{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"weaviate","slug":"weaviate","name":"Weaviate","type":"platform","url":"https://weaviate.io","page_url":"https://unfragile.ai/weaviate","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"weaviate__cap_0","uri":"capability://search.retrieval.semantic.search.with.text.embedding","name":"semantic-search-with-text-embedding","description":"Converts natural language queries to vector embeddings and retrieves semantically similar documents from the vector index without requiring exact keyword matches. Uses built-in embedding service (on Flex/Premium tiers) or custom ML models to transform text queries into dense vectors, then performs approximate nearest neighbor search across stored embeddings to surface contextually relevant results ranked by cosine similarity.","intents":["Find support tickets semantically similar to a user's problem description without exact keyword matching","Retrieve documentation pages relevant to a natural language question about a product","Build a semantic search feature that understands intent rather than just keywords"],"best_for":["Teams building RAG-powered chatbots and Q&A systems","Product teams needing semantic document retrieval across large corpora","Developers migrating from keyword-only search to semantic understanding"],"limitations":["Embedding quality depends on the underlying model; no control over model selection on Free tier","Requires pre-computed embeddings for all documents; real-time embedding of new documents incurs latency","Query Agent requests limited to 250/month on Free tier, 30,000/month on Flex tier","Specific embedding model names and dimensions not documented; max vector dimensions unknown"],"requires":["Weaviate instance (self-hosted or Weaviate Cloud)","Python 3.7+, Go 1.16+, TypeScript/JavaScript, or GraphQL client","API key for Weaviate Cloud (authentication mechanism not fully documented)","Pre-vectorized documents or access to embedding service"],"input_types":["text (natural language query string)","float array (pre-computed vector for direct vector search)"],"output_types":["JSON array of matching objects with similarity scores","GraphQL response with ranked results"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_1","uri":"capability://search.retrieval.hybrid.search.vector.keyword.fusion","name":"hybrid-search-vector-keyword-fusion","description":"Combines vector similarity search with traditional BM25 keyword matching using a weighted alpha parameter (0-1 range) to balance semantic and lexical relevance. Executes both vector and keyword queries in parallel, then fuses results using the alpha weight: alpha=0.75 means 75% vector similarity + 25% keyword relevance. Enables finding results that are both semantically similar AND contain important keywords, addressing the limitation of pure semantic search missing exact terminology.","intents":["Search for technical documentation where both semantic meaning and specific keywords matter (e.g., 'database connection timeout' should match docs with those exact terms)","Retrieve support tickets that are semantically similar but also contain critical error codes or product names","Balance recall (find everything relevant) with precision (avoid irrelevant semantic matches) in a single query"],"best_for":["Enterprise search systems requiring both semantic understanding and keyword precision","Technical documentation platforms where exact terminology is critical","Customer support teams needing to find similar tickets while respecting specific error codes or product versions"],"limitations":["Requires manual tuning of alpha parameter per use case; no automatic optimization","BM25 keyword matching still requires indexed text fields; no semantic understanding of keywords themselves","Fusion algorithm (linear weighted combination) is simplistic; no learning-to-rank or learned fusion weights","Performance cost of executing both vector and keyword queries in parallel not documented"],"requires":["Weaviate instance with both vector and keyword indexing enabled","Documents must be indexed with both vector embeddings and text fields","Python SDK, TypeScript SDK, or GraphQL API access","Understanding of alpha parameter tuning for your domain"],"input_types":["text query string","alpha parameter (float, 0-1)","limit parameter (integer, result count)"],"output_types":["JSON array of fused results ranked by combined score","GraphQL response with hybrid search results"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_10","uri":"capability://tool.use.integration.sdk.based.client.libraries.python.typescript.go","name":"sdk-based-client-libraries-python-typescript-go","description":"Official client libraries for Python, TypeScript, JavaScript, and Go providing method-chaining