Merge branch 'main' into litellm_security_fix

.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

docs/my-website/docs/proxy/alerting.md

@@ -62,6 +62,23 @@ curl -X GET 'http://localhost:4000/health/services?service=slack' \
   -H 'Authorization: Bearer sk-1234'
 ```
 
+## Advanced - Redacting Messages from Alerts
+
+By default alerts show the `messages/input` passed to the LLM. If you want to redact this from slack alerting set the following setting on your config
+
+
+```shell
+general_settings:
+  alerting: ["slack"]
+  alert_types: ["spend_reports"] 
+
+litellm_settings:
+  redact_messages_in_exceptions: True
+```
+
+
+
+
 ## Advanced - Opting into specific alert types
 
 Set `alert_types` if you want to Opt into only specific alert types

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,
@@ -60,6 +60,7 @@ _async_failure_callback: List[Callable] = (
 pre_call_rules: List[Callable] = []
 post_call_rules: List[Callable] = []
 turn_off_message_logging: Optional[bool] = False
+redact_messages_in_exceptions: Optional[bool] = False
 store_audit_logs = False  # Enterprise feature, allow users to see audit logs
 ## end of callbacks #############
 
@@ -233,6 +234,7 @@ 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 +768,7 @@ 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/_service_logger.py

@@ -1,10 +1,18 @@
-import litellm, traceback
+from datetime import datetime
+import litellm
 from litellm.proxy._types import UserAPIKeyAuth
 from .types.services import ServiceTypes, ServiceLoggerPayload
 from .integrations.prometheus_services import PrometheusServicesLogger
 from .integrations.custom_logger import CustomLogger
 from datetime import timedelta
-from typing import Union
+from typing import Union, Optional, TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from opentelemetry.trace import Span as _Span
+
+    Span = _Span
+else:
+    Span = Any
 
 
 class ServiceLogging(CustomLogger):
@@ -40,7 +48,13 @@ class ServiceLogging(CustomLogger):
             self.mock_testing_sync_failure_hook += 1
 
     async def async_service_success_hook(
-        self, service: ServiceTypes, duration: float, call_type: str
+        self,
+        service: ServiceTypes,
+        call_type: str,
+        duration: float,
+        parent_otel_span: Optional[Span] = None,
+        start_time: Optional[datetime] = None,
+        end_time: Optional[datetime] = None,
     ):
         """
         - For counting if the redis, postgres call is successful
@@ -61,6 +75,16 @@ class ServiceLogging(CustomLogger):
                     payload=payload
                 )
 
+        from litellm.proxy.proxy_server import open_telemetry_logger
+
+        if parent_otel_span is not None and open_telemetry_logger is not None:
+            await open_telemetry_logger.async_service_success_hook(
+                payload=payload,
+                parent_otel_span=parent_otel_span,
+                start_time=start_time,
+                end_time=end_time,
+            )
+
     async def async_service_failure_hook(
         self,
         service: ServiceTypes,

litellm/integrations/opentelemetry.py

@@ -1,9 +1,21 @@
 import os
-from typing import Optional
 from dataclasses import dataclass
+from datetime import datetime
 
 from litellm.integrations.custom_logger import CustomLogger
 from litellm._logging import verbose_logger
+from litellm.types.services import ServiceLoggerPayload
+from typing import Union, Optional, TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from opentelemetry.trace import Span as _Span
+    from litellm.proxy.proxy_server import UserAPIKeyAuth as _UserAPIKeyAuth
+
+    Span = _Span
+    UserAPIKeyAuth = _UserAPIKeyAuth
+else:
+    Span = Any
+    UserAPIKeyAuth = Any
 
 
 LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm")
@@ -77,6 +89,56 @@ class OpenTelemetry(CustomLogger):
     async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
         self._handle_failure(kwargs, response_obj, start_time, end_time)
 
+    async def async_service_success_hook(
+        self,
+        payload: ServiceLoggerPayload,
+        parent_otel_span: Optional[Span] = None,
+        start_time: Optional[datetime] = None,
+        end_time: Optional[datetime] = None,
+    ):
+        from opentelemetry import trace
+        from datetime import datetime
+        from opentelemetry.trace import Status, StatusCode
+
+        if parent_otel_span is not None:
+            _span_name = payload.service
+            service_logging_span = self.tracer.start_span(
+                name=_span_name,
+                context=trace.set_span_in_context(parent_otel_span),
+                start_time=self._to_ns(start_time),
+            )
+            service_logging_span.set_attribute(key="call_type", value=payload.call_type)
+            service_logging_span.set_attribute(
+                key="service", value=payload.service.value
+            )
+            service_logging_span.set_status(Status(StatusCode.OK))
+            service_logging_span.end(end_time=self._to_ns(end_time))
+
+    async def async_post_call_failure_hook(
+        self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth
+    ):
+        from opentelemetry.trace import Status, StatusCode
+        from opentelemetry import trace
+
+        parent_otel_span = user_api_key_dict.parent_otel_span
+        if parent_otel_span is not None:
+            parent_otel_span.set_status(Status(StatusCode.ERROR))
+            _span_name = "Failed Proxy Server Request"
+
+            # Exception Logging Child Span
+            exception_logging_span = self.tracer.start_span(
+                name=_span_name,
+                context=trace.set_span_in_context(parent_otel_span),
+            )
+            exception_logging_span.set_attribute(
+                key="exception", value=str(original_exception)
+            )
+            exception_logging_span.set_status(Status(StatusCode.ERROR))
+            exception_logging_span.end(end_time=self._to_ns(datetime.now()))
+
+            # End Parent OTEL Sspan
+            parent_otel_span.end(end_time=self._to_ns(datetime.now()))
+
     def _handle_sucess(self, kwargs, response_obj, start_time, end_time):
         from opentelemetry.trace import Status, StatusCode
 
@@ -85,15 +147,18 @@ class OpenTelemetry(CustomLogger):
             kwargs,
             self.config,
         )
+        _parent_context, parent_otel_span = self._get_span_context(kwargs)
 
         span = self.tracer.start_span(
             name=self._get_span_name(kwargs),
             start_time=self._to_ns(start_time),
-            context=self._get_span_context(kwargs),
+            context=_parent_context,
         )
         span.set_status(Status(StatusCode.OK))
