---
license: apache-2.0
size_categories: n<1K
dataset_info:
  features:
  - name: page_content
    dtype: string
  - name: parent_section
    dtype: string
  - name: url
    dtype: string
  - name: token_count
    dtype: int64
  splits:
  - name: train
    num_bytes: 3445987.2
    num_examples: 1656
  - name: test
    num_bytes: 861496.8
    num_examples: 414
  download_size: 1607895
  dataset_size: 4307484.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for rag_qa_embedding_questions_0_60_0

This dataset has been created with [distilabel](https://distilabel.argilla.io/).

## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "anchor": "\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u251b\n\nExplore Service Connector TypesService Connector Types are not only templates used to instantiate Service Connectors, they also form a body of knowledge that documents best security practices and guides users through the complicated world of authentication and authorization.\n\nZenML ships with a handful of Service Connector Types that enable you right out-of-the-box to connect ZenML to cloud resources and services available from cloud providers such as AWS and GCP, as well as on-premise infrastructure. In addition to built-in Service Connector Types, ZenML can be easily extended with custom Service Connector implementations.\n\nTo discover the Connector Types available with your ZenML deployment, you can use the zenml service-connector list-types CLI command:\n\nzenml service-connector list-types\n\nExample Command Output\n\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u252f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\n\u2503             NAME             \u2502 TYPE          \u2502 RESOURCE TYPES        \u2502 AUTH METHODS      \u2502 LOCAL \u2502 REMOTE \u2503\n\n\u2520\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2528\n\n\u2503 Kubernetes Service Connector \u2502 \ud83c\udf00 kubernetes \u2502 \ud83c\udf00 kubernetes-cluster \u2502 password          \u2502 \u2705    \u2502 \u2705     \u2503\n\n\u2503                              \u2502               \u2502                       \u2502 token             \u2502       \u2502        \u2503\n\n\u2520\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2528\n\n\u2503   Docker Service Connector   \u2502 \ud83d\udc33 docker     \u2502 \ud83d\udc33 docker-registry    \u2502 password          \u2502 \u2705    \u2502 \u2705     \u2503\n\n\u2520\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2528\n\n\u2503   Azure Service Connector    \u2502 \ud83c\udde6 azure      \u2502 \ud83c\udde6 azure-generic      \u2502 implicit          \u2502 \u2705    \u2502 \u2705     \u2503\n\n\u2503                              \u2502               \u2502 \ud83d\udce6 blob-container     \u2502 service-principal \u2502       \u2502        \u2503",
    "distilabel_metadata": {
        "raw_output_generate_sentence_pair_0": "## Positive\n\nWhat command can be used to list available Service Connector Types in ZenML?\n\n## Negative\n\nCan you provide a list of popular Azure services?"
    },
    "model_name": "gpt-4o",
    "negative": "Can you provide a list of popular Azure services?",
    "parent_section": "how-to",
    "positive": "What command can be used to list available Service Connector Types in ZenML?",
    "token_count": 499,
    "url": "https://docs.zenml.io/v/docs/how-to/auth-management/service-connectors-guide"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("zenml/rag_qa_embedding_questions_0_60_0", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("zenml/rag_qa_embedding_questions_0_60_0")
```


</details>