params["max_gen_len"] = v
+            if k == "temperature":
+                optional_params["temperature"] = v
+            if k == "top_p":
+                optional_params["top_p"] = v
+            if k == "stream":
+                optional_params["stream"] = v
+        return optional_params

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/amazon_mistral_transformation.py

@@ -0,0 +1,83 @@
+import types
+from typing import List, Optional
+
+from litellm.llms.base_llm.chat.transformation import BaseConfig
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    AmazonInvokeConfig,
+)
+
+
+class AmazonMistralConfig(AmazonInvokeConfig, BaseConfig):
+    """
+    Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-mistral.html
+    Supported Params for the Amazon / Mistral models:
+
+    - `max_tokens` (integer) max tokens,
+    - `temperature` (float) temperature for model,
+    - `top_p` (float) top p for model
+    - `stop` [string] A list of stop sequences that if generated by the model, stops the model from generating further output.
+    - `top_k` (float) top k for model
+    """
+
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    top_k: Optional[float] = None
+    stop: Optional[List[str]] = None
+
+    def __init__(
+        self,
+        max_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[int] = None,
+        top_k: Optional[float] = None,
+        stop: Optional[List[str]] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+        AmazonInvokeConfig.__init__(self)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not k.startswith("_abc")
+            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]:
+        return ["max_tokens", "temperature", "top_p", "stop", "stream"]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for k, v in non_default_params.items():
+            if k == "max_tokens":
+                optional_params["max_tokens"] = v
+            if k == "temperature":
+                optional_params["temperature"] = v
+            if k == "top_p":
+                optional_params["top_p"] = v
+            if k == "stop":
+                optional_params["stop"] = v
+            if k == "stream":
+                optional_params["stream"] = v
+        return optional_params

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py

@@ -0,0 +1,121 @@
+"""
+Handles transforming requests for `bedrock/invoke/{nova} models`
+
+Inherits from `AmazonConverseConfig`
+
+Nova + Invoke API Tutorial: https://docs.aws.amazon.com/nova/latest/userguide/using-invoke-api.html
+"""
+
+from typing import Any, List, Optional
+
+import httpx
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.types.llms.bedrock import BedrockInvokeNovaRequest
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ModelResponse
+
+from ..converse_transformation import AmazonConverseConfig
+from .base_invoke_transformation import AmazonInvokeConfig
+
+
+class AmazonInvokeNovaConfig(AmazonInvokeConfig, AmazonConverseConfig):
+    """
+    Config for sending `nova` requests to `/bedrock/invoke/`
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def get_supported_openai_params(self, model: str) -> list:
+        return AmazonConverseConfig.get_supported_openai_params(self, model)
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        return AmazonConverseConfig.map_openai_params(
+            self, non_default_params, optional_params, model, drop_params
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        _transformed_nova_request = AmazonConverseConfig.transform_request(
+            self,
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+        _bedrock_invoke_nova_request = BedrockInvokeNovaRequest(
+            **_transformed_nova_request
+        )
+        self._remove_empty_system_messages(_bedrock_invoke_nova_request)
+        bedrock_invoke_nova_request = self._filter_allowed_fields(
+            _bedrock_invoke_nova_request
+        )
+        return bedrock_invoke_nova_request
+
+    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,
+    ) -> litellm.ModelResponse:
+        return AmazonConverseConfig.transform_response(
+            self,
+            model,
+            raw_response,
+            model_response,
+            logging_obj,
+            request_data,
+            messages,
+            optional_params,
+            litellm_params,
+            encoding,
+            api_key,
+            json_mode,
+        )
+
+    def _filter_allowed_fields(
+        self, bedrock_invoke_nova_request: BedrockInvokeNovaRequest
+    ) -> dict:
+        """
+        Filter out fields that are not allowed in the `BedrockInvokeNovaRequest` dataclass.
+        """
+        allowed_fields = set(BedrockInvokeNovaRequest.__annotations__.keys())
+        return {
+            k: v for k, v in bedrock_invoke_nova_request.items() if k in allowed_fields
+        }
+
+    def _remove_empty_system_messages(
+        self, bedrock_invoke_nova_request: BedrockInvokeNovaRequest
+    ) -> None:
+        """
+        In-place remove empty `system` messages from the request.
+
+        /bedrock/invoke/ does not allow empty `system` messages.
