Table RAG in Python: Retrieve PDF Tables with Docling and DuckDB

Professor Py: AI Engineering · Beginner ·🧠 Large Language Models ·1mo ago

Skills: Supervised Learning53%

About this lesson

Preserve table structure in PDF RAG pipelines to avoid flattened tables and wrong answers — Docling and DuckDB keep rows intact. Get precise, cell-level answers with verifiable citations by extracting rows, normalizing into pandas, and indexing rows for retrieval. Demo uses Docling, pandas, DuckDB and TF‑IDF (scikit-learn) for fast, interpretable row matching; swap in dense embedders/ANNs as you scale. Subscribe for practical AI engineering and LLM retrieval tutorials with clear, runnable Python examples. #RAG #DocumentAI #Python #DuckDB #Docling #Pandas #TFIDF

Original Description

Preserve table structure in PDF RAG pipelines to avoid flattened tables and wrong answers — Docling and DuckDB keep rows intact. Get precise, cell-level answers with verifiable citations by extracting rows, normalizing into pandas, and indexing rows for retrieval. Demo uses Docling, pandas, DuckDB and TF‑IDF (scikit-learn) for fast, interpretable row matching; swap in dense embedders/ANNs as you scale. Subscribe for practical AI engineering and LLM retrieval tutorials with clear, runnable Python examples. #RAG #DocumentAI #Python #DuckDB #Docling #Pandas #TFIDF

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