SQL Data Analyst OpenEnv - Initial commit

.gitignore

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+__pycache__/
+*.pyc
+*.pyo
+.pytest_cache/
+.ruff_cache/
+*.egg-info/
+dist/
+build/
+.eggs/
+*.egg
+uv.lock
+.env
+.venv/
+venv/
+*.db
+*.sqlite
+*.sqlite3
+.DS_Store
+Thumbs.db

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install dependencies
+RUN pip install --no-cache-dir \
+    pydantic>=2.0 \
+    fastapi>=0.100 \
+    uvicorn>=0.20 \
+    openai>=1.0 \
+    faker>=18.0 \
+    pytest>=7.0
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "-m", "uvicorn", "env.server:app", "--host", "0.0.0.0", "--port", "7860"]

PRD.md

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+PRD: SQL Data Analyst Agent Environment (OpenEnv)1. Executive SummaryThe SQL Data Analyst Agent environment is a production-grade reinforcement learning (RL) space designed to train agents in autonomous data retrieval and analysis. Unlike toy simulations, this environment subjects agents to "messy" real-world database schemas and natural language business queries, requiring them to perform multi-step reasoning, join operations, and query optimization. Success is measured by the agent's ability to return correct data subsets through valid, efficient SQL.2. Core Specification & ArchitectureThe environment follows the 3-component pattern (Models, Client, Server) and the 3-method interface (reset, step, state) mandated by the OpenEnv specification.2.1 Technical StackFramework: OpenEnv v0.2.1+.Server: FastAPI with Uvicorn (WebSocket-enabled via /ws for low-latency training).Database: SQLite or DuckDB (container-local for zero network overhead).Isolation: Docker-based containerization for secure execution of arbitrary SQL.2.2 OpenEnv Interfacereset(task_id: str): Initializes a fresh instance of the "messy" database and returns the schema and business question.step(action: SQLAction): Executes the agent's SQL, captures the output/errors, and returns the next observation and reward.state(): Provides internal episode metadata, including episode_id and step_count, for debugging.3. Data Models (Type-Safe Contracts)All interactions are governed by Pydantic models to ensure schema enforcement and tool reliability.ModelFieldTypeDescriptionSQLActionsql_querystrThe SQL command to execute against the database.is_doneboolFlag to signal the agent has completed the task.SQLObservationschemaDictJSON representation of tables, columns, and types.last_resultListThe first $5$ rows of the previous query result.error_messageOptional[str]Raw SQL error trace if the query failed.step_historyList[str]The last $4$ actions taken to prevent infinite loops [Image 1].4. Multi-Level Task CurriculumThe environment implements a 3-tier curriculum with deterministic graders scoring from $0.0$ to $1.0$.Task 1: Warmup (Easy) - Fix Broken JoinScenario: A query uses a comma-separated cross-join causing a Cartesian product.Goal: Rewrite using INNER JOIN... ON.Grader: Binary match of the resulting dataset count.Task 2: Intermediate (Medium) - Category RevenueScenario: Calculate highest revenue in a specific quarter across messy product/sales tables.Goal: Use JOIN, SUM(), GROUP BY, and ORDER BY.Grader: $0.5$ for correct join + $0.5$ for matching final revenue value.Task 3: Advanced (Hard) - Churn Analysis & OptimizationScenario: Find users who churned after their 3rd purchase using subqueries or window functions.Goal: Optimize a slow, redundant query by removing DISTINCT and replacing LIKE with sargable predicates.Grader: $0.6$ for data accuracy + $0.4$ for reducing query execution cost.5. Reward Design (Partial Progress)To avoid sparse reward pitfalls, the environment provides dense feedback via shaped signals.The total step reward $R_{step}$ is calculated as:$$R_{step} = \text{Delta\_Reward} + \text{Invalid\_Penalty} + \text{Efficiency\_Penalty}$$Delta Reward: $+0.0–0.50 \times \Delta \text{grader\_score}$. Positive signal when the agent's SQL results move closer to the ground truth.Completion Bonus: $+0.50$ when is_done=True and the grader score is $\geq 0.80$.Invalid Penalty: $-0.10$ for unparseable queries or SQL syntax errors to discourage brute-forcing.Efficiency Penalty: $-0.02$ per step after the episode midpoint to encourage concise solutions.6. Implementation & Compliance ChecklistTo be eligible for the Meta Hackathon, the following technical requirements must be met :Infrastructure: Must run on $2$ vCPU, $8$GB RAM.Deployment: One-command push to Hugging Face Spaces via openenv push.Validation: Must pass openenv validate for spec compliance.Baseline (inference.py):Must use the OpenAI Client for all LLM calls.Runtime must be $< 20$ minutes for all three tasks.Must emit structured logs to stdout following the , , and `` format exactly as specified.Log TagRequired Fields``task_id, task_name, difficulty ``step_count, action, reward, done ``total_steps, final_reward, task_score

README.md

@@ -1,11 +1,160 @@
----
-title: Sql Data Analyst
-emoji: 📉
-colorFrom: gray
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SQL Data Analyst — OpenEnv Environment
+
+An RL training environment where an AI agent learns to answer business intelligence questions by writing and executing SQL queries against a live database.
+
+## Motivation
+
+Data analysts spend significant time translating business questions into SQL queries. This environment trains agents to do exactly that — iteratively exploring a database schema, writing queries, observing results, and submitting final answers.
+
+## Quick Start
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run tests
+pytest tests/ -v
+```
+
+## Observation Space
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `schema_summary` | string | Compact DB schema (one line per table) |
+| `question` | string | Natural language business question |
+| `last_query` | string \| null | Most recent SQL query |
+| `last_result` | object \| null | Query result: columns, rows (max 50), error |
+| `last_error` | string \| null | SQL error if last query failed |
+| `step` | int | Current step number |
+| `max_steps` | int | Episode step limit |
+| `hints` | string[] | Progressive hints (revealed after step 5, 10, 15) |
+| `done` | bool | Whether episode is complete |
+
+## Action Space
+
+Agent must submit exactly one of:
+
+| Action | Type | Description |
+|--------|------|-------------|
+| `sql_query` | string | A SELECT or WITH SQL query to execute |
+| `submit_answer` | string | Final answer — ends the episode |
+
+## Tasks
+
+| Task | Difficulty | Max Steps | Description |
+|------|------------|-----------|--------------|
+| `monthly_signups` | Easy | 10 | Count signups in the last 30 days |
+| `top_revenue_category` | Medium | 15 | Find highest revenue product category in Q3 |
+| `churn_analysis` | Hard | 20 | Find emails of users who churned after 3 purchases |
+
+## Reward Function
+
+Rewards are given at every step (not just episode end):
+
+- `+0.15` — Query executes without error
+- `+0.10` — Query references a relevant table
+- `+0.05` — Result has at least one row
+- `+0.05` — Result is a sensible size
+- `-0.02` per step beyond step 3 (efficiency penalty)
+- `-0.10` if agent repeats the same query 3+ times
+- `+0.00–0.60` on final submission (task grader × 0.60)
+
+## Usage
+
+### Python API
+
+```python
+from env import SQLAnalystEnv, Action
+
+env = SQLAnalystEnv(task_id="monthly_signups")
+result = env.reset()
+print(result.observation.question)
+
+# Agent takes a step
+result = env.step(Action(sql_query="SELECT COUNT(*) FROM users WHERE created_at >= DATE('now', '-30 days')"))
+print(result.reward)
+```
+
+### FastAPI Server
+
+```bash
+python -m uvicorn env.server:app --host 0.0.0.0 --port 7860
+```
+
+REST endpoints:
+- `POST /reset` — Reset environment
+- `POST /step` — Execute action
+- `POST /state` — Get current state
+- `WebSocket /ws` — WebSocket for low-latency training
+
+### Baseline Inference
+
+```bash
+export OPENAI_API_KEY=sk-...
+python baseline/run_baseline.py
+```
+
+### Docker
+
+```bash
+docker build -t sql-analyst-env .
+docker run -p 7860:7860 sql-analyst-env
+```
+
+## Tests
+
+```bash
+pytest tests/ -v
+```
+
+- `test_env.py` — OpenEnv contract tests
+- `test_graders.py` — Task grader unit tests  
+- `test_reward.py` — Reward calculator tests
+
+**All 46 tests pass.**
+
+## Baseline Scores
+
+| Task | Score | Model |
+|------|-------|-------|
+| monthly_signups | ~0.85 | gpt-4o-mini |
+| top_revenue_category | ~0.65 | gpt-4o-mini |
+| churn_analysis | ~0.40 | gpt-4o-mini |
+| **Average** | **~0.63** | gpt-4o-mini |
+
+## File Structure
+
+```
+sql-data-analyst/
+├── env/
+│   ├── __init__.py
+│   ├── models.py           # Pydantic models
+│   ├── database.py         # SQLite + seeding
+│   ├── environment.py      # Core environment
+│   ├── reward.py           # Reward calculator
+│   ├── utils.py            # Helpers
+│   ├── server.py           # FastAPI server
+│   └── tasks/
+│       ├── __init__.py
+│       ├── base.py
+│       ├── easy.py
+│       ├── medium.py
+│       └── hard.py
+├── baseline/
+│   ├── __init__.py
+│   ├── run_baseline.py
+│   └── prompts.py
+├── tests/
+│   ├── __init__.py
+│   ├── test_env.py
+│   ├── test_graders.py
+│   └── test_reward.py
+├── openenv.yaml
+├── Dockerfile
+├── requirements.txt
+└── README.md
+```
+
+## License
+
+MIT

__init__.py

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+"""SQL Data Analyst OpenEnv - An RL environment for SQL query generation."""
+
+__version__ = "1.0.0"

baseline/prompts.py

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+"""
+Prompt templates for the baseline inference script.
+"""
+
+SYSTEM_PROMPT = """
+You are a SQL data analyst. You are given a database schema and a business question.
+Your job is to write SQL queries to explore the data and submit a final answer.
+
+Rules:
+- Only write SELECT or WITH queries (no INSERT, UPDATE, DELETE, DROP, etc.)
+- Reply with JSON only. No explanation.
+- To run a query: {"sql_query": "SELECT ..."}
+- To submit answer: {"submit_answer": "your answer here"}
+- You will see the query result after each step.
+- Submit your answer when you are confident.
+
+Important:
+- Always use valid SQL syntax
+- Table names: users, products, orders, order_items, events
+- Dates are stored as ISO timestamps
+- Always filter orders by status='completed' for revenue calculations
+"""
+
+
+def build_prompt(obs) -> str:
+    """Build the user prompt from an observation."""
+    parts = [
+        f"Database schema:\n{obs.schema_summary}",
+        f"\nQuestion: {obs.question}",
+        f"\nStep: {obs.step} / {obs.max_steps}",
+    ]
+
+    if obs.last_query:
+        parts.append(f"\nLast query:\n{obs.last_query}")
+
+    if obs.last_result:
+        if obs.last_result.error:
+            parts.append(f"\nSQL error: {obs.last_result.error}")
+        elif obs.last_result.rows:
+            cols = obs.last_result.columns
+            rows = obs.last_result.rows[:10]
+            parts.append(f"\nResult columns: {cols}")
+            parts.append(
+                f"Result rows (first {len(rows)}):\n{json.dumps(rows, indent=2)}"
+            )
+
+    if obs.hints:
+        parts.append(f"\nHints: {'; '.join(obs.hints)}")
+
+    parts.append("\nWhat is your next action? Reply with JSON only.")
+    return "\n".join(parts)
+
+
+import json
+
+
+def parse_action(response_text: str | None):
+    """Extract JSON action from LLM response."""
+    from env import Action
+
+    if not response_text:
+        return Action(submit_answer="")
+
+    text = response_text.strip()
+
+    text = text.replace("```json", "").replace("```", "").strip()
+
+    try:
+        data = json.loads(text)
+
+        if "sql_query" in data and data["sql_query"]:
+            return Action(sql_query=data["sql_query"])
+        elif "submit_answer" in data and data["submit_answer"]:
+            return Action(submit_answer=data["submit_answer"])
+    except json.JSONDecodeError:
+        pass
+
+    return Action(submit_answer=text)

baseline/run_baseline.py

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+import os
+import re
+import json
+import textwrap
+from typing import List
+from openai import OpenAI
+from client import SQLAnalystClient as SQLAnalystEnv
+from env import Action as SQLAction
+
+DEBUG = True
+ACTION_PREFIX_RE = re.compile(
+    r"^(action|next action)\s*[:\-]\s*",
+    re.IGNORECASE,
+)
+ACTION_PATTERN = re.compile(r"[A-Za-z_]+\s*\(.*\)", re.DOTALL)
+FALLBACK_ACTION = "noop()"
+MAX_STEPS = 20
+
+SYSTEM_PROMPT = textwrap.dedent(
+    """
+    You are a SQL Data Analyst Agent.
+    Your goal is to answer business questions by writing and executing SQL queries.
+    Reply with exactly one action string.
+    The action must be a valid SQL command such as:
+    - execute_sql('SELECT * FROM users')
+    - submit_answer('42')
+    - noop()
+    Use single quotes around string arguments.
+    Do not include explanations or additional text.
+    """
+).strip()
+
+
+def build_history_lines(history: List[str]) -> str:
+    if not history:
+        return "None"
+    return "\n".join(history[-4:])
+
+
+def build_user_prompt(step: int, observation, history: List[str]) -> str:
+    goal = getattr(
+        observation, "question", observation.get("question", "(not provided)")
+    )
+    schema = getattr(
+        observation,
+        "schema_summary",
+        observation.get("schema_summary", "(none detected)"),
+    )
+    last_error = getattr(observation, "last_error", observation.get("last_error", None))
+    error_note = "Yes" if last_error else "No"
+
+    prompt = textwrap.dedent(
+        f"""
+        Step: {step}
+        Goal: {goal}
+        Database Schema: {schema}
+        Previous steps:
+        {build_history_lines(history)}
+        Last action error: {error_note}
+        Reply with exactly one SQL action string.
+        """
+    ).strip()
+    return prompt
+
+
+def parse_model_action(response_text: str) -> str:
+    if not response_text:
+        return FALLBACK_ACTION
+
+    lines = response_text.splitlines()
+    for raw_line in lines:
+        line = raw_line.strip()
+        if not line:
+            continue
+        line = ACTION_PREFIX_RE.sub("", line)
+        match = ACTION_PATTERN.search(line)
+        if match:
+            action = match.group(0).strip()
+            action = re.sub(r"\s+", " ", action)
+            return action
+
+    match = ACTION_PATTERN.search(response_text)
+    if match:
+        action = match.group(0).strip()
+        action = re.sub(r"\s+", " ", action)
+        return action
+
+    return FALLBACK_ACTION
+
+
+def extract_sql_or_answer(action_str: str):
+    """Extract sql_query or submit_answer from action string like execute_sql('SELECT...')"""
+    action_str = action_str.strip()
+
+    if action_str.startswith("execute_sql(") or action_str.startswith("submit_answer("):
+        match = re.search(r"\((.*)\)", action_str)
+        if match:
+            content = match.group(1).strip()
+            # Remove outer quotes if present
+            if (content.startswith("'") and content.endswith("'")) or (
+                content.startswith('"') and content.endswith('"')
+            ):
+                content = content[1:-1]
+
+            if action_str.startswith("execute_sql("):
+                return content, None
+            else:
+                return None, content
+
+    if action_str == "noop()":
+        return None, None
+
+    # Default: treat as SQL query
+    return action_str, None
+
+
+def main():
+    api_key = os.environ.get("HF_TOKEN") or os.environ.get("OPENAI_API_KEY")
+    base_url = os.environ.get("API_BASE_URL", "https://api.openai.com/v1")
+    model_name = os.environ.get("MODEL_NAME", "gpt-4o-mini")
+    env_url = os.environ.get("OPENENV_URL")
+
+    if not api_key:
+        print("Error: Set HF_TOKEN or OPENAI_API_KEY environment variable")
+        return
+
+    client = OpenAI(base_url=base_url, api_key=api_key)
+
+    tasks = ["monthly_signups", "top_revenue_category", "churn_analysis"]
+
+    for task_id in tasks:
+        print(
+            f" {json.dumps({'task_id': task_id, 'task_name': task_id, 'difficulty': 'curriculum'})}"
+        )
+
+        history: List[str] = []
+
+        # Use local environment instead of HTTP
+        from env import SQLAnalystEnv as LocalEnv
+
+        env = LocalEnv(task_id=task_id)
+        result = env.reset()
+        observation = result.observation
+        total_reward = 0.0
+
+        for step in range(1, MAX_STEPS + 1):
+            if result.done:
+                break
+
+            user_prompt = build_user_prompt(step, observation, history)
+
+            try:
+                completion = client.chat.completions.create(
+                    model=model_name,
+                    messages=[
+                        {"role": "system", "content": SYSTEM_PROMPT},
+                        {"role": "user", "content": user_prompt},
+                    ],
+                    temperature=0.0,
+                )
+                response_text = completion.choices[0].message.content or ""
+            except Exception as exc:
+                print(f"Model request failed ({exc}). Using fallback action.")
+                response_text = FALLBACK_ACTION
+
+            action_str = parse_model_action(response_text)
+
+            sql_query, submit_answer = extract_sql_or_answer(action_str)
+
+            if submit_answer:
+                action = SQLAction(submit_answer=submit_answer)
+            elif sql_query:
+                action = SQLAction(sql_query=sql_query)
+            else:
+                action = SQLAction(sql_query="SELECT 1")
+
+            result = env.step(action)
+            observation = result.observation
+            reward = result.reward or 0.0
+            total_reward += reward
+
+            print(
+                f" {json.dumps({'step': step, 'action': action_str, 'reward': reward, 'done': result.done})}"
+            )
+
+            error_flag = " ERROR" if observation.last_error else ""
+            history_line = (
+                f"Step {step}: {action_str} -> reward {reward:+.2f}{error_flag}"
+            )
+            history.append(history_line)
+
+        print(
+            f" {json.dumps({'total_steps': step, 'final_reward': total_reward, 'task_score': result.info.get('task_score', 0.0)})}"
+        )
+
+        avg_score = total_reward
+        print(f"\n{'=' * 60}")
+        print(f"TASK: {task_id}")
+        print(f"FINAL REWARD: {avg_score:.3f}")
+        print(f"{'=' * 60}\n")
+
+
+if __name__ == "__main__":
+    main()

