{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-jina-ai--serve","slug":"jina-ai--serve","name":"serve","type":"mcp","url":"https://jina.ai/serve","page_url":"https://unfragile.ai/jina-ai--serve","categories":["mcp-servers","deployment-infra"],"tags":["cloud-native","cncf","deep-learning","docker","fastapi","framework","generative-ai","grpc","jaeger","kubernetes","llmops","machine-learning","microservice","mlops","multimodal","neural-search","opentelemetry","orchestration","pipeline","prometheus"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-jina-ai--serve__cap_0","uri":"capability://data.processing.analysis.multimodal.document.centric.request.processing.with.automatic.batching","name":"multimodal document-centric request processing with automatic batching","description":"Jina-serve processes requests through a standardized Document/DocArray data layer that represents multimodal data (text, images, embeddings, metadata) with automatic request batching via dynamic batching logic. Executors receive batched Documents through @requests-decorated methods, enabling efficient processing of variable-sized request streams without manual batch management. The framework handles serialization/deserialization across gRPC, HTTP, and WebSocket protocols transparently.","intents":["I want to build an AI service that handles mixed media types (text + images + embeddings) without writing protocol-specific serialization code","I need automatic batching of incoming requests to maximize GPU utilization without manually managing batch sizes","I want to process requests from multiple protocols (gRPC, HTTP, WebSocket) with a single executor implementation"],"best_for":["ML engineers building multimodal inference services","teams deploying neural search or retrieval-augmented generation systems","developers migrating from single-protocol services to cloud-native architectures"],"limitations":["Dynamic batching adds latency variance — requests may wait up to configured batch timeout (default ~100ms) for batch formation","Document schema must be predefined; runtime schema changes require redeployment","Batching logic is opaque to executor code — no direct control over batch composition or timeout tuning per-request"],"requires":["Python 3.9+","DocArray package (included with Jina)","Executor class inheriting from jina.Executor with @requests decorator"],"input_types":["Document objects (BaseDoc instances)","DocList (collections of Documents)","Multimodal payloads: text, images (numpy arrays), embeddings (float arrays), metadata dicts"],"output_types":["Document objects with populated fields","DocList with results","Streaming responses via WebSocket/gRPC streaming"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_1","uri":"capability://automation.workflow.declarative.flow.orchestration.with.request.routing.and.composition","name":"declarative flow orchestration with request routing and composition","description":"Jina Flow provides a declarative YAML/Python API to compose Executors into directed acyclic graphs (DAGs) where requests flow through multiple processing stages. The Flow layer manages request routing, parallel execution paths, and result aggregation without requiring manual thread/async management. Flows support both sequential pipelines and branching topologies, with the Gateway component automatically routing requests through the defined execution graph and collecting results.","intents":["I want to compose multiple AI models (embedding, ranking, reranking) into a single inference pipeline without writing orchestration code","I need to define complex request routing logic (e.g., send to model A or B based on input type) declaratively","I want to parallelize independent processing steps and merge results automatically"],"best_for":["teams building multi-stage retrieval-augmented generation (RAG) systems","developers creating neural search pipelines with embedding + ranking stages","ML engineers prototyping complex inference workflows before optimization"],"limitations":["DAG must be acyclic — no feedback loops or conditional branching based on intermediate results","Flow composition is static at deployment time; dynamic routing requires custom Gateway logic","Debugging multi-stage flows requires tracing through multiple executor logs; limited built-in flow visualization"],"requires":["Python 3.9+","YAML configuration file or Python Flow API (jina.Flow class)","Executors deployed as Deployments or Pods"],"input_types":["YAML flow definitions","Python Flow object with .add() method calls","Executor references (by name or class)"],"output_types":["Routed requests to appropriate Executors","Aggregated results from all pipeline stages","Execution traces (with OpenTelemetry instrumentation)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_10","uri":"capability://tool.use.integration.client.side.request.building.with.streaming.support.and.async.sync.apis","name":"client-side