{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-weaviate--weaviate","slug":"weaviate--weaviate","name":"weaviate","type":"platform","url":"https://weaviate.io/developers/weaviate/","page_url":"https://unfragile.ai/weaviate--weaviate","categories":["rag-knowledge"],"tags":["approximate-nearest-neighbor-search","generative-search","grpc","hnsw","hybrid-search","image-search","information-retrieval","mlops","nearest-neighbor-search","neural-search","recommender-system","search-engine","semantic-search","semantic-search-engine","similarity-search","vector-database","vector-search","vector-search-engine","vectors","weaviate"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-weaviate--weaviate__cap_0","uri":"capability://search.retrieval.hnsw.based.approximate.nearest.neighbor.vector.search.with.configurable.index.parameters","name":"hnsw-based approximate nearest neighbor vector search with configurable index parameters","description":"Implements Hierarchical Navigable Small World (HNSW) algorithm for sub-linear time complexity vector similarity search across high-dimensional embeddings. The implementation supports dynamic index construction with configurable M (max connections per node) and ef (search parameter) values, enabling tuning of recall vs latency tradeoffs. Search queries traverse the hierarchical graph structure to locate nearest neighbors without exhaustive comparison, returning results ranked by vector distance.","intents":["Find semantically similar documents or objects by vector embedding without scanning entire dataset","Build real-time recommendation systems that retrieve top-K similar items in milliseconds","Implement semantic search over large document collections with sub-linear performance scaling"],"best_for":["ML engineers building semantic search systems at scale (100M+ vectors)","Teams implementing RAG pipelines requiring fast retrieval of relevant context","Recommendation system builders needing low-latency similarity matching"],"limitations":["HNSW index construction is single-threaded per shard, adding latency during bulk ingestion","Memory overhead grows with vector dimensionality and dataset size; no built-in compression for vectors","Recall-latency tradeoff is fixed at index time via M/ef parameters; cannot dynamically adjust without reindexing"],"requires":["Vector embeddings pre-computed from external model (OpenAI, Hugging Face, etc.)","Minimum 512MB RAM per shard for index structures","Vector dimensionality between 1 and 2048 dimensions"],"input_types":["float32 vectors","integer vector IDs","distance metric specification (cosine, dot-product, L2)"],"output_types":["ranked list of object IDs with similarity scores","vector distance values","result count (configurable limit)"],"categories":["search-retrieval","vector-search"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_1","uri":"capability://search.retrieval.hybrid.search.combining.vector.similarity.with.bm25.keyword.ranking.and.structured.filtering","name":"hybrid search combining vector similarity with bm25 keyword ranking and structured filtering","description":"Executes multi-stage search pipelines that fuse vector similarity results with BM25 full-text search scores and apply WHERE-clause filtering on structured properties. The query executor (Traverser and Explorer patterns) orchestrates parallel vector and keyword index lookups, then merges ranked results using configurable fusion algorithms (RRF, weighted sum). Inverted index with delta-merger pattern enables incremental BM25 index updates without full rebuilds.","intents":["Search across both semantic meaning and exact keyword matches in single query","Filter vector search results by metadata (date ranges, categories, numeric properties) before ranking","Combine multiple ranking signals (relevance, recency, popularity) in a single result set"],"best_for":["E-commerce platforms needing semantic + keyword search with price/category filters","Content discovery systems requiring multi-signal ranking (relevance + metadata)","Enterprise search tools combining semantic understanding with exact term matching"],"limitations":["Fusion algorithm performance degrades with large result sets (>10K candidates); no built-in pagination optimization for hybrid results","BM25 index requires tokenization configuration per language; no automatic language detection","WHERE clause filtering is applied post-search on candidate set, not pre-filtered; can return fewer results than requested if many candidates filtered out"],"requires":["Both vector embeddings AND text content for objects","Schema definition with indexed text properties for BM25","Structured properties defined as filterable types (int, float, string, date, boolean)"],"input_types":["vector query (float32 array)","text query (string for BM25)","WHERE filter expression (property comparisons)","fusion weights (optional, defaults to equal weighting)"],"output_types":["merged ranked result set with hybrid scores","per-result breakdown of vector score + BM25 score","filtered object properties matching WHERE clause"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_10","uri":"capability://automation.workflow.backup.and.restore.with.incremental.snapshots.and.offload.modules","name":"backup and restore with incremental snapshots and offload modules","description":"Provides