+        """
+        _system_message = bedrock_invoke_nova_request.get("system", None)
+        if isinstance(_system_message, list) and len(_system_message) == 0:
+            bedrock_invoke_nova_request.pop("system", None)
+        return

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/amazon_titan_transformation.py

@@ -0,0 +1,116 @@
+import re
+import types
+from typing import List, Optional, Union
+
+import litellm
+from litellm.llms.base_llm.chat.transformation import BaseConfig
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    AmazonInvokeConfig,
+)
+
+
+class AmazonTitanConfig(AmazonInvokeConfig, BaseConfig):
+    """
+    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-text-express-v1
+
+    Supported Params for the Amazon Titan models:
+
+    - `maxTokenCount` (integer) max tokens,
+    - `stopSequences` (string[]) list of stop sequence strings
+    - `temperature` (float) temperature for model,
+    - `topP` (int) top p for model
+    """
+
+    maxTokenCount: Optional[int] = None
+    stopSequences: Optional[list] = None
+    temperature: Optional[float] = None
+    topP: Optional[int] = None
+
+    def __init__(
+        self,
+        maxTokenCount: Optional[int] = None,
+        stopSequences: Optional[list] = None,
+        temperature: Optional[float] = None,
+        topP: 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)
+
+        AmazonInvokeConfig.__init__(self)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not k.startswith("_abc")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def _map_and_modify_arg(
+        self,
+        supported_params: dict,
+        provider: str,
+        model: str,
+        stop: Union[List[str], str],
+    ):
+        """
+        filter params to fit the required provider format, drop those that don't fit if user sets `litellm.drop_params = True`.
+        """
+        filtered_stop = None
+        if "stop" in supported_params and litellm.drop_params:
+            if provider == "bedrock" and "amazon" in model:
+                filtered_stop = []
+                if isinstance(stop, list):
+                    for s in stop:
+                        if re.match(r"^(\|+|User:)$", s):
+                            filtered_stop.append(s)
+        if filtered_stop is not None:
+            supported_params["stop"] = filtered_stop
+
+        return supported_params
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        return [
+            "max_tokens",
+            "max_completion_tokens",
+            "stop",
+            "temperature",
+            "top_p",
+            "stream",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for k, v in non_default_params.items():
+            if k == "max_tokens" or k == "max_completion_tokens":
+                optional_params["maxTokenCount"] = v
+            if k == "temperature":
+                optional_params["temperature"] = v
+            if k == "stop":
+                filtered_stop = self._map_and_modify_arg(
+                    {"stop": v}, provider="bedrock", model=model, stop=v
+                )
+                optional_params["stopSequences"] = filtered_stop["stop"]
+            if k == "top_p":
+                optional_params["topP"] = v
+            if k == "stream":
+                optional_params["stream"] = v
+        return optional_params

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude2_transformation.py

@@ -0,0 +1,90 @@
+import types
+from typing import Optional
+
+import litellm
+
+from .base_invoke_transformation import AmazonInvokeConfig
+
+
+class AmazonAnthropicConfig(AmazonInvokeConfig):
+    """
+    Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude
+
+    Supported Params for the Amazon / Anthropic models:
+
+    - `max_tokens_to_sample` (integer) max tokens,
+    - `temperature` (float) model temperature,
+    - `top_k` (integer) top k,
+    - `top_p` (integer) top p,
+    - `stop_sequences` (string[]) list of stop sequences - e.g. ["\\n\\nHuman:"],
+    - `anthropic_version` (string) version of anthropic for bedrock - e.g. "bedrock-2023-05-31"
+    """
+
+    max_tokens_to_sample: Optional[int] = litellm.max_tokens
+    stop_sequences: Optional[list] = None
+    temperature: Optional[float] = None
+    top_k: Optional[int] = None
+    top_p: Optional[int] = None
+    anthropic_version: Optional[str] = None
+
+    def __init__(
+        self,
+        max_tokens_to_sample: Optional[int] = None,
+        stop_sequences: Optional[list] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[int] = None,
+        anthropic_version: Optional[str] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @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):
+        return [
+            "max_tokens",
+            "max_completion_tokens",
+            "temperature",
+            "stop",
+            "top_p",
+            "stream",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ):
+        for param, value in non_default_params.items():
+            if param == "max_tokens" or param == "max_completion_tokens":
+                optional_params["max_tokens_to_sample"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if param == "stop":
+                optional_params["stop_sequences"] = value
