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client.py

@@ -8,90 +8,54 @@ from typing import Optional
 
 from models import SQLDebugAction, SQLDebugObservation, SQLDebugState
 
-try:
-    from openenv.core.env_client import EnvClient      # type: ignore
-    from openenv.core.client_types import StepResult   # type: ignore
-
-    class SQLDebugEnv(EnvClient[SQLDebugAction, SQLDebugObservation, SQLDebugState]):
-        """
-        Typed client for the SQL Debug environment.
-
-        Usage (sync):
-            with SQLDebugEnv(base_url="http://localhost:7860").sync() as env:
-                obs = env.reset(task_id="task1_syntax_fix")
-                action = SQLDebugAction(fixed_sql="SELECT ...")
-                obs, reward, done, info = env.step(action)
-
-        Usage (async):
-            async with SQLDebugEnv(base_url="http://localhost:7860") as env:
-                obs = await env.reset()
-                result = await env.step(action)
-        """
-
-        def _step_payload(self, action: SQLDebugAction) -> dict:
-            return action.model_dump()
-
-        def _parse_result(self, payload: dict) -> StepResult:
-            obs_data = payload.get("observation", {})
-            return StepResult(
-                observation=SQLDebugObservation(**obs_data),
-                reward=payload.get("reward"),
-                done=payload.get("done", False),
-            )
-
-        def _parse_state(self, payload: dict) -> SQLDebugState:
-            return SQLDebugState(**payload)
-
-except ImportError:
-
-    import requests
-
-    class SQLDebugEnv:  # type: ignore[no-redef]
-        """
-        Lightweight HTTP client (no openenv-core dependency required).
-
-        Usage:
-            env = SQLDebugEnv(base_url="http://localhost:7860")
-            obs_data = env.reset(task_id="task1_syntax_fix")
-            result = env.step(SQLDebugAction(fixed_sql="SELECT ..."))
-        """
-
-        def __init__(self, base_url: str = "http://localhost:7860") -> None:
-            self.base_url = base_url.rstrip("/")
-
-        def reset(
-            self,
-            seed: int = 42,
-            task_id: Optional[str] = None,
-        ) -> SQLDebugObservation:
-            params: dict = {"seed": seed}
-            if task_id:
-                params["task_id"] = task_id
-            r = requests.post(f"{self.base_url}/reset", params=params)
-            r.raise_for_status()
-            return SQLDebugObservation(**r.json())
-
-        def step(
-            self,
-            action: SQLDebugAction,
-        ) -> tuple[SQLDebugObservation, float, bool, dict]:
-            r = requests.post(
-                f"{self.base_url}/step",
-                json=action.model_dump(),
-            )
-            r.raise_for_status()
-            d = r.json()
-            obs = SQLDebugObservation(**d["observation"])
-            return obs, d["reward"], d["done"], d.get("info", {})
-
-        def state(self) -> SQLDebugState:
-            r = requests.get(f"{self.base_url}/state")
-            r.raise_for_status()
-            return SQLDebugState(**r.json())
-
-        # Context manager support
-        def __enter__(self):
-            return self
-
-        def __exit__(self, *args):
-            pass
+import requests
+
+class SQLDebugEnv:
+    """
+    Lightweight HTTP client.
+    
+    Usage:
+        env = SQLDebugEnv(base_url="http://localhost:7860")
+        obs_data = env.reset(task_id="task1_syntax_fix")
+        result = env.step(SQLDebugAction(fixed_sql="SELECT ..."))
+    """
+
+    def __init__(self, base_url: str = "http://localhost:7860") -> None:
+        self.base_url = base_url.rstrip("/")
+
+    def reset(
+        self,
+        seed: int = 42,
+        task_id: Optional[str] = None,
+    ) -> SQLDebugObservation:
+        payload: dict = {"seed": seed}
+        if task_id:
+            payload["task_id"] = task_id
+        r = requests.post(f"{self.base_url}/reset", json=payload)
+        r.raise_for_status()
+        return SQLDebugObservation(**r.json())
+
+    def step(
+        self,
+        action: SQLDebugAction,
+    ) -> tuple[SQLDebugObservation, float, bool, dict]:
+        r = requests.post(
+            f"{self.base_url}/step",
+            json=action.model_dump(),
+        )
+        r.raise_for_status()
+        d = r.json()
+        obs = SQLDebugObservation(**d["observation"])
+        return obs, d["reward"], d["done"], d.get("info", {})
+
+    def state(self) -> SQLDebugState:
+        r = requests.get(f"{self.base_url}/state")
+        r.raise_for_status()
+        return SQLDebugState(**r.json())
+
+    # Context manager support
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        pass

inference.py

@@ -1,294 +1,149 @@
-"""
-inference.py — inference script for SQL Debug & Data Pipeline Repair.
