{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-sciphi-ai--r2r","slug":"sciphi-ai--r2r","name":"R2R","type":"repo","url":"https://github.com/SciPhi-AI/R2R","page_url":"https://unfragile.ai/sciphi-ai--r2r","categories":["rag-knowledge","deployment-infra"],"tags":["artificial-intelligence","large-language-models","python","question-answering","rag","retrieval-augmented-generation","retrieval-systems","search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-sciphi-ai--r2r__cap_0","uri":"capability://data.processing.analysis.multimodal.document.ingestion.with.format.specific.parsing","name":"multimodal document ingestion with format-specific parsing","description":"Processes diverse document formats (PDF, DOCX, images, code files, web content) through a pluggable IngestionService that routes each format to specialized parsers (pypdf for PDFs, python-docx for Word docs, unstructured-client for mixed media). The system extracts text, metadata, and structural information, then chunks documents into semantically meaningful segments before vectorization. Supports streaming ingestion for large document batches.","intents":["I need to ingest a mixed collection of PDFs, Word docs, and images into a searchable knowledge base","I want to preserve document structure and metadata during ingestion for better retrieval","I need to process large document batches without blocking the API"],"best_for":["enterprise teams building document-centric RAG systems","organizations with heterogeneous document repositories (legal, medical, technical)","developers needing production-grade ingestion pipelines with error handling"],"limitations":["Chunking strategy is configurable but defaults to fixed-size windows, which may split semantic units in code or structured data","Image OCR quality depends on unstructured-client backend; handwritten text recognition is limited","Large PDFs (>500MB) may require memory optimization; streaming helps but doesn't eliminate memory overhead","No built-in deduplication across ingestion runs; requires external logic to detect duplicate documents"],"requires":["Python 3.9+","unstructured-client library","pypdf for PDF parsing","python-docx for Word documents","pillow for image processing","PostgreSQL 13+ for document metadata storage"],"input_types":["PDF files","DOCX/Word documents","Images (PNG, JPG, TIFF)","Plain text and code files","HTML/web content","Markdown"],"output_types":["chunked text segments with metadata","document embeddings (vector format)","structured metadata (title, source, creation date)","document-chunk relationships stored in PostgreSQL"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_1","uri":"capability://search.retrieval.hybrid.search.with.vector.and.full.text.ranking.fusion","name":"hybrid search with vector and full-text ranking fusion","description":"Combines dense vector search (pgvector embeddings) with sparse full-text search (PostgreSQL FTS) using Reciprocal Rank Fusion (RRF) to merge results from both modalities. Queries are embedded and matched against vector index, while simultaneously executed as full-text queries on indexed text columns. RRF algorithm normalizes and combines rankings, allowing semantic and keyword-based relevance to influence final ordering. Supports filtering by metadata, date ranges, and document tags.","intents":["I need search that handles both semantic queries ('what is machine learning') and exact phrase matches ('GDPR compliance')","I want to filter search results by document type, date, or custom metadata without separate queries","I need to rank results that combine semantic relevance with keyword frequency for better precision"],"best_for":["teams building enterprise search over mixed-content knowledge bases","applications requiring high precision (legal, medical, compliance domains)","developers needing configurable search strategies without custom ranking logic"],"limitations":["RRF weighting is fixed; no per-query tuning of vector vs. full-text balance without code changes","Full-text search limited to PostgreSQL FTS capabilities; no support for advanced NLP like lemmatization or synonym expansion without custom configuration","Metadata filtering requires indexed columns; adding new filterable fields requires schema migration","Performance degrades with very large result sets (>10k matches) before RRF fusion; pagination required"],"requires":["PostgreSQL 13+ with pgvector extension","Pre-computed embeddings for all documents","Full-text search indexes on document text columns","numpy for RRF computation"],"input_types":["natural language query strings","filter criteria (metadata key-value pairs, date ranges)","embedding vectors (768-1536 dimensions typical)"],"output_types":["ranked list of document chunks with relevance scores","metadata for each result (source, date, document ID)","combined score from vector + full-text fusion"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_10","uri":"capability://automation.workflow.docker.containerization.and.production.deployment","name":"docker containerization and production deployment","description":"Provides Docker configuration for containerized R2R deployment, including Dockerfile for building images and docker-compose for multi-container orchestration (R2R API, PostgreSQL, optional Redis for caching). Supports environment variable configuration for all settings, enabling deployment across different environments (dev, staging, production) without code changes. Includes health checks and graceful shutdown handling.","intents":["I want to deploy R2R to Kubernetes or Docker Swarm without manual configuration","I need to run R2R with PostgreSQL and other services in a single docker-compose command","I want to scale R2R horizontally by running multiple container instances"],"best_for":["teams deploying R2R to cloud platforms (AWS, GCP, Azure) or on-premise Kubernetes","organizations using containerized infrastructure and CI/CD pipelines","developers needing reproducible deployments across environments"],"limitations":["Docker image size is large (~2GB) due to dependencies; slow to pull in bandwidth-constrained environments","Multi-container orchestration (docker-compose) is not suitable for production; requires Kubernetes or similar","Health checks are basic (HTTP ping); no deep health checks for database connectivity or embedding model availability","Environment variable configuration is flat; complex nested configurations require custom parsing","No built-in secrets management; API keys and credentials must be injected via environment or secrets store"],"requires":["Docker 20.10+","docker-compose 2.0+ (for multi-container setup)","PostgreSQL 13+ (can be containerized)","Kubernetes (optional, for production orchestration)"],"input_types":["Dockerfile","docker-compose.yml","environment variables (.env file)"],"output_types":["Docker image","running containers with exposed ports","health check status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_11","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.tool.extension","name":"mcp (model context protocol) integration for tool extension","description":"Implements Model Context Protocol support, allowing R2R to expose its capabilities (document retrieval, search, entity lookup) as MCP tools that can be called by LLM clients (Claude, other MCP-compatible models). Tools are defined with JSON schemas and can be invoked by LLMs with automatic parameter validation. Enables seamless integration of R2R into LLM-native workflows without custom API wrappers.","intents":["I want Claude or another MCP-compatible LLM to directly call R2R search and retrieval functions","I need to expose R2R capabilities as tools without building a custom API wrapper","I want LLMs to have real-time access to my knowledge base without pre-loading context"],"best_for":["teams building LLM agents that need access to R2R knowledge bases","organizations using Claude or other MCP-compatible models","developers integrating R2R into LLM-native applications"],"limitations":["MCP support is relatively new; not all LLM providers support MCP yet (mainly Claude)","Tool schemas must match MCP specification exactly; mismatches cause LLM failures","No built-in rate limiting for tool calls; LLMs can make excessive calls without throttling","Tool results are limited to text; complex structured data (graphs, images) may not be representable","Debugging tool call failures requires MCP protocol knowledge; errors are not always clear"],"requires":["MCP-compatible LLM client (Claude, or custom implementation)","Python 3.9+","mcp library (if using Python MCP client)","JSON schema definitions for tools"],"input_types":["MCP tool call requests with parameters","tool schema definitions"],"output_types":["MCP tool results (text, structured data)","tool call logs"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_12","uri":"capability://data.processing.analysis.configurable.chunking.strategies.with.semantic.awareness","name":"configurable chunking strategies with semantic awareness","description":"Supports multiple document chunking strategies (fixed-size windows, semantic chunking, code-aware chunking) that can be selected via configuration. Semantic chunking uses embeddings to identify natural breakpoints in text, preserving semantic units. Code-aware chunking respects syntax boundaries (functions, classes) to avoid splitting logical units. Chunk size, overlap, and strategy are configurable per document type.","intents":["I want to chunk documents intelligently so that semantic units aren't split across chunks","I need different chunking strategies for code vs. prose documents","I want to control chunk size and overlap to balance context and retrieval precision"],"best_for":["teams with mixed document types (code, prose, technical docs) requiring different chunking","applications where chunk boundaries significantly impact retrieval quality","organizations needing fine-grained control over context window sizes"],"limitations":["Semantic chunking is computationally expensive (requires embeddings for every potential chunk boundary); slower than fixed-size chunking","Code-aware chunking requires language-specific parsers; not all languages are supported","Chunk overlap increases storage and retrieval cost; no automatic optimization for overlap size","Changing chunking strategy requires re-ingesting all documents; no in-place migration","No evaluation metrics to determine optimal chunking strategy; requires manual testing"],"requires":["Python 3.9+","Embedding model for semantic chunking","Language-specific parsers for code-aware chunking (tree-sitter, AST parsers)"],"input_types":["document text","chunking strategy configuration (strategy type, chunk size, overlap)","document type (for strategy selection)"],"output_types":["chunked text segments","chunk metadata (start/end positions, semantic