{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-weaviate-client","slug":"pypi-weaviate-client","name":"weaviate-client","type":"repo","url":"https://github.com/weaviate/weaviate-python-client","page_url":"https://unfragile.ai/pypi-weaviate-client","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-weaviate-client__cap_0","uri":"capability://tool.use.integration.synchronous.and.asynchronous.vector.database.client.initialization.with.connection.pooling","name":"synchronous and asynchronous vector database client initialization with connection pooling","description":"Provides dual WeaviateClient (sync) and WeaviateAsyncClient (async) classes that abstract HTTP connection management to a Weaviate vector database instance. Both inherit from _WeaviateClientExecutor base class implementing shared core functionality, with connection parameters (host, port, protocol) passed via ConnectionParams objects. Supports embedded Weaviate instances via EmbeddedOptions, custom headers, authentication credentials, and configurable timeouts through AdditionalConfig. Initialization can skip server health checks via skip_init_checks flag for faster startup in trusted environments.","intents":["Initialize a connection to a remote Weaviate server with custom authentication and headers","Set up an embedded Weaviate instance for local development without external dependencies","Choose between sync and async client paradigms based on application architecture","Configure connection timeouts and retry behavior for production deployments"],"best_for":["Python developers building RAG systems or semantic search applications","Teams deploying Weaviate in both cloud and embedded scenarios","Async-first applications using asyncio or FastAPI frameworks"],"limitations":["Async client requires Python 3.7+ with asyncio event loop running","Embedded Weaviate instances add ~50-200ms startup overhead vs remote connections","Connection pooling is handled by underlying HTTP library (requests/aiohttp), not explicitly configurable per client"],"requires":["Python 3.8+","Weaviate server 1.0+ (version compatibility matrix in docs)","Network connectivity to Weaviate instance or embedded binary for EmbeddedOptions"],"input_types":["ConnectionParams object (host, port, protocol)","AuthCredentials object (API key or username/password)","EmbeddedOptions object (for local instances)"],"output_types":["WeaviateClient or WeaviateAsyncClient instance with namespaced access to collections, batch, backup, cluster, roles, users"],"categories":["tool-use-integration","database-client"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_1","uri":"capability://data.processing.analysis.collection.based.schema.management.with.dynamic.property.definition","name":"collection-based schema management with dynamic property definition","description":"Exposes client.collections namespace for CRUD operations on Weaviate schema classes (collections). Allows creating collections with dynamic property definitions, vectorization settings (module selection), and indexing strategies without manual schema validation. Collections are created via fluent API accepting property objects with data types, vectorization hints, and indexing parameters. Supports retrieving existing collections, updating collection settings, and deleting collections with cascade options. Schema validation is performed server-side with detailed error messages returned to client.","intents":["Create a new collection with text, number, and reference properties in a single fluent call","Configure which vectorization module (text2vec-openai, text2vec-huggingface, etc.) applies to specific properties","Retrieve and inspect existing collection schemas to understand data structure","Update collection settings like vectorization parameters without data loss"],"best_for":["Data engineers setting up vector search pipelines with heterogeneous data types","Teams using multiple vectorization providers and needing per-property configuration","Rapid prototyping scenarios where schema evolution is frequent"],"limitations":["Schema changes (adding/removing properties) require collection recreation in most cases","Vectorization module selection is immutable after collection creation","No built-in schema versioning or migration tracking — requires external tooling"],"requires":["Weaviate server 1.0+","Write permissions on target Weaviate instance","Knowledge of available vectorization modules in connected Weaviate instance"],"input_types":["Collection name (string)","Property objects with name, data_type, vectorize_property_name flags","VectorIndexConfig objects (distance metric, ef_construction, etc.)"],"output_types":["Collection object with schema metadata","Boolean confirmation of create/delete operations","Error details if schema validation fails"],"categories":["data-processing-analysis","schema-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_10","uri":"capability://automation.workflow.cluster.management.and.node.status.inspection","name":"cluster management and node status inspection","description":"Exposes client.cluster namespace for inspecting Weaviate cluster topology and node health. Provides methods to list cluster nodes, retrieve node status (healthy/unhealthy), and inspect node metadata (shard count, vector count, memory usage). Node status is retrieved from Weaviate server and reflects current cluster state. No cluster modification operations are supported via client — cluster topology is managed via Weaviate server configuration.","intents":["Monitor cluster health and node status for operational visibility","Detect unhealthy nodes and trigger alerts or failover procedures","Inspect shard distribution and vector count across cluster