Using Codequiry with AI Assistants

AI coding assistants like ChatGPT, GitHub Copilot, and Claude can help you integrate Codequiry's API faster. Here's how to leverage them effectively.

Prompt Template for AI-Assisted Integration

I need to integrate the Codequiry plagiarism detection API into my application.

API Base URL: https://codequiry.com/api/v1
Authentication: API key in the "apikey" header

The workflow is:
1. POST /check/create - Create a check with {name, language}
2. POST /check/upload - Upload ZIP files with {check_id, file}
3. POST /check/start - Start analysis with {check_id, test_type}
4. POST /check/status - Poll until status_id = 4
5. POST /check/overview - Get results

Please build a complete integration class in [YOUR_LANGUAGE] that:
- Handles authentication
- Creates a check, uploads files, starts analysis
- Polls for completion with 30-second intervals and a 10-minute timeout
- Returns the plagiarism results
- Includes error handling for 401, 404, and 422 responses
        

            
AI Prompt Template
        
    

How AI Detection Works

Codequiry's AI detection analyzes code submissions to identify patterns consistent with AI-generated code from tools like ChatGPT, GitHub Copilot, Claude, and other LLMs.

Detection Methodology

Risk Level Thresholds

Complete Integration Example

A full example showing how to create a check, upload files, run analysis, and process both plagiarism and AI detection results.

class CodequiryClient {
    constructor(apiKey) {
        this.apiKey = apiKey;
        this.baseUrl = 'https://codequiry.com/api/v1';
    }

    async request(endpoint, data = {}) {
        const response = await fetch(`${this.baseUrl}${endpoint}`, {
            method: 'POST',
            headers: {
                'apikey': this.apiKey,
                'Content-Type': 'application/json'
            },
            body: JSON.stringify(data)
        });
        if (!response.ok) throw new Error(`API error: ${response.status}`);
        return response.json();
    }

    async runFullAnalysis(name, language, files) {
        // Step 1: Create check
        const check = await this.request('/check/create', { name, language });
        console.log(`Created check: ${check.id}`);

        // Step 2: Upload files
        for (const file of files) {
            const formData = new FormData();
            formData.append('check_id', check.id);
            formData.append('file', file);
            await fetch(`${this.baseUrl}/check/upload`, {
                method: 'POST',
                headers: { 'apikey': this.apiKey },
                body: formData
            });
        }

        // Step 3: Start analysis
        await this.request('/check/start', { check_id: check.id, test_type: 1 });

        // Step 4: Poll for completion
        const status = await this.pollStatus(check.id);

        // Step 5: Get results
        const overview = await this.request('/check/overview', { check_id: check.id });
        const aiResults = await this.request('/ai-results', { assignment_id: check.id });

        return { check, overview, aiResults };
    }

    async pollStatus(checkId, timeoutMs = 600000) {
        const start = Date.now();
        while (Date.now() - start < timeoutMs) {
            const status = await this.request('/check/status', { check_id: checkId });
            if (status.status_id === 4) return status;
            if (status.status_id === 5) throw new Error('Analysis failed');
            console.log(`Progress: ${status.progress}%`);
            await new Promise(r => setTimeout(r, 30000));
        }
        throw new Error('Timeout');
    }
}

// Usage
const client = new CodequiryClient('YOUR_API_KEY');
const results = await client.runFullAnalysis('Assignment 1', 14, files);

// Process AI detection results
results.aiResults.submissions.forEach(sub => {
    if (sub.ai_detection.risk_level === 'high') {
        console.log(`FLAGGED: ${sub.filename} - ${sub.ai_detection.avg_ai_probability}% AI`);
    }
});
        

            
Complete Integration
        
    

LMS Integration Patterns

Common patterns for integrating Codequiry with Learning Management Systems.

CI/CD Pipeline Integration

Add plagiarism detection to your CI/CD pipeline to automatically check code contributions.

GitHub Actions Example

name: Plagiarism Check
on:
  pull_request:
    types: [opened, synchronize]

jobs:
  plagiarism-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Create submission ZIP
        run: |
          zip -r submission.zip src/ -x "*.git*" "node_modules/*"

      - name: Run plagiarism check
        env:
          CODEQUIRY_API_KEY: ${{ secrets.CODEQUIRY_API_KEY }}
        run: |
          # Create check
          CHECK=$(curl -s -X POST "https://codequiry.com/api/v1/check/create" \
            -H "apikey: $CODEQUIRY_API_KEY" \
            -d "name=PR-${{ github.event.number }}" \
            -d "language=14")
          CHECK_ID=$(echo $CHECK | jq -r '.id')

          # Upload and start
          curl -s -X POST "https://codequiry.com/api/v1/check/upload" \
            -H "apikey: $CODEQUIRY_API_KEY" \
            -F "check_id=$CHECK_ID" \
            -F "[email protected]"

          curl -s -X POST "https://codequiry.com/api/v1/check/start" \
            -H "apikey: $CODEQUIRY_API_KEY" \
            -d "check_id=$CHECK_ID&test_type=1"

          # Poll for results (simplified)
          for i in $(seq 1 20); do
            sleep 30
            STATUS=$(curl -s -X POST "https://codequiry.com/api/v1/check/status" \
              -H "apikey: $CODEQUIRY_API_KEY" \
              -d "check_id=$CHECK_ID")
            STATUS_ID=$(echo $STATUS | jq -r '.status_id')
            if [ "$STATUS_ID" = "4" ]; then break; fi
          done

          echo "Plagiarism check complete for PR #${{ github.event.number }}"
        

            
GitHub Actions
        
    

Best Practices for AI Detection

What's Next?