diff --git a/.dockerignore b/.dockerignore index 5220c5b..3daeef7 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,6 +1,10 @@ .git +.github .venv +.env __pycache__ *.pyc *.egg-info +.pytest_cache +.ruff_cache .DS_Store diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..56f9b0f --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,31 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + +jobs: + test: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10", "3.12"] + steps: + - uses: actions/checkout@v4 + + - uses: astral-sh/setup-uv@v6 + with: + python-version: ${{ matrix.python-version }} + + - name: Install + run: uv sync --locked --extra dev + + - name: Lint + run: uv run ruff check . + + - name: Test + run: uv run pytest -q + + - name: Validate OpenEnv layout + run: uv run openenv validate . diff --git a/.gitignore b/.gitignore index 9d6023b..c0913e3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,21 @@ -.venv/ +# Python __pycache__/ +*.py[cod] *.egg-info/ -*.pyc +build/ +dist/ + +# Environments +.venv/ +.env + +# Tooling +.pytest_cache/ +.ruff_cache/ +.coverage +htmlcov/ + +# OS / editors .DS_Store -uv.lock +.idea/ +.vscode/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 33c42f8..3525e7e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,66 +1,57 @@ -# Development Notes +# Contributing -Thanks for checking out the codebase! I built this specifically for the Meta OpenEnv Hackathon 2026. If you want to run my local testing suite or add more SQL tasks later, here is my setup process. +Issues and pull requests are welcome. This guide covers local setup, the test suite, and how to add a task. ## Setup +Requires Python 3.10+ and [uv](https://docs.astral.sh/uv/). + ```bash -# clone and install -git clone +git clone https://github.com/rajdeepchatale/sql_query_env.git cd sql_query_env -uv sync +uv sync --extra dev ``` -## Running locally +## Run the server ```bash -# start the server -uv run server - -# test with curl +uv run server # http://localhost:8000 curl http://localhost:8000/health ``` -## Adding a new task - -1. Add the database seed data (if new domain) to `server/tasks.py` -2. Create a `Task` object with: - - Ground truth SQL query - - Expected output columns - - Difficulty level and hints -3. Add task ID to the appropriate list in `tasks.py` -4. Add task entry in `openenv.yaml` -5. Add task ID to `TASK_IDS` in `inference.py` (for baseline testing) -6. Test: start server, call `/reset` with the new task ID, verify grading +## Checks -## Testing grading +CI runs the same three commands on every push and pull request: -We have a quick sanity check you can run: +```bash +uv run ruff check . +uv run pytest -q +uv run openenv validate . +``` -```python -import sqlite3 -from server.tasks import create_database, TASK_MAP -from server.graders import grade_query +The test suite checks: -conn = create_database("company") -task = TASK_MAP["company_easy_1"] +- every reference query runs and is graded as correct +- the manifest, the task registry, and the baseline script list the same tasks +- the database rejects writes, `PRAGMA`, and `ATTACH` +- runaway queries are cut off +- known ways to game the reward stay closed -# should score ~0.95+ -result = grade_query(conn, task.ground_truth_query, task, []) -print(f"Ground truth score: {result.total_score}") +## Adding a task -# should score ~0.10 (syntax only) -result = grade_query(conn, "SELECT 1", task, []) -print(f"Bad query score: {result.total_score}") -``` +1. If the task needs a new domain, add its schema, seed data, and schema description to `server/tasks.py` and register it in `SCHEMAS`. +2. Add a `Task` to the matching list in `server/tasks.py` with: + - a `question` that states every filter the reference query applies. Hidden conditions make a task unfair, not harder. + - `ground_truth_query` and `expected_columns`, which must match the query's output column names. + - `difficulty`, `hints` (ordered from general to specific), and `max_steps`. +3. Add the task to `openenv.yaml`. +4. Add the task ID to `TASK_IDS` in `inference.py`. +5. Run `uv run pytest -q`. The consistency tests fail if you skipped step 3 or 4. -## Common issues +When the reference query sorts on a key that can tie, keep in mind that any tie order counts as correct. Row order is not part of the correctness check. -- **SQLite threading**: We use `check_same_thread=False` because FastAPI - is async. The in-memory databases are per-episode so there's no real - contention, but be aware of this if you change the session model. +## Things to know -- **Import errors**: The server can be run multiple ways (`uv run server`, - `python -m sql_query_env.server.app`, `uvicorn server.app:app`) and - each has a different package layout. That's why there are try/except - import blocks. Don't remove them. +- **Import fallbacks.** The server runs in several layouts (`uv run server`, `uvicorn server.app:app`, `python -m sql_query_env.server.app`), and each one resolves imports differently. That's why several modules have `try`/`except ImportError` import blocks. Keep them. +- **SQLite threading.** Connections use `check_same_thread=False` because the server calls the environment from a thread pool. Each episode has its own in-memory database, so there is no shared state. Revisit this if you change the session model. +- **Read-only database.** `create_database()` installs an SQLite authorizer after seeding. If a new task needs a feature the authorizer blocks, allow the specific action code rather than removing the authorizer. diff --git a/Dockerfile b/Dockerfile index d709091..9b4edc6 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,6 @@ -# Multi-stage build using openenv-base -# This Dockerfile is flexible and works for both: -# - In-repo environments (with local OpenEnv sources) -# - Standalone environments (with openenv from PyPI/Git) -# The build script (openenv build) handles context detection and sets appropriate build args. +# Multi-stage build on the OpenEnv base image. +# Used by Hugging Face Spaces (Docker SDK), `docker build .`, and `openenv build` +# (which passes BUILD_MODE / ENV_NAME as build args). ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest FROM ${BASE_IMAGE} AS builder @@ -31,9 +29,9 @@ RUN if ! command -v uv >/dev/null 2>&1; then \ mv /root/.local/bin/uv /usr/local/bin/uv && \ mv /root/.local/bin/uvx /usr/local/bin/uvx; \ fi - -# Install dependencies using uv sync -# If uv.lock exists, use it; otherwise resolve on the fly + +# Install dependencies from the committed lockfile (falls back to resolving +# on the fly if uv.lock is absent) RUN --mount=type=cache,target=/root/.cache/uv \ if [ -f uv.lock ]; then \ uv sync --frozen --no-install-project --no-editable; \ diff --git a/LICENSE b/LICENSE index 4b6389b..09774d5 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ BSD 3-Clause License -Copyright (c) 2025, sql_query_env contributors +Copyright (c) 2026, Rajdeep Chatale All rights reserved. Redistribution and use in source and binary forms, with or without diff --git a/README.md b/README.md index 495eae0..3663f7f 100644 --- a/README.md +++ b/README.md @@ -1,225 +1,221 @@ --- -title: Sql Query Env +title: SQL Query emoji: πŸ“Š colorFrom: blue colorTo: indigo sdk: docker app_port: 8000 pinned: false +short_description: Text-to-SQL RL environment with execution-based rewards +tags: + - openenv --- -# SQL Query Generation Environment -*Built for the Meta PyTorch OpenEnv Hackathon 2026 by Rajdeep Chatale* -An OpenEnv RL environment that trains AI agents to write SQL queries from natural language questions. +# SQL Query -## Motivation +[![CI](https://github.com/rajdeepchatale/sql_query_env/actions/workflows/ci.yml/badge.svg)](https://github.com/rajdeepchatale/sql_query_env/actions/workflows/ci.yml) +[![License: BSD-3-Clause](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)](LICENSE) +![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg) +[![Hugging Face Space](https://img.shields.io/badge/%F0%9F%A4%97-Space-yellow.svg)](https://huggingface.co/spaces/rajdeepchatale/sql_query_env) -I decided to build an SQL generation environment for this hackathon because writing complex SQL is something I struggle withβ€”and learn fromβ€”almost every day. Translating a business question like "show me the top customers last month who never returned an item" into actual SQL is surprisingly error-prone. You have to handle wrong JOINs, missing WHERE clauses, and edge cases around NULLs perfectly. +**SQL Query** is an [OpenEnv](https://github.com/meta-pytorch/OpenEnv) reinforcement-learning environment for **text-to-SQL**. The agent receives a database schema and a question in plain English, then submits SQLite queries. Each query is executed and graded against a reference result. The reward gives partial credit for every part the query gets right, and each step also returns structured diagnostics that say what to fix next. -I designed this to be a strong OpenEnv benchmark because: -- **It's a practical, real-world task** with clear right/wrong answers, not just a toy game. -- **Grading is deterministic** (I actually execute the query securely in-memory and compare the exact row outputs). -- **There's a natural difficulty curve**, scaling from simple SELECTs to brutal anti-joins. -- **Process Supervision is key.** I built a partial-credit system because binary pass/fail gives LLMs terrible learning signals. If an agent gets the tables right but the columns wrong, it deserves partial credit and a hint! +Built for the Meta PyTorch OpenEnv Hackathon 2026. -## How I built the workflow +## Why this environment -When the environment spins up, the agent receives a raw database schema and a natural language question. It writes SQL. My environment executes that query, compares the resulting dataset to the ground truth dataset, and returns a clamped score from 0.0 to 1.0 along with rich diagnostic feedback to supervise the agent's next step. +- **Verifiable answers.** Every task has a reference query. Grading executes the agent's query and compares the rows, so it is deterministic and needs no LLM judge. +- **Dense reward.** Five scoring components mean a query with the right tables but a wrong aggregate still earns signal, which binary pass/fail can't give. +- **Feedback an agent can act on.** Typed diagnostics such as `MISSING_JOIN`, `NULL_HANDLING` and `EXTRA_ROWS`, plus hints that get more specific after each failed attempt, support correction within an episode (process supervision). +- **Schemas to read, not memorize.** Three domains with different schemas and 14 tasks, from single-table filters to self-joins, anti-joins, and division-by-zero edge cases. +- **Safe to point an agent at.