score)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_13","uri":"capability://data.processing.analysis.vector.embedding.with.multi.model.support.and.batch.processing","name":"vector embedding with multi-model support and batch processing","description":"Supports multiple embedding models (OpenAI, Hugging Face, local models via Ollama) through a pluggable EmbeddingProvider interface. Processes documents in batches to maximize throughput and reduce API costs. Embeddings are stored in PostgreSQL with pgvector extension, enabling efficient similarity search. Supports re-embedding documents with different models without data loss.","intents":["I want to embed documents using different models (OpenAI for quality, local for cost) without code changes","I need to batch embed thousands of documents efficiently","I want to switch embedding models and re-embed my corpus without losing document data"],"best_for":["teams evaluating different embedding models for quality/cost tradeoff","organizations with on-premise requirements needing local embedding models","applications requiring frequent re-embedding (e.g., when new models are released)"],"limitations":["Embedding API costs scale with document volume; no built-in cost optimization","Batch processing adds latency; real-time embedding of single documents is slower than direct API calls","Embedding model switching requires re-embedding entire corpus; no incremental updates","pgvector performance degrades with very large embeddings (>1536 dimensions); index creation is slow","No built-in embedding quality evaluation; determining best model requires manual testing"],"requires":["Embedding model (OpenAI, Hugging Face, or local Ollama instance)","PostgreSQL 13+ with pgvector extension","Python 3.9+","API key for cloud embedding models (optional, for local models)"],"input_types":["document text chunks","embedding model selection","batch size configuration"],"output_types":["embedding vectors (768-1536 dimensions typical)","embedding metadata (model, timestamp, cost)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_2","uri":"capability://planning.reasoning.agentic.multi.step.reasoning.with.tool.integration","name":"agentic multi-step reasoning with tool integration","description":"Implements a Deep Research API that enables agents to iteratively fetch information from local knowledge bases and external web sources, synthesizing results through LLM-driven reasoning. Agents decompose complex queries into sub-tasks, call retrieval tools with refined prompts, and aggregate findings. The system supports tool calling via schema-based function registries compatible with OpenAI and Anthropic function-calling APIs. Streaming responses allow real-time visibility into agent reasoning steps.","intents":["I need an agent that can answer complex questions by searching my knowledge base, then web, then synthesizing results","I want to see the agent's reasoning process and intermediate retrieval steps in real-time","I need to extend the agent with custom tools (e.g., database queries, API calls) without modifying core logic"],"best_for":["teams building research assistants or question-answering systems over large knowledge bases","applications requiring multi-step reasoning with transparency into agent decisions","developers integrating R2R agents into larger LLM application stacks"],"limitations":["Agent reasoning quality depends heavily on LLM capability; weaker models (e.g., GPT-3.5) may fail at complex decomposition","Tool calling adds latency; each agent step requires LLM inference + retrieval, typically 2-5 seconds per step","No built-in memory persistence across agent sessions; requires external state store for multi-turn conversations","Tool schema validation is strict; mismatched function signatures cause agent failures without graceful fallback","Web search integration requires external API (e.g., Tavily, SerpAPI); adds cost and rate-limiting constraints"],"requires":["LLM API key (OpenAI, Anthropic, or local Ollama instance)","Python 3.9+","RetrievalService configured with vector/full-text search","Optional: web search API credentials (Tavily, SerpAPI, or Bing Search)"],"input_types":["natural language queries","tool schema definitions (JSON schema format)","context from previous retrieval steps"],"output_types":["streaming agent reasoning steps","final synthesized answer","tool call logs with inputs/outputs","source citations from retrieved documents"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_3","uri":"capability://memory.knowledge.knowledge.graph.construction.with.entity.extraction.and.community.detection","name":"knowledge graph construction with entity extraction and community detection","description":"Automatically extracts entities and relationships from ingested documents using LLM-based extraction or rule-based patterns, then constructs a knowledge graph stored as nodes and edges. Applies community detection algorithms (networkx-based) to identify clusters of related entities, enabling hierarchical knowledge organization. Supports querying the graph to find entity relationships, traverse paths between concepts, and retrieve context-rich information for RAG augmentation.","intents":["I want to automatically extract key entities (people, organizations, concepts) from my documents and map their relationships","I need to identify clusters of related topics to improve search and recommendation","I want to use the knowledge