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🍎 docs: add docs for v0.6.0 (#1964)
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21 changes: 14 additions & 7 deletions README.md
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Expand Up @@ -48,15 +48,25 @@ The purpose is to build infrastructure in the field of large models, through the

### AI-Native Data App
---
- 🔥🔥🔥 [Released V0.5.0 | Develop native data applications through workflows and agents](https://docs.dbgpt.site/docs/changelog/Released_V0.5.0)
- 🔥🔥🔥 [Released V0.6.0 | A set of significant upgrades](https://docs.dbgpt.cn/docs/changelog/Released_V0.6.0)
- [The AWEL upgrade to 2.0]()
- [GraphRAG]()
- [AI Native Data App construction and management]()
- [The GPT-Vis upgrade, supporting a variety of visualization charts]()
- [Support Text2NLU and Text2GQL fine-tuning]()
- [Support Intent recognition, slot filling, and Prompt management]()


- [Released V0.5.0 | Develop native data applications through workflows and agents](http://docs.dbgpt.cn/docs/changelog/Released_V0.5.0)
---

![Data-awels](https://github.com/eosphoros-ai/DB-GPT/assets/17919400/37d116fc-d9dd-4efa-b4df-9ab02b22541c)
![app_chat_v0 6](https://github.com/user-attachments/assets/a2f0a875-df8c-4f0d-89a3-eed321c02113)

![Data-Apps](https://github.com/eosphoros-ai/DB-GPT/assets/17919400/a7bf6d65-92d1-4f0e-aaf0-259ccdde22fd)
![app_manage_chat_data_v0 6](https://github.com/user-attachments/assets/c8cc85bb-e3c2-4fab-8fb9-7b4b469d0611)

![dashboard-images](https://github.com/eosphoros-ai/DB-GPT/assets/17919400/1849a79a-f7fd-40cf-bc9c-b117a041dd6a)
![chat_dashboard_display_v0 6](https://github.com/user-attachments/assets/b15d6ebe-54c4-4527-a16d-02fbbaf20dc9)

![agent_prompt_awel_v0 6](https://github.com/user-attachments/assets/40761507-a1e1-49d4-b49a-3dd9a5ea41cc)

## Contents
- [Introduction](#introduction)
Expand Down Expand Up @@ -197,8 +207,6 @@ At present, we have introduced several key features to showcase our current capa
- Support Datasources
- [Datasources](http://docs.dbgpt.site/docs/modules/connections)



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

Expand All @@ -216,7 +224,6 @@ At present, we have introduced several key features to showcase our current capa
</a>



## Licence
The MIT License (MIT)

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19 changes: 15 additions & 4 deletions README.zh.md
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Expand Up @@ -48,16 +48,27 @@

### AI原生数据智能应用
---
- 🔥🔥🔥 [V0.5.0发布——通过工作流与智能体开发原生数据应用](https://www.yuque.com/eosphoros/dbgpt-docs/owcrh9423f9rqkg2)
- [V0.6.0发布——一系列重大功能更新](https://www.yuque.com/eosphoros/dbgpt-docs/fho86kk4e9y4rkpd)
- AWEL协议升级2.0,支持更复杂的编排,同时优化了前端可视化与交互能力。
- 支持数据应用的创建与生命周期管理,提供多种应用构建模式。1. 多智能体自动规划模式、2. 任务流编排模式、3. 单一智能体模式、4. 原生应用模式
- GraphRAG支持图社区摘要与混合检索,性能与检索效果有显著优势,同时支持丰富的前端可视化。
- 支持意图识别、槽位填充与Prompt管理。
- GPT-Vis前端可视化升级,支持更丰富的可视化图表。
- 支持Text2NLU与Text2GQL微调, 即新增意图分类与从自然语言到图语言的微调。

- [V0.5.0发布——通过工作流与智能体开发原生数据应用](https://www.yuque.com/eosphoros/dbgpt-docs/owcrh9423f9rqkg2)
---

### Data Agents

![Data-awels](https://github.com/eosphoros-ai/DB-GPT/assets/17919400/37d116fc-d9dd-4efa-b4df-9ab02b22541c)
![app_chat_v0 6](https://github.com/user-attachments/assets/a2f0a875-df8c-4f0d-89a3-eed321c02113)

![Data-Apps](https://github.com/eosphoros-ai/DB-GPT/assets/17919400/a7bf6d65-92d1-4f0e-aaf0-259ccdde22fd)
![app_manage_chat_data_v0 6](https://github.com/user-attachments/assets/c8cc85bb-e3c2-4fab-8fb9-7b4b469d0611)

![chat_dashboard_display_v0 6](https://github.com/user-attachments/assets/b15d6ebe-54c4-4527-a16d-02fbbaf20dc9)

