{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"jina-embeddings","slug":"jina-embeddings","name":"Jina Embeddings","type":"api","url":"https://jina.ai/embeddings","page_url":"https://unfragile.ai/jina-embeddings","categories":["rag-knowledge","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"jina-embeddings__cap_0","uri":"capability://memory.knowledge.multilingual.text.embedding.generation.with.8k.token.context","name":"multilingual text embedding generation with 8k token context","description":"Generates dense vector embeddings for text input across 100+ languages using a unified encoder architecture that maintains semantic understanding across linguistic boundaries. The API accepts single strings or batch arrays, processes up to 8K tokens per input, and returns embeddings in configurable formats (float, binary, base64) with optional L2 normalization for efficient cosine similarity computation via dot product operations.","intents":["I need to embed customer support tickets in 15 different languages for multilingual semantic search","I want to build a cross-lingual RAG system that retrieves documents regardless of query language","I need to embed long-form documents (research papers, legal contracts) that exceed typical 512-token limits","I want to normalize embeddings for efficient similarity search in vector databases"],"best_for":["teams building multilingual search and RAG systems","organizations processing long-form documents requiring extended context windows","developers implementing semantic search across global user bases","vector database operators optimizing for cosine similarity with normalized embeddings"],"limitations":["8K token context window may truncate very long documents; requires preprocessing for documents exceeding this limit","No streaming or async API documented; batch processing requires synchronous request-response pattern with potential latency for large batches","Specific per-language performance characteristics and accuracy metrics not publicly disclosed","Binary and base64 output formats trade precision for storage efficiency; float format recommended for maximum semantic fidelity"],"requires":["Valid API key from Jina AI dashboard with active free trial or paid subscription","HTTP/2 capable client library for POST requests to api.jina.ai","Bearer token authentication support in client implementation"],"input_types":["text/plain (single string)","text/plain (array of strings for batch processing)","UTF-8 encoded text in any of 100+ supported languages"],"output_types":["float32 embeddings (default, full precision)","binary embeddings (compact 1-bit representation)","base64-encoded embeddings (efficient transmission)","L2-normalized vectors (unit length for cosine similarity via dot product)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_1","uri":"capability://data.processing.analysis.configurable.embedding.output.formats.with.normalization","name":"configurable embedding output formats with normalization","description":"Provides flexible output serialization for embedding vectors through three distinct formats (float, binary, base64) with optional L2 normalization applied server-side. The normalization flag scales embeddings to unit length, enabling efficient cosine similarity computation via simple dot product operations in downstream vector databases without client-side post-processing.","intents":["I need to reduce storage footprint of embeddings in my vector database by 90% using binary format","I want to transmit embeddings efficiently over the network using base64 encoding","I need normalized embeddings for cosine similarity search without post-processing overhead","I'm building a system where dot product similarity must equal cosine similarity for performance"],"best_for":["teams optimizing vector database storage costs with large embedding collections","systems with bandwidth constraints requiring compact embedding transmission","applications using vector databases (Pinecone, Weaviate, Milvus) that expect normalized embeddings","developers implementing similarity search where computational efficiency is critical"],"limitations":["Binary format (1-bit quantization) introduces precision loss; unsuitable for applications requiring maximum semantic fidelity","Base64 encoding increases payload size by ~33% compared to raw binary; primarily beneficial for text-based transmission protocols","L2 normalization is applied server-side; cannot be toggled per-request for cost optimization","No documentation on quantization method for binary format; unclear if uniform or learned quantization is used"],"requires":["Client capable of decoding base64 or binary formats if not using float output","Vector database supporting the chosen format (most support float; binary support varies)","Understanding of normalization implications for similarity metrics"],"input_types":["text/plain (any language)"],"output_types":["application/json with float32 array (default)","application/json with binary array (1-bit per dimension)","application/json with base64-encoded vector string"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_10","uri":"capability://tool.use.integration.cloud.service.provider.csp.regional.deployment.selection","name":"cloud service provider (csp) regional deployment selection","description":"Allows users to select which cloud service provider (AWS, Google Cloud, Azure, etc.) and region to use for API requests, enabling data residency compliance and latency optimization. A dropdown menu in the dashboard references 'On CSP' selection, suggesting users can choose deployment