redashify

Context-aware dash correction powered by LLMs.

Converts hyphens (-), double-hyphens (--), and triple-hyphens (---) to the typographically correct character — hyphen, en dash (–), or em dash (—) — based on surrounding context.

Unlike regex, redashify uses an LLM to determine the correct dash because the rules are too context-dependent for pattern matching alone. Only the dash contexts (not the full document) are sent to the LLM, making it token-efficient and privacy-conscious.

Install

npm install redashify

Quick start

import { redashify } from 'redashify'

const result = await redashify(
  'The court held--in a 5-4 decision--that pages 10-20 applied.',
  { apiKey: process.env.OPENAI_API_KEY, provider: 'openai' }
)

result.text
// → 'The court held—in a 5-4 decision—that pages 10–20 applied.'

result.corrections
// → [{ position: 14, original: '--', replacement: '—', context: '...' }, ...]

result.unchanged
// → false

Providers

Built-in support for any OpenAI-compatible API, plus a native Anthropic adapter.

| Provider | Default model | Notes | |---|---|---| | openai | gpt-4o-mini | | | anthropic | claude-haiku-4-5-20251001 | Native adapter (different API format) | | gemini | gemini-2.0-flash | OpenAI-compatible endpoint | | groq | llama-3.3-70b-versatile | | | together | meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo | | | mistral | mistral-small-latest | | | xai | grok-3-mini-fast | | | deepseek | deepseek-chat | | | openrouter | (none — must specify model) | |

Override the default model:

const result = await redashify(text, {
  apiKey: process.env.OPENAI_API_KEY,
  provider: 'openai',
  model: 'gpt-4o',
})

For unlisted providers that support the OpenAI chat completions format:

const result = await redashify(text, {
  apiKey: '...',
  baseURL: 'https://my-provider.com/v1',
  model: 'my-model',
})

Custom LLM function

Bypass the built-in client entirely:

const result = await redashify(text, {
  llm: async (messages) => {
    const res = await myLlmCall(messages)
    return res.text
  },
})

Options

| Option | Type | Default | Description | |---|---|---|---| | apiKey | string | — | API key for the LLM provider | | provider | Provider | — | Provider name (maps to base URL + default model) | | model | string | (per provider) | Model name. Required if no provider default. | | baseURL | string | — | Custom endpoint URL. Overrides provider mapping. | | llm | (messages) => Promise<string> | — | Custom LLM function. Overrides apiKey/provider/model. | | rules | string | “” | Custom rules prepended to the system prompt | | contextSize | number | 50 | Characters of context on each side of a dash | | batchSize | number | 50 | Maximum dashes per LLM call |

You must provide either apiKey (with provider or model) or llm.

Result

interface RedashifyResult {
  text: string          // The corrected text
  corrections: Array<{  // Only dashes that were changed
    position: number    // Index in original text
    original: string    // What was there (e.g. "--")
    replacement: string // What it became (e.g. "—")
    context: string     // Surrounding snippet for audit
  }>
  unchanged: boolean    // true if nothing was modified
}

No dashes in text: LLM is not called. Returns immediately with unchanged: true.

All dashes already correct: LLM is called (correctness can’t be pre-judged), but corrections is empty and unchanged is true.

Custom rules

Pass domain-specific rules via the rules option. Works with lexstyle for structured rule management:

import { rules, serialize } from 'lexstyle'
import { redashify } from 'redashify'

const result = await redashify(text, {
  apiKey: process.env.OPENAI_API_KEY,
  provider: 'openai',
  rules: serialize(rules, 'dashes'),
})

Or pass rules as a plain string:

const result = await redashify(text, {
  apiKey: '...',
  provider: 'openai',
  rules: `Use en dashes for page ranges (e.g. pp. 45–67).
Use em dashes for parenthetical asides with no spaces.
Keep hyphens in compound modifiers and vote tallies (5-4).`,
})

Design decisions

LLM over regex. Dash correction depends on semantic context — is “10-20” a range (en dash) or a compound modifier (hyphen)? Is “--” an em dash or a typo? Regex can’t answer these questions. An LLM can, given a few words of surrounding context.

Privacy by design. Only short context windows around each dash are sent to the LLM — never the full document. A 10,000-word document with 5 dashes sends ~5 small context snippets, regardless of document length.

Batch validation. Each batch response is validated against its expected IDs before merging. Cross-batch ID leakage or missing corrections are caught immediately, not silently swallowed.

Robust response parsing. LLM output is parsed via strict JSON first, with a hardened bracket-extraction fallback that skips stray brackets in preamble text. Every correction is validated for known dash characters and duplicate IDs.

Development

npm install
npm test
npm run typecheck
npm run build     # ESM + CJS + .d.ts

License

Apache-2.0