[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"page--\u002Fblog\u002Farticle":3,"schema--\u002Fblog\u002Farticle":48,"317e8e2a-912c-49a3-98d2-832985198aa9":49,"bc5d6217-b80c-499b-91a7-1dbca7208203":53,"blog-detail":89,"254171d1-0f9c-4a28-ba73-b88a0c100638":672,"ebe02389-31a7-4644-addc-9a469af71e79":1198,"related-blogs":1387},{"id":4,"name":5,"seo":6,"status":9,"schema":10,"template":11,"sections":35,"redirect":47},"289e4d80-4fe0-4497-8225-40d2b6d0fd37","Article ",{"title":7,"no_index":8,"no_follow":8},"Article",false,"published",[],{"name":12,"start_sections":13,"end_sections":19,"custom_codes":30},"Default",[14],{"collection":15,"id":16,"item":17},"section_harsh",12,{"id":18},"254171d1-0f9c-4a28-ba73-b88a0c100638",[20,25],{"collection":21,"id":22,"item":23},"section_hem",2,{"id":24},"bc5d6217-b80c-499b-91a7-1dbca7208203",{"collection":26,"id":27,"item":28},"section_bhavin",40,{"id":29},"ebe02389-31a7-4644-addc-9a469af71e79",[31,33,34],{"custom_codes":32},null,{"custom_codes":32},{"custom_codes":32},[36,42],{"collection":37,"id":38,"sort":39,"item":40},"section_bhargav",5576,1,{"id":41},"1b3ea167-b557-47ad-996c-6eeb0bcf3c08",{"collection":43,"id":44,"sort":22,"item":45},"section_krusha",5606,{"id":46},"317e8e2a-912c-49a3-98d2-832985198aa9",[],[],{"id":46,"title":50,"subtitle":51,"blogs":52,"selected_blogs":32},"\u003Cdiv>\n\u003Cdiv>More\u003C\u002Fdiv>\n\u003C\u002Fdiv>","\u003Cp>An interesting read? Here is more related to it.\u003C\u002Fp>","related",{"address":54,"contact":71},[55,64],{"hem_addresse":56},{"country":57,"address":58,"contact_number":59},"India (HQ)","\u003Cp>201, iSquare Corporate Park, Ahmedabad-380060, Gujarat, India\u003C\u002Fp>",{"type":60,"label":61,"url":62,"anchor_id":32,"target":63,"description":32,"click_id":32,"page":32,"icon":32,"section":32},"url","+91 77 97 977 977","tel:+917797977977","_blank",{"hem_addresse":65},{"country":66,"address":67,"contact_number":68},"Canada","\u003Cp>24 Merlot Court, Timberlea, NS B3T 0C2, Canada\u003C\u002Fp>",{"type":60,"label":69,"url":70,"anchor_id":32,"target":63,"description":32,"click_id":32,"page":32,"icon":32,"section":32},"+1 902-333-0067","tel:+19023330067",[72,78],{"hem_contact":73},{"label":74,"contact_value":75,"link":76},"\u003Cp>For Sales\u003C\u002Fp>","YO@7SPAN.COM",{"type":60,"label":75,"url":77,"anchor_id":32,"target":63,"description":32,"click_id":32,"page":32,"icon":32,"section":32},"mailto:YO@7SPAN.COM?subject=Inquiry",{"hem_contact":79},{"label":80,"contact_value":81,"link":82},"\u003Cp>Looking For Jobs\u003C\u002Fp>","Apply Now",{"type":83,"label":84,"url":32,"anchor_id":32,"target":32,"description":32,"click_id":32,"page":85,"icon":87,"section":32},"page","Career",{"path":86},"\u002Fcareers",{"name":84,"value":88,"svg":32},"mdi:briefcase-variant-outline",{"data":90},[91],{"id":92,"title":93,"date_published":94,"summary":32,"seo_description":95,"update_date":32,"status":9,"tags":96,"cover":102,"sections":105,"author":648,"category":32,"og_image":32,"color_primary":660},"97b49d9a-55c3-4526-a2c1-789c5644c42b","Document Ingestion for RAG: Parsing, Cleaning and Metadata","2026-11-06","Document ingestion turns PDFs into clean, labeled text for RAG. PyMuPDF parsed 7,031 pages in 8s. Learn parsing, cleaning, metadata and which tools to use.",[97],{"bg_tags_id":98},{"label":99,"slug":100,"id":101},"AI","ai","d85f2271-f26a-4da5-b9df-6c6630762c1c",{"id":103,"filename_disk":104},"63ddb761-dd4e-49a1-884c-ae1dbb81acd1","63ddb761-dd4e-49a1-884c-ae1dbb81acd1.png",[106,591,599],{"collection":107,"item":108},"bg_sections_content_blocks",{"id":109,"name":110,"content":111,"metadata":585},"4142258b-95e1-4a66-9ce7-2f99d2fa3766","1",{"time":112,"blocks":113,"version":584},1793612586015,[114,122,128,134,140,155,162,168,173,179,185,191,197,203,209,215,219,225,231,235,241,247,253,259,272,278,284,290,330,336,352,358,364,370,389,395,401,407,411,417,436,442,448,454,460,477,483,489,495,501,523,529,535,541,566,572,578],{"id":115,"data":116,"type":118,"tunes":119},"b194IJVSlE",{"text":117},"You've added an AI assistant to your HR portal. An employee asks about the hotel limit for managers, and it answers with the meal allowance. The hotel