{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ragflow","slug":"ragflow","name":"RAGFlow","type":"repo","url":"https://github.com/infiniflow/ragflow","page_url":"https://unfragile.ai/ragflow","categories":["rag-knowledge","documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ragflow__cap_0","uri":"capability://data.processing.analysis.template.based.intelligent.document.parsing.with.layout.aware.chunking","name":"template-based intelligent document parsing with layout-aware chunking","description":"RAGFlow implements a multi-strategy document parsing pipeline that uses configurable templates to understand document structure (headers, tables, lists, images) before chunking. The system combines OCR and layout recognition (vision processing) to preserve semantic boundaries, then applies intelligent chunking methods (recursive, sliding window, semantic) that respect document structure rather than naive token splitting. This approach maintains content coherence and enables accurate citation mapping back to source documents.","intents":["Parse complex PDFs with mixed layouts (tables, figures, text) while preserving structural relationships","Extract knowledge from unstructured documents without losing semantic context at chunk boundaries","Generate grounded citations that map LLM responses back to exact source locations","Handle documents in multiple formats (PDF, Word, images) with consistent quality"],"best_for":["Enterprise teams processing regulatory documents, research papers, or technical manuals","Organizations requiring audit trails and citation accuracy for compliance","Builders creating domain-specific RAG systems where document structure matters"],"limitations":["Template configuration requires domain expertise — generic templates may miss industry-specific layouts","OCR accuracy depends on document quality; scanned PDFs with poor resolution degrade parsing","Vision processing adds ~500ms-2s per document depending on page count and image density","No built-in support for handwritten content or non-standard document formats"],"requires":["Python 3.9+","Document processing dependencies (PyPDF2, python-docx, or equivalent)","Optional: GPU for accelerated OCR (CUDA 11.8+ recommended)","Storage for parsed document metadata and embeddings"],"input_types":["PDF files","Microsoft Word documents (.docx)","Images (PNG, JPG, TIFF)","Plain text files","Markdown documents"],"output_types":["Structured chunks with metadata (source location, page number, section hierarchy)","Embedding vectors (768-1536 dimensions depending on model)","Citation mappings (chunk → source document location)","Parsed document tree with semantic structure"],"categories":["data-processing-analysis","document-parsing","vision-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_1","uri":"capability://search.retrieval.hybrid.multi.tier.retrieval.with.semantic.and.keyword.search.fusion","name":"hybrid multi-tier retrieval with semantic and keyword search fusion","description":"RAGFlow implements a query processing pipeline that executes both semantic (embedding-based) and keyword (BM25/TF-IDF) retrieval in parallel, then applies learned re-ranking to fuse results. The system supports multiple recall strategies (dense retrieval, sparse retrieval, hybrid) with configurable weights, and includes a reranking layer that scores candidates using cross-encoder models or LLM-based scoring. This multi-tier approach captures both semantic similarity and lexical relevance, improving recall for diverse query types.","intents":["Retrieve relevant context for queries that mix semantic and keyword-heavy requirements","Improve recall on technical queries where exact terminology matters alongside semantic meaning","Reduce hallucinations by ensuring retrieved context is both semantically and lexically relevant","Support multi-language retrieval with language-agnostic semantic search"],"best_for":["Teams building RAG systems for technical documentation, legal contracts, or scientific papers","Applications requiring high recall (>90%) where missing relevant context is costly","Multi-language systems where keyword search alone is insufficient"],"limitations":["Parallel execution of semantic + keyword search adds ~200-500ms latency per query","Re-ranking with cross-encoders adds another ~100-300ms depending on candidate set size","Requires tuning of semantic/keyword weight balance per domain — no one-size-fits-all configuration","Embedding quality directly impacts semantic retrieval; poor embeddings degrade hybrid results"],"requires":["Vector database (Milvus, Weaviate, Pinecone, or compatible)","Keyword search backend (Elasticsearch, BM25 index, or equivalent)","Embedding model (768+ dimensional, e.g., OpenAI text-embedding-3-small, Sentence Transformers)","Optional: Cross-encoder model for re-ranking (adds ~50MB-500MB memory)"],"input_types":["Natural language queries (text)","Structured queries with filters (metadata-aware