russell@unturf.com
0192255f7d
The "permacomputer" query fell into title-relevance's empty-fallback
because "permacomputer" appears nowhere in any title. Fallback was
top-1 by raw BM25, getting one tangential source. Same problem for
any user-coined term: title-LIKE alone can't find it.
_filter_by_title_relevance now grows three accept paths:
1. Title-token overlap (existing).
2. TF-IDF core keyword overlap. A doc's distinctive low-frequency
terms get distilled into its tfidf-keywords-v1 core. A query for a
rare term ("permacomputer", "unturf") hits docs whose core lists
it as a keyword even when no title does.
3. Body density. Docs that mention a query token >= 3 times pass
regardless of title or core. Cheap proxy for "actually about it."
Empty-all-three fallback widens from top-1 to top-N (default 5) — the
LLM gets enough context to honestly say "I don't know" rather than
fabricating from a single tangential source.
Implementation:
- _docs_with_core_keyword_match: SQL join chunks -> documents -> derivations
finds source docs whose TF-IDF core content matches a token. Folded
into the per-shard search loop alongside FTS5 + title-LIKE.
- _body_density_passes: counts case-insensitive occurrences in
concatenated chunk content. Threshold 3.
- _search_corpus stashes core_match_roots + root_to_shard on the
returned hits list as side-channel info for _rerank.
Makefile: distill-shards-tfidf-parallel for the per-shard TF-IDF run.
Demo on the live cluster (after TF-IDF distill of 95k cur surface docs):
Q "tell me about permacomputer ?"
sources: MOAD Cheat Sheet + Unturf AI Infrastructure Expansion
answer: synthesized from both, bounds coverage honestly
Q "what is unturf about ?"
sources: Unturf Trademark + Unturf AI Infrastructure Expansion
answer: "grassroots, community-driven initiative ... ethical AI
hardware, software, and networks"
Both queries now find user-coined terms via TF-IDF cores even though
the terms never appear in any title.
79 tests passing.
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| aborist | ||
| bench | ||
| tests | ||
| .gitignore | ||
| CLAUDE.md | ||
| 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.