{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"turbopuffer","slug":"turbopuffer","name":"Turbopuffer","type":"product","url":"https://turbopuffer.com","page_url":"https://unfragile.ai/turbopuffer","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"turbopuffer__cap_0","uri":"capability://search.retrieval.approximate.nearest.neighbor.vector.search.with.warm.cold.tiering","name":"approximate nearest neighbor vector search with warm/cold tiering","description":"Executes sub-10ms vector similarity search on pre-computed embeddings using approximate nearest neighbor (ANN) algorithms with a two-tier memory architecture: hot data cached in NVMe SSD/memory for p50 latency of 8ms, cold data retrieved from S3 object storage on first access. Supports topk result limiting and operates at scale across 500M+ documents per namespace with observed throughput of 25k+ queries/second.","intents":["I need to find semantically similar documents from millions of embeddings in under 10ms for production RAG systems","I want to reduce query latency by keeping frequently accessed vectors in fast cache while storing the full dataset cheaply in S3","I need to scale vector search to billions of documents without proportional infrastructure cost increases"],"best_for":["teams building production RAG systems with cost-sensitive requirements","developers implementing semantic search over large document collections (1M+ vectors)","companies migrating from expensive vector databases like Pinecone or Weaviate"],"limitations":["Cold namespace queries (first access from S3) have unknown latency penalty — only warm cache p50/p90/p99 documented","Designed for first-stage retrieval to narrow millions to tens/hundreds, not exhaustive ranking or complex post-processing","Maximum vector dimensions not explicitly stated; tested at 768 dimensions only","Vector format (float32 vs float16 vs quantized) not documented — unclear if compression is automatic"],"requires":["Pre-computed vector embeddings from external embedding model (Turbopuffer does not provide embeddings)","AWS S3 bucket for object storage backend","API key for authentication","Vectors in 768-dimension format (or undocumented alternative dimensions)"],"input_types":["vector embeddings (float arrays)","topk parameter (integer)","optional metadata filter expressions (syntax unknown)"],"output_types":["ranked list of document IDs with similarity scores","document metadata if stored"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_1","uri":"capability://search.retrieval.bm25.full.text.search.with.metadata.filtering","name":"bm25 full-text search with metadata filtering","description":"Performs keyword-based document retrieval using BM25 ranking algorithm combined with optional metadata filtering to narrow result sets by document attributes. Operates independently from vector search or in hybrid mode, with measured p50 latency of 343ms on warm namespaces. Metadata filter syntax and exact filtering capabilities are undocumented but support structured attribute-based result narrowing.","intents":["I need to find documents by exact keyword matches or phrase queries in addition to semantic similarity","I want to filter search results by document metadata (date, category, author) before ranking","I need to combine keyword search with vector search in a hybrid approach for better recall"],"best_for":["RAG systems requiring both semantic and keyword-based retrieval","teams with structured document metadata (tags, categories, timestamps) that need filtering","applications where exact phrase matching is important alongside semantic similarity"],"limitations":["Full-text search latency significantly higher than vector search (p50 343ms vs 8ms) — not suitable for sub-100ms SLA requirements","Metadata filter query language and syntax completely undocumented — requires trial-and-error or support inquiry","Cold namespace full-text search latency not documented; only warm cache performance (p50 343ms, p90 444ms, p99 554ms) provided","No documented support for complex boolean queries, wildcards, or regex patterns"],"requires":["Documents indexed with full-text search enabled (indexing mechanism unknown)","Metadata fields defined and populated during document ingestion","API key for authentication"],"input_types":["keyword query string","optional metadata filter expression (syntax unknown)","topk parameter for result limiting"],"output_types":["ranked list of document IDs with BM25 relevance scores","document metadata if stored"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_10","uri":"capability://safety.moderation.api.authentication.and.access.control","name":"api authentication and access control","description":"Secures API access using API key-based authentication with undocumented header format and encoding. Supports role-based access control (RBPR) at Scale tier with SSO (single sign-on), and fine-grained permissions at Enterprise tier. Specific authentication mechanisms, token formats, and permission models are completely undocumented.","intents":["I need to authenticate API requests to Turbopuffer without exposing credentials in client code","I want to grant different team members different levels of access (read-only, write, admin)","I need to integrate Turbopuffer authentication with my company's identity provider (SSO)"],"best_for":["teams with multiple developers needing different access levels","enterprises with SSO/SAML requirements","applications requiring API key rotation and revocation"],"limitations":["API key format and authentication header completely undocumented — no examples or specifications provided","Role-based access control (RBAC) only available at Scale tier minimum — Launch tier has no documented permission model","SSO/SAML integration only at Scale tier — no documented support for OAuth2 or other standards","Fine-grained permissions (Enterprise tier) are undocumented — unclear what granularity is supported","No documented API key rotation, expiration, or revocation mechanisms","No documented audit trail for authentication events or permission changes"],"requires":["API key (generation mechanism undocumented)","Appropriate pricing tier for desired access control level (Scale for RBAC/SSO, Enterprise for fine-grained permissions)"],"input_types":["API key (format unknown)","authentication header (format unknown)"],"output_types":["authentication success/failure response (format unknown)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_11","uri":"capability://automation.workflow.multi.region.deployment.and.data.residency","name":"multi-region deployment and data residency","description":"Supports deployment across multiple AWS regions with data residency controls, but specific regions, latency characteristics, and failover behavior are completely undocumented. Region selection appears to be tied to S3 bucket location.","intents":["I need to deploy vector search in a specific AWS region for data residency compliance","I want to minimize latency by deploying close to my application servers","I need to replicate data across regions for disaster recovery"],"best_for":["companies with data residency requirements (GDPR, HIPAA, etc.)","global applications needing low-latency access from multiple regions","teams requiring disaster recovery and multi-region failover"],"limitations":["Supported regions completely undocumented — no list of available AWS regions provided","Cross-region replication and failover mechanisms undocumented","No documented latency characteristics between regions","No documented data residency guarantees or compliance controls","Unclear if multi-region is available at all pricing tiers or Enterprise-only","No documented pricing for cross-region data transfer"],"requires":["AWS region selection (available regions unknown)","S3 bucket in target region"],"input_types":["AWS region identifier"],"output_types":["region confirmation (format unknown)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_12","uri":"capability://automation.workflow.customer.support.and.sla.guarantees","name":"customer support and sla guarantees","description":"Provides tiered support with Launch offering community support, Scale offering 8-5 business hours support with private Slack channel, and Enterprise offering 24/7 support with 99.95% uptime SLA. Specific response times, escalation procedures, and SLA terms are undocumented.","intents":["I need guaranteed response times for production issues","I want direct communication with Turbopuffer support via Slack","I need 24/7 support for mission-critical applications"],"best_for":["startups using Launch tier with community support expectations","mid-market teams (Scale tier) needing business hours support","enterprises requiring 24/7 support and SLA guarantees"],"limitations":["Community support (Launch tier) has no documented response time or SLA","Business hours support (Scale tier) is 8-5 only — no weekend or holiday coverage documented","99.95% SLA (Enterprise tier) is undocumented in terms of exclusions, credits, or remedies","No documented escalation procedures or support ticket tracking","Private Slack channel (Scale tier) availability and response time not documented"],"requires":["Appropriate pricing tier (Launch for community, Scale for business hours, Enterprise for 24/7)"],"input_types":["support request (format unknown)"],"output_types":["support response (format unknown)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_2","uri":"capability://search.retrieval.hybrid.vector.full.text.search.with.combined.ranking","name":"hybrid vector + full-text search with combined ranking","description":"Executes simultaneous vector and full-text search queries and combines their ranking signals to produce a unified result set that balances semantic similarity with keyword relevance. Implementation details of ranking combination (weighted sum, learning-to-rank, etc.) are undocumented, but enables use cases requiring both semantic and keyword precision without separate round-trips.","intents":["I need search results that are both semantically similar AND contain relevant keywords, not just one or the other","I want to avoid tuning separate vector and keyword search pipelines and merging results in application code","I need to improve recall by combining multiple retrieval signals in a single query"],"best_for":["RAG systems with diverse query types (some semantic, some keyword-focused)","teams building search experiences where users expect both semantic and keyword relevance","applications with mixed structured and unstructured document content"],"limitations":["Ranking combination algorithm completely undocumented — no way to tune weights or understand result ordering","Latency is likely sum of vector + full-text latencies (8ms + 343ms = ~350ms+) making it unsuitable for sub-100ms requirements","No documented way to adjust balance between vector and full-text signals per query","Cold namespace performance unknown for hybrid queries"],"requires":["Pre-computed vector embeddings","Full-text indexed documents with metadata","API key for authentication"],"input_types":["vector embedding (float array)","keyword query string","optional metadata filters","topk parameter"],"output_types":["unified ranked list of document IDs with combined relevance scores","document metadata if