Lets Building an AI Knowledge Chatbot with Multiple Data Integrations: A Simple, Real-Life Guide

Let’s talk about how we built an AI chatbot that connects with platforms like Google Drive, Notion, and Slack. Our goal was simple – make a chatbot that can pull relevant information from these apps and provide answers based on that data. Imagine how useful it would be to have a chatbot that doesn’t just rely on surface-level information but taps into your personal files and messages to give more accurate, detailed responses.

If you're working on AI features for your app, creating a knowledge chatbot with the data you already have is just the start. The real magic happens when you integrate external data sources. This gives your chatbot a more personalized touch and makes it far more useful for your customers.

Tutorial Overview

In this tutorial, we’ll walk you through how we built our chatbot, which we named Parato. Parato pulls data from Slack messages, Google Drive files, and Notion pages to answer questions. It doesn’t just pull this data once—it updates in real-time. So, if you make a change in your Notion, Parato will be able to access the new information almost immediately and tell you exactly where it found the answer.

Video Walkthrough

We’ve made a quick video to demo Parato, walking you through how we structured and built it from start to finish.

How It Works: Chatbot Architecture

Our AI chatbot has three main components: the chatbot application (including both frontend and backend), an AI language model (LLM), and a database for storing data (usually a vector database). What makes Parato special is that it works with a fourth component—third-party data integrations. It can take information from Slack, Google Drive, and Notion, process it, and store it in the database so it’s ready when needed.

To make this possible, we had to interact with third-party APIs, which required dealing with authentication, data handling, and keeping everything updated. It’s not always easy, but it’s essential for making the chatbot as useful as possible.

Building the Chatbot: Meet Parato

We used LlamaIndex’s create-llama npm tool to get started. This tool gave us a full-stack framework, using NextJS for both the frontend and backend, and it integrates easily with OpenAI models and Pinecone’s vector database. With these tools, we could transform documents into vector data and store it in the database, allowing Parato to retrieve the information when needed.

The LlamaIndex pipeline handled document transformation, chunking, and loading into our database. We also created custom features to receive text data and metadata, run the pipelines, and store the data for future use.

Building the Data Ingestion Engine

We faced several challenges when working with third-party APIs and webhooks, but we used Paragon to help orchestrate the data ingestion process. Paragon managed many backend tasks, like authentication, load balancing, and scaling.

We created two types of pipelines: one for user-triggered events and one for webhook-triggered events. The user-triggered pipeline pulls data from sources like Slack when a customer connects their account. The webhook-triggered pipeline pulls new data automatically when events happen, like when a new Slack message is sent.

For user-triggered events, Parato can retrieve data from Google Drive, transform it, and send it to the chatbot’s database for use. For webhook-triggered events, Parato can automatically process new messages from Slack and make them available for the chatbot.

Wrapping Up

By the end of this tutorial, we hope you have a better understanding of how to build an AI chatbot that can pull data from multiple sources. Integrating external data is a bit tricky but absolutely worth it. It makes your chatbot far more powerful and personalized, giving your customers access to the information they need, exactly when they need it.

If you're looking to build something similar, combining the power of AI models with reliable third-party data integrations, tools like Paragon and LlamaIndex can make the process easier and more effective.