{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-infiniflow--ragflow","slug":"infiniflow--ragflow","name":"ragflow","type":"repo","url":"https://ragflow.io","page_url":"https://unfragile.ai/infiniflow--ragflow","categories":["rag-knowledge"],"tags":["agent","agentic","agentic-ai","agentic-workflow","ai","context-engineering","context-retrieval","deep-research","deepseek","deepseek-r1","document-understanding","graphrag","harness","llm","mcp","ollama","openai","openclaw","rag","retrieval-augmented-generation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-infiniflow--ragflow__cap_0","uri":"capability://data.processing.analysis.multi.strategy.document.parsing.with.format.aware.extraction","name":"multi-strategy document parsing with format-aware extraction","description":"RAGFlow implements a pluggable document parsing pipeline that selects parsing strategies based on document type (PDF, Word, HTML, images, etc.), using specialized handlers for each format. The system includes vision-based OCR and layout recognition for scanned documents, combined with structural parsing for native formats. This ensures high-fidelity extraction of text, tables, and metadata while preserving document structure and semantic relationships.","intents":["I need to ingest diverse document formats (PDFs, Word docs, images, web pages) and extract structured content accurately","I want OCR capabilities for scanned documents with layout understanding to preserve table structures","I need to handle complex document formats without losing semantic structure or metadata"],"best_for":["enterprises processing heterogeneous document collections","teams building knowledge bases from mixed-format sources","organizations requiring high-accuracy document understanding for compliance or research"],"limitations":["OCR accuracy depends on image quality; degraded scans may require preprocessing","Complex nested table structures may require manual post-processing in edge cases","Vision processing adds latency (~500ms-2s per document depending on page count)"],"requires":["Python 3.9+","Docker for containerized deployment","Optional: Tesseract or similar OCR engine for vision processing","Sufficient disk space for document staging and processing artifacts"],"input_types":["PDF (native and scanned)","Microsoft Word (.docx, .doc)","HTML/web content","Images (PNG, JPG, TIFF)","Plain text","Markdown"],"output_types":["structured JSON with extracted text, tables, and metadata","chunked document segments with position information","embedded vectors for semantic search"],"categories":["data-processing-analysis","document-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_1","uri":"capability://data.processing.analysis.intelligent.template.based.document.chunking.with.semantic.awareness","name":"intelligent template-based document chunking with semantic awareness","description":"RAGFlow provides multiple chunking strategies (fixed-size, semantic, layout-aware, and recursive) that can be configured per document type or knowledge base. The system analyzes document structure to identify natural boundaries (sections, paragraphs, tables) and chunks accordingly, rather than blindly splitting at token limits. Semantic chunking uses embeddings to ensure chunks maintain coherent meaning, while layout-aware chunking respects document structure to preserve table integrity and section relationships.","intents":["I need to chunk documents intelligently to preserve semantic meaning and avoid splitting mid-sentence or mid-table","I want different chunking strategies for different document types (e.g., technical docs vs. tables)","I need to control chunk size and overlap to balance retrieval granularity with context window constraints"],"best_for":["teams building RAG systems where chunk quality directly impacts retrieval relevance","organizations with diverse document types requiring format-specific chunking logic","developers optimizing for both retrieval accuracy and LLM context window efficiency"],"limitations":["Semantic chunking requires embedding computation, adding ~100-300ms per document","Layout-aware chunking may fail on malformed or non-standard document structures","Chunk overlap configuration requires tuning per use case; no universal optimal setting"],"requires":["Embedding model configured (local or API-based)","Document structure metadata from parsing stage","Configuration parameters for chunk size, overlap, and strategy selection"],"input_types":["parsed document segments with structure metadata","document type classification","semantic embeddings (for semantic chunking strategy)"],"output_types":["chunked text segments with position metadata","chunk-to-source document mapping","chunk embeddings for vector search"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_10","uri":"capability://data.processing.analysis.data.source.connectors.with.incremental.sync.and.change.detection","name":"data source connectors with incremental sync and change detection","description":"RAGFlow provides connectors for external data sources (databases, APIs, cloud storage, web crawlers) with incremental sync capabilities. The system detects changes in source data using timestamps, checksums, or API-provided change logs, syncing only modified documents to avoid redundant processing. Connectors support scheduling (periodic sync) and manual triggering, with error handling and retry logic for failed