embed

AI for language understanding 

Embed is Cohere’s leading text representation language model. It improves the accuracy of search results, retrieval-augmented generation (RAG), classification, and clustering.

Using Cohere to categorize FAQs in a dashboard

Leading embedding performance

Robust to noisy data


Noisy data often contains errors, outliers, and irrelevant information that hinder an embedding model’s ability to discern meaningful patterns or relationships within the data. Our Embed model understands your data’s nuances, making it highly accurate even when dealing with noisy real-world datasets.

Better retrievals for RAG


The effectiveness of RAG is dependent on multiple components, including embedding models that power search systems to retrieve relevant information. Embed’s elevated accuracy facilitates highly relevant and fewer search results, saving time and computational resources for retrievals.


What’s possible with Embed

semantic search

Embeddings enable searching by meaning, which leads to search systems that better incorporate context and user intent than previous keyword-matching systems.

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Retrieval-augmented generation

Improve RAG systems by using a performant embedding model especially tuned for search.

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Clustering

Make sense of large text archives by grouping similar texts based on their meaning (as captured by embeddings). Uncover patterns like often commonly asked questions or grouping similar types of issues.

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Text classification

Build systems that automatically categorize text and take action based on the type (e.g., route this message to sales, escalate that other message to tier 2 support).

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Language Models Optimized for Semantic Search

Use Embed with a wide variety of vector databases that directly integrate with the Embed model.

“From our experience, Cohere’s support is #1, and the embeddings endpoint has given us excellent results, outperforming other models”

Aaro Isosaari
Co-founder, CEO
flowrite

Embed resources

Cohere docs

Models

Continue in docs

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