hobbs-iac/docs/superpowers/plans/2026-05-28-linkedstorm-implementation.md

# LinkedStorm Implementation Plan

> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.

**Goal:** Build and deploy LinkedStorm — 3 Docker containers (pipeline, api, cron) that capture household speech via browser WebSocket, transcribe with Whisper, identify speakers with resemblyzer, and serve a searchable daily log dashboard.

**Architecture:** Browser tabs stream 250ms audio chunks over WebSocket to `linkedstorm-pipeline` (port 8300), which runs Silero VAD, calls Whisper STT + resemblyzer concurrently, matches voice embeddings against SQLite, and writes utterances. `linkedstorm-api` (port 8310) serves the FastAPI dashboard. `linkedstorm-cron` runs nightly Ollama summaries. All three mount the same NAS volume for SQLite + WAV clips.

**Tech Stack:** Python 3.12, FastAPI, uvicorn, SQLite WAL+FTS5, resemblyzer, silero-vad, httpx, livekit-api, pytest, pytest-asyncio, vanilla JS, Docker Compose.

**Code repo:** `http://192.168.86.100:3003/jeremy/linkedstorm` (clone to `/mnt/c/Users/Jeremy/projects/linkedstorm`)
**Compose repo:** `/mnt/c/Users/Jeremy/projects/docker/compose/hf-pdocker-01/linkedstorm/`

---

## File Map

```
/mnt/c/Users/Jeremy/projects/linkedstorm/
├── pyproject.toml
├── pytest.ini
├── .env.example
├── app/
│   ├── shared/
│   │   ├── __init__.py
│   │   ├── config.py          # Pydantic Settings — all env vars
│   │   ├── database.py        # SQLite thread-local connection, schema init
│   │   └── models.py          # Dataclasses: Room, Speaker, Utterance, etc.
│   ├── pipeline/
│   │   ├── __init__.py
│   │   ├── main.py            # FastAPI app: ws://:8300/ws/{room_id} + /internal/rooms/{id}/mute
│   │   ├── ingestion.py       # RoomVADBuffer: per-room VAD accumulator
│   │   ├── processor.py       # process_utterance: Whisper + resemblyzer + DB write
│   │   └── recognition.py     # embed_audio, find_best_speaker_match, pack/unpack embedding
│   ├── api/
│   │   ├── __init__.py
│   │   ├── main.py            # FastAPI app: mounts all routes, serves static/
│   │   ├── routes/
│   │   │   ├── __init__.py
│   │   │   ├── utterances.py  # GET/POST /api/utterances, /tag, /dismiss, /clip
│   │   │   ├── speakers.py    # GET/POST /api/speakers
│   │   │   ├── rooms.py       # GET /api/rooms, POST /api/rooms/{id}/mute
│   │   │   ├── search.py      # GET /api/search?q=
│   │   │   └── rag.py         # POST /api/ask, GET /api/summary/{date}
│   │   └── static/
│   │       ├── index.html     # Dashboard SPA (5 tabs)
│   │       ├── capture.html   # Minimal capture page (MediaRecorder + WS)
│   │       └── app.js         # Dashboard JS
│   └── cron/
│       ├── __init__.py
│       ├── main.py            # Sleep-loop entrypoint: runs all jobs at midnight
│       ├── daily_summary.py   # Fetch utterances → Ollama → daily_summaries row
│       ├── cluster_unknowns.py # Cluster unknown embeddings into Unknown #N groups
│       └── purge_clips.py     # Delete WAV files older than CLIP_RETENTION_DAYS
├── tests/
│   ├── conftest.py            # Shared fixtures: in-memory SQLite settings
│   ├── test_database.py
│   ├── test_recognition.py
│   ├── test_ingestion.py
│   ├── test_processor.py
│   ├── test_cron.py
│   └── routes/
│       ├── conftest.py        # TestClient fixture
│       ├── test_utterances.py
│       ├── test_speakers.py
│       ├── test_rooms.py
│       ├── test_search.py
│       └── test_rag.py
├── pipeline/
│   └── Dockerfile
├── api/
│   └── Dockerfile
└── cron/
    └── Dockerfile

/mnt/c/Users/Jeremy/projects/docker/
├── compose/hf-pdocker-01/linkedstorm/
│   ├── docker-compose.yml
│   └── .env.example
└── stacks/traefik/dynamic/linkedstorm.yml
```

---

## Task 1: Clone repo and scaffold

**Files:** `pyproject.toml`, `pytest.ini`, `.env.example`, all `__init__.py` files

- [ ] **Clone the linkedstorm repo locally**

```bash
git clone http://192.168.86.100:3003/jeremy/linkedstorm /mnt/c/Users/Jeremy/projects/linkedstorm
cd /mnt/c/Users/Jeremy/projects/linkedstorm
```

- [ ] **Create `pyproject.toml`**

```toml
[build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.backends.legacy:build"

[project]
name = "linkedstorm"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
    "fastapi==0.115.0",
    "uvicorn[standard]==0.30.6",
    "pydantic-settings==2.4.0",
    "httpx==0.27.2",
    "websockets==13.1",
    "resemblyzer==0.1.1.dev0",
    "silero-vad==5.1.2",
    "torch==2.4.1",
    "numpy==1.26.4",
    "livekit-api==0.7.2",
    "scipy==1.14.1",
]

[project.optional-dependencies]
dev = [
    "pytest==8.3.3",
    "pytest-asyncio==0.24.0",
    "pytest-mock==3.14.0",
    "httpx==0.27.2",
]

[tool.setuptools.packages.find]
where = ["."]
include = ["app*"]
```

- [ ] **Create `pytest.ini`**

```ini
[pytest]
asyncio_mode = auto
testpaths = tests
```

- [ ] **Create `.env.example`**

```bash
DB_PATH=/data/linkedstorm.db
CLIPS_DIR=/data/clips
SPEACHES_URL=http://192.168.86.150:8000
OLLAMA_URL=http://192.168.86.150:11434
OLLAMA_MODEL=qwen2.5:14b
LIVEKIT_URL=ws://192.168.86.150:7880
LIVEKIT_API_KEY=devkey
LIVEKIT_API_SECRET=devsecret
PIPELINE_INTERNAL_URL=http://linkedstorm-pipeline:8300
CLIP_RETENTION_DAYS=30
SPEAKER_KNOWN_THRESHOLD=0.85
SPEAKER_AMBIGUOUS_THRESHOLD=0.60
```

- [ ] **Create all `__init__.py` files**

```bash
mkdir -p app/shared app/pipeline app/api/routes app/api/static app/cron
mkdir -p tests/routes pipeline api cron
touch app/__init__.py app/shared/__init__.py app/pipeline/__init__.py
touch app/api/__init__.py app/api/routes/__init__.py
touch app/cron/__init__.py
touch tests/__init__.py tests/routes/__init__.py
```

- [ ] **Install dev dependencies**

```bash
pip install -e ".[dev]"
```

- [ ] **Commit**

```bash
git add .
git commit -m "feat: scaffold project structure"
git push origin master
```

---

## Task 2: Shared config and models

**Files:** `app/shared/config.py`, `app/shared/models.py`

- [ ] **Write `app/shared/config.py`**

```python
from pydantic_settings import BaseSettings


class Settings(BaseSettings):
    db_path: str = "/data/linkedstorm.db"
    clips_dir: str = "/data/clips"
    speaches_url: str = "http://192.168.86.150:8000"
    ollama_url: str = "http://192.168.86.150:11434"
    ollama_model: str = "qwen2.5:14b"
    livekit_url: str = "ws://192.168.86.150:7880"
    livekit_api_key: str = "devkey"
    livekit_api_secret: str = "devsecret"
    pipeline_internal_url: str = "http://linkedstorm-pipeline:8300"
    clip_retention_days: int = 30
    speaker_known_threshold: float = 0.85
    speaker_ambiguous_threshold: float = 0.60
    vad_silence_ms: int = 600
    vad_min_speech_ms: int = 500

    model_config = {"env_file": ".env", "env_file_encoding": "utf-8"}


def get_settings() -> Settings:
    return Settings()
```

- [ ] **Write `app/shared/models.py`**

```python
from dataclasses import dataclass
from datetime import datetime
from typing import Optional


@dataclass
class Room:
    id: int
    name: str
    device_label: Optional[str]
    is_active: bool
    last_seen: Optional[datetime]
    created_at: datetime


@dataclass
class Speaker:
    id: int
    name: str
    created_at: datetime
    updated_at: datetime


@dataclass
class VoiceEmbedding:
    id: int
    speaker_id: int
    embedding: bytes  # packed float32
    audio_sample: Optional[bytes]
    created_at: datetime


@dataclass
class Utterance:
    id: int
    room_id: int
    speaker_id: Optional[int]
    transcript: str
    embedding: Optional[bytes]
    match_status: str  # known | unknown | ambiguous | unreviewed
    match_confidence: Optional[float]
    start_time: datetime
    end_time: datetime
    audio_clip_path: Optional[str]
    livekit_room: Optional[str]
    created_at: datetime


@dataclass
class DailySummary:
    id: int
    date: str  # YYYY-MM-DD
    summary_text: str
    generated_at: datetime
```

- [ ] **Commit**

```bash
git add app/shared/
git commit -m "feat: add shared config and models"
git push origin master
```

---

## Task 3: Database layer (TDD)

**Files:** `app/shared/database.py`, `tests/conftest.py`, `tests/test_database.py`

- [ ] **Write failing tests first — `tests/conftest.py`**

```python
import pytest
import tempfile
import os
from app.shared.config import Settings
from app.shared.database import init_schema, get_db


@pytest.fixture
def settings(tmp_path):
    db_file = str(tmp_path / "test.db")
    clips_dir = str(tmp_path / "clips")
    os.makedirs(clips_dir, exist_ok=True)
    s = Settings(
        db_path=db_file,
        clips_dir=clips_dir,
        speaches_url="http://localhost:9999",
        ollama_url="http://localhost:9999",
        livekit_api_key="test",
        livekit_api_secret="test",
    )
    init_schema(s)
    return s


@pytest.fixture
def db(settings):
    return get_db(settings)
```

- [ ] **Write `tests/test_database.py`**

```python
def test_schema_creates_rooms_table(db):
    rows = db.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='rooms'").fetchall()
    assert len(rows) == 1


def test_schema_creates_utterances_fts(db):
    rows = db.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='utterances_fts'").fetchall()
    assert len(rows) == 1


def test_insert_and_fetch_room(db):
    db.execute("INSERT INTO rooms (name) VALUES (?)", ("Kitchen",))
    db.commit()
    row = db.execute("SELECT name FROM rooms WHERE name='Kitchen'").fetchone()
    assert row["name"] == "Kitchen"


def test_insert_speaker_and_embedding(db):
    db.execute("INSERT INTO speakers (name) VALUES (?)", ("Jeremy",))
    db.commit()
    speaker = db.execute("SELECT id FROM speakers WHERE name='Jeremy'").fetchone()
    db.execute(
        "INSERT INTO voice_embeddings (speaker_id, embedding) VALUES (?, ?)",
        (speaker["id"], b"\x00\x01\x02\x03"),
    )
    db.commit()
    emb = db.execute("SELECT speaker_id FROM voice_embeddings").fetchone()
    assert emb["speaker_id"] == speaker["id"]


def test_fts_trigger_on_utterance_insert(db):
    db.execute("INSERT INTO rooms (name) VALUES (?)", ("Living Room",))
    db.commit()
    room = db.execute("SELECT id FROM rooms WHERE name='Living Room'").fetchone()
    db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
        (room["id"], "hello world test", "unreviewed", "2026-01-01 10:00:00", "2026-01-01 10:00:03"),
    )
    db.commit()
    results = db.execute("SELECT rowid FROM utterances_fts WHERE transcript MATCH 'hello'").fetchall()
    assert len(results) == 1
```

- [ ] **Run tests — expect FAIL (database.py does not exist)**

```bash
cd /mnt/c/Users/Jeremy/projects/linkedstorm
pytest tests/test_database.py -v
```

Expected: `ModuleNotFoundError: No module named 'app.shared.database'`

- [ ] **Write `app/shared/database.py`**

```python
import sqlite3
import threading
from pathlib import Path
from app.shared.config import Settings

_local = threading.local()


def get_db(settings: Settings) -> sqlite3.Connection:
    if not hasattr(_local, "conn") or _local.conn is None:
        conn = sqlite3.connect(settings.db_path, check_same_thread=False)
        conn.row_factory = sqlite3.Row
        conn.execute("PRAGMA journal_mode=WAL")
        conn.execute("PRAGMA foreign_keys=ON")
        _local.conn = conn
    return _local.conn


def init_schema(settings: Settings) -> None:
    Path(settings.clips_dir).mkdir(parents=True, exist_ok=True)
    db = get_db(settings)
    db.executescript("""
        CREATE TABLE IF NOT EXISTS rooms (
            id          INTEGER PRIMARY KEY,
            name        TEXT NOT NULL UNIQUE,
            device_label TEXT,
            is_active   BOOLEAN DEFAULT 0,
            last_seen   DATETIME,
            created_at  DATETIME DEFAULT CURRENT_TIMESTAMP
        );

        CREATE TABLE IF NOT EXISTS speakers (
            id          INTEGER PRIMARY KEY,
            name        TEXT NOT NULL,
            created_at  DATETIME DEFAULT CURRENT_TIMESTAMP,
            updated_at  DATETIME DEFAULT CURRENT_TIMESTAMP
        );

        CREATE TABLE IF NOT EXISTS voice_embeddings (
            id          INTEGER PRIMARY KEY,
            speaker_id  INTEGER NOT NULL REFERENCES speakers(id) ON DELETE CASCADE,
            embedding   BLOB NOT NULL,
            audio_sample BLOB,
            created_at  DATETIME DEFAULT CURRENT_TIMESTAMP
        );

        CREATE TABLE IF NOT EXISTS utterances (
            id                INTEGER PRIMARY KEY,
            room_id           INTEGER NOT NULL REFERENCES rooms(id),
            speaker_id        INTEGER REFERENCES speakers(id),
            transcript        TEXT NOT NULL,
            embedding         BLOB,
            match_status      TEXT NOT NULL DEFAULT 'unreviewed',
            match_confidence  REAL,
            start_time        DATETIME NOT NULL,
            end_time          DATETIME NOT NULL,
            audio_clip_path   TEXT,
            livekit_room      TEXT,
            created_at        DATETIME DEFAULT CURRENT_TIMESTAMP
        );

        CREATE VIRTUAL TABLE IF NOT EXISTS utterances_fts USING fts5(
            transcript, content=utterances, content_rowid=id
        );

        CREATE TRIGGER IF NOT EXISTS utterances_ai AFTER INSERT ON utterances BEGIN
            INSERT INTO utterances_fts(rowid, transcript) VALUES (new.id, new.transcript);
        END;

        CREATE TRIGGER IF NOT EXISTS utterances_au AFTER UPDATE OF transcript ON utterances BEGIN
            INSERT INTO utterances_fts(utterances_fts, rowid, transcript)
                VALUES ('delete', old.id, old.transcript);
            INSERT INTO utterances_fts(rowid, transcript) VALUES (new.id, new.transcript);
        END;

        CREATE TRIGGER IF NOT EXISTS utterances_ad AFTER DELETE ON utterances BEGIN
            INSERT INTO utterances_fts(utterances_fts, rowid, transcript)
                VALUES ('delete', old.id, old.transcript);
        END;

        CREATE TABLE IF NOT EXISTS daily_summaries (
            id            INTEGER PRIMARY KEY,
            date          DATE NOT NULL UNIQUE,
            summary_text  TEXT NOT NULL,
            generated_at  DATETIME DEFAULT CURRENT_TIMESTAMP
        );
    """)
    db.commit()
```

- [ ] **Run tests — expect PASS**

```bash
pytest tests/test_database.py -v
```

Expected: `4 passed`

- [ ] **Commit**

```bash
git add app/shared/database.py tests/conftest.py tests/test_database.py
git commit -m "feat: add database schema with WAL mode and FTS5 triggers"
git push origin master
```

---

## Task 4: Voice recognition module (TDD)

**Files:** `app/pipeline/recognition.py`, `tests/test_recognition.py`

- [ ] **Write `tests/test_recognition.py`**

```python
import numpy as np
import struct
import pytest
from unittest.mock import patch, MagicMock


def test_pack_unpack_roundtrip():
    from app.pipeline.recognition import pack_embedding, unpack_embedding
    original = np.array([0.1, 0.2, 0.3, -0.5], dtype=np.float32)
    packed = pack_embedding(original)
    recovered = unpack_embedding(packed)
    np.testing.assert_allclose(recovered, original, rtol=1e-6)


def test_cosine_similarity_identical():
    from app.pipeline.recognition import cosine_similarity
    v = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    assert cosine_similarity(v, v) == pytest.approx(1.0)


def test_cosine_similarity_orthogonal():
    from app.pipeline.recognition import cosine_similarity
    a = np.array([1.0, 0.0], dtype=np.float32)
    b = np.array([0.0, 1.0], dtype=np.float32)
    assert cosine_similarity(a, b) == pytest.approx(0.0)


def test_cosine_similarity_zero_vector():
    from app.pipeline.recognition import cosine_similarity
    a = np.array([0.0, 0.0], dtype=np.float32)
    b = np.array([1.0, 0.0], dtype=np.float32)
    assert cosine_similarity(a, b) == 0.0


def test_find_best_match_known():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    ref = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    query = np.array([0.98, 0.2, 0.0], dtype=np.float32)
    query /= np.linalg.norm(query)
    ref /= np.linalg.norm(ref)
    status, sid, conf = find_best_speaker_match(
        query, [(1, pack_embedding(ref))], known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "known"
    assert sid == 1
    assert conf >= 0.85


def test_find_best_match_ambiguous():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    ref = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    # cos similarity ~0.707 (45 degrees)
    query = np.array([1.0, 1.0, 0.0], dtype=np.float32)
    query /= np.linalg.norm(query)
    ref /= np.linalg.norm(ref)
    status, sid, conf = find_best_speaker_match(
        query, [(1, pack_embedding(ref))], known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "ambiguous"
    assert sid == 1


def test_find_best_match_unknown():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    ref = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    query = np.array([0.0, 1.0, 0.0], dtype=np.float32)  # orthogonal = 0 similarity
    status, sid, conf = find_best_speaker_match(
        query, [(1, pack_embedding(ref))], known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "unknown"
    assert sid is None


def test_find_best_match_empty_embeddings():
    from app.pipeline.recognition import find_best_speaker_match
    query = np.array([1.0, 0.0], dtype=np.float32)
    status, sid, conf = find_best_speaker_match(query, [])
    assert status == "unknown"
    assert sid is None


def test_find_best_match_takes_max_per_speaker():
    from app.pipeline.recognition import find_best_speaker_match, pack_embedding
    # Speaker 1 has two embeddings — one poor, one good
    ref_bad = np.array([0.0, 1.0, 0.0], dtype=np.float32)
    ref_good = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    query = np.array([1.0, 0.0, 0.0], dtype=np.float32)
    stored = [(1, pack_embedding(ref_bad)), (1, pack_embedding(ref_good))]
    status, sid, conf = find_best_speaker_match(
        query, stored, known_threshold=0.85, ambiguous_threshold=0.60
    )
    assert status == "known"
    assert sid == 1


def test_embed_audio_calls_resemblyzer(mocker):
    from app.pipeline.recognition import embed_audio
    mock_wav = np.zeros(16000, dtype=np.float32)
    mock_emb = np.ones(256, dtype=np.float32)
    mocker.patch("app.pipeline.recognition.preprocess_wav", return_value=mock_wav)
    mock_encoder = MagicMock()
    mock_encoder.embed_utterance.return_value = mock_emb
    mocker.patch("app.pipeline.recognition.get_encoder", return_value=mock_encoder)
    result = embed_audio(b"\x00" * 100)
    assert result.shape == (256,)
    mock_encoder.embed_utterance.assert_called_once_with(mock_wav)
```

- [ ] **Run to confirm failure**

```bash
pytest tests/test_recognition.py -v
```

Expected: `ModuleNotFoundError: No module named 'app.pipeline.recognition'`

- [ ] **Write `app/pipeline/recognition.py`**

```python
import struct
import io
import numpy as np
from resemblyzer import VoiceEncoder, preprocess_wav

_encoder: VoiceEncoder | None = None


def get_encoder() -> VoiceEncoder:
    global _encoder
    if _encoder is None:
        _encoder = VoiceEncoder()
    return _encoder


def embed_audio(wav_bytes: bytes) -> np.ndarray:
    """Extract 256-dim d-vector from raw WAV bytes."""
    wav = preprocess_wav(io.BytesIO(wav_bytes))
    return get_encoder().embed_utterance(wav)


def pack_embedding(embedding: np.ndarray) -> bytes:
    n = len(embedding)
    return struct.pack(f"{n}f", *embedding)


def unpack_embedding(blob: bytes) -> np.ndarray:
    n = len(blob) // 4
    return np.array(struct.unpack(f"{n}f", blob), dtype=np.float32)


def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
    norm_a = np.linalg.norm(a)
    norm_b = np.linalg.norm(b)
    if norm_a == 0 or norm_b == 0:
        return 0.0
    return float(np.dot(a, b) / (norm_a * norm_b))


def find_best_speaker_match(
    utterance_embedding: np.ndarray,
    stored_embeddings: list[tuple[int, bytes]],
    known_threshold: float = 0.85,
    ambiguous_threshold: float = 0.60,
) -> tuple[str, int | None, float | None]:
    """
    Returns (match_status, speaker_id, confidence).
    Groups by speaker_id, takes MAX cosine similarity per speaker.
    """
    if not stored_embeddings:
        return "unknown", None, None

    best_by_speaker: dict[int, float] = {}
    for speaker_id, blob in stored_embeddings:
        ref = unpack_embedding(blob)
        sim = cosine_similarity(utterance_embedding, ref)
        if speaker_id not in best_by_speaker or sim > best_by_speaker[speaker_id]:
            best_by_speaker[speaker_id] = sim

    best_id = max(best_by_speaker, key=best_by_speaker.__getitem__)
    best_sim = best_by_speaker[best_id]

    if best_sim >= known_threshold:
        return "known", best_id, best_sim
    elif best_sim >= ambiguous_threshold:
        return "ambiguous", best_id, best_sim
    else:
        return "unknown", None, best_sim
```

- [ ] **Run tests — expect PASS**

```bash
pytest tests/test_recognition.py -v
```

Expected: `9 passed`

- [ ] **Commit**

```bash
git add app/pipeline/recognition.py tests/test_recognition.py
git commit -m "feat: voice recognition — resemblyzer embed + cosine similarity matching"
git push origin master
```

---

## Task 5: VAD ingestion buffer (TDD)

**Files:** `app/pipeline/ingestion.py`, `tests/test_ingestion.py`

- [ ] **Write `tests/test_ingestion.py`**

```python
import numpy as np
import pytest
from datetime import datetime, timezone
from unittest.mock import patch, MagicMock


def _make_chunk(samples: int = 4000, amplitude: float = 0.0) -> bytes:
    """Create a fake PCM int16 chunk."""
    audio = (np.ones(samples) * amplitude * 32767).astype(np.int16)
    return audio.tobytes()


@pytest.fixture
def buf(mocker):
    """RoomVADBuffer with mocked VAD model."""
    mocker.patch(
        "app.pipeline.ingestion.vad_prob",
        side_effect=lambda chunk: 0.9 if np.frombuffer(chunk, np.int16).max() > 100 else 0.1,
    )
    from app.pipeline.ingestion import RoomVADBuffer
    return RoomVADBuffer(silence_threshold_ms=600, min_speech_ms=500, chunk_ms=250)


def test_silence_produces_no_segment(buf):
    ts = datetime(2026, 1, 1, tzinfo=timezone.utc)
    for _ in range(10):
        result = buf.process_chunk(_make_chunk(amplitude=0.0), ts)
        assert result is None


def test_speech_then_silence_produces_segment(buf):
    ts = datetime(2026, 1, 1, tzinfo=timezone.utc)
    # 4 speech chunks = 1 second of speech
    for _ in range(4):
        result = buf.process_chunk(_make_chunk(amplitude=0.5), ts)
        assert result is None  # still accumulating
    # 3 silence chunks = 750ms silence (> 600ms threshold)
    segment = None
    for _ in range(3):
        segment = buf.process_chunk(_make_chunk(amplitude=0.0), ts)
    assert segment is not None
    assert isinstance(segment, tuple)
    audio_bytes, start_time = segment
    assert len(audio_bytes) > 0


def test_short_speech_discarded(buf):
    ts = datetime(2026, 1, 1, tzinfo=timezone.utc)
    # 1 speech chunk = 250ms (below 500ms min)
    buf.process_chunk(_make_chunk(amplitude=0.5), ts)
    # Now silence to trigger emission
    result = None
    for _ in range(3):
        result = buf.process_chunk(_make_chunk(amplitude=0.0), ts)
    assert result is None  # discarded as too short


def test_start_time_recorded(buf):
    ts = datetime(2026, 5, 1, 12, 0, 0, tzinfo=timezone.utc)
    buf.process_chunk(_make_chunk(amplitude=0.5), ts)
    for _ in range(3):
        result = buf.process_chunk(_make_chunk(amplitude=0.0), ts)
    # Should have started at ts
    # (segment would be discarded due to being too short in this test,
    # so test start_time tracking via internal state)
    # Reset and do a proper speech segment
    from app.pipeline.ingestion import RoomVADBuffer
    pass  # covered by test_speech_then_silence_produces_segment
```

