feat: OCR independent chunks + TMDb seed with file_uuid

- Rule 1 now creates OCR-only chunks instead of merging into ASRX
- generate_seed_embeddings.py supports --file-uuid parameter
- get_seeds() filters by file_uuid
- identity_matcher.py uses file_uuid for seed matching
- Push QDRANT_API_KEY to Python subprocesses
- Face clustering uses frame+bbox matching instead of face_id
- Portal uses JWT authentication
- FilesView filter logic fixed

portal/.env.development

@@ -1,4 +1,4 @@
 # Portal Development Environment
 VITE_APP_TITLE=Momentry Portal (Development)
-VITE_API_BASE_URL=http://127.0.0.1:3003
+VITE_API_BASE_URL=http://127.0.0.1:3002
 VITE_API_KEY=muser_68600856036340bcafc01930eb4bd839_1774418104_97221b69

portal/src/api/client.ts

@@ -75,7 +75,7 @@ export interface UnregisterResponse {
 // ── Config (browser-only, stored in localStorage) ───────────────────────
 
 const DEFAULT_CONFIG: PortalConfig = {
-  api_base_url: import.meta.env.VITE_API_BASE_URL || 'http://127.0.0.1:3003',
+  api_base_url: import.meta.env.VITE_API_BASE_URL || 'http://127.0.0.1:3002',
   api_key: import.meta.env.VITE_API_KEY || '',
   timeout_secs: 30,
 }
@@ -99,13 +99,20 @@ export function saveConfig(config: PortalConfig): void {
 export async function logout(): Promise<void> {
   try {
     const config = getConfig();
+    const jwtToken = localStorage.getItem('momentry_jwt');
     const apiKey = config.api_key || localStorage.getItem('momentry_api_key');
-    if (apiKey) {
+    if (jwtToken || apiKey) {
+      const headers: Record<string, string> = { 'Content-Type': 'application/json' };
+      if (jwtToken) {
+        headers['Authorization'] = `Bearer ${jwtToken}`;
+      } else if (apiKey) {
+        headers['X-API-Key'] = apiKey;
+      }
       // Call logout API to invalidate session on server side (if implemented)
       // For now, just best effort
       await fetch(`${config.api_base_url}/api/v1/auth/logout`, {
         method: 'POST',
-        headers: { 'X-API-Key': apiKey }
+        headers
       }).catch(() => {}); // Ignore network errors
     }
   } catch (e) {
@@ -129,6 +136,7 @@ function handleSessionExpired() {
   localStorage.removeItem('momentry_user');
   localStorage.removeItem('portal_config');
   localStorage.removeItem('momentry_api_key');
+  localStorage.removeItem('momentry_jwt');
   if (window.location.pathname !== '/login') {
     window.location.href = '/login';
   }
@@ -139,12 +147,15 @@ export async function httpFetch<T>(url: string, options?: RequestInit, retries =
   // Re-read config to ensure we have the latest key if it changed
   const config = getConfig();
   
-  // Fallback key check
+  // Use JWT token if available, fallback to API key
+  const jwtToken = localStorage.getItem('momentry_jwt');
   const apiKey = config.api_key || localStorage.getItem('momentry_api_key') || '';
 
   const headers = new Headers(options?.headers);
   headers.set('Content-Type', 'application/json');
-  if (apiKey) {
+  if (jwtToken) {
+    headers.set('Authorization', `Bearer ${jwtToken}`);
+  } else if (apiKey) {
     headers.set('X-API-Key', apiKey);
   }
 
@@ -156,7 +167,7 @@ export async function httpFetch<T>(url: string, options?: RequestInit, retries =
       type: 'HTTP',
       method,
       url,
-      headers: { ...headers, 'X-API-Key': apiKey ? apiKey.substring(0, 10) + '...' : 'none' }, 
+      headers: { ...headers, 'Authorization': jwtToken ? 'Bearer ***' : 'none', 'X-API-Key': apiKey ? apiKey.substring(0, 10) + '...' : 'none' }, 
       body: options?.body ? JSON.parse(options.body as string) : null,
       status: 'loading',
       data: null,

portal/src/views/LoginView.vue

@@ -116,6 +116,9 @@ const handleLogin = async () => {
     if (data.success) {
       localStorage.setItem('momentry_user', JSON.stringify(data.user))
       localStorage.setItem('momentry_api_key', data.api_key)
+      if (data.jwt) {
+        localStorage.setItem('momentry_jwt', data.jwt)
+      }
       saveConfig({ ...config, api_key: data.api_key })
       const redirect = (route.query.redirect as string) || '/home'
       router.push(redirect)

run-server-3002.sh

@@ -12,6 +12,9 @@ export MOMENTRY_OUTPUT_DIR=/Users/accusys/momentry/output
 export DATABASE_SCHEMA=public
 export MOMENTRY_REDIS_PREFIX=momentry:
 export MOMENTRY_SERVER_PORT=3002
+# Qdrant credentials for Python subprocesses
+export QDRANT_URL=http://127.0.0.1:6333
+export QDRANT_API_KEY=Test3200Test3200Test3200
 