limit is right there in the travel policy PDF. So what went wrong?","paragraph",{"alignment":120},{"alignment":121},"left",{"id":123,"data":124,"type":118,"tunes":126},"6eQbSfeMZy",{"text":125},"Often, nothing went wrong with the AI. The PDF had two columns, and when it was turned into text, lines from both columns got stitched together. The policy the assistant searched wasn't the policy your HR team wrote.",{"alignment":127},{"alignment":121},{"id":129,"data":130,"type":118,"tunes":132},"6JfPBNVOwl",{"text":131},"That conversion step is document ingestion. It doesn't happen when the employee asks a question. It runs earlier, once, when the document is added to the system, and again whenever it changes. Your ingestion pipeline (a script or scheduled job your team runs) reads each file, pulls out the text, cleans it, and labels it before anything gets chunked or searched. In this guide we'll cover what good ingestion looks like, the three parts to get right, which tools do the parsing, and how to check the result.",{"alignment":133},{"alignment":121},{"id":135,"data":136,"type":118,"tunes":138},"mcWOUsOPDR",{"text":137},"\u003Cb>Key Takeaways\u003C\u002Fb>",{"alignment":139},{"alignment":121},{"id":141,"data":142,"type":154},"J4b_25H64Y",{"items":143,"style":153},[144,147,150],{"items":145,"content":146},[],"Ingestion runs ahead of time, not at question time. If it loses or scrambles text, no later step can bring it back.",{"items":148,"content":149},[],"Parsing quality varies by document type. A CVPR 2025 benchmark found every parsing approach had strengths and weaknesses across its nine document sources (",{"items":151,"content":152},[],"Keep version, page, section and access metadata with every chunk, so answers cite the current policy and only reach people allowed to see it.","unordered","nestedlist",{"id":156,"data":157,"type":159,"tunes":160},"m_8Rb-npGk",{"text":158,"level":22},"Where Does Document Ingestion Sit in a RAG Pipeline?","header",{"alignment":161},{"alignment":121},{"id":163,"data":164,"type":118,"tunes":166},"cV8wHpnPds",{"text":165},"Ingestion is the first step on the indexing side of a RAG pipeline, the half that prepares documents before anyone asks anything. Every later step works on whatever text it produces. NVIDIA's tests found answer accuracy shifted from 0.603 to 0.648 just by changing how the same documents were split (\u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fblog\u002Ffinding-the-best-chunking-strategy-for-accurate-ai-responses\u002F\" target=\"_blank\">NVIDIA\u003C\u002Fa>, 2025), and splitting can only work with the text ingestion hands it.",{"alignment":167},{"alignment":121},{"id":169,"data":170,"type":172},"We7kAy3APP",{"code":171},"INDEXING (runs once, when documents are added or updated)\nDocuments → [ Ingestion ] → Chunking → Embeddings → Vector database\n              ▲ you are here\n\nANSWERING (runs on every question)\nUser question → Query transformation → Retrieval → Reranking → LLM answer","code",{"id":174,"data":175,"type":118,"tunes":177},"Car-tD423b",{"text":176},"The next steps each have their own guide: chunking strategies for RAG, then embeddings and vector databases. On the answering side, query transformation rewrites the question and reranking sorts the results.",{"alignment":178},{"alignment":121},{"id":180,"data":181,"type":118,"tunes":183},"bpBHDjzmQc",{"text":182},"Why does this order matter? Each step depends on the one before it. If parsing drops a sentence, cleaning can't restore it. If metadata is missing, the system can't tell which version is current or where an answer came from.",{"alignment":184},{"alignment":121},{"id":186,"data":187,"type":159,"tunes":189},"JMoWOKiRXR",{"text":188,"level":22},"What Is Ingestion Quality?",{"alignment":190},{"alignment":121},{"id":192,"data":193,"type":118,"tunes":195},"K4nBoy7EdZ",{"text":194},"Ingestion quality is how well a document survives being turned into text. Good ingestion keeps the words, their order and their meaning, removes noise like repeated headers, and attaches details such as source, page, version and permissions. It has three parts: parsing, cleaning and