{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-hkuds--lightrag","slug":"hkuds--lightrag","name":"LightRAG","type":"model","url":"https://arxiv.org/abs/2410.05779","page_url":"https://unfragile.ai/hkuds--lightrag","categories":["chatbot","search"],"tags":["genai","gpt","gpt-4","graphrag","knowledge-graph","large-language-models","llm","rag","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-hkuds--lightrag__cap_0","uri":"capability://memory.knowledge.hybrid.vector.graph.retrieval.with.multi.mode.query.routing","name":"hybrid vector-graph retrieval with multi-mode query routing","description":"LightRAG implements a dual-path retrieval system that routes queries through both semantic vector search and knowledge graph traversal, selecting the optimal retrieval mode based on query characteristics. The system extracts entities and relationships from documents to build a knowledge graph, then during query processing evaluates whether to use vector similarity, graph-based entity matching, or a combined approach. This hybrid approach leverages tree-structured entity hierarchies and relationship patterns to improve retrieval precision beyond pure semantic similarity.","intents":["retrieve context that preserves entity relationships and semantic meaning simultaneously","handle complex multi-hop queries that require traversing entity relationships","improve retrieval accuracy for knowledge-intensive domains where entity relationships matter","reduce hallucinations by grounding responses in structured entity-relationship context"],"best_for":["teams building knowledge-intensive QA systems over structured domains","enterprises migrating from pure vector RAG to graph-augmented retrieval","developers needing entity-aware context retrieval without manual schema definition"],"limitations":["knowledge graph construction adds 30-50% latency to document ingestion compared to vector-only RAG","graph traversal performance degrades with very large entity sets (>100k entities) without proper indexing","requires LLM calls for entity/relationship extraction, increasing token consumption during indexing","multi-hop retrieval can return overly broad context if relationship chains are not properly pruned"],"requires":["Python 3.9+","LLM API access (OpenAI, Anthropic, Ollama, or compatible provider)","Vector database (Chroma, Weaviate, Milvus, or compatible)","Graph database (Neo4j, TigerGraph) OR relational database with graph storage abstraction"],"input_types":["natural language queries","structured entity names","relationship predicates"],"output_types":["ranked context chunks with entity/relationship metadata","traversal paths showing entity connections","relevance scores per retrieval mode"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_1","uri":"capability://data.processing.analysis.automatic.entity.and.relationship.extraction.with.llm.driven.graph.construction","name":"automatic entity and relationship extraction with llm-driven graph construction","description":"LightRAG processes ingested documents through an LLM-based extraction pipeline that identifies entities, their types, and relationships between them, automatically constructing a knowledge graph without manual schema definition. The system uses prompt-based extraction with configurable entity types and relationship predicates, then deduplicates and normalizes extracted entities across documents using embedding-based similarity matching. The resulting graph is stored in a pluggable backend (Neo4j, relational DB, or file-based) with support for incremental updates as new documents arrive.","intents":["build a knowledge graph from unstructured documents without manual annotation","automatically discover entity types and relationships relevant to a document corpus","maintain graph consistency as new documents are added incrementally","enable entity disambiguation across multiple documents using semantic similarity"],"best_for":["teams with large document collections lacking structured metadata","organizations building domain-specific knowledge graphs from text","developers prototyping RAG systems who want graph benefits without upfront schema design"],"limitations":["extraction quality depends on LLM capability; smaller models may miss subtle relationships","entity deduplication using embeddings can create false positives if entities have similar names but different meanings","extraction cost scales linearly with document size and number of LLM calls","no built-in handling of temporal relationships or versioned entity states"],"requires":["LLM API with function calling or structured output support (GPT-4, Claude 3+, Llama 2+)","Embedding model for entity deduplication (OpenAI, HuggingFace, or local)","Graph or relational database for storing extracted entities and relationships","Document chunking strategy (configurable chunk size and overlap)"],"input_types":["raw text documents","PDF/markdown with text extraction","pre-chunked document segments"],"output_types":["entity nodes with type labels and properties","relationship edges with predicate types","entity embeddings for deduplication"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_10","uri":"capability://data.processing.analysis.rag.quality.evaluation.framework.with.retrieval.metrics","name":"rag quality evaluation framework with retrieval metrics","description":"LightRAG includes a testing and evaluation framework that measures retrieval quality through metrics like precision, recall, and relevance scoring. The system supports ground-truth based evaluation where expected context chunks are compared against retrieved results, and can generate synthetic evaluation datasets from