Merge pull request #4033 from BerriAI/litellm_bedrock_converse_api

feat(bedrock_httpx.py): add support for bedrock converse api

.gitignore

@@ -59,3 +59,4 @@ myenv/*
 litellm/proxy/_experimental/out/404/index.html
 litellm/proxy/_experimental/out/model_hub/index.html
 litellm/proxy/_experimental/out/onboarding/index.html
+litellm/tests/log.txt

litellm/__init__.py

@@ -5,7 +5,7 @@ warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*
 ### INIT VARIABLES ###
 import threading, requests, os
 from typing import Callable, List, Optional, Dict, Union, Any, Literal
-from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from litellm.caching import Cache
 from litellm._logging import (
     set_verbose,
@@ -233,6 +233,7 @@ max_end_user_budget: Optional[float] = None
 #### RELIABILITY ####
 request_timeout: float = 6000
 module_level_aclient = AsyncHTTPHandler(timeout=request_timeout)
+module_level_client = HTTPHandler(timeout=request_timeout)
 num_retries: Optional[int] = None  # per model endpoint
 default_fallbacks: Optional[List] = None
 fallbacks: Optional[List] = None
@@ -766,7 +767,7 @@ from .llms.sagemaker import SagemakerConfig
 from .llms.ollama import OllamaConfig
 from .llms.ollama_chat import OllamaChatConfig
 from .llms.maritalk import MaritTalkConfig
-from .llms.bedrock_httpx import AmazonCohereChatConfig
+from .llms.bedrock_httpx import AmazonCohereChatConfig, AmazonConverseConfig
 from .llms.bedrock import (
     AmazonTitanConfig,
     AmazonAI21Config,

litellm/llms/bedrock_httpx.py

@@ -38,6 +38,8 @@ from .prompt_templates.factory import (
     extract_between_tags,
     parse_xml_params,
     contains_tag,
+    _bedrock_converse_messages_pt,
+    _bedrock_tools_pt,
 )
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from .base import BaseLLM
@@ -45,6 +47,11 @@ import httpx  # type: ignore
 from .bedrock import BedrockError, convert_messages_to_prompt, ModelResponseIterator
 from litellm.types.llms.bedrock import *
 import urllib.parse
+from litellm.types.llms.openai import (
+    ChatCompletionResponseMessage,
+    ChatCompletionToolCallChunk,
+    ChatCompletionToolCallFunctionChunk,
+)
 
 
 class AmazonCohereChatConfig:
@@ -118,6 +125,8 @@ class AmazonCohereChatConfig:
             "presence_penalty",
             "seed",
             "stop",
+            "tools",
+            "tool_choice",
         ]
 
     def map_openai_params(
@@ -176,6 +185,37 @@ async def make_call(
     return completion_stream
 
 
+def make_sync_call(
+    client: Optional[HTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+):
+    if client is None:
+        client = HTTPHandler()  # Create a new client if none provided
+
+    response = client.post(api_base, headers=headers, data=data, stream=True)
+
+    if response.status_code != 200:
+        raise BedrockError(status_code=response.status_code, message=response.read())
+
+    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=completion_stream,  # Pass the completion stream for logging
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream
+
+
 class BedrockLLM(BaseLLM):
     """
     Example call
@@ -1000,12 +1040,12 @@ class BedrockLLM(BaseLLM):
                 if isinstance(timeout, float) or isinstance(timeout, int):
                     timeout = httpx.Timeout(timeout)
                 _params["timeout"] = timeout
-            self.client = AsyncHTTPHandler(**_params)  # type: ignore
+            client = AsyncHTTPHandler(**_params)  # type: ignore
         else:
-            self.client = client  # type: ignore
+            client = client  # type: ignore
 
         try:
-            response = await self.client.post(api_base, headers=headers, data=data)  # type: ignore
+            response = await client.post(api_base, headers=headers, data=data)  # type: ignore
             response.raise_for_status()
         except httpx.HTTPStatusError as err:
             error_code = err.response.status_code
@@ -1069,6 +1109,745 @@ class BedrockLLM(BaseLLM):
         return super().embedding(*args, **kwargs)
 
