Show HN: Tonic Validate Metrics – an open-source RAG evaluation metrics package

It's true that there are not a lot of datasets for benchmarking RAG. RAG applications are so tailored to the specific data being used as well as the use case, that a benchmark dataset is not useful across different RAG applications. The data used for a RAG application could be slack messages, technical documentation, insurance policies, internal company microsoft word documents, or a combination of these. For each of these different data source examples, the benchmark dataset would need to be very different.

We recommend that when building a RAG application, the developers build a benchmark dataset specifically tailored to the data being used for the RAG application, and the use case of the RAG application.

tvalmetrics is similar to ragas for sure, and we really like ragas. tvalmetrics has structural differences as well as differences in the specific metrics when compared to ragas.

With regards to the metrics, we have an end to end metric that scores how well your RAG response matches a reference correct response called answer similarity score. Last I saw, ragas did not have a score like this as they focus on scoring RAG responses and context without a reference correct answer. We also have a retrieval k-recall score that involves comparing the relevance of the retrieved context to the relevance of the top k context chunks where k is larger than the number of retrieved context chunks. Retrieval k-recall is a good score for tuning how many context chunks your RAG system should retrieve. I do not believe ragas has a score like this.

Structurally, tvalmetrics does not use langchain, while ragas does use langchain. We chose not to use langchain for our LLM API calls to keep the code in the package clear and make it easy for a user to understand exactly how the LLM-assisted evaluation works for each metric. Of course, the drawback of not using langchain means that our package is not integrated with as many LLMs. Currently, we only support using Open AI chat completion models as LLM evaluators to calcaulate the metrics. We plan on adding support for additional LLM evaluators very soon.

no questions - just want to say thanks for sharing!

Using LLMs to evaluate other LLMs sounds like a it would be dumb, but LLMs work in mysterious ways. I’ve found this approach useful though. In the context of RAG, using an LLM to evaluate whether a context chunk is relevant to answer a question is a nice complement to using the vector embedding semantic similarity search. Sometimes prompting the LLM gives better results than vector similarity.

Joe here. It's difficult to evaluate natural language responses that come from LLM applications - there are not hard metrics to measure performance like there are in say supervised machine learning tasks. For RAG, you have the response to evaluate as well as the retrieved context chunks. We found that using gpt-4 as an evaluator to measure the quality of RAG responses and the relevance of the context chunks gave similar results to using human evaluators at Tonic to do the same task. Some research also agrees that using LLMs as an evaluator for natural language tasks gives similar results to using human evaluators https://arxiv.org/abs/2306.05685.

As far as whether using gpt-4 is a safe approach, the best you could ask for is that gpt-4's evaluations match those of human evaluators, and that's what we've found as well as this research.

Right now, we are planning on adding more metrics in the future. We also have considered adding the ability of using vector embeddings instead of LLMs for analyzing the responses (which should bring some efficiency gains). Plus, we might add the ability to analyze the distribution of vectors in your vector db to compare it to the distribution of vectors for user queries.

We also have a UI for visualizing all of these metrics that is free at the moment with no paid tier at all. We are planning to add more features into that UI for better visualizations along with improving the ability to keep track of different versions of your LLM system for comparison.

Joe here. This is a good question, and always a challenge when using a test set to measure the performance of an LLM/AI/ML application. The answer is to make the test dataset consist of questions that come from users of your app - this is the best way to get a representative sample of questions to test your RAG application with. If you do this though, your test dataset will consist of questions withouth reference "correct" answers. In this case, you can still use tvalmetrics to evaluate the responses by using the metrics in tvalmetrics that do rely on reference "correct" answers.

tvalmetrics introduces 6 RAG metrics: answer similarity, retrieval precision, augmentation precision, augmentation accuracy, answer consistency, and retrieval k-recall. Of these 6 metrics, only answer similarity requires reference answers, so you can use the other metrics to measure the performance of your RAG system when you have a test dataset of questions without reference "correct" answers.