cur_text = c.text
snippets.append((cur_text,cur_fs))
# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as
# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info)
from langchain.docstore.document import Document
cur_idx = -1
semantic_snippets = []
# Assumption: headings have higher font size than their respective content
for s in snippets:
    # if current snippet's font size > previous section's heading => it is a new heading
    if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']:
        metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}
        metadata.update(data.metadata)
        semantic_snippets.append(Document(page_content='',metadata=metadata))
        cur_idx += 1
        continue
    
    # if current snippet's font size <= previous section's content => content belongs to the same section (one can also create
    # a tree like structure for sub sections if needed but that may require some more thinking and may be data specific)
    if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']:
        semantic_snippets[cur_idx].page_content += s[0]
        semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font'])
        continue
    
    # if current snippet's font size > previous section's content but less tha previous section's heading than also make a new
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/document_loaders/examples/pdf.html