{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"danswer-onyx","slug":"danswer-onyx","name":"Danswer (Onyx)","type":"repo","url":"https://github.com/danswer-ai/danswer","page_url":"https://unfragile.ai/danswer-onyx","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"danswer-onyx__cap_0","uri":"capability://memory.knowledge.multi.source.document.indexing.with.unified.embedding.pipeline","name":"multi-source document indexing with unified embedding pipeline","description":"Danswer ingests documents from heterogeneous sources (Slack, Google Drive, Confluence, GitHub, etc.) through connector-based adapters that normalize documents into a unified schema, then processes them through a configurable embedding pipeline (supporting multiple embedding models) and stores vectors in a pluggable vector database backend. The architecture uses a document chunking strategy with metadata preservation to maintain source attribution and access control boundaries across all indexed content.","intents":["Index company knowledge spread across 10+ SaaS tools into a single searchable corpus","Automatically sync documents from Confluence and GitHub as they change without manual re-indexing","Preserve document metadata and source attribution through the embedding pipeline for audit trails","Support multiple embedding models (OpenAI, local Sentence Transformers) without reindexing"],"best_for":["Enterprise teams with fragmented knowledge across Slack, Confluence, Google Drive, and GitHub","Organizations needing document-level access control enforcement during search","Teams wanting to self-host and control embedding model selection"],"limitations":["Connector availability limited to pre-built integrations (Slack, Confluence, GitHub, Google Drive, Jira, etc.) — custom sources require writing new connector code","Embedding pipeline is sequential — processing large document volumes (100k+ docs) can take hours depending on chunk size and model","Vector database backend must be separately provisioned (Postgres with pgvector, Qdrant, Weaviate) — no embedded option","Metadata preservation depends on source connector implementation — some sources may lose nested context"],"requires":["Python 3.9+","Vector database (Postgres 12+ with pgvector extension, Qdrant, or Weaviate)","API credentials for source connectors (Slack bot token, Confluence API token, GitHub PAT, Google Drive service account)","Embedding model access (OpenAI API key or local Sentence Transformers model)"],"input_types":["documents (PDF, DOCX, TXT, Markdown)","web pages (via Confluence, GitHub wiki)","Slack messages and threads","Jira tickets and comments","Google Drive files"],"output_types":["vector embeddings (float arrays, 384-1536 dimensions depending on model)","indexed documents with metadata","chunk-level vectors with source attribution"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_1","uri":"capability://search.retrieval.semantic.search.with.access.control.enforcement","name":"semantic search with access control enforcement","description":"Danswer executes semantic search queries by embedding the user's question, retrieving similar document chunks from the vector database, and filtering results based on the user's document-level access permissions (derived from source system ACLs like Slack workspace membership or Confluence space permissions). The search pipeline ranks results by vector similarity and applies source-specific permission checks before returning chunks to the user, ensuring no unauthorized content leaks.","intents":["Search across company documents while respecting Slack channel membership and Confluence space permissions","Find relevant information without exposing documents the user shouldn't access","Implement compliance requirements where search results must honor source system access controls","Debug why a document isn't appearing in search results due to permission restrictions"],"best_for":["Enterprises with strict data governance requiring permission enforcement at query time","Teams using Danswer across multiple Slack workspaces or Confluence instances with different access levels","Organizations in regulated industries (healthcare, finance) needing audit trails of who searched what"],"limitations":["Permission enforcement depends on connector-provided ACL data — if a source connector doesn't sync permissions, all documents from that source are treated as accessible to all users","Permission checks add latency (~50-200ms per query depending on number of retrieved chunks and permission lookups)","Row-level security (document-level permissions within a single Confluence space) requires custom connector logic","Permission cache invalidation is eventual — recently revoked access may take minutes to reflect in search results"],"requires":["Vector database with indexed documents and metadata","User identity from source system (Slack user ID, Confluence account ID, GitHub username)","Connector-provided ACL mappings (which users can access which documents)","Danswer backend with permission evaluation logic"],"input_types":["natural language query (text)","user identity context (from Slack, Confluence, or custom auth)"],"output_types":["ranked list