From 9061d577e728e83487268ef277b99c46e6403e8e Mon Sep 17 00:00:00 2001 From: Deven Joshi Date: Thu, 23 Oct 2025 17:45:28 +0200 Subject: [PATCH 01/12] Add AWS Bedrock function calling implementation - Implemented function calling for AWS Bedrock Realtime (Nova Sonic) - Added tool schema conversion to AWS Nova format - Implemented tool execution handlers for realtime API - Added audio resampling fix for simple_audio_response - Created example demonstrating function calling with AWS LLM - Updated README with function calling documentation - Added test for AWS Realtime function calling Note: AWS Nova Realtime toolConfiguration causes connection errors, likely an API limitation. Implementation is ready for when AWS adds support. --- plugins/aws/README.md | 60 +- .../aws_llm_function_calling_example.py | 88 + .../aws_realtime_function_calling_example.py | 141 + plugins/aws/example/pyproject.toml | 12 +- plugins/aws/example/uv.lock | 2441 +++++++---------- plugins/aws/tests/test_aws_realtime.py | 43 + .../vision_agents/plugins/aws/aws_realtime.py | 243 +- 7 files changed, 1579 insertions(+), 1449 deletions(-) create mode 100644 plugins/aws/example/aws_llm_function_calling_example.py create mode 100644 plugins/aws/example/aws_realtime_function_calling_example.py diff --git a/plugins/aws/README.md b/plugins/aws/README.md index e82e21f4..7569d345 100644 --- a/plugins/aws/README.md +++ b/plugins/aws/README.md @@ -28,9 +28,9 @@ agent = Agent( The full example is available in example/aws_qwen_example.py -### Realtime Text/Image Usage +### Realtime Audio Usage -Nova sonic audio realtime STS is also supported: +Nova Sonic audio realtime STS is also supported: ```python agent = Agent( @@ -41,6 +41,62 @@ agent = Agent( ) ``` +## 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 Create a `.env` file, or cp .env.example to .env and fill in 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..75dcdddc --- /dev/null +++ b/plugins/aws/example/aws_llm_function_calling_example.py @@ -0,0 +1,88 @@ +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, smart_turn + +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-3-5-sonnet-20241022-v2:0", + 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..58e1163b --- /dev/null +++ b/plugins/aws/example/aws_realtime_function_calling_example.py @@ -0,0 +1,141 @@ +import asyncio +import logging +from pathlib import Path +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", + voice_id="matthew" + ), + ) + + # 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) + + # 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/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..a3fbdc5b 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 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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_realtime.py b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py index 9c854b58..fb4ba754 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -5,6 +5,8 @@ import uuid from typing import Optional, List, Dict, Any from getstream.video.rtc.audio_track import AudioStreamTrack +from getstream.audio.utils import resample_audio +import numpy as np from vision_agents.core.llm import realtime from aws_sdk_bedrock_runtime.client import BedrockRuntimeClient, InvokeModelWithBidirectionalStreamOperationInput @@ -26,15 +28,15 @@ """ -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 @@ -43,6 +45,12 @@ 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 @@ -67,6 +75,14 @@ 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() @@ -166,9 +182,31 @@ async def simple_audio_response( """Send audio data to the model for processing.""" if not self.connected: 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) + if pcm.sample_rate != 24000: + # Convert to float32 for resampling + if pcm.samples.dtype == np.int16: + audio_float = pcm.samples.astype(np.float32) / 32768.0 + else: + audio_float = pcm.samples.astype(np.float32) + + # Resample + resampled_float = resample_audio(audio_float, pcm.sample_rate, 24000) + + # Convert back to int16 + resampled_samples = (resampled_float * 32768.0).astype(np.int16) + + # Create new PcmData with resampled audio + pcm = PcmData( + format=pcm.format, + sample_rate=24000, + samples=resampled_samples, + pts=pcm.pts, + dts=pcm.dts, + time_base=pcm.time_base + ) content_name = str(uuid.uuid4()) @@ -274,7 +312,21 @@ async def start_prompt(self): } } } - # TODO: tool support + + # 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") + self.logger.info(f"Tool schemas: {json_lib.dumps(tools, indent=2)}") + event["event"]["promptStart"]["toolUseOutputConfiguration"] = { + "mediaType": "application/json" + } + event["event"]["promptStart"]["toolConfiguration"] = { + "tools": tools + } + self.logger.info(f"Full promptStart event: {json_lib.dumps(event, indent=2)}") + await self.send_event(event) @@ -326,6 +378,155 @@ async def send_event(self, event_data: Dict[str, Any]) -> None: ) await self.stream.input_stream.send(event) + 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 {} + + self.logger.info(f"Tool {name} raw params: {json.dumps(params, indent=2)}") + + # 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 DICT (not a string) + # Reference: https://docs.aws.amazon.com/nova/latest/userguide/input-events.html + aws_tool = { + "toolSpec": { + "name": name, + "description": description, + "inputSchema": { + "json": params # This is a dict, not a JSON string + } + } + } + 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": "TOOL", + "interactive": False, + "role": "TOOL", + "toolResultInputConfiguration": { + "toolUseId": tool_use_id, + "type": "TEXT", + "textInputConfiguration": { + "mediaType": "text/plain" + } + } + } + } + } + await self.send_event(event) + + 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": { + "toolResult": { + "promptName": self.session_id, + "contentName": content_name, + "content": content_str # Stringified JSON, not an object/array + } + } + } + await self.send_event(event) + + async def _handle_tool_call(self, tool_name: str, tool_use_id: str, tool_input: 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_input: Input arguments for the tool + """ + try: + # Create normalized tool call + tool_call = { + "type": "tool_call", + "id": tool_use_id, + "name": tool_name, + "arguments_json": tool_input, + } + + logger.info(f"Executing tool call: {tool_name} with args: {tool_input}") + + # 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: + # Keep result as-is for AWS + response_data = result + logger.info(f"Tool call {tool_name} succeeded: {response_data}") + + # 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: {e}") + # Send error response back + 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) + async def close(self): if not self.connected: return @@ -406,25 +607,17 @@ async def _handle_events(self): 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']}") - # 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) + tool_use_data = json_data['event']['toolUse'] + tool_name = tool_use_data['toolName'] + tool_use_id = tool_use_data['toolUseId'] + tool_input = tool_use_data.get('input', {}) + + # Execute the tool immediately (don't wait for contentEnd) + asyncio.create_task(self._handle_tool_call( + tool_name=tool_name, + tool_use_id=tool_use_id, + tool_input=tool_input + )) elif 'contentEnd' in json_data['event']: From b1fca9543875fdf869723711ef5f8a8a0a8b2940 Mon Sep 17 00:00:00 2001 From: "Neevash Ramdial (Nash)" Date: Wed, 29 Oct 2025 16:13:19 -0600 Subject: [PATCH 02/12] Update converse format. Verbose logging --- .../aws_llm_function_calling_example.py | 5 +- .../aws/vision_agents/plugins/aws/aws_llm.py | 378 ++++++++++++++---- 2 files changed, 313 insertions(+), 70 deletions(-) diff --git a/plugins/aws/example/aws_llm_function_calling_example.py b/plugins/aws/example/aws_llm_function_calling_example.py index 75dcdddc..4c25c574 100644 --- a/plugins/aws/example/aws_llm_function_calling_example.py +++ b/plugins/aws/example/aws_llm_function_calling_example.py @@ -20,12 +20,13 @@ async def start_agent() -> None: 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-3-5-sonnet-20241022-v2:0", + 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), + # turn_detection=smart_turn.TurnDetection(buffer_duration=2.0, confidence_threshold=0.5), ) # Register custom functions diff --git a/plugins/aws/vision_agents/plugins/aws/aws_llm.py b/plugins/aws/vision_agents/plugins/aws/aws_llm.py index b347720d..b5d0ab20 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_llm.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_llm.py @@ -1,4 +1,5 @@ import os +import logging from typing import Optional, List, TYPE_CHECKING, Any, Dict import json import boto3 @@ -73,6 +74,10 @@ def __init__( self.client = boto3.client("bedrock-runtime", **session_kwargs) self.region_name = region_name + self.logger = logging.getLogger(__name__) + if not self.logger.handlers: + self.logger = logging.getLogger() + self.logger.setLevel(logging.INFO) async def simple_response( self, @@ -92,6 +97,9 @@ async def simple_response( await llm.simple_response("say hi to the user") """ + self.logger.debug(f"simple_response called with text: {text[:100]}...") + registered_tools = self.get_available_functions() + self.logger.debug(f"Available functions: {len(registered_tools)} - {[t.get('name') for t in registered_tools]}") return await self.converse_stream( messages=[{"role": "user", "content": [{"text": text}]}] ) @@ -107,9 +115,11 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: # Add tools if available tools = self.get_available_functions() if tools: + converted_tools = self._convert_tools_to_provider_format(tools) kwargs["toolConfig"] = { - "tools": self._convert_tools_to_provider_format(tools) + "tools": converted_tools } + self.logger.debug(f"Added {len(tools)} tools to converse request") # Combine original instructions with markdown file contents enhanced_instructions = self._build_enhanced_instructions() @@ -127,16 +137,37 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: self._conversation.messages.append(msg) try: + # Capture system message for use in follow-up calls + system_param = kwargs.get("system") + + # Log initial request details + self.logger.debug(f"Making converse request with {len(kwargs.get('messages', []))} messages") + if kwargs.get("toolConfig"): + tool_count = len(kwargs["toolConfig"].get("tools", [])) + self.logger.info(f"Request includes {tool_count} tools") + response = self.client.converse(**kwargs) # Extract text from response text = self._extract_text_from_response(response) llm_response = LLMResponseEvent(response, text) + self.logger.debug(f"Initial response text length: {len(text)}") + # Handle tool calls if present function_calls = self._extract_tool_calls_from_response(response) if function_calls: + self.logger.debug(f"Extracted {len(function_calls)} tool calls from response") + 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"][:] + + # Add assistant message from response (contains toolUse blocks) + assistant_msg_from_response = response.get('output', {}).get('message', {}) + if assistant_msg_from_response: + messages.append(assistant_msg_from_response) + self.logger.debug("Added assistant message from response to conversation history") + MAX_ROUNDS = 3 rounds = 0 seen: set[tuple[str, str, str]] = set() @@ -147,62 +178,131 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: triples, seen = await self._dedup_and_execute(current_calls, seen=seen, max_concurrency=8, timeout_s=30) # type: ignore[arg-type] if not triples: + self.logger.warning("No tool execution results despite tool calls") break + self.logger.debug(f"Executed {len(triples)} tool calls, processing results") + # Build tool result message + # According to AWS Bedrock Converse API, toolResult content should use {"text": "..."} format tool_result_blocks = [] for tc, res, err in triples: - payload = self._sanitize_tool_output(res) + 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({ "toolUseId": tc["id"], - "content": [{"json": payload if isinstance(payload, dict) else {"result": payload}}], + "content": [{"text": tool_response}], }) - # Add assistant message with tool use and user message with tool results - assistant_msg = { - "role": "assistant", - "content": [ - { - "toolUse": { - "toolUseId": tc["id"], - "name": tc["name"], - "input": tc["arguments_json"], - } - } - for tc, _, _ in triples - ] - } + # Add user message with tool results only + # Assistant message was already added from response before the loop user_tool_results_msg = { "role": "user", "content": [{"toolResult": tr} for tr in tool_result_blocks] } - messages = messages + [assistant_msg, user_tool_results_msg] + messages = messages + [user_tool_results_msg] + + self.logger.debug(f"Sending {len(tool_result_blocks)} tool results back to model") + # Build follow-up request parameters + follow_up_kwargs = { + "modelId": self.model, + "messages": messages, + "toolConfig": kwargs.get("toolConfig", {}), + } + # Include system message if it was provided + if system_param: + follow_up_kwargs["system"] = system_param + # Ask again WITH tools - follow_up_response = self.client.converse( - modelId=self.model, - messages=messages, - toolConfig=kwargs.get("toolConfig", {}), - ) + try: + self.logger.debug(f"Sending follow-up request with tool results (round {rounds + 1})") + 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 - # Extract new tool calls + # Extract new tool calls and text from follow-up response 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)) + follow_up_text = self._extract_text_from_response(follow_up_response) + llm_response = LLMResponseEvent(follow_up_response, follow_up_text) + + self.logger.debug(f"Follow-up response: {len(current_calls)} tool calls, text length: {len(follow_up_text)}") + + # Add assistant message from follow-up response if there are more tool calls + 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) + self.logger.debug("Added assistant message from follow-up response to conversation history") + + # If follow-up already has text and no more tool calls, we're done! + if follow_up_text and not current_calls: + self.logger.debug("Follow-up response contains text with no tool calls, using as final response") + text = follow_up_text + break + rounds += 1 - # Final pass without tools - if current_calls or rounds > 0: - final_response = self.client.converse( - modelId=self.model, - messages=messages, - ) - llm_response = LLMResponseEvent(final_response, self._extract_text_from_response(final_response)) + # Final pass without tools ONLY if we still have pending tool calls + if current_calls: + self.logger.debug("Performing final pass without tools to get text response") + final_kwargs = { + "modelId": self.model, + "messages": messages, + } + # Include system message if it was provided + if system_param: + final_kwargs["system"] = system_param + + try: + self.logger.info("Making final pass request without tools") + 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 + self.logger.info(f"Final response text length: {len(final_text)}") + elif rounds > 0: + # We had tool calls but follow-up gave us text, use that + text = llm_response.text if llm_response.text else text - self.events.send(LLMResponseCompletedEvent(original=response, text=text, plugin_name="aws")) + # Use final response text if available, otherwise original text + final_text_for_event = llm_response.text if hasattr(llm_response, 'text') and llm_response.text else text + original_for_event = llm_response.original if hasattr(llm_response, 'original') and llm_response.original else response + + self.logger.info(f"Emitting LLMResponseCompletedEvent with text length: {len(final_text_for_event)}") + 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)}" + 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}") + self.logger.error(f"Full error details: {e}") 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 @@ -210,15 +310,20 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: """ Streaming version of converse using Bedrock's ConverseStream API. """ + self.logger.info("converse_stream called") if "modelId" not in kwargs: kwargs["modelId"] = self.model # Add tools if available tools = self.get_available_functions() if tools: + converted_tools = self._convert_tools_to_provider_format(tools) kwargs["toolConfig"] = { - "tools": self._convert_tools_to_provider_format(tools) + "tools": converted_tools } + self.logger.info(f"Added {len(tools)} tools to converse_stream request: {[t.get('name') for t in tools]}") + else: + self.logger.info("No tools available for converse_stream request") # Ensure the AI remembers the past conversation new_messages = kwargs.get("messages", []) @@ -235,83 +340,181 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: kwargs["system"] = [{"text": enhanced_instructions}] try: - response = self.client.converse_stream(**kwargs) + # Capture system message for use in follow-up calls + system_param = kwargs.get("system") + + # Log initial request details + self.logger.info(f"Making converse_stream request with {len(kwargs.get('messages', []))} messages") + if kwargs.get("toolConfig"): + tool_count = len(kwargs["toolConfig"].get("tools", [])) + self.logger.info(f"Request includes {tool_count} tools") + + try: + response = self.client.converse_stream(**kwargs) + self.logger.info("converse_stream API call succeeded, processing stream...") + 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}") + self.logger.error(f"Full error details: {e}", 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 + event_count = 0 for event in stream: + event_count += 1 last_event = event self._process_stream_event(event, text_parts, accumulated_calls) + self.logger.info(f"Processed {event_count} stream events, accumulated {len(accumulated_calls)} tool calls, {len(''.join(text_parts))} chars of text") + # Handle multi-hop tool calling messages = kwargs["messages"][:] MAX_ROUNDS = 3 rounds = 0 seen: set[tuple[str, str, str]] = set() + if accumulated_calls: + self.logger.info(f"Extracted {len(accumulated_calls)} tool calls from stream") + # Build assistant message from accumulated tool calls + # This matches the format from AWS response['output']['message'] + 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) + self.logger.debug("Added assistant message from stream to conversation history") + 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] if not triples: + self.logger.warning("No tool execution results despite tool calls") break + self.logger.debug(f"Executed {len(triples)} tool calls, processing results") + # Build tool result messages + # According to AWS Bedrock Converse API, toolResult content should use {"text": "..."} format tool_result_blocks = [] for tc, res, err in triples: - payload = self._sanitize_tool_output(res) + 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({ "toolUseId": tc["id"], - "content": [{"json": payload if isinstance(payload, dict) else {"result": payload}}], + "content": [{"text": tool_response}], }) - assistant_msg = { - "role": "assistant", - "content": [ - { - "toolUse": { - "toolUseId": tc["id"], - "name": tc["name"], - "input": tc["arguments_json"], - } - } - for tc, _, _ in triples - ] - } + # Add user message with tool results only + # Assistant message was already added from stream before the loop user_tool_results_msg = { "role": "user", "content": [{"toolResult": tr} for tr in tool_result_blocks] } - messages = messages + [assistant_msg, user_tool_results_msg] + messages = messages + [user_tool_results_msg] + + self.logger.debug(f"Sending {len(tool_result_blocks)} tool results back to model") + # Build follow-up request parameters + follow_up_kwargs = { + "modelId": self.model, + "messages": messages, + "toolConfig": kwargs.get("toolConfig", {}), + } + # Include system message if it was provided + if system_param: + follow_up_kwargs["system"] = system_param + # Next round with tools - follow_up_response = self.client.converse_stream( - modelId=self.model, - messages=messages, - toolConfig=kwargs.get("toolConfig", {}), - ) + follow_up_response = self.client.converse_stream(**follow_up_kwargs) accumulated_calls = [] + 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) + + # Append follow-up text to main text parts + if follow_up_text_parts: + text_parts.extend(follow_up_text_parts) + + self.logger.debug(f"Follow-up response: {len(accumulated_calls)} tool calls, text length: {len(''.join(follow_up_text_parts))}") + + # Add assistant message from follow-up stream if there are more tool calls + if accumulated_calls: + # Build assistant message from accumulated tool calls from follow-up + 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) + self.logger.debug("Added assistant message from follow-up stream to conversation history") + + # If follow-up already has text and no more tool calls, we're done! + if follow_up_text_parts and not accumulated_calls: + self.logger.info("Follow-up response contains text with no tool calls, using as final response") + 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 pass without tools ONLY if we still have pending tool calls + if accumulated_calls: + self.logger.info("Performing final pass without tools to get text response") + final_kwargs = { + "modelId": self.model, + "messages": messages, + } + # Include system message if it was provided + 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) @@ -405,9 +608,21 @@ def _extract_tool_calls_from_response(self, response: Dict[str, Any]) -> List[No tool_calls = [] output = response.get('output', {}) + if not output: + self.logger.debug("Response has no 'output' field") + return tool_calls + message = output.get('message', {}) + if not message: + self.logger.debug("Response output has no 'message' field") + return tool_calls + content = message.get('content', []) + if not content: + self.logger.debug("Response message has no 'content' field") + return tool_calls + self.logger.debug(f"Checking {len(content)} content items for tool calls") for item in content: if 'toolUse' in item: tool_use = item['toolUse'] @@ -418,6 +633,7 @@ def _extract_tool_calls_from_response(self, response: Dict[str, Any]) -> List[No "arguments_json": tool_use.get('input', {}) } tool_calls.append(tool_call) + self.logger.debug(f"Found tool call: {tool_call['name']} (id: {tool_call['id']})") return tool_calls @@ -433,12 +649,38 @@ def _convert_tools_to_provider_format(self, tools: List[ToolSchema]) -> List[Dic """ 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 Bedrock Converse API expects toolSpec format with inputSchema.json as a DICT 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 } } } From 23f6d0171b0efcbd6ac2baa65cce921925e5c9be Mon Sep 17 00:00:00 2001 From: "Neevash Ramdial (Nash)" Date: Wed, 29 Oct 2025 16:33:04 -0600 Subject: [PATCH 03/12] Clean up for LLM --- .../aws/vision_agents/plugins/aws/aws_llm.py | 111 +----------------- 1 file changed, 6 insertions(+), 105 deletions(-) diff --git a/plugins/aws/vision_agents/plugins/aws/aws_llm.py b/plugins/aws/vision_agents/plugins/aws/aws_llm.py index b5d0ab20..d254aa0b 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_llm.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_llm.py @@ -75,9 +75,6 @@ def __init__( self.region_name = region_name self.logger = logging.getLogger(__name__) - if not self.logger.handlers: - self.logger = logging.getLogger() - self.logger.setLevel(logging.INFO) async def simple_response( self, @@ -97,9 +94,7 @@ async def simple_response( await llm.simple_response("say hi to the user") """ - self.logger.debug(f"simple_response called with text: {text[:100]}...") registered_tools = self.get_available_functions() - self.logger.debug(f"Available functions: {len(registered_tools)} - {[t.get('name') for t in registered_tools]}") return await self.converse_stream( messages=[{"role": "user", "content": [{"text": text}]}] ) @@ -119,7 +114,6 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: kwargs["toolConfig"] = { "tools": converted_tools } - self.logger.debug(f"Added {len(tools)} tools to converse request") # Combine original instructions with markdown file contents enhanced_instructions = self._build_enhanced_instructions() @@ -137,36 +131,23 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: self._conversation.messages.append(msg) try: - # Capture system message for use in follow-up calls system_param = kwargs.get("system") - # Log initial request details - self.logger.debug(f"Making converse request with {len(kwargs.get('messages', []))} messages") - if kwargs.get("toolConfig"): - tool_count = len(kwargs["toolConfig"].get("tools", [])) - self.logger.info(f"Request includes {tool_count} tools") - response = self.client.converse(**kwargs) # Extract text from response text = self._extract_text_from_response(response) llm_response = LLMResponseEvent(response, text) - self.logger.debug(f"Initial response text length: {len(text)}") - # Handle tool calls if present function_calls = self._extract_tool_calls_from_response(response) if function_calls: - self.logger.debug(f"Extracted {len(function_calls)} tool calls from response") for i, fc in enumerate(function_calls): - self.logger.debug(f" Tool call {i+1}: {fc.get('name')} with args: {fc.get('arguments_json')}") + self.logger.debug(f"Tool call {i+1}: {fc.get('name')} with args: {fc.get('arguments_json')}") messages = kwargs["messages"][:] - - # Add assistant message from response (contains toolUse blocks) assistant_msg_from_response = response.get('output', {}).get('message', {}) if assistant_msg_from_response: messages.append(assistant_msg_from_response) - self.logger.debug("Added assistant message from response to conversation history") MAX_ROUNDS = 3 rounds = 0 @@ -181,10 +162,7 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: self.logger.warning("No tool execution results despite tool calls") break - self.logger.debug(f"Executed {len(triples)} tool calls, processing results") - # Build tool result message - # According to AWS Bedrock Converse API, toolResult content should use {"text": "..."} format tool_result_blocks = [] for tc, res, err in triples: if err: @@ -204,29 +182,20 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: "content": [{"text": tool_response}], }) - # Add user message with tool results only - # Assistant message was already added from response before the loop user_tool_results_msg = { "role": "user", "content": [{"toolResult": tr} for tr in tool_result_blocks] } messages = messages + [user_tool_results_msg] - - self.logger.debug(f"Sending {len(tool_result_blocks)} tool results back to model") - - # Build follow-up request parameters follow_up_kwargs = { "modelId": self.model, "messages": messages, "toolConfig": kwargs.get("toolConfig", {}), } - # Include system message if it was provided if system_param: follow_up_kwargs["system"] = system_param - # Ask again WITH tools try: - self.logger.debug(f"Sending follow-up request with tool results (round {rounds + 1})") 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}") @@ -234,41 +203,30 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: self.logger.error(f"Error code: {error_code}, Full error: {str(e)}") raise - # Extract new tool calls and text from follow-up response current_calls = self._extract_tool_calls_from_response(follow_up_response) follow_up_text = self._extract_text_from_response(follow_up_response) llm_response = LLMResponseEvent(follow_up_response, follow_up_text) - self.logger.debug(f"Follow-up response: {len(current_calls)} tool calls, text length: {len(follow_up_text)}") - - # Add assistant message from follow-up response if there are more tool calls 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) - self.logger.debug("Added assistant message from follow-up response to conversation history") - # If follow-up already has text and no more tool calls, we're done! if follow_up_text and not current_calls: - self.logger.debug("Follow-up response contains text with no tool calls, using as final response") text = follow_up_text break rounds += 1 - # Final pass without tools ONLY if we still have pending tool calls if current_calls: - self.logger.debug("Performing final pass without tools to get text response") final_kwargs = { "modelId": self.model, "messages": messages, } - # Include system message if it was provided if system_param: final_kwargs["system"] = system_param try: - self.logger.info("Making final pass request without tools") final_response = self.client.converse(**final_kwargs) except ClientError as e: self.logger.error(f"AWS Bedrock API error in final pass: {e}") @@ -279,16 +237,12 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: final_text = self._extract_text_from_response(final_response) llm_response = LLMResponseEvent(final_response, final_text) text = final_text - self.logger.info(f"Final response text length: {len(final_text)}") elif rounds > 0: - # We had tool calls but follow-up gave us text, use that - text = llm_response.text if llm_response.text else text + text = llm_response.text or text - # Use final response text if available, otherwise original text - final_text_for_event = llm_response.text if hasattr(llm_response, 'text') and llm_response.text else text - original_for_event = llm_response.original if hasattr(llm_response, 'original') and llm_response.original else response + final_text_for_event = llm_response.text or text + original_for_event = llm_response.original or response - self.logger.info(f"Emitting LLMResponseCompletedEvent with text length: {len(final_text_for_event)}") if not final_text_for_event: self.logger.warning("Final response text is empty - model may not have responded") @@ -297,8 +251,7 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: 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: {error_code} - {error_msg}") - self.logger.error(f"Full error details: {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) @@ -310,7 +263,6 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: """ Streaming version of converse using Bedrock's ConverseStream API. """ - self.logger.info("converse_stream called") if "modelId" not in kwargs: kwargs["modelId"] = self.model @@ -321,9 +273,6 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: kwargs["toolConfig"] = { "tools": converted_tools } - self.logger.info(f"Added {len(tools)} tools to converse_stream request: {[t.get('name') for t in tools]}") - else: - self.logger.info("No tools available for converse_stream request") # Ensure the AI remembers the past conversation new_messages = kwargs.get("messages", []) @@ -340,23 +289,14 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: kwargs["system"] = [{"text": enhanced_instructions}] try: - # Capture system message for use in follow-up calls system_param = kwargs.get("system") - # Log initial request details - self.logger.info(f"Making converse_stream request with {len(kwargs.get('messages', []))} messages") - if kwargs.get("toolConfig"): - tool_count = len(kwargs["toolConfig"].get("tools", [])) - self.logger.info(f"Request includes {tool_count} tools") - try: response = self.client.converse_stream(**kwargs) - self.logger.info("converse_stream API call succeeded, processing stream...") 