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Map Your Embeddings

Atlas ingests unstructured data such as embeddings or text and organizes them. Once your data is in Atlas, you can view all of it at once on an interactive map. Any interaction you do on the map (e.g. tagging, topic labeling, vector search) you can programmatically access in this Python client.

Your first neural map

The following code snippet shows you how to map your embeddings with Atlas. Upload 10,000 random embeddings and see them instantly organized on an interactive map.

Random Embedding Map

map_embeddings.py
from nomic import atlas
import numpy as np

num_embeddings = 10000
embeddings = np.random.rand(num_embeddings, 256)

project = atlas.map_embeddings(embeddings=embeddings)

https://atlas.nomic.ai/map/82e15baf-5de2-4191-bc60-61ce9d76bd17/91e63b2d-b8af-4de2-a4d2-e6e96d879274

Add some colors

Now let's add colors. To do this, specify the data key in the map call. This field should contain a list of dictionaries - one for each of your embeddings. In the map_embeddings call, specify the key you want to be able to color by. In our example, this key is category.

map_embeddings_with_colors.py
from nomic import atlas
import numpy as np

num_embeddings = 10000
embeddings = np.random.rand(num_embeddings, 256)

categories = ['rhizome', 'cartography', 'lindenstrauss']
data = [{'category': categories[i % len(categories)], 'id': i}
            for i in range(len(embeddings))]

project = atlas.map_embeddings(embeddings=embeddings,
                                data=data,
                                id_field='id',
                                colorable_fields=['category']
                                )