Initial implementation

src/pipecat/adapters/services/bedrock_adapter.py

@@ -0,0 +1,38 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+from typing import Any, Dict, List, Union
+
+from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+
+
+class BedrockLLMAdapter(BaseLLMAdapter):
+    @staticmethod
+    def _to_bedrock_function_format(function: FunctionSchema) -> Dict[str, Any]:
+        return {
+            "toolSpec": {
+                "name": function.name,
+                "description": function.description,
+                "inputSchema": {
+                    "json": {
+                        "type": "object",
+                        "properties": function.properties,
+                        "required": function.required,
+                    },
+                }
+            }
+        }
+
+    def to_provider_tools_format(self, tools_schema: ToolsSchema) -> List[Dict[str, Any]]:
+        """Converts function schemas to Bedrock's function-calling format.
+
+        :return: Bedrock formatted function call definition.
+        """
+
+        functions_schema = tools_schema.standard_tools
+        return [self._to_bedrock_function_format(func) for func in functions_schema]

src/pipecat/services/aws/llm.py

@@ -0,0 +1,803 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import base64
+import copy
+import io
+import json
+import re
+from dataclasses import dataclass
+from typing import Any, Dict, List, Mapping, Optional, Union
+
+import boto3
+from botocore.config import Config
+import httpx
+from loguru import logger
+from PIL import Image
+from pydantic import BaseModel, Field
+
+from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
+from pipecat.frames.frames import (
+    Frame,
+    FunctionCallCancelFrame,
+    FunctionCallInProgressFrame,
+    FunctionCallResultFrame,
+    LLMFullResponseEndFrame,
+    LLMFullResponseStartFrame,
+    LLMMessagesFrame,
+    LLMTextFrame,
+    LLMUpdateSettingsFrame,
+    UserImageRawFrame,
+    VisionImageRawFrame,
+)
+from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantContextAggregator,
+    LLMUserContextAggregator,
+)
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+    OpenAILLMContextFrame,
+)
+from pipecat.processors.frame_processor import FrameDirection
+from pipecat.services.ai_services import LLMService
+
+try:
+    from anthropic import NOT_GIVEN, NotGiven
+except ModuleNotFoundError as e:
+    logger.error(f"Exception: {e}")
+    logger.error(
+        "In order to use Anthropic, you need to `pip install pipecat-ai[anthropic]`. "
+        + "Also, set `ANTHROPIC_API_KEY` environment variable."
+    )
+    raise Exception(f"Missing module: {e}")
+
+
+@dataclass
+class BedrockContextAggregatorPair:
+    _user: "BedrockUserContextAggregator"
+    _assistant: "BedrockAssistantContextAggregator"
+
+    def user(self) -> "BedrockUserContextAggregator":
+        return self._user
+
+    def assistant(self) -> "BedrockAssistantContextAggregator":
+        return self._assistant
+
+
+class BedrockLLMService(LLMService):
+    """This class implements inference with AWS Bedrock models including Amazon Nova and Anthropic Claude.
+    
+    Requires AWS credentials to be configured in the environment or through boto3 configuration.
