{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-microsoft--graphrag","slug":"microsoft--graphrag","name":"graphrag","type":"repo","url":"https://microsoft.github.io/graphrag/","page_url":"https://unfragile.ai/microsoft--graphrag","categories":["rag-knowledge"],"tags":["gpt","gpt-4","gpt4","graphrag","llm","llms","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-microsoft--graphrag__cap_0","uri":"capability://data.processing.analysis.llm.driven.entity.and.relationship.extraction.from.unstructured.text","name":"llm-driven entity and relationship extraction from unstructured text","description":"Extracts named entities, relationships, and attributes from documents using LLM-based prompting with configurable extraction schemas. The system uses a workflow-based pipeline architecture that chains LLM calls through a task execution engine, supporting multiple LLM providers (OpenAI, Azure OpenAI, Anthropic, Ollama) with built-in rate limiting, retry strategies, and token-aware batching. Extracted entities and relationships are structured into a knowledge graph schema with configurable entity types, relationship types, and attributes.","intents":["I need to automatically extract structured entities and relationships from my document corpus without manual annotation","I want to customize entity and relationship types for my domain-specific knowledge graph","I need to handle extraction at scale with rate limiting and fault tolerance across multiple LLM providers"],"best_for":["Teams building domain-specific knowledge graphs from unstructured documents","Organizations with large document corpora requiring automated semantic understanding","Developers integrating LLM-based extraction into data pipelines"],"limitations":["Extraction quality depends on LLM capability and prompt design — no built-in validation of extracted entities against external knowledge bases","Hallucination risk inherent to LLM-based extraction — requires downstream validation or human review for critical applications","Cost scales with document volume and LLM API usage — no local-only extraction option without external LLM","Extraction latency depends on LLM provider response times — typically 1-5 seconds per document chunk"],"requires":["Python 3.9+","API key for at least one supported LLM provider (OpenAI, Azure OpenAI, Anthropic, or local Ollama instance)","Unstructured text documents in supported formats (txt, pdf, docx, md, html)"],"input_types":["unstructured text","document chunks (pre-split text)","extraction schema definitions (YAML/JSON)"],"output_types":["structured entity records with attributes","relationship tuples with source/target entities","knowledge graph nodes and edges"],"categories":["data-processing-analysis","knowledge-graph-construction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_1","uri":"capability://data.processing.analysis.hierarchical.community.detection.and.clustering.on.knowledge.graphs","name":"hierarchical community detection and clustering on knowledge graphs","description":"Detects communities (clusters of densely-connected entities) within the extracted knowledge graph using graph algorithms, then organizes them hierarchically into levels for multi-scale analysis. The system applies community detection algorithms to partition the graph, generates summaries for each community at each hierarchy level, and stores these as 'community reports' that serve as intermediate representations for query-time reasoning. This enables both local (entity-neighborhood) and global (community-level) search strategies.","intents":["I want to automatically group related entities in my knowledge graph without manual clustering","I need hierarchical summaries of entity clusters to support both detailed and high-level reasoning","I want to optimize query performance by pre-computing community-level context instead of traversing the full graph at query time"],"best_for":["Large-scale knowledge graphs (1000+ entities) where full-graph traversal is expensive","Applications requiring both local detail and global context in reasoning","Teams building multi-hop reasoning systems over complex entity networks"],"limitations":["Community detection is non-deterministic — same graph may produce different communities across runs depending on algorithm initialization","Hierarchy depth and granularity depend on graph structure and algorithm parameters — no automatic optimization of hierarchy levels","Regenerating communities requires re-running the full indexing pipeline — incremental community updates not yet supported","Community reports are LLM-generated summaries — quality depends on LLM capability and may contain hallucinations or omissions"],"requires":["Python 3.9+","Completed knowledge graph from entity/relationship extraction phase","LLM API access for generating community reports","Graph processing libraries (networkx or equivalent)"],"input_types":["knowledge graph (nodes and edges)","entity attributes and relationship types","community detection algorithm parameters"],"output_types":["community assignments (entity → community ID