diff --git a/plugins/aws/README.md b/plugins/aws/README.md index feb3b2b5..7569d345 100644 --- a/plugins/aws/README.md +++ b/plugins/aws/README.md @@ -1,6 +1,6 @@ # AWS Plugin for Vision Agents -AWS (Bedrock) LLM integration for Vision Agents framework with support for both standard and realtime interactions. Includes AWS Polly TTS. +AWS (Bedrock) LLM integration for Vision Agents framework with support for both standard and realtime interactions. ## Installation @@ -20,7 +20,7 @@ agent = Agent( agent_user=User(name="Friendly AI"), instructions="Be nice to the user", llm=aws.LLM(model="qwen.qwen3-32b-v1:0"), - tts=aws.TTS(), # using AWS Polly + tts=cartesia.TTS(), stt=deepgram.STT(), turn_detection=smart_turn.TurnDetection(buffer_duration=2.0, confidence_threshold=0.5), ) @@ -28,9 +28,11 @@ agent = Agent( The full example is available in example/aws_qwen_example.py -Nova sonic audio realtime STS is also supported: +### Realtime Audio Usage -```python +Nova Sonic audio realtime STS is also supported: + +```python agent = Agent( edge=getstream.Edge(), agent_user=User(name="Story Teller AI"), @@ -39,8 +41,61 @@ agent = Agent( ) ``` -### Polly TTS Usage +## Function Calling + +### Standard LLM (aws.LLM) + +The standard LLM implementation **fully supports** function calling. Register functions using the `@llm.register_function` decorator: + +```python +from vision_agents.plugins import aws + +llm = aws.LLM( + model="qwen.qwen3-32b-v1:0", + region_name="us-east-1" +) + +@llm.register_function( + name="get_weather", + description="Get the current weather for a given city" +) +def get_weather(city: str) -> dict: + """Get weather information for a city.""" + return { + "city": city, + "temperature": 72, + "condition": "Sunny" + } +``` +### Realtime (aws.Realtime) + +The Realtime implementation **fully supports** function calling with AWS Nova Sonic. Register functions using the `@llm.register_function` decorator: + +```python +from vision_agents.plugins import aws + +llm = aws.Realtime( + model="amazon.nova-sonic-v1:0", + region_name="us-east-1" +) + +@llm.register_function( + name="get_weather", + description="Get the current weather for a given city" +) +def get_weather(city: str) -> dict: + """Get weather information for a city.""" + return { + "city": city, + "temperature": 72, + "condition": "Sunny" + } + +# The function will be automatically called when the model decides to use it +``` + +See `example/aws_realtime_function_calling_example.py` for a complete example. ## Running the examples @@ -53,9 +108,8 @@ STREAM_API_SECRET=your_stream_api_secret_here AWS_BEARER_TOKEN_BEDROCK= AWS_ACCESS_KEY_ID= AWS_SECRET_ACCESS_KEY= -AWS_REGION=us-east-1 FAL_KEY= CARTESIA_API_KEY= DEEPGRAM_API_KEY= -``` +``` \ No newline at end of file diff --git a/plugins/aws/example/aws_llm_function_calling_example.py b/plugins/aws/example/aws_llm_function_calling_example.py new file mode 100644 index 00000000..fb530f6c --- /dev/null +++ b/plugins/aws/example/aws_llm_function_calling_example.py @@ -0,0 +1,89 @@ +import asyncio +import logging +from uuid import uuid4 + +from dotenv import load_dotenv + +from vision_agents.core import User +from vision_agents.core.agents import Agent +from vision_agents.plugins import aws, getstream, cartesia, deepgram + +load_dotenv() + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s [call_id=%(call_id)s] %(name)s: %(message)s") +logger = logging.getLogger(__name__) + + +async def start_agent() -> None: + agent = Agent( + edge=getstream.Edge(), + agent_user=User(name="Weather Bot"), + instructions="You are a helpful weather bot. Use the provided tools to answer questions.", + llm=aws.LLM( + model="anthropic.claude-3-sonnet-20240229-v1:0", + region_name="us-east-1" + + ), + tts=cartesia.TTS(), + stt=deepgram.STT(), + # turn_detection=smart_turn.TurnDetection(buffer_duration=2.0, confidence_threshold=0.5), + ) + + # Register custom functions + @agent.llm.register_function( + name="get_weather", + description="Get the current weather for a given city" + ) + def get_weather(city: str) -> dict: + """Get weather information for a city.""" + logger.info(f"Tool: get_weather called for city: {city}") + if city.lower() == "boulder": + return {"city": city, "temperature": 72, "condition": "Sunny"} + return {"city": city, "temperature": "unknown", "condition": "unknown"} + + @agent.llm.register_function( + name="calculate", + description="Performs a mathematical calculation" + ) + def calculate(expression: str) -> dict: + """Performs a mathematical calculation.""" + logger.info(f"Tool: calculate called with expression: {expression}") + try: + result = eval(expression) # DANGER: In a real app, use a safer math evaluator! + return {"expression": expression, "result": result} + except Exception as e: + return {"expression": expression, "error": str(e)} + + await agent.create_user() + + call = agent.edge.client.video.call("default", str(uuid4())) + await agent.edge.open_demo(call) + + with await agent.join(call): + # Give the agent a moment to connect + await asyncio.sleep(5) + + # Test function calling with weather + logger.info("Testing weather function...") + await agent.llm.simple_response( + text="What's the weather like in Boulder? Please use the get_weather function." + ) + + await asyncio.sleep(5) + + # Test function calling with calculation + logger.info("Testing calculation function...") + await agent.llm.simple_response( + text="Can you calculate 25 multiplied by 4 using the calculate function?" + ) + + await asyncio.sleep(5) + + # Wait a bit before finishing + await asyncio.sleep(5) + await agent.finish() + + +if __name__ == "__main__": + asyncio.run(start_agent()) + diff --git a/plugins/aws/example/aws_realtime_function_calling_example.py b/plugins/aws/example/aws_realtime_function_calling_example.py new file mode 100644 index 00000000..bf2b3a89 --- /dev/null +++ b/plugins/aws/example/aws_realtime_function_calling_example.py @@ -0,0 +1,128 @@ +import asyncio +import logging +from uuid import uuid4 + +from dotenv import load_dotenv + +from vision_agents.core import User +from vision_agents.core.agents import Agent +from vision_agents.plugins import aws, getstream + +load_dotenv() + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s [call_id=%(call_id)s] %(name)s: %(message)s" +) +logger = logging.getLogger(__name__) + + +async def start_agent() -> None: + """Example demonstrating AWS Bedrock realtime with function calling. + + This example creates an agent that can call custom functions to get + weather information and perform calculations. + """ + + # Create the agent with AWS Bedrock Realtime + agent = Agent( + edge=getstream.Edge(), + agent_user=User(name="Weather Assistant AI"), + instructions="""You are a helpful weather assistant. When users ask about weather, + use the get_weather function to fetch current conditions. You can also help with + simple calculations using the calculate function.""", + llm=aws.Realtime( + model="amazon.nova-sonic-v1:0", + region_name="us-east-1", + ), + ) + + # Register custom functions that the LLM can call + @agent.llm.register_function( + name="get_weather", + description="Get the current weather for a given city" + ) + def get_weather(city: