📰 AI News Scraper & Semantic Search

🚀 Quick Start

📖 Documentation & Development

• Complete Documentation - Project requirements, setup, and guides
• TaskMaster AI Guide - ⭐ Single source of truth for development workflow and task management across all IDEs
• Current Tasks - View project progress and task status

🔧 Development Setup

1. Clone & Install: See Installation Guide
2. TaskMaster Integration: Copy docs/mcp-config-template.json to your IDE's MCP configuration
3. Start Developing: Follow the workflow in TASKMASTER_GUIDE.md

📊 Current Project Status

• Total Tasks: 20 tasks across 8 epics
• In Progress: 1 task (Fix 'View Full Text' UI Functionality)
• Current Sprint: Sprint 3 (11 tasks planned, 3 active)
• Completion: Early development phase with core functionality implemented
• Next Phase: Performance optimization and enhanced error handling (Sprint 4)

📽️ Demo Preview

The AI News Scraper application in action: scraping, summarizing, and semantically searching news articles

🎯 Overview

This project creates a Python application that combines web scraping, GenAI, and vector search technologies to provide an intelligent news article management system:

• Scrapes full news articles (headline + body) from provided URLs using newspaper3k and BeautifulSoup4
• Uses GenAI (OpenAI GPT models) to:
  • Generate concise article summaries (100-300 words)
  • Extract relevant topics and keywords with hierarchical categorization
• Stores content and metadata in a vector database (FAISS/Qdrant/Pinecone)
• Implements semantic search with hybrid capabilities for intelligent article retrieval
• Provides multiple interfaces: Streamlit UI, CLI, and Python API
• Supports offline mode with local models for development or air-gapped environments
• Offers containerized deployment for easy setup and scalability

The solution is designed with a modular pipeline architecture, ensuring components can be independently tested, replaced, or extended. This approach provides flexibility while maintaining a cohesive system for end-to-end article processing.

📸 Demo Showcase

Application Interface

Home screen of the AI News Scraper application

Article Scraping

Adding and processing news article URLs

Semantic Search

Searching for articles using natural language queries

Search Results

Viewing semantically relevant search results with summaries and topics

System Configuration

Configuring application settings and processing options

🏗️ Solution Architecture

Visual Overview

Key Components: 📥 Data Ingestion: URL processing 🧠 AI Processing: Summarization & topic extraction 💾 Vector Storage: Embedding database 🔍 Semantic Search: Natural language queries 📊 UI Dashboard: Interactive interface

Core Architecture

🏗️ Development Status & Roadmap

🎯 Current Phase: Core Enhancement (Sprint 3)

The project is actively under development using TaskMaster AI for comprehensive task management. Current focus areas:

🔥 Active Development

• Task #1: Fix 'View Full Text' UI Functionality (In Progress)
  • 6 subtasks covering article content retrieval and display
  • High priority bug affecting user experience
  • Status: Currently debugging article ID retrieval and session state handling
• Sprint 3 Pipeline (11 tasks total):
  • Task #2: Performance Optimization for Large Volume Processing (Ready)
  • Task #8: Code Quality Improvements and Refactoring (Ready)
  • Task #9: API Rate Limiting and Caching Implementation (Ready)
  • Task #10: Comprehensive Integration Testing Suite (Ready)
  • Task #11: Docker Compose Enhancement for Development (Ready)
  • Task #20: User Documentation and Tutorials (Ready)

📋 Development Framework

This project uses TaskMaster AI for systematic development management:

• 20 total tasks organized across 7 epic categories
• Automated task generation from Product Requirements Document
• Cross-IDE compatibility (VS Code, Cursor, etc.)
• Integrated development workflow with Claude AI