location. This feature enables compliance with data localization requirements (GDPR, HIPAA, etc.) and reduces latency for geographically distributed users by routing requests to nearby infrastructure.","intents":["I need to ensure embeddings are processed in a specific geographic region for data residency compliance","I want to reduce API latency by routing requests to the nearest cloud provider region","I need to comply with GDPR or other regulations requiring data processing in specific jurisdictions","I want to avoid cross-border data transfer for sensitive documents"],"best_for":["Organizations with data residency requirements (financial, healthcare, government sectors)","Global applications needing latency optimization across regions","Teams subject to GDPR, HIPAA, or other data localization regulations","Enterprises with multi-region deployments"],"limitations":["Supported CSPs and regions not documented — no list of available deployment locations","Regional pricing not documented — unclear if region selection affects pricing","Data residency guarantees not documented — unclear if data is stored in selected region or only processed there","Failover behavior unknown — unclear how requests are handled if selected region is unavailable","Region selection mechanism unclear — no documentation on how to specify region in API requests"],"requires":["Jina AI dashboard access to select CSP and region","Understanding of data residency requirements for your jurisdiction","API key associated with selected CSP/region configuration"],"input_types":["CSP and region selection (via dashboard)"],"output_types":["API endpoint configuration (mechanism not documented)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_2","uri":"capability://data.processing.analysis.batch.text.embedding.processing.with.array.input","name":"batch text embedding processing with array input","description":"Accepts arrays of text strings in a single API request and returns corresponding embeddings in parallel, enabling efficient bulk processing of documents, queries, or corpus items. The API processes multiple inputs synchronously within a single HTTP request-response cycle, reducing network overhead compared to sequential per-item requests.","intents":["I need to embed 10,000 product descriptions in one batch operation for initial vector database population","I want to embed a user's entire document library at once during onboarding","I need to process daily batches of customer feedback for semantic clustering","I'm building an ETL pipeline that embeds CSV rows in bulk"],"best_for":["data engineering teams performing bulk embedding operations during ETL pipelines","teams initializing vector databases with large document collections","applications with periodic batch processing requirements (daily, weekly)","developers building search indexing systems with offline processing"],"limitations":["Batch size limits not documented; unclear maximum array length per request (typical APIs support 100-1000 items per batch)","Synchronous processing means latency scales with batch size; no streaming or chunked response option for very large batches","No documented retry logic for partial batch failures; unclear if single failed item fails entire batch","No progress tracking or cancellation mechanism for long-running batch operations"],"requires":["API key with sufficient rate limit quota for batch size","Client capable of constructing JSON array in request body","Network timeout configuration appropriate for batch size (larger batches require longer timeouts)"],"input_types":["application/json array of strings","each string up to 8K tokens"],"output_types":["application/json array of embedding vectors","order preserved matching input array"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_3","uri":"capability://code.generation.editing.code.understanding.and.semantic.embedding","name":"code understanding and semantic embedding","description":"Encodes source code snippets and entire code files into semantic embeddings that capture syntactic structure and functional meaning, enabling code search, similarity detection, and clone identification. The embedding model understands programming language constructs, variable naming patterns, and algorithmic intent across multiple languages, producing vectors where semantically similar code clusters together regardless of formatting or variable names.","intents":["I need to find duplicate or similar code patterns across a large codebase for refactoring","I want to build a code search engine where developers can query by intent rather than keywords","I need to detect code clones and licensing violations in third-party dependencies","I'm building a code recommendation system that suggests similar implementations"],"best_for":["development teams managing large codebases seeking code deduplication","security teams scanning for code clones and licensing compliance","IDE and code editor vendors building semantic code search features","developers building code-to-code similarity detection systems"],"limitations":["Specific programming languages supported not documented; unclear if all languages or subset (Python, JavaScript, Java, etc.) are optimized","No documentation on handling of comments, docstrings, or non-code text within files; unclear if these are embedded separately or ignored","Code formatting normalization approach not specified; unclear if whitespace, indentation, or naming conventions affect embeddings","No benchmarks provided for code clone detection accuracy or false positive rates"],"requires":["Source code as plain