Basilica Python Client

class basilica.Connection(auth_key, server='https://api.basilica.ai', retries=2, backoff_factor=0.1, status_forcelist=500)[source]

A connection to basilica.ai that can be used to generate embeddings.

Parameters:
  • auth_key (str) – Your auth key. You can view your auth keys at https://basilica.ai/auth_keys.
  • server (str) – What URL to use to connect to the server.
  • retries (int) – Number of times to retry failed connections and requests.
  • backoff_factor (float) – See urllib3.util.retry.Retry.backoff_factor .
  • status_forcelist (Tuple[int]) – What HTTP response codes trigger a retry.
>>> with basilica.Connection('SLOW_DEMO_KEY') as c:
...   print(c.embed_sentence('A sentence.'))
[0.6246702671051025, ..., -0.03025037609040737]
embed_image(image, model='generic', version='default', opts={}, timeout=10)[source]

Generate the embedding for a JPEG image. The image should be passed as a byte string.

Parameters:
  • image (str) – The image to embed.
  • model (str) – What model to use (i.e. the kind of image being embedded).
  • version (str) – What version of that model to use.
  • opts (Dict[str, Any]) – Options specific to the model/version you chose.
  • opts["dimensions"] (int) – Number of dimensions to return. PCA will be used to reduce the number of dimensions with minimal information loss.
  • opts["normalize_l2"] (bool) – Whether or not each instance should be scaled to have unit L2 norm. (This is sometimes useful for instance retrieval tasks.) Defaults to False.
  • opts["normalize_mean"] (bool) – Whether or not to normalize each feature in the embedding to have mean 0 across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • opts["normalize_variance"] (bool) – Whether or not to normalize each feature in the embedding to have unit variance across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • timeout (int) – HTTP timeout for request.
Returns:

An embedding.

Return type:

List[float]

>>> with basilica.Connection('SLOW_DEMO_KEY') as c:
...   with open('img.jpg', 'rb') as f:
...     print(c.embed_image(f.read()))
[0.6246702671051025, ...]
embed_image_file(image_file, model='generic', version='default', opts={}, timeout=10)[source]

Generate the embedding for a JPEG image file. The file name should be passed as a path that can be understood by open.

Parameters:
  • image_file (str) – Path to the image to embed.
  • model (str) – What model to use (i.e. the kind of image being embedded).
  • version (str) – What version of that model to use.
  • opts (Dict[str, Any]) – Options specific to the model/version you chose.
  • opts["dimensions"] (int) – Number of dimensions to return. PCA will be used to reduce the number of dimensions with minimal information loss.
  • opts["normalize_l2"] (bool) – Whether or not each instance should be scaled to have unit L2 norm. (This is sometimes useful for instance retrieval tasks.) Defaults to False.
  • opts["normalize_mean"] (bool) – Whether or not to normalize each feature in the embedding to have mean 0 across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • opts["normalize_variance"] (bool) – Whether or not to normalize each feature in the embedding to have unit variance across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • timeout (int) – HTTP timeout for request.
Returns:

An embedding.

Return type:

List[float]

>>> with basilica.Connection('SLOW_DEMO_KEY') as c:
...   print(c.embed_image_file('img.jpg')
[0.6246702671051025, ...]
embed_image_files(image_files, model='generic', version='default', batch_size=32, opts={}, timeout=30)[source]

Generate embeddings for JPEG image files. The file names should be passed as paths that can be understood by open.

Parameters:
  • image_files (Iterable[str]) – An iterable (such as a list) of paths to the images to embed.
  • model (str) – What model to use (i.e. the kind of image being embedded).
  • version (str) – What version of that model to use.
  • batch_size (int) – How many instances to send to the server at a time.
  • opts (Dict[str, Any]) – Options specific to the model/version you chose.
  • opts["dimensions"] (int) – Number of dimensions to return. PCA will be used to reduce the number of dimensions with minimal information loss.
  • opts["normalize_l2"] (bool) – Whether or not each instance should be scaled to have unit L2 norm. (This is sometimes useful for instance retrieval tasks.) Defaults to False.
  • opts["normalize_mean"] (bool) – Whether or not to normalize each feature in the embedding to have mean 0 across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • opts["normalize_variance"] (bool) – Whether or not to normalize each feature in the embedding to have unit variance across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • timeout (int) – HTTP timeout for request.
Returns:

A generator of embeddings.

Return type:

Generator[List[float]]

>>> with basilica.Connection('SLOW_DEMO_KEY') as c:
...   for embedding in c.embed_image_files(['img1.jpg', 'img2.jpg']):
...     print(embedding)
[0.6246702671051025, ...]
[-0.03025037609040737, ...]
embed_images(images, model='generic', version='default', batch_size=32, opts={}, timeout=30)[source]

Generate embeddings for JPEG images. Images should be passed as byte strings, and will be sent to the server in batches to be embedded.

