{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-vespa-engine--vespa","slug":"vespa-engine--vespa","name":"vespa","type":"mcp","url":"https://vespa.ai","page_url":"https://unfragile.ai/vespa-engine--vespa","categories":["mcp-servers"],"tags":["ai","big-data","java","machine-learning","rag","search","search-engine","server","serving-recommendation","tensor","vector","vector-database","vector-search","vespa"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-vespa-engine--vespa__cap_0","uri":"capability://search.retrieval.distributed.vector.similarity.search.with.hnsw.indexing","name":"distributed vector similarity search with hnsw indexing","description":"Implements approximate nearest neighbor search across distributed clusters using Hierarchical Navigable Small World (HNSW) graph indexing built into the Proton search engine. Vectors are indexed as tensor attributes with configurable distance metrics (L2, angular, hamming) and query-time approximate matching that trades recall for latency. The distributed architecture partitions vector data across content nodes via consistent hashing, with each node maintaining its own HNSW graph and the dispatcher aggregating results from parallel searches.","intents":["Build RAG systems that retrieve semantically similar documents from billion-scale corpora in <100ms","Deploy multi-modal search combining dense vector embeddings with sparse BM25 ranking","Scale vector search across multiple data centers with automatic replication and failover"],"best_for":["ML teams building production RAG pipelines with strict latency SLAs","Search engineers migrating from Elasticsearch/Pinecone to self-hosted infrastructure","Organizations requiring vector search with strong consistency guarantees and ACID transactions"],"limitations":["HNSW graph construction is single-threaded per partition, limiting index rebuild speed on large vectors","Approximate search means recall is tunable but not 100% — requires benchmarking distance thresholds per use case","Vector dimension limits depend on memory allocation; very high-dimensional vectors (>2048) require careful memory planning","No built-in vector quantization — full-precision storage required unless custom preprocessing applied"],"requires":["Vespa cluster with content nodes (minimum 1, recommended 3+ for HA)","Tensor field definitions in schema with vector type (e.g., tensor<float>(x[384]))","Pre-computed embeddings from external model (OpenAI, Hugging Face, etc.) or Vespa embedder component"],"input_types":["tensor<float> or tensor<int8> vectors","query vectors as tensor input in YQL or REST API","document feed with vector fields in JSON/XML"],"output_types":["ranked result set with relevance scores","document summaries with matched vector similarity","raw tensor values for post-processing"],"categories":["search-retrieval","vector-database","distributed-indexing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_1","uri":"capability://data.processing.analysis.schema.driven.document.indexing.with.automatic.field.processing","name":"schema-driven document indexing with automatic field processing","description":"Defines document structure and indexing behavior through declarative schema files (Vespa Search Definition Language) that specify field types, indexing directives, and ranking features. The schema compiler (in config-model) transforms these declarations into concrete indexing pipelines that automatically handle tokenization, stemming, field weighting, and attribute creation. Document processing chains execute custom Java/C++ processors on inbound documents before indexing, enabling transformations like embedding generation, NLP annotation, or field extraction.","intents":["Define complex document schemas with nested structures, multi-language support, and custom field processing","Automatically generate embeddings for text fields during indexing using built-in or custom embedder components","Apply domain-specific NLP transformations (POS tagging, entity extraction) to documents before indexing"],"best_for":["Data engineers building search applications with complex document structures and multi-field ranking","Teams needing automatic embedding generation at index time rather than query time","Organizations with custom NLP pipelines that must run on every document ingest"],"limitations":["Schema changes require redeployment and may trigger full re-indexing of existing documents","Document processing chains are synchronous — slow processors block the indexing pipeline","No built-in schema versioning — backward compatibility requires careful migration planning","Custom processors must be compiled as Java/C++ plugins; no dynamic scripting language support"],"requires":["Vespa application package with schema files in src/main/application/schemas/","Java 11+ for custom document processor development","Understanding of Vespa's indexing language (index, attribute, summary directives)"],"input_types":["JSON documents with fields matching schema definition","XML documents with field mappings","streaming documents for real-time processing"],"output_types":["indexed fields (inverted indexes for text search)","attribute fields (columnar storage for filtering/sorting)","summary fields (stored for result rendering)","tensor fields (for ML features)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_10","uri":"capability://data.processing.analysis.attribute.based.filtering.and.sorting.with.columnar.storage","name":"attribute-based filtering and sorting with columnar storage","description":"Stores document fields as columnar attributes (dense arrays of values) rather than inverted indexes, enabling fast filtering