Overview
🤖 DB-GPT is an open source AI native data app development framework with AWEL(Agentic Workflow Expression Language) and agents.
The purpose is to build infrastructure in the field of large models, through the development of multiple technical capabilities such as multi-model management (SMMF), Text2SQL effect optimization, RAG framework and optimization, Multi-Agents framework collaboration, AWEL (agent workflow orchestration), etc. Which makes large model applications with data simpler and more convenient.
🚀 In the Data 3.0 era, based on models and databases, enterprises and developers can build their own bespoke applications with less code.
Features
Private Domain Q&A & Data Processing & RAG
- Supports custom construction of knowledge bases through methods such as built-in, multi-file format uploads, and plugin-based web scraping. Enables unified vector storage and retrieval of massive structured and unstructured data.
Multi-Data Source & GBI(Generative Business Intelligence)
- Supports interaction between natural language and various data sources such as Excel, databases, and data warehouses. Also supports analysis reporting.
SMMF(Service-oriented Multi-model Management Framework)
- Supports a wide range of models, including dozens of large language models such as open-source models and API proxies. Examples include LLaMA/LLaMA2, Baichuan, ChatGLM, ERNIE Bot, Qwen, Spark, etc.
Automated Fine-tuning
- Supports Text2SQL fine-tuning. Provides a lightweight automatic fine-tuning framework around the fields of LLM and Text2SQL, supporting methods such as LoRA/QLoRA/P-turning, making Text2SQL fine-tuning as convenient as a production line.
Data-Driven Multi-Agents & Plugins
- Supports executing tasks through custom plugins and natively supports the Auto-GPT plugin model. Agents protocol follows the Agent Protocol standard.
Privacy and Security
- Supports data privacy protection. Ensures data privacy and security through techniques such as privatizing large language models and proxy de-identification.
Getting Started
Terminology
| terminology | Description |
|---|---|
DB-GPT | DataBase Generative Pre-trained Transformer, an open source framework around databases and large language models |
Data App | an intelligent Data application built on DB-GPT. |
Text2SQL/NL2SQL | Text to SQL uses large language model capabilities to generate SQL statements based on natural language, or provide explanations based on SQL statements |
KBQA | Knowledge-Based Q&A system |
GBI | Generative Business Intelligence, based on large language models and data analysis, provides business intelligence analysis and decision-making through dialogue |
LLMOps | A large language model operation framework that provides a standard end-to-end workflow for training, tuning, deploying, and monitoring LLM to accelerate application deployment of generated AI models |
Embedding | Methods to convert text, audio, video and other materials into vectors |
RAG | Retrieval Augmented Generation |
AWEL | Agentic Workflow Expression Language, intelligent Workflow Expression Language |
AWEL Flow | workflow orchestration using the intelligent workflow Expression Language |
SMMF | a service-oriented multi-model management framework. |
Use Cases
Modules
SMMF
Service-oriented Multi-model Management Framework
Retrieval
Multi-Knowledge Enhanced Retrieval-Augmented Generation Framework
Agents
Data Driven Multi-Agents
Fine-tuning
Fine-tuning module for Text2SQL/Text2DSL
More
-
Connections Connect various data sources
-
Obvervablity Observing & monitoring
-
Evaluation Evaluate framework performance and accuracy
Community
If you encounter any problems during the process, you can submit an issue and communicate with us.
We welcome discussions in the community