fleet-memory/hindsight-api/tests/test_hnsw_indexes.py
Nicolò Boschi 43b3efc494
perf: replace window-function retrieval with UNION ALL + per-bank HNSW indexes (#541)
* 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
2026-03-11 12:09:50 +01:00

193 lines
7.2 KiB
Python

"""
Tests for per-bank HNSW index lifecycle and UNION ALL retrieval.
Covers:
- _hnsw_index_name deterministic naming
- Per-bank HNSW indexes created on bank creation (retain_async / ensure_bank_exists)
- Per-bank HNSW indexes dropped on bank deletion
- retrieve_semantic_bm25_combined groups results correctly by fact_type and source
"""
import uuid
from datetime import datetime, timezone
import pytest
from hindsight_api.engine.retain.bank_utils import _HNSW_FACT_TYPES, _hnsw_index_name
# ---------------------------------------------------------------------------
# Unit tests — no DB required
# ---------------------------------------------------------------------------
class TestHnswIndexName:
def test_deterministic(self):
uid = "550e8400-e29b-41d4-a716-446655440000"
assert _hnsw_index_name("world", uid) == _hnsw_index_name("world", uid)
def test_strips_dashes(self):
uid = "550e8400-e29b-41d4-a716-446655440000"
name = _hnsw_index_name("world", uid)
# uid16 should be hex chars only
assert "-" not in name
def test_uses_first_16_hex_chars(self):
uid = "550e8400-e29b-41d4-a716-446655440000"
uid16 = uid.replace("-", "")[:16] # "550e8400e29b41d4"
assert name_ends_with(name=_hnsw_index_name("world", uid), suffix=uid16)
def test_suffix_per_fact_type(self):
uid = "550e8400-e29b-41d4-a716-446655440000"
names = {ft: _hnsw_index_name(ft, uid) for ft in _HNSW_FACT_TYPES}
# All three names must be distinct
assert len(set(names.values())) == 3
def test_all_fact_types_covered(self):
assert set(_HNSW_FACT_TYPES) == {"world", "experience", "observation"}
def test_fits_pg_identifier_limit(self):
# PostgreSQL max identifier length is 63 chars
uid = "f" * 32 # simulated UUID without dashes
for ft in _HNSW_FACT_TYPES:
assert len(_hnsw_index_name(ft, uid)) <= 63
def name_ends_with(name: str, suffix: str) -> bool:
return name.endswith(suffix)
# ---------------------------------------------------------------------------
# Integration tests — require DB (memory fixture)
# ---------------------------------------------------------------------------
async def _get_bank_hnsw_indexes(pool, bank_id: str) -> list[str]:
"""Return index names for memory_units that match the per-bank pattern."""
async with pool.acquire() as conn:
rows = await conn.fetch(
"""
SELECT indexname
FROM pg_indexes
WHERE tablename = 'memory_units'
AND indexname LIKE 'idx_mu_emb_%'
AND indexdef LIKE $1
ORDER BY indexname
""",
f"%bank_id = '{bank_id}'%",
)
return [row["indexname"] for row in rows]
@pytest.mark.asyncio
async def test_retain_creates_per_bank_hnsw_indexes(memory, request_context):
"""retain_async on a new bank must create 3 per-(bank, fact_type) HNSW indexes."""
bank_id = f"test_hnsw_create_{uuid.uuid4().hex[:8]}"
try:
await memory.retain_async(
bank_id=bank_id,
content="Alice is a software engineer.",
request_context=request_context,
)
indexes = await _get_bank_hnsw_indexes(memory._pool, bank_id)
assert len(indexes) == 3, f"Expected 3 per-bank HNSW indexes, got: {indexes}"
for ft_short in _HNSW_FACT_TYPES.values():
assert any(ft_short in idx for idx in indexes), (
f"Missing index for fact_type short '{ft_short}' in {indexes}"
)
finally:
await memory.delete_bank(bank_id, request_context=request_context)
@pytest.mark.asyncio
async def test_delete_bank_drops_hnsw_indexes(memory, request_context):
"""delete_bank must drop all per-bank HNSW indexes."""
bank_id = f"test_hnsw_drop_{uuid.uuid4().hex[:8]}"
await memory.retain_async(
bank_id=bank_id,
content="Bob is a data scientist.",
request_context=request_context,
)
# Verify indexes exist before deletion
indexes_before = await _get_bank_hnsw_indexes(memory._pool, bank_id)
assert len(indexes_before) == 3
await memory.delete_bank(bank_id, request_context=request_context)
indexes_after = await _get_bank_hnsw_indexes(memory._pool, bank_id)
assert indexes_after == [], f"Indexes should be dropped after bank deletion, got: {indexes_after}"
@pytest.mark.asyncio
async def test_retain_idempotent_bank_creation(memory, request_context):
"""Retaining into the same bank twice must not error and still have exactly 3 indexes."""
bank_id = f"test_hnsw_idem_{uuid.uuid4().hex[:8]}"
try:
await memory.retain_async(
bank_id=bank_id,
content="Carol is a product manager.",
request_context=request_context,
)
await memory.retain_async(
bank_id=bank_id,
content="Carol joined the company in 2022.",
request_context=request_context,
)
indexes = await _get_bank_hnsw_indexes(memory._pool, bank_id)
assert len(indexes) == 3
finally:
await memory.delete_bank(bank_id, request_context=request_context)
@pytest.mark.asyncio
async def test_retrieve_semantic_bm25_grouped_by_fact_type(memory, request_context):
"""
retrieve_semantic_bm25_combined must return a dict keyed by fact_type with
(semantic_list, bm25_list) tuples. All returned facts must belong to their
declared fact_type.
"""
from hindsight_api.engine.search.retrieval import retrieve_semantic_bm25_combined
bank_id = f"test_retrieval_{uuid.uuid4().hex[:8]}"
try:
await memory.retain_async(
bank_id=bank_id,
content=(
"Alice is a software engineer at TechCorp. "
"She visited Paris in 2023 for a conference."
),
context="background",
event_date=datetime(2023, 6, 1, tzinfo=timezone.utc),
request_context=request_context,
)
query_emb = memory.embeddings.encode(["software engineer Alice"])
query_emb_str = str(query_emb[0])
fact_types = ["world", "experience"]
async with memory._pool.acquire() as conn:
results = await retrieve_semantic_bm25_combined(
conn=conn,
query_emb_str=query_emb_str,
query_text="software engineer Alice",
bank_id=bank_id,
fact_types=fact_types,
limit=5,
)
# Must return an entry for every requested fact_type
assert set(results.keys()) == set(fact_types)
for ft, (sem, bm25) in results.items():
# Semantic and BM25 lists must be lists
assert isinstance(sem, list)
assert isinstance(bm25, list)
# All semantic results must declare the correct fact_type
for r in sem:
assert r.fact_type == ft, f"Semantic result has wrong fact_type: {r.fact_type}"
# All BM25 results must declare the correct fact_type
for r in bm25:
assert r.fact_type == ft, f"BM25 result has wrong fact_type: {r.fact_type}"
finally:
await memory.delete_bank(bank_id, request_context=request_context)