This repository guides users through creating a product similarity search using Amazon SageMaker and Amazon RDS for PostgreSQL using the extension pgvector.
we have used pre-trained model all-MiniLM-L6-v2 from Hugging Face SentenceTransformers to generate fixed 384 length sentence embedding from feidegger, a zalandoresearch dataset. Then those feature vectors are stored in RDS for PostgreSQL using extension pgvector for product similarity search.
pgvector is an open-source extension designed to augment PostgreSQL databases with the capability to store and conduct searches on ML-generated embeddings to identify both exact and approximate nearest neighbors. It’s designed to work seamlessly with other PostgreSQL features, including indexing and querying.
To generate vector embeddings, you can use ML service such as Amazon SageMaker or Amazon Bedrock (limited preview). SageMaker allows you to easily train and deploy machine learning models, including models that generate vector embeddings for text data.
By utilizing the pgvector extension, PostgreSQL can effectively perform similarity searches on extensive vector embeddings, providing businesses with a speedy and proficient solution.
Please review pgvector documentation for additional details.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.