DB-GPT-Hub
Repository for DB-GPT models, datasets, and techniques aimed at Text-to-SQL performance enhancement.
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
DB-GPT-Hub offers resources for improving DB-GPT's Text-to-SQL capabilities through fine-tuning techniques and relevant datasets.
Good fit when
- Use when you need to improve Text-to-SQL performance specifically with the DB-GPT model, as this repository is specialized for enhancing its functionalities.
- Prefer it if you are engaged in projects requiring conversion of natural language queries into SQL syntax where domain-specific enhancements can leverage pre-existing fine-tuning methods.
Avoid when
- Avoid using when your project does not involve the DB-GPT model, as resources and techniques here are tailor-made for this specific model.
- Do not utilize if you require immediate results without the need for model customization or performance enhancement through fine-tuning.
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (444d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install DB-GPT-HubHow it fits your stack(1)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Includes models, datasets, fine-tuning methods to improve DB-GPT performance in converting natural language queries into SQL.
Capability facts
- Languages
- python
Source: github.language · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 19, 2026)
```python from dbgpt_hub_sql.data_process import preprocess_sft_dataSource link
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README
3.2 Quick Start Firstly, install with the following command Then, set up the arguments and run the whole process. ```python from dbgpt hub sql.data process import preprocess sft data from dbgpt hub sql.train import start sft from dbgpt hub sql.predict import start predict from db...
For agents
This page has a .md twin and JSON over the API.