feat: Initial v0.9 release with API Key authentication

## v0.9.20260325_144654

### Features
- API Key Authentication System
- Job Worker System
- V2 Backup Versioning

### Bug Fixes
- get_processor_results_by_job column mapping

Co-authored-by: OpenCode

scripts/compare_search.py

@@ -0,0 +1,131 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Search comparison script for PostgreSQL, MongoDB, and Qdrant
+"""
+
+import time
+import requests
+
+# Test queries
+TEST_QUERIES = [
+    "Charade",
+    "Paris",
+    " Audrey Hepburn",
+    "Cary Grant",
+]
+
+# PostgreSQL connection
+POSTGRES_CONFIG = {
+    "host": "localhost",
+    "port": 5432,
+    "user": "accusys",
+    "password": "Test3200",
+    "database": "momentry",
+}
+
+
+def test_postgres_text_search():
+    """Test text search in PostgreSQL"""
+    import psycopg2
+
+    results = {}
+    conn = psycopg2.connect(**POSTGRES_CONFIG)
+    cur = conn.cursor()
+
+    for query in TEST_QUERIES:
+        start = time.time()
+        cur.execute(
+            "SELECT chunk_id, content->>'text' FROM chunks WHERE chunk_type = 'sentence' AND content->>'text' ILIKE %s LIMIT 10",
+            (f"%{query}%",),
+        )
+        rows = cur.fetchall()
+        elapsed = (time.time() - start) * 1000
+
+        results[query] = {
+            "method": "PostgreSQL ILIKE",
+            "ms": round(elapsed, 2),
+            "rows": len(rows),
+        }
+        print(f"PostgreSQL text search '{query}': {elapsed:.2f}ms, {len(rows)} rows")
+
+    cur.close()
+    conn.close()
+    return results
+
+
+def test_qdrant_vector_search():
+    """Test vector search in Qdrant"""
+    results = {}
+
+    # First, generate query embeddings
+    for query in TEST_QUERIES:
+        # Get embedding from Ollama
+        embed_resp = requests.post(
+            "http://localhost:11434/api/embeddings",
+            json={"model": "nomic-embed-text", "prompt": query},
+        )
+        embedding = embed_resp.json()["embedding"]
+
+        # Search in Qdrant (using AccusysDB collection)
+        start = time.time()
+        resp = requests.post(
+            "http://localhost:6333/collections/AccusysDB/points/search",
+            headers={"api-key": "Test3200Test3200Test3200"},
+            json={"vector": embedding, "limit": 10},
+        )
+        elapsed = (time.time() - start) * 1000
+
+        data = resp.json()
+        result_count = len(data.get("result", []))
+
+        results[query] = {
+            "method": "Qdrant HNSW",
+            "ms": round(elapsed, 2),
+            "rows": result_count,
+        }
+        print(f"Qdrant vector search '{query}': {elapsed:.2f}ms, {result_count} rows")
+
+    return results
+
+
+def main():
+    print("=" * 60)
+    print("Search Performance Comparison Test")
+    print("=" * 60)
+
+    # Get chunk count
+    import psycopg2
+
+    conn = psycopg2.connect(**POSTGRES_CONFIG)
+    cur = conn.cursor()
+    cur.execute("SELECT COUNT(*) FROM chunks WHERE chunk_type = 'sentence'")
+    count = cur.fetchone()[0]
+    cur.close()
+    conn.close()
+
+    print(f"\nTotal sentence chunks: {count}")
+    print("\n" + "=" * 60)
+    print("A. Text Search Test (Priority a)")
+    print("=" * 60)
+    pg_results = test_postgres_text_search()
+
+    print("\n" + "=" * 60)
+    print("B. Vector Search Test (Priority b)")
+    print("=" * 60)
+    qdrant_results = test_qdrant_vector_search()
+
+    print("\n" + "=" * 60)
+    print("Summary")
+    print("=" * 60)
+    print(f"\n{'Query':<20} | {'PostgreSQL':<25} | {'Qdrant':<25}")
+    print("-" * 70)
+    for query in TEST_QUERIES:
+        pg = pg_results.get(query, {})
+        qd = qdrant_results.get(query, {})
+        print(
+            f"{query:<20} | {pg.get('ms', 0):.1f}ms ({pg.get('rows', 0)} rows) | {qd.get('ms', 0):.1f}ms ({qd.get('rows', 0)} rows)"
+        )
+
+
+if __name__ == "__main__":
+    main()