{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-infiniflow--infinity","slug":"infiniflow--infinity","name":"infinity","type":"product","url":"https://infiniflow.org","page_url":"https://unfragile.ai/infiniflow--infinity","categories":["rag-knowledge"],"tags":["ai-native","approximate-nearest-neighbor-search","bm25","cpp20","cpp20-modules","embedding","full-text-search","hnsw","hybrid-search","information-retrival","multi-vector","nearest-neighbor-search","rag","search-engine","tensor-database","vector","vector-database","vector-search","vectordatabase"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-infiniflow--infinity__cap_0","uri":"capability://search.retrieval.dense.vector.approximate.nearest.neighbor.search","name":"dense-vector-approximate-nearest-neighbor-search","description":"Executes approximate nearest neighbor (ANN) search on dense vector embeddings using HNSW (Hierarchical Navigable Small World) indexing, enabling sub-millisecond retrieval of semantically similar vectors from billion-scale datasets. The system maintains hierarchical graph structures with configurable layer counts and connection parameters, supporting both L2 and cosine distance metrics with SIMD-optimized distance computation.","intents":["Find semantically similar documents or embeddings from a large corpus in sub-second latency","Retrieve top-K nearest neighbors for a query vector with configurable recall-speed tradeoffs","Build RAG systems that can search embeddings generated by LLMs or sentence transformers"],"best_for":["LLM application builders implementing semantic search or RAG pipelines","Teams migrating from Pinecone or Weaviate seeking open-source alternatives","Researchers benchmarking vector search performance at scale"],"limitations":["HNSW index construction is single-threaded, adding ~O(n log n) overhead during bulk inserts","Recall-speed tradeoff controlled via ef_construction and ef_search parameters; higher recall requires larger search neighborhoods","Index must fit in memory or use memory-mapped storage; no native disk-based index streaming","Distance metrics limited to L2 and cosine; no learned distance functions or custom metrics"],"requires":["C++20 compiler with modules support (GCC 11+ or Clang 14+)","Dense vector embeddings pre-computed (e.g., from OpenAI, Hugging Face, or local models)","Vector dimension must be consistent across all inserted vectors"],"input_types":["float32 or float64 dense vectors","vector dimension (typically 384-1536 for modern embeddings)"],"output_types":["ranked list of vector IDs with distances","associated metadata for matched vectors"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_1","uri":"capability://search.retrieval.sparse.vector.bm25.full.text.search","name":"sparse-vector-bm25-full-text-search","description":"Executes BM25-based full-text search on sparse vector representations of documents, tokenizing text into terms, computing TF-IDF weights, and ranking results by relevance using the Okapi BM25 probabilistic model. The system maintains inverted indices mapping terms to document IDs with frequency statistics, enabling fast boolean and ranked retrieval without dense embeddings.","intents":["Search documents by keyword or phrase with traditional information retrieval ranking","Combine full-text search with vector search in hybrid queries for better recall","Index and retrieve text-heavy content (documents, articles, logs) without pre-computed embeddings"],"best_for":["RAG systems needing both semantic and keyword-based retrieval","Document search applications with large text corpora","Teams familiar with Elasticsearch or Solr seeking integrated vector+text search"],"limitations":["BM25 ranking is term-frequency based; cannot capture semantic relationships without dense vectors","Inverted index construction requires tokenization and stopword filtering; language-specific tuning needed for non-English text","Sparse vectors require explicit term vocabulary; out-of-vocabulary terms are ignored","No native support for phrase queries or complex boolean operators; requires query rewriting"],"requires":["Text tokenization pipeline (built-in or custom)","BM25 hyperparameters (k1, b) tuned for corpus characteristics","Stopword list for language of indexed documents"],"input_types":["raw text documents","pre-tokenized term lists","sparse vectors (term ID → TF-IDF weight)"],"output_types":["ranked list of document IDs with BM25 scores","term frequency statistics for matched documents"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_10","uri":"capability://data.processing.analysis.bulk.data.import.and.export","name":"bulk-data-import-and-export","description":"Supports bulk import of vectors and metadata from CSV, Parquet, or JSON files, with automatic schema inference and parallel loading across multiple threads. Export functionality writes query results to files in same formats; import uses buffered writes and batch index updates to minimize latency and memory overhead.","intents":["Load pre-computed embeddings from external sources (embedding services, offline models) into Infinity","Export search results or entire tables for analysis or backup","Migrate data from other vector databases to Infinity"],"best_for":["Data engineers loading large embedding datasets","Teams migrating from other vector databases","Batch processing workflows that periodically refresh vector indices"],"limitations":["Import requires data to fit in available disk space; no streaming import for infinite datasets","Schema inference is heuristic-based; complex nested structures require manual schema definition","Parquet import slower than CSV due to columnar