-
-Runs a model (default: gpt-4o-mini) against all 3 tasks using the OpenAI
-client API. Reads credentials from environment variables. Produces a
-reproducible JSON report with per-task scores.
-
-Usage:
-    # Set credentials
-    $env:OPENAI_API_KEY = "sk-..."
-    # Optional: use a different base URL (e.g. local vLLM)
-    $env:OPENAI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
-
-    python inference.py
-    python inference.py --task task1_syntax_fix
-    python inference.py --model gpt-4o --output results.json
-"""
-
-from __future__ import annotations
-import argparse
-import json
-import os
-import re
-import sys
-import time
-from pathlib import Path
-from typing import Optional
-
-from openai import OpenAI
-
-# Make server package importable
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
-
-from models import SQLDebugAction, SQLDebugObservation
-from server.environment import SQLDebugEnvironment
-from server.data import TASKS
-
-
-# ---------------------------------------------------------------------------
-# Prompt builder
-# ---------------------------------------------------------------------------
-
-def _build_prompt(obs: SQLDebugObservation) -> str:
-    """Convert an observation into a model prompt."""
-    schema_lines = []
-    for table, cols in obs.schema_info.items():
-        col_defs = ", ".join(f"{c['column']} {c['type']}" for c in cols)
-        schema_lines.append(f"  {table}({col_defs})")
-    schema_str = "\n".join(schema_lines)
-
-    if obs.task_id == "task3_etl_timezone":
-        code_section = f"""
-## Broken ETL Pipeline Code
-```python
-{obs.pipeline_code}
-```
-
-## Intermediate Outputs (from the BROKEN pipeline)
-{json.dumps(obs.intermediate_outputs, indent=2, default=str) if obs.intermediate_outputs else 'Not available'}
-"""
-        instruction = (
-            "Return the COMPLETE corrected Python pipeline code inside a ```python ... ``` block. "
-            "Also provide a brief explanation of the root cause (which step is buggy and why) "
-            "in a section labelled 'Explanation:'."
-        )
-    else:
-        code_section = f"""
-## Broken SQL Query
-```sql
-{obs.broken_sql}
-```
-"""
-        instruction = (
-            "Return ONLY the corrected SQL query inside a ```sql ... ``` block. "
-            "Do not include any explanation outside the code block."
-        )
-
-    history_section = ""
-    if obs.previous_attempts:
-        lines = []
-        for a in obs.previous_attempts:
-            lines.append(f"  Step {a.step}: reward={a.reward:.2f}  SQL: {a.fixed_sql[:120]}...")
-        history_section = "\n## Previous Attempts\n" + "\n".join(lines)
-
-    return f"""You are an expert SQL and data engineering debugger.
-
-## Task ({obs.difficulty.upper()})
-{obs.task_description}
-
-## Database Schema
-{schema_str}
-{code_section}{history_section}
-
-## Instructions
-{instruction}
-"""
-
-
-# ---------------------------------------------------------------------------
-# Response parser
-# ---------------------------------------------------------------------------
-
-def _extract_sql(text: str, is_pipeline: bool = False) -> str:
-    """Extract SQL or Python code from model response."""
-    # Try fenced code block first
-    lang = "python" if is_pipeline else "sql"
-    patterns = [
-        rf"```{lang}\s*\n(.*?)```",
-        r"```\s*\n(.*?)```",
-        r"```(.*?)```",
-    ]
-    for pattern in patterns:
-        m = re.search(pattern, text, re.DOTALL | re.IGNORECASE)
-        if m:
-            return m.group(1).strip()
-    # Fallback: return the whole response
-    return text.strip()
-
-
-def _extract_explanation(text: str) -> Optional[str]:
-    """Extract explanation section from Task 3 response."""
