How to Build AI ChatBot with Custom Knowledge Base in 10 mins

Introduction

Imagine transforming all your website's PDF documents, manuals, guides, eBooks, and reports into a helpful chatbot. Picture being able to ask questions and receive instant answers directly sourced from your data. The good news is, you can create your own AI chatbot with a custom knowledge base in less than 10 minutes—no coding skills required! This article will guide you through the process to set up a chatbot for your business or others, enabling you to potentially monetize this new tool.

Why Create a Custom ChatBot?

By building a chatbot, you can provide instant answers to your customers from your website or uploaded documents. This approach saves your users time and enhances their experience while enabling you to analyze their interactions for better service improvement. With this tutorial, you'll learn:

How to create a data store for your chatbot.
How to add structured data.
How to enable voice and chat experiences.
How to test your chatbot with realistic customer questions.
How to view conversation history and analytics.

Getting Started

Before beginning, it's essential to have the necessary APIs enabled in your Google Cloud account. Specifically, you should enable both the Vertex AI API and the Dialogflow API. Once you’ve done that, follow these steps:

Create an Agent:
- Go to Google Cloud Console.
- Log in and search for "Agent Builder."
- Click to create a new app, providing your company and agent name.
Create a Data Store:
- Click on "Create Data Store."
- You will be given options to choose a website URL for content crawling, import from a storage bucket, or manually upload data.
- For this tutorial, select the cloud storage option.
- Import either a folder or a file. Here, we'll use a previously uploaded PDF document (e.g., the "Budget of the US Government").
- After selecting the correct document, name the data store and create it.
Test Your Agent:
- Successfully verify the data was imported without errors.
- Click on "Test Agent" to begin an interactive session.
- You can enter different questions to see how the chatbot responds.
Enable Conversation History:
- To track user interactions, enable the logging option within your agent settings.
- This saves the conversation history, crucial for analyzing user responses and improving your chatbot.
Integrate the ChatBot:
- Navigate to integration settings.
- You can add functionality for phone access and speech capabilities or stick with text-based interactions.
- Enable an unauthenticated API to allow public interaction and get a code for embedding the chatbot on your website.

After embedding, you will be able to interact with your newly created chatbot and retrieve information sourced directly from your PDF documents.

Conclusion

Congratulations! You've built a custom AI chatbot with a knowledge base that can answer a variety of questions based on your documents—all within about 10 minutes. This tool can significantly enhance user engagement on your website and provide invaluable insights through analytics.

Keywords

AI Chatbot
Custom Knowledge Base
Google Cloud
Vertex AI API
Dialogflow API
Data Store
PDF Document
Conversation History
Integration

FAQ

Q1: Do I need coding skills to build this chatbot?
A: No, the process outlined in this tutorial does not require any coding skills.

Q2: Can I import data from different document formats?
A: Yes, you can import data from PDF, HTML, TXT, CSV, Word documents, and PowerPoint presentations.

Q3: How do I monitor the chatbot's performance?
A: You can enable conversation history to log user interactions, which can be reviewed in the analytics dashboard.

Q4: Where can I embed the chatbot?
A: The chatbot can be embedded into your website, Facebook Messenger, Slack, Discord, or Telegram.

Q5: How do I enhance the responses of my chatbot?
A: You can improve your chatbot's responses by adding more URLs or documents to its data source.