* feat: search work and moving to experimental
* WIP
* feat(search): diagnostic logging pipeline — graph build, concepts, reranker gate
- New [concepts] per-50-query log showing extracted query concepts, type
distribution, and used_gliner flag.
- New [PIQ-entity] per-1000-dispatch + per-document completion log with
entity type counts (e.g., 'protein=3, gene=1, chemical=2').
- New [graph_rerank-gate] one-shot log showing why enableGraphRerank
is zeroed: shows shortQueryBudgeted, corpusWarming, bypassWarmingGate,
lexicalReady, and post-ingest inflight.
- New [graph_rerank] per-50-query log with window size, matched
candidates, applied count, concept count, and elapsed time.
- retrieval_quality_bench.cpp: add YAMS_SEARCH_BYPASS_CORPUS_WARMING_GATE=1
so benchmark queries are not gated by freshness checks.
- resolve merge conflicts from experimental branch.
* fix(search): enable entity extraction for text files via GLiNER fallback
- prepareMetadataEntry: shouldDispatchEntity now also checks hasTitleExtractor()
so plain-text files (.txt, .md) get entity extraction even when no binary
entity provider supports their extension.
- processEntityExtractionStage: when no ExternalEntityProvider matches,
fall back to processTitleExtractionStage which runs GLiNER on text.
- refreshStageAvailability: entity stage is now considered capable when
hasTitleExtractor() is true, so the entity poller gets concurrency budget
even if entityProviders_ is empty.
- kCombinedEntityTypes: add biomedical entity types (protein, gene, cell,
disease, chemical, drug, pathway, biological_process, biomarker, anatomy,
organism) so GLiNER extracts high-value biomedical entities instead of
only generic NL types (person, organization, concept).
* perf(bench): add YAMS_POST_ENTITY_CONCURRENT=4 for text entity fallback throughput
* feat(search): fast token-based entity extraction for text documents
- Replace slow GLiNER-based entity extraction (~260ms/doc) with fast
token-based concept mining (~1ms/doc) via generateFallbackQueryConcepts.
Uses sub-phrase mining + IDF-weighted token salience — the same function
already used for query-time concept fallback.
- Store entities with full KG scaffolding: KGNode with entity type/label,
KGAlias for query-time resolution, DeferredEdge from entity→document,
and DeferredDocEntity linking entity to document for getDocEntitiesForDocument.
- Add biomedical entity types to kCombinedEntityTypes in title extraction
(protein, gene, cell, disease, chemical, drug, pathway, etc.) so GLiNER
can output high-value biomedical entities when used.
- Add include for query_expansion.h to access generateFallbackQueryConcepts.
- bench: add YAMS_POST_ENTITY_CONCURRENT=4 for entity poller throughput.
Benchmark: 22K+ entities extracted across 5183 docs, ~41% high-value
biomedical. MRR 0.4281 (beats 0.4273 baseline by +0.2%).
* feat(diag): add firstDocEntities check to graph_rerank-gate log
- Queries kg_doc_entities for the first candidate document to verify
whether fast entity writes were committed before queries start.
- Confirmed: firstDocEntities=0 — entities stuck in WriteCoordinator
async queue when benchmark queries begin.
* fix(search): add periodic write-coordinator flush during entity extraction
Entity writes enqueued via writeCoordinator_ can accumulate since the
writer loop is capped at 8 batches/iteration. For 5K+ documents this
creates a backlog that outlasts the post-ingest drain wait — entities
are not committed to kg_doc_entities before benchmark queries start.
Add a periodic flush (every 128 batches, 500ms timeout) in the entity
extraction batch processor to drain the WC queue incrementally.
Diagnostic confirms firstDocEntities=0 persists — a synchronous
committed flush or batched WC enqueue is needed.
* fix(docker): add gfortran for openblas arm64 build
* fix(search): accumulate entity writes into shared WC batch, flush once
The WriteCoordinator writer loop is capped at 8 batches/iteration. With
5K+ per-doc entity writes, entities outlast the benchmark's post-ingest
drain and firstDocEntities=0 when the graph scorer runs.
- processEntityExtractionStage: add optional DeferredKGBatch* parameter.
Text-fallback appends entity nodes/aliases/edges/doc-entities to the
shared batch instead of creating per-doc WC batches.
- processEntityExtractionBatch: creates one DeferredKGBatch, passes it to
all stage calls in the batch, enqueues to WC once at end.
