{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"voyage-ai","slug":"voyage-ai","name":"Voyage AI","type":"api","url":"https://www.voyageai.com","page_url":"https://unfragile.ai/voyage-ai","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"voyage-ai__cap_0","uri":"capability://memory.knowledge.general.purpose.text.embedding.generation.with.32k.token.context","name":"general-purpose text embedding generation with 32k token context","description":"Converts unstructured text into dense vector representations using the voyage-3.5 model, supporting up to 32K tokens of context per input. The model is optimized for retrieval-augmented generation (RAG) pipelines and produces 3x-8x shorter vectors than competing embeddings while maintaining superior accuracy on benchmark tasks. Handles arbitrary text length by chunking internally and returning normalized vector outputs compatible with any vector database.","intents":["I need to embed large documents with full context preservation for semantic search","I want shorter, more efficient vectors to reduce storage and compute costs in my vector database","I need embeddings that work out-of-the-box with any vector database without custom adapters"],"best_for":["Teams building RAG systems with large document collections","Developers optimizing for vector storage efficiency and query latency","Organizations migrating from other embedding providers to reduce infrastructure costs"],"limitations":["Context window capped at 32K tokens; longer documents require pre-chunking strategy","Specific vector dimensionality not disclosed in public documentation; may vary by model variant","No streaming support for real-time embedding generation; batch processing recommended for scale","Latency metrics not publicly specified; '4x faster' claim lacks independent verification"],"requires":["Valid Voyage AI API key","HTTP client or official SDK (language/version unknown)","Text input in UTF-8 encoding"],"input_types":["text (unstructured documents, paragraphs, sentences)","code snippets (via domain-specific code embedding model)"],"output_types":["dense vector (float array, dimensionality model-dependent)","normalized embeddings (L2 norm)"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_1","uri":"capability://memory.knowledge.lightweight.text.embedding.generation.with.reduced.model.footprint","name":"lightweight text embedding generation with reduced model footprint","description":"Provides the voyage-3.5-lite variant, a compressed version of the general-purpose embedding model optimized for inference speed and reduced computational requirements. Maintains competitive accuracy on retrieval benchmarks while consuming 4x less compute resources, enabling deployment on edge devices, serverless functions, and cost-constrained environments. Produces the same vector format as voyage-3.5 for seamless integration into existing RAG pipelines.","intents":["I need to embed documents in a serverless or edge environment with strict latency budgets","I want to reduce API costs by using a smaller model without sacrificing retrieval quality","I need to run embeddings locally or on-device without cloud infrastructure"],"best_for":["Startups and indie developers with cost-sensitive embedding workloads","Edge computing and mobile applications requiring low-latency embeddings","High-volume embedding operations where per-token costs are critical"],"limitations":["Accuracy trade-offs not quantified in public benchmarks; relative performance vs voyage-3.5 unknown","No local/on-device deployment option confirmed; still requires API calls","Dimensionality and vector size reduction not specified","No information on whether lite variant supports full 32K token context"],"requires":["Valid Voyage AI API key","HTTP client or official SDK","Text input in UTF-8 encoding"],"input_types":["text (unstructured documents, paragraphs, sentences)"],"output_types":["dense vector (float array, dimensionality model-dependent)","normalized embeddings (L2 norm)"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_10","uri":"capability://tool.use.integration.llm.agnostic.embedding.and.reranking.for.rag.pipelines","name":"llm-agnostic embedding and reranking for rag pipelines","description":"Voyage AI embeddings and reranking models are designed to integrate with any large language model (OpenAI, Anthropic, Ollama, open-source LLMs, etc.) without vendor-specific adapters. The embedding and reranking outputs conform to standard formats that any LLM can consume, enabling flexible RAG pipeline composition. Organizations can combine Voyage embeddings with their choice of LLM without architectural constraints or proprietary integrations.","intents":["I want to use Voyage embeddings with my preferred LLM without vendor lock-in","I need to build RAG pipelines that can switch between different LLM providers","I want to evaluate different LLM and embedding combinations without infrastructure changes"],"best_for":["Organizations building flexible RAG systems with multiple LLM options","Teams evaluating different LLM providers without embedding constraints","Developers prioritizing architecture flexibility and avoiding vendor lock-in"],"limitations":["No documented integration guides for specific LLM providers","No built-in prompt engineering or context formatting; requires manual integration","No LLM-specific optimizations or tuning; generic compatibility only","Organizations must handle context window management and token counting independently"],"requires":["Valid Voyage AI API key","HTTP client or official SDK","Any LLM API (OpenAI, Anthropic, Ollama, etc.)"],"input_types":["text (documents to embed and rerank)"],"output_types":["embeddings and reranked results (compatible with any LLM input format)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_2","uri":"capability://memory.knowledge.domain.specific.embedding.models.for.finance.legal.and.code","name":"domain-specific embedding models for finance, legal, and code","description":"Provides