fix: correct JavaScript field names to match API response schema

The web UI was referencing stale field names that didn't match the
actual API response fields returned by the backend:

  event.status         -> event.match_status
  event.identified_as  -> event.matched_object_name
  event.description    -> event.ai_description
  event.timestamp      -> event.created_at
  stats.known          -> stats.known_matches
  stats.unknown        -> stats.unknown_events
  stats.known_objects  -> stats.known_objects_count

This caused the review queue to display blank badges, timestamps,
descriptions, and identifications, and the dashboard stats to show
all zeros.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/cleanup.py

@@ -0,0 +1,35 @@
+"""Automatic event cleanup based on retention settings."""
+
+import asyncio
+import logging
+
+from database import get_connection
+
+logger = logging.getLogger(__name__)
+
+
+async def cleanup_old_events():
+    """Delete events older than the configured retention period. Runs hourly."""
+    while True:
+        try:
+            conn = get_connection()
+            row = conn.execute(
+                "SELECT value FROM settings WHERE key='event_retention_days'"
+            ).fetchone()
+            days = int(row["value"]) if row else 90
+
+            result = conn.execute(
+                "DELETE FROM events WHERE created_at < datetime('now', ? || ' days')",
+                (f"-{days}",),
+            )
+            conn.commit()
+            if result.rowcount > 0:
+                logger.info(
+                    "Cleaned up %d events older than %d days",
+                    result.rowcount,
+                    days,
+                )
+        except Exception:
+            logger.exception("Cleanup failed")
+
+        await asyncio.sleep(3600)

app/database.py

@@ -13,8 +13,9 @@ _db_path: str = "/data/frigate-notify.db"
 
 
 def set_db_path(path: str) -> None:
-    """Set the database path. Must be called before any other database operations."""
+    """Set the database path. Closes any existing connection first."""
     global _db_path
+    close_connection()
     _db_path = path
 
 

app/main.py

@@ -26,7 +26,10 @@ from matrix_client import MatrixClient
 from routes.events import router as events_router
 from routes.known_objects import router as known_objects_router
 from routes.settings import router as settings_router
-from vision import analyze_snapshot
+from ollama_client import OllamaClient
+from recognition import recognize_event, get_clip_embedding, embedding_to_bytes
+from vision import get_description
+from cleanup import cleanup_old_events
 
 # --- Logging ---
 logging.basicConfig(
@@ -67,7 +70,8 @@ async def fetch_snapshot(event_id: str) -> bytes | None:
 
 # --- Event processing ---
 async def process_event(
-    event_data: dict, claude: anthropic.Anthropic, matrix: MatrixClient
+    event_data: dict, claude: anthropic.Anthropic, matrix: MatrixClient,
+    ollama_client: OllamaClient = None,
 ):
     global events_processed, last_event_time
 
@@ -117,10 +121,63 @@ async def process_event(
     # --- Fetch snapshot ---
     snapshot = await fetch_snapshot(event_id)
 
-    # --- AI analysis ---
+    # --- Recognition ---
+    match_status = "unreviewed"
+    matched_id = None
+    match_conf = 0.0
+    matched_name = None
+
+    # Check if recognition is enabled
+    recognition_enabled = False
+    try:
+        conn = get_connection()
+        row = conn.execute("SELECT value FROM settings WHERE key='recognition_enabled'").fetchone()
+        if row:
+            recognition_enabled = row["value"].lower() == "true"
+    except Exception:
+        pass
+
+    if recognition_enabled and snapshot:
+        match_status, matched_id, match_conf, matched_name = await recognize_event(
+            snapshot, ollama_client
+        )
+        # Store embedding with event
+        embedding = await get_clip_embedding(snapshot)
+        if embedding:
+            embedding_blob = embedding_to_bytes(embedding)
+            try:
+                conn = get_connection()
+                conn.execute(
+                    "UPDATE events SET embedding=?, match_status=?, matched_object_id=?, match_confidence=? WHERE frigate_event_id=?",
+                    (embedding_blob, match_status, matched_id, match_conf, event_id),
+                )
+                conn.commit()
+            except Exception:
+                logger.exception("Failed to update event recognition")
+
+        # If known, check action - skip notification for quiet/suppress
+        if match_status == "known" and matched_id:
+            conn = get_connection()
+            obj = conn.execute("SELECT action FROM known_objects WHERE id=?", (matched_id,)).fetchone()
+            if obj and obj["action"] in ("quiet", "suppress"):
+                logger.info("Known object '%s' (%s), skipping notification", matched_name, obj["action"])
+                events_processed += 1
+                last_event_time = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+                return
+
+    # --- AI description ---
+    vision_backend = "haiku"
+    try:
+        conn = get_connection()
+        row = conn.execute("SELECT value FROM settings WHERE key='vision_backend'").fetchone()
+        if row:
+            vision_backend = row["value"]
+    except Exception:
+        pass
+
     description = None
     if snapshot:
-        description = analyze_snapshot(claude, snapshot)
+        description = await get_description(snapshot, vision_backend, claude, ollama_client)
     if not description:
         description = f"{label.capitalize()} detected on {camera}."
 
@@ -129,9 +186,9 @@ async def process_event(
         conn = get_connection()
         conn.execute(
             """INSERT OR IGNORE INTO events
-               (frigate_event_id, camera, label, score, snapshot, ai_description, notified)
-               VALUES (?, ?, ?, ?, ?, ?, 1)""",
-            (event_id, camera, label, score, snapshot, description),
+               (frigate_event_id, camera, label, score, snapshot, match_status, ai_description, notified)
+               VALUES (?, ?, ?, ?, ?, ?, ?, 1)""",
+            (event_id, camera, label, score, snapshot, match_status, description),
         )
         conn.commit()
     except Exception:
@@ -175,6 +232,7 @@ def create_mqtt_client(
     loop: asyncio.AbstractEventLoop,
     claude: anthropic.Anthropic,
     matrix: MatrixClient,
+    ollama_client: OllamaClient = None,
 ) -> mqtt.Client:
     global mqtt_connected
 
@@ -193,7 +251,7 @@ def create_mqtt_client(
         try:
             payload = json.loads(msg.payload)
             asyncio.run_coroutine_threadsafe(
-                process_event(payload, claude, matrix), loop
+                process_event(payload, claude, matrix, ollama_client), loop
             )
         except json.JSONDecodeError:
             logger.warning("Invalid JSON on %s", msg.topic)
@@ -217,8 +275,9 @@ def start_mqtt(loop: asyncio.AbstractEventLoop) -> None:
         config.MATRIX_ACCESS_TOKEN,
         config.MATRIX_ROOM_ID,
     )
+    ollama_client = OllamaClient(config.OLLAMA_URL, config.OLLAMA_MODEL)
 
-    mqtt_client_instance = create_mqtt_client(loop, claude, matrix)
+    mqtt_client_instance = create_mqtt_client(loop, claude, matrix, ollama_client)
     mqtt_client_instance.connect(config.MQTT_HOST, config.MQTT_PORT)
 
     thread = Thread(target=mqtt_client_instance.loop_forever, daemon=True)
@@ -240,6 +299,9 @@ async def lifespan(app: FastAPI):
     loop = asyncio.get_event_loop()
     start_mqtt(loop)
 
+    # Start periodic event cleanup
+    asyncio.create_task(cleanup_old_events())
+
     yield
 
