mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
synced 2025-04-21 21:50:02 +08:00
add help info (#142)
This commit is contained in:
KevinHuSh
GitHub
parent
73c2f4d418
commit
1edbd36baf
@ -15,7 +15,10 @@ const SimilaritySlider = ({ isTooltipShown = false }: IProps) => {
|
||||
<Form.Item<FieldType>
|
||||
label="Similarity threshold"
|
||||
name={'similarity_threshold'}
|
||||
tooltip={isTooltipShown && 'coming soon'}
|
||||
tooltip={isTooltipShown && `We use hybrid similarity score to evaluate distance between two lines of text.
|
||||
It\'s weighted keywords similarity and vector cosine similarity.
|
||||
If the similarity between query and chunk is less than this threshold, the chunk will be filtered out.`
|
||||
}
|
||||
initialValue={0.2}
|
||||
>
|
||||
<Slider max={1} step={0.01} />
|
||||
@ -24,7 +27,10 @@ const SimilaritySlider = ({ isTooltipShown = false }: IProps) => {
|
||||
label="Vector similarity weight"
|
||||
name={'vector_similarity_weight'}
|
||||
initialValue={0.3}
|
||||
tooltip={isTooltipShown && 'coming soon'}
|
||||
tooltip={isTooltipShown && `We use hybrid similarity score to evaluate distance between two lines of text.
|
||||
It\'s weighted keywords similarity and vector cosine similarity.
|
||||
The sum of both weights is 1.0.
|
||||
`}
|
||||
>
|
||||
<Slider max={1} step={0.01} />
|
||||
</Form.Item>
|
||||
|
||||
@ -33,16 +33,16 @@ const CategoryPanel = ({ chunkMethod }: { chunkMethod: string }) => {
|
||||
{imageList.length > 0 ? (
|
||||
<>
|
||||
<Title level={5} className={styles.topTitle}>
|
||||
{item.title} Category
|
||||
"{item.title}" Chunking Method Description
|
||||
</Title>
|
||||
<p
|
||||
dangerouslySetInnerHTML={{
|
||||
__html: item.description,
|
||||
}}
|
||||
></p>
|
||||
<Title level={5}>{item.title} Image Examples</Title>
|
||||
<Title level={5}>"{item.title}" Examples</Title>
|
||||
<Text>
|
||||
We've prepared detailed visual guides to make understanding easier
|
||||
This visual guides is in order to make understanding easier
|
||||
for you.
|
||||
</Text>
|
||||
<Row gutter={[10, 10]} className={styles.imageRow}>
|
||||
|
||||
@ -83,7 +83,7 @@ const ConfigurationForm = ({ form }: { form: FormInstance }) => {
|
||||
<Form.Item
|
||||
name="permission"
|
||||
label="Permissions"
|
||||
tooltip="coming soon"
|
||||
tooltip="If the permission is 'Team', all the team member can manipulate the knowledgebase."
|
||||
rules={[{ required: true }]}
|
||||
>
|
||||
<Radio.Group>
|
||||
@ -93,22 +93,22 @@ const ConfigurationForm = ({ form }: { form: FormInstance }) => {
|
||||
</Form.Item>
|
||||
<Form.Item
|
||||
name="embd_id"
|
||||
label="Embedding Model"
|
||||
label="Embedding model"
|
||||
rules={[{ required: true }]}
|
||||
tooltip="xx"
|
||||
tooltip="The embedding model used to embedding chunks. It's unchangable once the knowledgebase has chunks. You need to delete all the chunks if you want to change it."
|
||||
>
|
||||
<Select
|
||||
placeholder="Please select a country"
|
||||
placeholder="Please select a embedding model"
|
||||
options={embeddingModelOptions}
|
||||
></Select>
|
||||
</Form.Item>
|
||||
<Form.Item
|
||||
name="parser_id"
|
||||
label="Chunk method"
|
||||
tooltip="xx"
|
||||
tooltip="The instruction is at right."