search)","Multi-turn conversation context"],"output_types":["Ranked list of document chunks with relevance scores","Hybrid scores combining semantic and keyword components","Citation metadata (source document, page, section)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_10","uri":"capability://code.generation.editing.sandbox.code.execution.for.safe.tool.use.and.custom.logic","name":"sandbox code execution for safe tool use and custom logic","description":"RAGFlow includes a Sandbox Code Executor that safely executes Python code within isolated environments, enabling agents to run custom logic, data transformations, and computations without risking the main system. The sandbox enforces resource limits (CPU, memory, execution time) and restricts access to dangerous operations (file system, network). This capability integrates with the tool calling system, allowing agents to execute code as a tool with automatic error handling and output capture.","intents":["Enable agents to execute custom Python code for data transformation or computation","Safely run user-provided code without risking system stability or security","Support complex workflows requiring conditional logic or mathematical computations","Debug and test code snippets within the RAGFlow environment"],"best_for":["Agentic systems requiring custom computation or data transformation capabilities","Educational platforms teaching AI/ML where safe code execution is critical","Teams building domain-specific RAG systems with custom business logic"],"limitations":["Sandbox overhead adds ~500ms-2s per code execution due to isolation setup","Resource limits (CPU, memory, time) may be too restrictive for compute-heavy operations","No access to external libraries beyond Python standard library — requires pre-installation","Debugging sandboxed code is difficult; error messages may be limited","Network access is restricted — cannot call external APIs from sandboxed code"],"requires":["Python 3.9+ runtime","Sandbox implementation (e.g., E2B, Replit, or custom Docker-based sandbox)","Resource limits configuration (CPU, memory, execution time)","Security policy for allowed operations"],"input_types":["Python code as string","Input variables (dictionaries, lists, strings)","Execution context (environment variables, imports)"],"output_types":["Code execution result (return value or printed output)","Execution metadata (runtime, memory used, status)","Error messages and stack traces"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_11","uri":"capability://automation.workflow.admin.service.and.cli.for.system.configuration.and.operations","name":"admin service and cli for system configuration and operations","description":"RAGFlow provides an Admin Service and CLI tools for system-level operations: user and tenant management, model configuration, system health monitoring, database migrations, and backup/restore. The Admin CLI enables operators to configure RAGFlow without accessing the web UI, supporting automation and infrastructure-as-code workflows. The Admin Service exposes endpoints for programmatic system management, enabling integration with external admin dashboards or orchestration platforms.","intents":["Manage users, tenants, and access control in multi-tenant deployments","Configure LLM providers and models at the system level","Monitor system health, performance, and resource usage","Automate RAGFlow deployment and configuration via CLI or API"],"best_for":["DevOps teams managing RAGFlow deployments in production environments","SaaS platforms requiring tenant and user management at scale","Organizations with infrastructure-as-code practices"],"limitations":["CLI requires direct access to RAGFlow backend — not suitable for remote administration without VPN","Admin operations (user creation, model configuration) may require service restart","No built-in audit logging for admin actions — requires external logging integration","Limited role-based access control — all admin operations require full admin privileges"],"requires":["Direct access to RAGFlow backend (SSH, Docker exec, or local process)","Python 3.9+ for CLI usage","Database access for migrations and backups","Appropriate file system permissions for configuration and data directories"],"input_types":["CLI commands and arguments","Configuration files (TOML, YAML)","Admin API requests (JSON)"],"output_types":["CLI output (status messages, tables, logs)","Configuration updates (applied to system)","Health metrics and monitoring data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_12","uri":"capability://text.generation.language.internationalization.system.with.multi.language.ui.support","name":"internationalization system with multi-language ui support","description":"RAGFlow implements a comprehensive Internationalization (i18n) System that supports 12+ languages (English, Chinese, Japanese, Korean, Spanish, French, German, Italian, Portuguese, Russian, Vietnamese, Indonesian, Turkish, Arabic) through