Room: The Forum
Feedback: Leave feedback
The integration of embeddings generated using popular large language models (LLMs) with the pgvector open-source extension for PostgreSQL presents a powerful and efficient solution for optimizing the product catalog similarity search experience. By using pre-trained ML models and vector embeddings, businesses can enhance the accuracy and speed of similarity searches, personalized recommendations, question and answering bots, and fraud detection, which ultimately leads to improved user satisfaction and a more personalized experience. This session explores implementing vector databases in PostgreSQL using pgvector.