{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-tencent--weknora","slug":"tencent--weknora","name":"WeKnora","type":"repo","url":"https://weknora.weixin.qq.com","page_url":"https://unfragile.ai/tencent--weknora","categories":["rag-knowledge"],"tags":["agent","agentic","ai","chatbot","embeddings","evaluation","generative-ai","golang","knowledge-base","llm","multi-tenant","multimodel","ollama","openai","question-answering","rag","reranking","semantic-search","vector-search","wiki"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-tencent--weknora__cap_0","uri":"capability://data.processing.analysis.multi.format.document.ingestion.and.chunking.with.semantic.preservation","name":"multi-format document ingestion and chunking with semantic preservation","description":"Accepts heterogeneous document types (PDF, Word, images, structured data) and processes them through a document upload pipeline that extracts content, applies intelligent chunking strategies, and preserves semantic boundaries. Uses event-driven architecture with async task processing via Asynq to handle large-scale document ingestion without blocking the main service, storing chunks in a vector-indexed database with metadata tags for retrieval.","intents":["I need to upload diverse document formats and have them automatically indexed for semantic search","I want to ensure document chunks preserve semantic meaning rather than being split arbitrarily","I need to process large document batches asynchronously without blocking user interactions"],"best_for":["Enterprise teams building knowledge bases from mixed document sources","Organizations migrating from keyword-based search to semantic retrieval","Teams requiring async document processing at scale"],"limitations":["Multimodal document processing requires explicit configuration per document type","Chunking strategy is configurable but not adaptive — does not automatically adjust chunk size based on document density or domain","Large documents (>100MB) may require manual batching or custom preprocessing"],"requires":["Go 1.18+ (backend runtime)","PostgreSQL or compatible database for chunk metadata storage","Redis for Asynq task queue","Vector database (Milvus, Weaviate, or compatible) for embeddings storage"],"input_types":["PDF files","Microsoft Word documents","Images (with OCR support)","Structured data (JSON, CSV)","Plain text"],"output_types":["Indexed document chunks with embeddings","Chunk metadata (source, position, tags)","Vector embeddings stored in vector database"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_1","uri":"capability://search.retrieval.hybrid.retrieval.with.semantic.and.keyword.search.fusion","name":"hybrid retrieval with semantic and keyword search fusion","description":"Implements a hybrid retrieval strategy combining vector similarity search (semantic) with keyword-based matching, using a configurable reranking engine to fuse results from both approaches. The retrieval pipeline queries the vector database for semantic matches and applies optional reranking (e.g., BM25, cross-encoder models) to surface the most relevant chunks before passing them to the LLM context window.","intents":["I want to retrieve documents using both semantic similarity and exact keyword matches","I need to rerank retrieval results to improve answer quality without increasing latency significantly","I want to configure retrieval behavior per knowledge base (e.g., different reranking strategies)"],"best_for":["Teams building QA systems requiring high precision (legal, medical, technical documentation)","Organizations with domain-specific vocabularies where keyword matching is critical","Builders optimizing retrieval quality without retraining models"],"limitations":["Reranking adds latency (~100-300ms per query depending on reranker model size)","Hybrid retrieval requires tuning fusion weights — no automatic optimization","BM25 reranking requires pre-computed inverted indices, adding storage overhead"],"requires":["Vector database with semantic search capability (Milvus, Weaviate, Pinecone-compatible)","Optional: reranking model endpoint (local or cloud-based cross-encoder)","Configured embedding model (OpenAI, Ollama, or compatible)"],"input_types":["User query (text)","Knowledge base ID","Retrieval configuration (top-k, reranking strategy)"],"output_types":["Ranked list of document chunks with relevance scores","Chunk metadata and source references","Retrieval confidence scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_10","uri":"capability://automation.workflow.async.task.processing.with.asynq.for.background.document.and.embedding.operations","name":"async task processing with asynq for background document and embedding operations","description":"Uses Asynq (Redis-backed task queue) to handle long-running operations asynchronously, including document processing, embedding generation, and knowledge graph construction. Tasks are enqueued with configurable retry policies, priority levels, and deadlines. The system provides task status tracking and allows users to monitor progress without blocking the