Evaluating With `LabelledRagDataset`’s#

We have already gone through the core abstractions within the Evaluation module that enable various kinds of evaluation methodologies of LLM-based applications or systems, including RAG systems. Of course, to evaluate the system one needs an evaluation method, the system itself, as well as evaluation datasets. It is considered best practice to test the LLM application on several distinct datasets emanating from different sources and domains. Doing so helps to ensure the overall robustness (that is, the level in which the system will work in unseen, new cases) of the system.

To this end, we’ve included the LabelledRagDataset abstraction in our library. Their core purpose is to facilitate the evaluations of systems on various datasets, by making these easy to create, easy to use, and widely available.

This dataset consists of examples, where an example carries a query, a reference_answer, as well as reference_contexts. The main reason for using a LabelledRagDataset is to test a RAG system’s performance by first predicting a response to the given query and then comparing that predicted (or generated) response to the reference_answer.

from llama_index.llama_dataset import (
    LabelledRagDataset,
    CreatedBy,
    CreatedByType,
    LabelledRagDataExample,
)

example1 = LabelledRagDataExample(
    query="This is some user query.",
    query_by=CreatedBy(type=CreatedByType.HUMAN),
    reference_answer="This is a reference answer. Otherwise known as ground-truth answer.",
    reference_contexts=[
        "This is a list",
        "of contexts used to",
        "generate the reference_answer",
    ],
    reference_by=CreatedBy(type=CreateByType.HUMAN),
)

# a sad dataset consisting of one measely example
rag_dataset = LabelledRagDataset(examples=[example1])

Building A `LabelledRagDataset`#

As we just saw at the end of the previous section, we can build a LabelledRagDataset manually by constructing LabelledRagDataExample’s one by one. However, this is a bit tedious, and while human-annoted datasets are extremely valuable, datasets that are generated by strong LLMs are also very useful.

As such, the llama_dataset module is equipped with the RagDatasetGenerator that is able to generate a LabelledRagDataset over a set of source Document’s.

from llama_index.llama_dataset.generator import RagDatasetGenerator
from llama_index import ServiceContext
from llama_index.llm import OpenAI
import nest_asyncio

nest_asyncio.apply()

documents = ...  # a set of documents loaded by using for example a Reader

service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-4"))

dataset_generator = RagDatasetGenerator.from_documents(
    documents=documents,
    service_context=service_context,
    num_questions_per_chunk=10,  # set the number of questions per nodes
)

rag_dataset = dataset_generator.generate_dataset_from_nodes()

Using A `LabelledRagDataset`#

As mentioned before, we want to use a LabelledRagDataset to evaluate a RAG system, built on the same source Document’s, performance with it. Doing so would require performing two steps: (1) making predictions on the dataset (i.e. generating responses to the query of each individual example), and (2) evaluating the predicted response by comparing it to the reference answer. In step (2) we also evaluate the RAG system’s retrieved contexts and compare it to the reference contexts, to gain an assessment on the retrieval component of the RAG system.

For convenience, we have a LlamaPack called the RagEvaluatorPack that streamlines this evaluation process!

from llama_index.llama_pack import download_llama_pack

RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack")

rag_evaluator = RagEvaluatorPack(
    query_engine=query_engine,  # built with the same source Documents as the rag_dataset
    rag_dataset=rag_dataset,
)
benchmark_df = await rag_evaluator.run()

The above benchmark_df contains the mean scores for evaluation measures introduced previously: Correctness, Relevancy, Faithfulness as well as Context Similarity that measures the semantic similarity between the reference contexts as well as the contexts retrieved by the RAG system to generated the predicted response.

Where To Find `LabelledRagDataset`’s#

You can find all of the LabelledRagDataset’s in llamahub. You can browse each one of these and decide if you do decide that you’d like to use it to benchmark your RAG pipeline, then you can download the dataset as well as the source Document’s conveniently thru one of two ways: the llamaindex-cli or through Python code using the download_llama_dataset utility function.

# using cli
llamaindex-cli download-llamadataset PaulGrahamEssayDataset --download-dir ./data

# using python
from llama_index.llama_dataset import download_llama_dataset

# a LabelledRagDataset and a list of source Document's
rag_dataset, documents = download_llama_dataset(
    "PaulGrahamEssayDataset", "./data"
)

Contributing A `LabelledRagDataset`#

You can also contribute a LabelledRagDataset to llamahub. Contributing a LabelledRagDataset involves two high level steps. Generally speaking, you must create the LabelledRagDataset, save it as a json and submit both this json file and the source text files to our llama_datasets Github repository. Additionally, you’ll have to make a pull request, to upload required metadata of the dataset to our llama_hub Github repository.

Please refer to the “LlamaDataset Submission Template Notebook” linked below.

Now, Go And Build Robust LLM Applications#

This page hopefully has served as a good starting point for you to create, download and use LlamaDataset’s for building robust and performant LLM Applications. To learn more, we recommend reading the notebook guides provided below.

Evaluating With LabelledRagDataset’s#

Building A LabelledRagDataset#

Using A LabelledRagDataset#

Where To Find LabelledRagDataset’s#

Contributing A LabelledRagDataset#