This repository contains the working code for the example in the blog post

The code, contained in the notebook main.ipynb file will take you through the steps needed to:

• Retrieve the image included in any picture
• Calculate the embeddings from the faces
• Store the embedidngs in PostgreSQL in a vector column from pgvector
• Get a colleague picture from Slack
• Identify the face in the picture (needed since people can have all types of pictures in Slack)
• Calculate the embeddings in the Slack picture
• Use pgvector distance function to retrieve the closest faces and therefore photos

You need Python 3.8+ and to install the packages in the requirements.txt file with

pip install -r requirements.txt

You also need a PostgreSQL instance with pgvector. You can start one within few minutes from the Aiven Console.

Once the service is up and running, connect to PostgreSQL using psql and execute the dbsettings.sql file with:

psql <SERVICE_URI> -f dbsettings.sql

This will enable the pgvector extension and create the table to store embeddings

Face recognition with Python, OpenCV, OpenAI CLIP model and PostgreSQL pgvector is licensed under the Apache license, version 2.0. Full license text is available in the LICENSE file.

Please note that the project explicitly does not require a CLA (Contributor License Agreement) from its contributors.

Bug reports and patches are very welcome, please post them as GitHub issues and pull requests at https://github.com/aiven/pgvector-face-recognition . To report any possible vulnerabilities or other serious issues please see our security policy.