{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-onyx-dot-app--onyx","slug":"onyx-dot-app--onyx","name":"onyx","type":"product","url":"https://onyx.app","page_url":"https://unfragile.ai/onyx-dot-app--onyx","categories":["chatbots-assistants"],"tags":["ai","ai-chat","chatgpt","chatui","enterprise-search","gen-ai","information-retrieval","llm","llm-ui","nextjs","python","rag","self-hosted","vector-search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-onyx-dot-app--onyx__cap_0","uri":"capability://data.processing.analysis.multi.connector.document.indexing.with.unified.schema","name":"multi-connector document indexing with unified schema","description":"Onyx implements a pluggable connector framework that abstracts 20+ data sources (Slack, Google Drive, Confluence, GitHub, etc.) into a unified document ingestion pipeline. Each connector implements a standardized lifecycle (credential validation, document fetching, chunking, metadata extraction) and feeds into a Celery-based background task queue that coordinates with Vespa for full-text and semantic indexing. The system maintains connector state, handles incremental syncs, and manages credential encryption via a centralized credential store.","intents":["Index documents from multiple enterprise data sources into a single searchable knowledge base","Set up automated periodic syncs from Slack, Confluence, Google Drive without manual intervention","Manage connector credentials securely and rotate them without re-indexing","Track indexing progress and handle failures gracefully with retry logic"],"best_for":["Enterprise teams with data spread across Slack, Confluence, Google Workspace, GitHub","Organizations building internal knowledge management systems","Teams needing self-hosted document indexing without cloud vendor lock-in"],"limitations":["Connector development requires Python implementation of standardized interface; no low-code connector builder","Incremental sync logic varies by connector type; some sources require full re-index on schema changes","Credential rotation requires manual intervention in admin UI; no automated key rotation","Vespa indexing adds ~500ms-2s latency per document depending on size and chunking strategy"],"requires":["Python 3.9+","PostgreSQL 12+ for connector state and credentials","Redis for Celery task queue coordination","Vespa search engine deployed and accessible","Valid API credentials for each data source connector"],"input_types":["API credentials (OAuth tokens, API keys, service account JSON)","Connector configuration (sync frequency, document filters, metadata mappings)","Raw documents from source APIs (JSON, HTML, binary files)"],"output_types":["Indexed documents in Vespa with embeddings and metadata","Connector sync logs and error reports","Document chunks with citation metadata for retrieval"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_1","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.citation.tracking","name":"retrieval-augmented generation with citation tracking","description":"Onyx implements a RAG pipeline that retrieves relevant documents from Vespa using hybrid search (BM25 + semantic similarity), ranks results using LLM-based relevance scoring, and injects retrieved context into the LLM prompt with explicit citation metadata. The system tracks which documents contributed to each response, enables users to click through to source documents, and supports configurable retrieval strategies (dense-only, sparse-only, or hybrid). Retrieved chunks maintain document ID, source connector, and chunk position for precise citation.","intents":["Answer user questions by retrieving relevant documents and citing sources","Enable users to verify LLM responses by clicking through to original documents","Configure retrieval behavior per-assistant (number of results, similarity threshold, ranking strategy)","Support fact-checking workflows where citations are mandatory"],"best_for":["Teams building Q&A systems where source attribution is critical","Enterprise search applications requiring compliance-grade audit trails","Research and knowledge work where citation provenance matters"],"limitations":["Citation accuracy depends on chunk boundaries; mid-sentence splits can produce misleading citations","Hybrid search adds ~200-500ms latency vs. dense-only retrieval due to BM25 scoring overhead","LLM-based re-ranking requires additional API calls; can be disabled for cost optimization","No automatic citation deduplication; similar documents may appear as separate citations"],"requires":["Vespa instance with indexed documents and embeddings","LLM provider API key (OpenAI, Anthropic, or self-hosted via Ollama)","Document chunks with metadata (source ID, chunk position, document title)"],"input_types":["User query (text)","Retrieval configuration (top-k, similarity threshold, ranking strategy)","Assistant prompt template with {context} placeholder"],"output_types":["LLM response with inline citations","Citation metadata (document ID, source connector, chunk position, relevance score)","Retrieved document snippets for UI display"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_10","uri":"capability://text.generation.language.chat.frontend.with.real.time.message.streaming.and.ui.state.management","name":"chat frontend with real-time message streaming and ui state management","description":"Onyx provides a Next.js-based chat UI that streams LLM responses in real-time using Server-Sent Events (SSE), displaying tokens as they arrive. The frontend maintains local state for conversations, messages, and UI elements (input field, citation popups, research