syncs.","intents":["I need to ingest documents from external sources (databases, APIs, cloud storage) into my knowledge base","I want to keep my knowledge base synchronized with source data without reprocessing unchanged documents","I need to schedule periodic syncs and handle failures gracefully"],"best_for":["teams integrating RAG systems with existing data infrastructure","organizations with large, frequently-updated document collections","developers building knowledge bases from multiple heterogeneous sources"],"limitations":["Change detection accuracy depends on source system capabilities; some sources may not provide reliable change logs","Incremental sync requires state tracking (last sync timestamp, checksums); state corruption can cause missed updates","Connector implementation is source-specific; new sources require custom connector development"],"requires":["Connector implementation for target data source","Source system credentials and API access","State storage for tracking sync progress and change detection","Scheduling infrastructure (cron, message queue, or similar)"],"input_types":["data source configuration (credentials, connection parameters)","sync schedule or trigger","optional: change detection parameters (timestamp field, checksum algorithm)"],"output_types":["synced documents with metadata","sync status and error logs","change detection results (new, modified, deleted documents)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_11","uri":"capability://code.generation.editing.sandbox.code.execution.for.agent.tool.implementation","name":"sandbox code execution for agent tool implementation","description":"RAGFlow provides a sandboxed code execution environment enabling agents to execute Python code safely within isolated containers. The sandbox enforces resource limits (CPU, memory, execution time), prevents access to sensitive files or network resources, and captures output for agent observation. This enables agents to perform calculations, data transformations, or custom logic without exposing the host system.","intents":["I need agents to execute custom Python code (calculations, transformations) as part of their reasoning","I want to safely execute untrusted code without risking host system compromise","I need to capture code execution output for agent observation and decision-making"],"best_for":["teams building autonomous agents requiring computational capabilities","organizations implementing code-generation agents that need to verify generated code","developers creating AI systems that perform data analysis or transformations"],"limitations":["Sandbox overhead adds latency (~100-500ms per execution depending on code complexity)","Resource limits may prevent legitimate long-running computations; requires tuning per use case","Sandbox escape vulnerabilities are possible; requires regular security updates"],"requires":["Docker or similar containerization for sandbox isolation","Resource limit configuration (CPU, memory, timeout)","Python runtime within sandbox","Output capture and logging infrastructure"],"input_types":["Python code string","execution context (variables, imports)","resource limit configuration"],"output_types":["execution result (stdout, stderr)","execution status (success, timeout, error)","resource usage metrics"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_12","uri":"capability://text.generation.language.web.based.ui.for.knowledge.base.management.and.chat.interaction","name":"web-based ui for knowledge base management and chat interaction","description":"RAGFlow provides a full-featured web interface built with React and TypeScript, supporting document upload, knowledge base management, chat interaction, and workflow visualization. The UI includes a canvas editor for designing agentic workflows, a chat interface with streaming response display, and administrative dashboards for system monitoring. The system supports internationalization (12+ languages) and theming for customization.","intents":["I need a user-friendly interface for uploading documents and managing knowledge bases without coding","I want to chat with AI agents and see responses stream in real-time","I need to design and visualize agentic workflows using a visual editor"],"best_for":["non-technical users managing knowledge bases and interacting with AI","teams building internal tools requiring visual workflow design","organizations deploying RAGFlow as a managed service with multi-user access"],"limitations":["Web UI adds deployment complexity; requires Node.js and web server infrastructure","UI performance degrades with very large knowledge bases (>100k documents); requires pagination and lazy loading","Canvas editor has learning curve for complex workflow design"],"requires":["Node.js 18+ for frontend build and deployment","Web browser with modern JavaScript support","Backend API server for data persistence","Optional: reverse proxy (Nginx, Apache) for production deployment"],"input_types":["document files (PDF, Word, images, etc.)","user chat messages","workflow canvas definitions"],"output_types":["rendered chat responses with streaming","knowledge base management UI","workflow visualization and execution status"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_13","uri":"capability://tool.use.integration.rest.api.and.python.sdk.for.programmatic.integration","name":"rest api and python sdk for programmatic