         self.set_attributes(span, kwargs, response_obj)
         span.end(end_time=self._to_ns(end_time))
+        if parent_otel_span is not None:
+            parent_otel_span.end(end_time=self._to_ns(datetime.now()))
 
     def _handle_failure(self, kwargs, response_obj, start_time, end_time):
         from opentelemetry.trace import Status, StatusCode
@@ -122,17 +187,28 @@ class OpenTelemetry(CustomLogger):
         from opentelemetry.trace.propagation.tracecontext import (
             TraceContextTextMapPropagator,
         )
+        from opentelemetry import trace
 
         litellm_params = kwargs.get("litellm_params", {}) or {}
         proxy_server_request = litellm_params.get("proxy_server_request", {}) or {}
         headers = proxy_server_request.get("headers", {}) or {}
         traceparent = headers.get("traceparent", None)
+        _metadata = litellm_params.get("metadata", {})
+        parent_otel_span = _metadata.get("litellm_parent_otel_span", None)
+
+        """
+        Two way to use parents in opentelemetry
+        - using the traceparent header
+        - using the parent_otel_span in the [metadata][parent_otel_span]
+        """
+        if parent_otel_span is not None:
+            return trace.set_span_in_context(parent_otel_span), parent_otel_span
 
         if traceparent is None:
-            return None
+            return None, None
         else:
             carrier = {"traceparent": traceparent}
-            return TraceContextTextMapPropagator().extract(carrier=carrier)
+            return TraceContextTextMapPropagator().extract(carrier=carrier), None
 
     def _get_span_processor(self):
         from opentelemetry.sdk.trace.export import (

litellm/integrations/slack_alerting.py

@@ -326,8 +326,8 @@ class SlackAlerting(CustomLogger):
                 end_time=end_time,
             )
         )
-        if litellm.turn_off_message_logging:
-            messages = "Message not logged. `litellm.turn_off_message_logging=True`."
+        if litellm.turn_off_message_logging or litellm.redact_messages_in_exceptions:
+            messages = "Message not logged. litellm.redact_messages_in_exceptions=True"
         request_info = f"\nRequest Model: `{model}`\nAPI Base: `{api_base}`\nMessages: `{messages}`"
         slow_message = f"`Responses are slow - {round(time_difference_float,2)}s response time > Alerting threshold: {self.alerting_threshold}s`"
         if time_difference_float > self.alerting_threshold:
@@ -567,9 +567,12 @@ class SlackAlerting(CustomLogger):
             except:
                 messages = ""
 
-            if litellm.turn_off_message_logging:
+            if (
+                litellm.turn_off_message_logging
+                or litellm.redact_messages_in_exceptions
+            ):
                 messages = (
-                    "Message not logged. `litellm.turn_off_message_logging=True`."
+                    "Message not logged. litellm.redact_messages_in_exceptions=True"
                 )
             request_info = f"\nRequest Model: `{model}`\nMessages: `{messages}`"
         else:

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(
@@ -169,7 +178,38 @@ async def make_call(
     logging_obj.post_call(
         input=messages,
         api_key="",
-        original_response=completion_stream,  # Pass the completion stream for logging
+        original_response="first stream response received",
+        additional_args={"complete_input_dict": data},
+    )
+
+    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="first stream response received",
         additional_args={"complete_input_dict": data},
     )
 
@@ -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 +1109,738 @@ 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,
+            )
+
+            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 +1858,31 @@ 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 +1895,8 @@ 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:
-                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
+            return self.converse_chunk_parser(chunk_data=chunk_data)
         ######## bedrock.mistral mappings ###############
         elif "outputs" in chunk_data:
             if (
@@ -1137,11 +1923,11 @@ class AWSEventStreamDecoder:
             is_finished = True
             finish_reason = chunk_data["completionReason"]
         return GenericStreamingChunk(
-            **{
-                "text": text,
-                "is_finished": is_finished,
-                "finish_reason": finish_reason,
-            }
+            text=text,
+            is_finished=is_finished,
+            finish_reason=finish_reason,
+            tool_str="",
+            usage=None,
         )
 
     def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GenericStreamingChunk]:
@@ -1178,9 +1964,14 @@ 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
 
-        chunk = parsed_response.get("chunk")
-        if not chunk:
-            return None
-
-        return chunk.get("bytes").decode()  # type: ignore[no-any-return]
+            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 (
-    Any,
-    List,
-    Mapping,
-    MutableMapping,
-    Optional,
-    Sequence,
-)
+from typing import Any, List, Mapping, MutableMapping, Optional, Sequence, Tuple
 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(
-            "gemini image conversion failed please run `pip install Pillow`"
-        )
+        raise Exception("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/llms/vertex_ai.py

@@ -12,6 +12,7 @@ from litellm.llms.prompt_templates.factory import (
     convert_to_gemini_tool_call_result,
     convert_to_gemini_tool_call_invoke,
 )
+from litellm.types.files import get_file_mime_type_for_file_type, get_file_type_from_extension, is_gemini_1_5_accepted_file_type, is_video_file_type
 
 
 class VertexAIError(Exception):
@@ -297,29 +298,31 @@ def _convert_gemini_role(role: str) -> Literal["user", "model"]:
 
 def _process_gemini_image(image_url: str) -> PartType:
     try:
-        if ".mp4" in image_url and "gs://" in image_url:
-            # Case 1: Videos with Cloud Storage URIs
-            part_mime = "video/mp4"
-            _file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
-            return PartType(file_data=_file_data)
-        elif ".pdf" in image_url and "gs://" in image_url:
-            # Case 2: PDF's with Cloud Storage URIs
-            part_mime = "application/pdf"
-            _file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
-            return PartType(file_data=_file_data)
-        elif "gs://" in image_url:
-            # Case 3: Images with Cloud Storage URIs
-            # The supported MIME types for images include image/png and image/jpeg.