progress) using React hooks and TypeScript. The UI supports markdown rendering, code syntax highlighting, citation links, and responsive design. Real-time updates are coordinated via WebSocket or polling, and the frontend implements optimistic updates for better perceived latency.","intents":["Display LLM responses in real-time as they stream from the server","Show citations as clickable links to source documents","Render markdown and code with syntax highlighting","Manage conversation state and message history in the UI"],"best_for":["Teams building chat interfaces with real-time response streaming","Applications requiring responsive UI with optimistic updates","Organizations needing customizable chat UI (theming, branding)"],"limitations":["SSE streaming adds complexity; requires server-side streaming support and client-side event handling","Real-time updates via WebSocket require additional infrastructure (WebSocket server or polling fallback)","Citation rendering depends on document metadata; missing metadata produces broken links","Responsive design requires CSS media queries; mobile experience may be suboptimal without testing"],"requires":["Next.js 13+ for server-side rendering and API routes","React 18+ for hooks and state management","TypeScript for type safety","Backend API supporting SSE streaming"],"input_types":["User message (text input)","Conversation ID (to append to existing conversation)","Assistant selection (to choose which assistant to use)"],"output_types":["Streamed LLM response with real-time token display","Citation metadata with clickable links","Conversation state updates (message history, UI state)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_11","uri":"capability://tool.use.integration.mcp.server.integration.for.external.tool.execution","name":"mcp server integration for external tool execution","description":"Onyx implements a Model Context Protocol (MCP) server that exposes Onyx capabilities (search, retrieval, assistant management) to external LLM clients. External applications can call Onyx tools via MCP, enabling workflows where an external LLM orchestrates Onyx operations. The MCP server is implemented as a separate service that communicates with the main Onyx API, and supports standard MCP tool schemas for function calling. This enables integration with other AI systems and agents that support MCP.","intents":["Expose Onyx search and retrieval capabilities to external LLM clients via MCP","Enable external agents to orchestrate Onyx operations (search, retrieve, chat)","Integrate Onyx with other AI systems that support MCP","Build multi-agent workflows where Onyx is one component"],"best_for":["Teams building multi-agent systems with Onyx as a component","Organizations integrating Onyx with external LLM orchestration platforms","Developers building custom agents that need Onyx capabilities"],"limitations":["MCP server adds operational complexity; requires separate service deployment","Tool schemas must be manually defined; no automatic schema generation from Onyx API","MCP protocol overhead adds latency; not suitable for latency-sensitive applications","Limited to MCP-compatible clients; proprietary agent frameworks may not support MCP"],"requires":["MCP server implementation (included in Onyx)","MCP-compatible client (external LLM or agent framework)","Network connectivity between MCP client and Onyx MCP server","Tool schema definitions in MCP format"],"input_types":["MCP tool call with function name and arguments","Tool schema in MCP format"],"output_types":["Tool execution result (search results, retrieval output, chat response)","MCP-formatted response"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_12","uri":"capability://text.generation.language.embeddable.chat.widget.for.third.party.websites","name":"embeddable chat widget for third-party websites","description":"Onyx provides an embeddable chat widget that can be deployed on third-party websites via a simple script tag. The widget communicates with the Onyx backend via CORS-enabled API calls and maintains conversation state in the browser. The widget is customizable (colors, position, initial message) via configuration parameters, and supports authentication via JWT tokens or API keys. The widget is built with vanilla JavaScript (no framework dependencies) to minimize bundle size and compatibility issues.","intents":["Embed Onyx chat on external websites without building a custom UI","Provide customer-facing Q&A without exposing internal Onyx instance","Customize widget appearance to match website branding","Track user interactions and conversations for analytics"],"best_for":["SaaS companies embedding Onyx chat on customer websites","Organizations providing customer support via embedded chat","Teams wanting to offer Onyx without building custom UI"],"limitations":["Widget is limited to basic chat functionality; advanced features (research mode, assistants) are not exposed","CORS configuration must be permissive; restricting to specific domains requires manual configuration","Widget styling is limited to CSS variables; deep customization requires forking the widget code","No built-in analytics; tracking user interactions requires custom event handlers"],"requires":["Onyx instance with public API endpoint","CORS configuration allowing widget domain","JWT secret or API key for authentication","Third-party website with script tag injection capability"],"input_types":["Widget configuration (colors, position, initial message, assistant ID)","Authentication token (JWT or API key)","User message (text input in widget)"],"output_types":["Chat