Redis has announced new tools aimed at making it easier for AI developers to build applications and optimize large language model (LLM) outputs.
The new tools include LangCache, a fully-managed semantic caching service for applications and AI Agents that Redis said will help improve accuracy and speed.
Vector sets, meanwhile, are a new native data type that allows developers to more easily work with and scale vectors.
“Both give developers a simpler way to work with the complex data needed to build agentic apps,” said Redis CEO Rowan Trollope in a blog post.
LangCache looks to help developers integrate large language model (LLM) response caching into applications, with a REST API interface for easy implementation and optimizations included for accurate caching performance, the company said.
“Semantic caching is essential for GenAI applications, as it significantly reduces response latency and improves cost efficiency while maintaining high-quality user interactions,” said Trollope in the blog post.
LangCache lets developers take a user query that’s been asked before and return a relevant response that was cached, helping to save on costly calls to LLMs while also speeding up apps.
Redis said the tool will also help improve the accuracy of LLM cache retrieval using custom models and configurable search criteria, allow developers to choose a model provider of choice, and govern responses so apps bring back the right data approved for the current user.
“LangCache and vector sets give developers a simple way to handle the complex data needs that come with building agent-based AI apps,” said Trollope in a statement.
“Just as traditional apps need a cache that stores frequently accessed data, agents need fast access to the data that helps them make decisions to complete their tasks. LangCache speeds up responses and provides more accurate answers, while vector sets give them a simple, elegant way to store and retrieve the data.”
Vector sets
The other new announcement from Redis is vector sets: a new native data type that lets developers more easily work with vectors. Vector sets allow the storage and querying of high dimensional vector embeddings — crucial for AI and machine learning, the company said.
Vector sets were initially developed by Redis creator Salvatore Sanfilippo.
“They take inspiration from sorted sets, and extend this concept to store and query vector embeddings to search data semantically,” Trollope wrote.
“Like a sorted set, a vector set has string elements, but now they’re associated with a vector instead of a score. The fundamental goal of vector sets is to make it possible to add items, and later get a subset of the added items that are the most similar to a specified vector.”
They come with “exciting additional capabilities,” the company said, including quantization, dimensionality reduction, filtering, and multi-threading.
Vector sets are currently available in beta with Redis 8, which is itself available as a release candidate ahead of general availability in the next few weeks.
Last year Redis announced changes to its licensing that would shift it to a more restrictive software distribution approach that would see cloud providers required to enter into commercial agreements with the company. The move sparked the Linux Foundation to create rival Valkey.