+    """
+    class InputParams(BaseModel):
+        max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
+        temperature: Optional[float] = Field(default_factory=lambda: 0.7, ge=0.0, le=1.0)
+        top_p: Optional[float] = Field(default_factory=lambda: 0.999, ge=0.0, le=1.0)
+        stop_sequences: Optional[List[str]] = Field(default_factory=lambda: [])
+        latency: Optional[str] = Field(default_factory=lambda: "standard")
+        additional_model_request_fields: Optional[Dict[str, Any]] = Field(default_factory=dict)
+
+    def __init__(
+        self,
+        *,
+        aws_access_key: str,
+        aws_secret_key: str,
+        aws_session_token: Optional[str] = None,
+        aws_region: str = "us-east-1",
+        model: str,
+        params: InputParams = InputParams(),
+        client_config: Optional[Config] = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        
+        # Initialize the Bedrock client
+        if not client_config:
+            client_config = Config(
+                connect_timeout=300,  # 5 minutes
+                read_timeout=300,     # 5 minutes
+                retries={'max_attempts': 3}
+            )
+        session = boto3.Session(
+            aws_access_key_id=aws_access_key,
+            aws_secret_access_key=aws_secret_key,
+            aws_session_token=aws_session_token,
+            region_name=aws_region
+        )
+        self._client = session.client(
+            service_name='bedrock-runtime',
+            config=client_config
+        )
+        
+        self.set_model_name(model)
+        self._settings = {
+            "max_tokens": params.max_tokens,
+            "temperature": params.temperature,
+            "top_p": params.top_p,
+            "latency": params.latency,
+            "additional_model_request_fields": params.additional_model_request_fields if isinstance(params.additional_model_request_fields, dict) else {},
+        }
+        
+        # Determine model provider from model ID
+        self.model_provider = self._get_model_provider(model)
+        logger.info(f"Using AWS Bedrock model: {model} from provider: {self.model_provider}")
+
+    def _get_model_provider(self, model: str) -> str:
+        """Determine the model provider from the model ID"""
+        if "anthropic." in model:
+            return "anthropic"
+        elif "amazon." in model:
+            return "amazon"
+        else:
+            raise ValueError(f"Unsupported model: {model}. Only Anthropic Claude and Amazon Nova model families are supported.")
+
+    def can_generate_metrics(self) -> bool:
+        return True
+
+    def create_context_aggregator(
+        self,
+        context: OpenAILLMContext,
+        *,
+        user_kwargs: Mapping[str, Any] = {},
+        assistant_kwargs: Mapping[str, Any] = {},
+    ) -> BedrockContextAggregatorPair:
+        """Create an instance of BedrockContextAggregatorPair from an
+        OpenAILLMContext. Constructor keyword arguments for both the user and
+        assistant aggregators can be provided.
+
+        Args:
+            context (OpenAILLMContext): The LLM context.
+            user_kwargs (Mapping[str, Any], optional): Additional keyword
+                arguments for the user context aggregator constructor. Defaults
+                to an empty mapping.
+            assistant_kwargs (Mapping[str, Any], optional): Additional keyword
+                arguments for the assistant context aggregator
+                constructor. Defaults to an empty mapping.
+
+        Returns:
+            BedrockContextAggregatorPair: A pair of context aggregators, one
+            for the user and one for the assistant, encapsulated in an
+            BedrockContextAggregatorPair.
+        """
+        context.set_llm_adapter(self.get_llm_adapter())
+
+        if isinstance(context, OpenAILLMContext):
+            context = BedrockLLMContext.from_openai_context(context)
+                
+        user = BedrockUserContextAggregator(context, **user_kwargs)
+        assistant = BedrockAssistantContextAggregator(context, **assistant_kwargs)
+        return BedrockContextAggregatorPair(_user=user, _assistant=assistant)
+
+    async def _process_context(self, context: "BedrockLLMContext"):
+        # Usage tracking
+        prompt_tokens = 0
+        completion_tokens = 0
+        completion_tokens_estimate = 0
+        use_completion_tokens_estimate = False
+
+        try:
+            await self.push_frame(LLMFullResponseStartFrame())
+            await self.start_processing_metrics()
+
+            # logger.debug(
+            #     f"{self}: Generating chat with Bedrock model {self.model_name} | [{context.get_messages_for_logging()}]"
+            # )
+
+            await self.start_ttfb_metrics()
+            
+            # Set up inference config
+            inference_config = {
+                "maxTokens": self._settings["max_tokens"],
+                "temperature": self._settings["temperature"],
+                "topP": self._settings["top_p"],
+            }
+            
+            # Prepare request parameters
+            request_params = {
+                "modelId": self.model_name,
+                "messages": context.messages,
+                "inferenceConfig": inference_config,
+                "additionalModelRequestFields": self._settings["additional_model_request_fields"]
+            }
+            
+            # Add system message
+            request_params["system"] = [{"text": context.system}]
+                
+            # Add tools if present
+            if context.tools:
+                print(context.tools)
