AnnDB is a distributed approximate nearest neighbours database that allows you to provide a next-generation search experience in just a few lines of code. It's also fully hosted so there is no hassle with deploying and managing your infrastructure.
Fast: Map-Reduce like search allows us to achieve low latency and high throughput.
Highly available: AnnDB shards and replicates the data across multiple servers to ensure high availability and data resiliency.
CRUD: Unlike most solutions, AnnDB does not need to rebuild the index when the data changes and makes new/updated data available almost instantly.
Managed: Our software-as-a-service offering provides a managed solution for visual search, semantic search, question answering, and approximate nearest neighbours search in high-dimensional vector spaces with billions of items.
Vector embeddings are abstract numeric representations of images, natural language, etc. They are often generated using deep learning models that are trained to capture visual similarity between images, semantic similarity between sentences, or question-answer relevance of facts and natural language questions.
Unlike traditional search methods which use exact matching, inverted indices, etc., AnnDB leverages properties of vector representations to index and search items. This allows it to quickly find relevant items with high accuracy.
You can currently create datasets that support the following use-cases out-of-the-box:
AnnDB handles the embedding of images and strings to vectors internally, so that you do not have to deploy any machine learning models yourself. If you want to use your own embeddings, you can choose vector dataset type and use AnnDB as a hosted approximate nearest neighbours index.