snowspeeder
a66f09eb28
Rebuild & Deploy / rebuild (push) Successful in 55s
- Move task_dir assignment before VoiceAssistant construction in handle_job; UnboundLocalError was crashing every agent job before it joined the room, leaving the UI stuck on 'Opening a secure room' - Make settings sheet full-window height (100dvh) to match tasks panel
894 lines
36 KiB
Python
894 lines
36 KiB
Python
"""
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Voice Agent — real-time voice assistant.
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Pipeline: Azure STT → LLM (OpenWebUI, OpenAI-compatible) → Azure TTS
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Runs inside the single Docker container alongside LiveKit server and web frontend.
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"""
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import asyncio
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import json
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import logging
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import os
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import re
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import textwrap
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import uuid
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from dotenv import load_dotenv
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from livekit.agents import (
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Agent,
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AgentServer,
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AgentSession,
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JobContext,
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TurnHandlingOptions,
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cli,
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inference,
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llm as lk_llm,
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mcp,
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room_io,
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)
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from livekit.plugins import azure, openai
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logger = logging.getLogger("voice-agent")
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load_dotenv() # picks up /app/.env in the container
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# ── Configuration from environment ──────────────────────────────────────────
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AZURE_KEY = os.environ.get("AZURE_SPEECH_KEY", "")
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AZURE_REGION = os.environ.get("AZURE_SPEECH_REGION", "eastus")
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DEFAULT_VOICE = os.environ.get(
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"AZURE_TTS_VOICE", "en-US-Ava:DragonHDOmniLatestNeural"
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)
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LLM_BASE_URL = (
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os.environ.get("LLM_BASE_URL")
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or os.environ.get("GEMMA_BASE_URL")
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or "http://192.168.86.2:3005/api"
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)
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LLM_MODEL = (
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os.environ.get("LLM_MODEL")
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or os.environ.get("GEMMA_MODEL")
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or "gemma-4-e4b"
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)
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LLM_API_KEY = (
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os.environ.get("LLM_API_KEY")
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or os.environ.get("GEMMA_API_KEY")
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or "not-needed"
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)
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# Model ids that cannot hold a conversation (OpenWebUI hosts these too).
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NON_CHAT_MODEL_ID = re.compile(r"embed|rerank|whisper|tts-|stable-?diffusion|flux", re.I)
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WEB_MCP_ENABLED = os.environ.get("WEB_MCP_ENABLED", "true").lower() in ("1", "true", "yes")
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FIRECRAWL_BASE = os.environ.get("FIRECRAWL_BASE", "http://192.168.86.2:3002")
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SKILLS_DIR = os.environ.get("SKILLS_DIR", "/skills")
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VOICE_USERS_FILE = os.environ.get("VOICE_USERS_FILE", "/etc/hope/users.json")
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def load_user_profile(user_id: str) -> dict:
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"""Load one server-side user profile without exposing its credentials."""
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with open(VOICE_USERS_FILE, encoding="utf-8") as users_file:
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registry = json.load(users_file)
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profile = registry.get("users", {}).get(user_id)
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if not isinstance(profile, dict):
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raise ValueError(f"unknown voice user: {user_id}")
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memory = profile.get("memory")
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if not isinstance(memory, dict) or not memory.get("url") or not memory.get("api_key"):
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raise ValueError(f"voice user {user_id} has no valid memory profile")
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return {**profile, "user_id": user_id}
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def user_id_from_room(room_name: str) -> str:
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"""Extract the trusted user slug minted into a server-generated room name."""
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match = re.fullmatch(r"voice-([a-z0-9][a-z0-9_-]{1,30})-[0-9a-f]{8}", room_name)
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if not match:
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raise ValueError(f"invalid voice room name: {room_name}")
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return match.group(1)
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async def list_available_models() -> list[str]:
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"""Fetch chat-capable model ids from the OpenAI-compatible endpoint.
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Works with OpenWebUI (GET /api/models) and plain OpenAI-compatible
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servers (GET /v1/models) — both live under {base_url}/models. The
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configured default model is always first so it survives fetch failures.
