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- Implemented vector search for referencing similar historical content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.

data/README.md

@@ -0,0 +1,81 @@
+# Marketing Assistant AI - Data Directory
+
+This directory contains the data used by the Marketing Assistant AI system.
+
+## Structure
+
+- **past_campaigns/**: Contains JSON files of past marketing campaigns used for training and reference
+- **user_queries/**: Stores user queries and requests for analytics and model improvement
+- **style_guidelines/**: Contains brand tone and voice guidelines
+- **vector_store/**: Generated vector database for content retrieval (created automatically)
+
+## File Formats
+
+### Past Campaigns
+
+Past campaign files are stored as JSON with the following structure:
+
+```json
+{
+  "content": "The actual marketing content text",
+  "content_type": "email_campaign|social_media|blog_post|etc",
+  "metadata": {
+    "campaign_name": "Name of the campaign",
+    "performance_metrics": {
+      "metric1": value,
+      "metric2": value
+    },
+    "content_type": "Same as above",
+    "added_at": "ISO timestamp",
+    "training_data": true
+  },
+  "document_id": 0,
+  "timestamp": "ISO timestamp"
+}
+```
+
+### User Queries
+
+User query files store information about requests made to the AI:
+
+```json
+{
+  "prompt": "The user's prompt text",
+  "parameters": {
+    "content_type": "Type of content requested",
+    "tone": "Requested tone",
+    "length": "Requested length",
+    "include_cta": true|false
+  },
+  "timestamp": "ISO timestamp"
+}
+```
+
+### Brand Style Guidelines
+
+Brand style is stored as a JSON file with the following structure:
+
+```json
+{
+  "brand_name": "Adriana James",
+  "tone": ["professional", "friendly", "inspirational"],
+  "voice_characteristics": ["clear", "direct", "empowering"],
+  "taboo_words": ["cheap", "discount", "bargain"],
+  "preferred_terms": {
+    "customers": "clients",
+    "products": "solutions"
+  }
+}
+```
+
+## Adding New Data
+
+### Adding Past Campaigns
+
+1. Use the API endpoint `POST /training-data` with the appropriate JSON payload
+2. Alternatively, add a JSON file to the `past_campaigns` directory following the format above
+
+### Updating Brand Style
+
+1. Use the API endpoint `PUT /brand-style` with the updated style guidelines
+2. The system will automatically update the style file