LLM_LOGGING_ENHANCEMENT.md

LLM API Response Logging Enhancement

Overview

This document describes the comprehensive logging enhancements implemented to ensure all LLM API inference responses are printed without truncation in container logs.

Changes Made

1. Enhanced LLM Router Logging (src/llm_router.py and llm_router.py)

Complete API Request Logging

• Full Prompt Content: Logs the complete prompt sent to the LLM without truncation
• API Request Details: Logs all request parameters including:
  • API URL
  • Model ID
  • Task Type
  • Max Tokens
  • Temperature
  • Top P
  • User Message Length
• Complete API Payload: Logs the full JSON payload sent to the API

Complete API Response Logging

• Response Metadata: Logs complete API response metadata including:
  • Status Code
  • Response Headers
  • Response Size
  • Complete API Response JSON
• Full Response Content: Logs the complete LLM-generated response without any truncation
• Structured Format: Uses clear delimiters and formatting for easy identification in logs

2. Enhanced Synthesis Agent Logging (src/agents/synthesis_agent.py)

Response Synthesis Logging

• Complete LLM Response: Logs the full response received from LLM for synthesis
• Agent Context: Includes agent ID, task type, and response length
• Structured Format: Uses clear delimiters for easy log parsing

Narrative Summary Logging

• Complete Summary Response: Logs the full narrative summary generated by LLM
• Context Information: Includes interaction count and summary length
• Structured Format: Uses clear delimiters for easy identification

Log Format Examples

API Request Logging

================================================================================
LLM API REQUEST - COMPLETE PROMPT:
================================================================================
Model: mistralai/Mistral-7B-Instruct-v0.2
Task Type: response_synthesis
Prompt Length: 1234 characters
----------------------------------------
FULL PROMPT CONTENT:
----------------------------------------
[Complete prompt content here]
----------------------------------------
END OF PROMPT
================================================================================

API Response Logging

================================================================================
LLM API RESPONSE METADATA:
================================================================================
Status Code: 200
Response Headers: {'content-type': 'application/json', ...}
Response Size: 5678 characters
----------------------------------------
COMPLETE API RESPONSE JSON:
----------------------------------------
{
  "choices": [
    {
      "message": {
        "content": "[Full response content]"
      }
    }
  ]
}
----------------------------------------
END OF API RESPONSE METADATA
================================================================================

Complete Response Content Logging

================================================================================
COMPLETE LLM API RESPONSE:
================================================================================
Model: mistralai/Mistral-7B-Instruct-v0.2
Task Type: response_synthesis
Response Length: 1234 characters
----------------------------------------
FULL RESPONSE CONTENT:
----------------------------------------
[Complete LLM response without truncation]
----------------------------------------
END OF LLM RESPONSE
================================================================================

Container Log Benefits

1. Complete Visibility

• No Truncation: All LLM responses are logged in full
• Request/Response Pairs: Complete visibility into API interactions
• Metadata Included: Full context for debugging and monitoring

2. Easy Parsing

• Clear Delimiters: Uses consistent formatting with = and - characters
• Structured Sections: Each log section is clearly marked
• Searchable Format: Easy to grep for specific log sections

3. Debugging Support

• Full Context: Complete prompts and responses for debugging
• Error Tracking: Complete API response metadata for error analysis
• Performance Monitoring: Response sizes and timing information

Usage in Container Environments

Docker Logs

# View all logs
docker logs <container_id>

# Follow logs in real-time
docker logs -f <container_id>

# Search for LLM responses
docker logs <container_id> | grep "COMPLETE LLM API RESPONSE"

Kubernetes Logs

# View pod logs
kubectl logs <pod-name>

# Follow logs
kubectl logs -f <pod-name>

# Search for specific log sections
kubectl logs <pod-name> | grep "FULL RESPONSE CONTENT"

Files Modified

1. Research_AI_Assistant/src/llm_router.py

   • Added comprehensive API request logging
   • Added complete API response metadata logging
   • Added full response content logging

2. Research_AI_Assistant/llm_router.py

   • Added comprehensive API request logging
   • Added complete API response metadata logging
   • Added full response content logging

3. Research_AI_Assistant/src/agents/synthesis_agent.py

   • Added complete LLM response logging for synthesis
   • Added complete narrative summary logging
   • Added structured formatting for easy parsing

4. Research_AI_Assistant/test_logging.py (New)

   • Test script to verify logging configuration
   • Tests both basic logging and LLM-specific logging

Verification

The logging configuration has been tested and verified to:

• ✅ Log complete prompts without truncation
• ✅ Log complete API responses without truncation
• ✅ Include all metadata and context information
• ✅ Use clear, structured formatting
• ✅ Work in container environments
• ✅ Be easily searchable and parseable

Benefits

1. Complete Transparency: All LLM interactions are fully visible in logs
2. Debugging Support: Full context for troubleshooting issues
3. Performance Monitoring: Complete response data for analysis
4. Container Compatibility: Works seamlessly in Docker/Kubernetes environments
5. No Data Loss: No truncation means no loss of important information

This enhancement ensures that all LLM API inference responses are captured in full detail in container logs, providing complete visibility into the system's AI interactions.