metadata.",{"alignment":196},{"alignment":121},{"id":198,"data":199,"type":118,"tunes":201},"sbTRVQCAPq",{"text":200},"Think of it like taking notes from a book. If the pages get shuffled, a few key lines go missing, or you lose track of which chapter you're in, finding the right fact later gets hard. Search systems have the same problem, only they can't flip back to the original to check.",{"alignment":202},{"alignment":121},{"id":204,"data":205,"type":159,"tunes":207},"3ZvTBxuarz",{"text":206,"level":22},"What Happens in the Backend During Ingestion?",{"alignment":208},{"alignment":121},{"id":210,"data":211,"type":118,"tunes":213},"tpJxHqVRmJ",{"text":212},"Your pipeline opens each file, extracts the text page by page, strips the noise, attaches metadata, and hands the result to the chunker. PyMuPDF, a common Python PDF library, extracted text from 7,031 pages in about 8 seconds in its own benchmark, against 227 seconds for PDFMiner (\u003Ca href=\"https:\u002F\u002Fpymupdf.readthedocs.io\u002Fen\u002Flatest\u002Fabout.html\" target=\"_blank\">PyMuPDF docs\u003C\u002Fa>, vendor-published). \u003Cb>Note:\u003C\u002Fb> Developers can use different libraries depending on the programming language they are working with. Here's a simplified version for a 40-page travel policy:",{"alignment":214},{"alignment":121},{"id":216,"data":217,"type":172},"XzfZR4SBpy",{"code":218},"import pymupdf  # pip install pymupdf\n\nHEADER = \"Company Ltd - Internal Use Only\"\ndoc = pymupdf.open(\"employee-travel-policy-v3.pdf\")\npages = []\n\nfor page in doc:\n    text = page.get_text(\"text\", sort=True)   # read top-left to bottom-right\n\n    if len(text.strip()) \u003C 50:                # almost no text: probably a scanned image\n        send_to_ocr(doc, page.number)          # don't skip it silently\n        continue\n\n    lines = [l for l in text.splitlines() if l.strip() != HEADER]\n    if lines and lines[-1].strip().isdigit():  # drop the page number, last line only\n        lines = lines[:-1]\n\n    pages.append({\n        \"text\": \"\\n\".join(lines),\n        \"metadata\": {\n            \"doc_id\": \"travel-policy\",\n            \"title\": \"Employee Travel Policy\",\n            \"version\": \"v3\",\n            \"effective_date\": \"2026-01-01\",\n            \"page\": page.number + 1,\n            \"access\": \"all-employees\",\n        },\n    })\n\n# `pages` now goes to the chunker",{"id":220,"data":221,"type":118,"tunes":223},"LCqx6Al3L2",{"text":222},"Look at the page-number line. It only checks the last line. A lazier rule, \"drop every line that's just a number,\" would also delete a table cell holding \u003Ccode>4500\u003C\u002Fcode>, which might be the hotel limit you're trying to preserve. That's the kind of trade-off ingestion is full of.",{"alignment":224},{"alignment":121},{"id":226,"data":227,"type":118,"tunes":229},"pbC3t08Fb9",{"text":228},"And here's what goes wrong without a check on two-column pages. Say the left column covers hotels and the right covers meals (an illustrative example):",{"alignment":230},{"alignment":121},{"id":232,"data":233,"type":172},"1y259paLbs",{"code":234},"What the page shows                       What a naive extraction can produce\nHotel limits: Managers may book    |      Hotel limits: Managers may book Meal allowance:\nup to ₹6,000 per night.            |      up to ₹6,000 per night. ₹1,200 per day for all\nMeal allowance: ₹1,200 per day     |      grades.\nfor all grades.                    |",{"id":236,"data":237,"type":118,"tunes":239},"71EvLqA0Ce",{"text":238},"The words are all there. The sentences aren't. Sorting text by position helps, but it doesn't guarantee correct column order on every layout, so multi-column pages are worth checking by eye.",{"alignment":240},{"alignment":121},{"id":242,"data":243,"type":159,"tunes":245},"Oy7F4nSAXw",{"text":244,"level":22},"How Does Document Parsing Work?",{"alignment":246},{"alignment":121},{"id":248,"data":249,"type":118,"tunes":251},"yHQW9sL_-r",{"text":250},"Document parsing pulls text and structure out of a file. Different formats need different handling, and PDFs are the hardest. A PDF often stores text as characters placed at coordinates on a page, not as paragraphs, so the parser has to work out reading order, table rows and headings from position alone. OmniDocBench, a CVPR 2025 benchmark, built its test set from nine document sources, from academic papers to handwritten notes and dense newspapers, precisely because tools behave so differently across them (\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.07626\" target=\"_blank\">Ouyang et al.