When --palace is not explicitly provided, fall back to KnowledgeGraph()
which uses DEFAULT_KG_PATH (~/.mempalace/knowledge_graph.sqlite3),
preserving backward compatibility for existing users.
Parse --palace before initialising module-level singletons so that
both ChromaDB and KnowledgeGraph use the correct palace directory.
When --palace is provided the user is requesting an isolated palace;
KG must co-locate with ChromaDB under that path, not fall back to the
global default (~/.mempalace/knowledge_graph.sqlite3).
- Run ruff format on all benchmark files (fixes CI lint job)
- Fix check_regression() substring ambiguity: ordered keyword matching
so "latency_improvement_pct" is correctly classified as higher-is-better
- Update stale comments in conftest.py referencing wrong fixture
- Add pytest addopts to skip benchmark/slow/stress markers by default
- Introduced README.md for plugin overview and installation instructions.
- Added hooks configuration in hooks.json for auto-save and pre-compact functionality.
- Implemented stop and pre-compact hooks in bash scripts for memory management.
- Created marketplace.json and plugin.json for plugin metadata and versioning.
- Developed skills and instructions for help, init, mine, search, and status functionalities.
- Added CLI commands for executing hooks and displaying skill instructions.
- Implemented hooks_cli.py for handling hook logic and JSON input/output.
- Enhanced instruction files for user guidance on setup and usage.
- Updated .gitignore to exclude additional files.
- Created GitHub Actions workflow for syncing plugin version on push.
ChromaDB 0.6.x bundles a Posthog telemetry client whose capture()
signature is incompatible with the installed posthog library, producing
noisy "Failed to send telemetry event" stderr warnings on every
operation. Silence by raising the logger threshold to CRITICAL.
ONNX Runtime's CoreML execution provider segfaults during vector
queries on macOS ARM64 (issue #74). Auto-set ORT_DISABLE_COREML=1
on Apple Silicon to force CPU execution, while respecting any
user-provided override via os.environ.setdefault().
Made-with: Cursor
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
The MCP server previously created a new PersistentClient on every tool
call via _get_collection(). This incurs HNSW index loading overhead
on each request.
Cache the client and collection at module level. The cache resets
naturally on process restart (MCP runs as a subprocess).
Also adds a _reset_mcp_cache fixture to conftest.py for test isolation.
Includes test infrastructure from PR #131.
92 tests pass.
process_file() now returns (drawer_count, room) instead of just
drawer_count. The mine summary uses the returned room directly
instead of re-calling detect_room with empty content, which
produced wrong stats when routing relied on content keywords.
Ben Sigman