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"""
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ids: list[str] = []
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try:
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import httpx
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headers = {}
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if LLM_API_KEY and LLM_API_KEY != "not-needed":
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headers["Authorization"] = f"Bearer {LLM_API_KEY}"
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async with httpx.AsyncClient(timeout=10) as client:
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resp = await client.get(f"{LLM_BASE_URL}/models", headers=headers)
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resp.raise_for_status()
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data = resp.json()
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ids = [
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model_id
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for model in data.get("data", [])
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if isinstance(model, dict) and (model_id := model.get("id", ""))
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]
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except Exception as e: # noqa: BLE001
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logger.warning("Could not list models from %s: %s", LLM_BASE_URL, e)
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chat_models = [m for m in ids if not NON_CHAT_MODEL_ID.search(m)]
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if LLM_MODEL in chat_models:
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chat_models.remove(LLM_MODEL)
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ordered = [LLM_MODEL] + sorted(chat_models)
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return ordered or [LLM_MODEL]
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SYSTEM_PROMPT = textwrap.dedent("""\
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You are Hope, a warm, conversational voice assistant speaking with
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{user_display_name}. You are talking TO someone, not writing for them to
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read. Imagine you're having a natural conversation with a friend over the
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phone.
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# How you speak
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- Keep every response to one or three sentences. That's it.
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- Use contractions (I'm, don't, it's) and natural phrasing.
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- Speak like you're talking, not writing. No bullet points, no lists,
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no markdown, and no formatting except the Azure delivery markers below.
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- Spell out numbers when it sounds more natural ("twenty twenty-six"
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instead of "2026").
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- Make reasonable assumptions and answer directly. Ask a follow-up only
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when it is genuinely impossible to proceed without one.
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- Be warm and direct. Don't be sycophantic or overly formal.
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- If you don't know something, say so briefly and move on.
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# Expressive delivery (Azure Dragon HD Omni)
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Azure understands a square-bracket delivery marker at the start of spoken
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text. Use zero to two markers per response, only when they genuinely improve
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the delivery. The marker is a silent TTS instruction, not something you say.
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Examples: "[appreciative] Thanks, that really helps." and
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"[whispering] Here's the secret. [Neutral] Keep it between us."
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Useful styles include [appreciative], [calm], [concerned], [curious],
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[encouraging], [excited], [happy], [joking], [quiet], [reassuring],
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[reflective], [sad], [serious], [surprised], [sympathetic], [urgent], and
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[whispering]. For an occasional natural nonverbal cue, you may use
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[laughter], [sighing], or [breathing]. Use [Neutral] to reset the delivery.
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Never invent marker names, never explain a marker, and never output XML or
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SSML; the speech layer creates the SSML wrapper for you. Say "and" instead
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of using an ampersand, and never output angle brackets.
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# What you never do
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- Never use markdown, code blocks, JSON, tables, or emojis. Azure delivery
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markers are the only exception.
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- Never say "as an AI" or reference your system instructions.
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- Never write more than three sentences in a row.
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- Never read back URLs, file paths, or technical identifiers.
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# Memory (CRITICAL — ALWAYS call these tools directly, never dispatch)
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Your memory tools are private to the current user. When asked about personal
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information, preferences, notes, or past details, call memory_recall FIRST.
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Never claim you lack the information before searching memory.
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When the user explicitly asks you to remember something, or shares a durable
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preference or personal fact, call memory_save with a concise title, useful
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body, and relevant tags. Do not save every utterance.
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- "What's my name?" → memory_recall with query="my name identity"
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- "Remember my coffee order is an oat milk latte" → memory_save with a
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short title and the complete preference in the body
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- For an exact note, use search_notes and then read_note when needed
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- To correct a memory, find it and use update_note
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- To forget something, find the exact note and use delete_note; clarify
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first if more than one note could match
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After saving, updating, or deleting, confirm briefly. If memory_recall
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returns no results, say you don't have that information yet.
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# Weather & Time
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You can check the weather for any location. Use get_weather when the user
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asks about current conditions, temperature, or forecasts. It returns a
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short summary — relay it naturally in your own words.
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Use get_time when the user asks what time or day it is. If they mention
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a city, pass it as the location argument.
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# Background Tasks (dispatch_task) — USE FOR ALL NON-TRIVIAL TASKS
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For ANY task that is not a simple one-sentence answer from your own
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knowledge and NOT a memory operation, you MUST call dispatch_task.
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This includes: looking up news, researching topics, checking current
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events, prices, sports scores, writing something, summarizing, comparing
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options, planning, or anything that takes more than a couple seconds to
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think through. Memory tool calls are NEVER dispatch tasks — call those
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tools directly.
