import ollama import json import csv from ddgs import DDGS import time # --- Evaluation Benchmark --- BENCHMARK_TASKS = [ {"id": 1, "goal": "What is the boiling point of water at sea level in Celsius?"}, {"id": 2, "goal": "Who is the current CEO of Microsoft?"}, {"id": 3, "goal": "What year did the first moon landing occur?"}, {"id": 4, "goal": "Find the main ingredient in a traditional Japanese Miso soup."}, {"id": 5, "goal": "What is the capital city of Australia?"}, {"id": 6, "goal": "What is the population of the underwater city of Atlantis?"}, {"id": 7, "goal": "Find the official website for the Stark Industries corporation from the Iron Man movies."}, {"id": 8, "goal": "What is the chemical formula for Kryptonite?"}, {"id": 9, "goal": "Who is the king of the United States?"}, {"id": 10, "goal": "How many dragons are there in the wild in Germany?"}, {"id": 11, "goal": "What is the weather like?"}, {"id": 12, "goal": "Find a good recipe."}, {"id": 13, "goal": "How tall is the president?"}, {"id": 14, "goal": "Is it a holiday today?"}, {"id": 15, "goal": "What is the latest news?"}, {"id": 16, "goal": "What was the score of the 1955 Super Bowl?"}, {"id": 17, "goal": "Did Thomas Edison invent the light bulb?"}, {"id": 18, "goal": "Is water a good conductor of electricity?"}, {"id": 19, "goal": "What is the currency used in Switzerland?"}, {"id": 20, "goal": "Find the text of the 'Gettysburg Address' written by George Washington."}, ] # --- Agent Definitions --- def planner_agent(goal: str) -> dict: prompt = f"""You are a meticulous planner. Convert the high-level goal into a single, specific, and verifiable task. Create a Python list of simple, factual verification checks. Goal: "{goal}" Provide your output in a JSON format with two keys: "task" and "checklist".""" try: response = ollama.chat(model='phi', messages=[{'role': 'user', 'content': prompt}], format='json') return json.loads(response['message']['content']) except Exception as e: return {"task": "Error in planning", "checklist": [f"Error: {e}"]} def executor_agent(task: str) -> str: print(f" EXECUTOR 🛠️: Performing task: '{task}'") try: with DDGS() as ddgs: results = [r['body'] for r in ddgs.text(task, max_results=1)] return results[0] if results else "Error: No search results found." except Exception as e: return f"Error during execution: {e}" def verifier_agent(output: str, checklist: list) -> dict: prompt = f"""You are a scrupulous verifier. Check if the 'Executor Output' satisfies ALL conditions in the 'Verification Checklist'. Respond with a JSON object with two keys: "verified" (a boolean: true if all checks pass, otherwise false) and "reasoning" (a brief explanation). Executor Output: "{output}" Verification Checklist: {checklist}""" try: response = ollama.chat(model='phi', messages=[{'role': 'user', 'content': prompt}], format='json') return json.loads(response['message']['content']) except Exception as e: return {"verified": False, "reasoning": f"Error in verification: {e}"} def self_verifier_agent(output: str, original_task: str) -> dict: prompt = f"""You are an executor agent who must now verify your own work. Your original task was: "{original_task}" Your output was: "{output}" Critically evaluate if your output successfully and accurately completed the original task. Respond with a JSON object with two keys: "verified" (a boolean) and "reasoning" (a brief explanation of why you believe your work was or was not successful).""" try: response = ollama.chat(model='phi', messages=[{'role': 'user', 'content': prompt}], format='json') return json.loads(response['message']['content']) except Exception as e: return {"verified": False, "reasoning": f"Error in self-verification: {e}"} # --- Workflow Definitions --- def run_verifier_system(goal: str): plan = planner_agent(goal) task, checklist = plan.get("task"), plan.get("checklist", []) output = executor_agent(task) result = verifier_agent(output, checklist) return plan, output, result def run_no_verifier_system(goal: str): plan = planner_agent(goal) task = plan.get("task") output = executor_agent(task) # Baseline 1: Assumes success if no execution error occurs. result = {"verified": not output.startswith("Error:"), "reasoning": "No verifier present. Assumed success."} return plan, output, result def run_self_verifier_system(goal: str): plan = planner_agent(goal) task = plan.get("task") output = executor_agent(task) result = self_verifier_agent(output, task) return plan, output, result # --- Main Evaluation Loop --- def main(): """Runs the full evaluation and saves results to a CSV file.""" systems = { "Verifier_System": run_verifier_system, "No_Verifier_Baseline": run_no_verifier_system, "Self_Verifier_Baseline": run_self_verifier_system } csv_file_path = "evaluation_results.csv" csv_headers = ["task_id", "goal", "system_type", "planner_task", "planner_checklist", "executor_output", "system_reported_success", "verifier_reasoning"] with open(csv_file_path, 'w', newline='', encoding='utf-8') as f: writer = csv.DictWriter(f, fieldnames=csv_headers) writer.writeheader() for task_item in BENCHMARK_TASKS: for system_name, system_func in systems.items(): print(f"\n--- Running Task ID {task_item['id']} on {system_name} ---") print(f"GOAL: {task_item['goal']}") start_time = time.time() plan, output, result = system_func(task_item['goal']) end_time = time.time() print(f" RESULT: {result}") print(f" (Time taken: {end_time - start_time:.2f}s)") # Write result to CSV writer.writerow({ "task_id": task_item['id'], "goal": task_item['goal'], "system_type": system_name, "planner_task": plan.get('task'), "planner_checklist": json.dumps(plan.get('checklist')), # Store checklist as a JSON string "executor_output": output, "system_reported_success": result.get('verified'), "verifier_reasoning": result.get('reasoning') }) print(f"\n✅ Evaluation complete. Results saved to '{csv_file_path}'") if __name__ == "__main__": main()