** The database is read-only, and every query runs under a time limit and a row limit. -### Multi-domain testing +## How an episode works -To ensure agents learn general SQL reasoning rather than just memorizing one layout, I generated three distinct database domains: - -| Domain | Tables | Description | -|--------|--------|-------------| -| Company Analytics | departments, employees, products, customers, orders, reviews | Business intelligence queries | -| Hospital Management | wards, doctors, patients, appointments, medications, prescriptions | Healthcare data | -| E-Commerce Platform | sellers, categories, products, users, orders, order_items, returns | Retail analytics | - -### Action space - -The agent submits a single action per step: - -| Field | Type | Description | -|-------|------|-------------| -| `query` | string | A SQL SELECT query to execute against the database | - -Only SELECT statements are permitted. Destructive operations (DROP, DELETE, INSERT, UPDATE, etc.) are blocked and penalized. - -### Observation space - -After each step, the agent receives a rich observation: - -| Field | Type | Description | -|-------|------|-------------| -| `task_id` | string | Current task identifier | -| `difficulty` | string | easy, medium, or hard | -| `database_domain` | string | Which database domain this task uses | -| `question` | string | Natural language question to answer with SQL | -| `schema_description` | string | Full database schema with table definitions | -| `query_result` | string | Formatted result of the last query | -| `query_error` | string (nullable) | SQL error message, if any | -| `feedback` | string | Detailed grading feedback | -| `diagnostics` | array | Structured error diagnostics: type, severity, message, suggestion | -| `efficiency_notes` | array | SQL best-practice tips based on the submitted query | -| `expected_row_count` | integer | Expected number of result rows | -| `expected_columns` | array | Expected column names | -| `steps_remaining` | integer | Attempts left in this episode | -| `current_score` | number | Best score so far (0.0–1.0) | -| `history` | array | Previous queries and scores | - -### Scoring Structure - -I broke the scoring down into five distinct components so the agent always receives a highly dense learning signal, even on failure: - -| Component | Weight | What it measures | -|-----------|--------|-----------------| -| Syntax | 0.10 | Does the query run at all? | -| Tables | 0.15 | Right tables referenced? | -| Columns | 0.20 | Correct output columns? | -| Results | 0.45 | Do the rows match ground truth? | -| Efficiency | 0.10 | Is it well-written SQL? (aliases, no SELECT *, etc.) | - -There's also a -0.10 penalty for destructive SQL (DROP, DELETE) and -0.05 for repeating the same query. - -All scores are clamped to the range **[0.0, 1.0]**. - -### Diagnostic feedback - -Instead of just returning an arbitrary score, I engineered the grader to emit structured, actionable diagnostics back to the LLM: -- What exact tables are missing from the query -- Whether a JOIN is needed -- Column name mismatches -- Row count differences -- Efficiency tips (use aliases, avoid SELECT *, handle NULLs) - -I designed this specifically to excel at **process supervision**. The agent should be able to parse this feedback array and iteratively correct its SQL logic within a single episode. +1. `reset()` picks a task, or `reset(task_id=...)` selects one, and builds a fresh in-memory SQLite database for its domain. +2. The observation contains the schema, the question, the expected output columns and row count, and a first hint. +3. The agent submits `SqlQueryAction(query="SELECT ...")`. +4. The grader executes the query, compares the result with the reference, and returns a reward in `[0, 1]` with feedback and diagnostics. +5. The episode ends when the result matches exactly or the task's attempt budget (5–10) runs out. ## Tasks -14 tasks across three difficulty tiers and three database domains: - -### Full task list - -| Task ID | Domain | Difficulty | Description | Challenge Type | -|---------|--------|-----------|-------------|---------------| -| `company_easy_1` | Company | Easy | List active Engineering employees sorted by salary | Filter + sort | -| `company_easy_2` | Company | Easy | Filter electronics products by price | Single table filter | -| `hospital_easy_1` | Hospital | Easy | List patients still admitted (NULL discharge date) | NULL handling | -| `ecommerce_easy_1` | E-Commerce | Easy | List premium users with signup details | Simple filter | -| `company_medium_1` | Company | Medium | Total revenue per product category for completed orders | JOIN + GROUP BY | -| `company_medium_2` | Company | Medium | Departments with average salary exceeding $100K | GROUP BY + HAVING | -| `hospital_medium_1` | Hospital | Medium | Total medication cost for admitted patients | Multi-table JOIN + aggregation | -| `ecommerce_medium_1` | E-Commerce | Medium | Revenue per seller for delivered orders | JOIN + GROUP BY | -| `company_hard_1` | Company | Hard | Department salary-to-budget utilization percentage | Computed columns | -| `company_hard_2` | Company | Hard | Employees earning more than their direct manager | Self-join | -| `hospital_hard_1` | Hospital | Hard | Doctors prescribing to patients outside their ward | Double-join on wards | -| `hospital_hard_2` | Hospital | Hard | Patients with multiple appointments with the same doctor | GROUP BY + HAVING + COUNT | -| `ecommerce_hard_1` | E-Commerce | Hard | Return rate per product category with edge cases | Subquery + COALESCE | -| `ecommerce_hard_2` | E-Commerce | Hard | In-stock products that have never been ordered | LEFT JOIN anti-join | - -### Progressive hints - -When the agent gets stuck, my environment progressively reveals more helpful hints on each failed attempt. The first failure triggers a general hint, while later attempts reveal much more specific guidance about table relationships and edge cases. - -## Baseline scores - -Baseline agent: **Qwen/Qwen2.5-72B-Instruct** via Hugging Face Inference API. +| Domain | Tables | +|---|---| +| Company Analytics | departments, employees, products, customers, orders, reviews | +| Hospital Management | wards, doctors, patients, appointments, medications, prescriptions | +| E-Commerce Platform | sellers, categories, products, users, orders, order_items, returns | + +| Task ID | Difficulty | Task | Tests | Attempts | +|---|---|---|---|---| +| `company_easy_1` | Easy | Active Engineering employees by salary | Filter + JOIN + sort | 5 | +| `company_easy_2` | Easy | Electronics products over $50 | Single-table filter | 5 | +| `hospital_easy_1` | Easy | Patients still admitted | `IS NULL` | 5 | +| `ecommerce_easy_1` | Easy | Premium users by signup date | Boolean flag filter | 5 | +| `company_medium_1` | Medium | Revenue per category, completed orders | JOIN + GROUP BY | 6 | +| `company_medium_2` | Medium | Departments averaging > $100K (active staff) | GROUP BY + HAVING | 6 | +| `hospital_medium_1` | Medium | Active medication cost per admitted patient | 3-table JOIN + two NULL filters | 7 | +| `ecommerce_medium_1` | Medium | Revenue per seller, delivered orders | 4-table JOIN chain | 7 | +| `company_hard_1` | Hard | Salary-to-budget utilization per department | Computed percentage | 8 | +| `company_hard_2` | Hard | Employees earning more than their manager | Self-join | 8 | +| `hospital_hard_1` | Hard | Prescriptions to patients outside the doctor's ward | Same table joined twice | 10 | +| `hospital_hard_2` | Hard | Repeat visits to the same doctor | Pairwise GROUP BY + MIN/MAX | 8 | +| `ecommerce_hard_1` | Hard | Return rate per category | Subquery + LEFT JOIN + COALESCE | 10 | +| `ecommerce_hard_2` | Hard | In-stock products never ordered | Anti-join | 8 | + +## Reward + +| Component | Weight | Measures | +|---|---|---| +| Syntax | 0.10 | The query executes | +| Tables | 0.15 | Share of the reference query's tables that are referenced | +| Columns | 0.20 | Share of expected output columns present | +| Results | 0.45 | How closely the returned rows match the reference rows | +| Efficiency | 0.10 | SQL style: aliases, explicit columns, no needless `DISTINCT` or comma joins | + +Penalties: **βˆ’0.10** for destructive SQL, which is also rejected without being executed, and **βˆ’0.05** for resubmitting an identical query. The total is clamped to `[0, 1]`. + +How results are compared: + +- Rows are compared as a **multiset**, so rows duplicated by a join fan-out count against the query. +- Numbers are compared by value to two decimal places, so `INTEGER 145000` equals `REAL 145000.0`. Text is compared case-insensitively. +- Full result credit requires an **exact match**. Row order does not affect correctness, because several reference queries sort on keys with ties. For partially correct results, rows already in the reference position earn a small bonus. +- A query counts as **correct** when its rows match exactly and all expected columns are present. Correctness ends the episode, not a score threshold. A correct query scores 0.92–1.00 depending on its style points. +- Style bonuses that depend on the task, such as `COALESCE`, are only awarded when the reference query uses them. Sprinkling them into every query earns nothing. + +## Diagnostics + +Each step returns a list of `{type, severity, message, suggestion}` objects: + +| Stage | Types | +|---|---| +| Execution | `WRONG_TABLE_NAME`, `WRONG_COLUMN_NAME`, `AMBIGUOUS_COLUMN`, `SYNTAX_ERROR`, `NOT_ALLOWED`, `RESOURCE_LIMIT` | +| Structure | `MISSING_TABLE`, `MISSING_JOIN`, `MISSING_AGGREGATION`, `MISSING_HAVING`, `NULL_HANDLING` | +| Result | `MISSING_COLUMNS`, `EXTRA_COLUMNS`, `MISSING_ROWS`, `EXTRA_ROWS` | +| Behaviour | `DESTRUCTIVE_QUERY`, `REPEATED_QUERY` | + +```json +{ + "type": "MISSING_JOIN", + "severity": "warning", + "message": "This question requires data from multiple tables, but no JOIN was found.", + "suggestion": "Use JOIN to combine tables. Example: SELECT ... FROM t1 JOIN t2 ON t1.id = t2.t1_id" +} +``` -| Task ID | Difficulty | Baseline Score | -|---------|-----------|---------------| -| `company_easy_1` | Easy | 0.90 | -| `company_easy_2` | Easy | 0.90 | -| `hospital_easy_1` | Easy | 0.90 | -| `ecommerce_easy_1` | Easy | 0.85 | -| `company_medium_1` | Medium | 0.75 | -| `company_medium_2` | Medium | 0.70 | -| `hospital_medium_1` | Medium | 0.65 | -| `ecommerce_medium_1` | Medium | 0.70 | -| `company_hard_1` | Hard | 0.55 | -| `company_hard_2` | Hard | 0.45 | -| `hospital_hard_1` | Hard | 0.40 | -| `hospital_hard_2` | Hard | 0.50 | -| `ecommerce_hard_1` | Hard | 0.35 | -| `ecommerce_hard_2` | Hard | 0.45 | +## Action and observation spaces -**Average score: ~0.65** | Easy: ~0.89 | Medium: ~0.70 | Hard: ~0.45 +**Action:** `SqlQueryAction(query: str)`, a single SQLite `SELECT` statement. `WITH ... SELECT` is allowed. -*Scores are approximate and may vary slightly between runs due to LLM temperature.