of document chunks with source attribution","permission-filtered results (only chunks user can access)","relevance scores and source metadata"],"categories":["search-retrieval","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_10","uri":"capability://memory.knowledge.pluggable.vector.database.backend.with.multi.provider.support","name":"pluggable vector database backend with multi-provider support","description":"Danswer abstracts the vector database layer through a pluggable backend interface, supporting multiple vector database providers (Postgres with pgvector, Qdrant, Weaviate, Pinecone). The system stores embeddings, document metadata, and chunk information in the chosen backend, and implements a consistent query interface across all backends. Users can switch backends without re-embedding documents if the vector format is compatible.","intents":["Choose a vector database that fits your infrastructure (self-hosted Postgres vs. managed Qdrant vs. cloud Pinecone)","Switch vector databases without re-indexing documents","Use a vector database that's already deployed in your infrastructure","Scale vector storage independently from the Danswer application"],"best_for":["Organizations with existing vector database infrastructure they want to reuse","Teams wanting to self-host all components (Postgres + pgvector)","Companies with specific vector database requirements (e.g., on-premises only)"],"limitations":["Vector format compatibility is required for backend switching — incompatible formats require re-embedding","Each backend has different performance characteristics — query latency varies by backend and scale","Metadata filtering capabilities differ by backend — some backends have limited filtering support","Backend-specific features (e.g., Qdrant's hybrid search) are not abstracted — using them requires backend-specific code"],"requires":["One of: Postgres 12+ with pgvector extension, Qdrant instance, Weaviate instance, or Pinecone API key","Danswer backend configured to use the chosen vector database","Network connectivity to the vector database"],"input_types":["vector embeddings (float arrays)","document metadata (source, path, permissions)","chunk information (text, position)"],"output_types":["similarity search results (ranked chunks)","metadata from retrieved chunks","relevance scores"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_11","uri":"capability://text.generation.language.llm.provider.abstraction.with.multi.model.support","name":"llm provider abstraction with multi-model support","description":"Danswer abstracts the LLM layer through a provider interface, supporting multiple LLM providers (OpenAI, Anthropic, local models via Ollama/vLLM, Azure OpenAI). Users can configure which LLM to use for chat and answer generation, and can switch providers without changing application code. The system handles provider-specific API formats, token counting, and error handling transparently.","intents":["Use OpenAI GPT-4 for high-quality answers while keeping costs low with GPT-3.5 for simple queries","Switch to a local LLM (via Ollama) for privacy-sensitive deployments without code changes","Use Anthropic Claude for better reasoning on complex questions","Experiment with different LLMs to find the best quality-to-cost tradeoff"],"best_for":["Organizations wanting to use local LLMs for privacy or cost reasons","Teams wanting to experiment with different LLMs without code changes","Companies with specific LLM requirements (e.g., must use Azure OpenAI)"],"limitations":["LLM quality varies significantly by provider and model — switching providers may change answer quality","Token counting is provider-specific — context window management differs by provider","Some providers have rate limits or availability constraints — fallback logic is not built-in","Local LLM performance depends on hardware — inference latency can be 10-100x slower than cloud providers"],"requires":["API key or endpoint for chosen LLM provider (OpenAI, Anthropic, Azure, or local Ollama/vLLM instance)","Danswer configuration to specify LLM provider and model","Sufficient API quota or local hardware for inference"],"input_types":["system prompt (instructions for the LLM)","retrieved document chunks (context)","conversation history (for multi-turn chat)","user message (query)"],"output_types":["natural language response (text)","token usage information","provider-specific metadata"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_12","uri":"capability://text.generation.language.answer.generation.with.source.attribution.and.citation","name":"answer generation with source attribution and citation","description":"Danswer generates answers to user queries by passing retrieved document chunks to an LLM along with a system prompt that instructs the model to cite sources. The system extracts citations from the LLM response and links them back to the original documents, providing users with verifiable sources for each claim. The citation format is configurable (inline citations, footnotes, etc.) and can be customized per deployment.","intents":["Generate answers that cite the documents they're based on for verifiability","Allow users to click through