README.md

---
license: cc-by-sa-4.0
task_categories:
  - table-question-answering
  - text-generation
language:
  - en
tags:
  - text-to-sql
  - nl2sql
  - supply-chain
  - erp
  - odoo
  - multi-turn
  - domain-specific
pretty_name: SCM-SQL
size_categories:
  - n<1K
source_datasets:
  - original
paperswithcode_id: null
---

# SCM-SQL — a supply-chain natural-language-to-SQL evaluation set

**500 (question, gold SQL) pairs authored against the live Odoo 17 supply-chain
schema, spanning 6 explicit complexity levels including multi-turn dialogues.**

Built for the dissertation *Domain-Aware Multi-Agent Natural-Language-to-SQL for
Enterprise Supply Chain Intelligence* by Aniruddha Prakash Kawarase (BITS Pilani
WILP, 2026). Released as a public evaluation benchmark so other researchers can
compare domain-aware text-to-SQL systems on realistic enterprise-ERP queries.

## Why this dataset exists

Public text-to-SQL benchmarks like [BIRD](https://bird-bench.github.io) and
[Spider](https://yale-lily.github.io/spider) are open-domain: they test whether
a model generalises across many small schemas (typically 3-6 tables per
database). Enterprise supply-chain deployments look different: one deep
schema, many tables, dialect quirks, and analysts who iterate on their
questions over multiple turns. SCM-SQL is designed to stress-test that
enterprise setting.

## What's in the box

- **500 (question, gold SQL) pairs** — `data/pilot_500.yaml`
- **6 explicit complexity levels** — L1 (single-table filter) → L6 (multi-turn refinement)
- **4 supply-chain sub-domain tags** — demand, finance, inventory, logistics
- **Every gold SQL is execute-verified** against a stock Odoo 17 demo database
- **Multi-turn dialogues** — 50 L6 pairs consist of 2-3 turns each where turn N refines the SQL of turn N-1

### Distribution

| Level | Style | n | Multi-turn? |
|-------|-------|---|---|
| L1 | Single-table filter or aggregate | 100 | No |
| L2 | Two-table JOIN + GROUP BY | 100 | No |
| L3 | Nested subquery / semi-join / 3-table join | 130 | No |
| L4 | Window function | 60 | No |
| L5 | CTE + rollup / GROUPING SETS | 60 | No |
| L6 | Multi-turn conversational refinement | 50 | Yes (2-3 turns each) |
| **Total** | | **500** | **556 turn-level trials** |

Per-domain (a pair can carry multiple domain tags):

| Domain | Approx. count |
|--------|---------------|
| demand | ~ 145 |
| finance | ~ 140 |
| inventory | ~ 130 |
| logistics | ~ 130 |

## Target database

Every gold SQL is executable on **stock Odoo 17** (image
`odoo:17.0` from Docker Hub, unmodified). The Odoo demo dataset ships with
approximately 42 000 rows across 498 tables and is fetched by
`docker compose up` on the reference implementation.

**No modification is made to the Odoo demo data itself.** SCM-SQL is a new
artefact authored *on top of* the Odoo schema — it does not fork, edit, or
redistribute the underlying Odoo data.

## Loading

**With `datasets`:**
```python
from datasets import load_dataset
ds = load_dataset("AniruddhaAI/scm-sql", split="test")
print(ds[0])
```

**Directly from YAML:**
```python
import yaml
with open("data/pilot_500.yaml") as f:
    pairs = yaml.safe_load(f)["pairs"]
print(len(pairs), "pairs")
print(pairs[0])
```

See `examples/` for full loader and evaluation-harness snippets.

## Schema

Every pair is a YAML document with the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique identifier, e.g. `L1-001`, `L6-023` |
| `level` | int (1-6) | Complexity tier |
| `domains` | list[string] | One or more of `demand`, `finance`, `inventory`, `logistics` |
| `nl` | string | (L1-L5 only) The natural-language question |
| `gold_sql` | string | (L1-L5 only) The verified gold PostgreSQL statement |
| `turns` | list | (L6 only) Sequence of `{nl, gold_sql}` turns; turn N refines turn N-1 |
| `tags` | list[string] | Intent tags (`aggregate`, `filter`, `join`, `window`, `cte`, `multi_turn`, ...) |

Full schema documentation with examples: [`docs/SCHEMA.md`](docs/SCHEMA.md).

## Evaluation protocol

SCM-SQL follows the **Execution Accuracy (EX)** protocol standard in the
text-to-SQL literature: a predicted SQL is counted correct if and only if
executing it against the target Odoo database produces a row-equivalent
result set to the gold SQL (order-agnostic, column-name-agnostic row
multiset comparison).

An additional metric, **Soft-EX**, absorbs benign alias renames: a
prediction returning `revenue` where the gold returns `total_revenue`
still counts as correct as long as the row-values match.

See `examples/evaluate_predictions.py` for a reference implementation.

## Modifications to source datasets

**None.** SCM-SQL is a new artefact, not a fork of any existing dataset.
The Odoo 17 demo database itself is unchanged — no `INSERT`, `UPDATE`, or
`DELETE` statement is ever issued against it. The reference implementation
runs against the stock `odoo:17.0` Docker image, verifiable by pulling with
digest pinning.

## Companion project

The reference multi-agent NL-to-SQL implementation that this dataset was
built to evaluate lives at:

**https://github.com/AniruddhaPKawarase/scm-nl2sql**

That repository contains a LangGraph orchestrator (Router · Specialist ·
Composer · Compliance · Executor), a Next.js UI with live evaluation-metric
chips, and the full evaluation harness (`scripts/run_evaluation.py`) that
computes EX / Soft-EX / VES on this dataset.

## License

**CC BY-SA 4.0** — attribution + share-alike. This matches the licences
under which BIRD and Spider are released, so mixed benchmarking is
licence-consistent.

## Citation

If you use SCM-SQL in your research, please cite:

```bibtex
@misc{kawarase2026scmsql,
  title  = {SCM-SQL: A Supply-Chain Natural-Language-to-SQL Evaluation Set},
  author = {Kawarase, Aniruddha Prakash},
  year   = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/AniruddhaAI/scm-sql}},
  note   = {Companion repository: https://github.com/AniruddhaPKawarase/scm-nl2sql}
}
```

See [`CITATION.cff`](CITATION.cff) for the machine-readable citation file.

## Contact

Aniruddha Prakash Kawarase · BITS Pilani WILP · aniruddhakawarase@gmail.com