@@ -44,33 +57,7 @@
🚀 **データ3.0時代には、モデルとデータベースを基盤として、企業や開発者がより少ないコードで独自のアプリケーションを構築できます。**
-### AIネイティブデータアプリ
-- 🔥🔥🔥 [V0.7.0 リリース | 重要なアップグレードのセット](http://docs.dbgpt.cn/blog/db-gpt-v070-release)
- - [サポート MCP Protocol](https://github.com/eosphoros-ai/DB-GPT/pull/2497)
- - [サポート DeepSeek R1](https://github.com/deepseek-ai/DeepSeek-R1)
- - [サポート QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)
- - [基本モジュールをリファクタリングする]()
- - [dbgpt-app](./packages/dbgpt-app)
- - [dbgpt-core](./packages/dbgpt-core)
- - [dbgpt-serve](./packages/dbgpt-serve)
- - [dbgpt-client](./packages/dbgpt-client)
- - [dbgpt-accelerator](./packages/dbgpt-accelerator)
- - [dbgpt-ext](./packages/dbgpt-ext)
-
-
-
-
-
-
-
-## 目次
-- [紹介](#紹介)
-- [インストール](#インストール)
-- [特徴](#特徴)
-- [貢献](#貢献)
-- [連絡先](#連絡先情報)
-
-## 紹介
+### 紹介
DB-GPTのアーキテクチャは以下の図に示されています：
@@ -91,7 +78,7 @@ DB-GPTのアーキテクチャは以下の図に示されています：
- **データソース**：DB-GPTのコア機能に生産ビジネスデータをシームレスに接続するために、さまざまなデータソースを統合します。
-### サブモジュール
+#### サブモジュール
- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) 大規模言語モデル（LLM）上での教師ありファインチューニング（SFT）を適用することにより、高性能なText-to-SQLワークフロー。
- [dbgpts](https://github.com/eosphoros-ai/dbgpts) dbgptsは、DB-GPT上で構築されたいくつかのデータアプリ、AWELオペレータ、AWELワークフローテンプレート、およびエージェントを含む公式リポジトリです。
@@ -118,6 +105,31 @@ DB-GPTのアーキテクチャは以下の図に示されています：
- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) Auto-GPTプラグインを直接実行できるDB-GPTプラグイン
- [GPT-Vis](https://github.com/eosphoros-ai/GPT-Vis) 可視化プロトコル
+
+### AIネイティブデータアプリ
+- 🔥🔥🔥 [V0.7.0 リリース | 重要なアップグレードのセット](http://docs.dbgpt.cn/blog/db-gpt-v070-release)
+ - [サポート MCP Protocol](https://github.com/eosphoros-ai/DB-GPT/pull/2497)
+ - [サポート DeepSeek R1](https://github.com/deepseek-ai/DeepSeek-R1)
+ - [サポート QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)
+ - [基本モジュールをリファクタリングする]()
+ - [dbgpt-app](./packages/dbgpt-app)
+ - [dbgpt-core](./packages/dbgpt-core)
+ - [dbgpt-serve](./packages/dbgpt-serve)
+ - [dbgpt-client](./packages/dbgpt-client)
+ - [dbgpt-accelerator](./packages/dbgpt-accelerator)
+ - [dbgpt-ext](./packages/dbgpt-ext)
+
+---
+
+
+
+
+
+
+
+
+
+
## インストール
@@ -232,9 +244,6 @@ DB-GPTのアーキテクチャは以下の図に示されています：
## 画像
🌐 [AutoDLイメージ](https://www.codewithgpu.com/i/eosphoros-ai/DB-GPT/dbgpt)
-### 言語切り替え
- .env設定ファイルでLANGUAGEパラメータを変更して、異なる言語に切り替えることができます。デフォルトは英語です（中国語：zh、英語：en、他の言語は後で追加されます）。
-
## 貢献
- 新しい貢献のための詳細なガイドラインを確認するには、[貢献方法](https://github.com/eosphoros-ai/DB-GPT/blob/main/CONTRIBUTING.md)を参照してください。
diff --git a/README.md b/README.md
index be27a42b9..7c2bf7e53 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
-# DB-GPT: AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
+# 
DB-GPT: AI Native Data App Development framework with AWEL and Agents
-
+
@@ -45,8 +56,50 @@ The purpose is to build infrastructure in the field of large models, through the
🚀 **In the Data 3.0 era, based on models and databases, enterprises and developers can build their own bespoke applications with less code.**
-### DISCKAIMER
-- [disckaimer](./DISCKAIMER.md)
+### Introduction
+The architecture of DB-GPT is shown in the following figure:
+
+
+ 
+
+
+The core capabilities include the following parts:
+
+- **RAG (Retrieval Augmented Generation)**: RAG is currently the most practically implemented and urgently needed domain. DB-GPT has already implemented a framework based on RAG, allowing users to build knowledge-based applications using the RAG capabilities of DB-GPT.