- [ ] **Run to confirm failure**

```bash
pytest tests/test_ingestion.py -v
```

Expected: `ModuleNotFoundError: No module named 'app.pipeline.ingestion'`

- [ ] **Write `app/pipeline/ingestion.py`**

```python
import asyncio
import numpy as np
import torch
from datetime import datetime, timezone
from typing import Optional

# Load Silero VAD model once at module level
_vad_model = None
_vad_utils = None


def _load_vad():
    global _vad_model, _vad_utils
    if _vad_model is None:
        _vad_model, _vad_utils = torch.hub.load(
            repo_or_dir="snakers4/silero-vad",
            model="silero_vad",
            force_reload=False,
            trust_repo=True,
        )
    return _vad_model


def vad_prob(chunk: bytes, sample_rate: int = 16000) -> float:
    """Return speech probability (0-1) for a PCM int16 chunk."""
    model = _load_vad()
    audio = np.frombuffer(chunk, dtype=np.int16).astype(np.float32) / 32768.0
    tensor = torch.FloatTensor(audio)
    with torch.no_grad():
        prob = model(tensor, sample_rate).item()
    return prob


class RoomVADBuffer:
    """
    Accumulates audio chunks for a single room.
    Emits (audio_bytes, start_time) when a speech segment ends.
    """

    SPEECH_THRESHOLD = 0.5

    def __init__(
        self,
        silence_threshold_ms: int = 600,
        min_speech_ms: int = 500,
        chunk_ms: int = 250,
        sample_rate: int = 16000,
    ):
        self.silence_threshold_ms = silence_threshold_ms
        self.min_speech_ms = min_speech_ms
        self.chunk_ms = chunk_ms
        self.sample_rate = sample_rate
        self._buffer: list[bytes] = []
        self._speaking = False
        self._silence_ms = 0
        self._start_time: Optional[datetime] = None

    def process_chunk(
        self, chunk: bytes, timestamp: datetime
    ) -> Optional[tuple[bytes, datetime]]:
        """
        Returns (audio_bytes, start_time) when a complete speech segment is ready.
        Returns None while accumulating or during silence.
        """
        prob = vad_prob(chunk)

        if prob >= self.SPEECH_THRESHOLD:
            if not self._speaking:
                self._speaking = True
                self._start_time = timestamp
                self._buffer = []
            self._buffer.append(chunk)
            self._silence_ms = 0
        elif self._speaking:
            self._buffer.append(chunk)  # include trailing silence
            self._silence_ms += self.chunk_ms
            if self._silence_ms >= self.silence_threshold_ms:
                audio = b"".join(self._buffer)
                start = self._start_time
                self._buffer = []
                self._speaking = False
                self._silence_ms = 0
                self._start_time = None
                # Calculate duration (int16 = 2 bytes per sample)
                duration_ms = (len(audio) / 2 / self.sample_rate) * 1000
                if duration_ms >= self.min_speech_ms:
                    return audio, start

        return None


# Per-room registry: room_id -> (buffer, muted_flag)
_rooms: dict[str, dict] = {}


def get_or_create_room(room_id: str, silence_ms: int, min_speech_ms: int) -> dict:
    if room_id not in _rooms:
        _rooms[room_id] = {
            "buffer": RoomVADBuffer(silence_threshold_ms=silence_ms, min_speech_ms=min_speech_ms),
            "muted": False,
        }
    return _rooms[room_id]


def mute_room(room_id: str) -> None:
    if room_id in _rooms:
        _rooms[room_id]["muted"] = True


def unmute_room(room_id: str) -> None:
    if room_id in _rooms:
        _rooms[room_id]["muted"] = False


def is_muted(room_id: str) -> bool:
    return _rooms.get(room_id, {}).get("muted", False)
```

- [ ] **Run tests — expect PASS (VAD is mocked)**

```bash
pytest tests/test_ingestion.py -v
```

Expected: `3 passed` (the 4th test is a placeholder, it passes trivially)

- [ ] **Commit**

```bash
git add app/pipeline/ingestion.py tests/test_ingestion.py
git commit -m "feat: VAD ingestion buffer — Silero VAD, per-room accumulation"
git push origin master
```

---

## Task 6: Processing pipeline (TDD)

**Files:** `app/pipeline/processor.py`, `tests/test_processor.py`

- [ ] **Write `tests/test_processor.py`**

```python
import pytest
import numpy as np
from datetime import datetime, timezone
from unittest.mock import AsyncMock, MagicMock, patch


@pytest.fixture
def proc_settings(settings):
    return settings


@pytest.mark.asyncio
async def test_process_utterance_known_speaker(mocker, proc_settings, db):
    # Setup: insert a room and speaker with embedding
    db.execute("INSERT INTO rooms (name) VALUES (?)", ("Kitchen",))
    db.execute("INSERT INTO speakers (name) VALUES (?)", ("Jeremy",))
    db.commit()
    room = db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    speaker = db.execute("SELECT id FROM speakers WHERE name='Jeremy'").fetchone()

    from app.pipeline.recognition import pack_embedding
    ref_emb = np.array([1.0] + [0.0] * 255, dtype=np.float32)
    db.execute(
        "INSERT INTO voice_embeddings (speaker_id, embedding) VALUES (?,?)",
        (speaker["id"], pack_embedding(ref_emb)),
    )
    db.commit()

    # Mock Whisper HTTP call
    mocker.patch(
        "app.pipeline.processor.transcribe_audio",
        new=AsyncMock(return_value="hello world"),
    )
    # Mock resemblyzer — return a very similar embedding
    query_emb = np.array([0.99] + [0.0] * 255, dtype=np.float32)
    mocker.patch("app.pipeline.processor.embed_audio", return_value=query_emb)
    # Mock clip saving
    mocker.patch("app.pipeline.processor.save_clip", return_value="/data/clips/test.wav")

    from app.pipeline.processor import process_utterance
    start = datetime(2026, 1, 1, 10, 0, 0, tzinfo=timezone.utc)
    end = datetime(2026, 1, 1, 10, 0, 3, tzinfo=timezone.utc)

    utterance_id = await process_utterance(
        room_id=room["id"],
        audio_bytes=b"\x00" * 100,
        start_time=start,
        end_time=end,
        settings=proc_settings,
        db=db,
    )

    assert utterance_id is not None
    row = db.execute("SELECT * FROM utterances WHERE id=?", (utterance_id,)).fetchone()
    assert row["transcript"] == "hello world"
    assert row["match_status"] == "known"
    assert row["speaker_id"] == speaker["id"]


@pytest.mark.asyncio
async def test_process_utterance_unknown_speaker(mocker, proc_settings, db):
    db.execute("INSERT INTO rooms (name) VALUES (?)", ("Office",))
    db.commit()
    room = db.execute("SELECT id FROM rooms WHERE name='Office'").fetchone()

    mocker.patch(
        "app.pipeline.processor.transcribe_audio",
        new=AsyncMock(return_value="test transcript"),
    )
    mocker.patch(
        "app.pipeline.processor.embed_audio",
        return_value=np.zeros(256, dtype=np.float32),
    )
    mocker.patch("app.pipeline.processor.save_clip", return_value="/data/clips/test.wav")

    from app.pipeline.processor import process_utterance
    start = datetime(2026, 1, 1, 10, 0, 0, tzinfo=timezone.utc)
    end = datetime(2026, 1, 1, 10, 0, 2, tzinfo=timezone.utc)

    utterance_id = await process_utterance(
        room_id=room["id"],
        audio_bytes=b"\x00" * 100,
        start_time=start,
        end_time=end,
        settings=proc_settings,
        db=db,
    )

    row = db.execute("SELECT * FROM utterances WHERE id=?", (utterance_id,)).fetchone()
    assert row["match_status"] == "unknown"
    assert row["speaker_id"] is None
```

- [ ] **Run to confirm failure**

```bash
pytest tests/test_processor.py -v
```

Expected: `ModuleNotFoundError: No module named 'app.pipeline.processor'`

- [ ] **Write `app/pipeline/processor.py`**

```python
import asyncio
import sqlite3
import wave
import io
from datetime import datetime, timezone
from pathlib import Path
from typing import Optional

import httpx
import numpy as np

from app.shared.config import Settings
from app.pipeline.recognition import embed_audio, find_best_speaker_match, pack_embedding


async def transcribe_audio(wav_bytes: bytes, speaches_url: str) -> str:
    """POST audio to Speaches/Whisper, return transcript text."""
    async with httpx.AsyncClient(timeout=30.0) as client:
        resp = await client.post(
            f"{speaches_url}/v1/audio/transcriptions",
            files={"file": ("audio.wav", wav_bytes, "audio/wav")},
            data={"model": "Systran/faster-whisper-large-v3-turbo"},
        )
        resp.raise_for_status()
        return resp.json()["text"].strip()


def save_clip(audio_bytes: bytes, clips_dir: str, utterance_id: int, start_time: datetime) -> str:
    """Save raw PCM bytes as a WAV file, return the path."""
    date_str = start_time.strftime("%Y-%m-%d")
    clip_dir = Path(clips_dir) / date_str
    clip_dir.mkdir(parents=True, exist_ok=True)
    path = clip_dir / f"{utterance_id}.wav"
    with wave.open(str(path), "wb") as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)  # int16
        wf.setframerate(16000)
        wf.writeframes(audio_bytes)
    return str(path)


async def process_utterance(
    room_id: int,
    audio_bytes: bytes,
    start_time: datetime,
    end_time: datetime,
    settings: Settings,
    db: sqlite3.Connection,
) -> Optional[int]:
    """
    Full pipeline for one speech segment:
      1. Whisper STT + resemblyzer embed (concurrent)
      2. Speaker matching
      3. SQLite write + WAV clip save
    Returns the new utterance row id, or None on failure.
    """
    try:
        transcript, embedding = await asyncio.gather(
            transcribe_audio(audio_bytes, settings.speaches_url),
            asyncio.to_thread(embed_audio, audio_bytes),
        )
    except Exception:
        return None

    # Load all stored embeddings for matching
    rows = db.execute("SELECT speaker_id, embedding FROM voice_embeddings").fetchall()
    stored = [(r["speaker_id"], r["embedding"]) for r in rows]

    match_status, speaker_id, confidence = find_best_speaker_match(
        embedding,
        stored,
        known_threshold=settings.speaker_known_threshold,
        ambiguous_threshold=settings.speaker_ambiguous_threshold,
    )

    # Insert utterance row first to get the id
    cur = db.execute(
        """INSERT INTO utterances
           (room_id, speaker_id, transcript, embedding, match_status, match_confidence,
            start_time, end_time)
           VALUES (?,?,?,?,?,?,?,?)""",
        (
            room_id,
            speaker_id,
            transcript,
            pack_embedding(embedding),
            match_status,
            confidence,
            start_time.isoformat(),
            end_time.isoformat(),
        ),
    )
    db.commit()
    utterance_id = cur.lastrowid

    # Save WAV clip and update row with path
    clip_path = save_clip(audio_bytes, settings.clips_dir, utterance_id, start_time)
    db.execute(
        "UPDATE utterances SET audio_clip_path=? WHERE id=?",
        (clip_path, utterance_id),
    )
    db.commit()

    # If known match, store embedding as an additional reference for this speaker
    if match_status == "known" and speaker_id is not None:
        db.execute(
            "INSERT INTO voice_embeddings (speaker_id, embedding) VALUES (?,?)",
            (speaker_id, pack_embedding(embedding)),
        )
        db.commit()

    return utterance_id
```

- [ ] **Run tests — expect PASS**

```bash
pytest tests/test_processor.py -v
```

Expected: `2 passed`

- [ ] **Commit**

```bash
git add app/pipeline/processor.py tests/test_processor.py
git commit -m "feat: processing pipeline — Whisper + resemblyzer + speaker match + SQLite write"
git push origin master
```

---

## Task 7: Pipeline FastAPI app (WebSocket + internal HTTP)

**Files:** `app/pipeline/main.py`

*(No TDD — integration test requires running server; covered in Task 18 deployment verification)*

- [ ] **Write `app/pipeline/main.py`**

```python
import asyncio
import sqlite3
from contextlib import asynccontextmanager
from datetime import datetime, timezone

import uvicorn
from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException
from fastapi.responses import JSONResponse

from app.shared.config import get_settings, Settings
from app.shared.database import get_db, init_schema
from app.pipeline.ingestion import get_or_create_room, mute_room, unmute_room, is_muted
from app.pipeline.processor import process_utterance

_settings: Settings | None = None


def settings() -> Settings:
    global _settings
    if _settings is None:
        _settings = get_settings()
    return _settings


@asynccontextmanager
async def lifespan(app: FastAPI):
    s = settings()
    init_schema(s)
    yield


app = FastAPI(lifespan=lifespan)


@app.websocket("/ws/{room_name}")
async def audio_ws(websocket: WebSocket, room_name: str):
    await websocket.accept()
    s = settings()
    db = get_db(s)

    # Upsert room in DB
    db.execute(
        "INSERT INTO rooms (name, is_active, last_seen) VALUES (?,1,CURRENT_TIMESTAMP) "
        "ON CONFLICT(name) DO UPDATE SET is_active=1, last_seen=CURRENT_TIMESTAMP",
        (room_name,),
    )
    db.commit()
    room = db.execute("SELECT id FROM rooms WHERE name=?", (room_name,)).fetchone()
    room_id = room["id"]

    room_state = get_or_create_room(room_name, s.vad_silence_ms, s.vad_min_speech_ms)

    try:
        while True:
            data = await websocket.receive()
            if "bytes" in data:
                chunk = data["bytes"]
                if not is_muted(room_name):
                    db.execute(
                        "UPDATE rooms SET last_seen=CURRENT_TIMESTAMP WHERE id=?",
                        (room_id,),
                    )
                    now = datetime.now(timezone.utc)
                    result = room_state["buffer"].process_chunk(chunk, now)
                    if result is not None:
                        audio_bytes, start_time = result
                        end_time = datetime.now(timezone.utc)
                        asyncio.create_task(
                            process_utterance(room_id, audio_bytes, start_time, end_time, s, db)
                        )
            elif "text" in data:
                import json
                msg = json.loads(data["text"])
                if msg.get("type") == "mute":
                    mute_room(room_name)
                elif msg.get("type") == "unmute":
                    unmute_room(room_name)
    except WebSocketDisconnect:
        db.execute(
            "UPDATE rooms SET is_active=0 WHERE id=?", (room_id,)
        )
        db.commit()


@app.post("/internal/rooms/{room_name}/mute")
async def internal_mute(room_name: str):
    mute_room(room_name)
    return {"status": "muted", "room": room_name}


@app.post("/internal/rooms/{room_name}/unmute")
async def internal_unmute(room_name: str):
    unmute_room(room_name)
    return {"status": "unmuted", "room": room_name}


@app.get("/health")
async def health():
    return {"status": "ok"}


if __name__ == "__main__":
    uvicorn.run("app.pipeline.main:app", host="0.0.0.0", port=8300, reload=False)
```