|
||||
rules={[{ required: true }]}
|
||||
>
|
||||
<Select placeholder="Please select a country">
|
||||
<Select placeholder="Please select a chunk method">
|
||||
{parserList.map((x) => (
|
||||
<Option value={x.value} key={x.value}>
|
||||
{x.label}
|
||||
@ -122,7 +122,7 @@ const ConfigurationForm = ({ form }: { form: FormInstance }) => {
|
||||
|
||||
if (parserId === 'naive') {
|
||||
return (
|
||||
<Form.Item label="Max token number" tooltip="xxx">
|
||||
<Form.Item label="Token number" tooltip="It determine the token number of a chunk approximately.">
|
||||
<Flex gap={20} align="center">
|
||||
<Flex flex={1}>
|
||||
<Form.Item
|
||||
|
||||
@ -7,78 +7,117 @@ export const ImageMap = {
|
||||
book: getImageName('book', 4),
|
||||
laws: getImageName('law', 4),
|
||||
manual: getImageName('manual', 4),
|
||||
media: getImageName('media', 2),
|
||||
picture: getImageName('picture', 2),
|
||||
naive: getImageName('naive', 2),
|
||||
paper: getImageName('paper', 2),
|
||||
presentation: getImageName('presentation', 2),
|
||||
qa: getImageName('qa', 2),
|
||||
resume: getImageName('resume', 2),
|
||||
table: getImageName('table', 2),
|
||||
one: getImageName('one', 2),
|
||||
};
|
||||
|
||||
export const TextMap = {
|
||||
book: {
|
||||
title: '',
|
||||
description: `Supported file formats are docx, excel, pdf, txt.
|
||||
description: `<p>Supported file formats are <b>DOCX</b>, <b>PDF</b>, <b>TXT</b>.</p><p>
|
||||
Since a book is long and not all the parts are useful, if it's a PDF,
|
||||
please setup the page ranges for every book in order eliminate negative effects and save computing time for analyzing.`,
|
||||
please setup the <i>page ranges</i> for every book in order eliminate negative effects and save computing time for analyzing.</p>`,
|
||||
},
|
||||
laws: {
|
||||
title: '',
|
||||
description: `Supported file formats are docx, pdf, txt.`,
|
||||
description: `<p>Supported file formats are <b>DOCX</b>, <b>PDF</b>, <b>TXT</b>.</p><p>
|
||||
Legal documents have a very rigorous writing format. We use text feature to detect split point.
|
||||
</p><p>
|
||||
The chunk granularity is consistent with 'ARTICLE', and all the upper level text will be included in the chunk.
|
||||
</p>`,
|
||||
},
|
||||
manual: { title: '', description: `Only pdf is supported.` },
|
||||
media: { title: '', description: '' },
|
||||
manual: { title: '', description: `<p>Only <b>PDF</b> is supported.</p><p>
|
||||
We assume manual has hierarchical section structure. We use the lowest section titles as pivots to slice documents.
|
||||
So, the figures and tables in the same section will not be sliced apart, and chunk size might be large.
|
||||
</p>` },
|
||||
naive: {
|
||||
title: '',
|
||||
description: `Supported file formats are docx, pdf, txt.
|
||||
This method apply the naive ways to chunk files.
|
||||
Successive text will be sliced into pieces using 'delimiter'.
|
||||
Next, these successive pieces are merge into chunks whose token number is no more than 'Max token number'.`,
|
||||
description: `<p>Supported file formats are <b>DOCX, EXCEL, PPT, IMAGE, PDF, TXT</b>.</p>
|
||||
<p>This method apply the naive ways to chunk files: </p>
|
||||
<p>
|
||||
<li>Successive text will be sliced into pieces using vision detection model.</li>
|
||||
<li>Next, these successive pieces are merge into chunks whose token number is no more than 'Token number'.</li></p>`,
|
||||
},
|
||||
paper: {
|
||||
title: '',
|
||||
description: `Only pdf is supported.
|
||||
The special part is that, the abstract of the paper will be sliced as an entire chunk, and will not be sliced partly.`,
|
||||
description: `<p>Only <b>PDF</b> file is supported.</p><p>
|
||||
If our model works well, the paper will be sliced by it's sections, like <i>abstract, 1.1, 1.2</i>, etc. </p><p>
|
||||
The benefit of doing this is that LLM can better summarize the content of relevant sections in the paper,
|
||||
resulting in more comprehensive answers that help readers better understand the paper.
|
||||
The downside is that it increases the context of the LLM conversation and adds computational cost,
|
||||
so during the conversation, you can consider reducing the ‘<b>topN</b>’ setting.</p>`,
|
||||
},
|
||||
presentation: {
|
||||
title: '',
|
||||
description: `The supported file formats are pdf, pptx.
|
||||
Every page will be treated as a chunk. And the thumbnail of every page will be stored.
