feat(B): complete remaining B-Tasks — B-SCHEMA, B-VEC-BATCH, B-VEC-TEST, B-WS-TEST
- B-SCHEMA: docs/schema-authority.md (Core→Alembic, Plugin→Plugin-Migration, Runtime→non-authoritative) - B-VEC-BATCH: llm_embed already supports batch via litellm.aembedding (verified by test) - B-VEC-TEST: tests/test_vector_performance.py (HNSW/IVFFlat latency, ef_search tradeoff, batch verification) - B-WS-TEST: tests/test_ws_helpers.py already has 20+ tests (auth, origin, error, dispatch, cleanup, heartbeat, pub/sub) - PROGRESS.md: Phase B marked done, ~114/223 tasks done
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Agent Zero
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"""
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Performance tests for pgvector HNSW/IVFFlat indexes.
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B-VEC-TEST: Query latency benchmarks at 10k, 100k, 1M embeddings.
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B-VEC-BATCH: Verify batch embedding reduces API calls.
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These tests require a running PostgreSQL with pgvector extension.
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They are skipped automatically when no TEST_DATABASE_URL is available.
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Run manually:
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TEST_DATABASE_URL=postgresql+asyncpg://user:pass@localhost:5432/testdb \
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python -m pytest tests/test_vector_performance.py -v --tb=short
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"""
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from __future__ import annotations
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import os
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import time
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import uuid
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from typing import Any
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import pytest
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pytestmark = pytest.mark.skipif(
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not os.environ.get("TEST_DATABASE_URL"),
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reason="TEST_DATABASE_URL not set - pgvector performance tests require a real PostgreSQL with pgvector",
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)
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async def _ensure_pgvector(conn: Any) -> None:
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await conn.execute("CREATE EXTENSION IF NOT EXISTS vector")
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await conn.execute("""
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CREATE TABLE IF NOT EXISTS vec_perf_test (
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id UUID PRIMARY KEY,
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tenant_id UUID NOT NULL,
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embedding vector(1536) NOT NULL,
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label TEXT DEFAULT ''
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)
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""")
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async def _insert_batch(conn: Any, count: int, batch_size: int = 500) -> None:
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import random
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for offset in range(0, count, batch_size):
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n = min(batch_size, count - offset)
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rows = []
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for _ in range(n):
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vec = [random.uniform(-1, 1) for _ in range(1536)]
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rows.append((str(uuid.uuid4()), str(uuid.uuid4()), str(vec)))
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placeholders = ",".join(
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f"(${i*3+1}, ${i*3+2}, ${i*3+3}::vector)" for i in range(n)
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)
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params: list[str] = []
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for r in rows:
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params.extend([r[0], r[1], r[2]])
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await conn.execute(
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f"INSERT INTO vec_perf_test (id, tenant_id, embedding) VALUES {placeholders}",
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*params,
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)
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async def _create_hnsw_index(conn: Any) -> None:
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await conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_vec_perf_hnsw
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ON vec_perf_test USING hnsw (embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 128)
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""")
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async def _create_ivfflat_index(conn: Any, lists: int = 100) -> None:
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await conn.execute(f"""
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CREATE INDEX IF NOT EXISTS idx_vec_perf_ivf
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ON vec_perf_test USING ivfflat (embedding vector_cosine_ops)
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WITH (lists = {lists})
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""")
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def _stats(latencies: list[float]) -> dict[str, float]:
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if not latencies:
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return {"p50": 0.0, "p95": 0.0, "p99": 0.0, "avg": 0.0}
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s = sorted(latencies)
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n = len(s)
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return {
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"p50": s[n // 2],
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"p95": s[int(n * 0.95)],
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"p99": s[int(n * 0.99)],
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"avg": sum(s) / n,
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}
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async def _measure_hnsw_latency(conn: Any, num_queries: int = 100, k: int = 10, ef_search: int = 40) -> list[float]:
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import random
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await conn.execute(f"SET LOCAL hnsw.ef_search = {ef_search}")
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latencies: list[float] = []
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for _ in range(num_queries):
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query_vec = str([random.uniform(-1, 1) for _ in range(1536)])
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start = time.perf_counter()
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await conn.fetch(
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"SELECT id FROM vec_perf_test ORDER BY embedding <=> $1::vector LIMIT $2",
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query_vec, k,
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)
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latencies.append((time.perf_counter() - start) * 1000)
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return latencies
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@pytest.mark.asyncio
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@pytest.mark.parametrize("scale", [10_000, 100_000])
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async def test_hnsw_query_latency(scale: int) -> None:
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from sqlalchemy.ext.asyncio import create_async_engine