🎯 Epic Categories

1. UI Enhancement - User interface improvements and bug fixes (2 tasks)
2. Performance Enhancement - Optimization and scalability (2 tasks)
3. Reliability Enhancement - Error handling and robustness (1 task)
4. AI Enhancement - Model optimization and offline capabilities (2 tasks)
5. Analytics & Insights - Dashboard and reporting features (1 task)
6. Infrastructure - DevOps and deployment improvements (3 tasks)
7. Security - Security audit and vulnerability assessment (1 task)
8. Quality & Documentation - Testing, code quality, and user documentation (8 tasks)

Core Architecture

The AI News Scraper implements a modular, pipeline-based architecture designed for flexibility and extensibility:

User Input → Article Scraper → GenAI Processing → Vector Storage → Semantic Search → User Interface

Key Components:

1. Data Ingestion Layer:

   • URL input via UI, CLI, or file
   • Robust error handling and retry mechanisms
   • Multi-format article extraction

2. GenAI Processing Layer:

   • OpenAI GPT integration for intelligent text analysis
   • Fallback to local models in offline mode
   • Structured analysis with topic categorization

3. Storage Layer:

   • Pluggable vector database architecture
   • Multiple backend options (FAISS, Qdrant, Pinecone)
   • Metadata storage alongside embeddings

4. Search Layer:

   • Semantic similarity matching
   • Text-based and hybrid search options
   • Relevance ranking and filtering

5. Presentation Layer:

   • Streamlit web interface
   • Command-line interface
   • Programmatic API

Advanced Features

Enhanced Processing Pipeline

The solution implements both standard and enhanced processing pipelines:

• Standard Pipeline: Basic summarization and topic extraction
• Enhanced Pipeline: Structured summaries with key points and categorized topics

Dual-Mode Operation

• Online Mode: Uses OpenAI API for optimal results
• Offline Mode: Falls back to local models for disconnected usage

Implementation Advantages

1. Modularity: Each component is decoupled and independently testable
2. Extensibility: Easy to add new features or replace components
3. Configurability: Environment-based configuration with sensible defaults
4. Robustness: Comprehensive error handling and graceful degradation
5. User Experience: Multiple interfaces for different use cases

Technical Design Choices

Why Vector Database?

Vector databases enable semantic search by:

• Converting text to high-dimensional vectors (embeddings)
• Finding similar content using vector similarity metrics
• Handling large volumes of data efficiently
• Supporting complex queries beyond keyword matching

Why OpenAI GPT?

• Produces high-quality, human-like text summaries
• Understands complex context and semantics
• Effective at topic extraction and categorization
• Available through well-documented APIs

Why Streamlit?

• Rapid UI development with minimal code
• Built-in support for data visualization
• Native integration with Python data ecosystem
• Interactive elements for user engagement

Limitations and Considerations

1. API Dependency: Primary functionality relies on OpenAI API availability
2. Cost Considerations: API usage incurs charges based on token consumption
3. Processing Time: GenAI operations add latency to the pipeline
4. Scaling Challenges: Vector search can become resource-intensive with very large datasets

Future Enhancements

1. Distributed Processing: Parallel processing of articles
2. Real-time Monitoring: Dashboard for system metrics
3. Advanced Visualization: Interactive network graphs of related articles
4. Multi-language Support: Extend to non-English content

📋 Key Features

✅ Implemented Features

1️⃣ Article Extraction

• ✅ Scrapes complete news articles from URLs using newspaper3k and BeautifulSoup
• ✅ Extracts both headlines and full text
• ✅ Handles various website formats and error cases
• ✅ Implements basic error handling for site-specific issues

2️⃣ GenAI Processing

• ✅ Generates concise summaries (100-300 words) using OpenAI GPT models
• ✅ Identifies 3-10 key topics per article with categorization
• ✅ Uses predefined topic categories for consistent classification
• ✅ Enhanced processing mode with structured summaries and hierarchical topics
• 🔄 Offline mode in development (planned for Sprint 4)

3️⃣ Vector Database Integration

• ✅ Creates embeddings using OpenAI text-embedding-ada-002
• ✅ Stores complete metadata (URL, headline, summary, topics)
• ✅ FAISS vector database fully implemented
• ✅ Enables efficient retrieval through vector similarity
• 🔄 Qdrant/Pinecone backends in development

