Cohere Embed v3
Embeddings for semantic search and retrieval.
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What is Cohere Embed v3?
Cohere Embed v3 encodes text into vectors for semantic search and RAG pipelines.
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Technical Specifications
Context Window
512 tokens
Max Output
1024-dimensional vector
Training Cutoff
Not specified
Active
Active
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Capabilities
Embeddings
Semantic search
Clustering
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Benchmark Scores
MTEB Average
62.3%Dimension
1024Max Input
512Compression Quality
98%Languages
100+Throughput
High05
Pros & Cons
Pros
- Strong retrieval
- Scales well
- Vector DB friendly
Cons
- Not a chat model
- Requires vector infra
- Not specified
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Features
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Retrieval Ready
Optimized for search.
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Compact Vectors
Efficient for large corpora.
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Scalable
Built for indexing.
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Use Cases
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Semantic Search
Improve search relevance.
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Recommendations
Find similar items.
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RAG Indexing
Embed documents.
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FAQ
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