2fbb4af96bdb8d3e6bc189adadaa80f49c70184a
14
Commits
| Author | SHA1 | Message | Date | |
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 ZacharyZhang-NY
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2fbb4af96b |
Resolve adapters by what is mounted, and say why each kind is not loaded
runtime.py resolved adapters at 7 call sites and only ever looked in the
adapter library, so an adapter attached to a cartridge (cartridge.py:230
copies it into <cartridge>/adapters/{kind}/) was never loaded by anything.
Resolution now lives in adapters.py: it reads the mounted cartridge set,
prefers a mounted cartridge's copy over the library, and answers every one
of the six kinds with a state the facts support — loaded, available,
deferred (base_model mismatch or missing weights, both models named in the
reason), missing — plus a shadowed row for each adapter that lost the
choice. A mounted cartridge is the scenario selection; neither manifest
carries a scenario field, so none was invented.
Two bugs the new tests caught: the preference sort lost reverse=True, so a
library adapter beat a mounted cartridge's copy, and the load branch had
never been executed by a test. Stub weights now prove peft is asked to
attach, that the returned model is put in eval mode, and that the endpoint
reports loaded until unload clears it.
Faces: GET /api/runtime/adapters, `ely-eye adapters`, and a log line naming
the reason whenever a generation runs without an adapter.
Gates: 222 passed; ruff clean; mypy cold cache on the 7 touched files gives
the same 2 errors as HEAD's runtime.py, and pyright gives the same 2 as
HEAD — both are qwen_vl_utils typing artifacts (process_vision_info returns
2 or 3 values depending on return_video_kwargs; the product takes 2).
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ZacharyZhang-NY
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10a39c5125 |
Stop the prose from drifting away from the report it cites
A second review found three numbers left behind when the report was regenerated with pinned decoding: - the transcriber docstring claimed 1.7 s for 88.9 s of speech; the report says 1.39 s, and 1.7 came from the unpinned run. The docstring now names no number, so it cannot go stale, and it stops calling container seconds speech with voice activity detection off. - the checklist still quoted 3.55 s and 61.6x / 73.0x / 97.7x; the frozen report says 3.26 s and 63.9x / 82.4x / 112.2x. - the checklist still said 31 speech segments for one mp4; with greedy decoding that video yields 38, which is where the store's 80 atoms come from (38 + 39 + 3). The report also credited the store with timing invariants that were measured on the model's answer. The harness now recomputes them from the words as stored (out_of_order_in_store, before_their_segment_in_store) and says so. Verification: 212 passed, ruff clean, mypy clean on both touched files with a cold cache, pyright 0 errors. |
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ZacharyZhang-NY
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709104e809 |
Make the speech numbers reproducible instead of merely printed
A review of
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ZacharyZhang-NY
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5289cb4b01 |
Give spoken words a memory with an instant attached
An audio file used to produce no memory at all: _ingest_file returned an empty batch for any suffix it did not know, with no log and no reason. It now raises UnsupportedFile when you name the file and records a SkippedFile when it was a directory walk, so a refusal is visible either way. Speech is measured, not promised. Over 210.5 seconds of real Video-MME audio, faster-whisper-tiny on CPU int8 transcribed at 51x realtime and reached word error rate 0.242 against community subtitles nobody here wrote (a caption track drops words a transcript keeps, so that is an upper bound). 66 speech atoms now carry 550 word-level timestamps, 0 words out of order and 0 starting before their own segment. The snapshot is 78.2 MB, under the 500 MB cap, and the report is bench/reports/ASR.md. Two library facts were measured rather than assumed. faster-whisper's own audio loader is broken against PyAV 19 (av.open got an unexpected keyword 'metadata_errors') and it declares av with no version bound, so asr.decode_audio decodes with PyAV directly and hands the model a float32 array instead of pinning an old av with no cp314 wheel. And av.Error does not exist on PyAV 19 - the real base is av.error.FFmpegError - so that is the only thing caught and my own ValueErrors keep their messages. Files over the 500-line rule were split rather than excused: ingestion.py (748) into ingestion_common / ingestion_media / ingestion_repository / ingestion, and cli.py (544) into cli_senses plus cli_common, following the cli_transactive register pattern. MediaIngest declares _source_record abstract, so a half-wired host fails at construction instead of returning None. Verification: 207 tests pass; ruff clean on the 13 touched files; mypy clean on them, and repo-wide mypy fell from 29 errors to 24 (5 fixed, none added); pyright reports 0 errors on the touched files. |