- [ ] **Commit**

```bash
git add app/pipeline/main.py
git commit -m "feat: pipeline FastAPI app — WebSocket audio ingestion + internal mute API"
git push origin master
```

---

## Task 8: API routes — utterances (TDD)

**Files:** `app/api/routes/utterances.py`, `tests/routes/conftest.py`, `tests/routes/test_utterances.py`

- [ ] **Write `tests/routes/conftest.py`**

```python
import pytest
from fastapi.testclient import TestClient
from app.shared.database import init_schema, get_db
from app.shared.config import Settings


@pytest.fixture
def api_client(settings):
    from app.api.main import create_app
    app = create_app(settings)
    return TestClient(app)


@pytest.fixture
def seeded_db(db):
    """DB with a room and speaker pre-inserted."""
    db.execute("INSERT INTO rooms (name) VALUES (?)", ("Kitchen",))
    db.execute("INSERT INTO speakers (name) VALUES (?)", ("Jeremy",))
    db.commit()
    return db
```

- [ ] **Write `tests/routes/test_utterances.py`**

```python
from datetime import datetime


def test_list_utterances_empty(api_client):
    resp = api_client.get("/api/utterances")
    assert resp.status_code == 200
    assert resp.json() == []


def test_list_utterances_with_data(api_client, seeded_db):
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) "
        "VALUES (?,?,?,?,?)",
        (room["id"], "hello world", "unknown", "2026-01-01 10:00:00", "2026-01-01 10:00:03"),
    )
    seeded_db.commit()
    resp = api_client.get("/api/utterances")
    assert resp.status_code == 200
    data = resp.json()
    assert len(data) == 1
    assert data[0]["transcript"] == "hello world"
    assert data[0]["room_name"] == "Kitchen"


def test_filter_utterances_by_date(api_client, seeded_db):
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
        (room["id"], "today speech", "unknown", "2026-05-27 10:00:00", "2026-05-27 10:00:03"),
    )
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
        (room["id"], "yesterday speech", "unknown", "2026-05-26 10:00:00", "2026-05-26 10:00:03"),
    )
    seeded_db.commit()
    resp = api_client.get("/api/utterances?date=2026-05-27")
    data = resp.json()
    assert len(data) == 1
    assert data[0]["transcript"] == "today speech"


def test_tag_utterance_existing_speaker(api_client, seeded_db):
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    speaker = seeded_db.execute("SELECT id FROM speakers WHERE name='Jeremy'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time, embedding) VALUES (?,?,?,?,?,?)",
        (room["id"], "tag me", "unknown", "2026-01-01 10:00:00", "2026-01-01 10:00:03",
         b"\x00" * 1024),
    )
    seeded_db.commit()
    utt = seeded_db.execute("SELECT id FROM utterances").fetchone()
    resp = api_client.post(f"/api/utterances/{utt['id']}/tag", json={"speaker_id": speaker["id"]})
    assert resp.status_code == 200
    updated = seeded_db.execute("SELECT * FROM utterances WHERE id=?", (utt["id"],)).fetchone()
    assert updated["speaker_id"] == speaker["id"]
    assert updated["match_status"] == "known"
    assert updated["match_confidence"] == 1.0


def test_tag_utterance_create_new_speaker(api_client, seeded_db):
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
        (room["id"], "new speaker", "unknown", "2026-01-01 10:00:00", "2026-01-01 10:00:03"),
    )
    seeded_db.commit()
    utt = seeded_db.execute("SELECT id FROM utterances WHERE transcript='new speaker'").fetchone()
    resp = api_client.post(
        f"/api/utterances/{utt['id']}/tag",
        json={"name": "Alice", "create_new": True},
    )
    assert resp.status_code == 200
    alice = seeded_db.execute("SELECT id FROM speakers WHERE name='Alice'").fetchone()
    assert alice is not None


def test_dismiss_utterance(api_client, seeded_db):
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
        (room["id"], "dismiss me", "unreviewed", "2026-01-01 10:00:00", "2026-01-01 10:00:03"),
    )
    seeded_db.commit()
    utt = seeded_db.execute("SELECT id FROM utterances WHERE transcript='dismiss me'").fetchone()
    resp = api_client.post(f"/api/utterances/{utt['id']}/dismiss")
    assert resp.status_code == 200
    updated = seeded_db.execute("SELECT match_status FROM utterances WHERE id=?", (utt["id"],)).fetchone()
    assert updated["match_status"] == "unknown"
```

- [ ] **Run to confirm failure**

```bash
pytest tests/routes/test_utterances.py -v
```

Expected: `ModuleNotFoundError: No module named 'app.api.main'`

- [ ] **Write `app/api/routes/utterances.py`**

```python
import sqlite3
from typing import Optional
from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import FileResponse
from pydantic import BaseModel

from app.shared.config import Settings
from app.pipeline.recognition import pack_embedding, unpack_embedding

router = APIRouter(prefix="/api/utterances")


class TagRequest(BaseModel):
    speaker_id: Optional[int] = None
    name: Optional[str] = None
    create_new: bool = False


def _row_to_dict(row: sqlite3.Row) -> dict:
    return dict(row)


def get_utterances(
    db: sqlite3.Connection,
    date: Optional[str] = None,
    room_id: Optional[int] = None,
    speaker_id: Optional[int] = None,
    match_status: Optional[str] = None,
    limit: int = 200,
) -> list[dict]:
    query = """
        SELECT u.*, r.name as room_name, s.name as speaker_name
        FROM utterances u
        JOIN rooms r ON u.room_id = r.id
        LEFT JOIN speakers s ON u.speaker_id = s.id
        WHERE 1=1
    """
    params: list = []
    if date:
        query += " AND date(u.start_time) = ?"
        params.append(date)
    if room_id:
        query += " AND u.room_id = ?"
        params.append(room_id)
    if speaker_id:
        query += " AND u.speaker_id = ?"
        params.append(speaker_id)
    if match_status:
        query += " AND u.match_status = ?"
        params.append(match_status)
    query += " ORDER BY u.start_time DESC LIMIT ?"
    params.append(limit)
    rows = db.execute(query, params).fetchall()
    return [_row_to_dict(r) for r in rows]


def make_router(settings: Settings, db_factory):

    @router.get("")
    def list_utterances(
        date: Optional[str] = None,
        room_id: Optional[int] = None,
        speaker_id: Optional[int] = None,
        match_status: Optional[str] = None,
    ):
        db = db_factory()
        return get_utterances(db, date, room_id, speaker_id, match_status)

    @router.get("/{utterance_id}/clip")
    def get_clip(utterance_id: int):
        db = db_factory()
        row = db.execute(
            "SELECT audio_clip_path FROM utterances WHERE id=?", (utterance_id,)
        ).fetchone()
        if not row or not row["audio_clip_path"]:
            raise HTTPException(404, "Clip not found or expired")
        return FileResponse(row["audio_clip_path"], media_type="audio/wav")

    @router.post("/{utterance_id}/tag")
    def tag_utterance(utterance_id: int, body: TagRequest):
        db = db_factory()
        row = db.execute("SELECT * FROM utterances WHERE id=?", (utterance_id,)).fetchone()
        if not row:
            raise HTTPException(404, "Utterance not found")

        speaker_id = body.speaker_id
        if body.create_new and body.name:
            cur = db.execute("INSERT INTO speakers (name) VALUES (?)", (body.name,))
            db.commit()
            speaker_id = cur.lastrowid

        if speaker_id is None:
            raise HTTPException(400, "Provide speaker_id or name+create_new")

        db.execute(
            "UPDATE utterances SET speaker_id=?, match_status='known', match_confidence=1.0 WHERE id=?",
            (speaker_id, utterance_id),
        )
        db.commit()

        # Store utterance embedding as a reference for this speaker
        if row["embedding"]:
            db.execute(
                "INSERT INTO voice_embeddings (speaker_id, embedding) VALUES (?,?)",
                (speaker_id, row["embedding"]),
            )
            db.commit()

        return {"ok": True, "speaker_id": speaker_id}

    @router.post("/{utterance_id}/dismiss")
    def dismiss_utterance(utterance_id: int):
        db = db_factory()
        db.execute(
            "UPDATE utterances SET match_status='unknown' WHERE id=?", (utterance_id,)
        )
        db.commit()
        return {"ok": True}

    return router
```

- [ ] **Write minimal `app/api/main.py` (enough for tests to import)**

```python
import sqlite3
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from pathlib import Path

from app.shared.config import Settings
from app.shared.database import get_db, init_schema
from app.api.routes import utterances, speakers, rooms, search, rag


def create_app(settings: Settings) -> FastAPI:
    init_schema(settings)
    app = FastAPI(title="LinkedStorm")

    def db_factory() -> sqlite3.Connection:
        return get_db(settings)

    app.include_router(utterances.make_router(settings, db_factory))
    app.include_router(speakers.make_router(settings, db_factory))
    app.include_router(rooms.make_router(settings, db_factory))
    app.include_router(search.make_router(settings, db_factory))
    app.include_router(rag.make_router(settings, db_factory))

    static_dir = Path(__file__).parent / "static"
    if static_dir.exists():
        app.mount("/", StaticFiles(directory=str(static_dir), html=True), name="static")

    return app
```

Note: `speakers`, `rooms`, `search`, `rag` routers are stub files — write them now:

- [ ] **Write stub `app/api/routes/speakers.py`**

```python
from fastapi import APIRouter
router = APIRouter(prefix="/api/speakers")
def make_router(settings, db_factory):
    return router
```

- [ ] **Write stub `app/api/routes/rooms.py`**

```python
from fastapi import APIRouter
router = APIRouter(prefix="/api/rooms")
def make_router(settings, db_factory):
    return router
```

- [ ] **Write stub `app/api/routes/search.py`**

```python
from fastapi import APIRouter
router = APIRouter(prefix="/api/search")
def make_router(settings, db_factory):
    return router
```

- [ ] **Write stub `app/api/routes/rag.py`**

```python
from fastapi import APIRouter
router = APIRouter(prefix="/api")
def make_router(settings, db_factory):
    return router
```

- [ ] **Run tests — expect PASS**

```bash
pytest tests/routes/test_utterances.py -v
```

Expected: `6 passed`

- [ ] **Commit**

```bash
git add app/api/ tests/routes/
git commit -m "feat: utterances API routes — list, clip, tag, dismiss"
git push origin master
```

---

## Task 9: API routes — speakers, rooms, search, RAG (TDD)

**Files:** `app/api/routes/speakers.py`, `rooms.py`, `search.py`, `rag.py` (replace stubs), plus tests

- [ ] **Write `tests/routes/test_speakers.py`**

```python
def test_list_speakers_empty(api_client):
    resp = api_client.get("/api/speakers")
    assert resp.status_code == 200
    assert resp.json() == []


def test_create_speaker(api_client):
    resp = api_client.post("/api/speakers", json={"name": "Alice"})
    assert resp.status_code == 200
    data = resp.json()
    assert data["name"] == "Alice"
    assert "id" in data


def test_list_speakers_includes_utterance_count(api_client, seeded_db):
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    speaker = seeded_db.execute("SELECT id FROM speakers WHERE name='Jeremy'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, speaker_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?,?)",
        (room["id"], speaker["id"], "hi", "known", "2026-01-01 10:00:00", "2026-01-01 10:00:01"),
    )
    seeded_db.commit()
    resp = api_client.get("/api/speakers")
    data = resp.json()
    jeremy = next(s for s in data if s["name"] == "Jeremy")
    assert jeremy["utterance_count"] == 1
```

- [ ] **Write `tests/routes/test_rooms.py`**

```python
def test_list_rooms(api_client, seeded_db):
    resp = api_client.get("/api/rooms")
    assert resp.status_code == 200
    data = resp.json()
    assert any(r["name"] == "Kitchen" for r in data)


def test_mute_room_calls_pipeline(api_client, seeded_db, mocker):
    mock_post = mocker.patch("app.api.routes.rooms.httpx.post")
    mock_post.return_value.status_code = 200
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    resp = api_client.post(f"/api/rooms/{room['id']}/mute")
    assert resp.status_code == 200
    mock_post.assert_called_once()
```

- [ ] **Write `tests/routes/test_search.py`**

```python
def test_search_returns_matches(api_client, seeded_db):
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
        (room["id"], "the grocery list is ready", "unknown", "2026-01-01 10:00:00", "2026-01-01 10:00:03"),
    )
    seeded_db.commit()
    resp = api_client.get("/api/search?q=grocery")
    assert resp.status_code == 200
    data = resp.json()
    assert len(data) == 1
    assert "grocery" in data[0]["transcript"]


def test_search_no_results(api_client):
    resp = api_client.get("/api/search?q=xyznotfound")
    assert resp.status_code == 200
    assert resp.json() == []
```

- [ ] **Write `tests/routes/test_rag.py`**

```python
def test_ask_returns_response(api_client, seeded_db, mocker):
    mock_post = mocker.patch("app.api.routes.rag.httpx.post")
    mock_post.return_value.json.return_value = {
        "message": {"content": "You talked about groceries."}
    }
    mock_post.return_value.status_code = 200
    room = seeded_db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    seeded_db.execute(
        "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
        (room["id"], "we need milk from the store", "unknown", "2026-01-01 10:00:00", "2026-01-01 10:00:03"),
    )
    seeded_db.commit()
    resp = api_client.post("/api/ask", json={"question": "What did we need from the store?"})
    assert resp.status_code == 200
    data = resp.json()
    assert "answer" in data


def test_get_summary_not_found(api_client):
    resp = api_client.get("/api/summary/2026-01-01")
    assert resp.status_code == 404
```

- [ ] **Run to confirm failures**

```bash
pytest tests/routes/ -v
```

Expected: New test files fail. `test_utterances.py` still passes.

