igny8/docs/AI-NEW-DESIGN/STAGE2-EXECUTION-LOGGING-COMPLETE.md

Stage 2 - AI Execution & Logging Layer - COMPLETE ✅

Summary

Successfully created a centralized, consistent, and traceable execution layer for all AI requests with unified request handler and clean console-based logging.

✅ Completed Deliverables

1. Centralized Execution in ai_core.py

run_ai_request() Method

• Purpose: Single entry point for all AI text generation requests
• Features:
  • Step-by-step console logging with print() statements
  • Standardized request payload construction
  • Error handling with detailed logging
  • Token counting and cost calculation
  • Rate limit detection and logging
  • Timeout handling
  • JSON mode auto-enablement for supported models

Console Logging Format

[AI][function_name] Step 1: Preparing request...
[AI][function_name] Step 2: Using model: gpt-4o
[AI][function_name] Step 3: Auto-enabled JSON mode for gpt-4o
[AI][function_name] Step 4: Prompt length: 1234 characters
[AI][function_name] Step 5: Request payload prepared (model=gpt-4o, max_tokens=4000, temp=0.7)
[AI][function_name] Step 6: Sending request to OpenAI API...
[AI][function_name] Step 7: Received response in 2.34s (status=200)
[AI][function_name] Step 8: Received 150 tokens (input: 50, output: 100)
[AI][function_name] Step 9: Content length: 450 characters
[AI][function_name] Step 10: Cost calculated: $0.000123
[AI][function_name][Success] Request completed successfully

Error Logging Format

[AI][function_name][Error] OpenAI Rate Limit - waiting 60s
[AI][function_name][Error] HTTP 429 error: Rate limit exceeded (Rate limit - retry after 60s)
[AI][function_name][Error] Request timeout (60s exceeded)
[AI][function_name][Error] Failed to parse JSON response: ...

2. Image Generation with Logging

generate_image() Method

• Purpose: Centralized image generation with console logging
• Features:
  • Supports OpenAI DALL-E and Runware
  • Model and size validation
  • Step-by-step console logging
  • Error handling with detailed messages
  • Cost calculation

Console Logging Format

[AI][generate_images] Step 1: Preparing image generation request...
[AI][generate_images] Provider: OpenAI
[AI][generate_images] Step 2: Using model: dall-e-3, size: 1024x1024
[AI][generate_images] Step 3: Sending request to OpenAI Images API...
[AI][generate_images] Step 4: Received response in 5.67s (status=200)
[AI][generate_images] Step 5: Image generated successfully
[AI][generate_images] Step 6: Cost: $0.0400
[AI][generate_images][Success] Image generation completed

3. Updated All Function Files

functions/auto_cluster.py

• ✅ Uses AICore.extract_json() for JSON parsing
• ✅ Engine calls run_ai_request() (via engine.py)

functions/generate_ideas.py

• ✅ Updated generate_ideas_core() to use run_ai_request()
• ✅ Console logging enabled with function name

functions/generate_content.py

• ✅ Updated generate_content_core() to use run_ai_request()
• ✅ Console logging enabled with function name

functions/generate_images.py

• ✅ Updated to use run_ai_request() for prompt extraction
• ✅ Updated to use generate_image() with logging
• ✅ Console logging enabled

4. Updated Engine

engine.py

• ✅ Updated to use run_ai_request() instead of call_openai()
• ✅ Passes function name for logging context
• ✅ Maintains backward compatibility

5. Deprecated Old Code

processor.py

• ✅ Marked as DEPRECATED
• ✅ Redirects all calls to AICore
• ✅ Kept for backward compatibility only
• ✅ All methods now use AICore internally

6. Edge Case Handling

Implemented in run_ai_request():

• ✅ API Key Validation: Logs error if not configured
• ✅ Prompt Length: Logs character count
• ✅ Rate Limits: Detects and logs retry-after time
• ✅ Timeouts: Handles 60s timeout with clear error
• ✅ JSON Parsing Errors: Logs decode errors with context
• ✅ Empty Responses: Validates content exists
• ✅ Token Overflow: Max tokens enforced
• ✅ Model Validation: Auto-selects JSON mode for supported models

7. Standardized Request Schema

OpenAI Request Payload

{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": prompt}],
    "temperature": 0.7,
    "max_tokens": 4000,
    "response_format": {"type": "json_object"}  # Auto-enabled for supported models
}

All Functions Use Same Logic:

• Model selection (account default or override)
• JSON mode auto-enablement
• Token limits
• Temperature settings
• Error handling

8. Test Script Created

ai/tests/test_run.py

• ✅ Test script for all AI functions
• ✅ Tests run_ai_request() directly
• ✅ Tests JSON extraction
• ✅ Placeholder tests for all functions
• ✅ Can be run standalone to verify logging

📋 File Changes Summary

File	Changes	Status
ai_core.py	Complete rewrite with run_ai_request() and console logging	✅ Complete
engine.py	Updated to use run_ai_request()	✅ Complete
processor.py	Marked deprecated, redirects to AICore	✅ Complete
functions/auto_cluster.py	Uses AICore methods	✅ Complete
functions/generate_ideas.py	Uses run_ai_request()	✅ Complete
functions/generate_content.py	Uses run_ai_request()	✅ Complete
functions/generate_images.py	Uses run_ai_request() and generate_image()	✅ Complete
tests/test_run.py	Test script created	✅ Complete

🔄 Migration Path

Old Code (Deprecated)

from igny8_core.utils.ai_processor import AIProcessor
processor = AIProcessor(account=account)
result = processor._call_openai(prompt, model=model)

New Code (Recommended)

from igny8_core.ai.ai_core import AICore
ai_core = AICore(account=account)
result = ai_core.run_ai_request(
    prompt=prompt,
    model=model,
    function_name='my_function'
)

✅ Verification Checklist

• run_ai_request() created with console logging
• All function files updated to use run_ai_request()
• Engine updated to use run_ai_request()
• Old processor code deprecated
• Edge cases handled with logging
• Request schema standardized
• Test script created
• No linting errors
• Backward compatibility maintained

🎯 Benefits Achieved

1. Centralized Execution: All AI requests go through one method
2. Consistent Logging: Every request logs steps to console
3. Better Debugging: Clear step-by-step visibility
4. Error Handling: Comprehensive error detection and logging
5. Reduced Duplication: No scattered AI call logic
6. Easy Testing: Single point to test/mock
7. Future Ready: Easy to add retry logic, backoff, etc.

📝 Console Output Example

When running any AI function, you'll see:

[AI][generate_ideas] Step 1: Preparing request...
[AI][generate_ideas] Step 2: Using model: gpt-4o
[AI][generate_ideas] Step 3: Auto-enabled JSON mode for gpt-4o
[AI][generate_ideas] Step 4: Prompt length: 2345 characters
[AI][generate_ideas] Step 5: Request payload prepared (model=gpt-4o, max_tokens=4000, temp=0.7)
[AI][generate_ideas] Step 6: Sending request to OpenAI API...
[AI][generate_ideas] Step 7: Received response in 3.45s (status=200)
[AI][generate_ideas] Step 8: Received 250 tokens (input: 100, output: 150)
[AI][generate_ideas] Step 9: Content length: 600 characters
[AI][generate_ideas] Step 10: Cost calculated: $0.000250
[AI][generate_ideas][Success] Request completed successfully

🚀 Next Steps (Future Stages)

• Stage 3: Simplify logging (optional - console logging already implemented)
• Stage 4: Clean up legacy code (remove old processor completely)
• Future: Add retry logic, exponential backoff, request queuing