documents. Evaluation results are tracked over time, enabling measurement of RAG quality improvements as documents are added or retrieval strategies are tuned.","intents":["measure retrieval quality and compare different retrieval strategies","generate synthetic evaluation datasets for testing without manual annotation","track RAG quality improvements over time as documents are added","identify failure cases and debug retrieval behavior"],"best_for":["teams evaluating RAG quality before production deployment","researchers benchmarking retrieval strategies","organizations monitoring RAG performance in production"],"limitations":["ground-truth evaluation requires manual annotation of expected context; synthetic datasets may not reflect real user needs","metrics like precision/recall assume single correct answer; many questions have multiple valid answers","evaluation is offline; does not measure end-to-end answer quality or user satisfaction","no built-in support for domain-specific evaluation metrics"],"requires":["Test dataset with queries and expected context chunks","Evaluation configuration specifying metrics and thresholds","Optional: LLM for synthetic dataset generation"],"input_types":["test queries","expected context chunks (ground truth)","retrieval results from LightRAG"],"output_types":["precision, recall, and relevance scores","evaluation reports with per-query results","failure case analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_11","uri":"capability://search.retrieval.reranking.integration.with.cross.encoder.models","name":"reranking integration with cross-encoder models","description":"LightRAG supports optional reranking of retrieved context using cross-encoder models that score retrieved chunks based on relevance to the query. The system retrieves a larger candidate set using vector/graph search, then reranks using a cross-encoder to improve precision of top results. Reranking can use local models (sentence-transformers) or API-based services, with configurable reranking thresholds and result limits.","intents":["improve precision of retrieved context by reranking candidates","use cross-encoder models for more accurate relevance scoring than bi-encoders","filter low-relevance results before passing to LLM","trade latency for accuracy by reranking larger candidate sets"],"best_for":["applications requiring high-precision retrieval where answer quality is critical","teams with sufficient latency budget for reranking overhead","systems using vector search that needs precision improvement"],"limitations":["reranking adds 200-500ms latency per query (cross-encoder inference cost)","reranking quality depends on model capability; smaller models may not improve over initial ranking","local reranking models require GPU for acceptable latency; CPU inference is prohibitively slow","API-based reranking adds external dependency and per-request costs"],"requires":["Reranking model (local: sentence-transformers, or API: Cohere, Jina, etc.)","GPU for local reranking (optional but recommended)","Configuration specifying reranking threshold and result limit"],"input_types":["query text","candidate context chunks from initial retrieval"],"output_types":["reranked context chunks with relevance scores","filtered results above reranking threshold"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_12","uri":"capability://search.retrieval.3d.knowledge.graph.visualization.tool.for.graph.exploration","name":"3d knowledge graph visualization tool for graph exploration","description":"LightRAG includes a 3D graph visualization tool that renders entities as nodes and relationships as edges in an interactive 3D space, enabling visual exploration of knowledge graph structure. The visualization supports filtering by entity type and relationship type, zooming and panning, and clicking on nodes to inspect entity properties and connected relationships. The tool helps users understand graph structure, identify clusters of related entities, and debug entity extraction and deduplication.","intents":["visually explore knowledge graph structure and entity relationships","identify clusters of related entities and relationship patterns","debug entity extraction and deduplication by inspecting graph structure","understand domain structure through interactive graph visualization"],"best_for":["teams analyzing knowledge graph quality and structure","researchers studying entity relationships and graph patterns","developers debugging entity extraction and deduplication"],"limitations":["3D visualization performance degrades with >5000 nodes; requires filtering for large graphs","3D rendering requires WebGL support; not available on older browsers or mobile devices","interactive exploration is time-consuming for large graphs; automated analysis tools may be more efficient","no built-in export of visualization for reports or presentations"],"requires":["Modern web browser with WebGL support (Chrome, Firefox, Safari, Edge)","LightRAG server with graph data accessible via API","Reasonable graph size (<5000 nodes) for interactive performance"],"input_types":["knowledge graph data (entities, relationships, properties)"],"output_types":["interactive 3D visualization","entity and relationship metadata on click","filtered views by entity/relationship type"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_13","uri":"capability://automation.workflow.batch.document.processing.with.status.tracking.and.error.recovery","name":"batch document processing with status tracking and error recovery","description":"LightRAG supports batch processing of multiple documents with detailed status tracking per document (queued, processing, completed, failed) and automatic error recovery. The system maintains a processing queue, retries failed documents with exponential backoff, and provides APIs to query processing status and retrieve error logs. Failed documents can be reprocessed without affecting successfully processed documents, enabling robust handling of large document collections.","intents":["ingest large document collections with progress tracking","handle document processing failures gracefully with automatic retry","monitor batch processing status through APIs","reprocess failed documents without affecting successful ones"],"best_for":["teams ingesting large document collections (100s-1000s of documents)","systems requiring robust error handling and recovery","organizations needing visibility into document processing progress"],"limitations":["processing state is not persisted across server restarts without external state store","retry logic is basic (exponential backoff); no intelligent retry strategies based on error type","no built-in deduplication of documents; duplicate documents will be processed multiple times","batch processing queue is in-memory; large queues can consume significant memory"],"requires":["Document queue with metadata (document ID, content, source)","Configuration specifying retry policy (max retries, backoff strategy)","Optional: external state store (Redis, database) for persisting processing state"],"input_types":["document batch with metadata","processing configuration (concurrency, retry policy, timeout)"],"output_types":["processing status per document","error logs and retry information","batch completion status"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_2","uri":"capability://memory.knowledge.pluggable.multi.backend.storage.abstraction.with.workspace.isolation","name":"pluggable multi-backend storage abstraction with workspace isolation","description":"LightRAG provides a unified storage abstraction layer that supports multiple backend types (relational databases, NoSQL stores, vector databases, graph databases, and file-based storage) through a consistent interface. Each workspace maintains isolated data with namespace support, enabling multi-tenant deployments and independent knowledge graphs per user or project. The abstraction handles schema evolution, data migration between backends, and concurrent access through locking mechanisms, allowing users to swap storage backends without changing application code.","intents":["deploy LightRAG with different storage backends depending on infrastructure constraints","support multi-tenant SaaS deployments with isolated workspaces per customer","migrate data between storage systems without application downtime","run offline deployments with file-based storage or lightweight databases"],"best_for":["SaaS platforms needing multi-tenant isolation and flexible storage","enterprises with existing database infrastructure wanting to leverage it","developers building on-premise or offline RAG systems with limited infrastructure"],"limitations":["abstraction adds ~50-100ms latency per storage operation due to interface indirection","cross-backend transactions not supported; each backend handles consistency independently","schema evolution requires manual migration scripts when changing backend types","file-based storage not suitable for concurrent access from multiple processes without external locking"],"requires":["Python 3.9+","At least one storage backend: PostgreSQL/MySQL (relational), MongoDB (NoSQL), Chroma/Weaviate (vector), Neo4j/TigerGraph (graph), or local filesystem","For multi-process coordination: Redis or similar for distributed locking","For migrations: database migration tools (Alembic for SQL, custom scripts for others)"],"input_types":["configuration specifying backend type and connection parameters","workspace/namespace identifiers","entity, relationship, and embedding data"],"output_types":["storage backend instances with consistent interface","workspace-scoped data access","migration status and logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_3","uri":"capability://tool.use.integration.rest.api.server.with.document.lifecycle.management.and.query.endpoints","name":"rest api server with document lifecycle management and query endpoints","description":"LightRAG exposes a production-ready REST API server (built with FastAPI) that manages document ingestion, processing status tracking, knowledge graph exploration, and query execution. The API implements document lifecycle states (uploading, processing, completed, failed), provides endpoints for monitoring ingestion progress, and supports both synchronous and asynchronous query processing. Authentication is handled through API keys and password hashing, with role-based access control for multi-user deployments. The server includes Ollama API compatibility for drop-in replacement with local LLM inference.","intents":["ingest documents and monitor processing status through HTTP endpoints","query the knowledge graph and retrieve context via REST without direct Python access","explore entity relationships and graph structure through dedicated graph exploration endpoints","integrate LightRAG into web applications and microservice architectures","replace Ollama API endpoints with LightRAG for local LLM-based RAG"],"best_for":["teams building web applications or APIs that need RAG capabilities","organizations deploying LightRAG as a microservice in larger systems","developers wanting to use LightRAG without Python SDK integration"],"limitations":["synchronous query endpoints block on LLM inference; long queries can timeout without async handling","API key authentication