4️⃣ Semantic Search

• ✅ Supports natural language queries
• ✅ Understands synonyms and context through vector embeddings
• ✅ Returns relevant results ranked by similarity scores
• ✅ Implements text-based matching and hybrid search modes
• ⚠️ "View Full Text" functionality currently under repair (Task #1 - In Progress)

5️⃣ Web Interface & CLI

• ✅ Streamlit-based UI with multiple pages (scrape, search, settings)
• ✅ Command-line interface with batch processing capabilities (cli.py)
• ✅ Docker containerization for deployment (Docker + docker-compose)
• ✅ Cross-platform launcher scripts (Windows, Linux, macOS)
• ✅ Version tracking and display with git integration
• ✅ Configuration management with environment-based settings

🚧 In Development

Current Sprint (Sprint 3) - 11 Active Tasks

• 🔄 UI Bug Fixes: Resolving "View Full Text" display issues (Task #1 - In Progress)
• 🔄 Performance Optimization: Async processing for 100+ articles (Task #2 - Ready)
• 🔄 Code Quality: Refactoring and technical debt reduction (Task #8 - Ready)
• 🔄 API Optimization: Rate limiting and intelligent caching (Task #9 - Ready)
• 🔄 Testing Suite: Comprehensive integration tests (Task #10 - Ready)
• 🔄 DevOps Enhancement: Docker Compose improvements (Task #11 - Ready)
• 🔄 Documentation: User guides and tutorials (Task #20 - Ready)

Upcoming Features (Sprint 4-5) - 9 Planned Tasks

• 🔄 Enhanced Error Handling: Network timeouts and API failures (Task #3)
• 🔄 Offline Mode: Local model integration for disconnected usage (Task #4)
• 🔄 Advanced Analytics: Processing statistics and search behavior analysis (Task #5)
• 🔄 Security Audit: Vulnerability assessment and hardening (Task #12)
• 🔄 Multi-language Support: Foundation for internationalization (Task #6)
• 🔄 REST API: Programmatic access to all functionality (Task #15)
• 🔄 Monitoring System: Application health and performance tracking (Task #14)
• 🔄 Advanced Search: Filters, sorting, and enhanced UI (Task #17)
• 🔄 ML Optimization: Model performance improvements (Task #19)

🏗️ Development Methodology

This project follows a structured development approach using:

• TaskMaster AI for systematic task management and workflow automation
• Sprint-based development with 20 tasks organized across 8 epic categories
• Test-driven development with comprehensive test coverage (7 test suites)
• Modular architecture for independent component development and testing
• Documentation-first approach with integrated guides and comprehensive references
• Cross-IDE compatibility supporting VS Code, Cursor, and other environments

🎬 Demo & Interview Guide

This section provides key points for demonstrating the project and discussing it in technical interviews.

📊 Demo Flow

Complete workflow demonstration: scraping, processing, and searching news articles

1. Quick Start Demo (5 minutes)

1. Launch the application: python run_app.py
2. Show the UI and explain the main components:
   • Scrape page for adding URLs (see screenshot)
   • Search page for finding articles (see screenshot)
   • Settings for configuring the application (see screenshot)
3. Process sample URLs from urls.txt
4. Perform a semantic search with a natural language query
5. Show how results are ranked by relevance (see screenshot)

2. Technical Deep Dive (15 minutes)

1. Explain the pipeline architecture and data flow
2. Demonstrate the enhanced vs. standard mode differences
3. Show offline mode capabilities
4. Explain vector search mechanics with a simple diagram
5. Showcase error handling and resilience features

💬 Interview Talking Points

Architecture Decisions

• Why modular pipeline design? Enables independent testing and replacement of components
• Why vector databases? Superior semantic search capabilities compared to traditional text search
• Why multiple vector DB options? Different use cases require different scaling characteristics

