Baseline Enterprise RAG, From PDF to Highlighted Answer

2026-05-29 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

A minimal RAG pipeline, built with approximately one hundred lines of Python, processes PDFs like the "Attention Is All You Need" paper or the World Bank's "Commodity Markets Outlook" to return sourced answers with highlighted evidence. This system comprises four core "bricks": document parsing (using pymupdf to extract lines and bounding boxes into a pandas DataFrame), question parsing (using OpenAI's LLM to extract keywords), retrieval (employing keyword matching for transparency over embeddings), and generation (using OpenAI's LLM with pydantic to produce a structured AnswerWithEvidence JSON object, including page/line citations, confidence, and quotes). An optional PDF annotation step highlights the cited lines on the source document. The pipeline demonstrates verifiable answers, clean "not found" handling, and direct source linking, while also illustrating the limitations of simple keyword matching and embeddings for complex queries or non-standard document structures.

Key takeaway

For AI Engineers building enterprise RAG systems, you should prioritize auditable retrieval and structured, verifiable outputs. Implement a modular pipeline with explicit parsing, transparent keyword-based retrieval, and LLM generation that forces line-level citations. This approach ensures answers are grounded, prevents hallucination, and allows users to easily verify claims against source documents, fostering trust in the system.

Key insights

A minimal RAG pipeline can provide verifiable, sourced answers by structuring outputs and linking directly to document evidence.

Principles

• Retrieval must be auditable for enterprise contexts.
• Document structure is critical for effective parsing.
• Structured outputs prevent hallucination and enable verification.

Method

The proposed method involves a four-brick pipeline: document parsing (PDF to line_df), question parsing (keywords), retrieval (keyword matching for transparency), and generation (LLM to AnswerWithEvidence JSON with citations).

In practice

• Use pymupdf for PDF text and bounding box extraction.
• Employ pydantic for structured LLM output with citations.
• Implement keyword matching for auditable retrieval.

Topics

• Retrieval-Augmented Generation
• Document Parsing
• Keyword Matching
• LLM Generation
• PDF Annotation
• Enterprise AI

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.