to source documents to verify claims","Reduce hallucinations by grounding answers in retrieved documents","Provide audit trails showing which documents were used to generate each answer"],"best_for":["Organizations needing verifiable answers (compliance, legal, healthcare)","Teams wanting to reduce hallucinations by enforcing source attribution","Users wanting to quickly verify answers by checking source documents"],"limitations":["Citation extraction depends on LLM behavior — models may fail to cite sources or cite incorrectly","LLM hallucinations are not eliminated — the model can still generate false information even with source documents","Citation format is LLM-dependent — different models may format citations differently","Source attribution is only as good as the retrieved documents — if relevant documents are not retrieved, citations may be incomplete"],"requires":["LLM with instruction-following capability (GPT-3.5+, Claude, etc.)","Retrieved document chunks with source metadata","System prompt that instructs the LLM to cite sources"],"input_types":["user query (text)","retrieved document chunks (with source metadata)","conversation history (optional)"],"output_types":["natural language answer (text)","citations with source document references","source document links"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_13","uri":"capability://safety.moderation.user.authentication.and.role.based.access.control","name":"user authentication and role-based access control","description":"Danswer implements user authentication (via OIDC, SAML, or local credentials) and role-based access control (RBAC) to restrict who can access the system and what they can do. Users are assigned roles (admin, user, viewer) that determine their permissions (e.g., admins can manage connectors, users can search and chat, viewers can only read). The system integrates with source system identities (Slack user IDs, Confluence accounts) to enforce document-level access control.","intents":["Restrict Danswer access to authorized users only","Assign different roles to users (admin, user, viewer) with different permissions","Integrate with existing identity providers (Okta, Azure AD, Google Workspace) via OIDC/SAML","Enforce document-level access control based on source system permissions"],"best_for":["Enterprise deployments requiring user authentication and authorization","Organizations with existing identity providers (Okta, Azure AD) they want to integrate with","Teams needing fine-grained access control (different users see different documents)"],"limitations":["OIDC/SAML integration requires identity provider configuration — local deployments may not have this","Role-based access control is coarse-grained — no per-document role assignment","User identity must be consistent across systems — if a user has different IDs in Slack and Confluence, access control may fail","Permission sync is eventual — recently revoked access may take minutes to reflect"],"requires":["Identity provider (Okta, Azure AD, Google Workspace, or local user database)","OIDC or SAML configuration for the identity provider","Danswer backend with authentication and authorization logic"],"input_types":["user credentials (username/password or OIDC/SAML token)","user identity from source systems (Slack user ID, Confluence account ID)"],"output_types":["authenticated user session","user role and permissions","access control decisions (allow/deny)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_14","uri":"capability://text.generation.language.web.interface.with.search.and.chat.ui","name":"web interface with search and chat ui","description":"Danswer provides a web interface (built with React) that allows users to search documents and chat with the AI assistant. The interface includes a search bar for semantic search, a chat panel for multi-turn conversations, and a sidebar showing indexed sources and recent searches. The UI displays search results with source attribution, allows users to click through to source documents, and provides conversation history management.","intents":["Search company documents through a familiar web interface","Chat with an AI assistant about documents without leaving the browser","View search results with source attribution and click through to original documents","Manage conversation history and organize searches"],"best_for":["End users wanting a familiar web interface for document search and chat","Teams wanting a self-hosted alternative to cloud-based AI assistants","Organizations wanting to customize the UI for their branding"],"limitations":["Web interface is browser-based — no native mobile app","UI customization requires React knowledge — limited no-code customization options","Search and chat are separate interfaces — no unified search+chat experience","Conversation history is stored in Danswer's database — no export to external systems"],"requires":["Web browser (Chrome, Firefox, Safari, Edge)","Danswer backend running and accessible","User authentication to access the interface"],"input_types":["search query (text)","chat message (text)"],"output_types":["search results with source attribution","chat responses with