- [ ] **Replace stub `app/api/routes/speakers.py`**

```python
import sqlite3
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel

router = APIRouter(prefix="/api/speakers")


class CreateSpeakerRequest(BaseModel):
    name: str


def make_router(settings, db_factory):

    @router.get("")
    def list_speakers():
        db: sqlite3.Connection = db_factory()
        rows = db.execute("""
            SELECT s.id, s.name, s.created_at,
                   COUNT(u.id) as utterance_count
            FROM speakers s
            LEFT JOIN utterances u ON u.speaker_id = s.id
            GROUP BY s.id
            ORDER BY s.name
        """).fetchall()
        return [dict(r) for r in rows]

    @router.post("")
    def create_speaker(body: CreateSpeakerRequest):
        db: sqlite3.Connection = db_factory()
        cur = db.execute("INSERT INTO speakers (name) VALUES (?)", (body.name,))
        db.commit()
        row = db.execute("SELECT * FROM speakers WHERE id=?", (cur.lastrowid,)).fetchone()
        return dict(row)

    return router
```

- [ ] **Replace stub `app/api/routes/rooms.py`**

```python
import sqlite3
import httpx
from fastapi import APIRouter, HTTPException
from app.shared.config import Settings

router = APIRouter(prefix="/api/rooms")


def make_router(settings: Settings, db_factory):

    @router.get("")
    def list_rooms():
        db: sqlite3.Connection = db_factory()
        rows = db.execute(
            "SELECT id, name, device_label, is_active, last_seen, created_at FROM rooms ORDER BY name"
        ).fetchall()
        return [dict(r) for r in rows]

    @router.post("/{room_id}/mute")
    def mute_room(room_id: int):
        db: sqlite3.Connection = db_factory()
        row = db.execute("SELECT name FROM rooms WHERE id=?", (room_id,)).fetchone()
        if not row:
            raise HTTPException(404, "Room not found")
        httpx.post(f"{settings.pipeline_internal_url}/internal/rooms/{row['name']}/mute")
        return {"ok": True, "room": row["name"]}

    @router.post("/{room_id}/unmute")
    def unmute_room(room_id: int):
        db: sqlite3.Connection = db_factory()
        row = db.execute("SELECT name FROM rooms WHERE id=?", (room_id,)).fetchone()
        if not row:
            raise HTTPException(404, "Room not found")
        httpx.post(f"{settings.pipeline_internal_url}/internal/rooms/{row['name']}/unmute")
        return {"ok": True, "room": row["name"]}

    return router
```

- [ ] **Replace stub `app/api/routes/search.py`**

```python
import sqlite3
from typing import Optional
from fastapi import APIRouter, HTTPException

router = APIRouter(prefix="/api")


def make_router(settings, db_factory):

    @router.get("/search")
    def search(q: str, date: Optional[str] = None):
        if not q.strip():
            return []
        db: sqlite3.Connection = db_factory()
        fts_query = q.strip()
        rows = db.execute("""
            SELECT u.*, r.name as room_name, s.name as speaker_name
            FROM utterances_fts fts
            JOIN utterances u ON u.id = fts.rowid
            JOIN rooms r ON u.room_id = r.id
            LEFT JOIN speakers s ON u.speaker_id = s.id
            WHERE utterances_fts MATCH ?
            ORDER BY u.start_time DESC
            LIMIT 100
        """, (fts_query,)).fetchall()
        return [dict(r) for r in rows]

    return router
```

- [ ] **Replace stub `app/api/routes/rag.py`**

```python
import sqlite3
import httpx
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from app.shared.config import Settings

router = APIRouter(prefix="/api")


class AskRequest(BaseModel):
    question: str
    date: str | None = None


def make_router(settings: Settings, db_factory):

    @router.post("/ask")
    def ask(body: AskRequest):
        db: sqlite3.Connection = db_factory()
        # Fetch recent transcripts as context (last 200 utterances for the date or today)
        date_filter = body.date or "date('now')"
        rows = db.execute("""
            SELECT u.start_time, u.transcript, r.name as room_name, s.name as speaker_name
            FROM utterances u
            JOIN rooms r ON u.room_id = r.id
            LEFT JOIN speakers s ON u.speaker_id = s.id
            WHERE date(u.start_time) = date(?)
            ORDER BY u.start_time ASC
            LIMIT 200
        """, (body.date or "now",)).fetchall()

        context_lines = [
            f"[{r['room_name']} {r['start_time']}] {r['speaker_name'] or 'Unknown'}: {r['transcript']}"
            for r in rows
        ]
        context = "\n".join(context_lines) if context_lines else "(no transcripts for this date)"

        prompt = (
            f"You are a helpful assistant with access to household conversation transcripts.\n\n"
            f"Transcripts:\n{context}\n\n"
            f"Question: {body.question}\n\n"
            f"Answer based only on the transcripts above. Cite the time and room when relevant."
        )

        resp = httpx.post(
            f"{settings.ollama_url}/api/chat",
            json={
                "model": settings.ollama_model,
                "messages": [{"role": "user", "content": prompt}],
                "stream": False,
            },
            timeout=60.0,
        )
        resp.raise_for_status()
        answer = resp.json()["message"]["content"]
        return {"answer": answer, "context_count": len(rows)}

    @router.get("/summary/{date}")
    def get_summary(date: str):
        db: sqlite3.Connection = db_factory()
        row = db.execute(
            "SELECT * FROM daily_summaries WHERE date=?", (date,)
        ).fetchone()
        if not row:
            raise HTTPException(404, f"No summary for {date}")
        return dict(row)

    return router
```

- [ ] **Run all route tests**

```bash
pytest tests/routes/ -v
```

Expected: `~14 passed`

- [ ] **Commit**

```bash
git add app/api/routes/ tests/routes/
git commit -m "feat: API routes — speakers, rooms, search, RAG"
git push origin master
```

---

## Task 10: Cron jobs (TDD)

**Files:** `app/cron/daily_summary.py`, `app/cron/cluster_unknowns.py`, `app/cron/purge_clips.py`, `app/cron/main.py`, `tests/test_cron.py`

- [ ] **Write `tests/test_cron.py`**

```python
import pytest
import os
from datetime import datetime, timezone
from pathlib import Path
from unittest.mock import patch, MagicMock


def _seed_utterances(db, room_id, count=3):
    for i in range(count):
        db.execute(
            "INSERT INTO utterances (room_id, transcript, match_status, start_time, end_time) VALUES (?,?,?,?,?)",
            (room_id, f"utterance {i}", "unknown",
             "2026-05-27 10:00:00", "2026-05-27 10:00:03"),
        )
    db.commit()


def test_daily_summary_inserts_row(mocker, settings, db):
    db.execute("INSERT INTO rooms (name) VALUES (?)", ("Kitchen",))
    db.commit()
    room = db.execute("SELECT id FROM rooms WHERE name='Kitchen'").fetchone()
    _seed_utterances(db, room["id"])

    mock_post = mocker.patch("app.cron.daily_summary.httpx.post")
    mock_post.return_value.json.return_value = {"message": {"content": "Busy day."}}
    mock_post.return_value.raise_for_status = MagicMock()

    from app.cron.daily_summary import generate_daily_summary
    generate_daily_summary("2026-05-27", settings, db)

    row = db.execute("SELECT * FROM daily_summaries WHERE date='2026-05-27'").fetchone()
    assert row is not None
    assert row["summary_text"] == "Busy day."


def test_daily_summary_skips_if_no_utterances(mocker, settings, db):
    mock_post = mocker.patch("app.cron.daily_summary.httpx.post")
    from app.cron.daily_summary import generate_daily_summary
    generate_daily_summary("2026-05-27", settings, db)
    mock_post.assert_not_called()


def test_purge_clips_deletes_old_files(settings, db, tmp_path):
    from app.cron.purge_clips import purge_old_clips
    import shutil

    clips_dir = tmp_path / "clips"
    old_dir = clips_dir / "2026-04-01"
    old_dir.mkdir(parents=True)
    old_file = old_dir / "1.wav"
    old_file.write_bytes(b"\x00" * 100)

    new_dir = clips_dir / "2026-05-27"
    new_dir.mkdir(parents=True)
    new_file = new_dir / "2.wav"
    new_file.write_bytes(b"\x00" * 100)

    settings_override = type(settings)(
        db_path=settings.db_path,
        clips_dir=str(clips_dir),
        clip_retention_days=30,
        speaches_url=settings.speaches_url,
        ollama_url=settings.ollama_url,
        livekit_api_key=settings.livekit_api_key,
        livekit_api_secret=settings.livekit_api_secret,
    )

    purge_old_clips("2026-05-28", settings_override, db)
    assert not old_file.exists()
    assert new_file.exists()
```

- [ ] **Run to confirm failures**

```bash
pytest tests/test_cron.py -v
```

Expected: `ModuleNotFoundError: No module named 'app.cron.daily_summary'`

- [ ] **Write `app/cron/daily_summary.py`**

```python
import sqlite3
import httpx
from app.shared.config import Settings


def generate_daily_summary(date: str, settings: Settings, db: sqlite3.Connection) -> None:
    rows = db.execute("""
        SELECT u.start_time, u.transcript, r.name as room_name, s.name as speaker_name
        FROM utterances u
        JOIN rooms r ON u.room_id = r.id
        LEFT JOIN speakers s ON u.speaker_id = s.id
        WHERE date(u.start_time) = ?
        ORDER BY u.start_time ASC
    """, (date,)).fetchall()

    if not rows:
        return

    lines = [
        f"[{r['room_name']} {r['start_time']}] {r['speaker_name'] or 'Unknown'}: {r['transcript']}"
        for r in rows
    ]
    context = "\n".join(lines)

    prompt = (
        f"Summarize the following household conversations from {date} in 2-4 sentences. "
        f"Focus on key decisions, activities, and topics discussed. Do not transcribe verbatim.\n\n"
        f"{context}"
    )

    resp = httpx.post(
        f"{settings.ollama_url}/api/chat",
        json={
            "model": settings.ollama_model,
            "messages": [{"role": "user", "content": prompt}],
            "stream": False,
        },
        timeout=120.0,
    )
    resp.raise_for_status()
    summary = resp.json()["message"]["content"]

    db.execute(
        "INSERT INTO daily_summaries (date, summary_text) VALUES (?,?) "
        "ON CONFLICT(date) DO UPDATE SET summary_text=?, generated_at=CURRENT_TIMESTAMP",
        (date, summary, summary),
    )
    db.commit()
```

- [ ] **Write `app/cron/cluster_unknowns.py`**

```python
import sqlite3
import numpy as np
from app.shared.config import Settings
from app.pipeline.recognition import unpack_embedding, cosine_similarity


def cluster_unknown_speakers(settings: Settings, db: sqlite3.Connection) -> None:
    """
    Groups unknown/ambiguous utterances from the last 7 days by voice similarity.
    Assigns speaker_id from an existing 'Unknown #N' speaker or creates a new one.
    Threshold: 0.70 cosine similarity.
    """
    CLUSTER_THRESHOLD = 0.70

    rows = db.execute("""
        SELECT id, embedding FROM utterances
        WHERE match_status IN ('unknown', 'ambiguous')
          AND embedding IS NOT NULL
          AND date(start_time) >= date('now', '-7 days')
        ORDER BY start_time ASC
    """).fetchall()

    if not rows:
        return

    # Build clusters greedily
    clusters: list[list[int]] = []  # list of utterance id lists
    cluster_centroids: list[np.ndarray] = []

    for row in rows:
        emb = unpack_embedding(row["embedding"])
        best_cluster = None
        best_sim = 0.0
        for i, centroid in enumerate(cluster_centroids):
            sim = cosine_similarity(emb, centroid)
            if sim > best_sim and sim >= CLUSTER_THRESHOLD:
                best_sim = sim
                best_cluster = i
        if best_cluster is not None:
            clusters[best_cluster].append(row["id"])
            # Update centroid as mean
            n = len(clusters[best_cluster])
            cluster_centroids[best_cluster] = (
                (cluster_centroids[best_cluster] * (n - 1) + emb) / n
            )
        else:
            clusters.append([row["id"]])
            cluster_centroids.append(emb)

    # Assign Unknown #N speakers to clusters with > 1 utterance
    for i, cluster in enumerate(clusters):
        if len(cluster) < 2:
            continue
        name = f"Unknown #{i + 1}"
        existing = db.execute("SELECT id FROM speakers WHERE name=?", (name,)).fetchone()
        if existing:
            speaker_id = existing["id"]
        else:
            cur = db.execute("INSERT INTO speakers (name) VALUES (?)", (name,))
            db.commit()
            speaker_id = cur.lastrowid
        for utt_id in cluster:
            db.execute(
                "UPDATE utterances SET speaker_id=?, match_status='ambiguous' WHERE id=?",
                (speaker_id, utt_id),
            )
    db.commit()
```

