“Ultimate Step-by-Step Guide to Create a Mind-Blowing RAG AI Agent with n8n (No Code Required) Using Google Drive, Vector Database Integration, CH GPT-40 Language Model Mastery, PDF Information Extraction, and Automation Workflows for Limitless Document Management and Human-Like Response Generation!”

Step-by-Step Tutorial: Create an RAG AI Agent with n8n (No Code)

This tutorial is going to guide you through creating a Retrieval-Augmented Generation (RAG) AI agent utilising n8n, a vector database and Google Drive for document management. We’ll also be leveraging the capabilities of the CH GPT-40 language model to enhance the AI agent’s interaction and information extraction abilities.

1. Build an AI Agent Using n8n

n8n is an open-source workflow automation tool that lets you create automation flows without needing to write any code. Here’s how to start building the AI agent:

Sign up for n8n and create a new workflow.
Familiarise yourself with the n8n interface, focusing on triggers and nodes.

You’ll create a workflow that serves as the backbone of your AI agent, which handles processing data and user queries.

2. Incorporate a Vector Database and Google Drive

To store and retrieve processed data efficiently, we’ll be integrating a vector database, which allows for fast similarity searches, as well as Google Drive for storing documents:

Choose a suitable vector database. Popular options include Pinecone or Weaviate.
Create a Google Drive API project in order to enable file management.

To access your Google Drive and the vector database, you will require API keys.

Conclusion

By following this tutorial, you’ve learned how to set up an RAG AI agent using n8n, Google Drive, a vector database, and a language model. You now have a actionable framework you can build upon and customise according to specific needs and inquiries.

Bonus material: Here are some official documents on n8n, Google APIs, and embedding models for additional information: