temp hacking
This commit is contained in:
Kwindla Hultman Kramer
212
examples/foundational/22b-natural-conversation-anthropic.py
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212
examples/foundational/22b-natural-conversation-anthropic.py
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#
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# Copyright (c) 2024, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import aiohttp
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import os
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import sys
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMMessagesFrame, TextFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.gated_openai_llm_context import GatedOpenAILLMContextAggregator
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from pipecat.processors.aggregators.openai_llm_context import (
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OpenAILLMContext,
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OpenAILLMContextFrame,
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)
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from pipecat.processors.filters.null_filter import NullFilter
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from pipecat.processors.filters.wake_notifier_filter import WakeNotifierFilter
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from pipecat.processors.user_idle_processor import UserIdleProcessor
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.deepgram import DeepgramSTTService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.sync.event_notifier import EventNotifier
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.frames.frames import Frame
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from runner import configure
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from loguru import logger
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from dotenv import load_dotenv
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, _) = await configure(session)
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transport = DailyTransport(
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room_url,
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None,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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# This is the LLM that will be used to detect if the user has finished a
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# statement. This doesn't really need to be an LLM, we could use NLP
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# libraries for that, but it was easier as an example because we
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# leverage the context aggregators.
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statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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statement_messages = [
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{
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"role": "system",
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"content": "Determine if the user's statement is a complete sentence or question, ending in a natural pause or punctuation. Return 'YES' if it is complete and 'NO' if it seems to leave a thought unfinished.",
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},
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]
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statement_context = OpenAILLMContext(statement_messages)
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statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
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# This is the regular LLM.
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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"role": "system",
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"content": "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.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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# We have instructed the LLM to return 'YES' if it thinks the user
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# completed a sentence. So, if it's 'YES' we will return true in this
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# predicate which will wake up the notifier.
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async def wake_check_filter(frame):
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logger.debug(f"Completeness check frame: {frame}")
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return frame.text == "YES"
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# This is a notifier that we use to synchronize the two LLMs.
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notifier = EventNotifier()
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# This a filter that will wake up the notifier if the given predicate
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# (wake_check_filter) returns true.
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completeness_check = WakeNotifierFilter(
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notifier, types=(TextFrame,), filter=wake_check_filter
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)
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# This processor keeps the last context and will let it through once the
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# notifier is woken up.
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gated_context_aggregator = GatedOpenAILLMContextAggregator(notifier)
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# Notify if the user hasn't said anything.
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async def user_idle_notifier(frame):
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await notifier.notify()
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# Sometimes the LLM will fail detecting if a user has completed a
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# sentence, this will wake up the notifier if that happens.
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user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
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class StatementJudgeContextFilter(FrameProcessor):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, OpenAILLMContextFrame):
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logger.debug(f"Context Frame: {frame}")
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await self.push_frame(frame, direction)
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class GatedTTSOutput(FrameProcessor):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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await self.push_frame(frame, direction)
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# The ParallePipeline input are the user transcripts. We have two
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# contexts. The first one will be used to determine if the user finished
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# a statement and if so the notifier will be woken up. The second
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# context is simply the regular context but it's gated waiting for the
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# notifier to be woken up.
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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ParallelPipeline(
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[
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statement_context_aggregator.user(),
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statement_llm,
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completeness_check,
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NullFilter(),
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],
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[context_aggregator.user(), gated_context_aggregator, llm],
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),
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user_idle,
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tts, # TTS
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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pipeline_x = Pipeline(
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[
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transport.input(),
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stt,
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user_idle,
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context_aggregator.user(),
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ParallelPipeline(
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[
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StatementJudgeContextFilter(),
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statement_llm,
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completeness_check,
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NullFilter(),
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],
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[
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llm,
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tts,
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GatedTTSOutput(),
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],
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),
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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# pipeline,
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pipeline_x,
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PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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report_only_initial_ttfb=True,
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),
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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMMessagesFrame(messages)])
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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