warren/momentry_core
1
0
Fork 0
Code Issues 2 Pull Requests Actions Packages Projects Releases 2 Wiki Activity

feat: OCR independent chunks + TMDb seed with file_uuid

Browse Source 
- 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
This commit is contained in:
Accusys
2026-07-06 08:56:56 +08:00
parent cb604b74ec
commit 799ede5a0e
10 changed files with 147 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files
portal/.env.development
+1 -1
View File
@@ -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
+17 -6
View File
@@ -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 ${jwtToken}`);
} else if (apiKey) {
headers.set('X-API-Key', apiKey);
}
@@ -156,7 +167,7 @@ export async function httpFetch<T>(url: string, options?: RequestInit, retries =
type: 'HTTP',
method,
url,
headers: { ...headers, 'X-API-Key': apiKey ? apiKey.substring(0, 10) + '...' : 'none' },
headers: { ...headers, 'Authorization': jwtToken ? 'Bearer ***' : 'none', 'X-API-Key': apiKey ? apiKey.substring(0, 10) + '...' : 'none' },
body: options?.body ? JSON.parse(options.body as string) : null,
status: 'loading',
data: null,
portal/src/views/LoginView.vue
+3
View File
@@ -116,6 +116,9 @@ const handleLogin = async () => {
if (data.success) {
localStorage.setItem('momentry_user', JSON.stringify(data.user))
localStorage.setItem('momentry_api_key', data.api_key)
if (data.jwt) {
localStorage.setItem('momentry_jwt', data.jwt)
}
saveConfig({ ...config, api_key: data.api_key })
const redirect = (route.query.redirect as string) || '/home'
router.push(redirect)
run-server-3002.sh
+3
View File
@@ -12,6 +12,9 @@ export MOMENTRY_OUTPUT_DIR=/Users/accusys/momentry/output
export DATABASE_SCHEMA=public
export MOMENTRY_REDIS_PREFIX=momentry:
export MOMENTRY_SERVER_PORT=3002
# Qdrant credentials for Python subprocesses
export QDRANT_URL=http://127.0.0.1:6333
export QDRANT_API_KEY=Test3200Test3200Test3200
# Kill existing server on port 3002
PID=$(lsof -ti :3002 2>/dev/null || true)
run-worker-3002.sh
+3
View File
@@ -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
+48 -7
View File
@@ -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")
if face_id and face_id in embedding_map:
embeddings.append(embedding_map[face_id])
face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx, "face_id": face_id})
x = face.get("x", 0)
y = face.get("y", 0)
w = face.get("width", 0)
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
+33 -16
View File
@@ -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"""
SELECT id, uuid, name, tmdb_id, tmdb_profile
FROM {table}
WHERE source = 'tmdb' AND tmdb_profile IS NOT NULL
ORDER BY id
"""
if file_uuid:
query = f"""
SELECT DISTINCT i.id, i.uuid, i.name, i.tmdb_id, i.tmdb_profile
FROM {table} i
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
+2 -2
View File
@@ -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
+9 -6
View File
@@ -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"] = {
"must": [
{"key": "source", "match": {"value": source}}
]
}
filters.append({"key": "source", "match": {"value": source}})
if file_uuid:
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
+28
View File
@@ -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\",\"\")}');",