specialized embedding models fine-tuned for specific domains (finance, legal, code) that outperform general-purpose embeddings on domain-specific retrieval benchmarks. Each model is trained on domain-relevant corpora and optimized for terminology, context, and semantic relationships unique to that field. Integrates seamlessly into RAG pipelines by replacing the general-purpose embedding model while maintaining the same vector database interface.","intents":["I need to embed financial documents and retrieve relevant regulatory filings or market analysis with domain-aware semantics","I want to build a legal research system that understands case law, statutes, and contract language with precision","I need to embed source code and retrieve semantically similar functions or patterns across a codebase"],"best_for":["Financial services firms building compliance and market intelligence systems","Legal tech companies and law firms automating document discovery and research","Software development teams building code search and refactoring tools"],"limitations":["Specific model names and availability not documented; unclear which domains are currently supported","No public benchmarks comparing domain models to general-purpose alternatives","Context window support (32K tokens) not confirmed for domain-specific variants","Custom fine-tuning availability mentioned but requires sales contact; no self-service option"],"requires":["Valid Voyage AI API key","HTTP client or official SDK","Domain-relevant text input (financial documents, legal text, source code)"],"input_types":["text (domain-specific documents: financial reports, legal documents, source code)"],"output_types":["dense vector (float array, dimensionality model-dependent)","normalized embeddings (L2 norm)"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_3","uri":"capability://memory.knowledge.custom.company.specific.embedding.models.via.fine.tuning","name":"custom company-specific embedding models via fine-tuning","description":"Enables organizations to request custom fine-tuned embedding models tailored to their proprietary data, terminology, and domain-specific requirements. The fine-tuning process leverages Voyage AI's base models and adapts them to company-specific semantic relationships, enabling superior retrieval performance on internal knowledge bases and proprietary corpora. Custom models are deployed via the same API interface as standard models, requiring no changes to downstream RAG infrastructure.","intents":["I want to fine-tune embeddings on my company's proprietary documents to improve retrieval accuracy","I need embeddings that understand our internal terminology, product names, and domain-specific concepts","I want to optimize embedding performance for our specific use case without building a custom model from scratch"],"best_for":["Enterprise organizations with large proprietary document collections and custom terminology","Companies with domain-specific retrieval requirements that general models cannot satisfy","Teams with sufficient budget for custom model development and deployment"],"limitations":["Custom fine-tuning requires sales contact; no self-service API or pricing information available","Minimum data requirements, training time, and deployment timeline not documented","No information on model versioning, updates, or retraining workflows","Unclear whether custom models support 32K token context or other base model features"],"requires":["Sales contact with Voyage AI for custom model request","Proprietary training data (quantity and format requirements unknown)","Valid API key for deployed custom model"],"input_types":["text (proprietary documents, internal knowledge bases, company-specific corpora)"],"output_types":["dense vector (float array, dimensionality model-dependent)","normalized embeddings (L2 norm)"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_4","uri":"capability://memory.knowledge.multimodal.embedding.generation.for.text.and.images","name":"multimodal embedding generation for text and images","description":"The voyage-multimodal-3.5 model generates embeddings for both text and images in a shared vector space, enabling cross-modal retrieval where text queries can retrieve relevant images and vice versa. The model is trained to align text and image semantics, producing vectors that preserve both modalities' semantic relationships. Integrates into RAG pipelines to support hybrid document collections containing both text and visual content.","intents":["I need to embed documents with both text and images and retrieve relevant results across modalities","I want to search for images using text queries or find similar images to a reference image","I need to build a multimodal RAG system that understands documents with mixed content types"],"best_for":["E-commerce and product discovery platforms with mixed text and image content","Content management systems requiring cross-modal search and retrieval","Multimodal RAG applications combining documents, images, and structured data"],"limitations":["Model announced but not yet released; availability date and pricing unknown","Supported image formats, resolution limits, and preprocessing requirements not documented","Cross-modal retrieval accuracy and performance metrics not available","No information on whether multimodal model supports 32K token context for text"],"requires":["Valid Voyage AI API key (when model becomes available)","HTTP client or official SDK","Text and/or image input (formats and size limits unknown)"],"input_types":["text (arbitrary length, up to context window)","image (format and resolution requirements unknown)"],"output_types":["dense vector (float array, shared embedding space for text and images)","normalized embeddings (L2 norm)"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_5","uri":"capability://memory.knowledge.context.aware.chunk.level.embeddings.with.global.document.context","name":"context-aware chunk-level embeddings with global document