-            part_mime = "image/png" if "png" in image_url else "image/jpeg"
-            _file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
-            return PartType(file_data=_file_data)
+        # GCS URIs
+        if "gs://" in image_url:
+            # Figure out file type
+            extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png"
+            extension = extension_with_dot[1:] # Ex: "png"
+
+            file_type = get_file_type_from_extension(extension)
+
+            # Validate the file type is supported by Gemini
+            if not is_gemini_1_5_accepted_file_type(file_type):
+                raise Exception(f"File type not supported by gemini - {file_type}")
+            
+            mime_type = get_file_mime_type_for_file_type(file_type)
+            file_data = FileDataType(mime_type=mime_type, file_uri=image_url)
+
+            return PartType(file_data=file_data)
+
+        # Direct links
         elif "https:/" in image_url:
-            # Case 4: Images with direct links
             image = _load_image_from_url(image_url)
             _blob = BlobType(data=image.data, mime_type=image._mime_type)
             return PartType(inline_data=_blob)
+        
+        # Base64 encoding
         elif "base64" in image_url:
-            # Case 5: Images with base64 encoding
             import base64, re
 
             # base 64 is passed as data:image/jpeg;base64,<base-64-encoded-image>
@@ -426,112 +429,6 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
     return contents
 
 
-def _gemini_vision_convert_messages(messages: list):
-    """
-    Converts given messages for GPT-4 Vision to Gemini format.
-
-    Args:
-        messages (list): The messages to convert. Each message can be a dictionary with a "content" key. The content can be a string or a list of elements. If it is a string, it will be concatenated to the prompt. If it is a list, each element will be processed based on its type:
-            - If the element is a dictionary with a "type" key equal to "text", its "text" value will be concatenated to the prompt.
-            - If the element is a dictionary with a "type" key equal to "image_url", its "image_url" value will be added to the list of images.
-
-    Returns:
-        tuple: A tuple containing the prompt (a string) and the processed images (a list of objects representing the images).
-
-    Raises:
-        VertexAIError: If the import of the 'vertexai' module fails, indicating that 'google-cloud-aiplatform' needs to be installed.
-        Exception: If any other exception occurs during the execution of the function.
-
-    Note:
-        This function is based on the code from the 'gemini/getting-started/intro_gemini_python.ipynb' notebook in the 'generative-ai' repository on GitHub.
-        The supported MIME types for images include 'image/png' and 'image/jpeg'.
-
-    Examples:
-        >>> messages = [
-        ...     {"content": "Hello, world!"},
-        ...     {"content": [{"type": "text", "text": "This is a text message."}, {"type": "image_url", "image_url": "example.com/image.png"}]},
-        ... ]
-        >>> _gemini_vision_convert_messages(messages)
-        ('Hello, world!This is a text message.', [<Part object>, <Part object>])
-    """
-    try:
-        import vertexai
-    except:
-        raise VertexAIError(
-            status_code=400,
-            message="vertexai import failed please run `pip install google-cloud-aiplatform`",
-        )
-    try:
-        from vertexai.preview.language_models import (
-            ChatModel,
-            CodeChatModel,
-            InputOutputTextPair,
-        )
-        from vertexai.language_models import TextGenerationModel, CodeGenerationModel
-        from vertexai.preview.generative_models import (
-            GenerativeModel,
-            Part,
-            GenerationConfig,
-            Image,
-        )
-
-        # given messages for gpt-4 vision, convert them for gemini
-        # https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/intro_gemini_python.ipynb
-        prompt = ""
-        images = []
-        for message in messages:
-            if isinstance(message["content"], str):
-                prompt += message["content"]
-            elif isinstance(message["content"], list):
-                # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models
-                for element in message["content"]:
-                    if isinstance(element, dict):
-                        if element["type"] == "text":
-                            prompt += element["text"]
-                        elif element["type"] == "image_url":
-                            image_url = element["image_url"]["url"]
-                            images.append(image_url)
-        # processing images passed to gemini
-        processed_images = []
-        for img in images:
-            if "gs://" in img:
-                # Case 1: Images with Cloud Storage URIs
-                # The supported MIME types for images include image/png and image/jpeg.
-                part_mime = "image/png" if "png" in img else "image/jpeg"
-                google_clooud_part = Part.from_uri(img, mime_type=part_mime)
-                processed_images.append(google_clooud_part)
-            elif "https:/" in img:
-                # Case 2: Images with direct links
-                image = _load_image_from_url(img)
-                processed_images.append(image)
-            elif ".mp4" in img and "gs://" in img:
-                # Case 3: Videos with Cloud Storage URIs
-                part_mime = "video/mp4"
-                google_clooud_part = Part.from_uri(img, mime_type=part_mime)
-                processed_images.append(google_clooud_part)
-            elif "base64" in img:
-                # Case 4: 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 = img.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)
-                else:
-                    mime_type = "image/jpeg"
-                decoded_img = base64.b64decode(img_without_base_64)
-                processed_image = Part.from_data(data=decoded_img, mime_type=mime_type)
-                processed_images.append(processed_image)
-        return prompt, processed_images
-    except Exception as e:
-        raise e
-
-
 def _get_client_cache_key(model: str, vertex_project: str, vertex_location: str):
     _cache_key = f"{model}-{vertex_project}-{vertex_location}"
     return _cache_key

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 ################
 
@@ -2107,22 +2108,40 @@ def completion(
                             logging_obj=logging,
                         )
             else:
-                response = bedrock_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,
-                )
+                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,
+                        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,
+                    )
             if optional_params.get("stream", False):
                 ## LOGGING
                 logging.post_call(

litellm/proxy/_types.py

@@ -1,11 +1,20 @@
 from pydantic import BaseModel, Extra, Field, model_validator, Json, ConfigDict
 from dataclasses import fields
 import enum
-from typing import Optional, List, Union, Dict, Literal, Any, TypedDict
+from typing import Optional, List, Union, Dict, Literal, Any, TypedDict, TYPE_CHECKING
 from datetime import datetime
 import uuid, json, sys, os
 from litellm.types.router import UpdateRouterConfig
 from litellm.types.utils import ProviderField
+from typing_extensions import Annotated
+
+
+if TYPE_CHECKING:
+    from opentelemetry.trace import Span as _Span
+
+    Span = _Span
+else:
+    Span = Any
 
 
 class LitellmUserRoles(str, enum.Enum):
@@ -1195,6 +1204,7 @@ class UserAPIKeyAuth(
         ]
     ] = None
     allowed_model_region: Optional[Literal["eu"]] = None
+    parent_otel_span: Optional[Span] = None
 
     @model_validator(mode="before")
     @classmethod
@@ -1207,6 +1217,9 @@ class UserAPIKeyAuth(
                 values.update({"api_key": hash_token(values.get("api_key"))})
         return values
 
+    class Config:
+        arbitrary_types_allowed = True
+
 
 class LiteLLM_Config(LiteLLMBase):
     param_name: str

litellm/proxy/auth/auth_checks.py

@@ -17,10 +17,19 @@ from litellm.proxy._types import (
     LiteLLM_OrganizationTable,
     LitellmUserRoles,
 )
-from typing import Optional, Literal, Union
-from litellm.proxy.utils import PrismaClient
+from typing import Optional, Literal, TYPE_CHECKING, Any
+from litellm.proxy.utils import PrismaClient, ProxyLogging, log_to_opentelemetry
 from litellm.caching import DualCache
 import litellm
+from litellm.types.services import ServiceLoggerPayload, ServiceTypes
+from datetime import datetime
+
+if TYPE_CHECKING:
+    from opentelemetry.trace import Span as _Span
+
+    Span = _Span
+else:
+    Span = Any
 
 all_routes = LiteLLMRoutes.openai_routes.value + LiteLLMRoutes.management_routes.value
 
@@ -216,10 +225,13 @@ def get_actual_routes(allowed_routes: list) -> list:
     return actual_routes
 
 
+@log_to_opentelemetry
 async def get_end_user_object(
     end_user_id: Optional[str],
     prisma_client: Optional[PrismaClient],
     user_api_key_cache: DualCache,
+    parent_otel_span: Optional[Span] = None,
+    proxy_logging_obj: Optional[ProxyLogging] = None,
 ) -> Optional[LiteLLM_EndUserTable]:
     """
     Returns end user object, if in db.
