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"""
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Execute the complete 3-agent pipeline on all JSON files in mordor_dataset.
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This runs:
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1. Log Analysis Agent
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2. Retrieval Supervisor (with Database Agent and Grader)
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3. Response Agent
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Outputs are saved to final_response/ folder.
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Usage: python execute_pipeline.py [--model MODEL_NAME]
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"""
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import subprocess
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from pathlib import Path
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import sys
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import argparse
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def find_project_root(start: Path) -> Path:
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"""Find the project root by looking for common markers."""
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for p in [start] + list(start.parents):
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if (
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(p / "mordor_dataset").exists()
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or (p / "src").exists()
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or (p / ".git").exists()
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):
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return p
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return start.parent
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def main():
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"""Execute pipeline on all mordor dataset files"""
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parser = argparse.ArgumentParser(
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description="Execute pipeline on all mordor dataset files",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Run with default model (Gemini 2.0 Flash)
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python execute_pipeline.py
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# Run with specific model
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python execute_pipeline.py --model google_genai:gemini-2.0-flash
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python execute_pipeline.py --model groq:gpt-oss-120b
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python execute_pipeline.py --model groq:llama-3.1-8b-instant
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Available models:
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- google_genai:gemini-2.0-flash (default)
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- google_genai:gemini-1.5-flash
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- groq:gpt-oss-120b
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- groq:gpt-oss-20b
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- groq:llama-3.1-8b-instant
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- groq:llama-3.3-70b-versatile
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""",
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)
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parser.add_argument(
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"--model",
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default="google_genai:gemini-2.0-flash",
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help="Model to use for analysis (default: google_genai:gemini-2.0-flash)",
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)
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args = parser.parse_args()
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model_name = args.model
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current_file = Path(__file__).resolve()
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project_root = find_project_root(current_file.parent)
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mordor_dir = project_root / "mordor_dataset"
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datasets_dir = mordor_dir / "datasets"
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if not datasets_dir.exists():
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print(f"[ERROR] mordor_dataset/datasets not found at {datasets_dir}")
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sys.exit(1)
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files = sorted([p for p in datasets_dir.rglob("*.json")])
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if not files:
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print("[ERROR] No JSON files found in mordor_dataset/datasets")
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sys.exit(1)
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print("=" * 80)
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print("EXECUTING FULL PIPELINE ON ALL MORDOR FILES")
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print("=" * 80)
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print(f"Model: {model_name}")
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print(f"Found {len(files)} files to process\n")
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files_by_folder = {}
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for f in files:
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folder_name = f.parent.name
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if folder_name not in files_by_folder:
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files_by_folder[folder_name] = []
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files_by_folder[folder_name].append(f)
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total_success = 0
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total_failed = 0
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for folder_name in sorted(files_by_folder.keys()):
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folder_files = files_by_folder[folder_name]
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print(f"\n{'='*80}")
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print(f"Processing folder: {folder_name} ({len(folder_files)} files)")
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print(f"{'='*80}")
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for f in folder_files:
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pipeline_script = (
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project_root / "src" / "scripts" / "run_simple_pipeline.py"
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)
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if not pipeline_script.exists():
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print(f"[ERROR] Pipeline script not found: {pipeline_script}")
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sys.exit(1)
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output_dir = str(mordor_dir / "eval_output")
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cmd = [
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sys.executable,
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str(pipeline_script),
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str(f),
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"--model",
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model_name,
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"--output-dir",
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output_dir,
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]
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print(f"\n--- Processing: {f.relative_to(datasets_dir)}")
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print(f" Model: {model_name}")
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print(f" Output: {output_dir}")
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try:
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subprocess.run(cmd, check=True)
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total_success += 1
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except subprocess.CalledProcessError as e:
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print(f"[ERROR] Pipeline failed for {f.name}: {e}")
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total_failed += 1
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print("\n" + "=" * 80)
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print("PIPELINE EXECUTION COMPLETE")
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print("=" * 80)
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print(f"Model used: {model_name}")
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print(f"Total files processed: {len(files)}")
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print(f"Successful: {total_success}")
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print(f"Failed: {total_failed}")
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print(f"Results saved to: {mordor_dir / 'eval_output'}/")
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print("=" * 80 + "\n")
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if __name__ == "__main__":
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main() |