could i use llm_node hook to prevent some inputs from getting to the TTS and replace it with another input

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

can you provide me with a simple example of how to stop the llm response from getting to the tts and replace with another predefined text, and do i need to use tts_node in this example?

Hey there! If you want to stop the LLM response from going to the TTS and instead use some predefined text, you can easily override the

llm_node

in your agent. You actually don’t need the

tts_node

here since we’re making the change before the TTS step. Here’s a straightforward example to help you out:

from livekit.agents import Agent
from livekit.plugins import openai, deepgram, silero

class MyAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions="You are a helpful assistant.",
            stt=deepgram.STT(),
            llm=openai.LLM(),
            tts=openai.TTS(),
            vad=silero.VAD.load()
        )

    async def llm_node(self, chat_ctx, tools, model_settings=None):
        async def process_stream():
            async with self.llm.chat(chat_ctx=chat_ctx, tools=tools, tool_choice=None) as stream:
                async for chunk in stream:
                    if chunk is None:
                        continue

                    # Grab the content from the chunk
                    content = getattr(chunk.delta, 'content', None) if hasattr(chunk, 'delta') else str(chunk)
                    if content is None:
                        yield chunk
                        continue

                    # Swap out the original text with your predefined message
                    processed_content = "This is a predefined response."

                    if processed_content != content:
                        if hasattr(chunk, 'delta') and hasattr(chunk.delta, 'content'):
                            chunk.delta.content = processed_content
                        else:
                            chunk = processed_content

                    yield chunk

        return process_stream()

    async def on_enter(self):
        await self.session.generate_reply()

So, what’s happening here? • The

llm_node

catches the LLM’s output. • The

process_stream

function swaps the original content with your predefined response. • Then, this updated text gets sent to the TTS engine. This way, you’re sure the original LLM response gets replaced before it ever reaches the TTS. Hope this helps!