{"id":2320,"date":"2026-05-13T10:00:54","date_gmt":"2026-05-13T08:00:54","guid":{"rendered":"https:\/\/askem.eu\/?p=2320"},"modified":"2026-05-13T10:01:00","modified_gmt":"2026-05-13T08:01:00","slug":"ragas-mesurer-objectivement-la-qualite-dun-pipeline-rag-en-open-source","status":"publish","type":"post","link":"https:\/\/askem.eu\/en\/2026\/05\/13\/ragas-mesurer-objectivement-la-qualite-dun-pipeline-rag-en-open-source\/","title":{"rendered":"RAGAS : mesurer objectivement la qualit\u00e9 d&rsquo;un pipeline RAG en open source"},"content":{"rendered":"<h2 class=\"wp-block-heading\">RAGAS&nbsp;: mesurer objectivement la qualit\u00e9 d&rsquo;un pipeline RAG en open source<\/h2>\n\n\n\n<p>Un pipeline RAG (Retrieval-Augmented Generation) bien con\u00e7u repose sur une dizaine de leviers&nbsp;: choix de l&#8217;embedding, taille des chunks, strat\u00e9gie de d\u00e9coupage, reranker, mod\u00e8le de g\u00e9n\u00e9ration, prompt, fen\u00eatre de contexte. Quand l&rsquo;un de ces leviers bouge, comment savoir si la qualit\u00e9 progresse vraiment, ou si l&rsquo;on d\u00e9place simplement les erreurs&nbsp;? <strong><a href=\"https:\/\/github.com\/vibrantlabsai\/ragas\">RAGAS<\/a><\/strong> (Retrieval-Augmented Generation Assessment) est la r\u00e9ponse open source qui s&rsquo;est impos\u00e9e pour cette question&nbsp;: un framework Python sous licence Apache 2.0 qui produit des m\u00e9triques chiffr\u00e9es, comparables, sur la <em>fid\u00e9lit\u00e9<\/em>, la <em>pertinence<\/em> et la <em>pr\u00e9cision contextuelle<\/em> des r\u00e9ponses g\u00e9n\u00e9r\u00e9es. C&rsquo;est le cha\u00eenon manquant entre une d\u00e9mo qui marche bien sur trois questions et un syst\u00e8me RAG mis en production avec confiance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Le probl\u00e8me de fond&nbsp;: un RAG qui semble bon n&rsquo;est pas forc\u00e9ment bon<\/h3>\n\n\n\n<p>Les bugs les plus co\u00fbteux d&rsquo;un RAG ne se voient pas \u00e0 l&rsquo;\u0153il nu. Le mod\u00e8le peut produire une r\u00e9ponse fluide qui n&rsquo;utilise pas du tout les passages r\u00e9cup\u00e9r\u00e9s (faible <em>faithfulness<\/em>), ou s&rsquo;appuyer sur un passage hors-sujet en r\u00e9pondant juste par chance, ou rater syst\u00e9matiquement les questions o\u00f9 la r\u00e9ponse est dispers\u00e9e dans plusieurs documents. Sans m\u00e9trique, on optimise \u00e0 l&rsquo;aveugle&nbsp;: on change le top-k, on ajoute un reranker, on gonfle le prompt syst\u00e8me, et l&rsquo;on confond <em>am\u00e9lioration ressentie sur quelques exemples<\/em> avec <em>am\u00e9lioration r\u00e9elle<\/em>. Le co\u00fbt est connu&nbsp;: r\u00e9gression silencieuse en production, perte de confiance utilisateur, d\u00e9marche d&rsquo;am\u00e9lioration impossible \u00e0 objectiver aupr\u00e8s d&rsquo;un commanditaire.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Les m\u00e9triques que RAGAS apporte<\/h3>\n\n\n\n<p>RAGAS d\u00e9coupe l&rsquo;\u00e9valuation en m\u00e9triques orthogonales, chacune mesurant un d\u00e9faut diff\u00e9rent du pipeline&nbsp;:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Faithfulness<\/strong>&nbsp;: la r\u00e9ponse est-elle effectivement \u00e9tay\u00e9e par les passages r\u00e9cup\u00e9r\u00e9s, ou le mod\u00e8le hallucine-t-il&nbsp;? M\u00e9trique critique pour des SI publics ou r\u00e9glement\u00e9s.<\/li>\n\n\n\n<li><strong>Answer relevancy<\/strong>&nbsp;: la r\u00e9ponse traite-t-elle r\u00e9ellement la question pos\u00e9e, ou divague-t-elle&nbsp;?<\/li>\n\n\n\n<li><strong>Context precision<\/strong>&nbsp;: parmi les passages remont\u00e9s, lesquels \u00e9taient utiles&nbsp;? Mesure indirectement la qualit\u00e9 du retriever et du reranker.