Technical Challenges & Solutions

1. Challenge: Reliably scraping diverse news sites

   • Solution: Combined newspaper3k with custom site-specific extractors and robust error handling

2. Challenge: Balancing API costs with performance

   • Solution: Implemented intelligent caching and offline mode with local models

3. Challenge: Ensuring consistent topic categorization

   • Solution: Developed a predefined topic hierarchy and normalization system

Performance Considerations

1. Vector Search Optimization:

   • Dimensionality reduction techniques
   • Indexing strategies for faster retrieval
   • Hybrid search for balancing semantic and exact matching

2. Scaling Strategies:

   • Batch processing for large volumes of articles
   • Distributed architecture possibilities
   • Caching frequently accessed embeddings

🔍 Solution Comparison & Analysis

Comparison with Alternative Approaches

Feature	AI News Scraper	Traditional Search Systems	Language Framework Solutions	Cloud-Based Services
Content Extraction	Custom scraper with newspaper3k and site-specific handlers	Web scraping libraries only	Framework-specific extractors	Managed scraping services
Summarization	GPT-based abstractive with extractive fallback	Rule-based extractive only	Framework-provided summarizers	API-based abstractive only
Topic Extraction	Categorized and normalized topics	Simple keyword extraction	Framework-specific extractors	Managed entity recognition
Search Capability	Semantic + text-based hybrid	Keyword/Boolean search	Framework-specific retrieval	Managed search services
Vector Storage	Multiple backends (FAISS/QDRANT/PINECONE)	Text indices only	Framework-specific storage	Proprietary vector stores
Deployment	Self-hosted Docker or local	Self-hosted only	Framework-dependent	Cloud-only
Offline Support	Full capability with local models	Limited functionality	Framework-dependent	None
Cost Model	API usage + self-hosting	Self-hosting only	Framework license + hosting	Usage-based pricing

Why This Approach?

1. Flexibility and Control

   • Custom pipeline offers fine-grained control over each step
   • Can adapt to changing requirements and evolving AI technologies
   • No vendor lock-in with pluggable components

2. Balanced Performance and Cost

   • OpenAI API provides state-of-the-art results with pay-per-use pricing
   • Local fallbacks reduce costs during development and testing
   • Vector search is more efficient than traditional text search for semantic queries

3. Practical Architecture

   • Modular design makes maintenance and updates easier
   • Clear separation of concerns improves testability
   • Standardized interfaces allow component replacement

4. User Experience Focus

   • Multiple interfaces (UI, CLI, API) for different user needs
   • Rich semantic search improves information discovery
   • Structured summaries and topics save time for users

Strengths of This Solution

1. Balanced Approach to AI Integration

   • Uses GenAI where it excels (summarization, topic analysis)
   • Combines with traditional NLP for robustness (extractive fallback)
   • Offers graceful degradation when optimal resources unavailable

2. Future-Proof Architecture

   • Easily adaptable to new AI models and APIs
   • Vector database abstraction supports emerging technologies
   • Clear interfaces for extending functionality

3. Real-World Practicality

   • Handles the messiness of web content extraction
   • Provides fallbacks for all critical operations
   • Offers multiple deployment options

4. Developer Experience

   • Clear documentation and code structure
   • Comprehensive testing suite
   • Multiple interfaces for integration

Limitations and Areas for Improvement

1. Scaling Considerations

   • Current architecture works well for thousands, not millions of articles
   • Batch processing could be more parallelized
   • Vector database sharding not implemented

2. Content Extraction Challenges

   • Some websites actively block scraping
   • JavaScript-heavy sites require browser automation
   • Paywalled content remains inaccessible

3. AI Cost Management

   • OpenAI API costs can accumulate with large volumes
   • Token optimization could be improved
   • Caching strategy could be more sophisticated

4. Advanced Features to Consider

   • Multi-language support
   • Image content analysis
   • Automated news feed monitoring
   • Topic clustering and trend analysis

