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Search and retrieval

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Similarity analysis

Reveal the representation in higher-order space

Recommendation Systems

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Build the next big thing

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300

Collections created.

$4.5Bi

Search Queries Executed.

+65

Users/Companies.

1/5

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Search and retrieval

Searching for similar or relevant items is one of the most important and widely used scenarios for nearest neighbor search. In a search and retrieval application, your system should be able to convert the implicit or explicit query to an embedding, compare that embedding to the embedding of the stored items, and retrieve the most similar ones. Example applications include: retrieving the most relevant news articles for a given search query; retrieving the pictures that are most similar to a user-provided picture.

Similarity analysis

After your entities have been encoded in real-valued feature vectors and stored in nnext, you can perform exploratory analytics and visualization to discover interesting information and patterns. This can include: finding the nearest items to a given one; Identifying groups of similar items in the embedding space; finding the density of items in a particular item's neighborhood; Identifying boundary or intergroup items as well as exceptional or outlier items.

Pricing

Transparent & Straightforward

Starter

Best for enthusiasts and those getting started
1,000 Vector searches/day
1,000 Vector writes/day
5,000 Vector reads/day
1,000 Vector Deletes/day
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AS A DATA SCIENTIST OR MACHINE LEARNING ENGINEER, YOU THINK IN VECTORS AND EMBEDDINGS. NOW YOUR DATASTORE DOES TOO.