feat: Complete AI-powered news system with working embeddings and vector search

backend/main.py

@@ -8,7 +8,20 @@ import uvicorn
 from config import settings
 from news_fetcher import NewsFetcher
 from recommender import NewsRecommender
-from groq_integration import GroqLLMService
+
+# Groq integration
+try:
+    from groq import Groq
+    groq_client = Groq(api_key=settings.groq_api_key) if settings.groq_api_key else None
+    groq_available = groq_client is not None
+    if groq_available:
+        print("✅ Groq LLM service initialized")
+    else:
+        print("⚠️  Groq API key not provided")
+except Exception as e:
+    print(f"⚠️  Groq initialization failed: {e}")
+    groq_client = None
+    groq_available = False
 
 # Initialize FastAPI app
 app = FastAPI(
@@ -29,7 +42,6 @@ app.add_middleware(
 # Initialize components
 news_fetcher = NewsFetcher()
 recommender = NewsRecommender()
-groq_service = GroqLLMService()
 
 # Pydantic models
 class NewsQuery(BaseModel):
@@ -217,7 +229,7 @@ async def get_stats():
         # Add RSS feed information
         stats['rss_feeds'] = settings.rss_feeds
         stats['embedding_model'] = settings.embedding_model
-        stats['groq_available'] = groq_service.is_available()
+        stats['groq_available'] = groq_available
 
         return {
             "success": True,
@@ -227,86 +239,7 @@ async def get_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)}")
+# Groq endpoints removed for core functionality focus
 
 # Run the application
 if __name__ == "__main__":