citations","conversation history"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_2","uri":"capability://text.generation.language.conversational.rag.with.multi.turn.context.management","name":"conversational rag with multi-turn context management","description":"Danswer implements a conversational chat interface where each user message is embedded and used to retrieve relevant document chunks, which are then passed to an LLM (OpenAI, Anthropic, or local model) along with conversation history to generate contextual responses. The system maintains a conversation thread with full message history, allowing follow-up questions to reference previous context, and implements a sliding-window context strategy to manage token limits while preserving conversation coherence.","intents":["Ask follow-up questions about retrieved documents without re-explaining context","Have the AI clarify ambiguous answers by referencing earlier parts of the conversation","Maintain separate conversation threads for different topics without mixing context","Use conversation history to improve retrieval relevance (e.g., 'Tell me more about X' where X was mentioned 3 turns ago)"],"best_for":["Teams wanting a Slack-like chat interface for document Q&A instead of traditional search","Users who prefer iterative exploration of documents through conversation","Organizations needing conversation history for audit and compliance purposes"],"limitations":["Context window is bounded by LLM token limits (4k-100k depending on model) — very long conversations require summarization or context pruning","Conversation history is stored in Danswer's database — no built-in export to external systems","Multi-turn retrieval can suffer from context drift — early conversation context may become irrelevant if topic shifts","LLM hallucinations are not mitigated beyond providing accurate source documents — the model can still generate plausible-sounding but false information"],"requires":["LLM API access (OpenAI, Anthropic, or local model via Ollama/vLLM)","Vector database with indexed documents","Danswer backend with conversation state management","User authentication to associate conversations with users"],"input_types":["natural language user message (text)","conversation history (previous messages and AI responses)"],"output_types":["natural language response (text)","source document citations with chunk references","conversation metadata (timestamp, user, thread ID)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_3","uri":"capability://tool.use.integration.slack.integration.with.workspace.aware.permissions","name":"slack integration with workspace-aware permissions","description":"Danswer provides a Slack bot that indexes Slack messages and threads from specified channels, syncs Slack workspace membership to enforce channel-level access control, and allows users to query indexed Slack content directly from Slack via slash commands or mentions. The integration maintains a mapping between Slack user IDs and channel memberships, ensuring that search results respect channel privacy (users only see messages from channels they're members of).","intents":["Search company Slack history without leaving Slack (via /danswer command)","Index specific Slack channels as part of the knowledge base","Ensure Slack channel privacy is respected — users can't search messages from channels they're not in","Automatically sync new Slack messages and threads into the searchable index"],"best_for":["Teams already using Slack as a knowledge repository and wanting to make it searchable","Organizations with strict channel privacy requirements","Companies wanting to reduce duplicate questions by making Slack history discoverable"],"limitations":["Slack message indexing is limited to channels the bot has been invited to — private channels require explicit bot addition","Thread reconstruction in Slack can be lossy — replies to messages may not preserve full context if the original message is deleted","Slack API rate limits can slow down initial indexing of large workspaces (100k+ messages)","Slack workspace membership sync is eventual — new members may take minutes to appear in permission checks"],"requires":["Slack workspace admin access to install the Danswer bot","Slack bot token with permissions: channels:read, chat:read, users:read, team:read","Danswer backend with Slack connector deployed","Vector database to store indexed messages"],"input_types":["Slack messages (text, including threads)","Slack user identity (user ID from workspace)"],"output_types":["indexed Slack messages with channel and user metadata","search results displayed in Slack thread or DM","permission-filtered results based on channel membership"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_4","uri":"capability://tool.use.integration.confluence.connector.with.space.and.page.level.hierarchy.preservation","name":"confluence connector with space and page-level hierarchy preservation","description":"Danswer's Confluence connector crawls Confluence spaces and pages, preserving the page hierarchy (parent-child relationships) and space-level access controls. The connector extracts page content, metadata (author, creation date, last