* +**Observation** (`SqlQueryObservation`): -## Getting started +| Field | Description | +|---|---| +| `task_id`, `difficulty`, `database_domain` | Task identity | +| `question`, `schema_description` | What to answer, and the schema with relationships and NULL semantics | +| `expected_columns`, `expected_row_count` | Shape of the correct answer | +| `query_result`, `query_error` | Formatted rows (first 20) or the SQLite error | +| `feedback` | Score breakdown, quality notes, issues, and hints as text | +| `diagnostics`, `efficiency_notes` | Structured versions of the above | +| `steps_remaining`, `current_score`, `history` | Episode progress (best score so far, previous queries and scores) | -### Environment variables +## Sandboxing -Before running the inference script, set these required variables: +- **Read-only database.** After seeding, an SQLite authorizer allows only read operations. SQLite itself refuses writes, schema changes, `PRAGMA` and `ATTACH`. +- **Resource limits.** Each query gets 2 seconds and at most 10,000 rows, so an unbounded recursive CTE can't block a server worker. +- **Isolation.** Every episode gets its own in-memory database, and each step accepts exactly one statement. -```bash -export HF_TOKEN="your-huggingface-token" -export API_BASE_URL="https://router.huggingface.co/v1" # default -export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct" # default -``` +## Quickstart -### Running locally +Requires Python 3.10+ and [uv](https://docs.astral.sh/uv/). ```bash -# install dependencies +git clone https://github.com/rajdeepchatale/sql_query_env.git +cd sql_query_env uv sync - -# start the environment server -uv run server - -# run the baseline agent -export HF_TOKEN="your-token" -python inference.py +uv run server # serves on http://localhost:8000 ``` -## Client example +From Python: ```python import asyncio from sql_query_env import SqlQueryAction, SqlQueryEnv async def main(): - async with SqlQueryEnv(base_url="http://localhost:8000") as client: - result = await client.reset() + async with SqlQueryEnv(base_url="http://localhost:8000") as env: + result = await env.reset(task_id="company_easy_1") print(result.observation.question) - print(result.observation.schema_description) - - result = await client.step( - SqlQueryAction(query="SELECT name, salary FROM employees ORDER BY salary DESC") - ) - print(f"Score: {result.reward}") - print(f"Feedback: {result.observation.feedback}") + result = await env.step(SqlQueryAction( + query="SELECT name, salary FROM employees ORDER BY salary DESC" + )) + print(result.reward, result.done) for diag in result.observation.diagnostics: - print(f" [{diag['type']}] {diag['message']}") + print(f"[{diag['type']}] {diag['message']}") asyncio.run(main()) ``` -## Docker +With Docker: + +```bash +docker build -t sql_query_env . +docker run -p 8000:8000 sql_query_env +``` + +## Baseline agent + +`inference.py` runs an OpenAI-compatible chat model on all 14 tasks. The default is `Qwen/Qwen2.5-72B-Instruct` via the Hugging Face router. The script logs each episode in the `[START]` / `[STEP]` / `[END]` format expected by the OpenEnv evaluation pipeline, then prints per-task and per-domain averages. ```bash -docker build -t sql_query_env:latest . -docker run -p 8000:8000 sql_query_env:latest +export HF_TOKEN="hf_..." # or API_KEY for another provider +export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct" # optional +export API_BASE_URL="https://router.huggingface.co/v1" # optional +uv run python inference.py # expects the server on localhost:8000 ``` +Set `ENV_BASE_URL` to use a remote server, or `IMAGE_NAME` to start the environment from a Docker image. + +## Development + +```bash +uv sync --extra dev +uv run pytest -q # grader, sandbox, episode, and consistency tests +uv run ruff check . +uv run openenv validate . +``` + +See [CONTRIBUTING.md](CONTRIBUTING.md) for how to add a task. + ## Project structure ``` sql_query_env/ -β”œβ”€β”€ Dockerfile # Container build -β”œβ”€β”€ models.py # Action/Observation Pydantic models -β”œβ”€β”€ client.py # EnvClient for WebSocket connection -β”œβ”€β”€ inference.py # Baseline inference script -β”œβ”€β”€ openenv.yaml # Environment manifest (tasks, schemas) -└── server/ - β”œβ”€β”€ app.py # FastAPI entry point - β”œβ”€β”€ sql_query_env_environment.py # Core environment logic - β”œβ”€β”€ tasks.py # Database schemas + all task definitions - └── graders.py # 5-component grading + diagnostics +β”œβ”€β”€ models.py # Action / observation Pydantic models +β”œβ”€β”€ client.py # Async WebSocket client (EnvClient) +β”œβ”€β”€ inference.py # LLM baseline agent +β”œβ”€β”€ openenv.yaml # OpenEnv manifest: tasks, action and observation spaces +β”œβ”€β”€ Dockerfile # Container image (HF Spaces, openenv build) +β”œβ”€β”€ server/ +β”‚ β”œβ”€β”€ app.py # FastAPI app and `server` entry point +β”‚ β”œβ”€β”€ sql_query_env_environment.py # Episode logic: task selection, hints, termination +β”‚ β”œβ”€β”€ tasks.py # Schemas, seed data, read-only DB factory, task definitions +β”‚ └── graders.py # Execution sandbox, 5-component scoring, diagnostics +└── tests/ # pytest suite ``` +## Limitations + +- **Diagnostics reveal the reference query's structure.** They name its tables and flag a missing `JOIN`, `GROUP BY` or `HAVING`. That is by design for guided correction, but it means the scores measure *assisted* text-to-SQL, not blind generation. +- **Row order is not graded.** Tie-aware order checking would need an explicit sort specification per task. +- **Table matching is regex-based.** Table references are extracted without a full SQL parser, so CTE names are not resolved. +- **Small, synthetic data, SQLite dialect only.** Seed tables have 3–24 rows each. + ## License -BSD-3-Clause +BSD 3-Clause. See [LICENSE](LICENSE). + +## Acknowledgements + +Built on [OpenEnv](https://github.com/meta-pytorch/OpenEnv) for the Meta PyTorch OpenEnv Hackathon 2026. diff --git a/__init__.py b/__init__.py index 06d82ad..08ebfb2 100644 --- a/__init__.py +++ b/__init__.py @@ -1,7 +1,12 @@ """SQL Query Generation Environment.""" -from .client import SqlQueryEnv -from .models import SqlQueryAction, SqlQueryObservation +# The repo root doubles as the `sql_query_env` package (OpenEnv layout). Only +# re-export when imported as that package: tools that load this file as a bare +# module (e.g. pytest in a checkout named `sql_query_env-main`) have no parent +# package for the relative imports to resolve against. +if __package__: + from .client import SqlQueryEnv + from .models import SqlQueryAction, SqlQueryObservation __all__ = [ "SqlQueryAction", diff --git a/inference.py b/inference.py index 50ec10a..edff187 100644 --- a/inference.py +++ b/inference.py @@ -1,17 +1,25 @@ """ Baseline inference script for the SQL Query Environment. -Runs Qwen-72B across all task domains and difficulty levels, -printing results in the [START]/[STEP]/[END] format required -by the OpenEnv evaluation pipeline. +Runs an OpenAI-compatible chat model (default: Qwen2.5-72B-Instruct via the +Hugging Face router) on every task, printing results in the +[START]/[STEP]/[END] format required by the OpenEnv evaluation pipeline. Usage: export HF_TOKEN="your-token" - python inference.py + uv run python inference.py + +Environment variables: + HF_TOKEN / API_KEY API key for the model endpoint (required) + API_BASE_URL OpenAI-compatible endpoint (default: HF router) + MODEL_NAME Model id (default: Qwen/Qwen2.5-72B-Instruct) + ENV_BASE_URL Running environment server (default: http://localhost:8000) + IMAGE_NAME Start the environment from this Docker image instead """ import asyncio import os +import sys import textwrap from typing import List, Optional @@ -31,19 +39,21 @@ TEMPERATURE = 0.3 MAX_TOKENS = 500 -# one task from each domain at each difficulty level +# every task, grouped by difficulty (must match openenv.yaml) TASK_IDS = [ "company_easy_1", + "company_easy_2", "hospital_easy_1", "ecommerce_easy_1", "company_medium_1", + "company_medium_2", "hospital_medium_1", "ecommerce_medium_1", "company_hard_1", + "company_hard_2", # self-join "hospital_hard_1", - "ecommerce_hard_1", - "company_hard_2", # self-join challenge "hospital_hard_2", # repeat visits + "ecommerce_hard_1", "ecommerce_hard_2", # anti-join / dead stock ] @@ -184,7 +194,7 @@ async def run_task(client: OpenAI, env: SqlQueryEnv, task_id: str) -> float: log_start(task=task_id, env=BENCHMARK, model=MODEL_NAME) try: - result = await env.reset() + result = await env.reset(task_id=task_id) obs = result.observation feedback = obs.feedback @@ -231,6 +241,8 @@ async def run_task(client: OpenAI, env: SqlQueryEnv, task_id: str) -> float: async def main() -> None: """Run all tasks and print summary.""" + if not API_KEY: + sys.exit("Set HF_TOKEN (or API_KEY) to run the baseline.") client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY) if IMAGE_NAME: @@ -253,12 +265,12 @@ async def main() -> None: print(f"{'='*50}", flush=True) domains = {"company": [], "hospital": [], "ecommerce": []} - for tid, sc in zip(TASK_IDS, scores): + for tid, sc in zip(TASK_IDS, scores, strict=True): domain = tid.split("_")[0] domains[domain].append(sc) print(f" {tid}: {sc:.2f}", flush=True) - print(f"\nPer-domain averages:", flush=True) + print("\nPer-domain averages:", flush=True) for domain, dscores in domains.items(): avg = sum(dscores) / len(dscores) if dscores else 0 print(f" {domain}: {avg:.2f}", flush=True) diff --git a/models.py b/models.py index 4e9eb65..03c2dea 100644 --- a/models.py +++ b/models.py @@ -1,10 +1,9 @@ """ Pydantic models for the SQL environment's action/observation space. -We tried keeping the observation minimal at first but realized the agent -needs way more context to actually improve between steps - so we added -diagnostics, efficiency notes, history etc. Bit of a chonky payload but -it's all text so doesn't matter much for perf. +The observation is deliberately rich: besides the score, it carries the +query result, structured diagnostics, style notes, and the attempt history, +so an agent has what it needs to improve its query between steps. """ from typing import Dict, List, Optional @@ -24,18 +23,17 @@ class SqlQueryAction(Action): class SqlQueryObservation(Observation): """ - Full observation returned after each step. + Full observation returned by ``reset()`` and after each step. - We pack a lot in here because the agent needs to understand what went - wrong and how to fix it. The alternative was returning just a score, - but that gives basically zero learning signal. + A bare score says that a query is wrong