nodes","Validate cluster readiness before production traffic"],"best_for":["Operations teams managing multi-node Weaviate clusters","Monitoring and alerting systems requiring cluster health visibility","Deployment automation requiring cluster readiness validation"],"limitations":["No cluster modification operations — topology changes require Weaviate server configuration","Node status is point-in-time snapshot — no historical trend data","No built-in alerting — client must implement monitoring logic","Cluster inspection is read-only — no rebalancing or shard migration via client"],"requires":["Weaviate cluster deployment (single-node clusters also supported)","Weaviate server 1.0+ with cluster support","Admin credentials for cluster inspection"],"input_types":[],"output_types":["List of cluster nodes with metadata (node ID, status, shard count, vector count)","Node health status (healthy/unhealthy)","Cluster topology information"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_11","uri":"capability://tool.use.integration.embedded.weaviate.instance.lifecycle.management.for.local.development","name":"embedded weaviate instance lifecycle management for local development","description":"Supports embedded Weaviate instances via EmbeddedOptions, allowing developers to run Weaviate in-process without separate server. Embedded instance is started automatically on client initialization and stopped on client close. Supports configurable persistence (in-memory or disk-backed), port binding, and data directory. Embedded Weaviate is fully functional — supports all client operations (collections, queries, batch import) with same API as remote instances. Useful for local development, testing, and prototyping without Docker/Kubernetes overhead.","intents":["Develop and test Weaviate applications locally without external server setup","Run integration tests with isolated Weaviate instances per test","Prototype RAG systems without cloud infrastructure","Reduce development environment complexity for small teams"],"best_for":["Individual developers and small teams prototyping RAG applications","CI/CD pipelines requiring isolated test environments","Educational use cases and tutorials"],"limitations":["Embedded instance is single-node only — no clustering or replication","Performance is limited by local machine resources — not suitable for large-scale testing","Embedded instance lifecycle is tied to client — no separate server management","Data persistence requires explicit disk configuration — in-memory by default"],"requires":["Python 3.8+","Sufficient disk space for embedded binary (~100MB) and data","Weaviate embedded binary (downloaded automatically on first use)"],"input_types":["EmbeddedOptions object (persistence, port, data_dir)","Same collection and query APIs as remote instances"],"output_types":["WeaviateClient instance connected to embedded server","Same result types as remote instances"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_12","uri":"capability://memory.knowledge.vectorization.module.integration.with.external.embedding.providers","name":"vectorization module integration with external embedding providers","description":"Supports configurable vectorization modules (text2vec-openai, text2vec-huggingface, text2vec-cohere, etc.) at collection level, enabling automatic embedding generation for text properties. Vectorization module is selected at collection creation and applied to specified properties. Client does not perform embedding generation — Weaviate server handles vectorization using configured module and provider credentials. Supports per-property vectorization configuration (which properties trigger embedding, which skip). Vectorization is transparent to client — objects are inserted with text, embeddings are generated server-side.","intents":["Automatically generate embeddings for text properties using OpenAI, Hugging Face, or other providers","Configure which text properties trigger embedding generation vs remain text-only","Switch embedding providers by changing collection vectorization module","Avoid client-side embedding generation complexity — let Weaviate handle vectorization"],"best_for":["Teams using managed embedding services (OpenAI, Cohere) without local inference","Rapid prototyping where embedding provider switching is frequent","Production systems requiring consistent embedding generation across all data"],"limitations":["Vectorization module is immutable after collection creation — cannot switch providers without recreation","Embedding provider credentials must be configured in Weaviate server — not client-configurable","No client-side embedding caching or optimization — all embeddings generated server-side","Vectorization latency depends on provider API — no client-side control over embedding speed"],"requires":["Weaviate server with vectorization module installed (e.g., text2vec-openai)","API credentials for embedding provider configured in Weaviate server","Text properties in collection schema"],"input_types":["Collection name","Vectorization module name (text2vec-openai, text2vec-huggingface, etc.)","Properties to vectorize (list of property names)"],"output_types":["Collection with vectorization module configured","Objects with auto-generated embeddings on insertion"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_13","uri":"capability://data.processing.analysis.reference.property.management.for.object.relationships.and.graph.traversal","name":"reference property management for object relationships and graph traversal","description":"Supports reference properties that create relationships between objects in different collections, enabling graph-like queries. References are defined at collection