# Four things you can build with Appwrite VectorsDB

DevFeed: [Four things you can build with Appwrite VectorsDB](<https://devfeed.tech/articles/four-things-you-can-build-with-appwrite-vectorsdb-16513.md>)

Original publisher: [Read original article](<https://appwrite.io/blog/post/vectorsdb-use-cases>)

Author: Atharva Deosthale

Published: 2026-09-02T00:00:00Z

Content type: tutorial

Language: en

Sources: [Appwrite Blog](<https://devfeed.tech/sources/appwrite-blog.md>)

Topics: [Appwrite](<https://devfeed.tech/topics/appwrite.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Databases](<https://devfeed.tech/topics/databases.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [cache](<https://devfeed.tech/tags/cache.md>), [database](<https://devfeed.tech/tags/database.md>), [databases](<https://devfeed.tech/tags/databases.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [history](<https://devfeed.tech/tags/history.md>), [search](<https://devfeed.tech/tags/search.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>)

## AI overview

This tutorial explains how Appwrite VectorsDB uses vector embeddings to find documents by meaning rather than exact keywords. It demonstrates help-article search, filtered search, recommendations from user history, and an answer cache, including a 256-number vector approach used in the examples.

## Source excerpt

Appwrite VectorsDB finds documents by meaning, not by words. This post builds four features with it: a help article search, a filtered search, a recommendation from user history, and an answer cache.