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Decision brief
VectorAI stands out for its Python-centric approach and broad support of machine-learning models including TensorFlow and PyTorch.
Good fit when
- When you need to develop vector-based applications with comprehensive support for various ML frameworks like TensorFlow and PyTorch, VectorAI is a suitable choice.
- If your project involves deep learning encodings or embeddings that require high-precision vector similarity searches, consider using VectorAI.
Avoid when
- Avoid VectorAI if you seek a platform with native support for real-time data streaming, as its focus lies on static or batch processing of vector data.
- If your application demands an open-source database without commercial restrictions and you prefer tools not centered around Python ecosystems, you may find alternatives more fitting.
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
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Install
pip install vectoraiSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Platform to encode, query and analyse data using vectors.
Capability facts
- Languages
- python
Source: github.language · Aug 23, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 23, 2026)
- For Python Documentation: https://vector-ai.github.io/vectoraiSource link
Tags
README
(VectorAI is depreciated, and no longer maintained. We recommend using Relevance AI for using vector search, check out https://tryrelevance.com )
Vector AI is a framework designed to make the process of building production grade vector based applications as quickly and easily as possible. Create, store, manipulate, search and analyse vectors alongside json documents to power applications such as neural search, semantic search, personalised recommendations recommendations etc.
- Visit our website and sign up for an api-key: https://getvectorai.com
- For Python Documentation: https://vector-ai.github.io/vectorai
- For REST API Documentation: https://api.vctr.ai/documentation
- Join our discord: https://discord.gg/CbwUxyD
- For a more gentle introduction comparing our features, read https://getvectorai.com/production-ready-search-in-5-minutes/
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 db 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. Vector AI 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.
Quick Terminologies
- Models/Encoders (aka. Embedders) ~ Turns data into vectors e.g. Word2Vec turns words into vector
- Vector Similarity Search (aka. Nearest Neighbor Search, Distance Search)
- Collection (aka. Index, Table) ~ a collection is made up of multiple documents
- Documents (aka. Json, Item, Dictionary, Row) ~ a document can contain vectors, text and links to videos/images/audio.
QuickStart
Install via pip! Compatible with any OS.
pip install vectorai
If you require the nightly version due to on-going improvements, you can install the nightly version using:
pip install vectorai-nightly
Note: while the nightly version
For agents
This page has a .md twin and JSON over the API.