diff --git a/docs/authoring/dynamic_phrasing_based_on_values.md b/docs/authoring/dynamic_phrasing_based_on_values.md index a1264a511..0b034062a 100644 --- a/docs/authoring/dynamic_phrasing_based_on_values.md +++ b/docs/authoring/dynamic_phrasing_based_on_values.md @@ -6,7 +6,7 @@ slug: dynamic_phrasing_based_on_values --- Docassemble has several clever features that you can use in templates -and in the body of your interview to automatically rephrase text in a +and in the body of your interview to automatically rephrase text in a context specific way depending on: 1. the length of a list @@ -15,7 +15,7 @@ context specific way depending on: In general, these features are useful short-hand solutions for problems that you could also solve with ordinary conditional text (e.g., an `if` statement). -These are covered in more depth in the documentation for +These are covered in more depth in the documentation for [DAList](https://docassemble.org/docs/objects.html#DAList), although it may be hard to find the specific functions on that fairly long documentation page without internal anchors. The best solution to learn more may be to read about the method on this page, then visit the DAList documentation page @@ -31,6 +31,34 @@ It's often safe to use the grammar shortcuts inside a template (because it will to avoid them inside the interview itself. ::: +## What do to instead for text that will be translated + +Instead of using the special grammar functions below, use conditional logic at the sentence level +when the text might be translated in the future. + +You might want to use the special [`label`/`field` syntax](https://docassemble.org/docs/fields.html#label) to make it +easier to use conditional logic on the `field` level. This syntax allows you to use multi-line `mako` conditional +syntax. + +For example: + +```yaml +question: | + % if children.number_gathered() > 1: + Where do the children live? + % else: + Where does your child live? + % endif +fields: + - label: | + % if children.number_gathered() > 1: + Children's address + % else: + Child's address + % endif + field: children.shared_address +``` + ## Show the person's full name, without abbreviation By default, Docassemble abbreviates an individual's middle name, so diff --git a/docs/components/formfyxer/docx_wrangling.md b/docs/components/formfyxer/docx_wrangling.md deleted file mode 100644 index 47f745009..000000000 --- a/docs/components/formfyxer/docx_wrangling.md +++ /dev/null @@ -1,123 +0,0 @@ ---- -sidebar_label: docx_wrangling -title: formfyxer.docx_wrangling ---- - -#### update\_docx - -Update the document with the modified runs. - -Note: OpenAI is probabilistic, so the modified run indices may not be correct. -When the index of a run or paragraph is out of range, a new paragraph -will be inserted at the end of the document or a new run at the end of the -paragraph's runs. - -Take a careful look at the output document to make sure it is still correct. - -**Arguments**: - -- `document` - the docx.Document object, or the path to the DOCX file -- `modified_runs` - a tuple of paragraph number, run number, the modified text, a question (not used), and whether a new paragraph should be inserted (for conditional text) - - -**Returns**: - - The modified document. - -#### get\_docx\_repr - -Return a JSON representation of the paragraphs and runs in the DOCX file. - -**Arguments**: - -- `docx_path` - path to the DOCX file - - -**Returns**: - - A JSON representation of the paragraphs and runs in the DOCX file. - -#### get\_labeled\_docx\_runs - -Scan the DOCX and return a list of modified text with Jinja2 variable names inserted. - -**Arguments**: - -- `docx_path` - path to the DOCX file -- `docx_repr` - a string representation of the paragraphs and runs in the DOCX file, if docx_path is not provided. This might be useful if you want -- `custom_people_names` - a tuple of custom names and descriptions to use in addition to the default ones. Like: ("clients", "the person benefiting from the form") - - -**Returns**: - - A list of tuples, each containing a paragraph number, run number, and the modified text of the run. - -#### get\_modified\_docx\_runs - -Use GPT to rewrite the contents of a DOCX file paragraph by paragraph. Does not handle tables, footers, or -other structures yet. - -This is a light wrapper that provides the structure of DOCX paragraphs and runs to your prompt -to OpenAI to facilitate the rewriting of the document without disrupting formatting. - -For example, this could be used to: -* Remove any passive voice -* Replace placeholder text with variable names -* Rewrite to a 6th grade reading level -* Do an advanced search and replace, without requiring you to use a regex - -By default, the example prompt includes a sample like this: - -[ -[0, 0, "Dear "], -[0, 1, "John Smith:"], -[1, 0, "I hope this letter finds you well."], -] - -Your custom instructions should include an example of how the sample will be modified, like the one below: - -Example reply, indicating paragraph, run, the new text, and a number indicating if this changes the -current paragraph, adds one before, or adds one after (-1, 0, 1): - -\{"results": -[ -[0, 1, "Dear \{\{ other_parties[0] \}\}:", 0], -[2, 0, "\{%p if is_tenant %\}", -1], -[3, 0, "\{%p endif %\}", 1], -] -\} - -You may also want to customize the input example to better match your use case. - -**Arguments**: - -- `docx_path` _str_ - path to the DOCX file -- `docx_repr` _str_ - a string representation of the paragraphs and runs in the DOCX file, if docx_path is not provided. -- `custom_example` _Optional[str]_ - a string containing the purpose and overview of the task - instructions (str) a string containing specific instructions for the task -- `openai_client` _Optional[OpenAI]_ - an OpenAI client object. If not provided a new one will be created. -- `api_key` _Optional[str]_ - an OpenAI API key. If not provided, it will be obtained from the environment -- `temperature` _float_ - the temperature to use when generating text. Lower temperatures are more conservative. - - -**Returns**: - - A list of tuples, each containing a paragraph number, run number, and the modified text of the run. - -#### make\_docx\_plain\_language - -Convert a DOCX file to plain language with the help of OpenAI. - -#### modify\_docx\_with\_openai\_guesses - -Uses OpenAI to guess the variable names for a document and then modifies the document with the guesses. - -**Arguments**: - -- `docx_path` _str_ - Path to the DOCX file to modify. - - -**Returns**: - -- `docx.Document` - The modified document, ready to be saved to the same or a new path - diff --git a/docs/components/formfyxer/lit_explorer.md b/docs/components/formfyxer/lit_explorer.md deleted file mode 100644 index 9263ed35d..000000000 --- a/docs/components/formfyxer/lit_explorer.md +++ /dev/null @@ -1,257 +0,0 @@ ---- -sidebar_label: lit_explorer -title: formfyxer.lit_explorer ---- - -#### recursive\_get\_id - -Pull ID values out of the LIST/NSMI results from Spot. - -#### spot - -Call the Spot API (https://spot.suffolklitlab.org) to classify the text of a PDF using -the NSMIv2/LIST taxonomy (https://taxonomy.legal/), but returns only the IDs of issues found in the text. - -#### re\_case - -Capture PascalCase, snake_case and kebab-case terms and add spaces to separate the joined words - -#### regex\_norm\_field - -Apply some heuristics to a field name to see if we can get it to match AssemblyLine conventions. -See: https://assemblyline.suffolklitlab.org/docs/authoring/label_variables#fields-labels-and-variables - -#### reformat\_field - -Transforms a string of text into a snake_case variable close in length to `max_length` name by -summarizing the string and stitching the summary together in snake_case. -h/t https://medium.com/data-science/nlp-building-a-summariser-68e0c19e3a93 - -#### norm - -Normalize a word vector. - -#### vectorize - -Vectorize a string of text. - -**Arguments**: - -- `text` - a string of multiple words to vectorize -- `tools_token` - the token to tools.suffolklitlab.org, used for micro-service - to reduce the amount of memory you need on your machine. If - not passed, you need to have `en_core_web_lg` installed - -#### normalize\_name - -Normalize a field name, if possible to the Assembly Line conventions, and if -not, to a snake_case