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}") - self.logger.error(f"Full error details: {e}", exc_info=True) + self.logger.error(f"AWS Bedrock API error in converse_stream: {error_code} - {error_msg}", exc_info=True) raise stream = response.get('stream') @@ -369,25 +309,16 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: accumulated_calls: List[NormalizedToolCallItem] = [] last_event = None - # Process stream - event_count = 0 for event in stream: - event_count += 1 last_event = event self._process_stream_event(event, text_parts, accumulated_calls) - self.logger.info(f"Processed {event_count} stream events, accumulated {len(accumulated_calls)} tool calls, {len(''.join(text_parts))} chars of text") - - # Handle multi-hop tool calling messages = kwargs["messages"][:] MAX_ROUNDS = 3 rounds = 0 seen: set[tuple[str, str, str]] = set() if accumulated_calls: - self.logger.info(f"Extracted {len(accumulated_calls)} tool calls from stream") - # Build assistant message from accumulated tool calls - # This matches the format from AWS response['output']['message'] assistant_content = [] for tool_call in accumulated_calls: assistant_content.append({ @@ -402,7 +333,6 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: "content": assistant_content } messages.append(assistant_msg_from_stream) - self.logger.debug("Added assistant message from stream to conversation history") 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] @@ -411,10 +341,6 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: self.logger.warning("No tool execution results despite tool calls") break - self.logger.debug(f"Executed {len(triples)} tool calls, processing results") - - # Build tool result messages - # According to AWS Bedrock Converse API, toolResult content should use {"text": "..."} format tool_result_blocks = [] for tc, res, err in triples: if err: @@ -434,27 +360,19 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: "content": [{"text": tool_response}], }) - # Add user message with tool results only - # Assistant message was already added from stream before the loop user_tool_results_msg = { "role": "user", "content": [{"toolResult": tr} for tr in tool_result_blocks] } messages = messages + [user_tool_results_msg] - - self.logger.debug(f"Sending {len(tool_result_blocks)} tool results back to model") - - # Build follow-up request parameters follow_up_kwargs = { "modelId": self.model, "messages": messages, "toolConfig": kwargs.get("toolConfig", {}), } - # Include system message if it was provided if system_param: follow_up_kwargs["system"] = system_param - # Next round with tools follow_up_response = self.client.converse_stream(**follow_up_kwargs) accumulated_calls = [] @@ -464,15 +382,10 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: last_event = event self._process_stream_event(event, follow_up_text_parts, accumulated_calls) - # Append follow-up text to main text parts if follow_up_text_parts: text_parts.extend(follow_up_text_parts) - self.logger.debug(f"Follow-up response: {len(accumulated_calls)} tool calls, text length: {len(''.join(follow_up_text_parts))}") - - # Add assistant message from follow-up stream if there are more tool calls if accumulated_calls: - # Build assistant message from accumulated tool calls from follow-up follow_up_assistant_content = [] for tool_call in accumulated_calls: follow_up_assistant_content.append({ @@ -487,23 +400,17 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: "content": follow_up_assistant_content } messages.append(follow_up_assistant_msg) - self.logger.debug("Added assistant message from follow-up stream to conversation history") - # If follow-up already has text and no more tool calls, we're done! if follow_up_text_parts and not accumulated_calls: - self.logger.info("Follow-up response contains text with no tool calls, using as final response") break rounds += 1 - # Final pass without tools ONLY if we still have pending tool calls if accumulated_calls: - self.logger.info("Performing final pass without tools to get text response") final_kwargs = { "modelId": self.model, "messages": messages, } - # Include system message if it was provided if system_param: final_kwargs["system"] = system_param @@ -609,20 +516,16 @@ def _extract_tool_calls_from_response(self, response: Dict[str, Any]) -> List[No output = response.get('output', {}) if not output: - self.logger.debug("Response has no 'output' field") return tool_calls message = output.get('message', {}) if not message: - self.logger.debug("Response output has no 'message' field") return tool_calls content = message.get('content', []) if not content: - self.logger.debug("Response message has no 'content' field") return tool_calls - self.logger.debug(f"Checking {len(content)} content items for tool calls") for item in content: if 'toolUse' in item: tool_use = item['toolUse'] @@ -633,7 +536,6 @@ def _extract_tool_calls_from_response(self, response: Dict[str, Any]) -> List[No "arguments_json": tool_use.get('input', {}) } tool_calls.append(tool_call) - self.logger.debug(f"Found tool call: {tool_call['name']} (id: {tool_call['id']})") return tool_calls @@ -674,7 +576,6 @@ def _convert_tools_to_provider_format(self, tools: List[ToolSchema]) -> List[Dic if "additionalProperties" not in params: params["additionalProperties"] = False - # AWS Bedrock Converse API expects toolSpec format with inputSchema.json as a DICT aws_tool = { "toolSpec": { "name": name, From 32cdc5641c5ba33445d03c749085373b3cac2e54 Mon Sep 17 00:00:00 2001 From: "Neevash Ramdial (Nash)" Date: Wed, 29 Oct 2025 22:39:45 -0600 Subject: [PATCH 04/12] Working realtime function calling (pre cleanup) --- .../aws_realtime_function_calling_example.py | 16 +- .../vision_agents/plugins/aws/aws_realtime.py | 374 ++++++++++++------ 2 files changed, 247 insertions(+), 143 deletions(-) diff --git a/plugins/aws/example/aws_realtime_function_calling_example.py b/plugins/aws/example/aws_realtime_function_calling_example.py index 58e1163b..33a9c5a0 100644 --- a/plugins/aws/example/aws_realtime_function_calling_example.py +++ b/plugins/aws/example/aws_realtime_function_calling_example.py @@ -35,7 +35,6 @@ async def start_agent() -> None: llm=aws.Realtime( model="amazon.nova-sonic-v1:0", region_name="us-east-1", - voice_id="matthew" ), ) @@ -115,24 +114,13 @@ def calculate(operation: str, a: float, b: float) -> dict: # 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?" - ) + # Wait for AWS Nova to process the request and call the function + await asyncio.sleep(15) - await asyncio.sleep(5) - - # Wait a bit before finishing - await asyncio.sleep(5) await agent.finish() diff --git a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py index fb4ba754..66e418d9 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -98,6 +98,41 @@ def get_weather(city: str) -> dict: 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, @@ -134,6 +169,7 @@ def __init__( 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 @@ -156,25 +192,46 @@ 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 - - # Start listener task - self._stream_task = asyncio.create_task(self._handle_events()) + 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 + logger.info("Successfully connected to AWS Bedrock stream") - # send start and prompt event - await self.start_session() - await self.start_prompt() + # Start listener task + self._stream_task = asyncio.create_task(self._handle_events()) + logger.info("Started event handling task") - # 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") + # send start and prompt event + await self.start_session() + logger.info("Sent session start event") + + # Small delay between init events + await asyncio.sleep(0.1) + + await self.start_prompt() + logger.info("Sent prompt start event") + + # Give AWS Nova a moment to process the prompt start event + await asyncio.sleep(0.1) + logger.info("Waiting for AWS Nova to process prompt start...") + + # 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("Sent system instructions") + + logger.info("AWS Bedrock connection setup complete") + + 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 @@ -237,6 +294,7 @@ async def content_input(self, content: str, role: str): This method wraps the 3 events in one operation """ content_name = str(uuid.uuid4()) + logger.info(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) @@ -277,30 +335,39 @@ 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_json = '''{ "event": { "sessionStart": { "inferenceConfiguration": { - "maxTokens": 10240, # TODO: make configurable in init? + "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": { + + # 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": { + }, + "audioOutputConfiguration": { "mediaType": "audio/lpcm", "sampleRateHertz": 24000, "sampleSizeBits": 16, @@ -308,75 +375,80 @@ async def start_prompt(self): "voiceId": self.voice_id, "encoding": "base64", "audioType": "SPEECH" + }, + "toolUseOutputConfiguration": { + "mediaType": "application/json" + }, + "toolConfiguration": { + "tools": tools + } } + } } - } - } - - # 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") - self.logger.info(f"Tool schemas: {json_lib.dumps(tools, indent=2)}") - event["event"]["promptStart"]["toolUseOutputConfiguration"] = { - "mediaType": "application/json" - } - event["event"]["promptStart"]["toolConfiguration"] = { - "tools": tools - } - self.logger.info(f"Full promptStart event: {json_lib.dumps(event, indent=2)}") + 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_event(event) + await self.send_raw_event(event_json) async def text_content_start(self, content_name: str, role: str): - event = { - "event": { - "contentStart": { - "promptName": self.session_id, - "contentName": content_name, - "type": "TEXT", - "interactive": False, - "role": role, - "textInputConfiguration": { - "mediaType": "text/plain" - } - } - } - } - await self.send_event(event) + 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): - event = { - "event": { - "textInput": { - "promptName": self.session_id, - "contentName": content_name, - "content": content, - } - } - } - await self.send_event(event) + # 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 = { - "event": { - "contentEnd": { - "promptName": self.session_id, - "contentName": content_name, - } - } - } - await self.send_event(event) + 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: - event_json = json.dumps(event_data) - event = InvokeModelWithBidirectionalStreamInputChunk( - value=BidirectionalInputPayloadPart(bytes_=event_json.encode('utf-8')) - ) - await self.stream.input_stream.send(event) + 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: + logger.debug(f"Sending raw event: {event_json}") + event = InvokeModelWithBidirectionalStreamInputChunk( + value=BidirectionalInputPayloadPart(bytes_=event_json.encode('utf-8')) + ) + await self.stream.input_stream.send(event) + logger.debug("Successfully sent raw 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. @@ -393,8 +465,6 @@ def _convert_tools_to_provider_format(self, tools: List[Any]) -> List[Dict[str, description = tool.get("description", "") or "" params = tool.get("parameters_schema") or tool.get("parameters") or {} - self.logger.info(f"Tool {name} raw params: {json.dumps(params, indent=2)}") - # Normalize to a valid JSON Schema object if not isinstance(params, dict): params = {} @@ -416,14 +486,16 @@ def _convert_tools_to_provider_format(self, tools: List[Any]) -> List[Dict[str, if "additionalProperties" not in params: params["additionalProperties"] = False - # AWS Nova expects toolSpec format with inputSchema.json as a DICT (not a string) - # Reference: https://docs.aws.amazon.com/nova/latest/userguide/input-events.html + # 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": params # This is a dict, not a JSON string + "json": schema_json # This should be a JSON string, not a dict } } } @@ -482,15 +554,28 @@ async def send_tool_result(self, content_name: str, result: Any): } await self.send_event(event) - async def _handle_tool_call(self, tool_name: str, tool_use_id: str, tool_input: Dict[str, Any]): + 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_input: Input arguments for the tool + tool_use_content: Full tool use content from AWS """ try: + logger.info(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", @@ -499,8 +584,6 @@ async def _handle_tool_call(self, tool_name: str, tool_use_id: str, tool_input: "arguments_json": tool_input, } - logger.info(f"Executing tool call: {tool_name} with args: {tool_input}") - # Execute using existing tool execution infrastructure from base LLM tc, result, error = await self._run_one_tool(tool_call, timeout_s=30) @@ -509,23 +592,26 @@ async def _handle_tool_call(self, tool_name: str, tool_use_id: str, tool_input: response_data = {"error": str(error)} logger.error(f"Tool call {tool_name} failed: {error}") else: - # Keep result as-is for AWS response_data = result - logger.info(f"Tool call {tool_name} succeeded: {response_data}") # 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: {e}") + logger.error(f"Error handling tool call {tool_name}: {e}", exc_info=True) # Send error response back - 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) + 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) + logger.info(f"Sent error response for failed tool call {tool_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: @@ -557,6 +643,7 @@ async def close(self): async def _handle_events(self): """Process incoming responses from AWS Bedrock.""" + logger.info("Starting event handling loop") try: while True: try: @@ -566,6 +653,7 @@ 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: @@ -584,6 +672,7 @@ 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'] @@ -606,57 +695,84 @@ async def _handle_events(self): elif 'toolUse' in json_data['event']: - logger.info(f"Tool use from AWS Bedrock: {json_data['event']['toolUse']}") tool_use_data = json_data['event']['toolUse'] - tool_name = tool_use_data['toolName'] - tool_use_id = tool_use_data['toolUseId'] - tool_input = tool_use_data.get('input', {}) + tool_name = tool_use_data.get('toolName') + tool_use_id = tool_use_data.get('toolUseId') + + logger.info(f"Tool use event received: {tool_name} (id: {tool_use_id})") + + # 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}") - # Execute the tool immediately (don't wait for contentEnd) - asyncio.create_task(self._handle_tool_call( - tool_name=tool_name, - tool_use_id=tool_use_id, - tool_input=tool_input - )) 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(f"Content end from AWS Bedrock {stopReason}: {content_end_data}") elif 'completionEnd' in json_data['event']: logger.info(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.info("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) + logger.error("Response processing error: %s", e) + # Only reset connection on critical errors self.connected = False + finally: + # Only log connection state, don't unconditionally reset + if self.connected: + logger.info("Event handling loop ended, connection still active") + else: + logger.info("Event handling loop ended, connection was closed") From eb31eaa5eeb6ae73fa9300b751e95a642de26bb6 Mon Sep 17 00:00:00 2001 From: "Neevash Ramdial (Nash)" Date: Thu, 30 Oct 2025 21:34:06 -0600 Subject: [PATCH 05/12] Squashed commit of the following: commit