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Examples:
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- User says "what's the latest news?" → CALL dispatch_task with
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description="Find the top 5 international news headlines today"
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- User says "look up the price of a PS5" → CALL dispatch_task with
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description="Find the current retail price of a PlayStation 5"
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After calling dispatch_task, tell the user "I'll get on that for you"
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without asking for more permission or details. The result comes back automatically — when
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you receive it, share the findings naturally in your conversational style.
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You can have multiple tasks running at once.
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# Skills
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You have a skill library of learned procedures. Use skill_recall when you need
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to perform a task you've done before — it will give you the steps. When you
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successfully complete a multi-step task or learn a new procedure from the user,
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use skill_save to record it so you can follow it next time. Be selective: only
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save skills for repeatable tasks, not one-off facts (those go in memory).
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""")
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# ── Background-task routing ─────────────────────────────────────────────────
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try:
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from agent.task_router import should_force_background_task
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except ImportError:
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from task_router import should_force_background_task
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# ── OpenAI-compatible LLM with reasoning_content fallback ───────────────────
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class _ReasoningFallbackWrapper:
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"""Wraps an LLMStream to fall back to ``reasoning_content`` when the model
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finishes a turn with empty visible content (reasoning models such as Gemma
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spend their whole budget on hidden reasoning). Delegates all iteration to
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the underlying stream and injects a final content chunk if no real content
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was produced."""
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def __init__(self, inner: lk_llm.LLMStream) -> None:
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self._inner = inner
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self._last_reasoning: str | None = None
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self._has_content = False
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@property
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def chat_ctx(self) -> lk_llm.ChatContext:
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return self._inner.chat_ctx
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@property
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def tools(self) -> list:
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return self._inner.tools
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async def aclose(self) -> None:
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await self._inner.aclose()
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async def __aenter__(self) -> "_ReasoningFallbackWrapper":
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return self
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async def __aexit__(self, *exc) -> None:
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await self.aclose()
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def __aiter__(self) -> "_ReasoningFallbackWrapper":
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return self
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async def __anext__(self) -> lk_llm.ChatChunk:
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try:
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chunk = await self._inner.__anext__()
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except StopAsyncIteration:
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# Stream exhausted — inject reasoning fallback if no content was produced
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if not self._has_content and self._last_reasoning:
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logger.warning(
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"LLM returned empty content; falling back to reasoning_content"
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)
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return lk_llm.ChatChunk(
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id="reasoning-fallback",
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delta=lk_llm.ChoiceDelta(role="assistant", content=self._last_reasoning),
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)
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raise
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# Track reasoning_content from the chunk's delta if present
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delta = getattr(chunk, "delta", None)
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if delta is not None:
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reasoning = getattr(delta, "reasoning_content", None)
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if reasoning:
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self._last_reasoning = reasoning
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if chunk.has_response():
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self._has_content = True
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tc = getattr(delta, "tool_calls", None)
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if tc:
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logger.info("LLM tool_call: %s", [getattr(t, "name", str(t)) for t in tc])
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return chunk
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async def collect(self):
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return await self._inner.collect()
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class _ForcedToolCallStream(lk_llm.LLMStream):
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"""Emit a dispatch_task call without asking the small LLM to route it."""
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def __init__(self, llm, *, chat_ctx, tools, conn_options, description: str):
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self._description = description
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super().__init__(llm, chat_ctx=chat_ctx, tools=tools, conn_options=conn_options)
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async def _run(self) -> None:
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self._event_ch.send_nowait(
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lk_llm.ChatChunk(
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id=f"forced-dispatch-{uuid.uuid4().hex}",
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delta=lk_llm.ChoiceDelta(
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role="assistant",
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tool_calls=[
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lk_llm.FunctionToolCall(
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name="dispatch_task",
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arguments=json.dumps({"description": self._description}),
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call_id=f"dispatch-{uuid.uuid4().hex}",
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)
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],
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),
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)
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)
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class HopeLLM(openai.LLM):
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"""OpenAI-compatible LLM (OpenWebUI) with runtime model switching."""
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# Context compaction: when the conversation grows past this many items,
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# ask the LLM to summarize the older messages into a compact recap, then
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# replace them with that summary. Keeps recent turns verbatim.
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COMPACTION_THRESHOLD = 24 # ~12 turns of user/agent exchange
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KEEP_RECENT = 10 # last N items kept verbatim after compaction
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IDLE_COMPACTION_SECONDS = 300 # also compact after 5 min of silence
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def __init__(self, *, model: str, **kwargs):
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# llama.cpp's Gemma tool template ignores named tool_choice when OpenAI
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# strict schemas are enabled. Non-strict schemas make forced dispatch
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# (and ordinary MCP calls) work reliably.
