# # Copyright (c) 2024, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import aiohttp import asyncio import os import sys import time from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.frames.frames import LLMMessagesFrame, TextFrame from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.parallel_pipeline import ParallelPipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContext, ) from pipecat.services.cartesia import CartesiaTTSService from pipecat.services.deepgram import DeepgramSTTService from pipecat.services.anthropic import AnthropicLLMService from pipecat.sync.event_notifier import EventNotifier from pipecat.transports.services.daily import DailyParams, DailyTransport from pipecat.processors.frame_processor import FrameProcessor, FrameDirection from pipecat.frames.frames import ( CancelFrame, EndFrame, Frame, StartFrame, StartInterruptionFrame, StopInterruptionFrame, SystemFrame, TranscriptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame from pipecat.sync.base_notifier import BaseNotifier from pipecat.processors.filters.function_filter import FunctionFilter from pipecat.processors.user_idle_processor import UserIdleProcessor from runner import configure from loguru import logger from dotenv import load_dotenv load_dotenv(override=True) logger.remove(0) logger.add(sys.stderr, level="DEBUG") classifier_statement = """Determine if the user's statement ends with a complete thought and you should respond. The user text is transcribed speech. You are trying to determine if: 1. the user has finished talking and expects a response from you, or 2. this statement is incomplete and the user will continue talking A previous assistant response is provided for additional context. But you are only evaluating the user text. The user text may contain multiple fragments concatentated together. There may be repeated words or mistakes in the transcription. There may be grammatical errors. There may be extra punctuation. Ignore all of that. Interpret the transcribed text as text that would have been spoken. Then consider only whether the user has finished speaking and is expecting a response. Categorize the last user statement as either complete with the user now expecting a response, or incomplete. Return 'YES' if text is likely complete and the user is expecting a response. Return 'NO' if the text seems to be a partial expression or unfinished thought. If you are not sure, respond with your best guess. If the user is expecting a response, respond with YES. If the user is not expecting a response, respond with NO. Always output either YES or NO and no other text. Respond only YES or NO Examples: User: What's the capital of Assistant: NO User: What's the captial of France? Assistant: YES User: Tell me a story about Assistant: NO User: Tell me a story about a dragon Assistant YES User: Is there a Assistant: NO User: Is there a large Assistant: NO User: Is there a large lake near Chicago? Assistant: YES User: When is the longest day of the year? Assistant: YES User: When when is the longest day of the year Assistant: YES User: When when is the ASSISTANT: NO User: What is the um I u Assistant: NO User: What is the um i u largest city in the world Assistant: YES User: How much does a how much does an adult elephant weigh? Assistant: YES User: How much does a how much does Assistant: NO User: What can you tell me All the Assistant: NO User: What can you tell me All the prime numbers less than 100 Assistant: YES User: What's the what's the length of the Amazon River? Assistant: YES User: What's what's the length of the Amazon River? Assistant: YES User: What's what's the length of the Amazon River Assistant: YES User: What's what's the best way to get a coffee stain out of a white shirt Assistant: YES """ conversational_system_message = """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way. Please be very concise in your responses. Unless you are explicitly asked to do otherwise, give me the shortest complete answer possible without unnecessary elaboration. Generally you should answer with a single sentence. """ class StatementJudgeContextFilter(FrameProcessor): def __init__(self, notifier: BaseNotifier, **kwargs): super().__init__(**kwargs) self._notifier = notifier async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) # We must not block system frames. if isinstance(frame, SystemFrame): await self.push_frame(frame, direction) return # Just treat an LLMMessagesFrame as complete, no matter what. if isinstance(frame, LLMMessagesFrame): await self._notifier.notify() return # Otherwise, we only want to handle OpenAILLMContextFrames, and only want to push a simple # messages frame that contains a system prompt and the most recent user messages, # concatenated. if isinstance(frame, OpenAILLMContextFrame): # Take text content from the most recent user messages. messages = frame.context.messages user_text_messages = [] last_assistant_message = None for message in reversed(messages): if message["role"] != "user": if message["role"] == "assistant": last_assistant_message = message break if isinstance(message["content"], str): user_text_messages.append(message["content"]) elif isinstance(message["content"], list): for content in message["content"]: if content["type"] == "text": user_text_messages.insert(0, content["text"]) # If we have any user text content, push an LLMMessagesFrame if user_text_messages: user_message = " ".join(reversed(user_text_messages)) logger.debug(f"!!! {user_message}") messages = [ { "role": "system", "content": classifier_statement, } ] if last_assistant_message: messages.append(last_assistant_message) messages.append({"role": "user", "content": user_message}) await self.push_frame(LLMMessagesFrame(messages)) class CompletenessCheck(FrameProcessor): def __init__(self, notifier: BaseNotifier): super().