{"id":2374,"date":"2026-06-12T07:58:00","date_gmt":"2026-06-12T05:58:00","guid":{"rendered":"https:\/\/askem.eu\/?p=2374"},"modified":"2026-06-12T07:58:04","modified_gmt":"2026-06-12T05:58:04","slug":"ragflow-comprendre-un-document-avant-de-le-decouper","status":"publish","type":"post","link":"https:\/\/askem.eu\/en\/2026\/06\/12\/ragflow-comprendre-un-document-avant-de-le-decouper\/","title":{"rendered":"RAGFlow : comprendre un document avant de le d\u00e9couper"},"content":{"rendered":"

RAGFlow : comprendre un document avant de le d\u00e9couper<\/p>\n\n\n\n

La qualit\u00e9 d’un syst\u00e8me RAG se joue avant la recherche vectorielle, au moment o\u00f9 le document est lu et segment\u00e9. La plupart des cha\u00eenes RAG d\u00e9coupent les fichiers en blocs de taille fixe, ce qui d\u00e9truit les tableaux, m\u00e9lange les colonnes et coupe les paragraphes au milieu d’une phrase. RAGFlow<\/a><\/strong>, moteur RAG open source d’InfiniFlow sous licence Apache 2.0, prend le probl\u00e8me par l’autre bout : il analyse d’abord la structure r\u00e9elle du document, puis d\u00e9coupe en connaissant la mise en page.<\/p>\n\n\n\n

Le pari de la compr\u00e9hension profonde du document<\/h3>\n\n\n\n

Le c\u0153ur de RAGFlow s’appelle DeepDoc. Plut\u00f4t que de lire un PDF comme un flot de texte brut, il applique une analyse de mise en page : reconnaissance des titres, des paragraphes, des en-t\u00eates, des listes, et surtout des tableaux et des figures. Un tableau financier reste un tableau, une facture garde ses lignes align\u00e9es, un rapport conserve sa hi\u00e9rarchie. Cette \u00e9tape, acc\u00e9l\u00e9rable sur GPU, conditionne tout le reste de la cha\u00eene.<\/p>\n\n\n\n

Sur cette base, RAGFlow propose un d\u00e9coupage par gabarit : on choisit un mod\u00e8le de segmentation adapt\u00e9 au type de document (article, pr\u00e9sentation, manuel, tableau, facture, code) au lieu d’un d\u00e9coupage aveugle. Le d\u00e9coupage obtenu est visualisable dans l’interface, ce qui permet de v\u00e9rifier et corriger les blocs avant indexation. C’est une rupture avec l’approche habituelle o\u00f9 l’on ne voit jamais comment le document a \u00e9t\u00e9 tron\u00e7onn\u00e9.<\/p>\n\n\n\n

Une cha\u00eene RAG compl\u00e8te, pas seulement un parseur<\/h3>\n\n\n\n

RAGFlow ne s’arr\u00eate pas au parsing. Il int\u00e8gre l’ensemble de la cha\u00eene avec une interface web de gestion documentaire et de bases de connaissances. Les \u00e9l\u00e9ments cl\u00e9s \u00e0 conna\u00eetre :<\/p>\n\n\n\n

    \n
  • Citations v\u00e9rifiables<\/strong> : chaque r\u00e9ponse renvoie aux extraits sources pr\u00e9cis, ce qui r\u00e9duit le risque d’hallucination et rend les r\u00e9ponses auditables.<\/li>\n\n\n\n
  • Recherche multi-voies<\/strong> : combinaison de recherche lexicale et vectorielle, suivie d’un reranking pour affiner la pertinence.<\/li>\n\n\n\n
  • GraphRAG<\/strong> : construction d’un graphe de connaissances \u00e0 l’\u00e9chelle de toute la base documentaire, et non fichier par fichier, mis \u00e0 jour \u00e0 chaque nouvel import.<\/li>\n\n\n\n
  • RAPTOR et long contexte<\/strong> : agr\u00e9gation s\u00e9mantique hi\u00e9rarchique pour traiter de longs corpus et remonter du niveau document au niveau dataset.<\/li>\n\n\n\n
  • Capacit\u00e9s agentiques<\/strong> : raisonnement multi-\u00e9tapes pour les requ\u00eates complexes, avec un composant navigateur permettant \u00e0 l’agent de consulter des pages web de fa\u00e7on autonome.<\/li>\n<\/ul>\n\n\n\n