format overhead; CSV recommended for speed","Export to Parquet requires all results in memory; large result sets may cause OOM"],"requires":["Data files in CSV, Parquet, or JSON format","Schema definition (column names, types, vector dimensions)","Sufficient disk space for import files"],"input_types":["CSV, Parquet, or JSON files with vector and metadata columns"],"output_types":["imported record count and statistics","exported files in requested format"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_11","uri":"capability://automation.workflow.index.creation.and.management","name":"index-creation-and-management","description":"Creates and manages indices on vector and metadata columns, supporting HNSW indices for dense vectors, inverted indices for full-text search, and B-tree indices for metadata filtering. Index creation is asynchronous and can be cancelled; index statistics are maintained for query optimization and can be manually refreshed.","intents":["Create HNSW indices on vector columns to enable fast ANN search","Create full-text indices on text columns for BM25 ranking","Create metadata indices for efficient filtering during vector search"],"best_for":["Database administrators tuning Infinity performance","Teams optimizing query latency for production workloads","Data engineers managing index lifecycle and maintenance"],"limitations":["Index creation is single-threaded; large indices take hours to build","Index size can exceed vector data size (HNSW adds ~20-30% overhead); requires capacity planning","No incremental index updates; indices must be rebuilt after schema changes","Index statistics are approximate; cost-based query planning may be suboptimal"],"requires":["Table with vector or metadata columns","Index type selection (HNSW, inverted, B-tree)","Index parameters (HNSW: ef_construction, M; inverted: tokenizer)"],"input_types":["CREATE INDEX statements with index type and parameters"],"output_types":["index creation status and progress","index statistics (size, entry count, build time)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_12","uri":"capability://automation.workflow.snapshot.and.backup.recovery","name":"snapshot-and-backup-recovery","description":"Creates point-in-time snapshots of the entire database including vectors, metadata, and indices, enabling recovery to previous states or migration to other systems. Snapshots are incremental and can be stored locally or on remote storage; recovery is atomic and validates data integrity before committing.","intents":["Create backups before major operations (schema changes, bulk deletes) for rollback capability","Migrate data to new Infinity cluster or other vector databases","Implement disaster recovery with periodic snapshots to remote storage"],"best_for":["Production systems requiring disaster recovery and backup compliance","Teams migrating between Infinity versions or clusters","Applications with strict data retention and recovery requirements"],"limitations":["Snapshot creation requires copying all data; adds I/O overhead during backup window","Incremental snapshots require tracking changes; adds write-path overhead","Recovery is atomic but slow for large snapshots; may require downtime","Remote storage integration requires external configuration (S3, GCS, etc.)"],"requires":["Sufficient disk space for snapshot (equal to database size)","Remote storage credentials if using cloud backup","Snapshot schedule and retention policy"],"input_types":["snapshot name and destination","snapshot type (full or incremental)"],"output_types":["snapshot metadata (timestamp, size, data integrity hash)","recovery status and validation results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_13","uri":"capability://planning.reasoning.query.execution.with.cost.based.optimization","name":"query-execution-with-cost-based-optimization","description":"Optimizes query execution plans using cost-based optimization that estimates operation costs (I/O, CPU, memory) and selects lowest-cost plan. The optimizer considers index availability, data statistics, and filter selectivity to decide between sequential scan, index scan, and hybrid search paths; execution uses pipelined operators for memory efficiency.","intents":["Automatically select optimal query execution strategy without manual hints","Improve query latency by choosing efficient index paths based on data characteristics","Handle complex queries with multiple filters and search types efficiently"],"best_for":["Teams running diverse query workloads with varying selectivity","Production systems where query latency is critical","Applications with complex queries combining vector search, filtering, and aggregation"],"limitations":["Cost estimation is heuristic-based; poor estimates lead to suboptimal plans","Statistics collection requires periodic ANALYZE commands; stale statistics degrade optimization","No learned cost models; cannot adapt to workload-specific characteristics","Plan caching is limited; complex queries may be re-optimized on each execution"],"requires":["Table statistics (row count, column cardinality, value distributions)","Index availability on columns used in filters and search","Query complexity within reasonable bounds (deep nesting may timeout optimizer)"],"input_types":["SQL queries with vector search, filters, and aggregations"],"output_types":["optimized query execution plan","cost estimates for each plan step"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_2","uri":"capability://search.retrieval.multi.vector.tensor.search","name":"multi-vector-tensor-search","description":"Executes search over multi-vector (tensor) representations