{"id":7463,"date":"2026-08-19T19:10:06","date_gmt":"2026-08-19T19:10:06","guid":{"rendered":"https:\/\/www.wiven.ai\/wiven-llm\/docs\/utilisation\/documents\/"},"modified":"2026-08-19T19:10:06","modified_gmt":"2026-08-19T19:10:06","slug":"documents","status":"publish","type":"wvn_doc","link":"https:\/\/www.wiven.ai\/en\/wiven-llm\/docs\/utilisation\/documents\/","title":{"rendered":"Documents"},"content":{"rendered":"<h1>Documents<\/h1>\n<p>WivenLLM responds based on your documents. This page explains how to do this.<br \/>\nto enter, what happens to them, and how to achieve good results.<\/p>\n<hr \/>\n<h2>The two stages of importing<\/h2>\n<p>This is the point that most often causes confusion.<\/p>\n<p><strong>Step 1 \u2014 Extraction.<\/strong> The file is converted to text. It joins the<br \/>\n<strong>document library<\/strong>, common to the instance. At this stage, it is not<br \/>\nqueryable in any space.<\/p>\n<p><strong>Step 2 \u2014 Adding to a space.<\/strong> You select the document in the<br \/>\nAdd it to your library and workspace. It will then be split.,<br \/>\nvectorized and indexed for this space.<\/p>\n<p>The same document can be added to multiple spaces: it is only extracted once.<br \/>\ntimes, and indexed separately in each.<\/p>\n<p>Drag and drop directly into a conversation combines the two steps.<br \/>\nautomatically.<\/p>\n<hr \/>\n<h2>How to import<\/h2>\n<p><strong>Files.<\/strong> Drag and drop into the conversation, or upload from the<br \/>\nDocument manager. Multiple files at once are accepted.<\/p>\n<p><strong>Web pages.<\/strong> Paste a URL. The page content is extracted; ads and<br \/>\nNavigation menus are kept out of the way as much as possible.<\/p>\n<p><strong>Complete site.<\/strong> A recursive import follows links from a starting page,<br \/>\nto a depth and number of pages that you specify. Use with<br \/>\nMeasurement: depth makes volume grow very quickly.<\/p>\n<p><strong>External sources.<\/strong> Connectors import directly from:<\/p>\n<table>\n<thead>\n<tr>\n<th>Source<\/th>\n<th>What is imported<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Code repositories (GitHub, GitLab)<\/td>\n<td>Files in a repository, filterable by branch and by extension<\/td>\n<\/tr>\n<tr>\n<td>Confluence<\/td>\n<td>Pages of a space<\/td>\n<\/tr>\n<tr>\n<td>Company Wikis<\/td>\n<td>Pages and tree structure<\/td>\n<\/tr>\n<tr>\n<td>Document management (Paperless-ngx)<\/td>\n<td>Documents and metadata<\/td>\n<\/tr>\n<tr>\n<td>Note chests (Obsidian)<\/td>\n<td>Notes and links between notes<\/td>\n<\/tr>\n<tr>\n<td>YouTube videos<\/td>\n<td>Transcription<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>From the API.<\/strong> Uploading a file, plain text, or URL, for<br \/>\nPower WivenLLM from your own applications.<br \/>\n\u2192 <a href=\"\/en\/wiven-llm\/docs\/integrations\/api\/\">API developer<\/a><\/p>\n<hr \/>\n<h2>Accepted formats<\/h2>\n<p><strong>Text and office automation<\/strong> \u2014 <code>.txt<\/code>, <code>.md<\/code>, <code>.csv<\/code>, <code>.json<\/code>, <code>.html<\/code>, formats of<br \/>\ntechnical documentation, <code>.pdf<\/code>, <code>.docx<\/code>, <code>.pptx<\/code>, <code>.xlsx<\/code>, OpenDocument formats.<\/p>\n<p><strong>Messaging and books<\/strong> \u2014 archives <code>.mbox<\/code>, digital books <code>.epub<\/code>.<\/p>\n<p><strong>Images<\/strong> \u2014 <code>.png<\/code>, <code>.jpg<\/code>, <code>.webp<\/code>, from which the text is extracted by<br \/>\ncharacter recognition.<\/p>\n<p><strong>Audio and video<\/strong> \u2014 main audio formats and <code>.mp4<\/code>, transcribed<br \/>\nautomatically. A meeting can also be recorded directly from<br \/>\nthe application \u2192 <a href=\"\/en\/wiven-llm\/docs\/utilisation\/reunions\/\">Meetings<\/a>.<\/p>\n<p>\u2192 <a href=\"\/en\/wiven-llm\/docs\/reference\/formats\/\">Detailed list<\/a><\/p>\n<h3>What works less well<\/h3>\n<ul>\n<li><strong>Scanned PDFs without text layer<\/strong> \u2014 character recognition applies,<br \/>\n  with a result dependent on the quality of the scan. A tilted or low-resolution scan will result in poor results.