@@ -279,11 +291,14 @@ async def get_end_user_object(
         return None
 
 
+@log_to_opentelemetry
 async def get_user_object(
     user_id: str,
     prisma_client: Optional[PrismaClient],
     user_api_key_cache: DualCache,
     user_id_upsert: bool,
+    parent_otel_span: Optional[Span] = None,
+    proxy_logging_obj: Optional[ProxyLogging] = None,
 ) -> Optional[LiteLLM_UserTable]:
     """
     - Check if user id in proxy User Table
@@ -330,10 +345,13 @@ async def get_user_object(
         )
 
 
+@log_to_opentelemetry
 async def get_team_object(
     team_id: str,
     prisma_client: Optional[PrismaClient],
     user_api_key_cache: DualCache,
+    parent_otel_span: Optional[Span] = None,
+    proxy_logging_obj: Optional[ProxyLogging] = None,
 ) -> LiteLLM_TeamTable:
     """
     - Check if team id in proxy Team Table
@@ -372,10 +390,13 @@ async def get_team_object(
         )
 
 
+@log_to_opentelemetry
 async def get_org_object(
     org_id: str,
     prisma_client: Optional[PrismaClient],
     user_api_key_cache: DualCache,
+    parent_otel_span: Optional[Span] = None,
+    proxy_logging_obj: Optional[ProxyLogging] = None,
 ):
     """
     - Check if org id in proxy Org Table

litellm/proxy/litellm_pre_call_utils.py

@@ -0,0 +1,130 @@
+import copy
+from fastapi import Request
+from typing import Any, Dict, Optional, TYPE_CHECKING
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm._logging import verbose_proxy_logger, verbose_logger
+
+if TYPE_CHECKING:
+    from litellm.proxy.proxy_server import ProxyConfig as _ProxyConfig
+
+    ProxyConfig = _ProxyConfig
+else:
+    ProxyConfig = Any
+
+
+def parse_cache_control(cache_control):
+    cache_dict = {}
+    directives = cache_control.split(", ")
+
+    for directive in directives:
+        if "=" in directive:
+            key, value = directive.split("=")
+            cache_dict[key] = value
+        else:
+            cache_dict[directive] = True
+
+    return cache_dict
+
+
+async def add_litellm_data_to_request(
+    data: dict,
+    request: Request,
+    user_api_key_dict: UserAPIKeyAuth,
+    proxy_config: ProxyConfig,
+    general_settings: Optional[Dict[str, Any]] = None,
+    version: Optional[str] = None,
+):
+    """
+    Adds LiteLLM-specific data to the request.
+
+    Args:
+        data (dict): The data dictionary to be modified.
+        request (Request): The incoming request.
+        user_api_key_dict (UserAPIKeyAuth): The user API key dictionary.
+        general_settings (Optional[Dict[str, Any]], optional): General settings. Defaults to None.
+        version (Optional[str], optional): Version. Defaults to None.
+
+    Returns:
+        dict: The modified data dictionary.