graph to provide richer context for RAG queries by including related entities"],"best_for":["organizations with large document collections requiring semantic organization (research, legal, medical)","teams building knowledge discovery tools or recommendation systems","applications needing entity-centric search (e.g., 'find all documents mentioning Company X and its competitors')"],"limitations":["LLM-based entity extraction is expensive (cost per document) and slower than rule-based approaches; requires careful prompt engineering for domain-specific entities","Community detection is computationally expensive for graphs with >100k nodes; requires offline processing or incremental updates","Entity disambiguation is not automatic; homonyms (e.g., 'Apple' as company vs. fruit) require manual resolution or external knowledge bases","Graph updates are not real-time; adding new documents requires re-running extraction and community detection, which can take hours for large graphs","No built-in visualization; requires external tools (Neo4j, Gephi) to explore graph structure"],"requires":["LLM API for entity extraction (OpenAI, Anthropic, or local model)","networkx library for graph algorithms","PostgreSQL for storing graph nodes/edges","Python 3.9+"],"input_types":["document text chunks","entity type definitions (e.g., 'Person', 'Organization', 'Location')","relationship patterns (optional, for rule-based extraction)"],"output_types":["entity nodes with attributes (type, name, frequency)","relationship edges with types and confidence scores","community clusters with member entities","graph traversal results for path queries"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_4","uri":"capability://tool.use.integration.restful.api.with.versioned.endpoints.and.multi.client.support","name":"restful api with versioned endpoints and multi-client support","description":"Exposes R2R functionality through a FastAPI application with versioned endpoints (v1, v2, v3) supporting document management, retrieval, search, and administrative operations. Provides Python (R2RClient, R2RAsyncClient) and JavaScript (r2rClient) SDKs that abstract HTTP communication and handle request/response serialization. Supports both synchronous and asynchronous operations, enabling non-blocking integration into async frameworks.","intents":["I want to integrate R2R into my existing application via REST API without managing HTTP details","I need to use R2R from both Python and JavaScript/Node.js codebases","I want to upgrade R2R versions without breaking my client code (backward compatibility)"],"best_for":["teams building polyglot applications (Python backend + JavaScript frontend)","developers deploying R2R as a microservice with multiple client applications","organizations requiring API versioning for gradual migration strategies"],"limitations":["API versioning adds maintenance burden; older versions must be supported in parallel, increasing code complexity","Synchronous SDK calls block the event loop; async SDK required for high-concurrency scenarios (>100 concurrent requests)","Authentication is basic (API keys); no built-in OAuth2 or SAML support without custom middleware","Rate limiting is not enforced at API level; requires external API gateway (nginx, Kong) for production","JavaScript SDK is less feature-complete than Python SDK; some advanced features may only be available via REST API"],"requires":["FastAPI 0.100+","Python 3.9+ for Python SDK","Node.js 16+ for JavaScript SDK","HTTP client library (requests for Python, fetch for JavaScript)","API key for authentication"],"input_types":["JSON request bodies","multipart form data (for file uploads)","query parameters (filters, pagination)","HTTP headers (authentication, content-type)"],"output_types":["JSON responses","streaming responses (Server-Sent Events for agent reasoning)","file downloads (exported documents, graphs)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_5","uri":"capability://tool.use.integration.configurable.provider.system.for.llm.embedding.and.database.backends","name":"configurable provider system for llm, embedding, and database backends","description":"Implements a pluggable provider architecture where LLM, embedding, database, and ingestion providers are swappable via TOML configuration without code changes. Supports multiple LLM backends (OpenAI, Anthropic, Ollama, LM Studio), embedding models (OpenAI, Hugging Face, local), and databases (PostgreSQL, in-memory). Providers implement standard interfaces (e.g., LLMProvider, EmbeddingProvider) enabling runtime selection and fallback strategies.","intents":["I want to switch from OpenAI to a local Ollama instance without changing application code","I need to use different embedding models for different document types (e.g., code vs. text)","I want to test R2R with multiple LLM backends to find the best cost/quality tradeoff"],"best_for":["teams evaluating different LLM/embedding providers before committing to one","organizations with on-premise requirements needing local LLM support (Ollama, LM Studio)","developers building multi-tenant systems where each tenant uses different providers"],"limitations":["Provider configuration is static at startup; switching providers requires application restart","Not all providers support all features (e.g., function calling not available in all LLM backends); feature detection is manual","Embedding model switching requires re-embedding