agentdb-explainable-recall

# Explainable Recall Standard search returns scores; explainable recall returns *features* — which dimensions of the embedding (or which keywords in hybrid search) drove the match. ## When to use - Recall quality is off and you need to debug *why* - Auditing for bias / fairness - User asks "why this result?" — show the receipts - Building UIs that surface match rationale ## API ``` agentdb_explainable_recall( query: <embedding | string> k: 5 features: 'embedding-dims' | 'bm25-tokens' | 'hybrid-both' | 'metadata' ) Returns: [ { id, score, explanation: { topDims?: [{ dim: 12, contribution: 0.18 }, ...], topTokens?: [{ token: "jwt", contribution: 0.31 }, ...], metadataMatch?: { topic: 'auth', project: 'api' } } }, ... ] ``` ## Use cases | Use | Features setting | |---|---| | Debug an unexpected high-score | `embedding-dims` — see which dims spiked | | Verify keyword fall-back works | `bm25-tokens` — see if exact terms were the driver | | Confirm metadata filters fired | `metadata` — see which filter values matched | | Build user-facing UI | `hybrid-both` — show both text-level + dim-level signals | ## Don't - Don't pipe the full feature vector to the user — it's noisy. Pick top 3-5 contributing dims/tokens. - Don't use explainable recall for normal searches. The feature extraction adds 20-50ms per query.