diff --git a/examples/foundational/20a-persistent-context-openai-responses.py b/examples/foundational/20a-persistent-context-openai-responses.py new file mode 100644 index 000000000..730e90197 --- /dev/null +++ b/examples/foundational/20a-persistent-context-openai-responses.py @@ -0,0 +1,249 @@ +# +# Copyright (c) 2024-2026, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import glob +import json +import os +from datetime import datetime + +from dotenv import load_dotenv +from loguru import logger + +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.processors.aggregators.llm_context import LLMContext +from pipecat.processors.aggregators.llm_response_universal import ( + LLMContextAggregatorPair, + LLMUserAggregatorParams, +) +from pipecat.runner.types import RunnerArguments +from pipecat.runner.utils import create_transport +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.responses.llm import OpenAIResponsesLLMService +from pipecat.transports.base_transport import BaseTransport, TransportParams +from pipecat.transports.daily.transport import DailyParams +from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams + +load_dotenv(override=True) + + +BASE_FILENAME = "/tmp/pipecat_conversation_" + + +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.get_messages(), indent=4)}" + ) + try: + with open(filename, "w") as file: + messages = params.context.get_messages() + # 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.get_messages(), indent=4)}" + ) + await params.llm.queue_frame(TTSSpeakFrame("Ok, I've loaded that conversation.")) + except Exception as e: + await params.result_callback({"success": False, "error": str(e)}) + + +system_instruction = "You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way." + +weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather", + 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"], +) + +save_conversation_function = FunctionSchema( + name="save_conversation", + description="Save the current conversatione. Use this function to persist the current conversation to external storage.", + properties={}, + required=[], +) + +get_filenames_function = FunctionSchema( + 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.", + properties={}, + required=[], +) + +load_conversation_function = FunctionSchema( + name="load_conversation", + description="Load a conversation history. Use this function to load a conversation history into the current session.", + properties={ + "filename": { + "type": "string", + "description": "The filename of the conversation history to load.", + } + }, + required=["filename"], +) + +tools = ToolsSchema( + standard_tools=[ + weather_function, + save_conversation_function, + get_filenames_function, + load_conversation_function, + ] +) + + +# We use lambdas to defer transport parameter creation until the transport +# type is selected at runtime. +transport_params = { + "daily": lambda: DailyParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), + "twilio": lambda: FastAPIWebsocketParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), + "webrtc": lambda: TransportParams( + audio_in_enabled=True, + audio_out_enabled=True, + ), +} + + +async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): + logger.info(f"Starting bot") + + stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + settings=CartesiaTTSService.Settings( + voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ), + ) + + llm = OpenAIResponsesLLMService( + api_key=os.getenv("OPENAI_API_KEY"), + settings=OpenAIResponsesLLMService.Settings( + system_instruction=system_instruction, + ), + ) + + # 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 = LLMContext(tools=tools) + user_aggregator, assistant_aggregator = LLMContextAggregatorPair( + context, + user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()), + ) + + pipeline = Pipeline( + [ + transport.input(), # Transport user input + stt, # STT + user_aggregator, + llm, # LLM + tts, + transport.output(), # Transport bot output + assistant_aggregator, + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_metrics=True, + enable_usage_metrics=True, + ), + idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, + ) + + @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([LLMRunFrame()]) + + @transport.event_handler("on_client_disconnected") + async def on_client_disconnected(transport, client): + logger.info(f"Client disconnected") + await task.cancel() + + runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) + + await runner.run(task) + + +async def bot(runner_args: RunnerArguments): + """Main bot entry point compatible with Pipecat Cloud.""" + transport = await create_transport(runner_args, transport_params) + await run_bot(transport, runner_args) + + +if __name__ == "__main__": + from pipecat.runner.run import main + + main()