dc9cfb5a30f28ba124bfb20ac06c1c49338d7b45
8
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 ZacharyZhang-NY
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dc9cfb5a30 |
Give memories a working tier, a long-term tier, and a record of what disagrees
A memory used to have one state: present. A session could not be reloaded
without searching, nothing separated what a person said today from what the
store ingested a year ago, and two statements that disagree about a number
were both handed to the reader as if the store believed both.
Schema v10. Atoms gain session_id, importance, recall_count, last_recalled_at
and archived_at; two tables arrive beside them — consolidations (one watermark
per holder+scenario lane) and conflicts (a pair, the term they disagree on,
both values, and whether anyone has settled it). v_atoms_current now also
hides archived rows, and apply() drops the view before rebuilding it because
CREATE VIEW IF NOT EXISTS would keep the old body and ignore the archive.
Working memory is a read, not a store: `working <holder> <session>` returns
what that session recorded, in order, with no search involved. Consolidation
rolls a lane's new memories into one standing summary and moves the watermark
to the newest memory actually rolled up — not to the clock of the pass, which
made a second pass count the same memories twice. The archive rule is stated
in the command that runs it (recorded before N days, never returned by a
search, importance at or under a floor) rather than hidden in a decay formula
nobody measured; --dry-run names the victims first.
A conflict is recorded when a new memory repeats a label with a different
number in the same holder's same scenario. Retrieval drops the older side
while the conflict is open and both sides answer again once it is settled:
settling is not superseding, and a word-only disagreement is left alone
because a reworded sentence would fire on every paraphrase. Numbers are keyed
to both the memory's subject and the nearest preceding label, measured: the
two thermostat sentences share only their subject, so a nearest-word-only rule
found nothing.
Search now counts the memories it handed back, which is the only use estimate
the store has, and the archive rule reads it. The dense index applies the same
currency rules as the view, so an archived memory cannot stay findable through
one leg while the other hides it.
Two reads over HTTP (working, conflicts) and two tools over MCP
(working_set, memory_conflicts); curation stays with the operator because an
agent should not decide what its own past is worth. remember and supersede
carry a session id; importance does not cross MCP, because recall_count is
observed and importance is stated.
Gates: 366 passed; ruff clean; mypy 24 errors in 9 files, the recorded
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ZacharyZhang-NY
|
cd9851a12c |
Keep the vectors beside the atoms instead of rebuilding them per query
The dense leg used to be a pair of files next to the database: a full .npy re-encoded from every atom, re-normalized on every query, and stale the moment anything was ingested. It is now a vec0 table inside the same SQLite file, written in the same transaction as the atom it describes, so a memory is queryable the instant it is remembered. - vector_index.py maintains the table; the writer inserts, the reader never rebuilds. A repair scan (ely-eye build-dense-index) catches what a crash hid, and it is not on the search path. - Ranking stays in the engine; every boundary (closed, retired, not yet true, another holder's private memory, the mounted cartridge) is checked once against the atoms table afterwards. A visibility subquery inside the KNN cost 48.9s on 153,857 vectors; the same check over 200 candidates costs 0.2ms. When the boundaries reject enough candidates the ask widens once, never twice. - The Go mirror and the web panel carry the new status fields. - bench/dense_scale.py and bench/quant_probe.py measure the leg at corpus size; bench/scale_check.py now works on a copy so the frozen baseline stays frozen. Measured on the frozen 165,973-atom corpus. bench/reports/DENSE.md, QUANT.md and SCALE.md carry the numbers and the machine they were taken on. Gates: 285 pytest, ruff clean, pyright 0 on the touched files, go build/vet/test and gofmt clean, frontend build and eslint clean. mypy is 25 findings against the parent's 24: the new one is "Skipping analyzing sqlite_vec: missing library stubs", the same class as the two existing qwen_vl_utils lines, and the repo keeps no type: ignore anywhere. |
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ZacharyZhang-NY
|
9cb46c18d3 |
Keep every file inside the rules the repo already wrote down
The 500-line rule and the one-line-comment rule were enforced only from
memory, so they drifted. bench/filesize_check.py now checks the budget on
every tracked source file, and it passes.
Split, with behaviour proven unchanged by comparing each moved function body
against HEAD byte for byte (53 training bodies, 21 benchmark bodies, 0
differences):
db.py 503 -> 396 store_search.py 105
bench/run.py 609 -> 351 query_set.py 80, report.py 211
benchmarks.py 619 -> 424 benchmark_scoring.py 262
training.py 1630 -> 94 training_{common,encode,metrics,datasets,lora,retrieval}
TrainingService stays as a thin facade for the CLI; the facade's parameter
lists are pinned against the module functions by inspect, which is what
catches a leaked self.