request building with streaming support and async/sync apis","description":"Jina provides Client classes (sync and async) for building and sending requests to services via gRPC, HTTP, or WebSocket. Clients support streaming responses (useful for token-by-token LLM generation), batch request submission, and automatic retry logic. Request building is fluent (method chaining) and type-safe with Document objects. Async clients enable high-concurrency request submission.","intents":["I want to send requests to my Jina service from Python with automatic serialization","I need to stream responses back to clients (e.g., LLM token generation)","I want to submit batches of requests concurrently with automatic retry"],"best_for":["Python developers building client applications","teams building web frontends that stream LLM responses","developers testing Jina services programmatically"],"limitations":["Clients are Python-only; non-Python clients must use gRPC/HTTP directly","Streaming is protocol-dependent; HTTP streaming has higher latency than gRPC","Retry logic is basic (exponential backoff); no circuit breaker or advanced resilience patterns"],"requires":["Python 3.9+","Jina client library (included with Jina)","Running Jina service (local or remote)"],"input_types":["Document objects or dictionaries","Request parameters (timeout, retry count, etc.)"],"output_types":["Document responses","Streaming response iterators","Batch results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_11","uri":"capability://memory.knowledge.custom.indexer.integration.for.vector.database.and.search.backend.support","name":"custom indexer integration for vector database and search backend support","description":"Jina Executors can integrate with custom indexers (vector databases, search backends) via a pluggable indexer interface. Executors can implement index/search operations that delegate to external systems (Elasticsearch, Milvus, Weaviate, etc.). The framework provides base classes and patterns for indexer integration, with automatic batching of index/search operations. Indexers can be stateful (maintaining indices across requests) or stateless (delegating to external services).","intents":["I want to build a semantic search service backed by a vector database (Milvus, Weaviate, Pinecone)","I need to index documents and search them with custom similarity metrics","I want to integrate my Jina service with an existing search infrastructure (Elasticsearch, etc.)"],"best_for":["teams building RAG systems with external vector databases","developers integrating Jina with existing search infrastructure","ML engineers building custom indexing logic"],"limitations":["Indexer interface is executor-specific; no standardized indexer abstraction across Jina","External indexer failures are not automatically handled; custom error handling required","Indexer state is not automatically persisted; requires external state management"],"requires":["Python 3.9+","Executor class with custom indexer integration","External indexer/vector database (Milvus, Weaviate, Elasticsearch, etc.)","Client library for external indexer"],"input_types":["Documents with embeddings or text","Search queries with embeddings or text","Index configuration (dimension, metric, etc.)"],"output_types":["Indexed documents in external system","Search results with similarity scores","Metadata about indexed documents"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_12","uri":"capability://safety.moderation.request.filtering.and.validation.with.custom.middleware.and.decorators","name":"request filtering and validation with custom middleware and decorators","description":"Jina supports request filtering via custom middleware and decorators that intercept requests before executor processing. Filters can validate input (schema validation, size limits), transform requests (preprocessing), or reject requests (rate limiting, authentication). Filters are composable and can be applied at Gateway or Executor level. The framework provides base classes for common patterns (authentication, rate limiting).","intents":["I want to validate incoming requests (schema, size limits) before processing","I need to implement rate limiting or authentication for my service","I want to preprocess requests (e.g., normalize text) before executor processing"],"best_for":["teams building production services requiring input validation","developers implementing API authentication and rate limiting","ML engineers building robust inference services"],"limitations":["Middleware is executor-specific; no global middleware across all executors","Filter composition is manual; no declarative filter chains","Performance impact of validation is not transparent; requires profiling"],"requires":["Python 3.9+","Custom middleware/decorator classes","Executor