A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers

The paper presents a production-ready Retrieval-Augmented Generation (RAG) pipeline tailored for PDF documents, integrating three core bricks—document parsing, question parsing, and retrieval—each governed by a dedicated contract. The document parsing stage performs relational parsing to capture entity relationships and extracts a Table of Contents (TOC) to index hierarchical sections, while the retrieval module uses the TOC as a semantic index for efficient answer extraction. The generation brick produces typed answers, outputting structured responses (e.g., JSON) that align with the original document’s schema, enabling downstream systems to consume results without additional parsing. The authors highlight a trade‑off between retrieval latency and answer precision, noting that the TOC‑driven index reduces query time by up to 30 % at the cost of a modest increase in preprocessing overhead.

⚡ Key Takeaways

• Relational parsing captures entity relationships within PDFs, enabling context-aware retrieval.
• The TOC retrieval component indexes hierarchical sections, improving query efficiency.
• Typed answer generation outputs structured JSON, facilitating seamless integration with downstream services.
• The pipeline’s contract‑per‑brick design isolates concerns, simplifying maintenance and scaling.
• WhyItMatters: Engineers building production RAG systems can adopt this modular approach to

Engineers building production RAG systems can adopt this modular approach to

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