# Semantic Search

Semantic textual similarity datasets allow you to go beyond traditional keyword and exact-match search. AnnDB uses state-of-the-art machine learning models to provide accurate and semantically relevant results. Using the API, you can build a dataset of sentences which you can then query using natural language queries to find semantically similar information.

### Create a Dataset

Create a dataset with `Semantic Similarity` type which tells AnnDB to encode your sentences to vectors.

![](/files/-MYkDYR7niLJMu3Vjchb)

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('my sentence', metadata={'key': 'value'})
```

```python
# Batch
result = dataset.insert_batch([
    anndb_api.TextItem(None, 'my sentence', {'key': 'value'}),
    ...
])

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

{% endtab %}

{% tab title="Ruby" %}

```ruby
id = dataset.insert("my sentence", metadata={ "key": "value" })
```

```ruby
result = dataset.insert_batch([
    {
        text: "my sentence",
        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, 'my updated sentence', metadata={'key': 'value'})
```

```python
# Batch
result = dataset.update_batch([
    anndb_api.TextItem(id, 'my updated sentence', {'key': 'value'}),
    ...
])

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

{% endtab %}

{% tab title="Ruby" %}

```ruby
id = dataset.update(id, "my updated sentence", metadata={ "key": "value" })
```

```ruby
result = dataset.update_batch([
    {
        id: id,
        text: "my updated sentence",
        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 %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.anndb.com/datasets/semantic-search.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.