# # Copyright (c) 2024–2025, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import argparse import glob import json import os from datetime import datetime from dotenv import load_dotenv from loguru import logger from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.vad_analyzer import VADParams from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContext, ) from pipecat.services.cartesia.tts import CartesiaTTSService from pipecat.services.deepgram.stt import DeepgramSTTService from pipecat.services.llm_service import FunctionCallParams from pipecat.services.openai.llm import OpenAILLMService from pipecat.transports.base_transport import TransportParams from pipecat.transports.network.small_webrtc import SmallWebRTCTransport from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection load_dotenv(override=True) BASE_FILENAME = "/tmp/pipecat_conversation_" tts = None async def fetch_weather_from_api(params: FunctionCallParams): temperature = 75 if params.arguments["format"] == "fahrenheit" else 24 await params.result_callback( { "conditions": "nice", "temperature": temperature, "format": params.arguments["format"], "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), } ) async def get_saved_conversation_filenames(params: FunctionCallParams): # Construct the full pattern including the BASE_FILENAME full_pattern = f"{BASE_FILENAME}*.json" # Use glob to find all matching files matching_files = glob.glob(full_pattern) logger.debug(f"matching files: {matching_files}") await params.result_callback({"filenames": matching_files}) async def save_conversation(params: FunctionCallParams): timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S") filename = f"{BASE_FILENAME}{timestamp}.json" logger.debug( f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}" ) try: with open(filename, "w") as file: messages = params.context.get_messages_for_persistent_storage() # remove the last message, which is the instruction we just gave to save the conversation messages.pop() json.dump(messages, file, indent=2) await params.result_callback({"success": True}) except Exception as e: await params.result_callback({"success": False, "error": str(e)}) async def load_conversation(params: FunctionCallParams): global tts filename = params.arguments["filename"] logger.debug(f"loading conversation from {filename}") try: with open(filename, "r") as file: params.context.set_messages(json.load(file)) logger.debug( f"loaded conversation from {filename}\n{json.dumps(params.context.messages, indent=4)}" ) await tts.say("Ok, I've loaded that conversation.") except Exception as e: await params.result_callback({"success": False, "error": str(e)}) messages = [ { "role": "system", "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.", }, ] tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA", }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use. Infer this from the users location.", }, }, "required": ["location", "format"], }, }, }, { "type": "function", "function": { "name": "save_conversation", "description": "Save the current conversatione. Use this function to persist the current conversation to external storage.", "parameters": { "type": "object", "properties": {}, "required": [], }, }, }, { "type": "function", "function": { "name": "get_saved_conversation_filenames", "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.", "parameters": { "type": "object", "properties": {}, "required": [], }, }, }, { "type": "function", "function": { "name": "load_conversation", "description": "Load a conversation history. Use this function to load a conversation history into the current session.", "parameters": { "type": "object", "properties": { "filename": { "type": "string", "description": "The filename of the conversation history to load.", } }, "required": ["filename"], }, }, }, ] async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace): logger.info(f"Starting bot") global tts transport = SmallWebRTCTransport( webrtc_connection=webrtc_connection, params=TransportParams( audio_in_enabled=True, audio_out_enabled=True, vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)), ), ) stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady ) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY")) # you can either register a single function for all function calls, or specific functions # llm.register_function(None, fetch_weather_from_api) llm.register_function("get_current_weather", fetch_weather_from_api) llm.register_function("save_conversation", save_conversation) llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames) llm.register_function("load_conversation", load_conversation) context = OpenAILLMContext(messages, tools) context_aggregator = llm.create_context_aggregator(context) pipeline = Pipeline( [ transport.input(), # Transport user input stt, # STT context_aggregator.user(), llm, # LLM tts, transport.output(), # Transport bot output context_aggregator.assistant(), ] ) task = PipelineTask( pipeline, params=PipelineParams( allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True, report_only_initial_ttfb=True, ), ) @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): logger.info(f"Client connected") # Kick off the conversation. await task.queue_frames([context_aggregator.user().get_context_frame()]) @transport.event_handler("on_client_disconnected") async def on_client_disconnected(transport, client): logger.info(f"Client disconnected") @transport.event_handler("on_client_closed") async def on_client_closed(transport, client): logger.info(f"Client closed connection") await task.cancel() runner = PipelineRunner(handle_sigint=False) await runner.run(task) if __name__ == "__main__": from run import main main()