feat: user personalization with Honcho-backed preferences
- WelcomeScreen: first-time name entry with cute onboarding - identify WS message: sets user_id, loads saved prefs from Honcho - set_preference WS message: saves scene/outfit/accessory to Honcho metadata - Preferences auto-load on return visits via localStorage + Honcho peer meta - Kira uses the user's name in greeting and prompts - Backend: get/set preference methods in KiraMemory service - Frontend: optimistic preference updates, synced to backend on change
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-52
@@ -6,6 +6,7 @@ Real-time speech-to-speech pipeline:
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Honcho memory integration:
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Cross-session user context injected into LLM prompts,
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conversation exchanges stored for continuous learning.
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User preferences (name, scene, outfit, accessory) persisted in peer metadata.
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"""
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import json
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@@ -65,10 +66,8 @@ def build_system_prompt(user_id: str) -> dict:
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"""Build system prompt with Honcho memory context injected."""
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base = BASE_SYSTEM_PROMPT
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# Append memory context if Honcho is available
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if kira_memory.enabled:
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try:
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# Get user-specific context from Honcho
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kira_memory.ensure_peers(user_id)
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memory_suffix = kira_memory.build_system_prompt_suffix()
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if memory_suffix:
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@@ -79,35 +78,89 @@ def build_system_prompt(user_id: str) -> dict:
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return {"role": "system", "content": base}
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def handle_identify(msg: dict, session_id: str) -> dict | None:
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"""Handle user identification. Returns user preferences or None."""
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user_id = msg.get("user_id", "").strip()
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if not user_id:
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return {"type": "error", "message": "user_id is required"}
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user_name = msg.get("name", "").strip()
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if user_name:
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kira_memory.set_user_preference(user_id, "name", user_name)
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prefs = kira_memory.get_user_preferences(user_id)
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logger.info(f"[{session_id}] Identified as {user_id} (name={user_name or prefs.get('name', '')})")
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return {
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"type": "identified",
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"user_id": user_id,
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"preferences": prefs,
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}
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def handle_set_preference(msg: dict, session_id: str, user_id: str) -> dict | None:
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"""Handle preference update. Returns success status."""
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if not user_id or user_id == "default-user":
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return {"type": "error", "message": "Must identify first"}
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key = msg.get("key", "").strip()
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value = msg.get("value", "").strip()
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if not key:
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return {"type": "error", "message": "key is required"}
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ok = kira_memory.set_user_preference(user_id, key, value)
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return {
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"type": "preference_saved",
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"key": key,
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"success": ok,
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}
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@app.websocket("/api/ws")
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async def conversation_ws(websocket: WebSocket):
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await websocket.accept()
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session_id = str(uuid.uuid4())[:8]
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user_id = "default-user"
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identified = False
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logger.info(f"[{session_id}] WebSocket connected")
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# Audio buffer accumulates chunks from one utterance
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audio_buffer = bytearray()
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conversation_history: list[dict] = []
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# Initialize Honcho for this session
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if kira_memory.enabled:
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try:
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kira_memory.ensure_peers(user_id)
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kira_memory.ensure_session(session_id)
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logger.info(f"[{session_id}] Honcho session ready")
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except Exception as e:
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logger.warning(f"[{session_id}] Honcho setup failed: {e}")
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try:
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first_message = True
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while True:
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raw = await websocket.receive_text()
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msg = json.loads(raw)
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msg_type = msg.get("type", "")
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# Build system prompt fresh each turn to get latest Honcho context
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# ── Identity & Preferences ──
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if msg_type == "identify":
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response = handle_identify(msg, session_id)
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if response:
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await websocket.send_json(response)
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if response["type"] == "identified":
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user_id = response["user_id"]
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identified = True
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# Set up Honcho for this user
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if kira_memory.enabled:
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try:
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kira_memory.ensure_peers(user_id)
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kira_memory.ensure_session(session_id)
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logger.info(f"[{session_id}] Honcho session ready for {user_id}")
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except Exception as e:
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logger.warning(f"[{session_id}] Honcho setup failed: {e}")
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continue
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if msg_type == "set_preference":
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response = handle_set_preference(msg, session_id, user_id)
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if response:
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await websocket.send_json(response)
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continue
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# ── Conversation ──
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system_prompt = build_system_prompt(user_id)
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if msg_type == "audio_chunk":
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@@ -121,7 +174,6 @@ async def conversation_ws(websocket: WebSocket):
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logger.info(f"[{session_id}] Transcribing {len(audio_buffer)} bytes...")