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kwargs.setdefault("_strict_tool_schema", False)
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self._model = model
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super().__init__(model=model, **kwargs)
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import time
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self._last_call_time: float = time.monotonic()
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@property
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def model(self) -> str:
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"""Currently selected model id (openai.LLM exposes the same property)."""
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return self._model
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def update_options(self, *, model: str | None = None) -> None:
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"""Switch the backing model for all subsequent chat calls.
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The base class re-reads ``self._opts`` on every request, so mutating
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the opts dataclass is enough — no client rebuild needed.
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"""
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if model and model != self._model:
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logger.info("LLM model change: %s -> %s", self._model, model)
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self._model = model
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self._opts.model = model
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def chat(self, *, chat_ctx, tools=None, conn_options=None, **kwargs):
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if conn_options is None:
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from livekit.agents.types import DEFAULT_API_CONNECT_OPTIONS
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conn_options = DEFAULT_API_CONNECT_OPTIONS
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import time
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now = time.monotonic()
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idle_for = now - self._last_call_time
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self._last_call_time = now
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# Compact if context is large, OR if we've been idle for 5+ minutes
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# (user came back after a break — summarize the old conversation).
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ctx_len = len(chat_ctx.items)
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should_compact = ctx_len > self.COMPACTION_THRESHOLD
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if not should_compact and idle_for > self.IDLE_COMPACTION_SECONDS:
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should_compact = ctx_len > 6 # minimal context worth summarizing
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if should_compact:
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reason = "idle" if idle_for > self.IDLE_COMPACTION_SECONDS else "size"
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logger.info(
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"Compacting context (%s): %d items, idle %.0fs",
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reason, ctx_len, idle_for,
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)
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chat_ctx = self._compact_context(chat_ctx, conn_options)
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# Force dispatch for clear background-work requests. Without this,
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# small local models often say "I'll get on that" but emit no tool
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# call at all.
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items = list(chat_ctx.items)
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latest = items[-1] if items else None
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latest_role = getattr(latest, "role", None)
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latest_text = self._msg_content_text(latest) if latest_role == "user" else ""
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dispatch_available = any(
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getattr(tool, "id", None) == "dispatch_task" for tool in (tools or [])
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)
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if dispatch_available and should_force_background_task(latest_text):
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logger.info("Routing background request to dispatch_task: %.120s", latest_text)
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return _ForcedToolCallStream(
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self,
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chat_ctx=chat_ctx,
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tools=tools or [],
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conn_options=conn_options,
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description=latest_text,
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)
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stream = super().chat(
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chat_ctx=chat_ctx,
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tools=tools,