integration","description":"RAGFlow exposes a comprehensive REST API covering all major operations (document management, chat, retrieval, workflow execution, memory management) with OpenAPI documentation. A Python SDK provides type-safe bindings for the API, simplifying integration into Python applications. Both API and SDK support async operations, streaming responses, and pagination for large result sets.","intents":["I need to integrate RAGFlow into my application via REST API without building custom clients","I want to use Python SDK for type-safe, convenient access to RAGFlow capabilities","I need to build custom applications that leverage RAGFlow's retrieval and reasoning capabilities"],"best_for":["developers building applications that integrate RAGFlow as a backend service","Python teams leveraging RAGFlow for RAG and agentic workflows","organizations deploying RAGFlow as a managed service with programmatic access"],"limitations":["API latency adds overhead compared to in-process library usage; typical latency 50-200ms per request","SDK is Python-only; other languages require direct REST API usage","Rate limiting and authentication add complexity to client implementation"],"requires":["Running RAGFlow server instance","API key or authentication credentials","Python 3.9+ for SDK usage","HTTP client library (requests, httpx, or similar)"],"input_types":["HTTP requests with JSON payloads","file uploads for document management","query parameters for filtering and pagination"],"output_types":["JSON responses with structured data","streaming responses (Server-Sent Events or WebSocket)","file downloads (documents, exports)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_2","uri":"capability://search.retrieval.hybrid.search.with.multi.tier.retrieval.and.learned.reranking","name":"hybrid search with multi-tier retrieval and learned reranking","description":"RAGFlow implements a hybrid retrieval pipeline combining dense vector search (semantic), sparse BM25 search (lexical), and structured metadata filtering. Retrieved candidates are reranked using learned-to-rank models or cross-encoder networks that score relevance based on query-document interaction. The system supports configurable fusion strategies (RRF, weighted sum) to combine scores from multiple retrieval tiers, enabling both semantic and keyword-based recall with precision reranking.","intents":["I need to retrieve relevant documents using both semantic similarity and keyword matching to maximize recall","I want to rerank retrieved candidates to improve precision without re-querying the LLM","I need to filter results by metadata (date, source, category) while maintaining semantic relevance"],"best_for":["teams building production RAG systems requiring high retrieval precision","organizations with large document collections where multi-tier retrieval improves recall","developers optimizing for both coverage (recall) and relevance (precision) in search results"],"limitations":["Reranking adds latency (~50-200ms per query depending on candidate set size)","Learned reranking models require training data; cold-start systems may use heuristic reranking","Metadata filtering requires structured indexing; unstructured metadata reduces filter effectiveness"],"requires":["Vector database (Milvus, Weaviate, or similar) for dense search","BM25 index (Elasticsearch, Solr, or embedded implementation)","Reranking model (cross-encoder or learned-to-rank) with inference capability","Query embedding model for semantic search"],"input_types":["natural language query","optional metadata filters (date range, source, category)","optional query expansion terms"],"output_types":["ranked list of document chunks with relevance scores","citation metadata (source document, position, confidence)","retrieval explanation (which tier contributed to ranking)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_3","uri":"capability://planning.reasoning.agentic.workflow.orchestration.with.react.loop.and.tool.integration","name":"agentic workflow orchestration with react loop and tool integration","description":"RAGFlow provides a canvas-based workflow engine that orchestrates multi-step agentic processes using a ReAct (Reasoning + Acting) loop pattern. Agents decompose tasks into reasoning steps, select tools from a registry, execute them, and observe results in an iterative cycle. The system includes built-in tools (retrieval, calculation, code execution) and supports custom tool registration via a schema-based function calling interface compatible with OpenAI, Anthropic, and other LLM providers.","intents":["I need to build multi-step AI workflows where agents reason about tasks, select tools, and iterate based on results","I want to integrate external APIs and custom functions as tools that agents can invoke","I need to orchestrate complex reasoning tasks (research, analysis, planning) that require multiple tool calls and feedback loops"],"best_for":["teams building autonomous AI agents for research, analysis, or task automation","developers creating complex workflows that require reasoning and tool use","organizations implementing agentic RAG systems that combine retrieval with reasoning"],"limitations":["ReAct loops can be unpredictable; agents may enter infinite loops or take suboptimal paths without proper guardrails","Tool execution latency compounds with each step; complex workflows may exceed LLM context