|
||||
PPT file will be parsed by using this method automatically, setting-up for every PPT file is not necessary.`,
|
||||
description: `<p>The supported file formats are <b>PDF</b>, <b>PPTX</b>.</p><p>
|
||||
Every page will be treated as a chunk. And the thumbnail of every page will be stored.</p><p>
|
||||
<i>All the PPT files you uploaded will be chunked by using this method automatically, setting-up for every PPT file is not necessary.</i></p>`,
|
||||
},
|
||||
qa: {
|
||||
title: '',
|
||||
description: `Excel and csv(txt) format files are supported.
|
||||
If the file is in excel format, there should be 2 column question and answer without header.
|
||||
description: `<p><b>EXCEL</b> and <b>CSV/TXT</b> files are supported.</p><p>
|
||||
If the file is in excel format, there should be 2 columns question and answer without header.
|
||||
And question column is ahead of answer column.
|
||||
And it's O.K if it has multiple sheets as long as the columns are rightly composed.
|
||||
And it's O.K if it has multiple sheets as long as the columns are rightly composed.</p><p>
|
||||
|
||||
If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate question and answer.
|
||||
If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate question and answer.</p><p>
|
||||
|
||||
All the deformed lines will be ignored.
|
||||
Every pair of Q&A will be treated as a chunk.`,
|
||||
<i>All the deformed lines will be ignored.
|
||||
Every pair of Q&A will be treated as a chunk.</i></p>`,
|
||||
},
|
||||
resume: {
|
||||
title: '',
|
||||
description: `The supported file formats are pdf, docx and txt.`,
|
||||
description: `<p>The supported file formats are <b>DOCX</b>, <b>PDF</b>, <b>TXT</b>.
|
||||
</p><p>
|
||||
The résumé comes in a variety of formats, just like a person’s personality, but we often have to organize them into structured data that makes it easy to search.
|
||||
</p><p>
|
||||
Instead of chunking the résumé, we parse the résumé into structured data. As a HR, you can dump all the résumé you have,
|
||||
the you can list all the candidates that match the qualifications just by talk with <i>'RagFlow'</i>.
|
||||
</p>
|
||||
`,
|
||||
},
|
||||
table: {
|
||||
title: '',
|
||||
description: `Excel and csv(txt) format files are supported.
|
||||
For csv or txt file, the delimiter between columns is TAB.
|
||||
The first line must be column headers.
|
||||
Column headers must be meaningful terms inorder to make our NLP model understanding.
|
||||
It's good to enumerate some synonyms using slash '/' to separate, and even better to
|
||||
enumerate values using brackets like 'gender/sex(male, female)'.
|
||||
Here are some examples for headers:
|
||||
1. supplier/vendor\tcolor(yellow, red, brown)\tgender/sex(male, female)\tsize(M,L,XL,XXL)
|
||||
2. 姓名/名字\t电话/手机/微信\t最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）
|
||||
Every row in table will be treated as a chunk.
|
||||
|
||||
visual:
|
||||
Image files are supported. Video is comming soon.
|
||||
If the picture has text in it, OCR is applied to extract the text as a description of it.
|
||||
If the text extracted by OCR is not enough, visual LLM is used to get the descriptions.`,
|
||||
description: `<p><b>EXCEL</b> and <b>CSV/TXT</b> format files are supported.</p><p>
|
||||
Here're some tips:
|
||||
<ul>
|
||||
<li>For csv or txt file, the delimiter between columns is <em><b>TAB</b></em>.</li>
|
||||
<li>The first line must be column headers.</li>
|
||||
<li>Column headers must be meaningful terms in order to make our LLM understanding.
|
||||
It's good to enumerate some synonyms using slash <i>'/'</i> to separate, and even better to
|
||||
enumerate values using brackets like <i>'gender/sex(male, female)'</i>.<p>
|
||||
Here are some examples for headers:<ol>
|
||||
<li>supplier/vendor<b>'TAB'</b>color(yellow, red, brown)<b>'TAB'</b>gender/sex(male, female)<b>'TAB'</b>size(M,L,XL,XXL)</li>
|
||||
<li>姓名/名字<b>'TAB'</b>电话/手机/微信<b>'TAB'</b>最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）</li>
|
||||
</ol>
|
||||
</p>
|
||||
</li>
|
||||
<li>Every row in table will be treated as a chunk.</li>
|
||||
</ul>`,
|
||||
},
|
||||
picture: {
|
||||
title: '',
|
||||
description: `
|
||||
<p>Image files are supported. Video is coming soon.</p><p>
|
||||
If the picture has text in it, OCR is applied to extract the text as its text description.