     # Shutdown

app/ollama_client.py

@@ -0,0 +1,132 @@
+"""Ollama vision LLM client for frigate-notify v2."""
+
+import base64
+import logging
+
+import aiohttp
+
+logger = logging.getLogger(__name__)
+
+
+class OllamaClient:
+    """Client for Ollama vision models (e.g., llava)."""
+
+    def __init__(self, base_url: str, model: str):
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+
+    async def analyze_image(self, image_bytes: bytes, prompt: str) -> str | None:
+        """Send an image to Ollama for analysis.
+
+        Args:
+            image_bytes: Raw image bytes (JPEG).
+            prompt: Text prompt for the vision model.
+
+        Returns:
+            Response text or None on failure.
+        """
+        b64 = base64.b64encode(image_bytes).decode("utf-8")
+        payload = {
+            "model": self.model,
+            "prompt": prompt,
+            "images": [b64],
+            "stream": False,
+            "options": {"num_predict": 200},
+        }
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.post(
+                    f"{self.base_url}/api/generate",
+                    json=payload,
+                    timeout=aiohttp.ClientTimeout(total=30),
+                ) as resp:
+                    if resp.status == 200:
+                        data = await resp.json()
+                        text = data.get("response", "").strip()
+                        logger.info(
+                            "Ollama analysis complete: %d chars", len(text)
+                        )
+                        return text if text else None
+                    body = await resp.text()
+                    logger.error(
+                        "Ollama analyze_image failed %s: %s", resp.status, body
+                    )
+        except Exception:
+            logger.exception("Ollama analyze_image request failed")
+        return None
+
+    async def compare_images(
+        self,
+        image_bytes: bytes,
+        reference_bytes: bytes,
+        object_name: str,
+    ) -> bool | None:
+        """Compare two images to determine if they show the same object/person.
+
+        Args:
+            image_bytes: The new event image bytes.
+            reference_bytes: The reference (known) image bytes.
+            object_name: Name of the known object for context.
+
+        Returns:
+            True if same, False if different, None on failure/inconclusive.
+        """
+        b64_new = base64.b64encode(image_bytes).decode("utf-8")
+        b64_ref = base64.b64encode(reference_bytes).decode("utf-8")
+        prompt = (
+            f"Look at these two images. The first image is from a security camera. "
+            f"The second image is a reference photo of '{object_name}'. "
+            f"Are these the same person/vehicle/animal? "
+            f"Answer with ONLY 'yes' or 'no'."
+        )
+        payload = {
+            "model": self.model,
+            "prompt": prompt,
+            "images": [b64_new, b64_ref],
+            "stream": False,
+            "options": {"num_predict": 200},
+        }
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.post(
+                    f"{self.base_url}/api/generate",
+                    json=payload,
+                    timeout=aiohttp.ClientTimeout(total=30),
+                ) as resp:
+                    if resp.status == 200:
+                        data = await resp.json()
+                        text = data.get("response", "").strip().lower()
+                        logger.info("Ollama compare result: %s", text)
+                        if "yes" in text.split()[:3]:
+                            return True
+                        if "no" in text.split()[:3]:
+                            return False
+                        logger.warning(
+                            "Ollama compare inconclusive: %s", text
+                        )
+                        return None
+                    body = await resp.text()
+                    logger.error(
+                        "Ollama compare_images failed %s: %s",
+                        resp.status,
+                        body,
+                    )
+        except Exception:
+            logger.exception("Ollama compare_images request failed")
+        return None
+
+    async def is_available(self) -> bool:
+        """Check if Ollama is reachable and responding.
+
+        Returns:
+            True if Ollama responds with HTTP 200, False otherwise.
+        """
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    f"{self.base_url}/api/tags",
+                    timeout=aiohttp.ClientTimeout(total=3),
+                ) as resp:
+                    return resp.status == 200
+        except Exception:
+            return False

app/recognition.py

@@ -0,0 +1,232 @@
+"""Recognition pipeline using CLIP embeddings with Ollama vision fallback."""
+
+import base64
+import logging
+import struct
+from typing import Optional
+
+import aiohttp
+import numpy as np
+
+from config import config
+from database import get_connection
+from ollama_client import OllamaClient
+
+logger = logging.getLogger(__name__)
+
+
+def embedding_to_bytes(embedding: list[float]) -> bytes:
+    """Pack a float32 embedding list into bytes for SQLite storage.
+
+    Args:
+        embedding: List of float values (typically 768-dimensional).
+
+    Returns:
+        Packed bytes (len(embedding) * 4 bytes).
+    """
+    return struct.pack(f"{len(embedding)}f", *embedding)
+
+
+def bytes_to_embedding(data: bytes) -> np.ndarray:
+    """Unpack bytes back into a float32 numpy array.
+
+    Args:
+        data: Packed bytes from embedding_to_bytes().
+
+    Returns:
+        Numpy array of float32 values.
+    """
+    count = len(data) // 4
+    values = struct.unpack(f"{count}f", data)
+    return np.array(values, dtype=np.float32)
+
+
+async def get_clip_embedding(image_bytes: bytes) -> list[float] | None:
+    """Get CLIP embedding for an image from the clip-service sidecar.
+
+    Args:
+        image_bytes: Raw image bytes (JPEG).
+
+    Returns:
+        List of float embedding values, or None on failure.
+    """
+    b64 = base64.b64encode(image_bytes).decode("utf-8")
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.post(
+                f"{config.CLIP_SERVICE_URL}/embed",
+                json={"image": b64},
+                timeout=aiohttp.ClientTimeout(total=10),
+            ) as resp:
+                if resp.status == 200:
+                    data = await resp.json()
+                    embedding = data.get("embedding")
+                    if embedding:
+                        logger.info(
+                            "CLIP embedding received: %d dimensions",
+                            len(embedding),
+                        )
+                        return embedding
+                    logger.warning("CLIP response missing embedding field")
+                else:
+                    body = await resp.text()
+                    logger.error(
+                        "CLIP embed failed %s: %s", resp.status, body
+                    )
+    except Exception:
+        logger.exception("CLIP embedding request failed")
+    return None
+
+
+async def find_best_match(
+    query_embedding: list[float],
+) -> tuple[int | None, float, str | None]:
+    """Find the best matching known object for a query embedding.
+
+    Loads all embeddings from the database, computes cosine similarity,
+    groups by known_object_id and takes the max score per object.
+
+    Args:
+        query_embedding: The query embedding to match against.
+
+    Returns:
+        Tuple of (best_object_id, best_score, best_name).
+        Returns (None, 0.0, None) if no embeddings exist.
+    """
+    conn = get_connection()
+    cursor = conn.execute(
+        """
+        SELECT e.id, e.known_object_id, e.embedding, ko.name
+        FROM embeddings e
+        JOIN known_objects ko ON e.known_object_id = ko.id
+        """
+    )
+    rows = cursor.fetchall()
+
+    if not rows:
+        return (None, 0.0, None)
+
+    query_vec = np.array(query_embedding, dtype=np.float32)
+    query_norm = np.linalg.norm(query_vec)
+    if query_norm == 0:
+        return (None, 0.0, None)
+    query_vec = query_vec / query_norm
+
+    # Group scores by known_object_id, keeping max per object
+    best_per_object: dict[int, tuple[float, str]] = {}
+
+    for row in rows:
+        stored_vec = bytes_to_embedding(row["embedding"])
+        stored_norm = np.linalg.norm(stored_vec)
+        if stored_norm == 0:
+            continue
+        stored_vec = stored_vec / stored_norm
+
+        similarity = float(np.dot(query_vec, stored_vec))
+        obj_id = row["known_object_id"]
+        obj_name = row["name"]
+
+        if obj_id not in best_per_object or similarity > best_per_object[obj_id][0]:
+            best_per_object[obj_id] = (similarity, obj_name)
+
+    if not best_per_object:
+        return (None, 0.0, None)
+
+    best_id = max(best_per_object, key=lambda k: best_per_object[k][0])
+    best_score, best_name = best_per_object[best_id]
+
+    return (best_id, best_score, best_name)
+
+
+async def recognize_event(
+    image_bytes: bytes,
+    ollama_client: Optional[OllamaClient] = None,
+) -> tuple[str, int | None, float, str | None]:
+    """Full recognition pipeline for a Frigate event image.
+
+    Steps:
+        1. Get CLIP embedding for the image.
+        2. Find the best matching known object.
+        3. High confidence (>= threshold_high): return "known".
+        4. Low confidence (< threshold_low): return "unknown".
+        5. Ambiguous range: use Ollama vision LLM to confirm/deny.
+        6. If LLM unavailable or inconclusive: return "ambiguous".
+        7. If no embedding could be obtained: return "unreviewed".
+
+    Args:
+        image_bytes: Raw image bytes (JPEG).
+        ollama_client: Optional OllamaClient for ambiguous case fallback.
+
+    Returns:
+        Tuple of (match_status, object_id, score, object_name).
+        match_status is one of: "known", "unknown", "ambiguous", "unreviewed".
+    """
+    # Step 1: Get CLIP embedding
+    embedding = await get_clip_embedding(image_bytes)
+    if embedding is None:
+        logger.warning("Could not get CLIP embedding, returning unreviewed")
+        return ("unreviewed", None, 0.0, None)
+
+    # Step 2: Find best match
+    best_id, best_score, best_name = await find_best_match(embedding)
+
+    # No embeddings in DB at all
+    if best_id is None:
+        return ("unknown", None, 0.0, None)
+
+    high_threshold = config.RECOGNITION_THRESHOLD_HIGH
+    low_threshold = config.RECOGNITION_THRESHOLD_LOW
+
+    # Step 3: High confidence match
+    if best_score >= high_threshold:
+        logger.info(
+            "High confidence match: %s (%.3f)", best_name, best_score
+        )
+        return ("known", best_id, best_score, best_name)
+
+    # Step 4: Low confidence - clearly not a match
+    if best_score < low_threshold:
+        logger.info("Low confidence, no match (%.3f)", best_score)
+        return ("unknown", None, best_score, None)
+
+    # Step 5: Ambiguous range - try Ollama vision LLM
+    if ollama_client is not None:
+        logger.info(
+            "Ambiguous score %.3f for %s, trying Ollama comparison",
+            best_score,
+            best_name,
+        )
+        # Get a reference thumbnail from the embeddings table
+        conn = get_connection()
+        cursor = conn.execute(
+            """
+            SELECT thumbnail FROM embeddings
+            WHERE known_object_id = ? AND thumbnail IS NOT NULL
+            LIMIT 1
+            """,
+            (best_id,),
+        )
+        ref_row = cursor.fetchone()
+        if ref_row and ref_row["thumbnail"] is not None:
+            result = await ollama_client.compare_images(
+                image_bytes, ref_row["thumbnail"], best_name
+            )
+            if result is True:
+                logger.info(
+                    "Ollama confirmed match: %s (%.3f)",
+                    best_name,
+                    best_score,
+                )
+                return ("known", best_id, best_score, best_name)
+            elif result is False:
+                logger.info(
+                    "Ollama denied match: %s (%.3f)", best_name, best_score
+                )
+                return ("unknown", None, best_score, None)
+            # result is None: inconclusive, fall through to ambiguous
+
+    # Step 6: LLM unavailable or inconclusive
+    logger.info(
+        "Ambiguous match: %s (%.3f)", best_name, best_score
+    )
+    return ("ambiguous", best_id, best_score, best_name)

