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# Marketing Assistant AI
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A comprehensive AI-powered tool designed to streamline the process of ideation, copywriting, and marketing campaign creation for Adriana James. This system generates high-quality marketing content that maintains brand consistency while significantly reducing content creation time.
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## 🌟 Features
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- **AI-Powered Content Generation**: Create marketing content for various channels with customizable parameters
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- **Brand Voice Consistency**: Ensure all content adheres to Adriana James' unique brand tone and style
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- **Vector Search**: Find similar past content for reference and inspiration
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- **Content Improvement**: Refine generated content based on specific feedback
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- **Brand Style Management**: Configure and update brand guidelines through a user-friendly interface
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- **Training Data Management**: Add successful marketing content to improve the AI over time
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- **Content Performance Analysis**: Get insights into the potential effectiveness of your content
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- **History Tracking**: Keep track of all generated content and user interactions
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- **Pagination Support**: Efficiently browse through large datasets of historical content
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- **Error Handling**: Robust error handling and user feedback systems
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## 📋 Project Overview
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The Marketing Assistant AI combines a powerful backend built with FastAPI and a clean, intuitive frontend interface. It leverages advanced AI models through the Cohere API for text generation, embeddings, and reranking.
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### Tech Stack
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- **Backend**: Python with FastAPI
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- **AI Models**: Cohere's generation and embedding models
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- **Vector Database**: FAISS for efficient content similarity search
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- **Storage**:
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- JSON files for historical marketing data
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- SQLite for training data
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- FAISS vector store for content similarity search
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## 🚀 Getting Started
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### Prerequisites
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- Python 3.9+
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- Node.js and npm (optional, for development tools)
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- Cohere API key
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### Installation
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1. **Clone the repository**
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```bash
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git clone http://23.29.118.76:3000/task/marketing-assistant-ai.git
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cd marketing-assistant-ai
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```
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2. **Set up the backend**
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```bash
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cd backend
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pip install -r requirements.txt
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```
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3. **Configure environment variables**
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Create a `.env` file in the project root with the following variables:
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```
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# API Keys
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COHERE_API_KEY=your-api-key-here
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# LLM Configuration
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LLM_MODEL=llm-model-name # e.g., command
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LLM_API_KEY=your-api-key-here # if using a custom model
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# Server Configuration
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API_HOST=localhost
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API_PORT=8000
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# Database Configuration
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DATABASE_URL=sqlite:///./data/training_data.db
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# Brand Configuration
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BRAND_NAME=Adriana James
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```
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4. **Start the backend server**
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```bash
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cd backend
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python main.py
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```
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The API will be available at http://localhost:8000
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## 📁 Project Structure
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```
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Marketing_Assistant_AI/
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│-- backend/
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│ │-- main.py # FastAPI application
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│ │-- copywriter.py # AI content generation
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│ │-- vector_store.py # FAISS vector database
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│ │-- embeddings.py # Cohere embeddings
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│ │-- brand_style.py # Brand style management
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│ │-- config.py # Configuration settings
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│ │-- requirements.txt # Python dependencies
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│
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│-- data/
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│ │-- past_campaigns/ # Stored marketing campaigns
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│ │-- user_queries/ # Query history
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│ │-- style_guidelines/ # Brand style config
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│ │ │-- brand_style.json # Default style guidelines
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│
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│-- docs/
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│ │-- README.md # Developer documentation
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│ │-- API_Documentation.md # API reference
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│
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│-- .env # Environment variables
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│-- .gitignore # Git ignore rules
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│-- LICENSE # License information
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│-- README.md # This file
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```
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## 🔄 API Endpoints
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The backend provides several RESTful API endpoints:
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| `/generate-copy` | POST | Generate marketing content |
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| `/brand-style` | GET | Retrieve brand style guidelines |
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| `/brand-style` | PUT | Update brand style guidelines |
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| `/training-data` | POST | Add new training data |
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| `/training-data` | GET | List available training data |
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| `/training-data/{id}` | GET | Get specific training document |
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| `/training-data/{id}` | DELETE | Delete training document |
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| `/improve-content` | POST | Improve content based on feedback |
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| `/analyze-content` | POST | Analyze content performance |
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For detailed API documentation, see [API Documentation](docs/API_Documentation.md).
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## 💡 How It Works
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1. **Content Generation Process**
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- User submits a content request
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- System formats the prompt with brand style guidelines
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- Similar past content is retrieved from the vector database
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- The AI generates content using the formatted prompt and references
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- The system checks alignment with brand guidelines
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- Final content is returned with alignment score and suggestions
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2. **Brand Style Management**
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- Brand style guidelines are stored in JSON format
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- The system uses these guidelines to format prompts for the AI
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- Content is checked against taboo words and preferred terminology
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- An alignment score is calculated based on adherence to guidelines
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3. **Vector Search**
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- Marketing content is converted to vector embeddings using Cohere
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- FAISS enables efficient similarity search across thousands of documents
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- Similar content is reranked based on relevance to the query
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## 🛠️ Development
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### Backend Development
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The backend is built with FastAPI and follows a modular architecture:
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- `main.py`: API endpoints and request handling
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- `copywriter.py`: Core AI content generation
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- `vector_store.py`: Vector database operations
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- `embeddings.py`: Embedding generation and reranking
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- `brand_style.py`: Brand style management
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- `config.py`: Configuration settings
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## 🔍 Future Enhancements
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- User authentication and role-based access
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- Content performance analytics dashboard
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- A/B testing capabilities
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- Integration with marketing platforms
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- Custom model fine-tuning for even better results
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- Mobile application
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## 👥 Contributing
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Contributions are welcome! Please feel free to submit a Pull Request.
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1. Fork the repository
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2. Create your feature branch (`git checkout -b feature/amazing-feature`)
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3. Commit your changes (`git commit -m 'Add some 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](LICENSE) file for details.
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## 👏 Acknowledgements
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- [Cohere](https://cohere.ai/) for AI models
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- [FastAPI](https://fastapi.tiangolo.com/) for the API framework
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- [FAISS](https://github.com/facebookresearch/faiss) for vector search
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---
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Made with ❤️ for Adriana James
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