+                tool_config = {
+                    "tools": context.tools
+                }
+                
+                # Add tool_choice if specified
+                if context.tool_choice:
+                    if context.tool_choice == "auto":
+                        tool_config["toolChoice"] = {"auto": {}}
+                    elif context.tool_choice == "none":
+                        # Skip adding toolChoice for "none"
+                        pass
+                    elif isinstance(context.tool_choice, dict) and "function" in context.tool_choice:
+                        tool_config["toolChoice"] = {
+                            "tool": {
+                                "name": context.tool_choice["function"]["name"]
+                            }
+                        }
+                
+                request_params["toolConfig"] = tool_config
+            
+            # Add performance config if latency is specified
+            if self._settings["latency"] in ["standard", "optimized"]:
+                request_params["performanceConfig"] = {
+                    "latency": self._settings["latency"]
+                }
+            
+            logger.debug(f"Calling Bedrock model with: {request_params}")
+            
+            # Call Bedrock with streaming
+            response = self._client.converse_stream(**request_params)
+            
+            await self.stop_ttfb_metrics()
+            
+            # Process the streaming response
+            tool_use_block = None
+            json_accumulator = ""
+            
+            for event in response["stream"]:
+                # Handle text content
+                if "contentBlockDelta" in event:
+                    delta = event["contentBlockDelta"]["delta"]
+                    if "text" in delta:
+                        await self.push_frame(LLMTextFrame(delta["text"]))
+                        completion_tokens_estimate += self._estimate_tokens(delta["text"])
+                    elif "toolUse" in delta and "input" in delta["toolUse"]:
+                        # Handle partial JSON for tool use
+                        json_str = json.dumps(delta["toolUse"]["input"])
+                        json_accumulator += json_str
+                        completion_tokens_estimate += self._estimate_tokens(json_str)
+                
+                # Handle tool use start
+                elif "contentBlockStart" in event:
+                    content_block = event["contentBlockStart"]
+                    if content_block.get("type") == "toolUse":
+                        tool_use_block = {
+                            "id": content_block["toolUse"].get("toolUseId", ""),
+                            "name": content_block["toolUse"].get("name", "")
+                        }
+                        json_accumulator = ""
+                
+                # Handle message completion with tool use
+                elif "messageDelta" in event and "stopReason" in event["messageDelta"]:
+                    if event["messageDelta"]["stopReason"] == "toolUse" and tool_use_block:
+                        try:
+                            arguments = json.loads(json_accumulator) if json_accumulator else {}
+                            await self.call_function(
+                                context=context,
+                                tool_call_id=tool_use_block["id"],
+                                function_name=tool_use_block["name"],
+                                arguments=arguments,
+                            )
+                        except json.JSONDecodeError:
+                            logger.error(f"Failed to parse tool arguments: {json_accumulator}")
+                
+                # Handle usage metrics if available
+                if "usage" in event:
+                    usage = event["usage"]
+                    prompt_tokens += usage.get("inputTokens", 0)
+                    completion_tokens += usage.get("outputTokens", 0)
+
+        except asyncio.CancelledError:
+            # If we're interrupted, we won't get a complete usage report. So set our flag to use the
+            # token estimate. The reraise the exception so all the processors running in this task
+            # also get cancelled.
+            use_completion_tokens_estimate = True
+            raise
+        except httpx.TimeoutException:
+            await self._call_event_handler("on_completion_timeout")
+        except Exception as e:
+            logger.exception(f"{self} exception: {e}")
+        finally:
+            await self.stop_processing_metrics()
+            await self.push_frame(LLMFullResponseEndFrame())
+            comp_tokens = (
+                completion_tokens
+                if not use_completion_tokens_estimate
+                else completion_tokens_estimate
+            )
+            await self._report_usage_metrics(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=comp_tokens,
+            )
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        context = None
+        if isinstance(frame, OpenAILLMContextFrame):
+            context = BedrockLLMContext.upgrade_to_bedrock(frame.context)
+        elif isinstance(frame, LLMMessagesFrame):
+            context = BedrockLLMContext.from_messages(frame.messages)
+        elif isinstance(frame, VisionImageRawFrame):
+            # This is only useful in very simple pipelines because it creates
+            # a new context. Generally we want a context manager to catch
+            # UserImageRawFrames coming through the pipeline and add them
+            # to the context.