 
+class AmazonConverseConfig:
+    """
+    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]
+
+    def __init__(
+        self,
+        maxTokens: Optional[int] = None,
+        stopSequences: Optional[List[str]] = None,
+        temperature: Optional[int] = None,
+        topP: Optional[int] = None,
+    ) -> None:
+        locals_ = locals()
+        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) -> List[str]:
+        supported_params = [
+            "max_tokens",
+            "stream",
+            "stream_options",
+            "stop",
+            "temperature",
+            "top_p",
+            "extra_headers",
+        ]
+
+        if (
+            model.startswith("anthropic")
+            or model.startswith("mistral")
+            or model.startswith("cohere")
+        ):
+            supported_params.append("tools")
+
+        if model.startswith("anthropic") or model.startswith("mistral"):
+            # 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")
+
+        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 map_openai_params(
+        self,
+        model: str,
+        non_default_params: dict,
+        optional_params: dict,
+        drop_params: bool,
+    ) -> dict:
+        for param, value in non_default_params.items():
+            if param == "max_tokens":
+                optional_params["maxTokens"] = value
+            if param == "stream":
+                optional_params["stream"] = value
+            if param == "stop":
+                if isinstance(value, str):
+                    value = [value]
+                optional_params["stop_sequences"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["topP"] = value
+            if param == "tools":
+                optional_params["tools"] = value
+            if param == "tool_choice":
+                _tool_choice_value = self.map_tool_choice_values(
+                    model=model, tool_choice=value, drop_params=drop_params  # type: ignore
+                )
+                if _tool_choice_value is not None:
+                    optional_params["tool_choice"] = _tool_choice_value
+        return optional_params
+
+
+class BedrockConverseLLM(BaseLLM):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def process_response(
+        self,
+        model: str,
+        response: Union[requests.Response, httpx.Response],
+        model_response: ModelResponse,
+        stream: bool,
+        logging_obj: Logging,
+        optional_params: dict,
+        api_key: str,
+        data: Union[dict, str],
+        messages: List,
+        print_verbose,
+        encoding,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key=api_key,
+            original_response=response.text,
+            additional_args={"complete_input_dict": data},
+        )
+        print_verbose(f"raw model_response: {response.text}")
+
+        ## 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] = []
+        if message is not None:
+            for content in message["content"]:
+                """
+                - Content is either a tool response or text
+                """
+                if "text" in content:
+                    content_str += content["text"]
+                if "toolUse" in content:
+                    _function_chunk = ChatCompletionToolCallFunctionChunk(
+                        name=content["toolUse"]["name"],
+                        arguments=json.dumps(content["toolUse"]["input"]),
+                    )
+                    _tool_response_chunk = ChatCompletionToolCallChunk(
+                        id=content["toolUse"]["toolUseId"],
+                        type="function",
+                        function=_function_chunk,
+                    )
+                    tools.append(_tool_response_chunk)
+        chat_completion_message["content"] = content_str
+        chat_completion_message["tool_calls"] = tools
+
+        ## CALCULATING USAGE - bedrock returns usage in the headers
+        input_tokens = completion_response["usage"]["inputTokens"]
+        output_tokens = completion_response["usage"]["outputTokens"]
+        total_tokens = completion_response["usage"]["totalTokens"]
+
+        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
+        usage = Usage(
+            prompt_tokens=input_tokens,
+            completion_tokens=output_tokens,
+            total_tokens=total_tokens,
+        )
+        setattr(model_response, "usage", usage)
+
+        return model_response
+
+    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 get_credentials(
+        self,
+        aws_access_key_id: Optional[str] = None,
+        aws_secret_access_key: Optional[str] = None,
+        aws_region_name: Optional[str] = None,
+        aws_session_name: Optional[str] = None,
+        aws_profile_name: Optional[str] = None,
+        aws_role_name: Optional[str] = None,
+        aws_web_identity_token: Optional[str] = None,
+    ):
+        """
+        Return a boto3.Credentials object
+        """
+        import boto3
+
+        ## CHECK IS  'os.environ/' passed in
+        params_to_check: List[Optional[str]] = [
+            aws_access_key_id,
+            aws_secret_access_key,
+            aws_region_name,
+            aws_session_name,
+            aws_profile_name,
+            aws_role_name,
+            aws_web_identity_token,
+        ]
+
+        # Iterate over parameters and update if needed
+        for i, param in enumerate(params_to_check):
+            if param and param.startswith("os.environ/"):
+                _v = get_secret(param)
+                if _v is not None and isinstance(_v, str):
+                    params_to_check[i] = _v
+        # Assign updated values back to parameters
+        (
+            aws_access_key_id,
+            aws_secret_access_key,
+            aws_region_name,
+            aws_session_name,
+            aws_profile_name,
+            aws_role_name,
+            aws_web_identity_token,
+        ) = params_to_check
+
+        ### CHECK STS ###
+        if (
+            aws_web_identity_token is not None
+            and aws_role_name is not None
+            and aws_session_name is not None
+        ):
+            oidc_token = get_secret(aws_web_identity_token)
+
+            if oidc_token is None:
+                raise BedrockError(
+                    message="OIDC token could not be retrieved from secret manager.",
+                    status_code=401,
+                )
+
+            sts_client = boto3.client("sts")
+
+            # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
+            # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
+            sts_response = sts_client.assume_role_with_web_identity(
+                RoleArn=aws_role_name,
+                RoleSessionName=aws_session_name,
+                WebIdentityToken=oidc_token,
+                DurationSeconds=3600,
+            )
+
+            session = boto3.Session(
+                aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
+                aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
+                aws_session_token=sts_response["Credentials"]["SessionToken"],
+                region_name=aws_region_name,
+            )
+
+            return session.get_credentials()
+        elif aws_role_name is not None and aws_session_name is not None:
+            sts_client = boto3.client(
+                "sts",
+                aws_access_key_id=aws_access_key_id,  # [OPTIONAL]
+                aws_secret_access_key=aws_secret_access_key,  # [OPTIONAL]
+            )
+
+            sts_response = sts_client.assume_role(
+                RoleArn=aws_role_name, RoleSessionName=aws_session_name
+            )
+
+            # Extract the credentials from the response and convert to Session Credentials
+            sts_credentials = sts_response["Credentials"]
+            from botocore.credentials import Credentials
+
+            credentials = Credentials(
+                access_key=sts_credentials["AccessKeyId"],
+                secret_key=sts_credentials["SecretAccessKey"],
+                token=sts_credentials["SessionToken"],
+            )
+            return credentials
+        elif aws_profile_name is not None:  ### CHECK SESSION ###
+            # uses auth values from AWS profile usually stored in ~/.aws/credentials
+            client = boto3.Session(profile_name=aws_profile_name)
+
+            return client.get_credentials()
+        else:
+            session = boto3.Session(
+                aws_access_key_id=aws_access_key_id,
+                aws_secret_access_key=aws_secret_access_key,
+                region_name=aws_region_name,
+            )
+
+            return session.get_credentials()
+
+    async def async_streaming(
+        self,
+        model: str,
+        messages: list,
+        api_base: str,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        data: str,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> CustomStreamWrapper:
+        streaming_response = CustomStreamWrapper(
+            completion_stream=None,
+            make_call=partial(
+                make_call,
+                client=client,
+                api_base=api_base,
+                headers=headers,
+                data=data,
+                model=model,
+                messages=messages,
+                logging_obj=logging_obj,
+            ),
+            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,
+        print_verbose: Callable,
+        data: str,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params=None,
+        logger_fn=None,
+        headers={},
+        client: Optional[AsyncHTTPHandler] = None,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            client = AsyncHTTPHandler(**_params)  # type: ignore
+        else:
+            client = client  # type: ignore
+
+        try:
+            response = await client.post(api_base, headers=headers, data=data)  # 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 as e:
+            raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+        return self.process_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,
+            print_verbose=print_verbose,
+            optional_params=optional_params,
+            encoding=encoding,
+        )
+
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        custom_prompt_dict: dict,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        logging_obj,
+        optional_params: dict,
+        acompletion: bool,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        litellm_params=None,
+        logger_fn=None,
+        extra_headers: Optional[dict] = None,
+        client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
+    ):
+        try:
+            import boto3
+
+            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'.")
+
+        ## SETUP ##
+        stream = optional_params.pop("stream", None)
+        modelId = optional_params.pop("model_id", None)
+        if modelId is not None:
+            modelId = self.encode_model_id(model_id=modelId)
+        else:
+            modelId = model
+
+        provider = model.split(".")[0]
+
+        ## 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_region_name = optional_params.pop("aws_region_name", 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)
+
+        ### SET REGION NAME ###
+        if aws_region_name is None:
+            # check env #
+            litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
+
+            if litellm_aws_region_name is not None and isinstance(
+                litellm_aws_region_name, str
+            ):
+                aws_region_name = litellm_aws_region_name
+
+            standard_aws_region_name = get_secret("AWS_REGION", None)
+            if standard_aws_region_name is not None and isinstance(
+                standard_aws_region_name, str
+            ):
+                aws_region_name = standard_aws_region_name
+
+            if aws_region_name is None:
+                aws_region_name = "us-west-2"
+
+        credentials: Credentials = self.get_credentials(
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            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,
+        )
+
+        ### SET RUNTIME ENDPOINT ###
+        endpoint_url = ""
+        env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT")
+        if aws_bedrock_runtime_endpoint is not None and isinstance(
+            aws_bedrock_runtime_endpoint, str
+        ):
+            endpoint_url = aws_bedrock_runtime_endpoint
+        elif env_aws_bedrock_runtime_endpoint and isinstance(
+            env_aws_bedrock_runtime_endpoint, str
+        ):
+            endpoint_url = env_aws_bedrock_runtime_endpoint
+        else:
+            endpoint_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com"
+
+        if (stream is not None and stream is True) and provider != "ai21":
+            endpoint_url = f"{endpoint_url}/model/{modelId}/converse-stream"
+        else:
+            endpoint_url = f"{endpoint_url}/model/{modelId}/converse"
+
+        sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
+
+        # Separate system prompt from rest of message
+        system_prompt_indices = []
+        system_content_blocks: List[SystemContentBlock] = []
+        for idx, message in enumerate(messages):
+            if message["role"] == "system":
+                _system_content_block = SystemContentBlock(text=message["content"])
+                system_content_blocks.append(_system_content_block)
+                system_prompt_indices.append(idx)
+        if len(system_prompt_indices) > 0:
+            for idx in reversed(system_prompt_indices):
+                messages.pop(idx)
+
+        inference_params = copy.deepcopy(optional_params)
+        additional_request_keys = []
+        additional_request_params = {}
+        supported_converse_params = AmazonConverseConfig.__annotations__.keys()
+        supported_tool_call_params = ["tools", "tool_choice"]
+        ## TRANSFORMATION ##
+        # send all model-specific params in 'additional_request_params'
+        for k, v in inference_params.items():
+            if (
+                k not in supported_converse_params
+                and k not in supported_tool_call_params
+            ):
+                additional_request_params[k] = v
+                additional_request_keys.append(k)
+        for key in additional_request_keys:
+            inference_params.pop(key, None)
+
+        bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt(
+            messages=messages
+        )
+        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: RequestObject = {
+            "messages": bedrock_messages,
+            "additionalModelRequestFields": additional_request_params,
+            "system": system_content_blocks,
+            "inferenceConfig": InferenceConfig(**inference_params),
+        }
+        if bedrock_tool_config is not None:
+            _data["toolConfig"] = bedrock_tool_config
+        data = json.dumps(_data)
+        ## COMPLETION CALL
+
+        headers = {"Content-Type": "application/json"}
+        if extra_headers is not None:
+            headers = {"Content-Type": "application/json", **extra_headers}
+        request = AWSRequest(
+            method="POST", url=endpoint_url, data=data, headers=headers
+        )
+        sigv4.add_auth(request)
+        prepped = request.prepare()
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": prepped.url,
+                "headers": prepped.headers,
+            },
+        )
+
+        ### ROUTING (ASYNC, STREAMING, SYNC)
+        if acompletion:
+            if isinstance(client, HTTPHandler):
+                client = None
+            if stream is True and provider != "ai21":
+                return self.async_streaming(
+                    model=model,
+                    messages=messages,
+                    data=data,
+                    api_base=prepped.url,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    encoding=encoding,
+                    logging_obj=logging_obj,
+                    optional_params=optional_params,
+                    stream=True,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    headers=prepped.headers,
+                    timeout=timeout,
+                    client=client,
+                )  # type: ignore
+            ### ASYNC COMPLETION
+            return self.async_completion(
+                model=model,
+                messages=messages,
+                data=data,
+                api_base=prepped.url,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                encoding=encoding,
+                logging_obj=logging_obj,
+                optional_params=optional_params,
+                stream=stream,  # type: ignore
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                headers=prepped.headers,
+                timeout=timeout,
+                client=client,
+            )  # type: ignore
+
+        if (stream is not None and stream is True) and provider != "ai21":
+
+            streaming_response = CustomStreamWrapper(
+                completion_stream=None,
+                make_call=partial(
+                    make_sync_call,
+                    client=None,
+                    api_base=prepped.url,
+                    headers=prepped.headers,  # type: ignore
+                    data=data,
+                    model=model,
+                    messages=messages,
+                    logging_obj=logging_obj,
+                ),
+                model=model,
+                custom_llm_provider="bedrock",
+                logging_obj=logging_obj,
+            )
+
+            ## LOGGING
+            logging_obj.post_call(
+                input=messages,
+                api_key="",
+                original_response=streaming_response,
+                additional_args={"complete_input_dict": data},
+            )
+            return streaming_response
+        ### COMPLETION
+
+        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 = HTTPHandler(**_params)  # type: ignore
+        else:
+            client = client
+        try:
+            response = client.post(url=prepped.url, headers=prepped.headers, data=data)  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise BedrockError(status_code=error_code, message=response.text)
+        except httpx.TimeoutException:
+            raise BedrockError(status_code=408, message="Timeout error occurred.")
+
+        return self.process_response(
+            model=model,
+            response=response,
+            model_response=model_response,
+            stream=stream,
+            logging_obj=logging_obj,
+            optional_params=optional_params,
+            api_key="",
+            data=data,
+            messages=messages,
+            print_verbose=print_verbose,
+            encoding=encoding,
+        )
+
+
 def get_response_stream_shape():
     from botocore.model import ServiceModel
     from botocore.loaders import Loader
@@ -1086,6 +1865,31 @@ class AWSEventStreamDecoder:
         self.model = model
         self.parser = EventStreamJSONParser()
 