+            if param == "stream" and value is True:
+                optional_params["stream"] = value
+        return optional_params

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py

@@ -0,0 +1,100 @@
+from typing import TYPE_CHECKING, Any, List, Optional
+
+import httpx
+
+from litellm.llms.anthropic.chat.transformation import AnthropicConfig
+from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import (
+    AmazonInvokeConfig,
+)
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ModelResponse
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class AmazonAnthropicClaude3Config(AmazonInvokeConfig, AnthropicConfig):
+    """
+    Reference:
+        https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=claude
+        https://docs.anthropic.com/claude/docs/models-overview#model-comparison
+
+    Supported Params for the Amazon / Anthropic Claude 3 models:
+    """
+
+    anthropic_version: str = "bedrock-2023-05-31"
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        return AnthropicConfig.get_supported_openai_params(self, model)
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        return AnthropicConfig.map_openai_params(
+            self,
+            non_default_params,
+            optional_params,
+            model,
+            drop_params,
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        _anthropic_request = AnthropicConfig.transform_request(
+            self,
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
+        _anthropic_request.pop("model", None)
+        _anthropic_request.pop("stream", None)
+        if "anthropic_version" not in _anthropic_request:
+            _anthropic_request["anthropic_version"] = self.anthropic_version
+
+        return _anthropic_request
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        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 AnthropicConfig.transform_response(
+            self,
+            model=model,
+            raw_response=raw_response,
+            model_response=model_response,
+            logging_obj=logging_obj,
+            request_data=request_data,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            encoding=encoding,
+            api_key=api_key,
+            json_mode=json_mode,
+        )

.venv/lib/python3.10/site-packages/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py

@@ -0,0 +1,679 @@
+import copy
+import json
+import time
+import urllib.parse
+from functools import partial
+from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast, get_args
+
+import httpx
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    cohere_message_pt,
+    custom_prompt,
+    deepseek_r1_pt,
+    prompt_factory,
+)
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.llms.bedrock.chat.invoke_handler import make_call, make_sync_call
+from litellm.llms.bedrock.common_utils import BedrockError
+from litellm.llms.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    HTTPHandler,
+    _get_httpx_client,
+)
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ModelResponse, Usage
+from litellm.utils import CustomStreamWrapper
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+
+
+class AmazonInvokeConfig(BaseConfig, BaseAWSLLM):
+    def __init__(self, **kwargs):
+        BaseConfig.__init__(self, **kwargs)
+        BaseAWSLLM.__init__(self, **kwargs)
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        """
+        This is a base invoke model mapping. For Invoke - define a bedrock provider specific config that extends this class.
+        """
+        return [
+            "max_tokens",
+            "max_completion_tokens",
+            "stream",
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        This is a base invoke model mapping. For Invoke - define a bedrock provider specific config that extends this class.
+        """
+        for param, value in non_default_params.items():
+            if param == "max_tokens" or param == "max_completion_tokens":
+                optional_params["max_tokens"] = value
+            if param == "stream":
+                optional_params["stream"] = value
+        return optional_params
+
+    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:
+        """
+        Get the complete url for the request
+        """
+        provider = self.get_bedrock_invoke_provider(model)
+        modelId = self.get_bedrock_model_id(
+            model=model,
+            provider=provider,
+            optional_params=optional_params,
+        )
+        ### SET RUNTIME ENDPOINT ###
+        aws_bedrock_runtime_endpoint = optional_params.get(
+            "aws_bedrock_runtime_endpoint", None
+        )  # https://bedrock-runtime.{region_name}.amazonaws.com
+        endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint(
+            api_base=api_base,
+            aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint,
+            aws_region_name=self._get_aws_region_name(
+                optional_params=optional_params, model=model
+            ),
+        )
+
+        if (stream is not None and stream is True) and provider != "ai21":
+            endpoint_url = f"{endpoint_url}/model/{modelId}/invoke-with-response-stream"
+            proxy_endpoint_url = (