\u003C\u002Fa>, 2025).",{"alignment":252},{"alignment":121},{"id":254,"data":255,"type":118,"tunes":257},"q1lc8Xp2D6",{"text":256},"Three things trip parsers up most:",{"alignment":258},{"alignment":121},{"id":260,"data":261,"type":154},"RlkKU6oOqD",{"items":262,"style":153},[263,266,269],{"items":264,"content":265},[],"Multi-column pages can be read across the columns instead of down them.",{"items":267,"content":268},[],"Tables can lose the link between a value and its row and column headers.",{"items":270,"content":271},[],"Scanned pages are images, so they return no text at all unless they go through OCR (optical character recognition).",{"id":273,"data":274,"type":118,"tunes":276},"eoAvRR0pKV",{"text":275},"Word files, HTML and Markdown are far easier, because the structure is already in the file.",{"alignment":277},{"alignment":121},{"id":279,"data":280,"type":159,"tunes":282},"FPAtQ2rWfQ",{"text":281,"level":22},"Which Tools Parse Documents for RAG?",{"alignment":283},{"alignment":121},{"id":285,"data":286,"type":118,"tunes":288},"N_cTFBwdf2",{"text":287},"Most teams start with an open-source parser running on their own servers, and move to a heavier layout-aware tool or a hosted service when tables, scans or odd layouts start causing misses. The five below cover the usual options. Docling, for example, started at IBM Research and handles layout, reading order and table structure locally (\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fdocling-project\u002Fdocling\" target=\"_blank\">Docling on GitHub\u003C\u002Fa>).",{"alignment":289},{"alignment":121},{"id":291,"data":292,"type":329},"7h8FoAEoEl",{"content":293,"withHeadings":328},[294,300,306,311,317,323],[295,296,297,298,299],"Tool","What it is","Runs","License","Good for",[301,302,303,304,305],"\u003Ca href=\"https:\u002F\u002Fpymupdf.readthedocs.io\u002Fen\u002Flatest\u002Fabout.html\" target=\"_blank\">PyMuPDF\u003C\u002Fa>","Fast Python library for PDFs and other formats","Your servers","AGPL or commercial","Clean, text-based PDFs where speed matters",[307,308,303,309,310],"\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fdocling-project\u002Fdocling\" target=\"_blank\">Docling\u003C\u002Fa>","Layout-aware converter to Markdown or JSON, with OCR","MIT","Tables, reading order, mixed formats, sensitive data",[312,313,314,315,316],"\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FUnstructured-IO\u002Funstructured\" target=\"_blank\">Unstructured\u003C\u002Fa>","Library that splits files into typed elements","Your servers, or a paid API","Apache-2.0 (library)","Many file types through one \u003Ccode>partition()\u003C\u002Fcode> call",[318,319,320,321,322],"\u003Ca href=\"https:\u002F\u002Fwww.llamaindex.ai\u002Fllamaparse\" target=\"_blank\">LlamaParse\u003C\u002Fa>","Hosted parsing service from LlamaIndex, 90+ formats","Hosted","Commercial","Complex layouts without running models yourself",[324,325,303,326,327],"\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ftesseract-ocr\u002Ftesseract\" target=\"_blank\">Tesseract\u003C\u002Fa>","OCR engine, 100+ languages","Apache-2.0","Turning scanned pages into text",true,"table",{"id":331,"data":332,"type":118,"tunes":334},"sGSLy8dsa8",{"text":333},"\u003Cb>A few things worth knowing before you pick:\u003C\u002Fb>",{"alignment":335},{"alignment":121},{"id":337,"data":338,"type":154},"xs-0eMrMwx",{"items":339,"style":153},[340,343,346,349],{"items":341,"content":342},[],"Check the license. PyMuPDF's AGPL license has obligations for software you distribute or serve. Its commercial license removes them.",{"items":344,"content":345},[],"Tesseract reads images, not PDFs. Scanned PDF pages are rendered to images first, then passed to OCR. Most of the tools above