In this tutorial, we'll build a complete recommendation system to understand video content at a semantic level. By combining the power of Twelve Labs for generating video embeddings with Qdrant vector similarity search, we'll create a recommendation engine that can find truly relevant video content based on meaning rather than just matching keywords.
Check it out -
Github - https://github.com/Hrishikesh332/Twelve-Labs-Content-Recommendation
Demo Application - https://twelve-labs-content-recommendation.vercel.app/
Twelve Labs Playground - https://playground.twelvelabs.io/
Twelve Labs Embed API Docs - https://docs.twelvelabs.io/docs/guides/create-embeddings/video
Qdrant Cloud Quickstart - https://qdrant.tech/documentation/quickstart-cloud/