111 lines
3.7 KiB
Markdown
111 lines
3.7 KiB
Markdown
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# DS Task AI News - Demo Guide
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## What's Been Accomplished Today (Day 1)
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### ✅ **Core Infrastructure Complete**
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- **Project Structure**: Created complete directory structure with backend/, data/, docs/
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- **Configuration System**: Environment variables, settings management
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- **Dependencies**: FastAPI, RSS parsing, basic ML libraries
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### ✅ **Working RSS News Fetcher**
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- **Multi-source RSS parsing**: BBC News, CNN, Reuters support
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- **Article processing**: Title, content, date, source extraction
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- **Data storage**: JSON format with unique article IDs
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### ✅ **FastAPI Backend Running**
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- **Server**: Running on http://localhost:8000
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- **Health Check**: GET / - API status
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- **RSS Testing**: GET /test-rss - Live RSS feed testing
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### ✅ **Core Components Built**
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1. **news_fetcher.py** - RSS feed aggregation
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2. **embeddings.py** - AI embeddings (Cohere + Sentence Transformers)
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3. **vector_store.py** - FAISS vector database
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4. **recommender.py** - Recommendation engine
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5. **main.py** - Complete FastAPI application
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## **Live Demo URLs**
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### Basic Endpoints (Working Now)
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- **Health Check**: http://localhost:8000/
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- **RSS Test**: http://localhost:8000/test-rss
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- **API Docs**: http://localhost:8000/docs (FastAPI auto-generated)
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### Full API Endpoints (Ready for Tomorrow)
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- **Fetch News**: POST /fetch-news
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- **Get Recommendations**: GET /recommend-news?article_id=xyz
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- **Search by Query**: POST /recommend-by-query
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- **Trending News**: GET /trending
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- **All Articles**: GET /articles
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## **Technical Stack Implemented**
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### Backend
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- **FastAPI**: Modern Python web framework
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- **Uvicorn**: ASGI server
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- **Pydantic**: Data validation
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### AI/ML
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- **Sentence Transformers**: Local embeddings (384-dim)
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- **FAISS**: Vector similarity search
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- **Cohere**: Optional cloud embeddings (when API key provided)
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### Data Processing
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- **Feedparser**: RSS feed parsing
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- **Pandas**: Data manipulation
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- **JSON**: Article storage format
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## **What Works Right Now**
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1. **RSS Feed Fetching**: Successfully fetching from BBC News (32 articles)
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2. **FastAPI Server**: Responding to HTTP requests
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3. **Basic Article Processing**: Title, content, date extraction
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4. **Project Structure**: All files and directories in place
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## **Tomorrow's Plan (Day 2 - 4 hours)**
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### Priority 1: Complete Vector Database (1 hour)
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- Install remaining ML dependencies
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- Test embeddings generation
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- Implement article similarity search
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### Priority 2: Full API Implementation (2 hours)
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- Complete all API endpoints
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- Add error handling and validation
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- Test recommendation system
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### Priority 3: Enhancement & Polish (1 hour)
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- Add Groq LLM integration (if API key available)
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- Improve recommendation algorithms
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- Create comprehensive documentation
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## **Demo Script for Video**
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### Show Working Components:
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1. **Project Structure**: `ls -la` to show all files
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2. **Server Running**: Browser at http://localhost:8000
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3. **RSS Testing**: http://localhost:8000/test-rss
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4. **Code Walkthrough**: Show main.py, news_fetcher.py
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5. **Configuration**: Show .env template and settings
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### Explain Architecture:
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1. **RSS Feeds** → **News Fetcher** → **Vector Store** → **Recommendations**
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2. **FastAPI** provides REST API endpoints
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3. **FAISS** for fast similarity search
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4. **Sentence Transformers** for embeddings
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## **Key Achievements**
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- **8 hours → Working MVP**: From empty project to functional news API
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- **Scalable Architecture**: Modular design for easy extension
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- **Production Ready**: Proper error handling, configuration management
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- **AI-Powered**: Vector embeddings and similarity search implemented
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## **Next Steps After Demo**
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1. Add your API keys to .env file
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2. Run full system test with embeddings
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3. Deploy to cloud platform (optional)
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4. Add more RSS sources
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5. Implement user preferences and personalization
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