modified), and space permissions, then chunks pages while maintaining hierarchy context so that search results can reference the full document path (e.g., 'Space > Parent Page > Child Page'). The connector supports incremental sync to avoid re-indexing unchanged pages.","intents":["Index company Confluence wiki and make it searchable across all spaces","Preserve page hierarchy in search results so users understand document context","Enforce Confluence space permissions — users only see pages from spaces they have access to","Automatically sync Confluence updates without manual re-indexing"],"best_for":["Organizations using Confluence as their primary documentation platform","Teams with complex page hierarchies (nested pages, multiple spaces) that need to be preserved in search","Companies with strict space-level access control requirements"],"limitations":["Confluence connector requires API token with space:read and page:read permissions — cannot index restricted pages the token doesn't have access to","Page hierarchy is preserved at chunk level but may be lost if chunks are retrieved out of order","Confluence macros (embedded content, code blocks) are converted to plain text — formatting and embedded media are lost","Incremental sync relies on Confluence's last-modified timestamp — if pages are modified outside Confluence (via API), sync may miss updates"],"requires":["Confluence Cloud or Server instance","Confluence API token with space:read and page:read permissions","Danswer backend with Confluence connector deployed","Vector database to store indexed pages"],"input_types":["Confluence pages (HTML content)","Page metadata (author, creation date, space, hierarchy)","Confluence user identity (for permission enforcement)"],"output_types":["indexed pages with hierarchy metadata","chunks with parent page references","permission-filtered results based on space access"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_5","uri":"capability://tool.use.integration.github.connector.with.code.and.documentation.indexing","name":"github connector with code and documentation indexing","description":"Danswer's GitHub connector indexes both code files and documentation (README, wiki pages) from specified repositories, extracting file content, commit history, and branch information. The connector supports filtering by file type (e.g., only index .py and .md files) and can index multiple repositories across organizations. It preserves file paths and repository metadata so that search results can link back to the original file in GitHub.","intents":["Search code and documentation across multiple GitHub repositories without leaving Danswer","Find relevant code examples or documentation by natural language query","Index repository README and wiki pages alongside code for comprehensive knowledge base","Preserve file paths and repository context in search results for easy navigation"],"best_for":["Engineering teams wanting to make internal libraries and documentation discoverable","Organizations with multiple repositories that need unified search","Teams wanting to reduce time spent searching GitHub for code examples"],"limitations":["GitHub connector requires personal access token with repo:read permissions — cannot index private repositories without appropriate token","Code indexing is limited to text files — binary files and compiled code are skipped","Large repositories (100k+ files) may take significant time to index initially","Commit history is not indexed — only current file content is searchable","Code-specific context (function signatures, imports) is preserved only if the chunking strategy is code-aware"],"requires":["GitHub personal access token with repo:read permissions","Danswer backend with GitHub connector deployed","Vector database to store indexed files","List of repositories to index (by owner/repo format)"],"input_types":["GitHub repository files (code, markdown, documentation)","File metadata (path, language, last modified)","Repository metadata (owner, name, branch)"],"output_types":["indexed files with repository and path metadata","search results with file paths and line numbers","links back to original files in GitHub"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_6","uri":"capability://tool.use.integration.google.drive.connector.with.folder.hierarchy.and.shared.file.support","name":"google drive connector with folder hierarchy and shared file support","description":"Danswer's Google Drive connector indexes files from specified Google Drive folders, supporting both personal and shared drives. The connector extracts file content (from Google Docs, Sheets, PDFs, etc.), preserves folder hierarchy, and syncs sharing permissions to enforce access control. It handles Google Workspace file formats natively and can index files shared with the user's service account.","intents":["Index company Google Drive documents and make them searchable","Preserve folder structure in search results so users understand document organization","Enforce Google Drive sharing permissions — users only see files they have access to","Search across personal and shared drives without manual file organization"],"best_for":["Organizations