I am implementing only chat ai - and i want to have suggested-responses as a feature - as when agent messages - it also send structured response - containing some suggested next questions that the user can ask ?

Thanks for reaching out, Yashi. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

1. its dynamic - openai, gemini etc. 2. the agent is livekit agent - it is voice agent but - just removed the stt and tts layer 3. we can send an event 4. only for certain messages

Thanks — this is very clear and helpful. I’m responding to your plan to have a LiveKit voice agent (STT/TTS removed) send structured suggested-next-question suggestions for certain messages, using dynamic LLMs like OpenAI or Gemini, and delivered as events. Below is a compact, practical pattern you can implement plus example payloads and considerations so suggestions integrate reliably with your existing LiveKit agent flow. Design overview (what to do) • Let the agent generate two parts for targeted messages: ◦ Primary reply text (the agent’s message). ◦ Suggestions array (structured suggested next questions). • Send the suggestions as a LiveKit data/event message alongside or immediately after the agent message so the client can render tappable suggestion buttons. • Only generate suggestions for selected messages (your server or agent logic decides when). • Use the LLM to produce the suggestions (OpenAI/Gemini). Ask the LLM to return a small JSON array to make parsing deterministic. Recommended payload shape • Keep it simple and explicit so the client can render and attach a behavior when the user taps a suggestion. Example JSON event payload: { "type": "agent_message", "message_id": "msg_12345", "text": "Sure — I can help with that. Would you like steps or troubleshooting?", "suggestions": [ { "id": "sugg_1", "label": "Show steps", "intent": "show_steps", "metadata": { "topic": "install" } }, { "id": "sugg_2", "label": "Troubleshoot error", "intent": "troubleshoot", "metadata": { "error_code": "E42" } } ] } Implementation steps (high-level) 1. Decide trigger points ◦ Add logic in your agent backend to mark which agent messages need suggestions (e.g., when the message contains an offer to assist, or when a certain tool is available). 1. Ask the LLM for structured suggestions ◦ Send a prompt instructing the LLM to return JSON with a small list (2–4) of concise suggestions, each with an id, label, intent, and optional metadata. Example prompt pattern below. 1. Validate & sanitize ◦ Parse the LLM output, validate it’s valid JSON, normalize fields, and apply any safety filters or length limits. 1. Emit to clients via LiveKit data/events ◦ Send a LiveKit data message or event containing the agent message and suggestions payload. Clients receive it via the LiveKit data channel and render suggestions as buttons. 1. Handle user selection ◦ When a user taps a suggestion, the client sends that suggestion id/intent back to your backend (as an event). Your backend resolves the intent (run a tool, send a follow-up message, record analytics, etc.), then emits the subsequent agent message. Example LLM prompt pattern (concise) • System/instruction: "You are an assistant that, when appropriate, produces a JSON array named suggestions describing short next-steps users can take. Return only valid JSON." • User content (example): "User asked how to install X. Provide 2–3 suggested next questions. Each suggestion must have id, label (<=40 chars), intent, and optional metadata. Output only JSON array." Example expected response from LLM: [ {"id":"s1","label":"Show installation steps","intent":"show_install_steps"}, {"id":"s2","label":"Show common errors","intent":"show_errors","metadata":{"topic":"install"}} ] Practical considerations & tips • Ask LLM to “output only JSON” to avoid extraneous text. Still defensively parse and handle errors. • Use short labels for buttons. Clients should truncate if too long. • Include an

intent

field that maps to your backend action so tapping a suggestion triggers deterministic behavior. • Consider a TTL or ephemeral flag so old suggestions aren’t actionable after a certain time. • Include

message_id

so analytics and conversation state remain consistent. • Rate-limit suggestion generation if LLM costs are a concern; generate suggestions only for messages that need them. • If you’re worried about mis-parses, have the LLM return a fixed envelope like { "suggestions": [...] } to make parsing easier. LiveKit-specific note • Since you can send events, use the LiveKit data channel to emit a single event that contains both the agent text and the suggestions payload. Clients listening for data messages can render both seamlessly. Follow-up I can help with • Provide a concrete server-side code sample for calling OpenAI or Gemini and emitting a LiveKit data event (tell me which runtime/language you prefer: Node.js, Python, or Go). • Create a ready-to-use prompt template tuned for short, actionable suggestions. • Client-side example showing how to render suggestion buttons and send the selected intent back. Which would you like next — a Node.js snippet that calls OpenAI, validates JSON, and sends a LiveKit event, or a generic prompt template and client-side handling example?

where should i implement this in the llm_node or in the transcription_node?