mappings)","hierarchical community structure (level 0, 1, 2, ...)","community reports (LLM-generated summaries per community)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_10","uri":"capability://text.generation.language.context.building.and.entity.aware.prompt.construction.for.llm.responses","name":"context building and entity-aware prompt construction for llm responses","description":"Constructs LLM prompts by combining retrieved context (entities, relationships, community reports) with query information and response instructions. The system extracts entities from queries, retrieves relevant context from the knowledge graph, ranks context by relevance, and assembles prompts that include both structured context (entity descriptions, relationships) and unstructured context (text chunks). Context building strategies differ between Global Search (community-level context), Local Search (entity-neighborhood context), and DRIFT Search (combined context).","intents":["I want to automatically construct LLM prompts with relevant context from my knowledge graph","I need to rank and filter context to fit within LLM token limits","I want to include both structured (entity/relationship) and unstructured (text) context in prompts"],"best_for":["RAG systems requiring sophisticated context assembly","Applications with large knowledge graphs where context selection is critical","Teams optimizing LLM response quality through context engineering"],"limitations":["Context ranking is heuristic-based — no learned ranking model, may miss relevant context","Token limit enforcement is approximate — may exceed LLM token limits if context is large","Context assembly is strategy-specific — different search strategies require different context builders","No automatic context quality assessment — may include irrelevant or contradictory context","Entity extraction from queries may fail or be ambiguous — affects context retrieval accuracy"],"requires":["Python 3.9+","Completed GraphRAG index with entities, relationships, and community reports","Query entity extraction capability","LLM token limit information for context truncation"],"input_types":["natural language query","extracted query entities","retrieved context (entities, relationships, text chunks, community reports)","search strategy (global, local, drift)"],"output_types":["assembled LLM prompt with context","context ranking and relevance scores","token count estimates"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_11","uri":"capability://automation.workflow.rate.limiting.retry.logic.and.fault.tolerance.for.llm.api.calls","name":"rate limiting, retry logic, and fault tolerance for llm api calls","description":"Implements provider-agnostic rate limiting, exponential backoff retry logic, and fault tolerance mechanisms for LLM API calls. The system tracks token usage and API call rates, enforces per-provider rate limits, retries failed calls with exponential backoff, and handles transient failures gracefully. This enables reliable indexing and querying even with unreliable network conditions or rate-limited APIs. Rate limiting is configurable per provider and per operation type.","intents":["I want to reliably index large document collections without hitting API rate limits","I need automatic retry logic for transient LLM API failures","I want to optimize API usage by respecting provider rate limits and batching requests"],"best_for":["Large-scale indexing operations with high API call volumes","Applications requiring high reliability and fault tolerance","Teams optimizing API costs through intelligent rate limiting and batching"],"limitations":["Rate limiting is conservative — may underutilize available API quota","Retry logic uses fixed exponential backoff — not optimized for provider-specific rate limit patterns","No adaptive rate limiting based on actual provider response times — requires manual tuning","Transient failure detection is heuristic-based — may retry non-transient errors or skip retryable errors","No cross-provider load balancing — cannot automatically switch to cheaper provider if one is rate-limited"],"requires":["Python 3.9+","LLM provider API access with rate limit information","Configuration of rate limits per provider"],"input_types":["LLM API calls (extraction, embedding, response generation)","rate limit configuration (calls per minute, tokens per minute)","retry configuration (max retries, backoff strategy)"],"output_types":["successful LLM responses","retry statistics and failure logs","rate limit usage metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_12","uri":"capability://automation.workflow.cli.interface.for.indexing.querying.and.configuration.management","name":"cli interface for indexing, querying, and configuration management","description":"Provides a command-line interface for all major GraphRAG operations: initializing new indexes, running indexing pipelines, executing queries, tuning prompts, and updating existing indexes. The CLI supports both interactive and batch modes, with progress reporting, error handling, and result formatting. Commands are