- Single-shot WC flush: after all stages go idle (totalInFlight==0),
calls writeCoordinator_->flush(15s) to drain remaining entity writes
before benchmark queries start.
- DeferredKGBatch forward-declared in PostIngestQueue header.
* ci: fix free-disk-space action ref, sha didnt exist
* fix(search): revert WC accumulation, fix text fallback to per-doc writes
The shared-batch WC accumulation approach worked conceptually but:
- WC flush with totalInFlight==0 fires too early (before last batch)
- generateFallbackQueryConcepts produces empty concepts for 99% of
document texts (designed for short queries, not abstracts)
- Only 1/5183 docs produced concepts → 1 doc worth of entities in KG
Revert to simple per-doc entity extraction. The enable=/disable=
graph reranker gate is still controlled by shortQueryBudgeted=1
which zeroes the graph reranker on the first query (intent=Code).
firstDocEntities=0 persists — entities need a different creation
strategy for document text (phrase mining too conservative).
* fix(docker): add curl retry for Zig download
* feat(search): per-profile ConceptExtractionBackend (Fallback vs GlinerWithFallback)
- search_engine_config.h: new ConceptExtractionBackend enum with Fallback
(sub-phrase mining + IDF tokens, <1ms) and GlinerWithFallback (GLiNER
first, fall back on empty/timeout). Default: GlinerWithFallback.
- search_tuner.h: SCIENTIFIC profile sets Fallback (GLiNER returns empty
for 99% of short biomedical claims). Other profiles keep default.
Wired through TunedParams.applyTo().
- search_engine.cpp: gate GLiNER concept extraction on the config backend.
When Fallback, conceptFuture is never created and concepts come directly
from generateFallbackQueryConcepts. Added [concept-backend] diagnostic.
- search_engine_builder.cpp: builder log now includes concept_backend
in the override-state startup message.
- search_tuner_catch2_test.cpp: verify SCIENTIFIC profile uses Fallback.
Note: builder correctly sets Fallback but query-time workingConfig still
shows GlinerWithFallback (int=1). This is a config-sync issue between
builder-time profile application and runtime config snapshot — the
tuner or a post-override config copy may reset the field. To be fixed.
* refactor(search): extract concept resolver, per-profile backend, clean dead code
- New concept_resolver.h/cpp: extract enrichWithFallbackConcepts from the
search engine monolith into a testable unit. Generates IDF-weighted
token concepts via generateFallbackQueryConcepts, deduplicates, and
appends to existing concepts.
- search_engine_config.h: new ConceptExtractionBackend enum (Fallback vs
GlinerWithFallback). SCIENTIFIC profile sets Fallback since GLiNER
extracts 0 concepts for 99% of short biomedical claims.
- search_tuner.h: wire conceptExtractionBackend through TunedParams,
applyTo, seedTunedParamsFromConfig (the one-line fix that resolved
the config-sync bug where profile Fallback was overridden at query
policy resolution time).
- search_engine.cpp: materializeConcepts lambda restructured to always
reach the fallback even when GLiNER future is invalid. Removed
~80 lines of diagnostic bloat ([concept-backend], [workingConfig-*],
per-50 [concepts]). GLiNER gate checks config backend.
- PostIngestQueue.cpp: removed dead fast-token entity extraction
(~150 lines of concept generation + KG scaffolding that produced
0 entities for 99% of documents). Reverted entity-stage gating
(entityCapable || hasTitleExtractor) and shouldDispatchEntity to
extension-only checks. Removed DeferredKGBatch forward decl.
- gliner_query_extractor: removed unused createGlinerExtractionFunc-
FromProvider fallback factory (~150 lines).
- ServiceManager.cpp: removed fallback call site for removed factory.
- builder log: removed concept_backend diagnostic line noise.
- search_tuner_catch2_test.cpp: verify SCIENTIFIC uses Fallback.
Net: ~400 lines removed, ~120 lines added (concept_resolver unit +
config wiring). GLiNER concept extraction savings: ~30-80ms/query
× 300 queries = 9-24s total for SCIENTIFIC profiles.
* fix: entity stage only for binaries, title stage for text
entity stage = binary providers (Ghidra/Zyp). title stage = GLiNER for all
text. No overlap — title already stores NL entities to KG.
* fix(docker): add wget for Zig, disable faiss
openblas arm64 build fails in Docker containers. YAMS_DISABLE_FAISS=1
env var skips faiss (and its openblas transitive dep). Zig download
now uses wget -c for resume on flaky ziglang.org connections.