and sorting without decompressing entire documents. Attributes are loaded into memory and support range queries, equality filters, and sorting operations with O(1) lookup per document. The attribute system supports multiple data types (int, float, string, tensor) and can be imported from other document types via reference fields, enabling efficient joins without denormalization.","intents":["Filter search results by numeric ranges (price, date, rating) with sub-millisecond latency","Sort results by document attributes (freshness, popularity, user rating) without full-document decompression","Implement faceted search by counting documents in attribute value ranges"],"best_for":["e-commerce search requiring fast price/rating filtering and sorting","news search with date-based filtering and freshness ranking","applications with high-cardinality attributes (millions of unique values) requiring efficient filtering"],"limitations":["Attributes consume memory proportional to document count — very large attributes may exceed available RAM","String attributes are limited to ~64KB per value — very long strings require truncation or external storage","Attribute updates require rewriting the entire attribute vector — slow for high-update-rate fields","No built-in compression for sparse attributes — memory usage is high for attributes with many null values"],"requires":["schema definition with attribute directive on fields","sufficient memory to load all attributes into RAM","document feed with values for attribute fields"],"input_types":["document fields marked as attributes in schema","filter queries on attribute ranges","sort directives on attribute fields"],"output_types":["filtered result set based on attribute ranges","sorted results by attribute values","facet counts for attribute value ranges"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_11","uri":"capability://data.processing.analysis.document.summary.customization.with.field.selection","name":"document summary customization with field selection","description":"Allows defining multiple summary views (document summaries) that specify which fields are returned in search results, with optional field transformations (truncation, highlighting, dynamic snippets). Summaries are defined in schema and can be selected per-query, enabling different result formats for different use cases (mobile vs. desktop, preview vs. full details). The summary framework supports dynamic field computation (e.g., generating snippets from matched text) and field-level access control.","intents":["Return different result formats for different clients (mobile apps get minimal fields, web gets full details)","Generate dynamic snippets highlighting matched query terms in document text","Reduce result payload size by selecting only necessary fields for each query"],"best_for":["applications serving multiple client types with different result format requirements","search interfaces needing dynamic snippets or field highlighting","bandwidth-constrained scenarios (mobile apps) requiring minimal result payloads"],"limitations":["Summary computation is per-document — complex transformations slow down result rendering","Dynamic snippets require storing full text in summaries — increases storage overhead","No built-in field-level access control — security must be enforced at application level","Summary views are static — no dynamic selection of fields based on query context"],"requires":["summary directives in schema defining summary views","fields marked for inclusion in specific summaries","query parameter to select summary view"],"input_types":["summary view definitions in schema","query parameter specifying summary name","document fields to include in summary"],"output_types":["result documents with selected fields","dynamic snippets with query term highlighting","truncated field values"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_12","uri":"capability://safety.moderation.metrics.collection.and.monitoring.with.custom.metrics","name":"metrics collection and monitoring with custom metrics","description":"Collects operational metrics from all Vespa components (query latency, indexing throughput, memory usage, cache hit rates) and exposes them via Prometheus-compatible endpoints. The metrics system supports custom metrics defined by application code, enabling tracking of business-specific KPIs (e.g., 'queries with zero results', 'average result rank position'). Metrics are aggregated across the cluster and can be queried via REST API or scraped by monitoring systems.","intents":["Monitor search cluster health and performance (query latency, indexing throughput, resource utilization)","Track application-specific metrics (zero-result queries, result quality) for business intelligence","Integrate Vespa metrics with existing monitoring stacks (Prometheus, Grafana, CloudWatch)"],"best_for":["operations teams managing production Vespa clusters requiring visibility into system health","data scientists tracking search quality metrics (result relevance, user satisfaction)","organizations integrating Vespa monitoring with centralized observability platforms"],"limitations":["Metrics collection adds CPU overhead — high-cardinality metrics (per-query-type) can impact performance","Metrics are aggregated at cluster level — no per-node breakdown without additional queries","Custom metrics require application code changes — no dynamic metric definition","Metrics retention is limited — historical data requires external time-series database"],"requires":["Vespa cluster with metrics collection enabled (default)","monitoring system with Prometheus scrape support (optional)","custom