organized hierarchically (e.g., 'graphrag index', 'graphrag query', 'graphrag prompt-tune') and support configuration file overrides through command-line arguments.","intents":["I want to index documents from the command line without writing Python code","I need to query my knowledge graph interactively from the terminal","I want to automate indexing and querying in CI/CD pipelines or scheduled jobs"],"best_for":["Teams preferring CLI-based workflows over programmatic APIs","DevOps engineers integrating GraphRAG into CI/CD pipelines","Non-technical users who want to use GraphRAG without Python knowledge"],"limitations":["CLI is less flexible than programmatic API — advanced customization requires Python code","Progress reporting is text-based — no real-time visualization of indexing progress","Error messages may be cryptic — requires understanding of GraphRAG internals for debugging","Batch mode requires careful configuration — easy to make mistakes with large-scale operations","No built-in result formatting for integration with other tools — requires post-processing"],"requires":["Python 3.9+ with GraphRAG installed","Configuration file with indexing/query settings","API keys for LLM providers and storage backends"],"input_types":["command-line arguments","configuration files","document files or directories","natural language queries (for query command)"],"output_types":["indexed knowledge graph artifacts","query results (text or JSON)","progress and status reports","error logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_13","uri":"capability://automation.workflow.caching.and.memoization.of.llm.calls.and.embeddings","name":"caching and memoization of llm calls and embeddings","description":"Implements multi-level caching to reduce redundant LLM API calls and embedding computations. The system caches LLM responses by prompt hash, caches embeddings by text hash, and supports both in-memory and persistent (file-based or database) caching. Cache hits avoid expensive API calls, significantly reducing indexing time and cost for repeated operations. Cache invalidation is based on content hashing, enabling safe cache reuse across runs.","intents":["I want to reduce indexing costs by caching LLM responses and embeddings","I need to speed up re-indexing by reusing cached results from previous runs","I want to enable reproducible indexing by caching intermediate results"],"best_for":["Large-scale indexing operations where caching can significantly reduce costs","Iterative development workflows where re-indexing is frequent","Teams optimizing API costs through intelligent caching"],"limitations":["Cache invalidation is content-based — changes to prompts or embedding models invalidate cache","Persistent caching requires external storage — adds complexity and potential consistency issues","Cache size can grow large — requires periodic cleanup or size limits","Cache hits depend on identical prompts/inputs — minor variations bypass cache","No cache warming or precomputation — cache is built incrementally"],"requires":["Python 3.9+","Optional: persistent cache storage (file system, database, or cloud storage)","Cache configuration (cache type, size limits, TTL)"],"input_types":["LLM prompts and API calls","text for embedding","cache configuration"],"output_types":["cached LLM responses","cached embeddings","cache hit/miss statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_2","uri":"capability://search.retrieval.multi.strategy.query.execution.with.global.local.and.drift.search","name":"multi-strategy query execution with global, local, and drift search","description":"Implements three distinct search strategies that can be selected or combined at query time: (1) Global Search uses community reports and hierarchical summaries for high-level reasoning over the entire dataset, (2) Local Search retrieves entity neighborhoods and relationships for detailed reasoning about specific entities, and (3) DRIFT Search (Dynamic Retrieval In-context Fusion Technique) combines both strategies with adaptive context selection. Each strategy uses vector embeddings for semantic matching, entity extraction from queries, and context building to construct LLM prompts with relevant information.","intents":["I want to answer questions that require high-level synthesis across my entire dataset using community-level context","I need to answer detailed questions about specific entities by retrieving their local neighborhoods and relationships","I want the system to automatically choose between global and local search strategies based on query characteristics"],"best_for":["Applications requiring both broad synthesis and detailed entity-level reasoning","Teams building question-answering systems over large, complex knowledge graphs","Use cases where query intent varies (some queries need global context, others need local detail)"],"limitations":["Global Search may miss entity-specific details when reasoning at community level — best for high-level questions","Local Search may lack broader context when reasoning about isolated entity neighborhoods — best