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DB GPT
Overview :
DB-GPT is an open-source AI-native data application development framework that, utilizing AWEL (Agentic Workflow Expression Language) and agent technologies, simplifies the integration of large models with data. Through its capabilities in multi-model management, Text2SQL optimization, RAG framework optimization, and multi-agent framework collaboration, DB-GPT empowers enterprises and developers to build customized applications with less code. In the era of Data 3.0, DB-GPT, based on models and databases, provides foundational data intelligence technologies for building enterprise-level reporting, analysis, and business insights.
Target Users :
DB-GPT is primarily aimed at enterprise developers and data scientists looking to leverage AI technology to simplify database interactions and data analysis. It is particularly suitable for professionals who need to build customized applications, optimize database queries, and enhance the efficiency of data-driven decision-making.
Use Cases
Enterprises utilize DB-GPT to build customized data analysis and report generation applications.
Developers leverage DB-GPT's Text2SQL functionality to optimize database query processes.
Data scientists enhance model accuracy in specific domains through DB-GPT's fine-tuning framework.
Features
RAG (Retrieval-Augmented Generation) framework, supporting the construction of knowledge-based applications.
GBI (Generative Business Intelligence), providing foundational data intelligence technologies for enterprise reporting analysis and business insights.
A complete fine-tuning framework, enabling enterprises to achieve model fine-tuning in vertical and niche domains.
Data-driven self-evolving multi-agent framework, making continuous data-driven decisions and executions.
Data Factory, focusing on cleaning and processing trustworthy knowledge and data in the large model era.
Integration support for multiple data sources, seamlessly connecting production business data to DB-GPT's core capabilities.
How to Use
1. Access the DB-GPT GitHub page and clone or download the project code.
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2. Read the documentation to understand the framework's architecture and core capabilities.
3. Based on your needs, select appropriate models and data sources for integration.
4. Utilize AWEL to define workflows and agents to automate data processing and analysis.
5. Fine-tune selected models for training and optimization using the provided framework.
6. Deploy and test the developed application, ensuring it meets business requirements.
7. Iterate on the application development based on feedback, continuously improving its performance.
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