windows","Debugging multi-step workflows is challenging; requires detailed logging and trace analysis"],"requires":["LLM with function calling support (OpenAI, Anthropic, or compatible)","Tool registry with schema definitions for each callable function","State management for tracking agent progress and tool results","Timeout and retry configuration for tool execution"],"input_types":["task description or user query","tool registry with function schemas","optional context or constraints"],"output_types":["final answer or task result","execution trace with reasoning steps and tool calls","intermediate results from each reasoning-action cycle"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_4","uri":"capability://memory.knowledge.multi.modal.memory.system.with.conversation.history.and.knowledge.persistence","name":"multi-modal memory system with conversation history and knowledge persistence","description":"RAGFlow implements a tiered memory architecture supporting short-term conversation history, long-term knowledge persistence, and user/session-specific memory scopes. The system stores conversation turns with embeddings for semantic recall, maintains a knowledge graph of extracted facts and relationships, and supports memory operations (add, retrieve, update, forget) via API. Memory can be persisted to multiple backends (PostgreSQL, vector databases) and retrieved using hybrid search to augment LLM context.","intents":["I need to maintain conversation context across multiple turns while managing token limits","I want to extract and persist facts from conversations for long-term knowledge accumulation","I need to retrieve relevant past interactions or facts to augment current LLM responses"],"best_for":["teams building conversational AI systems requiring multi-turn context management","organizations implementing knowledge accumulation systems that learn from interactions","developers creating personalized AI assistants with user-specific memory"],"limitations":["Memory retrieval adds latency (~50-150ms per query depending on memory size)","Long-term memory can become stale; requires periodic refresh or decay mechanisms","Memory conflicts (contradictory facts) require manual resolution or conflict detection logic"],"requires":["Persistent storage backend (PostgreSQL, MongoDB, or similar)","Vector database for semantic memory retrieval","Embedding model for memory encoding","Memory management API or SDK"],"input_types":["conversation turns (user message, assistant response)","extracted facts or knowledge to persist","memory query (semantic or keyword-based)"],"output_types":["retrieved memory items with relevance scores","augmented context for LLM input","memory operation confirmation (add, update, delete)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_5","uri":"capability://memory.knowledge.graphrag.and.raptor.hierarchical.knowledge.graph.construction","name":"graphrag and raptor hierarchical knowledge graph construction","description":"RAGFlow implements advanced knowledge extraction techniques including GraphRAG (building entity-relationship graphs from documents) and RAPTOR (recursive abstraction and processing for tree-organized retrieval). These methods extract entities, relationships, and hierarchical summaries from documents, enabling retrieval at multiple abstraction levels. The system supports both graph-based and tree-based retrieval, allowing queries to match at entity, relationship, or summary levels depending on information need.","intents":["I need to extract structured knowledge (entities, relationships) from unstructured documents for graph-based retrieval","I want to support multi-level retrieval where queries can match at entity, relationship, or summary levels","I need to build hierarchical knowledge representations that enable both detailed and high-level reasoning"],"best_for":["teams building knowledge graph systems for complex domains (finance, healthcare, research)","organizations requiring multi-level retrieval for hierarchical reasoning","developers implementing advanced RAG systems that go beyond simple document retrieval"],"limitations":["GraphRAG construction is computationally expensive; requires LLM calls for entity/relationship extraction (~1-5 minutes per document)","RAPTOR tree construction requires recursive summarization; adds significant processing overhead","Graph quality depends on LLM extraction accuracy; hallucinations in entity/relationship extraction propagate to retrieval"],"requires":["LLM with strong entity/relationship extraction capabilities","Graph database (Neo4j, ArangoDB) or vector database with graph support","Significant computational resources for recursive processing","Configuration for entity types, relationship types, and abstraction levels"],"input_types":["parsed documents with semantic structure","entity and relationship type definitions","abstraction level configuration"],"output_types":["knowledge graph with entities and relationships","hierarchical tree structure with summaries at each level","multi-level retrieval results (entity matches, relationship matches, summary matches)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_6","uri":"capability://text.generation.language.multi.provider.llm.integration.with.unified.interface.and.fallback.handling","name":"multi-provider llm integration with unified interface and fallback handling","description":"RAGFlow abstracts LLM provider