+    def converse_chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk:
+        text = ""
+        tool_str = ""
+        is_finished = False
+        finish_reason = ""
+        usage: Optional[ConverseTokenUsageBlock] = None
+        if "delta" in chunk_data:
+            delta_obj = ContentBlockDeltaEvent(**chunk_data["delta"])
+            if "text" in delta_obj:
+                text = delta_obj["text"]
+            elif "toolUse" in delta_obj:
+                tool_str = delta_obj["toolUse"]["input"]
+        elif "stopReason" in chunk_data:
+            finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop"))
+        elif "usage" in chunk_data:
+            usage = ConverseTokenUsageBlock(**chunk_data["usage"])  # type: ignore
+        response = GenericStreamingChunk(
+            text=text,
+            tool_str=tool_str,
+            is_finished=is_finished,
+            finish_reason=finish_reason,
+            usage=usage,
+        )
+        return response
+
     def _chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk:
         text = ""
         is_finished = False
@@ -1098,19 +1902,8 @@ class AWSEventStreamDecoder:
             is_finished = True
             finish_reason = "stop"
         ######## bedrock.anthropic mappings ###############
-        elif "completion" in chunk_data:  # not claude-3
-            text = chunk_data["completion"]  # bedrock.anthropic
-            stop_reason = chunk_data.get("stop_reason", None)
-            if stop_reason != None:
-                is_finished = True
-                finish_reason = stop_reason
         elif "delta" in chunk_data:
-            if chunk_data["delta"].get("text", None) is not None:
+            return self.converse_chunk_parser(chunk_data=chunk_data)
-                text = chunk_data["delta"]["text"]
-            stop_reason = chunk_data["delta"].get("stop_reason", None)
-            if stop_reason != None:
-                is_finished = True
-                finish_reason = stop_reason
         ######## bedrock.mistral mappings ###############
         elif "outputs" in chunk_data:
             if (
@@ -1137,11 +1930,11 @@ class AWSEventStreamDecoder:
             is_finished = True
             finish_reason = chunk_data["completionReason"]
         return GenericStreamingChunk(
-            **{
+            text=text,
-                "text": text,
+            is_finished=is_finished,
-                "is_finished": is_finished,
+            finish_reason=finish_reason,
-                "finish_reason": finish_reason,
+            tool_str="",
-            }
+            usage=None,
         )
 
