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Put unstructured data to work with Redis
A vector database is a type of database that stores data in the form of vectors or mathematical representations of data points. This transformation of unstructured data into numeric representations (vectors) captures the meaning and context that complement natural language processing and computer vision.
Vector Search (VS) is the process of finding data points that are similar to a given query vector in a vector database. Popular VS uses go well beyond keyword matching and filtering to include recommendation systems, image and video search, natural language processing, and anomaly detection. (Need a deep dive? This should do it.)
Users expect search functionality in every application and website they encounter. Yet more than 80% of business data is unsearchable and stored across multiple formats. The time has come for organizations to reimagine the ways to make all kinds of data discoverable and exceed user expectations with powerful features to fuel the next generation of AI applications.
Every organization that stores non-textual data – and that’s just about everyone – can benefit from improving search functionality across unstructured data. That day is here.
New to Vector Search? Download our datasheet.
Real-time search performance
Search and recommendation systems have to run incredibly fast. The VS functionality in Redis Enterprise guarantees low search latency, regardless of the size of the data collection or number of database nodes.
Built-in fault tolerance and resilience
The proprietary shared-nothing cluster architecture avoids downtime and is fault tolerant with automated failover at the process level, for individual nodes, and across infrastructure availability zones. And all unstructured data and vectors are protected with tunable persistence and disaster recovery mechanisms.
Minimized architectural and application complexity
With caching needs already handled by Redis, Vector Search is an easy extension for any database. Developers can store vectors just as easily as any other field in a Redis hash or JSON object.
Flexibility across clouds and geographies
Databases with Redis can be deployed anywhere, on any cloud platform, on-premises, or in a multi-cloud or hybrid-cloud architecture.
Retrieval Augmented Generation (RAG)
Redis Enterprise stores external domain-specific knowledge and provides powerful semantic search capabilities to infuse relevant contextual data into a prompt before it’s sent to a LLM for improved result quality.
Semantic Caching
Redis Enterprise identifies and retrieves cached responses that are semantically similar enough to the input query, dramatically reducing the response time and number of requests sent to an LLM.
Recommendation Systems
Redis Enterprise helps recommendation engines deliver fresh, relevant suggestions to users at low-latency. It helps them find similar products to those that a shopper enjoys.
Document Search
Redis Enterprise makes it easier to discover and retrieve information from a large corpus of documents, using natural language and semantic search.
Redis Enterprise manages vectors in an index data structure to enable intelligent similarity search that balances search speed and search quality. Choose from two popular techniques, FLAT (a brute force approach) and HNSW (a faster, and approximate approach), based on your data and use cases.
Redis Enterprise uses a distance metric to measure the similarity between two vectors. Choose from three popular metrics – Euclidean, Inner Product, and Cosine Similarity – used to calculate how “close” or “far apart” two vectors are.
Take advantage of the full suite of search features available in Redis Enterprise query and search. Enhance your workflows by combining the power of vector search with more traditional numeric, text, and tag filters. Incorporate more business logic into queries and simplify client application code.
Real-time search and recommendation systems generate large volumes of changing data. New images, text, products, or metadata? Perform updates, insertions, and deletes to the search index seamlessly as your dataset changes overtime. Redis Enterprise reduces costly impacts of stagnant data.
Traditional vector search is performed by finding the “top K” most similar vectors. Redis Enterprise also enables the discovery of relevant content within a predefined similarity range or threshold for an alternative, and offers a more flexible search experience.