{"id":2371,"date":"2026-06-10T09:58:48","date_gmt":"2026-06-10T07:58:48","guid":{"rendered":"https:\/\/askem.eu\/?p=2371"},"modified":"2026-06-10T09:58:51","modified_gmt":"2026-06-10T07:58:51","slug":"recherche-hybride-avec-opensearch","status":"publish","type":"post","link":"https:\/\/askem.eu\/en\/2026\/06\/10\/recherche-hybride-avec-opensearch\/","title":{"rendered":"Recherche hybride avec OpenSearch"},"content":{"rendered":"

Recherche hybride avec OpenSearch : combiner BM25 et vectoriel pour de meilleurs r\u00e9sultats RAG<\/h2>\n\n\n\n

Une grande partie de la qualit\u00e9 d’un syst\u00e8me RAG ne se joue pas dans le mod\u00e8le de langage, mais en amont, au moment de retrouver les bons passages. Or les deux grandes familles de recherche ont chacune un angle mort. La recherche lexicale classique, type BM25, excelle sur les termes exacts, les r\u00e9f\u00e9rences, les codes produit ou les noms propres, mais \u00e9choue d\u00e8s que la question est formul\u00e9e autrement que le document. La recherche vectorielle, elle, capte le sens et les reformulations, mais peut passer \u00e0 c\u00f4t\u00e9 d’un mot-cl\u00e9 pr\u00e9cis ou d’un acronyme rare. La recherche hybride consiste \u00e0 faire tourner les deux en parall\u00e8le et \u00e0 fusionner leurs r\u00e9sultats. OpenSearch<\/a>, fork open source d’Elasticsearch sous licence Apache 2.0, int\u00e8gre nativement cette m\u00e9canique.<\/p>\n\n\n\n

Pourquoi combiner les deux approches<\/h3>\n\n\n\n

L’id\u00e9e est simple : les forces de l’une compensent les faiblesses de l’autre. Sur une requ\u00eate comme \u00ab\u00a0proc\u00e9dure de remboursement formulaire CERFA 14011\u00a0\u00bb, BM25 va verrouiller le num\u00e9ro exact du formulaire, l\u00e0 o\u00f9 le vectoriel risque de le diluer. \u00c0 l’inverse, sur \u00ab\u00a0comment annuler mon abonnement\u00a0\u00bb, le vectoriel rapprochera la question d’un document intitul\u00e9 \u00ab\u00a0r\u00e9siliation de contrat\u00a0\u00bb m\u00eame sans mot commun, l\u00e0 o\u00f9 BM25 ne trouvera rien. En production, un syst\u00e8me hybride est presque toujours plus robuste qu’un syst\u00e8me purement vectoriel, surtout sur des corpus techniques riches en jargon, en codes et en r\u00e9f\u00e9rences.<\/p>\n\n\n\n

Les briques d’OpenSearch<\/h3>\n\n\n\n

OpenSearch r\u00e9unit dans un m\u00eame moteur ce qui demande souvent plusieurs outils ailleurs :<\/p>\n\n\n\n

    \n
  • Recherche lexicale BM25<\/strong> : l’indexation plein texte historique, mature et rapide, avec gestion des analyseurs linguistiques, des synonymes et du stemming.<\/li>\n\n\n\n
  • Recherche vectorielle k-NN<\/strong> : un champ de type knn_vector<\/em> stocke les embeddings et les indexe avec des algorithmes de voisinage approch\u00e9 comme HNSW, pour une recherche s\u00e9mantique \u00e0 grande \u00e9chelle.<\/li>\n\n\n\n
  • Pipeline de recherche hybride<\/strong> : un objet de configuration qui normalise puis combine les scores des deux requ\u00eates en une liste unique de r\u00e9sultats.<\/li>\n\n\n\n
  • Ingest pipelines<\/strong> : la possibilit\u00e9 de g\u00e9n\u00e9rer les embeddings au moment de l’indexation via un mod\u00e8le h\u00e9berg\u00e9, pour ne pas avoir \u00e0 les calculer c\u00f4t\u00e9 application.<\/li>\n<\/ul>\n\n\n\n

    Le point d\u00e9licat : fusionner deux scores qui n’ont pas la m\u00eame \u00e9chelle<\/h3>\n\n\n\n

    Un score BM25 et une distance vectorielle ne vivent pas dans le m\u00eame monde : ni la m\u00eame plage de valeurs, ni la m\u00eame signification. Les additionner brutalement n’a aucun sens. Deux strat\u00e9gies dominent pour les r\u00e9concilier.<\/p>\n\n\n\n

    La premi\u00e8re est la normalisation puis combinaison<\/strong> : OpenSearch ram\u00e8ne les scores de chaque requ\u00eate sur une \u00e9chelle commune, par exemple entre 0 et 1, puis les agr\u00e8ge selon une pond\u00e9ration choisie, souvent une moyenne arithm\u00e9tique ou g\u00e9om\u00e9trique. On peut ainsi donner plus de poids au lexical ou au s\u00e9mantique selon la nature du corpus.<\/p>\n\n\n\n

