feat: Phase 2.6 edges migration to Qdrant (TKG-only architecture)
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
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Accusys
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v1.1/scripts/generate_asr1_v1.11.py
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v1.1/scripts/generate_asr1_v1.11.py
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#!/opt/homebrew/bin/python3.11
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"""
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Generate {uuid}.asr-1.json by comparing asr.json (3417) with DB chunks (4188).
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Identifies which ASR segments were split and records corrections.
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"""
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import json, os, subprocess, sys, time
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PG_BIN = "/Users/accusys/pgsql/18.3/bin"
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DB_USER = "accusys"
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DB_NAME = "momentry"
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OUTPUT_DIR = "/Users/accusys/momentry/output_dev"
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UUID = "aeed71342a899fe4b4c57b7d41bcb692"
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def psql(sql):
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r = subprocess.run([f"{PG_BIN}/psql", "-U", DB_USER, "-d", DB_NAME, "-t", "-A", "-F", chr(31), "-c", sql],
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capture_output=True, text=True, timeout=30)
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return r.stdout.strip()
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def main():
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t0 = time.time()
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print(f"Loading ASR segments from {UUID}.asr.json...")
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asr_path = os.path.join(OUTPUT_DIR, f"{UUID}.asr.json")
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with open(asr_path) as f:
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asr_data = json.load(f)
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asr_segs = asr_data["segments"]
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print(f" {len(asr_segs)} ASR segments")
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print("Loading DB sentence chunks...")
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rows = []
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raw = psql(
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f"SELECT chunk_index, start_frame, end_frame, start_time, end_time, chunk_id, text_content "
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f"FROM dev.chunks WHERE file_uuid='{UUID}' AND chunk_type='sentence' "
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f"ORDER BY chunk_index"
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)
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for line in raw.split("\n"):
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if not line.strip():
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continue
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parts = line.split(chr(31))
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rows.append(parts)
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db_chunks = []
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for r in rows:
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db_chunks.append({
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"chunk_index": int(r[0]),
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"start_frame": int(r[1]),
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"end_frame": int(r[2]),
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"start_time": float(r[3]),
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"end_time": float(r[4]),
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"chunk_id": r[5],
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"text_content": r[6] if len(r) > 6 and r[6] else "",
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})
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print(f" {len(db_chunks)} DB chunks")
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# For each DB chunk, find the best-matching ASR segment.
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# A DB chunk belongs to ASR segment i if chunk's time range
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# falls WITHIN ASR segment i's time range.
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asr_of_chunk = {} # chunk_index -> asr_idx
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for dc in db_chunks:
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ct_mid = (dc["start_time"] + dc["end_time"]) / 2
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best_asr = None
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for ai, a in enumerate(asr_segs):
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if a["start"] - 0.1 <= dc["start_time"] and dc["end_time"] <= a["end"] + 0.1:
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if best_asr is None:
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best_asr = ai
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else:
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prev_a = asr_segs[best_asr]
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prev_mid = (prev_a["start"] + prev_a["end"]) / 2
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if abs(ct_mid - prev_mid) > abs(ct_mid - (a["start"] + a["end"]) / 2):
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best_asr = ai
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if best_asr is not None:
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asr_of_chunk[dc["chunk_index"]] = best_asr
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print(f" Mapped: {len(asr_of_chunk)} / {len(db_chunks)} chunks to ASR segments")
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# Group DB chunks by ASR index
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from collections import defaultdict
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chunks_by_asr = defaultdict(list)
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for ci, ai in asr_of_chunk.items():
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chunks_by_asr[ai].append(ci)
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# Build kept + corrections
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corrections = []
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kept = []
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for ai, child_indices in sorted(chunks_by_asr.items()):
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if len(child_indices) < 2:
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dc = db_chunks[child_indices[0]]
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kept.append({
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"chunk_index": ai,
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"start_frame": dc["start_frame"],
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"end_frame": dc["end_frame"],
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"text_content": dc["text_content"],
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})
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continue
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a = asr_segs[ai]
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children = []
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for ci in child_indices:
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dc = db_chunks[ci]
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children.append({
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"chunk_id": dc["chunk_id"],
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"start_frame": dc["start_frame"],
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"end_frame": dc["end_frame"],
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"text_content": dc["text_content"],
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})
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children_sorted = sorted(children, key=lambda x: x["start_frame"])
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# Assign new chunk_id format based on chunk_index
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# The first child of parent ASR idx N gets "N-01", second "N-02", etc.
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for si, child in enumerate(children_sorted):
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child["new_chunk_id"] = f"{ai}-{si+1:02d}"
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corrections.append({
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"parent_chunk_index": ai,
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"reason": "split",
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"original": {
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"start_frame": int(a["start"] * 24),
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"end_frame": int(a["end"] * 24),
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"text_content": a["text"],
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},
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"corrected": children_sorted
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})
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total_corrected = sum(len(c["corrected"]) for c in corrections)
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print(f" Kept chunks: {len(kept)}")
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print(f" Corrected chunks: {total_corrected}")
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print(f" Total: {len(kept) + total_corrected} (should be {len(db_chunks)})\n")
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# Write output
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output = {
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"file_uuid": UUID,
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"asr_version": 1,
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"kept": kept,
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"corrections": corrections
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}
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output_path = os.path.join(OUTPUT_DIR, f"{UUID}.asr-1.json")
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with open(output_path, "w") as f:
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json.dump(output, f, indent=2, ensure_ascii=False)
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print(f"\nSaved: {output_path} ({os.path.getsize(output_path) / 1024:.0f} KB)")
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# Stats
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split_sizes = {}
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for c in corrections:
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n = len(c["corrected"])
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split_sizes[n] = split_sizes.get(n, 0) + 1
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print(f"\nSplit distribution:")
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for n in sorted(split_sizes):
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print(f" {n} children: {split_sizes[n]} ASR segments → {n * split_sizes[n]} chunks")
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elapsed = time.time() - t0
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print(f"\nElapsed: {elapsed:.1f}s")
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if __name__ == "__main__":
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main()
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