DB-GPT

Preview：

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Introduce：

DB-GPT is an open source AI native data application development framework that utilizes AWEL(Agentic Workflow Expression Language) and agent technology to simplify the integration of large model applications with data. Through multi-model management, Text2SQL effect optimization, RAG framework optimization, multi-agent framework collaboration and other technical capabilities, it enables enterprises and developers to build custom applications with less code. In the Data 3.0 era, DB-GPT provides the fundamental data intelligence technology for building enterprise-level reporting analytics and business insights based on models and databases.
DB-GPT
Stakeholders:
DB-GPT is aimed at enterprise developers and data scientists who want to use AI technology to simplify database interaction and data analysis. It is especially suitable for professionals who need to build customized applications, optimize database queries, and improve the efficiency of data-driven decisions.
Usage Scenario Examples:

Enterprises use DB-GPT to build customized data analysis and report generation applications.
Developers use DB-GPT’s Text2SQL capabilities to optimize the database query process.
Data scientists use DB-GPT’s fine-tuning framework to improve model accuracy in specific domains.

The features of the tool:

RAG(Search Enhanced Generation) framework that supports building knowledge-based applications.
GBI(Generative Business Intelligence), the underlying data intelligence technology that provides enterprise reporting analytics and business insights.
A complete fine-tuning framework that enables enterprises to fine-tune models in both vertical and subdivision domains.
Data-driven, self-evolving multi-agent framework for continuous decision making and execution based on data.
Data Factory, which focuses on cleaning and processing trusted knowledge and data in the age of large models.
Supports the integration of multiple data sources, seamlessly connecting production business data to DB-GPT’s core capabilities.

Steps for Use:

1. Visit DB-GPT’s GitHub page and clone or download the project code.
2. Read the documentation to understand the framework architecture and core capabilities.
3. Select the right model and data source for integration according to the requirements.
4. Define workflows and agents with AWEL to automate data processing and analysis.
5. Train and optimize the selected model through the fine-tuning framework.
6. Deploy and test developed applications to ensure they meet business requirements.
7. Conduct iterative development according to feedback to continuously improve application performance.

Tool’s Tabs: Security, Database