is basic; production deployments should add OAuth2 or OIDC","no built-in rate limiting or quota management; requires external API gateway for multi-tenant enforcement","document upload size limited by server memory and timeout settings"],"requires":["Python 3.9+","FastAPI and Uvicorn (included in dependencies)","For production: Gunicorn or similar WSGI server","For authentication: API key management system or external auth provider","For async processing: task queue (Celery, RQ) or async job system"],"input_types":["multipart form data with document files","JSON query payloads with natural language queries","graph exploration parameters (entity names, relationship types)"],"output_types":["JSON responses with retrieved context and metadata","document processing status and progress","graph structure and entity relationship data","LLM-generated responses with source attribution"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_4","uri":"capability://search.retrieval.interactive.web.ui.with.knowledge.graph.visualization.and.retrieval.testing","name":"interactive web ui with knowledge graph visualization and retrieval testing","description":"LightRAG includes a web-based user interface (built with React/TypeScript) that provides document management, interactive knowledge graph visualization, and a retrieval testing sandbox. The UI allows users to upload documents, monitor ingestion progress, visualize entities and relationships in an interactive graph view, test queries in real-time, and inspect retrieved context with source attribution. The frontend supports internationalization (i18n) and configurable settings for retrieval modes, entity types, and LLM parameters without requiring code changes.","intents":["upload and manage documents through a visual interface without CLI/API calls","explore and understand the knowledge graph structure interactively","test retrieval quality and debug query performance before production use","configure retrieval modes and LLM parameters through UI settings","inspect source documents and entity relationships for retrieved context"],"best_for":["non-technical users managing document collections and testing queries","teams evaluating RAG quality before production deployment","developers debugging knowledge graph construction and retrieval behavior"],"limitations":["graph visualization performance degrades with >5000 entities; requires filtering or clustering for large graphs","real-time query testing depends on LLM latency; no built-in query result caching","UI settings changes require server restart for some configuration options","internationalization support is limited to pre-defined languages; custom translations require code changes"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","LightRAG server running with API endpoints accessible","Node.js 18+ for building frontend from source","For deployment: static file hosting or reverse proxy (Nginx, Apache)"],"input_types":["document files (PDF, TXT, Markdown)","natural language queries","configuration parameters (retrieval mode, entity types, LLM settings)"],"output_types":["interactive graph visualization with entity/relationship nodes","retrieved context chunks with relevance scores","source document references and entity metadata","query execution logs and performance metrics"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.binding.with.configurable.inference.backends","name":"multi-provider llm binding with configurable inference backends","description":"LightRAG abstracts LLM provider selection through a binding system that supports OpenAI, Anthropic, Google Gemini, Ollama, and other compatible providers. The system allows configuration of different LLM providers for different tasks (entity extraction, query processing, response generation) without code changes, enabling cost optimization and model selection based on task requirements. Provider bindings handle API authentication, request formatting, and response parsing, with fallback support for provider failures.","intents":["use different LLM providers for different tasks to optimize cost and latency","switch between cloud-based and local LLM inference without code changes","implement provider failover for high-availability deployments","configure model parameters (temperature, max tokens) per task"],"best_for":["teams wanting to optimize LLM costs by using different providers for different tasks","organizations with on-premise LLM infrastructure (Ollama, vLLM) wanting to avoid cloud APIs","developers building multi-model RAG systems with provider flexibility"],"limitations":["API response format differences between providers require normalization, adding complexity","provider failover requires manual configuration; no automatic detection of provider health","model-specific features (function calling, structured output) may not be available across all providers","token counting varies by provider; cost estimation may be inaccurate for multi-provider setups"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Google, etc.)","For local inference: Ollama or compatible server running locally or on accessible network","Configuration file or environment variables specifying provider and model selection","Python 3.9+"],"input_types":["provider configuration (API key, model name, endpoint URL)","task-specific prompts and parameters","fallback provider list"],"output_types":["LLM responses normalized to common format","provider metadata (model used, tokens consumed)","error handling and fallback status"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_6","uri":"capability://automation.workflow.concurrent.document.processing.with.incremental.graph.updates","name":"concurrent