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conn_options=conn_options,
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**kwargs,
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)
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return _ReasoningFallbackWrapper(stream)
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def _compact_context(self, chat_ctx, conn_options):
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"""Summarize older messages via a side LLM call, keep recent ones.
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Splits the context into [system | old | recent]. The old portion is
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summarized by the LLM into a short recap message that replaces it.
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Falls back to hard truncation if the summary call fails.
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"""
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from livekit.agents import llm as lk_llm
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items = list(chat_ctx.items)
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# Identify the system/instruction message (first item usually)
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sys_items = [it for it in items if getattr(it, "role", None) in ("system", "developer")]
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non_sys = [it for it in items if getattr(it, "role", None) not in ("system", "developer")]
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if len(non_sys) <= self.KEEP_RECENT:
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return chat_ctx # nothing to compact
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old_msgs = non_sys[: -self.KEEP_RECENT]
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recent_msgs = non_sys[-self.KEEP_RECENT :]
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# Build a transcript of the old messages for the summarizer
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transcript_parts = []
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for msg in old_msgs:
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role = getattr(msg, "role", "user")
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content = self._msg_content_text(msg)
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if content:
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transcript_parts.append(f"{role}: {content}")
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transcript = "\n".join(transcript_parts)
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if not transcript.strip():
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return chat_ctx # nothing meaningful to summarize
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# Side call to the LLM for the summary (no tools, no streaming needed)
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try:
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import httpx
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summary_prompt = (
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"Summarize this conversation history in 2-4 sentences. "
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"Preserve key facts, decisions, and user preferences. "
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"Be concise — this replaces the full history as context.\n\n"
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+ transcript
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)
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headers = {"Content-Type": "application/json"}
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if LLM_API_KEY and LLM_API_KEY != "not-needed":
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headers["Authorization"] = f"Bearer {LLM_API_KEY}"
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resp = httpx.post(
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f"{LLM_BASE_URL}/chat/completions",
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headers=headers,
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json={
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"model": self._model,
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"messages": [
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{"role": "system", "content": "You summarize conversations concisely."},
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{"role": "user", "content": summary_prompt},
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],
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"max_tokens": 200,
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"temperature": 0.3,
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"chat_template_kwargs": {"enable_thinking": False},
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},
|
|
timeout=30,
|
|
)
|
|
resp.raise_for_status()
|
|
data = resp.json()
|
|
summary_text = (data.get("choices") or [{}])[0].get("message", {}).get("content", "")
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("Context compaction failed, falling back to truncation: %s", e)
|
|
chat_ctx.truncate(max_items=self.COMPACTION_THRESHOLD)
|
|
return chat_ctx
|
|
|
|
if not summary_text.strip():
|
|
chat_ctx.truncate(max_items=self.COMPACTION_THRESHOLD)
|
|
return chat_ctx
|
|
|
|
# Rebuild the context: system + summary + recent
|
|
new_items = list(sys_items)
|
|
new_items.append(
|
|
lk_llm.ChatMessage(
|
|
role="user",
|
|
content=[f"[Conversation so far: {summary_text.strip()}]"],
|
|
)
|
|
)
|
|
