TopK

Retrieve relevant context from billions of documents using multi-vector and hybrid search

Pricing model

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use TopK to store, index, and retrieve large volumes of text and visual data for artificial intelligence applications. The system brings together vector storage, embedding models, and inference into a single engine running on object storage. It supports multi-vector, dense, sparse, and regular expression searches across partitioned collections. You can execute low-latency queries across billions of records to feed accurate context into autonomous agents, enterprise search systems, and multimodal document workflows while keeping data secure inside your private cloud environment.

What TopK does

Index documents using dense and sparse vector embeddingsQuery partitioned collections using multi-vector search modelsFilter search results with regular expressions and metadata tagsDeploy dedicated search clusters inside a private VPCStream document updates into storage with sub-second indexingRetrieve contextual answers from visual diagrams and spreadsheetsMulti-vector and hybrid search engine combining dense, sparse, and regex queriesDirect architecture on scalable cloud object storagePartition capacity supporting over one billion documents per partitionSub-100 millisecond p99 query response times at scaleData ingestion throughput up to 70 MB per second per partitionSub-second data freshness for newly indexed recordsNative visual document retrieval across tables, figures, and chartsPrivate deployment capability inside your VPC or on-premise infrastructure