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

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

Ruby

Search

Python

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

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

Ruby

Insert

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

Update

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

Delete

Python

# Single item
dataset.delete(id)

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

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

Ruby