{"id":4740,"date":"2025-06-03T13:57:11","date_gmt":"2025-06-03T20:57:11","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/ai-applications-aws-bedrock-couchbase\/"},"modified":"2025-06-03T13:57:11","modified_gmt":"2025-06-03T20:57:11","slug":"ai-applications-aws-bedrock-couchbase","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/ai-applications-aws-bedrock-couchbase\/","title":{"rendered":"Unlocking the Power of AWS Bedrock with Couchbase"},"content":{"rendered":"\n<p>The explosion of <a target=\"_blank\" href=\"https:\/\/www.couchbase.com\/blog\/what-is-generative-ai\/\">generative AI<\/a> has made vector databases a crucial part of modern applications. As businesses seek scalable and efficient solutions for AI-powered search, recommendation, and knowledge retrieval, AWS Bedrock and Couchbase emerge as a compelling combination. AWS Bedrock simplifies access to powerful foundation models, while Couchbase\u2019s vector store capabilities provide the storage and retrieval efficiency needed to build high-performance AI applications.<\/p>\n\n\n\n<p>In this blog, we explore how Couchbase\u2019s vector store, when integrated with AWS Bedrock, creates a powerful, scalable, and cost-effective AI solution. We\u2019ll also showcase some key implementation details to help you get started.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why AWS Bedrock + Couchbase?<\/h2>\n\n\n\n<p>Let\u2019s dive deeper into the unique value each component provides and how they work together to power modern AI applications.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Seamless access to foundation models<\/h3>\n\n\n\n<p>AWS Bedrock offers access to multiple foundation models without the need for extensive infrastructure management. This makes it easy for businesses to experiment, deploy, and scale AI-powered applications without worrying about the underlying complexity of model hosting and fine-tuning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Scalable and cost-effective vector storage<\/h3>\n\n\n\n<p>Couchbase\u2019s vector store capabilities provide a fast and efficient way to store, retrieve, and search embeddings. With built-in indexing and high-performance queries, Couchbase ensures AI-driven applications can handle large-scale vector search efficiently.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bridging the gap between LLMs and enterprise data<\/h3>\n\n\n\n<p>By combining AWS Bedrock\u2019s model inference capabilities with Couchbase\u2019s vector database, businesses can create intelligent applications that understand and respond to user queries with high accuracy. Whether it&#8217;s chatbot applications, enterprise search, or personalized recommendations, this integration enables AI to work seamlessly with real-world business data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Seamless integration with AWS Ecosystem<\/h3>\n\n\n\n<p>Bedrock integrates effortlessly with other AWS services like Lambda, S3, and API Gateway, enabling low-latency, serverless AI workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The need for serverless<\/h2>\n\n\n\n<p>Serverless architectures provide a range of benefits that will naturally assist AI-powered solutions, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Zero infrastructure management &#8211; No need to provision, scale, or maintain servers, reducing operational complexity and accelerating AI deployment.<\/li>\n\n\n<li>Auto-scaling &amp; cost efficiency &#8211; Dynamically scales based on demand, ensuring optimal performance while following a pay-as-you-go model to minimize costs.<\/li>\n\n\n<li>Seamless integration &amp; low latency &#8211; Easily connects with AWS services (Lambda, API Gateway) and provides real-time AI inference with minimal latency.<\/li>\n\n<\/ul>\n\n\n\n<p>Now that we\u2019ve covered the benefits and architecture, let\u2019s look at a practical example that brings it all together.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">A RAG PDF chat application example<\/h2>\n\n\n\n<p>We show a demonstration of how to integrate AWS Bedrock with Couchbase with an LLM chat application. This app allows you to upload your own PDFs, and a RAG pipeline built with Couchbase enables the LLM to extract data from your PDFs to answer your questions. <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Find the code in <a target=\"_blank\" href=\"https:\/\/github.com\/couchbase-examples\/rag-aws-bedrock-serverless\">this GitHub repository<\/a>.<\/li>\n\n<\/ul>\n\n\n\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-17165\" src=\"https:\/\/www.couchbase.com\/wp-content\/uploads\/sites\/5\/2026\/05\/pdf-chat-couchbase-aws-bedrock-app-1024x462-1.png\" alt=\"\" width=\"900\" height=\"406\"><\/p>\n\n\n\n<p>A detailed discussion of the diagram can be found in this <a target=\"_blank\" href=\"https:\/\/developer.couchbase.com\/tutorial-bedrock-serverless-pdf-chat\">PDF chat using AWS Bedrock serverless tutorial<\/a>. In short, there are two flows: one involving the ingestion of PDF data, and the other pertaining to the user interacting with the chat application. Along the way, we use a couple key Couchbase services which are critical to the application: eventing and vector search.