    O\u00f9 il se situe par rapport aux autres outils<\/h3>\n\n\n\n

    Face \u00e0 des plateformes comme AnythingLLM ou Dify, qui visent une cha\u00eene LLM g\u00e9n\u00e9raliste, RAGFlow se distingue par sa profondeur de lecture documentaire. Il est particuli\u00e8rement pertinent quand les sources sont des PDF complexes, des documents scann\u00e9s, des tableaux ou des formulaires : rapports administratifs, jeux de donn\u00e9es ouverts document\u00e9s, archives techniques, notices r\u00e9glementaires. L\u00e0 o\u00f9 un d\u00e9coupage na\u00eff perd l’information, l’analyse de structure la pr\u00e9serve.<\/p>\n\n\n\n

    Pour d\u00e9marrer<\/h3>\n\n\n\n

    RAGFlow se d\u00e9ploie en auto-h\u00e9bergement via Docker Compose, avec une interface web pour cr\u00e9er ses bases de connaissances, importer les documents, visualiser le d\u00e9coupage et tester les r\u00e9ponses. On peut le brancher sur des mod\u00e8les locaux (Ollama, vLLM) comme sur des API distantes, ce qui permet de garder une cha\u00eene enti\u00e8rement souveraine si n\u00e9cessaire. La version v0.25.6, publi\u00e9e fin mai 2026, ajoute notamment le composant navigateur dans l’agent.<\/p>\n\n\n\n

    Une bonne premi\u00e8re exp\u00e9rimentation : prendre un corpus de PDF r\u00e9els avec tableaux et figures, comparer le d\u00e9coupage par gabarit de RAGFlow \u00e0 un d\u00e9coupage \u00e0 taille fixe, et mesurer l’\u00e9cart de qualit\u00e9 des r\u00e9ponses et de fiabilit\u00e9 des citations. C’est souvent l\u00e0 que se r\u00e9v\u00e8le la valeur de la compr\u00e9hension profonde du document.<\/p>","protected":false},"excerpt":{"rendered":"

    RAGFlow : comprendre un document avant de le d\u00e9couper La qualit\u00e9 d’un syst\u00e8me RAG se joue avant la recherche vectorielle, au moment o\u00f9 le document est lu et segment\u00e9. La plupart des cha\u00eenes RAG d\u00e9coupent les fichiers en blocs de taille fixe, ce qui d\u00e9truit les tableaux, m\u00e9lange les colonnes et coupe les paragraphes au milieu […]<\/p>\n","protected":false},"author":1,"featured_media":2375,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"","ocean_second_sidebar":"","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"","ocean_custom_header_template":"","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"","ocean_menu_typo_font_family":"","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"","osh_disable_topbar_sticky":"default","osh_disable_header_sticky":"default","osh_sticky_header_style":"default","osh_sticky_header_effect":"","osh_custom_sticky_logo":0,"osh_custom_retina_sticky_logo":0,"osh_custom_sticky_logo_height":0,"osh_background_color":"","osh_links_color":"","osh_links_hover_color":"","osh_links_active_color":"","osh_links_bg_color":"","osh_links_hover_bg_color":"","osh_links_active_bg_color":"","osh_menu_social_links_color":"","osh_menu_social_hover_links_color":"","ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"on","ocean_gallery_id":[],"footnotes":""},"categories":[16],"tags":[],"class_list":["post-2374","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","entry","has-media"],"yoast_head":"\nRAGFlow : comprendre un document avant de le d\u00e9couper - askem<\/title>\n<meta name=\"description\" content=\"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. 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