variable name of appropriate length. - -HACK: temporarily all we do is re-case it and normalize it using regex rules. -Will be replaced with call to LLM soon. - -#### cluster\_screens - -Groups the given fields into screens based on how much they are related. - -**Arguments**: - -- `fields` - a list of field names -- `damping` - a value >= 0.5 and < 1. Tunes how related screens should be -- `tools_token` - the token to tools.suffolklitlab.org, needed of doing - micro-service vectorization - -- `Returns` - a suggested screen grouping, each screen name mapped to the list of fields on it - -## InputType Objects - -```python -class InputType(Enum) -``` - -Input type maps onto the type of input the PDF author chose for the field. We only -handle text, checkbox, and signature fields. - -#### field\_types\_and\_sizes - -Transform the fields provided by get_existing_pdf_fields into a summary format. -Result will look like: -[ -\{ -"var_name": var_name, -"type": "text | checkbox | signature", -"max_length": n -\} -] - -## AnswerType Objects - -```python -class AnswerType(Enum) -``` - -Answer type describes the effort the user answering the form will require. -"Slot-in" answers are a matter of almost instantaneous recall, e.g., name, address, etc. -"Gathered" answers require looking around one's desk, for e.g., a health insurance number. -"Third party" answers require picking up the phone to call someone else who is the keeper -of the information. -"Created" answers don't exist before the user is presented with the question. They may include -a choice, creating a narrative, or even applying legal reasoning. "Affidavits" are a special -form of created answers. -See Jarret and Gaffney, Forms That Work (2008) - -#### classify\_field - -Apply heuristics to the field's original and "normalized" name to classify -it as either a "slot-in", "gathered", "third party" or "created" field type. - -#### get\_adjusted\_character\_count - -Determines the bracketed length of an input field based on its max_length attribute, -returning a float representing the approximate length of the field content. - -The function chunks the answers into 5 different lengths (checkboxes, 2 words, short, medium, and long) -instead of directly using the character count, as forms can allocate different spaces -for the same data without considering the space the user actually needs. - -**Arguments**: - -- `field` _FieldInfo_ - An object containing information about the input field, - including the "max_length" attribute. - - -**Returns**: - -- `float` - The approximate length of the field content, categorized into checkboxes, 2 words, short, - medium, or long based on the max_length attribute. - - -**Examples**: - - >>> get_adjusted_character_count(\{"type"\}: InputType.CHECKBOX) - 4.7 - >>> get_adjusted_character_count(\{"max_length": 100\}) - 9.4 - >>> get_adjusted_character_count(\{"max_length": 300\}) - 230 - >>> get_adjusted_character_count(\{"max_length": 600\}) - 115 - >>> get_adjusted_character_count(\{"max_length": 1200\}) - 1150 - -#### time\_to\_answer\_field - -Apply a heuristic for the time it takes to answer the given field, in minutes. -It is hand-written for now. -It will factor in the input type, the answer type (slot in, gathered, third party or created), and the -amount of input text allowed in the field. -The return value is a function that can return N samples of how long it will take to answer the field (in minutes) - -#### time\_to\_answer\_form - -Provide an estimate of how long it would take an average user to respond to the questions -on the provided form. -We use signals such as the field type, name, and space provided for the response to come up with a -rough estimate, based on whether the field is: -1. fill in the blank -2. gathered - e.g., an id number, case number, etc. -3. third party: need to actually ask someone the information - e.g., income of not the user, anything else? -4. created: -a. short created (3 lines or so?) -b. long created (anything over 3 lines) - -#### cleanup\_text - -Apply cleanup routines to text to provide more accurate readability statistics. - -#### text\_complete - -Run a prompt via