+                f"{proxy_endpoint_url}/model/{modelId}/invoke-with-response-stream"
+            )
+        else:
+            endpoint_url = f"{endpoint_url}/model/{modelId}/invoke"
+            proxy_endpoint_url = f"{proxy_endpoint_url}/model/{modelId}/invoke"
+
+        return endpoint_url
+
+    def sign_request(
+        self,
+        headers: dict,
+        optional_params: dict,
+        request_data: dict,
+        api_base: str,
+        model: Optional[str] = None,
+        stream: Optional[bool] = None,
+        fake_stream: Optional[bool] = None,
+    ) -> dict:
+        try:
+            from botocore.auth import SigV4Auth
+            from botocore.awsrequest import AWSRequest
+            from botocore.credentials import Credentials
+        except ImportError:
+            raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
+
+        ## CREDENTIALS ##
+        # pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them
+        aws_secret_access_key = optional_params.get("aws_secret_access_key", None)
+        aws_access_key_id = optional_params.get("aws_access_key_id", None)
+        aws_session_token = optional_params.get("aws_session_token", None)
+        aws_role_name = optional_params.get("aws_role_name", None)
+        aws_session_name = optional_params.get("aws_session_name", None)
+        aws_profile_name = optional_params.get("aws_profile_name", None)
+        aws_web_identity_token = optional_params.get("aws_web_identity_token", None)
+        aws_sts_endpoint = optional_params.get("aws_sts_endpoint", None)
+        aws_region_name = self._get_aws_region_name(
+            optional_params=optional_params, model=model
+        )
+
+        credentials: Credentials = self.get_credentials(
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            aws_session_token=aws_session_token,
+            aws_region_name=aws_region_name,
+            aws_session_name=aws_session_name,
+            aws_profile_name=aws_profile_name,
+            aws_role_name=aws_role_name,
+            aws_web_identity_token=aws_web_identity_token,
+            aws_sts_endpoint=aws_sts_endpoint,
+        )
+
+        sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
+        if headers is not None:
+            headers = {"Content-Type": "application/json", **headers}
+        else:
+            headers = {"Content-Type": "application/json"}
+
+        request = AWSRequest(
+            method="POST",
+            url=api_base,
+            data=json.dumps(request_data),
+            headers=headers,
+        )
+        sigv4.add_auth(request)
+
+        request_headers_dict = dict(request.headers)
+        if (
+            headers is not None and "Authorization" in headers
+        ):  # prevent sigv4 from overwriting the auth header
+            request_headers_dict["Authorization"] = headers["Authorization"]
+        return request_headers_dict
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        ## SETUP ##
+        stream = optional_params.pop("stream", None)
+        custom_prompt_dict: dict = litellm_params.pop("custom_prompt_dict", None) or {}
+        hf_model_name = litellm_params.get("hf_model_name", None)
+
+        provider = self.get_bedrock_invoke_provider(model)
+
+        prompt, chat_history = self.convert_messages_to_prompt(
+            model=hf_model_name or model,
+            messages=messages,
+            provider=provider,
+            custom_prompt_dict=custom_prompt_dict,
+        )
+        inference_params = copy.deepcopy(optional_params)
+        inference_params = {
+            k: v
+            for k, v in inference_params.items()
+            if k not in self.aws_authentication_params
+        }
+        request_data: dict = {}
+        if provider == "cohere":
+            if model.startswith("cohere.command-r"):
+                ## LOAD CONFIG
+                config = litellm.AmazonCohereChatConfig().get_config()
+                for k, v in config.items():
+                    if (
+                        k not in inference_params
+                    ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+                        inference_params[k] = v
+                _data = {"message": prompt, **inference_params}
+                if chat_history is not None:
+                    _data["chat_history"] = chat_history
+                request_data = _data
+            else:
+                ## LOAD CONFIG
+                config = litellm.AmazonCohereConfig.get_config()
+                for k, v in config.items():
+                    if (
+                        k not in inference_params
+                    ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+                        inference_params[k] = v
+                if stream is True:
+                    inference_params[
+                        "stream"
+                    ] = True  # cohere requires stream = True in inference params
+                request_data = {"prompt": prompt, **inference_params}
+        elif provider == "anthropic":
+            return litellm.AmazonAnthropicClaude3Config().transform_request(
+                model=model,
+                messages=messages,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                headers=headers,