d78a4a025bab3a71aec92280282c9fe6a800af29 Author: Dan Gusev Date: Thu Oct 30 20:44:19 2025 +0100 Logging cleanup (#133) * logging: use logging.getLogger(__name__) everywhere to simplify configuration * Clean up logging everywhere - Replaced "logging.info" usages with separate loggers - Lowered some info messages to debug - Replaced prints with logging - Added emojis where they're already used * Enable default logging for the SDK logs - Added a way to set the SDK log level at the Agent class - Set the default formatter and added level-based coloring - If the logs are already configured, they remain intact - Moved logging_utils.py to utils/logging.py * Remove "name" from the default logging formatter --------- Co-authored-by: Thierry Schellenbach --- agents-core/pyproject.toml | 2 +- .../vision_agents/core/agents/agents.py | 140 +- .../vision_agents/core/events/manager.py | 10 +- .../vision_agents/core/mcp/mcp_base.py | 105 +- .../vision_agents/core/mcp/mcp_manager.py | 17 +- .../core/processors/base_processor.py | 11 +- .../{logging_utils.py => utils/logging.py} | 42 +- examples/01_simple_agent_example/uv.lock | 14 + .../vision_agents/plugins/cartesia/tts.py | 4 +- .../vision_agents/plugins/elevenlabs/tts.py | 4 +- .../getstream/stream_edge_transport.py | 33 +- .../vision_agents/plugins/kokoro/tts.py | 5 +- .../plugins/krisp/turn_detection.py | 2 +- tests/test_utils.py | 69 +- uv.lock | 5399 +++++++++-------- 15 files changed, 3128 insertions(+), 2729 deletions(-) rename agents-core/vision_agents/core/{logging_utils.py => utils/logging.py} (59%) diff --git a/agents-core/pyproject.toml b/agents-core/pyproject.toml index 75039b0a..1ecde9e6 100644 --- a/agents-core/pyproject.toml +++ b/agents-core/pyproject.toml @@ -26,7 +26,7 @@ dependencies = [ "pillow>=11.3.0", "numpy>=1.24.0", "mcp>=1.16.0", - "torchvision>=0.23.0", + "colorlog>=6.10.1", ] [project.urls] diff --git a/agents-core/vision_agents/core/agents/agents.py b/agents-core/vision_agents/core/agents/agents.py index 57ede961..460cc55e 100644 --- a/agents-core/vision_agents/core/agents/agents.py +++ b/agents-core/vision_agents/core/agents/agents.py @@ -15,7 +15,12 @@ from getstream.video.rtc.pb.stream.video.sfu.models.models_pb2 import TrackType from ..edge import sfu_events -from ..edge.events import AudioReceivedEvent, TrackAddedEvent, TrackRemovedEvent, CallEndedEvent +from ..edge.events import ( + AudioReceivedEvent, + TrackAddedEvent, + TrackRemovedEvent, + CallEndedEvent, +) from ..edge.types import Connection, Participant, PcmData, User, OutputAudioTrack from ..events.manager import EventManager from ..llm import events as llm_events @@ -27,7 +32,6 @@ ) from ..llm.llm import LLM from ..llm.realtime import Realtime -from ..logging_utils import CallContextToken, clear_call_context, set_call_context from ..mcp import MCPBaseServer, MCPManager from ..processors.base_processor import Processor, ProcessorType, filter_processors from ..stt.events import STTTranscriptEvent, STTErrorEvent @@ -35,6 +39,12 @@ from ..tts.tts import TTS from ..tts.events import TTSAudioEvent from ..turn_detection import TurnDetector, TurnStartedEvent, TurnEndedEvent +from ..utils.logging import ( + CallContextToken, + clear_call_context, + set_call_context, + configure_default_logging, +) from ..utils.video_forwarder import VideoForwarder from ..utils.video_utils import ensure_even_dimensions from ..vad import VAD @@ -65,6 +75,16 @@ def _log_task_exception(task: asyncio.Task): logger.exception("Error in background task") +class _AgentLoggerAdapter(logging.LoggerAdapter): + """ + A logger adapter to include the agent_id to the logs + """ + + def process(self, msg: str, kwargs): + if self.extra: + return "[Agent: %s] | %s" % (self.extra["agent_id"], msg), kwargs + return super(_AgentLoggerAdapter, self).process(msg, kwargs) + # TODO: move me @dataclass class AgentOptions: @@ -132,22 +152,29 @@ def __init__( # MCP servers for external tool and resource access mcp_servers: Optional[List[MCPBaseServer]] = None, options: Optional[AgentOptions] = None, + tracer: Tracer = trace.get_tracer("agents"), + # Configure the default logging for the sdk here. Pass None to leave the config intact. + log_level: Optional[int] = logging.INFO, ): + if log_level is not None: + configure_default_logging(level=log_level) if options is None: options = default_agent_options() else: options = default_agent_options().update(options) self.options = options + self.instructions = instructions self.edge = edge self.agent_user = agent_user self._agent_user_initialized = False # only needed in case we spin threads + self.tracer = tracer self._root_span: Optional[Span] = None self._root_ctx: Optional[Context] = None - self.logger = logging.getLogger(f"Agent[{self.agent_user.id}]") + self.logger = _AgentLoggerAdapter(logger, {"agent_id": self.agent_user.id}) self.events = EventManager() self.events.register_events_from_module(getstream.models, "call.") @@ -183,7 +210,7 @@ def __init__( # Merge plugin events BEFORE subscribing to any events for plugin in [stt, tts, turn_detection, vad, llm, edge]: if plugin and hasattr(plugin, "events"): - self.logger.info(f"Registered plugin {plugin}") + self.logger.debug(f"Register events from plugin {plugin}") self.events.merge(plugin.events) self.llm._attach_agent(self) @@ -242,8 +269,8 @@ async def simple_response( """ Overwrite simple_response if you want to change how the Agent class calls the LLM """ - logger.info("asking LLM to reply to %s", text) - with self.span("simple_response") as span: + self.logger.info('🤖 Asking LLM to reply to "%s"', text) + with self.tracer.start_as_current_span("simple_response") as span: response = await self.llm.simple_response( text=text, processors=self.processors, participant=participant ) @@ -504,6 +531,13 @@ async def finish(self): Subscribes to the edge transport's `call_ended` event and awaits it. If no connection is active, returns immediately. """ + # If connection is None or already closed, return immediately + if not self._connection: + self.logger.info( + "🔚 Agent connection is already closed, finishing immediately" + ) + return + with self.span("agent.finish"): # If connection is None or already closed, return immediately @@ -631,7 +665,7 @@ async def create_user(self) -> None: return None def _on_vad_audio(self, event: VADAudioEvent): - self.logger.info(f"Vad audio event {self._truncate_for_logging(event)}") + self.logger.debug(f"Vad audio event {self._truncate_for_logging(event)}") def _on_rtc_reconnect(self): # update the code to listen? @@ -677,7 +711,7 @@ async def _on_agent_say(self, event: events.AgentSayEvent): ) ) - self.logger.info(f"Agent said: {event.text}") + self.logger.info(f"🤖 Agent said: {event.text}") else: self.logger.warning("No TTS available, cannot synthesize speech") @@ -764,14 +798,18 @@ async def on_track(event: TrackAddedEvent): # If track is already being processed, just switch to it if track_id in self._active_video_tracks: track_type_name = TrackType.Name(track_type) - self.logger.info(f"🎥 Track re-added: {track_type_name} ({track_id}), switching to it") - + self.logger.info( + f"🎥 Track re-added: {track_type_name} ({track_id}), switching to it" + ) + if self.realtime_mode and isinstance(self.llm, Realtime): # Get the existing forwarder and switch to this track _, _, forwarder = self._active_video_tracks[track_id] track = self.edge.add_track_subscriber(track_id) if track and forwarder: - await self.llm._watch_video_track(track, shared_forwarder=forwarder) + await self.llm._watch_video_track( + track, shared_forwarder=forwarder + ) self._current_video_track_id = track_id return @@ -796,10 +834,16 @@ async def on_track_removed(event: TrackRemovedEvent): # Clean up track metadata self._active_video_tracks.pop(track_id, None) - + # If this was the active track, switch to any other available track - if track_id == self._current_video_track_id and self.realtime_mode and isinstance(self.llm, Realtime): - self.logger.info("🎥 Active video track removed, switching to next available") + if ( + track_id == self._current_video_track_id + and self.realtime_mode + and isinstance(self.llm, Realtime) + ): + self.logger.info( + "🎥 Active video track removed, switching to next available" + ) self._current_video_track_id = None await self._switch_to_next_available_track() @@ -839,16 +883,23 @@ async def _switch_to_next_available_track(self) -> None: self.logger.info("🎥 No video tracks available") self._current_video_track_id = None return - + # Just pick the first available video track - for track_id, (track_type, participant, forwarder) in self._active_video_tracks.items(): + for track_id, ( + track_type, + participant, + forwarder, + ) in self._active_video_tracks.items(): # Only consider video tracks (camera or screenshare) - if track_type not in (TrackType.TRACK_TYPE_VIDEO, TrackType.TRACK_TYPE_SCREEN_SHARE): + if track_type not in ( + TrackType.TRACK_TYPE_VIDEO, + TrackType.TRACK_TYPE_SCREEN_SHARE, + ): continue - + track_type_name = TrackType.Name(track_type) self.logger.info(f"🎥 Switching to track: {track_type_name} ({track_id})") - + # Get the track and forwarder track = self.edge.add_track_subscriber(track_id) if track and forwarder and isinstance(self.llm, Realtime): @@ -858,16 +909,19 @@ async def _switch_to_next_available_track(self) -> None: return else: self.logger.error(f"Failed to switch to track {track_id}") - + self.logger.warning("🎥 No suitable video tracks found") async def _process_track(self, track_id: str, track_type: int, participant): raw_forwarder = None processed_forwarder = None - + try: # we only process video tracks (camera video or screenshare) - if track_type not in (TrackType.TRACK_TYPE_VIDEO, TrackType.TRACK_TYPE_SCREEN_SHARE): + if track_type not in ( + TrackType.TRACK_TYPE_VIDEO, + TrackType.TRACK_TYPE_SCREEN_SHARE, + ): return # subscribe to the video track @@ -877,14 +931,16 @@ async def _process_track(self, track_id: str, track_type: int, participant): return # Wrap screenshare tracks to ensure even dimensions for H.264 encoding - if track_type == TrackType.TRACK_TYPE_SCREEN_SHARE: + if track_type == TrackType.TRACK_TYPE_SCREEN_SHARE: + class _EvenDimensionsTrack(VideoStreamTrack): - def __init__(self, src): + def __init__(self, src): super().__init__() self.src = src - async def recv(self): + + async def recv(self): return ensure_even_dimensions(await self.src.recv()) - + track = _EvenDimensionsTrack(track) # type: ignore[arg-type] # Create a SHARED VideoForwarder for the RAW incoming track @@ -896,17 +952,21 @@ async def recv(self): name=f"raw_video_forwarder_{track_id}", ) await raw_forwarder.start() - self.logger.info("🎥 Created raw VideoForwarder for track %s", track_id) + self.logger.debug("🎥 Created raw VideoForwarder for track %s", track_id) # Track forwarders for cleanup self._video_forwarders.append(raw_forwarder) # Store track metadata - self._active_video_tracks[track_id] = (track_type, participant, raw_forwarder) + self._active_video_tracks[track_id] = ( + track_type, + participant, + raw_forwarder, + ) # If Realtime provider supports video, switch to this new track track_type_name = TrackType.Name(track_type) - + if self.realtime_mode: if self._video_track: # We have a video publisher (e.g., YOLO processor) @@ -931,7 +991,9 @@ async def recv(self): self._current_video_track_id = track_id else: # No video publisher, send raw frames - switch to this new track - self.logger.info(f"🎥 Switching to {track_type_name} track: {track_id}") + self.logger.info( + f"🎥 Switching to {track_type_name} track: {track_id}" + ) if isinstance(self.llm, Realtime): await self.llm._watch_video_track( track, shared_forwarder=raw_forwarder @@ -978,7 +1040,9 @@ async def recv(self): for processor in self.image_processors: try: - await processor.process_image(img, participant.user_id) + await processor.process_image( + img, participant.user_id + ) except Exception as e: self.logger.error( f"Error in image processor {type(processor).__name__}: {e}" @@ -999,26 +1063,28 @@ async def recv(self): except asyncio.CancelledError: # Task was cancelled (e.g., track removed) # Clean up forwarders that were created for this track - self.logger.debug(f"🎥 Cleaning up forwarders for cancelled track {track_id}") - + self.logger.debug( + f"🎥 Cleaning up forwarders for cancelled track {track_id}" + ) + # Stop and remove the raw forwarder if it was created - if raw_forwarder is not None and hasattr(self, '_video_forwarders'): + if raw_forwarder is not None and hasattr(self, "_video_forwarders"): if raw_forwarder in self._video_forwarders: try: await raw_forwarder.stop() self._video_forwarders.remove(raw_forwarder) except Exception as e: self.logger.error(f"Error stopping raw forwarder: {e}") - + # Stop and remove processed forwarder if it was created - if processed_forwarder is not None and hasattr(self, '_video_forwarders'): + if processed_forwarder is not None and hasattr(self, "_video_forwarders"): if processed_forwarder in self._video_forwarders: try: await processed_forwarder.stop() self._video_forwarders.remove(processed_forwarder) except Exception as e: self.logger.error(f"Error stopping processed forwarder: {e}") - + return async def _on_turn_event(self, event: TurnStartedEvent | TurnEndedEvent) -> None: diff --git a/agents-core/vision_agents/core/events/manager.py b/agents-core/vision_agents/core/events/manager.py index 8117e916..fc277e3a 100644 --- a/agents-core/vision_agents/core/events/manager.py +++ b/agents-core/vision_agents/core/events/manager.py @@ -353,7 +353,7 @@ async def handle_audio_events(event: VADSpeechStartEvent | VADSpeechEndEvent): subscribed = True self._handlers.setdefault(event_type, []).append(function) module_name = getattr(function, "__module__", "unknown") - logger.info( + logger.debug( f"Handler {function.__name__} from {module_name} registered for event {event_type}" ) elif not self._ignore_unknown_events: @@ -362,7 +362,7 @@ async def handle_audio_events(event: VADSpeechStartEvent | VADSpeechEndEvent): ) else: module_name = getattr(function, "__module__", "unknown") - logger.info( + logger.debug( f"Event {sub_event} - {event_type} is not registered – skipping handler {function.__name__} from {module_name}." ) return function @@ -400,7 +400,7 @@ def _prepare_event(self, event): else: # No matching event class found if self._ignore_unknown_events: - logger.info(f"Protobuf event not registered: {proto_type}") + logger.debug(f"Protobuf event not registered: {proto_type}") return else: raise RuntimeError(f"Protobuf event not registered: {proto_type}") @@ -410,7 +410,7 @@ def _prepare_event(self, event): # logger.info(f"Received event {_truncate_event_for_logging(event)}") return event elif self._ignore_unknown_events: - logger.info(f"Event not registered {_truncate_event_for_logging(event)}") + logger.debug(f"Event not registered {_truncate_event_for_logging(event)}") else: raise RuntimeError(f"Event not registered {event}") @@ -507,7 +507,7 @@ async def _process_events_loop(self): await self._process_single_event(event) except asyncio.CancelledError as exc: cancelled_exc = exc - logger.info( + logger.debug( f"Event processing task was cancelled, processing remaining events, {len(self._queue)}" ) await self._process_single_event(event) diff --git a/agents-core/vision_agents/core/mcp/mcp_base.py b/agents-core/vision_agents/core/mcp/mcp_base.py index 5356e7e6..55263eb4 100644 --- a/agents-core/vision_agents/core/mcp/mcp_base.py +++ b/agents-core/vision_agents/core/mcp/mcp_base.py @@ -9,46 +9,46 @@ class MCPBaseServer(ABC): """Base class for MCP server connections.""" - + def __init__(self, session_timeout: float = 300.0): """Initialize the base MCP server. - + Args: session_timeout: How long an established MCP session can sit idle with no tool calls, no traffic (in seconds) """ self.session_timeout = session_timeout - self.logger = logging.getLogger(f"{self.