@markschema/mdshape

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paragraphs ​

Type: section

Signature: section().paragraphs([...])

What It Is ​

section().paragraphs([...]) is used here as a contract-first parser powered by document-level structure checks, explicit section targeting, and boundary constraints for paragraphs scenarios. With document(), section(), paragraphs(), and string() in the schema, 1 h1 heading and 1 h2 section is converted into top-level keys context without manual paragraphs post-processing. Error cases report issue codes like missing_section, making operational diagnostics for paragraphs flows consistent across local runs and CI.

When to Use ​

This method is a strong fit for section-scoped extraction where headings anchor each data slice where deterministic paragraphs parsing matters more than free-form flexibility. Do not default to it for free-form notes with unstable section names around paragraphs; the main cost is more explicit schema maintenance to keep output deterministic. For best results, compose section().paragraphs([...]) with document(), section(), paragraphs(), and string() so paragraphs schema intent stays readable and output remains predictable.

section().paragraphs([...]) ​

Input Markdown ​

md
## 2. CONTEXT

First paragraph.

Second paragraph.

Schema ​

ts
import { md } from '@markschema/mdshape'

const schema = md.document({
  context: md.section('2. CONTEXT').paragraphs([md.string().min(5), md.string().min(5)]),
})

Result ​

Success ​

json
{
  "success": true,
  "data": {
    "context": [
      "First paragraph.",
      "Second paragraph."
    ]
  }
}

Error ​

Failure trigger: The input violates one or more constraints declared in the schema; use issues[].path and issues[].code to locate the exact failing node.

json
{
  "success": false,
  "error": {
    "issues": [
      {
        "code": "missing_section",
        "message": "Missing section \"2. CONTEXT\"",
        "path": [
          "context"
        ],
        "line": 1,
        "position": {
          "start": {
            "line": 1,
            "column": 1
          }
        }
      }
    ]
  }
}