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 192.168.1.38:8001 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.

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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