Thanks — good question. I’m responding to your ask about whether to implement suggested-response generation in the

llm_node

or the

transcription_node

. Short answer: implement it in the

llm_node

. Why: • The

llm_node

is the component that generates the agent’s reply from the conversation context and has access to

ChatContext

, tools, and model settings. That makes it the correct place to produce the primary reply and any structured suggestions alongside it. • The

transcription_node

is for post-processing transcribed text and TTS-aligned timing; it does not generate new conversational content or responses, so it’s not appropriate for creating suggestions. How to implement (brief guidance) 1. Override the

llm_node

in your custom Agent class. 2. When generating a reply, have the LLM produce two parts (for messages that need suggestions): ◦ The primary agent message text. ◦ A structured suggestions payload (e.g., an array of {id, label, intent, metadata}). 1. Parse and validate the LLM output in the

llm_node

, apply any safety/length limits, then include the suggestions in the message object you emit. 2. Emit the agent message + suggestions to clients via your existing event/data channel so the client can render tappable suggestion buttons. 3. On user selection, send the selected suggestion id/intent back to your backend for deterministic handling. If you’d like, I can: • Provide a short Node.js or Python snippet showing an

llm_node

override that calls an LLM, parses JSON suggestions, and emits a LiveKit data event. • Draft a prompt template that instructs OpenAI/Gemini to return a safe, parseable JSON suggestions array. Which would help you most next — a code snippet (which language) or the prompt template? Sources: Pipeline nodes and hooks | LiveKit Documentation | Text and transcriptions | LiveKit Documentation

can u give me the snippet using the groq.llm in the llm_node which generate this suggested responses

Thanks — I’m responding to your request for a snippet that overrides

llm_node

using the Groq LLM to produce the agent’s primary reply plus structured suggested responses. Below is a compact Python example (fits the LiveKit Agents flow) that: • Overrides

llm_node

in your agent session. • Calls

groq.LLM

asking for a JSON envelope with

response

and

suggestions

. • Parses the streamed output, validates JSON, and emits a LiveKit data event containing the agent message and suggestions so your client can render tappable suggestion buttons. Adjust model name, prompt wording, and event publishing to match your app.

import asyncio
import json
from typing import AsyncIterable, Dict, Any, List

from livekit import agents
from livekit.agents import AgentContext, JobContext
from livekit.plugins import groq

# Example helper to send a LiveKit data event (replace with your publish method)
async def publish_agent_event(session, payload: Dict[str, Any]):
    # session or context should provide data publishing; adapt to your API
    # For illustration only — replace with the actual LiveKit publish call you use.
    await session.send_data(json.dumps(payload))

async def create_agent_with_suggestions(job: JobContext):
    ctx = AgentContext(ctx=job, llm=groq.LLM())  # minimal; include stt/tts if used

    session = ctx.create_session()

    # Custom llm_node override
    async def llm_node(
        chat_ctx: agents.llm.ChatContext,
        tools: List[agents.llm.FunctionTool],
        model_settings: agents.llm.ModelSettings = None,
    ) -> AsyncIterable[agents.llm.ChatChunk | str]:
        """
        This llm_node:
        - Calls the Groq LLM with an instruction to return JSON like:
          { "response": "<text>", "suggestions": [ { "id": "...", "label": "...", "intent": "...", "metadata": {...} } ] }
        - Streams chunks through as usual.
        - On finalization, parse and return the structured payload so downstream code can emit events.
        """
        # Build a system/user instruction that enforces JSON output
        system_prompt = (
            "You are an assistant. For some replies, return a JSON object ONLY with keys: "
            "`response` (string) and `suggestions` (array). Each suggestion must include "
            "`id`, `label`, and `intent`, and may include `metadata`. Output only valid JSON."
        )