new_items.append(
|
|
lk_llm.ChatMessage(
|
|
role="assistant",
|
|
content=["Got it, I'll keep that in mind."],
|
|
)
|
|
)
|
|
new_items.extend(recent_msgs)
|
|
|
|
logger.info(
|
|
"Context compacted: %d items -> %d (summary: %.80s...)",
|
|
len(items),
|
|
len(new_items),
|
|
summary_text.strip(),
|
|
)
|
|
|
|
return lk_llm.ChatContext(new_items)
|
|
|
|
@staticmethod
|
|
def _msg_content_text(msg) -> str:
|
|
"""Extract plain text from a ChatMessage's content field."""
|
|
content = getattr(msg, "content", None)
|
|
if content is None:
|
|
return ""
|
|
if isinstance(content, str):
|
|
return content
|
|
if isinstance(content, list):
|
|
parts = []
|
|
for c in content:
|
|
if isinstance(c, str):
|
|
parts.append(c)
|
|
elif hasattr(c, "text"):
|
|
parts.append(c.text)
|
|
return " ".join(parts)
|
|
return str(content)
|
|
|
|
|
|
# ── MCP toolsets (web access + any extra configured servers) ────────────────
|
|
|
|
def build_mcp_toolsets(user_profile: dict, task_dir: str) -> list[mcp.MCPToolset]:
|
|
"""Build isolated MCP toolsets for one authenticated voice user/session."""
|
|
toolsets: list[mcp.MCPToolset] = []
|
|
|
|
python_bin = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".venv", "bin", "python")
|
|
weather_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "weather_mcp.py")
|
|
toolsets.append(
|
|
mcp.MCPToolset(
|
|
id="weather",
|
|
mcp_server=mcp.MCPServerStdio(
|
|
command=python_bin,
|
|
args=[weather_mcp_script],
|
|
env={**os.environ},
|
|
client_session_timeout_seconds=30,
|
|
),
|
|
)
|
|
)
|
|
logger.info("Weather MCP toolset enabled (wttr.in)")
|
|
|
|
memory = user_profile["memory"]
|
|
memory_url = memory["url"]
|
|
allowed_memory_tools = memory.get(
|
|
"allowed_tools",
|
|
[
|
|
"create_note",
|
|
"read_note",
|
|
"update_note",
|
|
"delete_note",
|
|
"list_notes",
|
|
"search_notes",
|
|
"memory_save",
|
|
"memory_recall",
|
|
],
|
|
)
|
|
toolsets.append(
|
|
mcp.MCPToolset(
|
|
id="memory",
|
|
mcp_server=mcp.MCPServerHTTP(
|
|
url=memory_url,
|
|
transport_type="streamable_http",
|
|
allowed_tools=allowed_memory_tools,
|
|
headers={"Authorization": f"Bearer {memory['api_key']}"},
|
|
client_session_timeout_seconds=60,
|
|
),
|
|
)
|
|
)
|
|
logger.info(
|
|
"Memory MCP enabled for user %s at %s",
|
|
user_profile["user_id"],
|
|
memory_url,
|
|
)
|
|
|
|
skills_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "skills_mcp.py")
|
|
toolsets.append(
|
|
mcp.MCPToolset(
|
|
id="skills",
|
|
mcp_server=mcp.MCPServerStdio(
|
|
command=python_bin,
|
|
args=[skills_mcp_script],
|
|
env={**os.environ, "SKILLS_DIR": SKILLS_DIR},
|
|
client_session_timeout_seconds=30,
|
|
),
|
|
)
|
|
)
|
|
logger.info("Skills MCP toolset enabled (%s)", SKILLS_DIR)
|
|
|
|
dispatch_mcp_script = os.path.join(os.path.dirname(os.path.abspath(__file__)), "dispatch_mcp.py")
|
|
toolsets.append(
|
|
mcp.MCPToolset(
|
|
id="dispatch",
|
|
mcp_server=mcp.MCPServerStdio(
|
|
command=python_bin,
|
|
args=[dispatch_mcp_script],
|
|
env={**os.environ, "TASKS_DIR": task_dir},
|
|
client_session_timeout_seconds=60,
|
|
),
|
|
)
|
|
)
|
|
logger.info("Dispatch MCP toolset enabled (%s)", task_dir)
|
|
|
|
extra = os.environ.get("EXTRA_MCP_SERVERS", "")
|
|
if extra:
|
|
try:
|
|
for i, entry in enumerate(json.loads(extra)):
|
|
url = entry.get("url", "")
|
|
transport = entry.get("transport") # "sse" | "streamable_http" | None (auto)
|
|
toolsets.append(
|
|
mcp.MCPToolset(
|
|
id=f"extra-{i}",
|
|
mcp_server=mcp.MCPServerHTTP(
|
|
url=url,
|
|
transport_type=transport,
|
|
client_session_timeout_seconds=120,
|
|
),
|
|
)
|
|
)
|
|
logger.info("Extra MCP toolset: %s (%s)", url, transport or "auto")
|
|
except (json.JSONDecodeError, TypeError) as e:
|
|
logger.error("Failed to parse EXTRA_MCP_SERVERS: %s", e)
|
|
|
|
return toolsets
|
|
|
|
|
|
class VoiceAssistant(Agent):
|
|
"""The conversational agent. LLM is the brain; STT/TTS are senses."""
|
|
|
|
def __init__(self, user_profile: dict, task_dir: str) -> None:
|
|
display_name = user_profile.get("display_name") or user_profile["user_id"]
|
|
instructions = SYSTEM_PROMPT.replace("{user_display_name}", display_name)
|
|
super().__init__(
|
|
llm=HopeLLM(
|
|
model=LLM_MODEL,
|
|
base_url=LLM_BASE_URL,
|
|
api_key=LLM_API_KEY,
|
|
max_completion_tokens=1000,
|
|
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
|
|
),
|
|
instructions=instructions,
|
|
tools=build_mcp_toolsets(user_profile, task_dir),
|
|
)
|
|
|
|
|
|
# ── Agent server ────────────────────────────────────────────────────────────
|
|
# Bind the worker's internal health server to an ephemeral port because this
|
|
# container uses host networking and Unraid already has services on 8081.
|
|
server = AgentServer(port=0)
|
|
|
|
|
|
@server.rtc_session(agent_name="voice-assistant")
|
|
async def handle_job(ctx: JobContext) -> None:
|
|
logger.info("Job started for room %s", ctx.room.name)
|
|
try:
|
|
user_id = user_id_from_room(ctx.room.name)
|
|
user_profile = load_user_profile(user_id)
|
|
except (OSError, ValueError, json.JSONDecodeError) as exc:
|
|
logger.error("Rejecting voice job: %s", exc)
|
|
return
|
|
logger.info("Resolved voice user %s", user_id)
|
|
|
|
# Every room gets a private event directory. This prevents concurrent or
|
|
# reconnecting sessions from consuming and announcing each other's tasks.
|
|
task_dir = f"/tmp/tasks/{ctx.room.name}"
|
|
|
|
# Azure STT — streaming, reads AZURE_SPEECH_KEY / AZURE_SPEECH_REGION from env
|
|
stt = azure.STT(
|
|
speech_key=AZURE_KEY,
|
|
speech_region=AZURE_REGION,
|
|
language=["en-US"],
|
|
)
