Cohere Cohere Embed v3
Cohere Embed v3 produces high-quality text embeddings for search, clustering, and recommendations.
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What is Cohere Embed v3?
Cohere Embed v3 from Cohere encodes text into dense vectors for retrieval and analytics. Use it for RAG pipelines, semantic search, recommendations, and topic detection across languages. Optimized for quality and latency so it scales to large corpora.
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Technical Specifications
Context Window
512 token
Max Output
1024 boyutlu vektör
Training Cutoff
2024
Active
Active
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Capabilities
High-quality text embeddings for search and clustering
Handles multi-language inputs
Optimized for semantic retrieval latency
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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 quality
- Fast inference and small vectors
- Works across languages
Cons
- Not a generative model
- Needs good chunking to avoid drift
- Quality depends on downstream index settings
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Features
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Semantic search
Encode queries and documents into the same vector space.
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Multi-task
Use one embedding for search, recommendations, and clustering.
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Scalable
Low latency and small vectors for large corpora.
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Use Cases
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RAG indexing
Embed knowledge bases for accurate retrieval-augmented generation.
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Recommendations
Cluster similar items and surface relevant content.
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Analytics
Detect topics, intent, and anomalies across text streams.
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FAQ
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