where each document contains multiple embedding vectors (e.g., different model outputs or chunked representations), aggregating relevance scores across vectors using configurable fusion strategies (max, mean, weighted sum). The system stores tensors as columnar data structures and applies ANN search independently per vector dimension before combining results.","intents":["Search documents represented by multiple embeddings from different models or perspectives","Retrieve results based on ensemble of embedding models without reranking overhead","Handle chunked documents where each chunk has its own embedding vector"],"best_for":["Advanced RAG systems using multi-model ensembles for robustness","Document retrieval where chunks need independent embeddings","Teams experimenting with different embedding models simultaneously"],"limitations":["Fusion strategy selection (max/mean/weighted) requires manual tuning; no learned fusion weights","Storage overhead increases linearly with number of vectors per document","Search latency scales with vector count; N vectors = N separate ANN searches","No native support for cross-vector dependencies or attention mechanisms"],"requires":["Multiple pre-computed embedding vectors per document","Consistent vector dimensions across all vectors in a tensor","Fusion strategy configuration (max, mean, or custom weights)"],"input_types":["tensor data (multiple float32/float64 vectors per document)","fusion strategy specification"],"output_types":["ranked list of document IDs with fused relevance scores","per-vector scores before fusion (for debugging)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_3","uri":"capability://search.retrieval.hybrid.search.with.configurable.fusion","name":"hybrid-search-with-configurable-fusion","description":"Combines dense vector search, sparse vector (BM25) search, and full-text search in a single query, executing each search path independently and fusing results using configurable strategies (weighted sum, RRF, learned fusion). The query planner routes subqueries to appropriate indices and merges ranked lists while maintaining result deduplication and score normalization across heterogeneous search types.","intents":["Execute single queries that balance semantic and keyword relevance without separate round-trips","Improve RAG recall by combining vector and BM25 results with tunable weights","Implement search systems where some queries need semantic matching and others need exact term matching"],"best_for":["Production RAG systems requiring high recall and precision","Search applications with mixed query types (semantic + keyword)","Teams migrating from Elasticsearch to unified vector+text systems"],"limitations":["Fusion weight tuning requires manual experimentation or labeled relevance data; no automatic optimization","Score normalization across BM25 and vector distances is heuristic-based; no principled probabilistic fusion","Latency is sum of slowest search path; no early termination if one path dominates","RRF fusion requires full result sets from all paths; cannot use approximate top-K from each path"],"requires":["Both dense vector indices and BM25 indices on same table","Fusion strategy selection (weighted sum, RRF, or custom)","Fusion weights or parameters tuned for domain"],"input_types":["query vector (for dense search)","query text (for BM25 search)","fusion strategy and weights"],"output_types":["ranked list of document IDs with fused scores","per-search-type scores (for transparency)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_4","uri":"capability://search.retrieval.sql.based.query.interface.with.vector.extensions","name":"sql-based-query-interface-with-vector-extensions","description":"Provides SQL query interface extended with vector-specific functions (KNN, MATCH, FUSION) that compile to optimized query execution plans. The SQL parser (built with C++20 modules) handles standard DDL/DML plus vector operations, the query planner applies cost-based optimization for index selection, and the executor dispatches to specialized vector operators (HNSW traversal, BM25 ranking, fusion).","intents":["Write SQL queries that combine vector search with traditional filtering and aggregation","Use familiar SQL syntax for vector operations without learning new APIs","Build complex queries with vector search, metadata filtering, and post-processing in one statement"],"best_for":["SQL developers adding vector search to existing applications","Teams building complex RAG queries with filtering and aggregation","Data engineers integrating Infinity into SQL-based data pipelines"],"limitations":["Vector functions are SQL extensions; not all standard SQL optimizations apply to vector operators","Query planner lacks learned cost models; index selection relies on heuristics and statistics","No support for vector UDFs or custom distance functions in SQL; requires C++ extension","Subquery optimization for vector operations is limited; complex nested queries may be inefficient"],"requires":["SQL knowledge (SELECT, WHERE, JOIN, GROUP BY syntax)","Understanding of vector-specific functions (KNN, MATCH, FUSION)","Metadata schema design for filtering alongside vector search"],"input_types":["SQL query strings with vector extensions","table schemas with vector columns"],"output_types":["result sets with document IDs, scores, and metadata","query execution plans (for debugging)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_5","uri":"capability://search.retrieval.metadata.filtering.with.vector.search","name":"metadata-filtering-with-vector-search","description":"Applies metadata filters (WHERE clauses on non-vector columns) during or after vector search, supporting range queries, equality checks, and boolean combinations on structured fields. The query executor can push filters into index traversal (early termination) or apply post-search filtering depending on selectivity; metadata is stored alongside vectors in columnar format for cache-efficient access.","intents":["Search vectors while filtering by document metadata (date, category, source, etc.)","Implement access control by filtering search results to user-owned documents","Combine vector similarity with business logic constraints (price range, availability, etc.)"],"best_for":["Multi-tenant RAG systems needing per-user document filtering","E-commerce or marketplace search combining similarity with product attributes","Document search with temporal or categorical constraints"],"limitations":["Filter selectivity estimation is heuristic-based; poor estimates lead to inefficient execution plans","Pushing filters into HNSW traversal requires metadata colocation with index; adds memory overhead","Complex boolean filters (OR, NOT) may require full index scan if selectivity is high","No native support for full-text metadata search; requires separate BM25 index on metadata fields"],"requires":["Metadata columns defined in table schema with appropriate types","Metadata values populated alongside vector inserts","Filter predicates in SQL WHERE clause"],"input_types":["query vector","metadata filter predicates (e.g., WHERE category='news' AND date > '2024-01-01')"],"output_types":["filtered ranked list of document IDs","execution plan showing filter application point"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_6","uri":"capability://automation.workflow.transactional.consistency.with.wal.and.mvcc","name":"transactional-consistency-with-wal-and-mvcc","description":"Provides ACID transaction semantics for vector and metadata operations using Write-Ahead Logging (WAL) for durability and Multi-Version Concurrency Control (MVCC) for isolation. Each transaction maintains a consistent snapshot of the database; writes are logged before applying to in-memory structures, enabling recovery from crashes and concurrent reads during writes without blocking.","intents":["Ensure vector inserts and metadata updates are durable across server restarts","Support concurrent reads and writes without locking or blocking queries","Implement transactional consistency for multi-step operations (insert vectors, update metadata, create indices)"],"best_for":["Production systems requiring data durability and crash recovery","High-concurrency RAG systems with concurrent reads and writes","Applications where data consistency is critical (financial, healthcare, legal)"],"limitations":["WAL writes add ~5-10ms latency per transaction; batch inserts recommended for throughput","MVCC maintains multiple versions in memory; garbage collection overhead increases with transaction duration","Snapshot isolation prevents dirty reads but allows phantom reads; no serializable isolation level","WAL recovery is sequential; large WAL files slow down server startup"],"requires":["Persistent storage for WAL (SSD recommended for durability)","Sufficient memory for MVCC version storage","Transaction management code in client (explicit commit/rollback or auto-commit)"],"input_types":["transaction operations (INSERT, UPDATE, DELETE, CREATE INDEX)","transaction boundaries (BEGIN, COMMIT, ROLLBACK)"],"output_types":["transaction status (success, conflict, abort)","WAL entries for recovery"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_7","uri":"capability://automation.workflow.distributed.cluster.deployment.with.peer.replication","name":"distributed-cluster-deployment-with-peer-replication","description":"Deploys Infinity across multiple nodes with automatic data replication, peer-to-peer synchronization via Thrift RPC, and cluster management for failover and load balancing. The ClusterManager coordinates node roles (leader, replica), distributes data shards across nodes, and handles peer communication for consistency; replication is asynchronous with configurable consistency levels.","intents":["Scale vector search across multiple machines for throughput and fault tolerance","Replicate data to multiple nodes for high availability and disaster recovery","Distribute large vector indices across cluster nodes to fit in aggregate memory"],"best_for":["Production RAG systems requiring high availability and scale","Teams deploying Infinity in cloud or on-premises clusters","Applications with strict uptime requirements (99.9%+ SLA)"],"limitations":["Asynchronous replication introduces replication lag; reads may see stale data","Cluster management requires external coordination service (Etcd, Zookeeper) for leader election","Data rebalancing during node failures is manual or requires custom orchestration","Network partitions can cause split-brain scenarios; requires manual intervention to resolve","Peer-to-peer Thrift communication adds ~10-50ms latency per cross-node operation"],"requires":["Multiple Infinity server instances (minimum 3 for quorum)","Network connectivity between all nodes (low-latency LAN recommended)","Shared storage or replication mechanism for WAL durability","Cluster configuration (node addresses, replication factor, consistency level)"],"input_types":["cluster topology (node addresses, roles)","replication configuration (factor, consistency level)","data distribution policy (sharding key)"],"output_types":["cluster status (node health, replication lag)","failover events and recovery logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_8","uri":"capability://tool.use.integration.python.sdk.with.async.client","name":"python-sdk-with-async-client","description":"Provides Python SDK for programmatic access to Infinity via async/await interface, supporting connection pooling, batch operations, and automatic retry logic. The SDK wraps Thrift RPC calls and HTTP API endpoints, offering high-level abstractions for table creation, vector insertion, and hybrid search while handling serialization and error handling transparently.","intents":["Build Python applications that insert vectors and execute searches without raw Thrift/HTTP calls","Integrate Infinity into LLM frameworks (LangChain, LlamaIndex) as vector store backend","Batch insert millions of vectors efficiently using async operations"],"best_for":["Python developers building RAG systems and LLM applications","Data scientists prototyping vector search workflows","Teams integrating Infinity into existing Python ML pipelines"],"limitations":["Async SDK requires Python 3.8+; synchronous wrapper adds overhead for blocking code","Batch operations are client-side buffered; no server-side batch optimization","Type hints are incomplete for complex nested structures; IDE autocomplete limited","Error messages from server are sometimes opaque; requires server logs for debugging"],"requires":["Python 3.8+","Infinity server running (local or remote)","Network connectivity to Infinity server (HTTP or Thrift port)"],"input_types":["table schemas (column names, types, vector dimensions)","vector data (numpy arrays, lists of floats)","query vectors and filter predicates"],"output_types":["search results (document IDs, scores, metadata)","operation status (success/failure with error details)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--infinity__cap_9","uri":"capability://tool.use.integration.http.rest.api.for.vector.operations","name":"http-rest-api-for-vector-operations","description":"Exposes HTTP REST API for vector insertion, search, and metadata management, accepting JSON payloads and returning JSON responses. The HTTP server (built with C++20 async networking) handles request parsing, routes to query executor, and serializes results; supports both synchronous and streaming responses for large result sets.","intents":["Access Infinity from non-Python languages (JavaScript, Go, Java, etc.) via standard HTTP","Integrate Infinity into web applications and microservices architectures","Build serverless functions (AWS Lambda, Google Cloud Functions) that call Infinity via HTTP"],"best_for":["Polyglot teams using multiple programming languages","Web applications and REST API backends","Serverless and containerized deployments"],"limitations":["HTTP overhead (~5-10ms per request) higher than direct Thrift RPC","JSON serialization/deserialization adds latency; binary protocols faster for large vectors","No built-in authentication; requires external API gateway or proxy for security","Streaming responses not supported for very large result sets; pagination required"],"requires":["Infinity server with HTTP port exposed (default 8113)","HTTP client library in target language","JSON serialization support"],"input_types":["JSON payloads with vector data, metadata, and query parameters"],"output_types":["JSON responses with search results, status codes, error messages"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["C++20 compiler with modules support (GCC 11+ or Clang 14+)","Dense vector embeddings pre-computed (e.g., from OpenAI, Hugging Face, or local models)","Vector dimension must be consistent across all inserted vectors","Text tokenization pipeline (built-in or custom)","BM25 hyperparameters (k1, b) tuned for corpus characteristics","Stopword list for language of indexed documents","Data files in CSV, Parquet, or JSON format","Schema definition (column names, types, vector dimensions)","Sufficient disk space for import files","Table with vector or metadata columns"],"failure_modes":["HNSW index construction is single-threaded, adding ~O(n log n) overhead during bulk inserts","Recall-speed tradeoff controlled via ef_construction and ef_search parameters; higher recall requires larger search neighborhoods","Index must fit in memory or use memory-mapped storage; no native disk-based index streaming","Distance metrics limited to L2 and cosine; no learned distance functions or custom metrics","BM25 ranking is term-frequency based; cannot capture semantic relationships without dense vectors","Inverted index construction requires tokenization and stopword filtering; language-specific tuning needed for non-English text","Sparse vectors require explicit term vocabulary; out-of-vocabulary terms are ignored","No native support for phrase queries or complex boolean operators; requires query rewriting","Import requires data to fit in available disk space; no streaming import for infinite datasets","Schema inference is heuristic-based; complex nested structures require manual schema definition","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.30841675758762616,"quality":0.5,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-04-24T13:49:00Z"},"community":{"stars":4498,"forks":418,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=infiniflow--infinity","compare_url":"https://unfragile.ai/compare?artifact=infiniflow--infinity"}},"signature":"+e6A1WyZWy/WGjaj1zFA9XIIzGO/eKKLSsQsuPehOKQT5GJ4JdR5vWJn6SqaFxSeJRTOt8Ew8GHcn8QvhHt8AA==","signedAt":"2026-06-21T05:08:04.340Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/infiniflow--infinity","artifact":"https://unfragile.ai/infiniflow--infinity","verify":"https://unfragile.ai/api/v1/verify?slug=infiniflow--infinity","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"}}