Why Document Parsing Matters

Before you can search documents, you need to extract their content. Parsing is the foundation of every RAG system - it transforms raw files into searchable text.

Common document formats:

• PDF (most common)
• Word documents (.docx)
• HTML/Web pages
• Markdown
• Plain text

Basic Parsing Workflow

DEVELOPERpython
# Simple text extraction
def parse_document(file_path):
    # 1. Detect file type
    file_type = detect_format(file_path)

    # 2. Extract text
    if file_type == "pdf":
        text = extract_pdf(file_path)
    elif file_type == "docx":
        text = extract_docx(file_path)

    # 3. Extract metadata
    metadata = {
        "title": extract_title(file_path),
        "author": extract_author(file_path),
        "date": extract_date(file_path)
    }

    return text, metadata

Parsing Challenges

1. Encoding Issues Different languages use different character encodings.

DEVELOPERpython
# Always specify encoding
with open(file_path, 'r', encoding='utf-8') as f:
    text = f.read()

2. Structure Preservation Keep headers, lists, and formatting.

3. Metadata Extraction Titles, authors, dates are valuable for filtering.

Popular Parsing Libraries (November 2025)

PyMuPDF (fitz)

Fast PDF parsing with excellent text extraction.

DEVELOPERpython
import fitz  # PyMuPDF

doc = fitz.open("document.pdf")
text = ""
for page in doc:
    text += page.get_text()

python-docx

For Word documents.

DEVELOPERpython
from docx import Document

doc = Document("document.docx")
text = "\n".join([p.text for p in doc.paragraphs])

BeautifulSoup

For HTML parsing.

DEVELOPERpython
from bs4 import BeautifulSoup

with open("page.html") as f:
    soup = BeautifulSoup(f, 'html.parser')
    text = soup.get_text()

Best Practices

1. Preserve structure: Keep headers, bullet points
2. Extract metadata: Use it for filtering later
3. Handle errors: Files can be corrupted
4. Normalize text: Remove extra whitespace
5. Keep source reference: Track which file each chunk came from

Next Steps

Once you've extracted text, you'll need to:

• Chunk it into smaller pieces (see Chunking guides)
• Embed it into vectors (see Embedding guides)
• Store it in a vector database (see Storage guides)

Master parsing fundamentals, then explore specialized techniques for PDFs, images, and complex documents.