        # Append conversation context to user prompt (chat_ctx contains messages)
        # Many groq integrations accept a chat-like API; adapt if your wrapper differs.
        async with groq.LLM(model="llama3-groq-8b-8192-tool-use-preview").chat(
            chat_ctx=chat_ctx,
            tools=tools,
            tool_choice="none",
            model_settings=model_settings,
            system_message=system_prompt,
        ) as stream:
            # Stream tokens/chunks back to caller as normal to support partial TTS/display
            collected_text = ""
            async for chunk in stream:
                # Pass chunks through so caller can use streaming behavior
                yield chunk
                # Collect text to attempt JSON parse at the end
                if isinstance(chunk, str):
                    collected_text += chunk
                elif hasattr(chunk, "text"):
                    collected_text += getattr(chunk, "text")

        # After streaming completes, try to parse JSON out of the collected text.
        # Defensive parsing: find first '{' and last '}' to extract JSON blob.
        try:
            start = collected_text.find("{")
            end = collected_text.rfind("}") + 1
            if start == -1 or end == -1 or end <= start:
                raise ValueError("no JSON object found in LLM output")
            json_blob = collected_text[start:end]
            payload = json.loads(json_blob)
        except Exception:
            # If parsing fails, fallback to a simple response envelope
            payload = {"response": collected_text.strip(), "suggestions": []}

        # Normalize suggestions: ensure list of dicts with id/label/intent
        suggestions = []
        raw_sugs = payload.get("suggestions") or []
        for i, s in enumerate(raw_sugs):
            if not isinstance(s, dict):
                continue
            suid = s.get("id") or f"sugg_{i}"
            suggestions.append({
                "id": suid,
                "label": s.get("label", "")[:80],
                "intent": s.get("intent", ""),
                "metadata": s.get("metadata", {}),
            })

        # Emit a structured event so clients can render suggestions.
        # Include message_id or other correlation if your app uses it.
        event_payload = {
            "type": "agent_message",
            "message_id": f"msg_{chat_ctx.turn_id or '0'}",
            "text": payload.get("response", ""),
            "suggestions": suggestions,
        }

        # Replace publish_agent_event with your actual data publish method.
        await publish_agent_event(session, event_payload)

        # Optionally yield the final response string so downstream nodes get it too
        yield payload.get("response", "")

    # Attach custom llm_node to session agent
    session.agent._llm_node = llm_node

    # Start the session/connect as your app requires
    await ctx.connect()
    await ctx.say("Hello — how can I help you today?", allow_interruptions=False)
    return ctx

Key notes and adjustments • Place suggestion generation in

llm_node

because that node is responsible for producing conversational content and has access to context and tools. • The example asks the LLM to output only JSON; still parse defensively and fallback if the LLM emits extra text. • The

publish_agent_event

function is a placeholder — replace it with your actual LiveKit data publish call or event-emission method used in your agent runtime. • Keep suggestion labels short and include an

intent

so the client can send a deterministic intent back when the user taps a suggestion. • Add validation, safety checks, TTL, and rate-limiting for production use. If you want, I can: • Convert this to a Node.js snippet, • Provide a tighter prompt template for OpenAI/Gemini style models, • Or show a client example that renders the suggestions and sends back selected intent. Which would be most helpful next? Sources: Function tool definition | LiveKit Documentation | Groq LLM plugin guide | LiveKit Documentation

no - can we use the concept of fast responses in this purpose ?

