Meeting Mind- An Hour Long Meeting Analyzed in Under 30 Seconds (Powered by Langflow)

MeetingMind is an AI-powered meeting assistant that helps you capture, analyze, and act on your meeting insights effortlessly. This project is built with Langflow, Next.js and Groq-based fast transcription service to analyze your meetings and generate insights.

Demo

Check out this demo video to see MeetingMind in action:

MeetingMind.mp4

Features

• Audio recording and file upload
• AI-powered transcription
• Automatic extraction of key information:
  • Tasks
  • Decisions
  • Questions
  • Insights
  • Deadlines
  • Attendees
  • Follow-ups
  • Risks
  • Agenda

Getting Started

Prerequisites

• Node.js 14.x or later
• npm or yarn
• A LangFlow server running locally
• Git (for cloning the repository)

Caution

⚠️ Important: Groq Whisper used for transcription and analysis, currently supports files up to 25 MB only. There is a compression step in the process to reduce the file size to a manageable level. If your audio file is still larger than 25 MB, you will need to compress it before uploading. This limitation may affect the processing of longer meetings or high-quality audio recordings.

To compress your audio files further, you can use tools like:

• Online audio compressors
• FFmpeg (command-line tool for audio/video processing)

Ensure your compressed audio maintains sufficient quality for accurate transcription while staying under the 25 MB limit.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/meetingmind.git
   cd meetingmind

2. Install dependencies:

   npm install
   # or
   yarn install

3. Set up LangFlow:

   • Install and run the LangFlow backend server
   • Upload the flow provided in the repo at utils/langflow_flow/Meeting Mind.json
   • Note the URL of your LangFlow server

4. Create a .env.local file in the root directory and add the LangFlow URL:

   LANGFLOW_FLOW_URL="http://127.0.0.1:7860/api/v1/run/5781a690-e689-4b26-b636-45da76a91915"
   

   Replace the URL with your actual LangFlow server URL if different.

   In the file app/api/transcribe/route.ts, locate the payload object and update the Groq component name to match your LangFlow component name. For example:

   const payload = {
     output_type: 'text',
     input_type: 'text',
     tweaks: {
       'YourGroqComponentName': {
         audio_file: filePath
       },         
     }
   }

   Replace 'YourGroqComponentName' with the actual name of your Groq component in LangFlow.

5. Set up the database:

   This project uses Prisma as an ORM. By default, it's configured to use SQLite as the database.

   a. To use the local SQLite database:

   • Ensure your .env file contains:

     DATABASE_URL="file:./dev.db"
     

   • Run the following commands to set up your database:

     npx prisma generate
     npx prisma migrate dev --name init

   b. To use a different database (e.g., PostgreSQL with Neon):

   • Update your .env file with the appropriate connection string:

     DATABASE_URL="postgresql://username:password@host:port/database?schema=public"
     

   • Update the provider in prisma/schema.prisma:

     datasource db {
       provider = "postgresql"
       url      = env("DATABASE_URL")
     }

   • Run the Prisma commands as mentioned above to generate the client and run migrations.

6. Run the development server:

   npm run dev
   # or
   yarn dev

7. Open http://localhost:3000 with your browser to see the result.

Usage

1. Navigate to the dashboard page.
2. Upload an audio file.
3. Wait for the AI to process and analyze the meeting.
4. Review the extracted information in the Dashboard.

Project Structure

• app/: Contains the main application code
  • components/: Reusable React components
  • api/: API routes for server-side functionality
  • dashboard/: Dashboard page component
  • page.tsx: Home page component
• public/: Static assets
• prisma/: Database schema and migrations
• utils/: Utility functions and configurations
• lib/: Shared libraries and modules

Technologies Used

• Langflow: For AI workflow management
• Next.js: React framework for building the web application
• React: JavaScript library for building user interfaces
• Tailwind CSS: Utility-first CSS framework
• Framer Motion: Animation library for React
• Axios: Promise-based HTTP client
• Prisma: ORM for database management
• SQLite: Default database (can be changed to PostgreSQL or others)
• Groq: AI model provider for transcription and analysis

Configuration

• The project uses environment variables for configuration. Ensure all necessary variables are set in your .env.local file.
• Tailwind CSS configuration can be found in tailwind.config.ts.
• TypeScript configuration is in tsconfig.json.

API Routes

• /api/meetings: Handles CRUD operations for meetings
• /api/transcribe: Handles audio file transcription and analysis

Debugging

• Use the browser's developer tools to debug client-side issues.
• For server-side debugging, use console.log statements or attach a debugger to your Node.js process.

Performance Considerations

• Large audio files may take longer to process. Consider implementing a progress indicator for better user experience.
• Optimize database queries and indexes for improved performance as the number of meetings grows.

Screenshots

Landing Page

Dashboard

Meeting Summary

Meeting Details

These screenshots provide a visual representation of the application's main interfaces. The landing page showcases the initial user experience, while the dashboard displays the core functionality where users can upload audio files and view the AI-processed meeting information.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Here are some ways you can contribute:

• Report bugs and issues
• Suggest new features
• Improve documentation
• Submit pull requests with bug fixes or new features

Please read our contributing guidelines before submitting a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

If you encounter any problems or have questions, please open an issue on the GitHub repository.

Acknowledgements

• Thanks to the Langflow team for providing the AI workflow management tool.
• Special thanks to all contributors who have helped shape this project.