+
+    """
+    query_params = dict(request.query_params)
+    if "api-version" in query_params:
+        data["api_version"] = query_params["api-version"]
+
+    # Include original request and headers in the data
+    data["proxy_server_request"] = {
+        "url": str(request.url),
+        "method": request.method,
+        "headers": dict(request.headers),
+        "body": copy.copy(data),  # use copy instead of deepcopy
+    }
+
+    ## Cache Controls
+    headers = request.headers
+    verbose_proxy_logger.debug("Request Headers: %s", headers)
+    cache_control_header = headers.get("Cache-Control", None)
+    if cache_control_header:
+        cache_dict = parse_cache_control(cache_control_header)
+        data["ttl"] = cache_dict.get("s-maxage")
+
+    verbose_proxy_logger.debug("receiving data: %s", data)
+    # users can pass in 'user' param to /chat/completions. Don't override it
+    if data.get("user", None) is None and user_api_key_dict.user_id is not None:
+        # if users are using user_api_key_auth, set `user` in `data`
+        data["user"] = user_api_key_dict.user_id
+
+    if "metadata" not in data:
+        data["metadata"] = {}
+    data["metadata"]["user_api_key"] = user_api_key_dict.api_key
+    data["metadata"]["user_api_key_alias"] = getattr(
+        user_api_key_dict, "key_alias", None
+    )
+    data["metadata"]["user_api_end_user_max_budget"] = getattr(
+        user_api_key_dict, "end_user_max_budget", None
+    )
+    data["metadata"]["litellm_api_version"] = version
+
+    if general_settings is not None:
+        data["metadata"]["global_max_parallel_requests"] = general_settings.get(
+            "global_max_parallel_requests", None
+        )
+
+    data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id
+    data["metadata"]["user_api_key_org_id"] = user_api_key_dict.org_id
+    data["metadata"]["user_api_key_team_id"] = getattr(
+        user_api_key_dict, "team_id", None
+    )
+    data["metadata"]["user_api_key_team_alias"] = getattr(
+        user_api_key_dict, "team_alias", None
+    )
+    data["metadata"]["user_api_key_metadata"] = user_api_key_dict.metadata
+    _headers = dict(request.headers)
+    _headers.pop(
+        "authorization", None
+    )  # do not store the original `sk-..` api key in the db
+    data["metadata"]["headers"] = _headers
+    data["metadata"]["endpoint"] = str(request.url)
+    # Add the OTEL Parent Trace before sending it LiteLLM
+    data["metadata"]["litellm_parent_otel_span"] = user_api_key_dict.parent_otel_span
+
+    ### END-USER SPECIFIC PARAMS ###
+    if user_api_key_dict.allowed_model_region is not None:
+        data["allowed_model_region"] = user_api_key_dict.allowed_model_region
+
+    ### TEAM-SPECIFIC PARAMS ###
+    if user_api_key_dict.team_id is not None:
+        team_config = await proxy_config.load_team_config(
+            team_id=user_api_key_dict.team_id
+        )
+        if len(team_config) == 0:
+            pass
+        else:
+            team_id = team_config.pop("team_id", None)
+            data["metadata"]["team_id"] = team_id
+            data = {
+                **team_config,
+                **data,
+            }  # add the team-specific configs to the completion call
+
+    return data

litellm/proxy/proxy_config.yaml

@@ -21,10 +21,14 @@ model_list:
 
 general_settings:
   master_key: sk-1234
+  alerting: ["slack"]
+
+litellm_settings:
+  callbacks: ["otel"]
+  store_audit_logs: true
+  redact_messages_in_exceptions: True
   enforced_params:  
     - user
     - metadata
     - metadata.generation_name
 
-litellm_settings:
-  store_audit_logs: true

litellm/proxy/utils.py

@@ -1,4 +1,4 @@
-from typing import Optional, List, Any, Literal, Union
+from typing import Optional, List, Any, Literal, Union, TYPE_CHECKING
 import os
 import subprocess
 import hashlib
@@ -46,6 +46,15 @@ from email.mime.text import MIMEText
 from email.mime.multipart import MIMEMultipart
 from datetime import datetime, timedelta
 from litellm.integrations.slack_alerting import SlackAlerting
+from typing_extensions import overload
+from functools import wraps
+
+if TYPE_CHECKING:
+    from opentelemetry.trace import Span as _Span
+
+    Span = _Span
+else:
+    Span = Any
 
 
 def print_verbose(print_statement):
@@ -63,6 +72,58 @@ def print_verbose(print_statement):
         print(f"LiteLLM Proxy: {print_statement}")  # noqa
 
 
+def safe_deep_copy(data):
+    """
+    Safe Deep Copy
+
+    The LiteLLM Request has some object that can-not be pickled / deep copied
+
+    Use this function to safely deep copy the LiteLLM Request
+    """
+
+    # Step 1: Remove the litellm_parent_otel_span
+    if isinstance(data, dict):
+        # remove litellm_parent_otel_span since this is not picklable
+        if "metadata" in data and "litellm_parent_otel_span" in data["metadata"]:
+            litellm_parent_otel_span = data["metadata"].pop("litellm_parent_otel_span")
+    new_data = copy.deepcopy(data)
+
+    # Step 2: re-add the litellm_parent_otel_span after doing a deep copy
+    if isinstance(data, dict):
+        if "metadata" in data:
+            data["metadata"]["litellm_parent_otel_span"] = litellm_parent_otel_span
+    return new_data
+
+
+def log_to_opentelemetry(func):
+    @wraps(func)
+    async def wrapper(*args, **kwargs):
+        start_time = datetime.now()
+        result = await func(*args, **kwargs)
+        end_time = datetime.now()
+
+        # Log to OTEL only if "parent_otel_span" is in kwargs and is not None
+        if (
+            "parent_otel_span" in kwargs
+            and kwargs["parent_otel_span"] is not None
+            and "proxy_logging_obj" in kwargs
+            and kwargs["proxy_logging_obj"] is not None
+        ):
+            proxy_logging_obj = kwargs["proxy_logging_obj"]
+            await proxy_logging_obj.service_logging_obj.async_service_success_hook(
+                service=ServiceTypes.DB,
+                call_type=func.__name__,
+                parent_otel_span=kwargs["parent_otel_span"],
+                duration=0.0,
+                start_time=start_time,
+                end_time=end_time,
+            )
+        # end of logging to otel
+        return result
+
+    return wrapper
+
+
 ### LOGGING ###
 class ProxyLogging:
     """
@@ -282,7 +343,7 @@ class ProxyLogging:
         """
         Runs the CustomLogger's async_moderation_hook()
         """
-        new_data = copy.deepcopy(data)
+        new_data = safe_deep_copy(data)
         for callback in litellm.callbacks:
             try:
                 if isinstance(callback, CustomLogger):
@@ -832,6 +893,7 @@ class PrismaClient:
         max_time=10,  # maximum total time to retry for
         on_backoff=on_backoff,  # specifying the function to call on backoff
     )
+    @log_to_opentelemetry
     async def get_data(
         self,
         token: Optional[Union[str, list]] = None,
@@ -858,6 +920,8 @@ class PrismaClient:
         limit: Optional[
             int
         ] = None,  # pagination, number of rows to getch when find_all==True
+        parent_otel_span: Optional[Span] = None,
+        proxy_logging_obj: Optional[ProxyLogging] = None,
     ):
         args_passed_in = locals()
         start_time = time.time()
@@ -2829,6 +2893,10 @@ missing_keys_html_form = """
     """
 
 
+def _to_ns(dt):
+    return int(dt.timestamp() * 1e9)
+
+
 def get_error_message_str(e: Exception) -> str:
     error_message = ""
     if isinstance(e, HTTPException):

litellm/py.typed

@@ -0,0 +1,2 @@
+# Marker file to instruct type checkers to look for inline type annotations in this package.