![agent_prompt_awel_v0 6](https://github.com/user-attachments/assets/40761507-a1e1-49d4-b49a-3dd9a5ea41cc)

![dashboard-images](https://github.com/eosphoros-ai/DB-GPT/assets/17919400/1849a79a-f7fd-40cf-bc9c-b117a041dd6a)

## 目录
- [架构方案](#架构方案)
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# DB-GPT V0.6.0, Defining new standards for AI-native data applications.
59 changes: 0 additions & 59 deletions docs/docs/application/app_usage.md

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15 changes: 15 additions & 0 deletions docs/docs/application/apps/app_chat.md
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# App Chat

The online Chat interface provides the main conversation capabilities, showing the historical conversation records and the application currently in conversation. As shown in the figure below, clicking any smart application will also jump to this interface.

<p align="center">
<img src={'/img/app/app_chat_v0.6.jpg'} width="800px" />
</p>

In the dialogue interface, a series of operations such as refreshing and pausing the dialogue are supported. The specific operation buttons are in the edit box at the bottom right. At the same time, the dialog box also provides a variety of parameter selections, such as model selection, temperature parameter adjustment, file upload, etc.

<p align="center">
<img src={'/img/app/app_chat_op_v0.6.jpg'} width="800" />
</p>

If you find new problems or have good ideas during use, you can also directly post them on Github [issue](https://github.com/eosphoros-ai/DB-GPT/issues) feedback.
17 changes: 17 additions & 0 deletions docs/docs/application/apps/app_explore.md
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# App Explore

In the new version of DB-GPT V0.6.0, the application management has been comprehensively upgraded. The search square module is mainly used to discover various interesting, fun and useful data applications.KeywordsIn addition to searching for apps, it also provides popular recommendations, comprehensive apps, my favorites, etc.

After the default installation, the previous six application scenarios are retained.

- [Chat Excel](chat_excel.md)
- Chat Normal
- [Chat DB](chat_db.md)
- [Chat DashBoard](chat_dashboard.md)
- [Chat Data](chat_data.md)
- [Chat Knowledge Base](chat_knowledge.md)


<p align="center">
<img src={'/img/app/app_explore_v0.6.jpg'} width="800px" />
</p>
55 changes: 55 additions & 0 deletions docs/docs/application/apps/app_manage.md
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# App Manage

The application management panel provides many capabilities. Here we mainly introduce the management of the data intelligence application life cycle, including application creation, editing, deletion, and use.

<p align="center">
<img src={'/img/app/app_manage_v0.6.jpg'} width="800px" />
</p>

As shown in the figure, the application management interface. First, let's take a look at the creation of an application. In DB-GPT, four application creation modes are provided.

- Multi-agent automatic planning mode
- Task flow orchestration mode
- Single Agent Mode
- Native application mode

<p align="center">
<img src={'/img/app/app_manage_mode_v0.6.jpg'} width="800px" />
</p>

Next, we will explain the creation of applications in each mode respectively. Native application mode In the early versions of DB-GPT, six types of native application scenarios were provided, such as `Chat DB`, `Chat Data`, `Chat Dashboard`, `Chat Knowledge Base`, `Chat Normal`, `Chat Excel`, etc.

By creating a data intelligence application in the native application mode, you can quickly build a similar application based on your own database, knowledge base and other parameters. Click the upper right cornerCreate an applicationbutton, select **Native application mode**, enter the application name and description, click **Sure**

<p align="center">
<img src={'/img/app/app_manage_chat_data_v0.6.jpg'} width="800px" />
</p>

After confirmation, enter the parameter selection panel. As shown in the figure below, we can see selection boxes such as application type, model, temperature, and recommended questions.

<p align="center">
<img src={'/img/app/app_manage_chat_data_editor_v0.6.jpg'} width="800px" />
</p>

Here, we select **Chat Data** Application, fill in the parameters in order according to the requirements. Note that in the data dialogue application, the parameter column needs to fill in the data source. If you do not have a data source, you need to follow [Data Source Tutorial](../datasources.md) to add it.


After completing the parameters, click **Save** to view related applications in the application panel.

<p align="center">
<img src={'/img/app/app_manage_app_v0.6.jpg'} width="800px" />
</p>

Please note that after creating an application, there is a **Publish Application** button. Only after the application is published can it be discovered and used by other users.

<p align="center">
<img src={'/img/app/app_manage_app_publish_v0.6.jpg'} width="800px" />
</p>

Finally, click the **Start a conversation** button to start a conversation with the application you just created.