     def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GenericStreamingChunk]:
@@ -1178,9 +1971,14 @@ class AWSEventStreamDecoder:
         parsed_response = self.parser.parse(response_dict, get_response_stream_shape())
         if response_dict["status_code"] != 200:
             raise ValueError(f"Bad response code, expected 200: {response_dict}")
-
+        if "chunk" in parsed_response:
             chunk = parsed_response.get("chunk")
             if not chunk:
                 return None
-
             return chunk.get("bytes").decode()  # type: ignore[no-any-return]
+        else:
+            chunk = response_dict.get("body")
+            if not chunk:
+                return None
+
+            return chunk.decode()  # type: ignore[no-any-return]

litellm/llms/custom_httpx/http_handler.py

@@ -156,12 +156,13 @@ class HTTPHandler:
         self,
         url: str,
         data: Optional[Union[dict, str]] = None,
+        json: Optional[Union[dict, str]] = None,
         params: Optional[dict] = None,
         headers: Optional[dict] = None,
         stream: bool = False,
     ):
         req = self.client.build_request(
-            "POST", url, data=data, params=params, headers=headers  # type: ignore
+            "POST", url, data=data, json=json, params=params, headers=headers  # type: ignore
         )
         response = self.client.send(req, stream=stream)
         return response

litellm/llms/prompt_templates/factory.py

@@ -3,14 +3,7 @@ import requests, traceback
 import json, re, xml.etree.ElementTree as ET
 from jinja2 import Template, exceptions, meta, BaseLoader
 from jinja2.sandbox import ImmutableSandboxedEnvironment
-from typing import (
+from typing import Any, List, Mapping, MutableMapping, Optional, Sequence, Tuple
-    Any,
-    List,
-    Mapping,
-    MutableMapping,
-    Optional,
-    Sequence,
-)
 import litellm
 import litellm.types
 from litellm.types.completion import (
@@ -24,7 +17,7 @@ from litellm.types.completion import (
 import litellm.types.llms
 from litellm.types.llms.anthropic import *
 import uuid
-
+from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
 import litellm.types.llms.vertex_ai
 
 
@@ -1460,9 +1453,7 @@ def _load_image_from_url(image_url):
     try:
         from PIL import Image
     except:
-        raise Exception(
+        raise Exception("image conversion failed please run `pip install Pillow`")
-            "gemini image conversion failed please run `pip install Pillow`"
-        )
     from io import BytesIO
 
     try:
@@ -1613,6 +1604,380 @@ def azure_text_pt(messages: list):
     return prompt
 
