Ask My Docs

Ask My Docs RAG Chatbot Example — ⌘ Langbase

Ask or generate anything from your Documentation, taking advantage of Langbase Memory and Pipes. Follow this guide to upload your documentation files into Memory and create a RAG Chatbot on it.

Check out the source code of the example chatbot app here.

Check out the live demo here, which is a RAG Chatbot on Langbase's documentation.

Features

• 💬 RAG Chatbot app — Built with an AI Pipe on ⌘ Langbase
• ⚡️ Streaming — Real-time chat experience with streamed responses
• 🗣️ Q/A — Ask questions and get pre-defined answers with your preferred AI model and tone
• 🔋 Responsive and open source — Works on all devices and platforms

Running the project

Follow this guide to upload and set up a RAG on your documentation using Langbase Memory and Pipes.

Once you have followed the guide, and setup your Memory and Pipe you can run this app locally:

1. cd into the project directory and open it in your code editor.

2. Duplicate the .env.example file in this project and rename it to .env.local.

3. Add the following environment variables:

   sh
   DownloadCopy code
   1# Replace `PIPE_API_KEY` with the copied API key.
   2LANGBASE_PIPE_API_KEY="YOUR_PIPE_API_KEY"

4. Install the dependencies using the following command:

sh
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1npm install

5. Run the project using the following command:

sh
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1npm run dev

Your app template should now be running on localhost:3000.

NOTE: This is a Next.js project, so you can build and deploy it to any platform of your choice, like Vercel, Netlify, Cloudflare, etc.

Learn more

1. Check the AI Chatbot Pipe on ⌘ Langbase
2. Read the source code on GitHub for this example
3. Go through Documentaion: Pipe Quick Start
4. Learn more about Pipes & Memory features on ⌘ Langbase

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Authors

This project is created by Langbase team members, with contributions from:

• Ahmad Bilal (@AhmadBilalDev) - Founding Engineer, Langbase

Built by ⌘ Langbase.com — Ship hyper-personalized AI assistants with memory!