flat-airline-46245
02/17/2025, 8:03 AMchatCtx.append({
role: llm.ChatRole.USER,
text: "Yeah!",
});
gray-monkey-83525
02/17/2025, 8:03 AMChatContext.append
method to add a new message with the role set to user
and the desired text. This approach is supported by LiveKit's chat context management.
Would you like more details on how to implement this?
Sources:
- LiveKit Chat Context Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.flat-airline-46245
02/17/2025, 8:05 AMgray-monkey-83525
02/17/2025, 8:05 AMChatContext
class to manage the conversation state. Here's a step-by-step breakdown:
1. Import Required Modules: Ensure you have the necessary LiveKit modules imported.
2. Create a Chat Context: Initialize a ChatContext
instance to manage the conversation.
3. Append User Message: Use the append
method of ChatContext
to add a user message.
4. Trigger Agent Response: After appending the message, you can trigger the agent to respond using the chat
method of the LLM.
Here's a code snippet demonstrating these steps:
const { ChatContext, ChatMessage, ChatRole } = require('livekit/agents/llm');
// Initialize chat context
const chatCtx = new ChatContext();
// Append a user message
chatCtx.append({
role: ChatRole.USER,
text: "Yeah!",
});
// Assuming you have an LLM instance
const llm = ...; // Initialize your LLM instance
// Trigger a response from the agent
llm.chat({ chatCtx }).then(response => {
console.log("Agent response:", response);
});
This code sets up a chat context, appends a user message, and then uses the LLM to generate a response based on the updated context.
Sources:
- https://docs.livekit.io/agents/openai/multimodal-agent/
- https://docs.livekit.io/agents/openai/multimodalagent/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.flat-airline-46245
02/17/2025, 8:07 AMVoicePipelineAgent
with its own chat context. Where should I put it?gray-monkey-83525
02/17/2025, 8:07 AMVoicePipelineAgent
with its own chat context, you should initialize the ChatContext
and pass it as a parameter when creating the VoicePipelineAgent
. This allows the agent to manage and utilize the conversation context effectively.
Here's how you can do it:
1. Initialize Chat Context: Create a ChatContext
instance and populate it with any initial messages or context you want the agent to have.
2. Create VoicePipelineAgent: Pass the ChatContext
to the VoicePipelineAgent
during its initialization.
Here's a code snippet demonstrating these steps:
from livekit.agents import llm, VoicePipelineAgent
from livekit.plugins import silero, deepgram, openai, cartesia
# Initialize chat context
initial_ctx = llm.ChatContext().append(role="system", text="Welcome to the voice assistant!")
# Create the VoicePipelineAgent with the chat context
agent = VoicePipelineAgent(
vad=silero.VAD.load(),
stt=deepgram.STT(model="nova-2-general"),
llm=openai.LLM(),
tts=cartesia.TTS(),
chat_ctx=initial_ctx, # Pass the chat context here
allow_interruptions=True,
interrupt_speech_duration=0.5,
interrupt_min_words=0,
min_endpointing_delay=0.5,
)
# Start the agent in a room with a participant
agent.start(room, participant)
This setup ensures that the VoicePipelineAgent
uses the provided chat context for managing the conversation flow.
Sources:
- VoicePipelineAgent Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.flat-airline-46245
02/17/2025, 8:09 AMchatCtx.append
? Use Node.js.gray-monkey-83525
02/17/2025, 8:09 AMchatCtx.append
whenever you want to update the conversation context with a new message. This is typically done in response to events such as receiving a new message from a user or when the agent needs to add its own messages to the context.
Here's a step-by-step guide on where to place `chatCtx.append`:
1. Initialize Chat Context: Create a ChatContext
instance at the start of your application.
2. Handle Incoming Messages: Use event listeners to detect when a new message is received, and append it to the chat context.
