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.

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

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

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

Ruby

Update

Python

# Single item
id = dataset.update(id, 'my updated sentence', metadata={'key': 'value'})

# Batch
result = dataset.update_batch([
    anndb_api.TextItem(id, 'my updated sentence', {'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