with filter integration"],"input_types":["Incoming requests (Documents)","Filter configuration (validation rules, rate limits, etc.)"],"output_types":["Validated/transformed requests","Rejection responses (if validation fails)","Metrics (validation pass/fail rates)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_13","uri":"capability://safety.moderation.graceful.degradation.and.fallback.handling.for.fault.tolerance","name":"graceful degradation and fallback handling for fault tolerance","description":"Jina supports graceful degradation via fallback executors and timeout-based request handling. If an executor fails or times out, requests can be routed to fallback executors or return partial results. The framework provides configurable timeouts per executor and automatic retry logic with exponential backoff. Failures are logged and can be monitored via OpenTelemetry metrics.","intents":["I want my service to return partial results if a non-critical executor fails","I need to handle slow executors gracefully (timeout and fallback)","I want to monitor and alert on executor failures"],"best_for":["teams building production services requiring high availability","developers building multi-stage pipelines with optional stages","ML engineers handling model inference failures gracefully"],"limitations":["Fallback logic is executor-specific; no global fallback strategy","Timeout values are static; no dynamic timeout adjustment based on load","Partial results may be inconsistent; application logic must handle incomplete data"],"requires":["Python 3.9+","Executor configuration with timeout and fallback settings","Fallback executor implementation (optional)"],"input_types":["Executor timeout configuration","Fallback executor specification"],"output_types":["Partial results (if primary executor fails)","Fallback executor results","Failure metrics and logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_2","uri":"capability://automation.workflow.horizontal.scaling.via.sharding.and.replication.with.load.balancing","name":"horizontal scaling via sharding and replication with load balancing","description":"Jina Deployments support both replication (multiple identical executor instances for load balancing) and sharding (partitioning data across executor instances based on document ID or custom logic). The HeadRuntime component distributes incoming requests to WorkerRuntimes using configurable load-balancing strategies (round-robin, least-loaded), while sharding enables horizontal scaling of stateful operations like indexing. Scaling configuration is declarative via YAML or Python API, with automatic process/container spawning.","intents":["I want to scale a single executor across multiple CPU/GPU cores without rewriting code","I need to partition a large index across multiple executor instances to fit in memory","I want automatic load balancing of requests across executor replicas"],"best_for":["teams deploying inference services requiring >1 GPU or high throughput (>100 req/s)","ML engineers building distributed indexing systems (e.g., vector databases)","developers scaling from single-machine to multi-machine deployments"],"limitations":["Sharding requires stateless or carefully managed state — shared state across shards requires external coordination (Redis, etc.)","Load balancing is request-level; no awareness of per-request compute cost — expensive requests may cause imbalance","Replication multiplies memory usage; no built-in deduplication of shared model weights across replicas"],"requires":["Python 3.9+","Deployment configuration with replicas or shards parameters","For sharding: custom sharding logic or default document ID-based partitioning"],"input_types":["Deployment YAML with replicas/shards count","Python Deployment object with replicas/shards arguments","Custom sharding function (optional)"],"output_types":["Multiple executor processes/containers","Load-balanced request distribution","Aggregated results from shards (if applicable)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_3","uri":"capability://automation.workflow.kubernetes.native.deployment.with.yaml.manifests.and.container.orchestration","name":"kubernetes-native deployment with yaml manifests and container orchestration","description":"Jina Deployments compile to Kubernetes YAML manifests (Services, Deployments, ConfigMaps) that integrate with the Kubernetes API for lifecycle management, scaling, and networking. The framework generates container images (via Docker) and orchestration configs automatically from Flow/Deployment definitions, enabling push-button deployment to Kubernetes clusters. Integration with Kubernetes service discovery, persistent volumes, and resource limits is transparent to executor code.","intents":["I want to deploy my Jina service to Kubernetes without writing YAML manifests manually","I need my service