@contensis/html-canvas

@contensis/html-canvas

Convert HTML content to Contensis canvas

Installation

Install with your project's preferred package manager

npm install --save @contensis/html-canvas

yarn add --save @contensis/html-canvas

Usage

Convert a simple HTML snippet to Canvas JSON

import { parseHtml } from '@contensis/html-canvas';

const html = '<h1>test</h1>';
const canvas = await parseHtml(html);

// Output raw canvas to console
console.log(JSON.stringify(canvas, null, 2));

Create a HTML Parser, passing in configuration to allow the parser to prepare the canvas according to a specific field configuration in a content type.

Prepare a Delivery API client so the parser can find the field configuration and attempt to match external resources (such as images and links) to existing content in this Contensis project.

import { createHtmlParser } from '@contensis/html-canvas';
import { Client } from 'contensis-delivery-api';

const html = '<h1>test</h1>';

// Connect a Delivery API client
const client = Client.create({
  clientDetails: { clientId: '', clientSecret: '' },
  clientType: 'client_credentials',
  projectId: '',
  rootUrl: '',
  versionStatus: 'latest'
});

// Create a HTML parser targeting the content type and field with the right Canvas configuration
const { parse } = await createHtmlParser({ client, contentTypeId: 'document', fieldId: 'canvas' });

// Use the parse function returned above to parse the HTML string and generate canvas JSON
const canvas = await parse(html);

Adding complexity

A more complete example of converting existing entry data in a rich text field type to a canvas representation

import { createHtmlParser, Block } from '@contensis/html-canvas';
import { Client, ZenqlQuery } from 'contensis-delivery-api';

// Connect a Delivery API client
const client = Client.create({
  clientDetails: { clientId: '', clientSecret: '' },
  clientType: 'client_credentials',
  projectId: '',
  rootUrl: '',
  versionStatus: 'latest'
});

// Create a HTML parser targeting the content type and field with the right Canvas configuration
const { parse } = await createHtmlParser({ client, contentTypeId: 'document', fieldId: 'canvas' });

// Get some entries containing richText field
const query = new ZenqlQuery(`sys.contentTypeId = document AND richText EXISTS`);
query.pageSize = 10;
const entries = await client.entries.search(query);

const canvasData: { [id: string]: Block[] } = {};

// Iterate through the page of search results
for (const entry of entries.items) {
  if (entry.richText) {
    // Parse the richText field content and convert to Canvas
    const canvas = await parse(entry.richText);

    // Keep a note of the updated entry data
    canvasData[entry.sys.id] = canvas;
  }
}

for (const [entryId, canvas] of Object.entries(canvasData)) {
  // Log canvas data to console
  console.log(`Entry: ${entryId}, Canvas: \n${JSON.stringify(canvas, null, 2)}`);
}

Further customisation is available by overriding specific parts of the parser and call the canvas parse function directly

// Import the standalone `parse` method, and the `DataResolver` interface
import { DataResolver, createHtmlParser, parse } from '@contensis/html-canvas';
import { Client } from 'contensis-delivery-api';

const html = '<h1>test</h1>';

// Connect a Delivery API client
const client = Client.create({
  clientDetails: { clientId: '', clientSecret: '' },
  clientType: 'client_credentials',
  projectId: '',
  rootUrl: '',
  versionStatus: 'latest'
});

// Create a HTML parser targeting the content type and field with the right Canvas configuration
// - returning the base elements to call the raw `parse` function
const { parserSettings, resolver, settings } = await createHtmlParser({ client, contentTypeId: 'document', fieldId: 'canvas' });

// Create a class implementing DataResolver, it needs to implement three methods for locating external resources
class CustomResolver implements DataResolver {
  constructor(public client: Client) {
    // Add what you need in the constructor here when an external reference is found
    // and your `get...ByPath` method is called e.g. API client
  }

  async getAssetByPath(path: string) {
    const asset = await resolver.getAssetByPath(path);
    if (!asset) {
      // implement custom logic for resolving / handling asset links
    }
    return asset;
  }
  async getEntryByPath(path: string) {
    return resolver.getEntryByPath(path);
  }
  async getNodeByPath(path: string) {
    const node = resolver.getNodeByPath(path);
    if (!node) {
      // implement custom logic for handling unresolved site view nodes
    }
    return node;
  }
}

// Disallow element type heading (for example)
settings['type.heading'] = false;

// Instantiate our custom resolver so it is ready to use
const customResolver = new CustomResolver(client);

// Call the imported `parse` function providing all the required arguments
const canvas = await parse(html, settings, parserSettings, resolver);

You try this out with the HTML-to-canvas example project

Development

Clone the @contenis/canvas repository and run npm install, cd into the packages/html-canvas directory and run npm run build then npm run test:mock to run through the tests

Testing

npm test will run the tests against a real API, for this you will need to create a .env file in the package root with the following details in it:

rootUrl=""
projectId=""
clientId=""
sharedSecret=""

Adding new tests

You will need to first ensure the .env file mentioned above is in place and all the existing tests are passing with npm test

When the new test(s) are added and passing:

• Run the tests again with npm run test:record *(you can limit this to run specific tests with describe.only(... in mocha)
  • The API calls made for this test run will be saved in JSON files for use later as mocks.
• Run the entire test suite again with npm run test:mock so we can see all tests working with the new mock data.
• Commit the tests and JSON mocks.