+# See PEP 561 for more information.

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:
@@ -1398,7 +1404,6 @@ def test_hf_test_completion_tgi():
 
 
 def mock_post(url, data=None, json=None, headers=None):
-
     print(f"url={url}")
     if "text-classification" in url:
         raise Exception("Model not found")
@@ -2241,9 +2246,6 @@ def test_re_use_openaiClient():
         pytest.fail("got Exception", e)
 
 
-# test_re_use_openaiClient()
-
-
 def test_completion_azure():
     try:
         print("azure gpt-3.5 test\n\n")

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_proxy_exception_mapping.py

@@ -210,7 +210,9 @@ def test_chat_completion_exception_any_model(client):
         )
         assert isinstance(openai_exception, openai.BadRequestError)
         _error_message = openai_exception.message
-        assert "chat_completion: Invalid model name passed in model=Lite-GPT-12" in str(_error_message)
+        assert "chat_completion: Invalid model name passed in model=Lite-GPT-12" in str(
+            _error_message
+        )
 
     except Exception as e:
         pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}")
@@ -238,7 +240,9 @@ def test_embedding_exception_any_model(client):
         print("Exception raised=", openai_exception)
         assert isinstance(openai_exception, openai.BadRequestError)
         _error_message = openai_exception.message
-        assert "embeddings: Invalid model name passed in model=Lite-GPT-12" in str(_error_message)
+        assert "embeddings: Invalid model name passed in model=Lite-GPT-12" in str(
+            _error_message
+        )
 
     except Exception as e:
         pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}")

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",
-        # "anthropic.claude-3-sonnet-20240229-v1:0",
-        # "anthropic.claude-instant-v1",
-        # "bedrock/ai21.j2-mid",
-        # "mistral.mistral-7b-instruct-v0:2",
-        # "bedrock/amazon.titan-tg1-large",
-        # "meta.llama3-8b-instruct-v1:0",
-        "cohere.command-text-v14"
+        "bedrock/cohere.command-r-plus-v1:0",
+        "anthropic.claude-3-sonnet-20240229-v1:0",
+        "anthropic.claude-instant-v1",
+        "bedrock/ai21.j2-mid",
+        "mistral.mistral-7b-instruct-v0:2",
+        "bedrock/amazon.titan-tg1-large",
+        "meta.llama3-8b-instruct-v1:0",
+        "cohere.command-text-v14",
     ],
 )
 @pytest.mark.asyncio

litellm/types/files.py

@@ -0,0 +1,281 @@
+from enum import Enum
+from types import MappingProxyType
+from typing import List, Set
+
+"""
+Base Enums/Consts
+"""
+
+
+class FileType(Enum):
+    AAC = "AAC"
+    CSV = "CSV"
+    DOC = "DOC"
+    DOCX = "DOCX"
+    FLAC = "FLAC"
+    FLV = "FLV"
+    GIF = "GIF"
+    GOOGLE_DOC = "GOOGLE_DOC"
+    GOOGLE_DRAWINGS = "GOOGLE_DRAWINGS"
+    GOOGLE_SHEETS = "GOOGLE_SHEETS"
+    GOOGLE_SLIDES = "GOOGLE_SLIDES"
+    HEIC = "HEIC"
+    HEIF = "HEIF"
+    HTML = "HTML"
+    JPEG = "JPEG"
+    JSON = "JSON"
+    M4A = "M4A"
+    M4V = "M4V"
+    MOV = "MOV"
+    MP3 = "MP3"
+    MP4 = "MP4"
+    MPEG = "MPEG"
+    MPEGPS = "MPEGPS"
+    MPG = "MPG"
+    MPA = "MPA"
+    MPGA = "MPGA"
+    OGG = "OGG"
+    OPUS = "OPUS"
+    PDF = "PDF"
+    PCM = "PCM"
+    PNG = "PNG"
+    PPT = "PPT"
+    PPTX = "PPTX"
+    RTF = "RTF"
+    THREE_GPP = "3GPP"
+    TXT = "TXT"
+    WAV = "WAV"
+    WEBM = "WEBM"
+    WEBP = "WEBP"
+    WMV = "WMV"
+    XLS = "XLS"
+    XLSX = "XLSX"
+
+
+FILE_EXTENSIONS: MappingProxyType[FileType, List[str]] = MappingProxyType(
+    {
+        FileType.AAC: ["aac"],
+        FileType.CSV: ["csv"],
+        FileType.DOC: ["doc"],
+        FileType.DOCX: ["docx"],
+        FileType.FLAC: ["flac"],
+        FileType.FLV: ["flv"],
+        FileType.GIF: ["gif"],
+        FileType.GOOGLE_DOC: ["gdoc"],
+        FileType.GOOGLE_DRAWINGS: ["gdraw"],
+        FileType.GOOGLE_SHEETS: ["gsheet"],
+        FileType.GOOGLE_SLIDES: ["gslides"],
+        FileType.HEIC: ["heic"],
+        FileType.HEIF: ["heif"],
+        FileType.HTML: ["html", "htm"],
+        FileType.JPEG: ["jpeg", "jpg"],
+        FileType.JSON: ["json"],
+        FileType.M4A: ["m4a"],
+        FileType.M4V: ["m4v"],
+        FileType.MOV: ["mov"],
+        FileType.MP3: ["mp3"],
+        FileType.MP4: ["mp4"],
+        FileType.MPEG: ["mpeg"],
+        FileType.MPEGPS: ["mpegps"],
+        FileType.MPG: ["mpg"],
+        FileType.MPA: ["mpa"],
+        FileType.MPGA: ["mpga"],
+        FileType.OGG: ["ogg"],
+        FileType.OPUS: ["opus"],
+        FileType.PDF: ["pdf"],
+        FileType.PCM: ["pcm"],
+        FileType.PNG: ["png"],
+        FileType.PPT: ["ppt"],
+        FileType.PPTX: ["pptx"],
+        FileType.RTF: ["rtf"],
+        FileType.THREE_GPP: ["3gpp"],
+        FileType.TXT: ["txt"],
+        FileType.WAV: ["wav"],
+        FileType.WEBM: ["webm"],
+        FileType.WEBP: ["webp"],
+        FileType.WMV: ["wmv"],
+        FileType.XLS: ["xls"],
+        FileType.XLSX: ["xlsx"],
+    }
+)
+
+FILE_MIME_TYPES: MappingProxyType[FileType, str] = MappingProxyType(
+    {
+        FileType.AAC: "audio/aac",
+        FileType.CSV: "text/csv",