metric definitions in application code (for business metrics)"],"input_types":["metric definitions (name, type, dimensions)","metric values from application code","query parameters for metric filtering"],"output_types":["Prometheus-format metrics","JSON metrics via REST API","aggregated metrics across cluster"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_13","uri":"capability://data.processing.analysis.embedder.components.for.automatic.embedding.generation","name":"embedder components for automatic embedding generation","description":"Provides pluggable embedder components that generate vector embeddings for text fields during indexing or query processing. Built-in embedders support integration with external embedding services (OpenAI, Hugging Face, local models) via HTTP or gRPC. Embeddings are computed once at index time and stored as tensor attributes, or computed at query time for query embeddings. The embedder framework supports batching for efficient inference and caching to avoid redundant computations.","intents":["Automatically generate embeddings for text fields during indexing without external ETL","Generate query embeddings at search time for vector similarity matching","Integrate with external embedding services (OpenAI, Hugging Face) or local models"],"best_for":["teams building RAG systems needing automatic embedding generation at index time","organizations using external embedding APIs (OpenAI, Hugging Face) without local model serving","applications requiring both text search and semantic similarity matching"],"limitations":["Embedder latency adds to indexing time — slow embedders can become indexing bottleneck","External embedder dependencies (API rate limits, service availability) can block indexing","Embedding model changes require re-indexing all documents — expensive for large corpora","No built-in embedding caching — duplicate text may generate embeddings multiple times"],"requires":["embedder component configured in schema or container","external embedding service credentials (API key for OpenAI, Hugging Face, etc.)","network connectivity to embedding service"],"input_types":["text fields to embed","embedder configuration (model, service endpoint)","query text for query embeddings"],"output_types":["vector embeddings (tensor<float>)","stored as document attributes or computed at query time"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_2","uri":"capability://planning.reasoning.multi.phase.ranking.with.onnx.model.integration","name":"multi-phase ranking with onnx model integration","description":"Implements a two-phase ranking architecture where first-phase ranking (BM25, vector similarity, simple expressions) quickly filters candidates, then second-phase ranking applies expensive ML models (ONNX, XGBoost, LightGBM) to re-rank top-K results. Ranking expressions are compiled to efficient C++ code and executed on content nodes. ONNX models are loaded into memory and executed natively without Python/TensorFlow overhead, with support for batched inference across multiple result candidates.","intents":["Implement learning-to-rank pipelines that combine fast first-phase filters with expensive neural models","Deploy pre-trained ONNX models (from scikit-learn, PyTorch, TensorFlow) for result re-ranking without model serving overhead","Build complex ranking logic combining multiple signals (text relevance, freshness, user features, embeddings) in a single expression"],"best_for":["ML teams with trained ranking models who need low-latency inference at query time","Search engineers optimizing for both relevance and latency using multi-phase ranking","Organizations deploying personalized ranking that requires user context and feature computation"],"limitations":["ONNX model inference is single-threaded per query; very large models (>1GB) may cause latency spikes","Ranking expressions have limited expressiveness — complex logic requires custom Java plugins","Feature computation for ranking must be pre-computed and stored as attributes; no dynamic feature generation","Model updates require application redeployment; no hot-swapping of ranking models without downtime"],"requires":["ONNX model exported from training framework (PyTorch, scikit-learn, TensorFlow)","Ranking expression syntax knowledge (Vespa's custom DSL)","Pre-computed features stored as document attributes or query parameters"],"input_types":["ONNX model files (.onnx format)","ranking expression definitions in schema","query context with user features and signals","document attributes for feature input"],"output_types":["re-ranked result set with model scores","relevance scores combining multiple ranking phases","feature contributions for explainability"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_3","uri":"capability://data.processing.analysis.distributed.document.feed.with.acid.transaction.semantics","name":"distributed document feed with acid transaction semantics","description":"Provides a Document API that accepts document operations (put, update, remove) through HTTP REST endpoints or Java/Python clients, with guaranteed ACID semantics across distributed content nodes. The feed processing pipeline (Document API → MessageBus → Distributor → Persistence Engine) ensures documents are replicated across configured redundancy factor and persisted to disk. Updates are applied as conditional operations with version tracking, and the system provides strong consistency guarantees with configurable durability levels (acknowledged when replicated vs. persisted to disk).","intents":["Ingest documents at scale (millions per second) with guaranteed durability and consistency","Update individual