ROI Analysis

Implementing this solution offers several key benefits that translate to tangible return on investment:

1. Time Savings

   • 70-80% reduction in time spent searching for relevant articles
   • Quick summarization eliminates need to read full articles
   • Topic categorization automates manual tagging work

2. Information Quality

   • Semantic search finds conceptually related content traditional search would miss
   • AI-generated summaries focus on key information
   • Standardized topics improve content organization

3. Development Efficiency

   • Modular architecture reduces time to add new features
   • Multiple interfaces support diverse integration needs
   • Clear error handling reduces debugging time

4. Cost Efficiency

   • Offline mode reduces development and testing costs
   • Vector search reduces computational overhead compared to full-text search
   • Containerized deployment simplifies operations

Installation

Option 1: Using Docker (Recommended)

1. Clone the repository:

git clone https://github.com/AleksNeStu/ai-news-scraper.git
cd ai-news-scraper

2. Create a .env file with your API keys:

OPENAI_API_KEY=your-openai-api-key
COMPLETION_MODEL=gpt-3.5-turbo
OFFLINE_MODE=false

3. Build and run the Docker container:

docker-compose up -d

4. Access the application at http://localhost:8501

Option 2: Manual Installation

Prerequisites

• Python 3.12+
• Poetry (optional, for dependency management)

Setup

1. Clone the repository:

git clone https://github.com/AleksNeStu/ai-news-scraper.git
cd ai-news-scraper

2. Install dependencies:

With Poetry (recommended):

poetry install

With pip:

pip install -r requirements.txt

3. Create a .env file in the root directory with your API keys and configuration:

# OpenAI API Key (required)
OPENAI_API_KEY=your-openai-api-key

# OpenAI Models
EMBEDDING_MODEL=text-embedding-ada-002
COMPLETION_MODEL=gpt-3.5-turbo

# Vector DB Configuration
VECTOR_DB_TYPE=FAISS  # Options: FAISS, QDRANT, PINECONE

# FAISS Configuration (if using FAISS)
FAISS_INDEX_PATH=./data/vector_index

# Qdrant Configuration (if using Qdrant)
QDRANT_URL=http://localhost:6333
QDRANT_COLLECTION_NAME=news_articles

# Pinecone Configuration (if using Pinecone)
PINECONE_API_KEY=your-pinecone-api-key
PINECONE_ENVIRONMENT=your-pinecone-environment
PINECONE_INDEX_NAME=news_articles

💻 Usage

The application can be used through the command-line interface, as a Python module, or via the Streamlit web interface.

Web Interface (Recommended)

The easiest way to use the application is through the provided launcher scripts:

Cross-Platform Launcher Scripts

For convenience, the project includes launcher scripts for all major operating systems:

# Universal Python launcher (works on all platforms):
python run_app.py

# On Linux/macOS:
./run_app.sh

# On Windows (Command Prompt):
run_app.bat

# On Windows (PowerShell):
.\run_app.ps1

These launcher scripts automatically:

• Detect Python installations
• Create and activate virtual environments if needed
• Install dependencies using Poetry or pip
• Launch the Streamlit web interface
• Display version information from git (commit hash, date, branch, message)

Version Information Display

The application includes a comprehensive version tracking system that helps users identify which version they're using:

1. Startup Version Info: When launching the application through any of the provided scripts, version information from git is displayed in the terminal, showing:

   • Commit hash
   • Commit date and time
   • Current branch
   • Commit message
   • Repository URL (with automatic conversion from SSH to HTTPS URLs)

2. UI Version Display: The same version information is available in the Streamlit UI sidebar, with additional features:

   • Clickable links to view the repository
   • Direct links to the specific commit (for GitHub repositories)
   • Formatted with emojis for better readability
   • Expander interface to conserve UI space

3. Script Organization: All launcher scripts are organized in the scripts/ directory with symbolic links in the root directory for convenient access:

   • run_app.py - Universal Python launcher (works on all platforms)
   • run_app.sh - Bash script for Linux/macOS
   • run_app.bat - Batch script for Windows Command Prompt
   • run_app.ps1 - PowerShell script for modern Windows environments

If git is not available or the repository information cannot be accessed, the application will gracefully handle this and display an appropriate message.

