Question

You are building a RAG pipeline over quarterly financial reports delivered as PDFs. Each report contains long narrative sections interspersed with large tabular data (balance sheets, income statements). During testing, users ask questions about specific line-item values, but retrieved chunks frequently return partial tables where column headers are separated from their data rows, producing wrong or unanswerable results. Which document preparation approach best addresses this problem?

Accepted Answer

Use a table-aware extraction step that detects and serializes each table as a self-contained unit (headers plus rows) into its own chunk, while chunking narrative text separately with recursive character splitting

Answer

Increase the fixed chunk size to 4000 tokens so entire tables are more likely to fit inside a single chunk

Answer

Lower the embedding dimension to capture tabular structure more compactly and improve retrieval recall

Answer

Strip all tables from the PDFs during ingestion and rely solely on the narrative sections for answering questions