widget UI embedded in third-party website","LLM response streamed to widget","Conversation data sent to Onyx backend"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_13","uri":"capability://text.generation.language.desktop.application.with.local.first.architecture","name":"desktop application with local-first architecture","description":"Onyx provides a desktop application (built with Electron or similar) that can run locally or connect to a remote Onyx instance. The desktop app maintains local conversation history and can work offline with cached documents. It supports keyboard shortcuts, system tray integration, and native file dialogs for document upload. The app is built with the same frontend code as the web UI, enabling code reuse and consistent UX across platforms.","intents":["Use Onyx on desktop with native application experience","Work offline with cached documents and conversations","Integrate with system clipboard and file system","Access Onyx without opening a web browser"],"best_for":["Power users who prefer native desktop applications","Teams working offline or in low-connectivity environments","Organizations with strict browser policies requiring native apps"],"limitations":["Desktop app requires separate build and distribution pipeline; increases maintenance burden","Offline mode requires local document caching; large corpora may exceed disk space","System integration (clipboard, file dialogs) is platform-specific; cross-platform support requires testing","Auto-updates add complexity; requires update server and version management"],"requires":["Electron or similar framework for desktop app","Build pipeline for Windows, macOS, Linux","Code signing certificates for distribution","Update server for auto-updates"],"input_types":["User message (text input in desktop app)","File upload (via native file dialog)","Clipboard content (via system clipboard integration)"],"output_types":["Chat response in desktop app","Local conversation history","System notifications"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_14","uri":"capability://automation.workflow.cli.tool.for.programmatic.access.and.automation","name":"cli tool for programmatic access and automation","description":"Onyx provides a command-line interface (onyx-cli) for programmatic access to Onyx capabilities: searching documents, creating conversations, managing assistants, and uploading documents. The CLI is built with Python and uses the Onyx API, enabling automation workflows and integration with shell scripts. The CLI supports output formatting (JSON, CSV, table) for easy parsing, and authentication via API keys or environment variables.","intents":["Search Onyx documents from the command line","Automate document uploads and indexing via shell scripts","Create and manage assistants programmatically","Integrate Onyx with CI/CD pipelines and automation tools"],"best_for":["DevOps teams automating document management","Developers integrating Onyx with CI/CD pipelines","Power users preferring command-line interfaces"],"limitations":["CLI is limited to basic operations; advanced features (research mode, deep customization) are not exposed","Output formatting is basic; complex data transformations require external tools (jq, awk)","Authentication via API keys is less secure than OAuth; keys must be managed carefully","No built-in pagination; large result sets may be truncated"],"requires":["Python 3.9+","Onyx API endpoint (local or remote)","API key for authentication"],"input_types":["CLI command and arguments (search, upload, create-assistant, etc.)","API key (via environment variable or flag)","Query or document path"],"output_types":["Search results (JSON, CSV, or table format)","Upload status","Assistant metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_15","uri":"capability://search.retrieval.chrome.extension.for.in.browser.document.search.and.chat","name":"chrome extension for in-browser document search and chat","description":"Onyx provides a Chrome extension that enables searching Onyx documents and chatting with Onyx directly from the browser. The extension adds a sidebar to the browser that communicates with the Onyx backend, allowing users to search without leaving their current page. The extension supports authentication via OAuth or API keys, and maintains conversation state across browser sessions. The extension can be configured to search specific assistants or document collections.","intents":["Search Onyx documents without leaving the current browser page","Chat with Onyx in a sidebar while browsing","Quickly reference documents while working in other applications","Maintain conversation history across browser sessions"],"best_for":["Knowledge workers who need quick access to Onyx while browsing","Teams using Onyx as a reference tool alongside other web applications","Organizations wanting to reduce context switching"],"limitations":["Extension is limited to Chrome; no Firefox or Safari support without separate builds","Sidebar UI is constrained by browser extension limitations; advanced features are not exposed","Authentication via OAuth requires extension-specific OAuth flow; API keys are simpler but less secure","Cross-origin requests are restricted by browser security; some Onyx features may not work"],"requires":["Chrome browser (version 90+)","Onyx instance with public API endpoint","OAuth or API key for authentication"],"input_types":["Search query (text input in extension sidebar)","User message (text input in extension chat)","Authentication token (OAuth or API key)"],"output_types":["Search results displayed in extension sidebar","Chat