+        FileType.DOC: "application/msword",
+        FileType.DOCX: "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+        FileType.FLAC: "audio/flac",
+        FileType.FLV: "video/x-flv",
+        FileType.GIF: "image/gif",
+        FileType.GOOGLE_DOC: "application/vnd.google-apps.document",
+        FileType.GOOGLE_DRAWINGS: "application/vnd.google-apps.drawing",
+        FileType.GOOGLE_SHEETS: "application/vnd.google-apps.spreadsheet",
+        FileType.GOOGLE_SLIDES: "application/vnd.google-apps.presentation",
+        FileType.HEIC: "image/heic",
+        FileType.HEIF: "image/heif",
+        FileType.HTML: "text/html",
+        FileType.JPEG: "image/jpeg",
+        FileType.JSON: "application/json",
+        FileType.M4A: "audio/x-m4a",
+        FileType.M4V: "video/x-m4v",
+        FileType.MOV: "video/quicktime",
+        FileType.MP3: "audio/mpeg",
+        FileType.MP4: "video/mp4",
+        FileType.MPEG: "video/mpeg",
+        FileType.MPEGPS: "video/mpegps",
+        FileType.MPG: "video/mpg",
+        FileType.MPA: "audio/m4a",
+        FileType.MPGA: "audio/mpga",
+        FileType.OGG: "audio/ogg",
+        FileType.OPUS: "audio/opus",
+        FileType.PDF: "application/pdf",
+        FileType.PCM: "audio/pcm",
+        FileType.PNG: "image/png",
+        FileType.PPT: "application/vnd.ms-powerpoint",
+        FileType.PPTX: "application/vnd.openxmlformats-officedocument.presentationml.presentation",
+        FileType.RTF: "application/rtf",
+        FileType.THREE_GPP: "video/3gpp",
+        FileType.TXT: "text/plain",
+        FileType.WAV: "audio/wav",
+        FileType.WEBM: "video/webm",
+        FileType.WEBP: "image/webp",
+        FileType.WMV: "video/wmv",
+        FileType.XLS: "application/vnd.ms-excel",
+        FileType.XLSX: "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+    }
+)
+
+"""
+Util Functions
+"""
+
+
+def get_file_mime_type_from_extension(extension: str) -> str:
+    for file_type, extensions in FILE_EXTENSIONS.items():
+        if extension in extensions:
+            return FILE_MIME_TYPES[file_type]
+    raise ValueError(f"Unknown mime type for extension: {extension}")
+
+
+def get_file_extension_from_mime_type(mime_type: str) -> str:
+    for file_type, mime in FILE_MIME_TYPES.items():
+        if mime == mime_type:
+            return FILE_EXTENSIONS[file_type][0]
+    raise ValueError(f"Unknown extension for mime type: {mime_type}")
+
+
+def get_file_type_from_extension(extension: str) -> FileType:
+    for file_type, extensions in FILE_EXTENSIONS.items():
+        if extension in extensions:
+            return file_type
+
+    raise ValueError(f"Unknown file type for extension: {extension}")
+
+
+def get_file_extension_for_file_type(file_type: FileType) -> str:
+    return FILE_EXTENSIONS[file_type][0]
+
+
+def get_file_mime_type_for_file_type(file_type: FileType) -> str:
+    return FILE_MIME_TYPES[file_type]
+
+
+"""
+FileType Type Groupings (Videos, Images, etc)
+"""
+
+# Images
+IMAGE_FILE_TYPES = {
+    FileType.PNG,
+    FileType.JPEG,
+    FileType.GIF,
+    FileType.WEBP,
+    FileType.HEIC,
+    FileType.HEIF,
+}
+
+
+def is_image_file_type(file_type):
+    return file_type in IMAGE_FILE_TYPES
+
+
+# Videos
+VIDEO_FILE_TYPES = {
+    FileType.MOV,
+    FileType.MP4,
+    FileType.MPEG,
+    FileType.M4V,
+    FileType.FLV,
+    FileType.MPEGPS,
+    FileType.MPG,
+    FileType.WEBM,
+    FileType.WMV,
+    FileType.THREE_GPP,
+}
+
+
+def is_video_file_type(file_type):
+    return file_type in VIDEO_FILE_TYPES
+
+
+# Audio
+AUDIO_FILE_TYPES = {
+    FileType.AAC,
+    FileType.FLAC,
+    FileType.MP3,
+    FileType.MPA,
+    FileType.MPGA,
+    FileType.OPUS,
+    FileType.PCM,
+    FileType.WAV,
+}
+
+
+def is_audio_file_type(file_type):
+    return file_type in AUDIO_FILE_TYPES
+
+
+# Text
+TEXT_FILE_TYPES = {FileType.CSV, FileType.HTML, FileType.RTF, FileType.TXT}
+
+
+def is_text_file_type(file_type):
+    return file_type in TEXT_FILE_TYPES
+
+
+"""
+Other FileType Groupings
+"""
+# Accepted file types for GEMINI 1.5 through Vertex AI
+# https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/send-multimodal-prompts#gemini-send-multimodal-samples-images-nodejs
+GEMINI_1_5_ACCEPTED_FILE_TYPES: Set[FileType] = {
+    # Image
+    FileType.PNG,
+    FileType.JPEG,
+    # Audio
+    FileType.AAC,
+    FileType.FLAC,
+    FileType.MP3,
+    FileType.MPA,
+    FileType.MPGA,
+    FileType.OPUS,
+    FileType.PCM,
+    FileType.WAV,
+    # Video
+    FileType.FLV,
+    FileType.MOV,
+    FileType.MPEG,
+    FileType.MPEGPS,
+    FileType.MPG,
+    FileType.MP4,
+    FileType.WEBM,
+    FileType.WMV,
+    FileType.THREE_GPP,
+    # PDF
+    FileType.PDF,
+}
+
+
+def is_gemini_1_5_accepted_file_type(file_type: FileType) -> bool:
+    return file_type in GEMINI_1_5_ACCEPTED_FILE_TYPES

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/types/services.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel, Field
 from typing import Optional
 
 
-class ServiceTypes(enum.Enum):
+class ServiceTypes(str, enum.Enum):
     """
     Enum for litellm + litellm-adjacent services (redis/postgres/etc.)
