feat: add image for chunk method (#139)

* feat: add image of chunk method

* feat: add image for chunk method

web/src/pages/add-knowledge/components/knowledge-setting/utils.ts

@@ -0,0 +1,84 @@
+const getImageName = (prefix: string, length: number) =>
+  new Array(length)
+    .fill(0)
+    .map((x, idx) => `chunk-method/${prefix}-0${idx + 1}`);
+
+export const ImageMap = {
+  book: getImageName('book', 4),
+  laws: getImageName('law', 4),
+  manual: getImageName('manual', 4),
+  media: getImageName('media', 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),
+};
+
+export const TextMap = {
+  book: {
+    title: '',
+    description: `Supported file formats are docx, excel, pdf, txt.
+  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.`,
+  },
+  laws: {
+    title: '',
+    description: `Supported file formats are docx, pdf, txt.`,
+  },
+  manual: { title: '', description: `Only pdf is supported.` },
+  media: { title: '', description: '' },
+  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'.`,
+  },
+  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.`,
+  },
+  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.`,
+  },
+  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.
+  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.
+
+  If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate question and answer.
+
+  All the deformed lines will be ignored.
+  Every pair of Q&A will be treated as a chunk.`,
+  },
+  resume: {
+    title: '',
+    description: `The supported file formats are pdf, docx and txt.`,
+  },
+  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.`,
+  },
+};