Neuron
315b2eff00
engram_search_json — the function /api/neuron/recall actually reaches — ranked only by distinct-token match count, so the embedding field on every node record was inert. Add the semantic leg as a UNION beside the lexical one, not a replacement for it: fused = (distinct_tokens_matched / query_tokens) + 0.90 * sem sem = clamp01((cos(q,n) - 0.60) / (1 - 0.60)) ; 0 when not comparable Holding the semantic weight strictly below 1.0 means a node matching every query token can never be displaced by semantics alone — the regression guard that PR #135 lacked when it swapped the read path to spreading activation and took phrase recall from 85.7% to 28.6%. No query embedding (embedder down, circuit breaker open) => sem == 0 for all nodes => fused == sc/ntok, a monotone map of the old integer score, so the ordering degrades to the historical behaviour exactly. Restores engram claim 24: 'maintain a vector similarity index over the semantic embedding vectors of all stored node records, and ... respond to embedding search queries by returning the node records whose embedding vectors have the highest cosine similarity to a query vector, independently of the spreading activation traversal.' Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>