Build Your Own RAG Pipeline for Intelligent Chatbots

This workflow empowers you to construct a powerful Retrieval Augmented Generation (RAG) pipeline for your AI applications. It streamlines the process of ingesting documents, transforming them into queryable data, and then leveraging an AI agent to answer questions based on that data. The workflow begins with a simple form submission to upload files, which are then processed using a default data loader and a recursive text splitter. These text chunks are embedded using Ollama and stored in a Qdrant vector store. Simultaneously, a chat trigger initiates an AI agent, powered by an Ollama chat model and enhanced with memory, that can query the vector store to retrieve relevant information and formulate intelligent responses.