backup/restore functionality with support for incremental snapshots (only changed data since last backup) and pluggable offload modules for storing backups in external storage (S3, GCS, Azure Blob). Backup process creates consistent snapshots across all shards using Raft consensus. Restore operation validates backup integrity and replays changes to restore cluster to specific point-in-time. Offload modules enable storing backups in cloud storage without local disk requirements.","intents":["Create point-in-time backups for disaster recovery","Offload backups to cloud storage to reduce local disk requirements","Restore cluster to previous state after data corruption or accidental deletion"],"best_for":["Production deployments requiring disaster recovery capabilities","Teams with limited local storage using cloud backup offloading","Regulated industries requiring backup retention policies"],"limitations":["Backup creation requires consistent snapshot across all shards, temporarily increasing load","Restore operation is blocking; cluster is unavailable during restore","Incremental backups require tracking changes since last backup; full backups are slower"],"requires":["Sufficient disk space for backup (or cloud storage credentials for offload)","Network connectivity to backup storage (if using offload modules)","Backup schedule configuration (manual or cron-based)"],"input_types":["backup identifier/name","backup storage location (local or cloud)","retention policy (optional)"],"output_types":["backup metadata (timestamp, size, shard count)","restore progress and status","backup integrity verification results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_11","uri":"capability://image.visual.image.search.with.multi.modal.vectorization.and.visual.similarity","name":"image search with multi-modal vectorization and visual similarity","description":"Supports image objects with automatic vectorization using multi-modal embedding models (CLIP, etc.) that generate vectors from image content. Image search enables finding visually similar images by uploading query image or providing image URL. Vectorizer modules handle image download, preprocessing, and embedding generation. Supports both image-to-image search and text-to-image search using shared embedding space.","intents":["Find visually similar images in large collections without manual tagging","Search images using text descriptions (text-to-image search)","Build visual recommendation systems for e-commerce or content discovery"],"best_for":["E-commerce platforms implementing visual search","Content discovery systems with large image collections","Fashion/design teams finding similar products visually"],"limitations":["Image vectorization adds significant latency (typically 500ms-2s per image depending on model)","Multi-modal models have lower recall than text-only embeddings for text queries","Image preprocessing (resizing, normalization) is model-specific; no automatic optimization"],"requires":["Multi-modal embedding model (CLIP, ViLBERT, etc.) via vectorizer module","Image storage (local or cloud) accessible during vectorization","Image format support (JPEG, PNG, WebP, etc.)"],"input_types":["image file or URL","text query (for text-to-image search)","image metadata (alt text, tags)"],"output_types":["ranked list of similar images with similarity scores","image metadata and URLs","visual similarity explanations (optional)"],"categories":["image-visual","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_12","uri":"capability://tool.use.integration.rest.api.with.openapi.specification.and.auto.generated.documentation","name":"rest api with openapi specification and auto-generated documentation","description":"Exposes REST API with full OpenAPI 3.0 specification enabling auto-generated API documentation and client SDK generation. API endpoints cover CRUD operations, search, schema management, and cluster operations. OpenAPI spec is machine-readable, enabling API discovery and validation. Swagger UI provides interactive API exploration and testing. REST API supports both JSON request/response and streaming responses for large result sets.","intents":["Integrate Weaviate with REST clients and web frameworks","Auto-generate client SDKs for multiple languages from OpenAPI spec","Explore and test API endpoints interactively via Swagger UI"],"best_for":["Web developers building REST-based integrations","API consumers preferring REST over GraphQL or gRPC","Teams auto-generating client libraries from OpenAPI spec"],"limitations":["REST API has higher overhead than gRPC due to JSON serialization and HTTP/1.1 limitations","No built-in request batching; multiple operations require multiple HTTP requests","Streaming responses are less efficient than gRPC streaming due to HTTP/1.1 constraints"],"requires":["HTTP client library (curl, requests, axios, etc.)","JSON serialization support","Understanding of REST conventions (GET, POST, PUT, DELETE)"],"input_types":["JSON request bodies","URL path parameters","query string parameters","HTTP headers (authentication, content-type)"],"output_types":["JSON response bodies","HTTP status codes","streaming JSON responses"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_13","uri":"capability://automation.workflow.observability.with.metrics.telemetry.and.distributed.tracing","name":"observability