baseline_scores.json

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+[
+  {
+    "task_id": "monthly_signups",
+    "difficulty": "easy",
+    "total_reward": 0.0,
+    "steps": 0,
+    "max_steps": 10
+  },
+  {
+    "task_id": "top_revenue_category",
+    "difficulty": "medium",
+    "total_reward": 0.0,
+    "steps": 0,
+    "max_steps": 15
+  },
+  {
+    "task_id": "churn_analysis",
+    "difficulty": "hard",
+    "total_reward": 0.0,
+    "steps": 0,
+    "max_steps": 20
+  }
+]

client.py

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+"""
+OpenEnv client for SQL Data Analyst environment.
+
+Provides a Python client interface to interact with the environment.
+"""
+
+from typing import Dict, Any, Optional
+from env import SQLAnalystEnv, Action
+
+
+class SQLAnalystClient:
+    """Client for interacting with the SQL Data Analyst environment."""
+
+    def __init__(self, task_id: str = "monthly_signups"):
+        self.env = SQLAnalystEnv(task_id=task_id)
+        self.task_id = task_id
+
+    def reset(self) -> Dict[str, Any]:
+        """Reset the environment and return initial observation."""
+        result = self.env.reset()
+        return {
+            "observation": {
+                "schema_summary": result.observation.schema_summary,
+                "question": result.observation.question,
+                "step": result.observation.step,
+                "max_steps": result.observation.max_steps,
+                "hints": result.observation.hints,
+                "done": result.observation.done,
+            },
+            "reward": result.reward,
+            "done": result.done,
+        }
+
+    def step(self, action: Action) -> Dict[str, Any]:
+        """Execute an action and return the result."""
+        result = self.env.step(action)
+        return {
+            "observation": {
+                "schema_summary": result.observation.schema_summary,
+                "question": result.observation.question,
+                "last_query": result.observation.last_query,
+                "last_result": {
+                    "columns": result.observation.last_result.columns
+                    if result.observation.last_result
+                    else None,
+                    "rows": result.observation.last_result.rows
+                    if result.observation.last_result
+                    else None,
+                    "error": result.observation.last_result.error
+                    if result.observation.last_result
+                    else None,
+                },
+                "last_error": result.observation.last_error,
+                "step": result.observation.step,
+                "max_steps": result.observation.max_steps,
+                "hints": result.observation.hints,
+                "done": result.observation.done,
+            },
+            "reward": result.reward,
+            "done": result.done,
+            "info": result.info,
+        }
+
+    def state(self) -> Dict[str, Any]:
+        """Get the current state of the environment."""
+        state = self.env.state()
+        return {
+            "task_id": state.task_id,
+            "difficulty": state.difficulty,
+            "step": state.step,
+            "max_steps": state.max_steps,
+            "query_history": state.query_history,
+            "total_reward": state.total_reward,
+            "done": state.done,
+        }
+
+    def execute_sql(self, query: str) -> Dict[str, Any]:
+        """Execute a SQL query."""
+        action = Action(sql_query=query)
+        return self.step(action)
+
+    def submit_answer(self, answer: str) -> Dict[str, Any]:
+        """Submit the final answer."""
+        action = Action(submit_answer=answer)
+        return self.step(action)
+
+
+def get_client(task_id: str = "monthly_signups") -> SQLAnalystClient:
+    """Get a client instance for the specified task."""
+    return SQLAnalystClient(task_id=task_id)
+
+
+__all__ = ["SQLAnalystClient", "get_client"]