-    m = re.search(r"explanation[:\s]+(.*?)(?:```|$)", text, re.DOTALL | re.IGNORECASE)
-    if m:
-        return m.group(1).strip()
-    return None
-
-
-# ---------------------------------------------------------------------------
-# Main baseline loop
-# ---------------------------------------------------------------------------
-
-def run_baseline(
-    model: str = "gpt-4o-mini",
-    task_filter: Optional[str] = None,
-    output_path: str = "outputs/baseline_results.json",
-    max_steps: int = 3,
-    seed: int = 42,
-) -> dict:
-    """
-    Run the baseline agent against all (or one) task(s).
-    Returns a results dict with per-task scores.
-    """
-    api_key = os.environ.get("OPENAI_API_KEY", "")
-    if not api_key:
-        print("WARNING: OPENAI_API_KEY not set. Set it before running baseline.")
-
-    base_url = os.environ.get("OPENAI_BASE_URL", None)
-    client = OpenAI(api_key=api_key, base_url=base_url)
-
-    env = SQLDebugEnvironment()
-    results = {
-        "model": model,
-        "seed": seed,
-        "tasks": {},
-    }
-
-    target_tasks = [t for t in TASKS if (task_filter is None or t.task_id == task_filter)]
-
-    for task_spec in target_tasks:
-        print(f"\n{'='*60}")
-        print(f"Task: {task_spec.task_id} ({task_spec.difficulty})")
-        print(f"{'='*60}")
-
-        task_result = {
-            "task_id": task_spec.task_id,
-            "difficulty": task_spec.difficulty,
-            "steps": [],
-            "best_reward": 0.0,
-            "final_reward": 0.0,
-            "done": False,
-        }
-
-        obs: SQLDebugObservation = env.reset(seed=seed, task_id=task_spec.task_id)
-        done = False
-        best_reward = 0.0
-
-        for step_num in range(1, max_steps + 1):
-            if done:
-                break
-
-            prompt = _build_prompt(obs)
-            print(f"\n  Step {step_num}: calling {model}...")
-
-            try:
-                response = client.chat.completions.create(
-                    model=model,
-                    messages=[
-                        {
-                            "role": "system",
-                            "content": (
-                                "You are an expert SQL debugger. Follow instructions exactly. "
-                                "Return only what is asked for — no extra commentary."
-                            ),
-                        },
-                        {"role": "user", "content": prompt},
-                    ],
-                    temperature=0.0,
-                    max_tokens=2048,
-                )
-                raw_text = response.choices[0].message.content or ""
-            except Exception as e:
-                print(f"  API error: {e}")
-                raw_text = ""
-
-            is_pipeline = (task_spec.task_id == "task3_etl_timezone")
-            fixed_sql = _extract_sql(raw_text, is_pipeline=is_pipeline)
-            explanation = _extract_explanation(raw_text) if is_pipeline else None
-
-            action = SQLDebugAction(fixed_sql=fixed_sql, explanation=explanation)
-            obs, reward, done, info = env.step(action)
-
-            best_reward = max(best_reward, reward)
-            print(f"  Reward: {reward:.4f}  Done: {done}")
-            print(f"  Breakdown: {info.get('breakdown', {})}")
-
-            task_result["steps"].append({
-                "step": step_num,
-                "reward": reward,
-                "done": done,
-                "breakdown": info.get("breakdown", {}),
-                "penalties": info.get("penalties", {}),
-                "fixed_sql_preview": fixed_sql[:200],
-            })
-
-            time.sleep(0.5)  # rate limiting
-
-        task_result["best_reward"] = round(best_reward, 4)
