# nomic-embed-vision-v1

Source: https://zilliz.com/ai-models/nomic-embed-vision-v1
Published: 2025-11-28T11:20:12.379Z

## Model attributes

| Attribute | Value |
| --- | --- |
| Provider | Nomic |
| Integration | Milvus Integrated |
| Task | Embedding |
| Modality | Image |
| Similarity metric | Cosine |
| License | Apache 2.0 |
| Dimensions | 768 |
| Price | Free |

## Introduction to nomic-embed-vision-v1

The **nomic-embed-vision-v1** model is a fully replicable vision embedding model that shares the same latent space as nomic-embed-text-v1, enabling multimodal use where text and image embeddings can query one another directly. With a 92M-parameter vision encoder, it supports image and text embedding, unimodal search within each modality, and multimodal search across both. 


## How to create embeddings with nomic-embed-vision-v1

There are two primary ways to generate vector embeddings:

- [PyMilvus](https://github.com/milvus-io/pymilvus): the Python SDK for [Milvus](https://zilliz.com/what-is-milvus) that seamlessly integrates the `nomic-embed-vision-v1` model.
- The `embed` module in the Nomic Python SDK provides embedding functionality using the [Nomic Embedding API](https://docs.nomic.ai/reference/python-api/generate-embeddings).

Once the vector embeddings are generated, they can be stored in [Zilliz Cloud](https://zilliz.com/cloud) (a fully managed vector database service powered by [Milvus](https://zilliz.com/what-is-milvus)) and used for [semantic similarity search](https://zilliz.com/glossary/semantic-search). Here are four key steps:

- [Sign up](https://cloud.zilliz.com/signup) for a Zilliz Cloud account for free.
- [Set up a serverless cluster](https://docs.zilliz.com/docs/create-cluster#set-up-a-free-cluster) and obtain the [Public Endpoint and API Key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details).
- Create a vector collection and insert your vector embeddings.
- Run a semantic search on the stored embeddings.

### Create embeddings via PyMilvus and insert them into Zilliz Cloud for semantic search

```
from pymilvus import MilvusClient
from nomic import embed

# Prepare images (can be local paths or URLs)
images = ["path/to/image1.jpg", "path/to/image2.jpg", "path/to/image3.jpg"]

# Generate embeddings for images using nomic-embed-vision-v1
image_embeddings = embed.image(images=images, model="nomic-embed-vision-v1")[
    "embeddings"
]

# Prepare query image
query_images = ["path/to/query_image.jpg"]

# Generate embeddings for query
query_embeddings = embed.image(images=query_images, model="nomic-embed-vision-v1")[
    "embeddings"
]

# Connect to Zilliz Cloud with Public Endpoint and API Key
client = MilvusClient(uri=ZILLIZ_PUBLIC_ENDPOINT, token=ZILLIZ_API_KEY)

COLLECTION = "nomic_vision_v1_images"

# Drop collection if it exists
if client.has_collection(collection_name=COLLECTION):
    client.drop_collection(collection_name=COLLECTION)

# Create collection with dimension 768 (nomic-embed-vision-v1 output dimension)
client.create_collection(collection_name=COLLECTION, dimension=768, auto_id=True)

# Insert images with embeddings
for image, embedding in zip(images, image_embeddings):
    client.insert(COLLECTION, {"image_path": image, "vector": embedding})

# Search for similar images
results = client.search(
    collection_name=COLLECTION,
    data=query_embeddings,
    # consistency_level="Strong",  # Strong consistency ensures accurate results but may increase latency
    output_fields=["image_path"],
    limit=3,
)

# Print search results
print("Similar images found:")
for result in results[0]:
    print(f"  - {result['entity']['image_path']} (distance: {result['distance']:.4f})")
```

For more information, refer to our [PyMilvus Embedding Model documentation](https://milvus.io/docs/embeddings.md).