__class__.__name__}") + self.logger = logging.getLogger(__name__) self._session: Optional[ClientSession] = None self._is_connected = False self._last_activity: Optional[float] = None self._timeout_task: Optional[asyncio.Task] = None - + @abstractmethod async def connect(self) -> None: """Connect to the MCP server.""" pass - + @abstractmethod async def disconnect(self) -> None: """Disconnect from the MCP server.""" pass - + @property def is_connected(self) -> bool: """Check if the server is connected.""" return self._is_connected - + async def _update_activity(self) -> None: """Update the last activity timestamp.""" self._last_activity = asyncio.get_event_loop().time() - + async def _start_timeout_monitor(self) -> None: """Start monitoring for session timeout.""" if self._timeout_task: self._timeout_task.cancel() - + self._timeout_task = asyncio.create_task(self._timeout_monitor()) - + async def _timeout_monitor(self) -> None: """Monitor for session timeout.""" while self._is_connected: @@ -56,23 +56,27 @@ async def _timeout_monitor(self) -> None: if self._last_activity and self._is_connected: idle_time = asyncio.get_event_loop().time() - self._last_activity if idle_time > self.session_timeout: - self.logger.warning(f"Session timeout after {idle_time:.1f}s of inactivity") + self.logger.warning( + f"Session timeout after {idle_time:.1f}s of inactivity" + ) await self.disconnect() break - + async def _stop_timeout_monitor(self) -> None: """Stop the timeout monitor.""" if self._timeout_task: self._timeout_task.cancel() self._timeout_task = None - + async def _ensure_connected(self) -> None: """Ensure the server is connected, reconnecting if necessary.""" if not self._is_connected: self.logger.info("Reconnecting to MCP server...") await self.connect() - - async def _call_with_retry(self, operation_name: str, operation_func, *args, **kwargs): + + async def _call_with_retry( + self, operation_name: str, operation_func, *args, **kwargs + ): """Call an MCP operation with auto-reconnect on failure.""" max_retries = 2 for attempt in range(max_retries + 1): @@ -82,85 +86,104 @@ async def _call_with_retry(self, operation_name: str, operation_func, *args, **k return await operation_func(*args, **kwargs) except Exception as e: if attempt < max_retries: - self.logger.warning(f"{operation_name} failed (attempt {attempt + 1}/{max_retries + 1}): {e}. Reconnecting...") + self.logger.warning( + f"{operation_name} failed (attempt {attempt + 1}/{max_retries + 1}): {e}. Reconnecting..." + ) await self.disconnect() await asyncio.sleep(1) # Brief delay before retry else: - self.logger.error(f"{operation_name} failed after {max_retries + 1} attempts: {e}") + self.logger.error( + f"{operation_name} failed after {max_retries + 1} attempts: {e}" + ) raise - + async def _list_tools_impl(self) -> List[types.Tool]: """Internal implementation of list_tools without retry logic.""" if not self._session or not self._is_connected: raise RuntimeError("Not connected to MCP server") - + await self._update_activity() response = await self._session.list_tools() return response.tools - - async def _call_tool_impl(self, name: str, arguments: Dict[str, Any]) -> types.CallToolResult: + + async def _call_tool_impl( + self, name: str, arguments: Dict[str, Any] + ) -> types.CallToolResult: """Internal implementation of call_tool without retry logic.""" if not self._session or not self._is_connected: raise RuntimeError("Not connected to MCP server") - + await self._update_activity() return await self._session.call_tool(name, arguments) - + async def _list_resources_impl(self) -> List[types.Resource]: """Internal implementation of list_resources without retry logic.""" if not self._session or not self._is_connected: raise RuntimeError("Not connected to MCP server") - + await self._update_activity() response = await self._session.list_resources() return response.resources - + async def _read_resource_impl(self, uri: str) -> types.ReadResourceResult: """Internal implementation of read_resource without retry logic.""" if not self._session or not self._is_connected: raise RuntimeError("Not connected to MCP server") - + await self._update_activity() from mcp.types import AnyUrl + return await self._session.read_resource(AnyUrl(uri)) - + async def _list_prompts_impl(self) -> List[types.Prompt]: """Internal implementation of list_prompts without retry logic.""" if not self._session or not self._is_connected: raise RuntimeError("Not connected to MCP server") - + await self._update_activity() response = await self._session.list_prompts() return response.prompts - - async def _get_prompt_impl(self, name: str, arguments: Dict[str, Any]) -> types.GetPromptResult: + + async def _get_prompt_impl( + self, name: str, arguments: Dict[str, Any] + ) -> types.GetPromptResult: """Internal implementation of get_prompt without retry logic.""" if not self._session or not self._is_connected: raise RuntimeError("Not connected to MCP server") - + await self._update_activity() return await self._session.get_prompt(name, arguments) async def list_tools(self) -> List[types.Tool]: """List available tools from the MCP server with auto-reconnect.""" return await self._call_with_retry("list_tools", self._list_tools_impl) - - async def call_tool(self, name: str, arguments: Dict[str, Any]) -> types.CallToolResult: + + async def call_tool( + self, name: str, arguments: Dict[str, Any] + ) -> types.CallToolResult: """Call a tool on the MCP server with auto-reconnect.""" - return await self._call_with_retry("call_tool", self._call_tool_impl, name, arguments) - + return await self._call_with_retry( + "call_tool", self._call_tool_impl, name, arguments + ) + async def list_resources(self) -> List[types.Resource]: """List available resources from the MCP server with auto-reconnect.""" return await self._call_with_retry("list_resources", self._list_resources_impl) - + async def read_resource(self, uri: str) -> types.ReadResourceResult: """Read a resource from the MCP server with auto-reconnect.""" - return await self._call_with_retry("read_resource", self._read_resource_impl, uri) - + return await self._call_with_retry( + "read_resource", self._read_resource_impl, uri + ) + async def list_prompts(self) -> List[types.Prompt]: """List available prompts from the MCP server with auto-reconnect.""" return await self._call_with_retry("list_prompts", self._list_prompts_impl) - - async def get_prompt(self, name: str, arguments: Dict[str, Any]) -> types.GetPromptResult: + + async def get_prompt( + self, name: str, arguments: Dict[str, Any] + ) -> types.GetPromptResult: """Get a prompt from the MCP server with auto-reconnect.""" - return await self._call_with_retry("get_prompt", self._get_prompt_impl, name, arguments) + return await self._call_with_retry( + "get_prompt", self._get_prompt_impl, name, arguments + ) diff --git a/agents-core/vision_agents/core/mcp/mcp_manager.py b/agents-core/vision_agents/core/mcp/mcp_manager.py index 89caa537..d6248793 100644 --- a/agents-core/vision_agents/core/mcp/mcp_manager.py +++ b/agents-core/vision_agents/core/mcp/mcp_manager.py @@ -8,10 +8,15 @@ class MCPManager: """Manages MCP server connections and tool registration for agents.""" - - def __init__(self, mcp_servers: List[MCPBaseServer], llm, logger: logging.Logger): + + def __init__( + self, + mcp_servers: List[MCPBaseServer], + llm, + logger: logging.Logger | logging.LoggerAdapter, + ): """Initialize the MCP manager. - + Args: mcp_servers: List of MCP servers to manage llm: LLM instance for tool registration @@ -20,7 +25,7 @@ def __init__(self, mcp_servers: List[MCPBaseServer], llm, logger: logging.Logger self.mcp_servers = mcp_servers self.llm = llm self.logger = logger - + async def connect_all(self): """Connect to all configured MCP servers and register their tools.""" if not self.mcp_servers: @@ -89,12 +94,12 @@ async def call_tool( self, server_index: int, tool_name: str, arguments: Dict[str, Any] ) -> Any: """Call a tool on a specific MCP server. - + Args: server_index: Index of the MCP server in the mcp_servers list tool_name: Name of the tool to call arguments: Arguments to pass to the tool - + Returns: The result of the tool call """ diff --git a/agents-core/vision_agents/core/processors/base_processor.py b/agents-core/vision_agents/core/processors/base_processor.py index 17bceb1c..16ef0fb0 100644 --- a/agents-core/vision_agents/core/processors/base_processor.py +++ b/agents-core/vision_agents/core/processors/base_processor.py @@ -19,6 +19,9 @@ """ +logger = logging.getLogger(__name__) + + class ProcessorType(Enum): """Enum for different processor types based on mixins.""" @@ -152,7 +155,7 @@ async def process_audio( if self.should_process(): self.audio_count += 1 - logging.info( + logger.info( f"🎵 Audio #{self.audio_count} from {user_id}: {len(audio_data)} bytes" ) @@ -169,7 +172,7 @@ def __init__( # Create output directory self.output_dir.mkdir(exist_ok=True) - logging.info(f"📁 Saving captured frames to: {self.output_dir.absolute()}") + logger.info(f"📁 Saving captured frames to: {self.output_dir.absolute()}") async def process_image( self, @@ -181,7 +184,7 @@ async def process_image( if not self.should_process(): return None - logging.info(f"📸 ImageCapture running process_image for user {user_id}") + logger.info(f"📸 ImageCapture running process_image for user {user_id}") timestamp = int(asyncio.get_event_loop().time()) filename = f"frame_{user_id}_{timestamp}_{self.frame_count:04d}.jpg" @@ -191,7 +194,7 @@ async def process_image( image.save(filepath, "JPEG", quality=90) self.frame_count += 1 - logging.info( + logger.info( f"📸 Captured frame {self.frame_count}: {filename} ({image.width}x{image.height})" ) diff --git a/agents-core/vision_agents/core/logging_utils.py b/agents-core/vision_agents/core/utils/logging.py similarity index 59% rename from agents-core/vision_agents/core/logging_utils.py rename to agents-core/vision_agents/core/utils/logging.py index a547f632..1f51c6f2 100644 --- a/agents-core/vision_agents/core/logging_utils.py +++ b/agents-core/vision_agents/core/utils/logging.py @@ -1,7 +1,10 @@ +import sys from contextvars import ContextVar, Token from dataclasses import dataclass import logging +import colorlog + logger = logging.getLogger(__name__) call_id_ctx: ContextVar[str | None] = ContextVar("call_id", default=None) @@ -9,6 +12,41 @@ _ORIGINAL_FACTORY = logging.getLogRecordFactory() _CALL_ID_ENABLED = True + +MAIN_LOGGER = logging.getLogger("vision_agents") +GETSTREAM_LOGGER = logging.getLogger("getstream") + + +def configure_default_logging(level: int) -> None: + """ + Sets the default configuration for "vision_agents" and "getstream" loggers to have a nice formatting + if it's not already configured. + """ + colored_formatter = colorlog.ColoredFormatter( + fmt="%(log_color)s%(asctime)s.%(msecs)03d | %(levelname)-8s | %(message)s", + datefmt="%H:%M:%S", + log_colors={ + "DEBUG": "white", + "INFO": "light_white", + "WARNING": "yellow", + "ERROR": "red", + "CRITICAL": "bold_red", + }, + ) + default_handler = logging.StreamHandler(sys.stderr) + default_handler.setLevel(level) + default_handler.setFormatter(colored_formatter) + + for _logger in [MAIN_LOGGER, GETSTREAM_LOGGER]: + # Set the default handler only if it's not already configured + if not _logger.handlers: + _logger.handlers = [default_handler] + _logger.propagate = False + # Do not override the level if it's set + if _logger.level == logging.NOTSET: + _logger.setLevel(level) + + @dataclass(slots=True) class CallContextToken: """Token capturing prior state for restoring logging context.""" @@ -61,8 +99,8 @@ def clear_call_context(token: CallContextToken) -> None: global _CURRENT_CALL_ID - #failing TODO: fix - #call_id_ctx.reset(token.context_token) + # failing TODO: fix + # call_id_ctx.reset(token.context_token) _CURRENT_CALL_ID = token.previous_global diff --git a/examples/01_simple_agent_example/uv.lock b/examples/01_simple_agent_example/uv.lock index ac870173..3621bbed 100644 --- a/examples/01_simple_agent_example/uv.lock +++ b/examples/01_simple_agent_example/uv.lock @@ -339,6 +339,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, ] +[[package]] +name = "colorlog" +version = "6.10.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162, upload-time = "2025-10-16T16:14:11.978Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743, upload-time = "2025-10-16T16:14:10.512Z" }, +] + [[package]] name = "cryptography" version = "46.0.1" @@ -2166,6 +2178,7 @@ wheels = [ name = "vision-agents" source = { editable = "../../agents-core" } dependencies = [ + { name = "colorlog" }, { name = "getstream", extra = ["telemetry", "webrtc"] }, { name = "mcp" }, { name = "numpy" }, @@ -2176,6 +2189,7 @@ dependencies = [ [package.metadata] requires-dist = [ { name = "click", marker = "extra == 'dev'" }, + { name = "colorlog", specifier = ">=6.10.1" }, { name = "getstream", extras = ["telemetry", "webrtc"], specifier = ">=2.5.5" }, { name = "mcp", specifier = ">=1.16.0" }, { name = "mypy", marker = "extra == 'dev'" }, diff --git a/plugins/cartesia/vision_agents/plugins/cartesia/tts.py b/plugins/cartesia/vision_agents/plugins/cartesia/tts.py index 62f5f580..e4ffc660 100644 --- a/plugins/cartesia/vision_agents/plugins/cartesia/tts.py +++ b/plugins/cartesia/vision_agents/plugins/cartesia/tts.py @@ -14,6 +14,8 @@ from vision_agents.core import tts from getstream.video.rtc.track_util import PcmData, AudioFormat +logger = logging.getLogger(__name__) + class TTS(tts.TTS): """Text-to-Speech plugin backed by the Cartesia Sonic model.""" @@ -89,4 +91,4 @@ async def stop_audio(self) -> None: Returns: None """ - logging.info("🎤 Cartesia TTS stop requested (no-op)") + logger.info("🎤 Cartesia TTS stop requested (no-op)") diff --git a/plugins/elevenlabs/vision_agents/plugins/elevenlabs/tts.py b/plugins/elevenlabs/vision_agents/plugins/elevenlabs/tts.py index 734cc47d..47196b22 100644 --- a/plugins/elevenlabs/vision_agents/plugins/elevenlabs/tts.py +++ b/plugins/elevenlabs/vision_agents/plugins/elevenlabs/tts.py @@ -6,6 +6,8 @@ from vision_agents.core import tts from getstream.video.rtc.track_util import PcmData, AudioFormat +logger = logging.getLogger(__name__) + class TTS(tts.TTS): def __init__( @@ -69,4 +71,4 @@ async def stop_audio(self) -> None: Returns: None """ - logging.info("🎤 ElevenLabs TTS stop requested (no-op)") + logger.info("🎤 ElevenLabs TTS stop requested (no-op)") diff --git a/plugins/getstream/vision_agents/plugins/getstream/stream_edge_transport.py b/plugins/getstream/vision_agents/plugins/getstream/stream_edge_transport.py index 8e94bd2d..81456b97 100644 --- a/plugins/getstream/vision_agents/plugins/getstream/stream_edge_transport.py +++ b/plugins/getstream/vision_agents/plugins/getstream/stream_edge_transport.py @@ -30,6 +30,8 @@ if TYPE_CHECKING: from vision_agents.core.agents.agents import Agent +logger = logging.getLogger(__name__) + class StreamConnection(Connection): def __init__(self, connection: ConnectionManager): @@ -54,7 +56,6 @@ def __init__(self, **kwargs): super().__init__() version = get_vision_agents_version() self.client = AsyncStream(user_agent=f"vision-agents-{version}") - self.logger = logging.getLogger(self.__class__.