4 Steps to Build a Video Search System

Searching for videos by image with Milvus, a vector database for AI

1. System overview

The following diagram illustrates the typical workflow of such a video search system.

2. Data preparation

In this article, we use about 100,000 GIF files from Tumblr as a sample dataset in building an end-to-end solution for searching for video. You can use your own video repositories.

3. Deployment

The code for building the video retrieval system in this article is on GitHub.

# Get the video search code
$ git clone -b 0.10.0 https://github.com/JackLCL/search-video-demo.git

# Build front-end interface docker and api docker images
$ cd search-video-demo & make all
/mnt/redis/data /mnt/minio/data /mnt/milvus/db
$ docker-compose up -d
$ cd deploy
$ python3 import_data.py

4. Interface display

Open your browser and enter to see the interface of the video search system as shown below.

5. Build your own

In this article, we used Milvus to build a system for searching for videos by images. This exemplifies the application of Milvus in unstructured data processing.



Scalable similarity search on unstructured data (such as image, video, and natural language) powered by https://milvus.io

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Open-source Vector Database Powering AI Applications. #SimilaritySearch #Embeddings #MachineLearning