diff --git a/examples/phone-chatbot/bot_daily_gemini.py b/examples/phone-chatbot/bot_daily_gemini.py index feb0f1d7a..b7c6175af 100644 --- a/examples/phone-chatbot/bot_daily_gemini.py +++ b/examples/phone-chatbot/bot_daily_gemini.py @@ -20,6 +20,7 @@ from pipecat.frames.frames import ( EndTaskFrame, Frame, InputAudioRawFrame, + StopTaskFrame, SystemFrame, TranscriptionFrame, UserStartedSpeakingFrame, @@ -44,6 +45,8 @@ logger.add(sys.stderr, level="DEBUG") daily_api_key = os.getenv("DAILY_API_KEY", "") daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1") +system_message = None + class UserAudioCollector(FrameProcessor): """This FrameProcessor collects audio frames in a buffer, then adds them to the @@ -120,21 +123,24 @@ class FunctionHandlers: self, function_name, tool_call_id, args, llm, context, result_callback ): """Function the bot can call to leave a voicemail message.""" - message = """You are Chatbot leaving a voicemail message. Say EXACTLY this message and nothing else: + print(f"!!! Got a voicemail response, llm is: {llm}") + system_message = """You are Chatbot leaving a voicemail message. Say EXACTLY this message and nothing else: "Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you." After saying this message, call the terminate_call function.""" - - await self.context_switcher.switch_context(system_instruction=message) - - await result_callback("Leaving a voicemail message") + print("!!! about to push stop task frame from voicemail") + await llm.queue_frame(StopTaskFrame(), FrameDirection.UPSTREAM) + print("!!! pushed stop task frame from voicemail") + await result_callback("Goodbye") async def human_conversation( self, function_name, tool_call_id, args, llm, context, result_callback ): """Function the bot can when it detects it's talking to a human.""" - message = """You are Chatbot talking to a human. Be friendly and helpful. + print(f"!!! Got a human response, llm is: {llm}") + + system_message = """You are Chatbot talking to a human. Be friendly and helpful. Start with: "Hello! I'm a friendly chatbot. How can I help you today?" @@ -147,17 +153,16 @@ class FunctionHandlers: - "Thank you, that's all I needed" THEN say: "Thank you for chatting. Goodbye!" and call the terminate_call function.""" - - await self.context_switcher.switch_context(system_instruction=message) - - await result_callback("Talking to the customer") + print("!!! about to push stop task frame from human") + await llm.queue_frame(StopTaskFrame(), FrameDirection.UPSTREAM) + print("!!! pushed stop task frame from human") + await result_callback("Goodbye") async def terminate_call( function_name, tool_call_id, args, llm: LLMService, context, result_callback ): """Function the bot can call to terminate the call upon completion of the call.""" - await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM) @@ -239,38 +244,87 @@ If it sounds like a human (saying hello, asking questions, etc.), call the funct DO NOT say anything until you've determined if this is a voicemail or human.""" - llm = GoogleLLMService( + greeting_llm = GoogleLLMService( model="models/gemini-2.0-flash-lite-preview-02-05", api_key=os.getenv("GOOGLE_API_KEY"), system_instruction=system_instruction, tools=tools, ) - context = GoogleLLMContext() - context_aggregator = llm.create_context_aggregator(context) - audio_collector = UserAudioCollector(context, context_aggregator.user()) + greeting_context = GoogleLLMContext() + greeting_context_aggregator = greeting_llm.create_context_aggregator(greeting_context) + greeting_audio_collector = UserAudioCollector( + greeting_context, greeting_context_aggregator.user() + ) - context_switcher = ContextSwitcher(llm, context_aggregator.user()) + context_switcher = ContextSwitcher(greeting_llm, greeting_context_aggregator.user()) handlers = FunctionHandlers(context_switcher) - llm.register_function("switch_to_voicemail_response", handlers.voicemail_response) - llm.register_function("switch_to_human_conversation", handlers.human_conversation) - llm.register_function("terminate_call", terminate_call) + greeting_llm.register_function("switch_to_voicemail_response", handlers.voicemail_response) + greeting_llm.register_function("switch_to_human_conversation", handlers.human_conversation) + greeting_llm.register_function("terminate_call", terminate_call) - pipeline = Pipeline( + greeting_pipeline = Pipeline( [ transport.input(), # Transport user input - audio_collector, # Collect audio frames - context_aggregator.user(), # User responses - llm, # LLM + greeting_audio_collector, # Collect audio frames + greeting_context_aggregator.user(), # User responses + greeting_llm, # LLM tts, # TTS transport.output(), # Transport bot output - context_aggregator.assistant(), # Assistant spoken responses + greeting_context_aggregator.assistant(), # Assistant spoken responses + ] + ) + greeting_pipeline_task = PipelineTask( + greeting_pipeline, + PipelineParams(allow_interruptions=True), + ) + runner = PipelineRunner() + + print("!!! starting greeting") + await runner.run(greeting_pipeline_task) + print("!!! Done with greeting") + + # Create conversation pipeline with new system message + conversation_llm = GoogleLLMService( + model="models/gemini-2.0-flash-lite-preview-02-05", + api_key=os.getenv("GOOGLE_API_KEY"), + system_instruction=system_message if system_message else "You are a helpful chatbot.", + tools=[ + { + "function_declarations": [ + { + "name": "terminate_call", + "description": "Call this function to terminate the call.", + } + ] + } + ], + ) + conversation_llm.register_function("terminate_call", terminate_call) + + conversation_context = GoogleLLMContext() + conversation_context_aggregator = conversation_llm.create_context_aggregator( + conversation_context + ) + conversation_audio_collector = UserAudioCollector( + conversation_context, conversation_context_aggregator.user() + ) + + conversation_pipeline = Pipeline( + [ + transport.input(), # Transport user input + conversation_audio_collector, # Collect audio frames + conversation_context_aggregator.user(), # User responses + conversation_llm, # LLM + tts, # TTS + transport.output(), # Transport bot output + conversation_context_aggregator.assistant(), # Assistant spoken responses ] ) - task = PipelineTask( - pipeline, + conversation_task = PipelineTask( + conversation_pipeline, PipelineParams(allow_interruptions=True), ) @@ -319,11 +373,11 @@ DO NOT say anything until you've determined if this is a voicemail or human.""" @transport.event_handler("on_participant_left") async def on_participant_left(transport, participant, reason): - await task.cancel() + await conversation_task.cancel() - runner = PipelineRunner() - - await runner.run(task) + print("!!! Starting conversation") + await runner.run(conversation_task) + print("!!! Done with conversation") if __name__ == "__main__":