response in extension sidebar","Conversation history"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_2","uri":"capability://planning.reasoning.deep.research.mode.with.iterative.refinement","name":"deep research mode with iterative refinement","description":"Onyx implements a multi-turn research workflow where the LLM can iteratively refine queries, retrieve additional documents, and synthesize findings across multiple retrieval rounds. The system maintains conversation context, tracks which documents have been retrieved, and prevents redundant searches. Each research iteration generates a new query, retrieves fresh results, and updates the synthesis. This is coordinated via the chat message processing flow with state maintained in PostgreSQL conversation records.","intents":["Conduct deep research on complex topics by iteratively refining search queries","Synthesize information across multiple document retrievals without redundant searches","Track research progress and allow users to review intermediate findings","Enable LLM to autonomously decide when to retrieve more information vs. synthesize"],"best_for":["Research teams investigating complex multi-faceted topics","Analysts requiring comprehensive synthesis across many documents","Knowledge workers building detailed reports with iterative refinement"],"limitations":["Each iteration adds 1-3 seconds latency (query generation + retrieval + synthesis); not suitable for real-time chat","LLM must be configured to output structured queries; unstructured refinement attempts fail silently","No built-in cost control; iterative retrievals can rapidly consume API quota","Conversation state grows linearly with iterations; very long research sessions may hit context limits"],"requires":["LLM with function-calling support (OpenAI, Anthropic, or compatible)","Vespa instance with indexed documents","PostgreSQL for conversation state persistence","Assistant configured with research mode enabled"],"input_types":["Initial user query (text)","Research configuration (max iterations, similarity threshold, synthesis instructions)","Conversation history with previous retrievals"],"output_types":["Iterative research findings with intermediate syntheses","Final comprehensive answer with citations from all retrieval rounds","Research trace showing query refinements and document selections"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_3","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.model.selection.hierarchy","name":"multi-provider llm abstraction with model selection hierarchy","description":"Onyx abstracts LLM provider differences (OpenAI, Anthropic, Ollama, Azure, etc.) through a unified factory pattern that normalizes API calls, token counting, and error handling. The system implements a model selection hierarchy where assistants can specify preferred models, fallback models, and provider-specific configurations. LiteLLM is used as the underlying abstraction layer with custom monkey patches for Onyx-specific behavior (cost tracking, token limits, provider-specific prompt formatting). Each LLM provider has configurable access controls and quota limits enforced at the API server level.","intents":["Switch between LLM providers (OpenAI, Anthropic, self-hosted Ollama) without changing application code","Configure fallback models when primary provider is unavailable or quota-exceeded","Track token usage and costs per model and provider","Enforce per-user or per-organization LLM quota limits"],"best_for":["Teams wanting to avoid vendor lock-in by supporting multiple LLM providers","Organizations using self-hosted models (Ollama, vLLM) alongside cloud providers","Enterprises needing cost control and usage tracking per user/org"],"limitations":["LiteLLM abstraction adds ~50-100ms latency per request due to provider-specific request transformation","Not all LLM capabilities are exposed; some provider-specific features (vision, function calling variants) require custom code","Token counting is approximate for some models; actual usage may differ from estimates","Fallback logic is sequential; no parallel provider attempts for latency optimization"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, Azure, etc.)","LiteLLM library (included in requirements)","PostgreSQL for storing provider configurations and usage logs","Network access to LLM provider APIs or self-hosted model server"],"input_types":["LLM provider credentials (API keys, endpoint URLs)","Model configuration (model name, temperature, max tokens, system prompt)","User query or message to be processed by LLM"],"output_types":["LLM response text","Token usage metrics (prompt tokens, completion tokens, total cost)","Provider and model metadata (latency, error status)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_4","uri":"capability://text.generation.language.assistant.configuration.with.prompt.engineering.and.tool.binding","name":"assistant configuration with prompt engineering and tool binding","description":"Onyx allows creation of custom assistants with configurable system prompts, model selection, retrieval behavior, and tool bindings. Each assistant is stored as a database record with versioning, and can be assigned to users or organizations. Assistants can be configured to use specific LLM providers, retrieval strategies (dense/sparse/hybrid), and can bind to external tools via a schema-based function registry. Prompt templates support variable injection ({context}, {user_query}, {conversation_history}) and can be versioned for A/B testing.","intents":["Create specialized assistants for different use cases (customer