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# 1. Speech-to-text
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transcript = await transcribe_audio(bytes(audio_buffer))
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audio_buffer.clear()
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@@ -129,45 +181,27 @@ async def conversation_ws(websocket: WebSocket):
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await websocket.send_json({"type": "error", "message": "Could not transcribe audio"})
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continue
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# Echo transcript
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await websocket.send_json({
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"type": "transcript",
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"text": transcript,
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})
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await websocket.send_json({"type": "transcript", "text": transcript})
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# 2. LLM call
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logger.info(f"[{session_id}] User: {transcript}")
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user_msg = {"role": "user", "content": transcript}
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conversation_history.append(user_msg)
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conversation_history.append({"role": "user", "content": transcript})
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messages = [system_prompt] + conversation_history[-10:]
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kira_text = await get_kira_response(messages)
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assistant_msg = {"role": "assistant", "content": kira_text}
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conversation_history.append(assistant_msg)
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conversation_history.append({"role": "assistant", "content": kira_text})
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logger.info(f"[{session_id}] Kira: {kira_text}")
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# 3. Store in Honcho
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if kira_memory.enabled:
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if kira_memory.enabled and identified:
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try:
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kira_memory.store_messages(transcript, kira_text)
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except Exception as e:
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logger.warning(f"[{session_id}] Failed to store messages: {e}")
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# 4. TTS
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await websocket.send_json({
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"type": "speaking_start",
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"text": kira_text,
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})
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await websocket.send_json({"type": "speaking_start", "text": kira_text})
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audio_bytes = await synthesize_speech(kira_text)
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audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
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await websocket.send_json({
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"type": "audio",
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"data": audio_b64,
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"text": kira_text,
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})
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await websocket.send_json({"type": "audio", "data": audio_b64, "text": kira_text})
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await websocket.send_json({"type": "speaking_end"})
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elif msg_type == "ping":
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@@ -179,32 +213,24 @@ async def conversation_ws(websocket: WebSocket):
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continue
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logger.info(f"[{session_id}] User (text): {user_text}")
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user_msg = {"role": "user", "content": user_text}
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conversation_history.append(user_msg)
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conversation_history.append({"role": "user", "content": user_text})
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messages = [system_prompt] + conversation_history[-10:]
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kira_text = await get_kira_response(messages)
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assistant_msg = {"role": "assistant", "content": kira_text}
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conversation_history.append(assistant_msg)
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conversation_history.append({"role": "assistant", "content": kira_text})
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logger.info(f"[{session_id}] Kira: {kira_text}")
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# Store in Honcho
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if kira_memory.enabled:
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if kira_memory.enabled and identified:
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try:
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kira_memory.store_messages(user_text, kira_text)
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except Exception as e:
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logger.warning(f"[{session_id}] Failed to store messages: {e}")
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# TTS
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await websocket.send_json({"type": "speaking_start", "text": kira_text})
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audio_bytes = await synthesize_speech(kira_text)
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audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
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await websocket.send_json({
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"type": "audio",
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"data": audio_b64,
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"text": kira_text,
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})
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await websocket.send_json({"type": "audio", "data": audio_b64, "text": kira_text})
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await websocket.send_json({"type": "speaking_end"})
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except WebSocketDisconnect:
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@@ -178,6 +178,54 @@ class KiraMemory:
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except Exception as e:
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logger.warning(f"Failed to store Kira message: {e}")
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# ─── User preferences (stored in Honcho peer metadata) ───
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DEFAULT_PREFERENCES: dict[str, str] = {
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"name": "",
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"scene": "cozy-room",
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"outfit": "cozy-hoodie",
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"accessory": "",
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}
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def get_user_preferences(self, user_id: str) -> dict[str, str]:
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"""Load user preferences from Honcho peer metadata."""
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if not self.enabled:
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return dict(self.DEFAULT_PREFERENCES)
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try:
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peer = self._honcho.peer(user_id)
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meta = peer.metadata or {}
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prefs = dict(self.DEFAULT_PREFERENCES)
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for key in prefs:
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val = meta.get(key)
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if val is not None:
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prefs[key] = str(val)
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logger.info(f"Loaded preferences for {user_id}: {prefs}")
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return prefs
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except Exception as e:
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logger.warning(f"Failed to load preferences: {e}")
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return dict(self.DEFAULT_PREFERENCES)
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def set_user_preference(
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self, user_id: str, key: str, value: str
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) -> bool:
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"""Save a single user preference to Honcho peer metadata."""
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if key not in self.DEFAULT_PREFERENCES:
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logger.warning(f"Unknown preference key: {key}")
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return False
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try:
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peer = self._honcho.peer(user_id)
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# Merge with existing metadata
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meta = dict(peer.metadata or {})
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meta[key] = value
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peer.set_metadata(meta)
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logger.info(f"Saved preference {user_id}.{key} = {value}")
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return True
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except Exception as e:
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logger.warning(f"Failed to save preference: {e}")
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return False
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# Singleton instance for the app
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kira_memory = KiraMemory()
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