feat: cheapest pipeline — gpt-4o-mini-transcribe + gpt-5.4-nano + TTS
Simple 3-step chat completions pipeline at ~/usr/bin/bash.019/min total. Streams PCM16 audio from frontend, transcribes on release, generates response via gpt-5.4-nano, speaks via OpenAI TTS. Cost breakdown: gpt-4o-mini-transcribe: /usr/bin/bash.003/min gpt-5.4-nano: ~/usr/bin/bash.001/min OpenAI TTS (nova): /usr/bin/bash.015/min Total: ~/usr/bin/bash.019/min (~/usr/bin/bash.57/day at 30min)
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@@ -1,6 +1,7 @@
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"""Kira — AI body double backend
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Hybrid pipeline: gpt-realtime-whisper (streaming STT) → gpt-5.4-nano (LLM) → OpenAI TTS
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Cheapest pipeline: gpt-4o-mini-transcribe STT → gpt-5.4-nano LLM → OpenAI TTS
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~$0.019/min total, simple 3-step chat completions.
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"""
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import json
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@@ -13,7 +14,6 @@ from fastapi import FastAPI, WebSocket, WebSocketDisconnect
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from fastapi.middleware.cors import CORSMiddleware
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from config import settings
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from services.hybrid import HybridPipeline
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from services.memory import kira_memory
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logging.basicConfig(level=logging.INFO)
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@@ -29,6 +29,25 @@ app.add_middleware(
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allow_headers=["*"],
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)
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# System prompt
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BASE_SYSTEM_PROMPT = (
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"You are Kira, a warm, kind, and encouraging AI body double. "
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"You speak in a friendly, girly-pop tone. You are helping someone with ADHD "
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"stay focused and on task. Keep responses short, supportive, and uplifting. "
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"Check in on them. Remind them to take breaks. Celebrate small wins. "
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"Use occasional emoji but don't overdo it. Never be judgmental."
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)
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_openai = None
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def get_openai():
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global _openai
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if _openai is None:
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from openai import AsyncOpenAI
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_openai = AsyncOpenAI(api_key=settings.openai_api_key)
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return _openai
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@app.on_event("startup")
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async def startup():
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@@ -44,6 +63,69 @@ async def health():
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return {"status": "ok", "name": "kira", "memory": mem_status}
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def build_system_prompt(user_id: str) -> str:
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prompt = BASE_SYSTEM_PROMPT
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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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suffix = kira_memory.build_system_prompt_suffix()
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if suffix:
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prompt += suffix
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except Exception as e:
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logger.warning(f"Memory context failed: {e}")
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return prompt
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async def run_conversation(text: str, user_id: str) -> str:
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"""STT → LLM → TTS using the cheapest models."""
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system_prompt = build_system_prompt(user_id)
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client = get_openai()
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# LLM
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resp = await client.chat.completions.create(
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model="gpt-5.4-nano",
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": text},
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],
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max_tokens=300,
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temperature=0.7,
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)
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kira_text = resp.choices[0].message.content or "Mhm, I'm here!"
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return kira_text
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async def transcribe_audio(audio_bytes: bytes) -> str | None:
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"""Transcribe audio bytes using cheapest STT model."""
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client = get_openai()
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try:
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transcript = await client.audio.transcriptions.create(
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model="gpt-4o-mini-transcribe",
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file=("audio.webm", audio_bytes, "audio/webm"),
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response_format="text",
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)
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return transcript.strip() if transcript and transcript.strip() else None
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except Exception as e:
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logger.warning(f"STT error: {e}")
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return None
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async def synthesize_speech(text: str) -> bytes:
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"""Generate TTS audio from text."""
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client = get_openai()
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try:
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resp = await client.audio.speech.create(
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model="tts-1",
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voice="nova",
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input=text,
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response_format="opus",
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)
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return resp.content
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except Exception as e:
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logger.warning(f"TTS error: {e}")
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return b""
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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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@@ -52,92 +134,8 @@ async def conversation_ws(websocket: WebSocket):
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identified = False
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logger.info(f"[{session_id}] WebSocket connected")
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pending_transcripts: list[str] = []
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pipeline: HybridPipeline | None = None
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pipeline_task: asyncio.Task | None = None
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pipeline_ready = asyncio.Event()
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audio_queue: asyncio.Queue[bytes] = asyncio.Queue()
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text_queue: asyncio.Queue[str] = asyncio.Queue()
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memory_suffix = ""
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async def on_ready():
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pipeline_ready.set()
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logger.info(f"[{session_id}] Pipeline ready")
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async def on_transcript_delta(delta: str):
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"""Streaming partial transcript."""
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await websocket.send_json({"type": "transcript_delta", "text": delta})
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async def on_transcript_done(full: str):
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"""Full utterance received."""
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await websocket.send_json({"type": "transcript", "role": "user", "text": full})
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async def on_audio_delta(audio_bytes: bytes):
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"""Forward TTS audio to client."""