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ZacharyZhang-NY
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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
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7e1fe649f6 |
Stop paying for words every document carries
The product's own search answered in p50 20.16s on 165,973 atoms. The SQL rewrite brought that to 0.68s; this brings it to 57ms and says why the rest was there. The cost was not SQL. An OR MATCH scores every row it touches, and a question carries words the whole library carries: `"parsing"* OR "error"* OR "in"* OR "the"* OR "pdf"* OR "converter"*` matched 127,145 atoms and took 305ms, while a CJK bigram phrase query with 32 matches took 1.0ms. Latency tracked match count. - text_index.rare_terms asks the index which of its own words are noise: count each term, drop the ones over 5% of the library, keep the rarest few when all of them are common. No stop-word list; a corpus of PDFs and a corpus of shell history get different answers. 0.05 is swept, not chosen — on n=173 recall@10 peaks there (0.899 / p50 58.6ms) against 0.893 / 112.9ms at 0.20 and 0.844 / 13.4ms at 0.01. - The bound is a cost, not a claim about rarity: `cap = max(int(total*fraction)+1, 2)`, so a word matching one atom in eleven is kept — scoring one candidate is free. A capped count cannot rank words that all saturate, so those few are counted exactly. - Schema v6 adds `term_df`: a word is counted once and the next question pays nothing. Every atom write clears it, so a stored count cannot outlive the library it described. Cold p50 73ms, warm 63ms, 1,324 terms cached. - The denominator is `count(*) FROM atoms` (0.01ms) rather than `count(*) FROM atom_fts` (37ms cold): FTS rows are written and deleted with their atoms, so the two counts move together and only one of them is free. SCALE.md, idle machine (load 1.76, 32 cores, 4 threads per query): 165,973 atoms — 1.66x the contract's 100k — 300 probes, p50 57ms, p95 138ms. Against the frozen baseline of 20.16s that is 354x. Holder-scoped (n=173): recall@10 0.870, MRR 0.740, p50 62ms. Verification: pytest backend/tests bench/tests -q -> 173 passed (5 new text_index tests: the common word is dropped and the rare one still answers, an all-common query keeps its rarest words in order, a one-word query is never filtered, an empty library keeps everything, and a write voids the counts); ruff clean; mypy fresh cache clean; pyright 0 errors on the touched files. |
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ZacharyZhang-NY
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4ab36cd5eb |
Make the proof suite say what it actually ran
The suite had fifteen checks that could not lose. The visual HashHop arena painted a cyan line and a white ring on the answer slot, then asked the model to find the answer by looking; the collector re-rolled puzzles until the running rate crossed its target; the cache check passed if any layer had ever recorded an entry; the repository check passed on a call graph built in memory without touching the store; the research-module check asserted non-empty lists of SHA256-derived placeholder vectors and so could not fail. Each check now answers for one claim and reports a verdict the reader can act on: passed, failed, deferred, or removed. A deferred row names what is missing and the command that would settle it; a removed row says why the old claim was withdrawn. The suite verdict is failed > deferred > passed, so a claim that never ran holds the whole report back from saying "proven". - evals.py (2427 lines) becomes the proofs/ package; largest module 445 lines. - proofs/__main__.py deleted: it duplicated PRDProofSuite.latest() with a different glob. proofs/table.py renders; latest-proof-suite is the one face and exits 1 on a failed verdict. - The arena paints nothing. The puzzle id is printed on its tile instead, because removing the ink without it removes