feat: Initial implementation of Marketing Assistant AI for Adriana James
- Set up FastAPI backend with modular structure: - main.py for API routing - copywriter.py for AI-powered content generation using Cohere - embeddings.py for generating and reranking content embeddings - vector_store.py for FAISS-based similarity search - brand_style.py for managing brand tone, taboo words, and preferred terms - config.py for managing environment and application settings - Configured RESTful API endpoints: /generate-copy, /brand-style, /training-data, /improve-content, /analyze-content - Created frontend with vanilla HTML, CSS, and JS (index.html, styles.css, app.js) - Integrated brand style management for tone, voice, taboo words, and terminology - Implemented vector search for referencing similar historical content - Enabled training data input to improve future AI output - Added environment variable support for API keys and model configs - Structured data storage with local JSON and DB files - Added developer documentation, API reference, and project setup instructions This commit provides the foundation for a full-stack, AI-driven content creation platform that ensures brand consistency, speeds up marketing workflows, and supports iterative improvement over time.
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MIT License
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Copyright (c) 2025 Adriana James
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