{"id":4024,"date":"2024-08-30T06:58:19","date_gmt":"2024-08-30T13:58:19","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/couchbase-bedrock-rag-applications\/"},"modified":"2024-08-30T06:58:19","modified_gmt":"2024-08-30T13:58:19","slug":"couchbase-bedrock-rag-applications","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/couchbase-bedrock-rag-applications\/","title":{"rendered":"Build Performant RAG Applications Using Couchbase Vector Search and Amazon Bedrock"},"content":{"rendered":"\n

Generative AI (GenAI)<\/a> has the potential to automate work activities that currently occupy 60 to 70 percent of employees’ time, leading to substantial productivity gains across various industries. However, a General Purpose (GP) LLM’s knowledge is confined to its training data, causing it to “hallucinate” by providing incomplete or inaccurate responses. This undermines trust and credibility, preventing many GenAI Proofs of Concept (POCs) from reaching production. By introducing the Retrieval-Augmented Generation (RAG) technique we can augment the LLM with proprietary data which grounds LLM responses in current and reliable facts. Successful RAG implementation requires a highly scalable and dependable database to serve as a knowledge base, vector database, and LLM cache, along with a robust platform for developing and scaling GenAI applications using leading foundation models.<\/span><\/p>\n\n\n\n

Couchbase and Amazon Bedrock<\/a> together offer an end-to-end platform to seamlessly build performant RAG applications across industries such as retail, healthcare, hospitality, gaming, and many others.<\/span><\/p>\n\n\n\n

Couchbase Capella<\/a>, a cloud-native high-performance DBaaS (Database-as-service), allows you to get started quickly with storing, indexing, and querying operational, vector, time series, textual, and geospatial data for your RAG applications while leveraging the flexibility of JSON. Further, it uniquely offers <\/span>hybrid search<\/span><\/a>, which takes semantic search to the next level by blending traditional and vector search within a single SQL++ query to power applications that require highly granular search with high select specificity. This capability spans the cloud, edge, and mobile. You can easily integrate Capella as a knowledge base, vector DB, or an <\/span>LLM cache <\/span><\/a>with a leading GenAI platform like Amazon Bedrock using orchestration frameworks such as LangChain or LlamaIndex to build a production-grade RAG pipeline. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon using a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.\u00a0\u00a0<\/span><\/p>\n\n\n\n

In this blog, we present a RAG-powered chatbot application built using <\/span>Couchbase Vector Search<\/span><\/a> and Amazon Bedrock. It leverages the Couchbase <\/span>travel-sample data <\/span><\/a>stored in Couchbase Capella and Anthropic Claude model from Amazon Bedrock. <\/span><\/p>\n\n\n\n

\"Build<\/a>
Figure 1: Chatbot application developed using Capella and Amazon Bedrock<\/figcaption><\/figure>\n\n\n\n

You can follow the steps below to develop your RAG application depicted in Figure 1.<\/span><\/p>\n\n\n\n

Step 1: Create a vector index<\/span><\/h2>\n\n\n\n

Step 1.1: Create a Capella cluster in a few minutes<\/span><\/p>\n\n\n\n