 
+###### AMAZON BEDROCK #######
+
+from litellm.types.llms.bedrock import (
+    ToolResultContentBlock as BedrockToolResultContentBlock,
+    ToolResultBlock as BedrockToolResultBlock,
+    ToolConfigBlock as BedrockToolConfigBlock,
+    ToolUseBlock as BedrockToolUseBlock,
+    ImageSourceBlock as BedrockImageSourceBlock,
+    ImageBlock as BedrockImageBlock,
+    ContentBlock as BedrockContentBlock,
+    ToolInputSchemaBlock as BedrockToolInputSchemaBlock,
+    ToolSpecBlock as BedrockToolSpecBlock,
+    ToolBlock as BedrockToolBlock,
+    ToolChoiceValuesBlock as BedrockToolChoiceValuesBlock,
+)
+
+
+def get_image_details(image_url) -> Tuple[str, str]:
+    try:
+        import base64
+
+        # Send a GET request to the image URL
+        response = requests.get(image_url)
+        response.raise_for_status()  # Raise an exception for HTTP errors
+
+        # Check the response's content type to ensure it is an image
+        content_type = response.headers.get("content-type")
+        if not content_type or "image" not in content_type:
+            raise ValueError(
+                f"URL does not point to a valid image (content-type: {content_type})"
+            )
+
+        # Convert the image content to base64 bytes
+        base64_bytes = base64.b64encode(response.content).decode("utf-8")
+
+        # Get mime-type
+        mime_type = content_type.split("/")[
+            1
+        ]  # Extract mime-type from content-type header
+
+        return base64_bytes, mime_type
+
+    except requests.RequestException as e:
+        raise Exception(f"Request failed: {e}")
+    except Exception as e:
+        raise e
+
+
+def _process_bedrock_converse_image_block(image_url: str) -> BedrockImageBlock:
+    if "base64" in image_url:
+        # Case 1: Images with base64 encoding
+        import base64, re
+
+        # base 64 is passed as data:image/jpeg;base64,<base-64-encoded-image>
+        image_metadata, img_without_base_64 = image_url.split(",")
+
+        # read mime_type from img_without_base_64=data:image/jpeg;base64
+        # Extract MIME type using regular expression
+        mime_type_match = re.match(r"data:(.*?);base64", image_metadata)
+        if mime_type_match:
+            mime_type = mime_type_match.group(1)
+            image_format = mime_type.split("/")[1]
+        else:
+            mime_type = "image/jpeg"
+            image_format = "jpeg"
+        _blob = BedrockImageSourceBlock(bytes=img_without_base_64)
+        supported_image_formats = (
+            litellm.AmazonConverseConfig().get_supported_image_types()
+        )
+        if image_format in supported_image_formats:
+            return BedrockImageBlock(source=_blob, format=image_format)  # type: ignore
+        else:
+            # Handle the case when the image format is not supported
+            raise ValueError(
+                "Unsupported image format: {}. Supported formats: {}".format(
+                    image_format, supported_image_formats
+                )
+            )
+    elif "https:/" in image_url:
+        # Case 2: Images with direct links
+        image_bytes, image_format = get_image_details(image_url)
+        _blob = BedrockImageSourceBlock(bytes=image_bytes)
+        supported_image_formats = (
+            litellm.AmazonConverseConfig().get_supported_image_types()
+        )
+        if image_format in supported_image_formats:
+            return BedrockImageBlock(source=_blob, format=image_format)  # type: ignore
+        else:
+            # Handle the case when the image format is not supported
+            raise ValueError(
+                "Unsupported image format: {}. Supported formats: {}".format(
+                    image_format, supported_image_formats
+                )
+            )
+    else:
+        raise ValueError(
+            "Unsupported image type. Expected either image url or base64 encoded string - \
+                e.g. 'data:image/jpeg;base64,<base64-encoded-string>'"
+        )
+
+
+def _convert_to_bedrock_tool_call_invoke(
+    tool_calls: list,
+) -> List[BedrockContentBlock]:
+    """
+    OpenAI tool invokes:
+    {
+      "role": "assistant",
+      "content": null,
+      "tool_calls": [
+        {
+          "id": "call_abc123",
+          "type": "function",
+          "function": {
+            "name": "get_current_weather",
+            "arguments": "{\n\"location\": \"Boston, MA\"\n}"
+          }
+        }
+      ]
+    },
+    """
+    """
+    Bedrock tool invokes: 
+    [   
+        {
+            "role": "assistant",
+            "toolUse": {
+                "input": {"location": "Boston, MA", ..},
+                "name": "get_current_weather",
+                "toolUseId": "call_abc123"
+            }
+        }
+    ]
+    """
+    """
+    - json.loads argument
+    - extract name 
+    - extract id
+    """
+
+    try:
+        _parts_list: List[BedrockContentBlock] = []
+        for tool in tool_calls:
+            if "function" in tool:
+                id = tool["id"]
+                name = tool["function"].get("name", "")
+                arguments = tool["function"].get("arguments", "")
+                arguments_dict = json.loads(arguments)
+                bedrock_tool = BedrockToolUseBlock(
+                    input=arguments_dict, name=name, toolUseId=id
+                )
+                bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool)
+                _parts_list.append(bedrock_content_block)
+        return _parts_list
+    except Exception as e:
+        raise Exception(
+            "Unable to convert openai tool calls={} to bedrock tool calls. Received error={}".format(
+                tool_calls, str(e)
+            )
+        )
+
+
+def _convert_to_bedrock_tool_call_result(
+    message: dict,
+) -> BedrockMessageBlock:
+    """
+    OpenAI message with a tool result looks like:
+    {
+        "tool_call_id": "tool_1",
+        "role": "tool",
+        "name": "get_current_weather",
+        "content": "function result goes here",
+    },
+
+    OpenAI message with a function call result looks like:
+    {
+        "role": "function",
+        "name": "get_current_weather",
+        "content": "function result goes here",
+    }
+    """
+    """
+    Bedrock result looks like this: 
+    {
+        "role": "user",
+        "content": [
+            {
+                "toolResult": {
+                    "toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q",
+                    "content": [
+                        {
+                            "json": {
+                                "song": "Elemental Hotel",
+                                "artist": "8 Storey Hike"
+                            }
+                        }
+                    ]
+                }
+            }
+        ]
+    }
+    """
+    """
+    - 
+    """
+    content = message.get("content", "")
+    name = message.get("name", "")
+    id = message.get("tool_call_id", str(uuid.uuid4()))
+
+    tool_result_content_block = BedrockToolResultContentBlock(text=content)
+    tool_result = BedrockToolResultBlock(
+        content=[tool_result_content_block],
+        toolUseId=id,
+    )
+    content_block = BedrockContentBlock(toolResult=tool_result)
+
+    return BedrockMessageBlock(role="user", content=[content_block])
+
+
+def _bedrock_converse_messages_pt(messages: List) -> List[BedrockMessageBlock]:
+    """
+    Converts given messages from OpenAI format to Bedrock format
+
+    - Roles must alternate b/w 'user' and 'model' (same as anthropic -> merge consecutive roles)
+    - Please ensure that function response turn comes immediately after a function call turn
+    """
+
+    contents: List[BedrockMessageBlock] = []
+    msg_i = 0
+    while msg_i < len(messages):
+        user_content: List[BedrockContentBlock] = []
+        init_msg_i = msg_i
+        ## MERGE CONSECUTIVE USER CONTENT ##
+        while msg_i < len(messages) and messages[msg_i]["role"] == "user":
+            if isinstance(messages[msg_i]["content"], list):
+                _parts: List[BedrockContentBlock] = []
+                for element in messages[msg_i]["content"]:
+                    if isinstance(element, dict):
+                        if element["type"] == "text":
+                            _part = BedrockContentBlock(text=element["text"])
+                            _parts.append(_part)
+                        elif element["type"] == "image_url":
+                            image_url = element["image_url"]["url"]
+                            _part = _process_bedrock_converse_image_block(  # type: ignore
+                                image_url=image_url
+                            )
+                            _parts.append(BedrockContentBlock(image=_part))  # type: ignore
+                user_content.extend(_parts)
+            else:
+                _part = BedrockContentBlock(text=messages[msg_i]["content"])
+                user_content.append(_part)
+
+            msg_i += 1
+
+        if user_content:
+            contents.append(BedrockMessageBlock(role="user", content=user_content))
+        assistant_content: List[BedrockContentBlock] = []
+        ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+        while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+            if isinstance(messages[msg_i]["content"], list):
+                assistants_parts: List[BedrockContentBlock] = []
+                for element in messages[msg_i]["content"]:
+                    if isinstance(element, dict):
+                        if element["type"] == "text":
+                            assistants_part = BedrockContentBlock(text=element["text"])
+                            assistants_parts.append(assistants_part)
+                        elif element["type"] == "image_url":
+                            image_url = element["image_url"]["url"]
+                            assistants_part = _process_bedrock_converse_image_block(  # type: ignore
+                                image_url=image_url
+                            )
+                            assistants_parts.append(
+                                BedrockContentBlock(image=assistants_part)  # type: ignore
+                            )
+                assistant_content.extend(assistants_parts)
+            elif messages[msg_i].get(
+                "tool_calls", []
+            ):  # support assistant tool invoke convertion
+                assistant_content.extend(
+                    _convert_to_bedrock_tool_call_invoke(messages[msg_i]["tool_calls"])
+                )
+            else:
+                assistant_text = (
+                    messages[msg_i].get("content") or ""
+                )  # either string or none
+                if assistant_text:
+                    assistant_content.append(BedrockContentBlock(text=assistant_text))
+
+            msg_i += 1
+
+        if assistant_content:
+            contents.append(
+                BedrockMessageBlock(role="assistant", content=assistant_content)
+            )
+
+        ## APPEND TOOL CALL MESSAGES ##
+        if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
+            tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i])
+            contents.append(tool_call_result)
+            msg_i += 1
+        if msg_i == init_msg_i:  # prevent infinite loops
+            raise Exception(
+                "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
+                    messages[msg_i]
+                )
+            )
+
+    return contents
+
+
+def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
+    """
+    OpenAI tools looks like:
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather in a given location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                    },
+                    "required": ["location"],
+                },
+            }
+        }
+    ]
+    """
+    """
+    Bedrock toolConfig looks like: 
+    "tools": [
+        {
+            "toolSpec": {
+                "name": "top_song",
+                "description": "Get the most popular song played on a radio station.",
+                "inputSchema": {
+                    "json": {
+                        "type": "object",
+                        "properties": {
+                            "sign": {
+                                "type": "string",
+                                "description": "The call sign for the radio station for which you want the most popular song. Example calls signs are WZPZ, and WKRP."
+                            }
+                        },
+                        "required": [
+                            "sign"
+                        ]
+                    }
+                }
+            }
+        }
+    ]
+    """
+    tool_block_list: List[BedrockToolBlock] = []
+    for tool in tools:
+        parameters = tool.get("function", {}).get("parameters", None)
+        name = tool.get("function", {}).get("name", "")
+        description = tool.get("function", {}).get("description", "")
+        tool_input_schema = BedrockToolInputSchemaBlock(json=parameters)
+        tool_spec = BedrockToolSpecBlock(
+            inputSchema=tool_input_schema, name=name, description=description
+        )
+        tool_block = BedrockToolBlock(toolSpec=tool_spec)
+        tool_block_list.append(tool_block)
+
+    return tool_block_list
+
+
 # Function call template
 def function_call_prompt(messages: list, functions: list):
     function_prompt = """Produce JSON OUTPUT ONLY! Adhere to this format {"name": "function_name", "arguments":{"argument_name": "argument_value"}} The following functions are available to you:"""

litellm/main.py

@@ -79,7 +79,7 @@ from .llms.anthropic import AnthropicChatCompletion
 from .llms.anthropic_text import AnthropicTextCompletion
 from .llms.huggingface_restapi import Huggingface
 from .llms.predibase import PredibaseChatCompletion
-from .llms.bedrock_httpx import BedrockLLM
+from .llms.bedrock_httpx import BedrockLLM, BedrockConverseLLM
 from .llms.vertex_httpx import VertexLLM
 from .llms.triton import TritonChatCompletion
 from .llms.prompt_templates.factory import (
@@ -122,6 +122,7 @@ huggingface = Huggingface()
 predibase_chat_completions = PredibaseChatCompletion()
 triton_chat_completions = TritonChatCompletion()
 bedrock_chat_completion = BedrockLLM()
+bedrock_converse_chat_completion = BedrockConverseLLM()
 vertex_chat_completion = VertexLLM()
 ####### COMPLETION ENDPOINTS ################
 
@@ -2102,6 +2103,24 @@ def completion(
                             custom_llm_provider="bedrock",
                             logging_obj=logging,
                         )
+            else:
+                if model.startswith("anthropic"):
+                    response = bedrock_converse_chat_completion.completion(
+                        model=model,
+                        messages=messages,
+                        custom_prompt_dict=custom_prompt_dict,
+                        model_response=model_response,
+                        print_verbose=print_verbose,
+                        optional_params=optional_params,
+                        litellm_params=litellm_params,
+                        logger_fn=logger_fn,
+                        encoding=encoding,
+                        logging_obj=logging,
+                        extra_headers=extra_headers,
+                        timeout=timeout,
+                        acompletion=acompletion,
+                        client=client,
+                    )
                 else:
                     response = bedrock_chat_completion.completion(
                         model=model,

litellm/tests/test_bedrock_completion.py

@@ -243,6 +243,7 @@ def test_completion_bedrock_claude_sts_oidc_auth():
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
+
 @pytest.mark.skipif(
     os.environ.get("CIRCLE_OIDC_TOKEN_V2") is None,
     reason="Cannot run without being in CircleCI Runner",
@@ -277,7 +278,15 @@ def test_completion_bedrock_httpx_command_r_sts_oidc_auth():
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
-def test_bedrock_claude_3():
+
+@pytest.mark.parametrize(
+    "image_url",
+    [
+        "data:image/png;base64,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",
+        "https://avatars.githubusercontent.com/u/29436595?v=",
+    ],
+)
+def test_bedrock_claude_3(image_url):
     try:
         litellm.set_verbose = True
         data = {
@@ -294,7 +303,7 @@ def test_bedrock_claude_3():
                         {
                             "image_url": {
                                 "detail": "high",
-                                "url": "data:image/png;base64,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",
+                                "url": image_url,
                             },
                             "type": "image_url",
                         },
@@ -313,7 +322,6 @@ def test_bedrock_claude_3():
         # Add any assertions here to check the response
         assert len(response.choices) > 0
         assert len(response.choices[0].message.content) > 0
-
     except RateLimitError:
         pass
     except Exception as e:
@@ -552,7 +560,7 @@ def test_bedrock_ptu():
         assert "url" in mock_client_post.call_args.kwargs
         assert (
             mock_client_post.call_args.kwargs["url"]
-            == "https://bedrock-runtime.us-west-2.amazonaws.com/model/arn%3Aaws%3Abedrock%3Aus-west-2%3A888602223428%3Aprovisioned-model%2F8fxff74qyhs3/invoke"
+            == "https://bedrock-runtime.us-west-2.amazonaws.com/model/arn%3Aaws%3Abedrock%3Aus-west-2%3A888602223428%3Aprovisioned-model%2F8fxff74qyhs3/converse"
         )
         mock_client_post.assert_called_once()
 

litellm/tests/test_completion.py

@@ -300,7 +300,11 @@ def test_completion_claude_3():
         pytest.fail(f"Error occurred: {e}")
 
 
-def test_completion_claude_3_function_call():
+@pytest.mark.parametrize(
+    "model",
+    ["anthropic/claude-3-opus-20240229", "anthropic.claude-3-sonnet-20240229-v1:0"],
+)
+def test_completion_claude_3_function_call(model):
     litellm.set_verbose = True
     tools = [
         {
@@ -331,13 +335,14 @@ def test_completion_claude_3_function_call():
     try:
         # test without max tokens
         response = completion(
-            model="anthropic/claude-3-opus-20240229",
+            model=model,
             messages=messages,
             tools=tools,
             tool_choice={
                 "type": "function",
                 "function": {"name": "get_current_weather"},
             },
+            drop_params=True,
         )
 
