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<a href="https://github.com/eosphoros-ai/DB-GPT">
<img alt="forks" src="https://img.shields.io/github/forks/eosphoros-ai/db-gpt?style=social" />
</a>
<a href="https://opensource.org/licenses/MIT">
<img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-yellow.svg" />
</a>
<a href="https://opensource.org/licenses/MIT">
<img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-yellow.svg" />
</a>
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</p>


[**简体中文**](README.zh.md) |[**Discord**](https://discord.gg/nASQyBjvY) |[**Documents**](https://db-gpt.readthedocs.io/en/latest/)|[**Wechat**](https://github.com/eosphoros-ai/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC)|[**Community**](https://github.com/eosphoros-ai/community)
[**简体中文**](README.zh.md) | [**Discord**](https://discord.gg/nASQyBjvY) | [**Documents**](https://db-gpt.readthedocs.io/en/latest/) | [**Wechat**](https://github.com/eosphoros-ai/DB-GPT/blob/main/README.zh.md#%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC) | [**Community**](https://github.com/eosphoros-ai/community)
</div>

## What is DB-GPT?

DB-GPT is an open-source framework for large models in the databases fields. It's purpose is to build infrastructure for the domain of large models, making it easier and more convenient to develop applications around databases. By developing various technical capabilities such as:
DB-GPT is an open-source framework designed for the realm of large language models (LLMs) within the database field. Its primary purpose is to provide infrastructure that simplifies and streamlines the development of database-related applications. This is accomplished through the development of various technical capabilities, including:

1. **SMMF(Service-oriented Multi-model Management Framework)**
2. **Text2SQL Fine-tuning**
3. **RAG(Retrieval Augmented Generation) framework and optimization**
4. **Data-Driven Agents framework collaboration**
5. **GBI(Generative Business intelligence)**

etc, DB-GPT simplifies the construction of large model applications based on databases.
DB-GPT simplifies the creation of these applications based on large language models (LLMs) and databases.

In the era of Data 3.0, enterprises and developers can build their own customized applications with less code, leveraging models and databases.
In the era of Data 3.0, enterprises and developers can take the ability to create customized applications with minimal coding, which harnesses the power of large language models (LLMs) and databases.


## Contents
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## Features

Currently, we have released multiple key features, which are listed below to demonstrate our current capabilities:
At present, we have introduced several key features to showcase our current capabilities:
- **Private Domain Q&A & Data Processing**

The DB-GPT project offers a range of features to enhance knowledge base construction and enable efficient storage and retrieval of both structured and unstructured data. These include built-in support for uploading multiple file formats, the ability to integrate plug-ins for custom data extraction, and unified vector storage and retrieval capabilities for managing large volumes of information.
The DB-GPT project offers a range of functionalities designed to improve knowledge base construction and enable efficient storage and retrieval of both structured and unstructured data. These functionalities include built-in support for uploading multiple file formats, the ability to integrate custom data extraction plug-ins, and unified vector storage and retrieval capabilities for effectively managing large volumes of information.

- **Multi-Data Source & GBI(Generative Business intelligence)**
The DB-GPT project enables seamless natural language interaction with various data sources, including Excel, databases, and data warehouses. It facilitates effortless querying and retrieval of information from these sources, allowing users to engage in intuitive conversations and obtain insights. Additionally, DB-GPT supports the generation of analysis reports, providing users with valuable summaries and interpretations of the data.

The DB-GPT project facilitates seamless natural language interaction with diverse data sources, including Excel, databases, and data warehouses. It simplifies the process of querying and retrieving information from these sources, empowering users to engage in intuitive conversations and gain insights. Moreover, DB-GPT supports the generation of analytical reports, providing users with valuable data summaries and interpretations.

- **Multi-Agents&Plugins**

It supports custom plug-ins to perform tasks, natively supports the Auto-GPT plug-in model, and the Agents protocol adopts the Agent Protocol standard.
It offers support for custom plug-ins to perform various tasks and natively integrates the Auto-GPT plug-in model. The Agents protocol adheres to the Agent Protocol standard.