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Couchbase Eventing<\/h3>\n\n\n\n<p>The Eventing Service handles data changes that happen when applications interact and has these features that help AI applications thrive:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><b>Real-time Data Processing<\/b> \u2013 Couchbase Eventing allows you to execute business logic in response to data mutations (inserts, updates, deletes) within a Couchbase bucket.<\/li>\n\n\n<li><b>Asynchronous &amp; scalable<\/b> \u2013 The Eventing service runs asynchronously and scales independently, ensuring efficient handling of high-throughput workloads.<\/li>\n\n\n<li><b>Integration with external systems<\/b> \u2013 You can trigger external APIs, call microservices, or interact with message queues using eventing functions.<\/li>\n\n\n<li><b>Low latency &amp; high performance<\/b> \u2013 Designed for minimal overhead with direct memory access to data.<\/li>\n\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Couchbase Vector Search<\/h3>\n\n\n\n<p>Vector Search builds on Couchbase Capella\u2019s <a class=\"xref page\" href=\"https:\/\/docs.couchbase.com\/cloud\/search\/search.html\">Search Service<\/a> to provide vector index support. You can use these new Vector Search indexes for Retrieval Augmented Generation (RAG) with an existing Large Language Model (LLM), with these architectural benefits:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><b>Hybrid search<\/b> \u2013 Combines semantic search with traditional keyword and metadata filtering for more relevant results.<\/li>\n\n\n<li><b>Low-latency &amp; high-performance<\/b> \u2013 Optimized for real-time AI applications, ensuring fast retrieval of similar items.<\/li>\n\n\n<li><b>Seamless integration with Couchbase<\/b> \u2013 Runs natively within Couchbase, allowing efficient vector storage, indexing, and retrieval alongside structured and unstructured data.<\/li>\n\n<\/ul>\n\n\n\n<p>We also use other AWS services which complement our application in various ways, as discussed below:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">AWS Lambda<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Zero infrastructure management &#8211; just upload code and it runs<\/li>\n\n\n<li>Pay-per-use pricing<\/li>\n\n\n<li>Auto-scaling to handle traffic spikes<\/li>\n\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Amazon SQS<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>High data throughput for handling large volumes of traffic<\/li>\n\n\n<li>High reliability &#8211; does not lose a message<\/li>\n\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Amazon ECR<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Easy to implement, secure home for container images<\/li>\n\n\n<li>Easy integration between AWS services<\/li>\n\n\n<li>Ensures consistent behavior between development and production environments<\/li>\n\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">For further reading<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a target=\"_blank\" href=\"https:\/\/aws.amazon.com\/bedrock\/\">Explore AWS Bedrock<\/a><\/li>\n\n\n<li>Learn more about <a target=\"_blank\" href=\"https:\/\/aws.amazon.com\/lambda\/\">AWS Lambda<\/a>, <a target=\"_blank\" href=\"https:\/\/aws.amazon.com\/sqs\/\">Amazon SQS<\/a>, <a target=\"_blank\" href=\"https:\/\/aws.amazon.com\/ecr\/\">Amazon ECR<\/a><\/li>\n\n\n<li>Check out <a target=\"_blank\" href=\"https:\/\/www.couchbase.com\/products\/capella\/\">Couchbase Capella<\/a>, a managed database, and <a target=\"_blank\" href=\"https:\/\/cloud.couchbase.com\/sign-up?ref=blog\">start using it today for free<\/a>!<\/li>\n\n<\/ul>\n\n\n\n<p>\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The explosion of generative AI has made vector databases a crucial part of modern applications. As businesses seek scalable and efficient solutions for AI-powered search, recommendation, and knowledge retrieval, AWS Bedrock and Couchbase emerge as a compelling combination. AWS Bedrock simplifies access to powerful foundation models, while Couchbase\u2019s vector store capabilities provide the storage and [&hellip;]<\/p>\n","protected":false},"author":85626,"featured_media":4737,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[754,301,54,715],"tags":[419,985,728],"ppma_author":[986],"class_list":["post-4740","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-services","category-cloud","category-couchbase-server","category-vector-search","tag-amazon-web-services-aws","tag-lambda","tag-llms"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.6 (Yoast SEO v27.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Unlocking the Power of AWS Bedrock with Couchbase<\/title>\n<meta name=\"description\" content=\"In this blog, we 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