5289cb4b01f52f8f0ec05ff02ad6f23a0417b9de
10
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 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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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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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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61f6506104 |
Explain the product path's zero overlap and fix the report writer
The frozen baseline showed the unmodified RetrievalService agreeing with an equivalent BM25 index on 0% of its top ten results. Reproduced by hand: its top ten atoms came from one document's pages, so a source-level ranking cannot overlap it. - bench/systems.py: live_product_rankings also counts distinct sources behind the product's top-k atoms. - bench/run.py: scale probes no longer filtered by gold (a source prefix keeps almost no gold, so the 500/2,000/10,000 rows were silently skipped); scale_arm_names reads only latency dicts, which is what crashed write_markdown on the note field; the unanswerable share is computed instead of hard-coded. - bench/tests/test_joint.py: 60 tests total, including the writer regression. Verification: pytest bench/tests -q -> 60 passed; ruff check bench/ -> all checks passed; mypy (fresh cache) -> no issues in 25 source files. |
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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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77219c930b |
Add third-party benchmark suite frozen at 50,789 sources / 5,308 queries
Ground truth comes only from third-party annotations (MMLongBench-Doc
evidence_page_ids, VisRAG qrels, ScreenSpot-v2 bboxes, Video-MME QA,
LongMemEval answer_session_ids, CodeSearchNet func_path+func_name) and
from document structure; no model judgement is used anywhere.
- bench/corpus/manifest.jsonl: 28 sources with url, retrieved_at, sha256,
annotation_source, license, files[]; verified by bench.manifest_check
- bench/sets/{pdf,image,ui_screenshot,video,code,conversation}.{jsonl} plus
corpus indexes and a manifest that freezes every file's sha256
- 2,928 answerable + 2,380 unanswerable queries over 854 holders; every
query carries scope {holder, scenario, as_of} for transactive routing
- bench/validate_sets.py fails on missing gold, holder mismatch between
gold and query, gold observed after as_of, and manifest digest drift
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