Level up RAG apps with Redis Vector Library

Redis has introduced the Redis Vector Library (RedisVL) to simplify the development of AI applications and support Large Language Models (LLMs). The library is a dedicated Python client for using Redis for AI, offering an opinionated set of commands that allow developers to focus on their app. RedisVL provides efficient management of chat history by selecting the most relevant context from the session, ensuring optimal performance and cost-effectiveness through the power of a super fast vector database. Additionally, semantic caching is introduced to improve the performance of LLM systems by returning cached responses instead of calling costly models repeatedly for similar queries. The benefits of semantic caching include substantial cost savings, faster response times, and enhanced user privacy control.