Introduction to AI Assistants

AI assistants are transforming the way we interact with technology. Imagine having an AI assistant that can answer questions using the documents in your Google Drive folder. This capability highlights the significant role AI plays in modern applications, offering personalized and efficient solutions.

In this section, we will explore the basics of AI assistants, their importance in today’s digital landscape, and introduce a powerful tool called N8n. N8n is a workflow automation tool that enhances the capabilities of AI assistants, making them more versatile and user-friendly.

“Imagine having an AI assistant that can answer questions using the documents in your Google Drive folder.”

Explore further in the next sections as we delve into creating RAG chatbots, setting up a knowledge base, building AI assistants, and sharing and embedding these chatbots seamlessly into your applications.

Creating RAG Chatbots

In this section, we focus on creating RAG (Retrieval-Augmented Generation) chatbots using AI agents in N8n. These intelligent chatbots are designed to answer questions from various data sources, such as databases or documents.

Definition of RAG Chatbots

RAG chatbots utilize AI to retrieve relevant information from existing data sources and generate accurate responses. They are particularly useful for automating responses to emails, powering website FAQs, or creating custom knowledge assistants.

Applications of RAG Chatbots

• Automating email responses
• Powering website FAQs
• Creating custom knowledge assistants

Benefits of Using N8n for Chatbot Development

N8n provides a robust platform for building these intelligent chatbots, allowing seamless integration with various data sources. This flexibility makes it easy to update the knowledge base by simply adding new files to a designated folder, such as Google Drive.

“These intelligent chatbots can answer questions from various data sources like databases or documents.”

In the tutorial, we explore building a chatbot that can answer questions from a knowledge base, demonstrating how easy it is to update and expand the chatbot’s capabilities. The chatbot can be shared via a URL or embedded into a website, providing 24/7 assistance to users.

This comprehensive guide aims to equip you with the foundational skills needed to create your own agent-driven applications. Let’s dive into the process and explore the possibilities of RAG chatbots in transforming user interactions.

Setting Up the Knowledge Base

This section focuses on setting up a knowledge base by creating a workflow that integrates data from a source into a vector store. This process is essential for building effective AI assistants.

“So what this workflow will do is grab the data from a data source and then up serve that data into a vector store.”

Steps to Create a Google Drive Folder

1. Open Google Drive: Navigate to your Google Drive account.
2. Create a New Folder: Click on “New” and select “Folder” to create a new folder for your documents.

Uploading Documents to the Folder

1. Select Files: Choose the documents you want to upload.
2. Upload Files: Drag and drop the files into the newly created folder or use the “Upload” option.

Triggering Workflows on File Uploads

1. Setup Workflow Automation: Use a tool like n8n to automate workflows.
2. Configure Triggers: Set triggers for when files are uploaded to the Google Drive folder.

By following these steps, you can effectively set up a knowledge base that integrates seamlessly with your AI assistant. This setup allows for efficient data retrieval and processing, enhancing the overall functionality of the AI.

For further details, please refer to the Building the AI Assistant section.

Building the AI Assistant

In this section, we will explore the exciting process of building an AI assistant. This involves creating a workflow, configuring AI nodes, and setting up the system to ensure the assistant functions effectively.

Creating a New Workflow

To begin building the AI assistant, we start by adding a new workflow. This workflow is named “Oak and Barrel Assistant.”

“Let’s add a new workflow. Let’s call it Oak and Barrel Assistant.”

Adding Chat Message and AI Agent Nodes

1. Chat Message Node:

   • Add the “on chat message” node. This node allows interaction with the chatbot through a chat button.

2. AI Agent Node:

   • Add an “AI agent” node from the advanced AI options. This node is preferred over the question and answer chain as it allows setting system messages and providing tools to the agent.

Configuring the AI Agent

• Agent Type: Select the “tool agent” type. Other types like SQL agent are available for specific tasks.

• Prompt: Use the message from the chat node and add a system message:

  “You are a customer support agent for a restaurant called Oak and Barrel. Your name is Max. Your tone should be friendly and helpful. When asked questions about the restaurant, use the provided context. If you don’t know the answer, then simply respond with, I don’t know.”

• Chat Model: Use the OpenAI chat model, specifically the GPT-4-O mini model, known for its speed and affordability.

• Memory Node: Add a memory node to recall information from previous messages, using the window buffer memory node.

Integrating with Vector Store

• Add a vector store tool named “oak and barrel information” to return documents related to the restaurant and its menu.
• Configure the tool with a Pinecone vector store and specify the namespace “oak and barrel.”

Testing the AI Assistant

The setup allows testing the AI assistant by interacting with the chat feature. The system displays transparency by showing the steps taken by the agent, such as accessing the vector store or retrieving conversation history.

“We are getting a response back from the LLM which means that everything is set up correctly.”

This concludes the setup of the AI assistant. The next step involves sharing and embedding the chatbot, which you can explore in the Sharing and Embedding the Chatbot section.

Sharing and Embedding the Chatbot

“Now that we have this workflow working, how do we actually share this agent with the world?”

Methods to Share the AI Assistant

Sharing your AI assistant involves several methods, each suited to different platforms and audiences. You can distribute it via direct links, integrate it within applications, or offer it through messaging platforms.

Embedding the Chatbot into a Website

To embed the chatbot into a website, you can use an iframe or JavaScript snippet provided by your chatbot platform. This allows the assistant to be accessible directly from your web pages, providing users with seamless interaction.

Customizing the Chatbot’s Appearance

Customizing the appearance of your chatbot ensures it aligns with your brand identity. Most platforms offer options to adjust colors, fonts, and chat window styles to match your website’s design.