APIs for Weaviate operations. SDKs abstract HTTP/GraphQL details and provide type-safe interfaces (in TypeScript/Go) for semantic search, hybrid search, filtering, and object management. Example pattern: `client.collections.get('SupportTickets').query.near_text('login issues').with_limit(10)`. SDKs handle authentication, connection pooling, and error handling, reducing boilerplate compared to raw HTTP clients.","intents":["Build Weaviate applications in Python, TypeScript, or Go with type-safe, idiomatic APIs","Reduce boilerplate by using method-chaining instead of constructing raw HTTP requests","Leverage IDE autocomplete and type checking for Weaviate operations"],"best_for":["Python developers building data pipelines and ML applications","TypeScript/JavaScript developers building Node.js backends and web applications","Go developers building high-performance services","Teams prioritizing developer experience and type safety"],"limitations":["SDK maturity levels not documented; unclear which SDKs are production-ready vs experimental","No SDK feature parity documentation; unclear if all Weaviate features are exposed in all SDKs","SDK versioning and upgrade paths not documented; unclear how breaking changes are handled","No SDK performance benchmarks; unclear if SDKs add latency vs direct HTTP","Limited to officially supported languages; no community SDKs documented"],"requires":["Python 3.7+ (for Python SDK) or Node.js 14+ (for TypeScript/JavaScript) or Go 1.16+ (for Go SDK)","Weaviate instance (self-hosted or Cloud)","API key for authentication","SDK package installed via pip, npm, or go get"],"input_types":["method parameters (query strings, filters, limits)","objects for insertion (JSON-serializable Python dicts, TypeScript objects, Go structs)"],"output_types":["query results (Python lists/dicts, TypeScript arrays/objects, Go slices/structs)","error exceptions (SDK-specific exception types)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_11","uri":"capability://automation.workflow.weaviate.cloud.managed.hosting.with.tiered.slas","name":"weaviate-cloud-managed-hosting-with-tiered-slas","description":"Managed Weaviate hosting on Weaviate Cloud with four tiers (Free Trial, Flex, Premium, Enterprise) offering different SLAs, features, and pricing. Free Trial provides 14-day access with 250 Query Agent requests/month. Flex (pay-as-you-go, $45/month minimum) offers 99.5% uptime and 7-day backups. Premium ($400/month minimum) provides 99.9% uptime, SSO/SAML, and 30-day backups. Enterprise offers 99.95% uptime, HIPAA compliance, and custom features. Eliminates self-hosting operational burden (deployment, scaling, backups) at the cost of vendor lock-in and pricing per vector dimension.","intents":["Launch a vector search application without managing infrastructure, scaling, or backups","Achieve high availability (99.5%-99.95% uptime) without building redundancy yourself","Meet compliance requirements (HIPAA, SSO/SAML) without custom implementation"],"best_for":["Startups and small teams without DevOps expertise","Applications requiring high availability and compliance (HIPAA, SOC 2)","Teams wanting to avoid operational overhead of self-hosted Weaviate","Enterprises with dedicated budgets for managed services"],"limitations":["Vendor lock-in: migrating away from Weaviate Cloud requires exporting data and re-hosting","Pricing scales with vector dimensions and storage; high-dimensional embeddings become expensive at scale","Free tier limited to 250 Query Agent requests/month; insufficient for production applications","Flex tier limited to 30,000 Query Agent requests/month; may be insufficient for high-traffic applications","Data transfer costs on Premium tier (rates not documented); unclear if egress charges apply","Geographic coverage limited: Free/Flex on GCP only, Premium on GCP/AWS (AWS coming soon), Enterprise on GCP/AWS/Azure","Backup retention limited: Free (none), Flex (7 days), Premium (30 days), Enterprise (45 days)"],"requires":["Weaviate Cloud account (free trial or paid subscription)","API key for authentication","Credit card for paid tiers (Flex, Premium, Enterprise)","Compliance certifications if using Premium/Enterprise for regulated data"],"input_types":["vector dimension count (pricing dimension)","storage size in GiB (pricing dimension)","query volume (Query Agent requests/month)"],"output_types":["managed Weaviate instance with SLA guarantees","automated backups and disaster recovery","monitoring and alerting dashboards"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_12","uri":"capability://automation.workflow.self.hosted.weaviate.open.source.deployment","name":"self-hosted-weaviate-open-source-deployment","description":"Open-source Weaviate deployment on your own infrastructure (Docker, Kubernetes, VMs) with