Embeddings

Nomic Atlas allows anyone to access the power of embeddings.

An embedding is a vector representation of an unstructured datapoint that enables computers to manipulate the data based on semantics and meaning.

Learn more about Nomic Embed Text, Nomic Embed Vision, and the Embedding Inference API.

New Release: Nomic Embedding Vision and API

We've launched Nomic Embed Vision, a vision model aligned to Nomic Embed Text! All existing Nomic Embed Text embeddings are now multimodel; Nomic Embed Text embeddings can be used query the new Nomic Embed Vision embeddings out of the box, and visa versa. Together, Nomic Embed Text and Nomic Embed Vision project data into the only unified embedding space that achieves state of the art performance on vision, language, and multimodal tasks.

You can use it as the image embedding model powering your AtlasDataset and it is available in the Nomic Embedding API.

Read more in our official blog post and learn how to use it in the API Reference.

Embeddings in Atlas

When an unstructured dataset is uploaded to Atlas, an embedding is associated with each datapoint using a Nomic Embedding Model.

Nomic Atlas operates over embeddings to enable its unstructured data capabilities.

2D Embeddings: All embeddings stored in Atlas have a corresponding 2D, human-interpretable representation. These 2D embeddings power the layout of the Atlas Map. They are generated with a Nomic Dimensionality Reduction model.

You can generate text and image embeddings with Nomic Atlas by using the Nomic Embedding API.

For text embeddings,

from nomic import embed
import numpy as np

output = embed.text(
    texts=['The text you want to embed.'],
    model='nomic-embed-text-v1.5',
    task_type='search_document',
)

embeddings = np.array(output['embeddings'])

and vision embeddings,

from nomic import embed
import numpy as np

output = embed.image(
    images=["paths to images"],
    model='nomic-embed-vision-v1.5',
)

embeddings = np.array(output['embeddings'])

Now, explore your embedding in Atlas.

from nomic import atlas
dataset = atlas.map_data(embeddings=embeddings)

Embedding task types

When using Nomic Text embeddings models you must specify a task_type. The task_type allows the embedding to specialize for specific use cases and defaults to search_document.

There are four task type options for Nomic Embed

Retrieval task types

• search_query: Use this when you want to encode a query for question-answering over text that was embedded with search_document.
• search_document: The default embedding task type. Any document you want to use for retrieval or store in a vector database should use this task type.

Semantic search

If you want to do semantic similarity search instead of question answering, you should encode both queries and document with the search_document task type.

Classification and clustering tasks

• classification: Use this if your embeddings are for classification (e.g. training a linear probe for a target classification task)
• clustering: Use this if your embeddings need very high linear separability (e.g. building a topic model on your embeddings)

Resizable embeddings

You can resize the output dimensionality of your Nomic embeddings with Nomic Embed v1.5. This model allows you to specify a dimensionality ranging from 64 to 768 for your embedding size.

Shorter embeddings tradeoff small amounts of performance for space so require less storage, memory and bandwidth to use.

Specify a dimensionality to control the embedding size.

from nomic import embed

output = embed.text(
    texts=['Nomic Embedding API', '#keepAIOpen'],
    model='nomic-embed-text-v1.5',
    task_type='search_document',
    dimensionality=512,
)

Local inference

The Nomic Python client can optionally use GPT4All to generate text embeddings locally. Local embeddings are fully compatible with embeddings from the Atlas API, and should be the same within a small margin of error.

GPT4All is not included by default, but it can be installed with pip install nomic[local].

To enable local mode, use inference_mode='local'. The local model will be downloaded automatically. The first call to embed.text will be slower due to initialization, but successive calls with the same model and parameters reuse the Embed4All instance.

from nomic import embed

output = embed.text(
    texts=['Nomic Embedding API', '#keepAIOpen'],
    model='nomic-embed-text-v1.5',
    task_type='search_document',
    inference_mode='local',
)

mode = output['inference_mode']  # 'local'

Dynamic local inference

Local embeddings are free to generate, but generally take longer than remote embeddings, especially on a less capable machine. Dynamic mode can be enabled with inference_mode='dynamic', which uses the local model to speed up smaller embedding requests, while automatically using the remote API for larger ones.

from nomic import embed

output = embed.text(
    texts=['Nomic Embedding API', '#keepAIOpen'],
    model='nomic-embed-text-v1.5',
    task_type='search_document',
    inference_mode='dynamic',
)

mode = output['inference_mode']  # 'local'

output = embed.text(
    texts=['Nomic Embedding API'] * 50,
    model='nomic-embed-text-v1.5',
    task_type='search_document',
    inference_mode='remote',
)

mode = output['inference_mode']  # 'remote'

Selecting a device

The device parameter of embed.text allows you to use a different device for local inference. By default, the CPU is used on Windows and Linux, while the Metal backend is used on Apple Silicon. You can use the GPU on Windows or Linux with device='gpu'. See the GPT4All documentation for more information.

Explore your embeddings

At it's core, Atlas is an engine for embedding exploration and understanding. This is crucial for debugging your data, finding anomalies and refining your embedding powered applications.

Dive into 10,000 Wikipedia documents through the latent space of nomic-embed-text-v1.5. Learn more about how to use Atlas Datasets.

from nomic import embed, atlas
import numpy as np
from datasets import load_dataset, Dataset

wiki_10k = Dataset.from_generator(
    lambda: load_dataset(
        'wikimedia/wikipedia',
        '20231101.en',
        split='train',
        streaming=True
    ).take(10_000)
)

wiki_10k_embeddings = embed.text(
    texts=[row['text'][:1200] for row in wiki_10k],
    model='nomic-embed-text-v1.5'
)['embeddings']

wiki_10k_embeddings = np.array(wiki_10k_embeddings)

dataset = atlas.map_data(
    embeddings=wiki_10k_embeddings,
    data=[{'text': row['text'][:1200]} for row in wiki_10k]
)