Cohere Embed v3

Generates 1024-dimensional dense vectors from text input across 100+ languages using a transformer-based architecture optimized for semantic similarity. The model produces language-agnostic embeddings that enable cross-lingual retrieval without explicit translation, allowing queries in one language to match documents in another by mapping all languages to a shared semantic space. Embeddings are computed server-side via Cohere's cloud API with support for batch processing.

Generates embeddings optimized for either search or classification tasks via separate input type parameters that adjust the model's internal representation strategy. When configured for search, the model emphasizes query-document relevance matching; when configured for classification, it optimizes for feature distinctiveness across categories. This dual-mode approach allows a single model to serve both retrieval and classification workloads without retraining.

Compresses embeddings from 1024 dimensions down to 256, 512, or 768 dimensions using Matryoshka representation learning, a technique where the model learns nested vector representations such that lower-dimensional projections preserve semantic information. The compression is lossless at inference time — the model outputs the full 1024-dim vector but clients can truncate to any supported dimension without recomputing, reducing storage by up to 96% and accelerating downstream similarity computations.

Generates unified embeddings for documents containing mixed content types (text, tables, graphs, images) by processing each modality through specialized encoders and fusing their representations into a single 1024-dimensional vector. This allows a single embedding to represent a complex document like a financial report with text, charts, and tables, enabling semantic search across all modalities simultaneously without separate indexing per content type.

Provides embeddings through Cohere's managed cloud API with automatic scaling, rate limiting, and pay-as-you-go billing. Requests are processed server-side with no local model deployment required, enabling immediate access to the latest model versions and automatic infrastructure management. The API supports both synchronous single-request and batch processing modes with trial keys for development and production keys for scaled workloads.

Deploys Embed v3 to a dedicated instance in Cohere's Model Vault with hourly billing, providing guaranteed capacity and isolation from other users' workloads. The deployment model supports multiple tier sizes (Small, Medium, etc.) with different throughput characteristics, allowing teams to right-size capacity for their embedding volume. Instances remain warm and ready for requests, eliminating cold-start latency compared to serverless APIs.

Enables deployment of Embed v3 within customer-controlled infrastructure including Virtual Private Clouds (VPCs) and on-premises data centers, maintaining data residency and network isolation. Cohere manages the deployment and updates while the customer controls network access, compliance boundaries, and data flow, providing a hybrid model between fully managed cloud APIs and self-hosted open-source models.

Achieves state-of-the-art performance on the Massive Text Embedding Benchmark (MTEB) evaluation suite, which measures semantic similarity, retrieval, clustering, and classification across diverse datasets and languages. The model is optimized for these benchmark tasks through training objectives and data selection that emphasize semantic relevance, enabling strong out-of-the-box performance on standard NLP evaluation metrics without task-specific fine-tuning.

Designed as a drop-in embedding layer for Retrieval-Augmented Generation (RAG) systems, providing semantic search capabilities for document retrieval before LLM generation. The model's multilingual support, task optimization, and compression options make it suitable for enterprise RAG architectures handling large document collections, multiple languages, and varying latency/cost tradeoffs. Integrates with vector databases (Pinecone, Weaviate, Milvus, etc.) via standard embedding API contracts.

Optimized for e-commerce use cases where product embeddings enable semantic search across product catalogs, matching customer queries to relevant products based on semantic similarity rather than keyword matching. The model handles product descriptions, titles, and attributes, creating embeddings that capture product semantics for both search and recommendation tasks. Task optimization for search mode ensures embeddings prioritize query-document relevance.