ZacharyZhang-NY
cd9851a12c
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.
75 lines
1.8 KiB
Python
75 lines
1.8 KiB
Python
"""What the machine reports about itself: runtime, memory, cache layers, senses."""
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from __future__ import annotations
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from datetime import datetime
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from typing import Any, Literal
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from pydantic import BaseModel
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from .schemas import TrainingAdapterKind
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class CacheLayerStatus(BaseModel):
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layer: str
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entries: int
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bytes_estimate: int
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hit_count: int
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miss_count: int
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last_event_at: datetime | None
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class MemoryStatus(BaseModel):
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active_atoms: int
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active_token_equivalent: int
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cartridges: int
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library_token_equivalent: int
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layers: list[CacheLayerStatus]
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class RuntimeStatus(BaseModel):
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backend: str
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model_id: str
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available: bool
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detail: str
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adapter_path: str | None = None
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adapter_kind: TrainingAdapterKind | None = None
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adapter_id: str | None = None
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adapter_sha256: str | None = None
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gpu_name: str | None = None
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cuda: str | None = None
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vram_total_mb: int | None = None
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vram_used_mb: int | None = None
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SenseState = Literal["absent", "missing", "ready"]
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VisualState = Literal["absent", "stale", "ready"]
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DenseState = Literal["off", "missing", "behind", "current"]
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class SenseStatus(BaseModel):
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"""What the machine can perceive now; absent means no model was given, missing means the one it was given is gone."""
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dense: DenseState
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dense_covered: int
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dense_expected: int
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picture_atoms: int
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picture_bytes_missing: int
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visual: VisualState
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audio_atoms: int
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speech: SenseState
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captioning: SenseState
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clip_path: str | None = None
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asr_path: str | None = None
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vlm_path: str | None = None
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class SystemStatus(BaseModel):
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app: str = "Ely-Eye"
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version: str
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runtime: RuntimeStatus
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memory: MemoryStatus
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senses: SenseStatus
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profiles: dict[str, dict[str, Any]]
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data_home: str
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