CI Postgres shard caught two issues invisible to the local SQLite suite:
1. Postgres _prepare_single_term regression: the new edge-punctuation
strip ran after special-character cleaning, so an all-special-char
term ("()&!:") collapsed to empty and skipped the existing
NOSPECIALCHARS:* guard, emitting a malformed ":*". Folded the strip
into the word handlers so every guard survives, and added a
single-word empty guard.
2. Backend-specific test assumptions. Four tests in
test_search_repository.py (run under both backends via the
search_repository fixture) asserted SQLite FTS5 syntax and
SQLite-only strict-miss behavior. Postgres to_tsquery('english', ...)
auto-strips stopwords, so "When did Melanie paint a sunrise?" already
matches under strict AND. Made the four tests backend-aware via the
existing is_postgres_backend() helper, and switched the relaxation
integration test to a query with a word absent from the doc
("hiking") so the strict miss holds on both backends.
Reproduced and fixed against real Postgres (testcontainers): full
search test surface green on both backends (53 passed Postgres,
2968 SQLite), ruff + ty clean.
Signed-off-by: Drew Cain <groksrc@gmail.com>
Hybrid search was silently running vector-only on natural-language
queries — the FTS branch contributed zero candidates. Two causes in the
SQLite (and parallel Postgres) FTS query preparation:
1. Sentence punctuation forced phrase matching. A question like
"When did Melanie paint a sunrise?" reached FTS5 as the exact phrase
'"When did Melanie paint a sunrise?"*', which matches no document.
The FTS5 tokenizer ignores this punctuation in the index, so
stripping it from word edges loses nothing — but leaving it disabled
the entire FTS contribution. _prepare_single_term now strips
?!.,;: from word edges of multi-word queries (interior characters —
hyphens, slashes in permalinks/paths — untouched).
2. No relaxation when strict all-terms-AND matched nothing. Questions
rarely have every word in one document, so even after (1) the
strict AND returned zero rows. The hybrid path now retries once with
an OR-joined, stopword-filtered, content-term query when the strict
query is empty. bm25/ts_rank still rank multi-term matches first, and
fusion with the vector branch keeps relaxed lexical candidates from
dominating precision.
The relaxation is gated behind a new allow_relaxed=False parameter on
SearchRepositoryBase.search; only _search_hybrid opts in. Strict FTS
behavior (search_type=text, title, permalink, link resolution) is
unchanged — the service layer keeps its own conservative fallback.
No config flag, default-safe.
Discovered via the benchmark harness: two different fusion algorithms
produced byte-identical rankings across 1,986 queries (impossible with
two live sources), and instrumentation confirmed fts=0 on 40/40
sampled LoCoMo queries.
Benchmark impact (corrected LoCoMo, 1,986 queries, same index,
retrieval metrics — every category improves, no regression):
recall@5 0.745 -> 0.823 (+7.9)
MRR 0.618 -> 0.718 (+10.0)
headline r5 0.734 -> 0.801, MRR 0.621 -> 0.706
Largest gains on open_domain (+0.10 r5) and adversarial (+0.12 r5);
smallest on temporal (+0.003 r5 / +0.02 MRR).
Tests: punctuation no longer phrase-quotes; relaxation builds the
expected OR query and respects boolean/quoted/short-query intent; the
hybrid opt-in surfaces a partial-overlap document while the default
strict path still returns empty. Parallel coverage for Postgres. Full
SQLite unit suite green (2968 passed); ty + ruff clean.
Signed-off-by: Drew Cain <groksrc@gmail.com>
Adds LiteLLM as a semantic embedding provider, including provider configuration, vector normalization, live-provider evaluation tooling, and documentation for OpenAI, Cohere, Azure Foundry, Azure OpenAI, and NVIDIA NIM-style cases.
Maintainer follow-up on this PR added provider hardening, asymmetric document/query embedding support, dimension-forwarding controls, SQLite/Postgres vector invalidation coverage, and the repeatable live LiteLLM harness.
Verification:
- Full base-repo Tests workflow passed for 3d4e092ceb: https://github.com/basicmachines-co/basic-memory/actions/runs/27072071785
- Live LiteLLM harness passed locally for OpenAI, Cohere, and Azure Foundry.
Co-authored-by: Aarish Alam <arishalam121@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signed-off-by: RheagalFire <arishalam121@gmail.com>
Signed-off-by: phernandez <paul@basicmachines.co>
RRF compressed all fused scores to ~0.016, destroying ranking differentiation.
The new formula `max(vec, fts) + FUSION_BONUS * min(vec, fts)` preserves
dominant signals and rewards dual-source agreement.
Changes:
- Remove RRF_K constant; add FUSION_BONUS (0.3) and FTS_GATE_THRESHOLD (0.0)
- Use raw vector similarity scores instead of re-normalizing by vec_max
- Zero-score results now produce zero fused score (no 0.1 weight floor)
- Rename test_hybrid_rrf.py → test_hybrid_fusion.py with updated assertions
- Update docs and docstrings to reflect score-based fusion
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Replace f-string interpolation with parameterized queries for note_types,
search_item_types, and metadata filter paths to prevent SQL injection.
Fixes#591
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Drew Cain