- [ ] **Write `app/cron/purge_clips.py`**

```python
import sqlite3
from datetime import datetime, timedelta
from pathlib import Path
from app.shared.config import Settings


def purge_old_clips(today: str, settings: Settings, db: sqlite3.Connection) -> None:
    cutoff = datetime.strptime(today, "%Y-%m-%d") - timedelta(days=settings.clip_retention_days)
    clips_root = Path(settings.clips_dir)
    deleted = 0
    for date_dir in clips_root.iterdir():
        if not date_dir.is_dir():
            continue
        try:
            dir_date = datetime.strptime(date_dir.name, "%Y-%m-%d")
        except ValueError:
            continue
        if dir_date < cutoff:
            for wav in date_dir.glob("*.wav"):
                wav.unlink()
                deleted += 1
            try:
                date_dir.rmdir()
            except OSError:
                pass
    # Null out audio_clip_path for purged clips
    cutoff_str = cutoff.strftime("%Y-%m-%d")
    db.execute(
        "UPDATE utterances SET audio_clip_path=NULL WHERE date(start_time) < ?",
        (cutoff_str,),
    )
    db.commit()
```

- [ ] **Write `app/cron/main.py`**

```python
import time
from datetime import datetime, timezone

from app.shared.config import get_settings
from app.shared.database import get_db, init_schema
from app.cron.daily_summary import generate_daily_summary
from app.cron.cluster_unknowns import cluster_unknown_speakers
from app.cron.purge_clips import purge_old_clips


def run_nightly(settings, db):
    today = datetime.now(timezone.utc).strftime("%Y-%m-%d")
    yesterday = (datetime.now(timezone.utc).date()).strftime("%Y-%m-%d")
    generate_daily_summary(yesterday, settings, db)
    cluster_unknown_speakers(settings, db)
    purge_old_clips(today, settings, db)


def main():
    settings = get_settings()
    init_schema(settings)
    db = get_db(settings)

    while True:
        now = datetime.now(timezone.utc)
        # Sleep until midnight UTC
        next_midnight = now.replace(hour=0, minute=0, second=0, microsecond=0)
        if next_midnight <= now:
            from datetime import timedelta
            next_midnight += timedelta(days=1)
        sleep_secs = (next_midnight - now).total_seconds()
        time.sleep(sleep_secs)
        run_nightly(settings, db)


if __name__ == "__main__":
    main()
```

- [ ] **Run tests — expect PASS**

```bash
pytest tests/test_cron.py -v
```

Expected: `3 passed`

- [ ] **Commit**

```bash
git add app/cron/ tests/test_cron.py
git commit -m "feat: cron jobs — daily summary, speaker clustering, clip purge"
git push origin master
```

---

## Task 11: Capture page

**Files:** `app/api/static/capture.html`

*(No unit tests — browser JS; verified manually in Task 18)*

- [ ] **Create `app/api/static/capture.html`**

```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LinkedStorm Capture</title>
<style>
  body { font-family: system-ui, sans-serif; background: #0f172a; color: #e2e8f0;
         display: flex; align-items: center; justify-content: center; min-height: 100vh; margin: 0; }
  .card { background: #1e293b; border-radius: 16px; padding: 32px; text-align: center;
          min-width: 280px; box-shadow: 0 4px 32px rgba(0,0,0,0.4); }
  h1 { font-size: 1.4em; margin: 0 0 8px; color: #f1f5f9; }
  .room-label { font-size: 1em; color: #94a3b8; margin-bottom: 24px; }
  .status { display: flex; align-items: center; justify-content: center; gap: 10px;
            font-size: 0.9em; margin-bottom: 24px; }
  .dot { width: 12px; height: 12px; border-radius: 50%; }
  .dot.green { background: #4ade80; box-shadow: 0 0 8px #4ade80; animation: pulse 2s infinite; }
  .dot.red { background: #ef4444; }
  .dot.gray { background: #475569; }
  @keyframes pulse { 0%,100%{opacity:1} 50%{opacity:0.5} }
  button { padding: 12px 28px; border-radius: 8px; border: none; font-size: 1em;
           cursor: pointer; transition: all 0.15s; }
  #muteBtn { background: #334155; color: #e2e8f0; }
  #muteBtn:hover { background: #475569; }
  #muteBtn.muted { background: #7f1d1d; color: #fca5a5; }
  .room-input { background: #334155; border: 1px solid #475569; border-radius: 8px;
                padding: 8px 12px; color: #e2e8f0; font-size: 0.9em; width: 100%;
                box-sizing: border-box; margin-bottom: 16px; }
</style>
</head>
<body>
<div class="card">
  <h1>⚡ LinkedStorm</h1>
  <div id="setup" style="display:none">
    <p style="color:#94a3b8;font-size:0.85em">Enter room name to start capturing</p>
    <input id="roomInput" class="room-input" placeholder="e.g. Kitchen" />
    <button onclick="startCapture()" style="background:#4ade80;color:#052e16">Start Capturing</button>
  </div>
  <div id="capture" style="display:none">
    <div class="room-label">📍 <span id="roomDisplay"></span></div>
    <div class="status">
      <div class="dot" id="statusDot"></div>
      <span id="statusText">Connecting…</span>
    </div>
    <button id="muteBtn" onclick="toggleMute()">🔇 Mute</button>
  </div>
</div>
<script>
  const params = new URLSearchParams(location.search);
  let roomName = params.get('room') || localStorage.getItem('linkedstorm_room');
  let ws = null, mediaRecorder = null, muted = false, reconnectTimer = null;

  function init() {
    if (roomName) {
      document.getElementById('setup').style.display = 'none';
      document.getElementById('capture').style.display = 'block';
      document.getElementById('roomDisplay').textContent = roomName;
      connect();
    } else {
      document.getElementById('setup').style.display = 'block';
    }
  }

  function startCapture() {
    roomName = document.getElementById('roomInput').value.trim();
    if (!roomName) return;
    localStorage.setItem('linkedstorm_room', roomName);
    document.getElementById('setup').style.display = 'none';
    document.getElementById('capture').style.display = 'block';
    document.getElementById('roomDisplay').textContent = roomName;
    connect();
  }

  function setStatus(state, text) {
    const dot = document.getElementById('statusDot');
    const label = document.getElementById('statusText');
    dot.className = 'dot ' + state;
    label.textContent = text;
  }

  async function connect() {
    setStatus('gray', 'Connecting…');
    try {
      const stream = await navigator.mediaDevices.getUserMedia({
        audio: { channelCount: 1, sampleRate: 16000 }
      });
      const wsProto = location.protocol === 'https:' ? 'wss:' : 'ws:';
      ws = new WebSocket(`${wsProto}//${location.host.replace(':8310','').replace(':80','')}:8300/ws/${encodeURIComponent(roomName)}`);
      ws.binaryType = 'arraybuffer';
      ws.onopen = () => {
        setStatus('green', muted ? 'Muted' : 'Streaming');
        startRecorder(stream);
      };
      ws.onclose = () => {
        setStatus('gray', 'Reconnecting…');
        reconnectTimer = setTimeout(connect, 3000);
      };
      ws.onerror = () => ws.close();
    } catch (e) {
      setStatus('red', 'Mic denied — allow microphone access');
    }
  }

  function startRecorder(stream) {
    mediaRecorder = new MediaRecorder(stream, { mimeType: 'audio/webm;codecs=pcm' });
    mediaRecorder.ondataavailable = e => {
      if (ws && ws.readyState === WebSocket.OPEN && !muted && e.data.size > 0) {
        ws.send(e.data);
      }
    };
    mediaRecorder.start(250); // 250ms chunks
  }

  function toggleMute() {
    muted = !muted;
    const btn = document.getElementById('muteBtn');
    if (muted) {
      btn.textContent = '🎤 Unmute';
      btn.classList.add('muted');
      setStatus('red', 'Muted');
      if (ws && ws.readyState === WebSocket.OPEN)
        ws.send(JSON.stringify({ type: 'mute' }));
    } else {
      btn.textContent = '🔇 Mute';
      btn.classList.remove('muted');
      setStatus('green', 'Streaming');
      if (ws && ws.readyState === WebSocket.OPEN)
        ws.send(JSON.stringify({ type: 'unmute' }));
    }
  }

  init();
</script>
</body>
</html>
```

- [ ] **Commit**

```bash
git add app/api/static/capture.html
git commit -m "feat: browser capture page — MediaRecorder WebSocket streaming + mute"
git push origin master
```

---

## Task 12: Dashboard SPA

**Files:** `app/api/static/index.html`, `app/api/static/app.js`

- [ ] **Create `app/api/static/index.html`**

```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LinkedStorm</title>
<style>
  *{box-sizing:border-box;margin:0;padding:0}
  body{font-family:system-ui,sans-serif;background:#0f172a;color:#e2e8f0;display:flex;height:100vh;overflow:hidden}
  .sidebar{width:180px;flex-shrink:0;background:#1e293b;display:flex;flex-direction:column;border-right:1px solid #334155}
  .brand{padding:16px;font-weight:700;font-size:1em;color:#4ade80;border-bottom:1px solid #334155}
  .nav-item{padding:10px 16px;font-size:0.85em;color:#94a3b8;cursor:pointer;display:flex;align-items:center;gap:8px;transition:all 0.1s}
  .nav-item:hover{background:#334155;color:#e2e8f0}
  .nav-item.active{background:rgba(74,222,128,0.1);color:#4ade80;border-left:3px solid #4ade80}
  .main{flex:1;overflow:auto;background:#0f172a}
  .panel{display:none;padding:20px;height:100%}
  .panel.active{display:block}
  .toolbar{display:flex;gap:8px;margin-bottom:16px;align-items:center;flex-wrap:wrap}
  input,select{background:#1e293b;border:1px solid #334155;border-radius:6px;padding:7px 10px;color:#e2e8f0;font-size:0.85em}
  input:focus,select:focus{outline:none;border-color:#4ade80}
  button{padding:7px 14px;border-radius:6px;border:none;cursor:pointer;font-size:0.85em;transition:all 0.15s}
  .btn-primary{background:#4ade80;color:#052e16;font-weight:600}
  .btn-secondary{background:#334155;color:#e2e8f0}
  .btn-small{padding:3px 8px;font-size:0.72em}
  .time-block{margin-bottom:16px}
  .time-label{font-size:0.7em;text-transform:uppercase;letter-spacing:0.06em;color:#475569;margin-bottom:6px;display:flex;align-items:center;gap:8px}
  .time-label::after{content:'';flex:1;height:1px;background:#1e293b}
  .utterance{display:flex;gap:10px;margin-bottom:6px;align-items:flex-start}
  .avatar{width:28px;height:28px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-size:0.75em;font-weight:700;flex-shrink:0;margin-top:2px}
  .utt-body{flex:1;background:#1e293b;border-radius:8px;padding:8px 12px}
  .utt-meta{display:flex;gap:8px;align-items:center;margin-bottom:3px;font-size:0.72em}
  .utt-name{font-weight:600}
  .utt-room{color:#64748b}
  .utt-time{color:#475569;margin-left:auto}
  .utt-text{font-size:0.82em;color:#cbd5e1;line-height:1.45}
  .utt-actions{display:flex;gap:4px;margin-top:5px}
  .badge{font-size:0.62em;padding:1px 5px;border-radius:4px;margin-left:4px}
  .badge-unknown{background:rgba(239,68,68,0.2);color:#f87171}
  .badge-ambiguous{background:rgba(245,158,11,0.2);color:#fbbf24}
  .search-result{background:#1e293b;border-radius:8px;padding:10px 12px;margin-bottom:8px;border-left:3px solid #334155}
  .search-result:hover{border-left-color:#4ade80}
  .sr-meta{font-size:0.7em;color:#64748b;margin-bottom:3px}
  .sr-text{font-size:0.82em;color:#cbd5e1}
  mark{background:rgba(74,222,128,0.2);color:#4ade80;border-radius:2px;padding:0 2px}
  .speaker-grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(150px,1fr));gap:10px}
  .speaker-card{background:#1e293b;border:1px solid #334155;border-radius:10px;padding:14px;text-align:center;cursor:pointer}
  .speaker-card:hover{border-color:#4ade80}
  .sp-avatar{width:44px;height:44px;border-radius:50%;margin:0 auto 8px;display:flex;align-items:center;justify-content:center;font-weight:700;font-size:1.1em}
  .sp-name{font-size:0.85em;font-weight:600}
  .sp-count{font-size:0.72em;color:#64748b;margin-top:2px}
  .room-grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(170px,1fr));gap:10px}
  .room-card{background:#1e293b;border:1px solid #334155;border-radius:10px;padding:14px}
  .room-card.active{border-color:#4ade80}
  .room-card.muted{border-color:#ef4444}
  .room-name{font-size:0.9em;font-weight:600;margin-bottom:6px}
  .status-row{display:flex;align-items:center;gap:6px;font-size:0.75em}
  .sdot{width:8px;height:8px;border-radius:50%}
  .rag-msg{margin-bottom:12px}
  .rag-bubble{border-radius:10px;padding:10px 14px;font-size:0.85em;line-height:1.5;color:#cbd5e1}
  .rag-user .rag-bubble{background:rgba(74,222,128,0.08);border:1px solid rgba(74,222,128,0.2);margin-left:15%}
  .rag-ai .rag-bubble{background:#1e293b;border:1px solid #334155;margin-right:15%}
  .rag-label{font-size:0.68em;color:#64748b;margin-bottom:3px;padding:0 4px}
  .rag-source{font-size:0.7em;color:#475569;margin-top:6px;padding-top:6px;border-top:1px solid #334155;font-style:italic}
  .review-banner{background:rgba(245,158,11,0.08);border:1px solid rgba(245,158,11,0.25);border-radius:8px;padding:10px 14px;margin-bottom:14px;display:flex;justify-content:space-between;align-items:center;font-size:0.82em;color:#fbbf24}
  h2{font-size:1em;font-weight:600;margin-bottom:16px;color:#f1f5f9}
  .empty{color:#475569;font-size:0.85em;padding:20px 0}
</style>
</head>
<body>
<div class="sidebar">
  <div class="brand">⚡ LinkedStorm</div>
  <div class="nav-item active" onclick="showPanel('log',this)">📋 Daily Log</div>
  <div class="nav-item" onclick="showPanel('search',this)">🔍 Search</div>
  <div class="nav-item" onclick="showPanel('speakers',this)">🧑 Speakers</div>
  <div class="nav-item" onclick="showPanel('rag',this)">🤖 Ask AI</div>
  <div class="nav-item" onclick="showPanel('rooms',this)">📍 Rooms</div>
</div>
<div class="main">
  <div id="panel-log" class="panel active"></div>
  <div id="panel-search" class="panel"></div>
  <div id="panel-speakers" class="panel"></div>
  <div id="panel-rag" class="panel"></div>
  <div id="panel-rooms" class="panel"></div>
</div>
<script src="/app.js"></script>
</body>
</html>
```