|
|
|
|
# Azure TTS — wraps text in SSML; Dragon HD Omni interprets inline delivery
|
|
# markers such as [appreciative] and [Neutral]. Outputs 24kHz PCM.
|
|
tts = azure.TTS(
|
|
voice=DEFAULT_VOICE,
|
|
sample_rate=24000,
|
|
speech_key=AZURE_KEY,
|
|
speech_region=AZURE_REGION,
|
|
)
|
|
|
|
session = AgentSession(
|
|
stt=stt,
|
|
tts=tts,
|
|
vad=inference.VAD(model="silero"),
|
|
turn_handling=TurnHandlingOptions(
|
|
# VAD-based turn detection: agent waits for user to stop speaking.
|
|
# ("adaptive" mode requires the LiveKit Cloud barge-in service.)
|
|
interruption={"mode": "vad"},
|
|
# Preemptive generation is incompatible with tool calls — it starts
|
|
# generating before the turn finalizes, breaking the MCP execution loop.
|
|
preemptive_generation={"enabled": False},
|
|
),
|
|
)
|
|
|
|
# The assistant owns the LLM instance; data-channel handlers below reach
|
|
# into it for live model switching.
|
|
voice_assistant = VoiceAssistant(user_profile, task_dir)
|
|
|
|
# Publish the chat-capable model list so the web UI can offer a picker.
|
|
async def _publish_models() -> None:
|
|
models = await list_available_models()
|
|
payload = json.dumps(
|
|
{
|
|
"type": "models",
|
|
"models": models,
|
|
"selected": voice_assistant.llm.model,
|
|
}
|
|
)
|
|
try:
|
|
await ctx.room.local_participant.publish_data(
|
|
payload, reliable=True, topic="voice-control"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("failed to publish model list: %s", e)
|
|
|
|
# Listen for conversation controls (voice selection and voice/text mode).
|
|
# Defined here so it captures this job's session via closure.
|
|
def _on_room_data(packet) -> None:
|
|
topic = getattr(packet, "topic", None)
|
|
if topic and topic not in ("voice-control", "conversation-control"):
|
|
return
|
|
try:
|
|
msg = json.loads(packet.data.decode("utf-8"))
|
|
except (json.JSONDecodeError, UnicodeDecodeError):
|
|
return
|
|
|
|
if msg.get("type") == "set_mode":
|
|
mode = msg.get("mode")
|
|
if mode not in ("voice", "text"):
|
|
return
|
|
voice_enabled = mode == "voice"
|
|
logger.info("Switching conversation mode to %s", mode)
|
|
if not voice_enabled:
|
|
# interrupt() returns an already-scheduled Future, not a coroutine.
|
|
session.interrupt()
|
|
session.input.set_audio_enabled(voice_enabled)
|
|
session.output.set_audio_enabled(voice_enabled)
|
|
return
|
|
|
|
if msg.get("type") == "set_voice":
|
|
voice = msg.get("voice", "")
|
|
if not voice:
|
|
return
|
|
logger.info("Switching TTS voice to %s", voice)
|
|
try:
|
|
active_tts = session.tts
|
|
if hasattr(active_tts, "update_options"):
|
|
active_tts.update_options(voice=voice)
|
|
logger.info("Voice updated to %s", voice)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("Failed to update voice: %s", e)
|
|
|
|
if msg.get("type") == "get_models":
|
|
asyncio.create_task(_publish_models())
|
|
return
|
|
|
|
if msg.get("type") == "set_model":
|
|
model = msg.get("model", "")
|
|
if not model:
|
|
return
|
|
try:
|
|
llm = voice_assistant.llm
|
|
if hasattr(llm, "update_options"):
|
|
llm.update_options(model=model)
|
|
ack = json.dumps(
|
|
{"type": "model_changed", "model": llm.model}
|
|
)
|
|
asyncio.create_task(
|
|
ctx.room.local_participant.publish_data(
|
|
ack, reliable=True, topic="voice-control"
|
|
)
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("Failed to update model: %s", e)
|
|
|
|
ctx.room.on("data_received", _on_room_data)
