feat: Implement AI-powered embeddings and vector similarity search system

backend/main.py

@@ -8,6 +8,7 @@ import uvicorn
 from config import settings
 from news_fetcher import NewsFetcher
 from recommender import NewsRecommender
+from groq_integration import GroqLLMService
 
 # Initialize FastAPI app
 app = FastAPI(
@@ -28,6 +29,7 @@ app.add_middleware(
 # Initialize components
 news_fetcher = NewsFetcher()
 recommender = NewsRecommender()
+groq_service = GroqLLMService()
 
 # Pydantic models
 class NewsQuery(BaseModel):
@@ -211,19 +213,101 @@ async def get_stats():
     """Get system statistics"""
     try:
         stats = recommender.get_store_stats()
-        
+
         # Add RSS feed information
         stats['rss_feeds'] = settings.rss_feeds
         stats['embedding_model'] = settings.embedding_model
-        
+        stats['groq_available'] = groq_service.is_available()
+
         return {
             "success": True,
             "statistics": stats
         }
-        
+
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Error getting stats: {str(e)}")
 
+@app.post("/enhance-article")
+async def enhance_article_with_ai(article_data: Dict[str, Any]):
+    """Enhance an article with AI-generated summary, sentiment, and keywords"""
+    try:
+        if not groq_service.is_available():
+            raise HTTPException(status_code=503, detail="Groq LLM service not available")
+
+        enhanced_article = groq_service.enhance_article(article_data)
+
+        return {
+            "success": True,
+            "original_article": article_data,
+            "enhanced_article": enhanced_article
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error enhancing article: {str(e)}")
+
+@app.post("/generate-insights")
+async def generate_news_insights():
+    """Generate insights from recent news articles"""
+    try:
+        if not groq_service.is_available():
+            raise HTTPException(status_code=503, detail="Groq LLM service not available")
+
+        # Get recent articles
+        recent_articles = recommender.get_trending_articles(top_k=10)
+
+        if not recent_articles:
+            raise HTTPException(status_code=404, detail="No recent articles found")
+
+        insights = groq_service.generate_insights(recent_articles)
+
+        return {
+            "success": True,
+            "insights": insights,
+            "based_on_articles": len(recent_articles)
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating insights: {str(e)}")
+
+@app.post("/fetch-and-enhance-news")
+async def fetch_and_enhance_news():
+    """Fetch news and enhance with AI features"""
+    try:
+        # Fetch news articles
+        result = news_fetcher.fetch_and_save_news()
+
+        if not result["success"]:
+            raise HTTPException(status_code=500, detail=result.get("message", "Failed to fetch news"))
+
+        articles = result["articles"]
+
+        # Enhance with AI if Groq is available
+        if groq_service.is_available():
+            # Enhance first 5 articles as example
+            enhanced_articles = groq_service.batch_enhance_articles(articles[:5])
+
+            # Add enhanced articles to vector store
+            store_result = recommender.add_articles_to_store(enhanced_articles)
+        else:
+            # Add regular articles to vector store
+            store_result = recommender.add_articles_to_store(articles)
+
+        if not store_result["success"]:
+            raise HTTPException(status_code=500, detail=store_result.get("message", "Failed to add articles to store"))
+
+        return {
+            "success": True,
+            "message": "News fetched and processed successfully",
+            "articles_fetched": result["articles_count"],
+            "articles_enhanced": 5 if groq_service.is_available() else 0,
+            "articles_stored": store_result["articles_added"],
+            "total_articles": store_result["total_articles"],
+            "ai_features_enabled": groq_service.is_available()
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error fetching and enhancing news: {str(e)}")
+
 # Run the application
 if __name__ == "__main__":
     uvicorn.run(