__name__) self.events = EventManager() self.events.register_events_from_module(events) self.events.register_events_from_module(sfu_events) @@ -108,17 +109,17 @@ async def _on_track_published(self, event: sfu_events.TrackPublishedEvent): # Skip processing the agent's own tracks - we don't subscribe to them if is_agent_track: - self.logger.debug(f"Skipping agent's own track: {track_type_int} from {user_id}") + logger.debug(f"Skipping agent's own track: {track_type_int} from {user_id}") return # First check if track already exists in map (e.g., from previous unpublish/republish) if track_key in self._track_map: self._track_map[track_key]["published"] = True track_id = self._track_map[track_key]["track_id"] - self.logger.info( + logger.info( f"Track re-published: {track_type_int} from {user_id}, track_id: {track_id}" ) - + # Emit TrackAddedEvent so agent can switch to this track self.events.send( events.TrackAddedEvent( @@ -161,7 +162,7 @@ async def _on_track_published(self, event: sfu_events.TrackPublishedEvent): if track_id: # Store with correct type from SFU self._track_map[track_key] = {"track_id": track_id, "published": True} - self.logger.info( + logger.info( f"Trackmap published: {track_type_int} from {user_id}, track_id: {track_id} (waited {elapsed:.2f}s)" ) @@ -215,7 +216,7 @@ async def _on_track_removed( event_desc = "Participant left" track_names = [TrackType.Name(t) for t in tracks_to_remove] - self.logger.info(f"{event_desc}: {user_id}, tracks: {track_names}") + logger.info(f"{event_desc}: {user_id}, tracks: {track_names}") # Mark each track as unpublished and send TrackRemovedEvent for track_type_int in tracks_to_remove: @@ -237,7 +238,7 @@ async def _on_track_removed( # Mark as unpublished instead of removing self._track_map[track_key]["published"] = False else: - self.logger.warning(f"Track not found in map: {track_key}") + logger.warning(f"Track not found in map: {track_key}") async def create_conversation(self, call: Call, user, instructions): chat_client: ChatClient = call.client.stream.chat @@ -281,7 +282,7 @@ async def join(self, agent: "Agent", call: Call) -> StreamConnection: async def on_track(track_id, track_type, user): # Store track in pending map - wait for SFU to confirm type before spawning TrackAddedEvent self._pending_tracks[track_id] = (user.user_id, user.session_id, track_type) - self.logger.info( + logger.info( f"Track received from WebRTC (pending SFU confirmation): {track_id}, type: {track_type}, user: {user.user_id}" ) @@ -326,9 +327,9 @@ async def publish_tracks(self, audio_track, video_track): """ await self._connection.add_tracks(audio=audio_track, video=video_track) if audio_track: - self.logger.info("🤖 Agent ready to speak") + logger.info("🤖 Agent ready to speak") if video_track: - self.logger.info("🎥 Agent ready to publish video") + logger.info("🎥 Agent ready to publish video") # In Realtime mode we directly publish the provider's output track; no extra forwarding needed def _get_subscription_config(self): @@ -407,20 +408,20 @@ async def open_demo(self, call: Call) -> str: "skip_lobby": "true", "user_name": name, "video_encoder": "h264", # Use H.264 instead of VP8 for better compatibility - "bitrate": 12000000, + "bitrate": 12000000, "w": 1920, - "h": 1080, + "h": 1080, "channel_type": self.channel_type, } url = f"{base_url}{call.id}?{urlencode(params)}" - print(f"🌐 Opening browser to: {url}") + logger.info(f"🌐 Opening browser to: {url}") try: webbrowser.open(url) - print("✅ Browser opened successfully!") + logger.info("✅ Browser opened successfully!") except Exception as e: - print(f"❌ Failed to open browser: {e}") - print(f"Please manually open this URL: {url}") + logger.error(f"❌ Failed to open browser: {e}") + logger.warning(f"Please manually open this URL: {url}") return url diff --git a/plugins/kokoro/vision_agents/plugins/kokoro/tts.py b/plugins/kokoro/vision_agents/plugins/kokoro/tts.py index 9eca1f64..bf6c6d29 100644 --- a/plugins/kokoro/vision_agents/plugins/kokoro/tts.py +++ b/plugins/kokoro/vision_agents/plugins/kokoro/tts.py @@ -15,6 +15,9 @@ KPipeline = None # type: ignore # noqa: N816 +logger = logging.getLogger(__name__) + + class TTS(tts.TTS): """Text-to-Speech plugin backed by the Kokoro-82M model.""" @@ -68,7 +71,7 @@ async def stop_audio(self) -> None: Clears the queue and stops playing audio. """ - logging.info("🎤 Kokoro TTS stop requested (no-op)") + logger.info("🎤 Kokoro TTS stop requested (no-op)") def _generate_chunks(self, text: str): for _gs, _ps, audio in self._pipeline( diff --git a/plugins/krisp/vision_agents/plugins/krisp/turn_detection.py b/plugins/krisp/vision_agents/plugins/krisp/turn_detection.py index 112735d7..b62d15e5 100644 --- a/plugins/krisp/vision_agents/plugins/krisp/turn_detection.py +++ b/plugins/krisp/vision_agents/plugins/krisp/turn_detection.py @@ -42,7 +42,7 @@ def __init__( confidence_threshold=confidence_threshold, provider_name="KrispTurnDetection", ) - self.logger = logging.getLogger("KrispTurnDetection") + self.logger = logging.getLogger(__name__) self.model_path = model_path self.frame_duration_ms = frame_duration_ms self.turn_start_threshold = ( diff --git a/tests/test_utils.py b/tests/test_utils.py index af9c0bc1..c8fb7c9b 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,11 +1,16 @@ +import logging import os import tempfile + import numpy as np import av +import pytest + from vision_agents.core.utils.utils import parse_instructions, Instructions from getstream.video.rtc.track_util import AudioFormat from vision_agents.core.utils.video_utils import ensure_even_dimensions from vision_agents.core.edge.types import PcmData +from vision_agents.core.utils.logging import configure_default_logging class TestParseInstructions: @@ -538,9 +543,69 @@ def test_timing_information_preserved(self): frame = av.VideoFrame(width=1921, height=1081, format="yuv420p") frame.pts = 12345 frame.time_base = Fraction(1, 30) - + result = ensure_even_dimensions(frame) - + assert result.pts == 12345 assert result.time_base == Fraction(1, 30) + +@pytest.fixture() +def make_logger(): + """ + A fixture to get logger and reset its settings back to the original state after the test. + """ + loggers = [] + + def factory(name: str): + logger_ = logging.getLogger(name) + original_level = logger_.level + original_handlers = logger_.handlers[:] + original_filters = logger_.filters[:] + loggers.append((logger_, original_level, original_handlers, original_filters)) + return logger_ + + yield factory + for logger, level, handlers, filters in loggers: + logger.setLevel(level) + logger.handlers = handlers + logger.filters = filters + + +class TestLogging: + def test_configure_default_logging_configures(self, make_logger): + vision_agents_logger = make_logger("vision_agents") + getstream_logger = make_logger("getstream") + + some_level = 23 + assert vision_agents_logger.level != some_level + assert getstream_logger.level != some_level + configure_default_logging(level=some_level) + assert vision_agents_logger.level == some_level + assert getstream_logger.level == some_level + + def test_configure_default_logging_handlers_already_configured(self, make_logger): + loggers = [make_logger("vision_agents"), make_logger("getstream")] + some_level = 23 + null_handler = logging.NullHandler() + + for logger in loggers: + logger.addHandler(null_handler) + + configure_default_logging(level=some_level) + + for logger in loggers: + assert logger.level == some_level + assert logger.handlers == [null_handler] + + def test_configure_default_logging_level_already_set(self, make_logger): + loggers = [make_logger("vision_agents"), make_logger("getstream")] + for logger in loggers: + logger.setLevel(logging.DEBUG) + assert not logger.handlers + + configure_default_logging(level=logging.INFO) + + for logger in loggers: + assert logger.level == logging.DEBUG + assert logger.handlers diff --git a/uv.lock b/uv.lock index 79e560cf..08c54f52 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 3 +revision = 2 requires-python = ">=3.12" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'win32'", @@ -53,23 +53,23 @@ dev = [ name = "addict" version = "2.4.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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+++++++++++------- .../vision_agents/plugins/aws/aws_realtime.py | 453 +++++++++-------- 2 files changed, 531 insertions(+), 384 deletions(-) diff --git a/plugins/aws/vision_agents/plugins/aws/aws_llm.py b/plugins/aws/vision_agents/plugins/aws/aws_llm.py index d254aa0b..68d4d232 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_llm.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_llm.py @@ -9,7 +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 @@ -26,9 +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", @@ -46,7 +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") @@ -58,7 +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: @@ -70,7 +73,7 @@ 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 @@ -84,17 +87,16 @@ 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") """ - registered_tools = self.get_available_functions() return await self.converse_stream( messages=[{"role": "user", "content": [{"text": text}]}] ) @@ -111,9 +113,7 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: tools = self.get_available_functions() if tools: converted_tools = self._convert_tools_to_provider_format(tools) - kwargs["toolConfig"] = { - "tools": converted_tools - } + kwargs["toolConfig"] = {"tools": converted_tools} # Combine original instructions with markdown file contents enhanced_instructions = self._build_enhanced_instructions() @@ -122,7 +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 @@ -132,41 +132,51 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: 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')}") + 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', {}) + 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( + current_calls, seen=seen, max_concurrency=8, timeout_s=30 + ) # type: ignore[arg-type] + if not triples: - self.logger.warning("No tool execution results despite tool calls") + self.logger.warning( + "No tool execution results despite tool calls" + ) break - + # Build tool result message tool_result_blocks = [] for tc, res, err in triples: if err: - self.logger.error(f"Tool {tc['name']} execution error: {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) @@ -176,15 +186,17 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: tool_response = res else: tool_response = str(res) - - tool_result_blocks.append({ - "toolUseId": tc["id"], - "content": [{"text": tool_response}], - }) + + tool_result_blocks.append( + { + "toolUseId": tc["id"], + "content": [{"text": tool_response}], + } + ) 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 + [user_tool_results_msg] follow_up_kwargs = { @@ -194,30 +206,44 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: } 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)}") + 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) - follow_up_text = self._extract_text_from_response(follow_up_response) + + current_calls = self._extract_tool_calls_from_response( + follow_up_response + ) + follow_up_text = self._extract_text_from_response( + follow_up_response + ) llm_response = LLMResponseEvent(follow_up_response, follow_up_text) - + if current_calls: - assistant_msg_from_follow_up = follow_up_response.get('output', {}).get('message', {}) + 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, @@ -225,38 +251,67 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: } 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)}") + 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")) + 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_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) + 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) - + 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]: @@ -270,13 +325,11 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: tools = self.get_available_functions() if tools: converted_tools = self._convert_tools_to_provider_format(tools) - kwargs["toolConfig"] = { - "tools": converted_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) @@ -290,57 +343,72 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: try: 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) + 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') + + 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 - + for event in stream: last_event = event self._process_stream_event(event, text_parts, accumulated_calls) - + 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_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 + "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( + accumulated_calls, seen=seen, max_concurrency=8, timeout_s=30 + ) # type: ignore[arg-type] + if not triples: self.logger.warning("No tool execution results despite tool calls") break - + tool_result_blocks = [] for tc, res, err in triples: if err: @@ -354,15 +422,17 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: tool_response = res else: tool_response = str(res) - - tool_result_blocks.append({ - "toolUseId": tc["id"], - "content": [{"text": tool_response}], - }) + + tool_result_blocks.append( + { + "toolUseId": tc["id"], + "content": [{"text": tool_response}], + } + ) 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 + [user_tool_results_msg] follow_up_kwargs = { @@ -372,40 +442,44 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: } if system_param: follow_up_kwargs["system"] = system_param - + follow_up_response = self.client.converse_stream(**follow_up_kwargs) - + accumulated_calls = [] follow_up_text_parts: List[str] = [] - follow_up_stream = follow_up_response.get('stream') + follow_up_stream = follow_up_response.get("stream") for event in follow_up_stream: last_event = event - self._process_stream_event(event, follow_up_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_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 + "content": follow_up_assistant_content, } messages.append(follow_up_assistant_msg) - + if follow_up_text_parts and not accumulated_calls: break - + rounds += 1 - + if accumulated_calls: final_kwargs = { "modelId": self.model, @@ -413,75 +487,82 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: } if system_param: final_kwargs["system"] = system_param - + final_response = self.client.converse_stream(**final_kwargs) - final_stream = final_response.get('stream') + final_stream = final_response.get("stream") final_text_parts: List[str] = [] for event in final_stream: last_event = event - self._process_stream_event(event, final_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 "{}" @@ -493,59 +574,63 @@ 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', {}) + + output = response.get("output", {}) if not output: return tool_calls - - message = output.get('message', {}) + + message = output.get("message", {}) if not message: return tool_calls - - content = message.get('content', []) + + 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 """ @@ -554,35 +639,35 @@ def _convert_tools_to_provider_format(self, tools: List[ToolSchema]) -> List[Dic 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 + "additionalProperties": False, } else: # Already has type, but ensure additionalProperties is set if "additionalProperties" not in params: params["additionalProperties"] = False - + aws_tool = { "toolSpec": { "name": name, "description": description, "inputSchema": { "json": params # This is a dict, not a JSON string - } + }, } } aws_tools.append(aws_tool) @@ -594,9 +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] @@ -608,8 +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) @@ -621,4 +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 66e418d9..8f75b63c 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -9,8 +9,14 @@ import numpy as np from vision_agents.core.llm import realtime -from aws_sdk_bedrock_runtime.client import BedrockRuntimeClient, InvokeModelWithBidirectionalStreamOperationInput -from aws_sdk_bedrock_runtime.models import InvokeModelWithBidirectionalStreamInputChunk, BidirectionalInputPayloadPart +from aws_sdk_bedrock_runtime.client import ( + BedrockRuntimeClient, + InvokeModelWithBidirectionalStreamOperationInput, +) +from aws_sdk_bedrock_runtime.models import ( + InvokeModelWithBidirectionalStreamInputChunk, + BidirectionalInputPayloadPart, +) from aws_sdk_bedrock_runtime.config import Config from smithy_aws_core.identity.environment import EnvironmentCredentialsResolver @@ -22,11 +28,9 @@ logger = logging.getLogger(__name__) - DEFAULT_MODEL = "amazon.nova-sonic-v1:0" DEFAULT_SAMPLE_RATE = 16000 - """ AWS Bedrock Realtime with Nova Sonic support. @@ -67,14 +71,14 @@ class Realtime(realtime.Realtime): Examples: - + from vision_agents.plugins import aws - + llm = aws.Realtime( model="us.amazon.nova-sonic-v1:0", region_name="us-east-1" ) - + # Register a custom function @llm.register_function( name="get_weather", @@ -82,24 +86,25 @@ class Realtime(realtime.Realtime): ) def get_weather(city: str) -> dict: return {"city": city, "temp": 72, "condition": "sunny"} - + # Connect to the session await llm.connect() - + # Simple text response await llm.simple_response("Describe what you see and say hi") - + # Send audio await llm.simple_audio_response(pcm_data) - + # Close when done await llm.close() """ - connected : bool = False - voice_id : str - # Event templates - TEXT_CONTENT_START_EVENT = '''{ + connected: bool = False + voice_id: str + + # Event templates + TEXT_CONTENT_START_EVENT = """{ "event": { "contentStart": { "promptName": "%s", @@ -112,9 +117,9 @@ def get_weather(city: str) -> dict: } } } - }''' + }""" - TEXT_INPUT_EVENT = '''{ + TEXT_INPUT_EVENT = """{ "event": { "textInput": { "promptName": "%s", @@ -122,27 +127,25 @@ def get_weather(city: str) -> dict: "content": "%s" } } - }''' + }""" - CONTENT_END_EVENT = '''{ + CONTENT_END_EVENT = """{ "event": { "contentEnd": { "promptName": "%s", "contentName": "%s" } } - }''' + }""" def __init__( - self, - model: str = DEFAULT_MODEL, - region_name: str = "us-east-1", - voice_id: str = "matthew", - **kwargs + self, + model: str = DEFAULT_MODEL, + region_name: str = "us-east-1", + voice_id: str = "matthew", + **kwargs, ) -> None: - """ - - """ + """ """ super().__init__(**kwargs) self.model = model self.region_name = region_name @@ -168,8 +171,12 @@ 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 + 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 @@ -208,10 +215,10 @@ async def connect(self): # send start and prompt event await self.start_session() logger.info("Sent session start event") - + # Small delay between init events await asyncio.sleep(0.1) - + await self.start_prompt() logger.info("Sent prompt start event") @@ -222,12 +229,14 @@ async def connect(self): # 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") + raise Exception( + "AWS Bedrock requires system instructions before sending regular user input" + ) await self.content_input(system_instructions, "SYSTEM") logger.info("Sent system instructions") - + logger.info("AWS Bedrock connection setup complete") - + except Exception as e: logger.error(f"Failed to connect to AWS Bedrock: {e}", exc_info=True) self.connected = False @@ -238,7 +247,9 @@ async def simple_audio_response( ): """Send audio data to the model for processing.""" if not self.connected: - self.logger.warning("realtime is not active. can't call simple_audio_response") + self.logger.warning( + "realtime is not active. can't call simple_audio_response" + ) return # Resample from 48kHz to 24kHz if needed @@ -248,13 +259,13 @@ async def simple_audio_response( audio_float = pcm.samples.astype(np.float32) / 32768.0 else: audio_float = pcm.samples.astype(np.float32) - + # Resample resampled_float = resample_audio(audio_float, pcm.sample_rate, 24000) - + # Convert back to int16 resampled_samples = (resampled_float * 32768.0).astype(np.int16) - + # Create new PcmData with resampled audio pcm = PcmData( format=pcm.format, @@ -262,21 +273,25 @@ async def simple_audio_response( samples=resampled_samples, pts=pcm.pts, dts=pcm.dts, - time_base=pcm.time_base + time_base=pcm.time_base, ) - + content_name = str(uuid.uuid4()) await self.audio_content_start(content_name) self._emit_audio_input_event(pcm.samples, sample_rate=pcm.sample_rate) # Convert PcmData to base64 encoded bytes - audio_base64 = base64.b64encode(pcm.samples).decode('utf-8') + audio_base64 = base64.b64encode(pcm.samples).decode("utf-8") await self.audio_input(content_name, audio_base64) await self.content_end(content_name) - async def simple_response(self, text: str, processors: Optional[List[Processor]] = None, - participant: Optional[Participant] = None): + async def simple_response( + self, + text: str, + processors: Optional[List[Processor]] = None, + participant: Optional[Participant] = None, + ): """ Simple response standardizes how to send a text instruction to this LLM. @@ -305,37 +320,37 @@ async def audio_input(self, content_name: str, audio_bytes: str): "audioInput": { "promptName": self.session_id, "contentName": content_name, - "content": audio_bytes + "content": audio_bytes, } } } await self.send_event(audio_event) - async def audio_content_start(self, content_name: str, role: str="USER"): + async def audio_content_start(self, content_name: str, role: str = "USER"): event = { - "event": { - "contentStart": { - "promptName": self.session_id, - "contentName": content_name, - "type": "AUDIO", - "interactive": True, - "role": role, - "audioInputConfiguration": { - "mediaType": "audio/lpcm", - "sampleRateHertz": 24000, - "sampleSizeBits": 16, - "channelCount": 1, - "audioType": "SPEECH", - "encoding": "base64" + "event": { + "contentStart": { + "promptName": self.session_id, + "contentName": content_name, + "type": "AUDIO", + "interactive": True, + "role": role, + "audioInputConfiguration": { + "mediaType": "audio/lpcm", + "sampleRateHertz": 24000, + "sampleSizeBits": 16, + "channelCount": 1, + "audioType": "SPEECH", + "encoding": "base64", + }, } } - } } await self.send_event(event) async def start_session(self): # subclass this to change the session start - event_json = '''{ + event_json = """{ "event": { "sessionStart": { "inferenceConfiguration": { @@ -345,45 +360,40 @@ async def start_session(self): } } } - }''' + }""" await self.send_raw_event(event_json) async def start_prompt(self): prompt_name = self.session_id - + # 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 - } + "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}, + } } - } } event_json = json_lib.dumps(event) else: @@ -407,19 +417,27 @@ async def start_prompt(self): }} }} }}''' - - await self.send_raw_event(event_json) - + 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) + 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) + 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): @@ -430,7 +448,7 @@ 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')) + value=BidirectionalInputPayloadPart(bytes_=event_json.encode("utf-8")) ) await self.stream.input_stream.send(event) except Exception as e: @@ -442,7 +460,7 @@ async def send_raw_event(self, event_json: str) -> None: try: logger.debug(f"Sending raw event: {event_json}") event = InvokeModelWithBidirectionalStreamInputChunk( - value=BidirectionalInputPayloadPart(bytes_=event_json.encode('utf-8')) + value=BidirectionalInputPayloadPart(bytes_=event_json.encode("utf-8")) ) await self.stream.input_stream.send(event) logger.debug("Successfully sent raw event") @@ -450,12 +468,14 @@ async def send_raw_event(self, event_json: str) -> None: 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]]: + 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 """ @@ -464,39 +484,39 @@ def _convert_tools_to_provider_format(self, tools: List[Any]) -> List[Dict[str, 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 + "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) @@ -504,7 +524,7 @@ def _convert_tools_to_provider_format(self, tools: List[Any]) -> List[Dict[str, 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 @@ -520,10 +540,8 @@ async def send_tool_content_start(self, content_name: str, tool_use_id: str): "toolResultInputConfiguration": { "toolUseId": tool_use_id, "type": "TEXT", - "textInputConfiguration": { - "mediaType": "text/plain" - } - } + "textInputConfiguration": {"mediaType": "text/plain"}, + }, } } } @@ -531,7 +549,7 @@ async def send_tool_content_start(self, content_name: str, tool_use_id: 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) @@ -542,40 +560,46 @@ async def send_tool_result(self, content_name: str, result: Any): content_str = result else: content_str = json.dumps(result) - + event = { "event": { "toolResult": { "promptName": self.session_id, "contentName": content_name, - "content": content_str # Stringified JSON, not an object/array + "content": content_str, # Stringified JSON, not an object/array } } } await self.send_event(event) - async def _handle_tool_call(self, tool_name: str, tool_use_id: str, tool_use_content: Dict[str, Any]): + 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.info(f"Starting tool call execution: {tool_name} (id: {tool_use_id})") - + logger.info( + 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: + if "content" in tool_use_content: try: - tool_input = json.loads(tool_use_content['content']) + 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')}") + 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'] - + elif "input" in tool_use_content: + tool_input = tool_use_content["input"] + # Create normalized tool call tool_call = { "type": "tool_call", @@ -583,24 +607,24 @@ async def _handle_tool_call(self, tool_name: str, tool_use_id: str, tool_use_con "name": tool_name, "arguments_json": tool_input, } - + # 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 @@ -611,7 +635,10 @@ async def _handle_tool_call(self, tool_name: str, tool_use_id: str, tool_use_con await self.content_end(content_name) logger.info(f"Sent error response for failed tool call {tool_name}") except Exception as send_error: - logger.error(f"Failed to send error response for tool call {tool_name}: {send_error}", exc_info=True) + 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: @@ -626,11 +653,7 @@ async def close(self): } await self.send_event(prompt_end) - session_end: Dict[str, Any] = { - "event": { - "sessionEnd": {} - } - } + session_end: Dict[str, Any] = {"event": {"sessionEnd": {}}} await self.send_event(session_end) await self.stream.input_stream.close() @@ -640,7 +663,6 @@ async def close(self): self.connected = False - async def _handle_events(self): """Process incoming responses from AWS Bedrock.""" logger.info("Starting event handling loop") @@ -651,101 +673,145 @@ async def _handle_events(self): result = await output[1].receive() if result.value and result.value.bytes_: try: - response_data = result.value.bytes_.decode('utf-8') + 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(f"Content start from AWS Bedrock: {content_start}") + if "event" in json_data: + if "contentStart" in json_data["event"]: + content_start = json_data["event"]["contentStart"] + logger.info( + f"Content start from AWS Bedrock: {content_start}" + ) # set role - self.role = content_start['role'] + self.role = content_start["role"] # Check for speculative content - if 'additionalModelFields' in content_start: + if "additionalModelFields" in content_start: try: - additional_fields = json.loads(content_start['additionalModelFields']) - if additional_fields.get('generationStage') == 'SPECULATIVE': + additional_fields = json.loads( + content_start["additionalModelFields"] + ) + if ( + additional_fields.get("generationStage") + == "SPECULATIVE" + ): self.display_assistant_text = True else: 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(f"Text output from AWS Bedrock: {text_content}") - elif 'completionStart' in json_data['event']: - logger.info("Completion start from AWS Bedrock", json_data['event']['completionStart']) - elif 'audioOutput' in json_data['event']: - audio_content = json_data['event']['audioOutput']['content'] + + elif "textOutput" in json_data["event"]: + text_content = json_data["event"]["textOutput"][ + "content" + ] + # role = json_data['event']['textOutput']['role'] + logger.info( + f"Text output from AWS Bedrock: {text_content}" + ) + elif "completionStart" in json_data["event"]: + logger.info( + "Completion start from AWS Bedrock", + json_data["event"]["completionStart"], + ) + elif "audioOutput" in json_data["event"]: + audio_content = json_data["event"]["audioOutput"][ + "content" + ] audio_bytes = base64.b64decode(audio_content) - #await self.audio_output_queue.put(audio_bytes) + # await self.audio_output_queue.put(audio_bytes) audio_event = RealtimeAudioOutputEvent( plugin_name="aws", audio_data=audio_bytes, - sample_rate=24000 + sample_rate=24000, ) self.events.send(audio_event) await self.output_track.write(audio_bytes) + elif "toolUse" in json_data["event"]: + tool_use_data = json_data["event"]["toolUse"] + tool_name = tool_use_data.get("toolName") + tool_use_id = tool_use_data.get("toolUseId") + + logger.info( + f"Tool use event received: {tool_name} (id: {tool_use_id})" + ) - elif 'toolUse' in json_data['event']: - tool_use_data = json_data['event']['toolUse'] - tool_name = tool_use_data.get('toolName') - tool_use_id = tool_use_data.get('toolUseId') - - logger.info(f"Tool use event received: {tool_name} (id: {tool_use_id})") - # 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 + "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']: - 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}") - + logger.warning( + f"Invalid tool use event - missing toolName or toolUseId: {tool_use_data}" + ) + + elif "contentEnd" in json_data["event"]: + 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 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'] - )) - + 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.debug(f"Content end from AWS Bedrock {stopReason}: {content_end_data}") + logger.debug( + f"Content end from AWS Bedrock {stopReason}: {content_end_data}" + ) - elif 'completionEnd' in json_data['event']: - logger.info(f"Completion end from AWS Bedrock: {json_data['event']['completionEnd']}") + elif "completionEnd" in json_data["event"]: + logger.info( + 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']: + elif "usageEvent" in json_data["event"]: pass else: - logger.warning(f"Unhandled event: {json_data['event']}") + logger.warning( + f"Unhandled event: {json_data['event']}" + ) except json.JSONDecodeError as e: logger.warning(f"Failed to parse JSON response: {e}") @@ -775,4 +841,3 @@ async def _handle_events(self): logger.info("Event handling loop ended, connection still active") else: logger.info("Event handling loop ended, connection was closed") - From babfe631e0384da5c30c09582802decbbf13b966 Mon Sep 17 00:00:00 2001 From: "Neevash Ramdial (Nash)" Date: Thu, 30 Oct 2025 21:51:27 -0600 Subject: [PATCH 07/12] Remove resample_audio in favor of PCM.resample --- .../vision_agents/plugins/aws/aws_realtime.py | 25 ++----------------- 1 file changed, 2 insertions(+), 23 deletions(-) diff --git a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py index 8f75b63c..518c5060 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -5,7 +5,6 @@ import uuid from typing import Optional, List, Dict, Any from getstream.video.rtc.audio_track import AudioStreamTrack -from getstream.audio.utils import resample_audio import numpy as np from vision_agents.core.llm import realtime @@ -252,29 +251,9 @@ async def simple_audio_response( ) return - # Resample from 48kHz to 24kHz if needed + # Resample to 24kHz if needed, as required by AWS Nova if pcm.sample_rate != 24000: - # Convert to float32 for resampling - if pcm.samples.dtype == np.int16: - audio_float = pcm.samples.astype(np.float32) / 32768.0 - else: - audio_float = pcm.samples.astype(np.float32) - - # Resample - resampled_float = resample_audio(audio_float, pcm.sample_rate, 24000) - - # Convert back to int16 - resampled_samples = (resampled_float * 32768.0).astype(np.int16) - - # Create new PcmData with resampled audio - pcm = PcmData( - format=pcm.format, - sample_rate=24000, - samples=resampled_samples, - pts=pcm.pts, - dts=pcm.dts, - time_base=pcm.time_base, - ) + pcm = pcm.resample(24000) content_name = str(uuid.uuid4()) From 17839ea88bd0ea3c7b18176d699e1d541f792d8d Mon Sep 17 00:00:00 2001 From: "Neevash Ramdial (Nash)" Date: Thu, 30 Oct 2025 21:54:02 -0600 Subject: [PATCH 08/12] Migrate RealtimeAudioOutputEvent to _emit_audio_output_event --- plugins/aws/vision_agents/plugins/aws/aws_realtime.