API.","intents":["I want to upload large documents without waiting for processing to complete","I need to generate embeddings in the background without blocking chat interactions","I want to track the status of long-running operations (document indexing, graph construction)"],"best_for":["Teams handling high-volume document ingestion","Organizations requiring responsive APIs even during heavy processing","Builders needing reliable task execution with retry guarantees"],"limitations":["Asynq requires Redis — adds operational complexity","Task execution order is not guaranteed (FIFO within priority level only)","No built-in task deduplication — duplicate uploads may create duplicate tasks","Task status is not persisted beyond Redis TTL — long-term audit trails require custom logging"],"requires":["Redis 5.0+ for Asynq task queue","Asynq client library (Go)","Task handler implementations for each operation type"],"input_types":["Task type (document-process, embed, graph-construct)","Task payload (document ID, parameters)","Priority level (low, medium, high)"],"output_types":["Task ID (for status tracking)","Task status (queued, processing, completed, failed)","Task result (processed document, embeddings, graph)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_11","uri":"capability://automation.workflow.event.driven.chat.pipeline.with.streaming.response.support","name":"event-driven chat pipeline with streaming response support","description":"Implements an event-driven architecture for chat interactions where user messages trigger events that flow through handlers (retrieval, reasoning, response generation). The pipeline supports streaming responses, allowing partial results to be sent to the client as they become available. Events are processed sequentially within a session to maintain conversation order.","intents":["I want to stream LLM responses to users in real-time rather than waiting for full completion","I need to process chat events through multiple stages (retrieval, reasoning, generation) reliably","I want to handle chat interactions without blocking other operations"],"best_for":["Teams building real-time chat interfaces with streaming responses","Organizations requiring reliable event processing for chat operations","Builders needing transparent event flow for debugging and monitoring"],"limitations":["Event ordering is per-session only — concurrent sessions may have interleaved events","Streaming adds complexity to error handling — partial responses may be sent before errors occur","No built-in event replay — failed events are not automatically retried","Streaming requires WebSocket or Server-Sent Events (SSE) — not compatible with simple HTTP polling"],"requires":["WebSocket or SSE support in frontend","Event handler implementations for each pipeline stage","Session management for event ordering"],"input_types":["User message (text)","Session ID","Chat mode (QA or Agent)"],"output_types":["Streamed response chunks (text)","Event metadata (stage, timestamp)","Final response with metadata"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_12","uri":"capability://memory.knowledge.configurable.embedding.model.selection.with.multi.provider.support","name":"configurable embedding model selection with multi-provider support","description":"Allows organizations to select and configure embedding models from multiple providers (OpenAI, Ollama, local models) at the knowledge base level. Embeddings are generated during document indexing and stored in the vector database. The system supports model switching with re-embedding of existing documents, and provides fallback mechanisms if the primary provider is unavailable.","intents":["I want to use a specific embedding model for my knowledge base (e.g., local model for privacy)","I need to switch embedding models without losing indexed documents","I want to compare embedding quality across different models"],"best_for":["Organizations with privacy requirements (local embedding models)","Teams optimizing embedding costs (cheaper models vs. quality tradeoff)","Builders experimenting with different embedding strategies"],"limitations":["Switching embedding models requires re-embedding all documents — expensive operation","No automatic embedding quality evaluation — switching decisions are manual","Embedding model compatibility is not validated — incompatible models may cause retrieval failures","Fallback mechanisms are not automatic — require manual configuration"],"requires":["Embedding model endpoint (OpenAI API, Ollama server, or local model)","Vector database supporting the embedding dimension of chosen model","Configuration per knowledge base specifying embedding model"],"input_types":["Document text (for embedding generation)","Embedding model name (OpenAI, Ollama, etc.)","Model parameters (dimension, pooling strategy)"],"output_types":["Embedding vectors (float arrays)","Embedding metadata (model, dimension, timestamp)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_13","uri":"capability://memory.knowledge.tag.based.document.organization.and.hierarchical.filtering","name":"tag-based