Building End-to-End RAG Applications With Couchbase Vector Search

Couchbase's new Vector Search feature, available from version 7.6.0, enhances the capabilities of Retrieval Augmented Generation (RAG) applications by allowing large language models (LLMs) to provide more contextually appropriate and up-to-date information. Instead of passing entire databases, data closely related to user queries are selected within token limits, improving response accuracy. The post explains the development of a RAG application using Couchbase without external libraries, focusing on generating vector embeddings and importing search indexes using the FAISS framework. Comprehensive steps for setting up, data loading, and querying are provided.