3. Trigger Agent Responses: After updating the chat context, trigger the agent to generate a response.
Here's a code snippet demonstrating these steps:
const { ChatContext, ChatMessage, ChatRole } = require('livekit/agents/llm');
const { VoicePipelineAgent } = require('livekit/agents/pipeline');
// Initialize chat context
const chatCtx = new ChatContext();
// Create the VoicePipelineAgent
const agent = new VoicePipelineAgent({
vad: silero.VAD.load(),
stt: deepgram.STT({ model: "nova-2-general" }),
llm: openai.LLM(),
tts: cartesia.TTS(),
chatCtx: chatCtx, // Pass the chat context here
allowInterruptions: true,
interruptSpeechDuration: 0.5,
interruptMinWords: 0,
minEndpointingDelay: 0.5,
});
// Event listener for incoming messages
agent.on('message_received', (msg) => {
// Append the user message to the chat context
chatCtx.append({
role: ChatRole.USER,
text: msg.content,
});
// Trigger a response from the agent
agent.llm.chat({ chatCtx }).then(response => {
console.log("Agent response:", response);
});
});
// Start the agent in a room with a participant
agent.start(room, participant);
In this setup, chatCtx.append
is called within the event listener for incoming messages, ensuring that the chat context is updated dynamically as new messages are received.
Sources:
- https://docs.livekit.io/python/livekit/agents/pipeline/pipeline_agent.html#args
- https://docs.livekit.io/python/livekit/agents/pipeline/index.html#args 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.flat-airline-46245
02/17/2025, 8:13 AM"message_received"
is not assignable to parameter of type keyof VPACallbacks
.gray-monkey-83525
02/17/2025, 8:13 AMVoicePipelineAgent
. The VoicePipelineAgent
uses specific event types defined in its VPACallbacks
interface, and "message_received"
might not be one of them.
To resolve this, you should ensure that you're using the correct event types supported by VoicePipelineAgent
. Here's a step-by-step guide:
1. Check Supported Events: Review the VoicePipelineAgent
documentation to identify the correct event types you can listen to. Common events include user_started_speaking
, user_stopped_speaking
, agent_started_speaking
, and agent_stopped_speaking
.
2. Update Event Listener: Replace "message_received"
with a supported event type from VPACallbacks
.
Here's an example of how you might set up an event listener with a supported event type:
// Assuming you have a VoicePipelineAgent instance named 'agent'
// Listen for a supported event, e.g., 'user_started_speaking'
agent.on('user_started_speaking', () => {
console.log("User has started speaking");
});
Would you like more details on the specific events supported by VoicePipelineAgent
?
Sources:
- https://docs.livekit.io/python/livekit/rtc/index.html#livekit.rtc.ChatManager
- https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729255034.540999 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.flat-airline-46245
02/17/2025, 9:01 AMagent.on(pipeline.VPAEvent.AGENT_SPEECH_COMMITTED, () => {
chatCtx.append({
role: llm.ChatRole.USER,
text: "Find me ...",
});
agent.llm.chat({ chatCtx });
});
gray-monkey-83525
02/17/2025, 9:01 AMchatCtx.append
and agent.llm.chat
methods are being used within the event listener. Here are a few potential reasons why the code might not be working as expected:
1. Event Type: Ensure that pipeline.VPAEvent.AGENT_SPEECH_COMMITTED
is a valid event type for the VoicePipelineAgent
. Double-check the documentation to confirm that this event is supported.
2. Chat Context Update: When you append a message to chatCtx
, make sure that the context is correctly updated and that the agent.llm.chat
method is called with the updated context.
3. Asynchronous Handling: If agent.llm.chat
is an asynchronous operation, ensure that you handle it properly using await
or .then()
to process the response.
Here's a revised version of your code with these considerations:
agent.on(pipeline.VPAEvent.AGENT_SPEECH_COMMITTED, async () => {
// Append the user message to the chat context
chatCtx.append({
role: llm.ChatRole.USER,
text: "Find me ...",
});
// Trigger a response from the agent
try {
const response = await agent.llm.chat({ chatCtx });
console.log("Agent response:", response);
} catch (error) {
console.error("Error during chat:", error);
}
});
This version ensures that the agent.llm.chat
method is awaited, allowing you to handle the response or any potential errors properly.
Would you like more details on handling asynchronous operations in Node.js?
Sources:
- https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729255778.230979
- https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729785405.894609
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.