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#!/usr/bin/env python3
#
# Agora Real Time Engagement
# Created by Cline in 2024-03.
# Copyright (c) 2024 Agora IO. All rights reserved.
#
import asyncio
import time
import traceback
from enum import Enum
from typing import Optional, List, Dict
import boto3
from ten import (
AsyncTenEnv,
Cmd,
StatusCode,
CmdResult,
Data,
)
from ten_ai_base.config import BaseConfig
from ten_ai_base.llm import AsyncLLMBaseExtension
from dataclasses import dataclass
from .utils import (
rgb2base64jpeg,
filter_images,
parse_sentence,
get_greeting_text,
merge_images
)
# Constants
MAX_IMAGE_COUNT = 20
ONE_BATCH_SEND_COUNT = 6
VIDEO_FRAME_INTERVAL = 0.5
# Command definitions
CMD_IN_FLUSH = "flush"
CMD_IN_ON_USER_JOINED = "on_user_joined"
CMD_IN_ON_USER_LEFT = "on_user_left"
CMD_OUT_FLUSH = "flush"
# Data property definitions
DATA_IN_TEXT_DATA_PROPERTY_IS_FINAL = "is_final"
DATA_IN_TEXT_DATA_PROPERTY_TEXT = "text"
DATA_OUT_TEXT_DATA_PROPERTY_TEXT = "text"
DATA_OUT_TEXT_DATA_PROPERTY_TEXT_END_OF_SEGMENT = "end_of_segment"
class Role(str, Enum):
"""Role definitions for chat participants."""
User = "user"
Assistant = "assistant"
@dataclass
class BedrockLLMConfig(BaseConfig):
"""Configuration for BedrockV2V extension."""
region: str = "us-east-1"
model_id: str = "us.amazon.nova-lite-v1:0"
access_key_id: str = ""
secret_access_key: str = ""
language: str = "en-US"
prompt: str = "You are an intelligent assistant with real-time interaction capabilities. You will be presented with a series of images that represent a video sequence. Describe what you see directly, as if you were observing the scene in real-time. Do not mention that you are looking at images or a video. Instead, narrate the scene and actions as they unfold. Engage in conversation with the user based on this visual input and their questions, maintaining a concise and clear."
temperature: float = 0.7
max_tokens: int = 256
tokP: str = 0.5
topK: str = 10
max_duration: int = 30
vendor: str = ""
stream_id: int = 0
dump: bool = False
max_memory_length: int = 10
is_memory_enabled: bool = False
is_enable_video: bool = False
greeting: str = "Hello, I'm here to help you. How can I assist you today?"
def build_ctx(self) -> dict:
"""Build context dictionary from configuration."""
return {
"language": self.language,
"model": self.model_id,
}
class BedrockLLMExtension(AsyncLLMBaseExtension):
"""Extension for handling video-to-video processing using AWS Bedrock."""
def __init__(self, name: str):
super().__init__(name)
self.config: Optional[BedrockLLMConfig] = None
self.stopped: bool = False
self.memory: list = []
self.users_count: int = 0
self.bedrock_client = None
self.image_buffers: list = []
self.image_queue = asyncio.Queue()
self.text_buffer: str = ""
self.input_start_time: float = 0
self.processing_times = []
self.ten_env = None
self.ctx = None
async def on_init(self, ten_env: AsyncTenEnv) -> None:
"""Initialize the extension."""
await super().on_init(ten_env)
ten_env.log_info("BedrockV2VExtension initialized")
async def on_start(self, ten_env: AsyncTenEnv) -> None:
"""Start the extension and set up required components."""
await super().on_start(ten_env)
ten_env.log_info("BedrockV2VExtension starting")
try:
self.config = await BedrockLLMConfig.create_async(ten_env=ten_env)
ten_env.log_info(f"Configuration: {self.config}")
if not self.config.access_key_id or not self.config.secret_access_key:
ten_env.log_error("AWS credentials (access_key_id and secret_access_key) are required")
return
await self._setup_components(ten_env)
except Exception as e:
traceback.print_exc()
ten_env.log_error(f"Failed to initialize: {e}")
async def _setup_components(self, ten_env: AsyncTenEnv) -> None:
"""Set up extension components."""
self.memory = []
self.ctx = self.config.build_ctx()
self.ten_env = ten_env
self.loop = asyncio.get_event_loop()
self.loop.create_task(self._on_video(ten_env))
async def on_stop(self, ten_env: AsyncTenEnv) -> None:
"""Stop the extension."""
await super().on_stop(ten_env)
ten_env.log_info("BedrockV2VExtension stopping")
self.stopped = True
async def on_data(self, ten_env: AsyncTenEnv, data) -> None:
"""Handle incoming data."""