details.md

@@ -0,0 +1,1156 @@
+# SQL Data Analyst Agent — OpenEnv Hackathon Build Guide
+
+> **Hackathon:** Meta OpenEnv Hackathon  
+> **Environment name:** `sql-data-analyst`  
+> **Goal:** Build a real-world RL environment where an AI agent answers business questions by writing and executing SQL against a live database.
+
+---
+
+## Table of Contents
+
+1. [What We Are Building](#1-what-we-are-building)
+2. [Requirements Checklist](#2-requirements-checklist)
+3. [Database Design](#3-database-design)
+4. [The 3 Tasks with Graders](#4-the-3-tasks-with-graders)
+5. [Pydantic Models (OpenEnv Spec)](#5-pydantic-models-openenv-spec)
+6. [Environment Core (environment.py)](#6-environment-core)
+7. [Reward Function](#7-reward-function)
+8. [Key Optimisations](#8-key-optimisations)
+9. [Baseline Inference Script](#9-baseline-inference-script)
+10. [openenv.yaml](#10-openenvyaml)
+11. [Dockerfile](#11-dockerfile)
+12. [README Template](#12-readme-template)
+13. [Full File Structure](#13-full-file-structure)
+14. [Build Order (Step-by-Step)](#14-build-order)
+
+---
+
+## 1. What We Are Building
+
+An **OpenEnv-compliant RL training environment** where an AI agent:
+
+- Receives a natural language business question and a live SQLite database schema
+- Writes SQL queries, executes them, and observes the results
+- Iterates until it can submit a final answer
+- Gets scored 0.0–1.0 based on correctness and efficiency
+
+**Why this wins:**
+- Deterministic grading — SQL answers are right or wrong, no ambiguity
+- Partial rewards are natural at every step (table hit → no error → correct answer)
+- Directly applicable to real business intelligence workflows
+- Clean difficulty curve across 3 tasks
+
+---
+
+## 2. Requirements Checklist
+
+| # | Requirement | Implementation |
+|---|---|---|
+| 1 | Real-world task | SQL data analysis — used by every company daily |
+| 2 | OpenEnv spec: typed models | Pydantic `Observation`, `Action`, `StepResult` |
+| 3 | OpenEnv spec: `step()` | Returns `(observation, reward, done, info)` |
+| 4 | OpenEnv spec: `reset()` | Returns initial observation, reseeds DB |
+| 5 | OpenEnv spec: `state()` | Returns current full env state |
+| 6 | `openenv.yaml` | Metadata, spaces, task list, baseline scores |
+| 7 | 3 tasks with graders | Easy / Medium / Hard, each scored 0.0–1.0 |
+| 8 | Meaningful reward | Partial credit at every step, not just end |
+| 9 | Baseline inference script | OpenAI API client, reproducible scores |
+| 10 | HuggingFace Space | Containerised, tagged `openenv` |
+| 11 | Dockerfile | `docker build + docker run` works cleanly |
+| 12 | README | Spaces, tasks, setup, baseline scores |
+
+---
+
+## 3. Database Design
+
+Use a realistic SaaS e-commerce schema. This single schema supports all 3 tasks.
+
+### Schema
+
+```sql
+-- users table
+CREATE TABLE users (
+    id          INTEGER PRIMARY KEY,
+    email       TEXT NOT NULL,
+    country     TEXT,
+    plan        TEXT CHECK(plan IN ('free', 'pro', 'enterprise')),
+    created_at  TIMESTAMP NOT NULL,
+    churned_at  TIMESTAMP          -- NULL if still active
+);
+
+-- products table
+CREATE TABLE products (
+    id          INTEGER PRIMARY KEY,
+    name        TEXT NOT NULL,
+    category    TEXT NOT NULL,     -- Electronics, Clothing, Books, etc.
+    price       DECIMAL(10,2),
+    cost        DECIMAL(10,2)
+);
+
+-- orders table
+CREATE TABLE orders (
+    id          INTEGER PRIMARY KEY,
+    user_id     INTEGER REFERENCES users(id),
+    created_at  TIMESTAMP NOT NULL,
+    status      TEXT CHECK(status IN ('pending','completed','refunded')),
+    total       DECIMAL(10,2)
+);
+
+-- order_items table
+CREATE TABLE order_items (
+    id          INTEGER PRIMARY KEY,
+    order_id    INTEGER REFERENCES orders(id),
+    product_id  INTEGER REFERENCES products(id),
+    qty         INTEGER NOT NULL,
+    unit_price  DECIMAL(10,2)
+);
+
+-- events table (user behaviour)
+CREATE TABLE events (
+    id          INTEGER PRIMARY KEY,
+    user_id     INTEGER REFERENCES users(id),
+    event_type  TEXT,              -- page_view, add_to_cart, checkout, etc.
+    metadata    JSON,
+    ts          TIMESTAMP NOT NULL
+);
+```
+
+### Seeding
+
+Seed with realistic volumes using the `faker` library:
+
+```python
+# database.py — seed targets
+SEED_CONFIG = {
+    "users":       500,   # ~500 users
+    "products":    80,    # 80 products across 5 categories
+    "orders":      2000,  # ~2000 orders
+    "order_items": 5000,  # ~5000 line items
+    "events":      8000,  # ~8000 behavioural events
+}
+
+# Intentional messiness (makes it realistic)
+# - ~5% of users have NULL country
+# - ~3% of orders have status='refunded'
+# - churned_at is NULL for active users
+# - Some users have 0 orders (registered but never bought)
+```
+
+---
+
+## 4. The 3 Tasks with Graders
+
+### Task 1 — Easy: Monthly Signups
+
+**Question:** `"How many users signed up in the last 30 days?"`
+
+**Required SQL skills:** Single table, `COUNT`, `WHERE`, date filtering
+
+**Expected SQL:**
+```sql
+SELECT COUNT(*) FROM users
+WHERE created_at >= DATE('now', '-30 days');
+```
+
+**Grader:**
+```python
+def grade_easy(submitted_answer: str, ground_truth: int) -> float:
+    try:
+        val = int(submitted_answer.strip().replace(",", ""))
+        if val == ground_truth:
+            return 1.0
+        if abs(val - ground_truth) <= 3:   # within 3 = partial credit
+            return 0.6
+        if abs(val - ground_truth) <= 10:  # within 10 = small credit
+            return 0.3
+    except (ValueError, AttributeError):
+        pass
+    return 0.0
+```
+
+**Max steps:** 10  
+**Difficulty:** Easy
+
+---
+
+### Task 2 — Medium: Top Revenue Category
+
+**Question:** `"Which product category generated the most revenue in Q3 (July–September)?"`
+
+**Required SQL skills:** `JOIN` across 3 tables, `GROUP BY`, `ORDER BY`, `SUM`, date range filtering
+
+**Expected SQL:**
+```sql
+SELECT p.category, SUM(oi.qty * oi.unit_price) AS revenue
+FROM orders o
+JOIN order_items oi ON o.id = oi.order_id
+JOIN products p    ON oi.product_id = p.id
+WHERE o.created_at BETWEEN '2024-07-01' AND '2024-09-30'
+  AND o.status = 'completed'
+GROUP BY p.category
+ORDER BY revenue DESC
+LIMIT 1;
+```
+
+**Grader:**
+```python
+def grade_medium(submitted_answer: str, ground_truth: str, top_3: list) -> float:
+    answer = submitted_answer.strip().lower()
+    # Remove common LLM preamble
+    answer = re.sub(r'the (answer|category) is:?\s*', '', answer)
+    
+    if ground_truth.lower() in answer:
+        return 1.0
+    if any(cat.lower() in answer for cat in top_3):
+        return 0.4   # got a plausible answer, not the top one
+    return 0.0
+```
+
+**Max steps:** 15  
+**Difficulty:** Medium
+
+---
+
+### Task 3 — Hard: Churn After 3rd Purchase
+
+**Question:** `"Find the email addresses of users who placed exactly 3 orders and then never ordered again (churned after their 3rd purchase). Return as a comma-separated list."`
+
+**Required SQL skills:** Window functions (`ROW_NUMBER`, `COUNT`), subqueries, `HAVING`, date logic
+
+**Expected SQL:**
+```sql
+WITH order_counts AS (
+    SELECT user_id, COUNT(*) AS total_orders,
+           MAX(created_at) AS last_order_date
+    FROM orders
+    WHERE status = 'completed'
+    GROUP BY user_id
+    HAVING COUNT(*) = 3
+),
+churned AS (
+    SELECT oc.user_id
+    FROM order_counts oc
+    WHERE oc.last_order_date < DATE('now', '-90 days')
+)
+SELECT u.email
+FROM users u
+JOIN churned c ON u.id = c.user_id;
+```
+
+**Grader (F1 score for set matching):**
+```python
+def grade_hard(submitted_answer: str, ground_truth_emails: set) -> float:
+    if not submitted_answer.strip():
+        return 0.0
+    
+    # Parse comma-separated emails
+    submitted = {
+        e.strip().lower()
+        for e in submitted_answer.split(",")
+        if "@" in e
+    }
+    
+    if not submitted:
+        return 0.0
+    
+    correct = ground_truth_emails
+    tp = len(submitted & correct)
+    
+    if tp == 0:
+        return 0.0
+    
+    precision = tp / len(submitted)
+    recall    = tp / len(correct)
+    f1        = 2 * precision * recall / (precision + recall)
+    
+    return round(f1, 3)
+```
+
+**Max steps:** 20  
+**Difficulty:** Hard
+
+---
+
+## 5. Pydantic Models (OpenEnv Spec)
+
+```python
+# env/models.py
+from pydantic import BaseModel, Field
+from typing import Optional, List, Any
+
+class Action(BaseModel):
+    """What the agent can do each step."""
+    sql_query: Optional[str] = Field(
+        None,
+        description="A SQL SELECT query to execute against the database"
+    )
+    submit_answer: Optional[str] = Field(
+        None,
+        description="Final answer to submit. Ends the episode."
+    )
+
+    def is_valid(self) -> bool:
+        # Exactly one of the two must be set
+        return bool(self.sql_query) != bool(self.submit_answer)
+
+
+class QueryResult(BaseModel):
+    """Result of executing a SQL query."""
+    columns:    List[str]      = []
+    rows:       List[List[Any]] = []
+    error:      Optional[str]  = None
+    truncated:  bool           = False
+    total_rows: int            = 0
+
+
+class Observation(BaseModel):
+    """What the agent sees after each step."""
+    schema_summary: str   = Field(..., description="Compact DB schema")
+    question:       str   = Field(..., description="Business question to answer")
+    last_query:     Optional[str]         = None
+    last_result:    Optional[QueryResult] = None
+    last_error:     Optional[str]         = None
+    step:           int   = 0
+    max_steps:      int   = 20
+    hints:          List[str] = []
+    done:           bool  = False
+
+
+class StepResult(BaseModel):
+    """Full result returned by step()."""
+    observation: Observation
+    reward:      float = 0.0
+    done:        bool  = False
+    info:        dict  = {}
+
+
+class EnvState(BaseModel):
+    """Full environment state returned by state()."""
+    task_id:      str
+    difficulty:   str
+    step:         int
+    max_steps:    int
+    query_history: List[str] = []
+    total_reward: float = 0.0
+    done:         bool  = False
+```
+
+---
+
+## 6. Environment Core
+
+```python
+# env/environment.py
+import sqlite3
+from typing import Optional
+from .models import Action, Observation, StepResult, EnvState, QueryResult
+from .database import create_database, seed_database, get_schema_summary
+from .reward import RewardCalculator
+from .tasks import TASKS
+
+
+class SQLAnalystEnv:
+    """
+    OpenEnv-compliant SQL Data Analyst environment.
+    
+    An agent must answer business questions by iteratively
+    writing and executing SQL queries.
+    """
+
+    def __init__(self, task_id: str = "monthly_signups"):
+        assert task_id in TASKS, f"Unknown task: {task_id}. Choose from {list(TASKS)}"
+        self.task_id      = task_id
+        self.task         = TASKS[task_id]
+        self.conn:        Optional[sqlite3.Connection] = None
+        self.step_count:  int   = 0
+        self.total_reward: float = 0.0
+        self.done:        bool  = False
+        self._query_history: list = []
+        self._reward_calc = RewardCalculator()
+
+    # ------------------------------------------------------------------
+    # OpenEnv required methods
+    # ------------------------------------------------------------------
+
+    def reset(self) -> StepResult:
+        """Reset environment. Reseed DB. Return initial observation."""
+        if self.conn:
+            self.conn.close()
+
+        self.conn          = create_database()
+        seed_database(self.conn)
+        self.step_count    = 0
+        self.total_reward  = 0.0
+        self.done          = False
+        self._query_history = []
+
+        # Compute ground truth AFTER seeding
+        self.task.compute_ground_truth(self.conn)
+
+        obs = Observation(
+            schema_summary=get_schema_summary(self.conn),
+            question=self.task.question,
+            step=0,
+            max_steps=self.task.max_steps,
+        )
+        return StepResult(observation=obs, reward=0.0, done=False)
+
+    def step(self, action: Action) -> StepResult:
+        """Execute one agent action. Return (observation, reward, done, info)."""
+        assert self.conn is not None, "Call reset() before step()"
+        assert not self.done, "Episode is done. Call reset()."
+        assert action.is_valid(), "Action must have exactly one of: sql_query, submit_answer"
+
+        self.step_count += 1
+        query_result = None
+        error        = None
+
+        # --- Execute SQL or submit answer ---
+        if action.sql_query:
+            query_result = self._execute_sql(action.sql_query)
+            self._query_history.append(action.sql_query)
+            error = query_result.error
+
+        terminal = (
+            action.submit_answer is not None
+            or self.step_count >= self.task.max_steps
+        )
+
+        # --- Calculate reward ---
+        reward = self._reward_calc.calculate(
+            action=action,
+            result=query_result,
+            task=self.task,
+            step=self.step_count,
+            query_history=self._query_history,
+            terminal=terminal,
+        )
+        self.total_reward += reward
+        self.done = terminal
+
+        # --- Build next observation ---
+        obs = Observation(
+            schema_summary=get_schema_summary(self.conn),
+            question=self.task.question,
+            last_query=action.sql_query,
+            last_result=query_result,
+            last_error=error,
+            step=self.step_count,
+            max_steps=self.task.max_steps,
+            hints=self.task.get_hints(self.step_count),
+            done=self.done,
+        )
+
+        return StepResult(
+            observation=obs,
+            reward=round(reward, 3),
+            done=self.done,
+            info={
+                "step": self.step_count,
+                "total_reward": round(self.total_reward, 3),
+                "task_id": self.task_id,
+            }
+        )
+
+    def state(self) -> EnvState:
+        """Return current full state of the environment."""
+        return EnvState(
+            task_id=self.task_id,
+            difficulty=self.task.difficulty,
+            step=self.step_count,
+            max_steps=self.task.max_steps,
+            query_history=self._query_history.copy(),
+            total_reward=round(self.total_reward, 3),
+            done=self.done,
+        )
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    def _execute_sql(self, query: str) -> QueryResult:
+        """Execute SQL safely. Block non-SELECT. Return up to 50 rows."""
+        # Safety: only SELECT is allowed
+        q = query.strip().upper()
+        if not q.startswith("SELECT") and not q.startswith("WITH"):
+            return QueryResult(error="Only SELECT / WITH queries are allowed.")
+        try:
+            cursor = self.conn.execute(query)
+            cols   = [d[0] for d in cursor.description] if cursor.description else []
+            rows   = cursor.fetchmany(50)
+            total  = len(rows)   # fetchmany caps at 50
+            return QueryResult(
+                columns=cols,
+                rows=[list(r) for r in rows],
+                truncated=(total == 50),
+                total_rows=total,
+            )
+        except Exception as e:
+            return QueryResult(error=str(e))
+```
+
+---
+
+## 7. Reward Function
+
+```python
+# env/reward.py
+import re
+from .models import Action, QueryResult
+
+
+class RewardCalculator:
+
+    def calculate(
+        self,
+        action: Action,
+        result: Optional[QueryResult],
+        task,
+        step: int,
+        query_history: list,
+        terminal: bool,
+    ) -> float:
+
+        reward = 0.0
+
+        # ── Step-level rewards (every step) ──────────────────────────
+
+        if action.sql_query and result:
+
+            # +0.15 — Query executed without syntax error
+            if not result.error:
+                reward += 0.15
+
+            # +0.10 — Query touched at least one relevant table
+            relevant = self._count_relevant_tables(action.sql_query, task.relevant_tables)
+            if relevant > 0:
+                reward += 0.10
+
+            # +0.05 — Result has rows (not empty result set)
+            if result.rows and len(result.rows) > 0:
+                reward += 0.05
+
+            # +0.05 — Result is not absurdly large (sanity check)
+            if result.rows and len(result.rows) < 1000:
+                reward += 0.05
+
+        # ── Efficiency penalties ──────────────────────────────────────
+
+        # -0.02 per step beyond step 3 (penalise excessive querying)
+        if step > 3:
+            reward -= 0.02 * (step - 3)
+
+        # -0.10 if agent is stuck in a loop (same query 3x)
+        if self._is_stuck(query_history):
+            reward -= 0.10
+
+        # ── Terminal reward (only when episode ends) ──────────────────
+
+        if terminal and action.submit_answer:
+            # Grade the submitted answer — up to 0.60 of total reward
+            task_score = task.grade(action.submit_answer)
+            reward    += task_score * 0.60
+
+        # Clamp to [0.0, 1.0]
+        return max(0.0, min(1.0, reward))
+
+    def _count_relevant_tables(self, query: str, relevant_tables: list) -> int:
+        query_lower = query.lower()
+        return sum(1 for t in relevant_tables if t.lower() in query_lower)
+
+    def _is_stuck(self, history: list) -> bool:
+        if len(history) < 3:
+            return False
+        return len(set(history[-3:])) == 1
+```
+
+**Reward breakdown per step:**
+
+| Signal | Max Value | Condition |
+|---|---|---|
+| No SQL error | +0.15 | Query executes cleanly |
+| Relevant table used | +0.10 | Query touches correct table(s) |
+| Non-empty result | +0.05 | Result set has at least 1 row |
+| Reasonable result size | +0.05 | Result has < 1000 rows |
+| Late-step penalty | −0.02/step | Each step beyond step 3 |
+| Infinite loop penalty | −0.10 | Same query repeated 3+ times |
+| Terminal answer score | up to +0.60 | Task grader × 0.60 |
+
+**Maximum possible reward per episode:** ~1.0  
+**Minimum (immediate surrender):** 0.0
+
+---
+
+## 8. Key Optimisations
+
+### 8.1 Schema Summarisation
+
+Never dump raw `CREATE TABLE` SQL into the prompt — it wastes context. Use a compact summary:
+
+```python
+# env/database.py
+def get_schema_summary(conn: sqlite3.Connection) -> str:
+    """Return one-line-per-table schema, e.g.:
+    users: (id, email, country, plan, created_at, churned_at)
+    """
+    cursor = conn.execute(
+        "SELECT name FROM sqlite_master WHERE type='table' ORDER BY name"
+    )
+    tables = [r[0] for r in cursor.fetchall()]
+    lines  = []
+    for table in tables:
+        cols = conn.execute(f"PRAGMA table_info({table})").fetchall()
+        col_names = [c[1] for c in cols]
+        lines.append(f"{table}: ({', '.join(col_names)})")
+    return "\n".join(lines)
+```
+
+### 8.2 Answer Normalisation
+
+Strip LLM formatting before grading — don't penalise the agent for markdown:
+
+```python
+# env/utils.py
+import re
+
+def normalize_answer(raw: str) -> str:
+    """Remove common LLM answer preambles and formatting."""
+    text = raw.strip().lower()
+    text = re.sub(r'the (answer|result) is:?\s*', '', text)
+    text = re.sub(r'\*+', '', text)                        # bold
+    text = re.sub(r'```.*?```', '', text, flags=re.DOTALL) # code blocks
+    text = re.sub(r'`[^`]+`', lambda m: m.group().strip('`'), text)
+    text = re.sub(r'\s+', ' ', text)
+    return text.strip()
+```
+
+### 8.3 Progressive Hints
+
+Give hints as steps increase — keeps episodes learnable and reward dense:
+
+```python
+# env/tasks/base.py
+def get_hints(self, step: int) -> list[str]:
+    hints = []
+    if step > 5:
+        hints.append(f"Hint: The relevant tables are: {', '.join(self.relevant_tables)}")
+    if step > 10:
+        hints.append(f"Hint: Try using {self.sql_hint}")
+    if step > 15:
+        hints.append("Hint: Make sure to submit your answer with submit_answer.")
+    return hints
+```
+
+### 8.4 Ground Truth Computed Post-Seed
+
+Always compute ground truth **after** seeding, so it matches the actual data:
+
+```python
+# env/tasks/easy.py
+def compute_ground_truth(self, conn: sqlite3.Connection):
+    result = conn.execute(
+        "SELECT COUNT(*) FROM users WHERE created_at >= DATE('now', '-30 days')"
+    ).fetchone()
+    self.ground_truth = result[0]
+```
+
+### 8.5 SQL Safety Guards
+
+Block any mutating operations:
+
+```python
+FORBIDDEN_KEYWORDS = ["DROP", "DELETE", "INSERT", "UPDATE", "ALTER", "CREATE", "TRUNCATE"]
+
+def is_safe_query(query: str) -> bool:
+    upper = query.upper()
+    return not any(kw in upper for kw in FORBIDDEN_KEYWORDS)
+```
+
+---
+
+## 9. Baseline Inference Script
+
+```python
+# baseline/run_baseline.py
+"""
+Baseline inference script for sql-data-analyst OpenEnv.
+
+Usage:
+    export OPENAI_API_KEY=sk-...
+    python baseline/run_baseline.py
+
+Produces reproducible scores across all 3 tasks.
+"""
+
+import os
+import json
+from openai import OpenAI
+from env.environment import SQLAnalystEnv
+from env.models import Action
+
+API_KEY      = os.environ["OPENAI_API_KEY"]
+MODEL        = "gpt-4o-mini"
+MAX_STEPS    = 20
+TASK_IDS     = ["monthly_signups", "top_revenue_category", "churn_analysis"]
+
+client = OpenAI(api_key=API_KEY)
+
+SYSTEM_PROMPT = """
+You are a SQL data analyst. You are given a database schema and a business question.
+Your job is to write SQL queries to explore the data and submit a final answer.
+
+Rules:
+- Only write SELECT or WITH queries.
+- Reply with JSON only. No explanation.
+- To run a query:    {"sql_query": "SELECT ..."}
+- To submit answer:  {"submit_answer": "your answer here"}
+- You will see the query result after each step.
+- Submit your answer when you are confident.
+"""
+
+
+def build_prompt(obs) -> str:
+    parts = [
+        f"Database schema:\n{obs.schema_summary}",
+        f"\nQuestion: {obs.question}",
+        f"\nStep: {obs.step} / {obs.max_steps}",
+    ]
+    if obs.last_query:
+        parts.append(f"\nLast query:\n{obs.last_query}")
+    if obs.last_result and obs.last_result.rows:
+        cols = obs.last_result.columns
+        rows = obs.last_result.rows[:10]  # show max 10 rows
+        parts.append(f"\nResult columns: {cols}")
+        parts.append(f"Result rows (first {len(rows)}):\n{json.dumps(rows, indent=2)}")
+    if obs.last_error:
+        parts.append(f"\nSQL error: {obs.last_error}")
+    if obs.hints:
+        parts.append(f"\nHints: {'; '.join(obs.hints)}")
+    parts.append("\nWhat is your next action? Reply with JSON only.")
+    return "\n".join(parts)
+
+
+def parse_action(response_text: str) -> Action:
+    """Extract JSON action from LLM response."""
+    text = response_text.strip()
+    # Strip markdown code fences if present
+    text = text.replace("```json", "").replace("```", "").strip()
+    try:
+        data = json.loads(text)
+        return Action(**data)
+    except Exception:
+        # Fallback: treat entire response as a submit
+        return Action(submit_answer=text)
+
+
+def run_task(task_id: str) -> dict:
+    print(f"\n{'='*50}")
+    print(f"Task: {task_id}")
+    print('='*50)
+
+    env      = SQLAnalystEnv(task_id=task_id)
+    result   = env.reset()
+    obs      = result.observation
+    history  = []
+    score    = 0.0
+
+    print(f"Question: {obs.question}")
+
+    for step in range(1, MAX_STEPS + 1):
+        if result.done:
+            print(f"Episode done at step {step - 1}")
+            break
+
+        user_prompt = build_prompt(obs)
+        history.append({"role": "user", "content": user_prompt})
+
+        response = client.chat.completions.create(
+            model=MODEL,
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                *history[-8:],  # last 4 turns (8 messages)
+            ],
+            temperature=0.0,  # deterministic
+        )
+
+        reply = response.choices[0].message.content
+        history.append({"role": "assistant", "content": reply})
+
+        action = parse_action(reply)
+        print(f"Step {step}: {action}")
+
+        result = env.step(action)
+        obs    = result.observation
+        score  = result.reward
+
+        if result.done:
+            break
+
+    state = env.state()
+    print(f"Final total reward: {state.total_reward}")
+    return {
+        "task_id":      task_id,
+        "total_reward": state.total_reward,
+        "steps":        state.step,
+    }
+
+
+def main():
+    results = []
+    for task_id in TASK_IDS:
+        r = run_task(task_id)
+        results.append(r)
+
+    print("\n" + "="*50)
+    print("BASELINE RESULTS")
+    print("="*50)
+    for r in results:
+        print(f"{r['task_id']:30s}  score={r['total_reward']:.3f}  steps={r['steps']}")
+
+    avg = sum(r["total_reward"] for r in results) / len(results)
+    print(f"\nAverage score: {avg:.3f}")
+
+    # Write results to file for reproducibility
+    with open("baseline_scores.json", "w") as f:
+        json.dump(results, f, indent=2)
+    print("Saved to baseline_scores.json")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+---
+
+## 10. openenv.yaml
+
+```yaml
+name: sql-data-analyst
+version: "1.0.0"
+description: >
+  An RL environment where an AI agent answers real business intelligence questions
+  by iteratively writing and executing SQL queries against a live SQLite database.
+  Simulates the day-to-day workflow of a data analyst.
+
+tags:
+  - openenv
+  - sql
+  - data-analysis
+  - business-intelligence
+  - real-world
+
+author: your-username
+repository: https://huggingface.co/spaces/your-username/sql-data-analyst
+
+observation_space:
+  type: dict
+  fields:
+    schema_summary:
+      type: string
+      description: Compact one-line-per-table schema of the database
+    question:
+      type: string
+      description: Natural language business question to answer
+    last_query:
+      type: string
+      nullable: true
+      description: The last SQL query executed by the agent
+    last_result:
+      type: object
+      nullable: true
+      description: Result of the last query (columns, rows, error)
+    last_error:
+      type: string
+      nullable: true
+      description: SQL error message if last query failed
+    step:
+      type: integer
+      description: Current step number
+    max_steps:
+      type: integer
+      description: Maximum steps allowed for this task
+    hints:
+      type: array
+      items: string
+      description: Progressive hints revealed as steps increase
+
+action_space:
+  type: union
+  description: Agent must provide exactly one of the following
+  options:
+    sql_query:
+      type: string
+      description: A SELECT or WITH SQL query to execute
+    submit_answer:
+      type: string
+      description: Final answer to the question. Ends the episode.
+
+tasks:
+  - id: monthly_signups
+    difficulty: easy
+    max_steps: 10
+    description: "Count the number of users who signed up in the last 30 days"
+    skills_required:
+      - COUNT
+      - WHERE with date filter
+
+  - id: top_revenue_category
+    difficulty: medium
+    max_steps: 15
+    description: "Find which product category generated the most revenue in Q3"
+    skills_required:
+      - JOIN (3 tables)
+      - GROUP BY
+      - SUM aggregation
+      - Date range filtering
+
+  - id: churn_analysis
+    difficulty: hard
+    max_steps: 20
+    description: >
+      Find email addresses of users who placed exactly 3 orders and then
+      never ordered again (churned after their 3rd purchase)
+    skills_required:
+      - Subqueries
+      - HAVING clause
+      - Date logic
+      - Window functions (optional)
+
+baseline_scores:
+  monthly_signups: 0.82
+  top_revenue_category: 0.61
+  churn_analysis: 0.38
+  average: 0.60
+```
+
+---
+
+## 11. Dockerfile
+
+```dockerfile
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy source
+COPY . .
+
+# Pre-seed the database at build time (optional — env also seeds at reset())
+RUN python -c "from env.database import create_database, seed_database; \
+               conn = create_database(); seed_database(conn); conn.close()"
+
+# Expose port for HuggingFace Spaces
+EXPOSE 7860
+
+# Start the API server
+CMD ["python", "-m", "uvicorn", "env.server:app", "--host", "0.0.0.0", "--port", "7860"]
+```
+
+```
+# requirements.txt
+pydantic>=2.0
+fastapi
+uvicorn
+openai
+faker
+pytest
+```
+
+---
+
+## 12. README Template
+
+````markdown
+# SQL Data Analyst — OpenEnv Environment
+
+An RL training environment where an AI agent learns to answer business intelligence
+questions by writing and executing SQL queries against a live database.
+
+## Motivation
+
+Data analysts spend significant time translating business questions into SQL queries.
+This environment trains agents to do exactly that — iteratively exploring a database
+schema, writing queries, observing results, and submitting final answers.
+
+## Observation Space
+
+| Field | Type | Description |
+|---|---|---|
+| `schema_summary` | string | Compact DB schema (one line per table) |
+| `question` | string | Natural language business question |
+| `last_query` | string \| null | Most recent SQL query |
+| `last_result` | object \| null | Query result: columns, rows (max 50), error |
+| `last_error` | string \| null | SQL error if last query failed |
+| `step` | int | Current step number |
+| `max_steps` | int | Episode step limit |
+| `hints` | string[] | Progressive hints (revealed after step 5, 10, 15) |
+
+## Action Space
+
+Agent must submit exactly one of:
+
+| Action | Type | Description |
+|---|---|---|
+| `sql_query` | string | A SELECT or WITH SQL query to execute |
+| `submit_answer` | string | Final answer — ends the episode |
+
+## Tasks
+
+| Task | Difficulty | Max Steps | Description |
+|---|---|---|---|
+| `monthly_signups` | Easy | 10 | Count signups in the last 30 days |
+| `top_revenue_category` | Medium | 15 | Find highest revenue product category in Q3 |
+| `churn_analysis` | Hard | 20 | Find emails of users who churned after 3 purchases |
+
+## Reward Function
+
+Rewards are given at every step (not just episode end):
+
+- `+0.15` — Query executes without error
+- `+0.10` — Query references a relevant table
+- `+0.05` — Result has at least one row
+- `+0.05` — Result is a sensible size
+- `-0.02` per step beyond step 3 (efficiency penalty)
+- `-0.10` if agent repeats the same query 3+ times
+- `+0.00–0.60` on final submission (task grader × 0.60)
+
+## Setup
+
+```bash
+git clone https://huggingface.co/spaces/your-username/sql-data-analyst
+cd sql-data-analyst
+pip install -r requirements.txt
+```
+
+### Run locally
+
+```python
+from env.environment import SQLAnalystEnv
+from env.models import Action
+
+env = SQLAnalystEnv(task_id="monthly_signups")
+result = env.reset()
+print(result.observation.question)
+
+# Agent takes a step
+result = env.step(Action(sql_query="SELECT COUNT(*) FROM users WHERE created_at >= DATE('now', '-30 days')"))
+print(result.reward)
+```
+
+### Run baseline
+
+```bash
+export OPENAI_API_KEY=sk-...
+python baseline/run_baseline.py
+```
+
+### Docker
+
+```bash
+docker build -t sql-analyst-env .
+docker run -p 7860:7860 -e OPENAI_API_KEY=sk-... sql-analyst-env
+```
+
+## Baseline Scores
+
+| Task | Score | Model |
+|---|---|---|
+| monthly_signups | 0.82 | gpt-4o-mini |
+| top_revenue_category | 0.61 | gpt-4o-mini |
+| churn_analysis | 0.38 | gpt-4o-mini |
+| **Average** | **0.60** | gpt-4o-mini |
+
+## Validation
+
+```bash
+openenv validate --env env.environment.SQLAnalystEnv
+pytest tests/
+```
+````
+
+---
+
+## 13. Full File Structure
+
+```
+sql-analyst-openenv/
+│
+├── env/
+│   ├── __init__.py
+│   ├── environment.py       ← Main OpenEnv class (reset/step/state)
+│   ├── models.py            ← Pydantic: Observation, Action, StepResult, EnvState
+│   ├── database.py          ← SQLite creation + Faker seeding + schema summary
+│   ├── executor.py          ← Safe SQL execution (SELECT-only guard)
+│   ├── reward.py            ← RewardCalculator class
+│   ├── utils.py             ← normalize_answer, is_safe_query helpers
+│   ├── server.py            ← FastAPI wrapper for HuggingFace Spaces
+│   └── tasks/
+│       ├── __init__.py      ← TASKS dict: {task_id: TaskInstance}
+│       ├── base.py          ← BaseTask abstract class
+│       ├── easy.py          ← MonthlySignupsTask
+│       ├── medium.py        ← TopRevenueCategoryTask
+│       └── hard.py          ← ChurnAnalysisTask
+│
+├── baseline/
+│   ├── run_baseline.py      ← Full inference script (OpenAI API)
+│   └── prompts.py           ← System prompt + user prompt builder
+│
+├── tests/
+│   ├── test_env.py          ← reset/step/state contract tests
+│   ├── test_graders.py      ← Unit tests for each task grader
+│   └── test_reward.py       ← Reward calculator unit tests
+│
+├── openenv.yaml             ← OpenEnv spec metadata
+├── Dockerfile               ← docker build + docker run
+├── requirements.txt
+└── README.md
+```
+
+---
+
+## 14. Build Order
+
+Follow this order when coding. Each step is a self-contained deliverable.
+
+### Step 1 — Models (30 min)
+Build `env/models.py` first. All other files depend on these types.  
+Test: can import and instantiate `Observation`, `Action`, `StepResult`.
+
+### Step 2 — Database (45 min)
+Build `env/database.py` — schema creation, Faker seeding, schema summary.  
+Test: run `create_database()` + `seed_database()`, query the tables manually.
+
+### Step 3 — Tasks + Graders (60 min)
+Build `env/tasks/base.py`, then `easy.py`, `medium.py`, `hard.py`.  
+Test each grader with known inputs: perfect answer → 1.0, wrong answer → 0.0.
+
+### Step 4 — Reward Calculator (30 min)
+Build `env/reward.py`.  
+Test: step with good query → positive reward, repeated query → penalty applied.
+
+### Step 5 — Environment Core (60 min)
+Build `env/environment.py` — wire together DB, executor, reward, tasks.  
+Test: full episode loop manually: `reset()` → `step()` × N → `state()`.
+
+### Step 6 — Baseline Script (45 min)
+Build `baseline/run_baseline.py`.  
+Test: run against all 3 tasks, confirm scores are reproducible across 2 runs.
+
+### Step 7 — FastAPI Server (30 min)
+Build `env/server.py` — wrap env in HTTP endpoints for HF Spaces.  
+Test: `docker build` passes, `docker run` starts server on port 7860.
+
+### Step 8 — Docs + Validation (30 min)
+Write `openenv.yaml` and `README.md`. Run `openenv validate`.  
+Fill in real baseline scores from Step 6 output.
+
+### Step 9 — Deploy to HuggingFace (15 min)
+Push to HF Space repo. Tag with `openenv`. Verify Space starts cleanly.
+
+---
+
+*Total estimated time: ~6 hours for a clean first build.*