app/static/app.js

@@ -0,0 +1,578 @@
+// Frigate Alerts - Web UI
+(function () {
+    'use strict';
+
+    let currentView = 'dashboard';
+    let currentTagEventId = null;
+    let refreshTimer = null;
+    let knownObjectsCache = [];
+
+    // --- Initialization ---
+
+    document.addEventListener('DOMContentLoaded', () => {
+        setupNavigation();
+        setupTagModal();
+        loadView('dashboard');
+        loadStats();
+    });
+
+    // --- Navigation ---
+
+    function setupNavigation() {
+        document.querySelectorAll('.sidebar-item').forEach(item => {
+            item.addEventListener('click', (e) => {
+                e.preventDefault();
+                const view = item.dataset.view;
+                loadView(view);
+            });
+        });
+    }
+
+    function setActiveNav(view) {
+        document.querySelectorAll('.sidebar-item').forEach(item => {
+            item.classList.toggle('active', item.dataset.view === view);
+        });
+    }
+
+    // --- View Router ---
+
+    function loadView(view) {
+        currentView = view;
+        setActiveNav(view);
+        clearAutoRefresh();
+
+        switch (view) {
+            case 'dashboard':
+                loadDashboard();
+                startAutoRefresh();
+                break;
+            case 'review':
+                loadReview();
+                startAutoRefresh();
+                break;
+            case 'known':
+                loadKnown();
+                break;
+            case 'settings':
+                loadSettings();
+                break;
+        }
+    }
+
+    // --- Auto Refresh ---
+
+    function startAutoRefresh() {
+        refreshTimer = setInterval(() => {
+            if (currentView === 'dashboard') {
+                loadDashboard();
+            } else if (currentView === 'review') {
+                loadReview();
+            }
+            loadStats();
+        }, 30000);
+    }
+
+    function clearAutoRefresh() {
+        if (refreshTimer) {
+            clearInterval(refreshTimer);
+            refreshTimer = null;
+        }
+    }
+
+    // --- Dashboard View ---
+
+    function loadDashboard() {
+        const content = document.getElementById('main-content');
+        content.innerHTML = '<div class="loading">Loading events</div>';
+
+        fetch('/api/events?limit=50')
+            .then(r => r.json())
+            .then(events => {
+                if (!events.length) {
+                    content.innerHTML = `
+                        <h1>Dashboard</h1>
+                        <div class="empty-state">
+                            <p>No events recorded yet.</p>
+                            <p>Events will appear here when Frigate detects objects.</p>
+                        </div>`;
+                    return;
+                }
+                content.innerHTML = `
+                    <div class="page-header">
+                        <h1>Dashboard</h1>
+                    </div>
+                    <div class="filters">
+                        <select id="filter-camera" onchange="window._applyFilters()">
+                            <option value="">All Cameras</option>
+                        </select>
+                        <select id="filter-label" onchange="window._applyFilters()">
+                            <option value="">All Labels</option>
+                        </select>
+                        <select id="filter-status" onchange="window._applyFilters()">
+                            <option value="">All Statuses</option>
+                            <option value="known">Known</option>
+                            <option value="unknown">Unknown</option>
+                            <option value="ambiguous">Ambiguous</option>
+                            <option value="unreviewed">Unreviewed</option>
+                        </select>
+                    </div>
+                    <div id="events-list"></div>`;
+
+                populateFilterOptions(events);
+                renderEvents(events);
+            })
+            .catch(err => {
+                content.innerHTML = `<h1>Dashboard</h1><div class="empty-state"><p>Error loading events: ${err.message}</p></div>`;
+            });
+    }
+
+    function populateFilterOptions(events) {
+        const cameras = [...new Set(events.map(e => e.camera).filter(Boolean))].sort();
+        const labels = [...new Set(events.map(e => e.label).filter(Boolean))].sort();
+
+        const cameraSelect = document.getElementById('filter-camera');
+        const labelSelect = document.getElementById('filter-label');
+
+        if (cameraSelect) {
+            cameras.forEach(c => {
+                const opt = document.createElement('option');
+                opt.value = c;
+                opt.textContent = c;
+                cameraSelect.appendChild(opt);
+            });
+        }
+
+        if (labelSelect) {
+            labels.forEach(l => {
+                const opt = document.createElement('option');
+                opt.value = l;
+                opt.textContent = l;
+                labelSelect.appendChild(opt);
+            });
+        }
+    }
+
+    // Expose filter function globally for inline onchange handlers
+    window._applyFilters = function () {
+        const camera = document.getElementById('filter-camera')?.value || '';
+        const label = document.getElementById('filter-label')?.value || '';
+        const status = document.getElementById('filter-status')?.value || '';
+
+        let url = '/api/events?limit=50';
+        if (camera) url += `&camera=${encodeURIComponent(camera)}`;
+        if (label) url += `&label=${encodeURIComponent(label)}`;
+        if (status) url += `&status=${encodeURIComponent(status)}`;
+
+        fetch(url)
+            .then(r => r.json())
+            .then(events => renderEvents(events))
+            .catch(err => console.error('Filter error:', err));
+    };
+
+    function renderEvents(events) {
+        const container = document.getElementById('events-list');
+        if (!container) return;
+
+        if (!events.length) {
+            container.innerHTML = '<div class="empty-state"><p>No events match the current filters.</p></div>';
+            return;
+        }
+
+        container.innerHTML = events.map(event => `
+            <div class="event-card" data-event-id="${event.id}">
+                <img src="/api/events/${event.id}/snapshot"
+                     onerror="this.style.display='none'"
+                     alt="${event.label || 'snapshot'}">
+                <div class="event-info">
+                    <div class="event-header">
+                        <span>
+                            <span class="event-camera">${escapeHtml(event.camera || 'unknown')}</span>
+                            <span class="event-label"> &mdash; ${escapeHtml(event.label || 'unknown')}</span>
+                            <span class="event-score">(${(event.score * 100).toFixed(0)}%)</span>
+                        </span>
+                        <span class="badge badge-${event.match_status || 'unreviewed'}">${event.match_status || 'unreviewed'}</span>
+                    </div>
+                    <div class="event-time">${formatTime(event.created_at)}</div>
+                    ${event.ai_description ? `<div class="event-description">${escapeHtml(event.ai_description)}</div>` : ''}
+                    ${event.matched_object_name ? `<div class="event-label">Identified as: <strong>${escapeHtml(event.matched_object_name)}</strong></div>` : ''}
+                    <div class="event-actions">
+                        <button class="btn btn-primary btn-sm" onclick="window._tagEvent('${event.id}')">Tag</button>
+                        <button class="btn btn-sm" onclick="window._dismissEvent('${event.id}')">Dismiss</button>
+                    </div>
+                </div>
+            </div>
+        `).join('');
+    }
+
+    // --- Review View ---
+
+    function loadReview() {
+        const content = document.getElementById('main-content');
+        content.innerHTML = '<div class="loading">Loading review queue</div>';
+
+        fetch('/api/events?limit=50&status=unreviewed')
+            .then(r => r.json())
+            .then(events => {
+                if (!events.length) {
+                    content.innerHTML = `
+                        <h1>Review Queue</h1>
+                        <div class="empty-state">
+                            <p>No events to review.</p>
+                            <p>All events have been reviewed or dismissed.</p>
+                        </div>`;
+                    return;
+                }
+                content.innerHTML = `
+                    <div class="page-header">
+                        <h1>Review Queue</h1>
+                        <span>${events.length} event${events.length !== 1 ? 's' : ''} to review</span>
+                    </div>
+                    <div id="events-list"></div>`;
+                renderEvents(events);
+            })
+            .catch(err => {
+                content.innerHTML = `<h1>Review Queue</h1><div class="empty-state"><p>Error loading events: ${err.message}</p></div>`;
+            });
+    }
+
+    // --- Known Objects View ---
+
+    function loadKnown() {
+        const content = document.getElementById('main-content');
+        content.innerHTML = '<div class="loading">Loading known objects</div>';
+
+        fetch('/api/known-objects')
+            .then(r => r.json())
+            .then(objects => {
+                knownObjectsCache = objects;
+
+                if (!objects.length) {
+                    content.innerHTML = `
+                        <h1>Known Objects</h1>
+                        <div class="empty-state">
+                            <p>No known objects registered.</p>
+                            <p>Tag events from the Dashboard to add known objects.</p>
+                        </div>`;
+                    return;
+                }
+
+                content.innerHTML = `
+                    <div class="page-header">
+                        <h1>Known Objects</h1>
+                        <span>${objects.length} object${objects.length !== 1 ? 's' : ''}</span>
+                    </div>
+                    <div class="known-grid">
+                        ${objects.map(obj => `
+                            <div class="known-card" data-object-id="${obj.id}">
+                                <h4>${escapeHtml(obj.name)}</h4>
+                                <span class="type-badge">${escapeHtml(obj.type)}</span>
+                                <div class="embedding-count">${obj.embedding_count || 0} embedding${(obj.embedding_count || 0) !== 1 ? 's' : ''}</div>
+                                ${obj.samples && obj.samples.length ? `
+                                    <div class="known-card-samples">
+                                        ${obj.samples.map(s => `<img src="${s}" onerror="this.style.display='none'" alt="sample">`).join('')}
+                                    </div>
+                                ` : ''}
+                                <div class="known-card-actions">
+                                    <button class="btn btn-danger btn-sm" onclick="window._deleteKnownObject('${obj.id}', '${escapeHtml(obj.name)}')">Delete</button>
+                                </div>
+                            </div>
+                        `).join('')}
+                    </div>`;
+            })
+            .catch(err => {
+                content.innerHTML = `<h1>Known Objects</h1><div class="empty-state"><p>Error loading objects: ${err.message}</p></div>`;
+            });
+    }
+
+    // --- Settings View ---
+
+    function loadSettings() {