document organization and hierarchical filtering","description":"Allows documents and chunks to be tagged with custom labels, enabling hierarchical organization and filtering during retrieval. Tags are stored in the database and indexed for fast filtering. Queries can be scoped to specific tags, and retrieval results can be filtered by tag combinations. Tags support hierarchical relationships (parent-child).","intents":["I want to organize documents by category or department","I need to restrict retrieval to specific document subsets (e.g., only Q1 reports)","I want to support hierarchical tag structures (e.g., Product > Features > API)"],"best_for":["Organizations with large knowledge bases requiring content organization","Teams needing fine-grained access control based on document categories","Builders supporting multi-tenant scenarios with per-tenant document filtering"],"limitations":["Tag filtering is applied after retrieval — does not reduce vector search scope","No automatic tag suggestion — tags must be manually assigned","Hierarchical tags require explicit parent-child relationships — no automatic inference","Tag cardinality explosion is possible — no built-in tag management or cleanup"],"requires":["PostgreSQL for tag metadata storage","Tag schema definition (flat or hierarchical)","Retrieval pipeline supporting tag filtering"],"input_types":["Document or chunk ID","Tag labels (strings)","Tag hierarchy (optional parent tag)"],"output_types":["Tagged documents/chunks","Tag metadata (created date, usage count)","Filtered retrieval results by tag"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_14","uri":"capability://data.processing.analysis.evaluation.framework.for.rag.quality.assessment.and.benchmarking","name":"evaluation framework for rag quality assessment and benchmarking","description":"Provides tools to evaluate RAG pipeline quality by measuring retrieval precision/recall, answer relevance, and end-to-end QA accuracy. Supports benchmark datasets and allows comparing performance across different retrieval strategies, embedding models, and LLM configurations. Evaluation results are stored and can be tracked over time.","intents":["I want to measure how well my retrieval pipeline is working","I need to compare different embedding models or retrieval strategies","I want to track RAG quality improvements over time as I update documents"],"best_for":["Teams optimizing RAG systems for production quality","Organizations requiring quantitative metrics for RAG performance","Builders benchmarking different configurations before deployment"],"limitations":["Evaluation requires labeled test sets — no automatic ground truth generation","Metrics are limited to standard RAG metrics (precision, recall, BLEU) — domain-specific metrics require custom implementation","Evaluation is offline — does not measure real-time user satisfaction","No built-in statistical significance testing — results may be noisy"],"requires":["Test dataset with queries and expected answers","Evaluation metrics implementation (precision, recall, BLEU, etc.)","PostgreSQL for storing evaluation results"],"input_types":["Test queries (text)","Expected answers (text or structured)","Configuration to evaluate (embedding model, retrieval strategy)"],"output_types":["Evaluation metrics (precision, recall, BLEU, etc.)","Evaluation report with comparisons","Detailed results per query"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_2","uri":"capability://planning.reasoning.react.agent.driven.reasoning.with.tool.orchestration","name":"react agent-driven reasoning with tool orchestration","description":"Implements a ReAct (Reasoning + Acting) agent engine that decomposes user queries into reasoning steps, selects appropriate tools (web search, knowledge base retrieval, MCP-integrated functions), executes them, and iterates until reaching a conclusion. The agent maintains conversation context across multiple turns, uses dependency injection to wire tools dynamically, and supports both synchronous and streaming responses.","intents":["I want the system to reason through complex questions by breaking them into sub-tasks","I need the agent to decide when to search the web vs. query the knowledge base vs. call external APIs","I want to extend the agent with custom tools without modifying core logic"],"best_for":["Teams building autonomous research assistants or complex QA systems","Organizations integrating multiple data sources (internal KB + web + APIs)","Builders requiring transparent reasoning traces for debugging and auditing"],"limitations":["Agent reasoning adds 2-5 LLM calls per query, increasing latency and token cost vs. simple RAG","Tool selection is LLM-driven and can fail if tool descriptions are ambiguous","No built-in loop-breaking mechanism — requires careful prompt engineering to avoid infinite reasoning cycles","Agent state is session-scoped; no persistent memory across sessions without custom implementation"],"requires":["LLM with function-calling support (OpenAI GPT-4, Claude 3+, or compatible)","Tool registry with schema definitions (JSON Schema format)","Session management backend (PostgreSQL or compatible) for context persistence"],"input_types":["User query (text)","Conversation history (previous turns)","Tool definitions (JSON Schema)"],"output_types":["Final answer (text)","Reasoning trace (intermediate steps and tool calls)","Tool execution results"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_3","uri":"capability://safety.moderation.multi.tenant.knowledge.base.isolation.with.organization.scoped.access.control","name":"multi-tenant knowledge base isolation with organization-scoped access control","description":"Enforces tenant isolation at the database and API layer, where each organization owns isolated knowledge bases, documents, and chat sessions. Access control is enforced via organization IDs in request contexts, with role-based permissions (admin, editor, viewer) managed through a security layer. The architecture uses dependency injection to inject tenant context into service handlers, ensuring no cross-tenant data leakage.","intents":["I need to host multiple organizations' knowledge bases on a single WeKnora instance","I want to ensure strict data isolation between tenants without separate deployments","I need fine-grained access control (who can edit documents, who can view chat history)"],"best_for":["SaaS platforms offering RAG as a service to multiple customers","Enterprise deployments with multiple business units requiring data isolation","Teams building white-label knowledge base solutions"],"limitations":["Tenant context must be passed through every request — no implicit tenant detection","Cross-tenant analytics or aggregation requires custom queries outside the standard API","Role-based access control is coarse-grained (admin/editor/viewer) — no attribute-based policies","Database schema is shared across tenants; no per-tenant schema isolation"],"requires":["Authentication system providing organization ID and user role claims","PostgreSQL with row-level security (RLS) policies or application-level filtering","API gateway or middleware injecting tenant context into requests"],"input_types":["Organization ID (from auth token)","User role (admin, editor, viewer)","Resource ID (knowledge base, document, session)"],"output_types":["Filtered resources scoped to organization","Access control decision (allow/deny)","Audit logs with tenant and user context"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_4","uri":"capability://memory.knowledge.knowledge.base.faq.management.with.automatic.indexing","name":"knowledge base faq management with automatic indexing","description":"Provides a dedicated FAQ subsystem where organizations can define frequently asked questions with curated answers, which are automatically indexed as high-priority chunks in the vector database. FAQs are tagged separately and can be weighted higher during retrieval, ensuring common questions are answered with pre-approved responses. The system supports FAQ versioning and allows marking answers as verified or outdated.","intents":["I want to ensure common questions are answered with pre-approved, high-quality responses","I need to manage FAQ versions and track when answers were last verified","I want FAQs to be prioritized in retrieval without manual prompt engineering"],"best_for":["Customer support teams managing knowledge bases with high-frequency questions","Organizations requiring answer consistency and compliance (legal, medical)","Teams using RAG for internal documentation where certain answers must be authoritative"],"limitations":["FAQ weighting is static — does not adapt based on query patterns or user feedback","No automatic FAQ generation from chat logs — FAQs must be manually created","FAQ versioning is basic (no branching or approval workflows)"],"requires":["Knowledge base created and configured","Vector database with tagging support","PostgreSQL for FAQ metadata storage"],"input_types":["Question (text)","Answer (text)","Tags (optional)","Verification status"],"output_types":["Indexed FAQ chunks with high priority weights","FAQ metadata (created date, last verified, version)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_5","uri":"capability://memory.knowledge.session.based.conversation.context.management.with.multi.turn.memory","name":"session-based conversation context management with multi-turn memory","description":"Manages conversation sessions where each chat maintains a history of user queries and assistant responses, with configurable context window management. Sessions are stored in PostgreSQL with optional compression, and context is passed to the LLM for multi-turn reasoning. The system supports session titles (auto-generated or user-defined), session forking, and context summarization to handle long conversations without exceeding token limits.","intents":["I want the assistant to remember previous questions in the same conversation","I need to manage conversation history without exceeding LLM token limits","I want to fork conversations or create variants without losing the original context"],"best_for":["Teams building conversational