context","description":"The voyage-context-3 model generates embeddings that preserve both chunk-level details and global document context, addressing the limitation of standard embeddings that lose document-level semantics when chunking. The model is trained to understand how individual chunks relate to the overall document structure and meaning, improving retrieval accuracy for systems that chunk documents into smaller units. Outputs embeddings compatible with standard vector databases while maintaining awareness of document-level context.","intents":["I need to chunk large documents for retrieval but preserve document-level context in embeddings","I want to improve retrieval accuracy by understanding how chunks relate to the overall document","I need embeddings that reduce false positives when retrieving from chunked document collections"],"best_for":["RAG systems that chunk documents into smaller units for vector database storage","Applications requiring high retrieval precision where chunk context matters","Teams building document understanding systems that need both local and global semantics"],"limitations":["Model name and availability not confirmed in public documentation","How document context is encoded and preserved not technically specified","No benchmarks comparing context-aware embeddings to standard chunking approaches","Unclear whether model supports 32K token context or has reduced context window"],"requires":["Valid Voyage AI API key","HTTP client or official SDK","Chunked text input with document structure information (format unknown)"],"input_types":["text (chunked documents with document-level context)"],"output_types":["dense vector (float array, dimensionality model-dependent)","normalized embeddings (L2 norm)"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_6","uri":"capability://search.retrieval.general.purpose.reranking.with.instruction.following.capability","name":"general-purpose reranking with instruction-following capability","description":"The rerank-2.5 model re-orders retrieved search results to improve relevance ranking, using instruction-following capabilities to adapt reranking behavior based on user intent. The model takes a query and a list of candidate documents, scores each document's relevance to the query, and returns a ranked list optimized for precision. Integrates into RAG pipelines as a post-retrieval step to refine results from vector database queries before passing to the LLM.","intents":["I need to improve retrieval quality by reranking vector database results based on relevance","I want to customize reranking behavior based on query intent or domain-specific criteria","I need to reduce false positives from semantic search by applying a second-stage ranking model"],"best_for":["RAG systems requiring high retrieval precision and low false positive rates","Search applications where ranking quality directly impacts user experience","Teams building domain-specific retrieval systems with custom ranking requirements"],"limitations":["Reranking latency not specified; adds processing overhead to retrieval pipeline","Maximum number of documents per reranking request not documented","Instruction-following capability not technically detailed; unclear what instructions are supported","No benchmarks comparing rerank-2.5 to alternatives (Cohere rerank, Jina reranker)"],"requires":["Valid Voyage AI API key","HTTP client or official SDK","Query text and list of candidate documents (format and size limits unknown)"],"input_types":["text (query string)","text (list of candidate documents to rerank)"],"output_types":["ranked list (documents ordered by relevance score)","relevance scores (numeric values per document)"],"categories":["search-retrieval","ranking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_7","uri":"capability://search.retrieval.lightweight.reranking.with.reduced.computational.overhead","name":"lightweight reranking with reduced computational overhead","description":"The rerank-2.5-lite variant provides a compressed reranking model optimized for inference speed and reduced computational requirements, enabling real-time reranking in latency-sensitive applications. Maintains competitive ranking accuracy compared to rerank-2.5 while consuming significantly less compute resources, making it suitable for high-throughput retrieval pipelines and edge deployments. Produces the same ranking output format as rerank-2.5 for seamless pipeline integration.","intents":["I need to rerank search results in real-time without adding significant latency to my retrieval pipeline","I want to reduce infrastructure costs by using a lighter reranking model","I need to rerank results at scale without overwhelming my compute budget"],"best_for":["High-throughput search and RAG systems where reranking latency is critical","Cost-sensitive applications requiring reranking without expensive compute","Real-time search applications with strict latency budgets"],"limitations":["Accuracy trade-offs not quantified; relative performance vs rerank-2.5 unknown","Latency improvements not specified with concrete metrics","Instruction-following capability not confirmed for lite variant","No information on maximum batch size or throughput capacity"],"requires":["Valid Voyage AI API key","HTTP client or official SDK","Query text and list of candidate documents"],"input_types":["text (query string)","text (list of candidate documents to rerank)"],"output_types":["ranked list (documents ordered by relevance score)","relevance scores (numeric values per document)"],"categories":["search-retrieval","ranking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_8","uri":"capability://automation.workflow.batch.api.for.large.scale.embedding.and.reranking.operations","name":"batch api for large-scale embedding and reranking operations","description":"Provides a batch processing API for embedding and reranking large volumes of documents asynchronously, optimizing for throughput and cost efficiency over latency. The