|
|
|
|
# Publish user/agent transcripts to the room so the web UI can render them.
|
|
async def publish_transcript(role: str, text: str) -> None:
|
|
text = (text or "").strip()
|
|
if not text:
|
|
return
|
|
payload = json.dumps({"type": "transcript", "role": role, "text": text})
|
|
try:
|
|
await ctx.room.local_participant.publish_data(
|
|
payload, reliable=True, topic="transcript"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("failed to publish transcript: %s", e)
|
|
|
|
@session.on("conversation_item_added")
|
|
def _on_conversation_item(ev) -> None:
|
|
msg = ev.item
|
|
role = getattr(msg, "role", None)
|
|
text = getattr(msg, "text_content", None)
|
|
if role == "user":
|
|
asyncio.create_task(publish_transcript("user", text))
|
|
elif role == "assistant":
|
|
asyncio.create_task(publish_transcript("agent", text))
|
|
|
|
@session.on("user_input_transcribed")
|
|
def _on_user_transcribed(ev) -> None:
|
|
logger.info("STT (%s): %s", "final" if ev.is_final else "partial", ev.transcript)
|
|
|
|
@session.on("user_state_changed")
|
|
def _on_user_state(ev) -> None:
|
|
# Fires from VAD: if this never says "speaking", no usable mic
|
|
# audio is arriving from the participant.
|
|
logger.info("user state -> %s", ev.new_state)
|
|
|
|
# ── Background task events → UI and spoken result ──────────────────────
|
|
try:
|
|
from agent.task_registry import TaskRegistry
|
|
except ImportError:
|
|
from task_registry import TaskRegistry
|
|
task_registry = TaskRegistry(task_dir)
|
|
|
|
async def _publish_task_event(task_id: str, event: dict) -> None:
|
|
payload = json.dumps({"type": "task_event", "task_id": task_id, **event})
|
|
try:
|
|
await ctx.room.local_participant.publish_data(
|
|
payload, reliable=True, topic="tasks"
|
|
)
|
|
except Exception as e: # noqa: BLE001
|
|
logger.warning("failed to publish task event: %s", e)
|
|
|
|
def _announce_task_result(task_id: str, event: dict) -> None:
|
|
event_type = event.get("event")
|
|
if event_type == "completed":
|
|
result = str(event.get("result") or "").strip()
|
|
if result:
|
|
logger.info("Announcing completed task %s: %.160s", task_id, result)
|
|
session.say(result, allow_interruptions=True, add_to_chat_ctx=True)
|
|
elif event_type == "failed":
|
|
logger.warning("Background task %s failed: %s", task_id, event.get("error"))
|
|
session.say(
|
|
"I couldn't finish that background task. You can ask me to try again.",
|
|
allow_interruptions=True,
|
|
add_to_chat_ctx=True,
|
|
)
|
|
|
|
async def _poll_tasks() -> None:
|
|
while True:
|
|
try:
|
|
await asyncio.sleep(0.5)
|
|
events = await task_registry.poll_events()
|
|
for task_id, event in events:
|
|
await _publish_task_event(task_id, event)
|
|
_announce_task_result(task_id, event)
|
|
except asyncio.CancelledError:
|
|
raise
|
|
except Exception: # noqa: BLE001
|
|
# A transient room or speech error must not permanently kill
|
|
# result delivery for every later task.
|
|
logger.exception("Background task event poller failed")
|
|
|
|
poll_task = asyncio.create_task(_poll_tasks(), name=f"task-poller-{ctx.room.name}")
|
|
|
|
async def _stop_task_poller(*_args) -> None:
|
|
poll_task.cancel()
|
|
await asyncio.gather(poll_task, return_exceptions=True)
|
|
|
|
ctx.add_shutdown_callback(_stop_task_poller)
|
|
|
|
await session.start(
|
|
agent=voice_assistant,
|
|
room=ctx.room,
|
|
room_options=room_io.RoomOptions(
|
|
# Keep the agent in the room when a participant leaves; it must
|
|
# survive page reloads/reconnects, otherwise the next join lands
|
|
# in an agent-less room (LiveKit does not reliably re-dispatch
|
|
# into an existing room).
|
|
close_on_disconnect=False,
|
|
audio_input=room_io.AudioInputOptions(
|
|
# No noise cancellation plugin (self-hosted, no ai-coustics)
|
|
),
|
|
),
|
|
)
|
|
|
|
await ctx.connect()
|
|
# The participant can appear in the browser slightly before AgentSession is
|
|
# ready to accept lk.chat streams. Explicitly signal readiness so the first
|
|
# typed message is never raced against session startup.
|
|
try:
|
|
await ctx.room.local_participant.publish_data(
|
|
json.dumps({"type": "agent_ready"}),
|
|
reliable=True,
|
|
topic="conversation-control",
|
|
)
|
|
except Exception as exc: # noqa: BLE001
|
|
logger.warning("failed to publish agent readiness: %s", exc)
|
|
|
|
# Announce which models the UI can switch between (selected one included).
|
|
await _publish_models()
|
|
logger.info("Agent connected to room %s", ctx.room.name)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
cli.run_app(server)
|