https://github.com/extrawest/ai-study-companion-app
Flutter mobile and web app for simplifying learning process. Capable of working with pdf, jpeg, txt, doc files. Can summarize documents and create quizes based on summary
https://github.com/extrawest/ai-study-companion-app
agent chat flutter langgraph nodejs openai pinecone rag typescript
Last synced: 6 months ago
JSON representation
Flutter mobile and web app for simplifying learning process. Capable of working with pdf, jpeg, txt, doc files. Can summarize documents and create quizes based on summary
- Host: GitHub
- URL: https://github.com/extrawest/ai-study-companion-app
- Owner: extrawest
- License: bsd-3-clause
- Created: 2025-02-04T16:51:14.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-06T16:38:11.000Z (8 months ago)
- Last Synced: 2025-03-30T17:46:37.460Z (7 months ago)
- Topics: agent, chat, flutter, langgraph, nodejs, openai, pinecone, rag, typescript
- Language: Dart
- Homepage:
- Size: 156 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI Study Companion App
[]()
[](mailto:oleksandr.samoilenko@extrawest.com)
[]()
## Overview
AI Study Companion is a powerful Flutter application that leverages artificial intelligence to enhance your learning experience. The app allows users to upload various document types (PDF, DOC, TXT, JPG, PNG) and receive AI-generated summaries and interactive quizzes based on the content.
## Preview
https://github.com/user-attachments/assets/b24e93ab-ff42-4f56-b9f1-f463469e61de
## Live Web link
https://67a4df0dd9f4e12a8327e0d3--stellular-marigold-95229f.netlify.app
## Key Features
- **Document Processing**
- Support for multiple file formats (PDF, DOC, TXT, JPG, PNG)
- Upload up to 5 files simultaneously
- AI-powered document summarization
- Text extraction from images using OCR
- **AI-Powered Learning**
- OpenAI GPT-3.5 and GPT-4 integration
- Intelligent document analysis
- Dynamic quiz generation
- **Advanced Technology Stack**
- Vector similarity search using Pinecone
- Multi-agent system with Langgraph
- Real-time processing and analysis
- OCR for text extraction with @google-cloud/vision
## Technical Architecture
### Mobile App (Flutter)
- BLoC pattern for state management
- File handling and processing
- Real-time communication with backend
### Backend Server (Node.js/TypeScript)
- OpenAI integration
- Pinecone vector database
- Multi-agent system using Langgraph
- Document processing pipeline
## Prerequisites
- Flutter SDK (>=3.4.3)
- Node.js (Latest LTS version)
- OpenAI API Key
- Pinecone API Key and Index
## Installation
### Mobile App Setup
1. Clone the repository:
```bash
git clone
```
2. Navigate to the mobile app directory:
```bash
cd
```
3. Install dependencies:
```bash
flutter pub get
```
### Server Setup
1. Navigate to the server directory:
```bash
cd
```
2. Install dependencies:
```bash
npm install
```
3. Create a `.env` file in the server root with the following variables:
Create account in Google Cloud and download the JSON file
```env
GOOGLE_APPLICATION_CREDENTIALS_JSON = {YOUR GOOGLE APPLICATION CREDENTIALS JSON}
```
5. Start the server:
```bash
npx tsx server.ts
```
6. Update the server URL in the Flutter app:
- Open `mobile/lib/services/api_service.dart`
- Update the `baseUrl` to match your server address
## Project Structure
```
.
├── mobile/ # Flutter application
│ ├── lib/ # Dart source files
│ ├── assets/ # App resources
│ └── pubspec.yaml # Flutter dependencies
│
└── server/ # Backend server
├── services/ # Business logic
├── routes/ # API endpoints
├── tools/ # Utility scripts
└── package.json # Node.js dependencies
```
## Dependencies
### Mobile App
- flutter_ai_toolkit: ^0.6.8
- flutter_bloc: ^9.0.0
- file_picker: ^8.1.7
- flutter_chat_ui: ^1.6.15
- http: ^1.3.0
- And more (see pubspec.yaml)
### Server
- @langchain/openai: ^0.3.14
- @langchain/pinecone: ^0.1.3
- @google-cloud/vision: 4.3.2
- express: ^4.21.1
- typescript: ^5.7.2
- And more (see package.json)
## Contributing
1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request
## License
This project is licensed under the MIT License - see the LICENSE.txt file for details.
Created by Oleksandr Samoilenko
[Extrawest.com](https://www.extrawest.com), 2025