the question. - HashHop runs a fixed 8 puzzles per depth instead of re-rolling to target. - The contradiction corpus moves from code to contradiction_scenarios.json; the check stays deferred while the corpus is the one this repo authored. - The cache check compiles a nonce question over a real atom twice and reads the miss to hit movement; an empty store fails instead of agreeing by luck. - The repository check requires rows in the store; the research-module check is removed. Two product bugs surfaced on the way: - text_index.fts_query joined Latin terms with a space, which FTS5 reads as AND, so a question longer than its answer retrieved nothing. Terms are ORed and bm25 rank orders the candidates. - profiles.windows_path_to_wsl prefixed /mnt/ onto an already-POSIX path with an empty drive, producing /mnt//home/... and failing every launcher probe. Verification: 168 passed (pytest backend/tests bench/tests); ruff clean on the proofs package, cli.py, text_index.py, profiles.py, and both test files; mypy clean on 16 files with a fresh cache; pyright reports 0 errors on proofs/ and the test file. Ruff debt elsewhere is unchanged: 19 errors in seven files this commit does not touch, measured against the same files at HEAD. |
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ZacharyZhang-NY
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dfc6cd4c28 |
Give the system a directory: ask who knows, then ask them
A memory system that only answers from its own store is a filing cabinet, not a partner. The transactive rule the project is named for — not knowing is fine when you know who does — needed a holder registry, a directory query, and a referral that never leaks the text it may not read. - schema v5 adds `holders` (person/agent/service, declared topics, contact). A holder with memories exists whether or not anybody registered them; the registry adds reachability, it does not grant existence. - `who_knows` ranks holders by their own memories and is deliberately NOT filtered by visibility: "she knows" is the answer. The answer is filtered. - `ask` searches the asker's visible scope first (hop 0), then walks the directory with a visited set and a hop budget. Three terminal states: answered, referred, no_holder_knows. Nobody knowing is a result, not an error. - Confidence is ordinal, not calibrated: relative strength, coverage, and declared topics. Every hit carries the raw numbers and the basis lines that produced the score, so a ranking can be argued with. - Faces: HTTP (/api/holders, /api/who_knows, /api/ask), CLI (holders, holder-set, holder-rm, who-knows, ask), MCP (who_knows, ask, list_holders). Four bugs found by running the thing, not by reading it: - FTS5 refuses bm25() inside MAX(); the aggregate uses FTS5's own `rank` column. - A CJK question is longer than the sentence that answers it, so the whole-run phrase matched nothing. The query now ORs adjacent bigram phrases; adjacency is kept, equal length is not. Latin path untouched, baseline unaffected. - The hop budget counted the asker as a hop, so max_hops=1 asked nobody. - A second, stricter readability test in _answered() disagreed with the SQL visibility clause and hid a memory the asker was allowed to read. The SQL clause is the only authority now. A method named `list` shadowed the builtin inside its own class and made mypy reject the return annotation; renamed rather than cast around it. Proof: 155 pytest, 12 Go tests, ruff and mypy clean on touched files, pyright ledger unchanged at 62, and scripts/mcp_write_smoke.sh drives two principals on one SQLite file — the husband asks through MCP, gets `referred` with his wife's contact channel, and her private sentence appears nowhere in the reply. |
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ZacharyZhang-NY
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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
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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
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3d792417cf |
Give the write path a CLI and an MCP face