+            )
+        elif provider == "nova":
+            return litellm.AmazonInvokeNovaConfig().transform_request(
+                model=model,
+                messages=messages,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                headers=headers,
+            )
+        elif provider == "ai21":
+            ## LOAD CONFIG
+            config = litellm.AmazonAI21Config.get_config()
+            for k, v in config.items():
+                if (
+                    k not in inference_params
+                ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+                    inference_params[k] = v
+
+            request_data = {"prompt": prompt, **inference_params}
+        elif provider == "mistral":
+            ## LOAD CONFIG
+            config = litellm.AmazonMistralConfig.get_config()
+            for k, v in config.items():
+                if (
+                    k not in inference_params
+                ):  # completion(top_k=3) > amazon_config(top_k=3) <- allows for dynamic variables to be passed in
+                    inference_params[k] = v
+
+            request_data = {"prompt": prompt, **inference_params}
+        elif provider == "amazon":  # amazon titan
+            ## LOAD CONFIG
+            config = litellm.AmazonTitanConfig.get_config()
+            for k, v in config.items():
+                if (
+                    k not in inference_params
+                ):  # completion(top_k=3) > amazon_config(top_k=3) <- allows for dynamic variables to be passed in
+                    inference_params[k] = v
+
+            request_data = {
+                "inputText": prompt,
+                "textGenerationConfig": inference_params,
+            }
+        elif provider == "meta" or provider == "llama" or provider == "deepseek_r1":
+            ## LOAD CONFIG
+            config = litellm.AmazonLlamaConfig.get_config()
+            for k, v in config.items():
+                if (
+                    k not in inference_params
+                ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+                    inference_params[k] = v
+            request_data = {"prompt": prompt, **inference_params}
+        else:
+            raise BedrockError(
+                status_code=404,
+                message="Bedrock Invoke HTTPX: Unknown provider={}, model={}. Try calling via converse route - `bedrock/converse/<model>`.".format(
+                    provider, model
+                ),
+            )
+
+        return request_data
+
+    def transform_response(  # noqa: PLR0915
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        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:
+        try:
+            completion_response = raw_response.json()
+        except Exception:
+            raise BedrockError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+        verbose_logger.debug(
+            "bedrock invoke response % s",
+            json.dumps(completion_response, indent=4, default=str),
+        )
+        provider = self.get_bedrock_invoke_provider(model)
+        outputText: Optional[str] = None
+        try:
+            if provider == "cohere":
+                if "text" in completion_response:
+                    outputText = completion_response["text"]  # type: ignore
+                elif "generations" in completion_response:
+                    outputText = completion_response["generations"][0]["text"]
+                    model_response.choices[0].finish_reason = map_finish_reason(
+                        completion_response["generations"][0]["finish_reason"]
+                    )
+            elif provider == "anthropic":
+                return litellm.AmazonAnthropicClaude3Config().transform_response(
+                    model=model,
+                    raw_response=raw_response,
+                    model_response=model_response,
+                    logging_obj=logging_obj,
+                    request_data=request_data,
+                    messages=messages,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    encoding=encoding,
+                    api_key=api_key,
+                    json_mode=json_mode,
+                )
+            elif provider == "nova":
+                return litellm.AmazonInvokeNovaConfig().transform_response(
+                    model=model,
+                    raw_response=raw_response,
+                    model_response=model_response,
+                    logging_obj=logging_obj,
+                    request_data=request_data,
+                    messages=messages,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    encoding=encoding,
+                )
+            elif provider == "ai21":
+                outputText = (
+                    completion_response.get("completions")[0].get("data").get("text")
+                )
+            elif provider == "meta" or provider == "llama" or provider == "deepseek_r1":
+                outputText = completion_response["generation"]
+            elif provider == "mistral":
+                outputText = completion_response["outputs"][0]["text"]
+                model_response.choices[0].finish_reason = completion_response[
+                    "outputs"
