git:20260810.8075e72 to git:20260822.94279fe

# mempalace — Project Guide for Claude Code

## Stack

- **Python** 3.11+ (supports 3.11–3.14)

- **Storage**: LanceDB (core, crash-safe vector DB — no server required)

- **Embeddings**: sentence-transformers (local, no API key)

- **Config**: PyYAML

- **Linting / formatting / typing**: Ruff + Pyright

- **Tests**: pytest

- **Package manager**: uv (preferred) or pip

## Dev Setup

```bash

# With uv (preferred)

uv pip install -e ".[dev]"

# With pip

pip install -e ".[dev]"

```

No Docker required. Everything runs locally in a venv or with pipx.

Optional extras:

- `.[spellcheck]` — autocorrect support for room/wing names

## Running Tests

**Important**: Use the Python from your mempalace virtualenv (pipx venv, `.venv`, etc.), not the system Python which may lack dev deps.

```bash

# Full suite (stop on first failure)

python -m pytest tests/ -x -q -m "not needs_network"

# Per-module

python -m pytest tests/test_storage.py -v

python -m pytest tests/test_mcp_server.py -v

python -m pytest tests/test_miner.py -v

python -m pytest tests/test_convo_miner.py -v

```

## Linting / Formatting

```bash

# Check

ruff check mempalace_code/ tests/ scripts/

# Format check

ruff format --check mempalace_code/ tests/ scripts/

# Type check (gating in CI — must exit 0)

python -m pyright --pythonpath "$(python -c 'import sys; print(sys.executable)')"

# Auto-fix lint

ruff check --fix mempalace_code/ tests/ scripts/

# Auto-fix format

ruff format mempalace_code/ tests/ scripts/

```

Line length: 100. Target: py311. Quote style: double.

## Key Modules

| Module | Purpose |

|--------|---------|

| `storage.py` | LanceDB vector storage — add, search, delete, health_check, recover |

| `backup.py` | Tarball backup/restore — `mempalace-code backup`, scheduled backups |

| `miner.py` | Code project miner — walks source files, extracts drawers |

| `convo_miner.py` | Conversation miner — ingests Claude/ChatGPT/Slack exports |

| `searcher.py` | Semantic search — query palace with optional wing/room filters |

| `knowledge_graph.py` | Temporal KG — entity-relationship triples with validity windows |

| `layers.py` | Tiered context loading — L0/L1/L2/L3 wake-up layers for local models |

| `palace_graph.py` | Graph traversal and tunnel detection across wings/rooms |

| `mcp_server.py` | MCP server — exposes palace tools to Claude Code and other MCP clients |

| `watcher.py` | File watcher — `watch_and_mine`, `watch_all`, launchd/cron schedule rendering |

| `cli.py` | `mempalace-code` CLI entry point — init, mine, mine-all, watch, search, health, repair, backup |

## Architecture Principles

1. **Verbatim-first** — store content exactly as provided; do not summarize or compress drawers.

2. **Local-first** — no external APIs required; all embeddings run on-device via sentence-transformers.

3. **Zero-API-by-default** — LanceDB and sentence-transformers work offline; API integrations are opt-in.

## Storage Backend

- **LanceDB** is the core backend (installed by default via `lancedb>=0.20`).

## Embedding Model Policy

- **Current default**: `all-MiniLM-L6-v2` (384d, sentence-transformers).

- **No-regression rule**: any embedding model change must match or beat MiniLM on LongMemEval R@5 (text retrieval). Text quality is non-negotiable — this is a code-first fork but natural language search (conversations, commits, decisions) must not degrade.

- **No code-only models**: CodeBERT, UniXcoder, etc. improve code at the expense of prose. Only general-purpose sentence-transformers that handle both are candidates.

- **Gate**: model upgrades are gated behind A/B benchmark results.

### Benchmark Results — 2026-04-09 (BENCH-EMBED-AB)

Code retrieval on the mempalace repo (20 known-answer queries, 469 chunks):

| Model | R@5 | R@10 | Embed(s) | Query(ms) | Index(MB) |

|--------------------|-------|-------|----------|-----------|-----------|

| all-MiniLM-L6-v2 | 0.950 | 1.000 | 15.2 | 15.9 | 17.0 |

| all-mpnet-base-v2 | 0.900 | 1.000 | 47.5 | 30.5 | 17.7 |

| nomic-embed-text-v1.5 | 0.950 | 1.000 | 85.4 | 45.8 | 17.7 |

Per-category R@5:

| Model | architecture | class_lookup | cross_file | function_lookup |

|--------------------|:---:|:---:|:---:|:---:|

| all-MiniLM-L6-v2 | 0.800 | 1.000 | 1.000 | 1.000 |

| all-mpnet-base-v2 | 0.600 | 1.000 | 1.000 | 1.000 |

| nomic-embed-text-v1.5 | 1.000 | 1.000 | 1.000 | 0.833 |

**Recommendation: minilm remains default.**

- mpnet regresses on code R@5 (0.900 vs 0.950) while being 3× slower to embed and 2× slower at query. Eliminated.