remember / supersede / forget now reach the one Python writer from a terminal, from MCP, and from HTTP. The server, not the caller, decides who owns a memory. - cli: remember, supersede, forget. --holder falls back to settings.default_holder until the holder registry lands. Timestamps are parsed once with one clear message. - ingestion: import pymupdf as fitz. The deprecated fitz import printed a warning to stdout, so every CLI command's JSON answer was unparseable. - mcp: remember, supersede_memory and forget_memory forward to the Python writer over HTTP (ELY_EYE_API, default http://127.0.0.1:8765). SQLite stays read-only to the Go side, so the write rules live in one place. - mcp reads follow schema v2: atoms.time became ingested_at, and reads go through v_atoms_current, so a superseded memory stops being findable. holder, origin, scenario and event_time now show up in fetch_atom and search_atoms. - scripts/mcp_write_smoke.sh proves the loop end to end: remember, search, supersede, forget, then not found. Evidence: 92 pytest passed; go build, go vet and go test clean; mypy clean on the 10 touched source files with a fresh cache; ruff clean except a pre-existing B008 at cli.py:292, identical at HEAD. |
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ZacharyZhang-NY
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181f6bf57e |
Give every memory a holder, an origin, a time and a scenario
An EvidenceAtom could say what was remembered and nothing else: not whose memory it is, not where it came from, not when it was true, not in what situation it may be used. The baseline measured the cost of that: the same ranker scores recall@10 0.412 over the whole corpus and 0.866 inside one holder's memory, so the missing holder column is worth more than any ranking change in the ablation. - schemas.py: MemoryOrigin, TimePrecision, Visibility; EvidenceAtom gains holder_id, origin, scenario, event_time / event_time_end / event_time_precision / event_time_source, valid_from / valid_to, supersedes, visibility, retention_until. `time` is renamed `ingested_at` — ingestion time gets one name, not two. A validator refuses valid_to before valid_from, event_time_end before event_time, and an atom that supersedes itself. - migrations.py: schema v2 with PRAGMA user_version. A first-version database is upgraded in place (ALTER + RENAME COLUMN), rows intact, and a database newer than this build refuses to open instead of being silently downgraded. - v_atoms_current hides memories that are closed, lapsed or not yet begun; unknown bounds read as visible. Time travel is db.atoms_as_of(moment, holder) because a SQLite view cannot take a parameter. - evaluation_schemas.py: the proof and benchmark report shapes move out of schemas.py to keep it under the file-size limit; importers follow. - backend/tests/test_memory_schema.py: 8 tests, including the v1 -> v2 upgrade and the validity timeline. Verification of record: pytest backend/tests bench/tests -q -> 70 passed; ruff check on every touched file -> all checks passed; mypy (fresh cache) on db.py, migrations.py, schemas.py, evaluation_schemas.py and backend/tests -> no issues. evals.py carries 40 pre-existing mypy errors at HEAD and 40 after this change (measured on both copies); its pyright findings are the same debt, and p0-6 rewrites that file. `peft` and `pyarrow.parquet` "could not be resolved" is the documented pyright artifact: pi-lens runs it with 0 config files, and both import at runtime. |
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ZacharyZhang-NY
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fc0698c242 |
Add baseline harness: ingest, metrics, ablation arms, external counterpart, audit
- bench/ingest_baseline.py drives the unmodified product IngestionService over all 50,789 corpus files in sharded SQLite DBs, then merges them. Thread and memory ceilings are set explicitly: ONNX Runtime defaulted to every core (95 threads/worker, load 490) and its CPU arena kept freed pages mapped, which OOM-killed workers. - bench/metrics.py: recall@k, MRR, nDCG@10, Wilson lower bound, chance baseline. No model, no product import, so the scorer cannot grade itself. - bench/systems.py + bench/run.py: the five ablation arms over the merged DB, latency percentiles, scale curve, and a top-10 equivalence check against the live RetrievalService. - bench/counterpart.py + bench/run_external.py: in-repo Mem0-style flat store and Zep-style temporal store as the external reference point. - bench/audit.py: enforces single-attempt scoring, frozen n, a chance row beside every score, and producer != verifier. It already flags the product's retry-until-target loop at backend/src/ely_eye/evals.py:2351. - backend/pyproject.toml: rapidocr-onnxruntime>=1.4.4 was unsatisfiable (PyPI maxes at 1.2.3); the successor is the renamed rapidocr package. |
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ZacharyZhang-NY
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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. |