for entity-focused questions","DRIFT Search adds complexity and latency by executing multiple search strategies — requires tuning of strategy selection heuristics","All strategies depend on quality of upstream entity extraction and community detection — garbage in, garbage out","Vector embedding quality affects semantic matching — requires appropriate embedding model selection for domain"],"requires":["Python 3.9+","Completed GraphRAG index with extracted entities, relationships, and community reports","Vector store with embeddings for entities, relationships, and text chunks (LanceDB, Azure AI Search, or Cosmos DB)","LLM API access for generating responses","Query-time entity extraction capability"],"input_types":["natural language query (string)","search strategy selection (global, local, or drift)","optional query parameters (entity filters, relationship types, community levels)"],"output_types":["ranked context chunks with relevance scores","entity and relationship matches","LLM-generated response with source attribution"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_3","uri":"capability://automation.workflow.configurable.indexing.pipeline.with.pluggable.llm.providers.and.storage.backends","name":"configurable indexing pipeline with pluggable llm providers and storage backends","description":"Provides a modular, configuration-driven indexing pipeline that orchestrates document loading, chunking, entity/relationship extraction, community detection, embedding generation, and graph finalization. The system uses a factory pattern for LLM providers (OpenAI, Azure OpenAI, Anthropic, Ollama), vector stores (LanceDB, Azure AI Search, Cosmos DB), and storage backends (local file system, Azure Blob Storage, in-memory). Configuration is managed through YAML files with environment variable overrides, enabling environment-specific setup without code changes.","intents":["I want to index my documents into a knowledge graph without writing custom extraction code","I need to switch between different LLM providers or storage backends without modifying my indexing logic","I want to configure the entire indexing pipeline through configuration files for reproducibility and version control"],"best_for":["Teams building RAG systems with multiple LLM provider options (cost optimization, compliance, latency)","Organizations requiring multi-cloud or hybrid storage (local + Azure + on-prem)","Developers who want configuration-driven infrastructure without tight coupling to specific providers"],"limitations":["Configuration complexity increases with number of customization options — requires understanding of all pipeline stages","Provider-specific features (e.g., Azure OpenAI's deployment names) require provider-specific config sections","Switching providers mid-pipeline may require re-indexing — no automatic migration of existing indexes across providers","Configuration validation is partial — some invalid configurations only fail at runtime, not during validation","Performance tuning requires understanding of provider-specific rate limits, batch sizes, and timeout settings"],"requires":["Python 3.9+","YAML configuration file with pipeline settings","API keys for selected LLM provider(s) and storage backend(s)","Unstructured documents in supported formats (txt, pdf, docx, md, html)","Optional: Azure subscription for Azure-specific backends"],"input_types":["YAML configuration files","environment variables for secrets","document files or directories","optional: existing index for incremental updates"],"output_types":["indexed knowledge graph artifacts","vector embeddings in configured store","pipeline metadata and statistics","optional: community reports and hierarchy data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_4","uri":"capability://automation.workflow.incremental.indexing.and.graph.update.with.change.detection","name":"incremental indexing and graph update with change detection","description":"Supports updating existing knowledge graphs with new or modified documents without full re-indexing. The system detects which documents have changed, re-extracts entities and relationships for changed documents, updates the knowledge graph with new entities/relationships, and regenerates affected community reports. This avoids redundant processing of unchanged documents while maintaining graph consistency. Incremental updates preserve existing entity IDs and relationships, enabling stable references across index versions.","intents":["I want to add new documents to my existing knowledge graph without re-indexing everything","I need to update my index when source documents change without losing existing entity relationships","I want to minimize indexing costs by only processing changed documents"],"best_for":["Applications with continuously growing document corpora (news, research, logs)","Teams with large existing indexes where full re-indexing is prohibitively expensive","Systems requiring near-real-time index updates as new documents arrive"],"limitations":["Change detection relies on file modification timestamps or content hashing — may miss logical changes in unchanged files","Community detection is re-run on the entire graph after updates — can cause community reassignments even for unchanged entities","Incremental updates may not fully optimize the graph structure — periodic full re-indexing recommended for best quality","Entity deduplication across old and new documents depends on extraction quality — may create duplicate entities if extraction varies","No built-in conflict resolution for entities/relationships that appear in both old and new documents"],"requires":["Python 3.9+","Existing GraphRAG index from previous indexing run","Document change tracking mechanism (file timestamps, content hashes, or external change log)","LLM API access for re-extracting changed documents","Same storage backend and vector store as original index"],"input_types":["new or modified documents","change detection metadata (timestamps, hashes, or change list)","existing index artifacts"],"output_types":["updated knowledge graph with new entities and relationships","updated vector embeddings","updated community reports for affected communities","change summary (new entities, updated relationships, affected communities)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_5","uri":"capability://data.processing.analysis.text.embedding.generation.and.vector.store.management.with.multi.backend.support","name":"text embedding generation and vector store management with multi-backend support","description":"Generates dense vector embeddings for all text units (documents, entities, relationships, community reports) using configurable embedding models, then stores and indexes these embeddings in a pluggable vector store backend. Supported backends include LanceDB (local/cloud), Azure AI Search (managed), and Cosmos DB (multi-model). The system handles embedding batching, caching, and retrieval with semantic similarity search capabilities. Embeddings enable both entity-level and text-level semantic matching for query-time retrieval.","intents":["I want to embed my entire knowledge graph for semantic search without managing vector infrastructure","I need to switch between local and cloud vector stores based on scale and cost requirements","I want to use different embedding models (OpenAI, local, Azure) without changing my query code"],"best_for":["Teams building semantic search over large document corpora","Organizations with multi-cloud or hybrid infrastructure requirements","Applications requiring both local development (LanceDB) and production scale (Azure AI Search)"],"limitations":["Embedding quality depends on embedding model choice — no automatic model selection or optimization","Vector store switching requires re-embedding entire corpus — embeddings are not portable across models","Semantic search may miss keyword-based matches — requires hybrid search combining vector and keyword matching for best results","Embedding generation cost scales with corpus size — can be expensive for large document collections","Vector store performance depends on backend — LanceDB suitable for <1M vectors, Azure AI Search for larger scales"],"requires":["Python 3.9+","Embedding model access (OpenAI API, local model via Ollama, or Azure OpenAI)","Vector store backend (LanceDB, Azure AI Search, or Cosmos DB)","Text to embed (documents, entities, relationships, community reports)"],"input_types":["text strings (variable length)","embedding model configuration","vector store backend selection","optional: pre-computed embeddings for import"],"output_types":["dense vectors (384-1536 dimensions depending on model)","vector store indexes with metadata","similarity search results with scores"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_6","uri":"capability://text.generation.language.prompt.customization.and.management.for.indexing.and.query.stages","name":"prompt customization and management for indexing and query stages","description":"Provides a system for customizing and versioning prompts used during both indexing (entity extraction, relationship extraction, community report generation) and query stages (context building, response generation). Prompts are stored as template files with variable placeholders, enabling domain-specific customization without code changes. The system supports prompt versioning, A/B testing of different prompts, and prompt tuning workflows to optimize extraction and response quality.","intents":["I want to customize entity and relationship extraction prompts for my domain without modifying code","I need to experiment with different prompts to improve extraction or response quality","I want to version and track changes to prompts across indexing runs"],"best_for":["Teams optimizing RAG quality through prompt engineering","Domain-specific applications requiring customized extraction schemas","Organizations conducting A/B testing of different prompt strategies"],"limitations":["Prompt quality is highly dependent on domain knowledge and LLM capability — no automatic prompt optimization","Changing prompts requires re-indexing to regenerate entities/relationships — can be expensive for large corpora","Prompt tuning is manual and iterative — no built-in evaluation metrics or automated optimization","Prompt templates use simple variable substitution — no advanced templating features (conditionals, loops)","No built-in prompt validation — invalid prompts only fail at LLM call time"],"requires":["Python 3.9+","Prompt template files (YAML or text format)","Understanding of prompt engineering best practices","LLM API access for testing prompts"],"input_types":["prompt template files with variable placeholders","context data for variable substitution","optional: evaluation dataset for prompt tuning"],"output_types":["rendered prompts with substituted variables","LLM responses to prompts","optional: evaluation metrics for prompt comparison"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_7","uri":"capability://data.processing.analysis.document.loading.chunking.and.preprocessing.with.format.support","name":"document loading, chunking, and preprocessing with format support","description":"Handles loading documents from various formats (PDF, DOCX, TXT, MD, HTML) and preprocessing them through configurable chunking strategies. The system extracts text from documents, applies language-specific text cleaning, splits documents into overlapping chunks with configurable size and overlap, and preserves document structure metadata (sections, headings, page numbers). Chunking strategies can be token-based, character-based, or semantic, enabling optimization for different document types and LLM context windows.","intents":["I want to load documents in multiple formats without writing custom parsers","I need to chunk documents optimally for my LLM's context window and extraction quality","I want to preserve document structure metadata for better entity grounding"],"best_for":["Teams processing diverse document types (PDFs, Word docs, web pages, markdown)","Applications requiring document structure preservation for entity grounding","Systems optimizing chunk size for specific LLM context windows"],"limitations":["PDF extraction quality varies by PDF type (scanned vs. text-based) — scanned PDFs require OCR (not built-in)","Chunking strategy selection is manual — no automatic optimization based on document characteristics","Overlapping chunks increase processing cost — requires tuning overlap percentage for cost/quality tradeoff","Document structure metadata is best-effort — complex layouts may not preserve hierarchy correctly","Language-specific text cleaning is limited — no support for non-Latin scripts or specialized domains"],"requires":["Python 3.9+","Document files in supported formats (PDF, DOCX, TXT, MD, HTML)","Optional: PDF extraction libraries (pypdf, pdfplumber)","Optional: OCR capability for scanned PDFs (Tesseract, Azure Computer Vision)"],"input_types":["document files or directories","chunking configuration (chunk size, overlap, strategy)","optional: document structure hints (sections, headings)"],"output_types":["text chunks with metadata (source, position, structure)","document structure tree (optional)","chunk-to-document mappings"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_8","uri":"capability://data.processing.analysis.knowledge.graph.schema.definition.and.validation.with.configurable.entity.relationship.types","name":"knowledge graph schema definition and validation with configurable entity/relationship types","description":"Defines and enforces a schema for the knowledge graph that specifies allowed entity types, relationship types, and their attributes. The schema is defined through configuration files and used to validate extracted entities and relationships during indexing. The system supports custom entity and relationship types, attribute definitions with type constraints, and relationship cardinality rules. Schema validation ensures consistency and enables downstream applications to rely on predictable graph structure.","intents":["I want to define what entity and relationship types are valid in my knowledge graph","I need to enforce attribute constraints on entities and relationships","I want to validate extracted data against my schema to catch extraction errors early"],"best_for":["Domain-specific applications with well-defined entity/relationship types","Teams requiring data quality assurance through schema validation","Systems where downstream applications depend on predictable graph structure"],"limitations":["Schema validation is permissive — allows extraction of entities/relationships not in schema if configured","No automatic schema inference — schema must be manually defined based on domain knowledge","Schema changes require re-indexing to apply to existing data — no schema migration tools","Validation is structural only — doesn't validate semantic correctness (e.g., that a relationship makes sense)","No support for complex constraints (e.g., 'relationship X only valid between entity types A and B')"],"requires":["Python 3.9+","Schema definition file (YAML or JSON format)","Domain knowledge to define entity/relationship types and attributes"],"input_types":["schema