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.
dataset = client.text('<DATASET_NAME>')
dataset = client.text("<DATASET_NAME>")
Search
result = dataset.search('query', 10)for item in result:print(item.id, item.metadata)
result = dataset.search("query",10)result.eachdo|item|puts item.id, item.metadataend
Insert
# Single itemid= dataset.insert('my sentence', metadata={'key': 'value'})
# Batchresult = dataset.insert_batch([ anndb_api.TextItem(None, 'my sentence', {'key': 'value'}), ...])for r in result:print(r.id, r.error)
id = dataset.insert("my sentence", metadata={ "key":"value" })