# Islamic Scholar Agent

A production-ready Islamic knowledge agent built with Agno framework, featuring RAG (Retrieval-Augmented Generation) capabilities for answering questions based on Islamic texts and knowledge.

## Project Structure

```
├── src/                          # Production source code
│   ├── agents/                   # Agent implementations
│   ├── knowledge/                # Knowledge base & RAG pipeline
│   ├── models/                   # LLM integrations
│   ├── api/                      # FastAPI routes
│   ├── core/                     # Core configurations
│   └── utils/                    # Utility functions
├── data/                         # Data files
│   ├── raw/                      # Original data files
│   ├── processed/                # Processed/cleaned data
│   └── embeddings/               # Pre-computed embeddings
├── scripts/                      # Utility scripts
├── tests/                        # Test files
├── config/                       # Configuration files
├── docker/                       # Docker files
└── docs/                         # Documentation
```

## Features

- **Islamic Knowledge Focus**: Specialized agent for Islamic knowledge queries
- **RAG Pipeline**: Retrieval-augmented generation using vector databases
- **Multiple Vector Stores**: Support for Qdrant and PostgreSQL with PgVector
- **Modular Architecture**: Clean separation of concerns
- **Production Ready**: Docker support, health checks, logging
- **Comprehensive Testing**: Unit and integration tests

## Quick Start

1. **Setup Environment**:
   ```bash
   cp .env.example .env
   # Edit .env with your API keys
   ```

2. **Install Dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

3. **Setup Vector Database**:
   ```bash
   python scripts/setup_vectordb.py
   ```

4. **Ingest Knowledge Data**:
   ```bash
   python scripts/ingest_excel.py
   ```

5. **Run Application**:
   ```bash
   python src/main.py
   ```

## Development

### Running Tests
```bash
pytest tests/
```

### Qdrant Connection Test
Test Qdrant vector database connectivity:
```bash
python test_qdrant_connection.py
```

For detailed documentation see: [Qdrant Connection Test Guide](docs/QDRANT_CONNECTION_TEST.md)

### Health Check
```bash
python scripts/health_check.py
```

### Docker Development
```bash
cd docker
docker-compose -f docker-compose.dev.yml up
```

## API Usage

### Health Check
```bash
curl http://localhost:8081/health
```

### Chat Endpoint
```bash
curl -X POST "http://localhost:8081/chat" \
     -H "Content-Type: application/json" \
     -d '{"message": "What is the importance of Islamic knowledge?"}'
```

## Configuration

- **Development**: `config/development.env`
- **Production**: `config/production.env`
- **Models**: `config/models.yaml`

## Documentation

- [Production Readiness Report](PRODUCTION_READINESS_REPORT.md)
- [API Documentation](docs/API.md)
- [Deployment Guide](docs/DEPLOYMENT.md)

## License

This project is licensed under the MIT License.