support, research, coding) with custom prompts","Configure retrieval behavior per-assistant (number of results, similarity threshold, ranking strategy)","Bind assistants to external tools (APIs, webhooks, MCP servers) for extended capabilities","Version and A/B test different prompt configurations"],"best_for":["Teams building multiple specialized chatbots from a single platform","Organizations needing role-based assistants (customer support vs. internal research)","Prompt engineers iterating on system prompts and retrieval strategies"],"limitations":["Prompt templates use simple string interpolation; no conditional logic or loops","Tool binding requires manual schema definition; no automatic schema generation from API specs","Assistant versioning is manual; no automatic rollback or canary deployment","No built-in prompt optimization; requires external tools for systematic prompt tuning"],"requires":["PostgreSQL for storing assistant configurations","At least one LLM provider configured","Vespa instance if retrieval is enabled","Tool schemas in OpenAI function-calling format if tools are bound"],"input_types":["Assistant name and description","System prompt template with variable placeholders","Model selection (provider, model name, temperature, max tokens)","Retrieval configuration (enabled/disabled, top-k, similarity threshold)","Tool schemas in OpenAI function-calling format"],"output_types":["Assistant record with ID, configuration, and versioning metadata","Rendered prompt with injected variables","Tool execution results (if tools are bound)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_5","uri":"capability://search.retrieval.semantic.search.with.hybrid.bm25.and.embedding.based.ranking","name":"semantic search with hybrid bm25 and embedding-based ranking","description":"Onyx implements hybrid search in Vespa that combines BM25 (sparse, keyword-based) and semantic similarity (dense, embedding-based) scoring. Documents are indexed with both full-text tokens and vector embeddings (768-dim by default), and queries are processed through both pathways with configurable weighting. Results are ranked using a combination of BM25 score and cosine similarity, with optional LLM-based re-ranking for final ordering. The system supports configurable similarity thresholds to filter low-relevance results.","intents":["Search for documents using both keyword matching and semantic similarity","Reduce hallucination by retrieving factually relevant documents before LLM generation","Configure search behavior per-assistant (dense-only, sparse-only, or hybrid)","Filter results by similarity threshold to avoid low-confidence retrievals"],"best_for":["Teams building knowledge bases where both keyword and semantic search matter","Organizations with diverse document types (structured data + unstructured text)","Applications requiring high-precision retrieval with low false-positive rate"],"limitations":["Hybrid search adds ~200-500ms latency vs. dense-only due to BM25 scoring overhead","Embedding quality depends on the embedding model; domain-specific embeddings may require fine-tuning","Similarity threshold tuning is manual; no automatic threshold optimization","Large result sets (1000+ documents) may hit Vespa performance limits without pagination"],"requires":["Vespa instance with hybrid search configuration","Embedding model (default: OpenAI text-embedding-3-small or configurable alternative)","Documents indexed with both full-text tokens and embeddings","Query embedding generation (via same embedding model as indexing)"],"input_types":["User query (text)","Search configuration (top-k, similarity threshold, BM25 weight, embedding weight)","Document corpus with full-text and embedding indices"],"output_types":["Ranked list of documents with relevance scores (BM25 + semantic)","Document snippets with highlighted query terms","Metadata (source connector, document ID, chunk position)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_6","uri":"capability://safety.moderation.multi.tenant.architecture.with.role.based.access.control","name":"multi-tenant architecture with role-based access control","description":"Onyx implements multi-tenancy at the database level with organization-scoped data isolation. Each user belongs to an organization, and all queries are filtered by organization ID at the database layer. Role-based access control (RBAC) is enforced via a permission matrix stored in PostgreSQL, with roles including admin, user, and custom roles. Assistants, documents, and conversations are scoped to organizations, and cross-organization access is prevented at the API server level. Authentication supports SAML, OAuth, and basic auth with session management via JWT tokens.","intents":["Deploy a single Onyx instance serving multiple organizations with data isolation","Enforce role-based permissions (admin, user, custom roles) per organization","Manage user access and permissions through an admin UI","Support SSO integration (SAML, OAuth) for enterprise deployments"],"best_for":["SaaS platforms built on Onyx serving multiple customers","Enterprise deployments with strict data isolation requirements","Organizations with complex permission hierarchies (admin, editor, viewer roles)"],"limitations":["Organization isolation is enforced at the query level; no database-level row security, so bugs can leak data across orgs","RBAC is coarse-grained (admin/user/custom); no fine-grained field-level permissions","SAML/OAuth integration requires manual configuration per identity provider","No built-in audit logging; compliance audits require external log aggregation"],"requires":["PostgreSQL