Optimise RAG applications with semantic caching on Databricks скачать в хорошем качестве

Optimise RAG applications with semantic caching on Databricks

Discover how to build a cost-optimized chatbot with semantic caching. Learn about the innovative approach to reduce costs and computational load in RAG systems while improving scalability and response moderation. Key moments: 0:00 - Introduction to Ellen and the topic 2:18 - Explanation of semantic caching and its benefits 4:18 - Overview of the system's process flow 6:57 - Discussion on embedding drift and cache eviction strategies 9:22 - Demo walkthrough of the solution accelerator 14:36 - Exploration of threshold setting and data distribution Ellen shares valuable insights on: • The motivation behind semantic caching for chatbots • The significance of embedding-based vector similarity • Strategies to mitigate embedding drift • How semantic caching reduces operational costs • Practical implementation using Databricks Vector Search Check out the accompanying blog post and GitHub repository for a hands-on experience with this cost-effective chatbot solution! #SemanticCaching #Chatbots #CostOptimization #Databricks #AITechnology #MachineLearning #VectorSearch #RAGSystems