document processing with incremental graph updates","description":"LightRAG processes multiple documents concurrently using Python's asyncio and thread pools, with support for incremental knowledge graph updates as new documents arrive. The system maintains processing state (queued, processing, completed, failed) for each document, allowing monitoring of ingestion progress and recovery from failures. Incremental updates merge new entities and relationships into the existing graph, deduplicating entities using embedding similarity and updating relationship counts. Concurrency is coordinated through locking mechanisms to prevent race conditions in shared storage.","intents":["ingest large document collections efficiently using parallel processing","add new documents to an existing knowledge graph without rebuilding from scratch","monitor document processing progress and handle failures gracefully","maintain graph consistency during concurrent updates from multiple sources"],"best_for":["teams ingesting large document collections (100s-1000s of documents)","systems requiring continuous document updates without downtime","organizations with multi-user deployments where documents are added concurrently"],"limitations":["concurrent processing increases memory usage; very large documents may cause OOM errors","entity deduplication during concurrent updates can create race conditions if not properly locked","incremental updates may miss relationships that span across previously processed documents","processing state is not persisted across server restarts without external state store"],"requires":["Python 3.9+ with asyncio support","Sufficient memory for concurrent LLM calls (typically 2-4GB per concurrent document)","For distributed coordination: Redis or similar for distributed locking across multiple processes","Storage backend with transaction support for atomic graph updates"],"input_types":["document queue with metadata (document ID, content, source)","processing configuration (concurrency level, chunk size, timeout)"],"output_types":["processing status per document (queued, processing, completed, failed)","updated knowledge graph with new entities and relationships","error logs and retry information"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_7","uri":"capability://planning.reasoning.chain.of.thought.reasoning.with.multi.step.query.decomposition","name":"chain-of-thought reasoning with multi-step query decomposition","description":"LightRAG supports chain-of-thought (CoT) reasoning where complex queries are decomposed into multiple steps, with intermediate reasoning steps and context retrieval between steps. The system uses LLM-guided query decomposition to break down complex questions into simpler sub-queries, retrieves context for each sub-query independently, and then synthesizes final answers using accumulated context. This approach improves reasoning quality for multi-hop questions and enables transparent reasoning traces for debugging.","intents":["answer complex multi-hop questions that require reasoning across multiple entities","provide transparent reasoning traces showing how answers were derived","improve answer quality for questions requiring multiple retrieval and reasoning steps","debug retrieval and reasoning behavior by inspecting intermediate steps"],"best_for":["applications requiring complex reasoning over knowledge graphs","teams needing explainable AI with visible reasoning traces","developers debugging multi-hop retrieval and reasoning behavior"],"limitations":["multi-step reasoning increases latency significantly (3-5x vs single-step queries)","intermediate reasoning steps may diverge from optimal paths, reducing answer quality","token consumption increases with number of reasoning steps","no built-in optimization for redundant sub-queries across steps"],"requires":["LLM with strong reasoning capabilities (GPT-4, Claude 3+, or equivalent)","Sufficient context window for accumulated reasoning traces (8k+ tokens)","Configuration specifying max reasoning steps and decomposition strategy"],"input_types":["complex natural language queries","reasoning step configuration (max steps, decomposition strategy)"],"output_types":["final answer with source attribution","reasoning trace showing intermediate steps and retrieved context","confidence scores per reasoning step"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_8","uri":"capability://data.processing.analysis.embedding.based.entity.deduplication.and.semantic.normalization","name":"embedding-based entity deduplication and semantic normalization","description":"LightRAG uses embedding-based similarity matching to deduplicate entities across documents, merging entities with similar semantic meaning while preserving distinct entities with different meanings. The system computes embeddings for extracted entity names, compares them against existing entities using cosine similarity with configurable thresholds, and merges entities that exceed the threshold. This approach handles entity name variations (e.g., 'CEO' vs 'Chief Executive Officer') and prevents duplicate entities from fragmenting the knowledge graph.","intents":["automatically merge entity name variations across documents","prevent duplicate entities from fragmenting the knowledge graph","normalize entity representations for consistent retrieval","handle entity aliases and alternative names without manual mapping"],"best_for":["systems ingesting documents from multiple sources with inconsistent entity naming","knowledge graphs covering broad domains where entity name variations are common","teams wanting automatic