+            context = BedrockLLMContext.from_image_frame(frame)
+        elif isinstance(frame, LLMUpdateSettingsFrame):
+            await self._update_settings(frame.settings)
+        else:
+            await self.push_frame(frame, direction)
+
+        if context:
+            await self._process_context(context)
+
+    def _estimate_tokens(self, text: str) -> int:
+        return int(len(re.split(r"[^\w]+", text)) * 1.3)
+
+    async def _report_usage_metrics(
+        self,
+        prompt_tokens: int,
+        completion_tokens: int,
+    ):
+        if prompt_tokens or completion_tokens:
+            tokens = LLMTokenUsage(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=completion_tokens,
+                total_tokens=prompt_tokens + completion_tokens,
+            )
+            await self.start_llm_usage_metrics(tokens)
+
+
+class BedrockLLMContext(OpenAILLMContext):
+    def __init__(
+        self,
+        messages: Optional[List[dict]] = None,
+        tools: Optional[List[dict]] = None,
+        tool_choice: Optional[dict] = None,
+        *,
+        system: Union[str, NotGiven] = NOT_GIVEN,
+    ):
+        super().__init__(messages=messages, tools=tools, tool_choice=tool_choice)
+        self.system = system
+
+    @staticmethod
+    def upgrade_to_bedrock(obj: OpenAILLMContext) -> "BedrockLLMContext":
+        logger.debug(f"Upgrading to Bedrock: {obj}")
+        if isinstance(obj, OpenAILLMContext) and not isinstance(obj, BedrockLLMContext):
+            obj.__class__ = BedrockLLMContext
+            obj._restructure_from_openai_messages()
+        else:
+            obj._restructure_from_bedrock_messages()
+        return obj
+
+    @classmethod
+    def from_openai_context(cls, openai_context: OpenAILLMContext):
+        self = cls(
+            messages=openai_context.messages,
+            tools=openai_context.tools,
+            tool_choice=openai_context.tool_choice,
+        )
+        self.set_llm_adapter(openai_context.get_llm_adapter())
+        self._restructure_from_openai_messages()
+        return self
+
+    @classmethod
+    def from_messages(cls, messages: List[dict]) -> "BedrockLLMContext":
+        self = cls(messages=messages)
+        # self._restructure_from_openai_messages()
+        return self
+
+    @classmethod
+    def from_image_frame(cls, frame: VisionImageRawFrame) -> "BedrockLLMContext":
+        context = cls()
+        context.add_image_frame_message(
+            format=frame.format, size=frame.size, image=frame.image, text=frame.text
+        )
+        return context
+
+    def set_messages(self, messages: List):
+        self._messages[:] = messages
+        # self._restructure_from_openai_messages()
+
+    # convert a message in Bedrock format into one or more messages in OpenAI format
+    def to_standard_messages(self, obj):
+        """Convert Bedrock message format to standard structured format.
+
+        Handles text content and function calls for both user and assistant messages.