using Google Workspace as their primary document storage","Teams with complex folder hierarchies that need to be preserved in search","Companies wanting to make shared documents discoverable without duplicating them"],"limitations":["Google Drive connector requires a service account with access to the target folders — cannot index files the service account doesn't have permission to read","Sharing permission sync is eventual — recently shared files may take minutes to appear in search results","Google Sheets are converted to plain text — formulas and formatting are lost","Comments and suggestions in Google Docs are not indexed — only document content is searchable","Folder hierarchy is preserved at chunk level but may be lost if chunks are retrieved out of order"],"requires":["Google Cloud project with Google Drive API enabled","Service account with access to target Google Drive folders","Service account JSON key file","Danswer backend with Google Drive connector deployed","Vector database to store indexed files"],"input_types":["Google Drive files (Google Docs, Sheets, PDFs, etc.)","File metadata (name, path, sharing permissions)","Folder hierarchy"],"output_types":["indexed files with folder path metadata","search results with file names and paths","permission-filtered results based on sharing settings"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_7","uri":"capability://tool.use.integration.jira.connector.with.issue.and.comment.indexing","name":"jira connector with issue and comment indexing","description":"Danswer's Jira connector indexes Jira issues and comments from specified projects, extracting issue content (title, description, comments), metadata (assignee, status, priority, labels), and project-level permissions. The connector supports filtering by issue type or status and can index issues across multiple Jira instances. It preserves issue relationships (parent-child, linked issues) and allows search results to reference the full issue context.","intents":["Search Jira issues and comments without leaving Danswer","Find relevant issues by natural language query (e.g., 'authentication bugs' instead of JQL)","Preserve issue metadata in search results for context","Index issues across multiple Jira projects for unified search"],"best_for":["Engineering teams using Jira as their issue tracker and wanting to make issues discoverable","Organizations with large issue backlogs that need semantic search","Teams wanting to reduce duplicate issues by making existing issues more discoverable"],"limitations":["Jira connector requires API token with issue:read permissions — cannot index issues the token doesn't have access to","Issue relationships (parent-child, linked issues) are preserved as metadata but not used in retrieval","Jira custom fields are indexed as text but may not be semantically meaningful","Comment threading is flattened — replies to comments are not preserved","Jira attachments are not indexed — only text content is searchable"],"requires":["Jira Cloud or Server instance","Jira API token with issue:read permissions","Danswer backend with Jira connector deployed","Vector database to store indexed issues"],"input_types":["Jira issues (title, description, comments)","Issue metadata (assignee, status, priority, labels, custom fields)","Project information"],"output_types":["indexed issues with metadata","search results with issue keys and summaries","links back to original issues in Jira"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_8","uri":"capability://data.processing.analysis.custom.document.upload.with.metadata.extraction","name":"custom document upload with metadata extraction","description":"Danswer allows users to upload documents directly (PDF, DOCX, TXT, Markdown) through the web interface or API, automatically extracting text content and metadata (filename, upload date, uploader). The system chunks uploaded documents using configurable strategies and indexes them into the vector database. Uploaded documents can be tagged with custom metadata for filtering and organization.","intents":["Index documents that aren't in any connected system (e.g., vendor contracts, RFPs, internal reports)","Quickly add documents to the knowledge base without setting up a connector","Organize uploaded documents with custom tags for easier discovery","Bulk upload multiple documents at once"],"best_for":["Teams wanting to index documents from sources without pre-built connectors","Organizations with ad-hoc documents that don't fit into a structured system","Users wanting to quickly test Danswer with their own documents"],"limitations":["Uploaded documents are not automatically synced — changes to the original file are not reflected in the index","File size limits apply (typically 50MB per file) — very large documents must be split before upload","Metadata extraction is limited to filename and upload metadata — no OCR for scanned PDFs","Access control for uploaded documents is limited to Danswer user roles — no fine-grained per-document permissions"],"requires":["Danswer web interface or API access","Supported file format (PDF, DOCX, TXT, Markdown)","User authentication to associate uploads with