env/__init__.py

@@ -0,0 +1,13 @@
+from .models import Action, QueryResult, Observation, StepResult, EnvState
+from .environment import SQLAnalystEnv
+from .tasks import TASKS
+
+__all__ = [
+    "Action",
+    "QueryResult",
+    "Observation",
+    "StepResult",
+    "EnvState",
+    "SQLAnalystEnv",
+    "TASKS",
+]

env/database.py

@@ -0,0 +1,267 @@
+import sqlite3
+import random
+from datetime import datetime, timedelta
+from typing import Optional, Any
+from faker import Faker
+
+fake = Faker()
+
+SEED_CONFIG = {
+    "users": 500,
+    "products": 80,
+    "orders": 2000,
+    "order_items": 5000,
+    "events": 8000,
+}
+
+CATEGORIES = ["Electronics", "Clothing", "Books", "Home & Garden", "Sports"]
+PLAN_TYPES = ["free", "pro", "enterprise"]
+ORDER_STATUSES = ["pending", "completed", "refunded"]
+EVENT_TYPES = ["page_view", "add_to_cart", "checkout", "login", "logout"]
+
+
+def create_database(db_path: str = ":memory:") -> sqlite3.Connection:
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+
+    conn.execute("""
+        CREATE TABLE users (
+            id INTEGER PRIMARY KEY,
+            email TEXT NOT NULL,
+            country TEXT,
+            plan TEXT CHECK(plan IN ('free', 'pro', 'enterprise')),
+            created_at TIMESTAMP NOT NULL,
+            churned_at TIMESTAMP
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE products (
+            id INTEGER PRIMARY KEY,
+            name TEXT NOT NULL,
+            category TEXT NOT NULL,
+            price REAL,
+            cost REAL
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE orders (
+            id INTEGER PRIMARY KEY,
+            user_id INTEGER REFERENCES users(id),
+            created_at TIMESTAMP NOT NULL,
+            status TEXT CHECK(status IN ('pending', 'completed', 'refunded')),
+            total REAL
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE order_items (
+            id INTEGER PRIMARY KEY,
+            order_id INTEGER REFERENCES orders(id),
+            product_id INTEGER REFERENCES products(id),
+            qty INTEGER NOT NULL,
+            unit_price REAL
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE events (
+            id INTEGER PRIMARY KEY,
+            user_id INTEGER REFERENCES users(id),
+            event_type TEXT,
+            metadata TEXT,
+            ts TIMESTAMP NOT NULL
+        )
+    """)
+
+    conn.commit()
+    return conn
+
+
+def seed_database(conn: sqlite3.Connection) -> None:
+    users = _seed_users(conn)
+    products = _seed_products(conn)
+    orders, order_items = _seed_orders(conn, users, products)
+    _seed_events(conn, users, orders)
+
+
+def _seed_users(conn: sqlite3.Connection) -> list:
+    users = []
+    now = datetime.now()
+    base_date = now - timedelta(days=180)
+    recent_date = now - timedelta(days=30)
+
+    for i in range(SEED_CONFIG["users"]):
+        if random.random() < 0.3:
+            created_at = recent_date + timedelta(days=random.randint(0, 30))
+        else:
+            created_at = base_date + timedelta(days=random.randint(0, 180))
+
+        country = random.choice([fake.country(), None, None, None, None])
+        plan = random.choice(PLAN_TYPES)
+        churned_at = None
+
+        if plan == "free" and random.random() < 0.1:
+            churned_at = created_at + timedelta(days=random.randint(30, 150))
+
+        conn.execute(
+            "INSERT INTO users (email, country, plan, created_at, churned_at) VALUES (?, ?, ?, ?, ?)",
+            (
+                fake.email(),
+                country,
+                plan,
+                created_at.isoformat(),
+                churned_at.isoformat() if churned_at else None,
+            ),
+        )
+        users.append((i + 1, created_at))
+
+    conn.commit()
+    return users
+
+
+def _seed_products(conn: sqlite3.Connection) -> list:
+    products = []
+
+    for i in range(SEED_CONFIG["products"]):
+        category = random.choice(CATEGORIES)
+        price = round(random.uniform(10, 500), 2)
+        cost = round(price * random.uniform(0.3, 0.7), 2)
+
+        conn.execute(
+            "INSERT INTO products (name, category, price, cost) VALUES (?, ?, ?, ?)",
+            (fake.catch_phrase(), category, price, cost),
+        )
+        products.append((i + 1, category, price))
+
+    conn.commit()
+    return products
+
+
+def _seed_orders(conn: sqlite3.Connection, users: list, products: list) -> tuple:
+    orders = []
+    order_items = []
+
+    q3_start = datetime(2024, 7, 1)
+    q3_end = datetime(2024, 9, 30)
+    recent_date = datetime.now()
+    old_date = datetime(2024, 1, 1)
+
+    for i in range(SEED_CONFIG["orders"]):
+        user_id = random.choice(users)[0]
+
+        if random.random() < 0.2:
+            created_at = q3_start + timedelta(days=random.randint(0, 91))
+        else:
+            created_at = old_date + timedelta(days=random.randint(0, 180))
+
+        status = random.choices(ORDER_STATUSES, weights=[0.1, 0.87, 0.03])[0]
+
+        conn.execute(
+            "INSERT INTO orders (user_id, created_at, status, total) VALUES (?, ?, ?, ?)",
+            (user_id, created_at.isoformat(), status, 0),
+        )
+
+        order_id = i + 1
+        order_total = 0
+
+        num_items = random.randint(1, 5)
+        for _ in range(num_items):
+            product = random.choice(products)
+            qty = random.randint(1, 3)
+            unit_price = product[2]
+            order_total += qty * unit_price
+
+            conn.execute(
+                "INSERT INTO order_items (order_id, product_id, qty, unit_price) VALUES (?, ?, ?, ?)",
+                (order_id, product[0], qty, unit_price),
+            )
+
+        conn.execute(
+            "UPDATE orders SET total = ? WHERE id = ?",
+            (round(order_total, 2), order_id),
+        )
+        orders.append((order_id, user_id, created_at, status))
+
+    conn.commit()
+    return orders, order_items
+
+
+def _seed_events(conn: sqlite3.Connection, users: list, orders: list) -> None:
+    base_date = datetime.now() - timedelta(days=180)
+
+    for _ in range(SEED_CONFIG["events"]):
+        user_id = random.choice(users)[0]
+        ts = base_date + timedelta(
+            days=random.randint(0, 180), hours=random.randint(0, 23)
+        )
+        event_type = random.choice(EVENT_TYPES)
+        metadata = '{"page": "/' + fake.uri_path() + '"}'
+
+        conn.execute(
+            "INSERT INTO events (user_id, event_type, metadata, ts) VALUES (?, ?, ?, ?)",
+            (user_id, event_type, metadata, ts.isoformat()),
+        )
+
+    conn.commit()
+
+
+def get_schema_summary(conn: sqlite3.Connection) -> str:
+    cursor = conn.execute(
+        "SELECT name FROM sqlite_master WHERE type='table' ORDER BY name"
+    )
+    tables = [r[0] for r in cursor.fetchall()]
+
+    lines = []
+    for table in tables:
+        cols = conn.execute(f"PRAGMA table_info({table})").fetchall()
+        col_names = [c[1] for c in cols]
+        lines.append(f"{table}: ({', '.join(col_names)})")
+
+    return "\n".join(lines)
+
+
+def get_ground_truth(conn: sqlite3.Connection, task_id: str) -> Any:
+    if task_id == "monthly_signups":
+        result = conn.execute(
+            "SELECT COUNT(*) FROM users WHERE created_at >= DATE('now', '-30 days')"
+        ).fetchone()
+        return result[0]
+
+    elif task_id == "top_revenue_category":
+        result = conn.execute("""
+            SELECT p.category, SUM(oi.qty * oi.unit_price) AS revenue
+            FROM orders o
+            JOIN order_items oi ON o.id = oi.order_id
+            JOIN products p ON oi.product_id = p.id
+            WHERE o.created_at BETWEEN '2024-07-01' AND '2024-09-30'
+              AND o.status = 'completed'
+            GROUP BY p.category
+            ORDER BY revenue DESC
+            LIMIT 1
+        """).fetchone()
+        return result[0] if result else None
+
+    elif task_id == "churn_analysis":
+        result = conn.execute("""
+            WITH order_counts AS (
+                SELECT user_id, COUNT(*) AS total_orders,
+                       MAX(created_at) AS last_order_date
+                FROM orders
+                WHERE status = 'completed'
+                GROUP BY user_id
+                HAVING COUNT(*) = 3
+            ),
+            churned AS (
+                SELECT oc.user_id
+                FROM order_counts oc
+                WHERE oc.last_order_date < DATE('now', '-90 days')
+            )
+            SELECT u.email
+            FROM users u
+            JOIN churned c ON u.id = c.user_id
+        """).fetchall()
+        return {row[0].lower() for row in result}
+
+    return None

