Spaces:
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Initial OpenEnv SQL debug environment
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README.md
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| 1 |
+
# SQL Debug Environment (`sql-debug-env`)
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+
An OpenEnv environment for a real task people do every day: **debugging SQL**. The agent gets a broken query, a live (in-memory) SQLite database, and a description of the expected output. It can inspect schema/errors/samples and submit fixed queries until it solves the task.
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## What’s in this repo
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- **FastAPI server**: `server/main.py` (endpoints: `/health`, `/tasks`, `/reset`, `/step`, `/state`)
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- **Environment logic**: `server/env.py` + `server/database.py`
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- **Tasks**: `server/tasks/` (easy → medium → hard, deterministic seed data)
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- **Baseline agent**: `inference.py` (OpenAI client + `[START]/[STEP]/[END]` logs)
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+
## Tech Stack
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- Python 3.11+
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- FastAPI + Uvicorn
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- Pydantic v2
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- SQLite (in-memory)
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- OpenEnv Core
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- Docker
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- OpenAI Python SDK (baseline inference)
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## Production Notes
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- Stateless HTTP API with per-session environment instances keyed by `X-Session-Id`
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- Deterministic task data (in-memory SQLite) for reproducible grading
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- Reward clamped to `[0.0, 1.0]` with partial-progress shaping
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- Docker-first deployment path (local and Hugging Face Spaces)
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- Local benchmark endpoint for live latency checks (`/benchmark`)
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## API Docs (FastAPI Auto Docs)
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Use these for interactive testing in browser:
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- Swagger UI: `http://localhost:7860/docs`
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- ReDoc: `http://localhost:7860/redoc`
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- OpenAPI spec: `http://localhost:7860/openapi.json`
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## Action Space
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| Action | Required fields | Cost / reward effect |
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|---|---|---|
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| `submit_query` | `query` | Main evaluation step (dense reward based on grading) |
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| `inspect_schema` | none | Free information action (small positive reward component) |
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| `inspect_error` | none | Free information action (small positive reward component) |
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| `inspect_sample` | `table_name` | Free information action (small positive reward component) |
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| `reset_query` | none | Penalty action (reduces reward for that step) |
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## Observation Space
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| Field | Type |
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|---|---|
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| `task_id` | `string` |
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| `task_description` | `string` |
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| `original_query` | `string` |
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| `current_query` | `string_or_null` |
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| `expected_description` | `string` |
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| `last_action_type` | `string` |
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| `last_query_result` | `object_or_null` |
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| `steps_taken` | `integer` |
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| `steps_remaining` | `integer` |
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| `current_score` | `float` |
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| `schema_info` | `object_or_null` |
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| `error_details` | `string_or_null` |
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| `sample_rows` | `array_or_null` |
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| `hint` | `string_or_null` |
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| `is_done` | `boolean` |
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| `success` | `boolean` |
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## Reward Function
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| Component | Range | Description |
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|---|---|---|
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| `correctness` | `[0.0, 0.6]` | Row-level match vs expected output |
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| `efficiency` | `[0.0, 0.2]` | Bonus for solving with fewer steps |
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| `syntax_progress` | `[0.0, 0.1]` | Small reward for producing syntactically valid SQL |
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| `schema_bonus` | `[0.0, 0.1]` | Bonus for referencing correct tables/columns |
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| `penalty` | `[0.0, 0.2]` | Deduction magnitude for resets/regressions/urgency near step limit |
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## Tasks
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### Task 1: Easy — Syntax Error Fix (`easy_syntax_fix`)
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Two straightforward issues: a misspelled keyword (`GRUP BY`) and an `ORDER BY` alias mismatch.
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### Task 2: Medium — Logic Error Fix (`medium_logic_fix`)
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Logic bugs around outer joins + filtering scope + aggregation scope.
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### Task 3: Hard — Multi-Bug Fix (`hard_multi_bug`)
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Five bugs across correlated subqueries, window functions, CTE scope, date logic, and duplication.
