Is there a way to increase timeout for generate_reply function? or disable timeout? or just plain retry after timeout to try to generate fresh response after cancelling the old one?

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

ok 5.0 is hardcoded. what solutions do i need to have to add retry logic? i will need to cancel current response generation and try for a new one.

Here’s a friendly guide to adding retry logic to your

generate_reply

function, including how to cancel any ongoing response generation before starting fresh: 1. Keep Track of Pending Generations: Use a variable like

self._pending_generation_fut

to remember the current generation future. 2. Cancel Any Ongoing Generation: Before kicking off a new generation, check if one’s already running. If it is, gently cancel it using

cancel()

. 3. Create a New Future: Start a new future for your new generation and save it in your tracking variable. 4. Set a Timeout: To avoid waiting forever, add a timeout with

asyncio.get_event_loop().call_later()

. This will cancel the generation if it takes too long, and you can cancel the timeout once the generation finishes. Here’s a simple example to illustrate:

def generate_reply(self, *, instructions: NotGivenOr[str] = NOT_GIVEN) -> asyncio.Future[llm.GenerationCreatedEvent]:
    if self._pending_generation_fut and not self._pending_generation_fut.done():
        self._pending_generation_fut.cancel("Superseded by new generate_reply call")

    fut = asyncio.Future[llm.GenerationCreatedEvent]()
    self._pending_generation_fut = fut

    # Set up timeout
    def _on_timeout() -> None:
        if not fut.done():
            fut.set_exception(llm.RealtimeError("generate_reply timed out."))
            if self._pending_generation_fut is fut:
                self._pending_generation_fut = None

    timeout_handle = asyncio.get_event_loop().call_later(5.0, _on_timeout)
    fut.add_done_callback(lambda _: timeout_handle.cancel())

    return fut

This way, you’re making sure any current generation gets canceled before starting a new one, and you’ve got a timeout to keep things moving smoothly. If you want to chat more about it or need a hand, just let me know—I’m here to help! Sources: livekit-plugins/livekit-plugins-google/livekit/plugins/google/beta/realtime...

this means i need to override the AgentSession class. its being used internally everywhere by livekit library, so thats not feasible. other classes using it like AgentActivity are internal and should be untouched.

If you want to add retry logic for

generate_reply

without having to override the

AgentSession

class, a great way to do this is by using the

FallbackAdapter

from LiveKit. It’s designed to handle automatic failover and retries across multiple LLM providers seamlessly. Here’s a simple way to get it going:

from livekit.agents import AgentSession
from livekit.agents.llm import FallbackAdapter
from livekit.plugins.openai import OpenAI
from livekit.plugins.groq import Groq

# Initialize multiple LLM instances
llm_instances = [
    OpenAI(model="gpt-4"),
    Groq(model="mixtral-8x7b-32768")
]

# Create FallbackAdapter with retry configuration
llm_adapter = FallbackAdapter(
    llm_instances,
    max_retry_per_llm=3,  # Number of retries per LLM instance
    retry_interval=1.0,  # Seconds between retries
    retry_on_chunk_sent=True  # Retry even if some chunks were already sent
)

# Use the adapter in your AgentSession
session = AgentSession(
    llm=llm_adapter,
    # ... other configurations
)

Here’s why this is handy: • No Need to Override: You don’t have to mess with internal classes like

AgentSession

or

AgentActivity

. • Automatic Retries: It’ll automatically retry failed calls with settings you control, like how many times and how often. • Failover Built-In: If one LLM provider isn’t working, it smoothly switches to a backup. If you want, I’m here to help you get this set up or answer any questions you might have!

does the fallback adapter keep context between LLMs? how does it?

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!