AI-Assisted Quantexa to NewsCatcher API Conversion Tool

Transform Quantexa News API v6.1.0 queries into NewsCatcher News API v3 format using AI assistants with comprehensive documentation context and tested system prompts.

How It Works

1. Load Context: Upload relevant files from this repo to your AI assistant.
2. Use System Prompt: Apply the tested conversion prompt from tools/.
3. Describe Your Needs: Use simple structured input format.
4. Get Production Queries: Receive working NewsCatcher v3 queries.

Quick Start

Step 1: Choose Your AI Assistant

• Claude (Anthropic) ✅ Tested
• ChatGPT (OpenAI)
• Other AI assistants with document context support

Step 2: Load Required Files

Upload to your AI conversation:

• Required: tools/query-converter-prompt.md - Main system prompt.
• Required: reference/quantexa-newscatcher-api-comparison.md - API mappings.
• Optional: Relevant docs from docs/quantexa-tutorial/ for complex scenarios.

Step 3: Use This Input Format

Use Case: "[Brief description of what you want to monitor]"
Current Quantexa: [Your existing AQL query if any]
Keywords: "[Key terms separated by commas]"
Industries: "[Target industries]"
Geographic Focus: "[Countries/regions]"
Special Requirements: "[Any specific filtering needs]"

Step 4: Get Your Results

The AI generates complete NewsCatcher implementations with production-ready JSON queries.

Proven Examples

Input	Generated Output	Domain
Financial crime monitoring	01-financial-risk-queries.md	Banking & Fintech
Supply chain disruption tracking	02-supply-chain-queries.md	Manufacturing
ESG controversy monitoring	03-esg-sustainability-queries.md	Corporate Reputation
Pharma regulatory intelligence	04-pharma-regulatory-queries.md	Healthcare & Biotech
Tech M&A monitoring	05-tech-ma-innovation-queries.md	Technology Sector

See exact inputs used: tools/use_case_inputs.md

Project Structure

quantexa-newscatcher-ai-converter/
├── docs/
│   ├── quantexa-tutorial/                     # Quantexa documentation
│   └── use-cases/                             # AI-generated conversion examples
├── reference/
│   ├── quantexa-api-v6.1-spec.yaml            # Quantexa OpenAPI specification
│   └── quantexa-newscatcher-api-comparison.md # Quantexa vs Newscatcher API feature mapping
└── tools/
    ├── query-converter-prompt.md              # Main system prompt (tested)
    └── use_case_inputs.md                     # Example inputs and templates

What You Actually Get

Simple Input:

Use Case: "Monitor financial crime affecting banks"
Keywords: "money laundering, sanctions violations, AML"
Industries: "Banking, Fintech"
Geographic Focus: "Global, especially US and EU"

AI Generates Complete Document:

• 📋 5 Strategic Query Variations - Comprehensive coverage, entity-focused, geographic-specific, high-precision, exclusion-based
• 🔧 Advanced Query Combinations - Real-time alerts, fintech-specific, breaking news
• 📚 Migration Notes - Direct conversion from existing Quantexa queries
• ⚙️ Implementation Guidance - Performance optimization, alert thresholds, best practices
• 📊 Production Examples - Complete configuration templates and monitoring setup

Sample Strategy (1 of 5 generated):

{
  "q": "(\"money laundering\" OR \"sanctions violations\" OR \"AML violations\") AND (banking OR fintech)",
  "search_in": "title_content,title_content_translated",
  "ORG_entity_name": "\"JPMorgan Chase\" OR \"Wells Fargo\" OR \"HSBC\"",
  "content_sentiment_max": -0.1,
  "theme": "Financial Crime",
  "clustering_enabled": true,
  "clustering_threshold": 0.7,
  "page_size": 1000,
  "from_": "7d"
}

Total Output: ~2,000+ lines of production-ready implementation with comprehensive documentation.

Understanding Quantexa API

For Quantexa API questions, add tutorial context to your AI conversation:

• docs/quantexa-tutorial/ - Complete 15-part Quantexa documentation
• Use any relevant tutorial docs to understand Quantexa features before conversion
• Ask the AI questions about Quantexa syntax, capabilities, or migration challenges

Best Practices

✅ Do

• Always include the system prompt and API comparison
• Be specific about your monitoring requirements
• Test generated queries before production use

❌ Avoid

• Vague use case descriptions
• Skipping the required context documents
• Using queries without testing

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🤖 Ready to start? Load the required files into your AI assistant and describe your use case!