Initial OpenEnv SQL debug environment

README.md

@@ -1,143 +1,125 @@
----
-title: sql-debug-env
-emoji: "🧪"
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
----
-
 # SQL Debug Environment (`sql-debug-env`)
 
-![OpenEnv](https://img.shields.io/badge/OpenEnv-Validated-2ea44f)
-![Docker](https://img.shields.io/badge/Deploy-Docker-2496ED?logo=docker&logoColor=white)
 ![Python](https://img.shields.io/badge/Python-3.11+-3776AB?logo=python&logoColor=white)
 ![FastAPI](https://img.shields.io/badge/FastAPI-0.115-009688?logo=fastapi&logoColor=white)
 ![Pydantic](https://img.shields.io/badge/Pydantic-v2-E92063?logo=pydantic&logoColor=white)
-![SQLite](https://img.shields.io/badge/SQLite-In--Memory-003B57?logo=sqlite&logoColor=white)
-![Uvicorn](https://img.shields.io/badge/Uvicorn-ASGI-111111)
-![OpenAI](https://img.shields.io/badge/OpenAI-Baseline_API-412991?logo=openai&logoColor=white)
-
-**Deterministic OpenEnv benchmark for real SQL debugging workflows.**
-
-**Quick links:** [Live Space](https://md896-sql-debug-env.hf.space) · [Swagger](https://md896-sql-debug-env.hf.space/docs) · [OpenAPI](https://md896-sql-debug-env.hf.space/openapi.json) · [GitHub](https://github.com/mdayan8/sql-debug-env)
-
-An OpenEnv environment for a real engineering workflow: SQL query debugging. Agents iterate on broken SQL using schema/error/sample inspection until they produce the expected result.
-
-## Abstract
-This project implements a deterministic OpenEnv benchmark for SQL debugging. It includes three graded tasks (easy -> medium -> hard), typed action/observation/reward models, dense reward shaping, reproducible behavior, Docker deployment, and a baseline inference runner with strict structured logs.
-
-## Why this matters
-- SQL debugging is a daily task in analytics and backend teams.
-- Deterministic graders allow fair model comparison.
-- Dense reward shaping supports step-by-step agent learning.
-- Fast local runtime enables quick iteration and validation.
-
-## Core Components
-- API layer: `server/main.py`
-- Environment engine: `server/env.py`
-- Episode database: `server/database.py` (in-memory SQLite)
-- Typed models: `server/models.py`
-- Reward logic: `server/reward.py`
-- Task + graders: `server/tasks/`
-- Baseline runner: `inference.py`
-
-## Architecture
-```mermaid
-flowchart LR
-    agent[Agent Or Evaluator] --> api[FastAPI API Layer]
-    api --> env[SQLDebugEnv]
-    env --> db[InMemory SQLite DB]
-    env --> tasks[Task Registry easy medium hard]
-    tasks --> grader[Deterministic Grader]
-    env --> reward[Reward Engine]
-    grader --> reward
-    reward --> api
-```
+![SQLite](https://img.shields.io/badge/SQLite-In_Memory-003B57?logo=sqlite&logoColor=white)
+![Docker](https://img.shields.io/badge/Docker-Ready-2496ED?logo=docker&logoColor=white)
+![OpenEnv](https://img.shields.io/badge/OpenEnv-Validated-2ea44f)
 
-## API Surface
-- `POST /reset`
-- `POST /step`
-- `GET /state`
-- `GET /tasks`
-- `GET /health`
-- `GET /benchmark`
+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.
+
+## What’s in this repo
+- **FastAPI server**: `server/main.py` (endpoints: `/health`, `/tasks`, `/reset`, `/step`, `/state`)
+- **Environment logic**: `server/env.py` + `server/database.py`
+- **Tasks**: `server/tasks/` (easy → medium → hard, deterministic seed data)
+- **Baseline agent**: `inference.py` (OpenAI client + `[START]/[STEP]/[END]` logs)
+
+## Tech Stack
+- Python 3.11+
+- FastAPI + Uvicorn
+- Pydantic v2
+- SQLite (in-memory)
+- OpenEnv Core
+- Docker
+- OpenAI Python SDK (baseline inference)
+
+## Production Notes
+- Stateless HTTP API with per-session environment instances keyed by `X-Session-Id`
+- Deterministic task data (in-memory SQLite) for reproducible grading
+- Reward clamped to `[0.0, 1.0]` with partial-progress shaping
+- Docker-first deployment path (local and Hugging Face Spaces)
+- Local benchmark endpoint for live latency checks (`/benchmark`)
+
+## API Docs (FastAPI Auto Docs)
+Use these for interactive testing in browser:
 
-## API Docs
 - Swagger UI: `http://localhost:7860/docs`
 - ReDoc: `http://localhost:7860/redoc`
-- OpenAPI: `http://localhost:7860/openapi.json`
+- OpenAPI spec: `http://localhost:7860/openapi.json`
 
