AWS has decided to make their Valkey-based services significantly cheaper than their Redis counterparts. Valkey is the successor fork of Redis spearheaded by AWS and others, offering the same features and APIs but at a lower price.
This article discusses the importance of real-time access for Retrieval Augmented Generation (RAG) and how Redis can enable this through its real-time vector database, semantic cache, and LLM memory capabilities, leading to faster and more accurate responses in GenAI applications.
The article discusses strategies for reducing latency and costs in distributed systems by using zone-aware routing techniques. It emphasizes the importance of optimizing network traffic and resource distribution across multiple availability zones to maintain high availability and performance while minimizing data transfer costs.
Explore how semantic caching, which understands the meaning behind user queries, can boost performance and relevance in AI applications by storing and retrieving data based on intent.
The author describes building a GitHub repository assistant capable of answering user issues using Large Language Models (LLMs), specifically Gemini, and Redis.
The use cases covered in the article include caching, queueing, locking, throttling, session store, and rate limiting.
Redis Data Types. Redis data types are the data structures composed of fields and values, where fields are unique keys and values are the content associated with the key. Redis has nine native data structures, which are the basic types of values you can manipulate.