openAI's API and return the result. - -**Arguments**: - -- `prompt` _str_ - The prompt to send to the API. -- `max_tokens` _int, optional_ - The number of tokens to generate. Defaults to 500. -- `creds` _Optional[OpenAiCreds], optional_ - The credentials to use. Defaults to None. -- `temperature` _float, optional_ - The temperature to use. Defaults to 0. - -#### complete\_with\_command - -Combines some text with a command to send to open ai. - -#### needs\_calculations - -A conservative guess at if a given form needs the filler to make math calculations, -something that should be avoided. If - -#### get\_passive\_sentences - -Return a list of tuples, where each tuple represents a -sentence in which passive voice was detected along with a list of the -starting and ending position of each fragment that is phrased in the passive voice. -The combination of the two can be used in the PDFStats frontend to highlight the -passive text in an individual sentence. - -Text can either be a string or a list of strings. -If provided a single string, it will be tokenized with NTLK and -sentences containing fewer than 2 words will be ignored. - -#### get\_citations - -Get citations and some extra surrounding context (the full sentence), if the citation is -fewer than 5 characters (often eyecite only captures a section symbol -for state-level short citation formats) - -#### substitute\_phrases - -Substitute phrases in the input string and return the new string and positions of substituted phrases. - -**Arguments**: - -- `input_string` _str_ - The input string containing phrases to be replaced. -- `substitution_phrases` _Dict[str, str]_ - A dictionary mapping original phrases to their replacement phrases. - - -**Returns**: - - Tuple[str, List[Tuple[int, int]]]: A tuple containing the new string with substituted phrases and a list of - tuples, each containing the start and end positions of the substituted - phrases in the new string. - - -**Example**: - - >>> input_string = "The quick brown fox jumped over the lazy dog." - >>> substitution_phrases = \{"quick brown": "swift reddish", "lazy dog": "sleepy canine"\} - >>> new_string, positions = substitute_phrases(input_string, substitution_phrases) - >>> print(new_string) - "The swift reddish fox jumped over the sleepy canine." - >>> print(positions) - [(4, 17), (35, 48)] - -#### substitute\_neutral\_gender - -Substitute gendered phrases with neutral phrases in the input string. -Primary source is https://github.com/joelparkerhenderson/inclusive-language - -#### substitute\_plain\_language - -Substitute complex phrases with simpler alternatives. -Source of terms is drawn from https://www.plainlanguage.gov/guidelines/words/ - -#### transformed\_sentences - -Apply a function to a list of sentences and return only the sentences with changed terms. -The result is a tuple of the original sentence, new sentence, and the starting and ending position -of each changed fragment in the sentence. - -#### parse\_form - -Read in a pdf, pull out basic stats, attempt to normalize its form fields, and re-write the -in_file with the new fields (if `rewrite=1`). If you pass a spot token, we will guess the -NSMI code. If you pass openai creds, we will give suggestions for the title and description. - -#### form\_complexity - -Gets a single number of how hard the form is to complete. Higher is harder. - diff --git a/docs/components/formfyxer/pdf_wrangling.md b/docs/components/formfyxer/pdf_wrangling.md deleted file mode 100644 index b5dca6cec..000000000 --- a/docs/components/formfyxer/pdf_wrangling.md +++ /dev/null @@ -1,323 +0,0 @@ ---- -sidebar_label: pdf_wrangling -title: formfyxer.pdf_wrangling ---- - -## FieldType Objects - -```python -class FieldType(Enum) -``` - -#### TEXT - -Text input Field - -#### AREA - -Text input Field, but an area - -#### LIST\_BOX - -allows multiple selection - -#### CHOICE - -allows only one selection - -## FormField Objects - -```python -class FormField() -``` - -A data holding class, used to easily specify how a PDF form field should be created. - -#### \_\_init\_\_ - -Constructor - -**Arguments**: - -- `x` - the x position of the lower left corner of the