full control over configuration, scaling, and data residency. Eliminates vendor lock-in and cloud costs, but requires managing deployment, scaling, backups, monitoring, and security. Suitable for teams with DevOps expertise or strict data residency requirements. Commercial support available but not included in open-source license.","intents":["Deploy Weaviate in your own data center or private cloud for data residency compliance","Avoid cloud vendor lock-in and egress costs by self-hosting","Run Weaviate on-premises for regulated industries (healthcare, finance) with strict data locality requirements"],"best_for":["Enterprise teams with DevOps expertise and infrastructure","Organizations with strict data residency or compliance requirements","Teams wanting to avoid cloud vendor lock-in","High-volume applications where self-hosting is more cost-effective than managed cloud"],"limitations":["Operational overhead: deployment, scaling, backups, monitoring, and security are your responsibility","No built-in SLA or uptime guarantees; you must architect redundancy and disaster recovery","No managed backups; you must implement backup and restore procedures","Scaling requires manual infrastructure provisioning or Kubernetes orchestration","Security patches and updates are your responsibility; no automatic patching","Commercial support not included; requires separate support contract","Deployment requirements and minimum infrastructure specs not documented"],"requires":["Docker or Kubernetes for containerized deployment","Linux/Unix server or Kubernetes cluster","DevOps expertise for deployment, scaling, and operations","Backup and disaster recovery infrastructure","Monitoring and alerting setup (Prometheus, Grafana, etc.)","Network security and access control (firewalls, VPNs, etc.)"],"input_types":["Docker image or Kubernetes manifests","configuration files (environment variables, YAML)","persistent storage for data (volumes, block storage)"],"output_types":["running Weaviate instance","persistent data storage","logs and metrics for monitoring"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_13","uri":"capability://data.processing.analysis.built.in.vectorization.service.with.custom.model.support","name":"built-in-vectorization-service-with-custom-model-support","description":"Weaviate Cloud (Flex/Premium tiers) includes a built-in vectorization service that automatically converts text to embeddings without requiring external embedding APIs. Eliminates the need to call OpenAI, Cohere, or other embedding providers separately. Supports custom models via bring-your-own-model pattern, allowing you to use proprietary or fine-tuned embeddings. Self-hosted Weaviate requires external embedding services or custom vectorization modules.","intents":["Automatically vectorize documents and queries without managing external embedding API calls","Reduce latency by eliminating round-trips to external embedding services","Use proprietary or fine-tuned embedding models without exposing data to third-party APIs"],"best_for":["Teams wanting to avoid external embedding API costs and latency","Organizations with proprietary embedding models or fine-tuned models","Applications with strict data privacy requirements (no data sent to external APIs)"],"limitations":["Built-in vectorization only available on Flex/Premium Cloud tiers; Free tier and self-hosted require external services","Specific embedding models not documented; unclear which models are available or how to select them","Model performance and quality not benchmarked; unclear how built-in embeddings compare to OpenAI/Cohere","Custom model support mechanism not documented; unclear how to bring your own embeddings","Vectorization cost not separated in pricing; unclear if it's included in Query Agent requests or charged separately","No control over model updates; Weaviate may change the underlying model without notice"],"requires":["Weaviate Cloud Flex or Premium tier (built-in service)","Text documents to vectorize","For custom models: custom vectorization module or external embedding service"],"input_types":["text documents (strings)","text queries (strings)","custom embeddings (float arrays) for bring-your-own-model"],"output_types":["vector embeddings (float arrays)","vectorized documents stored in database"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_14","uri":"capability://safety.moderation.role.based.access.control.rbac.with.multi.tier.support","name":"role-based-access-control-rbac-with-multi-tier-support","description":"Implements role-based access control (RBAC) across all Weaviate Cloud tiers, with escalating features: Free/Flex/Premium support basic RBAC, Premium/Enterprise add SSO/SAML integration, and Enterprise adds bring-your-own-IdP and fine-grained permissions. Enables multi-user access with role-based restrictions (read-only, read-write, admin) without requiring application-level authorization logic. Enterprise tier supports HIPAA compliance with encrypted volumes using customer-managed keys.","intents":["Control access to Weaviate data by user role without implementing custom authorization","Integrate Weaviate with corporate identity providers (Okta, Azure AD) via SSO/SAML","Meet HIPAA compliance requirements with encrypted storage and access controls"],"best_for":["Enterprise teams with multiple users requiring role-based access","Organizations with corporate SSO/SAML infrastructure (Okta, Azure AD)","Regulated industries (healthcare, finance) requiring HIPAA or SOC 2 compliance"],"limitations":["RBAC available on all tiers but limited to basic roles on Free/Flex; no fine-grained permissions","SSO/SAML only available on Premium/Enterprise tiers; Free/Flex limited to API keys","Bring-your-own-IdP only available on Enterprise tier; Premium limited to pre-integrated providers","HIPAA compliance only available on Enterprise tier; no HIPAA support on lower tiers","Specific RBAC roles and permissions not documented; unclear what granularity is supported","No audit logging documented; unclear if access is logged for compliance purposes"],"requires":["Weaviate Cloud account (RBAC on all tiers, SSO/SAML on Premium+, HIPAA on Enterprise)","User management interface (mechanism not documented)","For SSO/SAML: corporate identity provider (Okta, Azure AD, etc.)","For HIPAA: Enterprise tier subscription"],"input_types":["user identities (email, username)","role assignments (read-only, read-write, admin)","IdP configuration (for SSO/SAML)"],"output_types":["access control decisions (allow/deny)","audit logs (if available)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_15","uri":"capability://automation.workflow.replication.and.high.availability.clustering","name":"replication and high-availability clustering","description":"Supports replication across multiple nodes for fault tolerance and load distribution. Replication mechanism (master-slave, multi-master, quorum-based) not documented. Availability is provided via cloud deployment SLAs (99.5%-99.95% uptime depending on tier) and self-hosted replication configuration.","intents":["I need high availability for production Weaviate deployments","I want to distribute query load across multiple nodes","I need fault tolerance and automatic failover"],"best_for":["Production deployments requiring high availability","High-throughput applications needing load distribution","Mission-critical applications requiring fault tolerance"],"limitations":["Replication mechanism (master-slave, multi-master, etc.) not documented","Failover behavior and RTO/RPO not documented","Replication lag and consistency guarantees not documented","Configuration and management of replication not documented","Self-hosted replication setup complexity not specified"],"requires":["Multiple Weaviate nodes (self-hosted) or managed cloud deployment with HA enabled","Network connectivity between nodes","Shared storage or distributed consensus mechanism (details unknown)"],"input_types":["replication configuration (node count, replication factor, etc.)"],"output_types":["replicated data across nodes with automatic failover"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_2","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.pipeline","name":"retrieval-augmented-generation-rag-pipeline","description":"Integrates vector search with LLM generation to create grounded, factual responses by retrieving relevant documents from the vector database and passing them as context to an LLM. The pipeline: (1) convert user query to vector, (2) retrieve top-k similar documents, (3) construct prompt with retrieved context, (4) send to LLM for generation. Weaviate handles steps 1-2; integration with LLM providers (OpenAI, Anthropic, etc.) for step 4 requires external orchestration or Weaviate Agents product.","intents":["Build a chatbot that answers questions grounded in your company's internal documentation, avoiding hallucinations","Create a customer support agent that retrieves relevant tickets and knowledge base articles before generating responses","Generate summaries or reports based on retrieved data from your vector database"],"best_for":["Teams building trustworthy AI assistants that must cite sources and avoid hallucinations","Enterprise support and knowledge management systems","Product teams needing to ground LLM responses in proprietary data"],"limitations":["RAG pipeline orchestration not built into Weaviate; requires external framework (LangChain, LlamaIndex, custom code) or Weaviate Agents (undocumented, beta status unclear)","Retrieval quality directly impacts generation quality; poor vector search results lead to poor LLM outputs","No built-in prompt engineering or context window management; developers must handle token counting and truncation","LLM integration mechanism and supported providers not documented; unclear if Weaviate provides native bindings or requires manual API calls","No evaluation metrics or monitoring for RAG quality (retrieval precision, generation relevance)"],"requires":["Weaviate instance with pre-vectorized documents","External LLM (OpenAI, Anthropic, Ollama, etc.) or Weaviate Agents product","Orchestration framework (LangChain, LlamaIndex) or custom Python/TypeScript code","Prompt engineering expertise to format retrieved context effectively"],"input_types":["text query (user question)","retrieval parameters (limit, filters)"],"output_types":["generated text response grounded in retrieved documents","response with citations/source references"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_3","uri":"capability://memory.knowledge.multi.tenant.data.isolation.with.shared.infrastructure","name":"multi-tenant-data-isolation-with-shared-infrastructure","description":"Isolates data for multiple tenants within a single Weaviate instance using tenant-scoped collections and queries. Each tenant's data is logically separated; queries automatically filtered to tenant context without requiring separate database instances. Implemented via tenant parameter in collection operations and query filters, enabling cost-efficient multi-tenant SaaS applications where infrastructure is shared but data is isolated per customer.","intents":["Build a multi-tenant SaaS product where each customer's data must be isolated without provisioning separate databases","Reduce infrastructure costs by sharing a single Weaviate cluster across multiple customers","Ensure data privacy and compliance by preventing cross-tenant data leakage at the database layer"],"best_for":["SaaS platforms with multiple customers requiring data isolation","Cost-conscious teams needing to scale to many tenants without linear infrastructure growth","Compliance-focused organizations requiring strong data separation guarantees"],"limitations":["Multi-tenancy implementation details not documented; unclear if isolation is enforced at query layer or storage layer","No documented performance impact of multi-tenancy; unclear if queries are slower due to tenant filtering","Tenant-level RBAC and access control not documented; unclear how to prevent a tenant from querying another tenant's data","No built-in tenant provisioning or management APIs; developers must implement tenant lifecycle management","Backup and restore behavior with multi-tenancy not documented; unclear if backups are per-tenant or instance-wide"],"requires":["Weaviate instance (self-hosted or Cloud)","Application-level tenant context (user ID, organization ID) to pass to queries","Python SDK, TypeScript SDK, or GraphQL API","Careful query construction to include tenant filters"],"input_types":["tenant identifier (string)","query parameters with tenant context"],"output_types":["tenant-scoped query results","isolated collections per tenant"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_4","uri":"capability://data.processing.analysis.dynamic.schema.inference.and.auto.indexing","name":"dynamic-schema-inference-and-auto-indexing","description":"Automatically infers data schema from inserted objects and creates appropriate indexes without explicit schema definition. When you insert a JSON object, Weaviate detects field types (text, number, boolean, etc.), creates vector indexes for text fields, and sets up keyword indexes for filtering. Eliminates manual schema management and index tuning, enabling rapid prototyping and schema evolution without downtime.","intents":["Quickly prototype a vector search application without upfront schema design","Ingest heterogeneous data with varying fields without pre-defining a rigid schema","Evolve your data model over time by adding new fields to objects without schema migrations"],"best_for":["Rapid prototyping and MVP development where schema is not yet finalized","Data ingestion pipelines with semi-structured or evolving data","Teams without database expertise who want to avoid schema design complexity"],"limitations":["Automatic schema inference may create suboptimal indexes for your use case; no control over index type or parameters","Schema changes (adding/removing fields) may require re-indexing; cost and performance impact not documented","No schema validation or constraints; any field can be added to any object, risking data quality issues","Automatic indexing may consume unnecessary storage and memory for fields that don't need to be searchable","No schema versioning or migration tools; evolving schema across