can do that step for you.",{"items":347,"content":348},[],"Hosted means your documents leave your servers. For HR, legal or finance files, check the provider's data-handling terms first.",{"items":350,"content":351},[],"Test on your own worst documents. No tool wins everywhere, and the only ranking that counts is the one on your files.",{"id":353,"data":354,"type":159,"tunes":356},"_cVTTLs3jq",{"text":355,"level":22},"How Do You Clean Extracted Text?",{"alignment":357},{"alignment":121},{"id":359,"data":360,"type":118,"tunes":362},"cHRqL7RmmN",{"text":361},"Cleaning removes noise that would otherwise fill your chunks and make unrelated passages look alike. Repeated headers are the classic case: if every page of a 40-page policy starts with \"Company Ltd - Internal Use Only,\" that line ends up in dozens of chunks and nudges them all toward each other in search.",{"alignment":363},{"alignment":121},{"id":365,"data":366,"type":118,"tunes":368},"e4UOwD4BXT",{"text":367},"\u003Cb>Common cleaning jobs:\u003C\u002Fb>",{"alignment":369},{"alignment":121},{"id":371,"data":372,"type":154},"agkOa-S909",{"items":373,"style":153},[374,377,380,383,386],{"items":375,"content":376},[],"Remove repeated headers, footers, page numbers, navigation menus and email signatures.",{"items":378,"content":379},[],"Fix extra spaces and line breaks, and rejoin words hyphenated across lines when it's safe.",{"items":381,"content":382},[],"Repair encoding problems (garbled characters like ",{"items":384,"content":385},[],"Remove duplicate copies of the same document or section.",{"items":387,"content":388},[],"Keep tables readable, with each value still next to its headers.",{"id":390,"data":391,"type":118,"tunes":393},"bF2EwW7aD9",{"text":392},"Cleaning can go too far. A rule that deletes every repeated line might remove a real heading like \"Terms and Conditions.\" A rule that deletes lone numbers can wipe out a column of prices. Compare text before and after cleaning on a few real pages, and keep the raw extracted text so you can rerun with better rules later.",{"alignment":394},{"alignment":121},{"id":396,"data":397,"type":159,"tunes":399},"zXTjEJr5bA",{"text":398,"level":22},"What Metadata Should Every Chunk Carry?",{"alignment":400},{"alignment":121},{"id":402,"data":403,"type":118,"tunes":405},"TynDZEYvtj",{"text":404},"Metadata is information about the text, not the text itself: where it came from, which version it is, and who can see it. Adding a short note of document and section context to each chunk before embedding cut failed retrievals by 35% in Anthropic's tests (\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fcontextual-retrieval\" target=\"_blank\">Anthropic\u003C\u002Fa>, 2024), and that context comes from metadata captured at ingestion.",{"alignment":406},{"alignment":121},{"id":408,"data":409,"type":172},"WsCs116Rx1",{"code":410},"doc_id:          travel-policy\ntitle:           Employee Travel Policy\nversion:         v3\neffective_date:  2026-01-01\nsource:          HR knowledge base\npage:            12\nsection:         4.2 Hotel limits\nparent_id:       travel-policy\u002Fsection-4\naccess:          all-employees",{"id":412,"data":413,"type":118,"tunes":415},"wY2Axa__UD",{"text":414},"\u003Cb>Each field does a job later on:\u003C\u002Fb>",{"alignment":416},{"alignment":121},{"id":418,"data":419,"type":154},"K7CHZb20YG",{"items":420,"style":153},[421,424,427,430,433],{"items":422,"content":423},[],"doc_id and version let you find and replace every chunk from a document when it changes.",{"items":425,"content":426},[],"page and section let the answer cite exactly where it came from.",{"items":428,"content":429},[],"parent_id lets hierarchical chunking pull in the surrounding section when a small chunk isn't enough.",{"items":431,"content":432},[],"effective_date lets you filter out policies that are no longer in force.",{"items":434,"content":435},[],"access lets you filter results or separate them into namespaces so people only see documents they're allowed to read.",{"id":437,"data":438,"type":118,"tunes":440},"VUNRk3o2bs",{"text":439},"Don't trust a file's built-in metadata blindly. A PDF's \"author\" might