but not why; the diagnostics and + feedback fields say what to change on the next attempt. """ # task context task_id: str = Field(default="", description="Current task identifier") difficulty: str = Field(default="easy", description="easy, medium, or hard") database_domain: str = Field( - default="company", description="Which database domain this task uses" + default="", description="Which database domain this task uses" ) question: str = Field(default="", description="Natural language question to answer with SQL") schema_description: str = Field( @@ -47,7 +45,7 @@ class SqlQueryObservation(Observation): query_error: Optional[str] = Field(default=None, description="SQL error message, if any") feedback: str = Field(default="", description="Detailed grading feedback") - # structured diagnostics - these are the key differentiator + # structured, machine-readable feedback for process supervision diagnostics: List[Dict] = Field( default_factory=list, description="Structured error diagnostics: type, severity, message, suggestion", diff --git a/openenv.yaml b/openenv.yaml index ed5e234..d462e3f 100644 --- a/openenv.yaml +++ b/openenv.yaml @@ -1,16 +1,18 @@ spec_version: 1 name: sql_query_env description: > - Trains agents to generate SQL queries from natural language questions. - Tests across three database domains (company analytics, hospital - management, e-commerce) with efficiency scoring and diagnostic feedback. + Text-to-SQL environment: the agent receives a database schema and a + natural-language question, submits SQLite SELECT queries, and is rewarded + by executing them against a read-only database. Three domains (company + analytics, hospital management, e-commerce), 14 tasks, partial-credit + scoring, and structured diagnostic feedback. type: space runtime: fastapi app: server.app:app port: 8000 tasks: - # Company + # Company Analytics - id: company_easy_1 name: "Company β€” Engineering Employees" difficulty: easy @@ -32,7 +34,7 @@ tasks: - id: company_medium_2 name: "Company β€” Department Salaries" difficulty: medium - description: "Departments with average salary exceeding $100K" + description: "Departments whose active employees average more than $100K" domain: company - id: company_hard_1 @@ -41,7 +43,13 @@ tasks: description: "Department salary-to-budget utilization percentage" domain: company - # Hospital + - id: company_hard_2 + name: "Company β€” Salary vs Manager" + difficulty: hard + description: "Employees earning more than their direct manager (self-join)" + domain: company + + # Hospital Management - id: hospital_easy_1 name: "Hospital β€” Current Patients" difficulty: easy @@ -60,7 +68,13 @@ tasks: description: "Doctors prescribing to patients outside their ward" domain: hospital - # E-Commerce + - id: hospital_hard_2 + name: "Hospital β€” Repeat Visits" + difficulty: hard + description: "Patients with multiple appointments with the same doctor" + domain: hospital + + # E-Commerce Platform - id: ecommerce_easy_1 name: "E-Commerce β€” Premium Users" difficulty: easy @@ -79,19 +93,6 @@ tasks: description: "Return rate per product category with edge cases" domain: ecommerce - # Expert-level - - id: company_hard_2 - name: "Company β€” Salary vs Manager" - difficulty: hard - description: "Employees earning more than their direct manager (self-join)" - domain: company - - - id: hospital_hard_2 - name: "Hospital β€” Repeat Visits" - difficulty: hard - description: "Patients with multiple appointments with the same doctor" - domain: hospital - - id: ecommerce_hard_2 name: "E-Commerce β€” Dead Stock" difficulty: hard @@ -103,7 +104,7 @@ action_space: properties: query: type: string - description: "SQL SELECT query to execute against the database" + description: "A single SQLite SELECT statement (WITH ... SELECT allowed)" observation_space: type: object @@ -123,7 +124,7 @@ observation_space: query_result: type: string query_error: - type: string + type: [string, "null"] feedback: type: string diagnostics: diff --git a/pyproject.toml b/pyproject.toml index 2a8bf47..066cc16 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,26 +1,59 @@ [build-system] -requires = ["setuptools>=45", "wheel"] +requires = ["setuptools>=77", "wheel"] build-backend = "setuptools.build_meta" [project] name = "openenv-sql_query_env" -version = "0.1.0" -description = "SQL query generation environment for OpenEnv" +version = "0.2.0" +description = "OpenEnv reinforcement-learning environment for text-to-SQL with execution-based, partial-credit rewards" +readme = "README.md" +license = "BSD-3-Clause" +license-files = ["LICENSE"] +authors = [{ name = "Rajdeep Chatale" }] requires-python = ">=3.10" +keywords = ["openenv", "reinforcement-learning", "text-to-sql", "llm", "sqlite", "benchmark"] +classifiers = [ + "Programming Language :: Python :: 3", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Database", +] dependencies = [ "openenv-core[core]>=0.2.2", + "openai>=1.0.0", # used by inference.py ] [project.optional-dependencies] dev = [ "pytest>=8.0.0", "pytest-cov>=4.0.0", + "pyyaml>=6.0", + "ruff>=0.5.0", ] +[project.urls] +Repository = "https://github.com/rajdeepchatale/sql_query_env" +"Hugging Face Space" = "https://huggingface.co/spaces/rajdeepchatale/sql_query_env" + [project.scripts] server = "sql_query_env.server.app:main" [tool.setuptools] include-package-data = true packages = ["sql_query_env", "sql_query_env.server"] -package-dir = { "sql_query_env" = ".", "sql_query_env.server" = "server" } \ No newline at end of file +package-dir = { "sql_query_env" = ".", "sql_query_env.server" = "server" } + +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["."] +# the repo root is itself the package (OpenEnv layout), so avoid rootdir-relative imports +addopts = "--import-mode=importlib" + +[tool.ruff] +target-version = "py310" + +[tool.ruff.lint] +# explicit rule set so CI doesn't change with ruff's evolving defaults +select = ["E4", "E7", "E9", "F", "I", "B"] + +[tool.ruff.lint.isort] +known-first-party = ["sql_query_env"] diff --git a/server/Dockerfile b/server/Dockerfile deleted file mode 100644 index d709091..0000000 --- a/server/Dockerfile +++ /dev/null @@ -1,74 +0,0 @@ -# Multi-stage build using openenv-base -# This Dockerfile is flexible and works for both: -# - In-repo environments (with local OpenEnv sources) -# - Standalone environments (with openenv from PyPI/Git) -# The build script (openenv build) handles context detection and sets appropriate build args. - -ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest -FROM ${BASE_IMAGE} AS builder - -WORKDIR /app - -# Ensure git is available (required for installing dependencies from VCS) -RUN apt-get update && \ - apt-get install -y --no-install-recommends git && \ - rm -rf /var/lib/apt/lists/* - -# Build argument to control whether we're building standalone or in-repo -ARG BUILD_MODE=in-repo -ARG ENV_NAME=sql_query_env - -# Copy environment code (always at root of build context) -COPY . /app/env - -# For in-repo builds, openenv is already vendored in the build context -# For standalone builds, openenv will be installed via pyproject.toml -WORKDIR /app/env - -# Ensure uv is available (for local builds where base image lacks it) -RUN if ! command -v uv >/dev/null 2>&1; then \ - curl -LsSf https://astral.sh/uv/install.sh | sh && \ - mv /root/.local/bin/uv /usr/local/bin/uv && \ - mv /root/.local/bin/uvx /usr/local/bin/uvx; \ - fi - -# Install dependencies using uv sync -# If uv.lock exists, use it; otherwise resolve on the fly -RUN --mount=type=cache,target=/root/.cache/uv \ - if [ -f uv.lock ]; then \ - uv sync --frozen --no-install-project --no-editable; \ - else \ - uv sync --no-install-project --no-editable; \ - fi - -RUN --mount=type=cache,target=/root/.cache/uv \ - if [ -f uv.lock ]; then \ - uv sync --frozen --no-editable; \ - else \ - uv sync --no-editable; \ - fi - -# Final runtime stage -FROM ${BASE_IMAGE} - -WORKDIR /app - -# Copy the virtual environment from builder -COPY --from=builder /app/env/.venv /app/.venv - -# Copy the environment code -COPY --from=builder /app/env /app/env - -# Set PATH to use the virtual environment -ENV PATH="/app/.venv/bin:$PATH" - -# Set PYTHONPATH so imports work correctly -ENV PYTHONPATH="/app/env:$PYTHONPATH" - -# Health check -HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ - CMD curl -f http://localhost:8000/health || exit 1 - -# Run the FastAPI server -# The module path is constructed to work with the /app/env structure -CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 8000"] diff --git a/server/graders.py b/server/graders.py index 969b48e..7e821ff 100644 --- a/server/graders.py +++ b/server/graders.py @@ -1,24 +1,38 @@ """ Grading logic for SQL queries. -I split the scoring into five isolated components instead of a standard binary pass/fail. -I found during early local testing that if an agent referenced the correct tables but -messed up an aggregate column name, binary scoring gave it zero signal, and it couldn't learn. +Scoring is split into five components instead of binary pass/fail. With a +binary reward, a query that references the right tables but gets one +aggregate column wrong scores the same as garbage, which gives an agent no +gradient to follow. Partial credit keeps the signal dense: -Score weights were chosen through extensive trial and error on my end: syntax=0.10, tables=0.15, columns=0.20, results=0.45, efficiency=0.10 -I designed this so a query needs to be mostly correct to break 0.70, but the agent -will always retain *some* partial progress. +Result correctness dominates, so a query has to return mostly correct rows +to score above ~0.70. A query only counts as *correct* (and ends the +episode) when its rows match the reference result exactly. + +Agent queries run under a time and row budget against a read-only +connection (see ``tasks.create_database``). """ import re import sqlite3 +import time +from collections import Counter from dataclasses import dataclass, field from typing import Dict, List, Optional, Set, Tuple from .tasks import Task +# Execution budget for a single agent query. The seed data is tiny, so any +# legitimate query finishes in milliseconds; these limits only stop runaway +# queries (e.g. an unbounded recursive CTE) from blocking a server worker. +QUERY_TIMEOUT_S = 2.0 +MAX_RESULT_ROWS = 10_000 +_PROGRESS_INTERVAL = 10_000 # SQLite VM instructions between deadline checks +_LIMIT_PREFIX = "Resource limit exceeded" + # --------------------------------------------------------------------------- # Data structures @@ -27,7 +41,7 @@ @dataclass class Diagnostic: """Structured diagnostic for process supervision feedback.""" - type: str # e.g. MISSING_JOIN, WRONG_TABLE + type: str # e.g. MISSING_JOIN, WRONG_TABLE_NAME severity: str # error / warning / info message: str suggestion: str @@ -59,6 +73,13 @@ class GradeResult: rows_expected: int diagnostics: List[Diagnostic] = field(default_factory=list) efficiency_notes: List[str] = field(default_factory=list) + # True when the returned rows match the reference result exactly and all + # expected columns are present. Independent of the efficiency score. + is_correct: bool = False + + +class QueryLimitError(Exception): + """Raised when an agent query exceeds the time or row budget.""" # --------------------------------------------------------------------------- @@ -68,14 +89,16 @@ class GradeResult: def _extract_tables(query: str) -> Set[str]: """Pull table names out of FROM and JOIN clauses. - Quick regex approach β€” doesn't handle subquery aliases or CTEs but - it's good enough for our task set. Didn't want to pull in sqlparse - just for this. + Regex-based rather than a full parser: handles comma-separated FROM lists + and subqueries, but not CTE names. Good enough for the task set without + adding a dependency on sqlparse. """ q = " ".join(query.lower().split()) tables = set() - for match in re.finditer(r'\bfrom\s+(\w+)', q): - tables.add(match.group(1)) + from_list = r'\bfrom\s+(\w+(?:\s+(?:as\s+)?