+
+        Args:
+            obj: Message in Bedrock format:
+                {
+                    "role": "user/assistant",
+                    "content": [{"text": str} | {"toolUse": {...}} | {"toolResult": {...}}]
+                }
+
+        Returns:
+            List of messages in standard format:
+            [
+                {
+                    "role": "user/assistant/tool",
+                    "content": [{"type": "text", "text": str}]
+                }
+            ]
+        """
+        role = obj.get("role")
+        content = obj.get("content")
+        
+        if role == "assistant":
+            if isinstance(content, str):
+                return [{"role": role, "content": [{"type": "text", "text": content}]}]
+            elif isinstance(content, list):
+                text_items = []
+                tool_items = []
+                for item in content:
+                    if "text" in item:
+                        text_items.append({"type": "text", "text": item["text"]})
+                    elif "toolUse" in item:
+                        tool_use = item["toolUse"]
+                        tool_items.append(
+                            {
+                                "type": "function",
+                                "id": tool_use["toolUseId"],
+                                "function": {
+                                    "name": tool_use["name"],
+                                    "arguments": json.dumps(tool_use["input"]),
+                                },
+                            }
+                        )
+                messages = []
+                if text_items:
+                    messages.append({"role": role, "content": text_items})
+                if tool_items:
+                    messages.append({"role": role, "tool_calls": tool_items})
+                return messages
+        elif role == "user":
+            if isinstance(content, str):
+                return [{"role": role, "content": [{"type": "text", "text": content}]}]
+            elif isinstance(content, list):
+                text_items = []
+                tool_items = []
+                for item in content:
+                    if "text" in item:
+                        text_items.append({"type": "text", "text": item["text"]})
+                    elif "toolResult" in item:
+                        tool_result = item["toolResult"]
+                        # Extract content from toolResult
+                        result_content = ""
+                        if isinstance(tool_result["content"], list):
+                            for content_item in tool_result["content"]:
+                                if "text" in content_item:
+                                    result_content = content_item["text"]
+                                elif "json" in content_item:
+                                    result_content = json.dumps(content_item["json"])
+                        else:
+                            result_content = tool_result["content"]
+                            
+                        tool_items.append(
+                            {
+                                "role": "tool",
+                                "tool_call_id": tool_result["toolUseId"],
+                                "content": result_content,
+                            }
+                        )
+                messages = []
+                if text_items:
+                    messages.append({"role": role, "content": text_items})
+                messages.extend(tool_items)
+                return messages
+
+    def from_standard_message(self, message):
+        """Convert standard format message to Bedrock format.
+
+        Handles conversion of text content, tool calls, and tool results.
+        Empty text content is converted to "(empty)".
+
+        Args:
+            message: Message in standard format:
+                {
+                    "role": "user/assistant/tool",
+                    "content": str | [{"type": "text", ...}],
+                    "tool_calls": [{"id": str, "function": {"name": str, "arguments": str}}]
+                }
+
+        Returns:
+            Message in Bedrock format:
+            {
+                "role": "user/assistant",
+                "content": [
+                    {"text": str} |
+                    {"toolUse": {"toolUseId": str, "name": str, "input": dict}} |
+                    {"toolResult": {"toolUseId": str, "content": [...], "status": str}}
+                ]
+            }
+        """
+        print(message)
+        if message["role"] == "tool":
+            # Try to parse the content as JSON if it looks like JSON
+            try:
+                if message["content"].strip().startswith('{') and message["content"].strip().endswith('}'):
+                    content_json = json.loads(message["content"])
+                    tool_result_content = [{"json": content_json}]
+                else:
+                    tool_result_content = [{"text": message["content"]}]
+            except:
+                tool_result_content = [{"text": message["content"]}]
+                
+            return {
+                "role": "user",
+                "content": [
+                    {
+                        "toolResult": {
+                            "toolUseId": message["tool_call_id"],
+                            "content": tool_result_content
+                        },
+                    },
+                ],
+            }
+            
+        if message.get("tool_calls"):
+            tc = message["tool_calls"]
+            ret = {"role": "assistant", "content": []}
+            for tool_call in tc:
+                function = tool_call["function"]
+                arguments = json.loads(function["arguments"])
+                new_tool_use = {
+                    "toolUse": {
+                        "toolUseId": tool_call["id"],
+                        "name": function["name"],
+                        "input": arguments,
+                    }
+                }
+                ret["content"].append(new_tool_use)
+            return ret
+            
+        # Handle text content
+        content = message.get("content")
+        if isinstance(content, str):
+            if content == "":
+                return {"role": message["role"], "content": [{"text": "(empty)"}]}
+            else:
+                return {"role": message["role"], "content": [{"text": content}]}
+        elif isinstance(content, list):
+            new_content = []
+            for item in content:
+                if item.get("type", "") == "text":
+                    text_content = item["text"] if item["text"] != "" else "(empty)"
+                    new_content.append({"text": text_content})
+            return {"role": message["role"], "content": new_content}
+            
+        return message
+
+    def add_image_frame_message(
+        self, *, format: str, size: tuple[int, int], image: bytes, text: str = None
+    ):
+        buffer = io.BytesIO()
+        Image.frombytes(format, size, image).save(buffer, format="JPEG")
+        encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+        # Image should be the first content block in the message
+        content = [
+            {
+                "type": "image",
+                "format": "jpeg",
+                "source": {
+                    "bytes": encoded_image
+                }
+            }
+        ]
+        if text:
+            content.append({"text": text})
+        self.add_message({"role": "user", "content": content})
+
+    def add_message(self, message):
+        try:
+            if self.messages:
+                # Bedrock requires that roles alternate. If this message's role is the same as the
+                # last message, we should add this message's content to the last message.