a locale-based translation system. The frontend UI automatically detects user language preferences and loads appropriate translation files. The system is extensible for adding new languages without code changes, using standard i18n patterns (locale files, translation keys, pluralization rules).","intents":["Provide localized UI for global users without maintaining separate codebases","Support non-English speaking teams building RAG systems","Automatically detect and apply user language preferences"],"best_for":["Global SaaS platforms serving international users","Organizations with multilingual teams"],"limitations":["LLM responses are not automatically translated — requires separate translation service","Adding new languages requires translation of all UI strings","Right-to-left (RTL) languages require additional UI layout adjustments","Translation quality depends on translator expertise; machine translation may be inaccurate"],"requires":["Web browser with locale detection support","Translation files for target languages (JSON or YAML format)"],"input_types":["User language preference (browser locale, user settings)","Translation keys (UI component strings)"],"output_types":["Localized UI text","Locale-specific formatting (dates, numbers, currency)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_13","uri":"capability://automation.workflow.visual.theming.system.with.customizable.ui.components","name":"visual theming system with customizable ui components","description":"RAGFlow includes a Theming System that enables customization of UI appearance through configurable color schemes, typography, and component styles. The system supports light and dark themes with automatic switching based on user preferences or system settings. Theme configuration is stored as JSON/YAML, enabling white-label deployments where SaaS customers can customize the UI to match their brand. The UI Component Architecture uses a design system approach with reusable, themeable components.","intents":["Customize RAGFlow UI to match organizational branding in white-label deployments","Support user preferences for light/dark themes","Maintain consistent UI design across all pages and components"],"best_for":["SaaS platforms offering white-label RAG solutions","Organizations with strict brand guidelines"],"limitations":["Theme customization is limited to predefined variables — deep UI restructuring requires code changes","Custom components may not respect theme settings without additional styling","Theme switching may cause brief visual flicker during page load"],"requires":["Web browser supporting CSS custom properties (CSS variables)","Theme configuration file (JSON or YAML)"],"input_types":["Theme configuration (colors, fonts, spacing)","User theme preference (light/dark)"],"output_types":["Styled UI components","CSS custom properties applied to page"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_2","uri":"capability://automation.workflow.visual.pipeline.editor.with.canvas.based.workflow.composition","name":"visual pipeline editor with canvas-based workflow composition","description":"RAGFlow provides a web-based Canvas Engine that allows users to compose RAG and agentic workflows by dragging components onto a visual canvas and connecting them with data flow edges. The system includes a DSL (Domain-Specific Language) that translates visual workflows into executable task graphs, with built-in components for document ingestion, retrieval, LLM calling, tool use, and response generation. The Canvas API manages workflow state, variable passing between components, and streaming execution with real-time progress updates.","intents":["Build RAG pipelines without writing code by visually connecting document processing, retrieval, and LLM components","Compose agentic workflows with ReAct loops, tool calling, and memory management through a visual interface","Debug workflows by inspecting intermediate outputs and variable states at each pipeline step","Share and version control workflows as JSON/YAML configurations"],"best_for":["Non-technical domain experts (business analysts, product managers) building RAG systems","Teams prototyping multiple workflow variations quickly without engineering overhead","Organizations needing visual audit trails of AI pipeline logic for compliance"],"limitations":["Complex conditional logic and loops are harder to express visually than in code","Custom component development still requires Python/JavaScript coding","Large workflows (>50 components) become visually cluttered and harder to navigate","No built-in version control — requires external Git integration for workflow history"],"requires":["Web browser (Chrome, Firefox, Safari, Edge — modern versions)","RAGFlow backend service running (Docker or self-hosted)","API access to LLM providers (OpenAI, Anthropic, etc.) for LLM components"],"input_types":["Visual component selections