\w+)?(?:\s*,\s*\w+(?:\s+(?:as\s+)?\w+)?)*)' + for match in re.finditer(from_list, q): + for item in match.group(1).split(","): + tables.add(item.split()[0]) for match in re.finditer(r'\bjoin\s+(\w+)', q): tables.add(match.group(1)) return tables @@ -100,12 +123,15 @@ def _format_result(rows: List[Tuple], columns: List[str], max_rows: int = 20) -> def _is_destructive(query: str) -> bool: - """Check if query tries to modify data. We block everything except SELECT. + """Check whether a query tries to modify data or schema. - Double-checking here even though the environment also uses read-only - connections β€” better safe than sorry. + String literals and comments are stripped first so that e.g. + ``WHERE notes LIKE '%update%'`` is not flagged. This check only decides + the penalty; the read-only authorizer on the connection is what actually + prevents writes. """ - q = query.upper().strip() + q = re.sub(r"'(?:[^']|'')*'", "''", query) + q = re.sub(r"--[^\n]*|/\*.*?\*/", " ", q, flags=re.DOTALL).upper() bad_keywords = ["DROP", "DELETE", "TRUNCATE", "ALTER", "UPDATE", "INSERT", "CREATE"] for kw in bad_keywords: if re.search(rf'\b{kw}\b', q): @@ -113,32 +139,69 @@ def _is_destructive(query: str) -> bool: return False +def _run_query(conn: sqlite3.Connection, query: str) -> Tuple[List[str], List[Tuple]]: + """Execute an agent query under the time and row budget. + + Returns lower-cased column names and the fetched rows. Raises + ``QueryLimitError`` when a budget is exceeded and ``sqlite3.Error`` (or + ``ValueError`` for statements without a result set) otherwise. + """ + deadline = time.monotonic() + QUERY_TIMEOUT_S + conn.set_progress_handler(lambda: time.monotonic() > deadline, _PROGRESS_INTERVAL) + cursor = None + try: + cursor = conn.execute(query) + if cursor.description is None: + raise ValueError("Query did not return a result set. Submit a SELECT statement.") + columns = [desc[0].lower() for desc in cursor.description] + rows = cursor.fetchmany(MAX_RESULT_ROWS + 1) + except sqlite3.OperationalError as e: + if str(e) == "interrupted": + raise QueryLimitError( + f"{_LIMIT_PREFIX}: query ran longer than {QUERY_TIMEOUT_S:g}s." + ) from e + raise + finally: + conn.set_progress_handler(None, 0) + if cursor is not None: + cursor.close() + + if len(rows) > MAX_RESULT_ROWS: + raise QueryLimitError(f"{_LIMIT_PREFIX}: query returned more than {MAX_RESULT_ROWS} rows.") + return columns, rows + + def _normalize_value(v) -> str: """Normalize cell values for comparison. - Rounds floats to 2 decimal places so 3.1000001 matches 3.10. + Numbers are compared by value at 2 decimal places, so 3.1000001 matches + 3.10 and an INTEGER 145000 matches a REAL 145000.0. Text is compared + case-insensitively. """ if v is None: return "NULL" - if isinstance(v, float): - return f"{v:.2f}" + if isinstance(v, (int, float)): + return f"{float(v):.2f}" return str(v).strip().lower() def _compare_result_sets( actual_rows: List[Tuple], expected_rows: List[Tuple], - query: str = "", ground_truth_query: str = "", -) -> float: - """Compare actual vs expected results, returns similarity 0-1. - - Also checks row ordering if the ground truth has ORDER BY. +) -> Tuple[float, bool]: + """Compare actual vs expected rows. + + Returns ``(similarity, exact_match)``. Rows are compared as multisets, so + duplicated rows (e.g. from join fan-out) count against the query. Row + order does not affect an exact match: several reference queries sort on + keys with ties, where any tie order is equally valid. For partially + correct results, rows in the reference position earn a small bonus. """ if not expected_rows: - return 1.0 if not actual_rows else 0.0 + return (1.0, True) if not actual_rows else (0.0, False) if not actual_rows: - return 0.0 + return 0.0, False def normalize_row(row: Tuple) -> Tuple[str, ...]: return tuple(_normalize_value(v) for v in row) @@ -146,33 +209,33 @@ def normalize_row(row: Tuple) -> Tuple[str, ...]: actual_norm = [normalize_row(r) for r in actual_rows] expected_norm = [normalize_row(r) for r in expected_rows] - actual_set = set(actual_norm) - expected_set = set(expected_norm) - - matching = actual_set & expected_set - total_expected = len(expected_set) + actual_counts = Counter(actual_norm) + expected_counts = Counter(expected_norm) + if actual_counts == expected_counts: + return 1.0, True - if total_expected == 0: - return 1.0 + matching = sum((actual_counts & expected_counts).values()) + total_expected = len(expected_norm) # what fraction of expected rows did we get? - content_score = len(matching) / total_expected + content_score = matching / total_expected # dock points for extra rows the agent shouldn't have returned - extra = len(actual_set - expected_set) + extra = len(actual_norm) - matching if extra > 0: content_score = max(0.0, content_score - min(extra / total_expected * 0.3, 0.3)) - # bonus for correct ordering when ORDER BY is expected + # small bonus for rows already in the right position when ORDER BY is expected order_bonus = 0.0 has_order = "ORDER BY" in ground_truth_query.upper() if has_order and content_score > 0.5 and len(actual_norm) == len(expected_norm): correct_positions = sum( - 1 for a, e in zip(actual_norm, expected_norm) if a == e + 1 for a, e in zip(actual_norm, expected_norm, strict=True) if a == e ) order_bonus = (correct_positions / len(expected_norm)) * 0.1 - return min(content_score + order_bonus, 1.0) + # full credit is reserved for an exact match + return min(content_score + order_bonus, 0.95), False # --------------------------------------------------------------------------- @@ -190,17 +253,20 @@ def _diagnose_query( ) -> List[Diagnostic]: """Figure out what went wrong and give actionable feedback. - We check for ~17 different issue types. The goal is process - supervision: give the agent enough info to fix its query on - the next attempt. + Covers execution errors (unknown table/column, ambiguous column, + disallowed statement, resource limits, syntax) and structural mismatches + against the reference query (missing tables, JOIN, GROUP BY, HAVING, + NULL handling, columns, row counts). The goal is process supervision: + give the agent enough information to fix its query on the next attempt. """ diagnostics = [] q_upper = query.upper() gt_upper = task.ground_truth_query.upper() - # -- syntax errors (parsed from SQLite error messages) -- + # -- execution errors (parsed from SQLite error messages) -- if error: - if "no such table" in error.lower(): + err_lower = error.lower() + if "no such table" in err_lower: table_match = re.search(r'no such table: (\w+)', error, re.IGNORECASE) table_name = table_match.group(1) if table_match else "unknown" diagnostics.append(Diagnostic( @@ -209,7 +275,7 @@ def _diagnose_query( message=f"Table '{table_name}' does not exist in this database.", suggestion="Check the schema description for available table names.", )) - elif "no such column" in error.lower(): + elif "no such column" in err_lower: col_match = re.search(r'no such column: (\S+)', error, re.IGNORECASE) col_name = col_match.group(1) if col_match else "unknown" diagnostics.append(Diagnostic( @@ -218,13 +284,27 @@ def _diagnose_query( message=f"Column '{col_name}' does not exist.", suggestion="Check the table definition for correct column names.", )) - elif "ambiguous column" in error.lower(): + elif "ambiguous column" in err_lower: diagnostics.append(Diagnostic( type="AMBIGUOUS_COLUMN", severity="error", message="A column name is ambiguous - it exists in multiple tables.", suggestion="Use table aliases (e.g. e.name instead of just name).", )) + elif "not authorized" in err_lower: + diagnostics.append(Diagnostic( + type="NOT_ALLOWED", + severity="error", + message="The database is read-only; this statement is not allowed.", + suggestion="Submit a single SELECT statement (WITH ... SELECT is fine).", + )) + elif error.startswith(_LIMIT_PREFIX): + diagnostics.append(Diagnostic( + type="RESOURCE_LIMIT", + severity="error", + message=error, + suggestion="Check for unbounded recursion or an accidental cross join.", + )) else: diagnostics.append(Diagnostic( type="SYNTAX_ERROR", @@ -243,8 +323,8 @@ def _diagnose_query( diagnostics.append(Diagnostic( type="MISSING_TABLE", severity="warning", - message=f"Your query doesn't reference: {', '.join(missing_tables)}", - suggestion=f"You probably need to JOIN with {', '.join(missing_tables)} to get the required data.", + message=f"Your query doesn't reference: {', '.join(sorted(missing_tables))}", + suggestion=f"You probably need to JOIN with {', '.join(sorted(missing_tables))} to get the required data.", )) if "JOIN" in gt_upper and "JOIN" not in q_upper: @@ -280,7 +360,7 @@ def _diagnose_query( )) # -- result set checks -- - missing_cols = set(expected_cols) - set(actual_cols) + missing_cols = sorted(set(expected_cols) - set(actual_cols)) if missing_cols: diagnostics.append(Diagnostic( type="MISSING_COLUMNS", @@ -289,7 +369,7 @@ def _diagnose_query( suggestion=f"Add these to your SELECT: {', '.join(missing_cols)}", )) - extra_cols = set(actual_cols) - set(expected_cols) + extra_cols = sorted(set(actual_cols) - set(expected_cols)) if extra_cols: diagnostics.append(Diagnostic( type="EXTRA_COLUMNS", @@ -303,7 +383,7 @@ def _diagnose_query( type="EXTRA_ROWS", severity="warning", message=f"Got {len(actual_rows)} rows but expected {len(expected_rows)}.", - suggestion="Your WHERE/HAVING conditions may be too broad. Try adding more filters.", + suggestion="Your WHERE/HAVING conditions may be too broad, or a JOIN