         # Add any assertions, here to check response args
@@ -364,10 +369,11 @@ def test_completion_claude_3_function_call():
         )
         # In the second response, Claude should deduce answer from tool results
         second_response = completion(
-            model="anthropic/claude-3-opus-20240229",
+            model=model,
             messages=messages,
             tools=tools,
             tool_choice="auto",
+            drop_params=True,
         )
         print(second_response)
     except Exception as e:

litellm/tests/test_prompt_factory.py

@@ -15,6 +15,7 @@ from litellm.llms.prompt_templates.factory import (
     claude_2_1_pt,
     llama_2_chat_pt,
     prompt_factory,
+    _bedrock_tools_pt,
 )
 
 
@@ -128,3 +129,27 @@ def test_anthropic_messages_pt():
 
 
 # codellama_prompt_format()
+def test_bedrock_tool_calling_pt():
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather in a given location",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        },
+                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                    },
+                    "required": ["location"],
+                },
+            },
+        }
+    ]
+    converted_tools = _bedrock_tools_pt(tools=tools)
+
+    print(converted_tools)

litellm/tests/test_streaming.py

@@ -1284,18 +1284,18 @@ async def test_completion_replicate_llama3_streaming(sync_mode):
 #         pytest.fail(f"Error occurred: {e}")
 
 
-@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.parametrize("sync_mode", [True])  # False
 @pytest.mark.parametrize(
     "model",
     [
-        # "bedrock/cohere.command-r-plus-v1:0",
+        "bedrock/cohere.command-r-plus-v1:0",
-        # "anthropic.claude-3-sonnet-20240229-v1:0",
+        "anthropic.claude-3-sonnet-20240229-v1:0",
-        # "anthropic.claude-instant-v1",
+        "anthropic.claude-instant-v1",
-        # "bedrock/ai21.j2-mid",
+        "bedrock/ai21.j2-mid",
-        # "mistral.mistral-7b-instruct-v0:2",
+        "mistral.mistral-7b-instruct-v0:2",
-        # "bedrock/amazon.titan-tg1-large",
+        "bedrock/amazon.titan-tg1-large",
-        # "meta.llama3-8b-instruct-v1:0",
+        "meta.llama3-8b-instruct-v1:0",
-        "cohere.command-text-v14"
+        "cohere.command-text-v14",
     ],
 )
 @pytest.mark.asyncio

litellm/types/llms/bedrock.py

@@ -1,4 +1,4 @@
-from typing import TypedDict, Any, Union, Optional
+from typing import TypedDict, Any, Union, Optional, Literal, List
 import json
 from typing_extensions import (
     Self,
@@ -11,10 +11,137 @@ from typing_extensions import (
 )
 
 
+class SystemContentBlock(TypedDict):
+    text: str
+
+
+class ImageSourceBlock(TypedDict):
+    bytes: Optional[str]  # base 64 encoded string
+
+
+class ImageBlock(TypedDict):
+    format: Literal["png", "jpeg", "gif", "webp"]
+    source: ImageSourceBlock
+
+
+class ToolResultContentBlock(TypedDict, total=False):
+    image: ImageBlock
+    json: dict
+    text: str
+
+
+class ToolResultBlock(TypedDict, total=False):
+    content: Required[List[ToolResultContentBlock]]
+    toolUseId: Required[str]
+    status: Literal["success", "error"]
+
+
+class ToolUseBlock(TypedDict):
+    input: dict
+    name: str
+    toolUseId: str
+
+
+class ContentBlock(TypedDict, total=False):
+    text: str
+    image: ImageBlock
+    toolResult: ToolResultBlock
+    toolUse: ToolUseBlock
+
+
+class MessageBlock(TypedDict):
+    content: List[ContentBlock]
+    role: Literal["user", "assistant"]
+
+
+class ConverseMetricsBlock(TypedDict):
+    latencyMs: float  # time in ms
+
+
+class ConverseResponseOutputBlock(TypedDict):
+    message: Optional[MessageBlock]
+
+
+class ConverseTokenUsageBlock(TypedDict):
+    inputTokens: int
+    outputTokens: int
+    totalTokens: int
+
+
+class ConverseResponseBlock(TypedDict):
+    additionalModelResponseFields: dict
+    metrics: ConverseMetricsBlock
+    output: ConverseResponseOutputBlock
+    stopReason: (
+        str  # end_turn | tool_use | max_tokens | stop_sequence | content_filtered
+    )
+    usage: ConverseTokenUsageBlock
+
+
+class ToolInputSchemaBlock(TypedDict):
+    json: Optional[dict]
+
+
+class ToolSpecBlock(TypedDict, total=False):
+    inputSchema: Required[ToolInputSchemaBlock]
+    name: Required[str]
+    description: str
+
+
+class ToolBlock(TypedDict):
+    toolSpec: Optional[ToolSpecBlock]
+
+
+class SpecificToolChoiceBlock(TypedDict):
+    name: str
+
+
+class ToolChoiceValuesBlock(TypedDict, total=False):
+    any: dict
+    auto: dict
+    tool: SpecificToolChoiceBlock
+
+
+class ToolConfigBlock(TypedDict, total=False):
+    tools: Required[List[ToolBlock]]
+    toolChoice: Union[str, ToolChoiceValuesBlock]
+
+
+class InferenceConfig(TypedDict, total=False):
+    maxTokens: int
+    stopSequences: List[str]
+    temperature: float
+    topP: float
+
+
+class ToolBlockDeltaEvent(TypedDict):
+    input: str
+
+
+class ContentBlockDeltaEvent(TypedDict, total=False):
+    """
+    Either 'text' or 'toolUse' will be specified for Converse API streaming response.
+    """
+
+    text: str
+    toolUse: ToolBlockDeltaEvent
+
+
+class RequestObject(TypedDict, total=False):
+    additionalModelRequestFields: dict
+    additionalModelResponseFieldPaths: List[str]
+    inferenceConfig: InferenceConfig
+    messages: Required[List[MessageBlock]]
+    system: List[SystemContentBlock]
+    toolConfig: ToolConfigBlock
+
+
 class GenericStreamingChunk(TypedDict):
     text: Required[str]
+    tool_str: Required[str]
     is_finished: Required[bool]
     finish_reason: Required[str]
+    usage: Optional[ConverseTokenUsageBlock]
 
 
 class Document(TypedDict):

litellm/types/llms/openai.py

@@ -293,3 +293,20 @@ class ListBatchRequest(TypedDict, total=False):
     extra_headers: Optional[Dict[str, str]]
     extra_body: Optional[Dict[str, str]]
     timeout: Optional[float]
+
+
+class ChatCompletionToolCallFunctionChunk(TypedDict):
+    name: str
+    arguments: str
+
+
+class ChatCompletionToolCallChunk(TypedDict):
+    id: str
+    type: Literal["function"]
+    function: ChatCompletionToolCallFunctionChunk
+
+
+class ChatCompletionResponseMessage(TypedDict, total=False):
+    content: Optional[str]
+    tool_calls: List[ChatCompletionToolCallChunk]
+    role: Literal["assistant"]

litellm/utils.py

@@ -239,6 +239,8 @@ def map_finish_reason(
         return "length"
     elif finish_reason == "tool_use":  # anthropic
         return "tool_calls"
+    elif finish_reason == "content_filtered":
+        return "content_filter"
     return finish_reason
 