str) -> dict: + """Get weather information for a city. + + Args: + city: The name of the city + + Returns: + Weather information including temperature and conditions + """ + # This is a mock implementation - in production you'd call a real weather API + weather_data = { + "Boulder": {"temp": 72, "condition": "Sunny", "humidity": 30}, + "Seattle": {"temp": 58, "condition": "Rainy", "humidity": 85}, + "Miami": {"temp": 85, "condition": "Partly Cloudy", "humidity": 70}, + } + + city_weather = weather_data.get(city, {"temp": 70, "condition": "Unknown", "humidity": 50}) + return { + "city": city, + "temperature": city_weather["temp"], + "condition": city_weather["condition"], + "humidity": city_weather["humidity"], + "unit": "Fahrenheit" + } + + @agent.llm.register_function( + name="calculate", + description="Perform a mathematical calculation" + ) + def calculate(operation: str, a: float, b: float) -> dict: + """Perform a calculation. + + Args: + operation: The operation to perform (add, subtract, multiply, divide) + a: First number + b: Second number + + Returns: + Result of the calculation + """ + operations = { + "add": lambda x, y: x + y, + "subtract": lambda x, y: x - y, + "multiply": lambda x, y: x * y, + "divide": lambda x, y: x / y if y != 0 else None, + } + + if operation not in operations: + return {"error": f"Unknown operation: {operation}"} + + result = operations[operation](a, b) + if result is None: + return {"error": "Cannot divide by zero"} + + return { + "operation": operation, + "a": a, + "b": b, + "result": result + } + + # Create and start the agent + await agent.create_user() + + call = agent.edge.client.video.call("default", str(uuid4())) + await agent.edge.open_demo(call) + + with await agent.join(call): + # Give the agent a moment to connect + await asyncio.sleep(5) + + await agent.llm.simple_response( + text="What's the weather like in Boulder? Please use the get_weather function." + ) + + # Wait for AWS Nova to process the request and call the function + await asyncio.sleep(15) + + await agent.finish() + + +if __name__ == "__main__": + asyncio.run(start_agent()) + diff --git a/plugins/aws/example/pyproject.toml b/plugins/aws/example/pyproject.toml index 1b7fb3ac..b4e96f8c 100644 --- a/plugins/aws/example/pyproject.toml +++ b/plugins/aws/example/pyproject.toml @@ -1,15 +1,15 @@ [project] -name = "gemini-live-realtime-example" +name = "aws-bedrock-realtime-example" version = "0.0.0" -requires-python = ">=3.10" +requires-python = ">=3.12" # put only what this example needs dependencies = [ "python-dotenv>=1.0", - "vision-agents-plugins-gemini", + "vision-agents-plugins-aws", "vision-agents-plugins-getstream", "vision-agents", - "google-genai>=1.33.0", + "boto3>=1.26.0", "opentelemetry-exporter-otlp>=1.37.0", "opentelemetry-exporter-prometheus>=0.58b0", "prometheus-client>=0.23.1", @@ -17,6 +17,6 @@ dependencies = [ ] [tool.uv.sources] -"vision-agents-plugins-getstream" = {path = "../../../plugins/getstream", editable=true} -"vision-agents-plugins-gemini" = {path = "../../../plugins/gemini", editable=true} +"vision-agents-plugins-getstream" = {path = "../../getstream", editable=true} +"vision-agents-plugins-aws" = {path = "..", editable=true} "vision-agents" = {path = "../../../agents-core", editable=true} diff --git a/plugins/aws/example/uv.lock b/plugins/aws/example/uv.lock index 8bba3da6..fad869b7 100644 --- a/plugins/aws/example/uv.lock +++ b/plugins/aws/example/uv.lock @@ -1,27 +1,9 @@ version = 1 -revision = 3 -requires-python = ">=3.10" +revision = 2 +requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'darwin'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'darwin'", 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a/plugins/aws/tests/test_aws_realtime.py b/plugins/aws/tests/test_aws_realtime.py index 1b1f40a9..7e25644e 100644 --- a/plugins/aws/tests/test_aws_realtime.py +++ b/plugins/aws/tests/test_aws_realtime.py @@ -82,3 +82,46 @@ async def test_connection_lifecycle(self, realtime): await realtime.close() assert realtime.connected is False + @pytest.mark.integration + async def test_function_calling(self, realtime): + """Test that function calling works with AWS Bedrock Realtime""" + # Register a test function + @realtime.register_function( + name="get_test_data", + description="Get test data for a given key" + ) + def get_test_data(key: str) -> dict: + """Get test data. + + Args: + key: The key to look up + + Returns: + Test data for the key + """ + test_data = { + "weather": {"value": "sunny", "temp": 72}, + "time": {"value": "12:00 PM"}, + } + return test_data.get(key, {"error": "Key not found"}) + + events = [] + + @realtime.events.subscribe + async def on_audio(event: RealtimeAudioOutputEvent): + events.append(event) + + await asyncio.sleep(0.01) + await realtime.connect() + + # Ask the model to use the function + await realtime.simple_response( + "Please use the get_test_data function to get data for the weather key and tell me what you find." + ) + + # Wait for response + await asyncio.sleep(15.0) + + # Verify we got some audio output (meaning the model responded after function call) + assert len(events) > 0 + diff --git a/plugins/aws/vision_agents/plugins/aws/aws_llm.py b/plugins/aws/vision_agents/plugins/aws/aws_llm.py index b347720d..5fb3d677 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_llm.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_llm.py @@ -1,5 +1,6 @@ import os -from typing import Optional, List, TYPE_CHECKING, Any, Dict +import logging +from typing import Optional, List, TYPE_CHECKING, Any, Dict, cast import json import boto3 from botocore.exceptions import ClientError @@ -8,7 +9,10 @@ from vision_agents.core.llm.llm_types import ToolSchema, NormalizedToolCallItem -from vision_agents.core.llm.events import LLMResponseChunkEvent, LLMResponseCompletedEvent +from vision_agents.core.llm.events import ( + LLMResponseChunkEvent, + LLMResponseCompletedEvent, +) from vision_agents.core.processors import Processor from . import events from vision_agents.core.edge.types import Participant @@ -25,9 +29,9 @@ class BedrockLLM(LLM): https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime/client/converse.html Chat history has to be manually passed, there is no conversation storage. - + Examples: - + from vision_agents.plugins import aws llm = aws.LLM( model="anthropic.claude-3-5-sonnet-20241022-v2:0", @@ -45,7 +49,7 @@ def __init__( ): """ Initialize the BedrockLLM class. - + Args: model: The Bedrock model ID (e.g., "anthropic.claude-3-5-sonnet-20241022-v2:0") region_name: AWS region name (default: "us-east-1") @@ -57,7 +61,7 @@ def __init__( self.events.register_events_from_module(events) self.model = model self._pending_tool_uses_by_index: Dict[int, Dict[str, Any]] = {} - + # Initialize boto3 bedrock-runtime client session_kwargs = {"region_name": region_name} if aws_access_key_id: @@ -69,10 +73,11 @@ def __init__( if os.environ.get("AWS_BEDROCK_API_KEY"): session_kwargs["aws_session_token"] = os.environ["AWS_BEDROCK_API_KEY"] - + self.client = boto3.client("bedrock-runtime", **session_kwargs) self.region_name = region_name + self.logger = logging.getLogger(__name__) async def simple_response( self, @@ -82,14 +87,14 @@ async def simple_response( ): """ Simple response is a standardized way to create a response. - + Args: text: The text to respond to processors: list of processors (which contain state) about the video/voice AI participant: optionally the participant object - + Examples: - + await llm.simple_response("say hi to the user") """ return await self.converse_stream( @@ -107,9 +112,8 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: # Add tools if available tools = self.get_available_functions() if tools: - kwargs["toolConfig"] = { - "tools": self._convert_tools_to_provider_format(tools) - } + converted_tools = self._convert_tools_to_provider_format(tools) + kwargs["toolConfig"] = {"tools": converted_tools} # Combine original instructions with markdown file contents enhanced_instructions = self._build_enhanced_instructions() @@ -118,7 +122,7 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: # Ensure the AI remembers the past conversation new_messages = kwargs.get("messages", []) - if hasattr(self, '_conversation') and self._conversation: + if hasattr(self, "_conversation") and self._conversation: old_messages = [m.original for m in self._conversation.messages] kwargs["messages"] = old_messages + new_messages # Add messages to conversation @@ -127,83 +131,187 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: self._conversation.messages.append(msg) try: + system_param = kwargs.get("system") + response = self.client.converse(**kwargs) - + # Extract text from response text = self._extract_text_from_response(response) llm_response = LLMResponseEvent(response, text) - + # Handle tool calls if present function_calls = self._extract_tool_calls_from_response(response) if function_calls: + for i, fc in enumerate(function_calls): + self.logger.debug( + f"Tool call {i + 1}: {fc.get('name')} with args: {fc.get('arguments_json')}" + ) messages = kwargs["messages"][:] + assistant_msg_from_response = response.get("output", {}).get( + "message", {} + ) + if assistant_msg_from_response: + messages.append(assistant_msg_from_response) + MAX_ROUNDS = 3 rounds = 0 seen: set[tuple[str, str, str]] = set() current_calls = function_calls - + while current_calls and rounds < MAX_ROUNDS: # Execute calls concurrently with dedup - triples, seen = await self._dedup_and_execute(current_calls, seen=seen, max_concurrency=8, timeout_s=30) # type: ignore[arg-type] - + triples, seen = await self._dedup_and_execute( + cast(List[Dict[str, Any]], current_calls), seen=seen, max_concurrency=8, timeout_s=30 + ) + if not triples: + self.logger.warning( + "No tool execution results despite tool calls" + ) break - + # Build tool result message tool_result_blocks = [] for tc, res, err in triples: - payload = self._sanitize_tool_output(res) - tool_result_blocks.append({ - "toolUseId": tc["id"], - "content": [{"json": payload if isinstance(payload, dict) else {"result": payload}}], - }) - - # Add assistant message with tool use and user message with tool results - assistant_msg = { - "role": "assistant", - "content": [ + if err: + self.logger.error( + f"Tool {tc['name']} execution error: {err}" + ) + tool_response = str(err) + else: + # Convert result to string format (AWS expects text, not json content type) + if isinstance(res, (dict, list)): + tool_response = json.dumps(res) + elif isinstance(res, str): + tool_response = res + else: + tool_response = str(res) + + tool_result_blocks.append( { - "toolUse": { - "toolUseId": tc["id"], - "name": tc["name"], - "input": tc["arguments_json"], - } + "toolUseId": tc["id"], + "content": [{"text": tool_response}], } - for tc, _, _ in triples - ] - } + ) + user_tool_results_msg = { "role": "user", - "content": [{"toolResult": tr} for tr in tool_result_blocks] + "content": [{"toolResult": tr} for tr in tool_result_blocks], } - messages = messages + [assistant_msg, user_tool_results_msg] - - # Ask again WITH tools - follow_up_response = self.client.converse( - modelId=self.model, - messages=messages, - toolConfig=kwargs.get("toolConfig", {}), + messages = messages + [user_tool_results_msg] + follow_up_kwargs = { + "modelId": self.model, + "messages": messages, + "toolConfig": kwargs.get("toolConfig", {}), + } + if system_param: + follow_up_kwargs["system"] = system_param + + try: + follow_up_response = self.client.converse(**follow_up_kwargs) + except ClientError as e: + self.logger.error( + f"AWS Bedrock API error in follow-up call: {e}" + ) + error_code = ( + e.response.get("Error", {}).get("Code", "Unknown") + if hasattr(e, "response") + else "Unknown" + ) + self.logger.error( + f"Error code: {error_code}, Full error: {str(e)}" + ) + raise + + current_calls = self._extract_tool_calls_from_response( + follow_up_response ) - - # Extract new tool calls - current_calls = self._extract_tool_calls_from_response(follow_up_response) - llm_response = LLMResponseEvent(follow_up_response, self._extract_text_from_response(follow_up_response)) - rounds += 1 - - # Final pass without tools - if current_calls or rounds > 0: - final_response = self.client.converse( - modelId=self.model, - messages=messages, + follow_up_text = self._extract_text_from_response( + follow_up_response ) - llm_response = LLMResponseEvent(final_response, self._extract_text_from_response(final_response)) - - self.events.send(LLMResponseCompletedEvent(original=response, text=text, plugin_name="aws")) + llm_response = LLMResponseEvent(follow_up_response, follow_up_text) + + if current_calls: + assistant_msg_from_follow_up = follow_up_response.get( + "output", {} + ).get("message", {}) + if assistant_msg_from_follow_up: + messages.append(assistant_msg_from_follow_up) + + if follow_up_text and not current_calls: + text = follow_up_text + break + + rounds += 1 + + if current_calls: + final_kwargs = { + "modelId": self.model, + "messages": messages, + } + if system_param: + final_kwargs["system"] = system_param + + try: + final_response = self.client.converse(**final_kwargs) + except ClientError as e: + self.logger.error(f"AWS Bedrock API error in final pass: {e}") + error_code = ( + e.response.get("Error", {}).get("Code", "Unknown") + if hasattr(e, "response") + else "Unknown" + ) + self.logger.error( + f"Error code: {error_code}, Full error: {str(e)}" + ) + raise + + final_text = self._extract_text_from_response(final_response) + llm_response = LLMResponseEvent(final_response, final_text) + text = final_text + elif rounds > 0: + text = llm_response.text or text + + final_text_for_event = llm_response.text or text + original_for_event = llm_response.original or response + + if not final_text_for_event: + self.logger.warning( + "Final response text is empty - model may not have responded" + ) + + self.events.send( + LLMResponseCompletedEvent( + original=original_for_event, + text=final_text_for_event, + plugin_name="aws", + ) + ) except ClientError as e: - error_msg = f"AWS Bedrock API error: {str(e)}" - llm_response = LLMResponseEvent(None, error_msg, exception = e) - + error_code = ( + e.response.get("Error", {}).get("Code", "Unknown") + if hasattr(e, "response") + else "Unknown" + ) + error_msg = ( + e.response.get("Error", {}).get("Message", str(e)) + if hasattr(e, "response") + else str(e) + ) + self.logger.error( + f"AWS Bedrock API error: {error_code} - {error_msg}", exc_info=True + ) + llm_response = LLMResponseEvent(None, error_msg, exception=e) + except Exception as e: + self.logger.error( + f"Unexpected error in converse: {type(e).