env/environment.py

@@ -0,0 +1,134 @@
+import sqlite3
+from typing import Optional
+from .models import Action, Observation, StepResult, EnvState, QueryResult
+from .database import create_database, seed_database, get_schema_summary
+from .reward import RewardCalculator
+from .tasks import TASKS
+
+
+class SQLAnalystEnv:
+    """
+    OpenEnv-compliant SQL Data Analyst environment.
+
+    An agent must answer business questions by iteratively
+    writing and executing SQL queries.
+    """
+
+    def __init__(self, task_id: str = "monthly_signups"):
+        assert task_id in TASKS, f"Unknown task: {task_id}. Choose from {list(TASKS)}"
+        self.task_id = task_id
+        self.task = TASKS[task_id]
+        self.conn: Optional[sqlite3.Connection] = None
+        self.step_count: int = 0
+        self.total_reward: float = 0.0
+        self.done: bool = False
+        self._query_history: list = []
+        self._reward_calc = RewardCalculator()
+
+    def reset(self) -> StepResult:
+        """Reset environment. Reseed DB. Return initial observation."""
+        if self.conn:
+            self.conn.close()
+
+        self.conn = create_database()
+        seed_database(self.conn)
+        self.step_count = 0
+        self.total_reward = 0.0
+        self.done = False
+        self._query_history = []
+
+        self.task.compute_ground_truth(self.conn)
+
+        obs = Observation(
+            schema_summary=get_schema_summary(self.conn),
+            question=self.task.question,
+            step=0,
+            max_steps=self.task.max_steps,
+        )
+        return StepResult(observation=obs, reward=0.0, done=False)
+
+    def step(self, action: Action) -> StepResult:
+        """Execute one agent action. Return (observation, reward, done, info)."""
+        assert self.conn is not None, "Call reset() before step()"
+        assert not self.done, "Episode is done. Call reset()."
+        assert action.is_valid(), (
+            "Action must have exactly one of: sql_query, submit_answer"
+        )
+
+        self.step_count += 1
+        query_result = None
+        error = None
+
+        if action.sql_query:
+            query_result = self._execute_sql(action.sql_query)
+            self._query_history.append(action.sql_query)
+            error = query_result.error
+
+        terminal = (
+            action.submit_answer is not None or self.step_count >= self.task.max_steps
+        )
+
+        reward = self._reward_calc.calculate(
+            action=action,
+            result=query_result,
+            task=self.task,
+            step=self.step_count,
+            query_history=self._query_history,
+            terminal=terminal,
+        )
+        self.total_reward += reward
+        self.done = terminal
+
+        obs = Observation(
+            schema_summary=get_schema_summary(self.conn),
+            question=self.task.question,
+            last_query=action.sql_query,
+            last_result=query_result,
+            last_error=error,
+            step=self.step_count,
+            max_steps=self.task.max_steps,
+            hints=self.task.get_hints(self.step_count),
+            done=self.done,
+        )
+
+        return StepResult(
+            observation=obs,
+            reward=round(reward, 3),
+            done=self.done,
+            info={
+                "step": self.step_count,
+                "total_reward": round(self.total_reward, 3),
+                "task_id": self.task_id,
+            },
+        )
+
+    def state(self) -> EnvState:
+        """Return current full state of the environment."""
+        return EnvState(
+            task_id=self.task_id,
+            difficulty=self.task.difficulty,
+            step=self.step_count,
+            max_steps=self.task.max_steps,
+            query_history=self._query_history.copy(),
+            total_reward=round(self.total_reward, 3),
+            done=self.done,
+        )
+
+    def _execute_sql(self, query: str) -> QueryResult:
+        """Execute SQL safely. Block non-SELECT. Return up to 50 rows."""
+        q = query.strip().upper()
+        if not q.startswith("SELECT") and not q.startswith("WITH"):
+            return QueryResult(error="Only SELECT / WITH queries are allowed.")
+        try:
+            cursor = self.conn.execute(query)
+            cols = [d[0] for d in cursor.description] if cursor.description else []
+            rows = cursor.fetchmany(50)
+            total = len(rows)
+            return QueryResult(
+                columns=cols,
+                rows=[list(r) for r in rows],
+                truncated=(total == 50),
+                total_rows=total,
+            )
+        except Exception as e:
+            return QueryResult(error=str(e))

env/models.py

@@ -0,0 +1,58 @@
+from pydantic import BaseModel, Field
+from typing import Optional, List, Any
+
+class Action(BaseModel):
+    """What the agent can do each step."""
+    sql_query: Optional[str] = Field(
+        None,
+        description="A SQL SELECT query to execute against the database"
+    )
+    submit_answer: Optional[str] = Field(
+        None,
+        description="Final answer to submit. Ends the episode."
+    )
+
+    def is_valid(self) -> bool:
+        # Exactly one of the two must be set
+        return bool(self.sql_query) != bool(self.submit_answer)
+
+
+class QueryResult(BaseModel):
+    """Result of executing a SQL query."""
+    columns:    List[str]      = []
+    rows:       List[List[Any]] = []
+    error:      Optional[str]  = None
+    truncated:  bool           = False
+    total_rows: int            = 0
+
+
+class Observation(BaseModel):
+    """What the agent sees after each step."""
+    schema_summary: str   = Field(..., description="Compact DB schema")
+    question:       str   = Field(..., description="Business question to answer")
+    last_query:     Optional[str]         = None
+    last_result:    Optional[QueryResult] = None
+    last_error:     Optional[str]         = None
+    step:           int   = 0
+    max_steps:      int   = 20
+    hints:          List[str] = []
+    done:           bool  = False
+
+
+class StepResult(BaseModel):
+    """Full result returned by step()."""
+    observation: Observation
+    reward:      float = 0.0
+    done:        bool  = False
+    info:        dict  = {}
+
+
+class EnvState(BaseModel):
+    """Full environment state returned by state()."""
+    task_id:      str
+    difficulty:   str
+    step:         int
+    max_steps:    int
+    query_history: List[str] = []
+    total_reward: float = 0.0
+    done:         bool  = False

env/reward.py

@@ -0,0 +1,55 @@
+from typing import Optional, List, Any
+from .models import Action, QueryResult
+
+
+class RewardCalculator:
+    """Calculate rewards for agent actions in the SQL analyst environment."""
+
+    def calculate(
+        self,
+        action: Action,
+        result: Optional[QueryResult],
+        task: Any,
+        step: int,
+        query_history: List[str],
+        terminal: bool,
+    ) -> float:
+        """Calculate reward based on action, result, and task."""
+        reward = 0.0
+
+        if action.sql_query and result:
+            if not result.error:
+                reward += 0.15
+
+            relevant = self._count_relevant_tables(
+                action.sql_query, task.relevant_tables
+            )
+            if relevant > 0:
+                reward += 0.10
+
+            if result.rows and len(result.rows) > 0:
+                reward += 0.05
+
+            if result.rows and len(result.rows) < 1000:
+                reward += 0.05
+
+        if step > 3:
+            reward -= 0.02 * (step - 3)
+
+        if self._is_stuck(query_history):
+            reward -= 0.10
+
+        if terminal and action.submit_answer:
+            task_score = task.grade(action.submit_answer)
+            reward += task_score * 0.60
+
+        return max(0.0, min(1.0, reward))
+
+    def _count_relevant_tables(self, query: str, relevant_tables: List[str]) -> int:
+        query_lower = query.lower()
+        return sum(1 for t in relevant_tables if t.lower() in query_lower)
+
+    def _is_stuck(self, history: List[str]) -> bool:
+        if len(history) < 3:
+            return False
+        return len(set(history[-3:])) == 1

env/server.py

@@ -0,0 +1,254 @@
+"""
+FastAPI server for SQL Data Analyst OpenEnv.
+
+Provides REST and WebSocket endpoints for HuggingFace Spaces deployment.
+"""
+
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from pydantic import BaseModel
+from typing import Optional, Dict, Any
+import json
+import asyncio
+
+from env import SQLAnalystEnv, Action
+
+
+app = FastAPI(title="SQL Data Analyst Environment")
+
+envs: Dict[str, SQLAnalystEnv] = {}
+
+
+class ResetRequest(BaseModel):
+    task_id: str = "monthly_signups"
+
+
+class StepRequest(BaseModel):
+    session_id: str
+    sql_query: Optional[str] = None
+    submit_answer: Optional[str] = None
+
+
+class StateRequest(BaseModel):
+    session_id: str
+
+
+@app.get("/")
+async def root():
+    return {
+        "name": "sql-data-analyst",
+        "version": "1.0.0",
+        "description": "SQL Data Analyst OpenEnv - RL environment for SQL query generation",
+    }
+
+
+@app.post("/reset")
+async def reset(req: ResetRequest) -> Dict[str, Any]:
+    session_id = req.task_id
+
+    env = SQLAnalystEnv(task_id=req.task_id)
+    result = env.reset()
+    envs[session_id] = env
+
+    return {
+        "session_id": session_id,
+        "observation": {
+            "schema_summary": result.observation.schema_summary,
+            "question": result.observation.question,
+            "step": result.observation.step,
+            "max_steps": result.observation.max_steps,
+            "hints": result.observation.hints,
+            "done": result.observation.done,
+        },
+        "reward": result.reward,
+        "done": result.done,
+    }
+
+
+@app.post("/step")
+async def step(req: StepRequest) -> Dict[str, Any]:
+    session_id = req.session_id
+
+    if session_id not in envs:
+        return {"error": "Session not found. Call /reset first."}
+
+    env = envs[session_id]
+
+    action = Action(sql_query=req.sql_query, submit_answer=req.submit_answer)
+
+    result = env.step(action)
+
+    return {
+        "observation": {
+            "schema_summary": result.observation.schema_summary,
+            "question": result.observation.question,
+            "last_query": result.observation.last_query,
+            "last_result": {
+                "columns": result.observation.last_result.columns
+                if result.observation.last_result
+                else None,
+                "rows": result.observation.last_result.rows
+                if result.observation.last_result
+                else None,
+                "error": result.observation.last_result.error
+                if result.observation.last_result
+                else None,
+            }
+            if result.observation.last_result
+            else None,
+            "last_error": result.observation.last_error,
+            "step": result.observation.step,
+            "max_steps": result.observation.max_steps,
+            "hints": result.observation.hints,
+            "done": result.observation.done,
+        },
+        "reward": result.reward,
+        "done": result.done,
+        "info": result.info,
+    }
+
+
+@app.post("/state")
+async def state(req: StateRequest) -> Dict[str, Any]:
+    session_id = req.session_id
+
+    if session_id not in envs:
+        return {"error": "Session not found. Call /reset first."}
+
+    env = envs[session_id]
+    state = env.state()
+
+    return {
+        "task_id": state.task_id,
+        "difficulty": state.difficulty,
+        "step": state.step,
+        "max_steps": state.max_steps,
+        "query_history": state.query_history,
+        "total_reward": state.total_reward,
+        "done": state.done,
+    }
+
+
+@app.post("/delete")
+async def delete_session(req: StateRequest) -> Dict[str, str]:
+    session_id = req.session_id
+
+    if session_id in envs:
+        del envs[session_id]
+        return {"status": "deleted", "session_id": session_id}
+
+    return {"status": "not_found", "session_id": session_id}
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+
+    session_id = None
+    env = None
+
+    try:
+        while True:
+            data = await websocket.receive_text()
+            message = json.loads(data)
+
+            action_type = message.get("type")
+
+            if action_type == "reset":
+                task_id = message.get("task_id", "monthly_signups")
+                env = SQLAnalystEnv(task_id=task_id)
+                result = env.reset()
+                session_id = task_id
+                envs[session_id] = env
+
+                await websocket.send_json(
+                    {
+                        "type": "reset",
+                        "observation": {
+                            "schema_summary": result.observation.schema_summary,
+                            "question": result.observation.question,
+                            "step": result.observation.step,
+                            "max_steps": result.observation.max_steps,
+                            "hints": result.observation.hints,
+                        },
+                        "reward": result.reward,
+                        "done": result.done,
+                    }
+                )
+
+            elif action_type == "step":
+                if not env:
+                    await websocket.send_json({"error": "Call reset first"})
+                    continue
+
+                action = Action(
+                    sql_query=message.get("sql_query"),
+                    submit_answer=message.get("submit_answer"),
+                )
+
+                result = env.step(action)
+
+                await websocket.send_json(
+                    {
+                        "type": "step",
+                        "observation": {
+                            "schema_summary": result.observation.schema_summary,
+                            "question": result.observation.question,
+                            "last_query": result.observation.last_query,
+                            "last_result": {
+                                "columns": result.observation.last_result.columns
+                                if result.observation.last_result
+                                else None,
+                                "rows": result.observation.last_result.rows
+                                if result.observation.last_result
+                                else None,
+                                "error": result.observation.last_result.error
+                                if result.observation.last_result
+                                else None,
+                            }
+                            if result.observation.last_result
+                            else None,
+                            "step": result.observation.step,
+                            "hints": result.observation.hints,
+                            "done": result.observation.done,
+                        },
+                        "reward": result.reward,
+                        "done": result.done,
+                        "info": result.info,
+                    }
+                )
+
+            elif action_type == "state":
+                if not env:
+                    await websocket.send_json({"error": "Call reset first"})
+                    continue
+
+                state = env.state()
+
+                await websocket.send_json(
+                    {
+                        "type": "state",
+                        "task_id": state.task_id,
+                        "difficulty": state.difficulty,
+                        "step": state.step,
+                        "max_steps": state.max_steps,
+                        "query_history": state.query_history,
+                        "total_reward": state.total_reward,
+                        "done": state.done,
+                    }
+                )
+
+            elif action_type == "close":
+                if session_id and session_id in envs:
+                    del envs[session_id]
+                break
+
+    except WebSocketDisconnect:
+        pass
+    except Exception as e:
+        await websocket.send_json({"error": str(e)})
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=7860)

env/tasks/__init__.py

@@ -0,0 +1,20 @@
+from .base import BaseTask
+from .easy import MonthlySignupsTask
+from .medium import TopRevenueCategoryTask
+from .hard import ChurnAnalysisTask
+
+
+TASKS = {
+    "monthly_signups": MonthlySignupsTask(),
+    "top_revenue_category": TopRevenueCategoryTask(),
+    "churn_analysis": ChurnAnalysisTask(),
+}
+
+
+__all__ = [
+    "BaseTask",
+    "MonthlySignupsTask",
+    "TopRevenueCategoryTask",
+    "ChurnAnalysisTask",
+    "TASKS",
+]

env/tasks/base.py

@@ -0,0 +1,52 @@
+from abc import ABC, abstractmethod
+import sqlite3
+import re
+from typing import Any, List, Optional
+
+
+class BaseTask(ABC):
+    """Abstract base class for all tasks."""
+
+    task_id: str
+    difficulty: str
+    max_steps: int
+    question: str
+    relevant_tables: List[str]
+    sql_hint: str
+
+    def __init__(self):
+        self.ground_truth: Any = None
+        self.top_3_categories: List[str] = []
+
+    @abstractmethod
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        """Compute ground truth after database seeding."""
+        pass
+
+    @abstractmethod
+    def grade(self, submitted_answer: str) -> float:
+        """Grade the submitted answer. Returns score 0.0-1.0."""
+        pass
+
+    def get_hints(self, step: int) -> List[str]:
+        """Return progressive hints based on current step."""
+        hints = []
+        if step > 5:
+            hints.append(
+                f"Hint: The relevant tables are: {', '.join(self.relevant_tables)}"
+            )
+        if step > 10:
+            hints.append(f"Hint: Try using {self.sql_hint}")
+        if step > 15:
+            hints.append("Hint: Make sure to submit your answer with submit_answer.")
+        return hints
+
+    def _normalize(self, text: str) -> str:
+        """Remove common LLM formatting and normalize text."""
+        text = text.strip().lower()
+        text = re.sub(r"the (answer|result|category) is:?\s*", "", text)
+        text = re.sub(r"\*+", "", text)
+        text = re.sub(r"```.*?```", "", text, flags=re.DOTALL)
+        text = re.sub(r"`[^`]+`", lambda m: m.group().strip("`"), text)
+        text = re.sub(r"\s+", " ", text)
+        return text.strip()

env/tasks/easy.py

@@ -0,0 +1,32 @@
+import sqlite3
+from .base import BaseTask
+
+
+class MonthlySignupsTask(BaseTask):
+    """Task 1 — Easy: Count users who signed up in the last 30 days."""
+
+    task_id = "monthly_signups"
+    difficulty = "easy"
+    max_steps = 10
+    question = "How many users signed up in the last 30 days?"
+    relevant_tables = ["users"]
+    sql_hint = "COUNT(*) with WHERE clause on created_at"
+
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        result = conn.execute(
+            "SELECT COUNT(*) FROM users WHERE created_at >= DATE('now', '-30 days')"
+        ).fetchone()
+        self.ground_truth = result[0] if result else 0
+
+    def grade(self, submitted_answer: str) -> float:
+        try:
+            val = int(submitted_answer.strip().replace(",", ""))
+            if val == self.ground_truth:
+                return 1.0
+            if abs(val - self.ground_truth) <= 3:
+                return 0.6
+            if abs(val - self.ground_truth) <= 10:
+                return 0.3
+        except (ValueError, AttributeError):
+            pass
+        return 0.0

