* perf: replace window-function retrieval with UNION ALL + per-bank HNSW indexes
The previous retrieve_semantic_bm25_combined() used ROW_NUMBER() OVER (PARTITION
BY fact_type ...) which forced a full sequential scan — pgvector cannot use HNSW
indexes when a window function partitions on the same column as the ORDER BY.
Changes:
- retrieval.py: rewrite to UNION ALL of per-fact_type subqueries; each arm has
its own ORDER BY embedding <=> $1 LIMIT n, enabling partial HNSW index scans.
Semantic arms over-fetch 5x (min 100) for HNSW approximation; trimmed in Python.
- memory_engine.py: set hnsw.ef_search=200 at pool init (persistent per-connection,
no per-query SET/RESET overhead).
- bank_utils.py: add create_bank_hnsw_indexes / drop_bank_hnsw_indexes for
per-(bank_id, fact_type) partial HNSW index lifecycle management.
- fact_storage.py / bank_utils.py: create per-bank indexes on fresh bank insert.
- memory_engine.py delete_bank: drop per-bank indexes via DELETE...RETURNING to
avoid a separate round-trip.
- Migration a3b4c5d6e7f8: add interim fact_type-only partial indexes.
- Migration d5e6f7a8b9c0: add internal_id UUID UNIQUE to banks, replace
fact_type-only indexes with per-(bank, fact_type) partial HNSW indexes, drop
the global idx_memory_units_embedding that competed with them.
Why per-(bank, fact_type) not just per-fact_type:
The idx_memory_units_bank_id B-tree index always wins over fact_type-only partial
indexes when bank_id appears in the WHERE clause. Including bank_id in the partial
index predicate removes the B-tree from consideration and lets the planner choose
HNSW. The global HNSW index must also be dropped to avoid competing for the larger
fact_type partitions (world, observation).
* refactor: collapse two HNSW migrations into one
* refactor: generate bank internal_id in Python before insert
Instead of relying on DEFAULT gen_random_uuid() and RETURNING internal_id,
generate the UUID in application code before the INSERT. This means we
always know the value upfront and can call create_bank_hnsw_indexes
immediately without needing a DB round-trip to retrieve the assigned ID.
Also adds tests for HNSW index lifecycle and retrieve_semantic_bm25_combined.
* fix: correct migration and prevent global HNSW index recreation
Migration fixes:
- Add text() wrappers for raw SQL in d5e6f7a8b9c0 (SQLAlchemy 2.0 compat)
- Drop stale fact_type-only partial indexes (idx_mu_emb_world/observation/experience)
that may exist from prior migrations on the same DB
migrations.py fix:
- Skip global HNSW index creation when per-bank partial HNSW indexes already
exist on memory_units (idx_mu_emb_* pattern). Without this, the post-migration
vector index check detects no %embedding% named index and recreates the global
idx_memory_units_embedding, which defeats the per-bank index strategy.
Verified with EXPLAIN ANALYZE on 66K-row bank: all three fact_type arms use
their per-bank HNSW index scan (idx_mu_emb_worl/expr/obsv_<uid16>).
* fix: use correct embeddings.encode() in test
43b3efc494