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)
This commit is contained in:
+162
-120
@@ -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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@@ -1,236 +0,0 @@
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"""Hybrid pipeline: streaming STT (gpt-realtime-whisper) + cheap LLM + TTS.
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Uses gpt-realtime-whisper for low-latency streaming transcription,
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gpt-5.4-nano as the brain, and OpenAI TTS for voice output.
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"""
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import json
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import base64
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import logging
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import asyncio
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from typing import Callable, Awaitable
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from openai import AsyncOpenAI
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from config import settings
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logger = logging.getLogger("kira.hybrid")
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# ─── System instructions for Kira's personality ───
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KIRA_INSTRUCTIONS = (
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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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class HybridPipeline:
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"""Streaming STT via gpt-realtime-whisper → gpt-5.4-nano LLM → OpenAI TTS."""
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def __init__(
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self,
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on_transcript_delta: Callable[[str], Awaitable[None]],
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on_transcript_done: Callable[[str], Awaitable[None]],
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on_audio_delta: Callable[[bytes], Awaitable[None]],
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on_speech_start: Callable[[], Awaitable[None]],
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on_speech_end: Callable[[], Awaitable[None]],
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on_ready: Callable[[], Awaitable[None]],
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on_error: Callable[[str], Awaitable[None]],
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memory_suffix: str = "",
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):
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self._on_transcript_delta = on_transcript_delta
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self._on_transcript_done = on_transcript_done
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self._on_audio_delta = on_audio_delta
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self._on_speech_start = on_speech_start
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self._on_speech_end = on_speech_end
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self._on_ready = on_ready
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self._on_error = on_error
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self._memory_suffix = memory_suffix
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self._openai = None
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self._conn = None
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self._connected = False
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self._transcript_buffer = ""
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async def connect(self):
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"""Connect to gpt-realtime-whisper via OpenAI Realtime API."""
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if self._connected:
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return
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try:
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import websockets
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self._openai = AsyncOpenAI(api_key=settings.openai_api_key)
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logger.info("Connecting to gpt-realtime-whisper...")
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# Connect directly via websockets to avoid the OpenAI-Beta header
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url = f"wss://api.openai.com/v1/realtime?model=gpt-realtime-whisper"
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ws = await websockets.connect(
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url,
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additional_headers={
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"Authorization": f"Bearer {settings.openai_api_key}",
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},
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)
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async with ws as conn:
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self._conn = conn
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self._connected = True
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logger.info("Connected to gpt-realtime-whisper")
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# Configure session for transcription
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await self._send({
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"type": "session.update",
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"session": {
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"input_audio_format": "pcm16",
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"input_audio_transcription": {
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"enabled": True,
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},
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"turn_detection": {
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"type": "server_vad",
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"threshold": 0.5,
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"prefix_padding_ms": 300,
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"silence_duration_ms": 600,
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},
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||||
},
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||||
})
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await self._on_ready()
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# Listen for transcription events
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while self._connected:
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try:
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raw = await conn.recv()
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if isinstance(raw, (str, bytes)):
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data = json.loads(raw if isinstance(raw, str) else raw.decode())
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await self._handle_event(data)
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except Exception as e:
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if self._connected:
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logger.warning(f"recv error: {e}")
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break
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||||
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except Exception as e:
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logger.error(f"Connection error: {e}")
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await self._on_error(str(e))
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finally:
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self._connected = False
|
||||
self._conn = None
|
||||
|
||||
async def _handle_event(self, event):
|
||||
"""Process events from gpt-realtime-whisper."""
