VecDB

Welcome to the official documentation of the VecDB API

VecDB is a managed API for vectors designed to enable developers and data scientists to quickly and easily build production-grade vector-based applications at critical scales. Create, store, manipulate, search and analyse vectors alongside JSON documents to power applications such as neural search, semantic search, personalised recommendations, recommendations etc. No DevOps, No 3rd party metadata store, All through a simple API.

Use Cases

Using the VecDB API, developers are able to perform the following using just 1 endpoint:

Store data easily in our database
Predict easily using our models
Search their text/image/audio data using our models
Perform aggregations (e.g. top 5 categories)
Get clusters within data (grouping together similar text or image data)
Tag data
Recommendations

Features

Multimedia Data Vectorisation: Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning)
Document Orientated Store: Store your vectors alongside documents without having to do a database lookup for metadata about the vectors.
Vector Similarity Search: Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalisation, etc.
Hybrid Search: There are scenarios where vector search is not as effective as traditional search, e.g. searching for SKUs. VecDB lets you combine vector search with all the features of traditional search such as filtering, fuzzy search, keyword matching to create an even more powerful search.
Multi-Model Weighted Search: Our Vector search is highly customisable and you can peform searches with multiple vectors from multiple models and give them different weightings.
Vector Operations: Flexible search with out of the box operations on vectors. e.g. mean, median, sum, etc.
Aggregation: All the traditional aggregation you'd expect. e.g. group by mean, pivot tables, etc
Clustering: Interpret your vectors and data by allocating them to buckets and get statistics about these different buckets based on data you provide.
Vector Analytics: Get better understanding of your vectors by using out-of-the-box practical vector analytics, giving you better understanding of the quality of your vectors.

Why VecDB?

Production Ready: Our API is fully managed and can scale to power hundreds of millions of searches a day. Even at millions of searches it is blazing fast through edge caching, GPU utilisation and software optimisation so you never have to worry about scaling your infrastructure as your use case scales.
Simple to use. Quick to get started.: One of our core design principles is that we focus on how people can get started on using VecDB as quickly as possible, whilst ensuring there is still a tonne of functionality and customisability options.
A richer understanding of your vectors and their properties: Our library is designed to allow people to do more than just obtain nearest neighbors, but to actually experiment, analyse, interpret and improve on them the moment the data added to the index.
Store vector data with ease: The document-orientated nature for VecDB allows users to label, filter search and understand their vectors as much as possible.
Real-time access to data: VecDB data is accessible in real time, as soon as the data is inserted it is searchable straight away. No need to wait hours to build an index.
Framework agnostic: We are never going to force a specific framework on VecDB. If you have a framework of choice, you can use it - as long as your documents are JSON-serializable!

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Last updated 3 years ago

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Use Cases

Using the VecDB API, developers are able to perform the following using just 1 endpoint:

Store data easily in our database

Predict easily using our models

Search their text/image/audio data using our models

Perform aggregations (e.g. top 5 categories)

Get clusters within data (grouping together similar text or image data)

Tag data

Recommendations

Features

Multimedia Data Vectorisation: Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning)

Document Orientated Store: Store your vectors alongside documents without having to do a database lookup for metadata about the vectors.

Vector Similarity Search: Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalisation, etc.

Hybrid Search: There are scenarios where vector search is not as effective as traditional search, e.g. searching for SKUs. VecDB lets you combine vector search with all the features of traditional search such as filtering, fuzzy search, keyword matching to create an even more powerful search.

Multi-Model Weighted Search: Our Vector search is highly customisable and you can peform searches with multiple vectors from multiple models and give them different weightings.

Vector Operations: Flexible search with out of the box operations on vectors. e.g. mean, median, sum, etc.

Aggregation: All the traditional aggregation you'd expect. e.g. group by mean, pivot tables, etc

Clustering: Interpret your vectors and data by allocating them to buckets and get statistics about these different buckets based on data you provide.

Vector Analytics: Get better understanding of your vectors by using out-of-the-box practical vector analytics, giving you better understanding of the quality of your vectors.

Why VecDB?

Production Ready: Our API is fully managed and can scale to power hundreds of millions of searches a day. Even at millions of searches it is blazing fast through edge caching, GPU utilisation and software optimisation so you never have to worry about scaling your infrastructure as your use case scales.

Simple to use. Quick to get started.: One of our core design principles is that we focus on how people can get started on using VecDB as quickly as possible, whilst ensuring there is still a tonne of functionality and customisability options.

A richer understanding of your vectors and their properties: Our library is designed to allow people to do more than just obtain nearest neighbors, but to actually experiment, analyse, interpret and improve on them the moment the data added to the index.

Store vector data with ease: The document-orientated nature for VecDB allows users to label, filter search and understand their vectors as much as possible.

Real-time access to data: VecDB data is accessible in real time, as soon as the data is inserted it is searchable straight away. No need to wait hours to build an index.

Framework agnostic: We are never going to force a specific framework on VecDB. If you have a framework of choice, you can use it - as long as your documents are JSON-serializable!