<p align="center">
<img src={'/img/app/app_manage_chat_v0.6.jpg'} width="800px" />
</p>

In addition, you can also edit and delete applications. Just operate on the corresponding interface.
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Expand Up @@ -6,7 +6,7 @@ Report analysis corresponds to the `Chat Dashboard` scenario in DB-GPT, and inte
The following are the steps for using report analysis:
- 1.Data preparation
- 2.Add data source
- 3.Select Chat Dashboard
- 3.Select Chat Dashboard App
- 4.Start chat


Expand All @@ -27,38 +27,25 @@ python docker/examples/dashboard/test_case_mysql_data.py

The steps to add a data source are the same as [Chat Data](./chat_data.md). Select the corresponding database type in the data source management tab, then create it. Fill in the necessary information to complete the creation.

<p align="left">
<img src={'/img/chat_dashboard/add_data.png'} width="720px" />
</p>



### Select Chat Dashboard

After the data source is added, select `Chat Dashboard` on the home scene page to perform report analysis.

<p align="left">
<img src={'/img/chat_dashboard/choose_chat_dashboard.png'} width="720px" />
<p align="center">
<img src={'/img/app/chat_dashboard_v0.6.jpg'} width="800px" />
</p>



### Start chat
Enter specific questions in the dialog box on the right to start a data conversation.

<p align="left">
<img src={'/img/chat_dashboard/preview.png'} width="720px" />
</p>



:::info note

⚠️ Data dialogue has relatively high requirements on model capabilities, and `ChatGPT/GPT-4` has a high success rate. Other open source models you can try `Vicuna-13B`
⚠️ Data dialogue has relatively high requirements on model capabilities, and `ChatGPT/GPT-4` has a high success rate. Other open source models you can try `qwen2`
:::

Of course, in addition to `preview mode`, `editor mode` is also provided. In editor mode, SQL can be edited and modified. You can see the changes in the chart synchronously.

<p align="left">
<img src={'/img/chat_dashboard/edit.png'} width="720px" />
</p>
<p align="center">
<img src={'/img/app/chat_dashboard_display_v0.6.jpg'} width="800px" />
</p>
36 changes: 36 additions & 0 deletions docs/docs/application/apps/chat_data.md
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# Chat Data

Chat data capability is to dialogue with data through natural language. Currently, it is mainly dialogue between structured and semi-structured data, which can assist in data analysis and insight.

:::info note

Before starting the data conversation, we first need to add the data source
:::

## steps

To start a data conversation, you need to go through the following steps:
- 1.Add data source
- 2.Select ChatData
- 3.Select the corresponding database
- 4.Start a conversation

### Add data source

First, select the [data source](../datasources.md) on the left to add and add a database. Currently, DB-GPT supports multiple database types. Just select the corresponding database type to add. Here we choose MySQL as a demonstration. For the test data of the demonstration, see the [test sample](https://github.com/eosphoros-ai/DB-GPT/tree/main/docker/examples/sqls).


### Choose ChatData App

<p align="center">
<img src={'/img/app/chat_data_v0.6.jpg'} width="800px" />
</p>

### Start a conversation

<p align="center">
<img src={'/img/app/chat_data_display_v0.6.jpg'} width="800px" />
</p>



File renamed without changes.
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Expand Up @@ -11,46 +11,35 @@ Chat Excel means that you can interpret and analyze Excel data through natural l
## Steps

The steps to use Chat Excel are relatively simple and are mainly divided into the following steps:
- 1.Select Chat Excel dialogue scene
- 1.Select Chat Excel dialogue app
- 2.Upload Excel document
- 3.Start chat

### Select `Chat Excel`


<p align="left">
<img src={'/img/chat_excel/choose_excel.png'} width="720px" />
<p align="center">
<img src={'/img/app/chat_excel_v0.6.jpg'} width="800px" />
</p>

### Upload Excel document

<p align="center">
<img src={'/img/app/chat_excel_upload_succ_v0.6.jpg'} width="800px" />
</p>


:::info note

⚠️ the Excel file format is converted to `.csv` format
:::

<p align="left">
<img src={'/img/chat_excel/upload_excel.png'} width="720px" />
</p>

After the upload is successful, the content will be summarized by default and some questioning strategies will be recommended.

<p align="left">
<img src={'/img/chat_excel/upload_finish.png'} width="720px" />
</p>

### Start chat

You can then start a conversation based on the uploaded file.

<p align="left">
<img src={'/img/chat_excel/chat.gif'} width="720px" />
</p>


Use open source models

<p align="left">
<img src={'/img/chat_excel/use_vicuna.gif'} width="720px" />
</p>
<p align="center">
<img src={'/img/app/chat_excel_upload_v0.6.jpg'} width="800px" />
</p>
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