__name__}: {str(e)}", + exc_info=True, + ) + llm_response = LLMResponseEvent( + None, f"Unexpected error: {str(e)}", exception=e + ) + return llm_response async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: @@ -216,13 +324,12 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: # Add tools if available tools = self.get_available_functions() if tools: - kwargs["toolConfig"] = { - "tools": self._convert_tools_to_provider_format(tools) - } + converted_tools = self._convert_tools_to_provider_format(tools) + kwargs["toolConfig"] = {"tools": converted_tools} # Ensure the AI remembers the past conversation new_messages = kwargs.get("messages", []) - if hasattr(self, '_conversation') and self._conversation: + if hasattr(self, "_conversation") and self._conversation: old_messages = [m.original for m in self._conversation.messages] kwargs["messages"] = old_messages + new_messages normalized_messages = self._normalize_message(new_messages) @@ -235,143 +342,227 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: kwargs["system"] = [{"text": enhanced_instructions}] try: - response = self.client.converse_stream(**kwargs) - stream = response.get('stream') - + system_param = kwargs.get("system") + + try: + response = self.client.converse_stream(**kwargs) + except ClientError as e: + error_code = ( + e.response.get("Error", {}).get("Code", "Unknown") + if hasattr(e, "response") + else "Unknown" + ) + error_msg = ( + e.response.get("Error", {}).get("Message", str(e)) + if hasattr(e, "response") + else str(e) + ) + self.logger.error( + f"AWS Bedrock API error in converse_stream: {error_code} - {error_msg}", + exc_info=True, + ) + raise + + stream = response.get("stream") + if not stream: + self.logger.error("converse_stream response has no 'stream' field") + llm_response = LLMResponseEvent(None, "No stream in response") + return llm_response + text_parts: List[str] = [] accumulated_calls: List[NormalizedToolCallItem] = [] last_event = None - - # Process stream + for event in stream: last_event = event self._process_stream_event(event, text_parts, accumulated_calls) - - # Handle multi-hop tool calling + messages = kwargs["messages"][:] MAX_ROUNDS = 3 rounds = 0 seen: set[tuple[str, str, str]] = set() - + + if accumulated_calls: + assistant_content = [] + for tool_call in accumulated_calls: + assistant_content.append( + { + "toolUse": { + "toolUseId": tool_call["id"], + "name": tool_call["name"], + "input": tool_call["arguments_json"], + } + } + ) + assistant_msg_from_stream = { + "role": "assistant", + "content": assistant_content, + } + messages.append(assistant_msg_from_stream) + while accumulated_calls and rounds < MAX_ROUNDS: - triples, seen = await self._dedup_and_execute(accumulated_calls, seen=seen, max_concurrency=8, timeout_s=30) # type: ignore[arg-type] - + triples, seen = await self._dedup_and_execute( + cast(List[Dict[str, Any]], accumulated_calls), seen=seen, max_concurrency=8, timeout_s=30 + ) + if not triples: + self.logger.warning("No tool execution results despite tool calls") break - - # Build tool result messages + tool_result_blocks = [] for tc, res, err in triples: - payload = self._sanitize_tool_output(res) - tool_result_blocks.append({ - "toolUseId": tc["id"], - "content": [{"json": payload if isinstance(payload, dict) else {"result": payload}}], - }) - - assistant_msg = { - "role": "assistant", - "content": [ + if err: + self.logger.error(f"Tool {tc['name']} execution error: {err}") + tool_response = str(err) + else: + # Convert result to string format (AWS expects text, not json content type) + if isinstance(res, (dict, list)): + tool_response = json.dumps(res) + elif isinstance(res, str): + tool_response = res + else: + tool_response = str(res) + + tool_result_blocks.append( { - "toolUse": { - "toolUseId": tc["id"], - "name": tc["name"], - "input": tc["arguments_json"], - } + "toolUseId": tc["id"], + "content": [{"text": tool_response}], } - for tc, _, _ in triples - ] - } + ) + user_tool_results_msg = { "role": "user", - "content": [{"toolResult": tr} for tr in tool_result_blocks] + "content": [{"toolResult": tr} for tr in tool_result_blocks], } - messages = messages + [assistant_msg, user_tool_results_msg] + messages = messages + [user_tool_results_msg] + follow_up_kwargs = { + "modelId": self.model, + "messages": messages, + "toolConfig": kwargs.get("toolConfig", {}), + } + if system_param: + follow_up_kwargs["system"] = system_param + + follow_up_response = self.client.converse_stream(**follow_up_kwargs) - # Next round with tools - follow_up_response = self.client.converse_stream( - modelId=self.model, - messages=messages, - toolConfig=kwargs.get("toolConfig", {}), - ) - accumulated_calls = [] - follow_up_stream = follow_up_response.get('stream') + follow_up_text_parts: List[str] = [] + follow_up_stream = follow_up_response.get("stream") for event in follow_up_stream: last_event = event - self._process_stream_event(event, text_parts, accumulated_calls) - + self._process_stream_event( + event, follow_up_text_parts, accumulated_calls + ) + + if follow_up_text_parts: + text_parts.extend(follow_up_text_parts) + + if accumulated_calls: + follow_up_assistant_content = [] + for tool_call in accumulated_calls: + follow_up_assistant_content.append( + { + "toolUse": { + "toolUseId": tool_call["id"], + "name": tool_call["name"], + "input": tool_call["arguments_json"], + } + } + ) + follow_up_assistant_msg = { + "role": "assistant", + "content": follow_up_assistant_content, + } + messages.append(follow_up_assistant_msg) + + if follow_up_text_parts and not accumulated_calls: + break + rounds += 1 - - # Final pass without tools - if accumulated_calls or rounds > 0: - final_response = self.client.converse_stream( - modelId=self.model, - messages=messages, - ) - final_stream = final_response.get('stream') + + if accumulated_calls: + final_kwargs = { + "modelId": self.model, + "messages": messages, + } + if system_param: + final_kwargs["system"] = system_param + + final_response = self.client.converse_stream(**final_kwargs) + final_stream = final_response.get("stream") + final_text_parts: List[str] = [] for event in final_stream: last_event = event - self._process_stream_event(event, text_parts, accumulated_calls) - + self._process_stream_event( + event, final_text_parts, accumulated_calls + ) + if final_text_parts: + text_parts.extend(final_text_parts) + total_text = "".join(text_parts) llm_response = LLMResponseEvent(last_event, total_text) - self.events.send(LLMResponseCompletedEvent(original=last_event, text=total_text, plugin_name="aws")) - + self.events.send( + LLMResponseCompletedEvent( + original=last_event, text=total_text, plugin_name="aws" + ) + ) + except ClientError as e: error_msg = f"AWS Bedrock streaming error: {str(e)}" llm_response = LLMResponseEvent(None, error_msg) - + return llm_response def _process_stream_event( - self, - event: Dict[str, Any], + self, + event: Dict[str, Any], text_parts: List[str], - accumulated_calls: List[NormalizedToolCallItem] + accumulated_calls: List[NormalizedToolCallItem], ): """Process a streaming event from AWS.""" # Forward the native event - self.events.send(events.AWSStreamEvent( - plugin_name="aws", - event_data=event - )) - + self.events.send(events.AWSStreamEvent(plugin_name="aws", event_data=event)) + # Handle content block delta (text) - if 'contentBlockDelta' in event: - delta = event['contentBlockDelta']['delta'] - if 'text' in delta: - text_parts.append(delta['text']) - self.events.send(LLMResponseChunkEvent( - plugin_name="aws", - content_index=event['contentBlockDelta'].get('contentBlockIndex', 0), - item_id="", - output_index=0, - sequence_number=0, - delta=delta['text'], - )) - + if "contentBlockDelta" in event: + delta = event["contentBlockDelta"]["delta"] + if "text" in delta: + text_parts.append(delta["text"]) + self.events.send( + LLMResponseChunkEvent( + plugin_name="aws", + content_index=event["contentBlockDelta"].get( + "contentBlockIndex", 0 + ), + item_id="", + output_index=0, + sequence_number=0, + delta=delta["text"], + ) + ) + # Handle tool use - if 'contentBlockStart' in event: - start = event['contentBlockStart'].get('start', {}) - if 'toolUse' in start: - tool_use = start['toolUse'] - idx = event['contentBlockStart'].get('contentBlockIndex', 0) + if "contentBlockStart" in event: + start = event["contentBlockStart"].get("start", {}) + if "toolUse" in start: + tool_use = start["toolUse"] + idx = event["contentBlockStart"].get("contentBlockIndex", 0) self._pending_tool_uses_by_index[idx] = { - "id": tool_use.get('toolUseId', ''), - "name": tool_use.get('name', ''), - "parts": [] + "id": tool_use.get("toolUseId", ""), + "name": tool_use.get("name", ""), + "parts": [], } - - if 'contentBlockDelta' in event: - delta = event['contentBlockDelta']['delta'] - if 'toolUse' in delta: - idx = event['contentBlockDelta'].get('contentBlockIndex', 0) + + if "contentBlockDelta" in event: + delta = event["contentBlockDelta"]["delta"] + if "toolUse" in delta: + idx = event["contentBlockDelta"].get("contentBlockIndex", 0) if idx in self._pending_tool_uses_by_index: - input_data = delta['toolUse'].get('input', '') - self._pending_tool_uses_by_index[idx]['parts'].append(input_data) - - if 'contentBlockStop' in event: - idx = event['contentBlockStop'].get('contentBlockIndex', 0) + input_data = delta["toolUse"].get("input", "") + self._pending_tool_uses_by_index[idx]["parts"].append(input_data) + + if "contentBlockStop" in event: + idx = event["contentBlockStop"].get("contentBlockIndex", 0) pending = self._pending_tool_uses_by_index.pop(idx, None) if pending: buf = "".join(pending["parts"]).strip() or "{}" @@ -383,63 +574,100 @@ def _process_stream_event( "type": "tool_call", "id": pending["id"], "name": pending["name"], - "arguments_json": args + "arguments_json": args, } accumulated_calls.append(tool_call_item) def _extract_text_from_response(self, response: Dict[str, Any]) -> str: """Extract text content from AWS response.""" - output = response.get('output', {}) - message = output.get('message', {}) - content = message.get('content', []) - + output = response.get("output", {}) + message = output.get("message", {}) + content = message.get("content", []) + text_parts = [] for item in content: - if 'text' in item: - text_parts.append(item['text']) - + if "text" in item: + text_parts.append(item["text"]) + return "".join(text_parts) - def _extract_tool_calls_from_response(self, response: Dict[str, Any]) -> List[NormalizedToolCallItem]: + def _extract_tool_calls_from_response( + self, response: Dict[str, Any] + ) -> List[NormalizedToolCallItem]: """Extract tool calls from AWS response.""" - tool_calls = [] - - output = response.get('output', {}) - message = output.get('message', {}) - content = message.get('content', []) - + tool_calls: List[NormalizedToolCallItem] = [] + + output = response.get("output", {}) + if not output: + return tool_calls + + message = output.get("message", {}) + if not message: + return tool_calls + + content = message.get("content", []) + if not content: + return tool_calls + for item in content: - if 'toolUse' in item: - tool_use = item['toolUse'] + if "toolUse" in item: + tool_use = item["toolUse"] tool_call: NormalizedToolCallItem = { "type": "tool_call", - "id": tool_use.get('toolUseId', ''), - "name": tool_use.get('name', ''), - "arguments_json": tool_use.get('input', {}) + "id": tool_use.get("toolUseId", ""), + "name": tool_use.get("name", ""), + "arguments_json": tool_use.get("input", {}), } tool_calls.append(tool_call) - + return tool_calls - def _convert_tools_to_provider_format(self, tools: List[ToolSchema]) -> List[Dict[str, Any]]: + def _convert_tools_to_provider_format( + self, tools: List[ToolSchema] + ) -> List[Dict[str, Any]]: """ Convert ToolSchema objects to AWS Bedrock format. - + Args: tools: List of ToolSchema objects - + Returns: List of tools in AWS Bedrock format """ aws_tools = [] for tool in tools: + name = tool.get("name", "unnamed_tool") + description = tool.get("description", "") or "" + params = tool.get("parameters_schema") or {} + + # Normalize to a valid JSON Schema object + if not isinstance(params, dict): + params = {} + + # Ensure it has the required JSON Schema structure + if "type" not in params: + # Extract required fields from properties if they exist + properties = params if params else {} + required = list(properties.keys()) if properties else [] + + params = { + "type": "object", + "properties": properties, + "required": required, + "additionalProperties": False, + } + else: + # Already has type, but ensure additionalProperties is set + if "additionalProperties" not in params: + params["additionalProperties"] = False + aws_tool = { "toolSpec": { - "name": tool["name"], - "description": tool.get("description", ""), + "name": name, + "description": description, "inputSchema": { - "json": tool["parameters_schema"] - } + "json": params # This is a dict, not a JSON string + }, } } aws_tools.append(aws_tool) @@ -451,9 +679,7 @@ def _normalize_message(aws_messages: Any) -> List["Message"]: from vision_agents.core.agents.conversation import Message if isinstance(aws_messages, str): - aws_messages = [ - {"content": [{"text": aws_messages}], "role": "user"} - ] + aws_messages = [{"content": [{"text": aws_messages}], "role": "user"}] if not isinstance(aws_messages, (List, tuple)): aws_messages = [aws_messages] @@ -465,8 +691,8 @@ def _normalize_message(aws_messages: Any) -> List["Message"]: # Extract text from content blocks text_parts = [] for item in content_items: - if isinstance(item, dict) and 'text' in item: - text_parts.append(item['text']) + if isinstance(item, dict) and "text" in item: + text_parts.append(item["text"]) elif isinstance(item, str): text_parts.append(item) content = " ".join(text_parts) @@ -478,4 +704,3 @@ def _normalize_message(aws_messages: Any) -> List["Message"]: messages.append(message) return messages - diff --git a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py index d5ea9b67..83bc46cb 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -21,26 +21,23 @@ from vision_agents.core.utils.video_forwarder import VideoForwarder from vision_agents.core.processors import Processor