stored"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_3","uri":"capability://memory.knowledge.namespace.based.multi.tenancy.and.data.isolation","name":"namespace-based multi-tenancy and data isolation","description":"Isolates documents and queries into logical namespaces, enabling secure multi-tenant deployments where each tenant's data is completely segregated at the API level. Supports up to 100M+ namespaces with independent vector/full-text indexes, metadata schemas, and cache policies. Namespaces can be pinned (up to 256) to keep data in warm cache, or unpinned to use cold S3 storage for cost optimization.","intents":["I need to serve multiple customers from a single Turbopuffer account with complete data isolation and no cross-tenant leakage","I want to optimize costs by keeping high-traffic customer data warm while storing low-traffic data in cold S3","I need to manage per-tenant quotas, access controls, and billing separately"],"best_for":["SaaS platforms offering RAG/search features to multiple customers","teams building multi-tenant AI applications with strict data isolation requirements","companies needing per-customer cost tracking and resource management"],"limitations":["Namespace isolation is logical (API-level) not cryptographic — relies on API key authentication to prevent cross-tenant access","No documented per-namespace quotas, rate limits, or resource guarantees — unclear if one tenant can starve others","Pinned namespace limit of 256 means only 256 tenants can have guaranteed warm cache; others fall back to S3 latency","No documented namespace-level access controls or fine-grained permissions — appears to be all-or-nothing per API key"],"requires":["API key with multi-tenancy enabled (Launch or Scale tier minimum)","Unique namespace identifier per tenant","Mechanism to route tenant queries to correct namespace (application responsibility)"],"input_types":["namespace identifier (string)","documents with unique IDs and metadata","vector embeddings"],"output_types":["namespace metadata (size, document count, cache status)","list of all namespaces in account"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_4","uri":"capability://memory.knowledge.s3.backed.persistent.storage.with.tiered.caching","name":"s3-backed persistent storage with tiered caching","description":"Stores all vector and document data durably in AWS S3 object storage while maintaining a two-tier cache layer (NVMe SSD + memory) for hot data. On first query to a namespace, data is loaded from S3 into cache; subsequent queries hit the faster cache layer. Namespaces can be explicitly pinned to keep data in warm cache, or unpinned to allow cache eviction and S3 fallback for cost savings.","intents":["I need durable, cost-effective storage for vector data without paying for always-hot managed vector database storage","I want to control the cost/latency tradeoff by deciding which namespaces stay warm vs cold","I need to back up or export my vector data easily using standard S3 tools"],"best_for":["teams with large vector datasets (100M+ documents) where cost is a primary concern","applications with bursty query patterns where keeping all data warm is wasteful","companies requiring data residency in specific AWS regions or S3 buckets"],"limitations":["Cold query latency (first access from S3) is completely undocumented — only warm cache latencies provided (p50 8ms vector, p50 343ms full-text)","S3 bucket must be provided and managed by customer — Turbopuffer does not provide managed S3 storage","No documented encryption at rest, key management, or compliance controls for S3 data","Cache eviction policy and warm/cold transition mechanics are undocumented — unclear how long data stays warm after last query","No documented way to pre-warm specific namespaces or control cache layer size"],"requires":["AWS S3 bucket with appropriate IAM permissions for Turbopuffer service account","AWS region selection matching S3 bucket location","API key for authentication"],"input_types":["vector embeddings and documents","namespace identifier"],"output_types":["S3 object references (internal)","cache status (warm/cold) — undocumented if exposed via API"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_5","uri":"capability://data.processing.analysis.document.write.update.delete.operations.with.batch.support","name":"document write/update/delete operations with batch support","description":"Ingests documents into namespaces with vector embeddings, metadata, and unique IDs. Supports create, update, and delete operations to maintain document indexes. Specific HTTP methods, request schemas, batch size limits, and transaction semantics are completely undocumented, but the capability enables dynamic document management without full namespace reindexing.","intents":["I need to add new documents to my vector index without rebuilding the entire namespace","I want to update document metadata or embeddings when source data changes","I need to remove documents from search results when they become obsolete or deleted"],"best_for":["RAG systems with frequently updated source documents (news, product catalogs, knowledge bases)","teams needing to maintain document freshness without full reindexing","applications with document lifecycle management (creation, updates, deletion)"],"limitations":["Write API specification completely undocumented — no request schema, HTTP methods, or response format documented","Batch write size limits unknown — unclear if there are constraints on documents per request","Transaction semantics unknown — unclear if writes are atomic, if partial batch failures are possible, or how to handle failures","Write latency not documented — unknown if writes are synchronous or asynchronous","No documented support for conditional writes, versioning, or conflict resolution","Unclear if metadata schema is flexible or must be pre-defined per namespace"],"requires":["API key with write permissions","Document ID (unique per namespace)","Vector embedding (pre-computed)","Optional metadata fields (schema unknown)"],"input_types":["document ID (string)","vector embedding (float array)","metadata object (structure unknown)","optional document content/text"],"output_types":["write confirmation (format unknown)","error details if write fails (schema unknown)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_6","uri":"capability://data.processing.analysis.namespace.export.and.data.extraction","name":"namespace export and data extraction","description":"Exports documents and vectors from a namespace in an undocumented format for backup, migration, or external processing. Export mechanism, supported formats (JSON, Parquet, CSV), and constraints (size limits, rate limits) are completely undocumented.","intents":["I need to back up my vector data before migrating to another system","I want to export search results or specific documents for external analysis","I need to migrate my data from Turbopuffer to another vector database"],"best_for":["teams implementing disaster recovery and backup strategies","companies evaluating Turbopuffer and needing to test data portability","applications requiring periodic data exports for compliance or auditing"],"limitations":["Export API specification completely undocumented — no format options, size limits, or performance characteristics documented","Export latency unknown — unclear if large exports are asynchronous or synchronous","No documented support for incremental exports or change data capture (CDC)","Unclear if exports include metadata, embeddings, or both","No documented rate limits or quotas on export frequency"],"requires":["API key with read permissions","Namespace identifier","Sufficient storage/bandwidth for exported data"],"input_types":["namespace identifier","optional filter criteria (syntax unknown)"],"output_types":["exported documents and vectors (format unknown)","metadata if included (schema unknown)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_7","uri":"capability://automation.workflow.namespace.cache.warming.and.performance.optimization","name":"namespace cache warming and performance optimization","description":"Explicitly pre-loads namespace data from S3 into NVMe SSD and memory cache to guarantee sub-10ms query latency. Supports pinning up to 256 namespaces to keep data warm, or unpinning to allow cache eviction and S3 fallback. Cache warming mechanics and warm/cold transition behavior are undocumented.","intents":["I need to guarantee low latency for high-traffic customer namespaces by keeping them in warm cache","I want to optimize costs by keeping only frequently-accessed namespaces warm while others use cold S3","I need to pre-warm a namespace before a traffic spike or scheduled event"],"best_for":["SaaS platforms with tiered customer tiers (premium customers get warm cache, others use cold S3)","teams with predictable traffic patterns and scheduled high-traffic events","applications requiring guaranteed sub-10ms latency for specific namespaces"],"limitations":["Pinned namespace limit of 256 is a hard constraint — only 256 tenants can have guaranteed warm cache in a single account","Cache warming latency unknown — unclear how long it takes to warm a namespace from cold S3","Cache eviction policy undocumented — unclear how long data stays warm after last query or if manual unpinning is required","No documented way to monitor cache status, hit rates, or warm/cold transitions","No documented per-namespace cache size limits or memory guarantees"],"requires":["API key with namespace management permissions","Namespace identifier","Understanding of which namespaces need warm cache (application responsibility)"],"input_types":["namespace identifier","pin/unpin action"],"output_types":["cache status confirmation (format unknown)","namespace metadata including cache state (undocumented)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_8","uri":"capability://automation.workflow.pay.per.query.pricing.with.minimum.monthly.commitment","name":"pay-per-query pricing with minimum monthly commitment","description":"Charges customers based on query volume with a minimum monthly commitment tier (Launch $64, Scale $256, Enterprise $4,096). Per-query costs are undocumented, but pricing is claimed to be 10x cheaper than alternatives. Minimum commitments include query budget that resets monthly; overage pricing beyond minimum is undocumented.","intents":["I need to understand the cost of running vector search at scale before committing to infrastructure","I want to pay only for queries I actually run, not for provisioned capacity","I need to estimate monthly costs based on expected query volume"],"best_for":["startups and small teams with unpredictable query volumes","companies migrating from fixed-cost vector databases and wanting usage-based pricing","teams evaluating Turbopuffer and needing transparent cost modeling"],"limitations":["Per-query cost completely undocumented — no way to calculate exact costs for a given query volume","Per-write cost undocumented — unclear if document ingestion is charged separately","Per-byte storage cost undocumented — unclear if S3 storage is included in minimum or charged separately","Overage pricing beyond minimum commitment unknown — unclear what happens if you exceed monthly query budget","No documented cost calculator or pricing transparency — claims