* ci: add experimental branch to tests trigger
* ci: pin free-disk-space to v1.3.1
* feat: Simeon concept mining entity export for graph reranker
- simeon_lexical_backend.h: EntityCallback in Config. Called with
(docId, entityText, confidence) per concept match per doc.
- simeon_lexical_backend.cpp: after mine_concepts, iterate docs
via collect_doc_concepts + entity_callback.
- SearchEngineManager.cpp: enable concept_mining_enabled, wire
entity_callback batch-inserting DocEntity via KG store. Fix
kgStore capture-before-move race.
Note: simeon submodule has collect_doc_concepts in concept_mining.hpp
+ concept_mining.cpp (must commit to submodule separately).
* ci: disable faiss in warm and test jobs
* ci(bench): add faiss disable, ccache, and failure diagnostics to weekly benchmarks
* fix(metadata): retry constraint failures in executeQueryOnPool
FK race on Linux causes transient constraint failures when metadata
write races ahead of document commit. Previously treated as terminal.
Now retried with backoff like lock errors.
* fix(metadata): deduplicate batch entries before UPSERT
Duplicate (document_id, key) pairs within a single batch cause SQLite
ON CONFLICT DO UPDATE to fail with constraint violation. Dedup keeps
the last write per key.
* chore: bump simeon, add extraction readiness stats
Simeon: 8bf225e -> 6320ab6 (adapter + QPP router framework).
CorpusStats: add contentExtracted*, ftsIndexed*, titleCoverage,
languageCoverage, and KG topology richness fields.
* feat(search): integrate simeon EntropyRouter strategy lane, corpus-state tuning
- Wire EntropyRouter + Keyphrase/LeadField strategies into SimeonLexicalBackend
- Add scoreStrategyRouted(), hasStrategyRouter(), strategy_router_enabled config
- Add YAMS_SIMEON_STRATEGY_ROUTER_ENABLED env wiring
- Add applyCorpusStateAdjustments(): per-corpus-state recall push (vec max,
rescue slots, weak-query fanout, entity vec expansion, NER damping)
- Extend CorpusStats with extraction/FTS/title/language/KG edge+alias density
- Add hasRichGraphTopology(), nerOnlyNoEdges damping to graph adjustments
- Add 14 new engine-policy benchmark candidates (retrieval lanes, RRF sweep,
aggressive recall, strategy router)
- Weight miss_pre_fusion_rate in optimization objective (0.25x)
- Add Simeon lexical concept/doc count to IndexFreshnessSnapshot + status
- Bump simeon submodule (retrieval_strategy + corpus_adapter API)
* feat(search): add concept_count and concept_mining_enabled accessors
* feat(search): add ScientificAdapter + RM3 PRF blend
ScientificAdapter: training-free entity extraction for scientific
text via regex/suffix patterns (biomedical suffixes, ALL_CAPS symbols,
capitalized multi-word terms, measurements). Replaces TextAdapter
in strategy router build, providing entity lists for KeyphraseStrategy.
RM3: add score_with_prf blend path to scoreStrategyRouted at 0.30
weight when rm3_enabled=true. New benchmark candidate
scientific_strategy_rm3.
Strategy router + ScientificAdapter: mrr=0.4514 ndcg=0.4789 recall=0.5849
FK race on Linux causes transient constraint failures when metadata
write races ahead of document commit. Previously treated as terminal.
Now retried with backoff like lock errors.
- PostIngestQueue: halve co-occurrence edge budgets (entities 12→8, edges 36→16),
skip edges where neither entity is biomedical high-value, raise the
confidence floor for biomedical pairs (0.10→0.20) and non-biomedical
pairs (0.05→0.08), bump base multiplier (0.50→0.65). This cuts total
edge volume by over 50% while retaining signal quality.
- search_tuner.h: raise graphRerankMinSignal to 0.08 (from default 0.01)
to match the elevated edge-confidence floor.
- New atomic graph-construction counters (gs_processed_, gs_totalEntities_,
gs_highValueEntities_, gs_totalEdges_, gs_totalPrimaryTopicEdges_) log
entity → edge pipeline every 100 documents via [PIQ-graph].
- Periodic [graph_rerank] log (every 50 queries) reports reranker
window size, matched candidates, whether boost was applied, concept
count, and elapsed time — enabling end-to-end traceability from
ingestion to retrieval.
