LangChain & Supabase - Building a Chatbot for Your Website
LangChain & Supabase offers a powerful solution to easily create a ChatGpt chatbot for your website using a combination of advanced technologies: LangChain, Supabase, Typescript, OpenAI, and Next.js. This innovative project is ideal for developers looking to integrate AI-driven conversation capabilities into their web platforms.
About the Project
The core of this project revolves around LangChain, a framework designed to simplify the development of scalable AI and Language Model (LLM) applications. Accompanying it is Supabase, an open-source Postgres database enhanced with a pg vector extension, which is used to store the embeddings needed for the chatbot to understand and retrieve information efficiently.
Getting Started
The development process kicks off by cloning the project's repository from GitHub:
git clone [github https url]
After cloning, the required packages are installed using the command:
pnpm install
Next, developers set up the environment configuration. This involves creating a .env file by copying from .env.local.example and filling it with necessary API keys and database URLs. These keys can be obtained from OpenAI and Supabase following their documentation:
- OpenAI API Key: Used for the chatbot’s natural language processing capabilities.
- Supabase Keys: Essential for setting up the database that will store the chatbot's data.
Configuring and Customizing
The project includes robust customization options to tailor the chatbot to specific needs:
- Webpage URLs: In the
configfolder, developers replace URLs in an array with those of the websites needing chatbot interactivity. - Data Extraction: By modifying the
utils/custom_web_loader.tsfile, developers can specify which HTML elements from the webpage to extract for the chatbot to use. Cheerio, a fast, flexible, and lean implementation of jQuery, assists in scraping the desired content.
Database Setup
Once the configuration is complete, the database needs to be prepared to store the chatbot data. Developers run schema.sql in the Supabase SQL editor to ensure the creation of the necessary tables, such as the documents table.
Scraping and Embedding Data
With the configurations and database set up, developers can run the scraping and embedding script:
npm run scrape-embed
This script crawls through the specified URLs, extracts data based on the defined criteria, and converts this data into vector embeddings using OpenAI's Embeddings model (text-embedding-ada-002). These embeddings are vital for enabling the chatbot to process and reply to queries effectively.
Running the Application
Once the data is successfully embedded in the Supabase database, the application is run using:
npm run dev
The chatbot is now ready to accept questions, interactively engaging with website visitors by utilizing the data and embeddings stored in the database.
Acknowledgments
The frontend of this project draws inspiration from the langchain-chat-nextjs repository, reflecting modern web development practices. Additionally, this project incorporates Notion guides from productivity expert Thomas Frank’s website, aiding developers in efficient project setup and deployment.
By following this straightforward framework and utilizing powerful tools, developers can create a responsive and intelligent chatbot that significantly enhances website interactivity.