In this blog post, I describe how to use Langchain, NestJS, and Gemma 2 to build an RAG application about an Angular book in PDF format. Then, the HTMX and Handlebar template engine render the responses in a list. The application uses Langchain and its built-in PDF loader to load the PDF book and split the document into chunks. Then, Langchain uses the Gemini embedding text model to represent the documents into vectors and persist the vectors into a vector database.
