RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented
  Generation
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.
66b1dda98b1fc211c927e850
Daniel Fleischer
claimed_verified
2024-08-06T08:53:56.279Z
62d93cd728f9c86a4031562e
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Daniel Fleischer
user
danf
66b1dda98b1fc211c927e851
Moshe Berchansky
admin_assigned
2024-08-06T13:17:11.172Z
63e0c8875c6964861ebb0c49
https://cdn-avatars.huggingface.co/v1/production/uploads/63e0c8875c6964861ebb0c49/yzkhPSxgXtJCM62iMBOOK.jpeg
Moshe Berchansky
user
mber
66b1dda98b1fc211c927e852
Moshe Wasserblat
admin_assigned
2024-08-06T13:17:18.216Z
6013c64031d84a3bb7650038
/avatars/e05ca715004b39e79472399f75010bda.svg
Moshe Wasserblat
user
moshew
66b1dda98b1fc211c927e853
Peter Izsak
admin_assigned
2024-08-06T13:17:24.275Z
605714b1861b9d53d9c4b8db
https://cdn-avatars.huggingface.co/v1/production/uploads/605714b1861b9d53d9c4b8db/5kpnfrvnIKRBjmQsMJKk-.jpeg
Peter Izsak
user
peterizsak
RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented
  Generation
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.
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.
Retrieval-Augmented Generation (RAG) data creation training inference evaluation RAG Foundry data-augmented datasets Llama-3 Phi-3 knowledge-intensive datasets