+                ][0]["stop_reason"]
+            else:  # amazon titan
+                outputText = completion_response.get("results")[0].get("outputText")
+        except Exception as e:
+            raise BedrockError(
+                message="Error processing={}, Received error={}".format(
+                    raw_response.text, str(e)
+                ),
+                status_code=422,
+            )
+
+        try:
+            if (
+                outputText is not None
+                and len(outputText) > 0
+                and hasattr(model_response.choices[0], "message")
+                and getattr(model_response.choices[0].message, "tool_calls", None)  # type: ignore
+                is None
+            ):
+                model_response.choices[0].message.content = outputText  # type: ignore
+            elif (
+                hasattr(model_response.choices[0], "message")
+                and getattr(model_response.choices[0].message, "tool_calls", None)  # type: ignore
+                is not None
+            ):
+                pass
+            else:
+                raise Exception()
+        except Exception as e:
+            raise BedrockError(
+                message="Error parsing received text={}.\nError-{}".format(
+                    outputText, str(e)
+                ),
+                status_code=raw_response.status_code,
+            )
+
+        ## CALCULATING USAGE - bedrock returns usage in the headers
+        bedrock_input_tokens = raw_response.headers.get(
+            "x-amzn-bedrock-input-token-count", None
+        )
+        bedrock_output_tokens = raw_response.headers.get(
+            "x-amzn-bedrock-output-token-count", None
+        )
+
+        prompt_tokens = int(
+            bedrock_input_tokens or litellm.token_counter(messages=messages)
+        )
+
+        completion_tokens = int(
+            bedrock_output_tokens
+            or litellm.token_counter(
+                text=model_response.choices[0].message.content,  # type: ignore
+                count_response_tokens=True,
+            )
+        )
+
+        model_response.created = int(time.time())
+        model_response.model = model
+        usage = Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        )
+        setattr(model_response, "usage", usage)
+
+        return model_response
+
+    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:
+        return headers
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return BedrockError(status_code=status_code, message=error_message)
+
+    @track_llm_api_timing()
+    def get_async_custom_stream_wrapper(
+        self,
+        model: str,
+        custom_llm_provider: str,
+        logging_obj: LiteLLMLoggingObj,
+        api_base: str,
+        headers: dict,
+        data: dict,
+        messages: list,
+        client: Optional[AsyncHTTPHandler] = None,
+        json_mode: Optional[bool] = None,
+    ) -> CustomStreamWrapper:
+        streaming_response = CustomStreamWrapper(
+            completion_stream=None,
+            make_call=partial(
+                make_call,
+                client=client,
+                api_base=api_base,
+                headers=headers,
+                data=json.dumps(data),
+                model=model,
+                messages=messages,
+                logging_obj=logging_obj,
+                fake_stream=True if "ai21" in api_base else False,
+                bedrock_invoke_provider=self.get_bedrock_invoke_provider(model),
+                json_mode=json_mode,
+            ),
+            model=model,
+            custom_llm_provider="bedrock",
+            logging_obj=logging_obj,
+        )
+        return streaming_response
+
+    @track_llm_api_timing()
+    def get_sync_custom_stream_wrapper(
+        self,
+        model: str,
+        custom_llm_provider: str,
+        logging_obj: LiteLLMLoggingObj,
+        api_base: str,
+        headers: dict,
+        data: dict,
+        messages: list,
+        client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+        json_mode: Optional[bool] = None,
+    ) -> CustomStreamWrapper:
+        if client is None or isinstance(client, AsyncHTTPHandler):
+            client = _get_httpx_client(params={})
+        streaming_response = CustomStreamWrapper(
+            completion_stream=None,
+            make_call=partial(
+                make_sync_call,
+                client=client,
+                api_base=api_base,
+                headers=headers,
+                data=json.dumps(data),
+                model=model,
+                messages=messages,
+                logging_obj=logging_obj,
+                fake_stream=True if "ai21" in api_base else False,
+                bedrock_invoke_provider=self.get_bedrock_invoke_provider(model),
+                json_mode=json_mode,
+            ),
+            model=model,
+            custom_llm_provider="bedrock",
+            logging_obj=logging_obj,
+        )
+        return streaming_response
+
+    @property
+    def has_custom_stream_wrapper(self) -> bool:
+        return True
+
+    @property
+    def supports_stream_param_in_request_body(self) -> bool:
+        """
+        Bedrock invoke does not allow passing `stream` in the request body.