+        const content = document.getElementById('main-content');
+        content.innerHTML = '<div class="loading">Loading settings</div>';
+
+        fetch('/api/settings')
+            .then(r => r.json())
+            .then(settings => {
+                content.innerHTML = `
+                    <h1>Settings</h1>
+                    <div class="settings-form">
+                        <div class="settings-section">
+                            <h3>Vision Analysis</h3>
+                            <div class="form-group">
+                                <label>Vision Backend</label>
+                                <select id="setting-vision-backend">
+                                    <option value="haiku" ${settings.vision_backend === 'haiku' ? 'selected' : ''}>Claude Haiku</option>
+                                    <option value="ollama" ${settings.vision_backend === 'ollama' ? 'selected' : ''}>Ollama (Local)</option>
+                                    <option value="both" ${settings.vision_backend === 'both' ? 'selected' : ''}>Both (Haiku + Ollama)</option>
+                                </select>
+                                <div class="help-text">Which AI backend to use for snapshot analysis</div>
+                            </div>
+                        </div>
+
+                        <div class="settings-section">
+                            <h3>Recognition</h3>
+                            <div class="form-group">
+                                <label class="checkbox-label">
+                                    <input type="checkbox" id="setting-recognition-enabled" ${settings.recognition_enabled ? 'checked' : ''}>
+                                    Enable object recognition
+                                </label>
+                                <div class="help-text">Match detected objects against known objects database</div>
+                            </div>
+                        </div>
+
+                        <div class="settings-section">
+                            <h3>Data Retention</h3>
+                            <div class="form-group">
+                                <label>Event Retention (days)</label>
+                                <input type="number" id="setting-retention-days" value="${settings.event_retention_days || 30}" min="1" max="365">
+                                <div class="help-text">How long to keep event records and snapshots</div>
+                            </div>
+                        </div>
+
+                        <div class="form-group">
+                            <button class="btn btn-primary" onclick="window._saveSettings()">Save Settings</button>
+                        </div>
+                    </div>`;
+            })
+            .catch(err => {
+                content.innerHTML = `<h1>Settings</h1><div class="empty-state"><p>Error loading settings: ${err.message}</p></div>`;
+            });
+    }
+
+    window._saveSettings = function () {
+        const settings = {
+            vision_backend: document.getElementById('setting-vision-backend').value,
+            recognition_enabled: document.getElementById('setting-recognition-enabled').checked,
+            event_retention_days: parseInt(document.getElementById('setting-retention-days').value, 10)
+        };
+
+        fetch('/api/settings', {
+            method: 'PUT',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify(settings)
+        })
+            .then(r => {
+                if (!r.ok) throw new Error('Failed to save settings');
+                return r.json();
+            })
+            .then(() => showToast('Settings saved', 'success'))
+            .catch(err => showToast('Error: ' + err.message, 'error'));
+    };
+
+    // --- Stats ---
+
+    function loadStats() {
+        fetch('/api/stats')
+            .then(r => r.json())
+            .then(stats => {
+                const el = document.getElementById('stats-summary');
+                if (!el) return;
+
+                el.innerHTML = `
+                    <div class="stat-item">
+                        <span>Total Events</span>
+                        <span class="stat-value">${stats.total_events || 0}</span>
+                    </div>
+                    <div class="stat-item">
+                        <span>Unreviewed</span>
+                        <span class="stat-value">${stats.unreviewed || 0}</span>
+                    </div>
+                    <div class="stat-item">
+                        <span>Known</span>
+                        <span class="stat-value">${stats.known_matches || 0}</span>
+                    </div>
+                    <div class="stat-item">
+                        <span>Unknown</span>
+                        <span class="stat-value">${stats.unknown_events || 0}</span>
+                    </div>
+                    <div class="stat-item">
+                        <span>Known Objects</span>
+                        <span class="stat-value">${stats.known_objects_count || 0}</span>
+                    </div>`;
+            })
+            .catch(() => {
+                // Stats are non-critical, silently fail
+            });
+    }
+
+    // --- Tag Modal ---
+
+    function setupTagModal() {
+        document.getElementById('tag-cancel').addEventListener('click', closeTagModal);
+        document.getElementById('tag-save').addEventListener('click', saveTag);
+
+        document.getElementById('tag-existing-object').addEventListener('change', (e) => {
+            const newFields = document.getElementById('tag-new-fields');
+            newFields.style.display = e.target.value ? 'none' : 'block';
+        });
+
+        // Close modal on backdrop click
+        document.getElementById('tag-modal').addEventListener('click', (e) => {
+            if (e.target === document.getElementById('tag-modal')) {
+                closeTagModal();
+            }
+        });
+
+        // Close modal on Escape key
+        document.addEventListener('keydown', (e) => {
+            if (e.key === 'Escape') closeTagModal();
+        });
+    }
+
+    window._tagEvent = function (eventId) {
+        currentTagEventId = eventId;
+
+        // Show snapshot in modal
+        const snapshotContainer = document.getElementById('tag-modal-snapshot');
+        snapshotContainer.innerHTML = `<img src="/api/events/${eventId}/snapshot" onerror="this.style.display='none'" alt="snapshot">`;
+
+        // Load known objects for dropdown
+        fetch('/api/known-objects')
+            .then(r => r.json())
+            .then(objects => {
+                knownObjectsCache = objects;
+                const select = document.getElementById('tag-existing-object');
+                select.innerHTML = '<option value="">-- Create new --</option>';
+                objects.forEach(obj => {
+                    const opt = document.createElement('option');
+                    opt.value = obj.id;
+                    opt.textContent = `${obj.name} (${obj.type})`;
+                    select.appendChild(opt);
+                });
+            })
+            .catch(() => { });
+
+        // Reset form
+        document.getElementById('tag-name').value = '';
+        document.getElementById('tag-type').value = 'person';
+        document.getElementById('tag-new-fields').style.display = 'block';
+        document.getElementById('tag-existing-object').value = '';
+
+        document.getElementById('tag-modal').classList.remove('hidden');
+    };
+
+    function closeTagModal() {
+        document.getElementById('tag-modal').classList.add('hidden');
+        currentTagEventId = null;
+    }
+
+    function saveTag() {
+        if (!currentTagEventId) return;
+
+        const existingId = document.getElementById('tag-existing-object').value;
+        let body;
+
+        if (existingId) {
+            body = { known_object_id: existingId };
+        } else {
+            const name = document.getElementById('tag-name').value.trim();
+            const type = document.getElementById('tag-type').value;
+            if (!name) {
+                showToast('Please enter a name', 'error');
+                return;
+            }
+            body = { name: name, type: type };
+        }
+
+        fetch(`/api/events/${currentTagEventId}/tag`, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify(body)
+        })
+            .then(r => {
+                if (!r.ok) throw new Error('Failed to tag event');
+                return r.json();
+            })
+            .then(() => {
+                showToast('Event tagged successfully', 'success');
+                closeTagModal();
+                loadView(currentView);
+                loadStats();
+            })
+            .catch(err => showToast('Error: ' + err.message, 'error'));
+    }
+
+    // --- Event Actions ---
+
+    window._dismissEvent = function (eventId) {
+        fetch(`/api/events/${eventId}/dismiss`, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' }
+        })
+            .then(r => {
+                if (!r.ok) throw new Error('Failed to dismiss event');
+                return r.json();
+            })
+            .then(() => {
+                showToast('Event dismissed', 'success');
+                // Remove card from DOM for immediate feedback
+                const card = document.querySelector(`.event-card[data-event-id="${eventId}"]`);
+                if (card) {
+                    card.style.opacity = '0';
+                    card.style.transition = 'opacity 0.3s';
+                    setTimeout(() => card.remove(), 300);
+                }
+                loadStats();
+            })
+            .catch(err => showToast('Error: ' + err.message, 'error'));
+    };
+
+    // --- Known Object Actions ---
+
+    window._deleteKnownObject = function (objectId, name) {
+        if (!confirm(`Delete known object "${name}"? This cannot be undone.`)) return;
+
+        fetch(`/api/known-objects/${objectId}`, {
+            method: 'DELETE'
+        })
+            .then(r => {
+                if (!r.ok) throw new Error('Failed to delete object');
+                return r.json();
+            })
+            .then(() => {
+                showToast(`Deleted "${name}"`, 'success');
+                loadKnown();
+                loadStats();
+            })
+            .catch(err => showToast('Error: ' + err.message, 'error'));
+    };
+
+    // --- Utilities ---
+
+    function escapeHtml(str) {
+        if (!str) return '';
+        const div = document.createElement('div');
+        div.textContent = str;
+        return div.innerHTML;
+    }
+
+    function formatTime(timestamp) {
+        if (!timestamp) return '';
+        const date = new Date(timestamp);
+        const now = new Date();
+        const diffMs = now - date;
+        const diffMins = Math.floor(diffMs / 60000);
+        const diffHours = Math.floor(diffMs / 3600000);
+        const diffDays = Math.floor(diffMs / 86400000);
+
+        if (diffMins < 1) return 'Just now';
+        if (diffMins < 60) return `${diffMins}m ago`;
+        if (diffHours < 24) return `${diffHours}h ago`;
+        if (diffDays < 7) return `${diffDays}d ago`;
+
+        return date.toLocaleDateString() + ' ' + date.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' });
+    }
+
+    function showToast(message, type) {
+        const toast = document.createElement('div');
+        toast.className = `toast toast-${type}`;
+        toast.textContent = message;
+        document.body.appendChild(toast);
+        setTimeout(() => {
+            toast.style.opacity = '0';
+            toast.style.transition = 'opacity 0.3s';
+            setTimeout(() => toast.remove(), 300);
+        }, 3000);
+    }
+
+})();