text input (no binary or compiled code)","Code within 8K token limit; larger files require chunking strategy","Understanding that embeddings capture semantic similarity, not syntactic equivalence"],"input_types":["text/plain source code files","code snippets in any supported programming language","multi-file code samples (as separate API calls)"],"output_types":["float32 embeddings capturing code semantics","vectors where similar code has high cosine similarity"],"categories":["code-generation-editing","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_4","uri":"capability://search.retrieval.late.interaction.reranking.for.retrieval.quality.improvement","name":"late interaction reranking for retrieval quality improvement","description":"Provides a reranking mechanism that refines initial retrieval results by computing fine-grained relevance scores between queries and retrieved documents using late interaction architecture. Rather than recomputing full embeddings, the reranker leverages token-level interactions between query and document embeddings to produce more accurate relevance rankings, improving precision of top-k results in RAG pipelines.","intents":["I need to improve precision of my RAG system's top-5 results without recomputing all embeddings","I want to rerank BM25 keyword search results using semantic relevance","I need to filter out false positives from initial embedding-based retrieval","I'm building a multi-stage retrieval pipeline where reranking is the second stage"],"best_for":["teams operating RAG systems where initial retrieval precision is insufficient","hybrid search systems combining keyword and semantic search requiring reranking","applications where retrieval latency permits two-stage ranking (initial + rerank)","developers optimizing for precision-recall tradeoff in information retrieval"],"limitations":["Reranker API endpoint and request/response format not documented; unclear how to invoke or integrate","Late interaction architecture details not specified; unclear if token-level or chunk-level interactions are computed","No performance metrics provided; latency overhead of reranking step unknown","Integration with embedding API unclear; reranker may require separate API calls or batch processing","No documentation on maximum number of documents that can be reranked per request"],"requires":["Initial retrieval results from embedding-based or keyword search","Original query text for relevance computation","Separate API endpoint access (if not bundled with embedding API)","Understanding of late interaction ranking architecture"],"input_types":["query text (string)","candidate documents (array of strings)","document embeddings (optional, if pre-computed)"],"output_types":["relevance scores per document","reranked document order","confidence scores for ranking decisions"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_5","uri":"capability://tool.use.integration.elasticsearch.native.integration.via.elastic.inference.service","name":"elasticsearch native integration via elastic inference service","description":"Provides native integration with Elasticsearch through the Elastic Inference Service, enabling automatic embedding generation and indexing within Elasticsearch pipelines without external API calls. Documents are embedded at ingest time using Jina models, with embeddings stored in dense_vector fields for semantic search queries directly within Elasticsearch.","intents":["I want to embed documents automatically as they're indexed into Elasticsearch without external API calls","I need to run semantic search queries directly in Elasticsearch using Jina embeddings","I'm migrating from keyword search to semantic search while keeping data in Elasticsearch","I want to reduce latency of embedding generation by embedding at ingest time rather than query time"],"best_for":["teams already operating Elasticsearch clusters seeking semantic search capabilities","organizations wanting to avoid external embedding API calls for latency/cost reasons","developers building search applications where Elasticsearch is the primary data store","teams requiring tight integration between indexing and embedding pipelines"],"limitations":["Integration details not documented; unclear if this is Elasticsearch plugin, ingest processor, or external service","Elasticsearch version compatibility not specified; unclear if compatible with Elasticsearch 7.x, 8.x, or only latest versions","Configuration and setup instructions not provided; integration complexity unknown","Pricing model for Elastic Inference Service not documented; unclear if separate billing from API usage","No documentation on fallback behavior if Jina service is unavailable during indexing"],"requires":["Active Elasticsearch cluster (version unknown)","Jina API key configured in Elasticsearch environment","Elasticsearch ingest pipeline configuration (exact syntax not documented)","Network connectivity from Elasticsearch nodes to Jina API endpoints"],"input_types":["documents in Elasticsearch ingest pipeline","text fields to be embedded (configurable per pipeline)"],"output_types":["dense_vector fields in Elasticsearch documents","embeddings stored alongside original text for semantic search"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_6","uri":"capability://tool.use.integration.api.key.management.and.rate.limit.monitoring","name":"api key management and rate limit monitoring","description":"Provides dashboard-based API key generation, rotation, and rate limit tracking through the Jina AI console. Developers can create multiple API keys with independent