be whoever exported it, and its \"created\" date might just be when someone copied it. Where you can, take version, owner and access details from the system the file came from, like your HR portal or document management system.",{"alignment":441},{"alignment":121},{"id":443,"data":444,"type":159,"tunes":446},"yVuECvsWnm",{"text":445,"level":22},"When Should You Re-Ingest a Document?",{"alignment":447},{"alignment":121},{"id":449,"data":450,"type":118,"tunes":452},"_iJ3t8JuMS",{"text":451},"Re-ingest whenever a document changes, and delete the old version's chunks when you do. If the travel policy moves from v3 to v4 and you only add the new chunks, both versions sit in your index. Your assistant can then quote last year's hotel limit with complete confidence.",{"alignment":453},{"alignment":121},{"id":455,"data":456,"type":118,"tunes":458},"dYIfdJ58jj",{"text":457},"\u003Cb>A simple routine:\u003C\u002Fb>",{"alignment":459},{"alignment":121},{"id":461,"data":462,"type":154},"tyaSRmUAHt",{"items":463,"style":476},[464,467,470,473],{"items":465,"content":466},[],"Detect the change (a new upload, a modified date, or a changed file hash).",{"items":468,"content":469},[],"Find every chunk with that ",{"items":471,"content":472},[],"Ingest the new version from scratch, with the new ",{"items":474,"content":475},[],"Log what was replaced, so you can trace an old answer back if someone asks.","ordered",{"id":478,"data":479,"type":118,"tunes":481},"pXEdnUjZEO",{"text":480},"The same goes for deleted documents. If a policy is withdrawn, remove its chunks. Otherwise, it stays searchable long after nobody can find the PDF.",{"alignment":482},{"alignment":121},{"id":484,"data":485,"type":159,"tunes":487},"AtRdL5lXub",{"text":486,"level":22},"How Do You Check Ingestion Quality?",{"alignment":488},{"alignment":121},{"id":490,"data":491,"type":118,"tunes":493},"9r2SFpA0Nm",{"text":492},"Compare what the parser produced with what the document actually says, on a small set of your hardest files, every time the pipeline changes. You don't need a complex evaluation system to start. OmniDocBench's whole premise is that parsers are rarely evaluated on realistic, varied documents (\u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2412.07626\" target=\"_blank\">Ouyang et al.\u003C\u002Fa>, 2025), so build your own small version.",{"alignment":494},{"alignment":121},{"id":496,"data":497,"type":118,"tunes":499},"s0tuuzL3tc",{"text":498},"Keep a test set of 10 to 20 awkward files: scanned PDFs, two-column pages, tables, very long documents, and anything that has broken before. Then check:",{"alignment":500},{"alignment":121},{"id":502,"data":503,"type":154},"MKBAVJBug5",{"items":504,"style":153},[505,508,511,514,517,520],{"items":506,"content":507},[],"Reading order: do paragraphs on multi-column pages read in the right sequence?",{"items":509,"content":510},[],"Tables: does every value still sit next to its row and column headers, with units?",{"items":512,"content":513},[],"Scans: did OCR run, and is the result readable?",{"items":515,"content":516},[],"Empty or tiny output: pages with almost no text usually mean a missed scan or a failed parse.",{"items":518,"content":519},[],"Odd characters and repeats: garbled symbols or the same line on every page point to encoding or header problems.",{"items":521,"content":522},[],"Metadata coverage: does every chunk have its title, source, page, version and access label?",{"id":524,"data":525,"type":118,"tunes":527},"k9QSy3-7LR",{"text":526},"Finally, ask questions with known answers and check that search returns the right chunk. Our chunking guide's test plan shows how, with 30 to 50 real questions and a simple top-5 hit rate.",{"alignment":528},{"alignment":121},{"id":530,"data":531,"type":159,"tunes":533},"yMNumx00A-",{"text":532,"level":22},"What Are the Most Common Ingestion Mistakes?",{"alignment":534},{"alignment":121},{"id":536,"data":537,"type":118,"tunes":539},"h_n4N0_rIW",{"text":538},"Most ingestion mistakes are silent: nothing crashes, the text just ends up slightly wrong, and the assistant answers from it anyway. 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