and connections","Configuration JSON/YAML for workflow definition","Component parameter inputs (text, numbers, dropdowns)"],"output_types":["Executable workflow definition (JSON/YAML)","Streaming execution logs with component outputs","Final pipeline result (text, structured data, or file)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.unified.provider.abstraction","name":"multi-provider llm integration with unified provider abstraction","description":"RAGFlow abstracts LLM provider differences (OpenAI, Anthropic, Ollama, local models) behind a unified LLMBundle interface that handles model selection, API key management, error handling, and retry logic. The system supports tenant-level model configuration, allowing different users or teams to use different LLM providers without code changes. Provider implementations handle format translation (e.g., converting tool schemas to provider-specific formats), streaming response handling, and token counting for cost estimation.","intents":["Switch between LLM providers (OpenAI → Anthropic → Ollama) without modifying pipeline code","Support multi-tenant deployments where each tenant configures their own LLM provider and API keys","Implement fallback logic (e.g., use Ollama locally if OpenAI API is unavailable)","Track token usage and costs across different LLM providers for billing"],"best_for":["SaaS platforms offering RAG as a service to multiple customers with different LLM preferences","Enterprise deployments requiring on-premises LLM options (Ollama, vLLM) alongside cloud providers","Cost-conscious teams wanting to compare LLM providers without rewriting pipelines"],"limitations":["Provider-specific features (vision, function calling schemas) may not be fully abstracted — some code paths are provider-specific","Error handling varies by provider; timeout behavior, rate limiting, and error messages differ","Token counting is approximate for non-OpenAI models; actual usage may vary","Streaming response handling adds ~50-100ms latency overhead due to format translation"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Network access to LLM provider endpoints (or local Ollama instance)","Python 3.9+ with requests/httpx library for HTTP calls"],"input_types":["Prompt text with optional system message","Tool/function definitions (JSON schema format)","Chat history (list of messages with roles)"],"output_types":["Generated text response","Tool/function calls with arguments","Token usage metadata (input tokens, output tokens, total cost)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_4","uri":"capability://tool.use.integration.schema.based.function.calling.with.provider.native.format.translation","name":"schema-based function calling with provider-native format translation","description":"RAGFlow supports tool calling (function use) through a schema-based system that defines tools as JSON schemas, then translates them to provider-specific formats (OpenAI's function_calling, Anthropic's tool_use, etc.). The system includes a Tool Calling and Function Use layer that manages tool definitions, validates LLM-generated tool calls against schemas, and executes tools with error handling. Built-in tools include web search, code execution, and knowledge base retrieval; custom tools can be registered via the API.","intents":["Enable LLMs to call external tools (APIs, databases, code execution) with schema validation","Support agentic workflows where LLMs decide which tools to use and iterate based on results","Translate tool schemas to provider-specific formats (OpenAI vs Anthropic) automatically","Validate LLM-generated tool calls before execution to prevent malformed requests"],"best_for":["Agentic RAG systems where LLMs need to decide between retrieval, web search, and code execution","Multi-provider deployments where tool schemas must work across OpenAI, Anthropic, and other providers","Teams building complex workflows with conditional tool use and error recovery"],"limitations":["Tool schema translation is lossy — some provider-specific features (e.g., Anthropic's input_schema nesting) may not map perfectly","Tool execution errors are not automatically recovered; agents must handle failures via retry logic","No built-in sandboxing for code execution tools — requires external sandbox (e.g., E2B, Replit) for safety","Tool call validation adds ~50-100ms latency per tool invocation"],"requires":["LLM provider supporting tool calling (OpenAI, Anthropic, Ollama with function calling support)","Tool definitions as JSON schemas (JSON Schema Draft 7 or later)","Python 3.9+ with jsonschema library for validation"],"input_types":["Tool definitions (JSON schema format)","LLM-generated tool calls (provider-specific format)","Tool input arguments (JSON)"],"output_types":["Tool execution results (JSON or text)","Tool call validation errors","Execution status and metadata"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_5","uri":"capability://planning.reasoning.agentic.react.loop.with.memory.and.tool.use.orchestration","name":"agentic react loop with memory and tool use orchestration","description":"RAGFlow implements a ReAct (Reasoning + Acting) loop that orchestrates LLM reasoning, tool selection, execution, and observation cycles. The system manages agent state (current goal, tool history, observations), integrates with the memory system for context persistence, and handles tool execution with error recovery. The Canvas Engine provides visual composition of ReAct workflows, while the Agent API enables programmatic agent definition with custom reasoning strategies.","intents":["Build autonomous agents that reason about tasks, select appropriate tools, and iterate based on observations","Maintain agent memory across multiple turns (short-term conversation context, long-term knowledge)","Compose complex multi-step workflows where agents decide which tools to use and when to stop","Debug agent reasoning by inspecting thought processes and tool call decisions"],"best_for":["Teams building autonomous AI assistants that need to reason and act over multiple turns","Complex RAG systems where agents must decide between retrieval, web search, and computation","Research and experimentation with agentic patterns (ReAct, Chain-of-Thought, etc.)"],"limitations":["ReAct loops can be expensive — each reasoning step calls the LLM, multiplying token costs","Agent behavior is non-deterministic; same input may produce different tool sequences","No built-in convergence guarantees — agents may loop indefinitely without proper stopping conditions","Debugging agent decisions requires detailed logging; production visibility is challenging"],"requires":["LLM provider with tool calling support (OpenAI, Anthropic, Ollama)","Memory system configured (short-term conversation storage, long-term knowledge base)","Tool definitions for agent use (retrieval, web search, code execution, etc.)","Python 3.9+ for programmatic agent definition"],"input_types":["User query or task description","Agent configuration (model, tools, memory settings)","Tool definitions and execution handlers"],"output_types":["Final agent response (text or structured data)","Tool call history and reasoning trace","Memory updates (conversation context, learned facts)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_6","uri":"capability://memory.knowledge.multi.type.memory.system.with.conversation.and.knowledge.persistence","name":"multi-type memory system with conversation and knowledge persistence","description":"RAGFlow implements a Memory System with multiple storage types: short-term conversation memory (chat history), long-term knowledge memory (facts extracted from conversations), and session memory (user-specific context). The system provides Memory Management APIs and UI for viewing, editing, and clearing memory, with configurable retention policies and storage backends (database, vector store). Memory is automatically integrated into LLM context during retrieval and generation, enabling personalized responses and knowledge accumulation across conversations.","intents":["Maintain conversation history across multiple turns without exceeding LLM context limits","Extract and store facts from conversations for long-term knowledge accumulation","Personalize LLM responses based on user history and preferences","Implement memory management policies (e.g., forget after 30 days, summarize old conversations)"],"best_for":["Multi-turn conversational AI systems requiring context persistence","Personalized RAG systems where user history influences retrieval and generation","Applications with regulatory requirements for conversation logging and audit trails"],"limitations":["Memory retrieval adds latency (~100-200ms) to each query due to vector search","Long-term memory extraction requires LLM calls, increasing costs","Memory storage grows unbounded without retention policies — requires manual cleanup","Conflicting or outdated facts in memory can mislead LLM responses"],"requires":["Database for conversation history storage (PostgreSQL, MySQL, MongoDB, etc.)","Vector store for semantic memory retrieval (Milvus, Weaviate, Pinecone, etc.)","LLM provider for fact extraction and memory summarization","Python 3.9+ with SQLAlchemy or equivalent ORM"],"input_types":["User messages and LLM responses (conversation turns)","Memory queries (semantic search over facts)","Memory management commands (clear, summarize, export)"],"output_types":["Retrieved memory context (facts, conversation snippets)","Memory metadata (creation time, relevance score, source)","Memory management reports (storage usage, retention status)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_7","uri":"capability://data.processing.analysis.data.source.connectors.with.unified.ingestion.pipeline","name":"data source connectors with unified ingestion pipeline","description":"RAGFlow provides Data Source Connectors that enable ingestion from multiple sources (cloud storage, databases, APIs, web) through a unified pipeline. Each connector handles source-specific authentication, pagination, and format translation, then feeds documents into the parsing and chunking pipeline. The system includes built-in connectors for S3, Azure Blob, Google Drive, Notion, Salesforce, and others, with extensibility for custom sources via the Connector API.","intents":["Ingest documents from multiple cloud storage providers (S3, Azure, Google Drive) without custom code","Sync knowledge bases from SaaS platforms (Notion, Salesforce, Jira) with automatic updates","Build RAG systems over databases by querying and indexing structured data","Create multi-source RAG systems combining documents, databases, and APIs"],"best_for":["Enterprise teams with documents scattered across multiple cloud platforms","SaaS integrations where knowledge bases need to sync with external platforms","Organizations building knowledge bases over structured data (databases, APIs)"],"limitations":["Connector setup requires authentication credentials (API keys, OAuth tokens) for each source","Incremental sync is not supported for all connectors — full re-indexing may be required","Rate limiting on source APIs can slow ingestion for large datasets","Format translation is lossy — some source-specific metadata may be lost"],"requires":["Authentication credentials for data sources (API keys, OAuth tokens, database credentials)","Network access to data sources","Storage for ingested documents and metadata","Python 3.9+ with source-specific SDKs (boto3 for S3, azure-storage-blob, etc.)"],"input_types":["Data source configuration (type, credentials, query parameters)","Connector-specific settings (bucket names, database queries, API endpoints)"],"output_types":["Ingested documents with metadata (source, timestamp, format)","Ingestion status and error logs","Indexed chunks ready for retrieval"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_8","uri":"capability://memory.knowledge.graphrag.and.raptor.hierarchical.knowledge.graph.construction","name":"graphrag and raptor hierarchical knowledge graph construction","description":"RAGFlow implements advanced features for building hierarchical knowledge representations: GraphRAG constructs entity-relationship graphs from documents, enabling graph-based retrieval and reasoning, while RAPTOR builds recursive abstraction hierarchies that summarize documents at multiple levels of granularity. These features enable retrieval of both detailed facts and high-level summaries, improving context quality for complex queries. The system integrates graph construction with the parsing pipeline, automatically extracting entities and relationships during document processing.","intents":["Build knowledge graphs from unstructured documents for entity-aware retrieval and reasoning","Create hierarchical document summaries at multiple abstraction levels for flexible context retrieval","Answer complex queries that require reasoning over entity relationships and document structure","Improve retrieval quality for domain-specific knowledge bases with rich semantic structure"],"best_for":["Knowledge-intensive domains (legal, medical, scientific) where entity relationships matter","Complex document collections requiring multi-level reasoning (research papers, technical manuals)","Teams building specialized RAG systems where generic retrieval is insufficient"],"limitations":["Graph construction requires entity extraction and relationship inference — adds significant processing time (2-5x slower than basic chunking)","Graph quality depends on LLM entity extraction accuracy; poor extraction degrades graph structure","RAPTOR hierarchies require multiple LLM calls for summarization at each level, increasing costs","Graph storage and traversal add complexity; requires graph database (Neo4j) or specialized vector storage","No built-in graph visualization — requires external tools for graph exploration"],"requires":["LLM provider for entity extraction and relationship inference","Graph database (Neo4j) or vector store supporting graph operations","Python 3.9+ with graph processing libraries (NetworkX, PyTorch Geometric, etc.)","Significant computational resources for large-scale graph construction"],"input_types":["Documents (text, PDF, etc.)","Entity and relationship extraction prompts","Summarization prompts for RAPTOR hierarchies"],"output_types":["Knowledge graphs (nodes = entities, edges = relationships)","RAPTOR hierarchies (multi-level summaries with abstraction levels)","Graph-based retrieval results (entity-centric context)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__cap_9","uri":"capability://tool.use.integration.rest.api.and.python.sdk.with.authentication.and.multi.tenant.support","name":"rest api and python sdk with authentication and multi-tenant support","description":"RAGFlow exposes a comprehensive REST API