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store: Fix: decay filter bypass in semantic_recall() — cognitive-memory

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Cal Corum 2026-03-02 23:07:32 -06:00
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---
id: 96a6c125-bb69-45af-b1b6-a9b48654fb7e
type: fix
title: "Fix: decay filter bypass in semantic_recall() — cognitive-memory"
tags: [cognitive-memory, python, fix, decay, embeddings, semantic-recall]
importance: 0.6
confidence: 0.8
created: "2026-03-03T05:07:32.495008+00:00"
updated: "2026-03-03T05:07:32.495008+00:00"
---
## Problem
`semantic_recall()` in `embeddings.py` scored all memories in `_embeddings.json` against the query vector with no decay filter. Memories with `decay_score < THRESHOLD_DORMANT` (0.05) — archived/dormant — could re-surface if their semantic similarity was high enough, bypassing the decay system's intent.
The keyword path in `recall()` (`client.py`) already applied this filter correctly.
## Root Cause
`EmbeddingsMixin.semantic_recall()` only loaded the index (`_load_index()`), not the state (`_load_state()`). The decay threshold guard was missing entirely.
## Solution
Added `THRESHOLD_DORMANT` to `embeddings.py` imports from `common`. In `semantic_recall()`, loaded `_state.json` via `self._load_state()` and added a `decay_score < THRESHOLD_DORMANT` skip before computing cosine similarity.
## Files Changed
- `embeddings.py` — 6-line change: import + state load + filter
## PR
https://git.manticorum.com/cal/cognitive-memory/pulls/7