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Atlas Dataset

The AtlasDataset class manages your Atlas Dataset. Atlas Datasets store information server side and dynamically download it to your local environment with caching when necessary.

Any action you perform on your dataset in the web browser you can accomplish by interacting with an AtlasDataset object.

Modality Support

Atlas Datasets natively support text, image, and embedding datasets. Other modalities, such as video and audio, are supported by uploading user generated embeddings.

Creating an Atlas Dataset

from nomic import AtlasDataset

dataset = AtlasDataset(

Datasets are uniquely identified in your Nomic organization with a URL-safe name.

Adding data to an Atlas Dataset

from nomic import AtlasDataset

dataset = AtlasDataset(

data=[{'text': 'my first document'}, {'text': 'my second document'}]

Adding data to an Image Atlas Dataset

from nomic import AtlasDataset

dataset = AtlasDataset(

blobs=['cat.jpg', 'dog.jpg'],
data=[{'label': 'cat', 'id': 1}, {'label': 'dog', 'id': 2}]

Creating an Atlas Map

To structure your dataset in Atlas, you must index it. Indexing your dataset will create a map view of the data at a point in time, automatically detect topics, generate embeddings for unstructured data fields and augment it with metadata such as duplicate information

from nomic import AtlasDataset

dataset = AtlasDataset(

data=[{'text': 'my first document'}, {'text': 'my second document'}]

map = dataset.create_index(
topic_model: NomicTopicOptions = True,
duplicate_detection: NomicDuplicatesOptions = True,
projection: NomicProjectOptions = None,
embedding_model: NomicEmbedOptions = 'NomicEmbed'

There are several options you can configure for how Atlas will index your dataset:

Adding a Topic Model

Specifying topic_model during index creation will build a topic model over your datasets embeddings.

class NomicTopicOptions(BaseModel)

Options for Nomic Topic Model


  • build_topic_model - If True, builds a topic model over your dataset's embeddings.
  • topic_label_field - The dataset column (usually the column you embedded) that Atlas will use to assign a human-readable description to each topic.

Detecting Duplicate Datapoints

Specifying duplicate_detection during index creation will automatically identify datapoints in your data that are semantic duplicates.

class NomicDuplicatesOptions(BaseModel)

Options for Duplicate Detection


  • tag_duplicates - Should duplicate detection run over your datasets embeddings?
  • duplicate_cutoff - A hyperparameter of duplicate detection, smaller values capture more exact duplicates.

Modifying the 2D Reduction Algorithm

Specifying projection during index creation will allow you to configure the hyperparameters that define the 2D map layout.

class NomicProjectOptions(BaseModel)

Options for Nomic 2D Dimensionality Reduction Model


  • n_neighbors - The number of neighbors to use when approximating the high dimensional embedding space during reduction. Default: None (Auto-inferred).
  • n_epochs - How many dataset passes to train the projection model. Default: None (Auto-inferred).
  • model - The model to use when generating the 2D projected embedding space layout. Possible values: None or nomic-project-v1 or nomic-project-v2. Default: None.
  • local_neighborhood_size - Only used when model is nomic-project-v2. Controls the size of the neighborhood used in the local structure optimizing step of nomic-project-v2 algorithm. Min value: max(n_neighbors, 1); max value: 128. Default: None (Auto-inferred).
  • spread - Determines how tight together points appear. Larger values result a more spread out point layout. Min value: 0. It is recommended leaving this value as the default None (Auto-inferred).
  • rho - Only used when model is nomic-project-v2. Controls the spread in the local structure optimizing step of nomic-project-v2. Min value: 0; max value: 1. It is recommended to leave this value as the default None (Auto-inferred).

Customizing the embedding model

Specifying embedding_model during index creation will allow you to configure the hyperparameters of the embedding model. If you've uploaded your own embeddings, this is option is ignored.

class NomicEmbedOptions(BaseModel)

Options for Configuring the Nomic Embedding Model


  • model - The Nomic Embedding Model to use.

The AtlasDataset

class AtlasDataset(AtlasClass)


def __init__(identifier: Optional[str] = None,
description: Optional[str] = "A description for your map.",
unique_id_field: Optional[str] = None,
is_public: bool = True,

Creates or loads an AtlasDataset. AtlasDataset's store data (text, embeddings, etc) that you can organize by building indices. If the organization already contains a dataset with this name, it will be returned instead.