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py index 518c5060..1c883490 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -23,7 +23,6 @@ from vision_agents.core.processors import Processor from vision_agents.core.edge.types import Participant from vision_agents.core.edge.types import PcmData -from ...core.llm.events import RealtimeAudioOutputEvent logger = logging.getLogger(__name__) @@ -700,13 +699,10 @@ 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) From 5914a7ec14503243c33510d3d163839bd5520ee0 Mon Sep 17 00:00:00 2001 From: "Neevash Ramdial (Nash)" Date: Thu, 30 Oct 2025 21:55:16 -0600 Subject: [PATCH 09/12] unused import --- plugins/aws/vision_agents/plugins/aws/aws_realtime.py | 1 - 1 file changed, 1 deletion(-) diff --git a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py index 1c883490..a59f7641 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -5,7 +5,6 @@ import uuid from typing import Optional, List, Dict, Any from getstream.video.rtc.audio_track import AudioStreamTrack -import numpy as np from vision_agents.core.llm import realtime from aws_sdk_bedrock_runtime.client import ( From 0828afef2dee5342911579c9aa2618f0351b7014 Mon Sep 17 00:00:00 2001 From: Deven Joshi Date: Fri, 31 Oct 2025 17:23:14 +0100 Subject: [PATCH 10/12] Fix linting: remove unused imports --- .../aws_llm_function_calling_example.py | 2 +- .../aws_realtime_function_calling_example.py | 1 - plugins/aws/example/uv.lock | 28 +++++++++++++------ 3 files changed, 21 insertions(+), 10 deletions(-) diff --git a/plugins/aws/example/aws_llm_function_calling_example.py b/plugins/aws/example/aws_llm_function_calling_example.py index 4c25c574..fb530f6c 100644 --- a/plugins/aws/example/aws_llm_function_calling_example.py +++ b/plugins/aws/example/aws_llm_function_calling_example.py @@ -6,7 +6,7 @@ from vision_agents.core import User from vision_agents.core.agents import Agent -from vision_agents.plugins import aws, getstream, cartesia, deepgram, smart_turn +from vision_agents.plugins import aws, getstream, cartesia, deepgram load_dotenv() diff --git a/plugins/aws/example/aws_realtime_function_calling_example.py b/plugins/aws/example/aws_realtime_function_calling_example.py index 33a9c5a0..bf2b3a89 100644 --- a/plugins/aws/example/aws_realtime_function_calling_example.py +++ b/plugins/aws/example/aws_realtime_function_calling_example.py @@ -1,6 +1,5 @@ import asyncio import logging -from pathlib import Path from uuid import uuid4 from dotenv import load_dotenv diff --git a/plugins/aws/example/uv.lock b/plugins/aws/example/uv.lock index a3fbdc5b..fad869b7 100644 --- a/plugins/aws/example/uv.lock +++ b/plugins/aws/example/uv.lock @@ -449,6 +449,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, ] +[[package]] +name = "colorlog" +version = "6.10.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a2/61/f083b5ac52e505dfc1c624eafbf8c7589a0d7f32daa398d2e7590efa5fda/colorlog-6.10.1.tar.gz", hash = "sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321", size = 17162, upload-time = "2025-10-16T16:14:11.978Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6d/c1/e419ef3723a074172b68aaa89c9f3de486ed4c2399e2dbd8113a4fdcaf9e/colorlog-6.10.1-py3-none-any.whl", hash = "sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c", size = 11743, upload-time = "2025-10-16T16:14:10.512Z" }, +] + [[package]] name = "cryptography" version = "46.0.3" @@ -645,7 +657,7 @@ wheels = [ [[package]] name = "getstream" -version = "2.5.6" +version = "2.5.7" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "dataclasses-json" }, @@ -659,7 +671,7 @@ dependencies = [ { name = "python-dateutil" }, ] wheels = [ - { url = "https://files.pythonhosted.org/packages/88/c6/e1226dbfa237f061e6ef729113d81ef1fe23a662a9a8d93504689327618b/getstream-2.5.6-py3-none-any.whl", hash = "sha256:303a0ec7589cc8ae2eddd34b999cd9d97c568de217b982f2d0ea4b978a433ea5", size = 232577, upload-time = "2025-10-16T10:46:52.904Z" }, + { url = "https://files.pythonhosted.org/packages/de/7c/e592f640811f1ef14c41b6e648e817517ce079dfef55327bd635129b36d2/getstream-2.5.7-py3-none-any.whl", hash = "sha256:3c5af20e92f58076c1a6b033da88c5b12ebfb4c918fb0f61ed82260a55fda9cf", size = 239778, upload-time = "2025-10-30T13:58:09.714Z" }, ] [package.optional-dependencies] @@ -2604,6 +2616,7 @@ wheels = [ name = "vision-agents" source = { editable = "../../../agents-core" } dependencies = [ + { name = "colorlog" }, { name = "getstream", extra = ["telemetry", "webrtc"] }, { name = "mcp" }, { name = "numpy" }, @@ -2614,7 +2627,8 @@ dependencies = [ [package.metadata] requires-dist = [ { name = "click", marker = "extra == 'dev'" }, - { name = "getstream", extras = ["telemetry", "webrtc"], specifier = ">=2.5.5" }, + { name = "colorlog", specifier = ">=6.10.1" }, + { name = "getstream", extras = ["telemetry", "webrtc"], specifier = ">=2.5.7" }, { name = "mcp", specifier = ">=1.16.0" }, { name = "mypy", marker = "extra == 'dev'" }, { name = "numpy", specifier = ">=1.24.0" }, @@ -2638,12 +2652,10 @@ requires-dist = [ { name = "vision-agents-plugins-kokoro", marker = "extra == 'kokoro'", editable = "../../kokoro" }, { name = "vision-agents-plugins-krisp", marker = "extra == 'all-plugins'", editable = "../../krisp" }, { name = "vision-agents-plugins-krisp", marker = "extra == 'krisp'", editable = "../../krisp" }, - { name = "vision-agents-plugins-moonshine", marker = "extra == 'all-plugins'", editable = "../../moonshine" }, - { name = "vision-agents-plugins-moonshine", marker = "extra == 'moonshine'", editable = "../../moonshine" }, + { name = "vision-agents-plugins-moonshine", marker = "extra == 'all-plugins'" }, + { name = "vision-agents-plugins-moonshine", marker = "extra == 'moonshine'" }, { name = "vision-agents-plugins-openai", marker = "extra == 'all-plugins'", editable = "../../openai" }, { name = "vision-agents-plugins-openai", marker = "extra == 'openai'", editable = "../../openai" }, - { name = "vision-agents-plugins-silero", marker = "extra == 'all-plugins'", editable = "../../silero" }, - { name = "vision-agents-plugins-silero", marker = "extra == 'silero'", editable = "../../silero" }, { name = "vision-agents-plugins-smart-turn", marker = "extra == 'all-plugins'", editable = "../../smart_turn" }, { name = "vision-agents-plugins-smart-turn", marker = "extra == 'smart-turn'", editable = "../../smart_turn" }, { name = "vision-agents-plugins-ultralytics", marker = "extra == 'all-plugins'", editable = "../../ultralytics" }, @@ -2653,7 +2665,7 @@ requires-dist = [ { name = "vision-agents-plugins-xai", marker = "extra == 'all-plugins'", editable = "../../xai" }, { name = "vision-agents-plugins-xai", marker = "extra == 'xai'", editable = "../../xai" }, ] -provides-extras = ["all-plugins", "anthropic", "cartesia", "deepgram", "dev", "elevenlabs", "gemini", "getstream", "kokoro", "krisp", "moonshine", "openai", "silero", "smart-turn", "ultralytics", "wizper", "xai"] +provides-extras = ["all-plugins", "anthropic", "cartesia", "deepgram", "dev", "elevenlabs", "gemini", "getstream", "kokoro", "krisp", "moonshine", "openai", "smart-turn", "ultralytics", "wizper", "xai"] [[package]] name = "vision-agents-plugins-aws" From 30637a4c7e939ec93b75ef5d5bb8d0b01257694e Mon Sep 17 00:00:00 2001 From: Deven Joshi Date: Fri, 31 Oct 2025 17:29:53 +0100 Subject: [PATCH 11/12] Clean up excessive logging in AWS Bedrock realtime - Reduce connection setup logs from 7+ to 2 INFO messages - Change most event handling logs to DEBUG level instead of INFO - Remove duplicate 'Response processing error' log message - Remove unused _pending_tool_uses dictionary - Remove redundant debug logs in send_raw_event --- .../vision_agents/plugins/aws/aws_realtime.py | 38 ++++++------------- 1 file changed, 12 insertions(+), 26 deletions(-) diff --git a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py index a59f7641..83bc46cb 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_realtime.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_realtime.py @@ -168,9 +168,6 @@ 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 @@ -203,25 +200,20 @@ async def connect(self): InvokeModelWithBidirectionalStreamOperationInput(model_id=self.model) ) self.connected = True - logger.info("Successfully connected to AWS Bedrock stream") # Start listener task self._stream_task = asyncio.create_task(self._handle_events()) - logger.info("Started event handling task") # send start and prompt event await self.start_session() - logger.info("Sent session start event") # Small delay between init events await asyncio.sleep(0.1) await self.start_prompt() - logger.info("Sent prompt start event") # Give AWS Nova a moment to process the prompt start event await asyncio.sleep(0.1) - logger.info("Waiting for AWS Nova to process prompt start...") # next send system instructions system_instructions = self._build_enhanced_instructions() @@ -230,9 +222,8 @@ async def connect(self): "AWS Bedrock requires system instructions before sending regular user input" ) await self.content_input(system_instructions, "SYSTEM") - logger.info("Sent system instructions") - logger.info("AWS Bedrock connection setup complete") + logger.info("AWS Bedrock connection established") except Exception as e: logger.error(f"Failed to connect to AWS Bedrock: {e}", exc_info=True) @@ -286,7 +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.info(f"Sending content input: role={role}, content={content[:100]}...") + 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) @@ -435,12 +426,10 @@ async def send_event(self, event_data: Dict[str, Any]) -> None: async def send_raw_event(self, event_json: str) -> None: """Send a raw JSON event string to AWS Nova (matching working example approach).""" try: - logger.debug(f"Sending raw event: {event_json}") event = InvokeModelWithBidirectionalStreamInputChunk( value=BidirectionalInputPayloadPart(bytes_=event_json.encode("utf-8")) ) await self.stream.input_stream.send(event) - logger.debug("Successfully sent raw 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 @@ -560,7 +549,7 @@ async def _handle_tool_call( tool_use_content: Full tool use content from AWS """ try: - logger.info( + logger.debug( f"Starting tool call execution: {tool_name} (id: {tool_use_id})" ) @@ -610,7 +599,6 @@ async def _handle_tool_call( 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) - logger.info(f"Sent error response for failed tool call {tool_name}") except Exception as send_error: logger.error( f"Failed to send error response for tool call {tool_name}: {send_error}", @@ -642,7 +630,6 @@ async def close(self): async def _handle_events(self): """Process incoming responses from AWS Bedrock.""" - logger.info("Starting event handling loop") try: while True: try: @@ -658,7 +645,7 @@ async def _handle_events(self): 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 @@ -684,11 +671,11 @@ async def _handle_events(self): "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"], ) @@ -710,7 +697,7 @@ async def _handle_events(self): tool_name = tool_use_data.get("toolName") tool_use_id = tool_use_data.get("toolUseId") - logger.info( + logger.debug( f"Tool use event received: {tool_name} (id: {tool_use_id})" ) @@ -770,13 +757,13 @@ async def _handle_events(self): ) if stopReason == "INTERRUPTED": - logger.info("TODO: should flush audio buffer") + 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 @@ -791,7 +778,7 @@ async def _handle_events(self): logger.warning(f"Failed to parse JSON response: {e}") except StopAsyncIteration: # Stream has ended normally - logger.info("Stream ended normally") + logger.debug("Stream ended normally") break except Exception as e: logger.error("Error in event handling: %s", e) @@ -806,12 +793,11 @@ async def _handle_events(self): except Exception as e: logger.error("Critical error in event handling: %s", e) - logger.error("Response processing error: %s", e) # Only reset connection on critical errors self.connected = False finally: # Only log connection state, don't unconditionally reset if self.connected: - logger.info("Event handling loop ended, connection still active") + logger.debug("Event handling loop ended, connection still active") else: - logger.info("Event handling loop ended, connection was closed") + logger.debug("Event handling loop ended, connection was closed") From 9c0b4ee7bfec785dcd4a8ec9e640fe8884ed09ec Mon Sep 17 00:00:00 2001 From: Deven Joshi Date: Fri, 31 Oct 2025 17:32:12 +0100 Subject: [PATCH 12/12] Fix mypy type errors in AWS LLM plugin - Add type annotation for tool_calls list - Use cast() to properly type NormalizedToolCallItem lists for _dedup_and_execute - Import cast from typing module --- plugins/aws/vision_agents/plugins/aws/aws_llm.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/plugins/aws/vision_agents/plugins/aws/aws_llm.py b/plugins/aws/vision_agents/plugins/aws/aws_llm.py index 68d4d232..5fb3d677 100644 --- a/plugins/aws/vision_agents/plugins/aws/aws_llm.py +++ b/plugins/aws/vision_agents/plugins/aws/aws_llm.py @@ -1,6 +1,6 @@ import os import logging -from typing import Optional, List, TYPE_CHECKING, Any, Dict +from typing import Optional, List, TYPE_CHECKING, Any, Dict, cast import json import boto3 from botocore.exceptions import ClientError @@ -161,8 +161,8 @@ async def converse(self, *args, **kwargs) -> LLMResponseEvent[Any]: 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] + cast(List[Dict[str, Any]], current_calls), seen=seen, max_concurrency=8, timeout_s=30 + ) if not triples: self.logger.warning( @@ -402,8 +402,8 @@ async def converse_stream(self, *args, **kwargs) -> LLMResponseEvent[Any]: 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] + 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") @@ -595,7 +595,7 @@ def _extract_tool_calls_from_response( self, response: Dict[str, Any] ) -> List[NormalizedToolCallItem]: """Extract tool calls from AWS response.""" - tool_calls = [] + tool_calls: List[NormalizedToolCallItem] = [] output = response.get("output", {}) if not output: