openenv.yaml

name: sql-agent-openenv
version: "1.0.0"
description: >
  A SQL generation and repair environment where an AI agent learns to write
  correct SQL queries through a self-debugging loop powered by a LinUCB
  contextual bandit and GEPA prompt evolution. Models real-world data analyst
  workflows — querying databases with natural language, handling errors, and
  iteratively improving.

author: sql-agent-openenv-team
tags:
  - openenv
  - sql
  - rl
  - nlp
  - contextual-bandit

# ── Endpoints ────────────────────────────────────────────────────────────────
api:
  reset: /reset
  step: /step
  state: /state

# ── Action Space ─────────────────────────────────────────────────────────────
action_space:
  type: discrete
  n: 9
  actions:
    - name: generate
      description: "Generate SQL from scratch (first attempt)"
    - name: rewrite_full
      description: "Completely rewrite the query from scratch"
    - name: fix_column
      description: "Fix wrong column name references using schema"
    - name: fix_table
      description: "Fix wrong table name references or JOIN structure"
    - name: add_groupby
      description: "Add or fix GROUP BY / aggregation clauses"
    - name: rewrite_cte
      description: "Restructure CTEs or subqueries"
    - name: fix_syntax
      description: "Fix syntax errors (brackets, commas, keywords)"
    - name: change_dialect
      description: "Convert to SQLite-compatible functions"
    - name: relax_filter
      description: "Broaden or remove overly strict WHERE conditions"

# ── Observation Space ────────────────────────────────────────────────────────
observation_space:
  type: dict
  fields:
    - name: question
      type: string
      description: "Natural language question to answer with SQL"
    - name: schema_info
      type: string
      description: "Full database schema (tables, columns, types, FK relationships)"
    - name: current_sql
      type: string
      nullable: true
      description: "The SQL generated on the last attempt (null on first step)"
    - name: error_message
      type: string
      nullable: true
      description: "SQLite error message from the last attempt (null on success)"
    - name: error_class
      type: string
      nullable: true
      description: "Classified error type (e.g. no_such_column, syntax_error)"
    - name: attempt_number
      type: integer
      description: "Current attempt number (0 at reset, increments each step)"
    - name: max_attempts
      type: integer
      description: "Maximum allowed attempts per episode (5)"
    - name: task_id
      type: string
      description: "Active task identifier"
    - name: task_difficulty
      type: string
      description: "Task difficulty level: easy | medium | hard"

# ── Reward ───────────────────────────────────────────────────────────────────
reward:
  range: [0.05, 0.95]
  description: >
    Task score is the grader output clamped strictly inside (0, 1). Graders
    score partial progress (column correctness, row-count match) and apply
    attempt penalties for multi-step repair episodes.

# ── Tasks ────────────────────────────────────────────────────────────────────
tasks:
  - id: simple_queries
    name: Simple SQL Queries
    difficulty: easy
    description: >
      Single-table SELECT queries. Agent must retrieve correct rows by applying
      basic filters and projections on the marketplace database.
    question_count: 5
    grader: >
      Checks that required output columns are present and row count falls
      within expected bounds. Attempt penalty not applied.

  - id: join_queries
    name: SQL Join Queries
    difficulty: medium
    description: >
      Multi-table JOIN queries with GROUP BY and aggregation. Agent must
      correctly join tables and compute aggregates over the marketplace data.
    question_count: 5
    grader: >
      Correct columns + row count score multiplied by (1.0 - 0.1*(attempts-1)).
      Rewards efficient, first-try solutions.

  - id: complex_queries
    name: Complex SQL Queries
    difficulty: hard
    description: >
      Advanced queries using CTEs, window functions, nested aggregations, and
      multi-level joins. Requires precise SQLite syntax knowledge.
    question_count: 5
    grader: >
      Strict correctness required. Score capped at 0.8 without first-attempt
      bonus. Attempt penalty of 0.1*(attempts-1) applied. Hard tasks genuinely
      challenge frontier models.

# ── Environment Metadata ─────────────────────────────────────────────────────
metadata:
  max_steps_per_episode: 5
  database: SQLite (marketplace schema — users, products, orders, reviews, sellers)
  rl_algorithm: LinUCB contextual bandit (feature_dim=20, 8 repair actions)
  prompt_optimizer: GEPA (Generative Evolutionary Prompt Adaptation)
  runtime_estimate_minutes: 5
  compute_requirements:
    vcpu: 2
    memory_gb: 4