creation with target collection specification. Objects are inserted with reference values (target object IDs). Queries can traverse references via client.collections[name].query.near_vector().with_references() to include related objects in results. References are server-side relationships — no client-side graph construction. Supports bidirectional reference queries.","intents":["Create relationships between objects in different collections (e.g., documents referencing authors)","Traverse relationships in queries to include related objects in results","Build knowledge graphs with object relationships without separate graph database","Query across collections using reference relationships"],"best_for":["Knowledge graph applications with object relationships","Multi-collection systems requiring cross-collection queries","Teams building recommendation systems based on object relationships"],"limitations":["References are one-way — bidirectional queries require explicit reverse references","No reference validation — dangling references are not detected","Reference traversal depth is limited — no deep graph traversal in single query","No reference counting or relationship analytics — requires separate queries"],"requires":["Multiple collections with reference properties defined","Target collection must exist before reference creation","Weaviate server 1.0+ with reference support"],"input_types":["Reference property name (string)","Target collection name (string)","Target object ID (string, for reference values)"],"output_types":["Objects with reference properties populated","Related objects included in query results via with_references()"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_14","uri":"capability://safety.moderation.error.handling.and.exception.mapping.with.detailed.error.messages","name":"error handling and exception mapping with detailed error messages","description":"Implements comprehensive error handling via custom exception classes (WeaviateConnectionError, WeaviateInvalidInputError, WeaviateAuthenticationError, etc.) that map Weaviate server errors to Python exceptions. Error messages include server-side error details, HTTP status codes, and suggested remediation. Supports error recovery patterns (retry logic, connection pooling) at client level. Error handling is transparent — client code catches specific exceptions rather than parsing HTTP responses.","intents":["Distinguish between connection errors, authentication failures, and validation errors","Implement retry logic for transient failures (network timeouts, server unavailability)","Debug API errors with detailed error messages and server response details","Build resilient applications with proper error handling patterns"],"best_for":["Production systems requiring robust error handling and recovery","Teams building resilient applications with retry logic","Debugging and troubleshooting Weaviate integration issues"],"limitations":["Error messages are server-generated — client has limited control over error detail level","No built-in retry logic with exponential backoff — requires application-level implementation","Error recovery is application-specific — no generic recovery strategies","Connection pooling is handled by underlying HTTP library — not explicitly configurable"],"requires":["Python 3.8+","Weaviate server 1.0+"],"input_types":[],"output_types":["Custom exception objects with error details","HTTP status codes and server error messages"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_2","uri":"capability://search.retrieval.vector.similarity.search.with.configurable.distance.metrics.and.result.ranking","name":"vector similarity search with configurable distance metrics and result ranking","description":"Implements vector search via client.collections[name].query.near_vector() method, accepting a query vector and returning ranked results based on distance metric (cosine, L2, dot product, hamming). Search results include object data, distance scores, and optional metadata. Supports limiting result count, offset pagination, and result sorting by distance or other properties. Distance metric is configured at collection creation time and applied consistently across all queries. Results are returned as typed objects matching collection schema.","intents":["Find semantically similar documents by querying with an embedding vector","Retrieve top-K results with distance scores for relevance ranking","Implement pagination over large result sets without re-querying","Compare different distance metrics (cosine vs L2) for search quality tuning"],"best_for":["Semantic search and RAG applications requiring vector similarity matching","Teams benchmarking embedding quality across different vectorization models","Production systems needing efficient approximate nearest neighbor search"],"limitations":["Distance metric is immutable after collection creation — cannot switch metrics without recreation","No built-in approximate nearest neighbor optimization (relies on Weaviate server HNSW implementation)","Result ranking is single-metric only — no multi-factor relevance scoring in client"],"requires":["Collection with vector indexing enabled","Query vector matching collection's embedding dimension","Weaviate server with HNSW or other vector index backend"],"input_types":["Query vector (list of floats matching collection embedding dimension)","Limit (integer, default 25)","Offset (integer for pagination)","Where filter (optional, for pre-filtering before vector search)"],"output_types":["List of result objects with data, distance score, and metadata","Distance scores as floats (0-1 for normalized metrics, unbounded for dot product)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_3","uri":"capability://search.retrieval.hybrid.keyword.and.vector.search.with.bm25.ranking.fusion","name":"hybrid keyword and vector search with bm25 ranking fusion","description":"Provides client.collections[name].query.hybrid() method combining BM25 keyword search with vector similarity search, returning fused results ranked by configurable alpha parameter (0=pure BM25, 1=pure vector, 0.5=equal weight). Internally executes both keyword and vector queries against Weaviate server, then merges result sets using reciprocal rank fusion or other fusion algorithms. Supports filtering, limiting, and sorting on fused results. BM25 parameters (k1, b) are configured server-side and applied consistently.","intents":["Search for documents matching both keyword terms and semantic meaning in single query","Balance keyword precision (BM25) with semantic recall (vector search) via alpha tuning","Implement multi-modal search combining exact term matching with embedding similarity","Retrieve results ranked by fusion score rather than single metric"],"best_for":["Search applications requiring both keyword and semantic relevance (e.g., documentation search)","Teams tuning search quality by adjusting keyword/vector weight balance","RAG systems needing robust retrieval across diverse query types"],"limitations":["Fusion algorithm is server-side only — client cannot customize ranking formula","BM25 parameters (k1, b) are immutable per collection, not per-query configurable","Hybrid search requires both text properties and vector index — cannot use on vector-only collections"],"requires":["Collection with both text properties and vector indexing enabled","Query string (for BM25) and query vector (for vector search)","Weaviate server with hybrid search support (v1.0+)"],"input_types":["Query string (text for BM25 keyword matching)","Query vector (embedding for vector similarity)","Alpha parameter (float 0-1, default 0.5 for equal weighting)","Limit and offset for pagination"],"output_types":["List of result objects ranked by fusion score","Fusion score (float combining BM25 and vector scores)","Original BM25 and vector scores available in metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_4","uri":"capability://text.generation.language.generative.search.with.llm.powered.result.augmentation.and.summarization","name":"generative search with llm-powered result augmentation and summarization","description":"Implements client.collections[name].query.near_vector().with_generate() method that chains vector search with LLM-based generation, using retrieved objects as context. Supports multiple generation modes: single prompt applied to all results, per-result prompts, and grouped generation. LLM provider (OpenAI, Cohere, Hugging Face, etc.) is configured at collection level via generative module. Generated text is returned alongside search results without additional API calls from client. Supports prompt templating with result field substitution.","intents":["Retrieve documents and automatically generate summaries or answers using retrieved context","Implement RAG pipelines where LLM augmentation happens server-side without client orchestration","Generate multiple variations of answers by applying different prompts to same result set","Reduce latency by combining search and generation in single server round-trip"],"best_for":["RAG applications requiring server-side generation to reduce client latency","Teams using Weaviate's built-in generative modules (OpenAI, Cohere, etc.)","Summarization and question-answering systems with consistent LLM provider"],"limitations":["Generative module must be configured at Weaviate server level — not client-configurable","LLM provider is immutable per collection, cannot switch providers per-query","No streaming support for generated text — full response returned at once","Prompt engineering happens in client code, no built-in prompt optimization"],"requires":["Weaviate server with generative module enabled (e.g., text2vec-openai with generative-openai)","API credentials for LLM provider configured in Weaviate server","Collection with vector search capability"],"input_types":["Query vector (for initial search)","Prompt template (string with {property_name} placeholders for result field substitution)","Generation mode (single, per-result, grouped)"],"output_types":["Result objects with original data plus generated_text field","Generated text as string (not streamed)","Generation metadata (tokens used, model version if available)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_5","uri":"capability://data.processing.analysis.batch.object.import.with.configurable.batching.strategies.and.error.recovery","name":"batch object import with configurable batching strategies and error recovery","description":"Exposes client.batch namespace for optimized bulk data import via batch_objects() method, supporting multiple batching strategies: fixed-size batches, dynamic batching with timeout, and streaming mode. Internally queues objects and sends them in optimized HTTP requests to Weaviate server, with configurable batch size (default 100) and timeout (default 60s). Supports error handling modes: fail-fast, continue-on-error with error collection, and retry logic for transient failures. Returns batch results with per-object status (success/failure) and error details.","intents":["Import thousands of documents with embeddings in optimized batches without manual chunking","Handle import failures gracefully with per-object error tracking and retry logic","Tune batch size and timeout for different network conditions and object sizes","Monitor import progress with per-batch status reporting"],"best_for":["Data engineers bulk-loading vector