126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
#
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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 os
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import sys
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from typing import List
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import Frame, LLMMessagesFrame
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from pipecat.pipeline.pipeline import Pipeline
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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.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection
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from pipecat.services.anthropic import AnthropicLLMContext, AnthropicLLMService
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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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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class TestAnthropicLLMService(AnthropicLLMService):
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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if isinstance(frame, LLMMessagesFrame):
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logger.info("Original OpenAI format messages:")
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logger.info(frame.messages)
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# Convert to Anthropic format
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context = AnthropicLLMContext.from_messages(frame.messages)
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logger.info("Converted to Anthropic format:")
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logger.info(context.messages)
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# Convert back to OpenAI format
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openai_messages = []
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for msg in context.messages:
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converted = context.to_standard_messages(msg)
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openai_messages.extend(converted)
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logger.info("Converted back to OpenAI format:")
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logger.info(openai_messages)
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await super().process_frame(frame, direction)
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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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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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llm = TestAnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-opus-20240229"
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)
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# Test messages including various formats
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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, helpful, and brief way. Say hello.",
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},
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{
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"role": "assistant",
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"content": [
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{"type": "text", "text": "Hello! How can I help you today?"},
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{"type": "text", "text": "I'm ready to assist."},
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],
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},
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{"role": "user", "content": "Hi there!"},
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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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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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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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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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