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clawhdf5/CLAUDE.md
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osobhandClaude Opus 5.5 db9af7972c
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feat(agent): Ed25519-signed checkpoints
Makes the README's "cryptographically verifiable memory" true.

With HDF5Memory::set_signing_key(key), every checkpoint stores a signed
manifest of the store: a SHA-256 per memory record (text, embedding as
stored, channel, timestamp, session, tags, deleted flag, activation) in
a Merkle tree, plus hashes of the settings (and WAL mark), sessions and
knowledge graph. The signature, public key and manifest hashes go in
/meta; the per-record hashes in /integrity/record_hashes, so
HDF5Memory::verify(path, &public_key) can say which records changed, not
just that something did. A forged manifest fails the signature.

Decisions, as agreed:
- the key is set on the open store and never persisted;
- a signed store refuses to checkpoint without its key
  (MemoryError::SigningKeyRequired); remove_signature() is the
  deliberate way back to unsigned;
- checkpoints only: saves still in the WAL are not covered, and verify
  reports how many there are.

The hashes cover exactly what the file persists, in the form the loader
returns it (strings lose trailing NULs; an empty WAL mark is not
written), so untouched stores verify across any number of reopen and
checkpoint cycles. MemoryError becomes #[non_exhaustive] (it already
gains variants in this unreleased version).

CLI: keygen (owner-only key file), --signing-key / CLAWHDF5_SIGNING_KEY
on writing commands (create signs immediately), verify --public-key
(JSON; exit 2 if not valid), `signed` in create/stats output.

Tests: reopen/checkpoint cycles with awkward strings (f16 and f32),
refusal without the key, wrong and rotated keys, eight kinds of edit
each detected and located, a forged manifest, unsigned stores, NULs in
text, and an edit made in place with h5py that verify pinpoints.

Cost on tank (search_harness --signing-study --full, 3 runs): ~20% of a
checkpoint (+9 ms at 10K, +89-112 ms at 100K), verify 18.6 ms / 247 ms,
32 bytes per record in the file. New deps ed25519-dalek, sha2,
rand_core: pure Rust, the no-C check passes.

Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
2026-09-25 10:13:34 -05:00

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# clawhdf5
## Purpose
Pure-Rust HDF5 format implementation with HNSW vector search, WAL-backed persistence, agent memory storage, and GPU-accelerated I/O. Used by ZeroClaw as its persistent memory and knowledge graph backend.
## Architecture
Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal FFI bindings crate for the optional `szip` feature):
| Crate | Role |
|-------|------|
| `clawhdf5-format` | HDF5 binary spec parser (superblock, B-tree, heap) — also holds shared type definitions and physical constants |
| `clawhdf5-io` | Read/write implementation |
| `clawhdf5-filters` | Compression filters (gzip, LZ4, Zstd, Blosc) |
| `clawhdf5-derive` | Proc-macro derive for HDF5-serializable structs |
| `clawhdf5` | Main facade crate |
| `clawhdf5-netcdf4` | NetCDF-4 compatibility layer |
| `clawhdf5-ann` | HNSW approximate nearest-neighbor vector index |
| `clawhdf5-agent` | Agent memory, session history, knowledge graph storage |
| `clawhdf5-gpu` | GPU-accelerated I/O via wgpu (hand-written WGSL compute shaders) |
| `clawhdf5-accel` | CPU SIMD acceleration path |
| `clawhdf5-migrate` | SQLite → HDF5 agent-memory migration |
| `clawhdf5-android` | Android JNI bindings |
| `clawhdf5-cli` | Command-line interface |
| `clawhdf5-napi` | Node.js native addon bindings |
| `clawhdf5-py` | PyO3 Python bindings |
| `clawhdf5-bench` | Benchmark suite |
## Key Features
- Zero-C-dependency HDF5 read/write: no libhdf5, and deflate defaults to
pure-Rust zlib-rs (`fast-deflate` opts into zlib-ng, which needs cmake).
`ci-test.sh` fails if a C-building crate enters the core crates' default
tree. flate2 must keep `runtime_detection` with zlib-rs — without it zlib-rs
loses SIMD and inflates 3.5x slower. MSRV is 1.92 (`rust-version`, checked
in CI).
- HNSW vector index for semantic similarity search over agent memories — the
`clawhdf5-agent` `hnsw` feature is **on by default**, so `hybrid_search` uses
the approximate `clawhdf5-ann` index for the vector stage (the index mirrors
the cache and self-heals on drift). Build the agent with
`--no-default-features --features float16` to force the exact linear cosine scan.
The agent's `parallel` feature (also default) builds the index on a thread
pool; the graph is identical with or without it.
The index uses the HNSW paper's diversity heuristic for neighbour selection
(plain closest-M capped recall on clustered data: 0.31 recall@10 at 100K). Its
graph is saved to `<store>.h5.ann` at each checkpoint and reloaded by `open()`
(tied to the checkpoint by a generation id; stale/damaged sidecars are
ignored and the index rebuilt). `MemoryConfig::quantized_index` (**on by
default** for new stores, persisted; stores predating the setting load as
`false` and keep their f32 index — guarded by
`tests/fixtures/store_v2_5_0.h5`; CLI opt-out is `create --f32-index`)
stores the index's own copy of the embeddings as `i8`,
which roughly halves a loaded store's memory (2.72x -> 1.74x the raw vectors
at 100K); because quantised distances are approximate and `ef` cannot
compensate, the query path then re-scores the candidate pool against the
exact embeddings, which holds recall at the f32 index's level. It is also
faster at equal recall: 1.63x the QPS on x86-64 (AVX2) and 1.18x on a
Raspberry Pi 5 (`clawhdf5_accel::dot_i8`, NEON `SDOT` via inline asm since
the intrinsic is unstable; plain NEON on pre-dotprod cores). The aarch64
code is `cfg`'d out on x86, so x86 CI never compiles or lints it — test it
on real ARM (`rpivision02`, 10.0.2.3, is a Pi 5). `hybrid_search` keeps one incremental BM25
index for the life of the store and never writes the store: Hebbian
activation boosts are persisted by the next checkpoint (or on drop), not per
query. Measure any search-path change with
`cargo run --release -p clawhdf5-bench --bin search_harness` (baselines in
`BENCHMARKS.md`).
- WAL (write-ahead log) for crash-safe persistence, with a chained CRC32
trailer per entry (each entry's CRC folds in the previous entry's CRC) so a
corrupted, reordered, duplicated, or spliced entry stops replay cleanly
instead of loading bad or tampered data. The pre-chaining per-entry-CRC
format (v2) is still fully readable; the oldest no-CRC format (v1) is only
reachable through the one-time migration path in `HDF5Memory::open`, not
through the public `WalFile::read_entries`.
**What the WAL guarantees:** integrity, ordering, and recovery from a
*process* crash at any point — including between a checkpoint and the WAL
truncate (each checkpoint records a `WalMark` in `/meta`, and `open()` skips
the WAL prefix the `.h5` already contains, so entries are never applied
twice). Checkpoints and snapshots are made durable as a unit (temp file
synced, renamed, directory synced). **What it does not guarantee:**
individual WAL appends are *not* fsynced (a deliberate latency trade-off), so
saves made since the last checkpoint can be lost on power failure or kernel
panic. Current header version is 4 (adds the `Update` record used by
`save_or_update`); v3 files are read and upgraded in place.
- A store has a **single writer**: `HDF5Memory::create`/`open` hold an exclusive
advisory lock on `<store>.h5.lock` and a second opener gets
`MemoryError::Locked`. Use `HDF5Memory::open_read_only` for a lock-free,