<br \/>\n  The resolution produces a mediocre text, therefore mediocre answers.<\/li>\n<li><strong>complex spreadsheets<\/strong> \u2014 formulas, merged cells, and worksheets<br \/>\n  Very large datasets do not linearize well. For large tabular data,<br \/>\n  Exposing a database via the agent&#039;s SQL skill yields much better results.<br \/>\n  results that an indexed spreadsheet.<\/li>\n<li><strong>Documents with very rich layout<\/strong> \u2014 the text is extracted; the<br \/>\n  Information conveyed solely through visual presentation is lost.<\/li>\n<\/ul>\n<hr \/>\n<h2>What happens to the text: the segmentation<\/h2>\n<p>An entire document cannot fit within the context of a template. It is therefore broken down.<br \/>\nin <strong>fragments<\/strong>, and these are the fragments that are found when questioned.<\/p>\n<p>Two parameters, adjustable by an administrator in<br \/>\n<code>Settings \u2192 Text Clipping<\/code> :<\/p>\n<ul>\n<li><strong>Fragment size<\/strong> \u2014 the number of characters per fragment. Fragments<br \/>\n  Short videos give accurate results but sometimes lack context;<br \/>\n  Longer fragments retain the context but dilute the relevance.<\/li>\n<li><strong>Recovery<\/strong> \u2014 the number of characters repeated from one fragment to the next,<br \/>\n  so as not to split an idea in two.<\/li>\n<\/ul>\n<p>The default values are suitable for most corpora. Modify them<br \/>\n<strong>only affects documents indexed later<\/strong> The existing indexes retain<br \/>\ntheir segmentation up to re-indexing.<\/p>\n<hr \/>\n<h2>Organize the library<\/h2>\n<ul>\n<li><strong>Files.<\/strong> Organize documents by client, project, or type.<br \/>\n  The files organize the library; they do not change anything about the search.<\/li>\n<li><strong>Shift.<\/strong> A document can be moved between folders without losing its<br \/>\n  indexing.<\/li>\n<li><strong>Deletion.<\/strong> Deleting a document from the library removes it from <strong>all<\/strong><br \/>\n  removes spaces and erases the extracted text.<\/li>\n<\/ul>\n<hr \/>\n<h2>Automatic synchronization \u2014 <em>beta function<\/em><\/h2>\n<p>For documents imported from a URL or connector, a synchronization<br \/>\nPeriodic updates can detect changes at the source and reindex the content.<br \/>\nUpdated.<\/p>\n<p>She is active in <code>Settings \u2192 Beta features<\/code>, then settles the document by<br \/>\ndocument. Useful for dynamic documentation; useless for a static corpus.<\/p>\n<hr \/>\n<h2>Best practices<\/h2>\n<p><strong>Import what is relevant, not everything.<\/strong> A clean corpus provides better results.<br \/>\nanswers that an exhaustive corpus. Obsolete documents also provide answers.<br \/>\nthan the others \u2014 they come back with the same apparent authority.<\/p>\n<p><strong>Check the extracted text.<\/strong> After a difficult PDF import, ask a<br \/>\nquestion to which you know the answer. If the quoted excerpt is illegible, the<br \/>\nThe document is extracted incorrectly; converting it beforehand will give a better result.<\/p>\n<p><strong>Name your files explicitly.<\/strong> The document name appears in the<br \/>\nquotes. <code>Contrat-Dupont-2026.pdf<\/code> is useful; ; <code>scan_003.pdf<\/code> It is not.<\/p>\n<p><strong>Remove it before adding a new version.<\/strong> Two versions of the same contract<br \/>\nindexed simultaneously produce contradictory responses, without the<br \/>\nknow which model is the correct one.<\/p>\n<p><strong>Pin sparingly.<\/strong> A pinned document is injected in its entirety each time<br \/>\nmessage. \u2192 <a href=\"\/en\/wiven-llm\/docs\/utilisation\/espaces-de-travail\/\">Workspaces<\/a><\/p>","protected":false},"parent":7450,"menu_order":302,"template":"","meta":[],"class_list":["post-7463","wvn_doc","type-wvn_doc","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - 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