title: RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented
    Generation
summary: Implementing Retrieval-Augmented Generation (RAG) systems is inherently
  complex, requiring deep understanding of data, use cases, and intricate design
  decisions. Additionally, evaluating these systems presents significant
  challenges, necessitating assessment of both retrieval accuracy and generative
  quality through a multi-faceted approach. We introduce RAG Foundry, an
  open-source framework for augmenting large language models for RAG use cases.
  RAG Foundry integrates data creation, training, inference and evaluation into a
  single workflow, facilitating the creation of data-augmented datasets for
  training and evaluating large language models in RAG settings. This integration
  enables rapid prototyping and experimentation with various RAG techniques,
  allowing users to easily generate datasets and train RAG models using internal
  or specialized knowledge sources. We demonstrate the framework effectiveness by
  augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG
  configurations, showcasing consistent improvements across three
  knowledge-intensive datasets. Code is released as open-source in
  https://github.com/IntelLabs/RAGFoundry.
paper_authors._id: 66b1dda98b1fc211c927e850
paper_authors.name: Daniel Fleischer
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-06T08:53:56.279Z
paper_authors._id: 62d93cd728f9c86a4031562e
paper_authors.avatarUrl: /avatars/4619930d15512ec9b80b01c62e986217.svg
paper_authors.fullname: Daniel Fleischer
paper_authors.type: user
paper_authors.user: danf
paper_authors._id: 66b1dda98b1fc211c927e851
paper_authors.name: Moshe Berchansky
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T13:17:11.172Z
paper_authors._id: 63e0c8875c6964861ebb0c49
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paper_authors.fullname: Moshe Berchansky
paper_authors.type: user
paper_authors.user: mber
paper_authors._id: 66b1dda98b1fc211c927e852
paper_authors.name: Moshe Wasserblat
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T13:17:18.216Z
paper_authors._id: 6013c64031d84a3bb7650038
paper_authors.avatarUrl: /avatars/e05ca715004b39e79472399f75010bda.svg
paper_authors.fullname: Moshe Wasserblat
paper_authors.type: user
paper_authors.user: moshew
paper_authors._id: 66b1dda98b1fc211c927e853
paper_authors.name: Peter Izsak
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T13:17:24.275Z
paper_authors._id: 605714b1861b9d53d9c4b8db
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paper_authors.fullname: Peter Izsak
paper_authors.type: user
paper_authors.user: peterizsak
paper_title: RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented
    Generation
paper_summary: Implementing Retrieval-Augmented Generation (RAG) systems is inherently
  complex, requiring deep understanding of data, use cases, and intricate design
  decisions. Additionally, evaluating these systems presents significant
  challenges, necessitating assessment of both retrieval accuracy and generative
  quality through a multi-faceted approach. We introduce RAG Foundry, an
  open-source framework for augmenting large language models for RAG use cases.
  RAG Foundry integrates data creation, training, inference and evaluation into a
  single workflow, facilitating the creation of data-augmented datasets for
  training and evaluating large language models in RAG settings. This integration
  enables rapid prototyping and experimentation with various RAG techniques,
  allowing users to easily generate datasets and train RAG models using internal
  or specialized knowledge sources. We demonstrate the framework effectiveness by
  augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG
  configurations, showcasing consistent improvements across three
  knowledge-intensive datasets. Code is released as open-source in
  https://github.com/IntelLabs/RAGFoundry.
paper_ai_summary: RAG Foundry is an open-source framework integrating data creation, training, inference, and evaluation to facilitate the augmentation of large language models for RAG use cases, demonstrating improved performance across diverse datasets.
paper_ai_keywords: Retrieval-Augmented Generation (RAG) data creation training inference evaluation RAG Foundry data-augmented datasets Llama-3 Phi-3 knowledge-intensive datasets