scripts/optimize_prompt.py

import os
import json
import datetime
from pathlib import Path
from openai import OpenAI
from dotenv import load_dotenv

# --- Configuration & Initialization ---
load_dotenv()

# The script runs from the 'scripts/' directory, so its root is one level up.
PROJECT_ROOT = Path(__file__).resolve().parent.parent

# File Paths
MASTER_PROMPT_PATH = PROJECT_ROOT / "data" / "master_prompt.json"
FEEDBACK_LOG_PATH = PROJECT_ROOT / "data" / "feedback_log.json"
STATUS_LOG_PATH = PROJECT_ROOT / "data" / "status_log.txt" 
# LLM Model ID for Rewriting (Meta-LLM)
META_LLM_MODEL = "x-ai/grok-4-fast" 
# The minimum number of negative feedback entries required to trigger an update
MIN_NEGATIVE_FEEDBACK = 3 


def load_data(path: Path):
    """Safely loads JSON data, handling empty file content."""
    try:
        if path.exists():
            content = path.read_text().strip()
            
            # Handle empty content
            if not content:
                return []
            
            data = json.loads(content)
            
            # Ensure correct type based on file (MASTER_PROMPT is dict, FEEDBACK_LOG is list)
            if path == MASTER_PROMPT_PATH and not isinstance(data, dict):
                 return {}
            if path == FEEDBACK_LOG_PATH and not isinstance(data, list):
                 return []
            return data
            
        # Create initial state if file doesn't exist
        if path == MASTER_PROMPT_PATH:
             return {"system_message": "A critical error occurred.", "version": "1.0.0", "last_updated": datetime.datetime.now().isoformat()}
        return []
    
    except Exception as e:
        print(f"Error loading {path}: {e}")
        return []


def aggregate_negative_feedback(feedback_data: list) -> str:
    """

    Analyzes the feedback log to summarize only the negative (rating=0) feedback.

    """
    negative_feedback = [entry for entry in feedback_data if entry.get("rating") == 0]
    
    if len(negative_feedback) < MIN_NEGATIVE_FEEDBACK:
        print(f"INFO: Only {len(negative_feedback)} negative entries found. Skipping optimization.")
        return None

    # Summarize the negative prompts that led to user dislike
    summary = []
    for entry in negative_feedback:
        summary.append(
            f"User disliked the response (Rating 0) after input: '{entry['original_prompt']}' "
            f"The resulting OPTIMIZED PROMPT was: '{entry['optimized_prompt']}'"
        )
    
    print(f"INFO: Aggregated {len(negative_feedback)} negative feedback entries.")
    return "\n---\n".join(summary)


def optimize_system_prompt(current_system_message: str, feedback_summary: str) -> str:
    """

    Calls the Meta-LLM to rewrite the system message based on negative feedback.

    """
    
    # We define a strict Meta-Prompt for the Grok model to follow
    meta_prompt = (
        "You are the **System Prompt Optimizing Agent**. Your goal is to analyze the 'FAILED FEEDBACK' and rewrite the 'CURRENT SYSTEM MESSAGE' "
        "to address the problems identified. The new system message must aim to improve the quality of future responses, making them more accurate, "
        "detailed, or strictly adherent to formatting rules, based on the failure patterns. "
        "You must output **ONLY** the new system message text, nothing else. Do not use markdown quotes."
    )

    # The user message feeds the prompt and the negative data to the agent
    user_message = f"""

    CURRENT SYSTEM MESSAGE:

    ---

    {current_system_message}

    ---

    

    FAILED FEEDBACK (You must incorporate lessons from this data):

    ---

    {feedback_summary}

    ---

    

    Based ONLY on the above, rewrite the CURRENT SYSTEM MESSAGE to improve it.