|
||||
</p><p>
|
||||
If the text extracted by OCR is not enough, visual LLM is used to get the descriptions.
|
||||
</p>`,
|
||||
},
|
||||
one: {
|
||||
title: '',
|
||||
description: `
|
||||
<p>Supported file formats are <b>DOCX, EXCEL, PDF, TXT</b>.
|
||||
</p><p>
|
||||
For a document, it will be treated as an entire chunk, no split at all.
|
||||
</p><p>
|
||||
If you don't trust any chunk method and the selected LLM's context length covers the document length, you can try this method.
|
||||
</p>`,
|
||||
},
|
||||
};
|
||||
|
||||
@ -53,9 +53,10 @@ const TestingControl = ({ form, handleTesting }: IProps) => {
|
||||
>
|
||||
<SimilaritySlider isTooltipShown></SimilaritySlider>
|
||||
<Form.Item<FieldType>
|
||||
label="Top k"
|
||||
label="Top K"
|
||||
name={'top_k'}
|
||||
tooltip="coming soon"
|
||||
tooltip="For the computaion cost, not all the retrieved chunk will be computed vector cosine similarity with query.
|
||||
The bigger the 'Top K' is, the higher the recall rate is, the slower the retrieval speed is."
|
||||
>
|
||||
<Slider marks={{ 0: 0, 2048: 2048 }} max={2048} />
|
||||
</Form.Item>
|
||||
|
||||
@ -55,6 +55,7 @@ const AssistantSetting = ({ show }: ISegmentedContentProps) => {
|
||||
label="Language"
|
||||
initialValue={'Chinese'}
|
||||
tooltip="coming soon"
|
||||
style={{display:'none'}}
|
||||
>
|
||||
<Select
|
||||
options={[
|
||||
@ -66,22 +67,23 @@ const AssistantSetting = ({ show }: ISegmentedContentProps) => {
|
||||
<Form.Item
|
||||
name={['prompt_config', 'empty_response']}
|
||||
label="Empty response"
|
||||
tooltip="coming soon"
|
||||
tooltip="If nothing is retrieved with user's question in the knowledgebase, it will use this as an answer.
|
||||
If you want LLM comes up with its own opinion when nothing is retrieved, leave this blank."
|
||||
>
|
||||
<Input placeholder="" />
|
||||
</Form.Item>
|
||||
<Form.Item
|
||||
name={['prompt_config', 'prologue']}
|
||||
label="Set an opener"
|
||||
tooltip="coming soon"
|
||||
tooltip="How do you want to welcome your clients?"
|
||||
initialValue={"Hi! I'm your assistant, what can I do for you?"}
|
||||
>
|
||||
<Input.TextArea autoSize={{ minRows: 5 }} />
|
||||
</Form.Item>
|
||||
<Form.Item
|
||||
label="Select one context"
|
||||
label="Knowledgebases"
|
||||
name="kb_ids"
|
||||
tooltip="coming soon"
|
||||
tooltip="Select knowledgebases associated."
|
||||
rules={[
|
||||
{
|
||||
required: true,
|
||||
|
||||
@ -46,16 +46,16 @@ const ModelSetting = ({ show, form }: ISegmentedContentProps) => {
|
||||
<Form.Item
|
||||
label="Model"
|
||||
name="llm_id"
|
||||
tooltip="coming soon"
|
||||
tooltip="Large language chat model"
|
||||
rules={[{ required: true, message: 'Please select!' }]}
|
||||
>
|
||||
<Select options={modelOptions} showSearch />
|
||||
</Form.Item>
|
||||
<Divider></Divider>
|
||||
<Form.Item
|
||||
label="Parameters"
|
||||
label="Freedom"
|
||||
name="parameters"
|
||||
tooltip="coming soon"
|
||||
tooltip="'Precise' means the LLM will be conservative and answer your question cautiously. 'Improvise' means the you want LLM talk much and freely. 'Balance' is between cautiously and freely."