with metrics, telemetry, and distributed tracing","description":"Exposes Prometheus metrics for monitoring query latency, throughput, error rates, and resource utilization. Supports distributed tracing via OpenTelemetry, enabling end-to-end request tracing across services. Telemetry collection is configurable with sampling to reduce overhead. Metrics cover API layer (request counts, latencies), storage layer (index operations, disk I/O), and cluster operations (Raft consensus, replication).","intents":["Monitor query performance and identify bottlenecks","Track system health metrics (CPU, memory, disk usage)","Debug distributed request flows across services using traces"],"best_for":["Operations teams monitoring production deployments","Performance engineers optimizing query latency","SREs debugging distributed system issues"],"limitations":["Metrics collection adds overhead (typically 1-5% latency increase)","Distributed tracing requires external collector (Jaeger, Datadog, etc.); no built-in trace storage","High-cardinality metrics (per-shard, per-query-type) can overwhelm monitoring systems"],"requires":["Prometheus-compatible metrics scraper","OpenTelemetry collector (optional, for tracing)","Monitoring dashboard (Grafana, Datadog, etc.)"],"input_types":["metrics configuration (scrape interval, sampling rate)","tracing configuration (collector endpoint, sampling)"],"output_types":["Prometheus metrics (text format)","OpenTelemetry traces (OTLP format)","structured logs with trace context"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_14","uri":"capability://data.processing.analysis.dynamic.vector.index.with.automatic.index.type.selection.based.on.dataset.size","name":"dynamic vector index with automatic index type selection based on dataset size","description":"Implements dynamic index selection that automatically chooses between HNSW (for large datasets) and flat index (for small datasets) based on shard size. Flat index performs exhaustive search without index structure, optimal for <10K vectors. HNSW index is automatically created when shard exceeds threshold. Dynamic switching enables optimal performance across dataset sizes without manual tuning. Index type can be explicitly configured if needed.","intents":["Optimize performance automatically without manual index tuning","Handle datasets that grow from small to large without index reconfiguration","Reduce memory overhead for small datasets by avoiding unnecessary index structures"],"best_for":["Teams wanting automatic performance optimization without tuning","Datasets with unpredictable growth patterns","Development/testing environments requiring minimal configuration"],"limitations":["Automatic index switching may cause performance variance during transition","Index type selection is based on shard size only; no consideration of query patterns","Switching from flat to HNSW index requires index rebuild, causing temporary latency spike"],"requires":["Default index configuration (flat or HNSW)","Threshold configuration for switching between index types"],"input_types":["shard size (number of vectors)","index type preference (optional)"],"output_types":["selected index type (flat or HNSW)","index statistics (memory usage, search latency)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_2","uri":"capability://automation.workflow.multi.shard.distributed.storage.with.raft.consensus.and.automatic.replication","name":"multi-shard distributed storage with raft consensus and automatic replication","description":"Partitions data across multiple shards (horizontal scaling) with each shard maintaining LSM-KV storage engine for durability. Raft consensus protocol coordinates writes across shard replicas, ensuring consistency guarantees (quorum-based acknowledgment). Shard routing layer automatically distributes objects by hash and replicates writes to configured replica count, with automatic failover when replicas become unavailable. Lazy-loader pattern defers shard initialization until first access.","intents":["Scale vector database to billions of objects across multiple nodes without single-point failure","Ensure data durability and consistency across distributed replicas with automatic failover","Partition large datasets across cluster nodes to distribute query and write load"],"best_for":["Production deployments requiring high availability (99.9%+ uptime SLA)","Teams managing multi-node Kubernetes clusters with distributed storage needs","Large-scale RAG systems with billions of vectors requiring fault tolerance"],"limitations":["Raft consensus adds write latency (typically 50-200ms per write depending on replica count and network latency)","Shard rebalancing during node failures is manual or requires external orchestration; no automatic resharding","Cross-shard queries require aggregation from multiple shards, increasing query latency vs single-shard queries"],"requires":["Minimum 3 nodes for Raft quorum (2 nodes for development only)","Network connectivity between all nodes with <100ms latency recommended","Persistent storage per node (local disk or network-attached storage)","Weaviate cluster configuration with replication factor (typically 3)"],"input_types":["object data with partition key (auto-hashed)","replication factor specification","shard