--- title: Before Full Agentic RAG: Know How You Decide, and the... description: Presents a rule-based dispatcher for enterprise RAG document parsing that reads a PDF's 'nature' via six deterministic flags, plans an ordered sequence of... canonical: https://daily.dev/posts/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from-5m96jgogd twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From | daily.dev og:description: Presents a rule-based dispatcher for enterprise RAG document parsing that reads a PDF's 'nature' via six deterministic flags, plans an ordered sequence of... og:url: https://daily.dev/posts/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from-5m96jgogd og:image: https://api.daily.dev/og/posts/5M96JgOGD.png og:image:alt: Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From **[Towards Data Science](https://daily.dev/sources/tds)** · 14 min read · 1 upvotes · 0 comments ## Summary Presents a rule-based dispatcher for enterprise RAG document parsing that reads a PDF's 'nature' via six deterministic flags, plans an ordered sequence of parsing methods (fitz, Docling, Azure Document Intelligence, EasyOCR, vision LLM, TOC recovery), executes them in order, and merges outputs into one enriched corpus. Argues this is honestly 'rule-based routing plus LLM leaves' rather than true agentic parsing, since no LLM sits at the dispatch layer deciding what to run or re-planning based on feedback. True agentic parsing with observe-plan-act loops is reserved for a later volume. Includes a runnable notebook demonstrating the pipeline on the 'Attention Is All You Need' paper, plus a cost discussion contrasting this 'ex ante' full-parse approach against a lazy, retrieval-driven adaptive parsing alternative covered elsewhere in the series. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What does a rule-based document parsing dispatcher for RAG actually do differently from what vendors call agentic parsing? It reads deterministic document signals (scanned, native outline, composite structure, rich figures, tables) with no LLM involved, then picks a hard-coded ordered plan of parsing methods via if/elif logic, executes each method in sequence, and merges outputs into one corpus. LLMs only run inside individual method leaves like vision readers or OCR post-processors, never at the dispatch or re-planning layer, making the whole plan inspectable before execution. _Developers designing auditable RAG pipelines can compare dispatcher patterns like this one on daily.dev._ ### Which open source PDF parsing tools should I combine for enterprise RAG document ingestion covering text, tables, scanned pages, and figures? Use fitz (PyMuPDF) as the cheap baseline for the native text layer, Docling or Azure Document Intelligence for tables requiring cell-level extraction, a vision LLM for charts and diagrams, EasyOCR for scanned pages with no text layer, and dedicated TOC recovery methods for reconstructing outlines from a printed sommaire or body typography when no native outline exists. _Teams evaluating parsing stacks for RAG can track comparisons of tools like Docling and EasyOCR on daily.dev._ ### How much does agentic-style document parsing cost on a real PDF versus a large contract? On a 15-page paper, a four-step parsing plan (fitz text extraction, native TOC read, body-structure loop, image pipeline) costs a few hundred milliseconds. On a 300-page contract with tables, figures, and no native outline, the plan can grow to seven or eight methods including LLM calls, taking around a minute and costing real money, making it wasteful for corpus-scale ingestion where most pages are never queried. _Engineers weighing parsing cost tradeoffs for RAG ingestion can follow related benchmarks on daily.dev._ ## Similar posts on daily.dev - [Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It](https://daily.dev/posts/loop-engineering-with-adaptive-pdf-parsing-start-cheap-pay-for-a-heavier-parser-only-when-the-page-clzerc01t) · Towards Data Science · 0 upvotes · 0 comments - [Agentic RAG Explained in 3 Levels of Difficulty](https://daily.dev/posts/agentic-rag-explained-in-3-levels-of-difficulty-9v2stnekw) · Machine Learning Mastery · 0 upvotes · 0 comments - [Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit](https://daily.dev/posts/dispatching-the-parsed-rag-question-chunk-strategy-model-tier-activations-audit-qlbvnwlbj) · Towards Data Science · 0 upvotes · 0 comments - [How Agentic RAG Works?](https://daily.dev/posts/how-agentic-rag-works--84hyqba1y) · ByteByteGo · 10 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag) [View this post on daily.dev](https://daily.dev/posts/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from-5m96jgogd) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From","url":"https://daily.dev/posts/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from-5m96jgogd","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from-5m96jgogd"},"datePublished":"2026-08-12T16:41:30.578Z","dateModified":"2026-09-13T21:22:28.555Z","description":"Presents a rule-based dispatcher for enterprise RAG document parsing that reads a PDF's 'nature' via six deterministic flags, plans an ordered sequence of...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/a665785b241674c0e24cff2b91e046c2?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/a665785b241674c0e24cff2b91e046c2?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Towards Data Science","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from-5m96jgogd","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,ai-agents,rag","timeRequired":"PT14M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from-5m96jgogd#faq","mainEntity":[{"@type":"Question","name":"What does a rule-based document parsing dispatcher for RAG actually do differently from what vendors call agentic parsing?","acceptedAnswer":{"@type":"Answer","text":"It reads deterministic document signals (scanned, native outline, composite structure, rich figures, tables) with no LLM involved, then picks a hard-coded ordered plan of parsing methods via if/elif logic, executes each method in sequence, and merges outputs into one corpus. LLMs only run inside individual method leaves like vision readers or OCR post-processors, never at the dispatch or re-planning layer, making the whole plan inspectable before execution. Developers designing auditable RAG pipelines can compare dispatcher patterns like this one on daily.dev."}},{"@type":"Question","name":"Which open source PDF parsing tools should I combine for enterprise RAG document ingestion covering text, tables, scanned pages, and figures?","acceptedAnswer":{"@type":"Answer","text":"Use fitz (PyMuPDF) as the cheap baseline for the native text layer, Docling or Azure Document Intelligence for tables requiring cell-level extraction, a vision LLM for charts and diagrams, EasyOCR for scanned pages with no text layer, and dedicated TOC recovery methods for reconstructing outlines from a printed sommaire or body typography when no native outline exists. Teams evaluating parsing stacks for RAG can track comparisons of tools like Docling and EasyOCR on daily.dev."}},{"@type":"Question","name":"How much does agentic-style document parsing cost on a real PDF versus a large contract?","acceptedAnswer":{"@type":"Answer","text":"On a 15-page paper, a four-step parsing plan (fitz text extraction, native TOC read, body-structure loop, image pipeline) costs a few hundred milliseconds. On a 300-page contract with tables, figures, and no native outline, the plan can grow to seven or eight methods including LLM calls, taking around a minute and costing real money, making it wasteful for corpus-scale ingestion where most pages are never queried. Engineers weighing parsing cost tradeoffs for RAG ingestion can follow related benchmarks on daily.dev."}}]} ```