to auto-scale based on Kubernetes metrics (CPU, memory)","I want to manage multiple Jina services as a cohesive Kubernetes application"],"best_for":["teams with existing Kubernetes infrastructure (EKS, GKE, AKS)","DevOps engineers managing multi-service ML deployments","organizations requiring CNCF-compliant deployment patterns"],"limitations":["Kubernetes manifests are generated; custom modifications require post-generation editing or custom Helm charts","No built-in support for Kubernetes StatefulSets — sharded deployments may lose state on pod restart","Debugging Kubernetes-deployed services requires kubectl logs and port-forwarding; limited local debugging parity"],"requires":["Python 3.9+","Docker installed and configured","Kubernetes cluster (1.20+) with kubectl access","Container registry (Docker Hub, ECR, GCR) for image storage"],"input_types":["Jina Flow/Deployment Python definitions","Docker image specifications (base image, dependencies)","Kubernetes resource limits/requests (optional)"],"output_types":["Kubernetes YAML manifests (Deployment, Service, ConfigMap)","Docker container images","Kubernetes Service endpoints for client access"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_4","uri":"capability://tool.use.integration.grpc.http.and.websocket.protocol.support.with.automatic.serialization","name":"grpc, http, and websocket protocol support with automatic serialization","description":"Jina services expose a unified interface across three protocols (gRPC for low-latency RPC, HTTP/REST for broad compatibility, WebSocket for streaming) with automatic Protocol Buffer serialization/deserialization. The Gateway component handles protocol-specific details (HTTP request parsing, gRPC message framing, WebSocket frame handling) transparently, allowing executors to work with Document objects regardless of client protocol. Protocol selection is declarative via configuration.","intents":["I want to serve my AI model via both gRPC (for internal services) and HTTP (for web clients) without protocol-specific code","I need to stream results back to clients in real-time (e.g., token-by-token generation)","I want to support legacy HTTP clients while adding gRPC for performance-critical paths"],"best_for":["teams building polyglot microservices (Python, Go, Java clients)","developers serving both web and internal ML infrastructure","organizations requiring streaming inference (LLM token generation, video processing)"],"limitations":["HTTP/REST interface is auto-generated; custom REST semantics (e.g., specific status codes) require Gateway customization","WebSocket streaming requires client-side streaming support; not all HTTP clients support WebSocket upgrades","Protocol overhead: HTTP adds ~5-10ms latency vs gRPC; WebSocket adds connection setup latency"],"requires":["Python 3.9+","gRPC libraries (grpcio, grpcio-tools) for gRPC support","FastAPI (included) for HTTP/WebSocket support","Client libraries for each protocol (grpcio-client, requests, websocket-client)"],"input_types":["gRPC messages (Protocol Buffer format)","HTTP JSON payloads","WebSocket frames (JSON or binary)"],"output_types":["gRPC responses (Protocol Buffer format)","HTTP JSON responses","WebSocket streaming frames"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_5","uri":"capability://automation.workflow.opentelemetry.instrumentation.with.distributed.tracing.and.metrics.collection","name":"opentelemetry instrumentation with distributed tracing and metrics collection","description":"Jina integrates OpenTelemetry (OTEL) for automatic distributed tracing across Flow stages, with trace context propagation through request headers. Executors emit spans for each request, with automatic instrumentation of executor methods via decorators. Metrics (request latency, throughput, error rates) are collected and exported to Prometheus-compatible endpoints. Jaeger integration enables trace visualization across multi-stage pipelines.","intents":["I want to trace requests through my multi-stage pipeline to identify bottlenecks","I need to monitor executor latency and error rates in production","I want to correlate logs and traces across distributed services"],"best_for":["teams operating multi-stage ML pipelines in production","DevOps engineers monitoring SLA compliance (latency, error rates)","ML engineers debugging performance issues in distributed deployments"],"limitations":["Instrumentation overhead: tracing adds ~1-5ms per request; can be disabled but loses observability","Trace sampling is global; no per-request sampling based on latency or error status","Metrics cardinality can explode with high-dimensional labels (e.g., per-shard metrics); requires careful label design"],"requires":["Python 3.9+","OpenTelemetry SDK (opentelemetry-api, opentelemetry-sdk)","Jaeger agent or Prometheus scrape endpoint for metrics collection","Trace exporter (jaeger-exporter, otlp-exporter)"],"input_types":["Executor code with @requests decorators (auto-instrumented)","Custom span creation via OpenTelemetry API"],"output_types":["Distributed traces (Jaeger format)","Prometheus metrics (latency histograms, request counters, error rates)","Structured logs with trace context"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_6","uri":"capability://automation.workflow.executor.lifecycle.management.with.initialization.shutdown.and.state.persistence","name":"executor lifecycle management with initialization, shutdown, and state persistence","description":"Jina Executors support lifecycle hooks (@requests for request handling, __init__ for initialization, __del__ for cleanup) that enable stateful processing. Executors can load models on initialization (e.g., BERT embeddings) and persist state to disk or external stores. The framework manages executor process lifecycle, including graceful shutdown and resource cleanup. State can be serialized via pickle or custom serialization logic.","intents":["I want to load a large model once on startup and reuse it across requests","I need to persist executor state (e.g., indexed documents) between deployments","I want to ensure resources (GPU memory, file handles) are cleaned up on shutdown"],"best_for":["ML engineers building stateful services (e.g., vector databases, caches)","teams deploying long-running inference services with expensive initialization","developers requiring model checkpointing and recovery"],"limitations":["State persistence is executor-level; distributed state across shards requires external coordination","Pickle-based serialization has security implications; custom serialization required for untrusted data","Graceful shutdown timeout is configurable but may not guarantee all in-flight requests complete"],"requires":["Python 3.9+","Executor class with __init__ and optional __del__ methods","External storage (S3, local filesystem) for state persistence (optional)"],"input_types":["Model files (PyTorch, TensorFlow, ONNX)","Configuration files (YAML, JSON)","Serialized executor state (pickle, JSON)"],"output_types":["Loaded models in executor memory","Persisted state files","Executor process with initialized resources"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_7","uri":"capability://automation.workflow.yaml.based.configuration.with.python.overrides.for.declarative.service.definition","name":"yaml-based configuration with python overrides for declarative service definition","description":"Jina services can be defined declaratively via YAML configuration files (Flow, Deployment, Executor configs) that specify topology, scaling, and resource limits. Python API provides programmatic equivalents with full expressiveness. Configuration supports environment variable substitution and includes schema validation. YAML configs enable version control and GitOps workflows without code changes.","intents":["I want to define my service topology in version-controlled YAML without code changes","I need to adjust scaling (replicas, shards) and resource limits per environment (dev/staging/prod)","I want to enable GitOps workflows where config changes trigger deployments"],"best_for":["DevOps teams managing multiple Jina deployments","organizations using GitOps (ArgoCD, Flux) for infrastructure","teams separating infrastructure concerns from executor code"],"limitations":["YAML syntax is verbose for complex topologies; Python API is more concise","Schema validation errors are reported at deployment time, not edit time (no IDE support)","Environment variable substitution is limited; no templating engine (unlike Helm)"],"requires":["Python 3.9+","YAML configuration files","Jina CLI or Python API to load configs"],"input_types":["YAML files (Flow, Deployment, Executor configs)","Environment variables for substitution","Python dictionaries (for programmatic config)"],"output_types":["Parsed Flow/Deployment objects","Validated configuration with defaults applied","Kubernetes manifests (if using Kubernetes deployment)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_8","uri":"capability://tool.use.integration.jina.hub.integration.for.pre.built.executor.discovery.and.reuse","name":"jina hub integration for pre-built executor discovery and reuse","description":"Jina Hub is a registry of pre-built Executors (embeddings, rankers, indexers, etc.) that can be discovered, downloaded, and composed into Flows. Hub executors are containerized and versioned, with automatic dependency resolution. Executors can be referenced by name in Flow definitions, with the framework handling image pulls and instantiation. Hub enables rapid prototyping by reusing community-contributed components.","intents":["I want to use a pre-built embedding model (e.g., OpenAI, Hugging Face) without writing code","I need to discover and evaluate different