{"id":18982,"date":"2024-04-10T14:28:56","date_gmt":"2024-04-10T12:28:56","guid":{"rendered":"https:\/\/kairntech.com\/blog\/non-categorise\/kairntech-rag-pour-la-criminalistique\/"},"modified":"2026-04-17T00:08:14","modified_gmt":"2026-04-16T22:08:14","slug":"kairntech-rag-pour-la-criminalistique","status":"publish","type":"post","link":"https:\/\/kairntech.com\/fr\/blog\/articles-fr\/kairntech-rag-pour-la-criminalistique\/","title":{"rendered":"Kairntech RAG pour la criminalistique"},"content":{"rendered":"\n

Analyse des donn\u00e9es des courriels d’Enron <\/h2>\n\n

Kairntech RAG (Retrieval Augmented Generation) pour la criminalistique est l’un des cas d’utilisation les plus r\u00e9cents des grands mod\u00e8les de langage. Il permet d’analyser des informations internes tout en b\u00e9n\u00e9ficiant de la puissance des LLM. Et tout cela sans avoir \u00e0 partager les donn\u00e9es avec des tiers ou \u00e0 mener des campagnes co\u00fbteuses de recyclage ou de mise au point. <\/p>\n\n

Kairntech a mis en \u0153uvre RAG dans le cadre de sa solution. Ici, nous d\u00e9crivons bri\u00e8vement un cas d’utilisation autour de l’analyse d’un grand nombre de documents \u00e9lectroniques. Nous utilisons le corpus Enron Mail<\/a>: une vaste collection de courriels qui ont \u00e9t\u00e9 rendus publics apr\u00e8s l’affaire de fraude<\/a> Enron de 2001. <\/p>\n\n

Kairntech RAG pour la criminalistique – t\u00e9l\u00e9chargement de documents<\/h2>\n\n

Les documents peuvent \u00eatre t\u00e9l\u00e9charg\u00e9s dans Kairntech \u00e0 partir d’une vari\u00e9t\u00e9 de formats tels que PDF, HTML, DOC et bien d’autres. Dans ce cas, nous voulons nous assurer que nous conservons les riches m\u00e9tadonn\u00e9es qui accompagnent les documents envoy\u00e9s par courrier \u00e9lectronique. Qui a envoy\u00e9 quoi \u00e0 qui, quand et avec quel sujet, etc. Pour cela, nous traduisons les documents \u00e9lectroniques au format json de Kairntech : <\/p>\n\n

