168 lines
5.6 KiB
Python
168 lines
5.6 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.audio.vad.silero import SileroVADAnalyzer
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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 PipelineTask
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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from openai.types.chat import ChatCompletionToolParam
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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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video_participant_id = None
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async def get_weather(function_name, tool_call_id, arguments, llm, 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 get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
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logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}")
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question = arguments["question"]
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await llm.request_image_frame(user_id=video_participant_id, text_content=question)
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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 = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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llm.register_function("get_weather", get_weather)
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llm.register_function("get_image", get_image)
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tools = [
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "get_weather",
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"description": "Get the current weather",
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"parameters": {
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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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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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},
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},
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),
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ChatCompletionToolParam(
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type="function",
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function={
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"name": "get_image",
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"description": "Get an image from the video stream.",
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"parameters": {
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"type": "object",
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"properties": {
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"question": {
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"type": "string",
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"description": "The question to ask the AI to generate an image of",
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},
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},
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"required": ["question"],
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},
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},
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),
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]
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system_prompt = """\
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You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
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Your response will be turned into speech so use only simple words and punctuation.
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You have access to two tools: get_weather and get_image.
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You can respond to questions about the weather using the get_weather tool.
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You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
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indicate you should use the get_image tool are:
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- What do you see?
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- What's in the video?
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- Can you describe the video?
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- Tell me about what you see.
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- Tell me something interesting about what you see.
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- What's happening in the video?
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"""
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messages = [
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{"role": "system", "content": system_prompt},
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]
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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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[
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transport.input(),
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context_aggregator.user(),
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llm,
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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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task = PipelineTask(pipeline)
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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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global video_participant_id
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video_participant_id = participant["id"]
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await transport.capture_participant_transcription(participant["id"])
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await transport.capture_participant_video(video_participant_id, framerate=0)
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# Kick off the conversation.
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await tts.say("Hi! Ask me about the weather in San Francisco.")
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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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