{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"cohere-embed-v3","slug":"cohere-embed-v3","name":"Cohere Embed v3","type":"model","url":"https://cohere.com/embed","page_url":"https://unfragile.ai/cohere-embed-v3","categories":["rag-knowledge","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"cohere-embed-v3__cap_0","uri":"capability://data.processing.analysis.multilingual.dense.vector.embedding.generation","name":"multilingual dense vector embedding generation","description":"Converts text input across 100+ languages into 1024-dimensional dense vectors using a transformer-based architecture optimized for semantic similarity. The model generates language-agnostic embeddings that enable cross-lingual retrieval without explicit language identification or intermediate translation steps, leveraging contrastive learning patterns to align semantically similar content across language boundaries.","intents":["Generate embeddings for multilingual documents to enable cross-language semantic search without translation","Build a unified vector space for RAG systems serving global audiences in multiple languages","Create embeddings for non-English text that maintain semantic equivalence with English queries"],"best_for":["Enterprise teams building multilingual RAG pipelines","Global SaaS platforms requiring language-agnostic semantic search","Organizations with mixed-language document corpora (e.g., international financial records, healthcare systems)"],"limitations":["Specific language coverage list not published — '100+ languages' is unverified claim without enumeration","Cross-lingual retrieval accuracy varies by language pair and domain — no per-language benchmark data provided","No documented handling of code-mixed or transliterated text (e.g., Hinglish, Arabic numerals in non-Latin scripts)"],"requires":["Cohere API key (Trial or Production)","Text input in supported language (exact list unknown)","Network access to Cohere API endpoints"],"input_types":["plain text","UTF-8 encoded strings","mixed-language documents"],"output_types":["1024-dimensional float32 vectors","compressed vectors (256, 512, or 768 dimensions via Matryoshka)"],"categories":["data-processing-analysis","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_1","uri":"capability://data.processing.analysis.dimensionality.preserving.vector.compression.via.matryoshka.representation.learning","name":"dimensionality-preserving vector compression via matryoshka representation learning","description":"Compresses 1024-dimensional embeddings to 256, 512, or 768 dimensions using Matryoshka representation learning, a training technique that encodes nested vector hierarchies where lower-dimensional projections preserve semantic information from the full-dimensional space. This enables storage and latency optimization without requiring separate model inference or post-hoc dimensionality reduction (PCA/UMAP), maintaining embedding quality across compression ratios.","intents":["Reduce vector storage footprint in large-scale vector databases (e.g., Pinecone, Weaviate) by 75% without retraining","Lower embedding inference latency and memory consumption for edge deployment or high-throughput scenarios","Dynamically select embedding dimensionality at query time based on latency/accuracy tradeoffs"],"best_for":["Teams managing billion-scale vector indexes with storage cost constraints","Mobile or edge applications requiring sub-millisecond embedding lookups","Hybrid search systems balancing semantic accuracy with inference speed"],"limitations":["Quality loss from compression is claimed as 'minimal' but no ablation studies or MTEB scores provided for compressed variants","Compression is fixed at model training time — cannot dynamically adjust dimensionality per query without retraining","No guidance on optimal dimensionality selection for specific domains or task types"],"requires":["Cohere Embed v3/v4 API access","Vector database supporting arbitrary dimensionality (most modern DBs do)","Understanding of embedding quality tradeoffs for your use case"],"input_types":["1024-dimensional embeddings from Cohere API"],"output_types":["256-dimensional vectors","512-dimensional vectors","768-dimensional vectors"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_10","uri":"capability://search.retrieval.e.commerce.product.search.and.recommendation","name":"e-commerce product search and recommendation","description":"Enables semantic search and recommendation systems for e-commerce by embedding product descriptions, titles, images, and specifications into a unified vector space. Supports multimodal product data (text descriptions + product images + specification tables) and task-optimized embeddings for search-focused retrieval, enabling customers to find products by meaning rather than exact keyword matching.","intents":["Build semantic product search that understands customer intent (e.g., 'waterproof hiking boots' retrieves relevant products despite keyword mismatch)","Generate product recommendations based on embedding similarity without explicit collaborative filtering","Index large product catalogs (millions of SKUs) with multimodal content for fast semantic search"],"best_for":["E-commerce platforms with large product catalogs requiring semantic search","Marketplaces implementing product recommendations based on semantic similarity","Retailers migrating from keyword search to semantic search for improved discovery"],"limitations":["No published benchmarks on e-commerce search quality (click-through rate, conversion impact, etc.)","Multimodal product data handling (text + image + specs) not detailed — unclear how modalities are weighted","No guidance