with organization and user tables","Identity provider (SAML, OAuth, or basic auth)","JWT secret for session token signing","Redis for session management (optional but recommended)"],"input_types":["User credentials (email/password, SAML assertion, OAuth token)","Organization ID (implicit from user record)","Role assignment (admin, user, or custom role)"],"output_types":["JWT session token","User profile with organization and role","Filtered data scoped to user's organization"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_7","uri":"capability://automation.workflow.background.task.coordination.with.celery.and.redis","name":"background task coordination with celery and redis","description":"Onyx uses Celery workers coordinated via Redis to handle long-running tasks asynchronously: document indexing, connector syncs, embedding generation, and LLM inference. Tasks are enqueued with priority levels, and workers process them in parallel. Redis is used for task queue coordination, distributed locking (to prevent duplicate syncs), and caching of frequently-accessed data (embeddings, connector state). The system implements dynamic task scheduling where sync frequency can be adjusted without restarting workers, and failed tasks are retried with exponential backoff.","intents":["Index documents asynchronously without blocking the chat API","Sync connectors on a schedule (hourly, daily, weekly) without manual intervention","Generate embeddings in parallel across multiple workers","Handle long-running LLM inference without timing out HTTP requests"],"best_for":["Deployments with large document corpora requiring parallel indexing","Teams needing scheduled connector syncs without manual intervention","High-traffic applications where background processing prevents API latency"],"limitations":["Celery adds operational complexity; requires Redis and worker process management","Task visibility is limited; no built-in UI for monitoring task progress (requires external tools like Flower)","Distributed locking can cause deadlocks if not carefully managed; some edge cases require manual intervention","Failed tasks require manual retry; no automatic dead-letter queue for persistent failures"],"requires":["Redis instance for task queue and coordination","Celery library (included in requirements)","Worker processes running celery -A onyx.worker (can be scaled horizontally)","PostgreSQL for task state persistence"],"input_types":["Task definition (function name, arguments, priority, retry policy)","Schedule configuration (cron expression for periodic tasks)","Worker configuration (concurrency, task timeout, queue assignment)"],"output_types":["Task execution logs","Indexed documents in Vespa","Connector sync status and error reports","Embedding vectors for documents"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_8","uri":"capability://memory.knowledge.conversation.persistence.with.message.history.and.context.management","name":"conversation persistence with message history and context management","description":"Onyx stores conversations in PostgreSQL with full message history, including user messages, LLM responses, retrieved documents, and metadata (timestamps, token counts, costs). Each conversation maintains context across turns, enabling multi-turn interactions where the LLM can reference previous messages. The system implements context windowing to manage token limits: older messages are summarized or dropped when conversations exceed the LLM's context window. Conversations are scoped to users and organizations, and can be shared or exported.","intents":["Maintain conversation history for multi-turn interactions","Reference previous messages in follow-up questions","Track token usage and costs per conversation","Export conversations for compliance or analysis"],"best_for":["Applications requiring multi-turn dialogue with context awareness","Teams needing conversation audit trails for compliance","Organizations tracking LLM costs per conversation"],"limitations":["Context windowing is simplistic; older messages are dropped rather than intelligently summarized","No built-in conversation search; finding specific messages requires full-text search implementation","Conversation export is manual; no automated export pipeline","Very long conversations (100+ turns) may hit PostgreSQL performance limits without pagination"],"requires":["PostgreSQL for storing conversations and messages","User authentication (to scope conversations to users)","LLM provider for generating responses"],"input_types":["User message (text)","Conversation ID (to append to existing conversation)","Context window configuration (max tokens, summarization strategy)"],"output_types":["Conversation record with full message history","LLM response with metadata (tokens, cost, latency)","Retrieved documents and citations"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-onyx-dot-app--onyx__cap_9","uri":"capability://data.processing.analysis.document.chunking.and.metadata.extraction.with.configurable.strategies","name":"document chunking and metadata extraction with configurable strategies","description":"Onyx implements configurable document chunking strategies (fixed-size, semantic, recursive) that split documents into retrievable chunks while preserving context. Each chunk is assigned metadata (document ID, source connector, chunk position, document title) for citation tracking. The system supports metadata extraction via LLM-based summarization or rule-based patterns, enabling semantic search on extracted metadata. 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