| Titre : |
Reliable, fully local RAG agents with LLaMA3 |
| Type de document : |
document électronique |
| Auteurs : |
Ouassim Abdelmalek Ghribi, Auteur ; Oussama Arki, Directeur de thèse ; Hachem Betrouni, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2024 |
| Importance : |
1 fichier PDF (9 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet
Mémoire de Projet de Fin d’Etudes : Génie Industriel. Data Science-Intelligence Artificielle : Alger, Ecole Nationale Polytechnique : 2024
Bibliogr. p. 81 - 86 . - Annexe p. 87 - 89 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Natural language processing
Information retrieval
Information generation
Llama3
Complex queries |
| Index. décimale : |
PI01924 |
| Résumé : |
Recent advancements in natural language processing have highlighted the need for systems that can effectively retrieve and generate information to handle increasingly complex queries. Combining retrieval and generation processes addresses the limitations of each approach individually, leading to more comprehensive and accurate responses. This thesis presents the implementation of a Retrieval-Augmented Generation (RAG) agent utilizing Llama3 to enhance the accuracy and relevance of responses in complex query environments. The primary challenge is integrating effective information retrieval with advanced generative capabilities to provide precise and reliable answers. Our approach combines document retrieval, grading, and generation within a cohesive system. Queries are assessed for relevance, retrieving pertinent documents or conducting web searches as needed. The generated answers are rigorously evaluated to ensure they meet high standards of accuracy. This implementation demonstrates the potential of merging sophisticated retrieval mechanisms with powerful generative models, resulting in significant improvements in response quality and reliability. |
Reliable, fully local RAG agents with LLaMA3 [document électronique] / Ouassim Abdelmalek Ghribi, Auteur ; Oussama Arki, Directeur de thèse ; Hachem Betrouni, Directeur de thèse . - [S.l.] : [s.n.], 2024 . - 1 fichier PDF (9 Mo) : ill. Mode d'accès : accès au texte intégral par intranet
Mémoire de Projet de Fin d’Etudes : Génie Industriel. Data Science-Intelligence Artificielle : Alger, Ecole Nationale Polytechnique : 2024
Bibliogr. p. 81 - 86 . - Annexe p. 87 - 89 Langues : Anglais ( eng)
| Mots-clés : |
Natural language processing
Information retrieval
Information generation
Llama3
Complex queries |
| Index. décimale : |
PI01924 |
| Résumé : |
Recent advancements in natural language processing have highlighted the need for systems that can effectively retrieve and generate information to handle increasingly complex queries. Combining retrieval and generation processes addresses the limitations of each approach individually, leading to more comprehensive and accurate responses. This thesis presents the implementation of a Retrieval-Augmented Generation (RAG) agent utilizing Llama3 to enhance the accuracy and relevance of responses in complex query environments. The primary challenge is integrating effective information retrieval with advanced generative capabilities to provide precise and reliable answers. Our approach combines document retrieval, grading, and generation within a cohesive system. Queries are assessed for relevance, retrieving pertinent documents or conducting web searches as needed. The generated answers are rigorously evaluated to ensure they meet high standards of accuracy. This implementation demonstrates the potential of merging sophisticated retrieval mechanisms with powerful generative models, resulting in significant improvements in response quality and reliability. |
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