I am working on a feature in which I have multiple nodes, in each node I have a prompt and a path, now I want to process one node at a time and based on answer I'll jump to another one, is there any way in LiveKit to achieve this. Keep in consideration that I don't want send all nodes at once to LLM rather need to process nodes one by one.

It might be possible that LiveKit's framework for building agents could help you achieve this. The framework allows for creating programmable, multimodal AI agents that can orchestrate LLMs and other AI models to accomplish tasks. You can create agents using Python or Node.js, and they operate as stateful, long-running processes. This could potentially allow you to process nodes one at a time and transition between them based on the responses received. Though I'm not entirely certain, it might be that you can manage the state and transitions between nodes by updating the

chat_ctx

on the agent, which contains the entire context sent to LLMs. This context can be modified to reflect the current node's prompt and path, allowing you to process nodes sequentially. Would you like more detailed guidance on how to implement this using LiveKit's agent framework? Sources: - https://docs.livekit.io/python/livekit/plugins/openai/beta/assistant_llm.html#livekit.plugins.openai.beta.assistant_llm.AssistantLLMStream - https://docs.livekit.io/agents/overview/ Note: I wasn’t able to find highly relevant data sources, but above are a few potentially relevant links. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

can you help me in implementing Livekit's agent framework

I'm here to help with questions about Livekit. If you have specific questions about implementing Livekit's agent framework, please provide more details or clarify your question, and I'll do my best to assist you! 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

ok, can you help me to find out the documentation related to

WorkerOptions

@millions-hair-24956

Certainly! The

WorkerOptions

class is a data class used to describe worker behavior in LiveKit's agent framework. It provides various configuration options for setting up and managing workers. For detailed documentation on

WorkerOptions

, you can refer to the following sources: 1. Python WorkerOptions Documentation: This page provides information on the properties and usage of

WorkerOptions

in Python. 2. JavaScript WorkerOptions Documentation: This page details the

WorkerOptions

class for JavaScript, including its properties and constructors. Would you like more detailed guidance on how to use

WorkerOptions

in your project? Sources: - Python WorkerOptions Documentation - JavaScript WorkerOptions Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

what is

ingress

and

egress

I am bit confuse in these. Can you explain in detail. @millions-hair-24956

Ingress and egress in LiveKit refer to the processes of bringing media into and out of the LiveKit system, respectively. Ingress: This is the process of bringing external media streams into LiveKit. It allows you to ingest media from various sources, such as RTMP streams, and make them available within a LiveKit room. Ingress can be used to bring in live video feeds or other media content that needs to be shared with participants in a LiveKit session. According to the LiveKit Ingress API documentation, you can configure ingress settings to handle different types of media inputs. Egress: This refers to the process of taking media streams from LiveKit and sending them out to external destinations. Egress can be used to record sessions, stream to platforms like YouTube or Facebook, or save media to storage services. The LiveKit Egress API documentation provides details on how to configure egress to handle various output formats and destinations. Would you like more detailed information on how to set up ingress and egress in LiveKit? Sources: - LiveKit Ingress API documentation - LiveKit Egress API documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.