field. Should be in X,Y coordinates, - where (0, 0) is the lower left of the page, x goes to the right, and units are in - points (1/72th of an inch) -- `y` - the y position of the lower left corner of the field. Should be in X,Y coordinates, - where (0, 0) is the lower left of the page, y goes up, and units are in points - (1/72th of an inch) -- `config` - a dictionary containing any keyword argument to the reportlab field functions, - which will vary depending on what type of field this is. See section 4.7 of the - [reportlab User Guide](https://www.reportlab.com/docs/reportlab-userguide.pdf) -- `field_name` - the name of the field, exposed to via most APIs. Not the tooltip, but `users1_name__0` - -#### set\_fields - -Adds fields per page to the in_file PDF, writing the new PDF to a new file. - -Example usage: - -```python -set_fields('no_fields.pdf', 'four_fields_on_second_page.pdf', - [ - [], # nothing on the first page - [ # Second page - FormField('new_field', 'text', 110, 105, configs=\{'width': 200, 'height': 30\}), - # Choice needs value to be one of the possible options, and options to be a list of strings or tuples - FormField('new_choices', 'choice', 110, 400, configs=\{'value': 'Option 1', 'options': ['Option 1', 'Option 2']\}), - # Radios need to have the same name, with different values - FormField('new_radio1', 'radio', 110, 600, configs=\{'value': 'option a'\}), - FormField('new_radio1', 'radio', 110, 500, configs=\{'value': 'option b'\}) - ] - ] -) -``` - -**Arguments**: - -- `in_file` - the input file name or path of a PDF that we're adding the fields to -- `out_file` - the output file name or path where the new version of in_file will - be written. Doesn't need to exist. -- `fields_per_page` - for each page, a series of fields that should be added to that - page. -- `owerwrite` - if the input file already some fields (AcroForm fields specifically) - and this value is true, it will erase those existing fields and just add - `fields_per_page`. If not true and the input file has fields, this won't generate - a PDF, since there isn't currently a way to merge AcroForm fields from - different PDFs. - - -**Returns**: - - Nothing. - -#### rename\_pdf\_fields - -Given a dictionary that maps old to new field names, rename the AcroForm -field with a matching key to the specified value. - -**Example**: - -```python -rename_pdf_fields('current.pdf', 'new_field_names.pdf', - \{'abc123': 'user1_name', 'abc124', 'user1_address_city'\}) - -Args: - in_file: the filename of an input file - out_file: the filename of the output file. Doesn't need to exist, - will be overwritten if it does exist. - mapping: the python dict that maps from a current field name to the desired name - -Returns: - Nothing - -#### unlock\_pdf\_in\_place - -Try using pikePDF to unlock the PDF it it is locked. This won't work if it has a non-zero length password. - -#### has\_fields - -Check if a PDF has at least one form field using PikePDF. - -**Arguments**: - -- `pdf_file` _str_ - The path to the PDF file. - - -**Returns**: - -- `bool` - True if the PDF has at least one form field, False otherwise. - -#### get\_existing\_pdf\_fields - -Use PikePDF to get fields from the PDF - -#### swap\_pdf\_page - -(DEPRECATED: use copy_pdf_fields) Copies the AcroForm fields from one PDF to another blank PDF form. Optionally, choose a starting page for both -the source and destination PDFs. By default, it will remove any existing annotations (which include form fields) -in the destination PDF. If you wish to append annotations instead, specify `append_fields = True` - -#### copy\_pdf\_fields - -Copies the AcroForm fields from one PDF to another blank PDF form (without AcroForm fields). -Useful for getting started with an updated PDF form, where the old fields are pretty close to where -they should go on the new document. - -Optionally, you can choose a starting page for both -the source and destination PDFs. By default, it will remove any existing annotations (which include form fields) -in the destination PDF. If you wish to append annotations instead, specify `append_fields = True` - -**Example**: - -```python -new_pdf_with_fields = copy_pdf_fields( - source_pdf="old_pdf.pdf", - destination_pdf="new_pdf_with_no_fields.pdf") -new_pdf_with_fields.save("new_pdf_with_fields.pdf") -``` - - -**Arguments**: - -- `source_pdf` - a file name or path to a PDF that has AcroForm fields -- `destination_pdf` - a file name or path to a PDF without AcroForm fields. Existing fields will be removed. -- `source_offset` - the starting page that fields will be copied from. Defaults to 0. -- `destination_offset` - the starting page that fields will be copied to. Defaults to 0. -- `append_annotations` - controls whether formfyxer will try to append form fields instead of - overwriting. Defaults to false; when enabled may lead to undefined behavior. - - -**Returns**: - - A pikepdf.Pdf object with new fields. If `blank_pdf` was a pikepdf.Pdf object, the - same object is returned. - -#### get\_original\_text\_with\_fields - -Gets the original text of the document, with the names of the fields in jinja format (\{\{field_name\}\}) - -#### get\_textboxes\_in\_pdf - -Gets all of the text boxes found by pdfminer in a PDF, as well as their bounding boxes - -#### get\_bracket\_chars\_in\_pdf - -Gets all of the bracket characters ('[' and ']') found by pdfminer in a PDF, as well as their bounding boxes -TODO: Will eventually be used to find [ ] as checkboxes, but right now we can't tell the difference between [ ] and [i]. -This simply gets all of the brackets, and the characters of [hi] in a PDF and [ ] are the exact same distance apart. -Currently going with just "[hi]" doesn't happen, let's hope that assumption holds. - -#### intersect\_bbox - -bboxes are [left edge, bottom edge, horizontal length, vertical length] - -#### intersect\_bboxs - -Returns an iterable of booleans, one of each of the input bboxes, true if it collides with bbox_a - -#### contain\_boxes - -Given two bounding boxes, return a single bounding box that contains both of them. - -#### get\_dist\_sq - -returns the distance squared between two points. Faster than the true euclidean dist - -#### get\_dist - -euclidean (L^2 norm) distance between two points - -#### get\_connected\_edges - -point list is always ordered clockwise from the bottom left, -i.e. bottom left, top left, top right, bottom right - -#### bbox\_distance - -Gets our specific "distance measure" between two different bounding boxes. -This distance is roughly the sum of the horizontal and vertical difference in alignment of -the closest shared field-bounding box edge. We are trying to find which, given a list of text boxes -around a field, is the most likely to be the actual text label for the PDF field. - -bboxes are 4 floats, x, y, width and height - -#### get\_possible\_fields - -Given an input PDF, runs a series of heuristics to predict where there -might be places for user enterable information (i.e. PDF fields), and returns -those predictions. - -**Example**: - -```python -fields = get_possible_fields('no_field.pdf') -print(fields[0][0]) -# Type: FieldType.TEXT, Name: name, User name: , X: 67.68, Y: 666.0, Configs: \{'fieldFlags': 'doNotScroll', 'width': 239.4, 'height': 16\} -``` - - -**Arguments**: - -- `in_pdf_file` - the input PDF -- `textboxes` _optional_ - the location of various lines of text in the PDF. - If not given, will be calculated automatically. This allows us to - pass through expensive info to calculate through several functions. - - -**Returns**: - - For each page in the input PDF, a list of predicted form fields - -## LowestVertVisitor Objects - -```python -class LowestVertVisitor() -``` - -Gets just the closest text to the field, and returns that - -#### replace\_in\_original - -Given the original text of a PDF (extract_text(...)), adds the field's names in their best places. -Doesn't always work, especially with duplicate text. - -#### get\_possible\_checkboxes - -Uses boxdetect library to determine if there are checkboxes on an image of a PDF page. -Assumes the checkbox is square. - -find_small: if true, finds smaller checkboxes. Sometimes will "find" a checkbox in letters, -like O and D, if the font is too small - -#### get\_possible\_radios - -Even though it's called "radios", it just gets