A step-by-step guide to building a retrieval-augmented generation (RAG) system using Couchbase Capella AI Services and LangChain. The tutorial covers ingesting BBC News articles, generating vector embeddings with NVIDIA NeMo Retriever, storing and indexing vectors in Couchbase, performing semantic search, and generating answers using Mistral-7B LLM. The implementation demonstrates how to use Couchbase's unified platform for database storage, vectorization, and model inference through OpenAI-compatible endpoints.
Table of contents
Why Couchbase AI Services?Setting Up Couchbase AI ServicesPrerequisitesStep 1: Install DependenciesStep 2: Configuration & ConnectionStep 3: Set Up the Database StructureStep 4: Loading Couchbase Vector Search IndexStep 5: Initialize AI ModelsStep 6: Ingest DataStep 7: Build the RAG ChainStep 8: Run QueriesConclusionSort: