After spending way too many hours fighting with garbled PDF extractions and broken tables, I decided to document what actually works for parsing complex documents in RAG applications.
Most PDF parsers treat everything as plain text. They completely butcher tables with merged cells, miss embedded figures, and turn your carefully structured SEC filing into incomprehensible garbage. Then you wonder why your LLM can’t answer basic questions about the data.
What I built: A complete pipeline using LlamaParse + Llama Index that:
Extracts tables while preserving multi-level hierarchies
Handles merged cells, nested headers, footnotes
Maintains relationships between figures and references
Enables semantic search over both text AND structured data
test: I threw it at NCRB crime statistics…
After spending way too many hours fighting with garbled PDF extractions and broken tables, I decided to document what actually works for parsing complex documents in RAG applications.
Most PDF parsers treat everything as plain text. They completely butcher tables with merged cells, miss embedded figures, and turn your carefully structured SEC filing into incomprehensible garbage. Then you wonder why your LLM can’t answer basic questions about the data.
What I built: A complete pipeline using LlamaParse + Llama Index that:
Extracts tables while preserving multi-level hierarchies
Handles merged cells, nested headers, footnotes
Maintains relationships between figures and references
Enables semantic search over both text AND structured data
test: I threw it at NCRB crime statistics tables, the kind with multiple header levels, percentage calculations, and state-wise breakdowns spanning dozens of rows. Queries like "Which state had the highest percentage increase?" work perfectly because the structure is actually preserved.
The tutorial covers:
Complete setup (LlamaParse + Llama Index integration)
The parsing pipeline (PDF → Markdown → Nodes → Queryable index)
Vector store indexing for semantic search
Building query engines that understand natural language
Production considerations and evaluation strategies
Honest assessment: LlamaParse gets 85-95% accuracy on well-formatted docs, 70-85% on scanned/low-quality ones. It’s not perfect (nothing is), but it’s leagues ahead of standard parsers. The tutorial includes evaluation frameworks because you should always validate before production.
Free tier is 1000 pages/day, which is plenty for testing. The Llama Index integration is genuinely seamless—way less glue code than alternatives.
Full walkthrough with code and examples in the blog post. Happy to answer questions about implementation or share lessons learned from deploying this in production.
You can read it here: https://ubiai.tools/simplifying-document-parsing-with-agentic-ai-extracting-embedded-objects/