ten_env.log_info("on_data receive begin...")
data_name = data.get_name()
ten_env.log_info(f"on_data name {data_name}")
try:
is_final = data.get_property_bool(DATA_IN_TEXT_DATA_PROPERTY_IS_FINAL)
input_text = data.get_property_string(DATA_IN_TEXT_DATA_PROPERTY_TEXT)
if not is_final:
ten_env.log_info("ignore non-final input")
return
if not input_text:
ten_env.log_info("ignore empty text")
return
ten_env.log_info(f"OnData input text: [{input_text}]")
self.text_buffer = input_text
await self._handle_input_truncation("is_final")
except Exception as err:
ten_env.log_info(f"Error processing data: {err}")
async def on_video_frame(self, _: AsyncTenEnv, video_frame) -> None:
"""Handle incoming video frames."""
if not self.config.is_enable_video:
return
image_data = video_frame.get_buf()
image_width = video_frame.get_width()
image_height = video_frame.get_height()
await self.image_queue.put([image_data, image_width, image_height])
async def _on_video(self, ten_env: AsyncTenEnv):
"""Process video frames from the queue."""
while True:
try:
[image_data, image_width, image_height] = await self.image_queue.get()
#ten_env.log_info(f"image_width: {image_width}, image_height: {image_height}, image_size: {len(bytes(image_data)) / 1024 / 1024}MB")
frame_buffer = rgb2base64jpeg(image_data, image_width, image_height)
self.image_buffers.append(frame_buffer)
#ten_env.log_info(f"Processed frame, width: {image_width}, height: {image_height}, frame_buffer_size: {len(frame_buffer) / 1024 / 1024}MB")
while len(self.image_buffers) > MAX_IMAGE_COUNT:
self.image_buffers.pop(0)
# Skip remaining frames for the interval
while not self.image_queue.empty():
await self.image_queue.get()
await asyncio.sleep(VIDEO_FRAME_INTERVAL)
except Exception as e:
traceback.print_exc()
ten_env.log_error(f"Error processing video frame: {e}")
async def on_cmd(self, ten_env: AsyncTenEnv, cmd: Cmd) -> None:
"""Handle incoming commands."""
cmd_name = cmd.get_name()
ten_env.log_info(f"Command received: {cmd_name}")
try:
if cmd_name == CMD_IN_FLUSH:
await ten_env.send_cmd(Cmd.create(CMD_OUT_FLUSH))
elif cmd_name == CMD_IN_ON_USER_JOINED:
await self._handle_user_joined()
elif cmd_name == CMD_IN_ON_USER_LEFT:
self.users_count -= 1
else:
await super().on_cmd(ten_env, cmd)
return
cmd_result = CmdResult.create(StatusCode.OK)
cmd_result.set_property_string("detail", "success")
await ten_env.return_result(cmd_result, cmd)
except Exception as e:
traceback.print_exc()
ten_env.log_error(f"Error handling command {cmd_name}: {e}")
cmd_result = CmdResult.create(StatusCode.ERROR)
cmd_result.set_property_string("detail", str(e))
await ten_env.return_result(cmd_result, cmd)
async def _handle_user_left(self) -> None:
"""Handle user left event."""
self.users_count -= 1
if self.users_count == 0:
self._reset_state()
if self.users_count < 0:
self.users_count = 0
async def _handle_user_joined(self) -> None:
"""Handle user joined event."""
self.users_count += 1
if self.users_count == 1:
await self._greeting()
async def _handle_input_truncation(self, reason: str):
"""Handle input truncation events."""
try:
self.ten_env.log_info(f"Input truncated due to: {reason}")
if self.text_buffer:
await self._call_nova_model(self.text_buffer, self.image_buffers)
self._reset_state()
except Exception as e:
traceback.print_exc()
self.ten_env.log_error(f"Error handling input truncation: {e}")
def _reset_state(self):
"""Reset internal state."""
self.text_buffer = ""
self.image_buffers = []
self.input_start_time = 0
async def _initialize_aws_clients(self):
"""Initialize AWS clients."""
try:
if not self.bedrock_client:
self.bedrock_client = boto3.client('bedrock-runtime',
aws_access_key_id=self.config.access_key_id,
aws_secret_access_key=self.config.secret_access_key,
region_name=self.config.region
)
except Exception as e:
traceback.print_exc()
self.ten_env.log_error(f"Error initializing AWS clients: {e}")
raise
async def _greeting(self) -> None:
"""Send greeting message to the user."""
if self.users_count == 1:
text = self.config.greeting or get_greeting_text(self.config.language)
self.ten_env.log_info(f"send greeting {text}")
await self._send_text_data(text, True, Role.Assistant)
async def _send_text_data(self, text: str, end_of_segment: bool, role: Role):
"""Send text data to the user."""