__init__() self._notifier = notifier async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) if isinstance(frame, TextFrame) and frame.text == "YES": logger.debug("!!! Completeness check YES") await self.push_frame(UserStoppedSpeakingFrame()) await self._notifier.notify() elif isinstance(frame, TextFrame) and frame.text == "NO": logger.debug("!!! Completeness check NO") class OutputGate(FrameProcessor): def __init__(self, notifier: BaseNotifier, **kwargs): super().__init__(**kwargs) self._gate_open = False self._frames_buffer = [] self._notifier = notifier def close_gate(self): self._gate_open = False def open_gate(self): self._gate_open = True async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) # We must not block system frames. if isinstance(frame, SystemFrame): if isinstance(frame, StartFrame): await self._start() if isinstance(frame, (EndFrame, CancelFrame)): await self._stop() if isinstance(frame, StartInterruptionFrame): self._frames_buffer = [] self.close_gate() await self.push_frame(frame, direction) return # Ignore frames that are not following the direction of this gate. if direction != FrameDirection.DOWNSTREAM: await self.push_frame(frame, direction) return if self._gate_open: await self.push_frame(frame, direction) return self._frames_buffer.append((frame, direction)) async def _start(self): self._frames_buffer = [] self._gate_task = self.get_event_loop().create_task(self._gate_task_handler()) async def _stop(self): self._gate_task.cancel() await self._gate_task async def _gate_task_handler(self): while True: try: await self._notifier.wait() self.open_gate() for frame, direction in self._frames_buffer: await self.push_frame(frame, direction) self._frames_buffer = [] except asyncio.CancelledError: break async def main(): async with aiohttp.ClientSession() as session: (room_url, _) = await configure(session) transport = DailyTransport( room_url, None, "Respond bot", DailyParams( audio_out_enabled=True, vad_enabled=True, vad_analyzer=SileroVADAnalyzer(), vad_audio_passthrough=True, ), ) stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady ) # This is the LLM that will be used to detect if the user has finished a # statement. This doesn't really need to be an LLM, we could use NLP # libraries for that, but we have the machinery to use an LLM, so we might as well! statement_llm = AnthropicLLMService( api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-haiku-20241022", name="Haiku" ) # This is the regular LLM. llm = AnthropicLLMService( api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20241022", name="Sonnet", params=AnthropicLLMService.InputParams(enable_prompt_caching_beta=True), ) messages = [ { "role": "system", "content": conversational_system_message, }, ] context = OpenAILLMContext(messages) context_aggregator = llm.create_context_aggregator(context) # We have instructed the LLM to return 'YES' if it thinks the user # completed a sentence. So, if it's 'YES' we will return true in this # predicate which will wake up the notifier. async def wake_check_filter(frame): return frame.text == "YES" # This is a notifier that we use to synchronize the two LLMs. notifier = EventNotifier() # This turns the LLM context into an inference request to classify the user's speech # as complete or incomplete. statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier) # This sends a UserStoppedSpeakingFrame and triggers the notifier event completeness_check = CompletenessCheck(notifier=notifier) # # Notify if the user hasn't said anything. async def user_idle_notifier(frame): await notifier.notify() # Sometimes the LLM will fail detecting if a user has completed a # sentence, this will wake up the notifier if that happens. user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0) bot_output_gate = OutputGate(notifier=notifier) async def block_user_stopped_speaking(frame): return not isinstance(frame, UserStoppedSpeakingFrame) async def pass_only_llm_trigger_frames(frame): return ( isinstance(frame, OpenAILLMContextFrame) or isinstance(frame, LLMMessagesFrame) or isinstance(frame, StartInterruptionFrame) or isinstance(frame, StopInterruptionFrame) ) pipeline = Pipeline( [ transport.input(), stt, context_aggregator.user(), ParallelPipeline( [ # Pass everything except UserStoppedSpeaking to the elements after # this ParallelPipeline FunctionFilter(filter=block_user_stopped_speaking), ], [ # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed # LLMMessagesFrame to the statement classifier LLM. The only frame this # sub-pipeline will output is a UserStoppedSpeakingFrame. statement_judge_context_filter, statement_llm, completeness_check, ], [ # Block everything except OpenAILLMContextFrame and LLMMessagesFrame FunctionFilter(filter=pass_only_llm_trigger_frames), llm, bot_output_gate, # Buffer all llm/tts output until notified. ], ), tts, user_idle, transport.output(), context_aggregator.assistant(), ] ) task = PipelineTask( pipeline, PipelineParams( allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True, ), ) @transport.event_handler("on_first_participant_joined") async def on_first_participant_joined(transport, participant): await transport.capture_participant_transcription(participant["id"]) # Kick off the conversation. messages.append( { "role": "user", "content": "Start by just saying \"Hello I'm ready.\" Don't say anything else.", } ) await task.queue_frames([LLMMessagesFrame(messages)]) @transport.event_handler("on_app_message") async def on_app_message(transport, message, sender): logger.debug(f"Received app message: {message} - {sender}") if "message" not in message: return await task.queue_frames( [ UserStartedSpeakingFrame(), TranscriptionFrame( user_id=sender, timestamp=time.time(), text=message["message"] ), UserStoppedSpeakingFrame(), ] ) runner = PipelineRunner() await runner.run(task) if __name__ == "__main__": asyncio.run(main())