> ## Documentation Index
> Fetch the complete documentation index at: https://docs.athina.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Loading data via Llama-Index

You can load your data for evals using llama-index

```python
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:

```python
pd.DataFrame(llama_index_dataset)
```

#### Output Format[](#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.

```python
class RagDataPoint(TypedDict):
    query: str
    context: List[str]
    response: str
    expected_response: str
```