Updated README.md

README.md

@@ -6,4 +6,115 @@ emoji: 🚀
 app_port: 8501
 short_description: A one stop destination for all your finance problems
 ---
+
 # Multi-Source Multi-Agent Finance Assistant
+
+A powerful finance assistant that leverages multiple AI agents to provide comprehensive financial information and insights from various sources.
+
+## 🏗️ Architecture
+
+![Architecture Diagram](arch.jpg)
+
+The system is built using a multi-agent architecture with the following components:
+
+### Core Components
+
+1. **Orchestrator**
+   - Manages communication between different agents
+   - Handles request routing and response aggregation
+   - Built with FastAPI for high-performance API endpoints
+
+2. **Agents**
+   - **Voice Agent**: Handles speech-to-text and text-to-speech conversions
+   - **API Agent**: Interfaces with financial APIs (e.g., yfinance)
+   - **Retriever Agent**: Manages document retrieval and processing
+   - **Scraping Agent**: Extracts financial information from web sources
+
+3. **Data Ingestion**
+   - Handles data collection and preprocessing
+   - Supports multiple data sources and formats
+
+4. **Streamlit Frontend**
+   - User-friendly web interface
+   - Real-time interaction with the multi-agent system
+   - Voice input/output capabilities
+
+## 🛠️ Setup & Deployment
+
+### Prerequisites
+- Python 3.12+
+- Docker (for containerized deployment)
+- Google Cloud API credentials (for Gemini AI)
+
+### Local Development Setup
+
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/multi-source-multi-agent-finance-assistant.git
+cd multi-source-multi-agent-finance-assistant
+```
+
+2. Create and activate a virtual environment:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+4. Set up environment variables:
+```bash
+cp .env.example .env
+# Edit .env with your API keys and configuration
+```
+
+5. Run the application:
+```bash
+# Start the orchestrator
+uvicorn orchestrator.main:app --host 0.0.0.0 --port 8000
+
+# In a separate terminal, start the Streamlit app
+streamlit run streamlit_app/app.py
+```
+
+### Docker Deployment
+
+1. Build the Docker image:
+```bash
+docker build -t finance-assistant .
+```
+
+2. Run the container:
+```bash
+docker run -p 8000:8000 -p 8501:8501 finance-assistant
+```
+
+## 🚀 Framework & Toolkit Comparison
+
+| Component | Technology | Benefits |
+|-----------|------------|----------|
+| Backend Framework | FastAPI | High performance, async support, automatic API documentation |
+| Frontend | Streamlit | Rapid development, interactive widgets, easy deployment |
+| AI Framework | LangChain | Modular agent architecture, extensive tool integration |
+| Vector Store | FAISS | Efficient similarity search, optimized for large datasets |
+| Speech Processing | SpeechRecognition + gTTS | Cross-platform support, multiple language support |
+
+## 🔑 Key Features
+
+- Multi-source financial data aggregation
+- Voice-based interaction
+- Real-time market data processing
+- Document analysis and summarization
+- Interactive financial visualizations
+- Cross-platform compatibility
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.