@millions-hair-24956 proven ways to make vision faster.

It might be possible that optimizing the location where your agent is running could help improve the speed of vision processing. According to LiveKit documentation, running your agent closer to where the model servers are located, such as in the US, can reduce latency. Additionally, ensuring that your code efficiently handles video frames and does not retain unnecessary data in memory could also help. If you are experiencing memory issues, it could be beneficial to ensure that video streams are properly closed and cleaned up, as mentioned in a Slack discussion. Would you like more detailed steps on optimizing your setup for faster vision processing? Sources: - LiveKit documentation - Slack discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 When implementing a vision-enabled agent in LiveKit, I'm trying to capture video frames from a participant's screen share to analyze with an LLM. Currently we're using: 1. A function to get the video track:

python


for participant_id, participant in room.remote_participants.items():
    for track_id, track_publication in participant.track_publications.items():
        if track_publication.track and isinstance(track_publication.track, rtc.RemoteVideoTrack):
            video_track = track_publication.track
            return video_track

1. Another function to get a frame:

python


video_stream = rtc.VideoStream(video_track)
async for event in video_stream:
    return event.frame  # Return the actual frame

But the agent doesn't seem to be getting accurate frame data. Is there a better or more reliable way to capture frames from a video track in LiveKit? Are there any required settings or configuration to make vision work properly?

To capture video frames reliably from a video track in LiveKit, ensure that you are subscribing to the video track correctly and handling the video stream efficiently. According to the LiveKit documentation, you should listen for the

track_subscribed

event to ensure the track is ready before processing frames. Additionally, make sure to close the video stream properly to avoid memory leaks. Here's a refined approach: 1. Subscribe to the Track: Ensure you are subscribed to the video track using the

track_subscribed

event. 2. Process Video Stream: Use an

async for

loop to process frames from the

VideoStream

.

@ctx.room.on("track_subscribed")
def on_track_subscribed(track: rtc.Track, publication: rtc.TrackPublication, participant: rtc.RemoteParticipant):
    if track.kind == rtc.TrackKind.KIND_VIDEO:
        asyncio.create_task(process_video(track))

async def process_video(track: rtc.Track):
    video_stream = rtc.VideoStream(track)
    async for event in video_stream:
        frame = event.frame
        # Process the frame here
        # Ensure to close the stream when done
    await video_stream.aclose()

Ensure that your agent is configured to automatically subscribe to tracks or manually manage subscriptions as needed. This setup should help in capturing frames more reliably. Would you like more detailed guidance on any specific part of this process? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

how come the agent isnt joining the all immediately?

if __name__ == "__main__":

cli.run_app( WorkerOptions( entrypoint_fnc=entrypoint, prewarm_fnc=prewarm, ), ) I dont have a agent name specified @millions-hair-24956

If your agent isn't joining the room immediately, it could be due to the way the agent dispatching is configured. According to the LiveKit documentation, if you don't specify an

agent_name

in

WorkerOptions

, the agent should be automatically dispatched to new rooms. However, if the room already exists or if there are issues with room creation, the agent might not join as expected. Here are a few things to check: 1. Room Creation: Ensure that the room is being created successfully. The agent will only join if the room exists. 2. Agent Dispatch: Without an

agent_name

, the agent should be automatically dispatched. If it's not joining, check if there are any network issues or if the room is being reused too quickly, which can cause dispatching issues. 3. Logs: Enable detailed logging to see if there are any errors or warnings that might indicate why the agent isn't joining. Would you like more detailed troubleshooting steps or information on how to configure agent dispatching? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.