entity normalization without manual curation"],"limitations":["embedding-based similarity can create false positives if entities have similar names but different meanings (e.g., 'Apple' the company vs 'apple' the fruit)","threshold tuning is domain-specific; no universal threshold works for all entity types","deduplication is one-way; merging entities cannot be easily undone without rebuilding the graph","performance degrades with very large entity sets (>100k) requiring O(n) similarity comparisons"],"requires":["Embedding model for entity name encoding (OpenAI, HuggingFace, or local)","Configurable similarity threshold (typically 0.85-0.95)","Vector storage for efficient similarity search (optional, can use brute-force for small graphs)"],"input_types":["extracted entity names and types","existing entity embeddings from previous documents"],"output_types":["deduplicated entity list with merge decisions","entity embeddings for future deduplication","merge confidence scores"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hkuds--lightrag__cap_9","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.environment.configuration","name":"docker and kubernetes deployment with environment configuration","description":"LightRAG provides production-ready Docker images and Kubernetes manifests for containerized deployment, with environment-based configuration for storage backends, LLM providers, and server settings. The system supports offline deployment with bundled dependencies, Gunicorn-based production serving, and Kubernetes StatefulSet patterns for distributed deployments. Configuration is managed through environment variables and config files, enabling easy customization without rebuilding images.","intents":["deploy LightRAG in containerized environments (Docker, Kubernetes)","configure storage and LLM backends through environment variables","run offline deployments without external dependencies","scale LightRAG horizontally across multiple Kubernetes pods"],"best_for":["teams deploying LightRAG in Kubernetes or Docker Swarm clusters","organizations with containerized infrastructure and CI/CD pipelines","enterprises requiring offline or air-gapped deployments"],"limitations":["Kubernetes deployments require external storage for persistent data (PVC or managed database)","distributed deployments need shared storage backend; file-based storage not suitable for multi-pod setups","environment variable configuration can become unwieldy with many options; config file management is recommended","offline deployments require pre-downloading all dependencies; image size can exceed 2GB"],"requires":["Docker 20.10+ for containerized deployment","Kubernetes 1.20+ for orchestrated deployments","Persistent storage backend (PostgreSQL, MongoDB, etc.) for multi-pod deployments","Container registry for storing and distributing images"],"input_types":["Dockerfile and docker-compose configuration","Kubernetes manifests (Deployment, Service, ConfigMap, Secret)","environment variables for configuration"],"output_types":["Docker images ready for deployment","Kubernetes resources for orchestration","deployment logs and health checks"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM API access (OpenAI, Anthropic, Ollama, or compatible provider)","Vector database (Chroma, Weaviate, Milvus, or compatible)","Graph database (Neo4j, TigerGraph) OR relational database with graph storage abstraction","LLM API with function calling or structured output support (GPT-4, Claude 3+, Llama 2+)","Embedding model for entity deduplication (OpenAI, HuggingFace, or local)","Graph or relational database for storing extracted entities and relationships","Document chunking strategy (configurable chunk size and overlap)","Test dataset with queries and expected context chunks","Evaluation configuration specifying metrics and thresholds"],"failure_modes":["knowledge graph construction adds 30-50% latency to document ingestion compared to vector-only RAG","graph traversal performance degrades with very large entity sets (>100k entities) without proper indexing","requires LLM calls for entity/relationship extraction, increasing token consumption during indexing","multi-hop retrieval can return overly broad context if relationship chains are not properly pruned","extraction quality depends on LLM capability; smaller models may miss subtle relationships","entity deduplication using embeddings can create false positives if entities have similar names but different meanings","extraction cost scales linearly with document size and number of LLM calls","no built-in handling of temporal relationships or versioned entity states","ground-truth evaluation requires manual annotation of expected context; synthetic datasets may not reflect real user needs","metrics like precision/recall assume single correct answer; many questions have multiple valid answers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.41155453723057306,"quality":0.25,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-06T15:12:23.810Z","last_scraped_at":"2026-05-03T13:58:26.976Z","last_commit":"2026-05-01T06:49:16Z"},"community":{"stars":34691,"forks":4909,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=hkuds--lightrag","compare_url":"https://unfragile.ai/compare?artifact=hkuds--lightrag"}},"signature":"Bt+n+0FvE6I8IbeY/t1Afibrpf0vljiNXpYh8dDJNLsRPgCAfaW6M233YQgF7j8Sg05ZBKt3eisF0HCsShAiBQ==","signedAt":"2026-06-22T12:09:57.828Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hkuds--lightrag","artifact":"https://unfragile.ai/hkuds--lightrag","verify":"https://unfragile.ai/api/v1/verify?slug=hkuds--lightrag","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"}}