russell@unturf.com
727cb1bd96
Ship arborist corpus state to new peers and DVD-R archival via
point-in-time tar.zst packs. One artifact serves both channels —
bucket+CDN delivery and physical-media archival.
Bucket holds packs only. Pack key = hash_leaf(manifest_bytes), so same
chunk set on two writers produces the same pack_hash and upload is
idempotent. Each pack pins the corpus snapshot_root it covers in audit
+ result body — packs are delayed snapshots, not live mirrors;
falsifications between repacks produce new pack_hashes.
stream_packs runs streaming zstd over tarfile, peeking compressed-buffer
size after each chunk via FLUSH_BLOCK (preserves dictionary). Default
cap 4_400_000_000 — 4.4 GB DVD-R safe-fit, ~6.5% buffer below the
4.7 GB marketing capacity to absorb ISO9660 overhead, growisofs
lead-in/lead-out, media variance, and drive-edge refusal. Each disc
fills to ~4.4 GB recorded data, not the ~1.5 GB an uncompressed cap
produced.
One backend class (S3CompatibleBackend via boto3 + endpoint_url) covers
AWS S3, DO Spaces, R2, B2, GCS S3-interop, MinIO. Optional dep
[object-store] = boto3>=1.34; dev extras pull moto for the wire test.
Voyeur: credentials via AWS_ACCESS_KEY_ID/_SECRET_ACCESS_KEY env or
~/.aws/credentials, never printed; only endpoint URL + bucket name
surface in logs.
CLI: arborist cold {pack,unpack,stats}. Makefile: cold-pack,
cold-pack-dvd (local-dir output for growisofs), cold-unpack, cold-stats.
Sizing for current shards (14.1M chunks, ~17 GB compressed): ~4 packs
at the default cap, ~\$0.34/mo DO Spaces storage, ~\$0.0001/fresh-peer
hydrate.
Always-on raw-UTF-8 leaf store (per ticket "Hard invariants") deferred
— packs-only for now, backfill later.
2557 passed, 28 skipped, 1 xfailed.
164 lines
6 KiB
TOML
164 lines
6 KiB
TOML
[build-system]
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requires = ["setuptools>=68"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "arborist"
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version = "0.0.1"
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description = "An arborist for trees and forests of cross-linked information"
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readme = "README.md"
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license = { text = "AGPL-3.0-only" }
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requires-python = ">=3.10"
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authors = [
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{ name = "Russell Ballestrini", email = "russell@unturf.com" },
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{ name = "foxhop" },
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{ name = "TimeHexOn" },
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]
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dependencies = [
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"httpx>=0.27",
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"zstandard>=0.22",
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"cryptography>=42",
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]
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[project.optional-dependencies]
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html = [
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"selectolax>=0.3",
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]
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wikitext = [
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"mwparserfromhell>=0.6",
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]
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mesh = [
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# httpx is already in core deps; mesh wire only depends on stdlib +
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# cryptography (also core). This extras block exists as the documented
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# opt-in surface even though no extra packages are required today.
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]
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math = [
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# Symbolic algebra/calculus π* substrate (ticket #000030). SymPy is
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# ~30 MB installed; pulling it into core deps would inflate every
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# fresh checkout. Tests skip via pytest.importorskip when absent.
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"sympy>=1.13",
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]
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hessian = [
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# Phi_alignment_probe (ticket #000034 Phase 1a). Lanczos top-k +
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# bottom-k eigendecomposition for measuring whether v7's frozen
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# linear projection W aligns with the loss Hessian's low-eigenvalue
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# subspace. Numpy + scipy together ~80 MB; gated separately from
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# core to keep the default install lightweight. Tests skip via
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# pytest.importorskip when absent. Install with:
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# pip install 'arborist[hessian]'
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"numpy>=1.26",
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"scipy>=1.11",
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]
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crawler = [
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# Verbatim lift from agents.ai.unturf.com/core. Off by default — the
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# default test suite never imports the crawler. Install with:
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# pip install 'arborist[crawler]'
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# then run `make test-crawler`.
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"aiohttp>=3.8",
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"beautifulsoup4>=4.11",
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"lxml>=4.9",
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"html5lib>=1.1",
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"html2text>=2024.2.26",
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"miniuri>=1.1",
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"feedparser>=6.0",
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"Pillow>=10.0",
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"cairosvg>=2.7",
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"pypdf>=4.0",
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]
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vec = [
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# Optional sqlite-vec semantic retrieval backend (ticket #000039).