on handling product variants, SKU relationships, or inventory status in embeddings","Search quality depends on product data quality — no preprocessing or data cleaning guidance provided"],"requires":["Cohere Embed v3/v4 API access","Product data (descriptions, titles, images, specifications)","Vector database for storing product embeddings","Search interface to query embeddings and rank results"],"input_types":["product descriptions and titles","product images","specification tables","customer queries (natural language)"],"output_types":["ranked product recommendations with similarity scores","product IDs and metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_11","uri":"capability://search.retrieval.cross.lingual.information.retrieval.without.explicit.translation","name":"cross-lingual information retrieval without explicit translation","description":"Enables retrieval of documents in one language using queries in another language by embedding both into a shared cross-lingual vector space. The model aligns semantically equivalent content across languages without intermediate translation steps, leveraging contrastive learning to position similar meanings near each other regardless of language. Supports 100+ languages with documented cross-lingual retrieval capability.","intents":["Build multilingual search systems where users can query in their native language and retrieve documents in any supported language","Index global knowledge bases with mixed-language content and enable language-agnostic semantic search","Enable cross-lingual question-answering where queries and documents may be in different languages"],"best_for":["Global organizations with multilingual document repositories requiring unified search","International teams building RAG systems serving multiple language communities","Platforms supporting users in different languages without maintaining separate indexes"],"limitations":["Cross-lingual retrieval quality varies by language pair — no published per-pair benchmarks","Specific language coverage list not published — '100+ languages' unverified","No guidance on handling low-resource languages or language pairs with limited training data","Cross-lingual performance likely degrades for distant language pairs (e.g., English-Chinese vs. English-Spanish)"],"requires":["Cohere Embed v3/v4 API access","Documents and queries in supported languages","Vector database for storing multilingual embeddings"],"input_types":["text in any of 100+ supported languages","mixed-language documents"],"output_types":["cross-lingual embeddings in shared vector space","ranked retrieval results across languages"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_2","uri":"capability://data.processing.analysis.task.optimized.embedding.generation.with.input.type.parameters","name":"task-optimized embedding generation with input type parameters","description":"Generates embeddings optimized for specific downstream tasks (search vs. classification) via input type parameters that adjust the embedding geometry and attention patterns during inference. The model applies task-specific normalization and weighting to the transformer output, producing vectors that cluster more effectively for retrieval or discriminative tasks without requiring separate model checkpoints.","intents":["Generate embeddings specifically tuned for semantic search (maximize retrieval recall) vs. classification (maximize cluster separation)","Optimize embedding quality for a known downstream task without fine-tuning or training custom models","Reduce embedding quality variance across heterogeneous use cases by selecting task-appropriate parameters"],"best_for":["Teams using embeddings for both search and classification in the same pipeline","Developers optimizing for specific MTEB task categories without model retraining","Enterprise RAG systems where embedding quality directly impacts retrieval precision"],"limitations":["Specific input type parameter names and values not documented — exact API surface unknown","No published guidance on which task types are supported or how to select parameters","No quantitative comparison of embedding quality (recall, precision, F1) between task modes"],"requires":["Cohere API documentation specifying input type parameter names and valid values","Understanding of your downstream task (search vs. classification vs. other)","Cohere API key with Embed v3/v4 access"],"input_types":["text","task type parameter (search or classification, exact values unknown)"],"output_types":["1024-dimensional task-optimized vectors","compressed task-optimized vectors (256/512/768-dim)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_3","uri":"capability://image.visual.multimodal.document.embedding.with.text.image.table.fusion","name":"multimodal document embedding with text-image-table fusion","description":"Generates unified vector representations for mixed-modality business documents containing text, images, graphs, and tables by fusing embeddings from separate modality encoders (text transformer, vision transformer, table parser) into a single 1024-dimensional vector space. The fusion mechanism (architecture unknown) preserves semantic relationships across modalities, enabling retrieval of documents based on queries that reference any modality combination.","intents":["Index financial filings, healthcare records, and technical documentation containing mixed text and visual content in a single vector space","Retrieve documents based on queries mentioning both text content and visual elements (e.g., 'find reports with declining revenue charts')","Build RAG systems over unstructured enterprise documents without preprocessing