Build search and RAG systems on PostgreSQL using Cohere and pgai

Cohere, a leading generative AI company, has partnered with pgai, an open-source PostgreSQL extension, to provide enterprise-ready large language models (LLMs) for building search and retrieval augmented generation (RAG) systems. The collaboration allows developers to create embeddings using Cohere's Embed model directly within PostgreSQL tables without transferring data in and out of the database. Additionally, Cohere Rerank can be used to improve search quality by reranking results based on relevance to queries. Pgai supports Cohere's entire suite of models, enabling developers to build hybrid search systems for higher-quality results in search and RAG applications. The integration of Cohere's Command, Embed, and Rerank models into pgai aims to help PostgreSQL evolve into an AI database, offering powerful solutions for various enterprise use cases such as investment research assistants, support chatbots, executive AI assistants, document summarization tools, knowledge and project staffing assistants, regulatory compliance monitoring, sentiment analysis for brand management, and Research and Development assistance.