AnnDB
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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.
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Ruby
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dataset = client.text('<DATASET_NAME>')
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dataset = client.text("<DATASET_NAME>")
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Search
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Ruby
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result = dataset.search('query', 10)
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for item in result:
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print(item.id, item.metadata)
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result = dataset.search("query", 10)
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result.each do |item|
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puts item.id, item.metadata
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end
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Insert
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Ruby
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# Single item
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id = dataset.insert(
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'London has 8.9 million inhabitants.',
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metadata={'key': 'value'}
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)
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# Batch
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result = dataset.insert_batch([
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anndb_api.TextItem(
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None,
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'London has 8.9 million inhabitants.',
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{'key': 'value'}
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),
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...
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])
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for r in result:
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print(r.id, r.error)
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id = dataset.insert(
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"London has 8.9 million inhabitants.",
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metadata={ "key": "value" }
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)
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result = dataset.insert_batch([
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{
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text: "London has 8.9 million inhabitants.",
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metadata: { "key": "value" }
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},
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...
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])
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result.each { |r|
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puts r[:id], r[:error]
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}
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Update
Python
Ruby
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# Single item
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id = dataset.update(
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id,
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'London has 9 million inhabitants.',
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metadata={'key': 'value'}
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)
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# Batch
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result = dataset.update_batch([
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anndb_api.TextItem(
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id,
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'London has 9 million inhabitants.',
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{'key': 'value'}
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),
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...
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])
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for r in result:
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print(r.id, r.error)
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id = dataset.update(
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id,
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"London has 9 million inhabitants.",
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metadata={ "key": "value" }
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)
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result = dataset.update_batch([
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{
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id: id,
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text: "London has 9 million inhabitants.",
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metadata: { "key": "value" }
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},
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...
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])
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result.each { |r|
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puts r[:id], r[:error]
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}
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Delete
Python
Ruby
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# Single item
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dataset.delete(id)
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# Batch
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result = dataset.delete_batch([id, ...])
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for r in result:
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print(r.id, r.error)
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dataset.delete(id)
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result = dataset.delete_batch([id, ...])
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result.each { |r|
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puts r[:id], r[:error]
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}
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Last modified 7mo ago
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