to extract and separately embed modalities"],"best_for":["Enterprise RAG systems over financial, legal, or healthcare documents with embedded charts and tables","E-commerce product search combining product descriptions, images, and specification tables","Document management systems requiring semantic search across mixed-modality corpora"],"limitations":["Multimodal fusion mechanism and architecture completely undocumented — no details on how text/image/table embeddings are combined","No published benchmarks on multimodal retrieval quality or per-modality contribution to final embedding","Maximum image resolution, table complexity, and document length limits unknown","No guidance on handling documents with missing modalities (e.g., text-only or image-only)"],"requires":["Cohere Embed v3/v4 API access","Documents in supported formats (exact formats unknown)","Images embedded in documents or provided separately (API surface unknown)"],"input_types":["text","images (format and resolution limits unknown)","tables (format and complexity limits unknown)","mixed-modality documents"],"output_types":["1024-dimensional multimodal vectors","compressed multimodal vectors (256/512/768-dim)"],"categories":["image-visual","data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_4","uri":"capability://search.retrieval.semantic.search.and.retrieval.via.vector.similarity","name":"semantic search and retrieval via vector similarity","description":"Powers semantic search systems by computing cosine or dot-product similarity between query embeddings and document embeddings in the vector space, returning ranked results based on geometric proximity. The search operates on pre-computed embeddings stored in vector databases (Pinecone, Weaviate, Milvus, etc.), enabling sub-millisecond retrieval over billion-scale corpora without re-embedding at query time.","intents":["Build semantic search engines that retrieve documents by meaning rather than keyword matching","Implement the retrieval component of RAG systems that feed context to LLMs","Enable similarity-based recommendations (products, documents, users) using embedding distance"],"best_for":["Teams building RAG pipelines requiring semantic document retrieval","E-commerce and content platforms implementing semantic search and recommendations","Enterprise search systems over unstructured knowledge bases"],"limitations":["Search quality depends entirely on embedding quality — no semantic understanding beyond vector similarity","No built-in ranking beyond similarity score — requires external reranking for production quality","Retrieval is approximate in vector databases (ANN search) — may miss relevant documents at scale","No native support for filtering by metadata or hybrid search (requires vector DB integration)"],"requires":["Vector database (Pinecone, Weaviate, Milvus, Qdrant, etc.) with pre-computed embeddings","Query text to embed using Cohere API","Vector similarity computation (cosine, dot-product, Euclidean)"],"input_types":["query text (any language supported by Embed v3/v4)","pre-computed document embeddings in vector database"],"output_types":["ranked list of documents with similarity scores","document IDs and metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_5","uri":"capability://memory.knowledge.enterprise.rag.pipeline.integration.with.document.indexing","name":"enterprise rag pipeline integration with document indexing","description":"Integrates with enterprise RAG systems by providing embeddings for batch document indexing, enabling large-scale semantic search over knowledge bases. The integration pattern involves embedding documents offline (via batch API or Model Vault), storing vectors in a vector database, and using query embeddings for retrieval at inference time. Supports high-context business documents (financial filings, healthcare records) with multimodal content.","intents":["Index enterprise knowledge bases (financial reports, legal documents, technical specs) for semantic search","Build RAG systems that retrieve relevant context from large document corpora to augment LLM responses","Enable question-answering over proprietary documents without fine-tuning LLMs"],"best_for":["Enterprise teams deploying RAG systems over proprietary document collections","Financial services and healthcare organizations requiring semantic search over regulated documents","Organizations migrating from keyword search to semantic search without retraining LLMs"],"limitations":["Batch indexing latency unknown — no published throughput (docs/sec) or time-to-index metrics","No built-in document preprocessing or chunking — requires external pipeline to split long documents","Embedding quality on domain-specific documents (financial, medical) unverified — no domain benchmarks provided","No native integration with vector databases — requires custom code to orchestrate embedding → storage → retrieval"],"requires":["Cohere API key (Production for commercial use)","Vector database (Pinecone, Weaviate, etc.) for storing embeddings","Document preprocessing pipeline (chunking, cleaning, deduplication)","Orchestration layer to manage embedding → storage → retrieval workflow"],"input_types":["documents (text, images, tables, mixed-modality)","document metadata (title, source, date, etc.)"],"output_types":["indexed embeddings in vector database","document metadata with vector IDs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_6","uri":"capability://tool.use.integration.api.based.embedding.inference.with.rate.limited.trial.and.production.tiers","name":"api-based embedding inference with rate-limited trial and production tiers","description":"Provides embedding generation via REST