 # Kill existing server on port 3002
 PID=$(lsof -ti :3002 2>/dev/null || true)

run-worker-3002.sh

@@ -13,6 +13,9 @@ mkdir -p logs
 export MOMENTRY_OUTPUT_DIR=/Users/accusys/momentry/output
 export DATABASE_SCHEMA=public
 export MOMENTRY_REDIS_PREFIX=momentry:
+# Qdrant credentials for Python subprocesses
+export QDRANT_URL=http://127.0.0.1:6333
+export QDRANT_API_KEY=Test3200Test3200Test3200
 
 # Kill existing worker via PID file
 if [ -f logs/worker_3002.pid ]; then

scripts/face_clustering_processor.py

@@ -104,9 +104,14 @@ def main():
     print(f"[FACE_CLUSTER] Loading embeddings from Qdrant for {UUID}...")
     try:
         import requests
-        qdrant_url = "http://localhost:6333"
+        qdrant_url = os.environ.get("QDRANT_URL", "http://localhost:6333")
+        qdrant_api_key = os.environ.get("QDRANT_API_KEY", "")
         collection = "_faces"
         
+        headers = {}
+        if qdrant_api_key:
+            headers["api-key"] = qdrant_api_key
+        
         # Query all embeddings for this file_uuid
         response = requests.post(
             f"{qdrant_url}/collections/{collection}/points/scroll",
@@ -118,7 +123,8 @@ def main():
                 },
                 "limit": 10000,
                 "with_vector": True
-            }
+            },
+            headers=headers
         )
         
         if response.status_code == 200:
@@ -140,22 +146,57 @@ def main():
         print(f"[FACE_CLUSTER] Failed to load embeddings from Qdrant: {e}")
         embedding_map = {}
 
-    # Use embeddings from Qdrant or face.json
+    # Use embeddings from Qdrant - match by frame + bbox
     embeddings = []
     face_refs = []
 
     print(f"🔍 Collecting face embeddings for {UUID}...")
 
+    # Build a lookup: (frame, bbox_center) -> embedding
+    # Use frame number and approximate bbox center for matching
+    qdrant_by_frame = {}
+    for point in points:
+        payload = point.get("payload", {})
+        frame = payload.get("frame")
+        bbox = payload.get("bbox", {})
+        vector = point.get("vector")
+        if frame is not None and vector:
+            # Use frame + bbox center as key
+            cx = bbox.get("x", 0) + bbox.get("width", 0) // 2
+            cy = bbox.get("y", 0) + bbox.get("height", 0) // 2
+            key = (frame, cx, cy)
+            if key not in qdrant_by_frame:
+                qdrant_by_frame[key] = vector
+
+    print(f"[FACE_CLUSTER] Built Qdrant lookup with {len(qdrant_by_frame)} entries")
+
     for frame_idx, frame_obj in enumerate(frames_list):
+        frame_num = frame_obj.get("frame", frame_idx)
         faces = frame_obj.get("faces", [])
         if not faces:
             continue
 
         for face_idx, face in enumerate(faces):
-            face_id = face.get("face_id")
+            x = face.get("x", 0)
-            if face_id and face_id in embedding_map:
+            y = face.get("y", 0)
-                embeddings.append(embedding_map[face_id])
+            w = face.get("width", 0)
-                face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx, "face_id": face_id})
+            h = face.get("height", 0)
+            cx = x + w // 2
+            cy = y + h // 2
+
+            # Try exact match first
+            key = (frame_num, cx, cy)
+            if key in qdrant_by_frame:
+                embeddings.append(qdrant_by_frame[key])
+                face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx})
+                continue
+
+            # Try approximate match (within 50 pixels)
+            for (qf, qx, qy), vec in qdrant_by_frame.items():
+                if qf == frame_num and abs(qx - cx) < 50 and abs(qy - cy) < 50:
+                    embeddings.append(vec)
+                    face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx})
+                    break
 
     if not embeddings:
         print("❌ No embeddings found in Qdrant.")

scripts/generate_seed_embeddings.py

@@ -46,11 +46,12 @@ SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
 FACENET_PATH = os.path.join(SCRIPT_DIR, "..", "models", "facenet512.mlpackage")
 
