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1 |
+
# PanSea University Search
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An AI-powered RAG (Retrieval-Augmented Generation) system for searching ASEAN university admission requirements, designed to help prospective students find accurate and up-to-date information about study opportunities across Southeast Asia.
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## 🎯 Problem & Solution
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**Problem:** Prospective students worldwide seeking to study abroad face difficulty finding accurate, up-to-date university admission requirements. Information is scattered across PDFs, brochures, and outdated agency websites. Many waste time applying to unsuitable programs due to missing criteria and pay high agent fees.
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+
**Solution:** An LLM-powered, RAG-based platform powered by **SEA-LION multilingual models** that ingests official admissions documents from ASEAN universities. Students can query in any ASEAN language and receive ranked program matches with fees, entry requirements, deadlines, application windows, and source citations.
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+
## 🌟 Features
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- 📄 **PDF Document Ingestion**: Upload official university admission documents
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- 🔍 **Intelligent Search**: Natural language queries in multiple ASEAN languages
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- 🎯 **Accurate Responses**: AI-powered answers with source citations
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+
- 🔗 **Shareable Results**: Generate links to share query results
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+
- 🌏 **Multi-language Support**: English, Chinese, Malay, Thai, Indonesian, Vietnamese, Filipino
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+
- 💰 **Advanced Filtering**: Budget range, study level, country preferences
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## 🚀 Quick Start
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### Prerequisites
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- Python 3.11+
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- SEA-LION API Key
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- OpenAI API Key (optional, for fallback embeddings)
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### Installation
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1. **Clone and navigate to the project:**
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```bash
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cd pansea
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```
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2. **Activate virtual environment:**
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```bash
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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```
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3. **Install dependencies:**
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```bash
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pip install -r requirements.txt
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```
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4. **Set up environment variables:**
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```bash
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cp .env.example .env
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# Edit .env and add your SEA-LION API key (OpenAI key optional for fallback)
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```
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5. **Run the application:**
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```bash
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streamlit run app.py
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```
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6. **Open your browser to:** `http://localhost:8501`
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### Usage
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#### 1. Upload Documents
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- Go to the "Upload Documents" page
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- Enter university name and country
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- Select document type (admission requirements, tuition fees, etc.)
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- Upload PDF files containing university information
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- Click "Process Documents"
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#### 2. Search Universities
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- Go to the "Search Universities" page
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- Choose your response language
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- Enter questions like:
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- "Show me universities in Malaysia for master's degrees with tuition under 40,000 RMB per year"
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- "专科毕业,无雅思,想在马来西亚读硕士,学费不超过4万人民币/年"
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- "What are the English proficiency requirements for Singapore universities?"
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- Apply optional filters (budget, study level, countries)
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- Get AI-powered responses with source citations
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#### 3. Share Results
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- Each query generates a unique shareable link
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- Share results with friends, family, or education consultants
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- Access shared results without needing to upload documents again
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## 📁 Project Structure
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```
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pansea/
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├── app.py # Main Streamlit application
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├── rag_system.py # RAG system implementation
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├── requirements.txt # Python dependencies
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├── .env # Environment variables
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├── .venv/ # Virtual environment
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├── chroma_db/ # Vector database storage
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├── documents/ # Uploaded documents storage
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├── query_results/ # Shared query results
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└── README.md # This file
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```
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## 🛠️ Core Components
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### DocumentIngestion Class
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- Handles PDF text extraction using PyPDF2
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- Creates document chunks with metadata
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- Builds and persists ChromaDB vector store
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- Manages document preprocessing and storage
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### RAGSystem Class
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- Implements retrieval-augmented generation
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- Uses BGE-small-en-v1.5 embeddings for semantic search (with OpenAI fallback)
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- Leverages SEA-LION models for response generation:
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- **SEA-LION v3.5 Reasoning Model** for complex university queries
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- **SEA-LION v3 Instruct Model** for translation and simple questions
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- Provides multilingual query support with automatic model selection
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### Streamlit UI
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- Clean, intuitive interface
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- Multi-page navigation
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- File upload with progress tracking
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- Advanced search filters
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- Shareable query results
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## 🌏 Supported Languages
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The system supports queries and responses in:
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- **English** - Primary language
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- **中文 (Chinese)** - For Chinese-speaking students
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- **Bahasa Malaysia** - For Malaysian context
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- **ไทย (Thai)** - For Thai students
