sqlgym 0.1.1

SQLGym: A portable Gymnasium environment of SQLite database.

SQLGym

This is a portable Gymnasium environment of SQLite database. It is designed for platforms that are not able to use docker. (e.g. users without root privillege)

Setup

Simply pip install sqlgym. If you want to generate ReAct dataset and fine tune a model, please clone the repository and install from source.

# Clone this repository
git clone https://github.com/KYLN24/sqlgym.git
# or via SSH
# git clone [e-mail]:KYLN24/sqlgym.git

cd sqlgym

# Install this package
pip install ".[sft]"

Prepare Dataset

# Make a directory to save data
mkdir .data
cd .data

This project currently suppport the BIRD-SQL dataset.

mkdir bird
cd bird

# Download BIRD-SQL Dataset
wget -c https://bird-bench.oss-cn-beijing.aliyuncs.com/train.zip
unzip train.zip
cd train
unzip train_databases.zip
cd ..

wget -c https://bird-bench.oss-cn-beijing.aliyuncs.com/dev.zip
unzip dev.zip
cd dev
unzip dev_databases.zip
cd ..

Usage

from sqlgym import SqlGymEnv
from sqlgym.datasets import BirdDataset

dataset = BirdDataset(
    bird_path=".data/bird",
    mode="dev",
)

env = SqlGymEnv(dataset)

print(env.reset(0))
print(env.step(dataset[0].gt))

SFT

You can use scripts/make_datasets.py to generate a SFT dataset.

python -u scripts/make_datasets.py --bird_path=./data/bird # Dataset will be created at ./data/bird/train.jsonl and ./data/bird/dev.jsonl

You can use scripts/make_react_dataset.py to convert it to ReAct format with thought generated by GPT.

# Edit the script to add your OpenAI api_key.
# Change base_url and other generation parameters as you wish.
python -u scripts/make_react_dataset.py \
       --data_path=.data/bird/train.jsonl \
       --save_path=.data/bird/train_react.jsonl

Then, use scripts/train.py or scripts/train_react.py to fine tune a chat model. The tokenizer should support the apply_chat_template method.

torchrun --nproc_per_node=8 scripts/train.py \
         --model=meta-llama/Llama-2-7b-chat-hf \
         --train_set=.data/bird/train.jsonl \
         --output_dir=.data/output

torchrun --nproc_per_node=8 scripts/train.py \
         --model=meta-llama/Llama-2-7b-chat-hf \
         --train_set=.data/bird/train_react.jsonl \
         --output_dir=.data/output \
         --react