# Cosine Similarity Doesn't Know What Time It Is

> Source: <https://dev.to/devansh564/cosine-similarity-doesnt-know-what-time-it-is-5fck>
> Published: 2026-09-29 15:34:35+00:00

Vector search answers "what is most *similar* to my query?" Many real systems also need "what is most *true right now*?" Those are different questions.

While building a co-pilot for pediatric therapists, my plain vector index kept ranking a four-month-old note ("tolerated musical games well") above a note from 90 minutes earlier about an acute auditory crisis. The old note was semantically closer to the query, so it won. In a clinical setting, that's the wrong answer.

Embeddings encode meaning, not validity. A note's timestamp isn't part of the vector, so a stale fact and a fresh one compete only on wording.

Re-rank the retrieved hits with an exponential-decay recency term:

``` python
def rescore(hits, now, half_life_hours=48, alpha=0.6):
    ranked = []
    for h in hits:
        age_h = (now - h["ts"]) / 3600  # ts = epoch seconds
        recency = 0.5 ** (age_h / half_life_hours)
        score = alpha * h["sim"] + (1 - alpha) * recency
        ranked.append({**h, "score": score})
    return sorted(ranked, key=lambda x: x["score"], reverse=True)
```

A note loses half its recency weight every `half_life_hours`. `alpha` controls how much similarity matters versus freshness.

A crisis note goes stale in hours. A note like "weighted lap pad resolves agitation in about 4 minutes" is a durable protocol and shouldn't fade after a week. So give each note type its own half-life:

```
HALF_LIFE = {
    "acute_event": 6,        # hours
    "sleep_log": 72,
    "protocol": 24 * 90,
}

def rescore(hits, now, alpha=0.6, default_half_life=48):
    ranked = []
    for h in hits:
        half_life = HALF_LIFE.get(h["type"], default_half_life)
        age_h = (now - h["ts"]) / 3600
        recency = 0.5 ** (age_h / half_life)
        score = alpha * h["sim"] + (1 - alpha) * recency
        ranked.append({**h, "score": score})
    return sorted(ranked, key=lambda x: x["score"], reverse=True)
```

`alpha` and the half-lives by evaluating against real queries with known-good answers.`sim` and `recency` should both sit in roughly 0 to 1, or one term will silently dominate.
*(The examples here are illustrative, not clinical guidance, and contain no real patient data.)*