Initial commit of Marketing Assistant AI project, including backend setup with FastAPI, brand style management, and marketing copy generation features. Added .gitignore, README, and various data files for brand voice, past campaigns, and book excerpts. Implemented vector store for content retrieval and embeddings using Cohere API. Included HTML template for user interface.

backend/config.py

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+from dataclasses import dataclass
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+@dataclass
+class Settings:
+    # API Keys
+    COHERE_API_KEY: str
+    DEEPSEEK_API_KEY: str
+    
+    # Vector Store Settings
+    VECTOR_DIMENSION: int = 1024  # Cohere's embed-english-v3.0 model dimension
+    INDEX_PATH: str = "data/vector_store/index.faiss"
+    
+    # Content Settings
+    MAX_CONTEXT_LENGTH: int = 2000
+    DEFAULT_MODEL: str = "deepseek-chat"
+    
+    # Brand Settings
+    BRAND_TONE: str = "professional and empathetic"
+    BRAND_VOICE: str = "Adriana James"
+    
+    @classmethod
+    def from_env(cls):
+        """Create a Settings instance from environment variables."""
+        return cls(
+            COHERE_API_KEY=os.getenv("COHERE_API_KEY", ""),
+            DEEPSEEK_API_KEY=os.getenv("DEEPSEEK_API_KEY", ""),
+            VECTOR_DIMENSION=int(os.getenv("VECTOR_DIMENSION", "1024")),
+            INDEX_PATH=os.getenv("INDEX_PATH", "data/vector_store/index.faiss"),
+            MAX_CONTEXT_LENGTH=int(os.getenv("MAX_CONTEXT_LENGTH", "2000")),
+            DEFAULT_MODEL=os.getenv("DEFAULT_MODEL", "deepseek-chat"),
+            BRAND_TONE=os.getenv("BRAND_TONE", "professional and empathetic"),
+            BRAND_VOICE=os.getenv("BRAND_VOICE", "Adriana James")
+        )
+
+# Create a global settings instance
+settings = Settings.from_env()