differences through a unified LLMBundle interface that supports OpenAI, Anthropic, Ollama, DeepSeek, and other providers. The system handles provider-specific API differences (function calling schemas, streaming formats, error codes) transparently, enabling model switching without code changes. Built-in retry logic, timeout handling, and fallback strategies ensure resilience across provider outages or rate limits.","intents":["I need to use multiple LLM providers (OpenAI, Anthropic, local Ollama) without rewriting code for each","I want to switch models or providers based on cost, latency, or capability requirements","I need resilient LLM integration with automatic retries and fallback to alternative providers"],"best_for":["teams building multi-model AI systems for cost optimization or redundancy","organizations evaluating different LLM providers without vendor lock-in","developers requiring flexible model selection based on task requirements"],"limitations":["Function calling schemas differ across providers; complex schemas may not translate perfectly","Streaming format differences can cause latency variations across providers","Fallback strategies require pre-configured alternative models; no automatic model selection"],"requires":["API keys for desired LLM providers (OpenAI, Anthropic, etc.)","LLMBundle configuration with model names and provider settings","Retry and timeout configuration","Optional: local Ollama instance for on-premise models"],"input_types":["prompt or messages","function calling schema (optional)","model/provider selection parameters"],"output_types":["LLM response (text or structured)","function calls (if applicable)","usage metrics (tokens, latency)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_7","uri":"capability://memory.knowledge.citation.generation.with.source.attribution.and.confidence.scoring","name":"citation generation with source attribution and confidence scoring","description":"RAGFlow tracks document provenance throughout the retrieval and generation pipeline, maintaining position metadata (document ID, chunk ID, page number, character offset) for all retrieved content. When generating responses, the system maps LLM output back to source chunks and generates citations with confidence scores based on retrieval relevance and semantic similarity. Citations include document metadata, position information, and optional direct quotes for verification.","intents":["I need to generate AI responses with accurate citations to source documents for transparency and verification","I want to track confidence in citations based on retrieval relevance and semantic similarity","I need to provide users with direct links or quotes from source documents to verify AI-generated claims"],"best_for":["teams building trustworthy AI systems requiring source attribution","organizations in regulated industries (healthcare, finance, legal) requiring audit trails","developers implementing fact-checking or verification systems"],"limitations":["Citation accuracy depends on retrieval quality; poor retrieval leads to incorrect citations","Mapping LLM output to source chunks is heuristic-based; complex paraphrasing may break citation links","Confidence scoring is relative; absolute confidence values should not be interpreted as probability"],"requires":["Position metadata preserved through parsing and chunking pipeline","Retrieval system that returns source chunk identifiers","Citation mapping logic (semantic similarity or token overlap)","Document metadata (title, author, date, URL)"],"input_types":["LLM response text","retrieved source chunks with position metadata","document metadata"],"output_types":["response text with inline citation markers","citation list with source document, position, confidence score","optional: direct quotes from source for verification"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_8","uri":"capability://memory.knowledge.multi.tenant.knowledge.base.management.with.access.control.and.isolation","name":"multi-tenant knowledge base management with access control and isolation","description":"RAGFlow implements tenant-scoped knowledge bases with role-based access control (RBAC) and data isolation at the storage layer. Each tenant has isolated document collections, embeddings, and retrieval indices, with API-level access control enforcing permissions. The system supports tenant-specific LLM model configurations, enabling different teams to use different models or providers while sharing infrastructure.","intents":["I need to manage separate knowledge bases for different teams or customers with strict data isolation","I want to control who can upload, retrieve, or modify documents in each knowledge base","I need to configure different LLM models or providers per tenant for cost or capability optimization"],"best_for":["SaaS platforms serving multiple customers with isolated knowledge bases","enterprises with multiple teams requiring separate document collections","organizations implementing multi-tenant AI systems with strict data governance"],"limitations":["Tenant isolation adds complexity to deployment and scaling; requires careful database schema design","Cross-tenant queries are not supported; each query is scoped to a single tenant","Tenant-specific configurations (models, chunking strategies) require per-tenant tuning"],"requires":["Multi-tenant database schema with tenant_id foreign keys","RBAC