- nomic ties minilm on code R@5 (0.950) but is 5.6× slower to embed and 2.9× slower at query, with a 550 MB model vs 80 MB. No net gain.

- Text-gate (LongMemEval) evidence was not collected — prerequisites missing (`benchmarks/data/longmemeval_s_cleaned.json` not present, `fastembed` not installed). Any future upgrade must pass the text gate before switching.

- Full results: `benchmarks/results_embed_ab_2026-04-09.json`.

## Git Workflow

- **Branch naming**: `feat/<slug>`, `fix/<slug>`, `chore/<slug>`, `docs/<slug>`

- **Conventional commits**:

- `feat:` — new feature or capability

- `fix:` — bug fix

- `docs:` — documentation only

- `test:` — test additions or changes

- `bench:` — benchmarks

- `chore:` — maintenance, deps, tooling

- **No force-push to `main`**.

- PR merges go through the `feat/*` → `main` flow; squash if the branch is noisy.

## Operational Lessons

- **Keep this file public-safe.** `CLAUDE.md` is part of the publishable repository. Do not write private remotes, hostnames, credentials, local machine paths, customer/project details, incident specifics, or non-public operational history here. Put private or machine-local lessons in a local-only note outside the published tree.

- **Record reusable lessons only when they are public knowledge.** When a session exposes a project gotcha, publish step, verification boundary, or agent-behavior correction, add a concise durable note here only if it is safe for public readers and useful to future contributors.

- **Verify the environment that will actually run the change.** GitHub Actions runtime changes are not proven by Python tests alone. Use local YAML/static checks such as `actionlint`, then verify the real hosted workflow run when action runtime behavior matters.

- **Name the verification boundary.** If a workflow is tag-only or release-only, say that it was syntax-checked and version-checked but not execution-tested unless a real trigger was run. Do not imply full local coverage for hosted-only behavior.

- **Check the intended public release target.** Before publishing, verify the repository, branch, tag, and workflow that public users will see. Do not assume local remote names or private mirrors represent public release truth.

- **Treat release status as multiple independent facts.** Branch Tests, tag-triggered PyPI publish, GitHub Release creation, PyPI version visibility, and any deployment/release-environment status can diverge. Check all of them before calling a release published or latest; if one is red or missing, either fix it now or record the explicit remaining blocker.

- **Test dependency drift with a fresh resolver.** Local `.venv` and `uv.lock` success can hide what GitHub Actions or users get from an unlocked `pip install`. For dependency-sensitive failures, reproduce in a clean pip environment matching the hosted workflow before declaring the tests fixed.

- **Audit dependency targets before raising bounds.** For runtime, dev, and optional extras, check current and target versions against OSV or an equivalent advisory source, then run a resolver-level audit on a fresh environment. Do not raise optional legacy backends into advisory-affected ranges; hold or cap them and backlog the upgrade gate instead.

- **Separate public and local release information.** Public docs may name package ranges, workflow categories, advisory IDs, and reproducible commands. Private remotes, tokens, local paths, hostnames, and non-public incident details belong only in ignored local notes such as `.codex-local/LESSONS.md`.

- **Keep benchmark gates tied to measured baselines.** If a release benchmark fails, reproduce it locally against the pinned fixture, update the CI threshold only to the observed stable baseline, and backlog any desired quality increase separately.

- **Do not call tests "local feature testing."** When asked to test new features locally, run the public CLI/MCP/API behavior itself, not only pytest. For each new feature, exercise at least one success path and one important failure/guard path when safe, record the exact command or request, and name any behavior that was covered only by tests.

- **Exercise real integration surfaces before release claims.** Direct handler calls are useful for MCP compatibility, but they are not the same as a separate stdio MCP client. CLI help is not the same as executing the command. A release-readiness summary must distinguish unit tests, focused integration tests, direct API smoke, real CLI execution, and hosted/daemon behavior that was not run.

- **Clean up smoke-test artifacts immediately.** Real backup, cleanup, and benchmark smokes can create archives, temp palaces, and result JSON. Put them under disposable temp paths, verify success/failure, then remove artifacts or explicitly report what remains.

- **Treat palace disk growth as storage forensics first.** Compare backup size, live storage stats, row counts, and cleanup output before deleting anything. Preserve non-regenerable manual drawers and KG data, stop active writers when needed, use supported cleanup APIs, and verify health/status afterward.