Parameters:
  • images (Iterable[str]) – An iterable (such as a list) of the images to embed.
  • model (str) – What model to use (i.e. the kind of image being embedded).
  • version (str) – What version of that model to use.
  • batch_size (int) – How many instances to send to the server at a time.
  • opts (Dict[str, Any]) – Options specific to the model/version you chose.
  • opts["dimensions"] (int) – Number of dimensions to return. PCA will be used to reduce the number of dimensions with minimal information loss.
  • opts["normalize_l2"] (bool) – Whether or not each instance should be scaled to have unit L2 norm. (This is sometimes useful for instance retrieval tasks.) Defaults to False.
  • opts["normalize_mean"] (bool) – Whether or not to normalize each feature in the embedding to have mean 0 across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • opts["normalize_variance"] (bool) – Whether or not to normalize each feature in the embedding to have unit variance across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • timeout (int) – HTTP timeout for request.
Returns:

A generator of embeddings.

Return type:

Generator[List[float]]

>>> with basilica.Connection('SLOW_DEMO_KEY') as c:
...   images = []
...   for filename in ['img1.jpg', 'img2.jpg']:
...     with open(filename, 'rb') as f:
...     images.append(f.read())
...   for embedding in c.embed_images(images):
...     print(embedding)
[0.6246702671051025, ...]
[-0.03025037609040737, ...]
embed_sentence(sentence, model='english', version='default', opts={}, timeout=5)[source]

Generate the embedding for a sentence.

Parameters:
  • sentence (str) – The sentence to embed.
  • model (str) –

    What model to use (i.e. the kind of sentence being embedded).

    • generic: Generic English text embedding (the default.)
    • reddit: Text embedding specialized for English Reddit posts.
    • twitter: Text embedding specialized for English tweets.
    • email: Text embedding specialized for English emails.
    • product-reviews: Text embedding specialized for English product reviews.
  • version (str) – What version of that model to use.
  • opts (Dict[str, Any]) – Options specific to the model/version you chose.
  • opts["dimensions"] (int) – Number of dimensions to return. PCA will be used to reduce the number of dimensions with minimal information loss.
  • opts["normalize_l2"] (bool) – Whether or not each instance should be scaled to have unit L2 norm. (This is sometimes useful for instance retrieval tasks.) Defaults to False.
  • opts["normalize_mean"] (bool) – Whether or not to normalize each feature in the embedding to have mean 0 across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • opts["normalize_variance"] (bool) – Whether or not to normalize each feature in the embedding to have unit variance across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • timeout (int) – HTTP timeout for request.
Returns:

An embedding.

Return type:

List[float]

>>> with basilica.Connection('SLOW_DEMO_KEY') as c:
...   print(c.embed_sentence('This is a sentence.')
[0.6246702671051025, ...]
embed_sentences(sentences, model='english', version='default', batch_size=64, opts={}, timeout=15)[source]

Generate embeddings for sentences.

Parameters:
  • sentences (Iterable[str]) – An iterable (such as a list) of sentences to embed.
  • model (str) –

    What model to use (i.e. the kind of sentence being embedded).

    • generic: Generic English text embedding (the default.)
    • reddit: Text embedding specialized for English Reddit posts.
    • twitter: Text embedding specialized for English tweets.
    • email: Text embedding specialized for English emails.
    • product-reviews: Text embedding specialized for English product reviews.
  • version (str) – What version of that model to use.
  • batch_size (int) – How many instances to send to the server at a time.
  • opts (Dict[str, Any]) – Options specific to the model/version you chose.
  • opts["dimensions"] (int) – Number of dimensions to return. PCA will be used to reduce the number of dimensions with minimal information loss.
  • opts["normalize_l2"] (bool) – Whether or not each instance should be scaled to have unit L2 norm. (This is sometimes useful for instance retrieval tasks.) Defaults to False.
  • opts["normalize_mean"] (bool) – Whether or not to normalize each feature in the embedding to have mean 0 across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • opts["normalize_variance"] (bool) – Whether or not to normalize each feature in the embedding to have unit variance across our sample dataset. Defaults to True when dimensions is set, or False otherwise.
  • timeout (int) – HTTP timeout for request.
Returns:

A generator of embeddings.

Return type:

Generator[List[float]]

>>> with basilica.Connection('SLOW_DEMO_KEY') as c:
...   for embedding in c.embed_sentences(['Sentence one.', 'Sentence two.']):
...     print(embedding)
[0.6246702671051025, ...]
[-0.03025037609040737, ...]