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add help info (#142)
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KevinHuSh
@ -7,78 +7,117 @@ export const ImageMap = {
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book: getImageName('book', 4),
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laws: getImageName('law', 4),
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manual: getImageName('manual', 4),
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media: getImageName('media', 2),
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picture: getImageName('picture', 2),
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naive: getImageName('naive', 2),
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paper: getImageName('paper', 2),
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presentation: getImageName('presentation', 2),
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qa: getImageName('qa', 2),
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resume: getImageName('resume', 2),
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table: getImageName('table', 2),
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one: getImageName('one', 2),
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};
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export const TextMap = {
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book: {
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title: '',
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description: `Supported file formats are docx, excel, pdf, txt.
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description: `<p>Supported file formats are <b>DOCX</b>, <b>PDF</b>, <b>TXT</b>.</p><p>
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Since a book is long and not all the parts are useful, if it's a PDF,
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please setup the page ranges for every book in order eliminate negative effects and save computing time for analyzing.`,
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please setup the <i>page ranges</i> for every book in order eliminate negative effects and save computing time for analyzing.</p>`,
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},
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laws: {
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title: '',
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description: `Supported file formats are docx, pdf, txt.`,
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description: `<p>Supported file formats are <b>DOCX</b>, <b>PDF</b>, <b>TXT</b>.</p><p>
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Legal documents have a very rigorous writing format. We use text feature to detect split point.
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</p><p>
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The chunk granularity is consistent with 'ARTICLE', and all the upper level text will be included in the chunk.
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</p>`,
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},
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manual: { title: '', description: `Only pdf is supported.` },
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media: { title: '', description: '' },
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manual: { title: '', description: `<p>Only <b>PDF</b> is supported.</p><p>
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We assume manual has hierarchical section structure. We use the lowest section titles as pivots to slice documents.
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So, the figures and tables in the same section will not be sliced apart, and chunk size might be large.
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</p>` },
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naive: {
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title: '',
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description: `Supported file formats are docx, pdf, txt.
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This method apply the naive ways to chunk files.
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Successive text will be sliced into pieces using 'delimiter'.
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Next, these successive pieces are merge into chunks whose token number is no more than 'Max token number'.`,
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description: `<p>Supported file formats are <b>DOCX, EXCEL, PPT, IMAGE, PDF, TXT</b>.</p>
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<p>This method apply the naive ways to chunk files: </p>
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<p>
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<li>Successive text will be sliced into pieces using vision detection model.</li>
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<li>Next, these successive pieces are merge into chunks whose token number is no more than 'Token number'.</li></p>`,
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},
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paper: {
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title: '',
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description: `Only pdf is supported.
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The special part is that, the abstract of the paper will be sliced as an entire chunk, and will not be sliced partly.`,
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description: `<p>Only <b>PDF</b> file is supported.</p><p>
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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>
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The benefit of doing this is that LLM can better summarize the content of relevant sections in the paper,
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resulting in more comprehensive answers that help readers better understand the paper.
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The downside is that it increases the context of the LLM conversation and adds computational cost,
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so during the conversation, you can consider reducing the ‘<b>topN</b>’ setting.</p>`,
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},
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presentation: {
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title: '',
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description: `The supported file formats are pdf, pptx.
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Every page will be treated as a chunk. And the thumbnail of every page will be stored.
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PPT file will be parsed by using this method automatically, setting-up for every PPT file is not necessary.`,
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description: `<p>The supported file formats are <b>PDF</b>, <b>PPTX</b>.</p><p>
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Every page will be treated as a chunk. And the thumbnail of every page will be stored.</p><p>
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<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>`,
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},
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qa: {
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title: '',
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description: `Excel and csv(txt) format files are supported.
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If the file is in excel format, there should be 2 column question and answer without header.
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description: `<p><b>EXCEL</b> and <b>CSV/TXT</b> files are supported.</p><p>
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If the file is in excel format, there should be 2 columns question and answer without header.
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And question column is ahead of answer column.
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And it's O.K if it has multiple sheets as long as the columns are rightly composed.
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And it's O.K if it has multiple sheets as long as the columns are rightly composed.</p><p>
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If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate question and answer.
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If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate question and answer.</p><p>
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All the deformed lines will be ignored.
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Every pair of Q&A will be treated as a chunk.`,
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<i>All the deformed lines will be ignored.
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Every pair of Q&A will be treated as a chunk.</i></p>`,
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},
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resume: {
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title: '',
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description: `The supported file formats are pdf, docx and txt.`,
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description: `<p>The supported file formats are <b>DOCX</b>, <b>PDF</b>, <b>TXT</b>.
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</p><p>
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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.
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</p><p>
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Instead of chunking the résumé, we parse the résumé into structured data. As a HR, you can dump all the résumé you have,
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the you can list all the candidates that match the qualifications just by talk with <i>'RagFlow'</i>.
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</p>
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`,
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},
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table: {
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title: '',
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description: `Excel and csv(txt) format files are supported.
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For csv or txt file, the delimiter between columns is TAB.
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The first line must be column headers.
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Column headers must be meaningful terms inorder to make our NLP model understanding.
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It's good to enumerate some synonyms using slash '/' to separate, and even better to
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enumerate values using brackets like 'gender/sex(male, female)'.
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Here are some examples for headers:
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1. supplier/vendor\tcolor(yellow, red, brown)\tgender/sex(male, female)\tsize(M,L,XL,XXL)
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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）
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Every row in table will be treated as a chunk.
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visual:
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Image files are supported. Video is comming soon.
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If the picture has text in it, OCR is applied to extract the text as a description of it.
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If the text extracted by OCR is not enough, visual LLM is used to get the descriptions.`,
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description: `<p><b>EXCEL</b> and <b>CSV/TXT</b> format files are supported.</p><p>
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Here're some tips:
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<ul>
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<li>For csv or txt file, the delimiter between columns is <em><b>TAB</b></em>.</li>
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<li>The first line must be column headers.</li>
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<li>Column headers must be meaningful terms in order to make our LLM understanding.
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It's good to enumerate some synonyms using slash <i>'/'</i> to separate, and even better to
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enumerate values using brackets like <i>'gender/sex(male, female)'</i>.<p>
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Here are some examples for headers:<ol>
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<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>
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<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>
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</ol>
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</p>
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</li>
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<li>Every row in table will be treated as a chunk.</li>
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</ul>`,
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},
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picture: {
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title: '',
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description: `
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<p>Image files are supported. Video is coming soon.</p><p>
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If the picture has text in it, OCR is applied to extract the text as its text description.
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</p><p>
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If the text extracted by OCR is not enough, visual LLM is used to get the descriptions.
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</p>`,
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},
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one: {
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title: '',
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description: `
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<p>Supported file formats are <b>DOCX, EXCEL, PDF, TXT</b>.
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</p><p>
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For a document, it will be treated as an entire chunk, no split at all.
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</p><p>
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If you don't trust any chunk method and the selected LLM's context length covers the document length, you can try this method.
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</p>`,
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},
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};
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