An Open Source Vector Search Solution
Vektonn is a high-performance battle-tested vector search engine for your data science applications. It helps you manage vectors' lifecycle and radically reduces time to market.
We store not only embeddings but also their attributes, which are more interesting to users since they can use real-world objects. For example, they can identify objects using their real identificators.
Data filtering
You can adjust the indexing scheme for specific filter values for a more efficient search.
For horizontal scaling of the index during its creation, you can specify the attributes by which the vectors will be distributed into groups. When processing a search query, the results from multiple shards will be automatically combined.
We support changing indexes as new data arrives (delete, change, or add data to the index), in parallel with search queries.
Online changes
Seamless versioning
You can expand multiple indexes over a single data source (vectors and attributes) and seamlessly transition to new versions of indexes. You can expand different indexes with different parameters of the same data.
Dense and Sparse Vector Support
You can work with vectors of any type. For example, to solve word processing problems, you can use bag-of-words and load appropriate sparse vectors into Vektonn.
AkNN support provides you with the best balance between search speed, memory consumption, and search quality.
Approximate kNN support

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cconfigure the vector dimensions, the number of replicas, and more
Highly customizable
Resource configuration
adjust the amount of allocated memory and the number of processors
High availability
we use reliable solutions: k8s, mongo, kafka
configure the number of vector dimension, the number of replica and so on
Easy to use
Multi-language support
.net and python
automatically rebalance replicas
Integrations with