29ca1b7855
* processors(rtvi): rtvi 0.1 message protocol * added a single function call handler * wip - function calling * fixup * fixup * fixup * processors(rtvi): no need for configure_on_start() * processors(rtvi): add new option values if they haven't been set yet * Add the model name to the LLM usage metrics * wip - anthropic tool calling * still wip - anthropic tool use and vision * anthropic tools and vision working * anthropic tool calling and vision * Cartesia error handling * Anthropic tool use core Pipecat pieces refactored as per plan * aleix has good ideas * Usage metrics for Anthropic LLMs * fix function call result state not getting cleared bug * Pass **kwargs through from AnthropicLLMService constructor * about to tinker with anthropic * added openai function calling * openai function calling * fixup --------- Co-authored-by: Aleix Conchillo Flaqué <aleix@daily.co> Co-authored-by: Chad Bailey <chadbailey@gmail.com> Co-authored-by: mattie ruth backman <mattieruth@gmail.com> Co-authored-by: chadbailey59 <chadbailey59@users.noreply.github.com>
121 lines
4.1 KiB
Python
121 lines
4.1 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 aiohttp
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import os
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import sys
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from pipecat.frames.frames import 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.services.cartesia import CartesiaTTSService
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from pipecat.services.anthropic import AnthropicLLMService, AnthropicUserContextAggregator, AnthropicAssistantContextAggregator
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from pipecat.vad.silero import SileroVADAnalyzer
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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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 get_weather(function_name, tool_call_id, arguments, context, result_callback):
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location = arguments["location"]
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await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
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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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sample_rate=16000,
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)
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llm = AnthropicLLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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model="claude-3-5-sonnet-20240620"
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)
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llm.register_function("get_weather", get_weather)
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tools = [
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{
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"name": "get_weather",
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"description": "Get the current weather in a given location",
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"input_schema": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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}
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},
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"required": ["location"],
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},
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}
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]
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# todo: test with very short initial user message
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messages = [{"role": "system",
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"content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
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{"role": "user",
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"content": " Start the conversation by introducing yourself."}]
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context = OpenAILLMContext(messages, tools)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline([
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transport.input(), # Transport user input
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context_aggregator.user(), # User speech to text
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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 and tool context
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])
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=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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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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