PostgreSQL Hybrid Search Using pgvector and Cohere

This article discusses the creation of a hybrid search engine using Cohere and pgvector on PostgreSQL. Hybrid search combines keyword and semantic search methods to enhance result quality. The implementation involves generating dense and sparse embeddings, storing them in Timescale's PostgreSQL database, retrieving results, reranking them, and generating final lists of relevant documents for queries. The hybrid search engine can be applied to various applications, such as advanced retrieval-augmented generation (RAG) systems. The article also provides a step-by-step guide on setting up the necessary libraries, creating a table in PostgreSQL, inserting data, and implementing keyword and semantic search functions.