things shaped like circles, not -doing any semantic analysis yet. - -#### get\_possible\_text\_fields - -Uses openCV to attempt to find places where a PDF could expect an input text field. - -Caveats so far: only considers straight, normal horizonal lines that don't touch any vertical lines as fields -Won't find field inputs as boxes - -default_line_height: the default height (16 pt), in pixels (at 200 dpi), which is 45 - -#### auto\_add\_fields - -Uses [get_possible_fields](#get_possible_fields) and -[set_fields](#set_fields) to automatically add new detected fields -to an input PDF. - -**Example**: - -```python -auto_add_fields('no_fields.pdf', 'newly_added_fields.pdf') -``` - - -**Arguments**: - -- `in_pdf_file` - the input file name or path of the PDF where we'll try to find possible fields -- `out_pdf_file` - the output file name or path of the PDF where a new version of `in_pdf_file` will - be stored, with the new fields. Doesn't need to existing, but if a file does exist at that - filename, it will be overwritten. - - -**Returns**: - - Nothing - -#### is\_tagged - -Determines if the input PDF file is tagged for accessibility. - -**Arguments**: - -- `in_pdf_file` _Union[str, Path]_ - The path to the PDF file, as a string or a Path object. - - -**Returns**: - -- `bool` - True if the PDF is tagged, False otherwise. - diff --git a/docusaurus.config.js b/docusaurus.config.js index 45641c7a8..26fbe7642 100644 --- a/docusaurus.config.js +++ b/docusaurus.config.js @@ -129,9 +129,23 @@ module.exports = { from: '/docs/get_started/working_with_teams', to: '/docs/archive/working_with_teams' } - ] - } - ] + ], + + + }, + + ], + [ + 'docusaurus-plugin-papersaurus', + { + keepDebugHtmls: false, + sidebarNames: ['docs', 'get_started'], + addDownloadButton: true, + autoBuildPdfs: true, + ignoreDocs: ['licenses'], + author: 'Author name' + }, + ], ], themes: [ [ diff --git a/package-lock.json b/package-lock.json index 6f967ad84..9b8cd1ed6 100644 --- a/package-lock.json +++ b/package-lock.json @@ -17,6 +17,7 @@ "@easyops-cn/docusaurus-search-local": "^0.44.3", "@mdx-js/react": "^3.0", "clsx": "^1.1.1", + "docusaurus-plugin-papersaurus": "^2.0.3", "prism-react-renderer": "^2.1.0", "react": "^18.2.0", "react-dom": "^18.2.0" @@ -781,6 +782,18 @@ "@babel/core": "^7.0.0-0" } }, + "node_modules/@babel/plugin-syntax-object-rest-spread": { + "version": "7.8.3", + "resolved": "https://registry.npmjs.org/@babel/plugin-syntax-object-rest-spread/-/plugin-syntax-object-rest-spread-7.8.3.tgz", + "integrity": "sha512-XoqMijGZb9y3y2XskN+P1wUGiVwWZ5JmoDRwx5+3GmEplNyVM2s2Dg8ILFQm8rWM48orGy5YpI5Bl8U1y7ydlA==", + "license": "MIT", + "dependencies": { + "@babel/helper-plugin-utils": "^7.8.0" + }, + "peerDependencies": { + "@babel/core": "^7.0.0-0" + } + }, "node_modules/@babel/plugin-syntax-typescript": { "version": "7.27.1", "resolved": "https://registry.npmjs.org/@babel/plugin-syntax-typescript/-/plugin-syntax-typescript-7.27.1.tgz", @@ -3629,6 +3642,19 @@ "react-dom": "^18.0.0 || ^19.0.0" } }, + "node_modules/@docusaurus/react-loadable": { + "version": "5.5.2", + "resolved": "https://registry.npmjs.org/@docusaurus/react-loadable/-/react-loadable-5.5.2.tgz", + "integrity": "sha512-A3dYjdBGuy0IGT+wyLIGIKLRE+sAk1iNk0f1HjNDysO7u8lhL4N3VEm+FAubmJbAztn94F7MxBTPmnixbiyFdQ==", + "license": "MIT", + "dependencies": { + "@types/react": "*", + "prop-types": "^15.6.2" + }, + "peerDependencies": { + "react": "*" + } + }, "node_modules/@docusaurus/responsive-loader": { "version": "1.7.1", "resolved": "https://registry.npmjs.org/@docusaurus/responsive-loader/-/responsive-loader-1.7.1.tgz", @@ -4183,6 +4209,16 @@ "react": ">=16" } }, + "node_modules/@mdx-js/util": { + "version": "1.6.22", + "resolved": "https://registry.npmjs.org/@mdx-js/util/-/util-1.6.22.tgz", + "integrity": 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b/package.json index 90d27f6c3..696d75cb3 100644 --- a/package.json +++ b/package.json @@ -21,6 +21,7 @@ "@easyops-cn/docusaurus-search-local": "^0.44.3", "@mdx-js/react": "^3.0", "clsx": "^1.1.1", + "docusaurus-plugin-papersaurus": "^2.0.3", "prism-react-renderer": "^2.1.0", "react": "^18.2.0", "react-dom": "^18.2.0"