rate limit quotas, monitor usage in real-time, and adjust tier-based rate limits based on subscription level. The system tracks requests per minute/hour and provides visibility into quota consumption.","intents":["I need to generate separate API keys for different applications or environments","I want to monitor my embedding API usage to understand costs and optimize requests","I need to rotate API keys for security without downtime","I want to set different rate limits for development vs. production environments"],"best_for":["teams managing multiple applications or services using Jina embeddings","developers requiring API key rotation for security compliance","organizations tracking embedding costs and usage patterns","teams operating production systems requiring rate limit visibility"],"limitations":["Specific rate limit tiers and numeric limits not documented; unclear what free vs. paid tiers provide","Rate limit enforcement behavior not specified; unclear if requests are throttled, queued, or rejected when limits exceeded","No documentation on rate limit reset timing (per minute, hour, day, month)","API key rotation process not detailed; unclear if old keys are immediately revoked or have grace period","No programmatic API for rate limit management; appears to be dashboard-only"],"requires":["Jina AI account with email verification","Access to Jina AI console dashboard","Understanding of API key security best practices"],"input_types":["API key creation request (via dashboard UI)"],"output_types":["API key string (Bearer token format)","rate limit metrics (requests used, quota remaining)","usage analytics and cost estimates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_7","uri":"capability://tool.use.integration.bearer.token.authentication.with.api.key.based.access.control","name":"bearer token authentication with api key-based access control","description":"Implements OAuth 2.0 Bearer token authentication where API keys function as bearer tokens in HTTP Authorization headers. Each request requires the header `Authorization: Bearer <API_KEY>`, enabling stateless authentication without session management. API keys are generated per account and can be revoked independently, providing fine-grained access control.","intents":["I need to authenticate my application to Jina embeddings API securely","I want to use the same API key across multiple services without managing sessions","I need to revoke access to Jina API without changing application code","I'm building a multi-tenant application and need per-customer API keys"],"best_for":["developers building API-based applications requiring stateless authentication","teams implementing multi-tenant systems with per-customer API keys","organizations requiring API key rotation without session management","developers using standard HTTP client libraries without OAuth libraries"],"limitations":["No documented support for OAuth 2.0 flows (authorization code, client credentials); only bearer token supported","No API key scoping mechanism documented; unclear if keys can be restricted to specific endpoints or operations","No documented key expiration or automatic rotation; unclear if keys are permanent or have TTL","Bearer token transmitted in HTTP headers; requires HTTPS for security (not explicitly documented but assumed)","No documented support for API key versioning or multiple active keys per application"],"requires":["HTTPS client (HTTP not supported for security)","API key from Jina AI dashboard","HTTP client library supporting custom headers","Secure storage of API key (environment variables, secrets manager, not hardcoded)"],"input_types":["API key string (from dashboard)"],"output_types":["HTTP Authorization header with Bearer token","authenticated API requests"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_8","uri":"capability://tool.use.integration.free.tier.api.access.with.unknown.quota.limits","name":"free tier api access with unknown quota limits","description":"Provides free trial access to Jina Embeddings API without requiring payment, enabling developers to test embeddings before committing to paid usage. Free tier quota and limits are not documented in available materials. Billing is managed through the dashboard's 'API Key & Billing' section, with pay-as-you-go pricing model implied but not detailed. Free tier may have rate limits, token quotas, or usage caps that are not publicly specified.","intents":["I want to test Jina Embeddings in my application before committing to paid usage","I need to prototype a RAG system or semantic search feature with minimal upfront cost","I want to evaluate embedding quality and latency before deciding on a provider","I need to build a proof-of-concept without requesting budget approval"],"best_for":["Developers prototyping embedding-based applications","Startups evaluating embedding providers before scaling","Students and researchers building non-commercial projects","Teams conducting proof-of-concept evaluations"],"limitations":["Free tier quota not documented — no information on token limits, request limits, or monthly allowances","Rate limits unknown — no specification of requests per minute or concurrent request limits","Upgrade path unclear — no documentation on how to transition from free to paid tier","Quota enforcement mechanism unknown — unclear if quota is soft (warnings) or hard (rejections)","No SLA or uptime guarantees documented for free tier — production reliability unknown"],"requires":["Jina AI account (free signup required)","API key generation from dashboard","No payment method required for