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- **Bahasa Indonesia** - For Indonesian students
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- **Tiếng Việt (Vietnamese)** - For Vietnamese students
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- **Filipino** - For Philippines context
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## 🎯 Target ASEAN Countries
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- 🇸🇬 Singapore
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- 🇲🇾 Malaysia
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- 🇹🇭 Thailand
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- 🇮🇩 Indonesia
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- 🇵🇭 Philippines
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- 🇻🇳 Vietnam
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- 🇧🇳 Brunei
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- 🇰🇭 Cambodia
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- 🇱🇦 Laos
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- 🇲🇲 Myanmar
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## 🔧 Configuration
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### Environment Variables (.env)
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```bash
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# SEA-LION API Configuration
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SEA_LION_API_KEY=your_sea_lion_api_key_here
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SEA_LION_BASE_URL=https://api.sea-lion.ai/v1
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# OpenAI API Configuration (for embeddings)
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OPENAI_API_KEY=your_openai_api_key_here
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# Application Configuration
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APP_NAME=PanSea University Search
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APP_VERSION=1.0.0
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CHROMA_PERSIST_DIRECTORY=./chroma_db
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UPLOAD_FOLDER=./documents
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MAX_FILE_SIZE_MB=50
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```
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### Customization Options
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- **Chunk Size**: Adjust text splitting in `rag_system.py`
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- **Retrieval Count**: Modify number of retrieved documents (default: 5)
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- **Model Selection**: Configure SEA-LION model selection logic
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- **UI Themes**: Modify CSS in `app.py`
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- **Query Classification**: Adjust complex vs simple query detection
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## 📊 Example Queries
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Try these sample queries to test the system and see different model usage:
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### Complex Queries (Uses SEA-LION Reasoning Model)
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1. **Multi-criteria Search**: "Show me universities in Thailand and Malaysia for engineering master's programs with tuition under $15,000 per year"
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2. **Chinese Query**: "专科毕业,无雅思,想在马来西亚读硕士,学费不超过4万人民币/年"
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3. **Comparative Analysis**: "Compare MBA programs in Singapore and Indonesia with GMAT requirements and scholarship opportunities"
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### Simple Queries (Uses SEA-LION Instruct Model)
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4. **Translation**: "How do you say 'application deadline' in Thai and Indonesian?"
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5. **Definition**: "What is the difference between IELTS and TOEFL?"
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6. **Basic Information**: "What does GPA stand for and how is it calculated?"
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## 🔍 Technical Stack
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- **Backend**: Python 3.11, LangChain
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- **LLM Models**:
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- SEA-LION v3.5 8B Reasoning (complex queries)
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- SEA-LION v3 9B Instruct (simple queries & translation)
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- **Embeddings**: BGE-small-en-v1.5 (with OpenAI ada-002 fallback)
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- **Vector Database**: ChromaDB with persistence
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- **Frontend**: Streamlit with custom CSS
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- **Document Processing**: PyPDF2, PyCryptodome (for encrypted PDFs), RecursiveCharacterTextSplitter
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## 📈 Roadmap
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- [ ] Support for additional document formats (Word, Excel)
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- [x] Integration with SEA-LION multilingual models
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- [ ] Real-time web scraping of university websites
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- [ ] Mobile-responsive design
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- [ ] User authentication and query history
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- [ ] Advanced analytics and insights
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- [ ] Integration with university application systems
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- [ ] Fine-tuning SEA-LION models on university-specific data
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## 🤝 Contributing
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1. Fork the repository
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2. Create a feature branch (`git checkout -b feature/amazing-feature`)
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3. Commit your changes (`git commit -m 'Add amazing feature'`)
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4. Push to the branch (`git push origin feature/amazing-feature`)
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5. Open a Pull Request
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## 📄 License
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This project is licensed under the MIT License - see the LICENSE file for details.
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## 💡 Tips for Best Results
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1. **Upload Quality Documents**: Use official admission guides and requirements documents
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2. **Be Specific**: Include specific criteria in your queries (budget, location, program type)
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3. **Use Natural Language**: Ask questions as you would to a human counselor
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4. **Try Multiple Languages**: The system works well with mixed-language queries
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5. **Check Sources**: Always review the source documents cited in responses
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## 🆘 Troubleshooting
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### Common Issues
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**"No documents found"**: Upload PDF documents first in the Upload Documents page
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**"API Key not found"**: Add your SEA-LION API key to the .env file
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**"No embeddings available"**: BGE embeddings are used by default. If issues occur, add your OpenAI API key for fallback embeddings
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**"Import errors"**: Install dependencies using `pip install -r requirements.txt`
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**"ChromaDB errors"**: Delete the `chroma_db` folder and restart the application
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**"PyCryptodome is required for AES algorithm"**: This error occurs with encrypted PDFs. PyCryptodome is now included in requirements.txt
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**"Could not extract text from PDF"**: This can happen with:
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- Password-protected PDFs (provide unprotected versions)
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- Scanned PDFs or image-based documents (consider OCR tools)
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- Heavily encrypted or corrupted PDF files
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## 📞 Support
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For support, please create an issue on GitHub or contact the development team.
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---
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**Made with ❤️ for students seeking education opportunities in ASEAN** 🎓
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