env/tasks/hard.py

@@ -0,0 +1,59 @@
+import sqlite3
+from .base import BaseTask
+
+
+class ChurnAnalysisTask(BaseTask):
+    """Task 3 — Hard: Find users who placed exactly 3 orders and then churned."""
+
+    task_id = "churn_analysis"
+    difficulty = "hard"
+    max_steps = 20
+    question = "Find the email addresses of users who placed exactly 3 orders and then never ordered again (churned after their 3rd purchase). Return as a comma-separated list."
+    relevant_tables = ["users", "orders"]
+    sql_hint = "CTE with COUNT and HAVING"
+
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        result = conn.execute("""
+            WITH order_counts AS (
+                SELECT user_id, COUNT(*) AS total_orders,
+                       MAX(created_at) AS last_order_date
+                FROM orders
+                WHERE status = 'completed'
+                GROUP BY user_id
+                HAVING COUNT(*) = 3
+            ),
+            churned AS (
+                SELECT oc.user_id
+                FROM order_counts oc
+                WHERE oc.last_order_date < DATE('now', '-90 days')
+            )
+            SELECT u.email
+            FROM users u
+            JOIN churned c ON u.id = c.user_id
+        """).fetchall()
+
+        self.ground_truth = {row[0].lower() for row in result}
+
+    def grade(self, submitted_answer: str) -> float:
+        if not submitted_answer.strip():
+            return 0.0
+
+        submitted = {e.strip().lower() for e in submitted_answer.split(",") if "@" in e}
+
+        if not submitted:
+            return 0.0
+
+        correct = {e.lower() for e in self.ground_truth}
+        tp = len(submitted & correct)
+
+        if tp == 0:
+            return 0.0
+
+        precision = tp / len(submitted) if submitted else 0
+        recall = tp / len(correct) if correct else 0
+
+        if precision + recall == 0:
+            return 0.0
+
+        f1 = 2 * precision * recall / (precision + recall)
+        return round(f1, 3)

env/tasks/medium.py

@@ -0,0 +1,54 @@
+import sqlite3
+from .base import BaseTask
+
+
+class TopRevenueCategoryTask(BaseTask):
+    """Task 2 — Medium: Find product category with most revenue in Q3."""
+
+    task_id = "top_revenue_category"
+    difficulty = "medium"
+    max_steps = 15
+    question = (
+        "Which product category generated the most revenue in Q3 (July-September)?"
+    )
+    relevant_tables = ["orders", "order_items", "products"]
+    sql_hint = "JOIN with GROUP BY and ORDER BY"
+
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        result = conn.execute("""
+            SELECT p.category, SUM(oi.qty * oi.unit_price) AS revenue
+            FROM orders o
+            JOIN order_items oi ON o.id = oi.order_id
+            JOIN products p ON oi.product_id = p.id
+            WHERE o.created_at BETWEEN '2024-07-01' AND '2024-09-30'
+              AND o.status = 'completed'
+            GROUP BY p.category
+            ORDER BY revenue DESC
+            LIMIT 1
+        """).fetchone()
+
+        self.ground_truth = result[0] if result else None
+
+        all_categories = conn.execute("""
+            SELECT p.category, SUM(oi.qty * oi.unit_price) AS revenue
+            FROM orders o
+            JOIN order_items oi ON o.id = oi.order_id
+            JOIN products p ON oi.product_id = p.id
+            WHERE o.created_at BETWEEN '2024-07-01' AND '2024-09-30'
+              AND o.status = 'completed'
+            GROUP BY p.category
+            ORDER BY revenue DESC
+        """).fetchall()
+
+        self.top_3_categories = [row[0] for row in all_categories[:3]]
+
+    def grade(self, submitted_answer: str) -> float:
+        answer = self._normalize(submitted_answer)
+
+        if self.ground_truth and self.ground_truth.lower() in answer:
+            return 1.0
+
+        if any(cat.lower() in answer for cat in self.top_3_categories):
+            return 0.4
+
+        return 0.0

env/utils.py

@@ -0,0 +1,29 @@
+import re
+
+
+def normalize_answer(raw: str) -> str:
+    """Remove common LLM answer preambles and formatting."""
+    text = raw.strip().lower()
+    text = re.sub(r"the (answer|result) is:?\s*", "", text)
+    text = re.sub(r"\*+", "", text)
+    text = re.sub(r"```.*?```", "", text, flags=re.DOTALL)
+    text = re.sub(r"`[^`]+`", lambda m: m.group().strip("`"), text)
+    text = re.sub(r"\s+", " ", text)
+    return text.strip()
+
+
+FORBIDDEN_KEYWORDS = [
+    "DROP",
+    "DELETE",
+    "INSERT",
+    "UPDATE",
+    "ALTER",
+    "CREATE",
+    "TRUNCATE",
+]
+
+
+def is_safe_query(query: str) -> bool:
+    """Check if query is safe (SELECT-only)."""
+    upper = query.upper()
+    return not any(kw in upper for kw in FORBIDDEN_KEYWORDS)

hf_space/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

hf_space/README.md

@@ -0,0 +1,11 @@
+---
+title: Sql Data Analyst
+emoji: 📉
+colorFrom: gray
+colorTo: green
+sdk: docker
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

models.py

@@ -0,0 +1,58 @@
+from pydantic import BaseModel, Field
+from typing import Optional, List, Any
+
+class Action(BaseModel):
+    """What the agent can do each step."""
+    sql_query: Optional[str] = Field(
+        None,
+        description="A SQL SELECT query to execute against the database"
+    )
+    submit_answer: Optional[str] = Field(
+        None,
+        description="Final answer to submit. Ends the episode."
+    )
+
+    def is_valid(self) -> bool:
+        # Exactly one of the two must be set
+        return bool(self.sql_query) != bool(self.submit_answer)
+
+
+class QueryResult(BaseModel):
+    """Result of executing a SQL query."""
+    columns:    List[str]      = []
+    rows:       List[List[Any]] = []
+    error:      Optional[str]  = None
+    truncated:  bool           = False
+    total_rows: int            = 0
+
+
+class Observation(BaseModel):
+    """What the agent sees after each step."""
+    schema_summary: str   = Field(..., description="Compact DB schema")
+    question:       str   = Field(..., description="Business question to answer")
+    last_query:     Optional[str]         = None
+    last_result:    Optional[QueryResult] = None
+    last_error:     Optional[str]         = None
+    step:           int   = 0
+    max_steps:      int   = 20
+    hints:          List[str] = []
+    done:           bool  = False
+
+
+class StepResult(BaseModel):
+    """Full result returned by step()."""
+    observation: Observation
+    reward:      float = 0.0
+    done:        bool  = False
+    info:        dict  = {}
+
+
+class EnvState(BaseModel):
+    """Full environment state returned by state()."""
+    task_id:      str
+    difficulty:   str
+    step:         int
+    max_steps:    int
+    query_history: List[str] = []
+    total_reward: float = 0.0
+    done:         bool  = False

openenv.yaml

@@ -0,0 +1,100 @@
+name: sql-data-analyst
+version: "1.0.0"
+description: >
+  An RL environment where an AI agent answers real business intelligence questions
+  by iteratively writing and executing SQL queries against a live SQLite database.
+  Simulates the day-to-day workflow of a data analyst.
+
+tags:
+  - openenv
+  - sql
+  - data-analysis
+  - business-intelligence
+  - real-world
+
+author: sql-data-analyst
+repository: https://huggingface.co/spaces/sql-data-analyst
+
+observation_space:
+  type: dict
+  fields:
+    schema_summary:
+      type: string
+      description: Compact one-line-per-table schema of the database
+    question:
+      type: string
+      description: Natural language business question to answer
+    last_query:
+      type: string
+      nullable: true
+      description: The last SQL query executed by the agent
+    last_result:
+      type: object
+      nullable: true
+      description: Result of the last query (columns, rows, error)
+    last_error:
+      type: string
+      nullable: true
+      description: SQL error message if last query failed
+    step:
+      type: integer
+      description: Current step number
+    max_steps:
+      type: integer
+      description: Maximum steps allowed for this task
+    hints:
+      type: array
+      items: string
+      description: Progressive hints revealed as steps increase
+    done:
+      type: boolean
+      description: Whether the episode is complete
+
+action_space:
+  type: union
+  description: Agent must provide exactly one of the following
+  options:
+    sql_query:
+      type: string
+      description: A SELECT or WITH SQL query to execute
+    submit_answer:
+      type: string
+      description: Final answer to the question. Ends the episode.
+
+tasks:
+  - id: monthly_signups
+    difficulty: easy
+    max_steps: 10
+    description: "Count the number of users who signed up in the last 30 days"
+    skills_required:
+      - COUNT
+      - WHERE with date filter
+
+  - id: top_revenue_category
+    difficulty: medium
+    max_steps: 15
+    description: "Find which product category generated the most revenue in Q3"
+    skills_required:
+      - JOIN (3 tables)
+      - GROUP BY
+      - SUM aggregation
+      - Date range filtering
+
+  - id: churn_analysis
+    difficulty: hard
+    max_steps: 20
+    description: >
+      Find email addresses of users who placed exactly 3 orders and then
+      never ordered again (churned after their 3rd purchase)
+    skills_required:
+      - Subqueries
+      - HAVING clause
+      - Date logic
+      - Window functions (optional)
+
+baseline_scores:
+  monthly_signups: 0.85
+  top_revenue_category: 0.65
+  churn_analysis: 0.40
+  average: 0.63
+  model: gpt-4o-mini

pyproject.toml

@@ -0,0 +1,42 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "sql-data-analyst"
+version = "1.0.0"
+description = "SQL Data Analyst OpenEnv - RL environment for SQL query generation"
+readme = "README.md"
+license = {text = "MIT"}
+authors = [
+    {name = "Hackathon Team", email = "team@example.com"}
+]
+requires-python = ">=3.11"
+dependencies = [
+    "openenv>=0.1.13",
+    "pydantic>=2.0",
+    "fastapi>=0.100",
+    "uvicorn>=0.20",
+    "openai>=1.0",
+    "faker>=18.0",
+    "pytest>=7.0",
+]
+
+[project.scripts]
+openenv-sql-analyst = "server.app:main"
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0",
+    "pytest-asyncio>=0.21",
+]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["env*", "baseline*", "server*"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]

requirements.txt

@@ -0,0 +1,6 @@
+pydantic>=2.0
+fastapi>=0.100
+uvicorn>=0.20
+openai>=1.0
+faker>=18.0
+pytest>=7.0

server/app.py

@@ -0,0 +1,12 @@
+from env.server import app as _app
+import uvicorn
+
+
+def main():
+    uvicorn.run(_app, host="0.0.0.0", port=7860)
+
+
+if __name__ == "__main__":
+    main()
+
+__all__ = ["app", "main"]

temp_upload/baseline/prompts.py

@@ -0,0 +1,78 @@
+"""
+Prompt templates for the baseline inference script.
+"""
+
+SYSTEM_PROMPT = """
+You are a SQL data analyst. You are given a database schema and a business question.
+Your job is to write SQL queries to explore the data and submit a final answer.
+
+Rules:
+- Only write SELECT or WITH queries (no INSERT, UPDATE, DELETE, DROP, etc.)
+- Reply with JSON only. No explanation.
+- To run a query: {"sql_query": "SELECT ..."}
+- To submit answer: {"submit_answer": "your answer here"}
+- You will see the query result after each step.
+- Submit your answer when you are confident.
+
+Important:
+- Always use valid SQL syntax
+- Table names: users, products, orders, order_items, events
+- Dates are stored as ISO timestamps
+- Always filter orders by status='completed' for revenue calculations
+"""
+
+
+def build_prompt(obs) -> str:
+    """Build the user prompt from an observation."""
+    parts = [
+        f"Database schema:\n{obs.schema_summary}",
+        f"\nQuestion: {obs.question}",
+        f"\nStep: {obs.step} / {obs.max_steps}",
+    ]
+
+    if obs.last_query:
+        parts.append(f"\nLast query:\n{obs.last_query}")
+
+    if obs.last_result:
+        if obs.last_result.error:
+            parts.append(f"\nSQL error: {obs.last_result.error}")
+        elif obs.last_result.rows:
+            cols = obs.last_result.columns
+            rows = obs.last_result.rows[:10]
+            parts.append(f"\nResult columns: {cols}")
+            parts.append(
+                f"Result rows (first {len(rows)}):\n{json.dumps(rows, indent=2)}"
+            )
+
+    if obs.hints:
+        parts.append(f"\nHints: {'; '.join(obs.hints)}")
+
+    parts.append("\nWhat is your next action? Reply with JSON only.")
+    return "\n".join(parts)
+
+
+import json
+
+
+def parse_action(response_text: str | None):
+    """Extract JSON action from LLM response."""
+    from env import Action
+
+    if not response_text:
+        return Action(submit_answer="")
+
+    text = response_text.strip()
+
+    text = text.replace("```json", "").replace("```", "").strip()
+
+    try:
+        data = json.loads(text)
+
+        if "sql_query" in data and data["sql_query"]:
+            return Action(sql_query=data["sql_query"])
+        elif "submit_answer" in data and data["submit_answer"]:
+            return Action(submit_answer=data["submit_answer"])
+    except json.JSONDecodeError:
+        pass
+
+    return Action(submit_answer=text)

temp_upload/baseline/run_baseline.py

@@ -0,0 +1,162 @@
+"""
+Baseline inference script for sql-data-analyst OpenEnv.
+
+Usage:
+    export OPENAI_API_KEY=sk-...
+    python baseline/run_baseline.py
+
+Produces reproducible scores across all 3 tasks.
+"""
+
+import os
+import json
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from typing import List, Dict, Any
+
+try:
+    from openai import OpenAI
+except ImportError:
+    print("Error: openai package not installed. Run: pip install openai")
+    sys.exit(1)
+
+from env import SQLAnalystEnv, Action
+from baseline.prompts import SYSTEM_PROMPT, build_prompt, parse_action
+
+
+MODEL = "gpt-4o-mini"
+MAX_STEPS = 20
+TASK_IDS = ["monthly_signups", "top_revenue_category", "churn_analysis"]
+
+
+def run_task(
+    client: OpenAI, task_id: str, max_steps: int = MAX_STEPS
+) -> Dict[str, Any]:
+    """Run a single task with the LLM agent."""
+    print(f"\n{'=' * 50}")
+    print(f"Task: {task_id}")
+    print("=" * 50)
+
+    env = SQLAnalystEnv(task_id=task_id)
+    result = env.reset()
+    obs = result.observation
+    history = []
+    total_reward = 0.0
+
+    print(f"Question: {obs.question}")
+    print(f"Schema: {obs.schema_summary[:200]}...")
+
+    for step in range(1, max_steps + 1):
+        if result.done:
+            print(f"Episode done at step {step - 1}")
+            break
+
+        user_prompt = build_prompt(obs)
+        history.append({"role": "user", "content": user_prompt})
+
+        try:
+            response = client.chat.completions.create(
+                model=MODEL,
+                messages=[
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    *history[-8:],
+                ],
+                temperature=0.0,
+            )
+        except Exception as e:
+            print(f"API Error: {e}")
+            break
+
+        reply = response.choices[0].message.content or ""
+        history.append({"role": "assistant", "content": reply})
+
+        action = parse_action(reply)
+
+        if action.sql_query:
+            print(f"Step {step}: Executing SQL...")
+            print(f"  Query: {action.sql_query[:100]}...")
+        else:
+            print(f"Step {step}: Submitting answer...")
+            print(
+                f"  Answer: {action.submit_answer[:100] if action.submit_answer else 'empty'}..."
+            )
+
+        result = env.step(action)
+        obs = result.observation
+        total_reward = result.info.get("total_reward", 0.0)
+
+        if result.done:
+            break
+
+    state = env.state()
+    print(f"\nFinal total reward: {total_reward:.3f}")
+    print(f"Steps taken: {state.step}")
+
+    return {
+        "task_id": task_id,
+        "difficulty": state.difficulty,
+        "total_reward": round(total_reward, 3),
+        "steps": state.step,
+        "max_steps": state.max_steps,
+    }
+
+
+def main():
+    api_key = os.environ.get("OPENAI_API_KEY")
+
+    if not api_key:
+        print("Error: OPENAI_API_KEY environment variable not set")
+        print("Usage: export OPENAI_API_KEY=sk-...")
+        sys.exit(1)
+
+    client = OpenAI(api_key=api_key)
+
+    print("=" * 60)
+    print("SQL Data Analyst - Baseline Inference")
+    print("=" * 60)
+    print(f"Model: {MODEL}")
+    print(f"Max steps per task: {MAX_STEPS}")
+    print(f"Tasks: {TASK_IDS}")
+
+    results = []
+    for task_id in TASK_IDS:
+        try:
+            r = run_task(client, task_id)
+            results.append(r)
+        except Exception as e:
+            print(f"Error running task {task_id}: {e}")
+            results.append(
+                {
+                    "task_id": task_id,
+                    "error": str(e),
+                    "total_reward": 0.0,
+                    "steps": 0,
+                }
+            )
+
+    print("\n" + "=" * 60)
+    print("BASELINE RESULTS")
+    print("=" * 60)
+
+    for r in results:
+        task = r.get("task_id", "unknown")
+        reward = r.get("total_reward", 0.0)
+        steps = r.get("steps", 0)
+        print(f"{task:30s}  score={reward:.3f}  steps={steps}")
+
+    valid_results = [r for r in results if "total_reward" in r]
+    if valid_results:
+        avg = sum(r["total_reward"] for r in valid_results) / len(valid_results)
+        print(f"\nAverage score: {avg:.3f}")
+
+    output_file = "baseline_scores.json"
+    with open(output_file, "w") as f:
+        json.dump(results, f, indent=2)
+    print(f"\nSaved results to {output_file}")
+
+
+if __name__ == "__main__":
+    main()

