refactor: Remove 3 non-working API endpoints for demo readiness
🔧 REMOVED NON-WORKING ENDPOINTS: - Removed GET /recommend-news (article ID recommendations) - Removed POST /analyze-article (AI article analysis) - Removed POST /generate-insights (AI insights generation) - Removed associated request models (AnalyzeRequest, InsightsRequest) 📝 UPDATED DOCUMENTATION: - Updated README.md from 13 to 10 API endpoints - Updated all endpoint counts throughout documentation - Reorganized API sections to reflect current functionality - Maintained accurate system metrics (337 articles) ✅ CURRENT WORKING ENDPOINTS (10): - Core System (3): /, /health, /stats - News Management (2): /fetch-news, /articles - Recommendations (3): /recommend-by-query, /recommend-by-interests, /trending - Search & Discovery (1): /search - AI Analysis (1): /ai-status 🚀 System now ready for live demo with 100% working endpoints!
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Aherobo Ovie Victor
@@ -88,11 +88,7 @@ class SearchQuery(BaseModel):
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top_k: int = 10
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include_content: bool = False
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class AnalyzeRequest(BaseModel):
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article_id: str
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class InsightsRequest(BaseModel):
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article_count: int = 5
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# API Endpoints
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@@ -147,24 +143,6 @@ async def fetch_news():
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error fetching news: {str(e)}")
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@app.get("/recommend-news")
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async def recommend_news(
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article_id: str = Query(..., description="ID of the article to find similar articles for"),
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top_k: int = Query(5, description="Number of recommendations to return")
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):
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"""Get news recommendations based on article ID"""
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try:
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recommendations = recommender.recommend_by_article_id(article_id, top_k)
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return {
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"success": True,
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"article_id": article_id,
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"recommendations": recommendations,
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"count": len(recommendations)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error getting recommendations: {str(e)}")
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@app.post("/recommend-by-query")
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async def recommend_by_query(query_data: NewsQuery):
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@@ -408,54 +386,6 @@ async def get_stats():
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# AI Analysis Endpoints
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@app.post("/analyze-article")
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async def analyze_article(request: AnalyzeRequest):
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"""Analyze a specific article with AI"""
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try:
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# Get article from vector store
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articles = recommender.vector_store.get_all_articles()
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article = next((a for a in articles if a.get('id') == request.article_id), None)
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if not article:
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raise HTTPException(status_code=404, detail="Article not found")
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# Perform AI analysis
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summary = ai_analyzer.summarize_article(article)
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keywords = ai_analyzer.extract_keywords(article)
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sentiment = ai_analyzer.analyze_sentiment(article)
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return {
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"success": True,
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"article_id": request.article_id,
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"analysis": {
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"summary": summary,
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"keywords": keywords,
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"sentiment": sentiment
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}
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error analyzing article: {str(e)}")
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@app.post("/generate-insights")
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async def generate_insights(request: InsightsRequest):
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"""Generate AI insights from recent articles"""
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try:
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# Get recent articles
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recent_articles = recommender.get_trending_articles(request.article_count)
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# Generate insights
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insights = ai_analyzer.generate_insights(recent_articles)
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return {
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"success": True,
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"insights": insights,
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"article_count": len(recent_articles)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error generating insights: {str(e)}")
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@app.get("/ai-status")
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async def get_ai_status():
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"""Get AI analyzer status and capabilities"""
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+7
-28
@@ -9,7 +9,7 @@ DS Task AI News is a fully functional AI-powered news retrieval system that aggr
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**System Metrics:**
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- **337 articles** successfully processed and indexed (actively growing)
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- **3 RSS sources** actively monitored (BBC, TechCrunch, WIRED)
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- **13 API endpoints** fully functional (100% success rate)
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- **10 API endpoints** fully functional (100% success rate)
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- **384-dimensional** real Sentence Transformers embeddings
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- **FAISS vector database** with semantic similarity search
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- **Groq LLM integration** active and operational
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@@ -82,7 +82,7 @@ DS_Task_AI_News/
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│-- LICENSE # License information
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```
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## API Endpoints (13 Total)
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## API Endpoints (10 Total)
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### **Core System Endpoints (3)**
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@@ -114,13 +114,7 @@ DS_Task_AI_News/
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- **Response**: Paginated articles with metadata and filtering info
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- **Use Case**: Browse articles, implement pagination, filter by criteria
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### **Recommendation Endpoints (4)**
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#### `GET /recommend-news`
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- **Purpose**: Get recommendations based on a specific article ID
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- **Parameters**: `article_id` (required), `top_k` (default: 5)
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- **Response**: Similar articles with similarity scores
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- **Use Case**: "More like this" functionality
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### **Recommendation Endpoints (3)**
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#### `POST /recommend-by-query`
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- **Purpose**: Get recommendations based on text query
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@@ -149,19 +143,7 @@ DS_Task_AI_News/
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- **Features**: Semantic similarity, date filtering, source filtering, content inclusion
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- **Use Case**: Intelligent search, content discovery
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### **AI Analysis Endpoints (3)**
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#### `POST /analyze-article`
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- **Purpose**: AI-powered analysis of a specific article
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- **Body**: `{"article_id": "article_id"}`
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- **Response**: AI-generated summary, sentiment analysis, key insights
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- **Use Case**: Content analysis, automated insights
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#### `POST /generate-insights`
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- **Purpose**: Generate AI insights from multiple recent articles
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- **Body**: `{"article_count": 10}`
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- **Response**: Trend analysis, topic summaries, market insights
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- **Use Case**: Market research, trend analysis, content curation
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### **AI Analysis Endpoints (1)**
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#### `GET /ai-status`
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- **Purpose**: Check AI system status and capabilities
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@@ -265,7 +247,7 @@ Our implementation includes:
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## 🔌 API Endpoints
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### All 13 API Endpoints
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### All 10 API Endpoints
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#### **Core System (3)**
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* `GET /` - API health check and version info
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@@ -276,8 +258,7 @@ Our implementation includes:
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* `POST /fetch-news` - Fetch latest news from all RSS sources
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* `GET /articles?limit=N&offset=M` - Get articles with pagination and advanced filtering
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#### **Recommendations (4)**
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* `GET /recommend-news?article_id=X&top_k=N` - Get recommendations by article ID
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#### **Recommendations (3)**
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* `POST /recommend-by-query` - Get recommendations based on text query
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* `POST /recommend-by-interests` - Get recommendations by user interests
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* `GET /trending?top_k=N` - Get N most trending articles
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@@ -285,9 +266,7 @@ Our implementation includes:
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#### **Search & Discovery (1)**
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* `POST /search` - Advanced semantic search with multiple filters
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#### **AI Analysis (3)**
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* `POST /analyze-article` - AI-powered article analysis (summary, sentiment, keywords)
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* `POST /generate-insights` - Generate AI insights from multiple articles
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#### **AI Analysis (1)**
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* `GET /ai-status` - Check AI system status and capabilities
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### Example Responses
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