@millions-hair-24956 how knowledge base is extended of an agent

I'm not able to find any data sources to answer your question. You might want to specify the type of knowledge base or integration method you are referring to. If this doesn't help, please reach out to the Livekit support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

I want to build a multi-tanant system which each tanent will have its own data to work on

It seems likely that for a multi-tenant system using LiveKit, you can manage separate data for each tenant by using distinct API keys and secrets for each tenant. According to LiveKit Slack discussion, you can use a single LiveKit server and create separate API keys for each client or agent. This allows you to configure each agent with its specific prompt and domain knowledge while maintaining client-specific customizations. Additionally, you might consider using participant attributes and metadata to store application-specific state for each tenant. These attributes can be set in the participant’s access token and are automatically synchronized to new participants who join the room later. This approach helps in maintaining tenant-specific data within the same LiveKit server environment. Would you like more detailed guidance on implementing this setup? Sources: - LiveKit Slack discussion - participant attributes and metadata 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 I am asking like is there any integration available to use pinecone etc.

It seems likely that LiveKit does not have a direct integration with Pinecone mentioned in the available data sources. However, you can potentially integrate Pinecone or similar vector databases by using LiveKit's plugin framework to extend the functionality of your agents. According to the LiveKit documentation, the plugin framework is designed to be extensible, allowing you to integrate with various providers or directly load models for local inference. If you are looking to use Pinecone for vector retrieval in a RAG (Retrieval-Augmented Generation) pipeline, you might need to implement a custom solution that interfaces with Pinecone's API from within your agent's logic. This would involve setting up your agent to make API calls to Pinecone for vector searches and then using the results to inform the agent's responses. Would you like more detailed guidance on how to set up such an integration? Sources: - LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 Is there any boilerplate available for such system?

If you're looking for further help, please follow-up on one of the other channels available for community support!