try:
d = Data.create("text_data")
d.set_property_string(DATA_OUT_TEXT_DATA_PROPERTY_TEXT, text)
d.set_property_bool(DATA_OUT_TEXT_DATA_PROPERTY_TEXT_END_OF_SEGMENT, end_of_segment)
d.set_property_string("role", role)
asyncio.create_task(self.ten_env.send_data(d))
except Exception as e:
self.ten_env.log_error(f"Error sending text data: {e}")
async def _call_nova_model(self, input_text: str, image_buffers: List[bytes]) -> None:
"""Call Bedrock's Nova model with text and video input."""
try:
if not self.bedrock_client:
await self._initialize_aws_clients()
if not input_text:
self.ten_env.log_info("Text input is empty")
return
contents = []
# Process images
if image_buffers:
filtered_buffers = filter_images(image_buffers, ONE_BATCH_SEND_COUNT)
for image_data in filtered_buffers:
contents.append({
"image": {
"format": 'jpeg',
"source": {
"bytes": image_data
}
}
})
# Prepare memory
while len(self.memory) > self.config.max_memory_length:
self.memory.pop(0)
while len(self.memory) > 0 and self.memory[0]["role"] == "assistant":
self.memory.pop(0)
while len(self.memory) > 0 and self.memory[-1]["role"] == "user":
self.memory.pop(-1)
# Prepare request
contents.append({"text": input_text})
messages = []
for m in self.memory:
# Convert string content to list format if needed
m_content = m["content"]
if isinstance(m_content, str):
m_content = [{"text": m_content}]
messages.append({
"role": m["role"],
"content": m_content
})
messages.append({
"role": "user",
"content": contents
})
inf_params = {
"maxTokens": self.config.max_tokens,
"topP": self.config.tokP,
"temperature": self.config.temperature
}
additional_config = {
"inferenceConfig": {
"topK": self.config.topK
}
}
system = [{
"text": self.config.prompt
}]
# Make API call
start_time = time.time()
response = self.bedrock_client.converse_stream(
modelId=self.config.model_id,
system=system,
messages=messages,
inferenceConfig=inf_params,
additionalModelRequestFields=additional_config,
)
full_content = await self._process_stream_response(response, start_time)
# async append memory
async def async_append_memory():
if not self.config.is_memory_enabled:
return
image = merge_images(image_buffers)
contents = []
if image:
contents.append({
"image": {
"format": 'jpeg',
"source": {
"bytes": image
}
}
})
contents.append({"text": input_text})
self.memory.append({"role": Role.User, "content": contents})
self.memory.append({"role": Role.Assistant, "content": [{"text": full_content}]})
asyncio.create_task(async_append_memory())
except Exception as e:
traceback.print_exc()
self.ten_env.log_error(f"Error calling Nova model: {e}")
async def _process_stream_response(self, response: Dict, start_time: float):
"""Process streaming response from Nova model."""
sentence = ""
full_content = ""
first_sentence_sent = False
for event in response.get('stream'):
if "contentBlockDelta" in event:
if "text" in event["contentBlockDelta"]["delta"]:
content = event["contentBlockDelta"]["delta"]["text"]
full_content += content
while True:
sentence, content, sentence_is_final = parse_sentence(sentence, content)
if not sentence or not sentence_is_final:
break
self.ten_env.log_info(f"Processing sentence: [{sentence}]")
await self._send_text_data(sentence, False, Role.Assistant)
if not first_sentence_sent:
first_sentence_sent = True
self.ten_env.log_info(f"First sentence latency: {(time.time() - start_time)*1000}ms")
sentence = ""
elif any(key in event for key in ["internalServerException", "modelStreamErrorException",
"throttlingException", "validationException"]):
self.ten_env.log_error(f"Stream error: {event}")
break
elif 'metadata' in event:
if 'metrics' in event['metadata']:
self.ten_env.log_info(f"Nova model latency: {event['metadata']['metrics']['latencyMs']}ms")
# Send final sentence
await self._send_text_data(sentence, True, Role.Assistant)
self.ten_env.log_info(f"Final sentence sent: [{sentence}]")
# Update metrics
self.processing_times.append(time.time() - start_time)
return full_content
async def on_call_chat_completion(self, async_ten_env, **kargs):
raise NotImplementedError
async def on_data_chat_completion(self, async_ten_env, **kargs):
raise NotImplementedError
async def on_tools_update(
self, ten_env: AsyncTenEnv, tool
) -> None:
"""Called when a new tool is registered. Implement this method to process the new tool."""
ten_env.log_info(f"on tools update {tool}")
# await self._update_session() |