multiple environments is manual"],"requires":["Weaviate instance","JSON objects to insert (schema inferred from first object)","Python SDK, TypeScript SDK, or REST/GraphQL API"],"input_types":["JSON objects with arbitrary fields","text fields (auto-vectorized)","numeric, boolean, date fields (auto-indexed for filtering)"],"output_types":["automatically created collections with inferred schema","vector and keyword indexes on appropriate fields"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_5","uri":"capability://memory.knowledge.cross.reference.object.linking.and.traversal","name":"cross-reference-object-linking-and-traversal","description":"Links objects across collections using references (similar to foreign keys), enabling graph-like queries that traverse relationships. Define a reference field pointing to another collection, then query across collections using GraphQL syntax to fetch related objects in a single query. Implements a lightweight knowledge graph layer on top of the vector database, allowing you to model relationships between entities (e.g., support tickets → customers → accounts) and traverse them without multiple round-trips.","intents":["Link support tickets to customer profiles and retrieve customer context in a single query","Model product hierarchies (categories → products → variants) and traverse them for faceted search","Build knowledge graphs where documents reference each other and you need to fetch related documents transitively"],"best_for":["Applications with relational data that need both vector search and relationship traversal","Knowledge management systems with interconnected documents","E-commerce or marketplace platforms with hierarchical product relationships"],"limitations":["Cross-reference traversal performance not documented; unclear if joins are efficient or require multiple queries","No explicit support for circular references or cycle detection; risk of infinite traversal","Reference resolution happens at query time; no pre-computed materialized views or denormalization options","GraphQL query complexity may grow exponentially with deep relationship traversal; no query depth limits documented","No transaction support for maintaining referential integrity when updating linked objects"],"requires":["Weaviate instance with multiple collections","Reference fields defined in schema pointing to other collections","GraphQL API or SDK with reference query support","Understanding of GraphQL syntax for traversing relationships"],"input_types":["reference field definitions (collection name, property name)","GraphQL queries with nested reference traversal"],"output_types":["nested JSON objects with referenced data included","GraphQL response with related objects fetched in single query"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_6","uri":"capability://text.generation.language.generative.search.with.llm.result.synthesis","name":"generative-search-with-llm-result-synthesis","description":"Retrieves relevant documents from the vector database and automatically synthesizes them into a generated answer using an integrated LLM, returning both the generated text and source documents. Weaviate orchestrates the retrieval → LLM generation pipeline, handling prompt construction and context injection. Differs from basic RAG by being built into the query interface (single API call) rather than requiring external orchestration.","intents":["Get a synthesized answer to a question along with source documents in a single API call","Build a generative search feature that combines retrieval and generation without external orchestration","Reduce latency by eliminating round-trips between retrieval and LLM systems"],"best_for":["Teams building search-as-you-type features with generated summaries","Knowledge base systems that need to synthesize answers from multiple documents","Applications where latency is critical and eliminating orchestration overhead matters"],"limitations":["LLM provider integration mechanism not documented; unclear which LLMs are supported or how to configure them","Prompt engineering handled internally; no control over prompt template or context formatting","No streaming support documented; full generation must complete before response is returned","Source document attribution unclear; no explicit mechanism to cite which documents contributed to the generated answer","Cost of LLM generation not separated from retrieval costs in pricing; unclear if generative search incurs additional charges"],"requires":["Weaviate instance with generative search enabled","LLM provider configured (mechanism unknown)","Pre-vectorized documents in the database","Python SDK, TypeScript SDK, or GraphQL API"],"input_types":["text query","retrieval parameters (limit, filters)"],"output_types":["generated