documents or bulk-update with conditional logic (only update if version matches)","Maintain document versions and handle concurrent updates with conflict resolution"],"best_for":["teams building real-time search applications requiring strong consistency (e-commerce, financial search)","data engineers implementing ETL pipelines that feed Vespa from data lakes or streaming sources","organizations needing ACID guarantees for document mutations in distributed systems"],"limitations":["Feed throughput is limited by replication factor and disk I/O; high redundancy reduces write throughput","Bulk updates are not atomic — partial failures may leave cluster in inconsistent state without transaction rollback","Document size limits (default 100MB) require splitting very large documents","No built-in conflict resolution for concurrent updates — application must handle version conflicts"],"requires":["Vespa cluster with content nodes and distributor nodes","Document schema defined with document type","Feed client library (Java, Python, or HTTP REST API)","Network connectivity to container nodes (feed endpoints)"],"input_types":["JSON documents with document ID and fields","update operations with field modifications","remove operations with document ID","conditional operations with test conditions"],"output_types":["operation result with success/failure status","document version after mutation","error details if operation fails"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_4","uri":"capability://search.retrieval.streaming.search.for.unindexed.data","name":"streaming search for unindexed data","description":"Enables full-text search over documents without building inverted indexes by scanning document storage and applying ranking expressions at query time. The streaming search path uses the Visitor Framework to traverse stored documents, apply query filters, and execute ranking logic on-the-fly. This is useful for small datasets, frequently-changing data, or when index overhead is not justified. Streaming search supports the same ranking expressions and tensor operations as indexed search but with linear scan latency instead of logarithmic index lookup.","intents":["Search small document collections (<10M documents) without index overhead","Handle frequently-changing datasets where index maintenance cost exceeds search benefit","Perform complex filtering and ranking on unindexed data without pre-computing indexes"],"best_for":["teams with small-to-medium datasets where indexing overhead is not justified","applications with very high document churn where index maintenance is expensive","exploratory search scenarios where schema is not yet finalized"],"limitations":["Latency scales linearly with document count — unsuitable for billion-scale datasets","No term-based optimizations (inverted index) — all documents must be scanned for keyword queries","Memory usage is higher because documents are stored uncompressed in memory","Ranking expressions execute on every document, making complex ranking expensive"],"requires":["Vespa cluster with content nodes","streaming search mode enabled in schema (streaming: true)","documents stored in memory or fast disk storage"],"input_types":["query with filters and ranking expressions","documents in JSON/XML format"],"output_types":["ranked result set from linear scan","document summaries with computed features"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_5","uri":"capability://data.processing.analysis.tensor.based.feature.computation.and.ranking","name":"tensor-based feature computation and ranking","description":"Provides a tensor algebra system for computing ML features and ranking scores using multi-dimensional arrays. Tensors are defined in schema with dimensions (e.g., tensor<float>(x[10],y[20])) and can be stored as document attributes, computed from ranking expressions, or passed as query parameters. Ranking expressions support tensor operations (dot products, matrix multiplication, element-wise operations) compiled to optimized C++ code. This enables efficient computation of embedding-based features, neural network layers, and complex feature interactions without external computation.","intents":["Compute embedding-based ranking features (dot products, cosine similarity) efficiently in ranking expressions","Implement neural network layers (matrix multiplication, activation functions) as ranking logic","Store and query multi-dimensional feature matrices (e.g., user-item interaction tensors) for personalization"],"best_for":["ML engineers implementing neural ranking models without external model serving","teams building personalized search with user/item feature matrices","organizations needing efficient tensor operations for feature computation at query time"],"limitations":["Tensor operations are limited to basic linear algebra — no automatic differentiation or gradient computation","Very large tensors (>1GB per document) cause memory overhead and slow ranking","Tensor indexing is not supported — tensors cannot be used as search keys, only for ranking","No built-in tensor serialization format — requires custom encoding/decoding for complex tensor types"],"requires":["tensor field definitions in schema with explicit dimensions","ranking expressions using tensor operations (dot, matmul, etc.)","pre-computed tensor values stored as attributes or passed as query parameters"],"input_types":["tensor<float> or tensor<int8> with named dimensions","ranking expressions with tensor operations","query tensors passed as parameters"],"output_types":["computed tensor values from ranking expressions","scalar scores from tensor operations (dot