     """

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
 
 
@@ -5655,19 +5657,29 @@ def get_optional_params(
                 optional_params["stream"] = stream
         elif "anthropic" in model:
             _check_valid_arg(supported_params=supported_params)
-            # anthropic params on bedrock
-            # \"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(
+            if "aws_bedrock_client" in passed_params:  # deprecated boto3.invoke route.
+                if model.startswith("anthropic.claude-3"):
+                    optional_params = (
+                        litellm.AmazonAnthropicClaude3Config().map_openai_params(
+                            non_default_params=non_default_params,
+                            optional_params=optional_params,
+                        )
+                    )
+                else:
+                    optional_params = litellm.AmazonAnthropicConfig().map_openai_params(
                         non_default_params=non_default_params,
                         optional_params=optional_params,
                     )
-                )
-            else:
-                optional_params = litellm.AmazonAnthropicConfig().map_openai_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)
@@ -6445,20 +6457,7 @@ def get_supported_openai_params(
     - None if unmapped
     """
     if custom_llm_provider == "bedrock":
-        if model.startswith("anthropic.claude-3"):
-            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"]
+        return litellm.AmazonConverseConfig().get_supported_openai_params(model=model)
     elif custom_llm_provider == "ollama":
         return litellm.OllamaConfig().get_supported_openai_params()
     elif custom_llm_provider == "ollama_chat":
@@ -8558,7 +8557,11 @@ def exception_type(
                 extra_information = f"\nModel: {model}"
                 if _api_base:
                     extra_information += f"\nAPI Base: `{_api_base}`"
-                if messages and len(messages) > 0:
+                if (
+                    messages
+                    and len(messages) > 0
+                    and litellm.redact_messages_in_exceptions is False
+                ):
                     extra_information += f"\nMessages: `{messages}`"
 
                 if _model_group is not None:
@@ -9124,7 +9127,7 @@ def exception_type(
                 if "Unable to locate credentials" in error_str:
                     exception_mapping_worked = True
                     raise BadRequestError(
-                        message=f"SagemakerException - {error_str}",
+                        message=f"litellm.BadRequestError: SagemakerException - {error_str}",
                         model=model,
                         llm_provider="sagemaker",
                         response=original_exception.response,
@@ -9158,10 +9161,16 @@ def exception_type(
                 ):
                     exception_mapping_worked = True
                     raise BadRequestError(
-                        message=f"VertexAIException BadRequestError - {error_str}",
+                        message=f"litellm.BadRequestError: VertexAIException - {error_str}",
                         model=model,
                         llm_provider="vertex_ai",
-                        response=original_exception.response,
+                        response=httpx.Response(
+                            status_code=429,
+                            request=httpx.Request(
+                                method="POST",
+                                url=" https://cloud.google.com/vertex-ai/",
+                            ),
+                        ),
                         litellm_debug_info=extra_information,
                     )
                 elif (
@@ -9169,12 +9178,19 @@ def exception_type(
                     or "Content has no parts." in error_str
                 ):
                     exception_mapping_worked = True
-                    raise APIError(
-                        message=f"VertexAIException APIError - {error_str}",
+                    raise litellm.InternalServerError(
+                        message=f"litellm.InternalServerError: VertexAIException - {error_str}",
                         status_code=500,
                         model=model,
                         llm_provider="vertex_ai",
-                        request=original_exception.request,
+                        request=(
+                            original_exception.request
+                            if hasattr(original_exception, "request")
+                            else httpx.Request(
+                                method="POST",
+                                url=" https://cloud.google.com/vertex-ai/",
+                            )
+                        ),
                         litellm_debug_info=extra_information,
                     )
                 elif "403" in error_str:
@@ -9183,7 +9199,13 @@ def exception_type(
                         message=f"VertexAIException BadRequestError - {error_str}",
                         model=model,
                         llm_provider="vertex_ai",
-                        response=original_exception.response,
+                        response=httpx.Response(
+                            status_code=429,
+                            request=httpx.Request(
+                                method="POST",
+                                url=" https://cloud.google.com/vertex-ai/",
+                            ),
+                        ),
                         litellm_debug_info=extra_information,
                     )
                 elif "The response was blocked." in error_str:
@@ -9230,12 +9252,18 @@ def exception_type(
                             model=model,
                             llm_provider="vertex_ai",
                             litellm_debug_info=extra_information,
-                            response=original_exception.response,
+                            response=httpx.Response(
+                                status_code=429,
+                                request=httpx.Request(
+                                    method="POST",
+                                    url=" https://cloud.google.com/vertex-ai/",
+                                ),
+                            ),
                         )
                     if original_exception.status_code == 500:
                         exception_mapping_worked = True
-                        raise APIError(
-                            message=f"VertexAIException APIError - {error_str}",
+                        raise litellm.InternalServerError(
+                            message=f"VertexAIException InternalServerError - {error_str}",
                             status_code=500,
                             model=model,
                             llm_provider="vertex_ai",
@@ -11423,12 +11451,27 @@ 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)
@@ -11695,7 +11738,7 @@ 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)
@@ -11863,6 +11906,8 @@ class CustomStreamWrapper:
 
     def __next__(self):
         try:
+            if self.completion_stream is None:
+                self.fetch_sync_stream()
             while True:
                 if (
                     isinstance(self.completion_stream, str)
@@ -11937,6 +11982,14 @@ 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

pyproject.toml

@@ -5,6 +5,10 @@ description = "Library to easily interface with LLM API providers"
 authors = ["BerriAI"]
 license = "MIT"
 readme = "README.md"
+packages = [
+    { include = "litellm" },
+    { include = "litellm/py.typed"},
+]
 
 [tool.poetry.urls]
 homepage = "https://litellm.ai"

ruff.toml

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