feat: service inventory, ERP reports, sqlite-vec integration, visualize tool

- Add SERVICE_INVENTORY_V1.0.0.md (25 source-verified tools, 3.7GB)
- Add ERP_SELECTION_REPORT.md (Odoo CE vs ERPNext comparison)
- Add SFTPGO_ODOO_REPLACEMENT.md (SFTPGo migration plan)
- Add SERVICE_GO_GITEA_BUILD.md (Go compiler + Gitea build report)
- Add release visualize command (face trace heatmap + identity filter)
- Add sqlite-vec integration (160MB SQLite with vec0 vector tables)
- Add export_identities.py, export_sqlite.py, render_face_heatmap.py
- Add Go, Gitea, Rust/Cargo, Swift, yt-dlp, SQLite, sqlite-vec to service CLI
- Fix package to include identities and identity_bindings in data.sql
- Update release list to show all deployed video stats
- Add V1.0.0 YAML frontmatter to all docs (DOCS_STANDARD compliant)

scripts/export_identities.py

@@ -0,0 +1,74 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Export identity data for a video UUID as JSON (for offline analysis).
+Usage: python3 export_identities.py <file_uuid> [output.json]
+"""
+import sys, json, psycopg2
+
+UUID = sys.argv[1] if len(sys.argv) > 1 else "aeed71342a899fe4b4c57b7d41bcb692"
+OUT = sys.argv[2] if len(sys.argv) > 2 else f"/Users/accusys/momentry/output_dev/{UUID}.identities.json"
+
+conn = psycopg2.connect("dbname=momentry user=accusys")
+cur = conn.cursor()
+
+# Get identities referenced by this file's face_detections
+cur.execute("""
+    SELECT DISTINCT i.id, i.name, i.uuid, i.identity_type, i.source, i.status,
+           i.face_embedding, i.voice_embedding, i.reference_data, i.tmdb_id, i.tmdb_profile
+    FROM dev.identities i
+    INNER JOIN dev.face_detections fd ON fd.identity_id = i.id
+    WHERE fd.file_uuid = %s
+    ORDER BY i.id
+""", (UUID,))
+rows = cur.fetchall()
+
+identities = []
+for r in rows:
+    identities.append({
+        "id": r[0],
+        "name": r[1],
+        "uuid": str(r[2]) if r[2] else None,
+        "identity_type": r[3],
+        "source": r[4],
+        "status": r[5],
+        "tmdb_id": r[9],
+        "tmdb_profile": r[10],
+    })
+
+# Get identity_bindings for these identities' traces
+cur.execute("""
+    SELECT DISTINCT ib.identity_id, ib.identity_type, ib.identity_value, ib.confidence
+    FROM dev.identity_bindings ib
+    WHERE ib.identity_id IN (
+        SELECT DISTINCT fd.identity_id FROM dev.face_detections fd WHERE fd.file_uuid = %s
+    )
+""", (UUID,))
+bindings = [{"identity_id": r[0], "identity_type": r[1], "identity_value": r[2], "confidence": float(r[3])} for r in cur.fetchall()]
+
+# Get trace-to-identity mapping from face_detections
+cur.execute("""
+    SELECT DISTINCT trace_id, identity_id, COUNT(*) as face_count
+    FROM dev.face_detections
+    WHERE file_uuid = %s AND identity_id IS NOT NULL AND trace_id IS NOT NULL
+    GROUP BY trace_id, identity_id ORDER BY trace_id
+""", (UUID,))
+trace_map = [{"trace_id": r[0], "identity_id": r[1], "face_count": r[2]} for r in cur.fetchall()]
+
+cur.close(); conn.close()
+
+output = {
+    "file_uuid": UUID,
+    "identity_count": len(identities),
+    "binding_count": len(bindings),
+    "trace_mapping_count": len(trace_map),
+    "identities": identities,
+    "bindings": bindings,
+    "trace_to_identity": trace_map,
+}
+
+with open(OUT, 'w') as f:
+    json.dump(output, f, indent=2, ensure_ascii=False)
+
+size_kb = len(json.dumps(output)) / 1024
+print(f"Exported {len(identities)} identities, {len(bindings)} bindings, {len(trace_map)} trace mappings")
+print(f"  → {OUT} ({size_kb:.0f}KB)")