from vision_agents.core.edge.types import Participant -from getstream.video.rtc.track_util import PcmData, AudioFormat -from ...core.llm.events import RealtimeAudioOutputEvent +from vision_agents.core.edge.types import PcmData logger = logging.getLogger(__name__) - DEFAULT_MODEL = "amazon.nova-sonic-v1:0" DEFAULT_SAMPLE_RATE = 16000 - """ -TODO: -- Implement & cleanup function calling -- Cleanup chat integration +AWS Bedrock Realtime with Nova Sonic support. + +Supports real-time audio/video streaming and function calling (tool use). """ class Realtime(realtime.Realtime): """ - Realtime on AWS with support for audio/video streaming (uses AWS Bedrock). + Realtime on AWS with support for audio/video streaming and function calling (uses AWS Bedrock). A few things are different about Nova compared to other STS solutions @@ -49,6 +46,12 @@ class Realtime(realtime.Realtime): 3. input/text events are wrapped. so its common to do start event, text event, stop event 4. on close there is an session and a prompt end event + Function Calling: + This implementation supports AWS Nova's tool use feature. Register functions using + the @llm.register_function decorator and they will be automatically made available + to the model. When the model calls a function, it will be executed and the result + sent back to continue the conversation. + AWS Nova samples are the best docs: simple: https://github.com/aws-samples/amazon-nova-samples/blob/main/speech-to-speech/sample-codes/console-python/nova_sonic_simple.py @@ -73,6 +76,14 @@ class Realtime(realtime.Realtime): region_name="us-east-1" ) + # Register a custom function + @llm.register_function( + name="get_weather", + description="Get weather for a city" + ) + def get_weather(city: str) -> dict: + return {"city": city, "temp": 72, "condition": "sunny"} + # Connect to the session await llm.connect() @@ -89,6 +100,41 @@ class Realtime(realtime.Realtime): connected: bool = False voice_id: str + # Event templates + TEXT_CONTENT_START_EVENT = """{ + "event": { + "contentStart": { + "promptName": "%s", + "contentName": "%s", + "type": "TEXT", + "role": "%s", + "interactive": false, + "textInputConfiguration": { + "mediaType": "text/plain" + } + } + } + }""" + + TEXT_INPUT_EVENT = """{ + "event": { + "textInput": { + "promptName": "%s", + "contentName": "%s", + "content": "%s" + } + } + }""" + + CONTENT_END_EVENT = """{ + "event": { + "contentEnd": { + "promptName": "%s", + "contentName": "%s" + } + } + }""" + def __init__( self, model: str = DEFAULT_MODEL, @@ -114,7 +160,7 @@ def __init__( # Audio output track - Bedrock typically outputs at 16kHz self.output_track = AudioStreamTrack( - framerate=24000, stereo=False, format=AudioFormat.S16 + framerate=24000, stereo=False, format="s16" ) self._video_forwarder: Optional[VideoForwarder] = None @@ -122,9 +168,9 @@ def __init__( self._is_connected = False self._message_queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue() self._conversation_messages: List[Dict[str, Any]] = [] - self._pending_tool_uses: Dict[ - int, Dict[str, Any] - ] = {} # Track tool calls across stream events + self._pending_tool_calls: Dict[ + str, Dict[str, Any] + ] = {} # Store tool calls until contentEnd: key=toolUseId # Audio streaming configuration self.prompt_name = self.session_id @@ -147,27 +193,42 @@ async def connect(self): self.logger.warning("Already connected") return - # Initialize the stream - logger.info("Connecting to AWS Bedrock for model %s", self.model) - self.stream = await self.client.invoke_model_with_bidirectional_stream( - InvokeModelWithBidirectionalStreamOperationInput(model_id=self.model) - ) - self.connected = True + try: + # Initialize the stream + logger.info("Connecting to AWS Bedrock for model %s", self.model) + self.stream = await self.client.invoke_model_with_bidirectional_stream( + InvokeModelWithBidirectionalStreamOperationInput(model_id=self.model) + ) + self.connected = True - # Start listener task - self._stream_task = asyncio.create_task(self._handle_events()) + # Start listener task + self._stream_task = asyncio.create_task(self._handle_events()) - # send start and prompt event - await self.start_session() - await self.start_prompt() + # send start and prompt event + await self.start_session() - # next send system instructions - system_instructions = self._build_enhanced_instructions() - if not system_instructions: - raise Exception( - "AWS Bedrock requires system instructions before sending regular user input" - ) - await self.content_input(system_instructions, "SYSTEM") + # Small delay between init events + await asyncio.sleep(0.1) + + await self.start_prompt() + + # Give AWS Nova a moment to process the prompt start event + await asyncio.sleep(0.1) + + # next send system instructions + system_instructions = self._build_enhanced_instructions() + if not system_instructions: + raise Exception( + "AWS Bedrock requires system instructions before sending regular user input" + ) + await self.content_input(system_instructions, "SYSTEM") + + logger.info("AWS Bedrock connection established") + + except Exception as e: + logger.error(f"Failed to connect to AWS Bedrock: {e}", exc_info=True) + self.connected = False + raise async def simple_audio_response( self, pcm: PcmData, participant: Optional[Participant] = None @@ -177,9 +238,11 @@ async def simple_audio_response( self.logger.warning( "realtime is not active. can't call simple_audio_response" ) + return - # Resample from 48kHz to 24kHz if needed - pcm = pcm.resample(24000) + # Resample to 24kHz if needed, as required by AWS Nova + if pcm.sample_rate != 24000: + pcm = pcm.resample(24000) content_name = str(uuid.uuid4()) @@ -214,6 +277,7 @@ async def content_input(self, content: str, role: str): This method wraps the 3 events in one operation """ content_name = str(uuid.uuid4()) + logger.debug(f"Sending content input: role={role}, content={content[:100]}...") await self.text_content_start(content_name, role) await self.text_input(content_name, content) await self.content_end(content_name) @@ -254,86 +318,292 @@ async def audio_content_start(self, content_name: str, role: str = "USER"): async def start_session(self): # subclass this to change the session start - event = { - "event": { - "sessionStart": { - "inferenceConfiguration": { - "maxTokens": 10240, # TODO: make configurable in init? - "topP": 0.9, - "temperature": 0.7, - } - } + event_json = """{ + "event": { + "sessionStart": { + "inferenceConfiguration": { + "maxTokens": 1024, + "topP": 0.9, + "temperature": 0.7 + } } - } + } + }""" - await self.send_event(event) + await self.send_raw_event(event_json) async def start_prompt(self): prompt_name = self.session_id - event = { - "event": { - "promptStart": { - "promptName": prompt_name, - "textOutputConfiguration": {"mediaType": "text/plain"}, - "audioOutputConfiguration": { - "mediaType": "audio/lpcm", - "sampleRateHertz": 24000, - "sampleSizeBits": 16, - "channelCount": 1, - "voiceId": self.voice_id, - "encoding": "base64", - "audioType": "SPEECH", - }, + + # Add tool configuration if tools are available + tools = self._convert_tools_to_provider_format(self.get_available_functions()) + + if tools: + import json as json_lib + + self.logger.info(f"Adding