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- Implemented vector search for referencing similar historical content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.

backend/config.py

@@ -0,0 +1,87 @@
+"""
+Configuration module for the Marketing Assistant AI.
+Handles environment variables and application settings.
+"""
+
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Base paths
+BASE_DIR = Path(__file__).resolve().parent.parent
+DATA_DIR = BASE_DIR / "data"
+
+# Ensure data directories exist
+(DATA_DIR / "past_campaigns").mkdir(exist_ok=True)
+(DATA_DIR / "user_queries").mkdir(exist_ok=True)
+(DATA_DIR / "style_guidelines").mkdir(exist_ok=True)
+
+# API configuration
+API_HOST = os.getenv("API_HOST", "localhost")
+API_PORT = int(os.getenv("API_PORT", 8000))
+
+# LLM configuration
+LLM_MODEL = os.getenv("LLM_MODEL")
+LLM_API_KEY = os.getenv("LLM_API_KEY")
+
+# Cohere configuration
+COHERE_API_KEY = os.getenv("COHERE_API_KEY")
+
+# Vector database configuration
+VECTOR_DB_PATH = os.getenv("VECTOR_DB_PATH", str(DATA_DIR / "vector_store"))
+
+# Brand configuration
+BRAND_NAME = os.getenv("BRAND_NAME", "Adriana James")
+
+# Content types
+CONTENT_TYPES = [
+    "email_campaign",
+    "social_media",
+    "blog_post",
+    "website_copy",
+    "ad_copy",
+    "funnel_page",
+    "product_description",
+    "press_release"
+]
+
+# Tone options
+TONE_OPTIONS = [
+    "professional",
+    "friendly",
+    "excited",
+    "authoritative",
+    "casual",
+    "inspirational",
+    "empathetic",
+    "humorous"
+]
+
+# Content length options
+LENGTH_OPTIONS = [
+    "short",  # < 100 words
+    "medium",  # 100-300 words
+    "long",    # > 300 words
+]
+
+# Default brand style guidelines
+DEFAULT_BRAND_STYLE = {
+    "brand_name": BRAND_NAME,
+    "tone": ["professional", "friendly", "inspirational"],
+    "voice_characteristics": ["clear", "direct", "empowering"],
+    "taboo_words": ["cheap", "discount", "bargain"],
+    "preferred_terms": {
+        "customers": "clients",
+        "products": "solutions"
+    }
+}
+
+# Logging configuration
+LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
+LOG_FILE = os.getenv("LOG_FILE", str(BASE_DIR / "logs" / "app.log"))
+
+# Create logs directory if it doesn't exist
+(BASE_DIR / "logs").mkdir(exist_ok=True)