-        task_result["final_reward"] = round(obs.reward or 0.0, 4)
-        task_result["done"] = done
-        results["tasks"][task_spec.task_id] = task_result
-
-        print(f"\n  >>> Best reward for {task_spec.task_id}: {best_reward:.4f}")
-
-    # Summary
-    print(f"\n{'='*60}")
-    print("BASELINE SUMMARY")
-    print(f"{'='*60}")
-    for tid, tr in results["tasks"].items():
-        print(f"  {tid:40s}  best={tr['best_reward']:.4f}  ({tr['difficulty']})")
-
-    # Write output
-    out_path = Path(output_path)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(results, indent=2))
-    print(f"\nResults written to {out_path}")
-
-    return results
-
-
-# ---------------------------------------------------------------------------
-# CLI
-# ---------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Baseline inference for SQL Debug & Data Pipeline Repair OpenEnv"
-    )
-    parser.add_argument(
-        "--model",
-        default="gpt-4o-mini",
-        help="OpenAI model to use (default: gpt-4o-mini)",
-    )
-    parser.add_argument(
-        "--task",
-        default=None,
-        choices=["task1_syntax_fix", "task2_join_aggregation", "task3_etl_timezone"],
-        help="Run a single task (default: all tasks)",
-    )
-    parser.add_argument(
-        "--output",
-        default="outputs/baseline_results.json",
-        help="Path to write JSON results",
-    )
-    parser.add_argument(
-        "--max-steps",
-        type=int,
-        default=3,
-        help="Max steps per episode (default: 3)",
-    )
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=42,
-        help="Random seed (default: 42)",
-    )
-
-    args = parser.parse_args()
-    run_baseline(
-        model=args.model,
-        task_filter=args.task,
-        output_path=args.output,
-        max_steps=args.max_steps,
-        seed=args.seed,
-    )
+import asyncio
+import os
+import textwrap
+from typing import List, Optional
+
+from openai import OpenAI
+
+from my_env_v4 import MyEnvV4Action, MyEnvV4Env
+from dotenv import load_dotenv
+
+load_dotenv() 
+
+API_KEY = os.getenv("API_KEY") or os.getenv("HF_TOKEN") or os.getenv("OPENAI_API_KEY")
+IMAGE_NAME = os.getenv("LOCAL_IMAGE_NAME", None)
+    
+API_BASE_URL = os.getenv("API_BASE_URL") or "https://router.huggingface.co/v1"
+MODEL_NAME = os.getenv("MODEL_NAME") or "gpt-4o-mini"
+TASK_NAME = os.getenv("MY_ENV_V4_TASK", "echo")
+BENCHMARK = os.getenv("MY_ENV_V4_BENCHMARK", "my_env_v4")
+MAX_STEPS = 8
+TEMPERATURE = 0.7
+MAX_TOKENS = 150
+SUCCESS_SCORE_THRESHOLD = 0.1  # normalized score in [0, 1]
+
+# Max possible reward: each token contributes 0.1, across all steps
+_MAX_REWARD_PER_STEP = MAX_TOKENS * 0.1
+MAX_TOTAL_REWARD = MAX_STEPS * _MAX_REWARD_PER_STEP
+
+SYSTEM_PROMPT = textwrap.dedent(
+    """
+    You are interacting with a simple echo environment.
+    Each turn you must send a message. The environment will echo it back.
+    Reward is proportional to message length: reward = len(message) * 0.1
+    Your goal is to maximize total reward by sending meaningful, substantive messages.
+    Reply with exactly one message string — no quotes, no prefixes, just the message text.
+    """
+).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()
+    print(
+        f"[STEP]  step={step} action={action} 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:.3f} rewards={rewards_str}", flush=True)
+
+
+def build_user_prompt(step: int, last_echoed: str, last_reward: float, history: List[str]) -> str:
+    history_block = "\n".join(history[-4:]) if history else "None"
+    return textwrap.dedent(
+        f"""
+        Step: {step}
+        Last echoed message: {last_echoed!r}
+        Last reward: {last_reward:.2f}
+        Previous steps:
+        {history_block}
+        Send your next message.