free tier (but may be required to upgrade)"],"input_types":["text (same as paid tier)"],"output_types":["float (same as paid tier)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__cap_9","uri":"capability://code.generation.editing.auto.code.generation.for.ide.and.llm.copilot.integration","name":"auto code generation for ide and llm copilot integration","description":"Generates client code automatically for integrating Jina Embeddings into IDE copilots and LLM-based development tools. This feature (referenced as 'Auto codegen for your copilot IDE or LLM') likely generates function stubs, API call templates, or SDK bindings for popular IDEs and copilot platforms. Implementation details are not documented, but the intent is to reduce boilerplate code needed to integrate embeddings into development workflows.","intents":["I want my IDE copilot to generate Jina Embeddings integration code automatically","I need to reduce boilerplate code when integrating embeddings into my application","I want copilot suggestions that include proper API calls, error handling, and authentication","I need to accelerate development by auto-generating embedding integration code"],"best_for":["Developers using copilot-enabled IDEs (VS Code with Copilot, JetBrains IDEs, etc.)","Teams wanting to standardize embedding integration patterns across projects","Rapid prototyping scenarios where reducing boilerplate accelerates development"],"limitations":["Code generation implementation not documented — unclear if this is IDE plugin, LLM prompt injection, or API feature","Supported IDEs and copilots unknown — no list of compatible platforms","Generated code quality unknown — no examples or benchmarks on code correctness or best practices","Language support unknown — unclear which programming languages are supported","Customization options unknown — unclear if generated code can be tailored to specific patterns or frameworks"],"requires":["IDE or copilot platform supporting code generation (VS Code Copilot, JetBrains AI, etc.)","Integration with Jina Embeddings documentation or API schema (mechanism not documented)"],"input_types":["natural language prompts or IDE context (mechanism not documented)"],"output_types":["source code (Python, JavaScript, etc.)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jina-embeddings__headline","uri":"capability://memory.knowledge.high.performance.text.embedding.api","name":"high-performance text embedding api","description":"Jina Embeddings is a high-performance API for generating multilingual text embeddings, supporting up to 8K tokens, ideal for search and retrieval-augmented generation applications.","intents":["best text embedding API","text embeddings for multilingual applications","high-performance embeddings for RAG","API for generating text embeddings","embedding models for code understanding"],"best_for":["multilingual text processing","high token context requirements"],"limitations":["max input of 8K tokens"],"requires":["API key for authentication"],"input_types":["text"],"output_types":["embeddings"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Valid API key from Jina AI dashboard with active free trial or paid subscription","HTTP/2 capable client library for POST requests to api.jina.ai","Bearer token authentication support in client implementation","Client capable of decoding base64 or binary formats if not using float output","Vector database supporting the chosen format (most support float; binary support varies)","Understanding of normalization implications for similarity metrics","Jina AI dashboard access to select CSP and region","Understanding of data residency requirements for your jurisdiction","API key associated with selected CSP/region configuration","API key with sufficient rate limit quota for batch size"],"failure_modes":["8K token context window may truncate very long documents; requires preprocessing for documents exceeding this limit","No streaming or async API documented; batch processing requires synchronous request-response pattern with potential latency for large batches","Specific per-language performance characteristics and accuracy metrics not publicly disclosed","Binary and base64 output formats trade precision for storage efficiency; float format recommended for maximum semantic fidelity","Binary format (1-bit quantization) introduces precision loss; unsuitable for applications requiring maximum semantic fidelity","Base64 encoding increases payload size by ~33% compared to raw binary; primarily beneficial for text-based transmission protocols","L2 normalization is applied server-side; cannot be toggled per-request for cost optimization","No documentation on quantization method for binary format; unclear if uniform or learned quantization is used","Supported CSPs and regions not documented — no list of available deployment locations","Regional pricing not documented — unclear if region selection affects pricing","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":0.25,"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":"2026-05-24T12:16:23.327Z","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=jina-embeddings","compare_url":"https://unfragile.ai/compare?artifact=jina-embeddings"}},"signature":"MyghgPsiadc98xhPd671e7tP48aAQLsxh6HU8U1aZIeuzKp5zQV4NrqJno1bY6jqiiEq7rcyi7bA068jFZV1DA==","signedAt":"2026-06-22T06:50:31.673Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jina-embeddings","artifact":"https://unfragile.ai/jina-embeddings","verify":"https://unfragile.ai/api/v1/verify?slug=jina-embeddings","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"}}