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📰 AI News Scraper & Semantic Search - Product Requirements Document

📋 Document Information

  • Version: 2.0
  • Last Updated: June 1, 2025
  • Status: Active Development
  • Stakeholders: Development Team, Product Owner, QA Team

📽️ Executive Summary

Demo Preview

AI News Scraper Demo Complete workflow demonstration: scraping, processing, and searching news articles

Project Vision

Develop an intelligent news article management system that demonstrates advanced GenAI integration capabilities while providing practical value for content analysis and semantic search.

Key Value Propositions

  1. Automated Content Processing: Transform raw news URLs into structured, searchable knowledge
  2. GenAI-Powered Analysis: Leverage cutting-edge AI for summarization and topic extraction
  3. Semantic Search: Enable natural language queries across article collections
  4. Developer-Friendly: Clean, modular architecture with comprehensive APIs
  5. Production-Ready: Containerized deployment with CI/CD pipelines

🎯 Project Scope & Objectives

Primary Objectives

  • Technical Demonstration: Showcase GenAI integration and problem-solving skills
  • Practical Application: Create a useful tool for news analysis and research
  • Best Practices: Implement clean code, testing, and deployment practices
  • Scalability: Design for future enhancements and production use

Success Criteria

  • Process 95%+ of provided URLs successfully
  • Generate high-quality summaries (user satisfaction > 85%)
  • Achieve semantic search relevance > 80%
  • Maintain system uptime > 99%
  • Complete processing within 30 seconds per article

Out of Scope (Future Phases)

  • Real-time news monitoring
  • Multi-language support
  • Advanced analytics dashboard
  • Social media integration

👥 User Stories & Use Cases

Primary Personas

1. Research Analyst

  • Goal: Quickly analyze large volumes of news articles
  • Pain Points: Manual summarization is time-consuming
  • Use Cases: Topic trend analysis, competitive intelligence

2. Content Manager

  • Goal: Organize and categorize news content efficiently
  • Pain Points: Inconsistent categorization, duplicate content
  • Use Cases: Content curation, editorial planning

3. Developer/Data Scientist

  • Goal: Integrate news analysis into larger workflows
  • Pain Points: Complex setup, poor API design
  • Use Cases: Automated content pipelines, research projects

Core User Stories

Epic 1: Content Acquisition

Feature: News Article Scraping
  As a user
  I want to provide URLs and get structured article data
  So that I can analyze news content efficiently

  Scenario: Successful article extraction
    Given a valid news article URL
    When I submit it for processing
    Then I should receive structured data with headline, content, and metadata
    And the system should handle errors gracefully

Epic 2: AI-Powered Analysis

Feature: Article Summarization and Topic Extraction
  As a user
  I want AI-generated summaries and topics for articles
  So that I can quickly understand content without reading full text

  Scenario: Quality summarization
    Given a processed article
    When AI analysis is performed
    Then I should receive a 100-300 word summary
    And 3-10 relevant topic tags
    And the analysis should maintain factual accuracy

Epic 3: Semantic Search

Feature: Natural Language Search
  As a user
  I want to search articles using natural language
  So that I can find relevant content intuitively

  Scenario: Contextual search results
    Given a collection of processed articles
    When I search using natural language
    Then I should receive semantically relevant results
    And results should be ranked by relevance
    And the system should understand synonyms and context

📋 Functional Requirements

FR-001: Article Extraction System

Priority: P0 (Critical) Complexity: Medium Effort: 3-5 days

Description: Core web scraping functionality for news articles

Acceptance Criteria:

  • Extract headline, body text, publication date, and source
  • Handle multiple news site formats
  • Implement retry logic for network failures
  • Validate extracted content quality
  • Support batch processing of URLs
  • Maintain >95% success rate on valid URLs

Technical Requirements:

  • Use newspaper3k and BeautifulSoup4
  • Implement user-agent rotation
  • Add request rate limiting
  • Include content validation rules