+                if self.messages[-1]["role"] == message["role"]:
+                    # if the last message has just a content string, convert it to a list
+                    # in the proper format
+                    if isinstance(self.messages[-1]["content"], str):
+                        self.messages[-1]["content"] = [
+                            {"type": "text", "text": self.messages[-1]["content"]}
+                        ]
+                    # if this message has just a content string, convert it to a list
+                    # in the proper format
+                    if isinstance(message["content"], str):
+                        message["content"] = [{"text": message["content"]}]
+                    # append the content of this message to the last message
+                    self.messages[-1]["content"].extend(message["content"])
+                else:
+                    self.messages.append(message)
+            else:
+                self.messages.append(message)
+        except Exception as e:
+            logger.error(f"Error adding message: {e}")
+
+    def _restructure_from_bedrock_messages(self):
+        """Restructure messages in Bedrock format by handling system messages, 
+        merging consecutive messages with the same role, and ensuring proper content formatting.
+        """
+
+        print(self.messages)
+
+        # Handle system message if present at the beginning
+        if self.messages and self.messages[0]["role"] == "system":
+            if len(self.messages) == 1:
+                self.messages[0]["role"] = "user"
+            else:
+                system_content = self.messages.pop(0)["content"]
+                self.system = system_content[0]["text"] if isinstance(system_content, list) and system_content and isinstance(system_content[0], dict) and "text" in system_content[0] else str(system_content)
+
+        # Ensure content is properly formatted
+        for msg in self.messages:
+            if isinstance(msg["content"], str):
+                msg["content"] = [{"text": msg["content"]}]
+            elif not msg["content"]:
+                msg["content"] = [{"text": "(empty)"}]
+            elif isinstance(msg["content"], list):
+                for idx, item in enumerate(msg["content"]):
+                    if isinstance(item, dict) and "text" in item and item["text"] == "":
+                        item["text"] = "(empty)"
+                    elif isinstance(item, str) and item == "":
+                        msg["content"][idx] = {"text": "(empty)"}
+
+        # Merge consecutive messages with the same role
+        merged_messages = []
+        for msg in self.messages:
+            if merged_messages and merged_messages[-1]["role"] == msg["role"]:
+                merged_messages[-1]["content"].extend(msg["content"])
+            else:
+                merged_messages.append(msg)
+        
+        self.messages.clear()
+        self.messages.extend(merged_messages)
+
+    def _restructure_from_openai_messages(self):
+        # first, map across self._messages calling self.from_standard_message(m) to modify messages in place
+        try:
+            self._messages[:] = [self.from_standard_message(m) for m in self._messages]
+        except Exception as e:
+            logger.error(f"Error mapping messages: {e}")
+
+        # See if we should pull the system message out of our context.messages list. (For
+        # compatibility with Open AI messages format.)
+        if self.messages and self.messages[0]["role"] == "system":
+            if len(self.messages) == 1:
+                # If we have only have a system message in the list, all we can really do
+                # without introducing too much magic is change the role to "user".
