Semantic caching can use any vector store. If you’re already using a vector store such as Qdrant, you can use it to speed up semantically similar requests and reduce token usage without adding another database to your stack.

#SpringAI #Java #GenAI #SemanticCaching #VectorDatabase

https://medium.com/@thetalkingapp/spring-ai-recipe-semantic-caching-with-any-vector-store-8b5022be91cb

New research shows semantic caching can cut LLM inference costs by up to 73%—even when cache hits are misleading. The AdaptiveSemanticCache uses a QueryClassifier and similarity thresholds to decide when to reuse embeddings from a vector_store, dramatically reducing token usage. Curious how this works and how you can apply it to your own models? Read the full breakdown. #SemanticCaching #LLM #VectorStore #EmbeddingModel

🔗 https://aidailypost.com/news/semantic-caching-can-slash-llm-costs-by-73-despite-misleading-cache

Optimize LLM Costs with ScyllaDB Semantic Caching

https://techlife.blog/posts/cut-llm-costs-and-latency-with-scylladb-semantic-caching/

#AI #SemanticCaching #LLM #ScyllaDB #VectorSearch

Explore how #RetrievalAugmentedGeneration & #SemanticCaching can reduce #FalsePositives in AI-powered apps.

Insights come from a production-grade #CaseStudy testing 1,000 queries across 7 bi-encoder models.

📰 Read now: https://bit.ly/4nTPmso

#AI #LLMs #RAG #VectorDatabases #Infrastructure