russell@unturf.com
85ae761df8
Profile of the prior implementation (cProfile on 2000-doc ingest):
total 23.6s
parser 20.1s (85% of time, 27.7M list.append calls)
DB writes 2.9s
Three changes compound:
1. PRAGMA pack in store.connect():
synchronous=NORMAL skip per-commit fsync (durable under WAL replay)
cache_size=-65536 64 MB page cache
temp_store=MEMORY temp tables in RAM
mmap_size=256 MB page-cache reads bypass read() syscall
2. Batched transactions in ingest_source and distill_existing:
200 docs/tx default, --batch-size CLI flag.
One BEGIN/COMMIT per batch instead of per doc.
Audit chain SELECT-then-INSERT still chains correctly because
SQLite reads its own uncommitted writes within a transaction.
Idempotent on collision (INSERT OR IGNORE / pre-existence check)
so partial-batch re-runs are safe.
3. Parser rewrite (the actual bottleneck): replaced char-by-char
list-append loops with str.find / string slicing.
_decode_mysql_string fast-path: if no '\\\\' in input, return as-is.
_split_values_tuples uses _find_string_end (str.find with
backslash-parity check) instead of per-char scanning.
_iter_insert_statements buffered scan (str.find for ';' and "'")
crossing read-chunk boundaries via in_string flag.
~13x fewer Python function calls, semantics identical.
_upsert_edges now batches via executemany.
After: 2000 docs in 6.0s; 5000 old revisions in 12.8s wall (was 32.3s).
Throughput 85 -> 333 docs/sec. 49 tests still passing.
Cheaper to fail = cheaper to experiment.
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| aborist | ||
| tests | ||
| .gitignore | ||
| LICENSE | ||
| Makefile | ||
| pyproject.toml | ||
| README.md | ||
aborist
An arborist for trees and forests of cross-linked information.
Aborist ingests documents into a content-addressed, Merkle-committed store. Every
document carries a URI for backtrack and cross-link. Search hooks return hits with
declared audit modes (STRICT / HYBRID / VISUAL) so callers never overclaim
what a result actually proves.
Design
Aborist implements the runtime spec from Merkle-AGI v9.8 / Merkle Providence
Reverse RAG. The 8-dim admissibility key (source_root, question_hash,
model_profile_hash, conversation_hash, governance_policy_hash,
schema_version, canonicalization_version, chunking_version) plus
falsification state ensures cached records are never reused under drift.
Two layered document kinds:
- surface — diverse ingested content (full chunks, FTS-indexed)
- core — distilled records (haiku/equation/snippet) Merkle-signed back to
source surface roots via
derivations
Reversible eviction via chunk tier ∈ {hot, warm, cold}. Cold = leaf hash
- URI only; rehydratable from URI, identity verifiable via leaf hash.
Quick start
make all # bootstrap + fetch + ingest + verify + stats
make search Q='…'
License
AGPL-3.0-only.