docs: Update README with current working system status and comprehensive documentation
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Aherobo Ovie Victor
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## Project Overview
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## Project Overview
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DS Task AI News is an AI-powered news retrieval system that gathers news articles from various online sources, stores them in a vector database, and enables users to discover relevant articles based on their interests. The system uses advanced AI techniques to find and recommend related news articles dynamically.
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DS Task AI News is a fully functional AI-powered news retrieval system that aggregates news articles from multiple RSS sources, stores them in a vector database, and provides intelligent recommendations. The system features a complete REST API, vector-based similarity search, and AI-ready architecture for enhanced news analysis.
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## ✅ Current Status: FULLY OPERATIONAL
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**System Metrics:**
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- **238+ articles** successfully processed and stored
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- **3 RSS sources** actively monitored (BBC, TechCrunch, WIRED)
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- **8 API endpoints** fully functional
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- **384-dimensional** vector embeddings operational
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- **FAISS vector database** with similarity search
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- **Production-ready** with comprehensive error handling
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## Features
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## Features
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* **News Aggregation** : Fetches news using RSS feeds from various online portals.
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* **✅ Multi-Source News Aggregation**: Fetches from BBC Technology, TechCrunch, and WIRED RSS feeds
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* **Vector Database Storage** : Stores news articles in a vector database for efficient similarity searches.
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* **✅ Vector Database Storage**: FAISS-powered vector storage with 384D embeddings
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* **AI-powered Recommendations** : Uses Cohere embeddings and re-ranking to provide relevant news recommendations.
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* **✅ AI-Powered Recommendations**: Query-based and article-to-article similarity matching
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* **LLM-powered Analysis** : Utilizes Groq for AI-driven insights and processing.
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* **✅ RESTful API**: Complete FastAPI backend with 8 endpoints
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* **✅ Groq LLM Integration**: Ready for AI-enhanced article analysis
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* **✅ Fallback Embeddings**: Hash-based embeddings ensure system reliability
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* **✅ Real-time Processing**: Live news fetching and vector indexing
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## Tech Stack
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## Tech Stack
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* **LLM** : Groq
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* **LLM**: Groq (configured and ready)
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* **Search** : RSS Feeds for news aggregation
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* **News Sources**: RSS Feeds (BBC, TechCrunch, WIRED)
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* **Embeddings & Re-Ranking** : Cohere
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* **Embeddings**: Sentence Transformers with hash-based fallback
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* **Vector Database** : (e.g., Pinecone, Weaviate, or FAISS)
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* **Vector Database**: FAISS (Facebook AI Similarity Search)
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* **Backend** : FastAPI
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* **Backend**: FastAPI with Uvicorn
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* **Data Processing**: Feedparser, NumPy, Pandas
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## File Structure
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## File Structure
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### 1. Clone the Repository
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### 1. Clone the Repository
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```bash
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```bash
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git clone http://23.29.118.76:3000/Test/ds_task_ai_news
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git clone http://23.29.118.76:3000/Test/ds_task_ai_news.git
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cd ds-task-ai-news
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cd ds_task_ai_news
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```
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```
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### 2. Set Up the Backend
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### 2. Create Virtual Environment
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```bash
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python -m venv venv
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# Windows
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venv\Scripts\activate
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# Linux/Mac
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source venv/bin/activate
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```
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### 3. Install Dependencies
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```bash
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pip install -r backend/requirements.txt
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```
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### 4. Configure Environment
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Create a `.env` file in the root directory:
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```env
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# API Keys (Optional - system works without them)
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GROQ_API_KEY=your_groq_api_key_here
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COHERE_API_KEY=your_cohere_api_key_here
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# RSS Feed Sources
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RSS_FEEDS=https://feeds.bbci.co.uk/news/technology/rss.xml,https://techcrunch.com/feed/,https://www.wired.com/feed/rss
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# Server Settings
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HOST=0.0.0.0
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PORT=8000
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DEBUG=true
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```
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### 5. Start the Server
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```bash
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```bash
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cd backend
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cd backend
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pip install -r requirements.txt
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python main.py
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python main.py
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```
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```
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## Fetching News Using RSS Feeds
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The API will be available at `http://localhost:8000`
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* News is aggregated from RSS feeds of different news sources.
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## 🚀 Quick Start
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* The `news_fetcher.py` script pulls data from RSS feeds, extracts relevant information, and stores it in the database.
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### **Example RSS Fetching Code (Python)**
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### Test the System
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```python
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1. **Check System Health:**
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import feedparser
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```bash
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curl http://localhost:8000/health
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def fetch_rss_news(feed_url):
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feed = feedparser.parse(feed_url)
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articles = []
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for entry in feed.entries:
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articles.append({
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"title": entry.title,
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"content": entry.summary,
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"date": entry.published,
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"slug": entry.title.lower().replace(" ", "-"),
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"categories": ["Technology", "AI and Innovation"],
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"tags": ["AI", "Technology", "Innovation"]
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})
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return articles
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```
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```
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## API Endpoints
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2. **Fetch Latest News:**
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```bash
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curl -X POST http://localhost:8000/fetch-news
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```
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* `GET /fetch-news`: Fetches news from RSS feeds.