AI where multi-turn context is critical","Organizations requiring conversation audit trails and history","Builders optimizing token usage in long-running conversations"],"limitations":["Context window is fixed per LLM model — no automatic context optimization","Session history is not encrypted at rest by default","No built-in conversation summarization — requires external summarization model","Session forking creates data duplication; no deduplication strategy"],"requires":["PostgreSQL for session storage","LLM with context window >= 4K tokens (recommended 8K+)","Optional: summarization model for long conversations"],"input_types":["User query (text)","Session ID","Conversation history (previous turns)"],"output_types":["Assistant response (text)","Updated session history","Session metadata (title, created date, last updated)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_6","uri":"capability://tool.use.integration.mcp.model.context.protocol.tool.integration.with.schema.based.function.calling","name":"mcp (model context protocol) tool integration with schema-based function calling","description":"Integrates with MCP servers to expose external tools and functions as callable capabilities within the agent system. Tools are registered via JSON Schema definitions, and the agent can invoke them during reasoning. The system handles MCP protocol serialization/deserialization, manages tool execution timeouts, and returns results back to the agent for further reasoning.","intents":["I want to extend the agent with custom tools without modifying WeKnora core","I need to call external APIs or services as part of agent reasoning","I want to standardize tool definitions across multiple agents using MCP"],"best_for":["Teams building extensible agent systems with pluggable tools","Organizations integrating with multiple external services (Slack, Jira, databases)","Builders standardizing tool interfaces across heterogeneous systems"],"limitations":["MCP tool execution is synchronous — no built-in support for async tool chains","Tool timeout is fixed; no per-tool timeout configuration","Error handling is basic — tool failures may not gracefully degrade","No built-in tool caching — repeated calls to the same tool are not deduplicated"],"requires":["MCP server running and accessible (local or remote)","Tool definitions in JSON Schema format","Network connectivity to MCP server"],"input_types":["Tool name (string)","Tool arguments (JSON)","MCP server endpoint"],"output_types":["Tool execution result (JSON)","Execution status (success/failure)","Error messages (if applicable)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_7","uri":"capability://search.retrieval.web.search.integration.with.query.time.source.selection","name":"web search integration with query-time source selection","description":"Integrates web search capabilities (via configurable search providers like Google, Bing) into the agent reasoning loop, allowing agents to decide when to search the web vs. query the knowledge base. Search results are ranked and deduplicated before being passed to the LLM. The system supports search result caching to avoid redundant queries.","intents":["I want the agent to search the web for current information not in the knowledge base","I need to decide dynamically whether to use web search or KB retrieval based on the query","I want to cache search results to reduce API calls and latency"],"best_for":["Teams building research assistants requiring current information","Organizations combining internal knowledge with real-time web data","Builders optimizing search costs through intelligent caching"],"limitations":["Web search API calls incur per-query costs (Google, Bing, etc.)","Search result quality depends on query formulation — poor queries yield poor results","No built-in result verification — web results may contain misinformation","Search result caching is simple (TTL-based) — no semantic deduplication"],"requires":["Web search API key (Google Custom Search, Bing Search, or compatible)","Redis for search result caching (optional but recommended)","Network connectivity to search provider"],"input_types":["Search query (text)","Search provider (Google, Bing, etc.)","Number of results (top-k)"],"output_types":["Ranked search results with URLs and snippets","Search metadata (provider, timestamp, cache status)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_8","uri":"capability://image.visual.multimodal.document.processing.with.ocr.and.image.understanding","name":"multimodal document processing with ocr and image understanding","description":"Processes documents containing images and scanned PDFs by extracting text via OCR (Optical Character Recognition) and optionally analyzing images using vision models. Extracted text and image descriptions are indexed alongside document metadata, allowing semantic search across both text and visual content. The system supports configurable OCR engines and vision model backends.","intents":["I want to index scanned PDFs and extract text automatically","I need to understand images in documents (diagrams, charts, screenshots)","I want to search across both text and visual content in my knowledge base"],"best_for":["Organizations with legacy scanned documents or image-heavy content","Teams indexing technical documentation with diagrams and screenshots","Builders requiring comprehensive document understanding beyond text"],"limitations":["OCR accuracy depends on image quality — poor scans may have high error rates","Vision model analysis adds latency (~1-5 seconds per image) and cost","No built-in layout preservation — OCR may lose document structure (tables, columns)","Image descriptions are generated by LLM — quality depends on model capability"],"requires":["OCR engine (Tesseract, cloud-based OCR API, or compatible)","Vision model (GPT-4V, Claude 3 Vision, or compatible) for image understanding","Document processing pipeline configured for multimodal inputs"],"input_types":["PDF files with images","Scanned documents","Images (PNG, JPEG, etc.)","Mixed-media documents"],"output_types":["Extracted text from OCR","Image descriptions (from vision model)","Indexed chunks with multimodal metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tencent--weknora__cap_9","uri":"capability://memory.knowledge.knowledge.graph.and.graphrag.support.for.structured.reasoning","name":"knowledge graph and graphrag support for structured reasoning","description":"Builds and maintains knowledge graphs from indexed documents, representing entities and relationships extracted from text. The system supports graph-based retrieval where queries traverse the graph to find related entities and documents, enabling structured reasoning over interconnected knowledge. Graph construction is async and configurable per knowledge base.","intents":["I want to understand relationships between entities in my knowledge base","I need to answer questions that require traversing multiple document relationships","I want to visualize knowledge structure and dependencies"],"best_for":["Teams building knowledge management systems for complex domains (biomedical, legal)","Organizations requiring structured reasoning over interconnected documents","Builders needing entity-centric retrieval and relationship discovery"],"limitations":["Knowledge graph construction requires entity extraction — accuracy depends on NLP model quality","Graph traversal adds latency to retrieval (~200-500ms per query)","No automatic graph maintenance — requires periodic reindexing to reflect document updates","Graph visualization is not built-in — requires external tools"],"requires":["Graph database (Neo4j, ArangoDB, or compatible)","Entity extraction model (spaCy, transformer-based NER, or LLM-based)","Relationship extraction pipeline"],"input_types":["Document text","Entity definitions (optional)","Relationship types (optional)"],"output_types":["Knowledge graph (nodes and edges)","Entity-centric retrieval results","Relationship paths between entities"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Go 1.18+ (backend runtime)","PostgreSQL or compatible database for chunk metadata storage","Redis for Asynq task queue","Vector database (Milvus, Weaviate, or compatible) for embeddings storage","Vector database with semantic search capability (Milvus, Weaviate, Pinecone-compatible)","Optional: reranking model endpoint (local or cloud-based cross-encoder)","Configured embedding model (OpenAI, Ollama, or compatible)","Redis 5.0+ for Asynq task queue","Asynq client library (Go)","Task handler implementations for each operation type"],"failure_modes":["Multimodal document processing requires explicit configuration per document type","Chunking strategy is configurable but not adaptive — does not automatically adjust chunk size based on document density or domain","Large documents (>100MB) may require manual batching or custom preprocessing","Reranking adds latency (~100-300ms per query depending on reranker model size)","Hybrid retrieval requires tuning fusion weights — no automatic optimization","BM25 reranking requires pre-computed inverted indices, adding storage overhead","Asynq requires Redis — adds operational complexity","Task execution order is not guaranteed (FIFO within priority level only)","No built-in task deduplication — duplicate uploads may create duplicate tasks","Task status is not persisted beyond Redis TTL — long-term audit trails require custom logging","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7032134750295663,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:58:21.998Z","last_commit":"2026-04-30T09:47:40Z"},"community":{"stars":14190,"forks":1719,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tencent--weknora","compare_url":"https://unfragile.ai/compare?artifact=tencent--weknora"}},"signature":"nvYpdKKrjl+bAXEa/oC+0ZX8WoAHWAG7FgrtvqkB2dRCfXU+2oTkGGskWLDrwZsLZQAOM9+x795qWvUhjGiaCw==","signedAt":"2026-06-20T14:06:36.013Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tencent--weknora","artifact":"https://unfragile.ai/tencent--weknora","verify":"https://unfragile.ai/api/v1/verify?slug=tencent--weknora","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"}}