Lexical istr_contains alone can't surface a node whose words don't appear
in the query. This adds an optional dense-vector layer: node content and the
query are embedded through Ollama (nomic-embed-text), and nodes are ranked by
cosine similarity unioned with lexical hits, so a paraphrase query reaches the
right node.
Wired into all three query entry points in el_runtime.c:
- engram_search_json (HTTP /api/search): collect lexical ∪ semantic
candidates, score (lexical base 1.0 + cosine; pure-semantic = cosine),
rank, emit top-N. Stable sort preserves old order when semantic is off.
- engram_search (internal el_val twin): lexical ∪ semantic union.
- engram_activate seed loop (HTTP /api/activate): a node seeds if it
lexically matches OR clears the cosine threshold; pure-semantic seeds
enter scaled by cosine so paraphrase spreads without overpowering.
Degradable by design: the whole layer is gated on HAVE_CURL plus a one-shot
runtime probe. If curl is compiled out, Ollama is unreachable, or
ENGRAM_SEMANTIC=0, every entry point yields zero semantic signal and callers
fall back byte-for-byte to the pre-existing lexical search.
Node embeddings are cached in process memory keyed by node id with an FNV-1a
content hash for invalidation; the query is embedded once per call — so the
graph is not re-embedded on every query. nomic task prefixes
(search_query:/search_document:) are applied for retrieval separation.
Build steps gain -DHAVE_CURL so the engram artifact compiles the layer in
(-lcurl was already linked). Env: ENGRAM_SEMANTIC, ENGRAM_EMBED_URL,
ENGRAM_EMBED_MODEL, ENGRAM_SEMANTIC_MIN (cosine threshold, default 0.6).
will.anderson
b4967af13e