databases with millions of objects","Teams with unreliable network connections needing robust error recovery","Production systems requiring observable batch import with detailed error reporting"],"limitations":["Batch size is global per client — cannot vary batch size per-collection","No built-in deduplication — duplicate objects are imported as separate entries","Error recovery is retry-only — no exponential backoff or circuit breaker patterns","Batch results are in-memory only — no persistence of import state for resumption"],"requires":["Collection created with target schema","Objects matching collection property schema","Write permissions on target collection"],"input_types":["List of objects (dicts or typed objects matching collection schema)","Batch size (integer, default 100)","Timeout (integer seconds, default 60)","Error handling mode (fail_fast, continue_on_error)"],"output_types":["List of batch results with per-object status","Error details for failed objects (validation errors, network errors)","Summary statistics (total imported, failed, skipped)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_6","uri":"capability://data.processing.analysis.filtering.and.pre.filtering.with.where.clause.dsl.and.complex.boolean.logic","name":"filtering and pre-filtering with where clause dsl and complex boolean logic","description":"Provides where() method on query builders (near_vector, hybrid, etc.) accepting filter objects that compile to Weaviate WHERE clause JSON. Supports complex boolean logic (AND, OR, NOT) with nested conditions, comparison operators (==, !=, >, <, >=, <=, ~), and special operators (contains, like, in). Filters are applied server-side before vector search, reducing result set before ranking. Supports filtering on any collection property (text, number, date, reference). Filter compilation is transparent — client code uses Python objects, not raw JSON.","intents":["Filter search results to specific date ranges, categories, or metadata values before vector ranking","Implement complex multi-condition filters combining AND/OR/NOT logic","Reduce vector search scope to relevant subset of collection for faster retrieval","Build dynamic filters programmatically without string concatenation"],"best_for":["Multi-tenant or multi-category search systems requiring metadata filtering","Teams building complex query builders with dynamic filter construction","Production systems needing efficient pre-filtering before expensive vector search"],"limitations":["Filter compilation is client-side only — no server-side filter optimization or pushdown","Complex nested filters (3+ levels) can become verbose in Python code","No full-text search within filters — only exact/prefix/range matching on text properties","Filter performance depends on Weaviate server indexing strategy — client has no visibility"],"requires":["Collection with properties to filter on","Knowledge of property names and data types","Weaviate server 1.0+ with WHERE clause support"],"input_types":["Filter objects (Where, And, Or, Not classes)","Property names (strings matching collection schema)","Comparison values (strings, numbers, dates, lists)"],"output_types":["WHERE clause JSON compiled by client","Filtered result set from server"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_7","uri":"capability://data.processing.analysis.multi.tenancy.support.with.tenant.isolation.and.per.tenant.data.partitioning","name":"multi-tenancy support with tenant isolation and per-tenant data partitioning","description":"Enables multi-tenant data isolation via collection-level tenant configuration, where each tenant's data is logically partitioned within a single collection. Tenants are created via client.collections[name].tenants.create() and data is inserted with tenant context via batch_objects(tenant='tenant_id'). Queries automatically scope to specified tenant via near_vector(tenant='tenant_id'). Tenant isolation is enforced server-side — no cross-tenant data leakage. Supports tenant deletion with cascade options.","intents":["Isolate data for multiple customers/organizations in single collection without separate collections","Query only tenant-specific data without filtering across all tenants","Manage tenant lifecycle (create, delete) without collection recreation","Reduce storage overhead by sharing collection infrastructure across tenants"],"best_for":["SaaS platforms with multiple customer accounts sharing infrastructure","Teams requiring strong data isolation without separate collections per tenant","Cost-sensitive deployments needing efficient multi-tenant storage"],"limitations":["Tenant must be specified in every query — no default tenant context","Tenant isolation is logical only — no encryption-based isolation","Tenant deletion is permanent — no soft-delete or recovery","No built-in tenant quota management or rate limiting per tenant"],"requires":["Collection with multi-tenancy enabled at creation time","Tenant ID (string) for each tenant","Weaviate server 1.0+ with multi-tenancy support"],"input_types":["Tenant ID (string)","Objects for insertion (with tenant context)","Query parameters (with tenant context)"],"output_types":["Tenant-scoped results","Tenant creation/deletion confirmation"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_8","uri":"capability://safety.moderation.role.based.access.control.rbac.with.permission.management.and.user.assignment","name":"role-based access control (rbac) with permission management and user assignment","description":"Exposes client.roles and client.users namespaces for managing Weaviate RBAC system. Roles are created with specific permissions (read, write, delete, manage_rbac) on collections or cluster-wide. Users are assigned