Semantic caching stores vector embeddings of incoming API requests and their corresponding AI responses. When future requests arrive holding a similar semantic meaning (even if phrased entirely differently), the gateway can instantly reuse the prior cached result—drastically reducing token volume, slashing latency, and saving cost.
How Hyperion Implements It
- Layered Architecture: Hot data rests in Redis (exact match string cache), semantic vectors live in Qdrant/FAISS, and cold historical archives rest in S3.
- Granular Thresholds: The similarity threshold is completely configurable per route and per tenant.
- Cascading TTLs: Configure expiration windows separately per caching tier (e.g., 1 hour hot, 6 hour semantically fuzzy, custom limits for S3).
- Real-time Analytics: Built in dashboards provide clear reporting on hit/miss frequencies and similarity proximity histograms.
Example Configuration (YAML)
caching:
enabled: true
layers:
- type: redis
ttl: 3600
- type: qdrant
vector_threshold: 0.82
ttl: 21600
- type: s3
ttl: 259200Best Practices & Required KPIs
Semantic caching is incredibly effective for standard documentation retrieval augmentation, summarization of static objects, and general FAQ-like chatbots. However, you should disable fuzzy matching layers for explicitly personalized workflows or highly time-sensitive financial inputs.
Metrics You Can Track In-App
- Total Hit Rate (Overall & Endpoint)
- Average Semantic Similarity Score per hit
- Total Tokens Avoided (Generated vs Bypassed)
- Effective USD Savings per Month
- Average Latency Delta (Cache vs Live Generation)
Semantic Caching FAQs
It can if thresholds are too loose; tune by tracking similarity and human review.
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