October 2024 Summaries

Client-side caching support is now available in Redis official open-source client libraries, allowing users to read frequently accessed data, reduce latency, and use resources more efficiently. This feature enables near caching directly with the official client libraries, reducing network traffic and latency by serving frequent read operations from a cache on the same application server where the client is running. Client-side caching can be easily enabled with just a few lines of code during connection establishment time. The feature is fully compatible with Redis Software, Redis Cloud, and Azure Cache for Redis Enterprise, where it is currently in preview and will soon become generally available.

The first official Redis extension for Visual Studio Code is now available, allowing developers to work with Redis directly within their IDE. This extension supports real-time data browsing and interaction, as well as fast access to an enhanced Redis Command-Line Interface (CLI). It also offers features like syntax highlighting, auto-completion, and formatters for various data types. The extension is designed to keep up with the latest Redis features and can connect to any Redis server, including Redis Community Edition, Redis Cloud, Redis Software, and Redis on Azure Cache. Developers are encouraged to provide feedback through the GitHub repository.

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.

Redis has introduced a new AI-powered tool called Redis Copilot, designed to boost productivity and ease the learning curve for developers working with Redis databases. The tool offers two key features: a general knowledgebase chatbot and a context-aware chatbot that creates and runs queries to answer questions about data in a Redis database. Redis Copilot is accessible through the latest Redis Insight GUI dev tool, while the general chatbot can be found in public docs. The development of this natural language query builder chatbot involved using prompting and fine-tuning techniques on pre-trained models to generate accurate Redis queries based on user input.

Three security vulnerabilities in Redis have been published recently, including a high-risk Lua library command exploit (CVE-2024-31449), a moderate risk denial-of-service due to unbounded pattern matching (CVE-2024-31228), and another moderate risk denial-of-service due to malformed ACL selectors (CVE-2024-31227). These vulnerabilities require an attacker to gain access to the Redis instance. To protect against these threats, users should follow best practices such as upgrading their Redis versions and securely configuring, deploying, and using Redis. The Redis Cloud service has already been updated with fixes for these vulnerabilities.

Redis Copilot is an AI assistant designed to assist developers in building faster applications using Redis. It uses the RAG approach and vector database for real-time information retrieval, ensuring that relevant contextual information is provided based on user prompts. The platform also facilitates conversation history retention through preferred data types, enhancing interaction quality and customization. Redis Copilot stores input prompts and responses in a cache to reduce the computational expenses associated with ML-driven apps. It offers full-text search capabilities for retrieving information by matching words or phrases, as well as highlighting and summarization features for fast and readable results. The platform also supports microservice architecture and rate limiting to ensure stability and balanced usage across different sessions and IPs. Redis Copilot can be accessed through Redis Insight, with additional resources available for building AI apps.

In September, Redis introduced new features and improvements. Firstly, they launched Redis University for users to learn about Redis from beginner to expert level. Secondly, version 1.8.0 of the Redis Cloud registry in Terraform was released with enhancements and feature alignment. Thirdly, Redis Cloud Essentials was made available on Vercel Marketplace, along with a new Vercel template for quick deployment. Lastly, users can now configure built-in MFA without SMS, improving security and avoiding issues in restricted countries. Additionally, the Redis Released London event took place on September 12th, with upcoming events in NYC and a virtual event on November 6-7.