entire document corpus; no built-in migration tools","Provider-specific parameters (temperature, top_p) are not normalized; different providers have different tuning ranges","Fallback strategies (e.g., retry with different provider) are not automatic; requires custom orchestration logic"],"requires":["TOML configuration file (r2r.toml)","API keys for selected providers (OpenAI, Anthropic, etc.)","Python 3.9+","Provider-specific dependencies (e.g., ollama Python client for Ollama backend)"],"input_types":["TOML configuration with provider settings","provider-specific credentials (API keys, endpoints)"],"output_types":["instantiated provider objects at runtime","provider capabilities metadata (supported models, features)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_6","uri":"capability://safety.moderation.user.management.and.role.based.access.control","name":"user management and role-based access control","description":"Implements multi-user support with role-based access control (RBAC) where users have roles (admin, user, viewer) with different permissions for document management, search, and administrative operations. User authentication is API-key based; each user has a unique key for API requests. Permissions are enforced at the API endpoint level, preventing unauthorized access to documents or operations.","intents":["I need to restrict which users can upload documents, delete documents, or access sensitive information","I want to track which user performed which action (audit logging)","I need to support multi-tenant deployments where each tenant only sees their own documents"],"best_for":["enterprise deployments with multiple users and compliance requirements","multi-tenant SaaS applications built on R2R","organizations needing audit trails for document access and modifications"],"limitations":["API key authentication is basic; no OAuth2, SAML, or SSO integration without custom middleware","RBAC is coarse-grained (admin/user/viewer); no fine-grained permissions (e.g., 'can read but not delete')","No built-in audit logging; requires external logging system (ELK, Datadog) for compliance","User isolation is at the API level; no row-level security in database, so compromised database access bypasses RBAC","API keys are long-lived; no automatic rotation or expiration without custom implementation"],"requires":["PostgreSQL for user and permission storage","API key generation and validation logic","Python 3.9+"],"input_types":["user credentials (username, password for initial setup)","API keys (for subsequent requests)","role assignments (admin, user, viewer)"],"output_types":["API key for authenticated requests","permission validation results (allowed/denied)","audit logs (optional, requires external system)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_7","uri":"capability://data.processing.analysis.document.metadata.management.and.filtering","name":"document metadata management and filtering","description":"Stores and indexes document metadata (title, source, creation date, custom tags, document type) in PostgreSQL alongside document chunks. Metadata is extracted during ingestion or provided by users. Supports filtering search results by metadata using SQL WHERE clauses, enabling queries like 'find documents from 2024 with tag=legal'. Metadata can be updated without re-ingesting documents.","intents":["I want to filter search results by document type, date range, or custom tags","I need to track document provenance (source, version, last modified date)","I want to bulk-update metadata for a set of documents without re-ingesting them"],"best_for":["organizations with large document collections requiring fine-grained filtering","applications needing document versioning or audit trails","teams using custom metadata schemas (domain-specific tags, classifications)"],"limitations":["Metadata schema is fixed at database creation; adding new metadata fields requires schema migration","Metadata filtering is applied after vector search, reducing efficiency for highly selective filters","No full-text search on metadata values; filtering is exact match or range-based only","Bulk metadata updates are not transactional; partial failures may leave inconsistent state","No built-in metadata validation; invalid values can be stored without error"],"requires":["PostgreSQL 13+","Schema definition for metadata columns","Python 3.9+"],"input_types":["metadata key-value pairs (JSON or structured format)","filter criteria (key, operator, value)"],"output_types":["filtered document list with metadata","metadata update confirmations"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_8","uri":"capability://automation.workflow.streaming.ingestion.and.processing.with.async.support","name":"streaming ingestion and processing with async support","description":"Supports asynchronous document ingestion via streaming APIs, allowing large batches to be processed without blocking the main API thread. Uses async/await patterns throughout the ingestion pipeline (IngestionService, parsers, embedding). Clients can poll for ingestion status or receive webhooks when processing completes. Streaming responses enable real-time visibility into ingestion progress.","intents":["I want to upload 10,000 documents without blocking my application","I need to monitor ingestion progress in real-time as documents are processed","I want to ingest documents while simultaneously serving search