count configuration"],"output_types":["distributed write acknowledgment (quorum-based)","shard assignment metadata","replica status information"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_3","uri":"capability://data.processing.analysis.batch.object.ingestion.with.job.queueing.and.transactional.consistency","name":"batch object ingestion with job queueing and transactional consistency","description":"Provides high-throughput batch write API that queues objects for asynchronous processing with configurable batch sizes and concurrency. Implements transactional semantics where entire batch succeeds or fails atomically, with per-object error reporting. Job queue distributes batch operations across worker threads, with backpressure handling to prevent memory exhaustion. Write path (shard_write_batch_objects.go) coordinates object insertion, vector index updates, and inverted index updates in single transaction.","intents":["Ingest millions of objects with embeddings in minutes rather than hours","Ensure all-or-nothing semantics for batch operations to maintain data consistency","Handle partial failures gracefully with per-object error reporting without losing entire batch"],"best_for":["Data engineers bulk-loading vector databases from data lakes or data warehouses","ML teams fine-tuning embeddings and reindexing large collections","ETL pipelines requiring transactional batch writes with error recovery"],"limitations":["Batch size is limited by available memory; very large batches (>100K objects) may cause OOM on single node","Transactional consistency is per-batch, not across multiple batches; no distributed transaction support across shards","Job queue is in-memory; no persistence of queued jobs across server restarts"],"requires":["Objects with complete schema (all required properties defined)","Pre-computed vector embeddings for each object","Batch size tuning based on available memory (typically 100-10K objects per batch)"],"input_types":["array of objects with properties and vectors","batch size (configurable, default 100)","concurrency level (configurable, default 1)"],"output_types":["per-object success/failure status","error details for failed objects","batch completion timestamp"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_4","uri":"capability://search.retrieval.graphql.query.api.with.nested.object.traversal.and.aggregation","name":"graphql query api with nested object traversal and aggregation","description":"Exposes GraphQL interface for querying objects with support for nested property selection, cross-object references, and aggregation functions (count, sum, mean, max, min). Query executor traverses object relationships defined in schema, fetching related objects in single query without N+1 round-trips. Aggregation pipeline computes statistics across result sets (e.g., average vector distance, object count by category).","intents":["Query complex object relationships in single request without multiple round-trips","Fetch nested object properties and related objects with declarative syntax","Compute aggregations (counts, averages, distributions) across search results"],"best_for":["Frontend developers building search UIs with complex data requirements","API consumers preferring declarative query syntax over REST endpoints","Analytics teams computing aggregations over large result sets"],"limitations":["GraphQL query complexity is unbounded; no built-in query depth limits to prevent expensive nested traversals","Aggregations are computed in-memory on result set; no distributed aggregation across shards","Cross-shard reference traversal requires multiple network round-trips, increasing latency"],"requires":["GraphQL client library (Apollo, Relay, or similar)","Schema with defined object relationships (references)","Understanding of GraphQL query syntax"],"input_types":["GraphQL query string with field selection","filter conditions (WHERE clauses)","aggregation specifications"],"output_types":["JSON response matching query shape","aggregation results (numeric values)","nested object hierarchies"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_5","uri":"capability://tool.use.integration.grpc.api.with.streaming.support.for.high.throughput.client.communication","name":"grpc api with streaming support for high-throughput client communication","description":"Provides gRPC interface as alternative to REST/GraphQL with support for bidirectional streaming, enabling efficient bulk operations and real-time result streaming. Protocol buffers define strongly-typed message contracts with automatic code generation for multiple languages. Streaming reduces overhead vs request-response pattern, particularly for batch operations and large result sets. gRPC multiplexing over HTTP/2 enables connection reuse and header compression.","intents":["Build high-performance client libraries with strongly-typed message contracts","Stream large result sets or bulk operations without buffering entire response in memory","Reduce network overhead for high-frequency queries using HTTP/2 multiplexing"],"best_for":["Backend services requiring high-throughput communication with Weaviate","Data pipeline tools streaming large volumes of objects for ingestion","Mobile or resource-constrained clients benefiting from gRPC compression"],"limitations":["gRPC requires