    La seconde est la fusion par rang r\u00e9ciproque (Reciprocal Rank Fusion, ou RRF)<\/strong> : plut\u00f4t que de comparer les scores, on ne regarde que la position de chaque document dans chaque liste de r\u00e9sultats. Un document bien class\u00e9 par les deux m\u00e9thodes remonte fortement. L’avantage de RRF est de s’affranchir totalement des \u00e9chelles de score, ce qui la rend tr\u00e8s stable et peu sensible au r\u00e9glage. C’est souvent un excellent point de d\u00e9part.<\/p>\n\n\n\n

    Une cha\u00eene RAG type avec OpenSearch<\/h3>\n\n\n\n

    Dans une architecture concr\u00e8te, OpenSearch occupe la place du moteur de retrieval, en amont du mod\u00e8le de langage :<\/p>\n\n\n\n

      \n
    • Indexation<\/strong> : les documents sont d\u00e9coup\u00e9s en passages, chaque passage est index\u00e9 \u00e0 la fois en texte brut pour BM25 et sous forme d’embedding pour le k-NN.<\/li>\n\n\n\n
    • Interrogation<\/strong> : \u00e0 chaque question, une requ\u00eate hybride interroge simultan\u00e9ment les deux index et le pipeline fusionne les r\u00e9sultats.<\/li>\n\n\n\n
    • Affinage<\/strong> : les meilleurs passages peuvent passer par un reranker, un cross-encoder qui r\u00e9ordonne finement la s\u00e9lection avant de la transmettre au mod\u00e8le.<\/li>\n\n\n\n
    • G\u00e9n\u00e9ration<\/strong> : les passages retenus alimentent le contexte du LLM, qu’il soit auto-h\u00e9berg\u00e9 ou distant.<\/li>\n<\/ul>\n\n\n\n

      Cette cha\u00eene reste enti\u00e8rement compos\u00e9e de briques ouvertes et auto-h\u00e9bergeables, dans la continuit\u00e9 des approches d\u00e9j\u00e0 d\u00e9crites sur ce site pour l’inf\u00e9rence locale et les pipelines RAG.<\/p>\n\n\n\n

      Points de vigilance<\/h3>\n\n\n\n
        \n
      • Coh\u00e9rence des embeddings<\/strong> : le mod\u00e8le qui g\u00e9n\u00e8re les vecteurs \u00e0 l’indexation doit \u00eatre strictement le m\u00eame qu’\u00e0 l’interrogation. Changer de mod\u00e8le impose de r\u00e9indexer tout le corpus.<\/li>\n\n\n\n
      • R\u00e9glage de la pond\u00e9ration<\/strong> : le bon \u00e9quilibre lexical \/ s\u00e9mantique d\u00e9pend du corpus. Il se mesure sur un jeu de questions de r\u00e9f\u00e9rence, pas \u00e0 l’intuition.<\/li>\n\n\n\n
      • Ressources<\/strong> : l’index vectoriel HNSW consomme de la m\u00e9moire vive. Le dimensionnement doit anticiper le volume de passages et la taille des embeddings.<\/li>\n\n\n\n
      • \u00c9valuation<\/strong> : sans mesure objective de la pertinence, par exemple via un outil d’\u00e9valuation de RAG, il est impossible de savoir si l’hybride am\u00e9liore r\u00e9ellement les r\u00e9ponses.<\/li>\n<\/ul>\n\n\n\n

        Pourquoi ce sujet a sa place ici<\/h3>\n\n\n\n

        On a d\u00e9j\u00e0 couvert les bases vectorielles d\u00e9di\u00e9es et les rerankers comme second \u00e9tage de pr\u00e9cision. La recherche hybride compl\u00e8te ce tableau par l’\u00e9tage le plus structurant : la qualit\u00e9 du premier retrieval. OpenSearch a le m\u00e9rite de r\u00e9unir lexical et vectoriel dans un seul moteur open source mature, ce qui simplifie l’architecture tout en gardant la ma\u00eetrise compl\u00e8te des donn\u00e9es et de l’infrastructure.<\/p>","protected":false},"excerpt":{"rendered":"

        Recherche hybride avec OpenSearch : combiner BM25 et vectoriel pour de meilleurs r\u00e9sultats RAG Une grande partie de la qualit\u00e9 d’un syst\u00e8me RAG ne se joue pas dans le mod\u00e8le de langage, mais en amont, au moment de retrouver les bons passages. Or les deux grandes familles de recherche ont chacune un angle mort. La recherche […]<\/p>\n","protected":false},"author":1,"featured_media":2372,"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-2371","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","entry","has-media"],"yoast_head":"\nRecherche hybride avec OpenSearch - askem<\/title>\n<meta name=\"description\" content=\"ASKEM BUREAU D'\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. 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