superduper_ibis

Superduper ibis is a plugin for ibis-framework that allows you to use Superduper as a backend for your ibis queries.

This plugin cannot be used independently; it must be used together with superduper_ibis.

Superduper supports SQL databases via the ibis project. With superduper, queries may be built which conform to the ibis API, with additional support for complex data-types and vector-searches.

Installation

pip install superduper_ibis

API

Class	Description
`superduper_ibis.data_backend.IbisDataBackend`	Ibis data backend for the database.
`superduper_ibis.query.IbisQuery`	A query that can be executed on an Ibis database.
`superduper_ibis.db_helper.DBHelper`	Generic helper class for database.
`superduper_ibis.db_helper.ClickHouseHelper`	Helper class for ClickHouse database.
`superduper_ibis.field_types.FieldType`	Field type to represent the type of a field in a table.

Connection examples

MySQL

from superduper import superduper

db = superduper('mysql://<mysql-uri>')

Postgres

from superduper import superduper

db = superduper('postgres://<postgres-uri>')

Other databases

from superduper import superduper

db = superduper('<database-uri>')

Query examples

Inserting data

Table data must correspond to the Schema for that table. Either create a Schema and Table or use an auto-detected Schema. Once you've got a Schema, all data inserted must conform to that Schema:

import pandas

pandas.DataFrame([
    PIL.Image.open('image.jpg'), 'some text', 4,
    PIL.Image.open('other_image.jpg'), 'some other text', 3,
])

t.insert(dataframe.to_dict(orient='records'))

Selecting data

superduper supports selecting data via the ibis query API. For example:

db['my_table'].filter(t.rating > 3).limit(5).select(t.image).execute()

Vector-search

Vector-searches are supported via the like operator:

(
    db['my_table']
    .like({'text': 'something like this'}, vector_index='my-index')
    .filter(t.rating > 3)
    .limit(5)
    .select(t.image, t.id)
).execute()

Vector-searches are either first or last in a chain of operations:

(
    db['my_table']
    t.filter(t.rating > 3)
    .limit(5)
    .select(t.image, t.id)
    .like({'text': 'something like this'}, vector_index='my-index')
).execute()

Updating data

Updates are not covered for superduper SQL integrations.

Deleting data

db.databackend.drop_table('my-table')

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