batch API accepts bulk requests, processes them in optimized batches, and returns results via callback or polling mechanism. Enables cost-effective processing of millions of documents without hitting rate limits or incurring per-request overhead of synchronous API calls.","intents":["I need to embed millions of documents for initial vector database population without hitting rate limits","I want to rerank large result sets asynchronously without blocking my application","I need to process bulk embedding/reranking jobs cost-effectively with flexible timing"],"best_for":["Data engineering teams building initial RAG infrastructure with large document collections","Batch processing pipelines for periodic embedding updates and reranking","Cost-sensitive organizations processing high volumes of embeddings"],"limitations":["Batch API implementation details not documented; request format, polling mechanism, and result delivery unknown","Processing time and throughput guarantees not specified","Maximum batch size and job size limits not documented","No information on error handling, retries, or failure recovery for batch jobs"],"requires":["Valid Voyage AI API key","HTTP client or official SDK with batch API support","Bulk input data in supported format (format unknown)"],"input_types":["text (bulk documents for embedding)","text (bulk queries and documents for reranking)"],"output_types":["batch results (embeddings or reranking scores)","job status and completion notifications"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__cap_9","uri":"capability://tool.use.integration.vector.database.agnostic.embedding.integration","name":"vector database agnostic embedding integration","description":"Voyage AI embeddings are designed to be compatible with any vector database (Pinecone, Weaviate, Milvus, Qdrant, etc.) without custom adapters or format conversions. The API returns standard dense vectors in normalized format that conform to vector database input specifications, enabling plug-and-play integration. Organizations can switch between Voyage embedding models or migrate to other providers without modifying vector database schemas or retrieval code.","intents":["I want to use Voyage embeddings with my existing vector database without custom integration code","I need to evaluate different embedding providers without rewriting my vector database layer","I want to avoid vendor lock-in by using embeddings that work with any vector database"],"best_for":["Organizations building RAG systems with flexibility to switch embedding providers","Teams evaluating multiple embedding models without infrastructure changes","Developers prioritizing portability and avoiding vendor lock-in"],"limitations":["Vector dimensionality not specified; may vary by model, requiring schema adjustments when switching models","No documented integration guides for specific vector databases","Normalization and distance metric requirements not explicitly documented","No built-in connectors or adapters; integration requires manual API calls"],"requires":["Valid Voyage AI API key","HTTP client or official SDK","Vector database with support for dense vector storage (any major provider)"],"input_types":["text (documents to embed)"],"output_types":["dense vector (float array, compatible with standard vector database formats)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"voyage-ai__headline","uri":"capability://memory.knowledge.rag.framework.for.optimized.retrieval.and.embedding.generation","name":"rag framework for optimized retrieval and embedding generation","description":"Voyage AI provides state-of-the-art embedding models specifically designed for retrieval-augmented generation (RAG), delivering superior performance in various domains including code, legal, and finance.","intents":["best RAG framework","RAG for embedding generation","top embedding models for retrieval","RAG solutions for legal documents","best API for domain-specific embeddings"],"best_for":["developers needing domain-specific models","teams focusing on retrieval-augmented generation"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Valid Voyage AI API key","HTTP client or official SDK (language/version unknown)","Text input in UTF-8 encoding","HTTP client or official SDK","Any LLM API (OpenAI, Anthropic, Ollama, etc.)","Domain-relevant text input (financial documents, legal text, source code)","Sales contact with Voyage AI for custom model request","Proprietary training data (quantity and format requirements unknown)","Valid API key for deployed custom model","Valid Voyage AI API key (when model becomes available)"],"failure_modes":["Context window capped at 32K tokens; longer documents require pre-chunking strategy","Specific vector dimensionality not disclosed in public documentation; may vary by model variant","No streaming support for real-time embedding generation; batch processing recommended for scale","Latency metrics not publicly specified; '4x faster' claim lacks independent verification","Accuracy trade-offs not quantified in public benchmarks; relative performance vs voyage-3.5 unknown","No local/on-device deployment option confirmed; still requires API calls","Dimensionality and vector size reduction not specified","No information on whether lite variant supports full 32K token context","No documented integration guides for specific LLM providers","No built-in prompt engineering or context formatting; requires manual integration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","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=voyage-ai","compare_url":"https://unfragile.ai/compare?artifact=voyage-ai"}},"signature":"yDrBwSgQoZBAlR4KcLkglAZpMW6PnOHPUBRnu1E+8ApNDgkS25n9q+0lkJd0zEegkhgwqyq9ZB1NDPD50WSfCQ==","signedAt":"2026-06-21T12:59:37.175Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/voyage-ai","artifact":"https://unfragile.ai/voyage-ai","verify":"https://unfragile.ai/api/v1/verify?slug=voyage-ai","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"}}