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## Baseline
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The baseline script is intentionally simple: it loops `reset → step` and asks an OpenAI model to choose the next JSON action.
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## Reliability & Benchmarking
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### Verified status (local)
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- `openenv validate --verbose`: **PASS**
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- `python3 -m unittest discover -s tests -p "test_*.py"`: **10/10 PASS**
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- Docker smoke test: **PASS** (`/health`, `/tasks`, `/reset`, `/step`)
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- FastAPI docs available: **PASS** (`/docs`, `/redoc`, `/openapi.json`)
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### Endpoint benchmark (local Docker run, n=25)
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Measured with `scripts/benchmark_local.py` on a running local container:
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| Endpoint | avg | p50 | p95 |
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|---|---:|---:|---:|
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| `GET /health` | 0.69 ms | 0.67 ms | 0.76 ms |
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| `GET /tasks` | 0.82 ms | 0.81 ms | 0.90 ms |
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| `POST /reset` | 1.34 ms | 1.26 ms | 1.62 ms |
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| `POST /step` (`inspect_schema`) | 1.07 ms | 1.01 ms | 1.34 ms |
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Re-run anytime:
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```bash
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python3 scripts/benchmark_local.py
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```
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Notes:
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- These are local-machine numbers (single container, warm runtime).
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- For submission-grade reporting, also capture one run against your HF Space URL after deploy.
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## Setup & Usage
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### Local Development
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```bash
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pip install -r requirements.txt
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uvicorn server.main:app --host 0.0.0.0 --port 7860
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```
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### Docker
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```bash
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docker build -t sql-debug-env .
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docker run -p 7860:7860 sql-debug-env
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```
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### Quick smoke test
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```bash
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curl http://localhost:7860/health
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curl http://localhost:7860/tasks
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curl -X POST http://localhost:7860/reset -H "Content-Type: application/json" -d '{"task_id":"easy_syntax_fix"}'
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curl -X POST http://localhost:7860/step -H "Content-Type: application/json" -d '{"action":{"action_type":"inspect_schema"}}'
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curl "http://localhost:7860/benchmark?runs=20"
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```
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### Real-time benchmark API (for dashboards/web pages)
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This is a live endpoint, not static/dummy data. Every request runs fresh measurements.
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- Endpoint: `GET /benchmark?runs=20`
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- `runs` range: `1` to `100`
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- Returns JSON with `avg_ms`, `p50_ms`, `p95_ms`, `n`, and a fresh `timestamp_epoch_ms`
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Example:
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```bash
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curl "http://localhost:7860/benchmark?runs=30"
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```
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### Run Baseline
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```bash
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export API_BASE_URL="https://api.openai.com/v1"
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export MODEL_NAME="gpt-4o-mini"
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export OPENAI_API_KEY="your-key"
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export ENV_BASE_URL="http://localhost:7860"
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export HF_TOKEN="$OPENAI_API_KEY"
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export SEED="1"
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python inference.py
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```
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### OpenEnv Validation
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```bash
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pip install openenv-core
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openenv validate
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```
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### Suggested pre-submit check
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```bash
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openenv validate --verbose
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python3 -m unittest discover -s tests -p "test_*.py"
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docker build -t sql-debug-env .
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docker run --rm -p 7860:7860 sql-debug-env
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# in another terminal:
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curl -s http://localhost:7860/health
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curl -s http://localhost:7860/docs >/dev/null
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curl -s "http://localhost:7860/benchmark?runs=20"
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```
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## Hugging Face Spaces (Docker)
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1. Create a new **Space → Docker**.
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2. Push this repo.
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3. Update `openenv.yaml` → `api.base_url` to your Space URL: `https://<your-space>.hf.space`
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4. Wait for build, then verify:
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```bash
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curl -X POST https://<your-space>.hf.space/reset -H "Content-Type: application/json" -d '{}'
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```
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