+
+- **GBI (Generative Business Intelligence)**: Generative BI is one of the core capabilities of the DB-GPT project, providing the foundational data intelligence technology to build enterprise report analysis and business insights.
+
+- **Fine-tuning Framework**: Model fine-tuning is an indispensable capability for any enterprise to implement in vertical and niche domains. DB-GPT provides a complete fine-tuning framework that integrates seamlessly with the DB-GPT project. In recent fine-tuning efforts, an accuracy rate based on the Spider dataset has been achieved at 82.5%.
+
+- **Data-Driven Multi-Agents Framework**: DB-GPT offers a data-driven self-evolving multi-agents framework, aiming to continuously make decisions and execute based on data.
+
+- **Data Factory**: The Data Factory is mainly about cleaning and processing trustworthy knowledge and data in the era of large models.
+
+- **Data Sources**: Integrating various data sources to seamlessly connect production business data to the core capabilities of DB-GPT.
+
+#### SubModule
+- [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).
+
+- [dbgpts](https://github.com/eosphoros-ai/dbgpts) dbgpts is the official repository which contains some data apps、AWEL operators、AWEL workflow templates and agents which build upon DB-GPT.
+
+#### Text2SQL Finetune
+- support llms
+ - [x] LLaMA
+ - [x] LLaMA-2
+ - [x] BLOOM
+ - [x] BLOOMZ
+ - [x] Falcon
+ - [x] Baichuan
+ - [x] Baichuan2
+ - [x] InternLM
+ - [x] Qwen
+ - [x] XVERSE
+ - [x] ChatGLM2
+
+[More Information about Text2SQL finetune](https://github.com/eosphoros-ai/DB-GPT-Hub)
+
+- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins that can run Auto-GPT plugin directly
+- [GPT-Vis](https://github.com/eosphoros-ai/GPT-Vis) Visualization protocol
### AI-Native Data App
---
@@ -71,59 +124,8 @@ The purpose is to build infrastructure in the field of large models, through the
-## Contents
-- [Introduction](#introduction)
-- [Install](#install)
-- [Features](#features)
-- [Contribution](#contribution)
-- [Contact](#contact-information)
-## Introduction
-The architecture of DB-GPT is shown in the following figure:
-
-
-
-
-
-The core capabilities include the following parts:
-
-- **RAG (Retrieval Augmented Generation)**: RAG is currently the most practically implemented and urgently needed domain. DB-GPT has already implemented a framework based on RAG, allowing users to build knowledge-based applications using the RAG capabilities of DB-GPT.
-
-- **GBI (Generative Business Intelligence)**: Generative BI is one of the core capabilities of the DB-GPT project, providing the foundational data intelligence technology to build enterprise report analysis and business insights.
-
-- **Fine-tuning Framework**: Model fine-tuning is an indispensable capability for any enterprise to implement in vertical and niche domains. DB-GPT provides a complete fine-tuning framework that integrates seamlessly with the DB-GPT project. In recent fine-tuning efforts, an accuracy rate based on the Spider dataset has been achieved at 82.5%.
