Dev Hacks RAG Pipeline into Next.js App Using DynamoDB as 'Vector Store' – Proves AI MVPs Don't Need Big Bucks, Just Lambda Cold Starts

A recent experiment demonstrated the feasibility of implementing a Retrieval-Augmented Generation (RAG) pipeline in a production full-stack application without a vector database. The application, built using Next.js, Tailwind CSS, and AWS services, allows users to upload PDF documents, which are then indexed and made available for querying. The system utilizes AWS Cognito for user management and AWS Lambda for document indexing and querying. Testing revealed acceptable performance, with document indexing taking around 4 seconds per document and query latency increasing with the number of documents. The application confirms that a DynamoDB-based RAG system can be both cost-effective and usable, making it suitable for early-stage applications, internal tools, and experiments. With a total of 20 documents uploaded, the system demonstrated predictable performance, making it a viable option for developers seeking to implement RAG pipelines without breaking the bank. The experiment showcases the potential of using AWS services to build efficient and cost-effective RAG systems.