β‘ LLM Caching β’ Cosine Similarity β’ GPTCache & Redis VL β’ False-Positive Prevention
Semantic Cache & Similarity Threshold Calibrator
Exact match caches miss 90% of repeated LLM intents. Semantic caching matches queries by vector similarity, but setting the threshold too low returns hallucinated stale answers. Calibrate your optimal similarity boundary ($\tau$).
Test Query Comparison
Cache Threshold Boundary (τ) 0.88
0.75 (Permissive / High False Hit Risk) 0.88 (Production Sweet Spot) 0.98 (Near-Exact Match)
Embedding Model Baseline Profiles
- text-embedding-3-small (1536d): Recommended threshold τ = 0.88 - 0.91
- text-embedding-3-large (3072d): Recommended threshold τ = 0.86 - 0.90
- Cohere Embed v3 (1024d): Recommended threshold τ = 0.84 - 0.88
Estimated Semantic Match Score
TF-IDF & Jaccard token overlap heuristic
π’ CACHE HIT (Serve Instant Response)
Simulated Cosine Similarity
0.92
+0.04 above threshold (τ = 0.88)
Production GPTCache Init Code