- **Automated Fine-tuning text2SQL**
An automated fine-tuning lightweight framework built around large language models, Text2SQL data sets, LoRA/QLoRA/Pturning, and other fine-tuning methods, making TextSQL fine-tuning as convenient as an assembly line. [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub)

We've also developed an automated fine-tuning lightweight framework centred on large language models (LLMs), Text2SQL datasets, LoRA/QLoRA/Pturning, and other fine-tuning methods. This framework simplifies Text-to-SQL fine-tuning, making it as straightforward as an assembly line process. [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub)

- **SMMF(Service-oriented Multi-model Management Framework)**

Massive model support, including dozens of large language models such as open source and API agents. Such as LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, etc.
We offer extensive model support, including dozens of large language models (LLMs) from both open-source and API agents, such as LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, and many more.
- [Vicuna](https://huggingface.co/Tribbiani/vicuna-13b)
- [vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5)
- [LLama2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
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- **Privacy and Security**

The privacy and security of data are ensured through various technologies, such as privatized large models and proxy desensitization.
We ensure the privacy and security of data through the implementation of various technologies, including privatized large models and proxy desensitization.

- Support Datasources

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| [StarRocks](https://github.com/StarRocks/starrocks) | No | TODO |

## Introduction
The architecture of the entire DB-GPT is shown.
The architecture of DB-GPT is shown in the following figure:

<p align="center">
<img src="./assets/DB-GPT.png" width="800" />
</p>

The core capabilities mainly consist of the following parts:
1. Multi-Models: Support multi-LLMs, such as LLaMA/LLaMA2、CodeLLaMA、ChatGLM, QWen、Vicuna and proxy model ChatGPT、Baichuan、tongyi、wenxin etc
2. Knowledge-Based QA: You can perform high-quality intelligent Q&A based on local documents such as PDF, word, excel, and other data.
3. Embedding: Unified data vector storage and indexing, Embed data as vectors and store them in vector databases, providing content similarity search.
4. Multi-Datasources: Used to connect different modules and data sources to achieve data flow and interaction.
5. Multi-Agents: Provides Agent and plugin mechanisms, allowing users to customize and enhance the system's behavior.
6. Privacy & Secure: You can be assured that there is no risk of data leakage, and your data is 100% private and secure.
7. Text2SQL: We enhance the Text-to-SQL performance by applying Supervised Fine-Tuning (SFT) on large language models
The core capabilities primarily consist of the following components:
1. Multi-Models: We support multiple Large Language Models (LLMs) such as LLaMA/LLaMA2, CodeLLaMA, ChatGLM, QWen, Vicuna, and proxy models like ChatGPT, Baichuan, Tongyi, Wenxin, and more.
2. Knowledge-Based QA: Our system enables high-quality intelligent Q&A based on local documents such as PDFs, Word documents, Excel files, and other data sources.
3. Embedding: We offer unified data vector storage and indexing. Data is embedded as vectors and stored in vector databases, allowing for content similarity search.
4. Multi-Datasources: This feature connects different modules and data sources, facilitating data flow and interaction.
5. Multi-Agents: Our platform provides Agent and plugin mechanisms, empowering users to customize and enhance the system's behaviour.
6. Privacy & Security: Rest assured that there is no risk of data leakage, and your data is 100% private and secure.
7. Text2SQL: We enhance Text-to-SQL performance through Supervised Fine-Tuning (SFT) applied to Large Language Models (LLMs).

### SubModule
- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) Text-to-SQL performance by applying Supervised Fine-Tuning (SFT) on large language models.
- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins Can run autogpt plugin directly
- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) Text-to-SQL workflow with high performance by applying Supervised Fine-Tuning (SFT) on Large Language Models (LLMs).
- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins that can run Auto-GPT plugin directly
- [DB-GPT-Web](https://github.com/eosphoros-ai/DB-GPT-Web) ChatUI for DB-GPT

## Image
🌐 [AutoDL Image](https://www.codewithgpu.com/i/eosphoros-ai/DB-GPT/dbgpt)




### Language Switching
In the .env configuration file, modify the LANGUAGE parameter to switch to different languages. The default is English (Chinese: zh, English: en, other languages to be added later).

## Contribution

- Please run `black .` before submitting the code. Contributing guidelines, [how to contribute](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md)
- Please run `black .` before submitting the code.
- To check detailed guidelines for new contributions, please refer [how to contribute](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md)

## RoadMap

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- [x] ChatGLM2

- SFT Accuracy
As of October 10, 2023, by fine-tuning an open-source model of 13 billion parameters using this project, the execution accuracy on the Spider evaluation dataset has surpassed that of GPT-4!
As of October 10, 2023, through the fine-tuning of an open-source model with 13 billion parameters using this project, we have achieved execution accuracy on the Spider dataset that surpasses even GPT-4!

[More Information about Text2SQL finetune](https://github.com/eosphoros-ai/DB-GPT-Hub)

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