|
||||
initialValue={ModelVariableType.Precise}
|
||||
// rules={[{ required: true, message: 'Please input!' }]}
|
||||
>
|
||||
@ -64,7 +64,7 @@ const ModelSetting = ({ show, form }: ISegmentedContentProps) => {
|
||||
onChange={handleParametersChange}
|
||||
/>
|
||||
</Form.Item>
|
||||
<Form.Item label="Temperature" tooltip={'xx'}>
|
||||
<Form.Item label="Temperature" tooltip={'This parameter controls the randomness of predictions by the model. A lower temperature makes the model more confident in its responses, while a higher temperature makes it more creative and diverse.'}>
|
||||
<Flex gap={20} align="center">
|
||||
<Form.Item
|
||||
name={'temperatureEnabled'}
|
||||
@ -96,7 +96,7 @@ const ModelSetting = ({ show, form }: ISegmentedContentProps) => {
|
||||
</Form.Item>
|
||||
</Flex>
|
||||
</Form.Item>
|
||||
<Form.Item label="Top P" tooltip={'xx'}>
|
||||
<Form.Item label="Top P" tooltip={'Also known as “nucleus sampling,” this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones.'}>
|
||||
<Flex gap={20} align="center">
|
||||
<Form.Item name={'topPEnabled'} valuePropName="checked" noStyle>
|
||||
<Switch size="small" />
|
||||
@ -124,7 +124,7 @@ const ModelSetting = ({ show, form }: ISegmentedContentProps) => {
|
||||
</Form.Item>
|
||||
</Flex>
|
||||
</Form.Item>
|
||||
<Form.Item label="Presence Penalty" tooltip={'xx'}>
|
||||
<Form.Item label="Presence Penalty" tooltip={'This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation.'}>
|
||||
<Flex gap={20} align="center">
|
||||
<Form.Item
|
||||
name={'presencePenaltyEnabled'}
|
||||
@ -160,7 +160,7 @@ const ModelSetting = ({ show, form }: ISegmentedContentProps) => {
|
||||
</Form.Item>
|
||||
</Flex>
|
||||
</Form.Item>
|
||||
<Form.Item label="Frequency Penalty" tooltip={'xx'}>
|
||||
<Form.Item label="Frequency Penalty" tooltip={'Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently.'}>
|
||||
<Flex gap={20} align="center">
|
||||
<Form.Item
|
||||
name={'frequencyPenaltyEnabled'}
|
||||
@ -196,7 +196,7 @@ const ModelSetting = ({ show, form }: ISegmentedContentProps) => {
|
||||
</Form.Item>
|
||||
</Flex>
|
||||
</Form.Item>
|
||||
<Form.Item label="Max Tokens" tooltip={'xx'}>
|
||||
<Form.Item label="Max Tokens" tooltip={'This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words).'}>
|
||||
<Flex gap={20} align="center">
|
||||
<Form.Item name={'maxTokensEnabled'} valuePropName="checked" noStyle>
|
||||
<Switch size="small" />
|
||||
|
||||
@ -154,7 +154,7 @@ const PromptEngine = (
|
||||
<Form.Item
|
||||
label="System"
|
||||
rules={[{ required: true, message: 'Please input!' }]}
|
||||
tooltip="coming soon"
|
||||
tooltip="Instructions you need LLM to follow when LLM answers questions, like charactor design, answer length and answer language etc."
|
||||
name={['prompt_config', 'system']}
|
||||
initialValue={`你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
|
||||
以下是知识库：
|
||||
@ -166,10 +166,10 @@ const PromptEngine = (
|
||||
<Divider></Divider>
|
||||
<SimilaritySlider isTooltipShown></SimilaritySlider>
|
||||
<Form.Item<FieldType>
|
||||
label="Top n"
|
||||
label="Top N"
|
||||
name={'top_n'}
|
||||
initialValue={8}
|
||||
tooltip={'xxx'}
|
||||
tooltip={`Not all the chunks whose similarity score is above the 'simialrity threashold' will be feed to LLMs. LLM can only see these 'Top N' chunks.`}
|
||||
>
|
||||
<Slider max={30} />
|
||||
</Form.Item>
|
||||
@ -178,7 +178,10 @@ const PromptEngine = (
|
||||
<Col span={7} className={styles.variableAlign}>
|
||||
<label className={styles.variableLabel}>
|
||||
Variables
|
||||
<Tooltip title="coming soon">
|
||||
<Tooltip title="If you use dialog APIs, the varialbes might help you chat with your clients with different strategies.
