Add Word Wrangler demos
This commit is contained in:
Mark Backman
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examples/word-wrangler-gemini-live/server/bot.py
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examples/word-wrangler-gemini-live/server/bot.py
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#
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# Copyright (c) 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 os
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import sys
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from typing import Any, Dict
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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 pipecatcloud.agent import DailySessionArguments
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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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 OpenAILLMContext
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from pipecat.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
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from pipecat.processors.frameworks.rtvi import (
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RTVIConfig,
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RTVIObserver,
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RTVIProcessor,
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)
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from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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load_dotenv(override=True)
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# Check if we're in local development mode
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LOCAL_RUN = os.getenv("LOCAL_RUN")
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logger.add(sys.stderr, level="DEBUG")
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# Define conversation modes with their respective prompt templates
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game_prompt = """You are the AI host and player for a game of Word Wrangler.
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GAME RULES:
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1. The user will be given a word or phrase that they must describe to you
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2. The user CANNOT say any part of the word/phrase directly
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3. You must try to guess the word/phrase based on the user's description
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4. Once you guess correctly, the user will move on to their next word
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5. The user is trying to get through as many words as possible in 60 seconds
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6. The external application will handle timing and keeping score
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YOUR ROLE:
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1. Start with this exact brief introduction: "Welcome to Word Wrangler! I'll try to guess the words you describe. Remember, don't say any part of the word itself. Ready? Let's go!"
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2. Listen carefully to the user's descriptions
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3. Make intelligent guesses based on what they say
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4. When you think you know the answer, state it clearly: "Is it [your guess]?"
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5. If you're struggling, ask for more specific clues
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6. Keep the game moving quickly - make guesses promptly
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7. Be enthusiastic and encouraging
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IMPORTANT:
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- Keep all responses brief - the game is timed!
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- Make multiple guesses if needed
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- Use your common knowledge to make educated guesses
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- If the user indicates you got it right, just say "Got it!" and prepare for the next word
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- If you've made several wrong guesses, simply ask for "Another clue please?"
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Start with the exact introduction specified above, then wait for the user to begin describing their first word."""
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# Define personality presets
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PERSONALITY_PRESETS = {
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"friendly": "You have a warm, approachable personality. You use conversational language, occasional humor, and express enthusiasm for the topic. Make the user feel comfortable and engaged.",
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"professional": "You have a formal, precise personality. You communicate clearly and directly with a focus on accuracy and relevance. Your tone is respectful and business-like.",
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"enthusiastic": "You have an energetic, passionate personality. You express excitement about the topic and use dynamic language. You're encouraging and positive throughout the conversation.",
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"thoughtful": "You have a reflective, philosophical personality. You speak carefully, considering multiple angles of each point. You ask thought-provoking questions and acknowledge nuance.",
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"witty": "You have a clever, humorous personality. While remaining informative, you inject appropriate wit and playful language. Your goal is to be engaging and entertaining while still being helpful.",
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}
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async def main(transport: DailyTransport, config: Dict[str, Any]):
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# Use the provided session logger if available, otherwise use the default logger
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logger.debug("Configuration: {}", config)
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# Extract configuration parameters with defaults
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personality = config.get("personality", "witty")
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personality_prompt = PERSONALITY_PRESETS.get(personality, PERSONALITY_PRESETS["friendly"])
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system_instruction = f"""{game_prompt}
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{personality_prompt}
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Important guidelines:
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1. Your responses will be converted to speech, so keep them concise and conversational.
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2. Don't use special characters or formatting that wouldn't be natural in speech.
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3. Encourage the user to elaborate when appropriate."""
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intro_message = """Start with this exact brief introduction: "Welcome to Word Wrangler! I'll try to guess the words you describe. Remember, don't say any part of the word itself. Ready? Let's go!"""
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# Create the STT mute filter if we have strategies to apply
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stt_mute_filter = STTMuteFilter(
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config=STTMuteConfig(strategies={STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE})
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)
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llm = GeminiMultimodalLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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transcribe_user_audio=True,
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system_instruction=system_instruction,
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)
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# Set up the initial context for the conversation
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messages = [
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{
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"role": "user",
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"content": intro_message,
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},
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]
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# This sets up the LLM context by providing messages and tools
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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# RTVI events for Pipecat client UI
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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pipeline = Pipeline(
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[
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transport.input(),
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rtvi,
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stt_mute_filter,
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context_aggregator.user(),
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llm,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=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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),
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observers=[RTVIObserver(rtvi)],
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)
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@rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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logger.debug("Client ready event received")
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await rtvi.set_bot_ready()
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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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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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logger.info("First participant joined: {}", participant["id"])
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# Capture the participant's transcription
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await transport.capture_participant_transcription(participant["id"])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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logger.info("Participant left: {}", participant)
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await task.cancel()
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runner = PipelineRunner(handle_sigint=False, force_gc=True)
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await runner.run(task)
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async def bot(args: DailySessionArguments):
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"""Main bot entry point compatible with the FastAPI route handler.
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Args:
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room_url: The Daily room URL
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token: The Daily room token
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body: The configuration object from the request body
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session_id: The session ID for logging
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"""
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from pipecat.audio.filters.krisp_filter import KrispFilter
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logger.info(f"Bot process initialized {args.room_url} {args.token}")
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transport = DailyTransport(
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args.room_url,
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args.token,
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"Word Wrangler Bot",
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DailyParams(
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audio_in_filter=None if LOCAL_RUN else KrispFilter(),
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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try:
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await main(transport, args.body)
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logger.info("Bot process completed")
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except Exception as e:
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logger.exception(f"Error in bot process: {str(e)}")
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raise
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# Local development
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async def local_daily():
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"""Daily transport for local development."""
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from runner import configure
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try:
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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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bot_name="Bot",
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params=DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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vad_audio_passthrough=True,
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),
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)
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test_config = {
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"personality": "witty",
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}
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await main(transport, test_config)
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except Exception as e:
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logger.exception(f"Error in local development mode: {e}")
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# Local development entry point
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if LOCAL_RUN and __name__ == "__main__":
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try:
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asyncio.run(local_daily())
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except Exception as e:
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logger.exception(f"Failed to run in local mode: {e}")
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