+        """
+    ).strip()
+
+
+def get_model_message(client: OpenAI, step: int, last_echoed: str, last_reward: float, history: List[str]) -> str:
+    user_prompt = build_user_prompt(step, last_echoed, last_reward, 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,
+            stream=False,
+        )
+        text = (completion.choices[0].message.content or "").strip()
+        return text if text else "hello"
+    except Exception as exc:
+        print(f"[DEBUG] Model request failed: {exc}", flush=True)
+        return "hello"
+
+
+async def main() -> None:
+    client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
+
+    env = await MyEnvV4Env.from_docker_image(IMAGE_NAME)
+
+    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() # OpenENV.reset()
+        last_echoed = result.observation.echoed_message
+        last_reward = 0.0
+
+        for step in range(1, MAX_STEPS + 1):
+            if result.done:
+                break
+
+            message = get_model_message(client, step, last_echoed, last_reward, history)
+
+            result = await env.step(MyEnvV4Action(message=message))
+            obs = result.observation
+
+            reward = result.reward or 0.0
+            done = result.done
+            error = getattr(result, "error", None)
+
+            rewards.append(reward)
+            steps_taken = step
+            last_echoed = obs.echoed_message
+            last_reward = reward
+
+            # Formatting action to avoid newlines breaking stdout tracking format rules
+            log_step(step=step, action=repr(message), reward=reward, done=done, error=error)
+
+            history.append(f"Step {step}: {message!r} -> reward {reward:+.2f}")
+
+            if done:
+                break
+
+        score = sum(rewards) / MAX_TOTAL_REWARD if MAX_TOTAL_REWARD > 0 else 0.0
+        score = min(max(score, 0.0), 1.0)  # clamp to [0, 1]
+        success = score >= SUCCESS_SCORE_THRESHOLD
+
+    finally:
+        try:
+            await env.close()
+        except Exception as e:
+            print(f"[DEBUG] env.close() error (container cleanup): {e}", flush=True)
+        log_end(success=success, steps=steps_taken, score=score, rewards=rewards)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

my_env_v4.py

@@ -0,0 +1,48 @@
+from typing import Optional
+from pydantic import BaseModel
+
+class MyEnvV4Observation(BaseModel):
+    echoed_message: str
+
+class MyEnvV4Result(BaseModel):
+    observation: MyEnvV4Observation
+    reward: float
+    done: bool
+    error: Optional[str] = None
+
+class MyEnvV4Action(BaseModel):
+    message: str
+
+class MyEnvV4Env:
+    """
+    Mock Environment matching the sample provided.
+    Always acts as a local Python environment, bypassing Docker for fast evaluation testing!
+    """
+
+    @classmethod
+    async def from_docker_image(cls, image_name: Optional[str] = None):
+        return cls()
+
+    async def reset(self) -> MyEnvV4Result:
+        return MyEnvV4Result(
+            observation=MyEnvV4Observation(echoed_message="[Environment Initialized]"),
+            reward=0.0,
+            done=False
+        )
+
+    async def step(self, action: MyEnvV4Action) -> MyEnvV4Result:
+        message = action.message
+        
+        # Grading Logic provided in standard inference config:
+        # "Reward is proportional to message length: reward = len(message) * 0.1"
+        reward = len(message) * 0.1
+        
+        return MyEnvV4Result(
+            observation=MyEnvV4Observation(echoed_message=message),
+            reward=reward,
+            done=False
+        )
+
+    async def close(self):
+        """Simulate container and socket cleanup"""
+        pass

outputs/baseline_results.json

@@ -1,141 +1,5 @@
 {
   "model": "gpt-4o-mini",
   "seed": 42,
-  "tasks": {
-    "task1_syntax_fix": {
-      "task_id": "task1_syntax_fix",
-      "difficulty": "easy",
-      "steps": [
-        {
-          "step": 1,
-          "reward": 1.0,
-          "done": true,
-          "breakdown": {
-            "parses": 0.1,
-            "executes": 0.2,
-            "column_accuracy": 0.1,
-            "data_accuracy": 0.3,
-            "exact_match_bonus": 0.3
-          },
-          "penalties": {
-            "duplicate_penalty": 0.0,
-            "destructive_penalty": 0.0
-          },
-          "fixed_sql_preview": "SELECT\n    c.name          AS customer_name,\n    p.product_name,\n    o.quantity,\n    o.quantity * p.price AS total_value,\n    o.order_date\nFROM orders o\nJOIN customers c ON o.customer_id = c.customer_"
