diff --git a/examples/phone-chatbot/bot_daily_gemini.py b/examples/phone-chatbot/bot_daily_gemini.py index 3fe6e807d..e03d200cb 100644 --- a/examples/phone-chatbot/bot_daily_gemini.py +++ b/examples/phone-chatbot/bot_daily_gemini.py @@ -7,17 +7,18 @@ import argparse import asyncio import os import sys +from pprint import pprint from typing import Optional from dotenv import load_dotenv from loguru import logger from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.frames.frames import EndTaskFrame, LLMMessagesUpdateFrame +from pipecat.frames.frames import EndTaskFrame, LLMMessagesFrame, LLMMessagesUpdateFrame from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask -from pipecat.processors.frame_processor import FrameDirection +from pipecat.processors.frame_processor import FrameDirection, FrameProcessor from pipecat.services.ai_services import LLMService from pipecat.services.elevenlabs import ElevenLabsTTSService from pipecat.services.google import GoogleLLMContext, GoogleLLMService @@ -33,6 +34,55 @@ daily_api_key = os.getenv("DAILY_API_KEY", "") daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1") +class ContextSwitcher: + def __init__(self, llm, context_aggregator): + self._llm = llm + self._context_aggregator = context_aggregator + + async def switch_context(self, system_instruction): + # Create messages with updated system instruction + messages = [ + { + "role": "system", + "content": system_instruction, + } + ] + + # Update context with new messages + # await self._llm.push_frame(LLMMessagesUpdateFrame(messages)) + self._context_aggregator.set_messages(messages) + context_frame = self._context_aggregator.get_context_frame() + # Trigger LLM response by pushing a context frame + pprint(vars(context_frame.context)) + await self._llm.queue_frame(context_frame) + + +## Call result callback + + +class FunctionHandlers: + def __init__(self, context_switcher): + self.context_switcher = context_switcher + + async def respond_with_apple( + self, function_name, tool_call_id, args, llm, context, result_callback + ): + await self.context_switcher.switch_context(system_instruction="Always respond with Apple") + # await result_callback("") + + async def respond_with_banana( + self, function_name, tool_call_id, args, llm, context, result_callback + ): + await self.context_switcher.switch_context(system_instruction="Always respond with Banana") + # await result_callback("") + + async def respond_with_oranges( + self, function_name, tool_call_id, args, llm, context, result_callback + ): + await self.context_switcher.switch_context(system_instruction="Always respond with Oranges") + # await result_callback("") + + async def terminate_call( function_name, tool_call_id, args, llm: LLMService, context, result_callback ): @@ -40,54 +90,6 @@ async def terminate_call( await llm.queue_frame(EndTaskFrame(), FrameDirection.UPSTREAM) -async def respond_with_apple( - function_name, tool_call_id, args, llm: LLMService, context: GoogleLLMContext, result_callback -): - messages = [ - { - "role": "system", - "content": "Always respond with Apple", - } - ] - print("respond_with_apple") - # context.system_message = "Always respond with Apple" - print(f"context before: {context.tools}") - await llm.push_frame(LLMMessagesUpdateFrame(messages)) - print(f"context after: {context.tools}") - - -async def respond_with_banana( - function_name, tool_call_id, args, llm: LLMService, context: GoogleLLMContext, result_callback -): - messages = [ - { - "role": "system", - "content": "Always respond with Banana", - } - ] - print("respond_with_banana") - # context.system_message = "Always respond with Banana" - print(f"context before: {context.tools}") - await llm.push_frame(LLMMessagesUpdateFrame(messages)) - print(f"context after: {context.tools}") - - -async def respond_with_orange( - function_name, tool_call_id, args, llm: LLMService, context: GoogleLLMContext, result_callback -): - messages = [ - { - "role": "system", - "content": "Always respond with Orange", - } - ] - print("respond_with_orange") - # context.system_message = "Always respond with Orange" - print(f"context before: {context.tools}") - await llm.push_frame(LLMMessagesUpdateFrame(messages)) - print(f"context after: {context.tools}") - - async def main( room_url: str, token: str, @@ -141,57 +143,6 @@ async def main( } ] - system_instruction2 = """You are Chatbot, a friendly, helpful robot. - -IMPORTANT: You MUST use the terminate_call function to end the call in these situations: -1. After leaving a voicemail message -2. When the conversation with a human is finished - -VOICEMAIL DETECTION: -- Listen carefully for these exact phrases at the start of the call: - * "Please leave a message after the beep" - * "No one is available to take your call" - * "Record your message after the tone" - * "You have reached voicemail for..." - -IF VOICEMAIL DETECTED: -1. Wait for any beep sound if mentioned -2. Say EXACTLY: "Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you." -3. IMMEDIATELY call the terminate_call function after your message -4. Do not say anything else - -IF HUMAN DETECTED: -1. Say: "Oh, hello! I'm a friendly chatbot. Is there anything I can help you with?" -2. Keep responses short and helpful -3. When conversation ends, say: "Okay, thank you! Have a great day!" -4. IMMEDIATELY call the terminate_call function - -NEVER say these phrases yourself: -- "Please leave a message after the beep" -- "No one is available to take your call" -- "Record your message after the tone" -- "You have reached voicemail for..." -""" - - system_instruction3 = """ - You are Chatbot. Your MAIN GOAL is to call the terminate_call function at the end. - -After each response, YOU MUST call the terminate_call function. This is REQUIRED. - -If someone says "Please leave a message after the beep": -Say: "Hello, this is a message for Pipecat example user. This is Chatbot. Please call back on 123-456-7891. Thank you." - -If someone says anything else: -Say: "Hello, I'm Chatbot. Nice to meet you." - -IMPORTANT: YOU MUST CALL the terminate_call function after you respond. -terminate_call is the ONLY way to end the call properly. - """ - - system_instruction1 = """You are Chatbot. Follow these exact steps in order: -1. Say "Hi, I'm Chatbot! Here's a joke: Why don't scientists trust atoms? Because they make up everything!" -2. IMMEDIATELY after telling the joke, call the function terminate_call""" - system_instruction = """Always respond with the word Apple""" llm = GoogleLLMService( @@ -201,14 +152,15 @@ terminate_call is the ONLY way to end the call properly. tools=tools, ) - # llm.register_function("terminate_call", terminate_call) - llm.register_function("respond_with_apple", respond_with_apple) - llm.register_function("respond_with_banana", respond_with_banana) - llm.register_function("respond_with_orange", respond_with_orange) - context = GoogleLLMContext() context_aggregator = llm.create_context_aggregator(context) + context_switcher = ContextSwitcher(llm, context_aggregator.user()) + handlers = FunctionHandlers(context_switcher) + + llm.register_function("respond_with_apple", handlers.respond_with_apple) + llm.register_function("respond_with_banana", handlers.respond_with_banana) + llm.register_function("respond_with_orange", handlers.respond_with_oranges) pipeline = Pipeline( [