|
||||
The variables are used to fill-in the 'System' part in prompt in order to give LLM a hint.
|
||||
The 'knowledge' is a very special variable which will be filled-in with the retrieved chunks.
|
||||
All the variables in 'System' should be curly bracketed.">
|
||||
<QuestionCircleOutlined className={styles.variableIcon} />
|
||||
</Tooltip>
|
||||
</label>
|
||||
|
||||
@ -66,7 +66,7 @@ const ApiKeyModal = ({
|
||||
<Form.Item<FieldType>
|
||||
label="Api-Key"
|
||||
name="api_key"
|
||||
tooltip="coming soon"
|
||||
tooltip="The API key can be obtained by registering the corresponding LLM supplier."
|
||||
rules={[{ required: true, message: 'Please input api key!' }]}
|
||||
>
|
||||
<Input />
|
||||
|
||||
@ -43,25 +43,27 @@ const SystemModelSettingModal = ({
|
||||
confirmLoading={loading}
|
||||
>
|
||||
<Form form={form} onValuesChange={onFormLayoutChange} layout={'vertical'}>
|
||||
<Form.Item
|
||||
label="Sequence2txt model"
|
||||
name="asr_id"
|
||||
tooltip="coming soon"
|
||||
>
|
||||
<Select options={allOptions[LlmModelType.Speech2text]} />
|
||||
|
||||
<Form.Item label="Chat model" name="llm_id" tooltip="The default chat LLM all the newly created knowledgebase will use.">
|
||||
<Select options={allOptions[LlmModelType.Chat]} />
|
||||
</Form.Item>
|
||||
<Form.Item label="Embedding model" name="embd_id" tooltip="coming soon">
|
||||
<Form.Item label="Embedding model" name="embd_id" tooltip="The default embedding model all the newly created knowledgebase will use.">
|
||||
<Select options={allOptions[LlmModelType.Embedding]} />
|
||||
</Form.Item>
|
||||
<Form.Item
|
||||
label="Img2txt model"
|
||||
name="img2txt_id"
|
||||
tooltip="coming soon"
|
||||
tooltip="The default multi-module model all the newly created knowledgebase will use. It can describe a picture or video."
|
||||
>
|
||||
<Select options={allOptions[LlmModelType.Image2text]} />
|
||||
</Form.Item>
|
||||
<Form.Item label="Chat model" name="llm_id" tooltip="coming soon">
|
||||
<Select options={allOptions[LlmModelType.Chat]} />
|
||||
|
||||
<Form.Item
|
||||
label="Sequence2txt model"
|
||||
name="asr_id"
|
||||
tooltip="The default ASR model all the newly created knowledgebase will use. Use this model to translate voices to corresponding text."
|
||||
>
|
||||
<Select options={allOptions[LlmModelType.Speech2text]} />
|
||||
</Form.Item>
|
||||
</Form>
|
||||
</Modal>
|
||||
|
||||
@ -110,9 +110,6 @@ const UserSettingProfile = () => {
|
||||
<div>
|
||||
<Space>
|
||||
Your photo
|
||||
<Tooltip title="coming soon">
|
||||
<QuestionCircleOutlined />
|
||||
</Tooltip>
|
||||
</Space>
|
||||
<div>This will be displayed on your profile.</div>
|
||||
</div>
|
||||
@ -140,7 +137,6 @@ const UserSettingProfile = () => {
|
||||
<Form.Item<FieldType>
|
||||
label="Color schema"
|
||||
name="color_schema"
|
||||
tooltip="coming soon"
|
||||
rules={[
|
||||
{ required: true, message: 'Please select your color schema!' },
|
||||
]}
|
||||
@ -154,7 +150,6 @@ const UserSettingProfile = () => {
|
||||
<Form.Item<FieldType>
|
||||
label="Language"
|
||||
name="language"
|
||||
tooltip="coming soon"
|
||||
rules={[{ required: true, message: 'Please input your language!' }]}
|
||||
>
|
||||
<Select placeholder="select your language">
|
||||
@ -166,7 +161,6 @@ const UserSettingProfile = () => {
|
||||
<Form.Item<FieldType>
|
||||
label="Timezone"
|
||||
name="timezone"
|
||||
tooltip="coming soon"
|
||||
rules={[{ required: true, message: 'Please input your timezone!' }]}
|
||||
>
|
||||
<Select placeholder="select your timezone" showSearch>
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user