APAW/scripts/run-focused-eval.py

#!/usr/bin/env python3
"""
Focused Real-Fit Eval Runner v2
Evaluates key agents × models using real-fit-engine.py (the fixed version).
"""
import sqlite3, json, os, sys, importlib.util
from datetime import datetime, timezone
from concurrent.futures import ThreadPoolExecutor, as_completed

os.environ.setdefault("OLLAMA_KEY", "feaa56e2dff045af989346ca74cb33a6.xzJ-plOVSgTL1FbmL8PZZ3Wx")
os.environ.setdefault("OLLAMA_HOST", "https://ollama.com/v1")

# Import the dash-named real-fit-engine.py via importlib
_spec = importlib.util.spec_from_file_location("rfe", os.path.join(os.path.dirname(__file__), "real-fit-engine.py"))
_rfe = importlib.util.module_from_spec(_spec)
_spec.loader.exec_module(_rfe)

call_ollama = _rfe.call_ollama
evaluate_response = _rfe.evaluate_response
compute_aggregates = _rfe.compute_aggregates
generate_report = _rfe.generate_report
DB_PATH = _rfe.DB_PATH

AGENTS = [
    'code-skeptic',
    'lead-developer',
    'system-analyst',
    'sdet-engineer',
    'orchestrator',
    'devops-engineer',
    'workflow-cross-checker',
]

MODELS = [
    'kimi-k2.6',
    'deepseek-v4-pro-max',
    'qwen3-coder:480b',
    'glm-5.1',
]

def fetch_agent_tasks():
    conn = sqlite3.connect(DB_PATH)
    placeholders = ','.join('?' * len(AGENTS))
    rows = conn.execute(f"""
        SELECT id, agent_name, system_prompt, user_prompt, expected_keywords, rubric
        FROM test_prompts WHERE agent_name IN ({placeholders})
    """, tuple(AGENTS)).fetchall()
    conn.close()
    return rows

def eval_single(agent_name, model, prompt_id, system, user, expected_json, rubric_json):
    resp, latency, tokens = call_ollama(model, system, user)
    ev = evaluate_response(resp, expected_json, rubric_json)
    return {
        'agent': agent_name,
        'model': model,
        'prompt_id': prompt_id,
        'response_text': resp[:3000],
        'latency_ms': latency,
        'tokens_prompt': tokens['prompt'],
        'tokens_response': tokens['response'],
        'total_score': ev['total'],
        'scores_json': json.dumps(ev['scores']),
        'explanation': ev['explanation'],
        'evaluated_at': datetime.now(timezone.utc).isoformat(),
        'evaluator': 'rubric_v2'
    }

def save_single(res):
    conn = sqlite3.connect(DB_PATH)
    conn.execute("""
        INSERT INTO evaluations
        (agent_name, model, prompt_id, response, latency_ms, tokens_prompt, tokens_response,
         scores, total_score, explanation, evaluated_at, evaluator)
        VALUES (?,?,?,?,?,?,?,?,?,?,?,?)
    """, (res['agent'], res['model'], res['prompt_id'], res['response_text'], res['latency_ms'],
          res['tokens_prompt'], res['tokens_response'],
          res['scores_json'], res['total_score'], res['explanation'],
          res['evaluated_at'], res['evaluator']))
    conn.commit()
    conn.close()
    print(f"  [{res['agent']}] x [{res['model']}] score={res['total_score']:.1f} lat={res['latency_ms']}ms len={len(res['response_text'])}")

def run_focused_eval(max_workers=4):
    tasks = fetch_agent_tasks()
    print(f"[focused] Agents: {len(AGENTS)} | Models: {len(MODELS)} | Prompts: {len(tasks)}")
    print(f"[focused] Total evaluations: {len(tasks) * len(MODELS)}")

    work_items = []
    for pid, aname, system, user, exp_json, rub_json in tasks:
        for model in MODELS:
            work_items.append((aname, model, pid, system, user, exp_json, rub_json))

    completed = 0
    errors = 0
    with ThreadPoolExecutor(max_workers=max_workers) as ex:
        futures = {ex.submit(eval_single, *w): w for w in work_items}
        for future in as_completed(futures):
            try:
                res = future.result()
                save_single(res)
                completed += 1
                if completed % 4 == 0:
                    print(f"[focused] Progress: {completed}/{len(work_items)}")
            except Exception as e:
                import traceback
                traceback.print_exc()
                errors += 1

    print(f"[focused] Completed {completed}/{len(work_items)} (errs={errors})")
    compute_aggregates()

if __name__ == '__main__':
    print("="*60)
    print("FOCUSED REAL-FIT EVALUATION v2")
    print(f"Models: {', '.join(MODELS)}")
    print(f"Agents: {', '.join(AGENTS)}")
    print(f"API: {os.environ['OLLAMA_HOST']}")
    print("="*60)
    
    # Clean old evaluations
    conn = sqlite3.connect(DB_PATH)
    conn.execute("DELETE FROM evaluations WHERE agent_name IN ({})".format(','.join(f"'{a}'" for a in AGENTS)))
    conn.execute("DELETE FROM fit_scores WHERE agent_name IN ({})".format(','.join(f"'{a}'" for a in AGENTS)))
    conn.commit()
    conn.close()
    print("[focused] Cleaned old evaluations")

    run_focused_eval(max_workers=4)
    report = generate_report()
    print(f"[focused] Report generated with {len(report.get('agents',{}))} agents")