feat: Implement Pinecone vector store integration

- Update config.py with Pinecone settings and model configurations
- Implement VectorStore class with Pinecone backend
- Add comprehensive vector operations (add, search, delete)
- Set up proper error handling and metadata management
- Add .gitignore for Python project

backend/main.py

@@ -0,0 +1,93 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Optional, List
+import uvicorn
+from copywriter import generate_marketing_copy
+from vector_store import VectorStore
+from embeddings import CohereEmbeddings
+from brand_style import BrandStyleChecker
+from config import Settings
+from finetuned_model import finetuned_model
+
+app = FastAPI(title="Marketing Assistant AI")
+settings = Settings()
+vector_store = VectorStore()
+embeddings = CohereEmbeddings()
+brand_checker = BrandStyleChecker()
+
+class CopyRequest(BaseModel):
+    prompt: str
+    content_type: str
+    tone: Optional[str] = None
+    target_audience: Optional[str] = None
+
+class CopyResponse(BaseModel):
+    content: str
+    confidence_score: float
+    brand_alignment_score: float
+
+class DirectModelRequest(BaseModel):
+    prompt: str
+    max_length: Optional[int] = 200
+    num_return_sequences: Optional[int] = 1
+    temperature: Optional[float] = 0.7
+    top_p: Optional[float] = 0.9
+
+class DirectModelResponse(BaseModel):
+    generated_texts: List[str]
+
+@app.post("/generate-copy", response_model=CopyResponse)
+async def create_marketing_copy(request: CopyRequest):
+    try:
+        # Generate embeddings for the prompt
+        prompt_embedding = embeddings.generate(request.prompt)
+        
+        # Retrieve similar content from vector store
+        similar_content = vector_store.search(prompt_embedding)
+        
+        # Generate marketing copy
+        content = generate_marketing_copy(
+            prompt=request.prompt,
+            content_type=request.content_type,
+            similar_content=similar_content,
+            tone=request.tone,
+            target_audience=request.target_audience
+        )
+        
+        # Check brand alignment
+        brand_alignment = brand_checker.check_alignment(content)
+        
+        return CopyResponse(
+            content=content,
+            confidence_score=0.85,  # This should be calculated based on model confidence
+            brand_alignment_score=brand_alignment
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/direct-model", response_model=DirectModelResponse)
+async def direct_model_inference(request: DirectModelRequest):
+    """
+    Direct inference using the finetuned model without using the vector store or other components.
+    This endpoint is useful for testing the model directly.
+    """
+    try:
+        # Generate text using the finetuned model
+        generated_texts = finetuned_model.generate(
+            prompt=request.prompt,
+            max_length=request.max_length,
+            num_return_sequences=request.num_return_sequences,
+            temperature=request.temperature,
+            top_p=request.top_p
+        )
+        
+        return DirectModelResponse(generated_texts=generated_texts)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="localhost", port=8000, reload=True)