brief-vase-33757
02/28/2025, 2:15 AMgray-monkey-83525
02/28/2025, 2:15 AMmetrics_collected
event and using the log_metrics
helper function to format and log the metrics.
Additionally, if you are using a speech-to-speech agent, the logs for user and agent interactions are automatically logged in the console of the backend process. You can also enable more verbose logging by setting the log level to debug, as mentioned in a Slack discussion.
Would you like more detailed information on how to set up logging for specific types of agents or environments?
Sources:
- Capturing metrics | LiveKit Docs
- Slack discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.brief-vase-33757
02/28/2025, 2:25 AMgray-monkey-83525
02/28/2025, 2:26 AMtenant1_room1
, to easily distinguish between rooms.
Regarding agent names, it might be beneficial to have different agent names for each tenant to personalize the experience and maintain clarity in interactions. This can be achieved by configuring the agent's identity or metadata when generating the access token for the room connection, as suggested in the LiveKit documentation.
Would you like more detailed guidance on implementing these suggestions or any specific aspect of the multi-tenant setup?
Sources:
- Slack discussion
- LiveKit documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.brief-vase-33757
02/28/2025, 2:27 AMgray-monkey-83525
02/28/2025, 2:27 AMimport os
from livekit import rtc, agents
from livekit.agents import WorkerOptions
from livekit.server_sdk import RoomServiceClient, AccessToken
# Load environment variables
LIVEKIT_URL = os.getenv("LIVEKIT_URL")
API_KEY = os.getenv("LIVEKIT_API_KEY")
API_SECRET = os.getenv("LIVEKIT_API_SECRET")
# Function to create a room for a tenant
def create_room(tenant_id):
room_name = f"{tenant_id}_room"
room_client = RoomServiceClient(LIVEKIT_URL, API_KEY, API_SECRET)
room_client.create_room(name=room_name)
return room_name
# Function to generate a token for a tenant's agent
def generate_token(tenant_id, room_name):
identity = f"{tenant_id}_agent"
token = AccessToken(API_KEY, API_SECRET, identity=identity)
token.add_grant({
"room": room_name,
"roomJoin": True,
"canPublish": True,
"canSubscribe": True
})
return token.to_jwt()
# Example usage
tenant_id = "tenant1"
room_name = create_room(tenant_id)
token = generate_token(tenant_id, room_name)
print(f"Room created: {room_name}")
print(f"Token for agent: {token}")
This code snippet demonstrates how to create a room for each tenant and generate a token for an agent associated with that tenant. The room name and agent identity are customized based on the tenant ID.
Would you like more detailed information on any specific part of this setup?
Sources:
- https://livekit-users.slack.com/archives/C07FY8WHGPM/p1728956116.368569
- https://docs.livekit.io/home/server/managing-rooms/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.brief-vase-33757
02/28/2025, 2:29 AMbrief-vase-33757
02/28/2025, 2:30 AMgray-monkey-83525
02/28/2025, 2:30 AMempty_timeout
parameter when creating the room. This allows the room to persist for a while, reducing the need to recreate it for every call.
Regarding tokens, each participant needs a unique access token to join a room. Tokens are typically generated on the server side and are required for each new connection to ensure secure access. While this does involve an API call for each participant, it is necessary for maintaining security and managing permissions.
Would you like more detailed information on configuring room persistence or token generation?
Sources:
- LiveKit Room Management 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.brief-vase-33757
02/28/2025, 2:33 AMgray-monkey-83525
02/28/2025, 2:33 AMAnnoyIndex
class is used to manage and query vector data. This class allows you to load, query, and manage vector indices, which are essential for implementing RAG in an agent.
To implement RAG for an agent, you would typically:
1. Create and Load Index: Use the AnnoyIndex
class to create or load a vector index from a file.
2. Add Items: Add vector data to the index using the add_item
method.
3. Build Index: Use the build
method to prepare the index for querying.
4. Query Index: Retrieve similar vectors using the query
method, which returns the closest matches to a given vector.
Would you like more detailed guidance on setting up RAG with LiveKit?
Sources:
- LiveKit RAG Annoy Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.