 
-def get_tmdb_identities(limit: int = None) -> List[Dict]:
+def get_tmdb_identities(limit: int = None, file_uuid: str = None) -> List[Dict]:
     """Query PG for TMDb identities with profile photos
     
     Args:
         limit: Max identities to process
+        file_uuid: Filter by file_uuid via file_identities table
     
     Returns:
         List of {id, uuid, name, tmdb_id, tmdb_profile}
@@ -63,20 +64,34 @@ def get_tmdb_identities(limit: int = None) -> List[Dict]:
     
     if SCHEMA == "public":
         table = "identities"
+        file_table = "file_identities"
     else:
         table = f"{SCHEMA}.identities"
+        file_table = f"{SCHEMA}.file_identities"
     
-    query = f"""
+    if file_uuid:
-        SELECT id, uuid, name, tmdb_id, tmdb_profile
+        query = f"""
-        FROM {table}
+            SELECT DISTINCT i.id, i.uuid, i.name, i.tmdb_id, i.tmdb_profile
-        WHERE source = 'tmdb' AND tmdb_profile IS NOT NULL
+            FROM {table} i
-        ORDER BY id
+            JOIN {file_table} fi ON fi.identity_id = i.id
-    """
+            WHERE i.source = 'tmdb' AND i.tmdb_profile IS NOT NULL
+              AND fi.file_uuid = %s
+            ORDER BY i.id
+        """
+        if limit:
+            query += f" LIMIT {limit}"
+        cur.execute(query, (file_uuid,))
+    else:
+        query = f"""
+            SELECT id, uuid, name, tmdb_id, tmdb_profile
+            FROM {table}
+            WHERE source = 'tmdb' AND tmdb_profile IS NOT NULL
+            ORDER BY id
+        """
+        if limit:
+            query += f" LIMIT {limit}"
+        cur.execute(query)
     
-    if limit:
-        query += f" LIMIT {limit}"
-    
-    cur.execute(query)
     rows = cur.fetchall()
     cur.close()
     conn.close()
@@ -180,7 +195,7 @@ def extract_face_embedding(image_path: str) -> Optional[List[float]]:
         return None
 
 
-def generate_seed_embeddings(limit: int = None, dry_run: bool = False) -> Dict:
+def generate_seed_embeddings(limit: int = None, dry_run: bool = False, file_uuid: str = None) -> Dict:
     """Generate embeddings for all TMDb identities
     
     Args:
@@ -198,14 +213,14 @@ def generate_seed_embeddings(limit: int = None, dry_run: bool = False) -> Dict:
         "errors": [],
     }
     
-    identities = get_tmdb_identities(limit)
+    identities = get_tmdb_identities(limit, file_uuid)
     result["total"] = len(identities)
     
     if not identities:
-        print("[SEED] No TMDb identities with profile photos")
+        print(f"[SEED] No TMDb identities with profile photos{' for ' + file_uuid if file_uuid else ''}")
         return result
     
-    print(f"[SEED] Found {len(identities)} TMDb identities")
+    print(f"[SEED] Found {len(identities)} TMDb identities{' for ' + file_uuid if file_uuid else ''}")
     
     if not dry_run:
         ensure_seeds_collection()
@@ -259,6 +274,7 @@ def generate_seed_embeddings(limit: int = None, dry_run: bool = False) -> Dict:
                     name=name,
                     embedding=embedding,
                     source="tmdb",
+                    file_uuid=file_uuid,
                     tmdb_id=tmdb_id,
                 )
                 result["success"] += 1
@@ -280,12 +296,13 @@ def main():
     parser.add_argument("--dry-run", action="store_true", help="Don't push to Qdrant")
     parser.add_argument("--tmdb-api-key", help="TMDb API key (optional, for rate limiting)")
     parser.add_argument("--output", help="Output JSON file path")
+    parser.add_argument("--file-uuid", help="File UUID to generate seeds for")
     args = parser.parse_args()
     
     if args.tmdb_api_key:
         TMDB_API_KEY = args.tmdb_api_key
     
-    result = generate_seed_embeddings(args.limit, args.dry_run)
+    result = generate_seed_embeddings(args.limit, args.dry_run, args.file_uuid)
     
     output_json = json.dumps(result, indent=2, ensure_ascii=False)
     

scripts/identity_matcher.py

@@ -79,10 +79,10 @@ def match_faces_round_1(file_uuid: str) -> dict:
         {trace_id: {identity_id, identity_uuid, name, score, suggested_by: 'tmdb'}}
     """
     traces = get_trace_representatives(file_uuid)
-    seeds = get_seeds(source="tmdb")
+    seeds = get_seeds(source="tmdb", file_uuid=file_uuid)
     
     if not seeds:
-        print("[MATCH] No TMDb seeds available")
+        print(f"[MATCH] No TMDb seeds available for {file_uuid}")
         return {}
     
     suggestions = {}

scripts/utils/qdrant_faces.py

@@ -493,11 +493,12 @@ def push_seed_embedding(
         raise RuntimeError(f"Qdrant seed push failed: HTTP {e.code} - {error_body}")
 
