Principle:Neuml Txtai Text Extraction
| Knowledge Sources | |
|---|---|
| Domains | NLP, Information_Retrieval, RAG |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Text extraction and chunking is the process of converting raw documents in diverse formats (PDF, DOCX, HTML, URLs) into clean, segmented text suitable for embedding and retrieval in a RAG pipeline.
Description
Raw documents rarely arrive in a form that is directly usable for semantic search. PDFs contain layout markup, DOCX files embed XML structures, and HTML pages include navigation elements and scripts. Text extraction strips away these non-content elements and produces clean, readable text. The extracted text is then segmented (chunked) into smaller units -- sentences, paragraphs, sections, or custom chunks -- that become the atomic retrieval units in downstream indexing.
The chunking strategy has a significant impact on RAG quality. Chunks that are too large may contain multiple topics, reducing retrieval precision. Chunks that are too small may lack sufficient context for meaningful embedding. The ideal granularity depends on the domain, the average document length, and the embedding model's context window.
A typical extraction pipeline follows a three-stage architecture: first, convert the source format to an intermediate representation (commonly HTML); second, normalize the intermediate representation to a clean markup (commonly Markdown); third, segment the normalized text according to the chosen chunking strategy. This layered approach decouples format handling from segmentation logic, making it easy to support new file formats without changing the chunking code.
Usage
Use text extraction and chunking whenever you need to:
- Convert binary document formats (PDF, DOCX, XLSX, PPTX) into indexable text.
- Normalize HTML or web content by stripping boilerplate and retaining meaningful content.
- Segment long documents into retrieval-friendly chunks for embedding.
- Prepare text for any pipeline that requires uniform, clean text input from heterogeneous sources.
Theoretical Basis
Text extraction can be modeled as a composition of two functions:
E(d) = segment(normalize(parse(d)))
where:
- parse(d) converts a raw document d from its native format into an intermediate representation (typically HTML).
- normalize(h) transforms the intermediate HTML into a clean, standardized format (typically Markdown), stripping non-content elements.
- segment(t) divides the normalized text t into a list of chunks based on the configured strategy.
The segmentation function segment can operate in several modes:
- Sentence mode: splits text on sentence boundaries using natural language tokenization.
- Line mode: splits on line breaks, suitable for structured or tabular content.
- Paragraph mode: splits on double line breaks, preserving topical coherence.
- Section mode: splits on document structure markers (headings, page breaks), producing the coarsest chunks.
Each mode offers a different trade-off between context preservation (larger chunks retain more surrounding context) and retrieval precision (smaller chunks reduce noise in search results).
A minimum length filter removes segments below a character threshold, eliminating fragments such as page numbers, headers, or single-word lines that would produce low-quality embeddings.
In pseudocode:
FUNCTION extract_and_chunk(document, mode, min_length):
html = parse_to_html(document)
markdown = html_to_markdown(html)
segments = split(markdown, mode)
RETURN [s FOR s IN segments IF length(s) >= min_length]