<@U084APK40NS> Is it possible to reply to the agen...
# ask-ai
f
@millions-hair-24956 Is it possible to reply to the agent programatically?
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chatCtx.append({
      role: llm.ChatRole.USER,
      text: "Yeah!",
    });
g
Yes, you can programmatically reply to the agent by appending a message to the chat context. You can use the
ChatContext.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.
f
@millions-hair-24956 Yes, please, how do I implement this in Node.js?
g
To implement programmatic replies to an agent in Node.js using LiveKit, you can use the
ChatContext
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:
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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.
f
@millions-hair-24956 I have a
VoicePipelineAgent
with its own chat context. Where should I put it?
g
To set up a
VoicePipelineAgent
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:
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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.
f
@millions-hair-24956 Ok, but where should I programmatically call
chatCtx.append
? Use Node.js.
g
In a Node.js environment, you should programmatically call
chatCtx.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:
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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.
f
@millions-hair-24956 Argument of type
"message_received"
is not assignable to parameter of type
keyof VPACallbacks
.
g
It seems likely that the error you're encountering is due to a type mismatch in the event listener setup for the
VoicePipelineAgent
. 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:
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// 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.
f
@millions-hair-24956 Why does the following code not work?
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agent.on(pipeline.VPAEvent.AGENT_SPEECH_COMMITTED, () => {
    chatCtx.append({
      role: llm.ChatRole.USER,
      text: "Find me ...",
    });

    agent.llm.chat({ chatCtx });
  });
g
It seems likely that the issue with your code is related to how the
chatCtx.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:
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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.