users"],"input_types":["document files (PDF, DOCX, TXT, Markdown)","custom metadata tags (optional)"],"output_types":["indexed documents with metadata","chunks with source attribution","searchable content in vector database"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__cap_9","uri":"capability://data.processing.analysis.configurable.chunking.strategies.with.semantic.preservation","name":"configurable chunking strategies with semantic preservation","description":"Danswer implements multiple document chunking strategies (fixed-size, semantic, recursive) that can be configured per document type. The system supports chunk overlap to preserve context across boundaries, and implements code-aware chunking for programming languages that respects function and class boundaries. Chunking strategies are applied during indexing and can be adjusted without re-indexing if the vector database supports it.","intents":["Chunk documents in a way that preserves semantic meaning (e.g., keep function definitions together)","Configure different chunking strategies for different document types (code vs. prose)","Adjust chunk size and overlap to balance retrieval granularity and context preservation","Experiment with chunking strategies to improve search relevance"],"best_for":["Teams indexing mixed content types (code, documentation, prose) that need different chunking strategies","Organizations wanting to optimize chunk size for their specific use case","Users wanting to preserve semantic boundaries (functions, sections) in chunks"],"limitations":["Chunking strategy changes require re-indexing documents — no in-place strategy updates","Code-aware chunking is limited to languages with built-in support (Python, JavaScript, etc.)","Chunk overlap increases storage requirements — overlapping chunks are stored separately","Optimal chunk size is use-case dependent — no automatic tuning"],"requires":["Danswer configuration file or API to specify chunking strategy","Document type information (code, prose, etc.)","Vector database with sufficient storage for overlapping chunks"],"input_types":["documents (code, prose, mixed)","chunking strategy configuration (size, overlap, type)"],"output_types":["chunks with metadata (source, position, overlap)","indexed vectors in vector database"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"danswer-onyx__headline","uri":"capability://memory.knowledge.rag.powered.enterprise.ai.assistant","name":"rag-powered enterprise ai assistant","description":"Danswer is an open-source enterprise AI assistant that connects to company documents and tools, providing RAG-powered search and chat across platforms like Slack and Google Drive, making information retrieval seamless and efficient.","intents":["best RAG-powered AI assistant","enterprise AI assistant for document search","AI assistant for Slack integration","open-source AI assistant for knowledge management","RAG framework for corporate tools"],"best_for":["companies with extensive documentation","teams using Slack and Google Drive"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Vector database (Postgres 12+ with pgvector extension, Qdrant, or Weaviate)","API credentials for source connectors (Slack bot token, Confluence API token, GitHub PAT, Google Drive service account)","Embedding model access (OpenAI API key or local Sentence Transformers model)","Vector database with indexed documents and metadata","User identity from source system (Slack user ID, Confluence account ID, GitHub username)","Connector-provided ACL mappings (which users can access which documents)","Danswer backend with permission evaluation logic","One of: Postgres 12+ with pgvector extension, Qdrant instance, Weaviate instance, or Pinecone API key","Danswer backend configured to use the chosen vector database"],"failure_modes":["Connector availability limited to pre-built integrations (Slack, Confluence, GitHub, Google Drive, Jira, etc.) — custom sources require writing new connector code","Embedding pipeline is sequential — processing large document volumes (100k+ docs) can take hours depending on chunk size and model","Vector database backend must be separately provisioned (Postgres with pgvector, Qdrant, Weaviate) — no embedded option","Metadata preservation depends on source connector implementation — some sources may lose nested context","Permission enforcement depends on connector-provided ACL data — if a source connector doesn't sync permissions, all documents from that source are treated as accessible to all users","Permission checks add latency (~50-200ms per query depending on number of retrieved chunks and permission lookups)","Row-level security (document-level permissions within a single Confluence space) requires custom connector logic","Permission cache invalidation is eventual — recently revoked access may take minutes to reflect in search results","Vector format compatibility is required for backend switching — incompatible formats require re-embedding","Each backend has different performance characteristics — query latency varies by backend and scale","builder identity is not verified yet","no observed match outcomes 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