tool configuration with {len(tools)} tools") + + # Build the event with tools + event = { + "event": { + "promptStart": { + "promptName": prompt_name, + "textOutputConfiguration": {"mediaType": "text/plain"}, + "audioOutputConfiguration": { + "mediaType": "audio/lpcm", + "sampleRateHertz": 24000, + "sampleSizeBits": 16, + "channelCount": 1, + "voiceId": self.voice_id, + "encoding": "base64", + "audioType": "SPEECH", + }, + "toolUseOutputConfiguration": {"mediaType": "application/json"}, + "toolConfiguration": {"tools": tools}, + } } } - } - # TODO: tool support - await self.send_event(event) + event_json = json_lib.dumps(event) + else: + # Build the event without tools + event_json = f'''{{ + "event": {{ + "promptStart": {{ + "promptName": "{prompt_name}", + "textOutputConfiguration": {{ + "mediaType": "text/plain" + }}, + "audioOutputConfiguration": {{ + "mediaType": "audio/lpcm", + "sampleRateHertz": 24000, + "sampleSizeBits": 16, + "channelCount": 1, + "voiceId": "{self.voice_id}", + "encoding": "base64", + "audioType": "SPEECH" + }} + }} + }} + }}''' + + await self.send_raw_event(event_json) async def text_content_start(self, content_name: str, role: str): + event_json = self.TEXT_CONTENT_START_EVENT % ( + self.session_id, + content_name, + role, + ) + await self.send_raw_event(event_json) + + async def text_input(self, content_name: str, content: str): + # Escape content for JSON + escaped_content = ( + content.replace('"', '\\"').replace("\n", "\\n").replace("\r", "\\r") + ) + event_json = self.TEXT_INPUT_EVENT % ( + self.session_id, + content_name, + escaped_content, + ) + await self.send_raw_event(event_json) + + async def content_end(self, content_name: str): + event_json = self.CONTENT_END_EVENT % (self.session_id, content_name) + await self.send_raw_event(event_json) + + async def send_event(self, event_data: Dict[str, Any]) -> None: + try: + event_json = json.dumps(event_data) + event = InvokeModelWithBidirectionalStreamInputChunk( + value=BidirectionalInputPayloadPart(bytes_=event_json.encode("utf-8")) + ) + await self.stream.input_stream.send(event) + except Exception as e: + logger.error(f"Failed to send event to AWS Nova: {e}") + # Don't raise the exception, just log it to prevent connection reset + + async def send_raw_event(self, event_json: str) -> None: + """Send a raw JSON event string to AWS Nova (matching working example approach).""" + try: + event = InvokeModelWithBidirectionalStreamInputChunk( + value=BidirectionalInputPayloadPart(bytes_=event_json.encode("utf-8")) + ) + await self.stream.input_stream.send(event) + except Exception as e: + logger.error(f"Failed to send raw event to AWS Nova: {e}") + # Don't raise the exception, just log it to prevent connection reset + + def _convert_tools_to_provider_format( + self, tools: List[Any] + ) -> List[Dict[str, Any]]: + """Convert ToolSchema objects to AWS Nova Realtime format. + + Args: + tools: List of ToolSchema objects from the function registry + + Returns: + List of tools in AWS Nova Realtime format + """ + aws_tools = [] + for tool in tools or []: + name = tool.get("name", "unnamed_tool") + description = tool.get("description", "") or "" + params = tool.get("parameters_schema") or tool.get("parameters") or {} + + # Normalize to a valid JSON Schema object + if not isinstance(params, dict): + params = {} + + # Ensure it has the required JSON Schema structure + if "type" not in params: + # Extract required fields from properties if they exist + properties = params if params else {} + required = list(properties.keys()) if properties else [] + + params = { + "type": "object", + "properties": properties, + "required": required, + "additionalProperties": False, + } + else: + # Already has type, but ensure additionalProperties is set + if "additionalProperties" not in params: + params["additionalProperties"] = False + + # AWS Nova expects toolSpec format with inputSchema.json as a JSON STRING (matching official example) + # Convert the schema to a JSON string + schema_json = json.dumps(params) + + aws_tool = { + "toolSpec": { + "name": name, + "description": description, + "inputSchema": { + "json": schema_json # This should be a JSON string, not a dict + }, + } + } + aws_tools.append(aws_tool) + return aws_tools + + async def send_tool_content_start(self, content_name: str, tool_use_id: str): + """Send tool content start event. + + Args: + content_name: Unique content identifier + tool_use_id: The tool use ID from the toolUse event + """ event = { "event": { "contentStart": { "promptName": self.session_id, "contentName": content_name, - "type": "TEXT", + "type": "TOOL", "interactive": False, - "role": role, - "textInputConfiguration": {"mediaType": "text/plain"}, + "role": "TOOL", + "toolResultInputConfiguration": { + "toolUseId": tool_use_id, + "type": "TEXT", + "textInputConfiguration": {"mediaType": "text/plain"}, + }, } } } await self.send_event(event) - async def text_input(self, content_name: str, content: str): + async def send_tool_result(self, content_name: str, result: Any): + """Send tool result event. + + Args: + content_name: Unique content identifier + result: The result from executing the tool (will be stringified as JSON) + """ + # AWS Nova expects content as a stringified JSON string + # Reference: https://docs.aws.amazon.com/nova/latest/userguide/input-events.html + if isinstance(result, str): + content_str = result + else: + content_str = json.dumps(result) + event = { "event": { - "textInput": { + "toolResult": { "promptName": self.session_id, "contentName": content_name, - "content": content, + "content": content_str, # Stringified JSON, not an object/array } } } await self.send_event(event) - async def content_end(self, content_name: str): - event = { - "event": { - "contentEnd": { - "promptName": self.session_id, - "contentName": content_name, - } + async def _handle_tool_call( + self, tool_name: str, tool_use_id: str, tool_use_content: Dict[str, Any] + ): + """Handle tool call from AWS Bedrock. + + Args: + tool_name: Name of the tool to execute + tool_use_id: The tool use ID from AWS + tool_use_content: Full tool use content from AWS + """ + try: + logger.debug( + f"Starting tool call execution: {tool_name} (id: {tool_use_id})" + ) + + # Extract tool input from the tool use content (matching working example) + tool_input = {} + if "content" in tool_use_content: + try: + tool_input = json.loads(tool_use_content["content"]) + except (json.JSONDecodeError, TypeError): + logger.warning( + f"Could not parse tool input from content: {tool_use_content.get('content')}" + ) + tool_input = {} + elif "input" in tool_use_content: + tool_input = tool_use_content["input"] + + # Create normalized tool call + tool_call = { + "type": "tool_call", + "id": tool_use_id, + "name": tool_name, + "arguments_json": tool_input, } - } - await self.send_event(event) - async def send_event(self, event_data: Dict[str, Any]) -> None: - event_json = json.dumps(event_data) - event = InvokeModelWithBidirectionalStreamInputChunk( - value=BidirectionalInputPayloadPart(bytes_=event_json.encode("utf-8")) - ) - await self.stream.input_stream.send(event) + # Execute using existing tool execution infrastructure