diff --git a/docs/docs/application/apps/chat_dashboard.md b/docs/docs/application/apps/chat_dashboard.md
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--- a/docs/docs/application/apps/chat_dashboard.md
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-# Chat Dashboard
\ No newline at end of file
+# Chat Dashboard
+
+Report analysis corresponds to the `Chat Dashboard` scenario in DB-GPT, and intelligent report generation and analysis can be performed through natural language. It is one of the basic capabilities of generative BI (GBI). Let's take a look at how to use the report analysis capabilities.
+
+## Steps
+The following are the steps for using report analysis:
+- 1.Data preparation
+- 2.Add data source
+- 3.Select Chat Dashboard App
+- 4.Start chat
+
+
+### Data preparation
+
+In order to better experience the report analysis capabilities, we have built some test data into the code. To use this test data, we first need to create a test library.
+```SQL
+CREATE DATABASE IF NOT EXISTS dbgpt_test CHARACTER SET utf8;
+```
+
+After the test library is created, you can initialize the test data with one click through the script.
+
+```python
+python docker/examples/dashboard/test_case_mysql_data.py
+```
+
+### Add data source
+
+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.
+
+
+### Select Chat Dashboard
+
+After the data source is added, select `Chat Dashboard` on the home scene page to perform report analysis.
+
+
+ 
+
+
+
+### Start chat
+Enter specific questions in the dialog box on the right to start a data conversation.
+
+
+:::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 `qwen2`
+:::
+
+
+ 
+
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diff --git a/docs/docs/application/apps/chat_data.md b/docs/docs/application/apps/chat_data.md
index db166f187..2a7285d9a 100644
--- a/docs/docs/application/apps/chat_data.md
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@@ -1 +1,36 @@
-# Chat Data
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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
+
+
+ 
+
+
+### Start a conversation
+
+
+ 
+
+
+
+
diff --git a/docs/docs/application/apps/chat_excel.md b/docs/docs/application/apps/chat_excel.md
index 97c861478..fdbc7aaf6 100644
--- a/docs/docs/application/apps/chat_excel.md
+++ b/docs/docs/application/apps/chat_excel.md
@@ -1 +1,45 @@
-# Chat Excel
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+# Chat Excel
+
+Chat Excel means that you can interpret and analyze Excel data through natural language dialogue.
+
+
+
+ 
+
+
+
+## Steps
+
+The steps to use Chat Excel are relatively simple and are mainly divided into the following steps:
+- 1.Select Chat Excel dialogue app
+- 2.Upload Excel document
+- 3.Start chat
+
+### Select `Chat Excel`
+
+
+ 
+
+
+### Upload Excel document
+
+
+ 
+
+
+
+:::info note
+
+⚠️ the Excel file format is converted to `.csv` format
+:::
+
+
+After the upload is successful, the content will be summarized by default and some questioning strategies will be recommended.
+
+### Start chat
+
+You can then start a conversation based on the uploaded file.
+
+
+ 
+
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diff --git a/docs/docs/application/apps/chat_knowledge.md b/docs/docs/application/apps/chat_knowledge.md
index 53dda8e3d..33ff26b5b 100644
--- a/docs/docs/application/apps/chat_knowledge.md
+++ b/docs/docs/application/apps/chat_knowledge.md
@@ -1 +1,107 @@
-# Chat Knowledge
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+# Chat Knowledge Base
+
+`Chat knowledge Base` provides the ability to question and answer questions based on private domain knowledge, and can build intelligent question and answer systems, reading assistants and other products based on the `knowledge base`. `RAG` technology is also used in DB-GPT to enhance knowledge retrieval.
+
+
+## Noun explanation
+
+:::info note
+
+`Knowledge Space`: is a document space that manages a type of knowledge. Document knowledge of the same type can be uploaded to a knowledge space.
+:::
+
+
+## Steps
+The knowledge base operation process is relatively simple and is mainly divided into the following steps.
+- 1.Create knowledge space
+- 2.Upload documents
+- 3.Wait for document vectorization
+- 4.Select Knowledge Base App
+- 5.Chat With App
+
+
+### Create knowledge space
+
+At first open the `Construct App` and select the `Knowledge` on the top.
+
+
+ 
+
+
+Select the knowledge base, click the `Create` button, and fill in the necessary information to complete the creation of the knowledge space.
+
+
+
+ 
+
+
+### Upload documents
+
+Document addition currently supports multiple types, such as plain text, URL crawling, and various document types such as PDF, Word, and Markdown. Select a specific document to `upload`.
+
+
+ 
+
+
+
+Select one or more corresponding documents and click `next`.
+
+
+
+ 
+
+
+### Documents Segmentation
+
+Choose Document Segmentation, you can choose to segment the document by chunk size, separator, paragraph or markdown header. The default is to segment the document by chunk size.
+
+and click Process, it will take a few minutes to complete the document segmentation.
+
+
+ 
+
+
+:::tip
+**Automatic: The document is automatically segmented according to the document type.**
+
+**Chunk size: The number of words in each segment of the document. The default is 512 words.**
+ - chunk size: The number of words in each segment of the document. The default is 512 words.
+ - chunk overlap: The number of words overlapped between each segment of the document. The default is 50 words.
+** Separator:segmentation by separator **
+ - separator: The separator of the document. The default is `\n`.
+ - enable_merge: Whether to merge the separator chunks according to chunk_size after splits. The default is `False`.
+** Page: page segmentation, only support .pdf and .pptx document.**
+
+** Paragraph: paragraph segmentation, only support .docx document.**
+ - separator: The paragraph separator of the document. The default is `\n`.
+
+** Markdown header: markdown header segmentation, only support .md document.**
+:::
+
+
+### Waiting for document vectorization
+
+Click on the `knowledge space` and observe the document `slicing` + `vectorization` status in the lower left corner. When the status reaches `FINISHED`, you can start a knowledge base conversation.
+
+
+
+ 
+
+
+
+### Knowledge base chat
+
+Click the `Chat`button to start a conversation with the knowledge base.
+
+
+
+ 
+
+
+
+### Reading assistant
+In addition to the above capabilities, you can also upload documents directly in the knowledge base dialogue window, and the document will be summarized by default. This capability can be used as a `reading assistant` to assist document reading.
+
+
+ 
+
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