+                self.messages[0]["role"] = "user"
+            else:
+                # If we have more than one message, we'll pull the system message out of the
+                # list.
+                self.system = self.messages[0]["content"]
+                self.messages.pop(0)
+
+        # Merge consecutive messages with the same role.
+        i = 0
+        while i < len(self.messages) - 1:
+            current_message = self.messages[i]
+            next_message = self.messages[i + 1]
+            if current_message["role"] == next_message["role"]:
+                # Convert content to list of dictionaries if it's a string
+                if isinstance(current_message["content"], str):
+                    current_message["content"] = [
+                        {"type": "text", "text": current_message["content"]}
+                    ]
+                if isinstance(next_message["content"], str):
+                    next_message["content"] = [{"type": "text", "text": next_message["content"]}]
+                # Concatenate the content
+                current_message["content"].extend(next_message["content"])
+                # Remove the next message from the list
+                self.messages.pop(i + 1)
+            else:
+                i += 1
+
+        # Avoid empty content in messages
+        for message in self.messages:
+            if isinstance(message["content"], str) and message["content"] == "":
+                message["content"] = "(empty)"
+            elif isinstance(message["content"], list) and len(message["content"]) == 0:
+                message["content"] = [{"type": "text", "text": "(empty)"}]
+
+    def get_messages_for_persistent_storage(self):
+        messages = super().get_messages_for_persistent_storage()
+        if self.system:
+            messages.insert(0, {"role": "system", "content": self.system})
+        return messages
+
+    def get_messages_for_logging(self) -> str:
+        msgs = []
+        for message in self.messages:
+            msg = copy.deepcopy(message)
+            if "content" in msg:
+                if isinstance(msg["content"], list):
+                    for item in msg["content"]:
+                        if item.get("image"):
+                            item["source"]["bytes"] = "..."
+            msgs.append(msg)
+        return json.dumps(msgs)
+
+
+class BedrockUserContextAggregator(LLMUserContextAggregator):
+    pass
+
+
+class BedrockAssistantContextAggregator(LLMAssistantContextAggregator):
+    async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame):
+        # Format tool use according to Bedrock API
+        self._context.add_message(
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "toolUse": {
+                            "toolUseId": frame.tool_call_id,
+                            "name": frame.function_name,
+                            "input": frame.arguments
+                        }
+                    }
+                ],
+            }
+        )
+        self._context.add_message(
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "toolResult": {
+                            "toolUseId": frame.tool_call_id,
+                            "content": [
+                                {
+                                    "text": "IN_PROGRESS"
+                                }
+                            ],
+                        }
+                    }
+                ],
+            }
+        )
+
+    async def handle_function_call_result(self, frame: FunctionCallResultFrame):
+        if frame.result:
+            result = json.dumps(frame.result)
+            await self._update_function_call_result(frame.function_name, frame.tool_call_id, result)
+        else:
+            await self._update_function_call_result(
+                frame.function_name, frame.tool_call_id, "COMPLETED"
+            )
+
+    async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame):
+        await self._update_function_call_result(
+            frame.function_name, frame.tool_call_id, "CANCELLED"
+        )
+
+    async def _update_function_call_result(
+        self, function_name: str, tool_call_id: str, result: Any
+    ):
+        for message in self._context.messages:
+            if message["role"] == "user":
+                for content in message["content"]:
+                    if (
+                        isinstance(content, dict)
+                        and content.get("toolResult")
+                        and content["toolResult"]["toolUseId"] == tool_call_id
+                    ):
+                        content["toolResult"]["content"] = [{"text": result}]
+
+    async def handle_user_image_frame(self, frame: UserImageRawFrame):
+        await self._update_function_call_result(
+            frame.request.function_name, frame.request.tool_call_id, "COMPLETED"
+        )
+        self._context.add_image_frame_message(
+            format=frame.format,
+            size=frame.size,
+            image=frame.image,
+            text=frame.request.context,
+        )
+