# Agentar-Scale-SQL **Repository Path**: zhang_hua_322/Agentar-Scale-SQL ## Basic Information - **Project Name**: Agentar-Scale-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**: 2026-07-20 - **Last Updated**: 2026-07-20 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling
[![Product](https://img.shields.io/badge/Product-Website-purple)](https://antdigital.com/products/DataAgent) [![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2509.24403) [![Leaderboard](https://img.shields.io/badge/BIRD%20Leaderboard-%231-brightgreen)](https://bird-bench.github.io/) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow)](https://huggingface.co/collections/antgroup/agentar-scale-sql) [![ModelScope](https://img.shields.io/badge/ModelScope-Models-blue)](https://modelscope.cn/collections/Agentar-Scale-SQL-0c368e98f73f41)
## πŸ“ Introduction **Agentar-Scale-SQL** is a novel framework that leverages scalable computation to significantly improve Text-to-SQL performance on challenging benchmarks. By implementing an Orchestrated Test-Time Scaling strategy, our framework synergistically combines three distinct perspectives to bridge the gap between state-of-the-art models and human expert performance.
framework
Figure 1: The proposed Agentar-Scale-SQL framework.
--- ## ⚑️ Performance | Methods | EX (Dev) | **EX (Test)** | R-VES (%) | |:-----------------------------|:---:|:---:|:---------:| | **Agentar-Scale-SQL (Ours)** | **74.90** | **81.67** | **77.00** | | AskData + GPT-4o | 76.14 | 80.88 | 76.24 | | LongData-SQL | 74.32 | 77.53 | 71.89 | | CHASE-SQL + Gemini | 74.90 | 76.02 | 69.94 | | JoyDataAgent-SQL | 74.25 | 75.85 | 70.16 | | TCDataAgent-SQL | 74.12 | 75.74 | - | | Contextual-SQL | 73.50 | 75.63 | 70.02 | | XiYan-SQL | 73.34 | 75.63 | 71.41 | --- ## πŸŽ‰ News - πŸ“ `2026.05.07`: Our new paper **Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL** is available on [arXiv](https://arxiv.org/abs/2605.04719), introducing **FineStep** for fine-grained step-level credit assignment in tool-integrated Text-to-SQL. - πŸš€ `2025.11.27`: We are excited to release **Agentar-Scale-SQL-Generation-32B** on [Hugging Face](https://huggingface.co/antgroup/Agentar-Scale-SQL-Generation-32B) and [ModelScope](https://modelscope.cn/models/AntGroup/Agentar-Scale-SQL-Generation-32B)! Simultaneously, we have open-sourced the code for the **Light Schema Engine** and the **Offline Data Preprocessing Pipeline**! - 🎁 `2025.09.30`: Our paper is available on [arXiv](https://arxiv.org/abs/2509.24403). - πŸ† `2025.09.25`: We are proud to announce that we have achieved **#1 Rank** on the official [BIRD leaderboard](https://bird-bench.github.io/) with **81.67%** execution accuracy! --- ## πŸ—ΊοΈ Release Roadmap We are committed to continuously improving **Agentar-Scale-SQL**. Here is our plan for upcoming features and releases. - **Paper** - [x] Publish the **Paper** on arXiv. - **Model Releases** - [x] Release **Agentar-Scale-SQL-Generation-32B** on Hugging Face and ModelScope. - [ ] Release **Agentar-Scale-SQL-Selection-32B** on Hugging Face and ModelScope. - **Code Releases** - [x] Release the code for the **Light Schema Engine**. - [x] Release the code for the **Offline Data Preprocessing Pipeline**. - [ ] Release the code for **Task Understanding** and **Generating SQL Candidates with ICL Generators**. - [ ] Release the code for **Generating SQL Candidates with the Reasoning Generator**. - [ ] Release the code for the **Iterative Refinement** module. - [ ] Release the code for the **SQL Selection** module. --- ## πŸ“‚ Directory Structure ```bash Agentar-Scale-SQL/ β”œβ”€β”€ ScaleSQL/ # Core source code directory β”‚ └── workflows/ # Main workflow scripts β”‚ └── config/ # Configuration files β”œβ”€β”€ ddl_schema.sh β”œβ”€β”€ requirements.txt # Dependency list β”œβ”€β”€ .env # Environment variable β”œβ”€β”€ .env.example # Environment variable template β”œβ”€β”€ .gitignore β”œβ”€β”€ README.md # Current document β”œβ”€β”€ nltk_data.zip # For ddl schema generation ``` --- ## πŸ“š Usage ### 1. Installation and Environment Settings #### 1.1 Create Virtual Environment and Install Python Dependencies ```bash conda create -n ScaleSQL python=3.10 conda activate ScaleSQL ``` --- #### 1.2 Install PyTorch and Core Dependencies ```bash # Install PyTorch (CUDA 12.1) pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121 ``` --- #### 1.3 Install Project Dependencies ```bash pip install -r requirements.txt ``` --- #### 1.4 Install vLLM (for Inference Acceleration) ```bash pip install https://github.com/vllm-project/vllm/releases/download/v0.8.5.post1/vllm-0.8.5.post1+cu121-cp38-abi3-manylinux1_x86_64.whl ``` --- #### 1.5 Download Embedding Model ```bash modelscope download --model sentence-transformers/all-MiniLM-L6-v2 --local_dir ./ScaleSQL/model/all-MiniLM-L6-v2 ``` --- ### 2. Data Preparation #### 2.1 Configure Paths Modify the configuration file: `.ScaleSQL/workflows/config/pipeline_config.yaml`. Note that, we need column meaning file in the evaluation. You can find the file in [TA-SQL](https://github.com/quge2023/TA-SQL). ```yaml dataset_folder: /temp/bird_test # Change to the actual folder column_meaning_path: /your_path/column_meaning.json # Change to the actual path ``` --- ### 3. Preprocessing Pipeline --- #### 3.1 Generate Light Schema ```bash python -m ScaleSQL.workflows.schema_generation --evaluation_type test ``` > Output example: `.ScaleSQL/dataset/bird_test_light_schema.json` --- #### 3.2 Process Training Set Examples and Write to Vector Database ```bash ANONYMIZED_TELEMETRY=False python -m ScaleSQL.workflows.train_skeleton_process ``` > Output path: `/tmp/ScaleSQL/chroma/bird_train_skeleton` --- #### 3.3 Process Database Cell Values and Write to Vector Database ```bash ANONYMIZED_TELEMETRY=False python -m ScaleSQL.workflows.database_cell_process --evaluation_type test ``` > Output path: `/tmp/ScaleSQL/chroma/bird_test` --- #### 3.4 Build BM25 Index (Content-Based) and Generate DDL Schema (Requires Java Environment) ```bash bash ddl_schema.sh ``` > Output example: `.ScaleSQL/dataset/bird_test_ddl_schema.json` --- ## πŸ“¦ Try Our Product Unlock the power of your business data with natural language. 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Figure 2: The contact information.
--- ## πŸ“Ž Citation ```bibtex @misc{wang2025agentarscalesqladvancingtexttosqlorchestrated, title={Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling}, author={Pengfei Wang and Baolin Sun and Xuemei Dong and Yaxun Dai and Hongwei Yuan and Mengdie Chu and Yingqi Gao and Xiang Qi and Peng Zhang and Ying Yan}, year={2025}, eprint={2509.24403}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2509.24403}, } ```