Users expect search functionality in every application and website they encounter. Yet more than 80% of business data is unstructured, stored as text, images, audio, video, or other formats.
Organizations need to reimagine the ways to make every kind of data discoverable – not least of which is because users demand it. Powerful search features will fuel the next generation of applications.
A vector database is a type of database that stores data in the form of vectors or mathematical representations of data points. AI and machine learning is what is enabling this transformation of unstructured data into numeric representations (vectors) that capture meaning and context, benefiting from advances in natural language processing and computer vision.
Vector Similarity Search (VSS) is a key feature of a vector database. It is the process of finding data points that are similar to a given query vector in a vector database. Popular VSS uses include chatbots, recommendation systems, document search, image and video search, natural language processing, and anomaly detection. For example, if you build a recommendation system, you can use VSS to find (and suggest) products that are similar to a product in which a user previously showed interest. (Need a deep dive? This should do it.)
Traditional keyword matching and filtering only takes you so far. Sure, ordinary search algorithms are useful for text and document use cases. However, search results are limited when they do not incorporate meaning or context. The proliferation of unstructured data created a huge gap in the effectiveness of traditional keyword matching and filtering. Every organization that stores non-textual data – and that’s just about everyone – can benefit from improving search functionality across unstructured data. But until recently, only a handful of large cloud-native tech companies had this capability.
Redis Enterprise Cloud integration with AWS Bedrock now available
Real-time search performance
Search and recommendation systems have to run incredibly fast. The VSS functionality in Redis Enterprise guarantees low search latency, whether the data collection is tens of thousands or hundreds of millions of objects, distributed across a number of database nodes.
Built-in fault tolerance and resilience
To ensure your search applications never experience downtime, Redis Enterprise uses a shared-nothing cluster architecture. It is fault tolerant at all levels, with automated failover at the process level, for individual nodes, and across infrastructure availability zones. To ensure your unstructured data and vectors are never lost, Redis Enterprise includes tunable persistence and disaster recovery mechanisms.
Reduce architectural and application complexity
Most likely, your organization already benefits from Redis Enterprise for its caching needs. Instead of spinning up yet another costly single-point solution, extend your database to take advantage of VSS in your applications. Developers can store vectors just as easily as any other field in a Redis hash or JSON object.
Flexibility across clouds and geographies
Choose where your databases should run. Redis Enterprise can be deployed anywhere, on any cloud platform, on-premises, or in a multi-cloud or hybrid cloud architecture.
With Redis VSS, we can provide our customers with vector search, with the confidence that it’s reliable and extremely fast. We saw an improvement of 80% in latency, compared to our initial Lucene-based implementation. We’re glad to work with a trusted brand and a team that makes working with Enterprise easier.
CEO, Relevance AI
Our real-time recommender infrastructure needs to search and update vector embeddings in milliseconds to deliver a blazing fast experience for our social and marketplace customers. We benchmarked everything on the market and Redis came out on top. Plus it’s easy to use with the one-click Redis Enterprise Cloud setup.
Our retrieval, transformation, and enrichment platform heavily rely on vector databases to seamlessly integrate internal data with powerful language models, all while prioritizing data security. After an exhaustive evaluation of multiple vector database providers, Mantium selected Redis for its exceptional performance and extensive developer support. The speed and cost advantages offered by Redis were simply unparalleled.
CEO and Co-Founder, Mantium
Docugami’s Generative AI for Business Documents requires incredible speed, and efficiency for our LLM & ML Ops. Through Redis Enterprise, we’ve seen remarkable advances in Document XML Knowledge Graph writing performance, and a notable reduction in COGS. The integration of Redis Vector Database enables us to handle embeddings more efficiently, improving consistency and accuracy as we turn long-form documents into data for our customers.
CEO and Co-Founder, Docugami
At Metal, we chose Redis because of its versatility and reliability. It provides simple data structures for storing messages, methods for fetching slices, and fast vector similarity search and information retrieval – all of which are critical to our platform’s success. We’re thrilled to partner with Redis to further our mission of deploying LLM applications to production for the enterprise.
At Impact Analytics, we harness the power of Redis Vector DB for our AI-driven new product forecasting. Its vector-based image similarity search is unmatched, enabling us to quickly find similar products with both image and text embeddings. The real-time search performance handles vast indexes seamlessly, and with hybrid filtering, we can blend vector similarity with traditional search, offering a multifaceted analysis. Redis’s fault tolerance ensures our operations never miss a beat, even with massive datasets.
Divya G K
Project Leader – AI R&D, Impact Analytics