of '10x cheaper' are unverified","Minimum commitment is non-refundable and resets monthly — no annual discounts or volume commitments documented"],"requires":["Credit card for billing","Selection of pricing tier (Launch, Scale, or Enterprise)","Estimate of monthly query volume (for cost planning)"],"input_types":["pricing tier selection","estimated monthly query volume"],"output_types":["monthly cost estimate (format unknown)","billing invoice (format unknown)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__cap_9","uri":"capability://safety.moderation.soc2.gdpr.hipaa.compliance.and.security.certifications","name":"soc2/gdpr/hipaa compliance and security certifications","description":"Provides compliance certifications and security features across pricing tiers: Launch tier includes SOC2 and GDPR compliance; Scale tier adds HIPAA-readiness and SSO; Enterprise tier includes single-tenancy, BYOC (bring-your-own-compute), CMEK (customer-managed encryption keys), and private networking. Specific security controls, audit logging, and compliance verification mechanisms are undocumented.","intents":["I need to use a vector database that meets HIPAA requirements for healthcare data","I need SOC2 compliance certification for enterprise customer contracts","I want GDPR compliance guarantees for EU customer data"],"best_for":["healthcare and fintech companies requiring HIPAA compliance","enterprises with SOC2 audit requirements","companies serving EU customers with GDPR obligations","teams needing single-tenancy deployments for data isolation"],"limitations":["Specific security controls and audit logging mechanisms completely undocumented","HIPAA-readiness (Scale tier) is not full HIPAA compliance — unclear what additional work is required","CMEK and BYOC (Enterprise tier only) — no documented support for customer-managed encryption in lower tiers","No documented data residency guarantees — unclear if data stays in specific AWS regions","No documented encryption in transit or at rest specifications","Audit logging and compliance reporting mechanisms undocumented"],"requires":["Appropriate pricing tier (Launch for SOC2/GDPR, Scale for HIPAA-ready, Enterprise for BYOC/CMEK)","Compliance review and attestation process (undocumented)"],"input_types":["compliance requirements (HIPAA, SOC2, GDPR, etc.)"],"output_types":["compliance certification or attestation (format unknown)","audit logs (if available)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"turbopuffer__headline","uri":"capability://search.retrieval.cost.effective.vector.database","name":"cost-effective vector database","description":"Turbopuffer is a low-cost vector database designed for scalable, efficient search solutions, offering pay-per-query pricing and unique features like namespace isolation and metadata filtering.","intents":["best vector database","vector database for large-scale search","affordable vector database solutions","vector search database with pay-per-query pricing","cost-efficient vector database for developers"],"best_for":["developers","data engineers"],"limitations":["may lack advanced features of traditional search engines"],"requires":["API integration knowledge"],"input_types":["documents","queries","metadata"],"output_types":["search results"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Pre-computed vector embeddings from external embedding model (Turbopuffer does not provide embeddings)","AWS S3 bucket for object storage backend","API key for authentication","Vectors in 768-dimension format (or undocumented alternative dimensions)","Documents indexed with full-text search enabled (indexing mechanism unknown)","Metadata fields defined and populated during document ingestion","API key (generation mechanism undocumented)","Appropriate pricing tier for desired access control level (Scale for RBAC/SSO, Enterprise for fine-grained permissions)","AWS region selection (available regions unknown)","S3 bucket in target region"],"failure_modes":["Cold namespace queries (first access from S3) have unknown latency penalty — only warm cache p50/p90/p99 documented","Designed for first-stage retrieval to narrow millions to tens/hundreds, not exhaustive ranking or complex post-processing","Maximum vector dimensions not explicitly stated; tested at 768 dimensions only","Vector format (float32 vs float16 vs quantized) not documented — unclear if compression is automatic","Full-text search latency significantly higher than vector search (p50 343ms vs 8ms) — not suitable for sub-100ms SLA requirements","Metadata filter query language and syntax completely undocumented — requires trial-and-error or support inquiry","Cold namespace full-text search latency not documented; only warm cache performance (p50 343ms, p90 444ms, p99 554ms) provided","No documented support for complex boolean queries, wildcards, or regex patterns","API key format and authentication header completely undocumented — no examples or specifications provided","Role-based access control (RBAC) only available at Scale tier minimum — Launch tier has no documented permission model","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","last_scraped_at":null,"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=turbopuffer","compare_url":"https://unfragile.ai/compare?artifact=turbopuffer"}},"signature":"0+dt54MWhbaUrWiQJ4EahMO6ciKm+f1h1sm6EusXLTNKJ5Seshb74ZzM7dWiExoLkUoRVTjehIO+huHcg2HOAQ==","signedAt":"2026-06-20T21:24:49.402Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/turbopuffer","artifact":"https://unfragile.ai/turbopuffer","verify":"https://unfragile.ai/api/v1/verify?slug=turbopuffer","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"}}