Basic RAG Chatbot for Document-Based Sales Queries

A simple Retrieval-Augmented Generation (RAG) workflow that loads documents into a vector store and enables context-aware chat responses using Cohere embeddings and Groq LLM.

This n8n workflow implements a basic RAG pipeline divided into two main sections. Part 1 focuses on data ingestion: it reads files from disk or Google Drive, splits them into chunks using a Recursive Character Text Splitter, generates embeddings via the Cohere API, and stores them in an In-Memory Vector Store (easily swappable for Pinecone or Qdrant). This setup allows for efficient indexing of sales documents, CRM data, or knowledge bases. Part 2 handles interactive querying: user input from a
Platform
n8n
Category
Sales
Price
$19.99
Creator
Kaito Rahman

How to import this workflow into n8n

  1. 1Purchase or download the workflow to get the n8n workflow JSON file.
  2. 2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
  3. 3Open each node marked with a credential warning and connect your own accounts and API keys.
  4. 4Run the workflow once manually to verify the data flow, then toggle it to Active.

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