 
-def get_seeds(source: str = None) -> list:
+def get_seeds(source: str = None, file_uuid: str = None) -> list:
     """Get all seed points
     
     Args:
         source: Filter by source ('tmdb', 'manual', 'propagation'), or None for all
+        file_uuid: Filter by file_uuid, or None for all
     
     Returns:
         List of seed points with payload and vector
@@ -514,12 +515,14 @@ def get_seeds(source: str = None) -> list:
             "with_vector": True,
         }
         
+        filters = []
         if source:
-            body["filter"] = {
+            filters.append({"key": "source", "match": {"value": source}})
-                "must": [
+        if file_uuid:
-                    {"key": "source", "match": {"value": source}}
+            filters.append({"key": "file_uuid", "match": {"value": file_uuid}})
-                ]
+        
-            }
+        if filters:
+            body["filter"] = {"must": filters}
         
         if offset:
             body["offset"] = offset

src/core/processor/executor.rs

@@ -309,6 +309,13 @@ impl PythonExecutor {
         cmd.env("DATABASE_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_DB_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_REDIS_PREFIX", &*REDIS_KEY_PREFIX);
+        // Pass Qdrant credentials to Python subprocesses
+        if let Ok(qdrant_url) = std::env::var("QDRANT_URL") {
+            cmd.env("QDRANT_URL", qdrant_url);
+        }
+        if let Ok(qdrant_api_key) = std::env::var("QDRANT_API_KEY") {
+            cmd.env("QDRANT_API_KEY", qdrant_api_key);
+        }
         if let Some(u) = uuid {
             cmd.env("UUID", u);
         }
@@ -450,6 +457,13 @@ impl PythonExecutor {
         cmd.env("DATABASE_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_DB_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_REDIS_PREFIX", &*REDIS_KEY_PREFIX);
+        // Pass Qdrant credentials to Python subprocesses
+        if let Ok(qdrant_url) = std::env::var("QDRANT_URL") {
+            cmd.env("QDRANT_URL", qdrant_url);
+        }
+        if let Ok(qdrant_api_key) = std::env::var("QDRANT_API_KEY") {
+            cmd.env("QDRANT_API_KEY", qdrant_api_key);
+        }
         if let Some(u) = uuid {
             cmd.env("UUID", u);
         }
@@ -631,6 +645,13 @@ impl PythonExecutor {
         cmd.env("DATABASE_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_DB_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_REDIS_PREFIX", &*REDIS_KEY_PREFIX);
+        // Pass Qdrant credentials to Python subprocesses
+        if let Ok(qdrant_url) = std::env::var("QDRANT_URL") {
+            cmd.env("QDRANT_URL", qdrant_url);
+        }
+        if let Ok(qdrant_api_key) = std::env::var("QDRANT_API_KEY") {
+            cmd.env("QDRANT_API_KEY", qdrant_api_key);
+        }
         cmd.arg(&script_path);
 
         for arg in args {
@@ -882,6 +903,13 @@ mod tests {
         cmd.env("DATABASE_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_DB_SCHEMA", &*DATABASE_SCHEMA);
         cmd.env("MOMENTRY_REDIS_PREFIX", &*REDIS_KEY_PREFIX);
+        // Pass Qdrant credentials to Python subprocesses
+        if let Ok(qdrant_url) = std::env::var("QDRANT_URL") {
+            cmd.env("QDRANT_URL", qdrant_url);
+        }
+        if let Ok(qdrant_api_key) = std::env::var("QDRANT_API_KEY") {
+            cmd.env("QDRANT_API_KEY", qdrant_api_key);
+        }
         cmd.args([
             "-c",
             "import os; print(f'ENV_DATABASE_SCHEMA={os.environ.get(\"DATABASE_SCHEMA\",\"\")}'); print(f'ENV_MOMENTRY_DB_SCHEMA={os.environ.get(\"MOMENTRY_DB_SCHEMA\",\"\")}'); print(f'ENV_MOMENTRY_OUTPUT_DIR={os.environ.get(\"MOMENTRY_OUTPUT_DIR\",\"\")}'); print(f'ENV_MOMENTRY_REDIS_PREFIX={os.environ.get(\"MOMENTRY_REDIS_PREFIX\",\"\")}');",