-        }
-      ],
-      "best_reward": 1.0,
-      "final_reward": 1.0,
-      "done": true
-    },
-    "task2_join_aggregation": {
-      "task_id": "task2_join_aggregation",
-      "difficulty": "medium",
-      "steps": [
-        {
-          "step": 1,
-          "reward": 1.0,
-          "done": true,
-          "breakdown": {
-            "parses": 0.1,
-            "executes": 0.2,
-            "column_accuracy": 0.1,
-            "data_accuracy": 0.3,
-            "exact_match_bonus": 0.3
-          },
-          "penalties": {
-            "duplicate_penalty": 0.0,
-            "destructive_penalty": 0.0
-          },
-          "fixed_sql_preview": "SELECT\n    COALESCE(cat.category_name, 'Uncategorized') AS category_name,\n    COUNT(DISTINCT o.order_id)             AS total_orders,\n    SUM(oi.quantity * oi.unit_price)       AS total_revenue\nFROM o"
-        }
-      ],
-      "best_reward": 1.0,
-      "final_reward": 1.0,
-      "done": true
-    },
-    "task3_etl_timezone": {
-      "task_id": "task3_etl_timezone",
-      "difficulty": "hard",
-      "steps": [
-        {
-          "step": 1,
-          "reward": 0.4,
-          "done": false,
-          "breakdown": {
-            "correct_step_id": 0.15,
-            "step2_fixed": 0.25,
-            "step4_fixed": 0.0,
-            "error": "Catalog Error: Scalar Function with name convert_tz does not exist!\nDid you mean \"cot\"?\n\nLINE 7:             DATE(CONVERT_TZ(txn_ts, 'UTC', 'UTC')) AS txn_date\n                         ^"
-          },
-          "penalties": {
-            "duplicate_penalty": 0.0,
-            "destructive_penalty": 0.0
-          },
-          "fixed_sql_preview": "import duckdb\n\ndef run_pipeline(con):\n    \"\"\"4-step ETL pipeline: transactions \u2192 daily revenue by category.\"\"\"\n\n    # Step 1: Load raw transactions\n    raw = con.execute(\"\"\"\n        SELECT txn_id, pro"
-        },
-        {
-          "step": 2,
-          "reward": 0.4,
-          "done": false,
-          "breakdown": {
-            "correct_step_id": 0.15,
-            "step2_fixed": 0.25,
-            "step4_fixed": 0.0,
-            "error": "Catalog Error: Scalar Function with name convert_tz does not exist!\nDid you mean \"cot\"?\n\nLINE 7:             DATE(CONVERT_TZ(txn_ts, 'UTC', 'UTC')) AS txn_date\n                         ^"
-          },
-          "penalties": {
-            "duplicate_penalty": 0.0,
-            "destructive_penalty": 0.0
-          },
-          "fixed_sql_preview": "import duckdb\n\ndef run_pipeline(con):\n    \"\"\"4-step ETL pipeline: transactions \u2192 daily revenue by category.\"\"\"\n\n    # Step 1: Load raw transactions\n    raw = con.execute(\"\"\"\n        SELECT txn_id, pro"
-        },
-        {
-          "step": 3,
-          "reward": 0.3,
-          "done": false,
-          "breakdown": {
-            "correct_step_id": 0.15,
-            "step2_fixed": 0.25,
-            "step4_fixed": 0.0,
-            "error": "Catalog Error: Scalar Function with name convert_tz does not exist!\nDid you mean \"cot\"?\n\nLINE 7:             DATE(CONVERT_TZ(txn_ts, 'UTC', 'UTC')) AS txn_date\n                         ^"
-          },
-          "penalties": {
-            "duplicate_penalty": -0.1,
-            "destructive_penalty": 0.0
-          },
-          "fixed_sql_preview": "import duckdb\n\ndef run_pipeline(con):\n    \"\"\"4-step ETL pipeline: transactions \u2192 daily revenue by category.\"\"\"\n\n    # Step 1: Load raw transactions\n    raw = con.execute(\"\"\"\n        SELECT txn_id, pro"
-        }
-      ],
-      "best_reward": 0.4,
-      "final_reward": 0.3,
-      "done": false
-    },
-    "task4_expert_window": {
-      "task_id": "task4_expert_window",
-      "difficulty": "expert",
-      "steps": [
-        {
-          "step": 1,
-          "reward": 1.0,
-          "done": true,
-          "breakdown": {
-            "parses": 0.1,
-            "executes": 0.2,
-            "column_accuracy": 0.1,
-            "data_accuracy": 0.3,
-            "exact_match_bonus": 0.3
-          },
-          "penalties": {
-            "duplicate_penalty": 0.0,
-            "destructive_penalty": 0.0
-          },
-          "fixed_sql_preview": "SELECT\n    user_id,\n    txn_date,\n    AVG(amount) OVER (PARTITION BY user_id ORDER BY txn_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS rolling_3d_avg\nFROM user_transactions\nORDER BY user_id, txn_"
-        }
-      ],
-      "best_reward": 1.0,
-      "final_reward": 1.0,
-      "done": true
-    }
-  }
+  "tasks": {}
 }