Update README.md

README.md

@@ -1 +1,166 @@
+# SynSearch
 
+SynSearch is an advanced document processing and semantic search system that combines embedding generation, clustering, and summarization capabilities to effectively process and analyze large collections of text documents.
+
+## 🌟 Features
+
+- **Document Processing Pipeline**
+  - Domain-agnostic text preprocessing
+  - Supports multiple dataset formats
+  - Efficient batch processing capabilities
+
+- **Advanced Embedding Generation**
+  - Transformer-based embeddings
+  - Configurable model selection
+  - GPU acceleration support
+  - Optimized batch processing
+
+- **Dynamic Clustering**
+  - Adaptive clustering algorithms
+  - Theme-based document grouping
+  - Support for multiple clustering strategies
+
+- **Intelligent Summarization**
+  - Hybrid summarization approach
+  - Support for scientific and legal domains
+  - Cluster-based summary generation
+  - Configurable summary length
+
+- **ArXiv Integration**
+  - Direct ArXiv paper search
+  - Batch paper fetching
+  - Rate-limited API handling
+
+## 📋 Requirements
+
+- Python 3.8 or higher
+- CUDA-compatible GPU (optional, for acceleration)
+- Required Python packages:
+  - torch
+  - transformers
+  - pandas
+  - numpy
+  - spacy
+  - pyyaml
+
+## 🚀 Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/stochastic-sisyphus/synsearch.git
+cd synsearch
+```
+
+2. Set up a virtual environment (recommended):
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+4. Download required datasets:
+```bash
+make download-data
+```
+
+## ⚙️ Configuration
+
+The system is configured through YAML files located in the `config` directory. Key configuration areas include:
+
+- Data sources and paths
+- Embedding model settings
+- Preprocessing parameters
+- Clustering configuration
+- Summarization options
+
+Example configuration:
+```yaml
+data:
+  datasets:
+    - name: scisummnet
+      enabled: true
+  scisummnet_path: "path/to/dataset"
+
+embedding:
+  model_name: "bert-base-uncased"
+  dimension: 768
+  max_seq_length: 512
+  batch_size: 32
+
+preprocessing:
+  # Preprocessing settings
+
+clustering:
+  # Clustering settings
+
+summarization:
+  # Summarization settings
+```
+
+## 🔨 Usage
+
+1. Basic usage:
+```python
+from src.main import main
+
+# Run the complete pipeline
+main()
+```
+
+2. Using specific components:
+```python
+from src.preprocessing.domain_agnostic_preprocessor import DomainAgnosticPreprocessor
+from src.embedding_generator import EnhancedEmbeddingGenerator
+
+# Initialize components
+preprocessor = DomainAgnosticPreprocessor()
+embedding_generator = EnhancedEmbeddingGenerator(model_name="bert-base-uncased")
+
+# Process texts
+processed_texts = preprocessor.preprocess_texts(your_texts)
+embeddings = embedding_generator.generate_embeddings(processed_texts)
+```
+
+## 🧪 Testing
+
+Run the test suite:
+```bash
+pytest tests/
+```
+
+Key test areas include:
+- Preprocessing functionality
+- ArXiv API integration
+- Embedding generation
+- Clustering algorithms
+- Summarization quality
+
+## 📁 Project Structure
+
+```
+synsearch/
+├── src/
+│   ├── api/              # API integrations
+│   ├── preprocessing/    # Text preprocessing
+│   ├── clustering/       # Clustering algorithms
+│   ├── summarization/    # Summary generation
+│   └── utils/            # Utility functions
+├── tests/               # Test suite
+├── config/             # Configuration files
+├── data/              # Dataset storage
+└── outputs/           # Generated outputs
+```
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Commit your changes
+4. Push to the branch
+5. Open a Pull Request
+
+## 📝 License