You can load your data for evals using llama-index 
from athina.loaders import Loader
import pandas as pd

from llama_index import VectorStoreIndex, ServiceContext
from llama_index import download_loader

# create a llamaindex query engine
WikipediaReader = download_loader("WikipediaReader")
loader = WikipediaReader()
documents = loader.load_data(pages=['Berlin'])
vector_index = VectorStoreIndex.from_documents(
    documents, service_context=ServiceContext.from_defaults(chunk_size=512)
)

query_engine = vector_index.as_query_engine()

raw_data_llama_index = [
    {
        "query": "Where is Berlin?",
        "expected_response": "Berlin is the capital city of Germany"
    },
    {
        "query": "What is the main cuisine of Rome?",
        "expected_response": "Pasta dish with a sauce made with egg yolks"
    },
]

llama_index_dataset = Loader().load_from_llama_index(raw_data_llama_index, query_engine)
That’s all you need to do to load your data! To view the imported dataset as a pandas DataFrame: 
pd.DataFrame(llama_index_dataset)

​
Output Format

The output format will be different for different Loaders. The Loader will return a List[DataPoint] type after you call the load function of choice. 
class RagDataPoint(TypedDict):
    query: str
    context: List[str]
    response: str
    expected_response: str