title	SQL Data Analyst Investigation Environment
colorFrom	blue
colorTo	purple
sdk	docker
pinned	false
app_port	8000
base_path	/web
tags	openenv

🔍 SQL Data Analyst Investigation Environment

A multi-step data investigation environment where an AI agent analyzes a realistic e-commerce database through iterative SQL queries to answer complex analytical questions — simulating a real-world data analyst workflow.

🎯 What Makes This Special

• Dual Action Space: QueryAction (explore with SQL) vs AnswerAction (submit findings)
• Multi-Step Investigation: Agent must plan, hypothesize, and iteratively drill down
• Rich Reward Shaping: Partial rewards for productive exploration + multi-dimensional final grading
• Realistic Database: 8-table e-commerce schema with ~5K+ rows and planted anomalies
• 5 Investigation Tasks: From easy lookups to complex root cause analysis (easy → hard)

🚀 Quick Start

from sql_data_analyst import QueryAction, AnswerAction, SqlDataAnalystEnv

async with SqlDataAnalystEnv(base_url="http://localhost:8000") as env:
    # Start an investigation
    result = await env.reset(task_id="anomaly_diagnosis")
    print(result.observation.task_description)
    print(result.observation.schema_info)

    # Query the database
    result = await env.step(QueryAction(
        sql="SELECT quarter, SUM(revenue) FROM sales GROUP BY quarter"
    ))
    print(result.observation.query_result)

    # Submit your findings
    result = await env.step(AnswerAction(
        answer="Revenue dropped due to Electronics decline in APAC...",
        evidence=["Electronics down 45%", "APAC is the outlier region"]
    ))
    print(f"Score: {result.reward}")

📊 Investigation Tasks

#	Task	Difficulty	Steps	Description
1	basic_lookup	Easy	5	Top 5 products by revenue in Q4 2025
2	comparative_analysis	Medium	7	Compare return rates across categories
3	trend_investigation	Medium	8	Identify fastest growing customer segment
4	anomaly_diagnosis	Hard	10	Root cause of Q3 2025 revenue drop
5	strategic_recommendation	Hard	12	Recommend 3 products to discontinue

🏗️ Database Schema

8-table e-commerce analytics database:

• customers — segment, region, join date
• products — category, subcategory, price
• suppliers — country, reliability score
• orders — date, status, total amount
• order_items — quantity, price, discount
• shipments — ship/delivery dates, carrier
• returns — reason, refund amount
• reviews — rating (1-5), review text

🎯 Reward System

Per-step (QueryAction):

• Information gain: +0.0 to +0.2 (new tables, productive results)
• SQL error: -0.05
• Duplicate query: -0.1

Final (AnswerAction):

• Correctness × 0.6 (facts matched against ground truth)
• Completeness × 0.3 (answer depth, evidence, specific numbers)
• Efficiency × 0.1 (steps used vs budget)

Total episode score: clipped to [0.0, 1.0]

🛠️ Development

# Run locally
uvicorn server.app:app --reload --port 8000

# Run tests
python test_local.py

# Run baseline (requires OPENAI_API_KEY)
python baseline.py

# Build Docker image
docker build -t sql-data-analyst:latest -f server/Dockerfile .

📁 Project Structure

sql_data_analyst/
├── __init__.py              # Package exports
├── models.py                # QueryAction, AnswerAction, AnalystObservation, AnalystState
├── client.py                # SqlDataAnalystEnv(EnvClient)
├── openenv.yaml             # OpenEnv manifest
├── pyproject.toml           # Dependencies
├── baseline.py              # LLM inference agent
├── test_local.py            # Local test script
├── README.md                # This file
└── server/
    ├── __init__.py
    ├── app.py               # FastAPI server
    ├── environment.py        # Core environment logic
    ├── database.py           # SQLite schema + seed data
    ├── tasks.py              # 5 task definitions
    ├── grader.py             # Multi-dimensional grading
    ├── requirements.txt
    └── Dockerfile