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RAG Chatbot with n8n

5 min read
RAG Chatbot with n8n

Building a RAG Chatbot with n8n: A Step-by-Step Guide

The rise of Generative AI has paved the way for a powerful concept known as Retrieval-Augmented Generation (RAG). By combining the best of retrieval mechanisms with the capabilities of generative models, you can build chatbots that not only generate text but also retrieve accurate and relevant data from your resources. In this blog, we’ll guide you through building a RAG chatbot using n8n, an open-source workflow automation tool.

Here’s what we’ll cover:

  1. What is a RAG chatbot?
  2. Why use n8n for building a RAG chatbot?
  3. Step-by-step guide to create your RAG chatbot with n8n
  4. Using Google Drive for Chat with Documents
  5. Resources and links

What is a RAG Chatbot?

A Retrieval-Augmented Generation (RAG) chatbot enhances the traditional chatbot experience by leveraging an external knowledge base. It retrieves data from sources like databases, documents, or APIs, and then uses a large language model (LLM) to generate coherent and context-aware responses. This makes RAG chatbots highly accurate and domain-specific, ideal for business use cases like customer support, documentation assistance, or enterprise search.

Why Use n8n for Building a RAG Chatbot?

n8n is a fantastic platform for constructing a RAG chatbot due to its:

  • Ease of Use: A no-code/low-code interface that simplifies workflow automation.
  • Extensibility: Easily integrates with APIs, databases, cloud services, and more.
  • Cost-Effective: Open-source with optional paid hosting solutions.

With n8n, you can visually design your chatbot workflow and integrate components like webhooks, LLMs, and vector databases.

Step-by-Step Guide to Build a RAG Chatbot in n8n

1. Set Up the Workflow

Start with a blank workflow in n8n and add the following key nodes:

  • Webhook Node: This node acts as the entry point for your chatbot. It captures the user’s input (e.g., a query or question) and passes it into the workflow.

2. Connect an AI Model

  • Use the OpenAI Chat Model node to incorporate a powerful generative AI model. You’ll pass user input along with context retrieved from your knowledge base to this model.

3. Integrate with a Vector Store

  • Add a Vector Store Tool node (such as Supabase or Pinecone) to retrieve relevant data based on user queries. This acts as the retrieval mechanism of the RAG system.
  • In this example, we use Supabase Vector Store, as shown in the screenshot above.

4. Enable Memory Management

  • Include a memory component like Postgres Chat Memory to retain context across interactions, making the chatbot smarter over time.

5. Process Files

  • If your knowledge base consists of documents, use nodes like Google Drive, Extract from File, or others to process and embed the content into the vector store.

6. Respond Back to the User

  • Add the Respond to Webhook node to send the chatbot’s response back to the user. The response is generated by combining retrieved data with the output of the AI model.

7. Test the Workflow

Once all the nodes are connected, click on Test Workflow to debug and optimize your chatbot.

Using Google Drive for Chat with Documents

One of the key features of this setup is the ability to chat with documents stored in Google Drive. Here’s how you can achieve this:

  1. Connect Google Drive:

    • Use the Google Drive node to access your files. You can configure this node to monitor specific folders for new or updated documents.

  2. Extract Text:

    • Use the Extract from File node to read the content of the document. This node supports various file formats like PDFs, Word documents, and plain text files.

  3. Embed in Vector Store:

    • Pass the extracted text into the Supabase Vector Store (or any vector database). This allows your chatbot to retrieve contextually relevant information from the document during interactions.

  4. Query the Document:

    • When a user asks a question, the chatbot queries the vector store for relevant embeddings (document snippets) and combines them with the generative AI model’s response.

This setup makes it possible to interact with your document in a conversational manner, whether it’s for summarizing content, answering specific questions, or extracting key insights.

Resources and Links

Here are some resources to help you build your RAG chatbot:

Tutorials:

With these steps, you’re well on your way to creating a functional and efficient RAG chatbot using n8n. The combination of retrieval, AI generation, and n8n’s flexible workflows allows you to craft chatbots that truly stand out. Happy building!

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