Add backend functionality for news fetching, processing, and recommendations

- Implemented NewsFetcher class to fetch articles from RSS feeds and clean HTML content.
- Added EmbeddingGenerator for generating embeddings using Cohere API.
- Created VectorStore for storing and retrieving articles using Pinecone.
- Developed NewsRecommender for analyzing articles and generating insights with Groq.
- Set up FastAPI application with endpoints for fetching news and providing recommendations.
- Configured logging for better traceability and debugging.
- Updated .gitignore to include environment variables and data directories.
- Added requirements.txt for project dependencies.

backend/vector_store.py

@@ -0,0 +1,88 @@
+from pinecone import Pinecone, ServerlessSpec
+from typing import List, Dict, Any
+from config import (
+    PINECONE_API_KEY,
+    PINECONE_ENVIRONMENT,
+    PINECONE_INDEX_NAME,
+    VECTOR_DIMENSION,
+    TOP_K_RESULTS
+)
+
+class VectorStore:
+    def __init__(self):
+        self.pinecone = Pinecone(api_key=PINECONE_API_KEY)
+        self.index_name = PINECONE_INDEX_NAME
+        self._ensure_index()
+
+    def _ensure_index(self):
+        """Ensure the Pinecone index exists, create if it doesn't."""
+        if self.index_name not in self.pinecone.list_indexes().names():
+            self.pinecone.create_index(
+                name=self.index_name,
+                dimension=VECTOR_DIMENSION,
+                metric="cosine",
+                spec=ServerlessSpec(cloud="aws", region="us-east-1")
+            )
+        self.index = self.pinecone.Index(self.index_name)
+
+    def upsert_articles(self, articles: List[Dict[str, Any]]) -> bool:
+        """Upsert articles to the vector store."""
+        try:
+            vectors = []
+            for article in articles:
+                if "embedding" not in article:
+                    continue
+                    
+                vector = {
+                    "id": article["id"],
+                    "values": article["embedding"],
+                    "metadata": {
+                        "title": article["title"],
+                        "content": article["content"],
+                        "link": article["link"],
+                        "published": article["published"],
+                        "source": article["source"],
+                        "categories": article["categories"]
+                    }
+                }
+                vectors.append(vector)
+
+            if vectors:
+                self.index.upsert(vectors=vectors)
+            return True
+        except Exception as e:
+            print(f"Error upserting articles: {str(e)}")
+            return False
+
+    def search_similar(self, query_embedding: List[float], top_k: int = TOP_K_RESULTS) -> List[Dict[str, Any]]:
+        """Search for similar articles using the query embedding."""
+        try:
+            results = self.index.query(
+                vector=query_embedding,
+                top_k=top_k,
+                include_metadata=True
+            )
+            
+            articles = []
+            for match in results.matches:
+                article = {
+                    "id": match.id,
+                    "score": match.score,
+                    **match.metadata
+                }
+                articles.append(article)
+            
+            return articles
+        except Exception as e:
+            print(f"Error searching similar articles: {str(e)}")
+            return []
+
+    def delete_article(self, article_id: str) -> bool:
+        """Delete an article from the vector store."""
+        try:
+            self.index.delete(ids=[article_id])
+            return True
+        except Exception as e:
+            print(f"Error deleting article: {str(e)}")
+            return False
+