products, etc.)","multi-dimensional feature matrices"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_6","uri":"capability://automation.workflow.automatic.cluster.autoscaling.based.on.metrics","name":"automatic cluster autoscaling based on metrics","description":"Monitors resource utilization (CPU, memory, disk) and query latency metrics across content nodes, then automatically adjusts cluster size by provisioning or deprovisioning nodes to maintain target resource levels. The autoscaling system uses the Node Repository to track node state and the Cluster Controller to orchestrate node transitions. Autoscaling policies are defined in deployment.xml with target metrics (e.g., 'keep CPU at 70%'), and the system gradually scales up/down to avoid thrashing while respecting minimum/maximum cluster sizes.","intents":["Automatically scale search clusters up during traffic spikes and down during off-peak hours","Maintain consistent query latency by scaling based on resource utilization metrics","Reduce infrastructure costs by right-sizing clusters based on actual demand"],"best_for":["teams running Vespa on cloud infrastructure (AWS, GCP, Azure) with variable traffic patterns","organizations seeking to reduce operational overhead of manual cluster scaling","applications with predictable traffic patterns where autoscaling can optimize costs"],"limitations":["Autoscaling decisions lag behind traffic spikes — takes minutes to provision new nodes, causing temporary latency increase","Scaling down requires data redistribution across remaining nodes, which can temporarily impact query performance","Autoscaling policies are static — no dynamic adjustment based on query patterns or business logic","Minimum cluster size must be manually configured; no automatic detection of minimum viable cluster"],"requires":["Vespa cluster deployed on cloud infrastructure with auto-provisioning support","autoscaling policies defined in deployment.xml","metrics collection enabled (default)","Node Repository and Cluster Controller components running"],"input_types":["autoscaling policy definitions (target metrics, min/max nodes)","resource utilization metrics from content nodes","query latency metrics"],"output_types":["node provisioning/deprovisioning decisions","cluster size adjustments","scaling event logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_7","uri":"capability://search.retrieval.query.parsing.and.execution.with.yql.language","name":"query parsing and execution with yql language","description":"Parses user queries in Vespa Query Language (YQL) — a SQL-like syntax for expressing complex search logic including filters, ranking, grouping, and result pagination. The query parser (in container-search) converts YQL to an internal query tree, which is then executed by the dispatcher that routes sub-queries to content nodes, collects results, and applies second-phase ranking. YQL supports nested queries, aggregations, and tensor operations, enabling complex search workflows without application-level query construction.","intents":["Express complex search queries with filters, ranking, grouping, and pagination in a declarative language","Build search applications that accept user-friendly query syntax without exposing internal search logic","Implement faceted search and aggregations (e.g., 'group by category, count documents') in a single query"],"best_for":["search engineers building query interfaces that need to support complex filtering and aggregation","teams migrating from SQL-based search systems who prefer declarative query syntax","applications requiring faceted search, grouping, and result pagination"],"limitations":["YQL syntax is Vespa-specific — not compatible with SQL or other query languages","Query complexity is limited by expression depth — very deeply nested queries may hit parser limits","No query optimization — complex queries may execute inefficiently without careful construction","Debugging query performance requires understanding Vespa's internal query tree representation"],"requires":["understanding of YQL syntax (similar to SQL but with Vespa-specific extensions)","schema definition with indexed fields and attributes","container nodes with query processing enabled"],"input_types":["YQL query strings","query parameters (filters, ranking expressions, grouping directives)","user input converted to YQL by application"],"output_types":["ranked result set with documents and scores","aggregation results (facets, group counts)","result metadata (total hits, query time)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_8","uri":"capability://tool.use.integration.container.based.request.processing.with.custom.handlers","name":"container-based request processing with custom handlers","description":"Implements a request processing framework (JDisc Container) that routes HTTP requests to pluggable handler components written in Java. Handlers can perform custom logic (authentication, request transformation, result post-processing) and integrate with Vespa's search and document processing pipelines. The container supports dependency injection, component lifecycle management, and chaining of handlers for complex request workflows. This enables building custom APIs and business logic on top of Vespa's core search/feed capabilities.","intents":["Build custom REST APIs that wrap Vespa search with application-specific logic (authentication, logging, result transformation)","Implement request preprocessing (query expansion, user context injection) before sending to search","Post-process search results (filtering, formatting, enrichment) before returning to clients"],"best_for":["teams building search applications