RAG Performance & Fairness Evaluation Toolkit (OpenVINO + LangChain) #3114
+883
−0
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…. The toolkit computes standard metrics (BERT, BLEU, ROUGE, perplexity score) and a racial-bias indicator, and it is implemented using Optimum-Intel’s OVModelForCausalLM with the OpenVINO backend and LangChain for orchestration.
|
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This toolkit enables developers to build, evaluate, and optimize Retrieval-Augmented Generation (RAG) applications with comprehensive quality metrics including accuracy, bias detection, and perplexity analysis plus a racial-bias indicator. This uses RAG pipeline optimized with Intel OpenVINO for enhanced performance on CPU, GPU, and NPU. The pipeline leverages:
Optimum-Intel’s OVModelForCausalLM with the OpenVINO backend for efficient inference.
LangChain for orchestration of document loading, chunking, embedding, retrieval, reranking, and generation.
Goal: Provide a portable notebook-driven workflow for rapid experimentation, model comparison, and validation of RAG systems on custom/private corpora.