2cfcfdd1af
Phase 2.6.1: co_occurrence_edges migration - build_co_occurrence_edges_from_qdrant() - Qdrant embeddings → frame grouping → YOLO objects - Result: 6679 edges (vs 6701 PostgreSQL) Phase 2.6.2: face_face_edges migration - build_face_face_edges_from_qdrant() - Qdrant embeddings → frame grouping → face pairs - mutual_gaze detection preserved - Result: 6 edges (exact match) Phase 2.6.3: speaker_face_edges migration - build_speaker_face_edges_from_qdrant() - Qdrant embeddings → trace_id frame ranges - SPEAKS_AS edge creation Architecture: - All edges use Qdrant payload (no face_detections queries) - PostgreSQL fallback for empty Qdrant - Estimated 3.6x performance improvement Testing: - Playground (3003): ✓ All Phase 2.6 logs verified - Edge counts: ✓ Close match with PostgreSQL - Fallback: ✓ Working Docs: - docs_v1.0/DESIGN/TKG_PHASE2_6_EDGES_MIGRATION.md - docs_v1.0/M4_workspace/2026-06-21_phase2_6_test.md
132 lines
3.4 KiB
Python
132 lines
3.4 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Search comparison script for PostgreSQL, MongoDB, and Qdrant
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"""
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import time
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import requests
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# Test queries
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TEST_QUERIES = [
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"Charade",
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"Paris",
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" Audrey Hepburn",
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"Cary Grant",
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]
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# PostgreSQL connection
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POSTGRES_CONFIG = {
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"host": "localhost",
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"port": 5432,
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"user": "accusys",
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"password": "Test3200",
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"database": "momentry",
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}
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def test_postgres_text_search():
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"""Test text search in PostgreSQL"""
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import psycopg2
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results = {}
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conn = psycopg2.connect(**POSTGRES_CONFIG)
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cur = conn.cursor()
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for query in TEST_QUERIES:
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start = time.time()
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cur.execute(
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"SELECT chunk_id, content->>'text' FROM chunks WHERE chunk_type = 'sentence' AND content->>'text' ILIKE %s LIMIT 10",
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(f"%{query}%",),
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)
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rows = cur.fetchall()
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elapsed = (time.time() - start) * 1000
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results[query] = {
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"method": "PostgreSQL ILIKE",
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"ms": round(elapsed, 2),
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"rows": len(rows),
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}
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print(f"PostgreSQL text search '{query}': {elapsed:.2f}ms, {len(rows)} rows")
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cur.close()
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conn.close()
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return results
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def test_qdrant_vector_search():
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"""Test vector search in Qdrant"""
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results = {}
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# First, generate query embeddings
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for query in TEST_QUERIES:
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# Get embedding from Ollama
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embed_resp = requests.post(
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"http://localhost:11434/api/embeddings",
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json={"model": "nomic-embed-text", "prompt": query},
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)
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embedding = embed_resp.json()["embedding"]
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# Search in Qdrant (using AccusysDB collection)
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start = time.time()
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resp = requests.post(
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"http://localhost:6333/collections/AccusysDB/points/search",
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headers={"api-key": "Test3200Test3200Test3200"},
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json={"vector": embedding, "limit": 10},
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)
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elapsed = (time.time() - start) * 1000
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data = resp.json()
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result_count = len(data.get("result", []))
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results[query] = {
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"method": "Qdrant HNSW",
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"ms": round(elapsed, 2),
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"rows": result_count,
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}
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print(f"Qdrant vector search '{query}': {elapsed:.2f}ms, {result_count} rows")
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return results
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def main():
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print("=" * 60)
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print("Search Performance Comparison Test")
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print("=" * 60)
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# Get chunk count
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import psycopg2
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conn = psycopg2.connect(**POSTGRES_CONFIG)
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cur = conn.cursor()
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cur.execute("SELECT COUNT(*) FROM chunks WHERE chunk_type = 'sentence'")
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count = cur.fetchone()[0]
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cur.close()
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conn.close()
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print(f"\nTotal sentence chunks: {count}")
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print("\n" + "=" * 60)
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print("A. Text Search Test (Priority a)")
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print("=" * 60)
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pg_results = test_postgres_text_search()
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print("\n" + "=" * 60)
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print("B. Vector Search Test (Priority b)")
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print("=" * 60)
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qdrant_results = test_qdrant_vector_search()
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print("\n" + "=" * 60)
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print("Summary")
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print("=" * 60)
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print(f"\n{'Query':<20} | {'PostgreSQL':<25} | {'Qdrant':<25}")
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print("-" * 70)
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for query in TEST_QUERIES:
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pg = pg_results.get(query, {})
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qd = qdrant_results.get(query, {})
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print(
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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)"
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)
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if __name__ == "__main__":
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main()
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