| import os
|
| import asyncio
|
| from typing import List
|
| from openai import OpenAI
|
| from server.sql_env_environment import SqlEnvironment
|
| from models import SqlAction
|
| from dotenv import load_dotenv
|
| load_dotenv()
|
|
|
| API_BASE_URL = os.getenv("API_BASE_URL", "https://router.huggingface.co/v1")
|
| MODEL_NAME = os.getenv("MODEL_NAME", "meta-llama/Llama-3.1-8B-Instruct")
|
| HF_TOKEN = os.getenv("HF_TOKEN")
|
| MAX_STEPS = 10
|
| SUCCESS_SCORE_THRESHOLD = 0.6
|
|
|
|
|
| if not HF_TOKEN:
|
| raise ValueError("HF_TOKEN is required")
|
|
|
| def log_start(model):
|
| print(f"[START] env=sql_env model={model}", flush=True)
|
|
|
| def log_step(step, action, reward, done, error):
|
| err = error if error else "null"
|
| print(f"[STEP] step={step} action={repr(action)} reward={reward:.2f} done={str(done).lower()} error={err}", flush=True)
|
|
|
| def log_end(success, steps, score, rewards):
|
| r_str = ",".join(f"{r:.2f}" for r in rewards)
|
| print(f"[END] success={str(success).lower()} steps={steps} score={score:.2f} rewards={r_str}", flush=True)
|
|
|
| def get_model_query(client, task_description: str, last_output: str, history: List[str]) -> SqlAction:
|
| history_str = "\n".join(history[-4:]) if history else "None"
|
| prompt = f"""You are an expert SQL developer working with a SQLite database.
|
|
|
| The database has these tables:
|
| - employees (id, name, department, salary) -- department is a foreign key (integer ID)
|
| - departments (id, name, budget)
|
|
|
| IMPORTANT RULES:
|
| - employees.department is an INTEGER foreign key referencing departments.id
|
| - Always use table aliases to avoid ambiguous column names (e.g. e.name, d.name)
|
| - To get department name, JOIN departments and use d.name AS department_name
|
|
|
| YOUR TASK:
|
| {task_description}
|
|
|
| LAST QUERY RESULT:
|
| {last_output}
|
|
|
| RECENT HISTORY (learn from these, don't repeat low-scoring queries):
|
| {history_str}
|
|
|
| Instructions:
|
| - Write a single valid SQLite SQL query that completes the task above
|
| - Reward is between 0.0 and 1.0 — aim for 1.0
|
| - If last result was an ERROR, fix the query — do NOT fall back to SELECT *
|
| - Reply with ONLY one line:
|
| QUERY: <your sql here>
|
| """
|
| try:
|
| completion = client.chat.completions.create(
|
| model=MODEL_NAME,
|
| messages=[{"role": "user", "content": prompt}],
|
| max_tokens=200
|
| )
|
| text = completion.choices[0].message.content.strip()
|
| lines = {l.split(":")[0].strip(): ":".join(l.split(":")[1:]).strip()
|
| for l in text.splitlines() if ":" in l}
|
| query = lines.get("QUERY", "SELECT * FROM employees")
|
| return SqlAction(query=query)
|
| except Exception as e:
|
| print(f"[DEBUG] LLM error: {e}", flush=True)
|
| return SqlAction(query="SELECT * FROM employees")
|
|
|
| async def main():
|
| client = OpenAI(base_url=API_BASE_URL, api_key=HF_TOKEN)
|
| env = SqlEnvironment()
|
|
|
| rewards: List[float] = []
|
| history: List[str] = []
|
| steps_taken = 0
|
| score = 0.0
|
| success = False
|
|
|
| log_start(model=MODEL_NAME)
|
|
|
| try:
|
| obs = env.reset()
|
| task_description = obs.echoed_message
|
| last_output = obs.echoed_message
|
|
|
| for step in range(1, MAX_STEPS + 1):
|
| action = get_model_query(client, task_description, last_output, history)
|
| obs = env.step(action)
|
|
|
| metadata = obs.metadata or {}
|
| reward = metadata.get("reward", 0.0)
|
| error = metadata.get("error", None)
|
| done = reward >= 0.7
|
|
|
| rewards.append(reward)
|
| steps_taken = step
|
| last_output = f"ERROR: {error}" if error else obs.echoed_message
|
| history.append(f"Step {step}: {action.query[:80]} → reward {reward:.2f}")
|
|
|
| log_step(step=step, action=action.query[:100],
|
| reward=reward, done=done, error=error)
|
|
|
| if done:
|
| break
|
|
|
| score = sum(rewards) / max(len(rewards), 1)
|
| score = min(max(score, 0.0), 1.0)
|
| success = score >= SUCCESS_SCORE_THRESHOLD
|
|
|
| finally:
|
| log_end(success=success, steps=steps_taken, score=score, rewards=rewards)
|
|
|
| if __name__ == "__main__":
|
| asyncio.run(main()) |