- [ ] **Create `app/api/static/app.js`**

```javascript
// --- Utilities ---
const COLORS = ['#60a5fa','#a78bfa','#f472b6','#34d399','#fb923c','#38bdf8','#e879f9','#a3e635'];
const speakerColor = (id) => COLORS[(id || 0) % COLORS.length];
const speakerInitial = (name) => (name || '?')[0].toUpperCase();
const fmtTime = (iso) => new Date(iso).toLocaleTimeString([], {hour:'2-digit',minute:'2-digit',second:'2-digit'});
const fmtDate = (iso) => new Date(iso).toLocaleDateString([], {weekday:'long',month:'long',day:'numeric',year:'numeric'});
const today = () => new Date().toISOString().slice(0,10);

let _currentDate = today();

function showPanel(name, el) {
  document.querySelectorAll('.panel').forEach(p => p.classList.remove('active'));
  document.querySelectorAll('.nav-item').forEach(n => n.classList.remove('active'));
  document.getElementById('panel-' + name).classList.add('active');
  if (el) el.classList.add('active');
  if (name === 'log') loadLog();
  if (name === 'search') renderSearch();
  if (name === 'speakers') loadSpeakers();
  if (name === 'rag') renderRag();
  if (name === 'rooms') loadRooms();
}

// --- Daily Log ---
async function loadLog(date) {
  if (date) _currentDate = date;
  const panel = document.getElementById('panel-log');
  const utterances = await fetch(`/api/utterances?date=${_currentDate}`).then(r => r.json());
  
  const nav = `<div class="toolbar">
    <button class="btn-secondary btn-small" onclick="loadLog(shiftDate(-1))">← Prev</button>
    <span style="font-size:0.85em;font-weight:600">${fmtDate(_currentDate + 'T12:00:00')}</span>
    <button class="btn-secondary btn-small" onclick="loadLog(shiftDate(1))">Next →</button>
    <button class="btn-secondary btn-small" onclick="loadLog('${today()}')">Today</button>
  </div>`;

  if (!utterances.length) {
    panel.innerHTML = nav + '<div class="empty">No utterances recorded for this day.</div>';
    return;
  }

  // Group by hour
  const byHour = {};
  for (const u of utterances.reverse()) {
    const h = new Date(u.start_time).getHours();
    const label = new Date(u.start_time).toLocaleTimeString([], {hour:'2-digit',minute:'2-digit'});
    const key = label.slice(0,5);
    if (!byHour[key]) byHour[key] = [];
    byHour[key].push(u);
  }

  let html = nav;
  for (const [timeKey, utts] of Object.entries(byHour)) {
    html += `<div class="time-block"><div class="time-label">${timeKey}</div>`;
    for (const u of utts) {
      const color = speakerColor(u.speaker_id);
      const name = u.speaker_name || 'Unknown';
      const isUnknown = !u.speaker_id;
      const badge = u.match_status === 'unknown' ? '<span class="badge badge-unknown">unknown</span>'
                  : u.match_status === 'ambiguous' ? '<span class="badge badge-ambiguous">ambiguous</span>' : '';
      const actions = isUnknown ? `<div class="utt-actions">
        <button class="btn-small btn-secondary" onclick="playClip(${u.id})">▶ Play</button>
        <button class="btn-small btn-secondary" onclick="tagSpeaker(${u.id})">🏷 Tag</button>
        <button class="btn-small btn-secondary" onclick="dismiss(${u.id})">✕</button>
      </div>` : '';
      html += `<div class="utterance">
        <div class="avatar" style="background:${color}22;color:${color}">${speakerInitial(name)}</div>
        <div class="utt-body">
          <div class="utt-meta">
            <span class="utt-name" style="color:${color}">${name}${badge}</span>
            <span class="utt-room">${u.room_name}</span>
            <span class="utt-time">${fmtTime(u.start_time)}</span>
          </div>
          <div class="utt-text">${escHtml(u.transcript)}</div>
          ${actions}
        </div>
      </div>`;
    }
    html += '</div>';
  }
  panel.innerHTML = html;
}

function shiftDate(days) {
  const d = new Date(_currentDate + 'T12:00:00');
  d.setDate(d.getDate() + days);
  return d.toISOString().slice(0,10);
}

async function playClip(id) {
  const a = document.createElement('audio');
  a.src = `/api/utterances/${id}/clip`;
  a.controls = true;
  a.autoplay = true;
  document.body.appendChild(a);
  a.onended = () => a.remove();
}

async function tagSpeaker(utteranceId) {
  const speakers = await fetch('/api/speakers').then(r => r.json());
  const name = prompt('Speaker name (existing or new):' + speakers.map(s => '\n• ' + s.name).join(''));
  if (!name) return;
  const existing = speakers.find(s => s.name.toLowerCase() === name.toLowerCase());
  const body = existing
    ? { speaker_id: existing.id }
    : { name: name.trim(), create_new: true };
  await fetch(`/api/utterances/${utteranceId}/tag`, { method: 'POST', headers: {'Content-Type':'application/json'}, body: JSON.stringify(body) });
  loadLog();
}

async function dismiss(id) {
  await fetch(`/api/utterances/${id}/dismiss`, { method: 'POST' });
  loadLog();
}

// --- Search ---
function renderSearch() {
  const panel = document.getElementById('panel-search');
  panel.innerHTML = `<h2>Search Transcripts</h2>
    <div class="toolbar">
      <input id="searchQ" placeholder="Search…" style="flex:1" onkeydown="if(event.key==='Enter')doSearch()">
      <button class="btn-primary" onclick="doSearch()">Search</button>
    </div>
    <div id="searchResults"></div>`;
}

async function doSearch() {
  const q = document.getElementById('searchQ').value.trim();
  if (!q) return;
  const results = await fetch(`/api/search?q=${encodeURIComponent(q)}`).then(r => r.json());
  const container = document.getElementById('searchResults');
  if (!results.length) { container.innerHTML = '<div class="empty">No results.</div>'; return; }
  const highlight = (text) => text.replace(new RegExp(`(${q})`, 'gi'), '<mark>$1</mark>');
  container.innerHTML = results.map(r => `<div class="search-result">
    <div class="sr-meta">${r.speaker_name || 'Unknown'} · ${r.room_name} · ${fmtDate(r.start_time)} ${fmtTime(r.start_time)}</div>
    <div class="sr-text">${highlight(escHtml(r.transcript))}</div>
  </div>`).join('');
}

// --- Speakers ---
async function loadSpeakers() {
  const panel = document.getElementById('panel-speakers');
  const [speakers, unknowns] = await Promise.all([
    fetch('/api/speakers').then(r => r.json()),
    fetch('/api/utterances?match_status=unreviewed').then(r => r.json()),
  ]);
  let html = '';
  if (unknowns.length) {
    html += `<div class="review-banner">⚠ ${unknowns.length} utterances need speaker tags
      <button class="btn-small btn-secondary" onclick="showPanel('log',null);loadLog('${today()}')">Review now</button>
    </div>`;
  }
  html += `<div class="speaker-grid">` + speakers.map(s => {
    const color = speakerColor(s.id);
    return `<div class="speaker-card">
      <div class="sp-avatar" style="background:${color}22;color:${color}">${speakerInitial(s.name)}</div>
      <div class="sp-name">${escHtml(s.name)}</div>
      <div class="sp-count">${s.utterance_count} utterances</div>
    </div>`;
  }).join('') + `<div class="speaker-card" onclick="createSpeaker()" style="border-style:dashed;align-items:center;justify-content:center;display:flex;min-height:100px">
    <div style="color:#475569;font-size:0.8em">+ Add speaker</div>
  </div></div>`;
  panel.innerHTML = `<h2>Speakers</h2>` + html;
}

async function createSpeaker() {
  const name = prompt('Speaker name:');
  if (!name) return;
  await fetch('/api/speakers', { method: 'POST', headers: {'Content-Type':'application/json'}, body: JSON.stringify({name}) });
  loadSpeakers();
}

// --- Ask AI ---
const _ragHistory = [];
function renderRag() {
  const panel = document.getElementById('panel-rag');
  panel.innerHTML = `<h2>Ask AI</h2>
    <div id="ragMessages" style="margin-bottom:16px;min-height:200px"></div>
    <div class="toolbar">
      <input id="ragQ" placeholder="Ask about any conversation…" style="flex:1" onkeydown="if(event.key==='Enter')askRag()">
      <button class="btn-primary" onclick="askRag()">Ask</button>
    </div>`;
  renderRagMessages();
}

function renderRagMessages() {
  const el = document.getElementById('ragMessages');
  if (!el) return;
  if (!_ragHistory.length) { el.innerHTML = '<div class="empty">Ask a question about any conversation.</div>'; return; }
  el.innerHTML = _ragHistory.map(m => `<div class="rag-msg rag-${m.role}">
    <div class="rag-label">${m.role === 'user' ? 'You' : 'LinkedStorm AI'}</div>
    <div class="rag-bubble">${escHtml(m.content)}${m.sources ? `<div class="rag-source">Sources: ${m.sources}</div>` : ''}</div>
  </div>`).join('');
  el.scrollTop = el.scrollHeight;
}

async function askRag() {
  const q = document.getElementById('ragQ').value.trim();
  if (!q) return;
  document.getElementById('ragQ').value = '';
  _ragHistory.push({ role: 'user', content: q });
  renderRagMessages();
  const resp = await fetch('/api/ask', {
    method: 'POST', headers: {'Content-Type':'application/json'},
    body: JSON.stringify({ question: q, date: _currentDate }),
  }).then(r => r.json());
  _ragHistory.push({ role: 'ai', content: resp.answer, sources: `${resp.context_count} transcript segments` });
  renderRagMessages();
}

// --- Rooms ---
async function loadRooms() {
  const panel = document.getElementById('panel-rooms');
  const rooms = await fetch('/api/rooms').then(r => r.json());
  let html = `<h2>Capture Rooms</h2><div class="room-grid">`;
  for (const r of rooms) {
    const cls = r.is_active ? (r.muted ? 'muted' : 'active') : '';
    const dotColor = r.is_active ? (r.muted ? '#ef4444' : '#4ade80') : '#475569';
    const statusText = r.is_active ? (r.muted ? 'Muted' : 'Streaming') : 'Disconnected';
    html += `<div class="room-card ${cls}">
      <div class="room-name">${escHtml(r.name)}</div>
      <div class="status-row">
        <div class="sdot" style="background:${dotColor}${r.is_active&&!r.muted?';box-shadow:0 0 6px '+dotColor:''}"></div>
        <span style="color:${dotColor}">${statusText}</span>
      </div>
      ${r.last_seen ? `<div style="font-size:0.7em;color:#475569;margin-top:6px">Last: ${fmtTime(r.last_seen)}</div>` : ''}
      <button class="btn-small btn-secondary" style="margin-top:8px;width:100%"
        onclick="toggleMute(${r.id}, '${r.name}')">
        ${r.muted ? '🎤 Unmute' : '🔇 Mute'}
      </button>
      <a href="/capture?room=${encodeURIComponent(r.name)}" target="_blank"
         style="display:block;margin-top:6px;font-size:0.7em;color:#64748b;text-align:center">
        Open capture page ↗
      </a>
    </div>`;
  }
  html += `</div>`;
  panel.innerHTML = html;
}

async function toggleMute(roomId) {
  const rooms = await fetch('/api/rooms').then(r => r.json());
  const room = rooms.find(r => r.id === roomId);
  const action = room?.muted ? 'unmute' : 'mute';
  await fetch(`/api/rooms/${roomId}/${action}`, { method: 'POST' });
  loadRooms();
}

function escHtml(s) {
  return String(s).replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;');
}

// Initial load
loadLog();
setInterval(loadLog, 30000); // Refresh every 30s
```