app/static/index.html

@@ -0,0 +1,61 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Frigate Alerts</title>
+    <link rel="stylesheet" href="/static/style.css">
+</head>
+<body>
+    <nav id="sidebar">
+        <div class="sidebar-header">
+            <h2>Frigate Alerts</h2>
+        </div>
+        <div class="sidebar-section">
+            <a href="#" class="sidebar-item active" data-view="dashboard">Dashboard</a>
+            <a href="#" class="sidebar-item" data-view="review">Review Queue</a>
+            <a href="#" class="sidebar-item" data-view="known">Known Objects</a>
+            <a href="#" class="sidebar-item" data-view="settings">Settings</a>
+        </div>
+        <div class="sidebar-section sidebar-stats">
+            <div id="stats-summary"></div>
+        </div>
+    </nav>
+    <main id="content">
+        <div id="main-content"></div>
+    </main>
+
+    <div id="tag-modal" class="modal hidden">
+        <div class="modal-content">
+            <h3>Tag as Known Object</h3>
+            <div id="tag-modal-snapshot"></div>
+            <div class="form-group">
+                <label>Existing object:</label>
+                <select id="tag-existing-object">
+                    <option value="">-- Create new --</option>
+                </select>
+            </div>
+            <div id="tag-new-fields">
+                <div class="form-group">
+                    <label>Name:</label>
+                    <input type="text" id="tag-name" placeholder="e.g., Jeremy, Blue F-150, Max">
+                </div>
+                <div class="form-group">
+                    <label>Type:</label>
+                    <select id="tag-type">
+                        <option value="person">Person</option>
+                        <option value="vehicle">Vehicle</option>
+                        <option value="pet">Pet</option>
+                    </select>
+                </div>
+            </div>
+            <div class="modal-actions">
+                <button id="tag-save" class="btn btn-primary">Save</button>
+                <button id="tag-cancel" class="btn">Cancel</button>
+            </div>
+        </div>
+    </div>
+
+    <script src="/static/app.js"></script>
+</body>
+</html>