text answer","source documents used for generation"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_7","uri":"capability://planning.reasoning.weaviate.agents.agentic.ai.workflows","name":"weaviate-agents-agentic-ai-workflows","description":"Pre-built agents that interact with and improve your data autonomously, executing multi-step workflows without explicit orchestration. Agents can retrieve data, reason about it, call external tools, and update the database based on decisions. Positioned as a higher-level abstraction over raw vector search, enabling autonomous data management and decision-making workflows. Technical implementation details not documented; unclear if agents use ReAct pattern, tool-use APIs, or proprietary orchestration.","intents":["Automatically categorize and tag incoming support tickets using agent reasoning","Build autonomous data enrichment pipelines that fetch, analyze, and update records","Create intelligent workflows that make decisions based on retrieved data without human intervention"],"best_for":["Enterprise teams with complex data workflows requiring autonomous decision-making","Data enrichment and curation pipelines that need intelligent reasoning","Support and operations teams automating ticket triage and routing"],"limitations":["Weaviate Agents product is mentioned but not documented; unclear if it's GA, beta, or roadmap item","Agent capabilities, supported tools, and reasoning patterns not specified","No pricing information for Agents; unclear if it's included in Weaviate Cloud tiers or separate","No documentation on agent reliability, error handling, or failure modes","No control over agent behavior or decision-making; black-box orchestration","Unclear how agents interact with external systems or if they're limited to Weaviate operations"],"requires":["Weaviate instance (Cloud or self-hosted)","Weaviate Agents product (availability and pricing unknown)","Pre-configured agent workflows (configuration mechanism unknown)","External tools/APIs if agents need to call external services"],"input_types":["agent task definition (natural language or structured format unknown)","data context (documents, metadata)"],"output_types":["agent decisions and actions","updated database records","execution logs and reasoning traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_8","uri":"capability://tool.use.integration.graphql.api.with.nested.queries.and.mutations","name":"graphql-api-with-nested-queries-and-mutations","description":"Exposes Weaviate functionality via GraphQL API, enabling complex nested queries, mutations for data modification, and subscriptions for real-time updates. Supports semantic search, hybrid search, filtering, and cross-reference traversal through GraphQL syntax. Provides an alternative to REST API and SDKs for clients that prefer GraphQL's declarative query language and type safety.","intents":["Query Weaviate from a GraphQL client (Apollo, Relay) without learning Weaviate-specific SDK syntax","Fetch nested related objects in a single GraphQL query using cross-references","Integrate Weaviate into a GraphQL federation architecture"],"best_for":["Teams already using GraphQL for their API layer","Frontend developers familiar with GraphQL who want to query Weaviate directly","Applications requiring complex nested queries across multiple collections"],"limitations":["GraphQL schema not documented; unclear what queries, mutations, and subscriptions are available","No GraphQL introspection details provided; developers must discover schema through trial or documentation","Subscription support mentioned but not documented; unclear if real-time updates are supported","GraphQL query complexity and depth limits not specified; risk of expensive queries","No GraphQL-specific authentication or rate limiting documented; unclear how API keys map to GraphQL context"],"requires":["Weaviate instance with GraphQL API enabled","GraphQL client (Apollo, Relay, or curl/HTTP client)","API key for authentication (mechanism not documented)","Knowledge of GraphQL query syntax"],"input_types":["GraphQL query strings","GraphQL mutations for data modification","GraphQL subscriptions for real-time updates (if supported)"],"output_types":["JSON response matching GraphQL query shape","nested objects with cross-references resolved"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weaviate__cap_9","uri":"capability://tool.use.integration.rest.api.with.json.request.response","name":"rest-api-with-json-request-response","description":"Exposes Weaviate functionality via RESTful HTTP endpoints accepting JSON requests and returning JSON responses. Supports all core operations: object creation, semantic search, hybrid search, filtering, and deletion. 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