##  MATS: A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback

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MATS is a multi-agent framework for Text2SQL using small language models and execution feedback to improve query accuracy. It employs multiple specialized agents—including schema insight agent, planner, validator, fix agent, and selection agent. Some components of this framework are adapted from [CodeS](https://github.com/RUCKBReasoning/codes) (for schema filtering) and [alignment-handbook](https://github.com/huggingface/alignment-handbook) (for supervised fine-tuning and ORPO training).

**1. To set up the environment**
```
conda env create -n mats -f environment.yml
conda activate mats
```

**2. Run Evaluation on BIRD**:

First serve the models with VLLM.
```
CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-3b-bird-planner --host 0.0.0.0 --port 8003 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name planner --gpu-memory-utilization 0.3 --enable-prefix-caching

CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-1b-bird-validator --host 0.0.0.0 --port 8004 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name validator --gpu-memory-utilization 0.2  --enable-prefix-caching

CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-1b-bird-fixed --host 0.0.0.0 --port 8005 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name fixed --gpu-memory-utilization 0.2  --enable-prefix-caching

CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-3b-bird-selection --host 0.0.0.0 --port 8006 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name selection --gpu-memory-utilization 0.3  --enable-prefix-caching
```

Run evaluation:
```
eval_file=data/evaluate/orpo-llama-3-iter-2-end2end-bird_dev.jsonl
rm $eval_file
PYTHONPATH=. python evaluate_end2end.py \
	--input_file data/schema_insight_bird_with_evidence_dev_text2sql.json  \
	--output_file $eval_file \
	--model-name llama --mode test --n_return 10 --temperature 1.0 --api_host http://localhost:8003 --n_processes 16

python compute_acc.py --pred_file $eval_file
```




**3. To run evaluation on Spider**:

First serve the models with VLLM.
```
CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-3b-spider-planner --host 0.0.0.0 --port 8003 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name planner --gpu-memory-utilization 0.3 --enable-prefix-caching

CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-1b-spider-validator --host 0.0.0.0 --port 8004 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name validator --gpu-memory-utilization 0.2  --enable-prefix-caching

CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-1b-spider-fixed --host 0.0.0.0 --port 8005 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name fixed --gpu-memory-utilization 0.2  --enable-prefix-caching

CUDA_VISIBLE_DEVICES=0 vllm serve thanhdathoang/llama-3b-spider-selection --host 0.0.0.0 --port 8006 --dtype bfloat16 --max-model-len 4096 --disable-log-requests --served-model-name selection --gpu-memory-utilization 0.3  --enable-prefix-caching
```

Run evaluation:
```
eval_file=data/evaluate/orpo-llama-3-iter-2-end2end-spider_dev.jsonl
rm $eval_file
PYTHONPATH=. python evaluate_end2end.py \
	--input_file data/schema_insight_spider_dev_text2sql.json  \
	--output_file $eval_file \
	--model-name llama --mode test --n_return 10 --temperature 1.0 --api_host http://localhost:8003 --n_processes 16

python compute_acc.py --pred_file $eval_file
```


**4. For training agents**

The Schema Filtering is inherited from [CodeS](https://github.com/RUCKBReasoning/codes).

To train other agents, see the code in ***alignment-handbook/***, here we modified the repository [alignment-handbook](https://github.com/huggingface/alignment-handbook) for supervised-finetuning and ORPO on the completion part only. The config files could be found in **alignment-handbook/recipes/**.

**Note**: Currently this work is under review. The model and training dataset will be publicly available upon acceptance.

## Citation:
```
@article{hoang2025multi,
  title={A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback},
  author={Hoang, Thanh Dat and Huynh, Thanh Trung and Weidlich, Matthias and Nguyen, Thanh Tam and Chen, Tong and Yin, Hongzhi and Nguyen, Quoc Viet Hung},
  journal={arXiv preprint arXiv:2512.18622},
  year={2025}
}
```

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