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# sqlite-vec ships only the loadable SQLite extension (~1 MB);
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# fastembed pulls onnxruntime + tokenizers + huggingface-hub
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# (~150 MB) and downloads the bge-small-en-v1.5 ONNX model
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# (~130 MB) on first use. Gated separately so a fresh checkout
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# stays python3.12 + venv + sqlite3. CLI surfaces `arborist embed`
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# / `--backend vec` only when `sqlite_vec` imports. Install with:
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# pip install 'arborist[vec]'
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# (sentence-transformers is the heavier "official" embedder path
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# the ticket §5 names; fastembed is the lightweight ONNX one.)
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"sqlite-vec>=0.1.9",
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"fastembed>=0.4",
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]
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nli = [
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# Sentence-pair NLI for the #000049 Phase-2 *shadow* path
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# (arborist/qa/nli/) — measures whether a clause-level contradiction
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# veto would demote a weakly-grounded answer; never touches
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# audit_mode. transformers + a CPU torch is ~600 MB installed, so it
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# is gated hard out of core / dev — a fresh checkout stays
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# python3.12 + venv + sqlite3, and the default test suite skips the
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# NLI tests via pytest.importorskip when this extra is absent.
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# Install with:
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# pip install 'arborist[nli]'
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# `optimum[onnxruntime]` gives the ONNX-export + int8-quantize path
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# (`bench/scripts/export_nli_onnx.py`, `make export-nli-onnx`):
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# `onnxruntime` on a quantized cross-encoder is ~2-4x faster on CPU
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# than the torch forward path; `ShadowNLI._ensure_loaded` auto-prefers
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# an export if it finds one. torch is still here because `optimum`'s
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# exporter uses it, and it's the fallback when no export exists; a
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# Phase-3 runtime could ship an `[nli-onnx]`-only extra (onnxruntime,
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# no torch) once the export is committed/distributed (cf. [vec]).
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"transformers>=4.40",
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"torch>=2.2",
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"sentencepiece>=0.2",
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"protobuf>=4.0",
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"optimum[onnxruntime]>=1.20",
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]
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object-store = [
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# Cold-object eviction tier (ticket #000061). Pushes chunk bodies to an
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# S3-compatible bucket keyed by leaf_hash so the corpus can grow past
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# one machine while the Merkle tree stays intact. One backend covers
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# AWS S3, DigitalOcean Spaces, Cloudflare R2, Backblaze B2, GCS
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# (S3 interop), and MinIO — boto3 with a per-provider endpoint_url.
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# Gated separately so a fresh checkout stays python3.12 + venv +
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# sqlite3; tests skip via pytest.importorskip when absent. Install with:
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# pip install 'arborist[object-store]'
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# Credentials use boto3's standard discovery (env vars,
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# ~/.aws/credentials, IAM role); never read into arborist code. The
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# wire-level test gated additionally on `moto`.
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"boto3>=1.34",
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]
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mt = [
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# Local machine-translation engine for the #000056 "Operation
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# Sandwich" cross-language grounding edges (arborist/qa/mt/):
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# translate the query es->en (retrieval-side) and render the
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# *verified English* answer en->es for display only. Helsinki-NLP
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# opus-mt (MarianMT) is Apache-2.0, hash-pinnable by HF revision
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# (reproducible — provenance-critical for a Merkle-replay system),
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# and es<->en is its best-resourced pair. Hard out of core/dev like
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# [nli]: a fresh checkout stays python3.12 + venv + sqlite3, and the
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# default suite drives the sandwich via a deterministic
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# StubTranslator so it never needs these weights. Install with:
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# pip install 'arborist[mt]'
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# Weights are NOT in the repo; they cache under
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# ~/.arborist/models/mt/. Never Hermes-3-8B (8B unfit for
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# human-language translation); never an external API (zero egress /
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# Operation Voyeur; opaque API "versions" are provenance-hostile).
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"transformers>=4.40",
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"torch>=2.2",
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"sentencepiece>=0.2",
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"protobuf>=4.0",
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"sacremoses>=0.1",
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]
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dev = [
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"pytest>=8",
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"pytest-asyncio>=0.23",
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"pytest-xdist>=3.5",
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"arborist[html]",
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"arborist[wikitext]",
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"arborist[mesh]",
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"arborist[crawler]",
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"arborist[math]",
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"arborist[hessian]",
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"arborist[vec]",
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"arborist[object-store]",
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# moto is the wire-level boto3 test stub; only needed to run
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# tests/test_cold_object_boto3.py (the default suite uses MemoryBackend).
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"moto>=5.0",
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]
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[project.scripts]
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arborist = "arborist.cli:main"
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[tool.setuptools.packages.find]
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where = ["."]
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include = ["arborist*"]
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