The parse node extracts raw text from documents using optical character recognition (OCR). It does not interpret the content. It converts document pages into plain text that downstream nodes can process. Use parse before extract when you need more control over the text extraction step, or when you want to route based on document content.Documentation Index
Fetch the complete documentation index at: https://docs.docpipe.ai/llms.txt
Use this file to discover all available pages before exploring further.
When to use parse
- You want to route or classify documents based on raw text before deciding whether to extract them.
- You need the parsed text as a separate output for search, archiving, or audit.
- You’re running the same document through several extract schemas and want to OCR once.
- Skip parse when extract is the next step. Extract already runs OCR internally; chaining them runs OCR twice and bills both.
Configuration
| Field | Type | Required | Description |
|---|---|---|---|
| Engine | select | Yes | OCR engine to use: Standard, Vision, or KeyValuePair |
| Precision | select | No | Processing precision: Small, Medium, or High |
| Data source | text | No | JSONPath template expression for the input data. Default: {{payload}}. Shown when parse follows a route node |
Engines
- Standard: Traditional OCR. Best for clean, typed documents. 1 credit per page.
- Vision: AI-powered vision model. Handles handwriting, complex layouts, and low-quality scans. 2 credits per page.
- KeyValuePair: Extracts text as key-value pairs using layout analysis. 1 credit per page + LLM processing cost.
Inputs and outputs
Allowed inputs: Trigger nodes only (upload trigger, email trigger, webhook trigger). Output: Raw text content extracted from the document.Related
Parse best practices
Pick the right engine, control cost, and avoid pitfalls
Extract action
Extract structured data using AI after parsing
Route action
Route documents based on parsed text content
Credits
Per-engine credit costs