Cohere Embed v3 Multilingual
Text embedding model for semantic search and clustering.
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What is Cohere Embed v3 Multilingual?
Cohere Embed v3 Multilingual encodes text into vectors for search, clustering, and RAG pipelines. Use it to index documents and power similarity-based retrieval.
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Technical Specifications
Context Window
Not specified
Max Output
Not specified
Training Cutoff
Not specified
Active
Active
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Capabilities
Embeddings
Semantic search
Similarity matching
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Pros & Cons
Pros
- Strong retrieval quality
- Vector DB friendly
- Scales to large corpora
Cons
- Not a chat model
- Requires vector infrastructure
- Not specified
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Features
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Retrieval Ready
Optimized for semantic search and RAG.
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Vector Output
Dense embeddings for similarity matching.
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Scalable
Designed for large-scale indexing.
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Use Cases
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Semantic Search
Improve search relevance.
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RAG Indexing
Embed documents for retrieval.
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Recommendations
Find similar items and content.
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