with custom business logic beyond basic search","organizations needing to integrate Vespa with existing authentication/authorization systems","developers implementing complex request workflows (multi-stage search, result enrichment)"],"limitations":["Custom handlers are Java-only — no Python, Go, or other language support","Handler execution is synchronous — blocking operations slow down request processing","No built-in request queuing or rate limiting — must be implemented in handlers","Debugging custom handlers requires understanding JDisc container lifecycle and dependency injection"],"requires":["Java 11+ development environment","understanding of JDisc container framework and handler lifecycle","Maven build system for packaging custom handlers"],"input_types":["HTTP requests (GET, POST, PUT, DELETE)","request parameters and headers","request body (JSON, XML, form data)"],"output_types":["HTTP responses with custom status codes","JSON/XML response bodies","custom headers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-vespa-engine--vespa__cap_9","uri":"capability://automation.workflow.multi.datacenter.deployment.with.geo.replication","name":"multi-datacenter deployment with geo-replication","description":"Supports deploying Vespa clusters across multiple datacenters with automatic document replication and query routing. The deployment model (defined in deployment.xml) specifies which datacenters receive replicas, and the system automatically replicates documents across regions. Query routing can be configured to prefer local datacenters or failover to remote regions on latency/availability issues. The Cluster Controller manages fleet health across datacenters and coordinates node state transitions.","intents":["Deploy search clusters across multiple regions for disaster recovery and geographic redundancy","Route queries to nearest datacenter for lower latency while maintaining data consistency","Handle datacenter failures gracefully by failing over to replica regions"],"best_for":["organizations requiring high availability and disaster recovery across regions","global applications needing low-latency search from multiple geographic locations","teams with strict compliance requirements for data residency and replication"],"limitations":["Cross-datacenter replication adds latency to document writes — eventual consistency across regions","Network partitions between datacenters can cause temporary inconsistency — requires conflict resolution","Geo-replication increases storage costs (data replicated across regions) and network bandwidth","Query routing policies are static — no dynamic routing based on real-time latency measurements"],"requires":["Vespa clusters deployed in multiple datacenters with network connectivity","deployment.xml configured with multi-region deployment specifications","document replication factor set to span multiple regions"],"input_types":["deployment specifications with region definitions","document feed with replication directives","query routing policies"],"output_types":["replicated documents across regions","query results from preferred datacenter","replication status and lag metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Vespa cluster with content nodes (minimum 1, recommended 3+ for HA)","Tensor field definitions in schema with vector type (e.g., tensor<float>(x[384]))","Pre-computed embeddings from external model (OpenAI, Hugging Face, etc.) or Vespa embedder component","Vespa application package with schema files in src/main/application/schemas/","Java 11+ for custom document processor development","Understanding of Vespa's indexing language (index, attribute, summary directives)","schema definition with attribute directive on fields","sufficient memory to load all attributes into RAM","document feed with values for attribute fields","summary directives in schema defining summary views"],"failure_modes":["HNSW graph construction is single-threaded per partition, limiting index rebuild speed on large vectors","Approximate search means recall is tunable but not 100% — requires benchmarking distance thresholds per use case","Vector dimension limits depend on memory allocation; very high-dimensional vectors (>2048) require careful memory planning","No built-in vector quantization — full-precision storage required unless custom preprocessing applied","Schema changes require redeployment and may trigger full re-indexing of existing documents","Document processing chains are synchronous — slow processors block the indexing pipeline","No built-in schema versioning — backward compatibility requires careful migration planning","Custom processors must be compiled as Java/C++ plugins; no dynamic scripting language support","Attributes consume memory proportional to document count — very large attributes may exceed available RAM","String attributes are limited to ~64KB per value — very long strings require truncation or external storage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6235359858520749,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-03T13:35:22Z"},"community":{"stars":6904,"forks":711,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=vespa-engine--vespa","compare_url":"https://unfragile.ai/compare?artifact=vespa-engine--vespa"}},"signature":"gWdLeRp4/QoAYd9ypsQ71Y81sUa3WWLpv9zPULJhw5Dpkg6GDnuBJD8bNRIeWvrAhbgK9J0u5yBbGNTemL27AA==","signedAt":"2026-06-22T01:56:23.634Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vespa-engine--vespa","artifact":"https://unfragile.ai/vespa-engine--vespa","verify":"https://unfragile.ai/api/v1/verify?slug=vespa-engine--vespa","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"}}