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Multilingual Semantic Search

15 posts · Mar 2024 – Oct 2026

What this trail covers

Cohere's multimodal embeddings unify visual and text data, enabling semantic search and retrieval across both modalities. This capability enhances the understanding and organization of diverse datasets, building upon existing multilingual semantic search by extending its capabilities to include visual information for richer and more comprehensive search experiences. Embed v3 significantly enhances multimodal search for business, offering improved accuracy and efficiency. This post details the introduction of Embed 5, a new family of embedding models offering Pro and Fast tiers. Embed 5 Pro maximizes retrieval quality across multimodal, multilingual, financial, code, and parsed-document data. Embed 5 Fast provides competitive performance for latency- and cost-sensitive workloads. Both tiers share a single embedding space. Key features include 128K context window, support for text and image inputs, 100+ languages, and various output dimensions. Advanced techniques like Matryoshka representations and int8/binary embedding formats reduce storage and search costs. Embed 5 Pro achieves state-of-the-art performance on benchmarks like ViDoRe V3, FinanceBench, and FinQA, outperforming competitors in multimodal and multilingual retrieval. The new RCP-nDCG@10 methodology is introduced for more comprehensive performance evaluation.

All Cohere trails
  1. Embed 5: Frontier Embedding Models for Enterprise | CohereThis post introduces Cohere's Embed 5 model family, featuring 'Pro' and 'Fast' tiers. Embed 5 Pro offers maximum retrieval quality across multimodal, multilingual, financial, code, and parsed-document data, achieving state-of-the-art scores on benchmarks like ViDoRe V3 and FinanceBench. Embed 5 Fast provides competitive performance for latency- and cost-sensitive workloads. Both tiers share a unified embedding space, enabling indexing with Pro and querying with either model. Key technical advancements include a 128K token context window, support for multimodal inputs (text, images, fused text-image), 100+ languages, and various output dimensions (2048 down to 256). Matryoshka representations and int8/binary embedding formats are introduced to reduce vector storage and search costs. The post also introduces RCP-nDCG@10 as a new retrieval methodology for performance evaluation. Performance gains are detailed across visually rich documents, financial datasets, parsed PDFs, fused text-image retrieval, page-image retrieval, and multilingual capabilities, with specific benchmark scores and comparisons to competitors like Voyage 4 Large, Gemini Embedding 2, and OpenAI text-embedding-3-large.
  2. Multimodal embeddings: Unifying visual and text data | Cohere BlogThis post introduces Embed 3, a new multimodal embedding model that significantly enhances multimodal search for business, offering improved accuracy and efficiency. It details the technical implementation of this model, focusing on its ability to unify visual and text data for semantic search and retrieval. The post discusses the architecture and training of Embed 3, highlighting its performance improvements over previous versions and its application in enterprise search and RAG scenarios.
  3. Introducing Embed 4: Multimodal search for business | Cohere BlogThis post introduces Embed 4, Cohere's latest multimodal embedding model. It significantly enhances multimodal search for business by unifying visual and text data, offering improved accuracy and efficiency. The post details the technical advancements in Embed 4 that enable richer and more comprehensive search experiences across diverse datasets.
  4. Introducing multimodal Embed 3: Powering AI search | Cohere BlogIntroduces Embed 3, a new multimodal embedding model that significantly enhances multimodal search for business by unifying visual and text data for semantic search and retrieval across both modalities. This improves accuracy and efficiency in cross-modal retrieval.
  5. Multilingual Semantic Search with Cohere and Langchain | Cohere BlogThis post details the integration of Cohere's Embed v3 multimodal embeddings with Langchain for building multilingual semantic search applications. It highlights how Embed v3's ability to process both text and images enables richer search experiences and discusses the technical considerations for integrating these embeddings into a retrieval pipeline using Langchain's document loaders and vector stores. The post emphasizes the practical application of these models for enterprise search and RAG scenarios.
  7. The Embedding Archives: Multilingual Wiki Embeddings | Cohere BlogThis post details the creation and evaluation of multilingual Wiki embeddings using Cohere's Embed v3 model. It discusses the process of embedding a large corpus of Wikipedia articles across multiple languages, focusing on the challenges and techniques for achieving high-quality, semantically rich representations. The post highlights the performance of Embed v3 in capturing nuanced linguistic differences and enabling effective cross-lingual retrieval and analysis, demonstrating its utility for building sophisticated multilingual search and knowledge retrieval systems.
  8. Semantic Search with OpenSearch and Cohere: A Comprehensive DemoThis post demonstrates the integration of Cohere's embedding models with OpenSearch for semantic search. It covers the technical steps involved in ingesting data into OpenSearch, creating vector indexes using Cohere embeddings, and performing semantic searches. The demo highlights how to leverage OpenSearch's k-NN plugin and Cohere's API to enable powerful semantic search capabilities.
  9. Introducing Embed v3This post details the integration of Embed v3 with Pinecone for scalable semantic search applications, focusing on architectural considerations, performance optimizations, and best practices for efficient large-scale semantic search applications.
  10. Cohere int8 & binary Embeddings - Scale Your Vector Database to Large DatasetsThis post introduces int8 and binary embedding formats for Cohere's Embed 3 model, enabling significant reductions in vector database storage and improvements in retrieval speed. It details the trade-offs between precision and performance for these compressed formats, demonstrating their effectiveness in scaling semantic search to large datasets without sacrificing accuracy.
  11. Cohere Embed: New Features for More Efficient and Accurate Enterprise SearchThis post introduces Cohere Embed v3, a new generation of embedding models that significantly improve accuracy and efficiency for enterprise search and RAG applications. It highlights advancements in multilingual capabilities, multimodal understanding (unifying text and image data), and performance optimizations for faster inference and reduced costs. The new models offer improved performance on benchmarks like MTEB and are designed to enhance semantic search, knowledge retrieval, and overall AI-powered search experiences.