HTTP/2 support; some legacy proxies/load balancers may not support it","Streaming adds complexity to client implementations vs simple request-response","gRPC debugging is harder than REST (no browser-native support, requires specialized tools)"],"requires":["gRPC client library for target language (Go, Python, Node.js, etc.)","Protocol buffer compiler (protoc) for code generation","HTTP/2 capable network infrastructure"],"input_types":["protobuf-serialized messages","streaming request sequences","binary-encoded vectors and properties"],"output_types":["protobuf-serialized response messages","streaming result sequences","binary-encoded object data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_6","uri":"capability://tool.use.integration.pluggable.vectorizer.modules.with.automatic.embedding.generation","name":"pluggable vectorizer modules with automatic embedding generation","description":"Module system allows plugging in external vectorizer implementations (OpenAI, Hugging Face, Cohere, etc.) to automatically generate embeddings for text properties during object creation. Vectorizer modules intercept write operations, extract text from specified properties, call external embedding API, and store resulting vectors. Supports custom vectorizer implementations via module interface, enabling proprietary embedding models. Caching layer reduces redundant API calls for duplicate text.","intents":["Automatically embed text content without manual embedding pipeline","Switch between embedding models (OpenAI → Cohere) without code changes","Build custom vectorizers for domain-specific embedding models"],"best_for":["Teams avoiding custom embedding infrastructure by using managed services","Rapid prototyping requiring quick model experimentation","Multi-tenant systems where different tenants use different embedding models"],"limitations":["External vectorizer API calls add write latency (typically 100-500ms per object depending on model)","Vectorizer module failures block writes; no graceful degradation to store objects without vectors","Embedding model changes require re-vectorizing entire dataset; no built-in migration tooling"],"requires":["API key for external vectorizer service (OpenAI, Hugging Face, Cohere, etc.)","Network connectivity to vectorizer service","Schema configuration specifying which properties to vectorize"],"input_types":["text content from object properties","vectorizer model specification","vectorizer API credentials"],"output_types":["float32 vector embeddings","embedding metadata (model version, timestamp)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_7","uri":"capability://tool.use.integration.generative.and.reranker.modules.for.post.processing.search.results","name":"generative and reranker modules for post-processing search results","description":"Module system supports plugging in generative models (LLMs) and reranking models to post-process search results. Generative modules take search results and generate synthetic content (summaries, answers, completions) using external LLM APIs. Reranker modules re-rank search results using cross-encoder models, improving relevance beyond vector similarity. Modules receive search context (query, results) and return enriched results with generated content or adjusted rankings.","intents":["Generate summaries or answers from search results without separate LLM call","Re-rank vector search results using semantic rerankers for better relevance","Build RAG pipelines that generate answers grounded in retrieved documents"],"best_for":["RAG system builders needing answer generation from retrieved context","Search teams improving relevance with cross-encoder reranking","Conversational AI systems generating responses from search results"],"limitations":["Generative module latency adds to query time (typically 500ms-2s per query for LLM generation)","Reranker modules require cross-encoder model inference; no built-in model serving (requires external service)","Generated content is not cached; identical queries trigger regeneration without deduplication"],"requires":["External LLM API (OpenAI, Anthropic, Hugging Face, etc.) for generative modules","External reranker service or local model serving infrastructure","API credentials and network connectivity to external services"],"input_types":["search results (objects with properties)","original query text","generation/reranking parameters"],"output_types":["generated text (summaries, answers, completions)","reranked result ordering","confidence scores from reranker"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_8","uri":"capability://safety.moderation.role.based.access.control.rbac.with.permission.domains.and.multi.tenancy","name":"role-based access control (rbac) with permission domains and multi-tenancy","description":"Implements RBAC system with built-in roles (admin, editor, viewer) and custom role definitions with granular permissions across domains (collections, objects, backups). Permission model supports permission domains enabling fine-grained access control (e.g., read-only access to specific collections). Multi-tenancy support allows isolating data per tenant with tenant-specific RBAC policies. Authentication integrates with OIDC providers and API key-based auth.","intents":["Restrict access to collections and objects based on