<\/li>\n\n\n\n<li><strong>Context recall<\/strong>&nbsp;: a-t-on remont\u00e9 tous les passages n\u00e9cessaires pour r\u00e9pondre&nbsp;? D\u00e9tecte les questions multi-documents mal couvertes.<\/li>\n\n\n\n<li><strong>Answer correctness<\/strong>&nbsp;: la r\u00e9ponse correspond-elle \u00e0 la v\u00e9rit\u00e9 de r\u00e9f\u00e9rence (en pr\u00e9sence d&rsquo;un jeu de tests annot\u00e9)&nbsp;?<\/li>\n\n\n\n<li><strong>Noise sensitivity<\/strong> et <strong>response relevancy<\/strong>&nbsp;: robustesse face \u00e0 des passages distracteurs.<\/li>\n<\/ul>\n\n\n\n<p>Chaque m\u00e9trique est un score entre 0 et 1, calcul\u00e9 pour chaque question, agr\u00e9g\u00e9 pour l&rsquo;ensemble du jeu de tests. La combinaison faithfulness + context precision + answer relevancy suffit dans 80&nbsp;% des cas pour comparer deux versions d&rsquo;un pipeline.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Comment \u00e7a calcule, concr\u00e8tement<\/h3>\n\n\n\n<p>RAGAS s&rsquo;appuie sur un <strong>LLM juge<\/strong>&nbsp;: pour chaque question et chaque r\u00e9ponse, le framework appelle un mod\u00e8le (par d\u00e9faut OpenAI, mais on peut brancher Claude, un Llama local servi par vLLM, Mistral via LiteLLM, etc.) \u00e0 qui l&rsquo;on demande, par exemple, de d\u00e9composer la r\u00e9ponse en affirmations et de v\u00e9rifier laquelle est appuy\u00e9e par le contexte. Cette approche dite <em>LLM-as-a-judge<\/em> n&rsquo;est pas parfaite (il faut id\u00e9alement un mod\u00e8le juge plus puissant que le mod\u00e8le \u00e9valu\u00e9), mais elle a l&rsquo;avantage d&rsquo;\u00e9viter d&rsquo;avoir \u00e0 annoter manuellement des milliers d&rsquo;exemples. Pour des secteurs sensibles, on combine RAGAS avec un petit \u00e9chantillon not\u00e9 \u00e0 la main pour calibrer.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Mise en route en quelques lignes<\/h3>\n\n\n\n<p>L&rsquo;installation est triviale&nbsp;: <code>pip install ragas<\/code>. On constitue ensuite un jeu d&rsquo;\u00e9valuation au format Hugging Face Datasets ou simple liste de dictionnaires, avec quatre champs par exemple&nbsp;: <em>question<\/em>, <em>contexts<\/em> (les passages remont\u00e9s par le retriever), <em>answer<\/em> (r\u00e9ponse du syst\u00e8me), et optionnellement <em>ground_truth<\/em>.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from ragas import evaluate\nfrom ragas.metrics import faithfulness, answer_relevancy, context_precision\n\nresultats = evaluate(\n    dataset=mon_jeu_test,\n    metrics=&#91;faithfulness, answer_relevancy, context_precision],\n    llm=mon_juge,           # ex&nbsp;: Claude Sonnet via litellm\n    embeddings=mes_embeddings,\n)\nprint(resultats)<\/code><\/pre>\n\n\n\n<p>Le r\u00e9sultat est un DataFrame pandas que l&rsquo;on peut versionner, comparer entre deux runs, ou pousser dans un dashboard. RAGAS s&rsquo;int\u00e8gre nativement avec <strong><a href=\"https:\/\/askem.eu\/en\/2026\/04\/02\/langfuse-observer-et-evaluer-ses-pipelines-llm-open-source-en-production\/\" type=\"post\" id=\"2162\">Langfuse<\/a><\/strong>, ce qui permet d&rsquo;\u00e9valuer en continu sur des traces de production r\u00e9elles, et avec <strong>Hugging Face Datasets<\/strong> pour la constitution de jeux de tests r\u00e9utilisables.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Constituer son jeu de tests sans y passer un mois<\/h3>\n\n\n\n<p>La pierre d&rsquo;achoppement classique d&rsquo;un projet d&rsquo;\u00e9valuation, c&rsquo;est le jeu de questions. RAGAS propose un module de <strong>g\u00e9n\u00e9ration synth\u00e9tique<\/strong> qui parcourt un corpus, en extrait des questions plausibles \u00e0 diff\u00e9rents niveaux de difficult\u00e9 (simple, raisonnement, multi-contextes), et produit la v\u00e9rit\u00e9 de r\u00e9f\u00e9rence associ\u00e9e. On obtient en quelques heures un jeu de 100 \u00e0 500 questions exploitable, qu&rsquo;il reste \u00e0 filtrer manuellement pour \u00e9carter les questions mal form\u00e9es. Cette approche est imparfaite mais bien plus efficiente que d&rsquo;annoter \u00e0 froid, et permet de bootstrapper l&rsquo;\u00e9valuation d\u00e8s la phase POC.