🎯 New Features Added to Active Reading Demo

📂 Category Selection Feature

What It Does

Users can now manually select or override the document category detection:

Available Categories:

Auto-Detect (default) - AI detects domain automatically
Finance - Financial reports, earnings, budgets
Legal - Contracts, agreements, policies
Technical - API docs, manuals, specifications
Medical - Clinical trials, research, treatments
General - Any other document type

Category-Specific Extraction Patterns

📊 Finance Category

Revenue: $150 million revenue, sales of $2.5B
Profit: profit margin 25%, net profit $50M
Growth: 15% growth, increased by 20%
Dates: Q3 2024, fiscal year 2023
Employees: hire 200 engineers, workforce of 5000
Market Cap: market cap $10B

⚖️ Legal Category

Parties: between Company A and Company B
Terms: term of 36 months, duration 3 years
Liability: liability not to exceed $1M
Termination: 90 days written notice
Governing Law: governed by laws of Delaware
Effective Date: effective January 1, 2024

🔧 Technical Category

API Endpoints: GET /api/users, POST /auth/login
Versions: version 2.1.0, v3.5
Response Time: response time 150ms
Rate Limits: 1000 requests per minute
Authentication: OAuth 2.0, JWT tokens
Status Codes: HTTP 200, status code 404

🏥 Medical Category

Dosage: 50mg daily, 100ml twice daily
Duration: treatment for 12 weeks
Efficacy: 85% efficacy rate
Side Effects: side effects in 12% of patients
Patient Count: 500 patients enrolled
P-Values: p<0.001, p=0.025

🔑 Custom Keys Feature

What It Does

Users can specify their own extraction terms as comma-separated values:

Example Inputs:

CEO, budget, deadline, timeline
risk assessment, compliance, audit
performance, scalability, security
treatment, dosage, clinical trial

How It Works

Smart Extraction: Finds sentences containing the custom terms
Context Preservation: Returns full sentences, not just keywords
Confidence Scoring: Shows extraction confidence levels
JSON Output: Structured data for easy integration

🎯 New Strategy: Category-Specific Extraction

What's New

Added a specialized strategy that combines:

Category-specific patterns for targeted extraction
Custom key extraction for user-defined terms
Structured output with confidence scores
Domain expertise for each business category

Example Output

{
  "category": "Finance",
  "extracted_data": {
    "revenue": ["$150 million", "$2.5 billion sales"],
    "growth": ["15% increase", "20% growth rate"],
    "date": ["Q3 2024", "fiscal year 2023"]
  },
  "custom_extractions": {
    "CEO": ["CEO announced plans to expand", "CEO John Smith reported"],
    "investment": ["$50M investment in AI", "investment in new markets"]
  },
  "confidence_scores": {
    "revenue": 8.5,
    "custom_CEO": 6.2
  }
}

🎨 Enhanced UI Elements

New Input Controls

📂 Category Dropdown: Manual category selection
🔑 Custom Keys Input: Text field for custom extraction terms
📊 Enhanced Strategy Selection: Added "Category-Specific Extraction"

New Output Tabs

🎯 Category Analysis: Dedicated tab for category-specific results
Enhanced JSON: Structured category extraction data
Confidence Scores: Shows extraction reliability

Improved User Experience

Dynamic Help Text: Context-aware guidance
Example Suggestions: Sample custom keys for each category
Better Visual Organization: Clearer result presentation

🚀 Usage Examples

Finance Document Analysis

Document Category: Finance
Custom Keys: CEO, quarterly results, investment
Strategy: Category-Specific Extraction

Result: Extracts revenue figures, profit margins, growth rates PLUS CEO mentions, quarterly data, and investment information.

Legal Contract Review

Document Category: Legal  
Custom Keys: liability, termination, governing law
Strategy: Category-Specific Extraction

Result: Finds contract parties, terms, dates PLUS specific liability clauses, termination conditions, and jurisdiction details.

Technical Documentation

Document Category: Technical
Custom Keys: security, performance, scalability  
Strategy: Category-Specific Extraction

Result: Extracts API endpoints, versions, rate limits PLUS security features, performance metrics, and scalability considerations.

🎯 Why This Makes Active Reading Better

1. Adaptive Intelligence

AI now adapts not just to document type, but to user-specific needs
Combines automated domain detection with custom requirements

2. Enterprise Flexibility

Users can extract exactly what they need for their business case
Supports diverse enterprise document analysis workflows

3. Structured Output

Category-specific patterns ensure consistent extraction
Custom keys add user-defined flexibility
JSON format enables easy integration

4. Demonstrable Value

Shows how Active Reading adapts to different business domains
Proves the framework can handle real enterprise requirements
Highlights the superiority over one-size-fits-all approaches

🎨 Implementation Impact

What Changed in Code

Added: extract_category_specific_info() method
Enhanced: process_document() function with category/custom key parameters
New: Category-specific regex patterns for each domain
Improved: UI with additional input controls and output tabs

Backward Compatibility

✅ All existing strategies still work
✅ Auto-detection remains the default
✅ Original demo functionality preserved
✅ Enhanced with new capabilities

This makes your Active Reading demo much more interactive and showcases the adaptive intelligence that makes it superior to traditional document processing approaches! 🚀