Deleted the eight top-level functions no file called, after fixing the scan
that had reported 201 false positives by skipping the defining file. The
v1/v2/v3 sweep found nothing to delete and says so: every remaining version
tag is an on-disk format marker, a migration step, a fixture name, or a
third-party model name.
Collapsed 39 multi-line comment blocks (86 lines) to one line each, keeping
the measured facts and error strings that justify the code.
269 tests pass; ruff clean; mypy 24 errors in 9 files against a 24-in-6
baseline, net zero; pyright 4, the same four HEAD measures.
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ZacharyZhang-NY
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56b94cd58e |
Delete the eight functions nobody called
Measured, not guessed: every top-level def in every tracked .py file was counted against every other tracked file, including markdown, scripts, Go and TypeScript. Eight names had zero references anywhere: cartridges_holding_atom and clear_mounts (memory.py reads each cartridge's own sources.json, and set_mounts already clears by replacing the set), gradient_checkpointing_enabled, group_hits_by_source, ingested_paths, is_finite_number, parse_runtime_json (the runtime answers in prose now, and the JSON path was never wired back), write_sources_json. Typer commands and pytest cases were excluded from the count because a framework calls them by name. The same scan now returns zero. 263 tests pass; repo-wide mypy is still 24 errors in 6 files, unchanged. |
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ZacharyZhang-NY
|
03ca35763f |
Give wordless pictures a way to be found
A screenshot with no OCR-able words used to be a memory nothing could reach. It now has a vector, and a question can be asked with it. - picture space: Xenova/chinese-clip-vit-base-patch16 fp16 ONNX, 361MB, sha256 0fad978faa89b85f…, CPU-runnable on the onnxruntime the repo already ships. Text 76ms, image 29ms/image at batch 8. The ONNX contract was measured, not assumed: it is the fused contrastive graph, so the unused tower is fed a placeholder row, and the tokenizer must be BertTokenizer — a CLIP tokenizer emits ids past the embedding table. - visual_index.py keeps picture vectors in their own family beside the text ones, keyed by atom id, and refuses to answer when the map and the array disagree. retrieval.py stays under 500 lines. - a search can be asked with a picture, and when the words come up thin the picture ranking joins the fusion — but only then, so a text query never pays for the encoder. - visual tokens are counted at 32x32 patches (migration v7): 21,325 image atoms went from 5,331 to 34,618 tokens, and the library from 29.9M to 84.2M. The 100M capsule fits this 50,278-file corpus and nothing much larger — that is what the claim actually means. - the vision-language describer is written and gated: with no snapshot an atom records "deferred: no vision-language model snapshot"; a broken model becomes a note on the atom instead of a dead ingest. - forget now voids the picture index and the copy this process holds, so a forgotten picture stops answering from RAM. Measured, six arms, ScreenSpot-v2 (554 icon crops, 408 whole screenshots), bench/reports/VISUAL.md: icon pool picture 0.208 words 0.175 chance 0.018 screenshot pool words 0.384 picture 0.215 screenshot to icon 0.108 — CLIP is a whole-image model, not a grounding model: it cannot point at one small icon inside a screen. within one screen MRR 0.908 (recall@10 is 1.000 there only because chance is 1.000 too, so MRR is the only honest number in that row). ScreenSpot-v2 instructions are all English, so this run does not test the bilingual claim and the report says so. Gates: 199 tests, ruff clean on 17 files, mypy clean on 16 (fresh cache), the repo's own pyright 0 errors on the 12 files touched. |
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ZacharyZhang-NY
|
9fe13fffb3 |
Make a cartridge a scope you mount, not only a bundle you export
A cartridge used to be an export: materialize it, hash it, and never let it
change behaviour again. The transactive-memory promise needs the opposite —
mounting the home cartridge must make the work cartridge's memories
unfindable, and appending to a cartridge must make a new memory findable
through it.
Cartridge scope (schema v2 -> v3):
- cartridge_atoms (which atoms are in which cartridge) and cartridge_mounts
(which cartridges this machine has mounted now); both rebuilt on reopen,
because a mount is a machine-local state, not a fact about the data.
- mount / switch / unmount / append refuse a cartridge that was built for a
different runtime, naming every differing field instead of a boolean.
