{"id":2285,"date":"2026-04-24T14:08:31","date_gmt":"2026-04-24T12:08:31","guid":{"rendered":"https:\/\/askem.eu\/?p=2285"},"modified":"2026-04-24T14:08:35","modified_gmt":"2026-04-24T12:08:35","slug":"graphrag-quand-le-graphe-de-connaissances-remplace-ou-complete-la-base-vectorielle","status":"publish","type":"post","link":"https:\/\/askem.eu\/en\/2026\/04\/24\/graphrag-quand-le-graphe-de-connaissances-remplace-ou-complete-la-base-vectorielle\/","title":{"rendered":"GraphRAG : quand le graphe de connaissances remplace (ou compl\u00e8te) la base vectorielle"},"content":{"rendered":"

GraphRAG : quand le graphe de connaissances remplace (ou compl\u00e8te) la base vectorielle<\/h2>\n\n\n\n

Le RAG classique fonctionne bien quand la question porte sur un passage pr\u00e9cis d’un document. Il \u00e9choue d\u00e8s qu’il faut relier<\/em> plusieurs informations dispers\u00e9es : \u00ab quels sont les sujets r\u00e9currents dans ce corpus ? \u00bb, \u00ab qui collabore avec qui ? \u00bb, \u00ab comment tel acteur influence-t-il tel autre ? \u00bb. GraphRAG<\/a><\/strong> r\u00e9pond \u00e0 ce besoin en indexant le corpus sous forme de graphe : entit\u00e9s, relations, communaut\u00e9s, plut\u00f4t que (ou en plus de) chunks vectoris\u00e9s. Microsoft Research a popularis\u00e9 l’approche en 2024 avec son projet open source ; l’\u00e9cosyst\u00e8me s’est depuis \u00e9largi \u00e0 Neo4j, LlamaIndex et LangChain, tous disponibles en licence \u00ab\u00a0permissive\u00a0\u00bb.<\/p>\n\n\n\n

Ce que le vecteur ne sait pas faire<\/h3>\n\n\n\n

Une base vectorielle r\u00e9pond tr\u00e8s bien aux questions locales<\/em> (quel paragraphe parle de X), mais elle \u00e9choue sur les questions globales<\/em> qui exigent d’agr\u00e9ger des dizaines de documents. Exemple : sur un corpus de comptes-rendus de projet, retrouver \u00ab les trois risques structurels qui reviennent depuis deux ans \u00bb suppose de regrouper les signaux faibles, pas de pointer un chunk isol\u00e9. Un graphe r\u00e9sout ce probl\u00e8me en rendant les relations explicites et navigables.<\/p>\n\n\n\n

Anatomie d’un pipeline GraphRAG<\/h3>\n\n\n\n

Le sch\u00e9ma classique, quel que soit l’outillage, comporte quatre \u00e9tapes :<\/p>\n\n\n\n

    \n
  • Extraction<\/strong> : un LLM parcourt chaque document et en extrait entit\u00e9s (personnes, lieux, concepts, organisations) et relations ( \u00ab A finance B \u00bb, \u00ab B d\u00e9pend de C \u00bb). Sortie typique : triplets sujet-pr\u00e9dicat-objet.<\/li>\n\n\n\n
  • Construction du graphe<\/strong> : les triplets sont consolid\u00e9s (d\u00e9duplication des entit\u00e9s, fusion des alias) et persist\u00e9s dans une base graphe : Neo4j, Memgraph, ou un simple format JSON\/Parquet pour les petits volumes.<\/li>\n\n\n\n
  • D\u00e9tection de communaut\u00e9s<\/strong> : un algorithme comme Leiden regroupe les n\u0153uds fortement connect\u00e9s en clusters th\u00e9matiques. Chaque communaut\u00e9 re\u00e7oit ensuite un r\u00e9sum\u00e9 g\u00e9n\u00e9r\u00e9 par LLM, c’est la brique-cl\u00e9 qui permet les questions globales.<\/li>\n\n\n\n
  • Interrogation hybride<\/strong> : selon la question, le syst\u00e8me choisit une recherche locale (voisinage d’un n\u0153ud), globale (r\u00e9sum\u00e9s de communaut\u00e9s) ou mixte.<\/li>\n<\/ul>\n\n\n\n

