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/embeddings.py

@@ -0,0 +1,37 @@
+import cohere
+from typing import List
+import numpy as np
+from config import settings
+
+class CohereEmbeddings:
+    def __init__(self):
+        self.settings = settings
+        self.client = cohere.Client(self.settings.COHERE_API_KEY)
+    
+    def generate_embedding(self, text: str) -> np.ndarray:
+        """Generate embeddings for a single text using Cohere."""
+        response = self.client.embed(
+            texts=[text],
+            model="embed-english-v3.0",
+            input_type="search_document"
+        )
+        return np.array(response.embeddings[0])
+    
+    def rerank_results(self, query: str, documents: List[str], top_n: int = 5) -> List[str]:
+        """Rerank documents based on relevance to the query."""
+        results = self.client.rerank(
+            query=query,
+            documents=documents,
+            top_n=top_n,
+            model="rerank-english-v2.0"
+        )
+        
+        # Extract the reranked documents in order
+        reranked_docs = []
+        for result in results.results:
+            # Get the document at the index returned by the rerank API
+            doc_index = result.index
+            if 0 <= doc_index < len(documents):
+                reranked_docs.append(documents[doc_index])
+        
+        return reranked_docs