-
-- **Data-Driven Multi-Agents Framework**: DB-GPT offers a data-driven self-evolving multi-agents framework, aiming to continuously make decisions and execute based on data.
-
-- **Data Factory**: The Data Factory is mainly about cleaning and processing trustworthy knowledge and data in the era of large models.
-
-- **Data Sources**: Integrating various data sources to seamlessly connect production business data to the core capabilities of DB-GPT.
-
-### SubModule
-- [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).
-
-- [dbgpts](https://github.com/eosphoros-ai/dbgpts) dbgpts is the official repository which contains some data apps、AWEL operators、AWEL workflow templates and agents which build upon DB-GPT.
-
-#### Text2SQL Finetune
-- support llms
- - [x] LLaMA
- - [x] LLaMA-2
- - [x] BLOOM
- - [x] BLOOMZ
- - [x] Falcon
- - [x] Baichuan
- - [x] Baichuan2
- - [x] InternLM
- - [x] Qwen
- - [x] XVERSE
- - [x] ChatGLM2
-
-[More Information about Text2SQL finetune](https://github.com/eosphoros-ai/DB-GPT-Hub)
-
-- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT Plugins that can run Auto-GPT plugin directly
-- [GPT-Vis](https://github.com/eosphoros-ai/GPT-Vis) Visualization protocol
-
-## Install
+## Installation / Quick Start
@@ -239,8 +241,6 @@ At present, we have introduced several key features to showcase our current capa
🌐 [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
@@ -255,10 +255,13 @@ At present, we have introduced several key features to showcase our current capa
## Licence
The MIT License (MIT)
-## Citation
-If you want to understand the overall architecture of DB-GPT, please cite
paper and
Paper
+## DISCKAIMER
+- [disckaimer](./DISCKAIMER.md)
-If you want to learn about using DB-GPT for Agent development, please cite the
paper
+## Citation
+If you want to understand the overall architecture of DB-GPT, please cite
Paper and
Paper
+
+If you want to learn about using DB-GPT for Agent development, please cite the
Paper
```bibtex
@article{xue2023dbgpt,
title={DB-GPT: Empowering Database Interactions with Private Large Language Models},
@@ -287,7 +290,12 @@ If you want to learn about using DB-GPT for Agent development, please cite the <
## Contact Information
-We are working on building a community, if you have any ideas for building the community, feel free to contact us.
-[](https://discord.gg/7uQnPuveTY)
+Thanks to everyone who has contributed to DB-GPT! Your ideas, code, comments, and even sharing them at events and on social platforms can make DB-GPT better.
+We are working on building a community, if you have any ideas for building the community, feel free to contact us.
+
+- [Github Issues](https://github.com/eosphoros-ai/DB-GPT/issues) ⭐️：For questions about using GB-DPT, see the CONTRIBUTING.
+- [Github Discussions](https://github.com/orgs/eosphoros-ai/discussions) ⭐️：Share your experience or unique apps.
+- [Twitter](https://x.com/DBGPT_AI) ⭐️：Please feel free to talk to us.
