Michael Ikehi
6fd7213076
- 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style management for tone, voice, taboo words, and terminology - Implemented vector search for referencing similar historical content - Enabled training data input to improve future AI output - Added environment variable support for API keys and model configs - Structured data storage with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.
87 lines
2.0 KiB
Python
87 lines
2.0 KiB
Python
"""
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Configuration module for the Marketing Assistant AI.
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Handles environment variables and application settings.
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"""
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import os
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from pathlib import Path
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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# Base paths
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BASE_DIR = Path(__file__).resolve().parent.parent
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DATA_DIR = BASE_DIR / "data"
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# Ensure data directories exist
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(DATA_DIR / "past_campaigns").mkdir(exist_ok=True)
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(DATA_DIR / "user_queries").mkdir(exist_ok=True)
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(DATA_DIR / "style_guidelines").mkdir(exist_ok=True)
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# API configuration
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API_HOST = os.getenv("API_HOST", "localhost")
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API_PORT = int(os.getenv("API_PORT", 8000))
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# LLM configuration
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LLM_MODEL = os.getenv("LLM_MODEL")
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LLM_API_KEY = os.getenv("LLM_API_KEY")
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# Cohere configuration
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COHERE_API_KEY = os.getenv("COHERE_API_KEY")
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# Vector database configuration
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VECTOR_DB_PATH = os.getenv("VECTOR_DB_PATH", str(DATA_DIR / "vector_store"))
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# Brand configuration
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BRAND_NAME = os.getenv("BRAND_NAME", "Adriana James")
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# Content types
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CONTENT_TYPES = [
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"email_campaign",
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"social_media",
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"blog_post",
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"website_copy",
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"ad_copy",
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"funnel_page",
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"product_description",
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"press_release"
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]
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# Tone options
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TONE_OPTIONS = [
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"professional",
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"friendly",
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"excited",
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"authoritative",
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"casual",
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"inspirational",
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"empathetic",
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"humorous"
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]
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# Content length options
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LENGTH_OPTIONS = [
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"short", # < 100 words
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"medium", # 100-300 words
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"long", # > 300 words
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]
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# Default brand style guidelines
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DEFAULT_BRAND_STYLE = {
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"brand_name": BRAND_NAME,
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"tone": ["professional", "friendly", "inspirational"],
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"voice_characteristics": ["clear", "direct", "empowering"],
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"taboo_words": ["cheap", "discount", "bargain"],
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"preferred_terms": {
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"customers": "clients",
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"products": "solutions"
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}
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}
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# Logging configuration
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LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
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LOG_FILE = os.getenv("LOG_FILE", str(BASE_DIR / "logs" / "app.log"))
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# Create logs directory if it doesn't exist
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(BASE_DIR / "logs").mkdir(exist_ok=True) |