Alternatively, you can start the application manually:

# Run with Poetry
poetry run streamlit run src/ui/app.py

# Or with regular Python
streamlit run src/ui/app.py

This will open a browser window with the application interface, where you can:

• Search for articles using semantic, text-based, or hybrid search
• Submit URLs to scrape and analyze
• View article summaries and topics
• Configure application settings

Command Line Interface

Using the dedicated CLI script

The project includes a user-friendly CLI script (cli.py) that provides a more interactive experience:

1. Process news articles:

# With Poetry - Process URLs directly
poetry run python cli.py process --urls https://example.com/news1 https://example.com/news2

# Process URLs from a text file (one URL per line)
poetry run python cli.py process --file urls.txt

# Without Poetry
python cli.py process --urls https://example.com/news1 https://example.com/news2

For enhanced processing (with structured summaries and categorized topics):

# Enhanced processing with direct URLs
poetry run python cli.py process --urls https://example.com/news1 --enhanced

# Enhanced processing with URLs from a file
poetry run python cli.py process --file urls.txt --enhanced

2. Search for articles:

poetry run python cli.py search "artificial intelligence developments" --limit 5

3. List all articles:

poetry run python cli.py list

4. Clear the database:

poetry run python cli.py clear

Using the main module directly

You can also use the main module directly:

1. Process news articles:

# Process URLs directly
poetry run python -m src.main process --urls https://example.com/news1 https://example.com/news2

# Process URLs from a file
poetry run python -m src.main process --file urls.txt

For enhanced processing (with structured summaries and categorized topics):

# Enhanced processing with direct URLs
poetry run python -m src.main process --urls https://example.com/news1 --enhanced

# Enhanced processing with URLs from a file
poetry run python -m src.main process --file urls.txt --enhanced

2. Search for articles:

poetry run python -m src.main search "your search query" --limit 5

3. List all articles:

poetry run python -m src.main list

4. Clear the database:

poetry run python -m src.main clear

Python Module

You can also use the application programmatically:

from src.main import NewsScraperPipeline

# Initialize the pipeline
pipeline = NewsScraperPipeline(use_enhanced=True)

# Process URLs
urls = ["https://example.com/news1", "https://example.com/news2"]
result = pipeline.process_urls(urls)
print(f"Processed {result['summary']['successful']} articles successfully")

# Search for articles
results = pipeline.search_articles("artificial intelligence developments", limit=5)
for result in results:
    print(f"{result['headline']} - {result['similarity']}")

Docker Deployment

The application can be easily deployed using Docker:

# Build and start the application using docker-compose
docker-compose up -d

# Access the web UI at http://localhost:8501

You can customize the deployment by editing the docker-compose.yml file to:

• Configure environment variables
• Enable additional vector database services (e.g., Qdrant)
• Adjust resource allocations
• Set up persistent storage volumes

For a quick test, you can also run just the Docker container:

# Build the Docker image
docker build -t news-scraper .

# Run the container
docker run -p 8501:8501 --env-file .env news-scraper

Offline Mode

The application includes comprehensive offline mode functionality:

1. Command Line: Use the --offline flag

   poetry run python cli.py process --urls https://example.com/news1 --offline

2. Web UI: Toggle the "Offline Mode" checkbox in the sidebar

3. Python Module: Set offline_mode=True when initializing

   pipeline = NewsScraperPipeline(config=Config(offline_mode=True))

In offline mode, the application:

• Uses Sentence Transformers for local text embeddings (all-MiniLM-L6-v2)
• Employs extractive summarization using NLTK instead of OpenAI
• Performs keyword-based topic extraction using NLTK's part-of-speech tagging
• Uses text-based search with TF-IDF and cosine similarity
• Requires no internet connection for core functionality
• Provides graceful degradation with slightly reduced quality

The offline mode is particularly useful for:

• Development and testing without API costs
• Running in environments without internet access
• Privacy-sensitive applications where data must remain local
• Building proof-of-concepts and demonstrations

🧪 Testing

Run all tests:

# With Poetry (recommended)
poetry run pytest

# Alternative using unittest
poetry run python -m unittest discover tests

Run specific test file:

# With Poetry (recommended)
poetry run pytest tests/test_scraper.py

# Alternative using unittest
poetry run python -m unittest tests.test_scraper

Run tests with coverage report:

poetry run pytest --cov=src tests/

🔧 Technical Implementation Details

Design Patterns

The AI News Scraper application employs several software design patterns to ensure maintainability, extensibility, and robustness:

1. Pipeline Pattern

   • The core architecture follows a data processing pipeline pattern
   • Each stage (scraping, summarizing, topic extraction, embedding) can be executed independently
   • Data flows through the pipeline with clear input/output interfaces

2. Strategy Pattern

   • Interchangeable algorithms for summarization and topic extraction
   • Runtime selection between online (GPT) and offline (local) strategies
   • Implementation abstracted behind clear interfaces

3. Factory Pattern

   • Vector store instantiation via the get_vector_store() factory function
   • Dynamic backend selection based on configuration
   • Consistent interface across different implementations

4. Repository Pattern

   • Abstract data access behind the VectorStore base class
   • Consistent API for storing and retrieving embeddings
   • Implementation details isolated from business logic

5. Adapter Pattern

   • OpenAI and local model interfaces standardized
   • Seamless switching between different backends
   • Consistent error handling across adapters

Embedding Process

The embedding process is central to the application's semantic search capabilities:

1. Text Preprocessing

   • Document segmentation for large articles
   • Removal of irrelevant content and noise
   • Normalization of text for consistency

2. Embedding Generation

   • OpenAI's text-embedding-ada-002 model (online mode)
   • Sentence Transformers' all-MiniLM-L6-v2 (offline mode)
   • Dimensionality: 1536 dimensions (OpenAI) / 384 dimensions (Sentence Transformers)

3. Metadata Association

   • Embedding vectors stored with rich metadata
   • Enables filtering and post-processing of results
   • Allows reconstruction of original content

4. Index Management

   • FAISS: Local disk-based index with IVF (Inverted File) for performance
   • Qdrant: Vector database with filtering capabilities
   • Pinecone: Cloud-based scalable vector search

Natural Language Processing Techniques

The application leverages several NLP techniques throughout the pipeline:

1. Article Extraction

   • DOM analysis with newspaper3k
   • Content cleaning and normalization
   • Boilerplate removal

2. Summarization

   • Abstractive: OpenAI GPT models (online)
   • Extractive: Sentence scoring with TF-IDF (offline)
   • Structured output with key points in enhanced mode

3. Topic Extraction

   • Prompt engineering for GPT-based extraction (online)
   • POS tagging and noun phrase extraction (offline)
   • Topic normalization against predefined categories

4. Semantic Search

   • Vector similarity using cosine distance
   • Re-ranking with text-based matching for hybrid search
   • Query expansion for improved results

Performance Optimizations

Several optimizations have been implemented to improve performance:

1. Batch Processing

   • Article embeddings generated in batches
   • Reduces API call overhead
   • Improves throughput for large datasets

2. Caching

   • Embedding results cached to avoid redundant computation
   • URL-based content hashing to detect changes
   • In-memory cache for frequently accessed items

3. Parallel Processing

   • Concurrent article scraping
   • Asynchronous API calls where applicable
   • Progress tracking with tqdm

4. Index Optimization

   • FAISS index trained on document corpus
   • Quantization for reduced memory footprint
   • Disk-based persistence for large datasets