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try:
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audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
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await websocket.send_json({"type": "audio", "data": audio_b64})
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except Exception:
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pass
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async def on_speech_start():
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await websocket.send_json({"type": "speaking_start"})
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async def on_speech_end():
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await websocket.send_json({"type": "speaking_end"})
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async def on_error(msg: str):
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await websocket.send_json({"type": "error", "message": msg})
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# Create pipeline
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pipeline = HybridPipeline(
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on_transcript_delta=on_transcript_delta,
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on_transcript_done=on_transcript_done,
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on_audio_delta=on_audio_delta,
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on_speech_start=on_speech_start,
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on_speech_end=on_speech_end,
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on_ready=on_ready,
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on_error=on_error,
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memory_suffix=memory_suffix,
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)
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pipeline_task = asyncio.create_task(pipeline.connect())
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try:
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await asyncio.wait_for(pipeline_ready.wait(), timeout=15)
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except asyncio.TimeoutError:
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logger.error(f"[{session_id}] Pipeline failed to connect")
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await websocket.send_json({"type": "error", "message": "Failed to connect to AI"})
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pipeline_task.cancel()
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return
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# Forward audio/text from client to pipeline
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async def forward_audio():
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while pipeline and pipeline._connected:
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try:
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pcm16 = await asyncio.wait_for(audio_queue.get(), timeout=1)
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await pipeline.send_audio(pcm16)
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except asyncio.TimeoutError:
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continue
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except Exception:
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break
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async def forward_text():
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while pipeline and pipeline._connected:
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try:
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text = await asyncio.wait_for(text_queue.get(), timeout=1)
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await pipeline.send_text(text)
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# Store in Honcho
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if kira_memory.enabled and identified:
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kira_memory.store_user_message(text)
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except asyncio.TimeoutError:
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continue
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except Exception:
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break
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fwd_audio = asyncio.create_task(forward_audio())
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fwd_text = asyncio.create_task(forward_text())
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audio_buffer = bytearray()
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conversation_history: list[dict] = []
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try:
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while True:
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@@ -145,7 +143,7 @@ async def conversation_ws(websocket: WebSocket):
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msg = json.loads(raw)
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msg_type = msg.get("type", "")
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# ── Identity ──
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# ── Identity & Preferences ──
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if msg_type == "identify":
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user_id = msg.get("user_id", "").strip()
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user_name = msg.get("name", "").strip()
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@@ -159,16 +157,6 @@ async def conversation_ws(websocket: WebSocket):
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kira_memory.ensure_peers(user_id)
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kira_memory.ensure_session(session_id)
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# Build memory context and update pipeline
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if kira_memory.enabled:
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try:
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ctx = kira_memory.build_system_prompt_suffix()
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if ctx:
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pipeline._memory_suffix = ctx
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memory_suffix = ctx
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except Exception:
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pass
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await websocket.send_json({
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"type": "identified",
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"user_id": user_id,
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@@ -176,40 +164,94 @@ async def conversation_ws(websocket: WebSocket):
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})
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continue
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# ── Preferences ──
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if msg_type == "set_preference":
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key = msg.get("key", "").strip()
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value = msg.get("value", "").strip()
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if key and user_id and user_id != "default-user":
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kira_memory.set_user_preference(user_id, key, value)
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await websocket.send_json({
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"type": "preference_saved",
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"key": key,
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"success": True,
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})
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continue
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# ── Audio (PCM16) ──
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# ── Conversation ──
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if msg_type == "audio":
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audio_b64 = msg.get("data", "")
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if audio_b64:
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pcm16 = base64.b64decode(audio_b64)
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await audio_queue.put(pcm16)
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continue
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# Accumulate PCM16 audio chunks
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chunk = base64.b64decode(msg["data"])
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audio_buffer.extend(chunk)
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# ── Text input ──
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if msg_type == "conversation_text":
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text = msg.get("text", "").strip()
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if text:
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await text_queue.put(text)
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continue
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elif msg_type == "transcribe":
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if not audio_buffer:
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await websocket.send_json({"type": "error", "message": "No audio data"})
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continue
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if msg_type == "ping":
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logger.info(f"[{session_id}] Transcribing {len(audio_buffer)} bytes...")
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# 1. STT
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transcript = await transcribe_audio(bytes(audio_buffer))
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audio_buffer.clear()
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if not transcript:
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await websocket.send_json({"type": "error", "message": "Could not transcribe"})
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continue
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await websocket.send_json({"type": "transcript", "role": "user", "text": transcript})
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conversation_history.append({"role": "user", "content": transcript})
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# 2. LLM
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logger.info(f"[{session_id}] User: {transcript}")
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kira_text = await run_conversation(transcript, user_id)
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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 and identified:
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try:
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kira_memory.store_messages(transcript, kira_text)
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except Exception:
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pass
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# 3. 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({"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 == "conversation_text":
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user_text = msg.get("text", "").strip()
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if not user_text:
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continue
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conversation_history.append({"role": "user", "content": user_text})
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logger.info(f"[{session_id}] User (text): {user_text}")
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kira_text = await run_conversation(user_text, user_id)
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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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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:
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pass
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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({"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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await websocket.send_json({"type": "pong"})
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except WebSocketDisconnect:
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logger.info(f"[{session_id}] Disconnected")
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except Exception as e:
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logger.error(f"[{session_id}] Error: {e}")
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finally:
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fwd_audio.cancel()
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fwd_text.cancel()
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if pipeline:
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await pipeline.disconnect()
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if pipeline_task:
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pipeline_task.cancel()
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try:
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await websocket.send_json({"type": "error", "message": str(e)})
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except Exception:
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pass
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