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/main.py

@@ -0,0 +1,112 @@
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from typing import List, Dict, Any
+import json
+import os
+
+from news_fetcher import NewsFetcher
+from embeddings import EmbeddingGenerator
+from vector_store import VectorStore
+from recommender import NewsRecommender
+from config import RAW_NEWS_DIR, PROCESSED_NEWS_DIR
+
+app = FastAPI(title="DS Task AI News API")
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Initialize components
+news_fetcher = NewsFetcher()
+embedding_generator = EmbeddingGenerator()
+vector_store = VectorStore()
+recommender = NewsRecommender()
+
+@app.get("/")
+async def root():
+    """Root endpoint returning API information."""
+    return {
+        "name": "DS Task AI News API",
+        "version": "1.0.0",
+        "description": "AI-powered news retrieval and recommendation system"
+    }
+
+@app.get("/fetch-news")
+async def fetch_news():
+    """Fetch news from RSS feeds and store in vector database."""
+    try:
+        result = news_fetcher.process()
+        if result["status"] == "error":
+            raise HTTPException(status_code=404, detail=result["message"])
+        
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/recommend-news")
+async def recommend_news(article_id: str = None, query: str = None):
+    """Get news recommendations based on article ID or search query."""
+    try:
+        if article_id:
+            # Get article from vector store
+            article = vector_store.search_similar([0] * 4096, top_k=1)  # Placeholder vector
+            if not article:
+                raise HTTPException(status_code=404, detail="Article not found")
+            
+            # Generate query embedding from article content
+            query_embedding = embedding_generator.get_query_embedding(
+                f"{article[0]['title']} {article[0]['content']}"
+            )
+        elif query:
+            # Generate query embedding from search query
+            query_embedding = embedding_generator.get_query_embedding(query)
+        else:
+            raise HTTPException(
+                status_code=400,
+                detail="Either article_id or query parameter is required"
+            )
+
+        # Search for similar articles
+        similar_articles = vector_store.search_similar(query_embedding)
+        if not similar_articles:
+            raise HTTPException(status_code=404, detail="No similar articles found")
+
+        # Generate insights for the articles
+        insights = recommender.analyze_articles(similar_articles)
+
+        return {
+            "articles": similar_articles,
+            "insights": insights
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/article/{article_id}")
+async def get_article(article_id: str):
+    """Get a specific article and its summary."""
+    try:
+        # Search for the article
+        articles = vector_store.search_similar([0] * 4096, top_k=1)  # Placeholder vector
+        if not articles:
+            raise HTTPException(status_code=404, detail="Article not found")
+
+        article = articles[0]
+        
+        # Generate summary
+        summary = recommender.generate_summary(article)
+
+        return {
+            "article": article,
+            "summary": summary
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)