API with two deployment tiers: Trial API (free, rate-limited, non-commercial) and Production API (pay-as-you-go billing). Requests are processed synchronously, returning 1024-dimensional vectors (or compressed variants) with latency dependent on request size and API load. Trial tier enforces rate limits and prohibits commercial use; Production tier offers higher throughput and SLA guarantees.","intents":["Generate embeddings on-demand for documents and queries without managing inference infrastructure","Prototype RAG systems and semantic search with minimal setup using free Trial API","Scale embedding generation to production workloads with pay-as-you-go pricing and SLA guarantees"],"best_for":["Developers prototyping RAG systems and semantic search applications","Teams without GPU infrastructure or expertise to self-host embedding models","Startups and small teams requiring cost-effective embedding generation at scale"],"limitations":["Trial API rate limits and exact limits unknown — documentation does not specify requests/min or tokens/sec","Trial API explicitly prohibits production/commercial use — requires migration to Production tier","API pricing structure not disclosed — no per-request or per-token pricing published","Inference latency not published — no p50, p95, p99 latency benchmarks provided","No batch API for efficient large-scale indexing — requires sequential API calls or custom batching logic"],"requires":["Cohere API key (Trial or Production)","HTTP client library (curl, Python requests, etc.)","Network access to Cohere API endpoints","Production API key for commercial use"],"input_types":["text (any language supported by Embed v3/v4)","images and tables (multimodal support)","task type parameter (search vs. classification)"],"output_types":["JSON response with 1024-dimensional vector","compressed vectors (256/512/768-dim) if requested"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_7","uri":"capability://tool.use.integration.dedicated.model.vault.deployment.with.fixed.and.flexible.pricing","name":"dedicated model vault deployment with fixed and flexible pricing","description":"Provides fully managed dedicated deployment of Embed v3/v4 via Cohere's Model Vault platform, offering isolated inference infrastructure with fixed hourly or monthly pricing. Deployments run on Cohere-managed hardware (GPU/CPU specs unknown) with guaranteed availability and performance SLAs. Supports VPC, on-premises, and multi-cloud deployment options (AWS/Azure/GCP implied but unconfirmed).","intents":["Deploy embeddings to production with guaranteed SLA and isolated infrastructure","Reduce per-request API costs for high-volume embedding workloads via fixed pricing","Maintain data privacy by running embeddings in VPC or on-premises without sending data to Cohere's shared API"],"best_for":["Enterprise teams with high-volume embedding workloads (millions of embeddings/day)","Organizations with data residency or privacy requirements prohibiting cloud API calls","Teams requiring guaranteed SLA and performance isolation"],"limitations":["Pricing is fixed hourly/monthly regardless of usage — uneconomical for low-volume workloads","Minimum deployment cost ($2,500/month for Embed 4 Small) may exceed API costs for small teams","Hardware specifications (GPU memory, CPU cores, throughput) not published — impossible to estimate cost/performance tradeoff","Deployment and scaling procedures unknown — no documentation on provisioning, autoscaling, or failover","VPC and on-premises deployment details unknown — unclear if supported or requires custom engineering"],"requires":["Cohere Model Vault account with enterprise contract","Minimum monthly commitment ($2,500 for Embed 4 Small)","Infrastructure for VPC or on-premises deployment (if applicable)","Custom integration code to route requests to dedicated endpoint"],"input_types":["text","images and tables (multimodal)","task type parameters"],"output_types":["1024-dimensional vectors","compressed vectors (256/512/768-dim)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_8","uri":"capability://data.processing.analysis.mteb.benchmark.evaluation.and.competitive.positioning","name":"mteb benchmark evaluation and competitive positioning","description":"Cohere Embed v3/v4 is positioned as outperforming OpenAI text-embedding-3 and Voyage AI on MTEB (Massive Text Embedding Benchmark), a standardized evaluation suite covering retrieval, clustering, classification, and semantic similarity tasks across multiple languages and domains. The claim is based on MTEB benchmark scores, though specific scores and task breakdowns are not published in available documentation.","intents":["Evaluate embedding quality for specific MTEB task categories (retrieval, clustering, etc.) before deployment","Compare Cohere Embed v3/v4 performance against OpenAI and Voyage on standardized benchmarks","Justify embedding model selection to stakeholders based on published benchmark results"],"best_for":["Teams evaluating embedding models for production deployment","Organizations requiring benchmark-backed model selection decisions","Developers optimizing for specific MTEB task categories (e.g., retrieval vs. clustering)"],"limitations":["Specific MTEB scores not published — claim of superiority is unverified and unquantified","No per-task breakdown (retrieval vs. clustering vs. classification) — unclear which tasks Cohere excels at","No per-language breakdown — multilingual superiority claim unverified","MTEB benchmarks may not reflect real-world performance on proprietary documents or domains","No published ablation studies showing impact of task-specific parameters or compression on MTEB