Implement a Retrieval-Augmented Generation Chatbot with n8n

The workflow is divided into two main parts: loading data into a vector store and interacting with it via a chat interface. Initially, documents are read from a source, split into manageable chunks, and embedded using the Cohere API. These embeddings are stored in an in-memory vector store. The second part involves taking user input, embedding the query, retrieving similar content from the vector store, and generating a response using a Groq-hosted language model. This setup allows for efficient and contextually relevant interactions, ideal for applications requiring dynamic content retrieval and response generation.