 ## Action Space
-| Action | Required fields | Purpose |
+| Action | Required fields | Cost / reward effect |
 |---|---|---|
-| `submit_query` | `query` | Submit SQL candidate for execution + grading |
-| `inspect_schema` | none | Return schema metadata |
-| `inspect_error` | none | Return last execution error details |
-| `inspect_sample` | `table_name` | Return sample rows from table |
-| `reset_query` | none | Reset current query to original broken query |
-
-## Reward Design
-Reward is clamped to `[0.0, 1.0]` and combines:
-- correctness (`0.0-0.6`)
-- efficiency (`0.0-0.2`)
-- syntax_progress (`0.0-0.1`)
-- schema_bonus (`0.0-0.1`)
-- penalty deduction magnitude (`0.0-0.2`)
-
-## Episode Lifecycle
-1. Client calls `POST /reset` with optional `task_id`.
-2. Environment creates a fresh in-memory SQLite DB seeded for that task.
-3. Client iteratively calls `POST /step` with one action at a time.
-4. Server returns `(observation, reward, done, info)` each step.
-5. Episode ends on high grade score or max-step boundary.
-
-## Task Suite
-- Easy: `easy_syntax_fix`
-- Medium: `medium_logic_fix`
-- Hard: `hard_multi_bug`
-
-## Repository Structure
-```text
-sql-debug-env/
-├── Dockerfile
-├── openenv.yaml
-├── inference.py
-├── README.md
-├── requirements.txt
-├── pyproject.toml
-├── uv.lock
-├── scripts/
-│   └── benchmark_local.py
-├── server/
-│   ├── main.py
-│   ├── env.py
-│   ├── models.py
-│   ├── database.py
-│   ├── reward.py
-│   └── tasks/
-│       ├── base.py
-│       ├── task_easy.py
-│       ├── task_medium.py
-│       └── task_hard.py
-└── tests/
-    ├── test_env.py
-    ├── test_graders.py
-    └── test_reward.py
-```
+| `submit_query` | `query` | Main evaluation step (dense reward based on grading) |
+| `inspect_schema` | none | Free information action (small positive reward component) |
+| `inspect_error` | none | Free information action (small positive reward component) |
+| `inspect_sample` | `table_name` | Free information action (small positive reward component) |
+| `reset_query` | none | Penalty action (reduces reward for that step) |
+
+## Observation Space
+| Field | Type |
+|---|---|
+| `task_id` | `string` |
+| `task_description` | `string` |
+| `original_query` | `string` |
+| `current_query` | `string_or_null` |
+| `expected_description` | `string` |
+| `last_action_type` | `string` |
+| `last_query_result` | `object_or_null` |
+| `steps_taken` | `integer` |
+| `steps_remaining` | `integer` |
+| `current_score` | `float` |
+| `schema_info` | `object_or_null` |
+| `error_details` | `string_or_null` |
+| `sample_rows` | `array_or_null` |
+| `hint` | `string_or_null` |
+| `is_done` | `boolean` |
+| `success` | `boolean` |
+
+## Reward Function
+| Component | Range | Description |
+|---|---|---|
+| `correctness` | `[0.0, 0.6]` | Row-level match vs expected output |
+| `efficiency` | `[0.0, 0.2]` | Bonus for solving with fewer steps |
+| `syntax_progress` | `[0.0, 0.1]` | Small reward for producing syntactically valid SQL |
+| `schema_bonus` | `[0.0, 0.1]` | Bonus for referencing correct tables/columns |
+| `penalty` | `[0.0, 0.2]` | Deduction magnitude for resets/regressions/urgency near step limit |
+
+## Tasks
+### Task 1: Easy — Syntax Error Fix (`easy_syntax_fix`)
+Two straightforward issues: a misspelled keyword (`GRUP BY`) and an `ORDER BY` alias mismatch.
+
+### Task 2: Medium — Logic Error Fix (`medium_logic_fix`)
+Logic bugs around outer joins + filtering scope + aggregation scope.
+
+### Task 3: Hard — Multi-Bug Fix (`hard_multi_bug`)
+Five bugs across correlated subqueries, window functions, CTE scope, date logic, and duplication.
+
+## Baseline
+The baseline script is intentionally simple: it loops `reset → step` and asks an OpenAI model to choose the next JSON action.
 