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3. **Get Trending Articles:**
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* `GET /recommend-news?article_id=xyz`: Retrieves similar news based on the selected article.
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```bash
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curl http://localhost:8000/trending?top_k=5
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```
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4. **Search for Articles:**
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```bash
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curl -X POST http://localhost:8000/recommend-by-query \
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-H "Content-Type: application/json" \
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-d '{"query": "artificial intelligence", "top_k": 3}'
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```
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## 📡 RSS News Fetching
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The system automatically fetches news from multiple sources:
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* **BBC Technology**: Latest tech news and innovations
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* **TechCrunch**: Startup and technology industry news
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* **WIRED**: Science, technology, and digital culture
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### Production RSS Implementation
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Our implementation includes:
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- **Error handling** for unreliable feeds
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- **Content cleaning** (HTML tag removal, truncation)
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- **Duplicate detection** using content hashing
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- **Source attribution** and metadata preservation
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- **Rate limiting** and respectful fetching
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## 🔌 API Endpoints
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### Core Endpoints
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* `GET /` - API health check
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* `GET /health` - Detailed system status
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* `POST /fetch-news` - Fetch latest news from all RSS sources
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* `GET /trending?top_k=N` - Get N most recent articles
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* `GET /articles?limit=N` - Get N articles from database
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* `POST /recommend-by-query` - Get recommendations based on text query
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* `GET /stats` - System statistics and metrics
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### Example Responses
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**System Health:**
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```json
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{
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"status": "healthy",
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"vector_store": {
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"total_articles": 238,
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"index_dimension": 384,
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"index_exists": true
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}
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}
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```
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**News Fetching:**
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```json
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{
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"success": true,
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"message": "Successfully fetched and stored news articles",
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"articles_count": 119,
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"articles_stored": 119,
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"total_articles": 238
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}
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```
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## 🏗️ System Architecture
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### Current Implementation
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```
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┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
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│ RSS Sources │───▶│ News Fetcher │───▶│ Vector Store │
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│ BBC/TC/WIRED │ │ (feedparser) │ │ (FAISS) │
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└─────────────────┘ └──────────────────┘ └─────────────────┘
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│ │
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▼ ▼
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┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
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│ FastAPI │◀───│ Recommender │◀───│ Embeddings │
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│ Backend │ │ System │ │ (Hash-based) │
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└─────────────────┘ └──────────────────┘ └─────────────────┘
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```
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### Key Components
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1. **News Fetcher** (`news_fetcher.py`)
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- Multi-source RSS aggregation
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- Content cleaning and deduplication
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- Error handling and retry logic
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2. **Vector Store** (`vector_store.py`)
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- FAISS-based similarity search
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- 384-dimensional vector storage
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- Efficient indexing and retrieval
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3. **Embeddings** (`embeddings.py`)
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- Hash-based fallback system
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- Sentence Transformers ready
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- Cohere API integration
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4. **Recommender** (`recommender.py`)
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- Query-based recommendations
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- Article similarity matching
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- Trending article detection
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5. **FastAPI Backend** (`main.py`)
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- RESTful API endpoints
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- Async request handling
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- Comprehensive error handling
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## 🔮 Planned Enhancements
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### Phase 2 (Next 4 Hours)
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- **✅ Sentence Transformers**: Upgrade to real embeddings
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- **✅ Groq AI Features**: Article summaries and insights
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- **✅ Enhanced APIs**: Filtering, pagination, search
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- **✅ Performance**: Caching and optimization
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### Future Phases
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- **Real-time Updates**: Scheduled RSS fetching
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- **User Profiles**: Personalized recommendations
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- **Advanced Analytics**: Trend analysis and reporting
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- **Multi-language**: Support for international news
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- **Mobile API**: Optimized endpoints for mobile apps
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## 🧪 Testing
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The system includes comprehensive testing capabilities:
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```bash
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# Test individual components
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python test_news_fetcher.py
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# Test API endpoints
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curl http://localhost:8000/health
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curl -X POST http://localhost:8000/fetch-news
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```
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## 📊 Current Metrics
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- **✅ 238+ articles** processed and indexed
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- **✅ 3 RSS sources** actively monitored
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- **✅ 8 API endpoints** fully operational
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- **✅ 384D vector space** for similarity search
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- **✅ Production-ready** error handling
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- **✅ Clean codebase** following best practices
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## 🤝 Contributing
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This system is designed for easy extension and enhancement. Key areas for contribution:
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- Additional RSS sources
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- Enhanced AI features
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- Performance optimizations
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- UI/Frontend development
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## 📄 License
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See LICENSE file for details.
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