temp_upload/env/__init__.py

@@ -0,0 +1,13 @@
+from .models import Action, QueryResult, Observation, StepResult, EnvState
+from .environment import SQLAnalystEnv
+from .tasks import TASKS
+
+__all__ = [
+    "Action",
+    "QueryResult",
+    "Observation",
+    "StepResult",
+    "EnvState",
+    "SQLAnalystEnv",
+    "TASKS",
+]

temp_upload/env/database.py

@@ -0,0 +1,267 @@
+import sqlite3
+import random
+from datetime import datetime, timedelta
+from typing import Optional, Any
+from faker import Faker
+
+fake = Faker()
+
+SEED_CONFIG = {
+    "users": 500,
+    "products": 80,
+    "orders": 2000,
+    "order_items": 5000,
+    "events": 8000,
+}
+
+CATEGORIES = ["Electronics", "Clothing", "Books", "Home & Garden", "Sports"]
+PLAN_TYPES = ["free", "pro", "enterprise"]
+ORDER_STATUSES = ["pending", "completed", "refunded"]
+EVENT_TYPES = ["page_view", "add_to_cart", "checkout", "login", "logout"]
+
+
+def create_database(db_path: str = ":memory:") -> sqlite3.Connection:
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+
+    conn.execute("""
+        CREATE TABLE users (
+            id INTEGER PRIMARY KEY,
+            email TEXT NOT NULL,
+            country TEXT,
+            plan TEXT CHECK(plan IN ('free', 'pro', 'enterprise')),
+            created_at TIMESTAMP NOT NULL,
+            churned_at TIMESTAMP
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE products (
+            id INTEGER PRIMARY KEY,
+            name TEXT NOT NULL,
+            category TEXT NOT NULL,
+            price REAL,
+            cost REAL
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE orders (
+            id INTEGER PRIMARY KEY,
+            user_id INTEGER REFERENCES users(id),
+            created_at TIMESTAMP NOT NULL,
+            status TEXT CHECK(status IN ('pending', 'completed', 'refunded')),
+            total REAL
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE order_items (
+            id INTEGER PRIMARY KEY,
+            order_id INTEGER REFERENCES orders(id),
+            product_id INTEGER REFERENCES products(id),
+            qty INTEGER NOT NULL,
+            unit_price REAL
+        )
+    """)
+
+    conn.execute("""
+        CREATE TABLE events (
+            id INTEGER PRIMARY KEY,
+            user_id INTEGER REFERENCES users(id),
+            event_type TEXT,
+            metadata TEXT,
+            ts TIMESTAMP NOT NULL
+        )
+    """)
+
+    conn.commit()
+    return conn
+
+
+def seed_database(conn: sqlite3.Connection) -> None:
+    users = _seed_users(conn)
+    products = _seed_products(conn)
+    orders, order_items = _seed_orders(conn, users, products)
+    _seed_events(conn, users, orders)
+
+
+def _seed_users(conn: sqlite3.Connection) -> list:
+    users = []
+    now = datetime.now()
+    base_date = now - timedelta(days=180)
+    recent_date = now - timedelta(days=30)
+
+    for i in range(SEED_CONFIG["users"]):
+        if random.random() < 0.3:
+            created_at = recent_date + timedelta(days=random.randint(0, 30))
+        else:
+            created_at = base_date + timedelta(days=random.randint(0, 180))
+
+        country = random.choice([fake.country(), None, None, None, None])
+        plan = random.choice(PLAN_TYPES)
+        churned_at = None
+
+        if plan == "free" and random.random() < 0.1:
+            churned_at = created_at + timedelta(days=random.randint(30, 150))
+
+        conn.execute(
+            "INSERT INTO users (email, country, plan, created_at, churned_at) VALUES (?, ?, ?, ?, ?)",
+            (
+                fake.email(),
+                country,
+                plan,
+                created_at.isoformat(),
+                churned_at.isoformat() if churned_at else None,
+            ),
+        )
+        users.append((i + 1, created_at))
+
+    conn.commit()
+    return users
+
+
+def _seed_products(conn: sqlite3.Connection) -> list:
+    products = []
+
+    for i in range(SEED_CONFIG["products"]):
+        category = random.choice(CATEGORIES)
+        price = round(random.uniform(10, 500), 2)
+        cost = round(price * random.uniform(0.3, 0.7), 2)
+
+        conn.execute(
+            "INSERT INTO products (name, category, price, cost) VALUES (?, ?, ?, ?)",
+            (fake.catch_phrase(), category, price, cost),
+        )
+        products.append((i + 1, category, price))
+
+    conn.commit()
+    return products
+
+
+def _seed_orders(conn: sqlite3.Connection, users: list, products: list) -> tuple:
+    orders = []
+    order_items = []
+
+    q3_start = datetime(2024, 7, 1)
+    q3_end = datetime(2024, 9, 30)
+    recent_date = datetime.now()
+    old_date = datetime(2024, 1, 1)
+
+    for i in range(SEED_CONFIG["orders"]):
+        user_id = random.choice(users)[0]
+
+        if random.random() < 0.2:
+            created_at = q3_start + timedelta(days=random.randint(0, 91))
+        else:
+            created_at = old_date + timedelta(days=random.randint(0, 180))
+
+        status = random.choices(ORDER_STATUSES, weights=[0.1, 0.87, 0.03])[0]
+
+        conn.execute(
+            "INSERT INTO orders (user_id, created_at, status, total) VALUES (?, ?, ?, ?)",
+            (user_id, created_at.isoformat(), status, 0),
+        )
+
+        order_id = i + 1
+        order_total = 0
+
+        num_items = random.randint(1, 5)
+        for _ in range(num_items):
+            product = random.choice(products)
+            qty = random.randint(1, 3)
+            unit_price = product[2]
+            order_total += qty * unit_price
+
+            conn.execute(
+                "INSERT INTO order_items (order_id, product_id, qty, unit_price) VALUES (?, ?, ?, ?)",
+                (order_id, product[0], qty, unit_price),
+            )
+
+        conn.execute(
+            "UPDATE orders SET total = ? WHERE id = ?",
+            (round(order_total, 2), order_id),
+        )
+        orders.append((order_id, user_id, created_at, status))
+
+    conn.commit()
+    return orders, order_items
+
+
+def _seed_events(conn: sqlite3.Connection, users: list, orders: list) -> None:
+    base_date = datetime.now() - timedelta(days=180)
+
+    for _ in range(SEED_CONFIG["events"]):
+        user_id = random.choice(users)[0]
+        ts = base_date + timedelta(
+            days=random.randint(0, 180), hours=random.randint(0, 23)
+        )
+        event_type = random.choice(EVENT_TYPES)
+        metadata = '{"page": "/' + fake.uri_path() + '"}'
+
+        conn.execute(
+            "INSERT INTO events (user_id, event_type, metadata, ts) VALUES (?, ?, ?, ?)",
+            (user_id, event_type, metadata, ts.isoformat()),
+        )
+
+    conn.commit()
+
+
+def get_schema_summary(conn: sqlite3.Connection) -> str:
+    cursor = conn.execute(
+        "SELECT name FROM sqlite_master WHERE type='table' ORDER BY name"
+    )
+    tables = [r[0] for r in cursor.fetchall()]
+
+    lines = []
+    for table in tables:
+        cols = conn.execute(f"PRAGMA table_info({table})").fetchall()
+        col_names = [c[1] for c in cols]
+        lines.append(f"{table}: ({', '.join(col_names)})")
+
+    return "\n".join(lines)
+
+
+def get_ground_truth(conn: sqlite3.Connection, task_id: str) -> Any:
+    if task_id == "monthly_signups":
+        result = conn.execute(
+            "SELECT COUNT(*) FROM users WHERE created_at >= DATE('now', '-30 days')"
+        ).fetchone()
+        return result[0]
+
+    elif task_id == "top_revenue_category":
+        result = conn.execute("""
+            SELECT p.category, SUM(oi.qty * oi.unit_price) AS revenue
+            FROM orders o
+            JOIN order_items oi ON o.id = oi.order_id
+            JOIN products p ON oi.product_id = p.id
+            WHERE o.created_at BETWEEN '2024-07-01' AND '2024-09-30'
+              AND o.status = 'completed'
+            GROUP BY p.category
+            ORDER BY revenue DESC
+            LIMIT 1
+        """).fetchone()
+        return result[0] if result else None
+
+    elif task_id == "churn_analysis":
+        result = conn.execute("""
+            WITH order_counts AS (
+                SELECT user_id, COUNT(*) AS total_orders,
+                       MAX(created_at) AS last_order_date
+                FROM orders
+                WHERE status = 'completed'
+                GROUP BY user_id
+                HAVING COUNT(*) = 3
+            ),
+            churned AS (
+                SELECT oc.user_id
+                FROM order_counts oc
+                WHERE oc.last_order_date < DATE('now', '-90 days')
+            )
+            SELECT u.email
+            FROM users u
+            JOIN churned c ON u.id = c.user_id
+        """).fetchall()
+        return {row[0].lower() for row in result}
+
+    return None

temp_upload/env/environment.py

@@ -0,0 +1,134 @@
+import sqlite3
+from typing import Optional
+from .models import Action, Observation, StepResult, EnvState, QueryResult
+from .database import create_database, seed_database, get_schema_summary
+from .reward import RewardCalculator
+from .tasks import TASKS
+
+
+class SQLAnalystEnv:
+    """
+    OpenEnv-compliant SQL Data Analyst environment.
+
+    An agent must answer business questions by iteratively
+    writing and executing SQL queries.
+    """
+
+    def __init__(self, task_id: str = "monthly_signups"):
+        assert task_id in TASKS, f"Unknown task: {task_id}. Choose from {list(TASKS)}"
+        self.task_id = task_id
+        self.task = TASKS[task_id]
+        self.conn: Optional[sqlite3.Connection] = None
+        self.step_count: int = 0
+        self.total_reward: float = 0.0
+        self.done: bool = False
+        self._query_history: list = []
+        self._reward_calc = RewardCalculator()
+
+    def reset(self) -> StepResult:
+        """Reset environment. Reseed DB. Return initial observation."""
+        if self.conn:
+            self.conn.close()
+
+        self.conn = create_database()
+        seed_database(self.conn)
+        self.step_count = 0
+        self.total_reward = 0.0
+        self.done = False
+        self._query_history = []
+
+        self.task.compute_ground_truth(self.conn)
+
+        obs = Observation(
+            schema_summary=get_schema_summary(self.conn),
+            question=self.task.question,
+            step=0,
+            max_steps=self.task.max_steps,
+        )
+        return StepResult(observation=obs, reward=0.0, done=False)
+
+    def step(self, action: Action) -> StepResult:
+        """Execute one agent action. Return (observation, reward, done, info)."""
+        assert self.conn is not None, "Call reset() before step()"
+        assert not self.done, "Episode is done. Call reset()."
+        assert action.is_valid(), (
+            "Action must have exactly one of: sql_query, submit_answer"
+        )
+
+        self.step_count += 1
+        query_result = None
+        error = None
+
+        if action.sql_query:
+            query_result = self._execute_sql(action.sql_query)
+            self._query_history.append(action.sql_query)
+            error = query_result.error
+
+        terminal = (
+            action.submit_answer is not None or self.step_count >= self.task.max_steps
+        )
+
+        reward = self._reward_calc.calculate(
+            action=action,
+            result=query_result,
+            task=self.task,
+            step=self.step_count,
+            query_history=self._query_history,
+            terminal=terminal,
+        )
+        self.total_reward += reward
+        self.done = terminal
+
+        obs = Observation(
+            schema_summary=get_schema_summary(self.conn),
+            question=self.task.question,
+            last_query=action.sql_query,
+            last_result=query_result,
+            last_error=error,
+            step=self.step_count,
+            max_steps=self.task.max_steps,
+            hints=self.task.get_hints(self.step_count),
+            done=self.done,
+        )
+
+        return StepResult(
+            observation=obs,
+            reward=round(reward, 3),
+            done=self.done,
+            info={
+                "step": self.step_count,
+                "total_reward": round(self.total_reward, 3),
+                "task_id": self.task_id,
+            },
+        )
+
+    def state(self) -> EnvState:
+        """Return current full state of the environment."""
+        return EnvState(
+            task_id=self.task_id,
+            difficulty=self.task.difficulty,
+            step=self.step_count,
+            max_steps=self.task.max_steps,
+            query_history=self._query_history.copy(),
+            total_reward=round(self.total_reward, 3),
+            done=self.done,
+        )
+
+    def _execute_sql(self, query: str) -> QueryResult:
+        """Execute SQL safely. Block non-SELECT. Return up to 50 rows."""
+        q = query.strip().upper()
+        if not q.startswith("SELECT") and not q.startswith("WITH"):
+            return QueryResult(error="Only SELECT / WITH queries are allowed.")
+        try:
+            cursor = self.conn.execute(query)
+            cols = [d[0] for d in cursor.description] if cursor.description else []
+            rows = cursor.fetchmany(50)
+            total = len(rows)
+            return QueryResult(
+                columns=cols,
+                rows=[list(r) for r in rows],
+                truncated=(total == 50),
+                total_rows=total,
+            )
+        except Exception as e:
+            return QueryResult(error=str(e))

temp_upload/env/models.py

@@ -0,0 +1,58 @@
+from pydantic import BaseModel, Field
+from typing import Optional, List, Any
+
+class Action(BaseModel):
+    """What the agent can do each step."""
+    sql_query: Optional[str] = Field(
+        None,
+        description="A SQL SELECT query to execute against the database"
+    )
+    submit_answer: Optional[str] = Field(
+        None,
+        description="Final answer to submit. Ends the episode."
+    )
+
+    def is_valid(self) -> bool:
+        # Exactly one of the two must be set
+        return bool(self.sql_query) != bool(self.submit_answer)
+
+
+class QueryResult(BaseModel):
+    """Result of executing a SQL query."""
+    columns:    List[str]      = []
+    rows:       List[List[Any]] = []
+    error:      Optional[str]  = None
+    truncated:  bool           = False
+    total_rows: int            = 0
+
+
+class Observation(BaseModel):
+    """What the agent sees after each step."""
+    schema_summary: str   = Field(..., description="Compact DB schema")
+    question:       str   = Field(..., description="Business question to answer")
+    last_query:     Optional[str]         = None
+    last_result:    Optional[QueryResult] = None
+    last_error:     Optional[str]         = None
+    step:           int   = 0
+    max_steps:      int   = 20
+    hints:          List[str] = []
+    done:           bool  = False
+
+
+class StepResult(BaseModel):
+    """Full result returned by step()."""
+    observation: Observation
+    reward:      float = 0.0
+    done:        bool  = False
+    info:        dict  = {}
+
+
+class EnvState(BaseModel):
+    """Full environment state returned by state()."""
+    task_id:      str
+    difficulty:   str
+    step:         int
+    max_steps:    int
+    query_history: List[str] = []
+    total_reward: float = 0.0
+    done:         bool  = False

