embeddings

Uncover trends and compare languages easily

For ML teams looking to build their own text analysis applications, Embeddings offers high-performance and accuracy in English and 100+ languages.

Code sample that runs the Cohere API embed endpoint with only 9 lines
Using Cohere to categorize FAQs in a dashboard

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What's possible with Embeddings

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Semantic search

Build semantic search capability using conversational language.
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Topic modeling

Cluster similar topics and discover thematic trends across a body of text sources.
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Recommendations

Build a recommendation engine and engage your users with more relevant content.
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Multilingual Embeddings

Run topic modeling, semantic search, and recommendations across 100+ languages with just one model.

"It's next to impossible to gain access to Language AI and the experts building the technology. That’s why working with Cohere has been such a great experience. Anytime we have a new idea, their incredible team works with us to drive projects forward."

Carlos Perez
CFO

Eficiencia Informativa

Why Embeddings

1
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Embeddings performance

Cohere’s Embed model leads the industry in accuracy and performance, and works well with noisy datasets

2
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Multilingual support

Over 100 languages are supported, so the same topics, products and issues are identified the same way in each


3
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Scalability

Cohere Embed supports data compression, reducing storage and compute requirements

4
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Flexible deployment

Cohere models can be accessed through a SaaS API, on cloud services (e.g. OCI, AWS SageMaker, Bedrock) and soon through private deployments (VPC and on-premise)

Simple APIs, powerful results

No matter your level of experience with ML/AI, the Cohere Platform makes it easy to classify text in your applications.

1import cohere
2co = cohere.Client('{apiKey}')
3
4faq_questions=[
5     "How much is a burger?",
6     "When do you close?",
7     "What are the hours",
8     "Do you have vegan options",
9     "What is the closest route"]
10
11response=co.embed(texts=faq_questions, input_type="search_query", model="embed-english-v3.0")
12print('Embeddings: {}'.format(response.embeddings))

Embeddings resources

Don’t want to code? Try our playground instead

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Get started with Cohere today!

Reach out to us and let’s discuss your embedding needs.