ruvnet 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.