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| """ | |
| Baseline inference script for the SQL Query Optimizer OpenEnv. | |
| Produces reproducible baseline scores on all 3 tasks using deterministic | |
| hardcoded optimal rewrites. Optionally uses the OpenAI API if OPENAI_API_KEY | |
| is set. | |
| Prints structured [START]/[STEP]/[END] output to stdout as required by the | |
| OpenEnv validation pipeline. | |
| Usage: | |
| python inference.py | |
| # or with LLM: | |
| OPENAI_API_KEY=sk-... python inference.py | |
| """ | |
| import os | |
| import sys | |
| from env.environment import SQLEnv | |
| from env.models import Action | |
| from env.tasks import TASKS | |
| # Deterministic baseline rewrites that score well on the graders | |
| BASELINE_REWRITES = { | |
| 1: "SELECT users.name, orders.amount FROM users JOIN orders ON users.id = orders.user_id;", | |
| 2: "SELECT e.name FROM employees e JOIN departments d ON e.dept_id = d.id WHERE d.name = 'Engineering';", | |
| 3: "SELECT s.id, s.product_id, s.sale_date, s.amount FROM sales s /* USE INDEX (idx_sales_date) */ WHERE s.sale_date = '2023-01-01';", | |
| } | |
| def get_rewrite_llm(obs, task_id: int) -> str: | |
| """Try to get a rewrite from the OpenAI API; fall back to baseline.""" | |
| api_key = os.environ.get("OPENAI_API_KEY") | |
| if not api_key: | |
| return BASELINE_REWRITES[task_id] | |
| try: | |
| from openai import OpenAI | |
| client = OpenAI(api_key=api_key) | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": ( | |
| "You are an expert SQL DBA. You rewrite SQL queries " | |
| "to be correct, optimized, and performant." | |
| ), | |
| }, | |
| { | |
| "role": "user", | |
| "content": ( | |
| f"Task #{obs.task_id}\n" | |
| f"Original Query: {obs.query}\n" | |
| f"Database Schema Context: {obs.schema_context}\n" | |
| f"Hint: {obs.hint}\n\n" | |
| "Please provide the optimized query. " | |
| "Output ONLY the raw SQL query, no markdown formatting, no explanation." | |
| ), | |
| }, | |
| ] | |
| response = client.chat.completions.create( | |
| model="gpt-3.5-turbo", | |
| messages=messages, | |
| temperature=0.0, | |
| ) | |
| rewritten = response.choices[0].message.content.strip() | |
| # Strip markdown fences if present | |
| if rewritten.startswith("```sql"): | |
| rewritten = rewritten[6:] | |
| if rewritten.startswith("```"): | |
| rewritten = rewritten[3:] | |
| if rewritten.endswith("```"): | |
| rewritten = rewritten[:-3] | |
| return rewritten.strip() | |
| except Exception as e: | |
| print(f"LLM call failed ({e}), using deterministic baseline", flush=True) | |
| return BASELINE_REWRITES[task_id] | |
| def run_task(env: SQLEnv, task_id: int, task_name: str) -> float: | |
| """Run a single task and print structured output.""" | |
| print(f"[START] task={task_name}", flush=True) | |
| obs = env.reset(task_id=task_id) | |
| rewritten_query = get_rewrite_llm(obs, task_id) | |
| action = Action( | |
| rewritten_query=rewritten_query, | |
| explanation="Baseline inference rewrite", | |
| is_done=True, | |
| ) | |
| result_obs = env.step(action) | |
| reward = result_obs.reward | |
| grader_score = env.final_grader_score | |
| step_count = env.step_number - 1 # step_number was incremented after step() | |
| print(f"[STEP] step=1 reward={reward}", flush=True) | |
| print(f"[END] task={task_name} score={grader_score} steps={step_count}", flush=True) | |
| return grader_score | |
| def main(): | |
| env = SQLEnv() | |
| scores = {} | |
| for task_id, task_info in TASKS.items(): | |
| task_name = task_info["name"] | |
| score = run_task(env, task_id, task_name) | |
| scores[task_id] = score | |
| # Summary | |
| print("\n=== Baseline Evaluation Results ===", flush=True) | |
| for tid, score in scores.items(): | |
| print(f" Task {tid} ({TASKS[tid]['name']}): {score}/1.0", flush=True) | |
| if __name__ == "__main__": | |
| main() | |