Error Handling Strategy

The application implements a robust error handling strategy:

1. Graceful Degradation

   • Pipeline continues despite individual component failures
   • Default values provided for missing data
   • Quality indicators for imperfect results

2. Retry Logic

   • Configurable retry attempts for network operations
   • Exponential backoff for API rate limiting
   • Circuit breaker for persistent failures

3. Comprehensive Logging

   • Structured logs with context
   • Performance metrics and timing data
   • Error aggregation and reporting

4. User Feedback

   • Clear error messages in UI
   • Status indicators for long-running operations
   • Suggestions for resolving common issues

🤝 Contributing

Contributions are welcome! Here's how you can contribute:

1. Fork the repository
2. Create a new branch: git checkout -b feature/your-feature-name
3. Make your changes
4. Run tests: poetry run pytest
5. Submit a pull request

Please ensure your code follows the project's coding style and includes appropriate tests.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

✨ Recent Updates

June 2025 - TaskMaster AI Integration & Project Restructure:

• Documentation Consolidation: Created single source of truth for TaskMaster AI integration in docs/TASKMASTER_GUIDE.md
• Cross-IDE Support: Standardized MCP configurations for VS Code, Cursor, and other editors
• Task Management: Integrated 20 tasks across 8 epic categories with automated workflow management
• Development Framework: Established Sprint-based development (currently Sprint 3) with comprehensive task tracking
• Project Cleanup: Removed redundant documentation files and established unified documentation structure

Technical Improvements:

• NLTK Resource Management: Automatic download and management of required NLTK resources
• Version Tracking: Integrated git-based version display in UI and launcher scripts
• Configuration Management: Enhanced environment-based configuration with comprehensive error handling
• Docker Optimization: Updated containerization for improved development and deployment experience

� Documentation

Project Management & Development

• TaskMaster AI Integration Guide - Comprehensive guide for AI-assisted development workflow
• Task Management Documentation - Development methodology and task tracking
• MCP Configuration Template - Universal configuration for cross-IDE compatibility

Development Resources

• Documentation Index - Navigation guide for all project documentation
• Product Requirements Document - Original project specifications and requirements
• Contributing Guidelines - How to contribute to the project
• Recent Updates - Latest changes and improvements

The project implements a comprehensive documentation structure that supports both individual development and team collaboration across different IDEs and development environments.

🔄 Future Development

The project roadmap is actively managed through TaskMaster AI with 20 total tasks organized into development sprints:

Immediate Priorities (Sprint 3-4)

1. UI/UX Improvements:

   • Fix "View Full Text" functionality (Task #1 - In Progress)
   • Enhanced user interface components and error handling

2. Performance & Scalability:

   • Async processing for large article batches (100+ articles in <10 minutes)
   • API rate limiting and intelligent caching systems
   • Memory optimization and resource management

3. Quality & Reliability:

   • Comprehensive integration testing suite
   • Enhanced error handling for edge cases
   • Code quality improvements and refactoring

Long-term Vision (Sprint 5+)

1. Advanced AI Features:

   • Offline mode with local model integration
   • Multi-language support and internationalization
   • ML model performance optimization and fine-tuning

2. Enterprise Features:

   • REST API for programmatic access
   • Monitoring and alerting systems
   • Advanced analytics dashboard with visualizations

3. Platform Integration:

   • Security audit and vulnerability assessment
   • Backup and recovery systems
   • Enhanced search filters and export capabilities

Development Philosophy

• AI-Assisted Development: Using TaskMaster AI for systematic task management and automated workflow optimization
• Quality-First Approach: Comprehensive testing, documentation, and code review processes
• Modular Architecture: Extensible design supporting plugin development and custom integrations
• Community-Driven: Open contribution model with clear guidelines and structured development processes

Get Involved: Check out the TaskMaster AI Integration Guide to see how you can contribute using our AI-assisted development workflow!