FR-002: GenAI Summarization Engine

Priority: P0 (Critical) Complexity: High Effort: 5-7 days

Description: AI-powered article summarization using OpenAI models

Acceptance Criteria:

  • Generate 100-300 word summaries
  • Maintain factual accuracy
  • Handle articles of varying lengths
  • Provide offline fallback mode
  • Include confidence scoring
  • Support configurable summary lengths

Technical Requirements:

  • OpenAI GPT-3.5/4 integration
  • Token optimization for cost efficiency
  • Local model fallback (e.g., BART)
  • Prompt engineering for consistency

FR-003: Topic Extraction & Classification

Priority: P0 (Critical) Complexity: High Effort: 4-6 days

Description: Automated topic identification and categorization

Acceptance Criteria:

  • Extract 3-10 relevant topics per article
  • Use predefined topic taxonomy
  • Implement topic normalization
  • Support hierarchical categorization
  • Provide topic confidence scores
  • Handle domain-specific terminology

Technical Requirements:

  • Predefined topic categories (Politics, Technology, Health, etc.)
  • Topic mapping and normalization rules
  • Machine learning-based classification backup

FR-004: Vector Database Integration

Priority: P0 (Critical) Complexity: Medium Effort: 3-4 days

Description: Efficient storage and retrieval using vector embeddings

Acceptance Criteria:

  • Generate semantic embeddings for articles
  • Store metadata alongside vectors
  • Support similarity search queries
  • Handle large-scale data efficiently
  • Provide database backup/restore
  • Support multiple vector DB backends

Technical Requirements:

  • OpenAI text-embedding-ada-002
  • FAISS for development, Qdrant for production
  • Metadata indexing strategy
  • Vector dimensionality optimization

FR-005: Semantic Search Engine

Priority: P0 (Critical) Complexity: High Effort: 5-8 days

Description: Natural language search with contextual understanding

Acceptance Criteria:

  • Process natural language queries
  • Return ranked relevant results
  • Handle synonyms and context
  • Support complex query types
  • Provide search result explanations
  • Include search analytics

Technical Requirements:

  • Query embedding generation
  • Similarity scoring algorithms
  • Result ranking and filtering
  • Search result caching

FR-006: Web User Interface

Priority: P1 (High) Complexity: Medium Effort: 4-6 days

Description: Intuitive Streamlit-based web interface

Acceptance Criteria:

  • URL input with multiple methods (text, file, samples)
  • Real-time processing status
  • Search interface with filters
  • Article visualization and management
  • Settings and configuration panel
  • Responsive design

Technical Requirements:

  • Streamlit framework
  • Component-based architecture
  • State management
  • Error handling and user feedback

FR-007: System Configuration

Priority: P1 (High) Complexity: Low Effort: 2-3 days

Description: Flexible configuration management

Acceptance Criteria:

  • Environment-based configuration
  • Secure API key management
  • Runtime configuration updates
  • Configuration validation
  • Default fallback values
  • Configuration documentation

Technical Requirements:

  • python-dotenv integration
  • Pydantic for validation
  • Environment variable mapping
  • Configuration file support

⚙️ Non-Functional Requirements

Performance Requirements

  • Response Time: Search queries < 2 seconds
  • Throughput: Process 100 articles per hour
  • Scalability: Support up to 10,000 articles
  • Availability: 99% uptime during operation

Security Requirements

  • API Key Protection: No hardcoded credentials
  • Input Validation: Sanitize all user inputs
  • Error Handling: No sensitive data in error messages
  • Access Control: Basic authentication for admin features

Quality Requirements

  • Code Coverage: Minimum 80%
  • Documentation: Complete API and user documentation
  • Maintainability: Modular, well-commented code
  • Testability: Comprehensive unit and integration tests

🏗️ System Architecture

High-Level Architecture

┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Frontend  │────│   API       │────│  Processing │────│   Storage   │
│  (Streamlit)│    │  Gateway    │    │   Pipeline  │    │  (Vector DB)│
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
                           │                  │                  │
                   ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
                   │   Config    │    │   GenAI     │    │   Search    │
                   │ Management  │    │  Services   │    │   Engine    │
                   └─────────────┘    └─────────────┘    └─────────────┘