system with user roles and permissions","Tenant-scoped API authentication (API keys, OAuth)","Isolated storage for embeddings and indices per tenant"],"input_types":["tenant identifier (from authentication context)","user role and permissions","document upload or query request"],"output_types":["tenant-scoped retrieval results","access control decision (allow/deny)","tenant-specific metadata"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__cap_9","uri":"capability://text.generation.language.streaming.response.generation.with.token.level.control.and.cancellation","name":"streaming response generation with token-level control and cancellation","description":"RAGFlow supports streaming LLM responses with token-level control, enabling real-time response delivery and user cancellation. The system streams tokens from the LLM provider while maintaining retrieval context and citation information, allowing clients to display responses incrementally. Streaming can be cancelled mid-generation, and the system gracefully handles provider disconnections or timeouts.","intents":["I need to deliver LLM responses in real-time without waiting for full generation","I want users to be able to cancel long-running generations to save latency and cost","I need to maintain citation and context information while streaming responses"],"best_for":["web and mobile applications requiring responsive user interfaces","teams building conversational AI with real-time interaction","developers optimizing for perceived latency and user experience"],"limitations":["Streaming adds complexity to error handling; partial responses may be incomplete if generation fails","Citation generation is delayed until full response is available; cannot cite partial responses","Streaming requires compatible LLM provider (most modern providers support it)"],"requires":["LLM provider with streaming API support","WebSocket or Server-Sent Events (SSE) for client-server streaming","Client-side streaming response handler","Timeout and cancellation handling"],"input_types":["prompt or messages","streaming configuration (chunk size, timeout)"],"output_types":["token stream (via WebSocket or SSE)","final response with citations (after generation completes)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-infiniflow--ragflow__headline","uri":"capability://data.processing.analysis.retrieval.augmented.generation.rag.engine.with.agentic.capabilities","name":"retrieval-augmented generation (rag) engine with agentic capabilities","description":"RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that integrates advanced document understanding with agentic workflows, enabling the creation of high-fidelity AI systems that leverage contextual grounding and explainability.","intents":["best RAG engine","RAG framework for document understanding","open-source RAG solutions","RAG for AI workflows","best retrieval-augmented generation tools"],"best_for":["developers needing document processing","teams building AI workflows"],"limitations":[],"requires":[],"input_types":["documents in various formats"],"output_types":["contextual AI responses","structured data outputs"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Docker for containerized deployment","Optional: Tesseract or similar OCR engine for vision processing","Sufficient disk space for document staging and processing artifacts","Embedding model configured (local or API-based)","Document structure metadata from parsing stage","Configuration parameters for chunk size, overlap, and strategy selection","Connector implementation for target data source","Source system credentials and API access","State storage for tracking sync progress and change detection"],"failure_modes":["OCR accuracy depends on image quality; degraded scans may require preprocessing","Complex nested table structures may require manual post-processing in edge cases","Vision processing adds latency (~500ms-2s per document depending on page count)","Semantic chunking requires embedding computation, adding ~100-300ms per document","Layout-aware chunking may fail on malformed or non-standard document structures","Chunk overlap configuration requires tuning per use case; no universal optimal setting","Change detection accuracy depends on source system capabilities; some sources may not provide reliable change logs","Incremental sync requires state tracking (last sync timestamp, checksums); state corruption can cause missed updates","Connector implementation is source-specific; new sources require custom connector development","Sandbox overhead adds latency (~100-500ms per execution depending on code complexity)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8778359553210454,"quality":0.5,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-04-22T08:01:50.815Z","last_commit":"2026-04-22T07:59:41Z"},"community":{"stars":78708,"forks":8892,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=infiniflow--ragflow","compare_url":"https://unfragile.ai/compare?artifact=infiniflow--ragflow"}},"signature":"yurNCXCMGdDT6L12fxrGK2qBhu1gzk5q8ValE4KhNQh87QkPjnq81nv414JbwJTMGboP5tFiYrRvSfBc2dwDBw==","signedAt":"2026-06-20T17:37:25.759Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/infiniflow--ragflow","artifact":"https://unfragile.ai/infiniflow--ragflow","verify":"https://unfragile.ai/api/v1/verify?slug=infiniflow--ragflow","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"}}