Using Semantic Search to Find GIFs
Vector search powers some of the most popular services in the world. It serves your Google results, delivers the best podcasts on Spotify, and accounts for at least 35% of consumer purchases on Amazon.

In this article, we will use vector search applied to language, called semantic search, to build a GIF search engine. Unlike more traditional search where we rely on keyword matching, semantic search enables search based on the human meaning behind text and images. That means we can find highly relevant GIFs with natural language prompts.

The pipeline for a project like this is simple, yet powerful. It can easily be adapted to tasks as diverse as video search or answering Super Bowl questions, or as we’ll see, finding GIFs.

🌲 Pinecone article:
https://www.pinecone.io/learn/gif-search

🔗 Code:
https://github.com/pinecone-io/examples/tree/master/learn/projects/gif-search

🤖 70% Discount on the NLP With Transformers in Python course:
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00:00 Intro
00:17 GIF Search Demo
01:56 Pipeline Overview
05:33 Data Preparation
08:17 Vector Database and Retriever
12:37 Querying
15:42 Streamlit App Code