app/static/style.css

@@ -0,0 +1,534 @@
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+    background: #1a1a2e;
+    color: #e0e0e0;
+    display: flex;
+    min-height: 100vh;
+}
+
+/* Sidebar */
+#sidebar {
+    width: 220px;
+    background: #16213e;
+    padding: 20px 0;
+    position: fixed;
+    height: 100vh;
+    overflow-y: auto;
+}
+
+.sidebar-header {
+    padding: 0 20px 20px;
+    border-bottom: 1px solid #0f3460;
+}
+
+.sidebar-header h2 {
+    color: #00b4d8;
+    font-size: 1.2em;
+}
+
+.sidebar-section {
+    padding: 10px 0;
+}
+
+.sidebar-item {
+    display: block;
+    padding: 10px 20px;
+    color: #a0a0a0;
+    text-decoration: none;
+    transition: all 0.2s;
+}
+
+.sidebar-item:hover {
+    background: #0f3460;
+    color: #fff;
+}
+
+.sidebar-item.active {
+    background: #0f3460;
+    color: #00b4d8;
+    border-left: 3px solid #00b4d8;
+}
+
+.sidebar-stats {
+    padding: 20px;
+    font-size: 0.85em;
+    color: #888;
+}
+
+/* Main content */
+#content {
+    margin-left: 220px;
+    flex: 1;
+    padding: 20px;
+}
+
+/* Page headers */
+h1 {
+    margin-bottom: 20px;
+}
+
+.page-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 20px;
+}
+
+/* Filters */
+.filters {
+    display: flex;
+    gap: 10px;
+    margin-bottom: 20px;
+    flex-wrap: wrap;
+}
+
+.filters select,
+.filters input {
+    padding: 6px 10px;
+    background: #16213e;
+    border: 1px solid #0f3460;
+    color: #e0e0e0;
+    border-radius: 4px;
+}
+
+.filters select:focus,
+.filters input:focus {
+    outline: none;
+    border-color: #00b4d8;
+}
+
+/* Event cards */
+.event-card {
+    background: #16213e;
+    border-radius: 8px;
+    padding: 15px;
+    margin-bottom: 15px;
+    display: flex;
+    gap: 15px;
+    transition: border-color 0.2s;
+    border: 1px solid transparent;
+}
+
+.event-card:hover {
+    border-color: #0f3460;
+}
+
+.event-card img {
+    width: 160px;
+    height: 120px;
+    object-fit: cover;
+    border-radius: 4px;
+    background: #1a1a2e;
+}
+
+.event-info {
+    flex: 1;
+}
+
+.event-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 8px;
+}
+
+.event-camera {
+    font-weight: bold;
+    color: #00b4d8;
+}
+
+.event-label {
+    color: #a0a0a0;
+}
+
+.event-score {
+    color: #888;
+    font-size: 0.85em;
+}
+
+.event-description {
+    color: #ccc;
+    margin: 8px 0;
+    font-size: 0.9em;
+    line-height: 1.4;
+}
+
+.event-time {
+    color: #666;
+    font-size: 0.8em;
+    margin-bottom: 6px;
+}
+
+.event-actions {
+    margin-top: 10px;
+    display: flex;
+    gap: 8px;
+}
+
+/* Status badges */
+.badge {
+    padding: 3px 10px;
+    border-radius: 12px;
+    font-size: 0.8em;
+    font-weight: bold;
+}
+
+.badge-known {
+    background: #2d6a4f;
+    color: #95d5b2;
+}
+
+.badge-unknown {
+    background: #9a4c1a;
+    color: #f4a261;
+}
+
+.badge-ambiguous {
+    background: #7c6f09;
+    color: #ffd166;
+}
+
+.badge-unreviewed {
+    background: #444;
+    color: #aaa;
+}
+
+/* Buttons */
+.btn {
+    padding: 6px 14px;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 0.85em;
+    transition: all 0.2s;
+    color: #ccc;
+    background: #2a2a4a;
+}
+
+.btn:hover {
+    background: #3a3a5a;
+    color: #fff;
+}
+
+.btn-primary {
+    background: #0f3460;
+    color: #fff;
+}
+
+.btn-primary:hover {
+    background: #00b4d8;
+}
+
+.btn-danger {
+    background: #7c2929;
+    color: #fff;
+}
+
+.btn-danger:hover {
+    background: #c0392b;
+}
+
+.btn-sm {
+    padding: 4px 10px;
+    font-size: 0.8em;
+}
+
+/* Modal */
+.modal {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background: rgba(0, 0, 0, 0.7);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 1000;
+}
+
+.modal.hidden {
+    display: none;
+}
+
+.modal-content {
+    background: #16213e;
+    border-radius: 8px;
+    padding: 25px;
+    width: 450px;
+    max-width: 90vw;
+}
+
+.modal-content h3 {
+    margin-bottom: 15px;
+    color: #00b4d8;
+}
+
+.modal-actions {
+    display: flex;
+    gap: 10px;
+    justify-content: flex-end;
+    margin-top: 15px;
+}
+
+#tag-modal-snapshot {
+    margin-bottom: 15px;
+}
+
+#tag-modal-snapshot img {
+    width: 100%;
+    max-height: 200px;
+    object-fit: cover;
+    border-radius: 4px;
+}
+
+/* Form elements */
+.form-group {
+    margin-bottom: 12px;
+}
+
+.form-group label {
+    display: block;
+    margin-bottom: 4px;
+    color: #a0a0a0;
+    font-size: 0.9em;
+}
+
+.form-group input,
+.form-group select {
+    width: 100%;
+    padding: 8px 10px;
+    background: #1a1a2e;
+    border: 1px solid #0f3460;
+    color: #e0e0e0;
+    border-radius: 4px;
+}
+
+.form-group input:focus,
+.form-group select:focus {
+    outline: none;
+    border-color: #00b4d8;
+}
+
+.form-group input[type="checkbox"] {
+    width: auto;
+    margin-right: 8px;
+}
+
+.form-group .checkbox-label {
+    display: flex;
+    align-items: center;
+    cursor: pointer;
+}
+
+.form-group .help-text {
+    font-size: 0.8em;
+    color: #666;
+    margin-top: 4px;
+}
+
+/* Known objects grid */
+.known-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));
+    gap: 15px;
+}
+
+.known-card {
+    background: #16213e;
+    border-radius: 8px;
+    padding: 15px;
+    border: 1px solid transparent;
+    transition: border-color 0.2s;
+}
+
+.known-card:hover {
+    border-color: #0f3460;
+}
+
+.known-card h4 {
+    color: #00b4d8;
+    margin-bottom: 5px;
+}
+
+.known-card .type-badge {
+    font-size: 0.8em;
+    color: #a0a0a0;
+    text-transform: capitalize;
+}
+
+.known-card .embedding-count {
+    font-size: 0.8em;
+    color: #666;
+    margin-top: 6px;
+}
+
+.known-card .known-card-actions {
+    margin-top: 10px;
+    display: flex;
+    gap: 8px;
+}
+
+.known-card .known-card-samples {
+    display: flex;
+    gap: 5px;
+    margin-top: 10px;
+    overflow-x: auto;
+}
+
+.known-card .known-card-samples img {
+    width: 60px;
+    height: 60px;
+    object-fit: cover;
+    border-radius: 4px;
+    background: #1a1a2e;
+}
+
+/* Settings */
+.settings-form {
+    max-width: 600px;
+}
+
+.settings-form .form-group {
+    margin-bottom: 15px;
+}
+
+.settings-section {
+    margin-bottom: 30px;
+}
+
+.settings-section h3 {
+    color: #00b4d8;
+    margin-bottom: 15px;
+    padding-bottom: 8px;
+    border-bottom: 1px solid #0f3460;
+}
+
+/* Stats in sidebar */
+.stat-item {
+    display: flex;
+    justify-content: space-between;
+    margin-bottom: 8px;
+}
+
+.stat-value {
+    color: #00b4d8;
+    font-weight: bold;
+}
+
+/* Empty state */
+.empty-state {
+    text-align: center;
+    color: #666;
+    padding: 60px 20px;
+}
+
+.empty-state p {
+    margin-top: 10px;
+    font-size: 0.9em;
+}
+
+/* Loading spinner */
+.loading {
+    text-align: center;
+    padding: 40px;
+    color: #666;
+}
+
+.loading::after {
+    content: '';
+    display: inline-block;
+    width: 20px;
+    height: 20px;
+    border: 2px solid #0f3460;
+    border-top-color: #00b4d8;
+    border-radius: 50%;
+    animation: spin 0.8s linear infinite;
+    margin-left: 10px;
+    vertical-align: middle;
+}
+
+@keyframes spin {
+    to { transform: rotate(360deg); }
+}
+
+/* Toast notifications */
+.toast {
+    position: fixed;
+    bottom: 20px;
+    right: 20px;
+    padding: 12px 20px;
+    border-radius: 6px;
+    color: #fff;
+    font-size: 0.9em;
+    z-index: 2000;
+    animation: slideIn 0.3s ease-out;
+}
+
+.toast-success {
+    background: #2d6a4f;
+}
+
+.toast-error {
+    background: #7c2929;
+}
+
+@keyframes slideIn {
+    from {
+        transform: translateY(20px);
+        opacity: 0;
+    }
+    to {
+        transform: translateY(0);
+        opacity: 1;
+    }
+}
+
+/* Responsive */
+@media (max-width: 768px) {
+    #sidebar {
+        width: 100%;
+        height: auto;
+        position: relative;
+    }
+
+    .sidebar-section {
+        display: flex;
+        flex-wrap: wrap;
+    }
+
+    .sidebar-item {
+        padding: 8px 15px;
+    }
+
+    .sidebar-stats {
+        display: none;
+    }
+
+    #content {
+        margin-left: 0;
+    }
+
+    body {
+        flex-direction: column;
+    }
+
+    .event-card {
+        flex-direction: column;
+    }
+
+    .event-card img {
+        width: 100%;
+        height: auto;
+    }
+
+    .known-grid {
+        grid-template-columns: 1fr;
+    }
+}

app/vision.py

@@ -1,7 +1,11 @@
 import base64
 import logging
+from typing import Optional
+
 import anthropic
 
+from ollama_client import OllamaClient
+
 logger = logging.getLogger(__name__)
 
 PROMPT = (
@@ -40,3 +44,66 @@ def analyze_snapshot(client: anthropic.Anthropic, image_bytes: bytes) -> str | N
     except Exception:
         logger.exception("Claude Vision API failed")
         return None
+
+
+async def analyze_snapshot_ollama(
+    ollama_client: OllamaClient, image_bytes: bytes
+) -> str | None:
+    """Send a snapshot to Ollama vision model for description.
+
+    Args:
+        ollama_client: Configured OllamaClient instance.
+        image_bytes: Raw image bytes (JPEG).
+
+    Returns:
+        Description string or None on failure.
+    """
+    return await ollama_client.analyze_image(image_bytes, PROMPT)
+
+
+async def get_description(
+    image_bytes: bytes,
+    vision_backend: str,
+    claude_client: Optional[anthropic.Anthropic] = None,
+    ollama_client: Optional[OllamaClient] = None,
+) -> str | None:
+    """Get a scene description using the configured vision backend.
+
+    Args:
+        image_bytes: Raw image bytes (JPEG).
+        vision_backend: One of "haiku", "ollama", or "both".
+            "both" tries Ollama first, falls back to Haiku.
+        claude_client: Anthropic client (required for "haiku" and "both").
+        ollama_client: OllamaClient instance (required for "ollama" and "both").
+
+    Returns:
+        Description string or None if all backends fail.
+    """
+    if vision_backend == "ollama":
+        if ollama_client is None:
+            logger.error("Ollama backend selected but no client provided")
+            return None
+        return await analyze_snapshot_ollama(ollama_client, image_bytes)
+
+    if vision_backend == "haiku":
+        if claude_client is None:
+            logger.error("Haiku backend selected but no client provided")
+            return None
+        return analyze_snapshot(claude_client, image_bytes)
+
+    if vision_backend == "both":
+        # Try Ollama first, fall back to Haiku
+        if ollama_client is not None:
+            result = await analyze_snapshot_ollama(ollama_client, image_bytes)
+            if result is not None:
+                return result
+            logger.info("Ollama failed, falling back to Haiku")
+
+        if claude_client is not None:
+            return analyze_snapshot(claude_client, image_bytes)
+
+        logger.error("Both backend selected but no clients provided")
+        return None
+
+    logger.error("Unknown vision backend: %s", vision_backend)
+    return None

clip-service/Dockerfile

@@ -0,0 +1,15 @@
+FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime
+
+RUN apt-get update && apt-get install -y --no-install-recommends curl \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY app/requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app/ .
+
+EXPOSE 5200
+
+CMD ["python", "-u", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "5200"]