queries"],"best_for":["applications with large, frequent document uploads","teams needing non-blocking ingestion for responsive user interfaces","systems requiring high throughput (documents per second)"],"limitations":["Async ingestion adds complexity; debugging failures is harder than synchronous processing","Streaming responses require long-lived HTTP connections; not compatible with some proxies or load balancers","Progress tracking requires polling or webhooks; no built-in progress bar without client-side implementation","Memory usage is not reduced by async; large documents still require buffering in memory","Concurrent ingestion of the same document may cause race conditions; requires external locking"],"requires":["Python 3.9+ with async/await support","FastAPI with async endpoint support","asyncio event loop","Optional: webhook endpoint for completion notifications"],"input_types":["document files (streamed via multipart/form-data)","ingestion configuration (chunking strategy, metadata)"],"output_types":["streaming ingestion progress updates","final ingestion status (success/failure)","document IDs for ingested documents"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sciphi-ai--r2r__cap_9","uri":"capability://automation.workflow.orchestration.and.workflow.management.with.hatchet.integration","name":"orchestration and workflow management with hatchet integration","description":"Integrates with Hatchet workflow orchestration platform to manage complex, multi-step document processing pipelines. Workflows are defined as DAGs (directed acyclic graphs) where each node is a processing step (ingestion, embedding, entity extraction, graph construction). Hatchet handles task scheduling, retries, error handling, and distributed execution across worker nodes. R2R provides SimpleOrchestrationProvider for basic workflows and HatchetOrchestrationProvider for advanced scenarios.","intents":["I need to process documents through a complex pipeline (ingest → chunk → embed → extract entities → build graph)","I want automatic retries if a step fails, without manual intervention","I need to scale document processing across multiple worker nodes"],"best_for":["organizations processing large volumes of documents with complex pipelines","teams needing distributed processing across multiple machines","applications requiring reliable, auditable document processing workflows"],"limitations":["Hatchet integration adds operational complexity; requires Hatchet cluster setup and maintenance","Workflow definitions are code-based (Python); no visual workflow builder","Debugging failed workflows requires Hatchet UI or logs; limited visibility into intermediate state","Cost increases with Hatchet usage (per-task pricing); not suitable for small-scale deployments","Workflow state is not automatically cleaned up; requires manual archival or deletion"],"requires":["Hatchet account and API key","hatchet-sdk Python library","Python 3.9+","Hatchet worker nodes (can be local or cloud-hosted)"],"input_types":["workflow definitions (Python DAG)","input documents and configuration"],"output_types":["workflow execution status","processed documents with all pipeline steps applied","execution logs and timing information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","unstructured-client library","pypdf for PDF parsing","python-docx for Word documents","pillow for image processing","PostgreSQL 13+ for document metadata storage","PostgreSQL 13+ with pgvector extension","Pre-computed embeddings for all documents","Full-text search indexes on document text columns","numpy for RRF computation"],"failure_modes":["Chunking strategy is configurable but defaults to fixed-size windows, which may split semantic units in code or structured data","Image OCR quality depends on unstructured-client backend; handwritten text recognition is limited","Large PDFs (>500MB) may require memory optimization; streaming helps but doesn't eliminate memory overhead","No built-in deduplication across ingestion runs; requires external logic to detect duplicate documents","RRF weighting is fixed; no per-query tuning of vector vs. full-text balance without code changes","Full-text search limited to PostgreSQL FTS capabilities; no support for advanced NLP like lemmatization or synonym expansion without custom configuration","Metadata filtering requires indexed columns; adding new filterable fields requires schema migration","Performance degrades with very large result sets (>10k matches) before RRF fusion; pagination required","Docker image size is large (~2GB) due to dependencies; slow to pull in bandwidth-constrained environments","Multi-container orchestration (docker-compose) is not suitable for production; requires Kubernetes or similar","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6280294097825221,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2025-11-07T01:02:44Z"},"community":{"stars":7796,"forks":627,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sciphi-ai--r2r","compare_url":"https://unfragile.ai/compare?artifact=sciphi-ai--r2r"}},"signature":"PP8iYDLggM8hQqcYjzVSj/lEg7WVD3aviFu3P7XYUHPZYZjlzReVNFucgFu4yKL79QetkQ3lqA8AP+t6zyExCA==","signedAt":"2026-06-20T08:36:29.572Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sciphi-ai--r2r","artifact":"https://unfragile.ai/sciphi-ai--r2r","verify":"https://unfragile.ai/api/v1/verify?slug=sciphi-ai--r2r","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"}}