290 lines
10 KiB
Python
290 lines
10 KiB
Python
#
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# Copyright (c) 2024–2025, 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 glob
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import json
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import os
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import sys
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from datetime import datetime
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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.audio.vad.vad_analyzer import VADParams
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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 (
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OpenAILLMContext,
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)
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.google import GoogleLLMService
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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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video_participant_id = None
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BASE_FILENAME = "/tmp/pipecat_conversation_"
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tts = None
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async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
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temperature = 75 if args["format"] == "fahrenheit" else 24
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await result_callback(
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{
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"conditions": "nice",
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"temperature": temperature,
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"format": args["format"],
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"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
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}
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)
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async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
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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 get_saved_conversation_filenames(
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function_name, tool_call_id, args, llm, context, result_callback
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):
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# Construct the full pattern including the BASE_FILENAME
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full_pattern = f"{BASE_FILENAME}*.json"
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# Use glob to find all matching files
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matching_files = glob.glob(full_pattern)
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logger.debug(f"matching files: {matching_files}")
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await result_callback({"filenames": matching_files})
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async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
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timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
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filename = f"{BASE_FILENAME}{timestamp}.json"
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logger.debug(
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f"writing conversation to {filename}\n{json.dumps(context.get_messages_for_logging(), indent=4)}"
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)
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try:
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with open(filename, "w") as file:
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# todo: extract 'system' into the first message in the list
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messages = context.get_messages_for_persistent_storage()
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# remove the last message (the instruction to save the context)
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messages.pop()
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json.dump(messages, file, indent=2)
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await result_callback({"success": True})
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except Exception as e:
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logger.debug(f"error saving conversation: {e}")
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await result_callback({"success": False, "error": str(e)})
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async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
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global tts
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filename = args["filename"]
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logger.debug(f"loading conversation from {filename}")
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try:
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with open(filename, "r") as file:
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context.set_messages(json.load(file))
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await result_callback(
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{
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"success": True,
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"message": "The most recent conversation has been loaded. Awaiting further instructions.",
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}
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)
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except Exception as e:
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await result_callback({"success": False, "error": str(e)})
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# Test message munging ...
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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
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capabilities in a succinct way. Your output will be converted to audio so don't include special
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characters in your answers. Respond to what the user said in a creative and helpful way.
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You have several tools you can use to help you.
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You can respond to questions about the weather using the get_weather tool.
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You can save the current conversation using the save_conversation tool. This tool allows you to save
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the current conversation to external storage. If the user asks you to save the conversation, use this
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save_conversation too.
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You can load a saved conversation using the load_conversation tool. This tool allows you to load a
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conversation from external storage. You can get a list of conversations that have been saved using the
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get_saved_conversation_filenames 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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},
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# {"role": "user", "content": ""},
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# {"role": "assistant", "content": []},
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# {"role": "user", "content": "Tell me"},
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# {"role": "user", "content": "a joke"},
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]
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tools = [
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{
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"function_declarations": [
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{
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"name": "get_current_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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"name": "save_conversation",
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"description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
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"parameters": {
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"type": "object",
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"properties": {
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"user_request_text": {
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"type": "string",
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"description": "The text of the user's request to save the conversation.",
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}
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},
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"required": ["user_request_text"],
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},
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},
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{
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"name": "get_saved_conversation_filenames",
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"description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
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"parameters": None,
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},
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{
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"name": "load_conversation",
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"description": "Load a conversation history. Use this function to load a conversation history into the current session.",
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"parameters": {
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"type": "object",
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"properties": {
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"filename": {
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"type": "string",
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"description": "The filename of the conversation history to load.",
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}
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},
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"required": ["filename"],
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},
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},
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{
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"name": "get_image",
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"description": "Get and image from the camera or 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 to use when running inference on the acquired image.",
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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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]
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async def main():
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global tts
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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(params=VADParams(stop_secs=0.8)),
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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 = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
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# you can either register a single function for all function calls, or specific functions
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# llm.register_function(None, fetch_weather_from_api)
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llm.register_function("get_current_weather", fetch_weather_from_api)
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llm.register_function("save_conversation", save_conversation)
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llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
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llm.register_function("load_conversation", load_conversation)
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llm.register_function("get_image", get_image)
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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(), # Transport user input
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context_aggregator.user(),
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llm, # LLM
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tts,
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context_aggregator.assistant(),
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transport.output(), # Transport bot output
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]
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)
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task = PipelineTask(
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pipeline,
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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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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 task.queue_frames([context_aggregator.user().get_context_frame()])
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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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