CodeLab: Building a RAG Application With Couchbase Capella Model Services and LangChain

The tutorial explains how to build a retrieval-augmented generation (RAG) application using Couchbase AI Services, which involves storing data, generating embeddings, and performing large language model (LLM) inference. The process includes ingesting news articles from the BBC News dataset, generating vector embeddings with the NVIDIA NeMo Retriever model, storing and indexing these vectors in Couchbase Capella, performing semantic searches to retrieve relevant contexts, and generating answers using the Mistral-7B LLM. Couchbase AI Services offer a unified platform for database, vectorization, search, and model integration, along with OpenAI-compatible endpoints for LLM inference and embeddings. The guide covers setting up Couchbase AI Services, creating a cluster, enabling AI services, configuring the database structure, initializing AI models, ingesting data, and building a RAG chain to test queries, demonstrating the capabilities of Couchbase’s platform in creating contextually-aware AI applications.