clip-service/app/main.py

@@ -0,0 +1,194 @@
+"""CLIP embedding service for frigate-notify.
+
+Loads OpenAI ViT-L/14 CLIP model and exposes endpoints for generating
+image embeddings and computing cosine similarity between embeddings.
+Designed to run on a GPU machine (RTX 3090) as a sidecar service.
+"""
+
+import base64
+import io
+import logging
+import time
+from contextlib import asynccontextmanager
+
+import numpy as np
+import open_clip
+import torch
+from fastapi import FastAPI, HTTPException
+from PIL import Image
+from pydantic import BaseModel
+
+logger = logging.getLogger("clip-service")
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+
+# --- Pydantic models ---
+
+
+class EmbedRequest(BaseModel):
+    image: str  # base64-encoded image
+
+
+class EmbedResponse(BaseModel):
+    embedding: list[float]
+    inference_ms: float
+
+
+class SimilarityRequest(BaseModel):
+    embedding1: list[float]
+    embedding2: list[float]
+
+
+class SimilarityResponse(BaseModel):
+    score: float
+
+
+class BatchSimilarityRequest(BaseModel):
+    query_embedding: list[float]
+    candidate_embeddings: list[list[float]]
+
+
+class BatchSimilarityResponse(BaseModel):
+    scores: list[float]
+    best_index: int
+    best_score: float
+
+
+# --- Globals ---
+
+model = None
+preprocess = None
+device = None
+model_name = "ViT-L-14"
+pretrained = "openai"
+
+
+def load_model():
+    """Load the CLIP model onto the best available device."""
+    global model, preprocess, device
+
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        logger.info("CUDA available — using GPU: %s", torch.cuda.get_device_name(0))
+    else:
+        device = torch.device("cpu")
+        logger.warning("CUDA not available — falling back to CPU")
+
+    logger.info("Loading CLIP model %s (pretrained=%s)...", model_name, pretrained)
+    t0 = time.monotonic()
+    model, _, preprocess = open_clip.create_model_and_transforms(
+        model_name, pretrained=pretrained, device=device
+    )
+    model.eval()
+    elapsed = time.monotonic() - t0
+    logger.info("Model loaded in %.1fs on %s", elapsed, device)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    load_model()
+    yield
+
+
+app = FastAPI(title="CLIP Embedding Service", version="1.0.0", lifespan=lifespan)
+
+
+# --- Utility functions ---
+
+
+def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    """Compute cosine similarity between two vectors."""
+    dot = np.dot(a, b)
+    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(dot / (norm_a * norm_b))
+
+
+# --- Endpoints ---
+
+
+@app.get("/health")
+async def health():
+    return {
+        "status": "healthy",
+        "device": str(device) if device else "not_loaded",
+        "model": f"{model_name}/{pretrained}",
+    }
+
+
+@app.post("/embed", response_model=EmbedResponse)
+async def embed(request: EmbedRequest):
+    if model is None or preprocess is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+
+    # Decode base64 image
+    try:
+        image_bytes = base64.b64decode(request.image)
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid image data: {e}")
+
+    # Preprocess and encode
+    try:
+        image_tensor = preprocess(image).unsqueeze(0).to(device)
+
+        t0 = time.monotonic()
+        with torch.no_grad():
+            embedding = model.encode_image(image_tensor)
+        inference_ms = (time.monotonic() - t0) * 1000
+
+        # Normalize the embedding
+        embedding = embedding / embedding.norm(dim=-1, keepdim=True)
+        embedding_list = embedding.squeeze(0).cpu().numpy().tolist()
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Inference failed: {e}")
+
+    return EmbedResponse(embedding=embedding_list, inference_ms=round(inference_ms, 2))
+
+
+@app.post("/similarity", response_model=SimilarityResponse)
+async def similarity(request: SimilarityRequest):
+    a = np.array(request.embedding1, dtype=np.float32)
+    b = np.array(request.embedding2, dtype=np.float32)
+
+    if a.shape != b.shape:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Embedding dimensions must match: {a.shape} vs {b.shape}",
+        )
+
+    score = cosine_similarity(a, b)
+    return SimilarityResponse(score=round(score, 6))
+
+
+@app.post("/batch-similarity", response_model=BatchSimilarityResponse)
+async def batch_similarity(request: BatchSimilarityRequest):
+    query = np.array(request.query_embedding, dtype=np.float32)
+
+    if not request.candidate_embeddings:
+        raise HTTPException(status_code=400, detail="No candidate embeddings provided")
+
+    candidates = np.array(request.candidate_embeddings, dtype=np.float32)
+
+    if query.shape[0] != candidates.shape[1]:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Dimension mismatch: query={query.shape[0]}, candidates={candidates.shape[1]}",
+        )
+
+    # Compute cosine similarity for all candidates at once
+    query_norm = query / (np.linalg.norm(query) + 1e-10)
+    candidate_norms = candidates / (
+        np.linalg.norm(candidates, axis=1, keepdims=True) + 1e-10
+    )
+    scores = candidate_norms @ query_norm
+
+    scores_list = [round(float(s), 6) for s in scores]
+    best_index = int(np.argmax(scores))
+    best_score = scores_list[best_index]
+
+    return BatchSimilarityResponse(
+        scores=scores_list, best_index=best_index, best_score=best_score
+    )

clip-service/app/requirements.txt

@@ -0,0 +1,7 @@
+fastapi>=0.115.0
+uvicorn[standard]>=0.34.0
+open-clip-torch>=2.29.0
+torch>=2.5.0
+Pillow>=10.0.0
+numpy>=1.26.0
+python-multipart>=0.0.18

clip-service/docker-compose.yml

@@ -0,0 +1,27 @@
+services:
+  clip-service:
+    build: .
+    container_name: clip-service
+    restart: unless-stopped
+    ports:
+      - "5200:5200"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    mem_limit: 4G
+    memswap_limit: 4G
+    healthcheck:
+      test: ["CMD-SHELL", "curl -sf http://localhost:5200/health || exit 1"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 120s
+    logging:
+      driver: "json-file"
+      options:
+        max-size: "50m"
+        max-file: "3"

clip-service/tests/conftest.py

@@ -0,0 +1,65 @@
+"""Test configuration: mock heavy dependencies that aren't needed for unit tests."""
+
+import sys
+from unittest.mock import MagicMock
+
+# Mock modules that are not installed in the test environment.
+# The tests only exercise pure numpy math, not model inference.
+for mod_name in [
+    "torch",
+    "open_clip",
+    "fastapi",
+    "fastapi.responses",
+    "pydantic",
+    "PIL",
+    "PIL.Image",
+    "uvicorn",
+]:
+    if mod_name not in sys.modules:
+        sys.modules[mod_name] = MagicMock()
+
+# Provide a real BaseModel stand-in so Pydantic models can be defined
+# (they are referenced at import time in main.py).
+import types
+
+pydantic_mock = sys.modules["pydantic"]
+
+
+class _FakeBaseModel:
+    def __init_subclass__(cls, **kwargs):
+        pass
+
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+
+
+pydantic_mock.BaseModel = _FakeBaseModel
+
+# Provide a fake FastAPI and HTTPException so the module loads
+fastapi_mock = sys.modules["fastapi"]
+
+
+class _FakeFastAPI:
+    def __init__(self, **kwargs):
+        pass
+
+    def get(self, *a, **kw):
+        def decorator(fn):
+            return fn
+        return decorator
+
+    def post(self, *a, **kw):
+        def decorator(fn):
+            return fn
+        return decorator
+
+
+class _FakeHTTPException(Exception):
+    def __init__(self, status_code=500, detail=""):
+        self.status_code = status_code
+        self.detail = detail
+
+
+fastapi_mock.FastAPI = _FakeFastAPI
+fastapi_mock.HTTPException = _FakeHTTPException