ranking algorithms for my pipeline","I want to compose my service from community-contributed components"],"best_for":["teams prototyping RAG systems quickly","developers without ML expertise building AI services","organizations standardizing on community-vetted components"],"limitations":["Hub executor quality varies; no SLA or support guarantees for community executors","Dependency conflicts can occur when composing multiple Hub executors; resolution is manual","Hub executors are containerized; customization requires forking or wrapping"],"requires":["Python 3.9+","Internet access to Jina Hub registry","Docker for running Hub executors","Jina CLI or Python API to reference Hub executors"],"input_types":["Hub executor names (e.g., 'jinahub://OpenAIEmbedder')","Executor configuration (model name, API key, etc.)"],"output_types":["Downloaded executor containers","Instantiated executors in Flow","Executor metadata (version, dependencies, documentation)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jina-ai--serve__cap_9","uri":"capability://automation.workflow.docker.compose.support.for.local.multi.service.development.and.testing","name":"docker compose support for local multi-service development and testing","description":"Jina can generate Docker Compose files from Flow definitions, enabling local development and testing of multi-service deployments without Kubernetes. Compose files include service definitions, networking, and volume mounts. The framework handles service discovery and inter-service communication via Docker networks. Compose deployment is useful for CI/CD testing before Kubernetes deployment.","intents":["I want to test my multi-service Flow locally before deploying to Kubernetes","I need to develop and debug services with realistic networking (service-to-service communication)","I want to run integration tests with all services in Docker Compose"],"best_for":["developers testing multi-service deployments locally","CI/CD pipelines validating services before Kubernetes deployment","teams without Kubernetes clusters for development"],"limitations":["Docker Compose is single-machine; no distributed testing across multiple hosts","Resource limits in Compose are less sophisticated than Kubernetes (no CPU/memory guarantees)","Debugging Compose services requires docker logs; no Kubernetes-level observability"],"requires":["Python 3.9+","Docker and Docker Compose installed","Jina CLI or Python API to generate Compose files"],"input_types":["Jina Flow definitions","Docker image specifications"],"output_types":["docker-compose.yml file","Running containers with service discovery"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","DocArray package (included with Jina)","Executor class inheriting from jina.Executor with @requests decorator","YAML configuration file or Python Flow API (jina.Flow class)","Executors deployed as Deployments or Pods","Jina client library (included with Jina)","Running Jina service (local or remote)","Executor class with custom indexer integration","External indexer/vector database (Milvus, Weaviate, Elasticsearch, etc.)","Client library for external indexer"],"failure_modes":["Dynamic batching adds latency variance — requests may wait up to configured batch timeout (default ~100ms) for batch formation","Document schema must be predefined; runtime schema changes require redeployment","Batching logic is opaque to executor code — no direct control over batch composition or timeout tuning per-request","DAG must be acyclic — no feedback loops or conditional branching based on intermediate results","Flow composition is static at deployment time; dynamic routing requires custom Gateway logic","Debugging multi-stage flows requires tracing through multiple executor logs; limited built-in flow visualization","Clients are Python-only; non-Python clients must use gRPC/HTTP directly","Streaming is protocol-dependent; HTTP streaming has higher latency than gRPC","Retry logic is basic (exponential backoff); no circuit breaker or advanced resilience patterns","Indexer interface is executor-specific; no standardized indexer abstraction across Jina","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7423602091883784,"quality":0.35,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:21.997Z","last_commit":"2025-03-24T13:59:54Z"},"community":{"stars":21872,"forks":2236,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jina-ai--serve","compare_url":"https://unfragile.ai/compare?artifact=jina-ai--serve"}},"signature":"lpLzbPjBjAVG/mIpG4T023cnZRGFVISqo2cyUaERKYTckx+8nv/vDdIyuxeZU1ZbVeRxXkn7g9k/U6p/o9tyAA==","signedAt":"2026-06-20T21:28:43.474Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jina-ai--serve","artifact":"https://unfragile.ai/jina-ai--serve","verify":"https://unfragile.ai/api/v1/verify?slug=jina-ai--serve","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"}}