A document loader or parser is a component that takes a file or data source and turns it into text and metadata an AI system can actually use.
LLMs do not read PDFs, Word files, HTML pages, or emails directly in their native form. If you want to do retrieval-augmented generation (RAG), search, summarization, or extraction over real-world documents, you first need a reliable way to ingest them.
That is where loaders and parsers come in:
In practice, most teams start with a simple loader/parser pipeline before adding chunking, embeddings, or downstream QA.
Load the source
The loader opens the document source and reads the raw bytes or response. For example, it might download a PDF, read a .docx file, or fetch an HTML page.
Parse the content
The parser converts the raw input into a normalized representation. That often means:
Normalize for downstream use
The result is usually a list of document objects, text chunks, or records that other pipeline steps can use. A good parser tries to balance fidelity and simplicity: enough structure to be useful, not so much complexity that downstream steps break.
Hand off to the next stage
The parsed output is usually fed into chunking, indexing, embeddings, search, or an LLM prompt. The loader/parser itself is rarely the final step.
Scenario: you want to answer questions over a folder of PDF reports.
report_q4.pdfVery small pseudo-example:
doc = load("report_q4.pdf")
parsed = parse(doc)
print(parsed.text[:200])
print(parsed.metadata["title"])
Possible output:
Q4 Revenue Report...
Q4 Revenue Report