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">L&rsquo;ancrage c\u00f4t\u00e9 Askem&nbsp;: ce que \u00e7a change dans un projet RAG<\/h3>\n\n\n\n<p>RAGAS permet de transformer trois conversations difficiles en exercices objectivables&nbsp;:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>D\u00e9cider des arbitrages techniques<\/strong>&nbsp;: passer de pgvector \u00e0 Qdrant, ajouter un reranker BGE, passer de chunks de 512 \u00e0 1024 tokens \u2014 chaque hypoth\u00e8se devient un test mesurable, pas une intuition.<\/li>\n\n\n\n<li><strong>Choisir un mod\u00e8le<\/strong>&nbsp;: comparer Claude, Mistral, Llama et Qwen sur le m\u00eame jeu, le m\u00eame contexte, le m\u00eame prompt, et publier le tableau r\u00e9sultant. Le d\u00e9bat sort du registre des opinions.<\/li>\n\n\n\n<li><strong>Garantir la non-r\u00e9gression<\/strong>&nbsp;: int\u00e9grer RAGAS dans la CI au m\u00eame titre que les tests unitaires. Une mise \u00e0 jour de prompt qui d\u00e9grade la faithfulness de 5 points devient bloquante avant la mise en prod.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Limites et compl\u00e9ments<\/h3>\n\n\n\n<p>RAGAS reste un outil statistique&nbsp;: il \u00e9value un comportement moyen, pas la conformit\u00e9 ligne \u00e0 ligne. Pour des secteurs o\u00f9 chaque r\u00e9ponse doit \u00eatre trac\u00e9e et d\u00e9fendable, il faut le combiner avec des garde-fous d&rsquo;ex\u00e9cution (NeMo Guardrails, Guardrails AI), une journalisation compl\u00e8te (Langfuse), et une revue humaine cibl\u00e9e sur les cas \u00e0 faible faithfulness. \u00c0 noter aussi&nbsp;: <strong><a href=\"https:\/\/github.com\/confident-ai\/deepeval\">DeepEval<\/a><\/strong> et <strong><a href=\"https:\/\/github.com\/promptfoo\/promptfoo\">Promptfoo<\/a><\/strong> sont deux alternatives open source pertinentes \u00e0 conna\u00eetre, le premier proche de RAGAS dans l&rsquo;esprit, le second plus orient\u00e9 tests de prompts fa\u00e7on pytest.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ce qu&rsquo;il faut retenir<\/h3>\n\n\n\n<p>On ne pilote pas ce qu&rsquo;on ne mesure pas. RAGAS donne, en quelques lignes de Python et sans d\u00e9pendance cloud, les m\u00e9triques objectives qui transforment un pipeline RAG bricol\u00e9 en syst\u00e8me ma\u00eetris\u00e9. C&rsquo;est l&rsquo;un des outils \u00e0 int\u00e9grer d\u00e8s la phase POC, pas apr\u00e8s la mise en production.<\/p>","protected":false},"excerpt":{"rendered":"<p>RAGAS&nbsp;: mesurer objectivement la qualit\u00e9 d&rsquo;un pipeline RAG en open source Un pipeline RAG (Retrieval-Augmented Generation) bien con\u00e7u repose sur une dizaine de leviers&nbsp;: choix de l&#8217;embedding, taille des chunks, strat\u00e9gie de d\u00e9coupage, reranker, mod\u00e8le de g\u00e9n\u00e9ration, prompt, fen\u00eatre de contexte. Quand l&rsquo;un de ces leviers bouge, comment savoir si la qualit\u00e9 progresse vraiment, ou [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2321,"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-2320","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","entry","has-media"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>RAGAS : mesurer objectivement la qualit\u00e9 d&#039;un pipeline RAG en open source - askem<\/title>\n<meta name=\"description\" content=\"ASKEM BUREAU D&#039;\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. 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