README.md

---
license: apache-2.0
pipeline_tag: text-ranking
library_name: transformers
---

# GRAST-SQL: Scaling Text-to-SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers

GRAST-SQL is a lightweight, open-source schema-filtering framework that scales Text-to-SQL to real-world, very wide schemas by compacting prompts without sacrificing accuracy. It ranks columns with a query-aware LLM encoder enriched by values/metadata, reranks them via a graph transformer over a functional-dependency (FD) graph to capture inter-column structure, and then guarantees joinability with a Steiner-tree spanner to produce a small, connected sub-schema. This approach delivers near-perfect recall with substantially higher precision and maintains sub-second median latency while scaling to schemas with 23,000+ columns.

This model was presented in the paper: [Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers](https://huggingface.co/papers/2512.16083).

For more details, code, and further usage instructions, please visit the [official GitHub repository](https://github.com/thanhdath/grast-sql).

## Sample Usage

To apply GRAST-SQL to your own database and filter the most relevant columns for a given question, follow these two simple steps. Ensure your environment is set up as described in the [GitHub repository](https://github.com/thanhdath/grast-sql).

### Step 1: Initialize (ONE-TIME per database) - Functional Dependency Graph Construction & Metadata Completion

Extract schema information, generate table/column meanings, predict missing keys, and build the functional dependency graph. Make sure your OpenAI API key is set in `.env` if you are using an OpenAI model for meaning generation.

```bash
python init_schema.py \
    --db-path /path/to/your/database.sqlite \
    --output your_database.pkl \
    --model gpt-4.1-mini
```

**Arguments:**
- `--db-path`: Path to your SQLite database file (required)
- `--output`: Output path for the graph pickle file (default: `schema_graph.pkl`)
- `--model`: OpenAI model to use for meaning generation and key prediction (default: `gpt-4.1-mini`)

### Step 2: Filter Top-K Columns

Use the GRAST-SQL model to filter the most relevant columns for a given question:

```bash
python filter_columns.py \
    --graph your_database.pkl \
    --question "Show name, country, age for all singers ordered by age from the oldest to the youngest." \
    --top-k 5
```

**Arguments:**
- `--graph`: Path to the graph pickle file from Step 1 (required)
- `--question`: Natural language question about the database (required)
- `--top-k`: Number of top columns to retrieve (default: 10)
- `--checkpoint`: Path to GNN checkpoint (default: `griffith-bigdata/GRAST-SQL-0.6B-BIRD-Reranker/layer-3-hidden-2048.pt`)
- `--encoder-path`: Path to encoder model (default: `griffith-bigdata/GRAST-SQL-0.6B-BIRD-Reranker`)
- `--max-length`: Maximum sequence length (default: 4096)
- `--batch-size`: Batch size for embedding generation (default: 32)
- `--hidden-dim`: Hidden dimension for GNN (default: 2048)
- `--num-layers`: Number of GNN layers (default: 3)

## Citation

If you use GRAST-SQL in your research, please cite the following paper:

```bibtex
@misc{hoang2025scalingtext2sqlllmefficientschema,
      title={Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers}, 
      author={Thanh Dat Hoang and Thanh Tam Nguyen and Thanh Trung Huynh and Hongzhi Yin and Quoc Viet Hung Nguyen},
      year={2025},
      eprint={2512.16083},
      archivePrefix={arXiv},
      primaryClass={cs.DB},
      url={https://arxiv.org/abs/2512.16083}, 
}
```