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

Discover how a vector store can act as a model's local memory in our new LLMOps guide. Learn to set up FAISS with LangChain, generate embeddings in Python, and boost your OpenAI workflows. Turn your LLM into a smarter, self‑retrieving system—read the full walkthrough now! #LLMOps #VectorStore #FAISS #LangChain

🔗 https://aidailypost.com/news/llmops-guide-shows-how-vector-store-becomes-models-local-memory

[LangChain編] 新リリース Oracle Database 23ai と Cohere で実装するエンタープライズRAG
https://qiita.com/ksonoda/items/d434aca84d6e6dacb3f1?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #oracle #GenerativeAI #langchain #LLM #VectorStore

新リリース Oracle Database 23ai と Cohere で実装するエンタープライズRAG
https://qiita.com/ksonoda/items/c300e734a5b1bef7b872?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #OpenAI #rag #LLM #VectorStore #Cohere

生成AIでセキュリティの課題をどこまで改善できるか考える
https://developers.cyberagent.co.jp/blog/archives/45548/

#developers #エンジニア #Bedrock #LLM #Qdrant #RAG #SSG #VectorStore #セキュリティ #生成AI