Thanks — good question. You’re asking whether you can reuse LiveKit’s “fast responses” concept to generate suggested next questions for users. Short answer: No — fast responses are for preemptive reply generation to reduce latency, not for producing user-facing suggested questions. Use the

llm_node

approach instead to generate structured suggestions and emit them as events. Why fast responses aren’t a fit • Fast responses (e.g., preemptive generation or

session.generate_reply()

) are designed to start generating the agent’s reply earlier (while the user is still speaking) to reduce perceived latency. • They are not meant to produce separate, structured suggestion payloads for the client to render as tappable next-question buttons. Fast responses focus on response latency, not on producing deterministic suggestion metadata or intent mapping. What to do instead • Implement suggestion generation in your

llm_node

. The

llm_node

is responsible for generating conversational content and has access to

ChatContext

, tools, and model settings — so it’s the correct place to ask the LLM to produce both the primary response and a structured suggestions envelope. • Have the LLM return a deterministic JSON structure (or use a typed response format) with fields like

response

,

suggestions: [{id,label,intent,metadata}]

. • Parse and validate the JSON defensively in the

llm_node

, then publish it to clients over LiveKit data/events so the client can render suggestion buttons. • Only trigger suggestion generation for the messages you choose (your logic decides when to ask the LLM for suggestions). Practical reasons this is better • You get a stable

intent

value for each suggestion so tapping can trigger deterministic backend actions. • You can validate, sanitize, and TTL suggestions server-side. • You control costs by generating suggestions only for selected messages (not every utterance). • The client receives a single structured event (message + suggestions) and can render reliably. If you want, I can: • Provide a focused code snippet showing the

llm_node

using

groq.LLM

that asks for a JSON envelope (response + suggestions), parses it, and emits a LiveKit data event (I can give Node.js or Python). • Give a tight prompt template that forces JSON-only output for OpenAI/Gemini/Groq models. • Show a client-side example to render suggestions and send selected

intent

back to the backend. Which of the three would you like next — Node.js snippet, Python snippet, or the prompt template? Sources: Amazon Nova Sonic integration guide | LiveKit Documentation | Agent speech and audio | LiveKit Documentation | Pipeline nodes and hooks | LiveKit Documentation | LLM Output Replacement | LiveKit Documentation | Live conversations with AI using ChatGPT and WebRTC | LiveKit

ok so in the

async def llm_node(self, chat_ctx: ChatContext, tools: list[FunctionTool], model_settings: ModelSettings):

I don't want to use the same llm, i want to use the grok llm just for generating the suggested reponses - normal responses should go with the exising configured llm

Thanks — this is a clear ask. You want your existing configured LLM to generate the main reply, while using a Groq LLM only to produce structured suggested responses, all inside your

llm_node

. That’s a good approach and fully doable. Below is a compact Python example that shows the pattern: • Call the agent’s configured LLM as usual to produce the primary reply (streamed). • After or in parallel, call Groq (one-shot or streaming) to generate a small JSON suggestions envelope. • Parse/validate suggestions defensively. • Publish a single LiveKit data event containing the agent text and suggestions so the client can render tappable buttons. Adjust model names, publish method, and error handling to match your runtime.

import json
import asyncio
from typing import AsyncIterable, List, Dict, Any

from livekit.agents import llm as llm_module, ChatContext, FunctionTool, ModelSettings
from livekit.plugins import groq

# Replace with your actual publish method to send LiveKit data/events
async def publish_data_event(session, payload: Dict[str, Any]):
    # session.publish_data(...) or equivalent in your agent runtime
    await session.send_data(json.dumps(payload))

async def llm_node(
    self,
    chat_ctx: ChatContext,
    tools: List[FunctionTool],
    model_settings: ModelSettings = None,
) -> AsyncIterable[llm_module.ChatChunk | str]:
    """
    Use the agent's configured LLM for the primary reply (streamed),
    and call Groq LLM separately to generate structured suggestions.
    Emit a data event with both text and suggestions.
    """

    # 1) Generate primary reply using the agent's configured/default LLM
    # Use the existing/default llm_node implementation to preserve behavior and streaming.
    # This yields chunks so downstream nodes (e.g., TTS) receive streamed text.
    primary_text_chunks = []
    async for chunk in self.__class__.llm_node(self, chat_ctx, tools, model_settings):
        # Forward stream chunks to caller
        yield chunk
        # Collect text for later correlation
        if isinstance(chunk, str):
            primary_text_chunks.append(chunk)
        elif hasattr(chunk, "text"):
            primary_text_chunks.append(getattr(chunk, "text"))

    primary_text = "".join(primary_text_chunks).strip()

