Transform Your Notion Documents with an AI Chatbot

Table of Contents

1. Introduction
2. Building a Knowledge-Based Chatbot for Slack
3. Accessing Dynamic Information with Notion as an Example
4. Setting Up the Slack Application
5. Creating a Notion Integration Application
6. Connecting the Notion Integration to a Page
7. Retrieving Information from Notion
8. Writing the OpenAI Function to Retrieve Information from the Page
9. Combining Everything into the Slack Application
10. Conclusion

Building a Knowledge-Based Chatbot for Slack

In this tutorial, we will explore how to Create a knowledge-based chatbot for your Slack application using Notion as an example. Traditionally, we have looked at how to chat with static documents like PDFs, but what if you want to access dynamic information that is constantly changing? By using an API connection, such as the one provided by Notion, we can build a chatbot that can provide answers based on the information stored in a Notion page.

Introduction

In the introduction, we will provide an overview of the tutorial and explain the purpose of building a knowledge-based chatbot for Slack using Notion as an example. We will discuss the benefits of using dynamic information and highlight the limitations of traditional document-based chatbots.

Building a Knowledge-Based Chatbot for Slack

In this section, we will walk through the process of building a knowledge-based chatbot for Slack. We will start by setting up the Slack application and creating a Notion integration application. Then, we will connect the Notion integration to a specific page and write a function to retrieve information from the page. Finally, we will write the OpenAI function to synthesize the information into a response.

Setting Up the Slack Application

To get started, we need to create a Slack application. We will go to the Slack API Website and click on the "Create New App" button. We will follow the steps to create the app using the app manifest style, and we will export the manifest information from the GitHub repository provided. After creating the app, we will obtain the necessary tokens for authentication and install the app in our Slack workspace.

Creating a Notion Integration Application

Next, we will create a Notion integration application. We will go to the Notion Integrations page and click on the "New Integration" button. We will set the workspace and give the application a name. We will then copy the internal integration secrets and keep them somewhere safe, as we will need them later.

Connecting the Notion Integration to a Page

Once the Notion integration application is created, we need to connect it to the page from which we want to retrieve information. We will go to the page and click on the three dots menu. Then, we will select "Add Connection" and choose our Notion integration application. This will give our application access to the page.

Retrieving Information from Notion

Now, we can retrieve information from the Notion page. We will write a function to query the blocks on the page and extract the necessary content. We will loop through the blocks, extract the text content, and store it in a list. We will also handle code blocks separately to ensure the code is preserved. Finally, we will return the list of documents containing the extracted information.

Writing the OpenAI Function to Retrieve Information from the Page

To use the extracted information from Notion, we will write an OpenAI function. We will pass the documents and the user query to the function, which will use the GPT-3.5 model to generate a response. We will provide a prompt that sets the Context and instructs the model to return information in the desired format. The function will return the generated answer.

Combining Everything into the Slack Application

Now that we have all the necessary components, we can combine them into our Slack application. We will initialize the Slack app, set up an event handler for message events, and define a function to handle incoming messages. In the message handler function, we will retrieve the user query, pass it to the OpenAI function, and update the message with the generated answer.

Conclusion

In conclusion, building a knowledge-based chatbot for Slack using Notion as an example allows us to access dynamic information and provide accurate responses to user queries. By integrating the Slack application with Notion, we can create a scalable chatbot that can easily adapt to changes in the underlying knowledge base. This tutorial has provided a step-by-step guide to building such a chatbot and highlighted the benefits of using dynamic information sources. Now, it's up to You to explore and build your own chatbot using this knowledge.

Highlights

• Learn how to build a knowledge-based chatbot for Slack
• Access dynamic information using Notion as an example
• Set up the Slack application and create a Notion integration application
• Connect the Notion integration to a specific page
• Retrieve information from Notion and synthesize it into responses using OpenAI
• Combine all components into a functional Slack application

FAQ

Q: Can I use a different API connection instead of Notion? A: Yes, you can use any API connection that provides access to dynamic information. Notion is used as an example in this tutorial, but the principles can be applied to other systems as well.

Q: Is GPT-3.5 the only model I can use for the OpenAI function? A: No, you can choose any model provided by OpenAI that suits your needs. The GPT-3.5 model was chosen in this tutorial due to its long context window and fast response time.

Q: How can I ensure the chatbot returns information in the desired format? A: By providing the necessary instructions and context in the prompt, you can guide the chatbot to return information in the desired format. For example, if you want code snippets to be returned as code blocks, you can specify this in the prompt.