import asyncio import os import textwrap from typing import List, Optional from openai import OpenAI from client import SQLArenaEnv, SQLArenaAction IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME") or os.getenv("IMAGE_NAME") API_KEY = os.getenv("HF_TOKEN") or os.getenv("API_KEY") API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1") MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-72B-Instruct") TASK_NAME = os.getenv("SQLARENA_TASK", "medium_001") BENCHMARK = "sql_arena_env" MAX_STEPS = 8 TEMPERATURE = 0.0 MAX_TOKENS = 512 SUCCESS_SCORE_THRESHOLD = 0.5 SYSTEM_PROMPT = textwrap.dedent(""" You are an expert SQL agent working with a SQLite database. You will be given a natural language question and a schema description. Your job is to write correct SQLite SQL to answer the question. Rules: - Use only standard SQLite syntax (no MySQL/PostgreSQL specific features) - IMPORTANT: Never use SELECT * — always specify exact column names that match what the question asks for - Read the question carefully to know exactly which columns to return - You can run EXPLORE queries first to understand the data structure - When ready, submit your final answer with query_type="submit" - SQLite date functions: SUBSTR(date,1,7) for YYYY-MM, julianday() for date math - Window functions available: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), SUM() OVER(), AVG() OVER(), PERCENT_RANK() Response format — respond with ONLY a JSON object like this: {"sql": "SELECT ...", "query_type": "explore"} or {"sql": "SELECT ...", "query_type": "submit"} No explanation, no markdown, just the JSON. """).strip() def log_start(task: str, env: str, model: str) -> None: print(f"[START] task={task} env={env} model={model}", flush=True) def log_step(step: int, action: str, reward: float, done: bool, error: Optional[str]) -> None: error_val = error if error else "null" done_val = str(done).lower() action_safe = action.replace("\n", " ").replace("\r", "")[:200] print( f"[STEP] step={step} action={action_safe} reward={reward:.2f} done={done_val} error={error_val}", flush=True, ) def log_end(success: bool, steps: int, score: float, rewards: List[float]) -> None: rewards_str = ",".join(f"{r:.2f}" for r in rewards) print( f"[END] success={str(success).lower()} steps={steps} score={score:.2f} rewards={rewards_str}", flush=True, ) def build_user_prompt(obs, step: int, history: List[str]) -> str: history_block = "\n".join(history[-3:]) if history else "None" result_str = str(obs.query_result[:5]) if obs.query_result else "No results yet" return textwrap.dedent(f""" QUESTION: {obs.question} SCHEMA: {obs.schema_info} Step: {step} Explore steps remaining: {obs.explore_steps_remaining} Last query result (first 5 rows): {result_str} Last error: {obs.query_error or 'None'} Previous actions: {history_block} {"No more explore steps — you MUST submit now (query_type='submit')" if obs.explore_steps_remaining == 0 else "You can explore more or submit your final answer."} Respond with ONLY a JSON object: {{"sql": "...", "query_type": "explore" or "submit"}} """).strip() def get_model_action(client: OpenAI, obs, step: int, history: List[str]): import json user_prompt = build_user_prompt(obs, step, history) try: completion = client.chat.completions.create( model=MODEL_NAME, messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": user_prompt}, ], temperature=TEMPERATURE, max_tokens=MAX_TOKENS, ) text = (completion.choices[0].message.content or "").strip() # remove markdown block if model wrapped it if text.startswith("```"): text = text.split("```")[1] if text.startswith("json"): text = text[4:] text = text.strip() parsed = json.loads(text) sql = parsed.get("sql", "SELECT 1") query_type = parsed.get("query_type", "explore") # force submit if no explore budget left if obs.explore_steps_remaining == 0: query_type = "submit" return SQLArenaAction(sql=sql, query_type=query_type) except Exception as exc: print(f"[DEBUG] Model parse error: {exc} | raw: {text[:200] if 'text' in dir() else 'N/A'}", flush=True) # fallback query on parse error return SQLArenaAction( sql=f"SELECT * FROM sqlite_master WHERE type='table'", query_type="explore" if obs.explore_steps_remaining > 0 else "submit" ) async def main() -> None: client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY) # connect to env (docker or hf space) if IMAGE_NAME: env = await SQLArenaEnv.from_docker_image(IMAGE_NAME) else: hf_space_url = os.getenv("HF_SPACE_URL", "http://localhost:8000") env = SQLArenaEnv(base_url=hf_space_url) history: List[str] = [] rewards: List[float] = [] steps_taken = 0 score = 0.0 success = False log_start(task=TASK_NAME, env=BENCHMARK, model=MODEL_NAME) try: result = await env.reset(task_id=TASK_NAME) obs = result.observation for step in range(1, MAX_STEPS + 1): if result.done: break action = get_model_action(client, obs, step, history) result = await env.step(action) obs = result.observation reward = result.reward or 0.0 done = result.done error = obs.query_error rewards.append(reward) steps_taken = step log_step( step=step, action=f"{action.query_type}:{action.sql[:100]}", reward=reward, done=done, error=error, ) history.append( f"Step {step} [{action.query_type}]: {action.sql[:80]} → " f"rows={obs.rows_returned} reward={reward:.2f}" ) if done: break final_reward = rewards[-1] if rewards else 0.0 score = final_reward success = score >= SUCCESS_SCORE_THRESHOLD except Exception as exc: print(f"[DEBUG] Episode error: {exc}", flush=True) finally: try: await env.close() except Exception as e: print(f"[DEBUG] env.close() error: {e}", flush=True) log_end(success=success, steps=steps_taken, score=score, rewards=rewards) if __name__ == "__main__": asyncio.run(main())