    # 2) Decide when to generate suggestions
    # Example rule: only generate for certain messages — you can use chat_ctx, primary_text, etc.
    should_generate_suggestions = True  # Replace with your logic

    suggestions: List[Dict[str, Any]] = []
    if should_generate_suggestions:
        try:
            # 3) Call Groq to produce structured suggestions (ask for JSON only)
            system_prompt = (
                "You are a helper that returns ONLY valid JSON. Return an object with keys: "
                "\"response_summary\" (short string) and \"suggestions\" (array). "
                "Each suggestion must be an object with id, label, intent, and optional metadata. "
                "Labels should be <= 40 chars. Output only JSON."
            )

            # Build a minimal chat context for Groq; you can pass the last N messages or the reply
            groq_chat_ctx = chat_ctx.copy()  # or build a new ChatContext containing relevant messages
            # Optionally append the agent reply so Groq knows the behavior to suggest from:
            groq_chat_ctx.messages.append({"role": "assistant", "content": primary_text})

            # One-shot Groq call (non-streaming) to get compact JSON suggestions.
            # Adjust model name/config as needed.
            groq_llm = groq.LLM(model="llama3-groq-8b-8192-tool-use-preview")
            groq_response = await groq_llm.generate(
                chat_ctx=groq_chat_ctx,
                system_message=system_prompt,
                max_tokens=150,
            )

            raw_text = getattr(groq_response, "text", "") if groq_response else ""
            # Defensive JSON extraction (strip any surrounding text)
            start = raw_text.find("{")
            end = raw_text.rfind("}") + 1
            if start != -1 and end != -1 and end > start:
                json_blob = raw_text[start:end]
                payload = json.loads(json_blob)
            else:
                # if Groq didn't return clean JSON, fallback to no suggestions
                payload = {"response_summary": "", "suggestions": []}

            raw_sugs = payload.get("suggestions", [])
            for i, s in enumerate(raw_sugs):
                if not isinstance(s, dict):
                    continue
                suggestions.append({
                    "id": s.get("id") or f"sugg_{i}",
                    "label": s.get("label", "")[:80],
                    "intent": s.get("intent", ""),
                    "metadata": s.get("metadata", {}),
                })

        except Exception as exc:
            # Fail-safe: log and continue without blocking primary reply
            # Replace with your logging
            print("Suggestion generation failed:", exc)
            suggestions = []

    # 4) Publish a single data event with primary text and suggestions for the client
    event_payload = {
        "type": "agent_message",
        "message_id": f"msg_{chat_ctx.turn_id or '0'}",
        "text": primary_text,
        "suggestions": suggestions,
    }
    # session reference: use the session/context object you have access to (self.session or similar)
    await publish_data_event(self.session, event_payload)

    # Optionally yield the primary text one more time (or nothing) depending on your pipeline needs
    return

Key implementation notes and best practices • Keep primary reply streaming intact: using the existing

llm_node

preserves streaming behavior for TTS or realtime UIs. • Run Groq separately: do a small one-shot request for JSON suggestions (faster and cheaper). You can run it after the main reply finishes or concurrently if desired. • Defensive parsing: always parse JSON defensively (find braces, fallback) since models can emit extra text. • Control when suggestions are produced: base

should_generate_suggestions

on rules (message intent, message length, tool availability) to save cost and noise. • Include an

intent

per suggestion so the client can send back deterministic commands when tapped. • Publish a single event containing both

text

and

suggestions

so the client has one canonical source for UI rendering and correlation. • Rate-limit or cache suggestions for repeated prompts if cost/performance matters. • Ensure

self.session

(or your agent session object) is the correct handle for publishing LiveKit data events in your environment. If you’d like, I can: • Convert this into a concrete snippet that runs Groq concurrently (async.gather) so suggestion generation overlaps with the main reply. • Provide a tighter prompt you can drop into Groq that returns only valid JSON with a 2–3 suggestion limit. • Show the small client-side handler to render buttons and forward selected intent back to your backend. Which next step would help most — concurrent generation snippet, a ready-to-use Groq prompt, or a client handler example?