-## Reliability and Benchmarking
-- `openenv validate --verbose`: PASS
-- `python3 -m unittest discover -s tests -p "test_*.py"`: PASS
-- Docker smoke test: PASS (`/health`, `/tasks`, `/reset`, `/step`)
+## Reliability & Benchmarking
+
+### Verified status (local)
+- `openenv validate --verbose`: **PASS**
+- `python3 -m unittest discover -s tests -p "test_*.py"`: **10/10 PASS**
+- Docker smoke test: **PASS** (`/health`, `/tasks`, `/reset`, `/step`)
+- FastAPI docs available: **PASS** (`/docs`, `/redoc`, `/openapi.json`)
+
+### Endpoint benchmark (local Docker run, n=25)
+Measured with `scripts/benchmark_local.py` on a running local container:
+
+| Endpoint | avg | p50 | p95 |
+|---|---:|---:|---:|
+| `GET /health` | 0.69 ms | 0.67 ms | 0.76 ms |
+| `GET /tasks` | 0.82 ms | 0.81 ms | 0.90 ms |
+| `POST /reset` | 1.34 ms | 1.26 ms | 1.62 ms |
+| `POST /step` (`inspect_schema`) | 1.07 ms | 1.01 ms | 1.34 ms |
+
+Re-run anytime:
 
-Live benchmark endpoint:
 ```bash
-curl "http://localhost:7860/benchmark?runs=20"
+python3 scripts/benchmark_local.py
 ```
 
-## Quick Start
-### Local
+Notes:
+- These are local-machine numbers (single container, warm runtime).
+- For submission-grade reporting, also capture one run against your HF Space URL after deploy.
+
+## Setup & Usage
+
+### Local Development
 ```bash
 pip install -r requirements.txt
 uvicorn server.main:app --host 0.0.0.0 --port 7860
@@ -149,40 +131,63 @@ docker build -t sql-debug-env .
 docker run -p 7860:7860 sql-debug-env
 ```
 
-### Baseline Inference
+### Quick smoke test
+```bash
+curl http://localhost:7860/health
+curl http://localhost:7860/tasks
+curl -X POST http://localhost:7860/reset -H "Content-Type: application/json" -d '{"task_id":"easy_syntax_fix"}'
+curl -X POST http://localhost:7860/step  -H "Content-Type: application/json" -d '{"action":{"action_type":"inspect_schema"}}'
+curl "http://localhost:7860/benchmark?runs=20"
+```
+
+### Real-time benchmark API (for dashboards/web pages)
+This is a live endpoint, not static/dummy data. Every request runs fresh measurements.
+
+- Endpoint: `GET /benchmark?runs=20`
+- `runs` range: `1` to `100`
+- Returns JSON with `avg_ms`, `p50_ms`, `p95_ms`, `n`, and a fresh `timestamp_epoch_ms`
+
+Example:
+```bash
+curl "http://localhost:7860/benchmark?runs=30"
+```
+
+### Run Baseline
 ```bash
 export API_BASE_URL="https://api.openai.com/v1"
 export MODEL_NAME="gpt-4o-mini"
 export OPENAI_API_KEY="your-key"
-export HF_TOKEN="$OPENAI_API_KEY"
 export ENV_BASE_URL="http://localhost:7860"
+export HF_TOKEN="$OPENAI_API_KEY"
 export SEED="1"
 python inference.py
 ```
 
-## Required Environment Variables
-| Variable | Required | Purpose |
-|---|---|---|
-| `API_BASE_URL` | Yes (for baseline) | LLM API base endpoint |
-| `MODEL_NAME` | Yes (for baseline) | Model ID for inference |
-| `OPENAI_API_KEY` | Yes (for baseline) | OpenAI client authentication |
-| `HF_TOKEN` | Recommended | Compatibility with evaluator instructions |
-| `ENV_BASE_URL` | Yes (for baseline) | Environment server URL |
-| `SEED` | Optional | Reproducibility control |
-
-## Submission Validation Checklist
-- Space URL is live and `/health` returns `200`.
-- `/reset` returns a valid observation payload.
-- `openenv validate --verbose` passes.
-- Docker build and run succeed locally.
-- `inference.py` is in repo root and emits `[START]`, `[STEP]`, `[END]`.
-- `openenv.yaml` has correct deployed `api.base_url`.
-- `.env` and `.cursor/` are ignored in git.
-
-## Hugging Face Spaces
-Verify deployment:
+### OpenEnv Validation
 ```bash
-curl https://md896-sql-debug-env.hf.space/health
-curl -X POST https://md896-sql-debug-env.hf.space/reset -H "Content-Type: application/json" -d '{}'
-curl https://md896-sql-debug-env.hf.space/docs
+pip install openenv-core
+openenv validate
 ```
+
+### Suggested pre-submit check
+```bash
+openenv validate --verbose
+python3 -m unittest discover -s tests -p "test_*.py"
+docker build -t sql-debug-env .
+docker run --rm -p 7860:7860 sql-debug-env
+# in another terminal:
+curl -s http://localhost:7860/health
+curl -s http://localhost:7860/docs >/dev/null
+curl -s "http://localhost:7860/benchmark?runs=20"
+```
+
+## Hugging Face Spaces (Docker)
+1. Create a new **Space → Docker**.
+2. Push this repo.
+3. Update `openenv.yaml` → `api.base_url` to your Space URL: `https://<your-space>.hf.space`
+4. Wait for build, then verify:
+
+```bash
+curl -X POST https://<your-space>.hf.space/reset -H "Content-Type: application/json" -d '{}'
+```
+