 
@@ -4064,7 +4066,9 @@ def openai_token_counter(
                     for c in value:
                         if c["type"] == "text":
                             text += c["text"]
-                            num_tokens += len(encoding.encode(c["text"], disallowed_special=()))
+                            num_tokens += len(
+                                encoding.encode(c["text"], disallowed_special=())
+                            )
                         elif c["type"] == "image_url":
                             if isinstance(c["image_url"], dict):
                                 image_url_dict = c["image_url"]
@@ -5637,8 +5641,7 @@ def get_optional_params(
                 optional_params["stream"] = stream
         elif "anthropic" in model:
             _check_valid_arg(supported_params=supported_params)
-            # anthropic params on bedrock
+            if "aws_bedrock_client" in passed_params:  # deprecated boto3.invoke route.
-            # \"max_tokens_to_sample\":300,\"temperature\":0.5,\"top_p\":1,\"stop_sequences\":[\"\\\\n\\\\nHuman:\"]}"
                 if model.startswith("anthropic.claude-3"):
                     optional_params = (
                         litellm.AmazonAnthropicClaude3Config().map_openai_params(
@@ -5651,6 +5654,17 @@ def get_optional_params(
                         non_default_params=non_default_params,
                         optional_params=optional_params,
                     )
+            else:  # bedrock httpx route
+                optional_params = litellm.AmazonConverseConfig().map_openai_params(
+                    model=model,
+                    non_default_params=non_default_params,
+                    optional_params=optional_params,
+                    drop_params=(
+                        drop_params
+                        if drop_params is not None and isinstance(drop_params, bool)
+                        else False
+                    ),
+                )
         elif "amazon" in model:  # amazon titan llms
             _check_valid_arg(supported_params=supported_params)
             # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large
@@ -6427,20 +6441,7 @@ def get_supported_openai_params(
     - None if unmapped
     """
     if custom_llm_provider == "bedrock":
-        if model.startswith("anthropic.claude-3"):
+        return litellm.AmazonConverseConfig().get_supported_openai_params(model=model)
-            return litellm.AmazonAnthropicClaude3Config().get_supported_openai_params()
-        elif model.startswith("anthropic"):
-            return litellm.AmazonAnthropicConfig().get_supported_openai_params()
-        elif model.startswith("ai21"):
-            return ["max_tokens", "temperature", "top_p", "stream"]
-        elif model.startswith("amazon"):
-            return ["max_tokens", "temperature", "stop", "top_p", "stream"]
-        elif model.startswith("meta"):
-            return ["max_tokens", "temperature", "top_p", "stream"]
-        elif model.startswith("cohere"):
-            return ["stream", "temperature", "max_tokens"]
-        elif model.startswith("mistral"):
-            return ["max_tokens", "temperature", "stop", "top_p", "stream"]
     elif custom_llm_provider == "ollama":
         return litellm.OllamaConfig().get_supported_openai_params()
     elif custom_llm_provider == "ollama_chat":
@@ -11405,12 +11406,27 @@ class CustomStreamWrapper:
                 if response_obj["is_finished"]:
                     self.received_finish_reason = response_obj["finish_reason"]
             elif self.custom_llm_provider == "bedrock":
+                from litellm.types.llms.bedrock import GenericStreamingChunk
+
                 if self.received_finish_reason is not None:
                     raise StopIteration
-                response_obj = self.handle_bedrock_stream(chunk)
+                response_obj: GenericStreamingChunk = chunk
                 completion_obj["content"] = response_obj["text"]
+
                 if response_obj["is_finished"]:
                     self.received_finish_reason = response_obj["finish_reason"]
+
+                if (
+                    self.stream_options
+                    and self.stream_options.get("include_usage", False) is True
+                    and response_obj["usage"] is not None
+                ):
+                    self.sent_stream_usage = True
+                    model_response.usage = litellm.Usage(
+                        prompt_tokens=response_obj["usage"]["inputTokens"],
+                        completion_tokens=response_obj["usage"]["outputTokens"],
+                        total_tokens=response_obj["usage"]["totalTokens"],
+                    )
             elif self.custom_llm_provider == "sagemaker":
                 print_verbose(f"ENTERS SAGEMAKER STREAMING for chunk {chunk}")
                 response_obj = self.handle_sagemaker_stream(chunk)
@@ -11677,7 +11693,7 @@ class CustomStreamWrapper:
                 and hasattr(model_response, "usage")
                 and hasattr(model_response.usage, "prompt_tokens")
             ):
-                if self.sent_first_chunk == False:
+                if self.sent_first_chunk is False:
                     completion_obj["role"] = "assistant"
                     self.sent_first_chunk = True
                 model_response.choices[0].delta = Delta(**completion_obj)
@@ -11845,6 +11861,8 @@ class CustomStreamWrapper:
 
     def __next__(self):
         try:
+            if self.completion_stream is None:
+                self.fetch_sync_stream()
             while True:
                 if (
                     isinstance(self.completion_stream, str)
@@ -11919,6 +11937,14 @@ class CustomStreamWrapper:
                     custom_llm_provider=self.custom_llm_provider,
                 )
 
+    def fetch_sync_stream(self):
+        if self.completion_stream is None and self.make_call is not None:
+            # Call make_call to get the completion stream
+            self.completion_stream = self.make_call(client=litellm.module_level_client)
+            self._stream_iter = self.completion_stream.__iter__()
+
+        return self.completion_stream
+
     async def fetch_stream(self):
         if self.completion_stream is None and self.make_call is not None:
             # Call make_call to get the completion stream

ruff.toml

@@ -1 +1,3 @@
-ignore = ["F403", "F401"]
+ignore = ["F405"]
+extend-select = ["E501"]
+line-length = 120