- [ ] **Commit**

```bash
git add app/api/static/
git commit -m "feat: dashboard SPA — 5-tab log/search/speakers/RAG/rooms UI"
git push origin master
```

---

## Task 13: Dockerfiles

**Files:** `pipeline/Dockerfile`, `api/Dockerfile`, `cron/Dockerfile`

- [ ] **Write `pipeline/Dockerfile`**

```dockerfile
FROM python:3.12-slim

RUN apt-get update && apt-get install -y --no-install-recommends \
    ffmpeg libsndfile1 && rm -rf /var/lib/apt/lists/*

WORKDIR /app

COPY pyproject.toml .
RUN pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu \
 && pip install --no-cache-dir -e "." \
 && pip uninstall -y torch && pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu

COPY app/ app/

# Pre-download Silero VAD and resemblyzer models into the image layer
RUN python -c "import torch; torch.hub.load('snakers4/silero-vad','silero_vad',trust_repo=True)" \
 && python -c "from resemblyzer import VoiceEncoder; VoiceEncoder()"

ENV PYTHONUNBUFFERED=1
CMD ["python", "-m", "app.pipeline.main"]
```

- [ ] **Write `api/Dockerfile`**

```dockerfile
FROM python:3.12-slim

WORKDIR /app
COPY pyproject.toml .
RUN pip install --no-cache-dir fastapi uvicorn[standard] pydantic-settings httpx livekit-api

COPY app/ app/

ENV PYTHONUNBUFFERED=1
CMD ["uvicorn", "app.api.main:app", "--host", "0.0.0.0", "--port", "8310", "--factory"]
```

Update `app/api/main.py` to add an `app` factory callable at module level:

```python
# At the bottom of app/api/main.py, add:
import os
_app_instance = None
def app_factory():
    global _app_instance
    if _app_instance is None:
        from app.shared.config import get_settings
        _app_instance = create_app(get_settings())
    return _app_instance

# Uvicorn --factory needs this to be importable as app.api.main:app
# Use: uvicorn app.api.main:app_factory --factory
```

Change CMD in api/Dockerfile:
```dockerfile
CMD ["uvicorn", "app.api.main:app_factory", "--factory", "--host", "0.0.0.0", "--port", "8310"]
```

- [ ] **Write `cron/Dockerfile`**

```dockerfile
FROM python:3.12-slim

RUN apt-get update && apt-get install -y --no-install-recommends \
    libsndfile1 && rm -rf /var/lib/apt/lists/*

WORKDIR /app
COPY pyproject.toml .
RUN pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu \
 && pip install --no-cache-dir -e "."
COPY app/ app/

# Pre-download resemblyzer model
RUN python -c "from resemblyzer import VoiceEncoder; VoiceEncoder()"

ENV PYTHONUNBUFFERED=1
CMD ["python", "-m", "app.cron.main"]
```

- [ ] **Commit**

```bash
git add pipeline/ api/ cron/
git commit -m "feat: Dockerfiles for pipeline, api, and cron containers"
git push origin master
```

---

## Task 14: Docker Compose + Traefik + deploy

**Files:**
- `compose/hf-pdocker-01/linkedstorm/docker-compose.yml` (in docker repo)
- `compose/hf-pdocker-01/linkedstorm/.env.example` (in docker repo)
- `stacks/traefik/dynamic/linkedstorm.yml` (in docker repo)

- [ ] **Create `compose/hf-pdocker-01/linkedstorm/docker-compose.yml`** (in the docker repo at `/mnt/c/Users/Jeremy/projects/docker/`)

```yaml
services:
  linkedstorm-pipeline:
    build:
      context: /opt/linkedstorm
      dockerfile: pipeline/Dockerfile
    container_name: linkedstorm-pipeline
    restart: unless-stopped
    ports:
      - "8300:8300"
    volumes:
      - /mnt/docker-data/configs/linkedstorm:/data
    environment:
      - DB_PATH=/data/linkedstorm.db
      - CLIPS_DIR=/data/clips
      - SPEACHES_URL=${SPEACHES_URL}
      - OLLAMA_URL=${OLLAMA_URL}
      - LIVEKIT_API_KEY=${LIVEKIT_API_KEY}
      - LIVEKIT_API_SECRET=${LIVEKIT_API_SECRET}
    networks:
      - linkedstorm-net
    labels:
      - com.centurylinklabs.watchtower.enable=false

  linkedstorm-api:
    build:
      context: /opt/linkedstorm
      dockerfile: api/Dockerfile
    container_name: linkedstorm-api
    restart: unless-stopped
    ports:
      - "8310:8310"
    volumes:
      - /mnt/docker-data/configs/linkedstorm:/data
    environment:
      - DB_PATH=/data/linkedstorm.db
      - CLIPS_DIR=/data/clips
      - PIPELINE_INTERNAL_URL=http://linkedstorm-pipeline:8300
      - OLLAMA_URL=${OLLAMA_URL}
      - OLLAMA_MODEL=${OLLAMA_MODEL:-qwen2.5:14b}
      - LIVEKIT_API_KEY=${LIVEKIT_API_KEY}
      - LIVEKIT_API_SECRET=${LIVEKIT_API_SECRET}
    networks:
      - linkedstorm-net
    depends_on:
      - linkedstorm-pipeline
    labels:
      - com.centurylinklabs.watchtower.enable=false

  linkedstorm-cron:
    build:
      context: /opt/linkedstorm
      dockerfile: cron/Dockerfile
    container_name: linkedstorm-cron
    restart: unless-stopped
    volumes:
      - /mnt/docker-data/configs/linkedstorm:/data
    environment:
      - DB_PATH=/data/linkedstorm.db
      - CLIPS_DIR=/data/clips
      - OLLAMA_URL=${OLLAMA_URL}
      - OLLAMA_MODEL=${OLLAMA_MODEL:-qwen2.5:14b}
      - CLIP_RETENTION_DAYS=${CLIP_RETENTION_DAYS:-30}
    networks:
      - linkedstorm-net
    labels:
      - com.centurylinklabs.watchtower.enable=false

networks:
  linkedstorm-net:
    driver: bridge
```

- [ ] **Create `compose/hf-pdocker-01/linkedstorm/.env.example`**

```bash
SPEACHES_URL=http://192.168.86.150:8000
OLLAMA_URL=http://192.168.86.150:11434
OLLAMA_MODEL=qwen2.5:14b
LIVEKIT_API_KEY=your-livekit-api-key
LIVEKIT_API_SECRET=your-livekit-api-secret
CLIP_RETENTION_DAYS=30
```

- [ ] **Create `stacks/traefik/dynamic/linkedstorm.yml`**

```yaml
http:
  routers:
    linkedstorm:
      rule: "Host(`linkedstorm.hobbs.farm`)"
      entryPoints:
        - websecure
      service: linkedstorm
      tls:
        certResolver: letsencrypt

  services:
    linkedstorm:
      loadBalancer:
        servers:
          - url: "http://192.168.86.192:8310"
```

- [ ] **Commit to docker repo**

```bash
cd /mnt/c/Users/Jeremy/projects/docker
git add compose/hf-pdocker-01/linkedstorm/ stacks/traefik/dynamic/linkedstorm.yml
git commit -m "feat: linkedstorm docker-compose and Traefik route"
git push origin master
```

---

## Task 15: Deploy and verify on hf-pdocker-01

- [ ] **SSH to hf-pdocker-01**

```bash
ssh glyptic@192.168.86.192
```

- [ ] **Clone linkedstorm repo and create data directory**

```bash
sudo git clone http://192.168.86.100:3003/jeremy/linkedstorm /opt/linkedstorm
sudo mkdir -p /mnt/docker-data/configs/linkedstorm/clips
sudo chown -R 1000:1000 /opt/linkedstorm
```

- [ ] **Copy compose files from NAS**

```bash
# Compose files are already synced to NAS via gitea-backup
cd /mnt/docker-data/compose/hf-pdocker-01/linkedstorm
# OR pull from docker repo:
sudo git clone http://192.168.86.100:3003/jeremy/hobbs-iac /opt/hobbs-iac
# then copy compose dir to /mnt/docker-data/compose/hf-pdocker-01/linkedstorm/
```

- [ ] **Create `.env` from example and fill in real credentials**

```bash
cd /mnt/docker-data/compose/hf-pdocker-01/linkedstorm
cp .env.example .env
# Edit .env: add real LIVEKIT_API_KEY and LIVEKIT_API_SECRET from Vaultwarden
nano .env
```

- [ ] **Build and start all 3 containers**

```bash
cd /mnt/docker-data/compose/hf-pdocker-01/linkedstorm
docker compose up -d --build
```

Expected: 3 containers start: `linkedstorm-pipeline`, `linkedstorm-api`, `linkedstorm-cron`

- [ ] **Verify 1 — all containers healthy**

```bash
docker ps --format "table {{.Names}}\t{{.Status}}" | grep linkedstorm
```

Expected:
```
linkedstorm-pipeline   Up X minutes
linkedstorm-api        Up X minutes
linkedstorm-cron       Up X minutes
```

- [ ] **Verify 2 — health endpoint responds**

```bash
curl -s http://localhost:8310/health  # Note: add /health to api/main.py
curl -s http://localhost:8300/health
```

Expected: `{"status":"ok"}` from both

Note: Add health endpoint to `app/api/main.py` before deploying:
```python
@app.get("/health")
async def health():
    return {"status": "ok"}
```

- [ ] **Verify 3 — open capture page and speak**

On any device on the LAN, open: `http://192.168.86.192:8310/capture?room=Kitchen`

Allow microphone access, speak a sentence. Check dashboard at `http://192.168.86.192:8310`:

```bash
# Or check directly:
curl -s "http://localhost:8310/api/utterances" | python3 -m json.tool | head -40
```

Expected: At least one utterance with a transcript appears within a few seconds.

- [ ] **Verify 4 — speaker labeling**

In the dashboard Speakers tab, click "Review now", tag the unknown utterance as your name. Speak again. Confirm the next utterance resolves to your name automatically.

- [ ] **Verify 5 — search**

Search for a word you spoke. Confirm it appears with keyword highlighting.

- [ ] **Verify 6 — Ask AI**

Ask "What did we talk about today?" in the Ask AI tab. Confirm Ollama returns a coherent response.

- [ ] **Verify 7 — mute**

Click mute on the capture page. Speak. Confirm no new utterances appear in the log. Unmute and speak again — utterance appears.

- [ ] **Verify 8 — Traefik route**

```bash
curl -s https://linkedstorm.hobbs.farm/health
```

Expected: `{"status":"ok"}` (may take a minute for Let's Encrypt cert)

- [ ] **Verify 9 — manual cron test**

```bash
docker exec linkedstorm-cron python -c "
from app.shared.config import get_settings
from app.shared.database import get_db
from app.cron.daily_summary import generate_daily_summary
import datetime
s = get_settings()
db = get_db(s)
generate_daily_summary(datetime.date.today().isoformat(), s, db)
print('Done')
"
```

Expected: `Done` (if Ollama reachable, a row appears in daily_summaries)

- [ ] **Verify 10 — add Prometheus probe** (in docker repo)

```bash
cd /mnt/c/Users/Jeremy/projects/docker
```

Edit `compose/hf-pdocker-01/monitoring/prometheus.yml` — add to the `http-pdocker01` job's `static_configs` targets:

```yaml
- http://192.168.86.192:8310/health
```

Then hot-reload Prometheus:

```bash
ssh glyptic@192.168.86.192 "curl -s -X POST http://localhost:9095/-/reload"
```

- [ ] **Final commit to docker repo**

```bash
cd /mnt/c/Users/Jeremy/projects/docker
git add compose/hf-pdocker-01/monitoring/
git commit -m "monitoring: add Prometheus probe for linkedstorm-api"
git push origin master
```

---

## Self-Review Notes

**Spec coverage check:**
- ✅ 3 containers: pipeline (Task 7), api (Tasks 8-9, 12-13), cron (Task 10)
- ✅ Browser capture page with WebSocket + mute (Task 11)
- ✅ Silero VAD (Task 5)
- ✅ Whisper STT (Task 6, processor.py)
- ✅ resemblyzer voice embedding + cosine similarity (Task 4)
- ✅ Three-tier matching (Task 4, recognition.py)
- ✅ SQLite schema + FTS5 (Task 3)
- ✅ Speaker labeling workflow — tag endpoint (Task 8, utterances.py)
- ✅ Daily log timeline (Task 12, app.js)
- ✅ Full-text search (Task 9, search.py + Task 12)
- ✅ RAG via Ollama (Task 9, rag.py + Task 12)
- ✅ Room status + mute relay (Task 9, rooms.py + Task 12)
- ✅ Nightly daily summary (Task 10)
- ✅ Unknown speaker clustering (Task 10)
- ✅ 30-day clip purge (Task 10)
- ✅ Docker Compose (Task 14)
- ✅ Traefik route (Task 14)
- ✅ Prometheus probe (Task 15)
- ⚠️ LiveKit publishing from pipeline — simplified: capture page connects directly to LiveKit via JS SDK for live monitoring (future work, out of scope for v1). Replay uses stored WAV clips via /api/utterances/{id}/clip.

**Type consistency:** `find_best_speaker_match` returns `tuple[str, int | None, float | None]` — used consistently in recognition.py (Task 4) and processor.py (Task 6). `pack_embedding`/`unpack_embedding` used in recognition.py, processor.py, and utterances.py — same import path `app.pipeline.recognition` throughout.