scores"],"requires":["Access to MTEB benchmark results (public leaderboard or Cohere documentation)","Understanding of MTEB task definitions and evaluation methodology","Ability to run custom MTEB evaluations on your own data for validation"],"input_types":["MTEB benchmark datasets (public)"],"output_types":["MTEB scores (retrieval, clustering, classification, semantic similarity)","comparative rankings vs. OpenAI and Voyage"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__cap_9","uri":"capability://data.processing.analysis.enterprise.document.handling.with.high.context.business.content","name":"enterprise document handling with high-context business content","description":"Optimizes embedding generation for high-context business documents (financial filings, healthcare records, legal contracts, technical specifications) containing dense text, tables, charts, and domain-specific terminology. The model is trained to preserve semantic nuance in specialized vocabularies and maintain coherence across long, complex documents without documented context window limits or chunking requirements.","intents":["Index financial reports, 10-K filings, and earnings call transcripts for semantic search and analysis","Build RAG systems over healthcare records and medical literature with domain-specific terminology","Enable semantic search over legal contracts and regulatory documents with precise language requirements"],"best_for":["Financial services firms building semantic search over earnings reports and regulatory filings","Healthcare organizations indexing medical records and clinical literature","Legal departments enabling semantic search over contract repositories"],"limitations":["Domain-specific embedding quality unverified — no published benchmarks on financial, legal, or medical documents","Maximum document length and context window unknown — no guidance on handling very long documents","No published evaluation on domain-specific terminology preservation or accuracy","Unclear how multimodal content (charts, tables) in business documents affects embedding quality"],"requires":["Cohere Embed v3/v4 API access","Business documents in supported formats (text, images, tables)","Understanding of domain-specific terminology and context for your use case"],"input_types":["financial documents (10-K, 10-Q, earnings transcripts, etc.)","healthcare records (clinical notes, medical literature, etc.)","legal documents (contracts, regulatory filings, etc.)","technical specifications and documentation"],"output_types":["1024-dimensional domain-aware embeddings","compressed domain-aware embeddings (256/512/768-dim)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cohere-embed-v3__headline","uri":"capability://search.retrieval.multilingual.embedding.model.for.semantic.search.and.classification","name":"multilingual embedding model for semantic search and classification","description":"Cohere Embed v3 is a state-of-the-art multilingual embedding model that generates high-quality embeddings for semantic search and classification tasks, optimized for performance across 100+ languages.","intents":["best multilingual embedding model","embedding model for semantic search","embedding model for classification tasks","Cohere Embed vs OpenAI embeddings","best embeddings for enterprise RAG pipelines"],"best_for":["enterprise RAG pipelines","multilingual applications"],"limitations":[],"requires":[],"input_types":["text","images"],"output_types":["1024-dimensional vectors","compressed embeddings"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Cohere API key (Trial or Production)","Text input in supported language (exact list unknown)","Network access to Cohere API endpoints","Cohere Embed v3/v4 API access","Vector database supporting arbitrary dimensionality (most modern DBs do)","Understanding of embedding quality tradeoffs for your use case","Product data (descriptions, titles, images, specifications)","Vector database for storing product embeddings","Search interface to query embeddings and rank results","Documents and queries in supported languages"],"failure_modes":["Specific language coverage list not published — '100+ languages' is unverified claim without enumeration","Cross-lingual retrieval accuracy varies by language pair and domain — no per-language benchmark data provided","No documented handling of code-mixed or transliterated text (e.g., Hinglish, Arabic numerals in non-Latin scripts)","Quality loss from compression is claimed as 'minimal' but no ablation studies or MTEB scores provided for compressed variants","Compression is fixed at model training time — cannot dynamically adjust dimensionality per query without retraining","No guidance on optimal dimensionality selection for specific domains or task types","No published benchmarks on e-commerce search quality (click-through rate, conversion impact, etc.)","Multimodal product data handling (text + image + specs) not detailed — unclear how modalities are weighted","No guidance on handling product variants, SKU relationships, or inventory status in embeddings","Search quality depends on product data quality — no preprocessing or data cleaning guidance provided","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cohere-embed-v3","compare_url":"https://unfragile.ai/compare?artifact=cohere-embed-v3"}},"signature":"y2eRpdsdrOj9jF98RABUuBeAcR9mrscUbS06GyDg6i4fPD7fUmQ1ZlIP5aqAAAtghHwRqGnlXFzgIERaq3UkCg==","signedAt":"2026-06-22T16:39:03.694Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cohere-embed-v3","artifact":"https://unfragile.ai/cohere-embed-v3","verify":"https://unfragile.ai/api/v1/verify?slug=cohere-embed-v3","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}