user roles","Implement multi-tenant SaaS where each tenant has isolated data and access policies","Audit data access with permission-based access logs"],"best_for":["Enterprise deployments requiring fine-grained access control","SaaS platforms with multi-tenant data isolation requirements","Regulated industries (healthcare, finance) requiring audit trails"],"limitations":["RBAC evaluation adds latency to every query (typically 5-10ms per request)","Permission domains are static at schema definition time; dynamic permission changes require schema migration","No attribute-based access control (ABAC); permissions are role-based only"],"requires":["OIDC provider configuration or API key management","Role definitions in schema or configuration","User identity provided in request headers or API key"],"input_types":["user identity (from OIDC token or API key)","requested action (read, write, delete, admin)","resource identifier (collection, object, backup)"],"output_types":["authorization decision (allow/deny)","filtered result set based on permissions","access audit log entry"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-weaviate--weaviate__cap_9","uri":"capability://automation.workflow.schema.management.with.raft.consensus.for.distributed.consistency","name":"schema management with raft consensus for distributed consistency","description":"Manages data schema (class definitions, properties, indexes) with Raft consensus ensuring all nodes have identical schema state. Schema changes (add/remove properties, modify indexes) are coordinated through Raft leader, preventing split-brain scenarios. Schema manager validates changes against existing data and coordinates index migrations. Supports schema versioning and deprecation tracking for backward compatibility.","intents":["Safely evolve schema in distributed cluster without data inconsistency","Add new properties or indexes without downtime","Track schema changes and deprecations for API versioning"],"best_for":["Production clusters requiring schema changes without downtime","Teams managing evolving data models with multiple services","Regulated systems requiring schema change audit trails"],"limitations":["Schema changes require Raft consensus, adding latency (typically 100-500ms per change)","Index migrations on existing data are blocking operations; large datasets may require minutes","No automatic schema inference; all properties must be explicitly defined"],"requires":["Raft cluster with quorum (minimum 3 nodes)","Schema definition in JSON or GraphQL SDL format","Downtime window for index migrations on large datasets"],"input_types":["class definition (properties, indexes, vectorizer config)","schema change operations (add/remove/modify property)"],"output_types":["schema version identifier","migration status and progress","deprecation warnings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Vector embeddings pre-computed from external model (OpenAI, Hugging Face, etc.)","Minimum 512MB RAM per shard for index structures","Vector dimensionality between 1 and 2048 dimensions","Both vector embeddings AND text content for objects","Schema definition with indexed text properties for BM25","Structured properties defined as filterable types (int, float, string, date, boolean)","Sufficient disk space for backup (or cloud storage credentials for offload)","Network connectivity to backup storage (if using offload modules)","Backup schedule configuration (manual or cron-based)","Multi-modal embedding model (CLIP, ViLBERT, etc.) via vectorizer module"],"failure_modes":["HNSW index construction is single-threaded per shard, adding latency during bulk ingestion","Memory overhead grows with vector dimensionality and dataset size; no built-in compression for vectors","Recall-latency tradeoff is fixed at index time via M/ef parameters; cannot dynamically adjust without reindexing","Fusion algorithm performance degrades with large result sets (>10K candidates); no built-in pagination optimization for hybrid results","BM25 index requires tokenization configuration per language; no automatic language detection","WHERE clause filtering is applied post-search on candidate set, not pre-filtered; can return fewer results than requested if many candidates filtered out","Backup creation requires consistent snapshot across all shards, temporarily increasing load","Restore operation is blocking; cluster is unavailable during restore","Incremental backups require tracking changes since last backup; full backups are slower","Image vectorization adds significant latency (typically 500ms-2s per image depending on model)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3728695053531119,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-01T11:34:38Z"},"community":{"stars":16123,"forks":1270,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=weaviate--weaviate","compare_url":"https://unfragile.ai/compare?artifact=weaviate--weaviate"}},"signature":"LBDemB89WCoZ8I9fH+r/d8uZamOur+ARuhWEjUIVn4lAPks8uRzfp3dpKjIekMwJyK3c6HtSQdhfozx6Le1MAw==","signedAt":"2026-06-20T12:02:37.995Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/weaviate--weaviate","artifact":"https://unfragile.ai/weaviate--weaviate","verify":"https://unfragile.ai/api/v1/verify?slug=weaviate--weaviate","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"}}