to roles, inheriting permissions. Permissions are enforced server-side on all API calls. Supports role creation, permission assignment, user-role binding, and role deletion. RBAC is optional — disabled by default, enabled via Weaviate server configuration.","intents":["Create read-only roles for analytics users without write access","Assign collection-specific permissions to different user groups","Manage user lifecycle (create, assign roles, revoke access) programmatically","Audit access control configuration across Weaviate instance"],"best_for":["Enterprise deployments requiring fine-grained access control","Teams with multiple user roles (analysts, engineers, admins) needing permission separation","Compliance-sensitive systems requiring auditable access control"],"limitations":["RBAC must be enabled at Weaviate server level — not client-configurable","Permissions are coarse-grained (read/write/delete/manage_rbac) — no field-level access control","No built-in audit logging — access control changes are not logged by client","Role inheritance is flat — no role hierarchies or nested roles"],"requires":["Weaviate server with RBAC enabled","Admin credentials to create roles and assign permissions","Weaviate server 1.0+ with RBAC support"],"input_types":["Role name (string)","Permissions (list of permission strings: read, write, delete, manage_rbac)","User ID (string)","Collection name (for collection-specific permissions)"],"output_types":["Role creation confirmation","Permission assignment confirmation","User-role binding confirmation"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-weaviate-client__cap_9","uri":"capability://automation.workflow.backup.and.restore.with.multi.backend.support.filesystem.s3.gcs.azure","name":"backup and restore with multi-backend support (filesystem, s3, gcs, azure)","description":"Provides client.backup namespace for creating and restoring Weaviate backups across multiple storage backends. Supports filesystem (local disk), S3 (AWS), GCS (Google Cloud), and Azure Blob Storage as backup destinations. Backup creation is asynchronous — client initiates backup and polls for completion status. Restore operations restore entire Weaviate instance or specific collections from backup. Backup metadata (timestamp, size, collections included) is returned with backup status.","intents":["Create point-in-time backups of Weaviate data for disaster recovery","Restore data from backup to recover from data loss or corruption","Archive backups to cloud storage (S3, GCS, Azure) for long-term retention","Automate backup creation and retention policies via scheduled client calls"],"best_for":["Production Weaviate deployments requiring disaster recovery capabilities","Teams with compliance requirements for data backup and retention","Multi-region deployments needing cross-region backup replication"],"limitations":["Backup creation is asynchronous — client must poll for completion status","No built-in backup scheduling or retention policies — requires external orchestration","Restore is all-or-nothing per collection — no selective property restoration","Backup size and duration depend on Weaviate instance size — no client-side optimization"],"requires":["Weaviate server 1.0+ with backup support","Storage backend credentials (S3 access key, GCS service account, Azure connection string)","Sufficient storage space for backup destination"],"input_types":["Backup path (string, filesystem or cloud URI)","Backend type (filesystem, s3, gcs, azure)","Backend credentials (optional, if not configured in Weaviate server)"],"output_types":["Backup status (pending, in_progress, completed, failed)","Backup metadata (timestamp, size, collections included)","Restore confirmation"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Weaviate server 1.0+ (version compatibility matrix in docs)","Network connectivity to Weaviate instance or embedded binary for EmbeddedOptions","Weaviate server 1.0+","Write permissions on target Weaviate instance","Knowledge of available vectorization modules in connected Weaviate instance","Weaviate cluster deployment (single-node clusters also supported)","Weaviate server 1.0+ with cluster support","Admin credentials for cluster inspection","Sufficient disk space for embedded binary (~100MB) and data"],"failure_modes":["Async client requires Python 3.7+ with asyncio event loop running","Embedded Weaviate instances add ~50-200ms startup overhead vs remote connections","Connection pooling is handled by underlying HTTP library (requests/aiohttp), not explicitly configurable per client","Schema changes (adding/removing properties) require collection recreation in most cases","Vectorization module selection is immutable after collection creation","No built-in schema versioning or migration tracking — requires external tooling","No cluster modification operations — topology changes require Weaviate server configuration","Node status is point-in-time snapshot — no historical trend data","No built-in alerting — client must implement monitoring logic","Cluster inspection is read-only — no rebalancing or shard migration via client","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:16.568Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-weaviate-client","compare_url":"https://unfragile.ai/compare?artifact=pypi-weaviate-client"}},"signature":"JNYCmDlScm+fH5ZhnyXDSm0dojDrOR/PDdmSiVjNk13D9/G0nDrTvvvryIJqFwuydHhQbjqL9KDAWpGAdtS5Aw==","signedAt":"2026-06-16T17:04:05.224Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-weaviate-client","artifact":"https://unfragile.ai/pypi-weaviate-client","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-weaviate-client","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"}}