+
[](https://star-history.com/#csunny/DB-GPT)
diff --git a/README.zh.md b/README.zh.md
index 3410c6225..c692d7b51 100644
--- a/README.zh.md
+++ b/README.zh.md
@@ -1,11 +1,11 @@
-# DB-GPT: AI原生数据应用开发框架
+#
DB-GPT: AI原生数据应用开发框架
-
- 
+
+ 
-
+
## DB-GPT 是什么？
@@ -44,6 +56,38 @@
🚀 **数据3.0 时代，基于模型、数据库，企业/开发者可以用更少的代码搭建自己的专属应用。**
+### 架构方案
+
+
+
+
+
+核心能力主要有以下几个部分:
+- **RAG(Retrieval Augmented Generation)**，RAG是当下落地实践最多，也是最迫切的领域，DB-GPT目前已经实现了一套基于RAG的框架，用户可以基于DB-GPT的RAG能力构建知识类应用。
+
+- **GBI**：生成式BI是DB-GPT项目的核心能力之一，为构建企业报表分析、业务洞察提供基础的数智化技术保障。
+
+- **微调框架**: 模型微调是任何一个企业在垂直、细分领域落地不可或缺的能力，DB-GPT提供了完整的微调框架，实现与DB-GPT项目的无缝打通，在最近的微调中，基于spider的准确率已经做到了82.5%
+
+- **数据驱动的Multi-Agents框架**: DB-GPT提供了数据驱动的自进化Multi-Agents框架，目标是可以持续基于数据做决策与执行。
+
+- **数据工厂**: 数据工厂主要是在大模型时代，做可信知识、数据的清洗加工。
+
+- **数据源**: 对接各类数据源，实现生产业务数据无缝对接到DB-GPT核心能力。
+
+#### 子模块
+- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) 通过微调来持续提升Text2SQL效果
+- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT 插件仓库, 兼容Auto-GPT
+- [GPT-Vis](https://github.com/eosphoros-ai/DB-GPT-Web) 可视化协议
+
+- [dbgpts](https://github.com/eosphoros-ai/dbgpts) dbgpts 是官方提供的数据应用仓库, 包含数据智能应用, 智能体编排流程模版, 通用算子等构建在DB-GPT之上的资源。
+
+#### RAG生产落地实践架构
+
+ 
+
+
+
## 效果演示
### AI原生数据智能应用
@@ -69,45 +113,6 @@
-## 目录
-- [架构方案](#架构方案)
-- [安装](#安装)
-- [特性简介](#特性一览)
-- [贡献](#贡献)
-- [路线图](#路线图)
-- [联系我们](#联系我们)
-
-## 架构方案
-
-
-
-
-
-核心能力主要有以下几个部分:
-- **RAG(Retrieval Augmented Generation)**，RAG是当下落地实践最多，也是最迫切的领域，DB-GPT目前已经实现了一套基于RAG的框架，用户可以基于DB-GPT的RAG能力构建知识类应用。
-
-- **GBI**：生成式BI是DB-GPT项目的核心能力之一，为构建企业报表分析、业务洞察提供基础的数智化技术保障。
-
-- **微调框架**: 模型微调是任何一个企业在垂直、细分领域落地不可或缺的能力，DB-GPT提供了完整的微调框架，实现与DB-GPT项目的无缝打通，在最近的微调中，基于spider的准确率已经做到了82.5%
-
-- **数据驱动的Multi-Agents框架**: DB-GPT提供了数据驱动的自进化Multi-Agents框架，目标是可以持续基于数据做决策与执行。
-
-- **数据工厂**: 数据工厂主要是在大模型时代，做可信知识、数据的清洗加工。
-
-- **数据源**: 对接各类数据源，实现生产业务数据无缝对接到DB-GPT核心能力。
-
-### RAG生产落地实践架构
-
-
-
-
-### 子模块
-- [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) 通过微调来持续提升Text2SQL效果
-- [DB-GPT-Plugins](https://github.com/eosphoros-ai/DB-GPT-Plugins) DB-GPT 插件仓库, 兼容Auto-GPT
-- [GPT-Vis](https://github.com/eosphoros-ai/DB-GPT-Web) 可视化协议
-
-- [dbgpts](https://github.com/eosphoros-ai/dbgpts) dbgpts 是官方提供的数据应用仓库, 包含数据智能应用, 智能体编排流程模版, 通用算子等构建在DB-GPT之上的资源。
-
## 安装
@@ -251,10 +256,6 @@
🌐 [小程序云部署](https://www.yuque.com/eosphoros/dbgpt-docs/ek12ly8k661tbyn8)
-### 多语言切换
-
-在.env 配置文件当中，修改LANGUAGE参数来切换使用不同的语言，默认是英文(中文zh, 英文en, 其他语言待补充)
-
## 使用说明
### 多模型使用
@@ -281,6 +282,10 @@
The MIT License (MIT)
+### 免责声明
+
+- [免责声明](./DISCKAIMER.md)
+
## 引用
如果您发现`DB-GPT`对您的研究或开发有用，请引用以下论文，其中：
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