is duplicating rows.", )) elif len(actual_rows) < len(expected_rows) and len(actual_rows) > 0: diagnostics.append(Diagnostic( @@ -329,12 +409,14 @@ def _score_efficiency( """Score SQL best practices (0.0 - 0.10). Rewards good habits and penalizes bad ones. The weights are small - (max 0.10 total) so this doesn't dominate scoring, but it adds up - and teaches the agent to write cleaner SQL. + (max 0.10 total) so style never outweighs correctness. Bonuses that + depend on the task (COALESCE, DISTINCT) are judged against the reference + query, so they can't be farmed by adding the construct everywhere. """ notes = [] score = 0.0 q_upper = query.upper().strip() + gt_upper = task.ground_truth_query.upper() # -- good practices -- if re.search(r'\bAS\s+\w+', query, re.IGNORECASE): @@ -342,7 +424,7 @@ def _score_efficiency( notes.append("Good: using column aliases (AS)") if "SELECT *" not in q_upper and len(expected_cols) > 0: - if set(actual_cols) == set(expected_cols) or set(actual_cols).issuperset(set(expected_cols)): + if set(actual_cols).issuperset(set(expected_cols)): score += 0.02 notes.append("Good: selecting specific columns instead of SELECT *") @@ -351,7 +433,8 @@ def _score_efficiency( score += 0.02 notes.append("Good: using table aliases for readability") - if re.search(r'\bCOALESCE\b|\bIFNULL\b', query, re.IGNORECASE): + null_funcs = r'\bCOALESCE\b|\bIFNULL\b' + if re.search(null_funcs, query, re.IGNORECASE) and re.search(null_funcs, gt_upper): score += 0.03 notes.append("Good: proper NULL handling with COALESCE/IFNULL") @@ -360,7 +443,7 @@ def _score_efficiency( score -= 0.02 notes.append("Avoid SELECT * when you only need specific columns") - if "DISTINCT" in q_upper and "DISTINCT" not in task.ground_truth_query.upper(): + if "DISTINCT" in q_upper and "DISTINCT" not in gt_upper: score -= 0.01 notes.append("DISTINCT may not be needed here - check if duplicates are actually expected") @@ -378,6 +461,31 @@ def _score_efficiency( # Main grading function # --------------------------------------------------------------------------- +def _failed_result( + penalty: float, + feedback_parts: List[str], + error: str, + diagnostics: List[Diagnostic], +) -> GradeResult: + """Build the result for a query that was blocked or failed to execute.""" + return GradeResult( + total_score=max(0.0, penalty), + syntax_score=0.0, + table_score=0.0, + column_score=0.0, + result_score=0.0, + efficiency_score=0.0, + penalty=penalty, + feedback="\n".join(feedback_parts), + query_result=None, + error=error, + rows_returned=0, + rows_expected=0, + diagnostics=diagnostics, + efficiency_notes=[], + ) + + def grade_query( conn: sqlite3.Connection, query: str, @@ -393,6 +501,8 @@ def grade_query( results: 0.45 (correct data returned) efficiency: 0.10 (query quality) penalties: -0.10 for destructive SQL, -0.05 for repeated queries + + Destructive queries are rejected without being executed. """ feedback_parts = [] syntax_score = 0.0 @@ -402,14 +512,14 @@ def grade_query( efficiency_score = 0.0 penalty = 0.0 query_result_str = None - error_msg = None rows_returned = 0 rows_expected = 0 diagnostics = [] efficiency_notes = [] # check for destructive queries - if _is_destructive(query): + destructive = _is_destructive(query) + if destructive: penalty -= 0.10 feedback_parts.append("PENALTY: Destructive SQL detected. Only SELECT statements are allowed.") diagnostics.append(Diagnostic( @@ -433,13 +543,15 @@ def grade_query( )) break + if destructive: + return _failed_result( + penalty, feedback_parts, + "Blocked: only read-only SELECT statements are allowed.", diagnostics, + ) + # 1. Syntax check (0.10) - actual_rows = [] - actual_cols = [] try: - cursor = conn.execute(query) - actual_cols = [desc[0].lower() for desc in cursor.description] if cursor.description else [] - actual_rows = cursor.fetchall() + actual_cols, actual_rows = _run_query(conn, query) rows_returned = len(actual_rows) syntax_score = 0.10 feedback_parts.append("Query executed successfully.") @@ -447,40 +559,15 @@ def grade_query( except Exception as e: error_msg = str(e) feedback_parts.append(f"SQL Error: {error_msg}") - - diagnostics = _diagnose_query( - query, task, [], [], [], [], error_msg - ) - - total = max(0.0, penalty) - return GradeResult( - total_score=total, - syntax_score=0.0, - table_score=0.0, - column_score=0.0, - result_score=0.0, - efficiency_score=0.0, - penalty=penalty, - feedback="\n".join(feedback_parts), - query_result=None, - error=error_msg, - rows_returned=0, - rows_expected=0, - diagnostics=diagnostics, - efficiency_notes=[], - ) + diagnostics.extend(_diagnose_query(query, task, [], [], [], [], error_msg)) + return _failed_result(penalty, feedback_parts, error_msg, diagnostics) # get ground truth results try: - expected_cursor = conn.execute(task.ground_truth_query) - expected_rows = expected_cursor.fetchall() - expected_cols_from_query = [ - desc[0].lower() for desc in expected_cursor.description - ] if expected_cursor.description else [] + expected_rows = conn.execute(task.ground_truth_query).fetchall() rows_expected = len(expected_rows) except Exception: expected_rows = [] - expected_cols_from_query = [] rows_expected = 0 # 2. Table references (0.15) @@ -496,12 +583,13 @@ def grade_query( feedback_parts.append("Correct tables referenced.") elif table_ratio > 0: missing = expected_tables - actual_tables - feedback_parts.append(f"Partially correct tables. Missing: {', '.join(missing)}") + feedback_parts.append(f"Partially correct tables. Missing: {', '.join(sorted(missing))}") else: - feedback_parts.append(f"Wrong tables. Consider using: {', '.join(expected_tables)}") + feedback_parts.append(f"Wrong tables. Consider using: {', '.join(sorted(expected_tables))}") # 3. Column check (0.20) expected_cols_lower = [c.lower() for c in task.expected_columns] + col_ratio = 1.0 if expected_cols_lower: matching_cols = set(actual_cols) & set(expected_cols_lower) @@ -511,7 +599,7 @@ def grade_query( if col_ratio == 1.0: feedback_parts.append("Correct output columns.") elif col_ratio > 0: - missing_cols = set(expected_cols_lower) - set(actual_cols) + missing_cols = sorted(set(expected_cols_lower) - set(actual_cols)) feedback_parts.append(f"Missing columns: {', '.join(missing_cols)}") else: feedback_parts.append( @@ -519,12 +607,12 @@ def grade_query( ) # 4. Result set comparison (0.45) - similarity = _compare_result_sets( - actual_rows, expected_rows, query, task.ground_truth_query + similarity, exact_match = _compare_result_sets( + actual_rows, expected_rows, task.ground_truth_query ) result_score = 0.45 * similarity - if similarity == 1.0: + if exact_match: feedback_parts.append("Perfect result set - all rows match.") elif similarity > 0.5: feedback_parts.append( @@ -547,10 +635,10 @@ def grade_query( ) # generate diagnostics - diagnostics = _diagnose_query( + diagnostics.extend(_diagnose_query( query, task, actual_rows, expected_rows, actual_cols, expected_cols_lower, None, - ) + )) # compute final score total = syntax_score + table_score + column_score + result_score + efficiency_score + penalty @@ -584,9 +672,10 @@ def grade_query( penalty=penalty, feedback="\n".join(feedback_parts), query_result=query_result_str, - error=error_msg, + error=None, rows_returned=rows_returned, rows_expected=rows_expected, diagnostics=diagnostics, efficiency_notes=efficiency_notes, + is_correct=exact_match and col_ratio == 1.0, ) diff --git a/server/requirements.txt b/server/requirements.txt deleted file mode 100644 index 65b1c22..0000000 --- a/server/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -openenv[core]>=0.2.0 -fastapi>=0.115.0 -uvicorn>=0.24.0 - - - diff --git a/server/sql_query_env_environment.py b/server/sql_query_env_environment.py index 42155f9..1faab24 100644 --- a/server/sql_query_env_environment.py +++ b/server/sql_query_env_environment.py @@ -2,17 +2,13 @@ Core environment logic for the SQL query environment. Each episode creates a fresh in-memory SQLite database for the chosen -domain so there's no state leaking between episodes. SQLite in-memory -databases are cheap to spin up so this doesn't hurt performance. - -TODO(team): might want to add a "curriculum mode" that auto-selects -tasks of increasing difficulty within a session. For now we just let -the caller pick or we cycle through randomly. +domain, so no state leaks between episodes. The database is read-only once +seeded, and in-memory SQLite databases are cheap enough to build per episode. """ import random import sqlite3 -from typing import Dict, List, Optional +from typing import List, Optional from uuid import uuid4 from openenv.core.env_server.interfaces import Environment @@ -26,8 +22,8 @@ from models import SqlQueryAction, SqlQueryObservation try: + from .graders import grade_query from .tasks import ( - ALL_TASKS, ALL_TASKS_LIST, SCHEMAS, TASK_MAP, @@ -35,10 +31,9 @@ create_database, get_expected_result, ) - from .graders import grade_query, GradeResult except ImportError: + from server.graders import grade_query from server.tasks import ( - ALL_TASKS, ALL_TASKS_LIST, SCHEMAS, TASK_MAP, @@ -46,15 +41,17 @@ create_database, get_expected_result, ) - from server.graders import grade_query, GradeResult class SqlQueryEnvironment(Environment): """Main environment class - presents schema + question, grades SQL. - Steps are independent in terms of DB state (no writes accumulate). - We track query history and best score across steps for penalty logic - and progressive hints. + ``reset(task_id=...)