Collection knowledge_base usa named vectors (dense + sparse). O Qdrant
exige o parâmetro 'using' em collections com múltiplos vetores nomeados.
Sem ele, query_batch_points retornava 'Not existing vector name error'
em 100% das batches (44/44). O script reportava falsos 0 near-duplicates.
Correção: 1 linha — using='dense' em models.QueryRequest (linha 176).
Validado com --dry-run --threshold 0.95 contra 8886 pontos reais:
0 erros, 1776 near-duplicate pairs detectados.
V4.01: Replace O(n²) brute-force cosine similarity in semantic_dedup.py
with Qdrant query_batch_points() — delegates nearest-neighbor search
to the native HNSW index. Batch size auto-scales (min 200, adjusted
for collection size). Self-match excluded server-side via
HasIdCondition filter. Graceful fallback if qdrant-client not installed.
V4.03: Replace glob+parse in fabric-retrieve.py retrieve() with SQLite
fabric_index + fabric_fts (FTS5). _ensure_fabric_index() rebuilds the
index lazily when any fabric .md mtime exceeds the stored max mtime.
Transactional rebuild (BEGIN; DELETE; DELETE; ...; COMMIT) prevents
index corruption on partial failure. All score_entry fields preserved
as columns — no YAML parsing after initial index build.
ClaudioDrews