Principle:Marker Inc Korea AutoRAG Document Parsing
| Knowledge Sources | |
|---|---|
| Domains | Natural Language Processing, Information Retrieval, Document Understanding |
| Last Updated | 2026-02-12 00:00 GMT |
Overview
Document parsing is the process of extracting structured text content from raw document files, serving as the foundational first step in any document-based natural language processing or retrieval-augmented generation pipeline.
Description
Document parsing transforms unstructured or semi-structured files such as PDFs, plain text, CSV spreadsheets, HTML pages, Markdown, and XML into a normalized textual representation that downstream components can consume. The quality and completeness of the parsing step directly determines the ceiling of performance for all subsequent stages in a RAG pipeline, including chunking, indexing, retrieval, and generation.
Multiple parsing strategies exist, each suited to different document characteristics. Rule-based parsers (e.g., PDFMiner, BeautifulSoup for HTML) rely on explicit layout heuristics and structural markers to extract text. OCR-based parsers (e.g., Clova OCR, Tesseract) are necessary for scanned documents or image-embedded PDFs where text is not directly accessible. Cloud-based parsers (e.g., LlamaParse) offload the extraction to external services that may use proprietary models. Hybrid table/text parsers combine table extraction logic with standard text parsing to preserve tabular structure.
Preserving document structure during parsing is crucial. This includes maintaining page boundaries, section delineations, and metadata such as file paths and last-modified timestamps. A well-structured parsed output enables accurate downstream chunking by providing the positional information (page numbers, character offsets) needed to trace every chunk back to its source location in the original document. This traceability is essential for corpus remapping when the chunking strategy changes.
Usage
Document parsing should be applied at the very beginning of the evaluation data creation workflow, before any chunking or QA generation takes place. It is used whenever raw document files need to be converted into a tabular format with columns for text content, source file path, page number, and modification timestamp. Parsing configuration is specified via a YAML file that declares which parser modules to use for each file type, enabling heterogeneous document collections to be processed in a single run.
Theoretical Basis
The general document parsing pipeline can be expressed as:
INPUT: Set of raw document files D = {d_1, d_2, ..., d_n}
OUTPUT: Table T with columns (texts, path, page, last_modified_datetime)
For each file d_i in D:
1. Identify file type t_i (pdf, csv, md, html, xml, json)
2. Select parser function P(t_i) from configuration
3. Extract text segments: segments = P(t_i)(d_i)
4. For each segment s_j in segments:
- Record text content s_j.text
- Record source path d_i.path
- Record page number s_j.page (or -1 if not applicable)
- Record last modified datetime from file metadata
5. Append rows to T
Default parser mapping assigns standard parser implementations to common file types when no explicit override is provided:
| File Type | Default Parser | Method |
|---|---|---|
| langchain_parse | pdfminer | |
| CSV | langchain_parse | csv |
| Markdown | langchain_parse | unstructuredmarkdown |
| HTML | langchain_parse | bshtml |
| XML | langchain_parse | unstructuredxml |
The parser_node decorator pattern wraps each parsing function, ensuring that:
- Only files matching the declared file type are passed to the parser.
- The output is normalized into a consistent four-tuple of lists: (texts, paths, pages, last_modified_datetimes).
- File metadata is automatically appended via filesystem stat calls.
For OCR-based approaches, the theoretical model additionally involves an image-to-text recognition step before the standard text extraction, increasing computational cost but enabling parsing of non-digital-native documents.