Implement a Retrieval-Augmented Generation Chatbot with n8n - n8n Workflow | Neura Market | Neura Market
Implement a Retrieval-Augmented Generation Chatbot with n8n
This workflow sets up a Retrieval-Augmented Generation (RAG) pipeline using n8n, enabling dynamic interaction with a vector store to provide context-aware responses.
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