A framework for enhancing LLMs for RAG use-cases by enabling users to create data-augmented datasets for tuning and evaluation of LLMs, using RAG workflows.
RAG Foundry is a library designed to improve LLMs ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library helps create the data for training, given a RAG technique, helps easily train models using parameter-efficient finetuning (PEFT), and finally can help users measure the improved performance using various, RAG-specific metrics. The library is modular, workflows are customizable using configuration files.
Clone locally and run:
pip install -r requirements.txtThe RAG Foundry framework facilitates fast prototyping and experimentation with various RAG settings and configurations, including data selection and filtering, processing, retrieval, ranking, query manipulation, prompt generation, training, inference, output processing and evaluation. The library is comprised of 4 modules: dataset creation, training, inference and evaluation.
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Dataset Creation: The processing module creates datasets, persisting RAG interactions, to be used for RAG training and inference. RAG interactions include dataset loading, columns normalization, data aggregation (fewshot creation), information retrieval using external tools and frameworks, API integration, template-based prompt creation and any other form of pre-processing. The data is saved in a consistent, model-independent, input-output format, along with all other fields and metadata. See Processing.md.
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Training: using PEFT for efficient training and TRL (e.g. supervised FT) users can train any model on the augmented datasets. Training is done on the completions. Models can be pushed to HF Hub. See Training.md.
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Inference: generating predictions using the augmented datasets with trained or untrained LLMs. See Inference.md.
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Evaluation: running evaluation on the generated output from the inference module. Users can provide a list of metrics to run; custom metrics can be implemented easily. Current metrics include EM, F1, ROUGE, BERTScore, Deepeval, RAGAS, HF
evaluateand classification. Metrics can be local—run on each example, or global—run on the entire dataset, e.g. recall. Metrics can utilize any feature in the dataset, like retrieval results, reasoning, citations and attributions, not just the input and output texts. See Evaluation.md.
The 4 modules are represented as scripts: processing.py, training.py, inference.py and evaluation.py at the top
level. Every call has the form python SCRIPT options....
The library utilizes the Hydra configuration tool; it enables the use of hierarchical
configurations, easily overridden of values in the CLI and the ability to run multiple jobs remotely (e.g. integrations with
SLURM and Ray). It represents a configuration-as-code approach, as it can instantiate python classes according to
configuration (the _target_ keyword indicates the python class to use in a given context).
There are default configurations for each module in the configs folder. A configuration file can be overridden like so:
python processing -cp configs/paper -cn processing-asqa-retrievalIndividual keywords can be overridden as well:
python processing -cp configs/paper -cn processing-asqa-retrieval \
output_path=/store/data/here \
cache=trueFor a complete set of configurations, reproducing the experimentation in the paper with the ASQA dataset, see the configurations in the Paper folder.
The code is licensed under the Apache 2.0 License.
This is not an official Intel product.