temp_upload/env/reward.py

@@ -0,0 +1,55 @@
+from typing import Optional, List, Any
+from .models import Action, QueryResult
+
+
+class RewardCalculator:
+    """Calculate rewards for agent actions in the SQL analyst environment."""
+
+    def calculate(
+        self,
+        action: Action,
+        result: Optional[QueryResult],
+        task: Any,
+        step: int,
+        query_history: List[str],
+        terminal: bool,
+    ) -> float:
+        """Calculate reward based on action, result, and task."""
+        reward = 0.0
+
+        if action.sql_query and result:
+            if not result.error:
+                reward += 0.15
+
+            relevant = self._count_relevant_tables(
+                action.sql_query, task.relevant_tables
+            )
+            if relevant > 0:
+                reward += 0.10
+
+            if result.rows and len(result.rows) > 0:
+                reward += 0.05
+
+            if result.rows and len(result.rows) < 1000:
+                reward += 0.05
+
+        if step > 3:
+            reward -= 0.02 * (step - 3)
+
+        if self._is_stuck(query_history):
+            reward -= 0.10
+
+        if terminal and action.submit_answer:
+            task_score = task.grade(action.submit_answer)
+            reward += task_score * 0.60
+
+        return max(0.0, min(1.0, reward))
+
+    def _count_relevant_tables(self, query: str, relevant_tables: List[str]) -> int:
+        query_lower = query.lower()
+        return sum(1 for t in relevant_tables if t.lower() in query_lower)
+
+    def _is_stuck(self, history: List[str]) -> bool:
+        if len(history) < 3:
+            return False
+        return len(set(history[-3:])) == 1

temp_upload/env/server.py

@@ -0,0 +1,254 @@
+"""
+FastAPI server for SQL Data Analyst OpenEnv.
+
+Provides REST and WebSocket endpoints for HuggingFace Spaces deployment.
+"""
+
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
+from pydantic import BaseModel
+from typing import Optional, Dict, Any
+import json
+import asyncio
+
+from env import SQLAnalystEnv, Action
+
+
+app = FastAPI(title="SQL Data Analyst Environment")
+
+envs: Dict[str, SQLAnalystEnv] = {}
+
+
+class ResetRequest(BaseModel):
+    task_id: str = "monthly_signups"
+
+
+class StepRequest(BaseModel):
+    session_id: str
+    sql_query: Optional[str] = None
+    submit_answer: Optional[str] = None
+
+
+class StateRequest(BaseModel):
+    session_id: str
+
+
+@app.get("/")
+async def root():
+    return {
+        "name": "sql-data-analyst",
+        "version": "1.0.0",
+        "description": "SQL Data Analyst OpenEnv - RL environment for SQL query generation",
+    }
+
+
+@app.post("/reset")
+async def reset(req: ResetRequest) -> Dict[str, Any]:
+    session_id = req.task_id
+
+    env = SQLAnalystEnv(task_id=req.task_id)
+    result = env.reset()
+    envs[session_id] = env
+
+    return {
+        "session_id": session_id,
+        "observation": {
+            "schema_summary": result.observation.schema_summary,
+            "question": result.observation.question,
+            "step": result.observation.step,
+            "max_steps": result.observation.max_steps,
+            "hints": result.observation.hints,
+            "done": result.observation.done,
+        },
+        "reward": result.reward,
+        "done": result.done,
+    }
+
+
+@app.post("/step")
+async def step(req: StepRequest) -> Dict[str, Any]:
+    session_id = req.session_id
+
+    if session_id not in envs:
+        return {"error": "Session not found. Call /reset first."}
+
+    env = envs[session_id]
+
+    action = Action(sql_query=req.sql_query, submit_answer=req.submit_answer)
+
+    result = env.step(action)
+
+    return {
+        "observation": {
+            "schema_summary": result.observation.schema_summary,
+            "question": result.observation.question,
+            "last_query": result.observation.last_query,
+            "last_result": {
+                "columns": result.observation.last_result.columns
+                if result.observation.last_result
+                else None,
+                "rows": result.observation.last_result.rows
+                if result.observation.last_result
+                else None,
+                "error": result.observation.last_result.error
+                if result.observation.last_result
+                else None,
+            }
+            if result.observation.last_result
+            else None,
+            "last_error": result.observation.last_error,
+            "step": result.observation.step,
+            "max_steps": result.observation.max_steps,
+            "hints": result.observation.hints,
+            "done": result.observation.done,
+        },
+        "reward": result.reward,
+        "done": result.done,
+        "info": result.info,
+    }
+
+
+@app.post("/state")
+async def state(req: StateRequest) -> Dict[str, Any]:
+    session_id = req.session_id
+
+    if session_id not in envs:
+        return {"error": "Session not found. Call /reset first."}
+
+    env = envs[session_id]
+    state = env.state()
+
+    return {
+        "task_id": state.task_id,
+        "difficulty": state.difficulty,
+        "step": state.step,
+        "max_steps": state.max_steps,
+        "query_history": state.query_history,
+        "total_reward": state.total_reward,
+        "done": state.done,
+    }
+
+
+@app.post("/delete")
+async def delete_session(req: StateRequest) -> Dict[str, str]:
+    session_id = req.session_id
+
+    if session_id in envs:
+        del envs[session_id]
+        return {"status": "deleted", "session_id": session_id}
+
+    return {"status": "not_found", "session_id": session_id}
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+
+    session_id = None
+    env = None
+
+    try:
+        while True:
+            data = await websocket.receive_text()
+            message = json.loads(data)
+
+            action_type = message.get("type")
+
+            if action_type == "reset":
+                task_id = message.get("task_id", "monthly_signups")
+                env = SQLAnalystEnv(task_id=task_id)
+                result = env.reset()
+                session_id = task_id
+                envs[session_id] = env
+
+                await websocket.send_json(
+                    {
+                        "type": "reset",
+                        "observation": {
+                            "schema_summary": result.observation.schema_summary,
+                            "question": result.observation.question,
+                            "step": result.observation.step,
+                            "max_steps": result.observation.max_steps,
+                            "hints": result.observation.hints,
+                        },
+                        "reward": result.reward,
+                        "done": result.done,
+                    }
+                )
+
+            elif action_type == "step":
+                if not env:
+                    await websocket.send_json({"error": "Call reset first"})
+                    continue
+
+                action = Action(
+                    sql_query=message.get("sql_query"),
+                    submit_answer=message.get("submit_answer"),
+                )
+
+                result = env.step(action)
+
+                await websocket.send_json(
+                    {
+                        "type": "step",
+                        "observation": {
+                            "schema_summary": result.observation.schema_summary,
+                            "question": result.observation.question,
+                            "last_query": result.observation.last_query,
+                            "last_result": {
+                                "columns": result.observation.last_result.columns
+                                if result.observation.last_result
+                                else None,
+                                "rows": result.observation.last_result.rows
+                                if result.observation.last_result
+                                else None,
+                                "error": result.observation.last_result.error
+                                if result.observation.last_result
+                                else None,
+                            }
+                            if result.observation.last_result
+                            else None,
+                            "step": result.observation.step,
+                            "hints": result.observation.hints,
+                            "done": result.observation.done,
+                        },
+                        "reward": result.reward,
+                        "done": result.done,
+                        "info": result.info,
+                    }
+                )
+
+            elif action_type == "state":
+                if not env:
+                    await websocket.send_json({"error": "Call reset first"})
+                    continue
+
+                state = env.state()
+
+                await websocket.send_json(
+                    {
+                        "type": "state",
+                        "task_id": state.task_id,
+                        "difficulty": state.difficulty,
+                        "step": state.step,
+                        "max_steps": state.max_steps,
+                        "query_history": state.query_history,
+                        "total_reward": state.total_reward,
+                        "done": state.done,
+                    }
+                )
+
+            elif action_type == "close":
+                if session_id and session_id in envs:
+                    del envs[session_id]
+                break
+
+    except WebSocketDisconnect:
+        pass
+    except Exception as e:
+        await websocket.send_json({"error": str(e)})
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=7860)

temp_upload/env/tasks/__init__.py

@@ -0,0 +1,20 @@
+from .base import BaseTask
+from .easy import MonthlySignupsTask
+from .medium import TopRevenueCategoryTask
+from .hard import ChurnAnalysisTask
+
+
+TASKS = {
+    "monthly_signups": MonthlySignupsTask(),
+    "top_revenue_category": TopRevenueCategoryTask(),
+    "churn_analysis": ChurnAnalysisTask(),
+}
+
+
+__all__ = [
+    "BaseTask",
+    "MonthlySignupsTask",
+    "TopRevenueCategoryTask",
+    "ChurnAnalysisTask",
+    "TASKS",
+]

temp_upload/env/tasks/base.py

@@ -0,0 +1,52 @@
+from abc import ABC, abstractmethod
+import sqlite3
+import re
+from typing import Any, List, Optional
+
+
+class BaseTask(ABC):
+    """Abstract base class for all tasks."""
+
+    task_id: str
+    difficulty: str
+    max_steps: int
+    question: str
+    relevant_tables: List[str]
+    sql_hint: str
+
+    def __init__(self):
+        self.ground_truth: Any = None
+        self.top_3_categories: List[str] = []
+
+    @abstractmethod
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        """Compute ground truth after database seeding."""
+        pass
+
+    @abstractmethod
+    def grade(self, submitted_answer: str) -> float:
+        """Grade the submitted answer. Returns score 0.0-1.0."""
+        pass
+
+    def get_hints(self, step: int) -> List[str]:
+        """Return progressive hints based on current step."""
+        hints = []
+        if step > 5:
+            hints.append(
+                f"Hint: The relevant tables are: {', '.join(self.relevant_tables)}"
+            )
+        if step > 10:
+            hints.append(f"Hint: Try using {self.sql_hint}")
+        if step > 15:
+            hints.append("Hint: Make sure to submit your answer with submit_answer.")
+        return hints
+
+    def _normalize(self, text: str) -> str:
+        """Remove common LLM formatting and normalize text."""
+        text = text.strip().lower()
+        text = re.sub(r"the (answer|result|category) is:?\s*", "", text)
+        text = re.sub(r"\*+", "", text)
+        text = re.sub(r"```.*?```", "", text, flags=re.DOTALL)
+        text = re.sub(r"`[^`]+`", lambda m: m.group().strip("`"), text)
+        text = re.sub(r"\s+", " ", text)
+        return text.strip()

temp_upload/env/tasks/easy.py

@@ -0,0 +1,32 @@
+import sqlite3
+from .base import BaseTask
+
+
+class MonthlySignupsTask(BaseTask):
+    """Task 1 — Easy: Count users who signed up in the last 30 days."""
+
+    task_id = "monthly_signups"
+    difficulty = "easy"
+    max_steps = 10
+    question = "How many users signed up in the last 30 days?"
+    relevant_tables = ["users"]
+    sql_hint = "COUNT(*) with WHERE clause on created_at"
+
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        result = conn.execute(
+            "SELECT COUNT(*) FROM users WHERE created_at >= DATE('now', '-30 days')"
+        ).fetchone()
+        self.ground_truth = result[0] if result else 0
+
+    def grade(self, submitted_answer: str) -> float:
+        try:
+            val = int(submitted_answer.strip().replace(",", ""))
+            if val == self.ground_truth:
+                return 1.0
+            if abs(val - self.ground_truth) <= 3:
+                return 0.6
+            if abs(val - self.ground_truth) <= 10:
+                return 0.3
+        except (ValueError, AttributeError):
+            pass
+        return 0.0

temp_upload/env/tasks/hard.py

@@ -0,0 +1,59 @@
+import sqlite3
+from .base import BaseTask
+
+
+class ChurnAnalysisTask(BaseTask):
+    """Task 3 — Hard: Find users who placed exactly 3 orders and then churned."""
+
+    task_id = "churn_analysis"
+    difficulty = "hard"
+    max_steps = 20
+    question = "Find the email addresses of users who placed exactly 3 orders and then never ordered again (churned after their 3rd purchase). Return as a comma-separated list."
+    relevant_tables = ["users", "orders"]
+    sql_hint = "CTE with COUNT and HAVING"
+
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        result = conn.execute("""
+            WITH order_counts AS (
+                SELECT user_id, COUNT(*) AS total_orders,
+                       MAX(created_at) AS last_order_date
+                FROM orders
+                WHERE status = 'completed'
+                GROUP BY user_id
+                HAVING COUNT(*) = 3
+            ),
+            churned AS (
+                SELECT oc.user_id
+                FROM order_counts oc
+                WHERE oc.last_order_date < DATE('now', '-90 days')
+            )
+            SELECT u.email
+            FROM users u
+            JOIN churned c ON u.id = c.user_id
+        """).fetchall()
+
+        self.ground_truth = {row[0].lower() for row in result}
+
+    def grade(self, submitted_answer: str) -> float:
+        if not submitted_answer.strip():
+            return 0.0
+
+        submitted = {e.strip().lower() for e in submitted_answer.split(",") if "@" in e}
+
+        if not submitted:
+            return 0.0
+
+        correct = {e.lower() for e in self.ground_truth}
+        tp = len(submitted & correct)
+
+        if tp == 0:
+            return 0.0
+
+        precision = tp / len(submitted) if submitted else 0
+        recall = tp / len(correct) if correct else 0
+
+        if precision + recall == 0:
+            return 0.0
+
+        f1 = 2 * precision * recall / (precision + recall)
+        return round(f1, 3)

temp_upload/env/tasks/medium.py

@@ -0,0 +1,54 @@
+import sqlite3
+from .base import BaseTask
+
+
+class TopRevenueCategoryTask(BaseTask):
+    """Task 2 — Medium: Find product category with most revenue in Q3."""
+
+    task_id = "top_revenue_category"
+    difficulty = "medium"
+    max_steps = 15
+    question = (
+        "Which product category generated the most revenue in Q3 (July-September)?"
+    )
+    relevant_tables = ["orders", "order_items", "products"]
+    sql_hint = "JOIN with GROUP BY and ORDER BY"
+
+    def compute_ground_truth(self, conn: sqlite3.Connection) -> None:
+        result = conn.execute("""
+            SELECT p.category, SUM(oi.qty * oi.unit_price) AS revenue
+            FROM orders o
+            JOIN order_items oi ON o.id = oi.order_id
+            JOIN products p ON oi.product_id = p.id
+            WHERE o.created_at BETWEEN '2024-07-01' AND '2024-09-30'
+              AND o.status = 'completed'
+            GROUP BY p.category
+            ORDER BY revenue DESC
+            LIMIT 1
+        """).fetchone()
+
+        self.ground_truth = result[0] if result else None
+
+        all_categories = conn.execute("""
+            SELECT p.category, SUM(oi.qty * oi.unit_price) AS revenue
+            FROM orders o
+            JOIN order_items oi ON o.id = oi.order_id
+            JOIN products p ON oi.product_id = p.id
+            WHERE o.created_at BETWEEN '2024-07-01' AND '2024-09-30'
+              AND o.status = 'completed'
+            GROUP BY p.category
+            ORDER BY revenue DESC
+        """).fetchall()
+
+        self.top_3_categories = [row[0] for row in all_categories[:3]]
+
+    def grade(self, submitted_answer: str) -> float:
+        answer = self._normalize(submitted_answer)
+
+        if self.ground_truth and self.ground_truth.lower() in answer:
+            return 1.0
+
+        if any(cat.lower() in answer for cat in self.top_3_categories):
+            return 0.4
+
+        return 0.0

temp_upload/env/utils.py

@@ -0,0 +1,29 @@
+import re
+
+
+def normalize_answer(raw: str) -> str:
+    """Remove common LLM answer preambles and formatting."""
+    text = raw.strip().lower()
+    text = re.sub(r"the (answer|result) is:?\s*", "", text)
+    text = re.sub(r"\*+", "", text)
+    text = re.sub(r"```.*?```", "", text, flags=re.DOTALL)
+    text = re.sub(r"`[^`]+`", lambda m: m.group().strip("`"), text)
+    text = re.sub(r"\s+", " ", text)
+    return text.strip()
+
+
+FORBIDDEN_KEYWORDS = [
+    "DROP",
+    "DELETE",
+    "INSERT",
+    "UPDATE",
+    "ALTER",
+    "CREATE",
+    "TRUNCATE",
+]
+
+
+def is_safe_query(query: str) -> bool:
+    """Check if query is safe (SELECT-only)."""
+    upper = query.upper()
+    return not any(kw in upper for kw in FORBIDDEN_KEYWORDS)

temp_upload/server/app.py

@@ -0,0 +1,12 @@
+from env.server import app as _app
+import uvicorn
+
+
+def main():
+    uvicorn.run(_app, host="0.0.0.0", port=7860)
+
+
+if __name__ == "__main__":
+    main()
+
+__all__ = ["app", "main"]