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    Ask Documents QnA RAG by ⌘ Langbase

    License: MIT Fork to ⌘ Langbase

    Build RAG Application for QnA with Documents — ⌘ Langbase

    A RAG application example to help you create a powerful QnA system for your documents. This application is built by using a RAG Pipe on Langbase, it works with 30+ LLMs (OpenAI, Gemini, Mistral, Llama, Gemma, etc), any Data (10M+ context with Memory sets), and any Framework (standard web API you can use with any software).

    Follow step-by-step instructions to build your own RAG application with Langbase. Check out the live demo here.

    Features

    • 🤔 Question & Answer: Ask questions from the content of your files.
    • 🔗 Langbase Integration: Utilize Langbase's powerful Pipes and Memory services
    • ⚡️ Streaming — Get real-time answers with streamed responses
    • 🧩 Customizable: Easily extend and modify the application to fit your needs.

    Learn more

    1. Check the Documents QnA 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

    Get started

    Let's get started with the project:

    To get started with Langbase, you'll need to create a free personal account on Langbase.com and verify your email address. Done? Cool, cool!

    1. Fork the Documents QnA Pipe on ⌘ Langbase.
    2. Go to the API tab to copy the Pipe's API key (to be used on server-side only).
    3. Download the example project folder from here or clone the repository.
    4. cd into the project directory and open it in your code editor.
    5. Duplicate the .env.example file in this project and rename it to .env.local.
    6. Add the following environment variables:
    sh
    1# Replace `LANGBASE_PIPE_API_KEY` with the copied API key.
2LANGBASE_PIPE_API_KEY="LANGBASE_PIPE_API_KEY"
3
4# Install the dependencies using the following command:
5npm install
6
7# Run the project using the following command:
8npm 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.

    Authors

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

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