|
||||
event_type = getattr(event, "type", None) or (event.get("type") if isinstance(event, dict) else "")
|
||||
|
||||
if event_type == "input_audio_buffer.speech_started":
|
||||
self._transcript_buffer = ""
|
||||
|
||||
elif event_type == "input_audio_buffer.speech_stopped":
|
||||
if self._transcript_buffer.strip():
|
||||
await self._process_transcript(self._transcript_buffer.strip())
|
||||
self._transcript_buffer = ""
|
||||
|
||||
elif event_type == "input_audio_buffer.transcription_delta":
|
||||
delta_text = self._get_field(event, "delta", "")
|
||||
if delta_text:
|
||||
self._transcript_buffer += delta_text
|
||||
|
||||
elif event_type == "conversation.item.created":
|
||||
item = self._get_field(event, "item", {})
|
||||
content = self._get_field(item, "content", [])
|
||||
for part in (content or []):
|
||||
part_type = self._get_field(part, "type", "")
|
||||
part_transcript = self._get_field(part, "transcript", "")
|
||||
if part_type == "transcript" and part_transcript:
|
||||
self._transcript_buffer = part_transcript
|
||||
await self._on_transcript_delta(part_transcript)
|
||||
|
||||
elif event_type == "error":
|
||||
err = self._get_field(event, "error", {})
|
||||
msg = self._get_field(err, "message", str(event))
|
||||
logger.warning(f"Whisper error: {msg}")
|
||||
|
||||
async def _process_transcript(self, transcript: str):
|
||||
"""Full utterance received. Call LLM, then TTS."""
|
||||
await self._on_transcript_done(transcript)
|
||||
logger.info(f"User: {transcript}")
|
||||
|
||||
# Build system prompt with optional memory context
|
||||
system_content = KIRA_INSTRUCTIONS
|
||||
if self._memory_suffix:
|
||||
system_content += self._memory_suffix
|
||||
|
||||
# Call gpt-5.4-nano
|
||||
try:
|
||||
resp = await self._openai.chat.completions.create(
|
||||
model="gpt-5.4-nano",
|
||||
messages=[
|
||||
{"role": "system", "content": system_content},
|
||||
{"role": "user", "content": transcript},
|
||||
],
|
||||
max_tokens=300,
|
||||
temperature=0.7,
|
||||
)
|
||||
kira_text = resp.choices[0].message.content or "Mhm, I'm here!"
|
||||
except Exception as e:
|
||||
logger.error(f"LLM error: {e}")
|
||||
kira_text = "Sorry, let me try that again!"
|
||||
await self._on_error(str(e))
|
||||
|
||||
logger.info(f"Kira: {kira_text}")
|
||||
|
||||
# Call TTS
|
||||
await self._on_speech_start()
|
||||
try:
|
||||
tts_resp = await self._openai.audio.speech.create(
|
||||
model="tts-1",
|
||||
voice="nova",
|
||||
input=kira_text,
|
||||
response_format="opus",
|
||||
)
|
||||
audio_bytes = tts_resp.content
|
||||
if audio_bytes:
|
||||
await self._on_audio_delta(audio_bytes)
|
||||
except Exception as e:
|
||||
logger.error(f"TTS error: {e}")
|
||||
|
||||
await self._on_speech_end()
|
||||
|
||||
async def send_audio(self, pcm16_bytes: bytes):
|
||||
"""Send PCM16 audio chunk for transcription."""
|
||||
if not self._connected or not self._conn:
|
||||
return
|
||||
try:
|
||||
audio_b64 = base64.b64encode(pcm16_bytes).decode("utf-8")
|
||||
await self._send({
|
||||
"type": "input_audio_buffer.append",
|
||||
"audio": audio_b64,
|
||||
})
|
||||
except Exception as e:
|
||||
logger.warning(f"Send audio error: {e}")
|
||||
|
||||
async def send_text(self, text: str):
|
||||
"""Process text input directly (no transcription needed)."""
|
||||
await self._process_transcript(text)
|
||||
|
||||
async def _send(self, data: dict):
|
||||
try:
|
||||
await self._conn.send(json.dumps(data))
|
||||
except Exception as e:
|
||||
logger.warning(f"Send error: {e}")
|
||||
|
||||
async def disconnect(self):
|
||||
self._connected = False
|
||||
if self._conn:
|
||||
try:
|
||||
await self._conn.close()
|
||||
except Exception:
|
||||
pass
|
||||
self._conn = None
|
||||
|
||||
@staticmethod
|
||||
def _get_field(obj, field: str, default=None):
|
||||
"""Get a field from either an object or dict."""
|
||||
if hasattr(obj, field):
|
||||
return getattr(obj, field, default)
|
||||
if isinstance(obj, dict):
|
||||
return obj.get(field, default)
|
||||
return default
|
||||
Reference in New Issue
Block a user