Concentrates all drawers into a single wing+room to isolate the
embedding model's retrieval limit independent of palace filtering.
Confirms recall degrades to ~0.4-0.5 at 5K drawers per room even
with wing+room filters applied — the spatial structure helps by
keeping buckets small, but can't fix the underlying embedding ceiling.
Benchmark mempalace at configurable scale (1K–100K drawers) to find
real-world performance limits. Tests cover MCP tool OOM thresholds,
ChromaDB query degradation, search recall@k, mining throughput,
knowledge graph concurrency, memory leak detection, palace boost
quantification, and Layer1 unbounded fetch behavior.
- tests/benchmarks/ with 8 test modules + data generator + report system
- Deterministic data factory with planted needles for recall measurement
- JSON report output with regression detection (--bench-report flag)
- CI benchmark job on PRs at small scale
- psutil added as dev dependency for RSS tracking
- Switch CI install step from `pip install -r requirements.txt` to
`pip install -e ".[dev]"` since requirements.txt was removed
- Add noqa: E402 to intentionally-late imports in conftest.py
(HOME must be isolated before mempalace imports)
- Remove unused KnowledgeGraph import in test_knowledge_graph.py
- Apply ruff formatting to test files
Igor Lins e Silva