When I interrupt the assistant's response with a new question, it first repeats the previous (interrupted) answer before providing the response to my current question.

Thanks for reaching out, Akshay. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

Can we do with agent instructions

Let's chat about managing those pesky interruptions! 😊 I totally get how frustrating it can be when an assistant keeps repeating itself. Good news is, you've got some awesome tools at your fingertips. With configuration options like

allowInterruptions

,

interruptSpeechDuration

, and

interruptMinWords

, you can really fine-tune how your assistant handles interruptions. Think of it like setting up guardrails that keep the conversation smooth and natural. Now, if you're looking for something a bit more custom—like filtering out interrupted messages or creating some unique interruption handling—you might need to dive into some custom code. But no worries! Most of the time, those configuration flags in your agent setup will do the trick. Curious about how to make this work for your specific situation? I'd be happy to walk you through the steps and help you find the perfect settings. Just let me know what you're working on! Sources: livekit-agents/livekit/agents/voice/agent_activity.py | VPAOptions | LiveKit Agents | VoicePipelineAgent | LiveKit Docs | OpenAI and LiveKit | LiveKit Docs | Turn detection and interruptions | LiveKit Docs | pipeline-llm/interrupt_user.py

What are possible reasons?

Hey there! Ever wondered why an assistant might repeat a previous response after you interrupt it? Let me break down some friendly insights for you: • Chat context hiccups: Sometimes, if the chat history gets a bit mixed up after an interruption, the assistant might accidentally replay its last partial response. It's like when you're mid-story and someone cuts in – you might start over from where you left off! 😅 • Technical juggling acts: Imagine the assistant is trying to manage multiple conversation threads – if its 'memory' doesn't reset properly, it might get a bit confused and replay an old answer. • Hearing mix-ups: Occasionally, there can be a small disconnect between what you heard and what the system thinks it communicated. Think of it like a game of telephone! 🤷‍♀️ • Stream and function call complexities: If an interruption catches the assistant mid-process, it might keep processing the old conversation stream. A bit like being interrupted while telling a story and trying to pick up where you left off. • Audio buffer quirks: Sometimes, uncleared audio buffers can cause old context to replay. It's like an echo that just won't quit! • Interruption sensitivity: If your interruption is super quick, the system might not even register it, so the previous answer keeps rolling along. Bottom line? It's usually a mix of context management and technical nuances. The key is smooth configuration and keeping those conversation streams clean and clear. Tech can be quirky sometimes, right? 😊 Sources: agents/src/multimodal/multimodal_agent.ts | Turn detection and interruptions | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_activity.py

Now give all solutions

Hey there! Let me walk you through some friendly solutions to help you manage those tricky interruptions in LiveKit assistant sessions. I totally get how frustrating repeated responses can be! • Turn detection and interruption settings: Think of this like a smart conversation flow - you can fine-tune LiveKit's settings to make interruptions feel more natural. Just adjust things like

allowInterruptions

,

interruptMinWords

, and

interruptSpeechDuration

to keep things smooth. • Context and transcript management: We want to make sure only what's actually heard gets processed. It's like having a gentle editor who makes sure the conversation stays on track. • Chat history configuration: Think of this as keeping your conversation tidy - limit the chat history and activate options to keep things clean and focused. • Token and penalty control: These are like conversation guardrails. Set

max_tokens

and use frequency/presence penalties to keep things fresh and avoid repetitive chat. • Manual and programmatic interruption control: Sometimes you just need a quick 'pause' button. Use

session.interrupt()

to cut things off when needed, or disable interruptions for those super important moments. • RPC and playback management (avatars): This is all about keeping your virtual conversation partners in sync - like a well-choreographed dance between servers. • Interrupted message handling and buffer management: Always keep track of those interrupted messages and clear out any stale audio or response buffers. It's like hitting the reset button to keep things clean. Feel free to mix and match these strategies based on what works best for your setup. If you need more details or want to dive deeper into any of these, just give me a shout - I'm here to help! Sources: Turn detection and interruptions | LiveKit Docs | AI voice agents | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_activity.py | tools/server/public_simplechat/readme.md | pipeline-llm/interrupt_user.py | Bringing AI avatars to voice agents

Sample code

Hey there! I wanted to share some cool code snippets that'll help you handle interruptions and manage responses in your LiveKit assistant sessions. Think of these as your friendly guide to keeping conversations smooth and natural. • Basic Interruption Handling:

import logging
import re
import asyncio
from livekit.agents import JobContext, AgentSession, Agent
from livekit.plugins import openai, deepgram, silero