never-writing point-in-time view (the CLI's `recall`/`stats`/`agents-md`/
`export` do). An unreadable WAL (torn header, bad magic) is quarantined to
`<store>.h5.wal.corrupt-<ts>` rather than blocking `open()`; a WAL with an
unknown *newer* version still fails and is left untouched.
- `MemoryConfig::float16` (**on by default** for new stores, persisted;
existing stores keep their recorded `false` — guarded by the v2.5.0
fixture in `tests/float16_store.rs`; CLI opt-out is `create --f32`) writes
`/memory/embeddings` as IEEE half precision (48% smaller file at 100K;
LongMemEval with real MiniLM embeddings identical to f32).
`MemoryCache::half_precision` rounds each embedding as it enters the cache (push, update, WAL replay, and on load of a store still
`f32` on disk), so memory and file agree bit for bit; the conversions live
in `clawhdf5_format::float16` and must stay the single implementation.
Values beyond ±65504 are `MemoryError::InvalidEntry`. Interop: every file
must open in h5py — `f32` datasets and empty datasets did not until
2026-09-23 (see `docs/known-issues.md`); the agent's `h5py_interop` test
guards a whole store.
- `HDF5Memory::search(query_emb, text, &SearchOptions)` is the full search
path: optional source-channel filter (applied before ranking; exact scan of
the allowed records whenever cheaper than `pool × M` index distance
evaluations, and as the fallback when the pool comes back short), fusion,
activation scaling, optional re-ranking and confidence rejection.
`hybrid_search`/`hybrid_search_with` are thin wrappers; the OpenClaw
backend is `search` with re-rank + confidence on. Measure changes with
`search_harness --options-study`.
- `MemoryConfig::compression` is off by default; when on, embeddings are
deflate-compressed, or Zstd with the agent's `zstd` feature (links libzstd).
- Signed checkpoints (`clawhdf5-agent` `signing` module): with
`HDF5Memory::set_signing_key` every checkpoint stores an Ed25519-signed
manifest (SHA-256 per record in a Merkle tree + settings/sessions/graph
hashes; per-record hashes in `/integrity/record_hashes`);
`HDF5Memory::verify(path, &pk)` locates edits. The hashes must cover exactly
what the file persists in the form the loader returns it (strings lose
trailing NULs; an empty WAL mark is not written) or untouched stores stop
verifying — `tests/signed_store.rs` round-trips awkward strings. The key is
never persisted; a signed store refuses to checkpoint without it
(`MemoryError::SigningKeyRequired`, and `MemoryError` is `#[non_exhaustive]`).
WAL entries after the checkpoint are not covered.
- `Dataset::verify_provenance()` (clawhdf5 facade, `provenance` feature, on by
default) recomputes a dataset's SHA-256 and compares it against the
`_provenance_sha256` attribute written automatically on save when
`DatasetBuilder::with_provenance` is used. It's opt-in per call, not run
automatically on open — it decodes and hashes the whole dataset. The hash
is unkeyed (tamper-*evident*, not tamper-*proof*): it detects accidental
corruption, not a deliberate actor able to modify both the data and the
stored hash.
- `clawhdf5-agent`'s `HDF5Memory::save`/`save_batch`/`save_or_update` run every
write through an in-memory (session-scoped, not persisted to disk)
provenance ledger and write-anomaly detector: a content hash per record
(`provenance.rs`) for detecting accidental mid-session corruption, plus
rate-limit/injection-pattern/source-distribution checks (`anomaly.rs`).
Alerts never block a save — drain them with `HDF5Memory::take_anomaly_alerts`.
`MemorySource` for this bookkeeping is inferred from the caller-supplied
`source_channel` string (a heuristic, not an authenticated trust boundary).
- GPU-accelerated batch I/O for large dataset processing
- Python and Node.js bindings for cross-language use
- NetCDF-4 compatibility for scientific data interop
## Workflows
### Build
```bash
cargo build --release
```
### Test
```bash
cargo test --workspace
```
### CI
`.gitea/workflows/ci.yml` has two jobs, both green as of 2026-09-22:
- **`test`** (`ubuntu-latest`, in `rust:latest`) runs `scripts/ci-test.sh` with
the h5py/netCDF4 interop suites required (`CLAWHDF5_REQUIRE_INTEROP=1`).
Served by the `tank` and `architect` runners.
- **`test-arm64`** (`linux_arm64`) lints and tests the aarch64 code — the NEON
kernels are `cfg`'d out on x86, so this is the only place they are built.
Served by `vision-01` (host mode) and `vision-02` (Docker), so steps must
work in both.
Keep workflows free of JavaScript actions (`actions/checkout`, `actions/cache`,
…): `rust:latest` has no `node`, and not every runner reaches GitHub, where
they are fetched from. Check out with plain `git` instead. The `test` job
installs `cmake` for the opt-in `fast-deflate` (zlib-ng) steps; the default
build needs no C toolchain, so `test-arm64` does not.
All runners are on `gitea-runner` 3.5.0, from `docker.gitea.com/act_runner`
— `gitea/act_runner:latest` on Docker Hub is frozen at 0.6.1.
### CLI
```bash
cargo run -p clawhdf5-cli -- --help
# create, save, search, recall, stats, flush-wal, agents-md, export, snapshot subcommands
```
### Python bindings
```bash
cd crates/clawhdf5-py
maturin develop
python -c "import clawhdf5; print(clawhdf5.__version__)"
```
## Integration
ZeroClaw imports this as a Cargo feature (`clawhdf5` feature flag) to persist agent memory with HNSW vector search for context retrieval.