from base LLM + tc, result, error = await self._run_one_tool(tool_call, timeout_s=30) + + # Prepare response data + if error: + response_data = {"error": str(error)} + logger.error(f"Tool call {tool_name} failed: {error}") + else: + response_data = result + + # Send tool result back to AWS using Nova's format + content_name = str(uuid.uuid4()) + + await self.send_tool_content_start(content_name, tool_use_id) + await self.send_tool_result(content_name, response_data) + await self.content_end(content_name) + + except Exception as e: + logger.error(f"Error handling tool call {tool_name}: {e}", exc_info=True) + # Send error response back + try: + content_name = str(uuid.uuid4()) + await self.send_tool_content_start(content_name, tool_use_id) + await self.send_tool_result(content_name, {"error": str(e)}) + await self.content_end(content_name) + except Exception as send_error: + logger.error( + f"Failed to send error response for tool call {tool_name}: {send_error}", + exc_info=True, + ) async def close(self): if not self.connected: @@ -369,12 +639,13 @@ async def _handle_events(self): try: response_data = result.value.bytes_.decode("utf-8") json_data = json.loads(response_data) + logger.debug(f"Received event: {json_data}") # Handle different response types if "event" in json_data: if "contentStart" in json_data["event"]: content_start = json_data["event"]["contentStart"] - logger.info( + logger.debug( f"Content start from AWS Bedrock: {content_start}" ) # set role @@ -394,16 +665,17 @@ async def _handle_events(self): self.display_assistant_text = False except json.JSONDecodeError: pass + elif "textOutput" in json_data["event"]: text_content = json_data["event"]["textOutput"][ "content" ] # role = json_data['event']['textOutput']['role'] - logger.info( + logger.debug( f"Text output from AWS Bedrock: {text_content}" ) elif "completionStart" in json_data["event"]: - logger.info( + logger.debug( "Completion start from AWS Bedrock", json_data["event"]["completionStart"], ) @@ -413,93 +685,119 @@ async def _handle_events(self): ] audio_bytes = base64.b64decode(audio_content) # await self.audio_output_queue.put(audio_bytes) - - audio_event = RealtimeAudioOutputEvent( - plugin_name="aws", + self._emit_audio_output_event( audio_data=audio_bytes, sample_rate=24000, ) - self.events.send(audio_event) await self.output_track.write(audio_bytes) elif "toolUse" in json_data["event"]: - logger.info( - f"Tool use from AWS Bedrock: {json_data['event']['toolUse']}" - ) - self.toolUseContent = json_data["event"]["toolUse"] - self.toolName = json_data["event"]["toolUse"][ - "toolName" - ] - self.toolUseId = json_data["event"]["toolUse"][ - "toolUseId" - ] - # Add tool use to chat history - # TODO - # self.chat_history.add_tool_call( - # tool_use_content=self.toolUseContent - # ) - elif ( - "contentEnd" in json_data["event"] - and json_data["event"] - .get("contentEnd", {}) - .get("type") - == "TOOL" - ): - logger.info( - f"Tool end from AWS Bedrock: {json_data['event']['contentEnd']}" + tool_use_data = json_data["event"]["toolUse"] + tool_name = tool_use_data.get("toolName") + tool_use_id = tool_use_data.get("toolUseId") + + logger.debug( + f"Tool use event received: {tool_name} (id: {tool_use_id})" ) - # TODO: Implement tool support - # toolResult = await self.processToolUse(self.toolName, self.toolUseContent) - # self.chat_history.add_tool_result(self.toolUseId, toolResult) - # toolContent = str(uuid.uuid4()) - # await self.send_tool_start_event(toolContent) - # await self.send_tool_result_event(toolContent, toolResult) - # await self.send_tool_content_end_event(toolContent) + + # Store tool call info until contentEnd (matching working example) + if tool_use_id and tool_name: + self._pending_tool_calls[tool_use_id] = { + "toolName": tool_name, + "toolUseId": tool_use_id, + "toolUseContent": tool_use_data, + } + else: + logger.warning( + f"Invalid tool use event - missing toolName or toolUseId: {tool_use_data}" + ) + elif "contentEnd" in json_data["event"]: - stopReason = json_data["event"]["contentEnd"][ - "stopReason" - ] + content_end_data = json_data["event"]["contentEnd"] + stopReason = content_end_data.get("stopReason") + content_type = content_end_data.get("type") + + logger.debug( + f"Content end event: type={content_type}, stopReason={stopReason}" + ) + + # Process tool calls on contentEnd with type == 'TOOL' (matching reference implementation) + if content_type == "TOOL": + tool_use_id = content_end_data.get("toolUseId") + + # If toolUseId not in contentEnd, process most recent pending tool call + if not tool_use_id and self._pending_tool_calls: + # Get the most recently added tool call + tool_use_id = list( + self._pending_tool_calls.keys() + )[-1] + + if ( + tool_use_id + and tool_use_id in self._pending_tool_calls + ): + tool_call_info = ( + self._pending_tool_calls.pop( + tool_use_id + ) + ) + asyncio.create_task( + self._handle_tool_call( + tool_name=tool_call_info[ + "toolName" + ], + tool_use_id=tool_call_info[ + "toolUseId" + ], + tool_use_content=tool_call_info[ + "toolUseContent" + ], + ) + ) + if stopReason == "INTERRUPTED": - logger.info("TODO: should flush audio buffer") - logger.info( - f"Content end from AWS Bedrock {stopReason}: {json_data['event']['contentEnd']}" + logger.debug("TODO: should flush audio buffer") + logger.debug( + f"Content end from AWS Bedrock {stopReason}: {content_end_data}" ) elif "completionEnd" in json_data["event"]: - logger.info( + logger.debug( f"Completion end from AWS Bedrock: {json_data['event']['completionEnd']}" ) # Handle end of conversation, no more response will be generated elif "usageEvent" in json_data["event"]: - # logger.info(f"Usage event from AWS Bedrock: {json_data['event']['usageEvent']}") pass else: logger.warning( f"Unhandled event: {json_data['event']}" ) - # Put the response in the output queue for other components - # await self.output_queue.put(json_data) - except json.JSONDecodeError: - pass - # await self.output_queue.put({"raw_data": response_data}) + except json.JSONDecodeError as e: + logger.warning(f"Failed to parse JSON response: {e}") except StopAsyncIteration: - # Stream has ended - logger.error("Stop async iteration exception") + # Stream has ended normally + logger.debug("Stream ended normally") break except Exception as e: - logger.error("Error, %s", e) + logger.error("Error in event handling: %s", e) # Handle ValidationException properly if "ValidationException" in str(e): error_message = str(e) - print(f"Validation error: {error_message}") + logger.error(f"Validation error: {error_message}") else: - print(f"Error receiving response: {e}") - break + logger.error(f"Error receiving response: {e}") + # Don't break immediately, try to continue processing + continue except Exception as e: - logger.error("Error, %s", e) - print(f"Response processing error: {e}") - finally: + logger.error("Critical error in event handling: %s", e) + # Only reset connection on critical errors self.connected = False + finally: + # Only log connection state, don't unconditionally reset + if self.connected: + logger.debug("Event handling loop ended, connection still active") + else: + logger.debug("Event handling loop ended, connection was closed")