+        """
+        return False
+
+    @staticmethod
+    def get_bedrock_invoke_provider(
+        model: str,
+    ) -> Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL]:
+        """
+        Helper function to get the bedrock provider from the model
+
+        handles 4 scenarios:
+        1. model=invoke/anthropic.claude-3-5-sonnet-20240620-v1:0 -> Returns `anthropic`
+        2. model=anthropic.claude-3-5-sonnet-20240620-v1:0 -> Returns `anthropic`
+        3. model=llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n -> Returns `llama`
+        4. model=us.amazon.nova-pro-v1:0 -> Returns `nova`
+        """
+        if model.startswith("invoke/"):
+            model = model.replace("invoke/", "", 1)
+
+        _split_model = model.split(".")[0]
+        if _split_model in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
+            return cast(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL, _split_model)
+
+        # If not a known provider, check for pattern with two slashes
+        provider = AmazonInvokeConfig._get_provider_from_model_path(model)
+        if provider is not None:
+            return provider
+
+        # check if provider == "nova"
+        if "nova" in model:
+            return "nova"
+
+        for provider in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
+            if provider in model:
+                return provider
+        return None
+
+    @staticmethod
+    def _get_provider_from_model_path(
+        model_path: str,
+    ) -> Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL]:
+        """
+        Helper function to get the provider from a model path with format: provider/model-name
+
+        Args:
+            model_path (str): The model path (e.g., 'llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n' or 'anthropic/model-name')
+
+        Returns:
+            Optional[str]: The provider name, or None if no valid provider found
+        """
+        parts = model_path.split("/")
+        if len(parts) >= 1:
+            provider = parts[0]
+            if provider in get_args(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL):
+                return cast(litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL, provider)
+        return None
+
+    def get_bedrock_model_id(
+        self,
+        optional_params: dict,
+        provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL],
+        model: str,
+    ) -> str:
+        modelId = optional_params.pop("model_id", None)
+        if modelId is not None:
+            modelId = self.encode_model_id(model_id=modelId)
+        else:
+            modelId = model
+
+        modelId = modelId.replace("invoke/", "", 1)
+        if provider == "llama" and "llama/" in modelId:
+            modelId = self._get_model_id_from_model_with_spec(modelId, spec="llama")
+        elif provider == "deepseek_r1" and "deepseek_r1/" in modelId:
+            modelId = self._get_model_id_from_model_with_spec(
+                modelId, spec="deepseek_r1"
+            )
+        return modelId
+
+    def _get_model_id_from_model_with_spec(
+        self,
+        model: str,
+        spec: str,
+    ) -> str:
+        """
+        Remove `llama` from modelID since `llama` is simply a spec to follow for custom bedrock models
+        """
+        model_id = model.replace(spec + "/", "")
+        return self.encode_model_id(model_id=model_id)
+
+    def encode_model_id(self, model_id: str) -> str:
+        """
+        Double encode the model ID to ensure it matches the expected double-encoded format.
+        Args:
+            model_id (str): The model ID to encode.
+        Returns:
+            str: The double-encoded model ID.
+        """
+        return urllib.parse.quote(model_id, safe="")
+
+    def convert_messages_to_prompt(
+        self, model, messages, provider, custom_prompt_dict
+    ) -> Tuple[str, Optional[list]]:
+        # handle anthropic prompts and amazon titan prompts
+        prompt = ""
+        chat_history: Optional[list] = None
+        ## CUSTOM PROMPT
+        if model in custom_prompt_dict:
+            # check if the model has a registered custom prompt
+            model_prompt_details = custom_prompt_dict[model]
+            prompt = custom_prompt(
+                role_dict=model_prompt_details["roles"],
+                initial_prompt_value=model_prompt_details.get(
+                    "initial_prompt_value", ""
+                ),
+                final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
+                messages=messages,
+            )
+            return prompt, None
+        ## ELSE
+        if provider == "anthropic" or provider == "amazon":
+            prompt = prompt_factory(
+                model=model, messages=messages, custom_llm_provider="bedrock"
+            )
+        elif provider == "mistral":
+            prompt = prompt_factory(
+                model=model, messages=messages, custom_llm_provider="bedrock"
+            )
+        elif provider == "meta" or provider == "llama":
+            prompt = prompt_factory(
+                model=model, messages=messages, custom_llm_provider="bedrock"
+            )
+        elif provider == "cohere":
+            prompt, chat_history = cohere_message_pt(messages=messages)
+        elif provider == "deepseek_r1":
+            prompt = deepseek_r1_pt(messages=messages)
+        else:
+            prompt = ""
+            for message in messages:
+                if "role" in message:
+                    if message["role"] == "user":
+                        prompt += f"{message['content']}"
+                    else:
+                        prompt += f"{message['content']}"
+                else:
+                    prompt += f"{message['content']}"
+        return prompt, chat_history  # type: ignore