# RSL-SQL
**Repository Path**: godspeedotc/RSL-SQL
## Basic Information
- **Project Name**: RSL-SQL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-18
- **Last Updated**: 2025-08-18
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
Please give us a star ⭐ for the latest update.
[](https://arxiv.org/abs/2411.00073)
## Overview
## Main Results
### Execution Accuracy on BIRD Dev Set
## Ablaition Study
## Project directory structure
- Download `pytorch_model.bin` and place it in the `few_shot/sentence_transformers/` folder. Download address: https://huggingface.co/sentence-transformers/all-mpnet-base-v2/tree/main
- Download the `column_meaning.json` file and place it in the `data/` folder. Download address: https://github.com/quge2023/TA-SQL/blob/master/outputs/column_meaning.json
- Download the `dev.json` file and `dev_tables.json` file of the development set in the `data/` folder. Download address: https://bird-bench.github.io/
- Download the `train-00000-of-00001-fe8894d41b7815be.parquet` file and place it in the `few_shot/` folder. Download address: https://huggingface.co/datasets/xu3kev/BIRD-SQL-data-train/tree/main/data
```plaintext
RSL-SQL/
├── README.md
├── requirements.txt
│
├── data/
│ ├── column_meaning.json
│ ├── dev.json
│ └── dev_tables.json
│
├── database/
│ └── dev_databases/
│
├── few_shot/
│ ├── sentence_transformers/
│ └── train-00000-of-00001-fe8894d41b7815be.parquet
│
└── src/
└── configs/
└── config.py
```
## environment
```bash
conda create -n rsl_sql python=3.10
conda activate rsl_sql
pip install -r requirements.txt
```
Modify parameter configuration in `src/configs/config.py`
```python
dev_databases_path = 'database/dev_databases'
dev_json_path = 'data/dev.json'
api = '..'
base_url = 'http://'
```
## RUN
### 1. Data Preprocessing
```bash
# Construct `ppl_dev.json`.
python src/data_construct.py
#Construct few-shot examples pairs
python few_shot/construct_QA.py
# Generate few-shot examples
python few_shot/slg_main.py --dataset src/information/ppl_dev.json --out_file src/information/example.json --kshot 3
# add few-shot examples to ppl_dev.json
python src/information/add_example.py
```
### 2. preliminary sql generation and bidirectional schema linking
```bash
# step 1: preliminary sql
# There are two output files in this step, one is `src/sql_log/preliminary_sql.txt` and the other is `src/schema_linking/LLM.json`
# If an error occurs, you need to save these two files in time, then continue running and save the subsequent results.
python src/step_1_preliminary_sql.py --ppl_file src/information/ppl_dev.json --sql_out_file src/sql_log/preliminary_sql.txt --Schema_linking_LLM src/schema_linking/LLM.json --start_index 0
# schema linking
python src/bid_schema_linking.py --pre_sql_file src/sql_log/preliminary_sql.txt --sql_sl_output src/schema_linking/sql.json --hint_sl_output src/schema_linking/hint.json --LLM_sl_output src/schema_linking/LLM.json --Schema_linking_output src/schema_linking/schema.json
cp src/schema_linking/schema.json src/information
# add schema linking to ppl_dev.json
python src/information/add_sl.py
```
### 3. SQL Generation based simplified schema and Information augmentation
```bash
# step 2: sql generation
# There are two output files in this step, one is `src/sql_log/step_2_information_augmentation.txt` and the other is `src/information/augmentation.json`
# If an error occurs, you need to save these two files in time, then continue running and save the subsequent results.
python src/step_2_information_augmentation.py --ppl_file src/information/ppl_dev.json --sql_2_output src/sql_log/step_2_information_augmentation.txt --information_output src/information/augmentation.json --start_index 0
# add augmentation to ppl_dev.json
python src/information/add_augmentation.py
```
### 4. SQL selection
```bash
# step 3: sql selection
# There is one output files in this step, one is `src/sql_log/step_3_binary.txt`.
# If an error occurs, you need to save these two files in time, then continue running and save the subsequent results.
python src/step_3_binary_selection.py --ppl_file src/information/ppl_dev.json --sql_3_output src/sql_log/step_3_binary.txt --sql_1 src/sql_log/preliminary_sql.txt --sql_2 src/sql_log/step_2_information_augmentation.txt --start_index 0
```
### 5. SQL refinement
```bash
# step 4: sql refinement
# There is one output files in this step, one is `src/sql_log/final_sql.txt`.
python src/step_4_self_correction.py --ppl_file src/information/ppl_dev.json --sql_4_output src/sql_log/final_sql.txt --sql_refinement src/sql_log/step_3_binary.txt --start_index 0
```
## Evaluation
### Execution (EX) Evaluation:
Refer to the official evaluation script, the link is: https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/bird
### Strict Recall Rate Evaluation:
The script is in the `evaluation/evaluation_SL.py` file, and the usage is as follows:
We should organize the output of the database elements in the following format:
```json
{
"tables": [
"frpm"
],
"columns": [
"frpm.`Free Meal Count (K-12)`",
"frpm.`Enrollment (K-12)`",
"frpm.`School Name`",
"frpm.`County Name`"
]
}
```
# Citation
```citation
@article{cao2024rsl,
title={RSL-SQL: Robust Schema Linking in Text-to-SQL Generation},
author={Cao, Zhenbiao and Zheng, Yuanlei and Fan, Zhihao and Zhang, Xiaojin and Chen, Wei},
journal={arXiv preprint arXiv:2411.00073},
year={2024}
}
```