{\n    \"metadata\": {\n      \"Source\": \"maildir\/allen-p\/all_documents\/100.\",\n      \"Date\": \"Mon, 9 Oct 2000 07:16:00 -0700 (PDT)\",\n      \"From\": \"phillip.allen@enron.com\",\n      \"To\": [\"keith.holst@enron.com\"],\n      \"Subject\": \"Consolidated positions: Issues\"\n    },\n    \"text\": \" \u2026 Below is the issues & to do list as we go forward with documenting the requirements for consolidated physical\/financial positions and transport trade capture. What we need to focus on is the first bullet in Allan's list \u2026\u201d\n}<\/code><\/pre>\n\n
Les documents contiennent, outre un \u00e9l\u00e9ment \u00ab\u00a0texte\u00a0\u00bb, un champ \u00ab\u00a0m\u00e9tadonn\u00e9es\u00a0\u00bb dans lequel nous pouvons stocker les informations pertinentes relatives \u00e0 ce courrier \u00e9lectronique sp\u00e9cifique. Le fait d’avoir les m\u00e9tadonn\u00e9es \u00e0 c\u00f4t\u00e9 du texte deviendra important pour l’analyse m\u00e9dico-l\u00e9gale. <\/p>\n\n
Apr\u00e8s l’importation des documents, il est temps de les analyser en posant des questions en langage naturel.<\/p>\n\n
Parlez aux documents en langage naturel<\/h2>\n\n
RAG effectue une analyse s\u00e9mantique de la question et la compare aux r\u00e9sultats de l’analyse s\u00e9mantique pr\u00e9alable des documents import\u00e9s. Il s\u00e9lectionne ensuite ceux qui contiennent des informations pertinentes pour r\u00e9pondre \u00e0 la question. Le sous-ensemble de correspondances extraites est ensuite r\u00e9sum\u00e9 par un LLM pour fournir une r\u00e9ponse finale.  <\/p>\n\n
\"\"<\/figure>\n\n
Souvent, une r\u00e9ponse RAG renvoie des informations condens\u00e9es qu’un expert humain mettrait des heures, voire des jours, \u00e0 obtenir en utilisant uniquement les m\u00e9thodes de recherche textuelle traditionnelles.<\/p>\n\n
Int\u00e9grer des mod\u00e8les<\/h2>\n\n
Les utilisateurs peuvent acc\u00e9der \u00e0 un certain nombre d’options pour affiner le processus de recherche et de g\u00e9n\u00e9ration de r\u00e9ponses : Par exemple, diff\u00e9rents LLM peuvent diff\u00e9rer par leur qualit\u00e9, leur comportement en cours d’ex\u00e9cution ou leur prix par requ\u00eate. Dans la configuration ci-dessous, un utilisateur peut choisir le LLM \u00e0 utiliser : GTP3.5, le GPT4, plus r\u00e9cent, plus puissant et plus co\u00fbteux, ou le mod\u00e8le Dolphin de Mixtral, moins co\u00fbteux mais comp\u00e9titif. <\/p>\n\n
\"\"<\/figure>\n\n
Texte et m\u00e9tadonn\u00e9es : une combinaison gagnante<\/h2>\n\n
Comme nous avons enregistr\u00e9 les m\u00e9tadonn\u00e9es des courriers \u00e9lectroniques, nous pouvons les utiliser pour restreindre l’ensemble des documents pour notre prochaine question \u00e0 l’ensemble des documents dont nous avons besoin. Voici la question : Quels \u00e9taient les sujets abord\u00e9s dans les courriers \u00e9lectroniques envoy\u00e9s par Ann Schmidt \u00e0 Karen Denne ? <\/p>\n\n
\"\"<\/figure>\n\n
Savoir o\u00f9 chercher<\/h2>\n\n
Comme nous disposons des m\u00e9tadonn\u00e9es pour chaque document, nous pouvons \u00e9galement les combiner avec le contenu du courrier. Cela permet de poser des questions qui doivent \u00e9tudier \u00e0 la fois le contenu et les m\u00e9tadonn\u00e9es afin que le LLM puisse y r\u00e9pondre correctement. Par exemple, dans la question ci-dessous, le LLM \u00ab\u00a0comprend\u00a0\u00bb que les \u00ab\u00a0courriers envoy\u00e9s par Michelle Cash\u00a0\u00bb n\u00e9cessitent de rechercher des documents contenant Michelle Cash dans le champ \u00ab\u00a0From :\u00a0\u00bb.  <\/p>\n\n
\"\"<\/figure>\n\n
Cliquez ici pour<\/a> voir comment faire en sorte que le RAG prenne en compte le texte et les m\u00e9tadonn\u00e9es.<\/p>\n\n
<\/div>\n","protected":false},"excerpt":{"rendered":"
Analyse des donn\u00e9es des courriels d’Enron Kairntech RAG (Retrieval Augmented Generation) pour la criminalistique est l’un des cas d’utilisation les plus r\u00e9cents des grands mod\u00e8les de langage. Il permet d’analyser des informations internes tout en b\u00e9n\u00e9ficiant de la puissance des LLM. Et tout cela sans avoir \u00e0 partager les donn\u00e9es avec des tiers ou \u00e0 […]<\/p>\n","protected":false},"author":8,"featured_media":18984,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[479],"tags":[],"class_list":["post-18982","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles-fr"],"yoast_head":"\nRAG pour la criminalistique<\/title>\n<meta name=\"description\" content=\"Kairntech RAG pour la criminalistique : exploitez la puissance des LLM sur vos documents internes. 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