    Trois impl\u00e9mentations open source cr\u00e9dibles<\/h3>\n\n\n\n
      \n
    • Microsoft GraphRAG<\/strong> (licence MIT) : l’impl\u00e9mentation de r\u00e9f\u00e9rence, tr\u00e8s compl\u00e8te mais co\u00fbteuse en tokens \u00e0 l’indexation. Id\u00e9ale pour explorer l’approche sur un corpus ferm\u00e9.<\/li>\n\n\n\n
    • Neo4j + LLM Graph Builder<\/strong> : pile mature, graphe natif, requ\u00eates Cypher pour les questions complexes, int\u00e9gration directe avec LangChain et LlamaIndex. Plus artisanale mais plus contr\u00f4lable.<\/li>\n\n\n\n
    • LightRAG<\/strong> (licence MIT, HKU) : alternative plus l\u00e9g\u00e8re \u00e0 Microsoft GraphRAG, incr\u00e9mentale (on peut ajouter des documents sans tout r\u00e9indexer) et moins gourmande en LLM calls.<\/li>\n<\/ul>\n\n\n\n

      Co\u00fbts et limites \u00e0 conna\u00eetre<\/h3>\n\n\n\n

      GraphRAG n’est pas gratuit. L’\u00e9tape d’extraction consomme beaucoup de tokens, il faut compter plusieurs appels LLM par document, ce qui rend une version 100 % auto-h\u00e9berg\u00e9e (Ollama, vLLM) tr\u00e8s recommand\u00e9e. La d\u00e9tection de communaut\u00e9s et leurs r\u00e9sum\u00e9s ajoutent un co\u00fbt d’indexation non n\u00e9gligeable, mais qui s’amortit sur la dur\u00e9e d’exploitation. Sur un petit corpus (< 100 documents), un RAG vectoriel classique suffit souvent ; GraphRAG prend tout son sens sur des corpus volumineux, h\u00e9t\u00e9rog\u00e8nes, ou quand la valeur est dans les liens<\/em> entre documents.<\/p>\n\n\n\n

      Hybrider plut\u00f4t que remplacer<\/h3>\n\n\n\n

      Le pattern qui \u00e9merge en 2025 n’est pas \u00ab graphe contre vecteur \u00bb mais les deux simultan\u00e9ment : un pgvector ou un Qdrant pour le retrieval local classique, un Neo4j pour la navigation relationnelle, et un orchestrateur (LangGraph, Dify) qui route la requ\u00eate vers la bonne strat\u00e9gie selon son type. Le reranker vu dans le sujet pr\u00e9c\u00e9dent trouve sa place naturellement en sortie du retrieval vectoriel, tandis que le graphe alimente les questions agr\u00e9g\u00e9es.<\/p>\n\n\n\n

      Par o\u00f9 commencer<\/h3>\n\n\n\n

      Pour un POC rapide : Neo4j Community Edition en Docker, LlamaIndex ou LangChain comme couche d’int\u00e9gration, Ollama en backend LLM pour la phase d’extraction. Pr\u00e9voir un premier corpus de test bien d\u00e9limit\u00e9 (quelques dizaines de documents th\u00e9matiques) et mesurer la valeur ajout\u00e9e sur trois \u00e0 cinq questions typiques qui sont aujourd’hui mal trait\u00e9es par le RAG vectoriel. Si la r\u00e9ponse est nettement meilleure, c’est un signal fort pour passer \u00e0 l’\u00e9chelle.<\/p>\n\n\n\n

      GraphRAG n’est pas une \u00e9ni\u00e8me mode : c’est le moment o\u00f9 le RAG sort de la logique \u00ab chercher un passage \u00bb pour entrer dans celle de \u00ab comprendre un ensemble \u00bb. Un passage oblig\u00e9 pour toute organisation qui veut exploiter s\u00e9rieusement un patrimoine documentaire.<\/p>","protected":false},"excerpt":{"rendered":"

      GraphRAG : quand le graphe de connaissances remplace (ou compl\u00e8te) la base vectorielle Le RAG classique fonctionne bien quand la question porte sur un passage pr\u00e9cis d’un document. Il \u00e9choue d\u00e8s qu’il faut relier plusieurs informations dispers\u00e9es : \u00ab quels sont les sujets r\u00e9currents dans ce corpus ? \u00bb, \u00ab qui collabore avec qui ? \u00bb, \u00ab comment tel acteur influence-t-il tel autre ? \u00bb. GraphRAG […]<\/p>\n","protected":false},"author":1,"featured_media":2286,"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-2285","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","entry","has-media"],"yoast_head":"\nGraphRAG : quand le graphe de connaissances remplace (ou compl\u00e8te) la base vectorielle - askem<\/title>\n<meta name=\"description\" content=\"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. 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