- append rewrites atoms.parquet and sparse_index.bm25.zst, recomputes DNA and
the KV fingerprint, and keeps the cartridges row in one SQLite transaction;
the files are written first so a failed export cannot leave a half-cartridge
in the database.
- RetrievalService.search() now ranks inside the mounted scope by default, and
an explicit cartridge_ids filter overrides the mount. Nothing mounted means
the whole library, so an empty store behaves as it did before.
- forget() removes the forgotten atoms from every cartridge's membership:
forgetting must not leave a memory retrievable through a cartridge.
KV fingerprint (kv_fingerprint.py, stored in the manifest):
- model / tokenizer / chat-template hashes, the RoPE block (factor,
original_max_position_embeddings, mrope_interleaved, mrope_section,
partial_rotary_factor), the adapter hash, the context length, the engine
version, and the fingerprint format version.
- A snapshot we cannot read is recorded as unverifiable and listed in
unverifiable_fields; a blind machine refuses to mount rather than pretending
the runtime matches, because a wrong RoPE factor silently corrupts retrieval.
Two defects found by running the new tests, not by reading:
- atoms.parquet could not export a memory at all: pyarrow refuses a struct with
no child field, so metadata={} failed the whole table. The six JSON columns
are now written as JSON text and decoded by the cartridge reader.
- materialize() inserted cartridge membership before the cartridges row existed,
so the foreign key rejected every cartridge.
api.py crossed 500 lines, so the cartridge and mount routes moved to
api_cartridges.py. That module must not use `from __future__ import
annotations`: FastAPI then resolves Depends(settings_dep) against module
globals, where the injected dependency is only a parameter of register(), and
every route starts demanding settings as a query parameter.
Verification: 138 pytest passed (26 new: 10 fingerprint, 16 scope), Go
build/vet/gofmt/test clean, and scripts/mcp_write_smoke.sh now proves the whole
loop across two processes - mount narrows search, a memory written after the
upload is invisible until append_to_cartridge, unmount restores the library,
and rewriting the RoPE factor makes mount_cartridge refuse with "rope: factor:
cartridge 4.0 vs local 2.0". ruff clean on every touched file (the 20 remaining
package errors are in files this change did not touch; HEAD had 25). mypy: one
error, the pre-existing np.savez stub artifact at cartridge_assets.py:329
(HEAD:320). Repo pyright: 62 errors, unchanged from the p0-3 ledger.
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ZacharyZhang-NY
|
674c88e84e |
Rank memory search in SQL instead of rebuilding BM25 in Python
The product path pulled every atom into Python and rebuilt a BM25Okapi
index per query: p50 20.16s over 165,973 atoms. It now runs one SQLite
statement with LIMIT/OFFSET.
- text_index.py: one normalizer for both sides of the index. CJK runs are
split per character (unicode61 cannot segment them), camelCase and
separators are split so "search atoms" finds SearchAtoms, latin tokens
get a prefix match. Rows written before this existed hold raw text in
atom_fts, so schema v3 rebuilds the index on upgrade.
- retrieval.py: filters (holder / scenario / source / modality / since /
until / min_trust), a base64 cursor, and reciprocal rank fusion in place
of the 0.55/0.35/0.10 blend whose graph term was always zero.
- An empty result is {status: "no_match"}, not a bare array.
- Go: search_atoms and compile_context rank through POST /api/search. The
second Go tokenizer is deleted; two implementations could drift, which
is what the MCP/HTTP parity requirement forbids.
- db.py passed 500 lines, so the row codec moved to row_codec.py.
- Tooling: installed the repo's own pyright. pyrightconfig.json had no
exclude and swept the downloaded corpus (1171 errors); with excludes the
count is 62, all pre-existing (evals 48, profiles 4, training 4,
ingestion 3, runtime 2, cartridge_assets 1). backend/pyproject.toml
carried a second [tool.pyright] that shadowed the root config; deleted.
- bench/scale_check.py measures the product search at corpus size. The
100k-atom p50 number is not written until it is measured on an idle
machine.
Verification: 112 pytest passed; go build/vet/test clean, gofmt clean;
ruff clean on every touched file; mypy clean on the 14 touched Python
files; repo pyright 62 errors, unchanged per-file ledger.
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ZacharyZhang-NY
|
0ee99e271d |
Ely-Eye — local multimodal context operating system on Qwen3.5-9B
Real-models implementation: multimodal ingestion, Context Cartridge memory, sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard. Verified by a 15-check PRD proof suite on the live model. |