Vectorless RAG Implementation: Document Tree Generation
The goal is to convert a PDF document into a structured hierarchical tree representation that can be navigated and reasoned over. We'll use the pymupdf4llm library for layout-aware parsing, which helps in preserving the structure of headings and content.
Step 1: Extract Structured Markdown
First, we extract markdown text from the PDF while preserving its layout and heading levels using pymupdf4llm.
python1import fitz # PyMuPDF 2from pymupdf4llm import to_markdown 3 4def extract_markdown(pdf_path): 5 doc = fitz.open(pdf_path) 6 markdown_content = "" 7 8 for page_num in range(doc.page_count): 9 text = doc.load_page(page_num).get_text("text") 10 markdown_content += to_markdown(text) + "\n" 11 12 return markdown_content
Step 2: Parse Markdown Headers into Tree Hierarchy
Next, we parse the extracted markdown content to build a hierarchical tree structure.
python1import re 2 3def parse_markdown_to_tree(markdown_content): 4 lines = markdown_content.split("\n") 5 stack = [] 6 root 7 8[Read the full article at Towards AI - Medium](https://pub.towardsai.net/vectorless-rag-how-i-built-a-rag-system-without-embeddings-databases-or-vector-similarity-efccf21e42ff?source=rss----98111c9905da---4) 9 10--- 11 12**Want to create content about this topic?** [Use Nemati AI tools](https://nemati.ai) to generate articles, social posts, and more.

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