Build a Notion ChatBot with LangChain

Table of Contents

1. Introduction
2. The Importance of Chatting with Notion Docs
3. Architecture of Chatting with Notion Docs
4. Extracting Documents from Notion
   • Exporting in Markdown and CSV Format
   • Splitting Text into Chunks
   • Using Embeddings for Document Representation
5. Storing Documents in a Vector Store
   • Ingestion Phase
   • Using Pine Cone for Storing Embeddings
   • Retrieving Documents from the Store
6. Asking Questions to Notion Docs
   • Converting Queries into Embeddings
   • Searching Relevant Documents
   • Combining Question and Documents
7. Generating Response with GPT-3
   • Using Lang Chain Framework
   • Sending Prompt to the Language Model
8. Practical Example: Using Chat with Notion Docs
   • Working with Kran Documentation
   • Searching for Information
   • Interactive Chatbot Experience
9. Code Walkthrough: Building the Chatbot
   • Setting up Configuration
   • Ingesting Data into Pine Cone
   • Implementing the Chat Endpoint
   • Using Lang Chain for Query Processing
10. Conclusion

Introduction

Chatting with Notion Docs can greatly improve the experience of interacting with your documents. Notion is a versatile platform for storing information, whether it's for personal knowledge base or business support documentation. In this article, we will explore the architecture and steps involved in effectively searching and interacting with Notion documents using chat capabilities.

The Importance of Chatting with Notion Docs

Notion provides a rich set of features for organizing and structuring information. However, finding and extracting relevant information from your documents can sometimes be challenging. Chatting with Notion Docs allows you to quickly search and Interact with the content, making it easier to extract the information you need. Whether you are the user or a potential customer, having a chatbot that can interact with your brand's documents can greatly enhance the user experience.

Architecture of Chatting with Notion Docs

The architecture of chatting with Notion Docs involves several key steps. First, we need to extract the documents from Notion in a format that can be easily processed. This typically involves exporting the documents in Markdown and CSV format. Once we have the exported files, we split the text into chunks to ensure they can be efficiently processed by language models. We then use embeddings, which are number representations of words, to Create a numerical representation of the documents. These embeddings make it easier for computers to analyze and retrieve data. The embeddings are stored in a vector store, such as Pine Cone, which allows for efficient retrieval of document information. When a question is asked, the query is converted into embeddings and compared to the embeddings in the store. Relevant documents are retrieved and combined with the question to generate a prompt for the language model, such as GPT-3. The language model then generates a response Based on the combined information.

Extracting Documents from Notion

To effectively chat with Notion Docs, the first step is to extract the documents from Notion in a format that can be easily processed. When exporting from Notion, the documents are typically exported in Markdown and CSV format. Markdown is a lightweight markup language that is easy to work with. The exported files can be split into chunks to ensure efficient processing by language models. By splitting the text into smaller chunks, we can maximize the amount of information that can be processed by the models. This also helps to ensure that we do not lose Context between different chunks of text.

Storing Documents in a Vector Store

Once the documents are extracted, we need to store them in a vector store for efficient retrieval. A vector store is a place where numerical representations of documents, known as embeddings, can be stored and retrieved later. Storing the documents in a vector store allows for faster and more efficient analysis and retrieval of data. The ingestion phase involves converting the documents into embeddings and storing them in the vector store. Pine Cone is an example of a vector store that can be used for this purpose. By ingesting the documents into Pine Cone, we can easily search and retrieve relevant information when a question is asked.

Asking Questions to Notion Docs

The next step in the process is asking questions to Notion Docs. When a question is asked, it needs to be converted into embeddings for effective comparison with the embeddings of the documents in the vector store. This conversion allows for efficient searching and retrieval of relevant documents. By comparing the embeddings of the question with the embeddings in the store, we can identify the most relevant documents that match the query.

Generating Response with GPT-3

Once we have retrieved the relevant documents, we need to generate a response using a language model like GPT-3. Lang Chain is a framework that facilitates the interaction with language models like GPT-3. By combining the relevant documents and the question, we create a prompt that is sent to the language model for generating a response. The response can be a recommendation, an answer, or any other relevant information based on the context of the question and the documents.

Practical Example: Using Chat with Notion Docs

To better understand how to use chat with Notion Docs, let's consider a practical example using the Kran documentation. Kran is a Calendar App that utilizes the Notion platform for organization. By implementing a chatbot, users can easily search for information related to the app's functionality, setup, and FAQs. The chatbot interacts with the Kran documentation, retrieves relevant information, and provides responses to user queries. This interactive experience improves the overall user experience and enables quick access to the required information.

Code Walkthrough: Building the Chatbot

To build a chatbot for interacting with Notion Docs, certain steps need to be followed. Setting up the configuration involves providing the necessary keys for accessing the vector store and the language model. Ingesting data into the vector store, such as Pine Cone, allows for efficient retrieval of embeddings and documents. The chat endpoint is implemented to handle user queries and retrieve relevant information. Lang Chain, a framework for interacting with language models, is utilized to simplify the process of searching, combining, and generating responses. By following these steps, a functional chatbot can be created for interacting with Notion Docs.

Conclusion

Chatting with Notion Docs provides a powerful way to search, retrieve, and interact with your documents. By following the architecture and steps outlined in this article, you can effectively implement a chatbot that enhances the user experience. Whether for personal knowledge base or business support documentation, chat capabilities enable quick access to information and seamless interaction with your brand's documents. By utilizing technologies like embeddings, vector stores, and language models, you can empower your users with a chatbot that understands and responds to their queries.

Highlights:

• Chatting with Notion Docs allows for efficient searching and interaction with documents.
• Extracting documents from Notion involves exporting in Markdown and CSV format.
• Storing documents in a vector store like Pine Cone facilitates efficient retrieval.
• Asking questions involves converting queries into embeddings for comparison with embeddings in the store.
• Generating responses with language models like GPT-3 enhances the interactive chat experience.
• Practical example: Using chat with Kran documentation to retrieve information.
• Code walkthrough: Building a chatbot for Notion Docs using Lang Chain framework.
• Chatting with Notion Docs improves the user experience and provides quick access to information.

FAQ

Q: Can I chat with my Notion Docs using different languages? A: Yes, the chatbot can be configured to support different languages based on the training data and language model used.

Q: What if my Notion Docs have complex formatting and structure? A: The chatbot can handle complex formatting and structure by using Markdown and CSV format for exporting and storing the documents.

Q: Can the chatbot provide real-time updates to the Notion Docs? A: The chatbot can be configured to provide real-time updates by periodically syncing with the Notion Docs and updating the vector store with the latest information.

Q: Is it possible to have multiple chatbots for different Notion documents? A: Yes, multiple chatbots can be created to interact with different sets of Notion documents, each with its own vector store and language model configuration.