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/embeddings.py

@@ -0,0 +1,50 @@
+import cohere
+from typing import List, Dict, Any
+from config import COHERE_API_KEY
+
+class EmbeddingGenerator:
+    def __init__(self):
+        self.client = cohere.Client(COHERE_API_KEY)
+
+    def generate_embeddings(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for a list of texts using Cohere."""
+        try:
+            response = self.client.embed(
+                texts=texts,
+                model="embed-english-v3.0",
+                input_type="search_document"
+            )
+            return response.embeddings
+        except Exception as e:
+            print(f"Error generating embeddings: {str(e)}")
+            return []
+
+    def process_articles(self, articles: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Process articles and add embeddings to them."""
+        # Prepare texts for embedding
+        texts = [
+            f"{article['title']} {article['content']}" 
+            for article in articles
+        ]
+        
+        # Generate embeddings
+        embeddings = self.generate_embeddings(texts)
+        
+        # Add embeddings to articles
+        for article, embedding in zip(articles, embeddings):
+            article["embedding"] = embedding
+        
+        return articles
+
+    def get_query_embedding(self, query: str) -> List[float]:
+        """Generate embedding for a search query."""
+        try:
+            response = self.client.embed(
+                texts=[query],
+                model="embed-english-v3.0",
+                input_type="search_query"
+            )
+            return response.embeddings[0]
+        except Exception as e:
+            print(f"Error generating query embedding: {str(e)}")
+            return []