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title: SQL Correction RL Environment
emoji: "🛠️"
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false
tags:
- openenv
---
# SQL Correction RL Environment
An **OpenEnv-compliant** reinforcement learning environment where an AI agent
learns to fix broken SQL queries — a real task that developers face every day.
---
## Description & Motivation
SQL errors are one of the most common and costly mistakes in software
development. This environment trains agents to identify and correct SQL syntax
and logical errors, ranging from simple typos to complex multi-join query
reconstruction including column name mismatches.
The environment provides **partial progress signals** at every step; the agent
receives graded feedback even for near-correct answers, enabling meaningful
learning across the full trajectory rather than sparse end-of-episode rewards.
A **stagnation penalty** further discourages agents from repeating the same
wrong answer across steps.
---
## Observation Space
| Field | Type | Description |
|--------------------|-----------------|--------------------------------------------------------------------|
| `task_id` | string | Unique identifier for the current task instance |
| `broken_query` | string | The malformed SQL query the agent must fix |
| `schema_context` | string or null | Table and column definitions (hard tasks only) |
| `error_hint` | string or null | Plain-language hint about the error (easy tasks only) |
| `step_number` | integer | Current step within the episode (0 = initial) |
| `steps_remaining` | integer | Steps left before the episode ends |
| `previous_attempt` | string or null | The agent's SQL output from the previous step |
| `feedback` | string or null | Grader feedback on the previous attempt |
## Action Space
| Field | Type | Description |
|--------------------|--------|---------------------------------|
| `corrected_query` | string | The agent's corrected SQL query |
---
## Tasks
| Name | Difficulty | Count | Max Steps | Description |
|----------|------------|-------|-----------|-------------|
| `easy` | Easy | 15 | 5 | Fix a single keyword typo (e.g. `FORM` → `FROM`). Hint provided. |
| `medium` | Medium | 15 | 5 | Fix multiple errors including missing keywords and wrong clauses. No hint. |
| `hard` | Hard | 10 | 4 | Fix complex multi-join queries with subtle errors and wrong column names. Schema provided, no hint. |
---
## Reward Function
| Score | Condition |
|-------|-----------|
| `0.99` | Exact match after normalization (perfect fix) |
| `0.7` | All correct tokens present, structure slightly off |
| `0.4` | Most keywords correct and token overlap is high (≥85% keywords, ≥75% tokens) |
| `0.3` | Partial keyword and structure match (≥65% keywords, ≥50% tokens) |
| `0.2` | Basic `SELECT ... FROM ...` structure present |
| `0.01` | Response is not valid SQL |
A **stagnation penalty** of `−0.1` is applied when the agent submits the same
reward-equivalent answer for two or more consecutive steps, encouraging active
correction rather than looping.
Episodes terminate when reward reaches `0.99` (success) or max steps is reached.
---
## Setup & Usage
### Local Development
```bash
# Clone and install
git clone https://huggingface.co/spaces/YOUR_USERNAME/sql-correction-env
cd sql-correction-env
pip install -r requirements.txt
# Start the server
uvicorn server:app --host 0.0.0.0 --port 7860
# Test endpoints
curl -X POST http://localhost:7860/reset \
-H "Content-Type: application/json" -d '{"difficulty": "easy"}'
curl -X POST http://localhost:7860/step \
-H "Content-Type: application/json" \
-d '{"action": {"corrected_query": "SELECT * FROM users WHERE id = 1"}}'
curl http://localhost:7860/tasks
```
### Run Tests
```bash
pip install pytest
pytest tests/ -v
```
### Docker
```bash
docker build -t sql-correction-env .
docker run -p 7860:7860 sql-correction-env
```
### Running Inference
```bash
export HF_TOKEN=your_token
export API_BASE_URL=https://router.huggingface.co/v1
export MODEL_NAME=Qwen/Qwen2.5-72B-Instruct
export ENV_URL=http://localhost:7860
# Run all tasks
python inference.py
```
---
## Baseline Scores
| Task | Model | Avg Score | Notes |
|--------|------------------|-----------|-------|
| easy | Qwen/Qwen2.5-72B | ~0.85 | Single typo fix, hint provided |
| medium | Qwen/Qwen2.5-72B | ~0.62 | Multi-error correction |
| hard | Qwen/Qwen2.5-72B | ~0.38 | Complex multi-join, schema-guided |
*Run `inference.py` against the live Space to reproduce these scores.*
---
## API Endpoints
| Method | Path | Description |
|--------|-----------|----------------------------------------|
| POST | `/reset` | Start new episode, returns observation |
| POST | `/step` | Submit action, returns result |
| POST | `/state` | Get current episode state |
| GET | `/health` | Health check |
| GET | `/tasks` | List available task difficulties |
|