Component Breakdown

Data Layer

  • Vector Database: FAISS (dev) / Qdrant (prod)
  • Metadata Storage: Embedded with vectors
  • Configuration: Environment variables + files

Business Logic Layer

  • Scraping Engine: newspaper3k + BeautifulSoup
  • AI Processing: OpenAI API integration
  • Search Engine: Vector similarity + text matching
  • Pipeline Orchestrator: Async processing coordination

Presentation Layer

  • Web UI: Streamlit application
  • CLI Interface: Command-line tools
  • API Endpoints: REST-like interface

Infrastructure Layer

  • Containerization: Docker + docker-compose
  • CI/CD: GitLab CI / Azure DevOps
  • Monitoring: Logging + health checks

🧪 Testing Strategy

Testing Pyramid

Unit Tests (70%)

  • Individual component functionality
  • Mock external dependencies
  • Fast execution (< 1 second each)
  • High code coverage

Integration Tests (20%)

  • Component interaction testing
  • Database integration
  • API endpoint testing
  • Moderate execution time

End-to-End Tests (10%)

  • Full user workflow testing
  • UI automation
  • Performance validation
  • Longer execution time

Test Categories

Functional Testing

  • Article scraping accuracy
  • Summarization quality
  • Search relevance
  • UI functionality

Performance Testing

  • Load testing (concurrent users)
  • Stress testing (resource limits)
  • Volume testing (large datasets)
  • Endurance testing (extended operation)

Security Testing

  • Input validation
  • Authentication mechanisms
  • Data protection
  • Error handling

🚀 Deployment & DevOps

Deployment Strategy

Development Environment

  • Local Docker containers
  • File-based vector storage
  • Development API keys
  • Hot reloading enabled

Staging Environment

  • Container orchestration (docker-compose)
  • Shared vector database
  • Production-like configuration
  • Automated testing

Production Environment

  • Managed container service
  • Distributed vector database
  • Production API keys
  • Monitoring and alerting

CI/CD Pipeline

Build Stage

stages:
  - lint          # Code quality checks
  - test          # Unit and integration tests
  - security      # Security scanning
  - build         # Docker image creation
  - deploy        # Environment deployment

Quality Gates

  • All tests pass (100%)
  • Code coverage > 80%
  • Security scan clear
  • Performance benchmarks met

📊 Metrics & KPIs

Development Metrics

  • Velocity: Story points per sprint
  • Quality: Bug rate, test coverage
  • Efficiency: Lead time, cycle time

Product Metrics

  • Usage: Active users, session duration
  • Performance: Response times, error rates
  • Quality: User satisfaction, search relevance

Business Metrics

  • Value: Feature adoption, user retention
  • Cost: Infrastructure costs, development time
  • Growth: User acquisition, feature requests

🔄 Future Roadmap

Phase 1: MVP (Current)

  • Core functionality implementation
  • Basic UI and search capabilities
  • Container deployment

Phase 2: Enhancement

  • Advanced analytics dashboard
  • Multi-language support
  • Performance optimization
  • Advanced search filters

Phase 3: Scale

  • Real-time processing
  • Machine learning improvements
  • API monetization
  • Enterprise features

📚 Appendices

A. Glossary

  • Vector Database: Storage system optimized for similarity search
  • Semantic Search: Search based on meaning rather than keywords
  • GenAI: Generative Artificial Intelligence
  • Embedding: Numerical representation of text content

B. References

  • OpenAI API Documentation
  • FAISS Documentation
  • Streamlit Documentation
  • Best Practices for Vector Databases

C. Risk Register

  • High: API rate limits, model accuracy
  • Medium: Performance scaling, data quality
  • Low: UI usability, documentation gaps

This PRD serves as the single source of truth for the AI News Scraper project, combining technical requirements, user needs, and implementation guidance in a comprehensive document.