# Question Answering

Question answering datasets provide an out-of-the-box solution for a machine learning powered search engine. AnnDB uses state-of-the-art machine learning models to provide highly relevant search results. Using the API, you can build a dataset of facts that you can later query using natural language queries.

### Create a Dataset

Create a dataset with `Question Answering` type which tells AnnDB to encode your facts and queries to vectors.

![](https://1514770980-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MBjJDID-9E2lsbXx9on%2F-MYkDju8UK5m8Rywh23Q%2F-MYkDtqLyiHJZ4buhnc5%2Fimage.png?alt=media\&token=eef34c28-e0f1-4392-a29f-4dc0520f7e25)

In order to manage data in your dataset, create a corresponding dataset instance using the client.

{% tabs %}
{% tab title="Python" %}

```python
dataset = client.text('<DATASET_NAME>')
```

{% endtab %}

{% tab title="Ruby" %}

```ruby
dataset = client.text("<DATASET_NAME>")
```

{% endtab %}
{% endtabs %}

### Search

{% tabs %}
{% tab title="Python" %}

```python
result = dataset.search('query', 10)

for item in result:
    print(item.id, item.metadata)
```

{% endtab %}

{% tab title="Ruby" %}

```ruby
result = dataset.search("query", 10)

result.each do |item|
    puts item.id, item.metadata
end
```

{% endtab %}
{% endtabs %}

### Insert

{% tabs %}
{% tab title="Python" %}

```python
# Single item
id = dataset.insert(
    'London has 8.9 million inhabitants.',
    metadata={'key': 'value'}
)
```

```python
# Batch
result = dataset.insert_batch([
    anndb_api.TextItem(
        None,
        'London has 8.9 million inhabitants.',
        {'key': 'value'}
    ),
    ...
])

for r in result:
    print(r.id, r.error)
```

{% endtab %}

{% tab title="Ruby" %}

```ruby
id = dataset.insert(
    "London has 8.9 million inhabitants.",
    metadata={ "key": "value" }
)
```

```ruby
result = dataset.insert_batch([
    {
        text: "London has 8.9 million inhabitants.",
        metadata: { "key": "value" }
    },
    ...
])

result.each { |r|
    puts r[:id], r[:error]
}
```

{% endtab %}
{% endtabs %}

### Update

{% tabs %}
{% tab title="Python" %}

```python
# Single item
id = dataset.update(
    id,
    'London has 9 million inhabitants.',
    metadata={'key': 'value'}
)
```

```python
# Batch
result = dataset.update_batch([
    anndb_api.TextItem(
        id,
        'London has 9 million inhabitants.',
        {'key': 'value'}
    ),
    ...
])

for r in result:
    print(r.id, r.error)
```

{% endtab %}

{% tab title="Ruby" %}

```ruby
id = dataset.update(
    id,
    "London has 9 million inhabitants.",
    metadata={ "key": "value" }
)
```

```ruby
result = dataset.update_batch([
    {
        id: id,
        text: "London has 9 million inhabitants.",
        metadata: { "key": "value" }
    },
    ...
])

result.each { |r|
    puts r[:id], r[:error]
}
```

{% endtab %}
{% endtabs %}

### Delete

{% tabs %}
{% tab title="Python" %}

```python
# Single item
dataset.delete(id)
```

```python
# Batch
result = dataset.delete_batch([id, ...])

for r in result:
    print(r.id, r.error)
```

{% endtab %}

{% tab title="Ruby" %}

```ruby
dataset.delete(id)
```

```ruby
result = dataset.delete_batch([id, ...])

result.each { |r|
    puts r[:id], r[:error]
}
```

{% endtab %}
{% endtabs %}