---
title: "How to build a movie recommendation app without the complexities of vector databases"
url: https://daily.dev/posts/how-to-build-a-movie-recommendation-app-without-the-complexities-of-vector-databases-fo0yp6f8z
source_url: https://blog.streamlit.io/how-to-recommendation-app-vector-database-weaviate/
type: article
source: "Streamlit"
published: 2024-09-10T21:28:45.417Z
updated: 2024-12-09T12:16:25.476Z
tags: ["ai", "machine-learning", "python", "weaviate"]
reading_time: 7
upvotes: 62
comments: 0
language: en
---

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# How to build a movie recommendation app without the complexities of vector databases

**[Streamlit](https://daily.dev/sources/streamlit)** · 7 min read · 62 upvotes · 0 comments

## Summary

Learn how to create a movie recommendation app using Weaviate for backend data management and Streamlit for frontend interactions. This tutorial walks you through setting up Weaviate, connecting it with a Streamlit app, and performing various types of searches including keyword, semantic, and hybrid. Key technologies include Weaviate and Streamlit, and the app leverages the Cohere API for natural language processing.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.streamlit.io/how-to-recommendation-app-vector-database-weaviate/>

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---

Tags: [#ai](https://daily.dev/tags/ai), [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#weaviate](https://daily.dev/tags/weaviate)

[View this post on daily.dev](https://daily.dev/posts/how-to-build-a-movie-recommendation-app-without-the-complexities-of-vector-databases-fo0yp6f8z)