  • identifier - The dataset identifier in the form dataset or organization/dataset. If no organization is passed, your default organization will be used.
  • description - A description for the dataset.
  • unique_id_field - The field that uniquely identifies each data point.
  • is_public - Should this dataset be publicly accessible for viewing (read only). If False, only members of your Nomic organization can view.
  • dataset_id - An alternative way to load a dataset is by passing the dataset_id directly. This only works if a dataset exists.


def delete()

Deletes an atlas dataset with all associated metadata.


def id() -> str

The UUID of the dataset.


def total_datums() -> int

The total number of data points in the dataset.


def name() -> str

The customizable name of the dataset.


def slug() -> str

The URL-safe identifier for this dataset.


def identifier() -> str

The Atlas globally unique, URL-safe identifier for this dataset


def is_accepting_data() -> bool

Checks if the dataset can accept data. Datasets cannot accept data when they are being indexed.


True if dataset is unlocked for data additions, false otherwise.


def wait_for_dataset_lock()

Blocks thread execution until dataset is in a state where it can ingest data.


def get_map(name: Optional[str] = None,
atlas_index_id: Optional[str] = None,
projection_id: Optional[str] = None) -> AtlasProjection

Retrieves a map.


  • name - The name of your map. This defaults to your dataset name but can be different if you build multiple maps in your dataset.
  • atlas_index_id - If specified, will only return a map if there is one built under the index with the id atlas_index_id.
  • projection_id - If projection_id is specified, will only return a map if there is one built under the index with id projection_id.


The map or a ValueError.


def create_index(
name: Optional[str] = None,
indexed_field: Optional[str] = None,
modality: Optional[str] = None,
projection: Union[bool, Dict, NomicProjectOptions] = True,
topic_model: Union[bool, Dict, NomicTopicOptions] = True,
duplicate_detection: Union[bool, Dict, NomicDuplicatesOptions] = True,
embedding_model: Optional[Union[str, Dict, NomicEmbedOptions]] = None,
reuse_embeddings_from_index: Optional[str] = None
) -> Optional[AtlasProjection]

Creates an index in the specified dataset.


  • name - The name of the index and the map.
  • indexed_field - For text datasets, name the data field corresponding to the text to be mapped.
  • reuse_embeddings_from_index - the name of the index to reuse embeddings from.
  • modality - The data modality of this index. Currently, Atlas supports either text, image, or embedding indices.
  • projection - Options for configuring the 2D projection algorithm
  • topic_model - Options for configuring the topic model
  • duplicate_detection - Options for configuring semantic duplicate detection
  • embedding_model - Options for configuring the embedding model


The projection this index has built.


def get_data(ids: List[str]) -> List[Dict]

Retrieve the contents of the data given ids.


  • ids - a list of datum ids


A list of dictionaries corresponding to the data.


def delete_data(ids: List[str]) -> bool

Deletes the specified datapoints from the dataset.


  • ids - A list of data ids to delete


True if data deleted successfully.


def add_data(data=Union[DataFrame, List[Dict], pa.Table],
embeddings: Optional[np.ndarray] = None,
blobs: Optional[List[Union[str, bytes, Image.Image]]] = None,

Adds data of varying modality to an Atlas dataset.


  • data - A pandas DataFrame, list of dictionaries, or pyarrow Table matching the dataset schema.
  • embeddings - A numpy array of embeddings: each row corresponds to a row in the table. Use if you already have embeddings for your datapoints.
  • blobs - A list of image paths, bytes, or PIL Images. Use if you want to create an AtlasDataset using image embeddings over your images. Note: Blobs are stored locally only.
  • pbar - (Optional). A tqdm progress bar to update.


def update_maps(data: List[Dict],
embeddings: Optional[np.ndarray] = None,
num_workers: int = 10)

Utility method to update a project's maps by adding the given data.


  • data - An [N,] element list of dictionaries containing metadata for each embedding.
  • embeddings - An [N, d] matrix of embeddings for updating embedding dataset. Leave as None to update text dataset.
  • shard_size - Data is uploaded in parallel by many threads. Adjust the number of datums to upload by each worker.
  • num_workers - The number of workers to use when sending data.


def update_indices(rebuild_topic_models: bool = False)

Rebuilds all maps in a dataset with the latest state dataset data state. Maps will not be rebuilt to reflect the additions, deletions or updates you have made to your data until this method is called.


  • rebuild_topic_models - (Default False) - If true, will create new topic models when updating these indices.