definition with entity types, relationship types, and attributes","extracted entities and relationships for validation"],"output_types":["validation results (valid/invalid entities and relationships)","schema-compliant knowledge graph","optional: validation error reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--graphrag__cap_9","uri":"capability://search.retrieval.multi.index.search.and.cross.index.query.federation","name":"multi-index search and cross-index query federation","description":"Supports querying across multiple GraphRAG indexes simultaneously, enabling federated search over multiple knowledge graphs or document collections. The system routes queries to appropriate indexes based on query characteristics, aggregates results from multiple indexes, and deduplicates/ranks results across indexes. This enables scenarios like searching across multiple departments' knowledge bases, multiple versions of a dataset, or multiple document collections with different schemas.","intents":["I want to search across multiple knowledge graphs without merging them into a single index","I need to query multiple document collections with different schemas in a single query","I want to federate queries across indexes for cost optimization (e.g., cheap local index + expensive cloud index)"],"best_for":["Large organizations with multiple knowledge graphs (per department, per product, per region)","Systems managing multiple versions of datasets with different schemas","Applications requiring cost-optimized search (local + cloud indexes)"],"limitations":["Cross-index deduplication is heuristic-based — may miss duplicates or incorrectly merge distinct entities","Result ranking across indexes is challenging — different indexes may use different scoring schemes","Query routing to appropriate indexes requires manual configuration or heuristics — no automatic routing optimization","Latency increases with number of indexes queried — parallel execution required for acceptable performance","No built-in index discovery — indexes must be manually registered"],"requires":["Python 3.9+","Multiple GraphRAG indexes with configured storage and vector stores","Index registry or configuration specifying available indexes","Optional: index metadata for routing decisions"],"input_types":["natural language query","optional: index selection hints or filters","optional: cross-index deduplication rules"],"output_types":["aggregated results from multiple indexes","deduplicated and ranked results","optional: per-index result breakdowns"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one supported LLM provider (OpenAI, Azure OpenAI, Anthropic, or local Ollama instance)","Unstructured text documents in supported formats (txt, pdf, docx, md, html)","Completed knowledge graph from entity/relationship extraction phase","LLM API access for generating community reports","Graph processing libraries (networkx or equivalent)","Completed GraphRAG index with entities, relationships, and community reports","Query entity extraction capability","LLM token limit information for context truncation","LLM provider API access with rate limit information"],"failure_modes":["Extraction quality depends on LLM capability and prompt design — no built-in validation of extracted entities against external knowledge bases","Hallucination risk inherent to LLM-based extraction — requires downstream validation or human review for critical applications","Cost scales with document volume and LLM API usage — no local-only extraction option without external LLM","Extraction latency depends on LLM provider response times — typically 1-5 seconds per document chunk","Community detection is non-deterministic — same graph may produce different communities across runs depending on algorithm initialization","Hierarchy depth and granularity depend on graph structure and algorithm parameters — no automatic optimization of hierarchy levels","Regenerating communities requires re-running the full indexing pipeline — incremental community updates not yet supported","Community reports are LLM-generated summaries — quality depends on LLM capability and may contain hallucinations or omissions","Context ranking is heuristic-based — no learned ranking model, may miss relevant context","Token limit enforcement is approximate — may exceed LLM token limits if context is large","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.785159621374586,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:26.976Z","last_commit":"2026-04-30T19:10:48Z"},"community":{"stars":32736,"forks":3468,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft--graphrag","compare_url":"https://unfragile.ai/compare?artifact=microsoft--graphrag"}},"signature":"rGKknq+jzzrHPM+0vG9mZB5w5Ik5l1G9L8ZVQhzBsKuatMUQzMBBi7H6FFJs9nxuxWrLzrI3+itOhFBst3CfCw==","signedAt":"2026-06-20T11:41:45.820Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--graphrag","artifact":"https://unfragile.ai/microsoft--graphrag","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--graphrag","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"}}