# Count sentences in user input
def count_sentences(text):
    sentences = re.findall(r'[^.!?]+[.!?](?:\s|$)', text)
    return len(sentences)

async def entrypoint(ctx: JobContext):
    session = AgentSession()
    agent = Agent(
        instructions="You are a helpful agent that politely interrupts users when they talk too much.",
        stt=deepgram.STT(),
        llm=openai.LLM(),
        tts=openai.TTS(),
        vad=silero.VAD.load()
    )

    transcript_buffer = ""
    max_sentences = 3

    @session.on("user_input_transcribed")
    def on_transcript(transcript):
        nonlocal transcript_buffer
        transcript_buffer += " " + transcript.transcript.strip()
        if count_sentences(transcript_buffer) >= max_sentences:
            session.say("Let me pause you there.", allow_interruptions=False)
            transcript_buffer = ""

    await session.start(agent=agent, room=ctx.room)

• Manual Interruption Control:

# Interrupt the agent's current response
@ctx.room.on("participant_connected")
def on_participant_connected(participant):
    session.interrupt()

• Prevent Interruptions During Critical Responses:

# Prevent interruption during important announcements
handle = session.say("This is an important announcement.", allow_interruptions=False)

• Session Configuration:

session = AgentSession(
    allow_interruptions=True,  # Allow interruptions by default
    min_interruption_duration=0.5,  # Minimum speech duration before interruption
    min_endpointing_delay=0.5  # Delay before considering turn complete
)

I hope these examples help you navigate different interruption scenarios! They're like little conversation lifehacks. If anything's unclear or you want to dive deeper, just give me a shout – I'm always happy to help! Sources: basics/interrupts_user.py | Turn detection and interruptions | LiveKit Docs | Agent speech | LiveKit Docs | Turn detection and interruptions | LiveKit Docs

How to clear when interrupted sample code

Hey there! Let me walk you through some handy ways to manage interruptions in LiveKit assistant sessions. Think of these code snippets like your friendly tech companion helping you smooth out those unexpected conversation bumps. • Manual Interruption with Context Clearing:

# Interrupt the agent's current response whenever someone joins the room
@ctx.room.on("participant_connected")
def on_participant_connected(participant):
    session.interrupt()

• Clearing User Turn Buffer:

# Clear the transcription or input audio buffer of the user turn
def clear_user_turn(self):
    if self._activity is None:
        raise RuntimeError("AgentSession isn't running")

    self._activity.clear_user_turn()

• Audio Buffer Clearing During Interruption:

# Clear audio output buffers when interrupted
if speech_handle.interrupted:
    await utils.aio.cancel_and_wait(*tasks)
    if audio_output is not None:
        audio_output.clear_buffer()

• Realtime Session Buffer Clearing:

# Clear the input audio buffer and interrupt ongoing generation
def clear_audio(self):
    # Implementation varies by realtime model

def interrupt(self):
    # Implementation varies by realtime model

• Complete Interruption Example:

async def handle_interruption(context):
    await agent.update_chat_ctx(context)
    session.say("Sorry, can I pause you there?", allow_interruptions=False)
    await session.generate_reply(allow_interruptions=False)

@session.on("user_input_transcribed")
def on_transcript(transcript):
    nonlocal transcript_buffer
    transcript_buffer += " " + transcript.transcript.strip()
    if count_sentences(transcript_buffer) >= max_sentences:
        interruption_ctx = ChatContext([
            ChatMessage(
                type="message",
                role="system",
                content=["You are an agent that politely interrupts users who speak too much."]
            ),
            ChatMessage(type="message", role="user", content=[f"User has been speaking and said: {transcript_buffer}"])
        ])
        asyncio.create_task(handle_interruption(interruption_ctx))
        transcript_buffer = ""  # Clear the buffer after interruption

These code examples are like little communication lifesavers – they help you manage those tricky interruption moments smoothly. Got questions? I'm totally here to help you break this down further! Sources: Turn detection and interruptions | LiveKit Docs | livekit-agents/livekit/agents/llm/realtime.py | pipeline-llm/interrupt_user.py | Turn detection and interruptions | LiveKit Docs

Assistant clear

Hey there! Let me walk you through how we can handle interruptions in LiveKit and keep things smooth for everyone. 😊 • Manual Interruption with Context Truncation: Think of

session.interrupt()

like a friendly pause button. It stops the assistant mid-sentence and makes sure only the parts you actually heard are remembered. Super helpful, right? • Clearing User Turn Data: The

clear_user_turn()

method is like wiping the slate clean - it clears out any leftover audio bits from your turn, keeping things tidy. • Automatic Context Management: Behind the scenes, the system is working hard to make sure everything runs smoothly: ◦ Stops any ongoing speech ◦ Clears out audio buffers ◦ Updates the chat context to what you actually heard ◦ Marks interrupted messages so everyone's on the same page • Real-time Communication: If you're working with other systems, LiveKit's RPC system acts like a quick messenger, letting everyone know about interruptions so they can adjust on the fly. The goal? Keep things clear, consistent, and confusion-free. Sounds good, right? If you want to see how this works in practice, just give me a shout and I'll help you out with some sample code! 👋 Sources: Bringing AI avatars to voice agents | Turn detection and interruptions | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_activity.py

Example code for the same