    New System Message:

    """
    
    try:
        # Call OpenRouter API with the Meta-LLM
        client = OpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key=os.getenv("OPENROUTER_API_KEY"),
        )

        response = client.chat.completions.create(
            model=META_LLM_MODEL,
            messages=[
                {"role": "system", "content": meta_prompt},
                {"role": "user", "content": user_message}
            ],
            temperature=0.1, # Low temperature for reliable instruction following
            max_tokens=512,
        )
        
        new_prompt = response.choices[0].message.content.strip()
        print("SUCCESS: New System Prompt generated by Meta-LLM.")
        return new_prompt

    except Exception as e:
        print(f"CRITICAL ERROR: Meta-LLM API call failed: {e}")
        return current_system_message # Return original prompt on failure

def increment_version(version_str: str) -> str:
    """Safely increments the minor version (Y in X.Y.Z) of a version string."""
    try:
        # Assumes format X.Y.Z
        parts = version_str.split('.')
        if len(parts) < 2:
            return "1.0.0" 
            
        # Increment the second part (the minor version)
        new_minor_version = int(parts[1]) + 1
        
        # Rebuild the version string
        # Uses parts[2:] for safety, handles missing Z value if needed
        new_parts = [parts[0], str(new_minor_version)] + parts[2:]
        return ".".join(new_parts)
        
    except Exception:
        # If any part fails (non-integer, etc.), reset to a safe, known state.
        return "1.0.0" 


def run_optimization():
    """Main function for the MLOps pipeline script."""
    print(f"--- Running Prompt Optimization Pipeline at {datetime.datetime.now()} ---")
    
    # 1. Load Data
    current_config = load_data(MASTER_PROMPT_PATH)
    feedback_data = load_data(FEEDBACK_LOG_PATH)
    current_system_message = current_config.get("system_message", "")
    
    if not feedback_data:
        print("INFO: Feedback log is empty. Exiting optimization.")
        # Update the status log to reflect that the scheduled job ran but was skipped.
        update_status_log("MLOps Optimization skipped (No new feedback).")
        return

    # 2. Aggregate Feedback
    feedback_summary = aggregate_negative_feedback(feedback_data)
    
    if feedback_summary is None:
        # Optimization was skipped because not enough negative feedback was found
        update_status_log("MLOps Optimization skipped (Insufficient negative feedback).")
        return

    # 3. Optimize Prompt
    new_system_message = optimize_system_prompt(current_system_message, feedback_summary)
    
    # 4. Check if prompt actually changed before committing
    if new_system_message != current_system_message:
        print("\n*** PROMPT UPDATED ***")
        
        # 5. Update Master Prompt File
        current_config["system_message"] = new_system_message
        current_config["version"] = increment_version(current_config.get("version", "1.0.0"))
        current_config["last_updated"] = datetime.datetime.now().isoformat()
        
        with open(MASTER_PROMPT_PATH, 'w') as f:
            json.dump(current_config, f, indent=4)
        print(f"Successfully wrote new prompt version {current_config['version']} to master_prompt.json")

        # 6. Clear Feedback Log (Ready for next cycle)
        with open(FEEDBACK_LOG_PATH, 'w') as f:
            json.dump([], f)
        print("Feedback log cleared.")
        
        # 7. Update the Status Log (Rewrites the file with new timestamp)
        update_status_log(f"MLOps Prompt Optimization deployed successfully. New version: {current_config['version']}")
        
    else:
        print("\nINFO: No significant change or API error. Master prompt remains the same.")
        update_status_log("MLOps Optimization failed to deploy (API error or no meaningful change detected).")

def update_status_log(status_message: str):
    """Writes the current status to the status log file, overwriting the previous entry."""
    current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S IST")
    log_content = f"[{current_time}] {status_message}"
    
    try:
        with open(STATUS_LOG_PATH, 'w') as f:
            f.write(log_content)
        print(f"Status log updated: {log_content}")
    except Exception as e:
        print(f"CRITICAL ERROR: Failed to write to status log: {e}")


if __name__ == '__main__':
    run_optimization()