covering dataset management, document ingestion, chat/conversation, agent execution, and memory management. The API includes built-in authentication (API keys, OAuth), tenant isolation for multi-tenant deployments, and rate limiting. A Python SDK wraps the REST API with type hints and convenience methods, enabling programmatic access to all RAGFlow features. The API Architecture supports both synchronous and asynchronous operations, with streaming support for long-running tasks (document processing, agent execution).","intents":["Integrate RAGFlow into existing applications via REST API without embedding the full framework","Build multi-tenant SaaS platforms where each customer has isolated knowledge bases and configurations","Automate document ingestion and RAG pipeline execution via Python scripts or CI/CD workflows","Monitor RAG system health and performance via System Status and Health Monitoring endpoints"],"best_for":["Teams integrating RAGFlow into existing backend services or microservices architectures","SaaS platforms offering RAG as a service with multi-tenant isolation requirements","DevOps teams automating RAG pipeline deployment and monitoring"],"limitations":["REST API adds network latency (~50-200ms per request) compared to in-process library calls","Rate limiting may throttle high-volume ingestion or query workloads","API versioning requires careful management for backward compatibility","Streaming responses require WebSocket or Server-Sent Events support; some clients may not support these"],"requires":["RAGFlow backend service running (Docker or self-hosted)","API key or OAuth credentials for authentication","Python 3.9+ for SDK usage (optional; REST API works with any HTTP client)","Network access to RAGFlow API endpoint"],"input_types":["HTTP requests (JSON payloads for REST API)","Python objects and method calls (SDK)","File uploads (documents, configuration files)"],"output_types":["JSON responses (datasets, documents, chat results, agent outputs)","Streaming responses (document processing progress, agent reasoning traces)","File downloads (exported datasets, conversation logs)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ragflow__headline","uri":"capability://data.processing.analysis.open.source.rag.engine.for.document.understanding","name":"open-source rag engine for document understanding","description":"RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine designed for deep document understanding, enabling intelligent parsing and multi-recall retrieval through a visual pipeline editor.","intents":["best RAG framework","RAG engine for document parsing","open-source solution for document understanding","RAG tool for AI workflows","RAGFlow alternatives for document processing"],"best_for":["developers needing document parsing solutions","teams building AI applications"],"limitations":[],"requires":[],"input_types":["PDF","DOCX","Excel","PPT"],"output_types":["parsed data","AI-generated responses"],"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+","Document processing dependencies (PyPDF2, python-docx, or equivalent)","Optional: GPU for accelerated OCR (CUDA 11.8+ recommended)","Storage for parsed document metadata and embeddings","Vector database (Milvus, Weaviate, Pinecone, or compatible)","Keyword search backend (Elasticsearch, BM25 index, or equivalent)","Embedding model (768+ dimensional, e.g., OpenAI text-embedding-3-small, Sentence Transformers)","Optional: Cross-encoder model for re-ranking (adds ~50MB-500MB memory)","Python 3.9+ runtime","Sandbox implementation (e.g., E2B, Replit, or custom Docker-based sandbox)"],"failure_modes":["Template configuration requires domain expertise — generic templates may miss industry-specific layouts","OCR accuracy depends on document quality; scanned PDFs with poor resolution degrade parsing","Vision processing adds ~500ms-2s per document depending on page count and image density","No built-in support for handwritten content or non-standard document formats","Parallel execution of semantic + keyword search adds ~200-500ms latency per query","Re-ranking with cross-encoders adds another ~100-300ms depending on candidate set size","Requires tuning of semantic/keyword weight balance per domain — no one-size-fits-all configuration","Embedding quality directly impacts semantic retrieval; poor embeddings degrade hybrid results","Sandbox overhead adds ~500ms-2s per code execution due to isolation setup","Resource limits (CPU, memory, time) may be too restrictive for compute-heavy operations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ragflow","compare_url":"https://unfragile.ai/compare?artifact=ragflow"}},"signature":"9l3L8qBz6G3nqCNuBIoP6RHuPVhvq5eeqRWwvca1XfmJh6Ce6KJff0m2qJH/lXIjvJ2bSQdhPc68fB43qZGGDw==","signedAt":"2026-06-20T18:15:37.020Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ragflow","artifact":"https://unfragile.ai/ragflow","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}