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.
In order to manage data in your dataset, create a corresponding dataset instance using the client.
Python
Ruby
Python
dataset = client.text('<DATASET_NAME>')
Ruby
dataset = client.text("<DATASET_NAME>")
Search
Python
Ruby
Python
result = dataset.search('query', 10)
​
for item in result:
    print(item.id, item.metadata)
Ruby
result = dataset.search("query", 10)
​
result.each do |item|
    puts item.id, item.metadata
end
Insert
Python
Ruby
Python
# Single item
id = dataset.insert(
    'London has 8.9 million inhabitants.',
    metadata={'key': 'value'}
)
# 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)
Ruby
id = dataset.insert(
    "London has 8.9 million inhabitants.",
    metadata={ "key": "value" }
)
result = dataset.insert_batch([
    {
        text: "London has 8.9 million inhabitants.",
        metadata: { "key": "value" }
    },
    ...
])
​
result.each { |r|
    puts r[:id], r[:error]
}
Update
Python
Ruby
Python
# Single item
id = dataset.update(
    id,
    'London has 9 million inhabitants.',
    metadata={'key': 'value'}
)
# 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)
Ruby
id = dataset.update(
    id,
    "London has 9 million inhabitants.",
    metadata={ "key": "value" }
)
result = dataset.update_batch([
    {
        id: id,
        text: "London has 9 million inhabitants.",
        metadata: { "key": "value" }
    },
    ...
])
​
result.each { |r|
    puts r[:id], r[:error]
}
Delete
Python
Ruby
Python
# Single item
dataset.delete(id)
# Batch
result = dataset.delete_batch([id, ...])
​
for r in result:
    print(r.id, r.error)
Ruby
dataset.delete(id)
result = dataset.delete_batch([id, ...])
​
result.each { |r|
    puts r[:id], r[:error]
}
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Semantic Search
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Vector
Last updated 2 months ago