`` selects a specific task; without one, tasks are + served from a shuffled queue that cycles through all tasks (pass + ``seed`` for a reproducible order). The episode ends when the query's + result matches the reference exactly or the task's attempt budget runs + out. Query history and best score are tracked across steps for the + repeated-query penalty and progressive hints. """ SUPPORTS_CONCURRENT_SESSIONS: bool = True @@ -64,26 +61,27 @@ def __init__(self): self._db: Optional[sqlite3.Connection] = None self._current_task: Optional[Task] = None self._current_schema_id: Optional[str] = None + self._expected_row_count: int = 0 self._previous_queries: List[str] = [] self._best_score: float = 0.0 self._step_rewards: List[float] = [] + self._rng = random.Random() self._task_queue: List[str] = [] self._current_task_index: int = 0 - def _get_task_list(self) -> List[str]: - """Shuffle task IDs for random ordering.""" - all_ids = [t.id for t in ALL_TASKS_LIST] - random.shuffle(all_ids) - return all_ids - def _select_task(self, task_id: Optional[str] = None) -> Task: - """Pick a specific task or grab the next one from the queue.""" - if task_id and task_id in TASK_MAP: + """Pick a specific task or grab the next one from the shuffled queue.""" + if task_id is not None: + if task_id not in TASK_MAP: + raise ValueError( + f"Unknown task_id {task_id!r}. Valid ids: {', '.join(sorted(TASK_MAP))}" + ) return TASK_MAP[task_id] # lazy-init the queue if not self._task_queue: - self._task_queue = self._get_task_list() + self._task_queue = [t.id for t in ALL_TASKS_LIST] + self._rng.shuffle(self._task_queue) self._current_task_index = 0 if self._current_task_index >= len(self._task_queue): @@ -93,13 +91,28 @@ def _select_task(self, task_id: Optional[str] = None) -> Task: self._current_task_index += 1 return task - def reset(self, task_id: Optional[str] = None) -> SqlQueryObservation: + def reset( + self, + seed: Optional[int] = None, + episode_id: Optional[str] = None, + task_id: Optional[str] = None, + **kwargs, + ) -> SqlQueryObservation: """Start a new episode. + Args: + seed: Reseeds the task queue so un-targeted resets are reproducible. + episode_id: Optional custom episode identifier. + task_id: Run a specific task (see ``openenv.yaml`` for ids). + Creates a fresh DB for the task's domain and returns the initial - observation with schema, question, and hints. + observation with schema, question, and the first hint. """ - self._state = State(episode_id=str(uuid4()), step_count=0) + if seed is not None: + self._rng.seed(seed) + self._task_queue = [] + + self._state = State(episode_id=episode_id or str(uuid4()), step_count=0) self._previous_queries = [] self._best_score = 0.0 self._step_rewards = [] @@ -116,7 +129,9 @@ def reset(self, task_id: Optional[str] = None) -> SqlQueryObservation: self._db = create_database(self._current_schema_id) schema = SCHEMAS[self._current_schema_id] - expected_rows = get_expected_result(self._db, self._current_task.ground_truth_query) + self._expected_row_count = len( + get_expected_result(self._db, self._current_task.ground_truth_query) + ) return SqlQueryObservation( task_id=self._current_task.id, @@ -135,7 +150,7 @@ def reset(self, task_id: Optional[str] = None) -> SqlQueryObservation: ), diagnostics=[], efficiency_notes=[], - expected_row_count=len(expected_rows), + expected_row_count=self._expected_row_count, expected_columns=self._current_task.expected_columns, steps_remaining=self._current_task.max_steps, current_score=0.0, @@ -165,7 +180,12 @@ def _progressive_hints(self, step: int) -> str: return "" return "\n".join(f" - {h}" for h in shown) - def step(self, action: SqlQueryAction) -> SqlQueryObservation: + def step( + self, + action: SqlQueryAction, + timeout_s: Optional[float] = None, + **kwargs, + ) -> SqlQueryObservation: """Evaluate a submitted SQL query and return graded feedback.""" if self._current_task is None or self._db is None: return SqlQueryObservation( @@ -190,8 +210,8 @@ def step(self, action: SqlQueryAction) -> SqlQueryObservation: self._step_rewards.append(grade_result.total_score) self._best_score = max(self._best_score, grade_result.total_score) - # done if near-perfect or out of steps - is_done = grade_result.total_score >= 0.95 or steps_remaining <= 0 + # done if the result is exactly right or out of attempts + is_done = grade_result.is_correct or steps_remaining <= 0 # build history for the observation history = [ @@ -201,26 +221,25 @@ def step(self, action: SqlQueryAction) -> SqlQueryObservation: "score": round(s, 2), } for i, (q, s) in enumerate( - zip(self._previous_queries, self._step_rewards) + zip(self._previous_queries, self._step_rewards, strict=True) ) ] feedback = grade_result.feedback # add hints if the agent is struggling - if not is_done and grade_result.total_score < 0.95: + if not is_done: hint_text = self._progressive_hints(step) if hint_text: feedback += f"\n\nHints (attempt {step}):\n{hint_text}" if is_done: - if grade_result.total_score >= 0.95: - feedback += "\n\nNear-perfect score achieved." + if grade_result.is_correct: + feedback += "\n\nCorrect: the result set matches the expected output." else: feedback += f"\n\nEpisode finished. Best score: {self._best_score:.2f}" feedback += f"\nScore progression: {' -> '.join(f'{s:.2f}' for s in self._step_rewards)}" - expected_rows = get_expected_result(self._db, self._current_task.ground_truth_query) schema = SCHEMAS[self._current_schema_id] return SqlQueryObservation( @@ -234,7 +253,7 @@ def step(self, action: SqlQueryAction) -> SqlQueryObservation: feedback=feedback, diagnostics=[d.to_dict() for d in grade_result.diagnostics], efficiency_notes=grade_result.efficiency_notes, - expected_row_count=len(expected_rows), + expected_row_count=self._expected_row_count, expected_columns=self._current_task.expected_columns, steps_remaining=max(0, steps_remaining), current_score=self._best_score, @@ -247,6 +266,7 @@ def step(self, action: SqlQueryAction) -> SqlQueryObservation: "database_domain": self._current_schema_id, "step": step, "best_score": self._best_score, + "is_correct": grade_result.is_correct, "syntax_score": grade_result.syntax_score, "table_score": grade_result.table_score, "column_score": grade_result.column_score, diff --git a/server/tasks.py b/server/tasks.py index 18a1bde..2a0ed35 100644 --- a/server/tasks.py +++ b/server/tasks.py @@ -1,22 +1,21 @@ """ Database schemas, seed data, and task definitions. -Three separate database domains so the agent can't just memorize one -schema. We experimented with more domains but three felt like the sweet -spot for coverage without making the task set unmanageable. +Tasks span three independent database domains so an agent has to read the +schema it is given rather than memorize a single layout. -Seed data is intentionally small (~10-15 rows per table). Bigger datasets -would be more realistic but for an RL environment the grading speed matters -more. We can always scale up the data later if needed. +Seed data is intentionally small (3-24 rows per table): grading runs +the reference query on every step, so small tables keep episodes fast while +still containing the edge cases the tasks depend on (NULLs, inactive rows, +cancelled orders, rows with no matches). -NOTE: if you add a new task, make sure to also add it to the openenv.yaml -manifest and update the TASK_IDS list in inference.py. +When adding a task, also add it to ``openenv.yaml`` and to ``TASK_IDS`` in +``inference.py``; ``tests/test_tasks.py`` fails if the three drift apart. """ import sqlite3 from dataclasses import dataclass, field -from typing import Dict, List, Optional, Tuple - +from typing import Dict, List, Tuple # --------------------------------------------------------------------------- # Data structures @@ -44,7 +43,7 @@ class Task: description: str = "" hints: List[str] = field(default_factory=list) max_steps: int = 5 - challenge_type: str = "" # e.g., "ambiguous", "temporal", "efficiency" + challenge_type: str = "" # e.g. "null_handling", "self_join", "anti_join" # --------------------------------------------------------------------------- @@ -639,13 +638,34 @@ class Task: } +# Authorizer action codes a plain SELECT needs. Everything else (writes, +# schema changes, PRAGMA, ATTACH, transactions) is denied. SQLITE_RECURSIVE +# (33) is needed for recursive CTEs; the constant only exists on Python 3.11+. +_READ_ONLY_ACTIONS = frozenset({ + sqlite3.SQLITE_SELECT, + sqlite3.SQLITE_READ, + sqlite3.SQLITE_FUNCTION, + getattr(sqlite3, "SQLITE_RECURSIVE", 33), +}) + + +def _read_only_authorizer(action: int, *_args) -> int: + return sqlite3.SQLITE_OK if action in _READ_ONLY_ACTIONS else sqlite3.SQLITE_DENY + + def create_database(schema_id: str = "company") -> sqlite3.Connection: - """Create an in-memory SQLite database for a specific domain.""" + """Create a seeded, read-only in-memory SQLite database for a domain. + + The authorizer is installed after seeding, so every statement the agent + submits is compiled against it: anything other than a read is rejected + by SQLite before it runs, and the episode's data cannot be modified. + """ schema = SCHEMAS[schema_id] conn = sqlite3.connect(":memory:", check_same_thread=False) conn.execute("PRAGMA foreign_keys = ON") conn.executescript(schema.schema_sql) conn.executescript(schema.seed_sql) + conn.set_authorizer(_read_only_authorizer) return conn @@ -655,12 +675,6 @@ def get_expected_result(conn: sqlite3.Connection, query: str) -> List[Tuple]: return cursor.fetchall() -def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: - """Get column names from a query result.""" - cursor = conn.execute(query) - return [desc[0] for desc in cursor.description] if cursor.description else [] - - # --------------------------------------------------------------------------- # Company tasks # --------------------------------------------------------------------------- @@ -723,7 +737,7 @@ def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: id="company_medium_2", difficulty="medium", schema_id="company", - question="Find the average salary per department, but only show departments where the average salary exceeds $100,000. Show department name and average salary, sorted by average salary descending.", + question="Considering only active employees, find the average salary per department, but only show departments where that average exceeds $100,000. Show department name and average salary, sorted by average salary descending.", ground_truth_query=""" SELECT d.name, AVG(e.salary) AS avg_salary FROM employees e @@ -842,7 +856,7 @@ def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: ORDER BY d.name """, expected_columns=["doctor_name", "doctor_ward", "patient_name", "patient_ward", "medication"], - description="Cross-ward prescription analysis β€” 5-table JOIN with self-referencing comparison", + description="Cross-ward prescription analysis β€” 5-table JOIN with the wards table joined twice", hints=[ "Join prescriptions with doctors, patients, medications, and wards (twice!)", "Join wards once for doctor's ward and once for patient's ward", @@ -909,7 +923,7 @@ def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: id="ecommerce_hard_1", difficulty="hard", schema_id="ecommerce", - question="Calculate the return rate for each product category. Show the category name, total items sold (from delivered orders), total items returned, and the return rate as a percentage (rounded to 1 decimal). Include categories with zero returns. Sort by return rate descending.", + question="Calculate the return rate for each product category that has delivered sales. Show the category name, total items sold (sum of quantities from delivered orders), total items returned (the number of return records for the category, regardless of order or return status), and the return rate as a percentage (returned / sold * 100, rounded to 1 decimal). Include categories with zero returns. Sort by return rate descending.", ground_truth_query=""" SELECT c.name AS category, @@ -951,7 +965,7 @@ def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: # --------------------------------------------------------------------------- -# Expert tasks β€” designed to genuinely challenge frontier models +# Additional hard tasks β€” one per domain, each built around a classic pattern # --------------------------------------------------------------------------- EXPERT_TASKS = [ @@ -985,7 +999,7 @@ def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: challenge_type="self_join", ), - # Temporal reasoning: readmission within 30 days (hospital) + # Repeat visits: patient-doctor pairs with several completed appointments (hospital) Task( id="hospital_hard_2", difficulty="hard", @@ -1007,7 +1021,7 @@ def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: ORDER BY visit_count DESC """, expected_columns=["patient_name", "doctor_name", "visit_count", "first_visit", "last_visit"], - description="Temporal analysis with GROUP BY HAVING on joined data", + description="Pairwise grouping with HAVING and MIN/MAX date range", hints=[ "Join appointments with patients and doctors", "Group by patient-doctor pairs", @@ -1018,7 +1032,7 @@ def get_column_names(conn: sqlite3.Connection, query: str) -> List[str]: challenge_type="temporal_grouping", ), - # Multi-step: products in stock but never ordered (ecommerce) + # Anti-join: products in stock but never ordered (ecommerce) Task( id="ecommerce_hard_2", difficulty="hard", diff --git a/tests/test_environment.py b/tests/test_environment.py new file mode 100644 index 0000000..b5685fc --- /dev/null +++ b/tests/test_environment.py @@ -0,0 +1,81 @@ +"""Episode-level tests for SqlQueryEnvironment.""" + +import pytest + +from models import SqlQueryAction +from server.sql_query_env_environment import SqlQueryEnvironment +from server.tasks import TASK_MAP + + +@pytest.fixture +def env(): + environment = SqlQueryEnvironment() + yield environment + environment.close() + + +def test_reset_selects_requested_task(env): + obs = env.reset(task_id="hospital_hard_2") + + assert obs.task_id == "hospital_hard_2" + assert obs.expected_columns == TASK_MAP["hospital_hard_2"].expected_columns + assert obs.expected_row_count > 0 + assert obs.steps_remaining == TASK_MAP["hospital_hard_2"].max_steps + assert not obs.done + + +def test_reset_rejects_unknown_task(env): + with pytest.raises(ValueError, match="Unknown task_id"): + env.reset(task_id="company_easy_99") + + +def test_seeded_resets_are_reproducible(): + def task_order(seed): + environment = SqlQueryEnvironment() + return [environment.reset(seed=seed if i == 0 else None).task_id for i in range(5)] + + assert task_order(7) == task_order(7) + + +def test_unseeded_resets_cycle_through_every_task(env): + seen = {env.reset().task_id for _ in range(len(TASK_MAP))} + + assert seen == set(TASK_MAP) + + +def test_correct_answer_ends_episode(env): + env.reset(task_id="company_easy_2") + obs = env.step(SqlQueryAction(query=TASK_MAP["company_easy_2"].ground_truth_query)) + + assert obs.done + assert obs.reward >= 0.9 + assert obs.metadata["is_correct"] + + +def test_episode_ends_when_attempts_run_out(env): + obs = env.reset(task_id="company_easy_1") + for i in range(obs.steps_remaining): + obs = env.step(SqlQueryAction(query=f"SELECT {i}")) + + assert obs.done + assert obs.steps_remaining == 0 + + +def test_writes_cannot_be_used_to_game_the_reward(env): + # Deleting every row would make an empty answer "match" the reference. + env.reset(task_id="company_easy_1") + obs = env.step(SqlQueryAction(query="DELETE FROM employees")) + assert obs.reward == 0.0 + + obs = env.step(SqlQueryAction(query="SELECT e.name AS name, e.salary AS salary FROM employees e")) + assert not obs.done + assert obs.reward < 0.9 + + +def test_dropped_table_does_not_break_the_episode(env): + env.reset(task_id="ecommerce_hard_1") + env.step(SqlQueryAction(query="DROP TABLE returns")) + obs = env.step(SqlQueryAction(query=TASK_MAP["ecommerce_hard_1"].ground_truth_query)) + + assert obs.done + assert obs.metadata["is_correct"] diff --git a/tests/test_graders.py b/tests/test_graders.py new file mode 100644 index 0000000..64b03c8 --- /dev/null +++ b/tests/test_graders.py @@ -0,0 +1,155 @@ +"""Behavioural tests for the grader: correctness, sandboxing, and anti-gaming.""" + +import time + +import pytest + +from server import graders +from server.graders import grade_query +from server.tasks import ALL_TASKS_LIST, TASK_MAP, create_database + + +def _grade(task_id, query, previous=None): + task = TASK_MAP[task_id] + return grade_query(create_database(task.schema_id), query, task, previous or []) + + +@pytest.mark.parametrize("task", ALL_TASKS_LIST, ids=lambda t: t.id) +def test_reference_query_is_graded_correct(task): + result = grade_query(create_database(task.schema_id), task.ground_truth_query, task, []) + + assert result.is_correct + assert result.result_score == pytest.approx(0.45) + assert result.total_score >= 0.9 + + +def test_unrelated_query_scores_low(): + result = _grade("company_easy_1", "SELECT 1") + + assert not result.is_correct + assert result.total_score <= 0.15 + + +def test_row_order_does_not_block_correctness(): + task = TASK_MAP["company_easy_1"] + reversed_order = task.ground_truth_query.replace("DESC", "ASC") + + assert _grade("company_easy_1", reversed_order).is_correct + + +def test_duplicated_rows_are_not_correct(): + task = TASK_MAP["ecommerce_hard_2"] + body = task.ground_truth_query.split("ORDER BY")[0] + result = _grade("ecommerce_hard_2", f"{body} UNION ALL {body}") + + assert not result.is_correct + assert result.result_score < 0.45 + assert "EXTRA_ROWS" in {d.type for d in result.diagnostics} + + +def test_integer_and_real_values_compare_equal(): + # salary is REAL in the reference; an INTEGER cast must still match + query = """ + SELECT e.name, CAST(e.salary AS INTEGER) AS salary + FROM employees e JOIN departments d ON e.department_id = d.id + WHERE d.name = 'Engineering' AND e.is_active = 1 + """ + assert _grade("company_easy_1", query).is_correct + + +@pytest.mark.parametrize("query", [ + "DELETE FROM employees", + "DROP TABLE employees", + "UPDATE employees SET salary = 0", + "INSERT INTO departments VALUES (9, 'X', 1, 'Y')", +]) +def test_destructive_queries_are_blocked_and_penalized(query): + task = TASK_MAP["company_easy_1"] + conn = create_database(task.schema_id) + before = conn.execute("SELECT COUNT(*), SUM(salary) FROM employees").fetchone() + + result = grade_query(conn, query, task, []) + + assert result.total_score == 0.0 + assert result.penalty == pytest.approx(-0.10) + assert "DESTRUCTIVE_QUERY" in {d.type for d in result.diagnostics} + assert conn.execute("SELECT COUNT(*), SUM(salary) FROM employees").fetchone() == before + + +@pytest.mark.parametrize("query", [ + "PRAGMA table_info(employees)", + "ATTACH DATABASE ':memory:' AS probe", + "REPLACE INTO departments VALUES (1, 'X', 1, 'Y')", +]) +def test_database_rejects_non_read_statements(query): + result = _grade("company_easy_1", query) + + assert result.total_score == 0.0 + assert result.error + assert result.diagnostics + + +def test_keywords_inside_literals_are_not_destructive(): + query = "SELECT name, salary FROM employees WHERE email LIKE '%update%' -- drop later" + result = _grade("company_easy_1", query) + + assert result.penalty == 0.0 + assert result.error is None + + +def test_runaway_query_is_interrupted(monkeypatch): + monkeypatch.setattr(graders, "QUERY_TIMEOUT_S", 0.2) + query = "WITH RECURSIVE r(x) AS (SELECT 1 UNION ALL SELECT x + 1 FROM r) SELECT COUNT(*) FROM r" + + start = time.monotonic() + result = _grade("company_easy_1", query) + + assert time.monotonic() - start < 5 + assert "RESOURCE_LIMIT" in {d.type for d in result.diagnostics} + + +def test_oversized_result_is_rejected(): + query = "WITH RECURSIVE r(x) AS (SELECT 1 UNION ALL SELECT x + 1 FROM r) SELECT x FROM r" + result = _grade("company_easy_1", query) + + assert "RESOURCE_LIMIT" in {d.type for d in result.diagnostics} + + +def test_repeated_query_is_penalized(): + query = "SELECT name, salary FROM employees" + result = _grade("company_easy_1", query, previous=[query.lower()]) + + assert result.penalty == pytest.approx(-0.05) + assert "REPEATED_QUERY" in {d.type for d in result.diagnostics} + + +def test_penalty_diagnostics_survive_execution_errors(): + query = "SELECT nme FROM employees" + result = _grade("company_easy_1", query, previous=[query]) + + types = {d.type for d in result.diagnostics} + assert {"REPEATED_QUERY", "WRONG_COLUMN_NAME"} <= types + + +def test_coalesce_bonus_only_when_task_needs_it(): + query = """ + SELECT COALESCE(p.name, '') AS name, p.price AS price + FROM products p + WHERE p.category = 'Electronics' AND p.price > 50 + ORDER BY p.price + """ + result = _grade("company_easy_2", query) + + assert result.is_correct + assert not any("COALESCE" in note for note in result.efficiency_notes) + + +def test_comma_join_tables_are_detected(): + query = """ + SELECT e.name, e.salary FROM employees e, departments d + WHERE e.department_id = d.id AND d.name = 'Engineering' AND e.is_active = 1 + """ + result = _grade("company_easy_1", query) + + assert result.table_score == pytest.approx(0.15) + assert "MISSING_TABLE" not in {d.type for d in result.diagnostics} diff --git a/tests/test_tasks.py b/tests/test_tasks.py new file mode 100644 index 0000000..a70afec --- /dev/null +++ b/tests/test_tasks.py @@ -0,0 +1,57 @@ +"""Consistency checks for the task set, the manifest, and the baseline script.""" + +import ast +from pathlib import Path + +import pytest +import yaml + +from server.tasks import ALL_TASKS_LIST, SCHEMAS, TASK_MAP, create_database + +ROOT = Path(__file__).resolve().parent.parent + + +@pytest.mark.parametrize("task", ALL_TASKS_LIST, ids=lambda t: t.id) +def test_ground_truth_runs_and_matches_expected_columns(task): + conn = create_database(task.schema_id) + cursor = conn.execute(task.ground_truth_query) + columns = [d[0].lower() for d in cursor.description] + rows = cursor.fetchall() + + assert rows, "reference query must return at least one row" + assert columns == [c.lower() for c in task.expected_columns] + + +@pytest.mark.parametrize("task", ALL_TASKS_LIST, ids=lambda t: t.id) +def test_task_metadata(task): + assert task.difficulty in {"easy", "medium", "hard"} + assert task.schema_id in SCHEMAS + assert task.id.startswith(f"{task.schema_id}_{task.difficulty}_") + assert task.hints, "every task needs at least one hint" + assert task.max_steps >= 1 + + +def test_task_ids_are_unique(): + ids = [t.id for t in ALL_TASKS_LIST] + assert len(ids) == len(set(ids)) + + +def test_manifest_lists_every_task(): + manifest = yaml.safe_load((ROOT / "openenv.yaml").read_text()) + manifest_tasks = {t["id"]: t for t in 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