clip-service/tests/test_clip.py

@@ -0,0 +1,130 @@
+"""Tests for CLIP service math utilities.
+
+These tests exercise the cosine similarity logic using pure numpy --
+no GPU, no model loading, no network calls required.
+"""
+
+import numpy as np
+import pytest
+
+# Import the cosine_similarity function directly from the app module.
+# We add the app directory to sys.path so the import works without
+# installing the package.
+import sys
+import os
+
+sys.path.insert(
+    0, os.path.join(os.path.dirname(__file__), "..", "app")
+)
+
+from main import cosine_similarity
+
+
+class TestCosineSimilarity:
+    """Test the cosine_similarity helper function."""
+
+    def test_identical_vectors(self):
+        """Identical vectors should have cosine similarity of 1.0."""
+        v = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
+        score = cosine_similarity(v, v)
+        assert score == pytest.approx(1.0, abs=1e-6)
+
+    def test_identical_unit_vectors(self):
+        """Identical unit vectors should also yield 1.0."""
+        v = np.array([0.0, 1.0, 0.0], dtype=np.float32)
+        score = cosine_similarity(v, v)
+        assert score == pytest.approx(1.0, abs=1e-6)
+
+    def test_orthogonal_vectors(self):
+        """Orthogonal vectors should have cosine similarity of 0.0."""
+        a = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+        b = np.array([0.0, 1.0, 0.0], dtype=np.float32)
+        score = cosine_similarity(a, b)
+        assert score == pytest.approx(0.0, abs=1e-6)
+
+    def test_opposite_vectors(self):
+        """Opposite vectors should have cosine similarity of -1.0."""
+        a = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+        b = np.array([-1.0, -2.0, -3.0], dtype=np.float32)
+        score = cosine_similarity(a, b)
+        assert score == pytest.approx(-1.0, abs=1e-6)
+
+    def test_zero_vector_returns_zero(self):
+        """A zero vector should return 0.0 (guarded against division by zero)."""
+        a = np.array([0.0, 0.0, 0.0], dtype=np.float32)
+        b = np.array([1.0, 2.0, 3.0], dtype=np.float32)
+        score = cosine_similarity(a, b)
+        assert score == pytest.approx(0.0, abs=1e-6)
+
+    def test_high_dimensional(self):
+        """Test with 768-dimensional vectors (CLIP ViT-L/14 output size)."""
+        rng = np.random.RandomState(42)
+        a = rng.randn(768).astype(np.float32)
+        # Similarity with itself should be 1.0
+        score = cosine_similarity(a, a)
+        assert score == pytest.approx(1.0, abs=1e-5)
+
+
+class TestBatchSimilarity:
+    """Test batch similarity logic (matching the endpoint's numpy math)."""
+
+    def _batch_similarity(
+        self, query: np.ndarray, candidates: np.ndarray
+    ) -> tuple[list[float], int, float]:
+        """Replicate the batch-similarity endpoint logic."""
+        query_norm = query / (np.linalg.norm(query) + 1e-10)
+        candidate_norms = candidates / (
+            np.linalg.norm(candidates, axis=1, keepdims=True) + 1e-10
+        )
+        scores = candidate_norms @ query_norm
+        scores_list = [float(s) for s in scores]
+        best_index = int(np.argmax(scores))
+        best_score = scores_list[best_index]
+        return scores_list, best_index, best_score
+
+    def test_best_match_index(self):
+        """The candidate most similar to the query should be identified."""
+        query = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+        candidates = np.array(
+            [
+                [0.0, 1.0, 0.0],  # orthogonal
+                [0.9, 0.1, 0.0],  # close match
+                [1.0, 0.0, 0.0],  # exact match
+                [0.5, 0.5, 0.0],  # partial match
+            ],
+            dtype=np.float32,
+        )
+        scores, best_idx, best_score = self._batch_similarity(query, candidates)
+
+        assert best_idx == 2
+        assert best_score == pytest.approx(1.0, abs=1e-5)
+        assert len(scores) == 4
+        # Orthogonal candidate should score ~0
+        assert scores[0] == pytest.approx(0.0, abs=1e-5)
+
+    def test_single_candidate(self):
+        """Batch with a single candidate should work correctly."""
+        query = np.array([1.0, 1.0], dtype=np.float32)
+        candidates = np.array([[1.0, 1.0]], dtype=np.float32)
+
+        scores, best_idx, best_score = self._batch_similarity(query, candidates)
+        assert best_idx == 0
+        assert best_score == pytest.approx(1.0, abs=1e-5)
+
+    def test_scores_ordering(self):
+        """Scores should reflect relative similarity to the query."""
+        query = np.array([1.0, 0.0], dtype=np.float32)
+        candidates = np.array(
+            [
+                [0.5, 0.866],  # ~60 degrees
+                [0.866, 0.5],  # ~30 degrees
+                [1.0, 0.0],    # 0 degrees (exact)
+            ],
+            dtype=np.float32,
+        )
+        scores, best_idx, _ = self._batch_similarity(query, candidates)
+
+        # Exact match should be best
+        assert best_idx == 2
+        # 30 degrees should score higher than 60 degrees
+        assert scores[1] > scores[0]

docker-compose.yml

@@ -15,6 +15,12 @@ services:
       - MQTT_HOST=192.168.86.192
       - MQTT_PORT=1883
       - MATRIX_HOMESERVER=http://192.168.86.192:8008
+      - CLIP_SERVICE_URL=http://192.168.86.196:5200
+      - OLLAMA_URL=http://192.168.86.196:11434
+      - OLLAMA_MODEL=llava:13b
+      - DB_PATH=/data/frigate-notify.db
+    volumes:
+      - /mnt/docker-data/configs/frigate-notify:/data
     mem_limit: 256M
     memswap_limit: 256M
     healthcheck:

tests/test_cleanup.py

@@ -0,0 +1,65 @@
+"""Tests for event retention cleanup."""
+
+import sqlite3
+
+from database import init_db, set_db_path
+
+
+def test_cleanup_deletes_old_events(db_path):
+    """Old events beyond retention should be deleted."""
+    set_db_path(db_path)
+    init_db()
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+
+    # Insert an old event (100 days ago)
+    conn.execute(
+        """INSERT INTO events (frigate_event_id, camera, label, score, match_status, created_at)
+           VALUES ('old-1', 'cam', 'person', 0.9, 'unknown', datetime('now', '-100 days'))"""
+    )
+    # Insert a recent event
+    conn.execute(
+        """INSERT INTO events (frigate_event_id, camera, label, score, match_status)
+           VALUES ('new-1', 'cam', 'person', 0.9, 'unknown')"""
+    )
+    conn.commit()
+
+    # Simulate cleanup with 90-day retention
+    result = conn.execute(
+        "DELETE FROM events WHERE created_at < datetime('now', '-90 days')"
+    )
+    conn.commit()
+    assert result.rowcount == 1
+
+    remaining = conn.execute("SELECT COUNT(*) as c FROM events").fetchone()["c"]
+    assert remaining == 1
+    conn.close()
+
+
+def test_cleanup_preserves_recent_events(db_path):
+    """Events within retention period should not be deleted."""
+    set_db_path(db_path)
+    init_db()
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+
+    # Insert events from 30 and 60 days ago
+    conn.execute(
+        """INSERT INTO events (frigate_event_id, camera, label, score, match_status, created_at)
+           VALUES ('mid-1', 'cam', 'person', 0.9, 'unknown', datetime('now', '-30 days'))"""
+    )
+    conn.execute(
+        """INSERT INTO events (frigate_event_id, camera, label, score, match_status, created_at)
+           VALUES ('mid-2', 'cam', 'car', 0.8, 'known', datetime('now', '-60 days'))"""
+    )
+    conn.commit()
+
+    result = conn.execute(
+        "DELETE FROM events WHERE created_at < datetime('now', '-90 days')"
+    )
+    conn.commit()
+    assert result.rowcount == 0
+
+    remaining = conn.execute("SELECT COUNT(*) as c FROM events").fetchone()["c"]
+    assert remaining == 2
+    conn.close()

tests/test_recognition.py

@@ -0,0 +1,82 @@
+"""Tests for the recognition module — embedding serialization helpers."""
+
+import numpy as np
+import pytest
+
+from recognition import bytes_to_embedding, embedding_to_bytes
+
+
+class TestEmbeddingToBytes:
+    """Tests for embedding_to_bytes()."""
+
+    def test_roundtrip_preserves_values(self):
+        """Packing and unpacking should return the same values."""
+        original = [0.1, 0.2, 0.3, -0.5, 1.0, 0.0]
+        packed = embedding_to_bytes(original)
+        recovered = bytes_to_embedding(packed)
+        np.testing.assert_allclose(recovered, original, rtol=1e-6)
+
+    def test_correct_byte_length_768(self):
+        """A 768-dim embedding should produce 768 * 4 = 3072 bytes."""
+        embedding = [0.0] * 768
+        packed = embedding_to_bytes(embedding)
+        assert len(packed) == 768 * 4
+
+    def test_correct_byte_length_arbitrary(self):
+        """Byte length should always be len(embedding) * 4."""
+        for dim in [1, 10, 512, 1024]:
+            embedding = [1.0] * dim
+            packed = embedding_to_bytes(embedding)
+            assert len(packed) == dim * 4
+
+    def test_empty_embedding(self):
+        """An empty embedding should produce 0 bytes."""
+        packed = embedding_to_bytes([])
+        assert len(packed) == 0
+        recovered = bytes_to_embedding(packed)
+        assert len(recovered) == 0
+
+
+class TestBytesToEmbedding:
+    """Tests for bytes_to_embedding()."""
+
+    def test_correct_shape(self):
+        """Should return a 1-D array with the correct number of elements."""
+        embedding = [0.5] * 768
+        packed = embedding_to_bytes(embedding)
+        recovered = bytes_to_embedding(packed)
+        assert recovered.shape == (768,)
+
+    def test_correct_dtype(self):
+        """Should return float32 numpy array."""
+        embedding = [0.1, 0.2, 0.3]
+        packed = embedding_to_bytes(embedding)
+        recovered = bytes_to_embedding(packed)
+        assert recovered.dtype == np.float32
+
+    def test_negative_values(self):
+        """Negative values should survive roundtrip."""
+        original = [-1.0, -0.5, 0.0, 0.5, 1.0]
+        packed = embedding_to_bytes(original)
+        recovered = bytes_to_embedding(packed)
+        np.testing.assert_allclose(recovered, original, rtol=1e-6)
+
+    def test_large_values(self):
+        """Large float values should survive roundtrip within float32 precision."""
+        original = [1e10, -1e10, 3.14159, 2.71828]
+        packed = embedding_to_bytes(original)
+        recovered = bytes_to_embedding(packed)
+        np.testing.assert_allclose(recovered, original, rtol=1e-5)
+
+    def test_realistic_clip_embedding(self):
+        """Simulate a realistic CLIP embedding (768-dim, normalized)."""
+        rng = np.random.default_rng(42)
+        vec = rng.standard_normal(768).astype(np.float32)
+        vec = vec / np.linalg.norm(vec)  # normalize
+
+        packed = embedding_to_bytes(vec.tolist())
+        recovered = bytes_to_embedding(packed)
+
+        assert recovered.shape == (768,)
+        assert recovered.dtype == np.float32
+        np.testing.assert_allclose(recovered, vec, rtol=1e-6)