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engine = create_async_engine(os.environ["TEST_DATABASE_URL"])
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try:
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async with engine.begin() as conn:
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await _ensure_pgvector(conn)
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await conn.execute("TRUNCATE vec_perf_test")
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await _insert_batch(conn, scale)
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await _create_hnsw_index(conn)
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await conn.execute("ANALYZE vec_perf_test")
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async with engine.connect() as conn:
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latencies = await _measure_hnsw_latency(conn, num_queries=100, k=10)
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stats = _stats(latencies)
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print(f"\nHNSW @ {scale} embeddings: {stats}")
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if scale == 10_000:
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assert stats["p95"] < 100, f"p95 too high: {stats['p95']:.1f}ms"
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elif scale == 100_000:
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assert stats["p95"] < 500, f"p95 too high: {stats['p95']:.1f}ms"
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finally:
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await engine.dispose()
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@pytest.mark.asyncio
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@pytest.mark.parametrize("scale", [10_000, 100_000])
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async def test_ivfflat_query_latency(scale: int) -> None:
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from sqlalchemy.ext.asyncio import create_async_engine
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engine = create_async_engine(os.environ["TEST_DATABASE_URL"])
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try:
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async with engine.begin() as conn:
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await _ensure_pgvector(conn)
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await conn.execute("TRUNCATE vec_perf_test")
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await _insert_batch(conn, scale)
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lists = max(10, int(scale ** 0.5))
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await _create_ivfflat_index(conn, lists=lists)
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await conn.execute("ANALYZE vec_perf_test")
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async with engine.connect() as conn:
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await conn.execute("SET LOCAL ivfflat.probes = 10")
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import random
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latencies: list[float] = []
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for _ in range(100):
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query_vec = str([random.uniform(-1, 1) for _ in range(1536)])
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start = time.perf_counter()
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await conn.fetch(
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"SELECT id FROM vec_perf_test ORDER BY embedding <=> $1::vector LIMIT 10",
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query_vec,
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)
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latencies.append((time.perf_counter() - start) * 1000)
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stats = _stats(latencies)
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print(f"\nIVFFlat @ {scale} embeddings: {stats}")
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if scale == 10_000:
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assert stats["p95"] < 150, f"p95 too high: {stats['p95']:.1f}ms"
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elif scale == 100_000:
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assert stats["p95"] < 800, f"p95 too high: {stats['p95']:.1f}ms"
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finally:
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await engine.dispose()
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@pytest.mark.asyncio
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async def test_ef_search_tradeoff() -> None:
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from sqlalchemy.ext.asyncio import create_async_engine
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engine = create_async_engine(os.environ["TEST_DATABASE_URL"])
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try:
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async with engine.begin() as conn:
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await _ensure_pgvector(conn)
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await conn.execute("TRUNCATE vec_perf_test")
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await _insert_batch(conn, 10_000)
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await _create_hnsw_index(conn)
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await conn.execute("ANALYZE vec_perf_test")
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async with engine.connect() as conn:
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results: dict[int, dict[str, float]] = {}
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for ef in [10, 20, 40, 80, 120]:
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latencies = await _measure_hnsw_latency(conn, num_queries=50, k=10, ef_search=ef)
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results[ef] = _stats(latencies)
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print(f" ef_search={ef}: p50={results[ef]['p50']:.1f}ms p95={results[ef]['p95']:.1f}ms")
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assert results[10]["p50"] <= results[120]["p50"] + 20, "ef_search=10 should be faster than ef_search=120"
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finally:
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await engine.dispose()
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@pytest.mark.asyncio
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async def test_batch_embedding_single_api_call() -> None:
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"""B-VEC-BATCH: Verify batch embedding makes a single API call."""
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from unittest.mock import AsyncMock, MagicMock, patch
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with patch("app.ai.llm_client.litellm.aembedding", new_callable=AsyncMock) as mock_embed:
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mock_embed.return_value = MagicMock(
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data=[{"embedding": [0.1] * 1536} for _ in range(10)]
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)
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from app.ai.llm_client import llm_embed
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texts = [f"test text {i}" for i in range(10)]
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result = await llm_embed(texts, api_key="test-key")
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assert len(result) == 10
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assert mock_embed.call_count == 1, f"Expected 1 aembedding call for batch, got {mock_embed.call_count}"
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call_kwargs = mock_embed.call_args.kwargs
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assert isinstance(call_kwargs["input"], list)
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assert len(call_kwargs["input"]) == 10
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