Automated PDF to RAG System with Embeddings and Qdrant Search

Overview

This n8n workflow is designed to transform unstructured PDF documents into a structured, searchable knowledge base using a Retrieval Augmented Generation (RAG) approach. It starts by fetching a PDF, extracting its text content, and then intelligently splitting it into sections and chunks. These chunks are then converted into vector embeddings using Mistral AI's cloud service. Finally, the embeddings and associated metadata are stored in a Qdrant vector database, enabling fast and accurate semantic searches. This is particularly useful for building intelligent Q&A systems, knowledge retrieval tools, or any application that requires searching through large volumes of text documents.