{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-quivr","slug":"quivr","name":"quivr","type":"repo","url":"https://github.com/StanGirard/quivr","page_url":"https://unfragile.ai/quivr","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-quivr__cap_0","uri":"capability://data.processing.analysis.multi.format.document.ingestion.and.chunking","name":"multi-format document ingestion and chunking","description":"Accepts diverse file types (PDF, DOCX, TXT, CSV, JSON, Markdown) and automatically chunks them into semantically meaningful segments using configurable chunk sizes and overlap strategies. The system parses each format with specialized loaders, then applies sliding-window or recursive chunking to prepare documents for embedding without losing context boundaries.","intents":["I want to upload my entire knowledge base (mix of PDFs, spreadsheets, and text files) without manual preprocessing","I need documents split intelligently so embeddings capture semantic meaning rather than arbitrary byte boundaries","I want to preserve document structure (headers, tables, code blocks) during ingestion"],"best_for":["teams building RAG systems with heterogeneous document sources","non-technical users who want to upload files without format conversion","enterprises managing large document repositories (legal, medical, technical)"],"limitations":["No native support for image-heavy PDFs or scanned documents — requires OCR preprocessing","Chunking strategy is fixed per document type — no dynamic adjustment based on content density","Large files (>100MB) may require manual splitting to avoid memory overhead during parsing"],"requires":["Python 3.9+","File system access with read permissions","Optional: OCR library (Tesseract) for scanned PDFs"],"input_types":["PDF","DOCX","TXT","CSV","JSON","Markdown","HTML"],"output_types":["chunked text segments","metadata (source, page number, chunk index)","structured document trees"],"categories":["data-processing-analysis","document-parsing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_1","uri":"capability://memory.knowledge.vector.embedding.generation.and.storage","name":"vector embedding generation and storage","description":"Converts chunked text into dense vector embeddings using pluggable embedding models (OpenAI, Hugging Face, local models) and stores them in a vector database (Supabase pgvector, Pinecone, or Weaviate). The system manages embedding batching, caching, and metadata association to enable semantic search without re-computing embeddings on every query.","intents":["I want to embed my documents once and reuse those embeddings for multiple queries","I need to switch embedding models (e.g., from OpenAI to open-source) without re-ingesting documents","I want embeddings stored alongside document metadata for filtering and ranking"],"best_for":["developers building semantic search or RAG pipelines","teams with cost constraints wanting to use local/open-source embeddings","applications requiring sub-second retrieval over large document collections (>10K documents)"],"limitations":["Embedding model selection is fixed at initialization — switching models requires re-embedding all documents","No built-in deduplication of semantically similar chunks — may store redundant embeddings","Vector database choice locks you into that provider's ecosystem (Supabase pgvector vs Pinecone have different scaling characteristics)"],"requires":["Python 3.9+","API key for embedding service (OpenAI, Hugging Face, or local model server)","Vector database instance (Supabase, Pinecone, Weaviate, or local Chroma)","Network access to embedding service"],"input_types":["text chunks","document metadata (source, page number)"],"output_types":["vector embeddings (float arrays, typically 1536 dimensions for OpenAI)","vector IDs and metadata mappings"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_10","uri":"capability://automation.workflow.analytics.and.usage.tracking","name":"analytics and usage tracking","description":"Collects metrics on user interactions (queries, responses, document access) and system performance (retrieval latency, embedding quality, LLM token usage, cost). Provides dashboards or APIs to query usage patterns, identify popular documents, and monitor system health. Enables cost tracking per user/workspace and performance optimization based on real usage data.","intents":["I want to understand which documents are being used most and which are dead weight","I need to track LLM costs per user or workspace for billing","I want to identify performance bottlenecks (slow retrievals, expensive models) and optimize"],"best_for":["SaaS deployments needing usage-based billing","teams optimizing knowledge base quality and relevance","operations teams monitoring system health and performance"],"limitations":["Analytics collection adds latency to every request — requires careful instrumentation to avoid performance impact","Privacy concerns with tracking user queries — requires anonymization or explicit consent","Cost tracking is approximate — depends on accurate token counting and pricing data from LLM providers"],"requires":["Analytics backend (Mixpanel, Segment, custom database)","Time-series database for metrics (InfluxDB, Prometheus, or similar)","Dashboard tool (Grafana, Metabase, or custom)"],"input_types":["user query","retrieved documents","LLM response","system metrics (latency, tokens, cost)"],"output_types":["usage metrics (queries per user, documents accessed)","performance metrics (retrieval latency, LLM latency)","cost metrics (tokens used, cost per user/workspace)","dashboard visualizations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_2","uri":"capability://search.retrieval.semantic.search.and.retrieval.with.context.windowing","name":"semantic search and retrieval with context windowing","description":"Executes similarity search against stored embeddings to find relevant document chunks, then expands results with configurable context windows (preceding/following chunks) to provide LLMs with richer context. Uses cosine similarity or other distance metrics to rank results and optionally applies metadata filtering (date range, source, document type) before returning top-K results.","intents":["I want to find the most relevant documents for a user query without keyword matching limitations","I need to retrieve context around search results (e.g., surrounding paragraphs) to avoid orphaned snippets","I want to filter search results by document source or metadata before ranking"],"best_for":["RAG systems where semantic relevance matters more than keyword matching","applications with large document collections requiring fast retrieval (millisecond latency)","teams building conversational AI that needs rich context for coherent responses"],"limitations":["Retrieval quality depends entirely on embedding model quality — poor embeddings produce poor search results","Context windowing can bloat LLM input tokens, increasing latency and cost for large context windows","No built-in reranking — top-K results are returned in similarity order, not relevance order (requires separate reranker model)"],"requires":["Pre-computed embeddings in vector database","Vector database with similarity search API (Supabase, Pinecone, Weaviate)","Embedding model compatible with stored vectors"],"input_types":["user query (text)","optional metadata filters (JSON)"],"output_types":["ranked list of document chunks","similarity scores","expanded context (surrounding chunks)","metadata (source, page number)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_3","uri":"capability://text.generation.language.multi.turn.conversational.chat.with.memory.management","name":"multi-turn conversational chat with memory management","description":"Maintains conversation history across multiple turns, using a sliding-window or summary-based memory strategy to keep context within LLM token limits. Each user message is processed through the retrieval pipeline to fetch relevant documents, then combined with conversation history and system prompts to generate coherent responses. The system tracks conversation state (user ID, session ID, turn count) to enable multi-user and multi-session support.","intents":["I want users to have natural multi-turn conversations with my knowledge base without repeating context","I need to manage conversation memory efficiently so token usage doesn't explode with long conversations","I want to support multiple concurrent users with isolated conversation histories"],"best_for":["teams building chatbot interfaces over document collections","applications requiring conversational context awareness (e.g., follow-up questions)","multi-tenant systems where conversation isolation is critical"],"limitations":["Memory strategy is fixed (sliding window or summary) — no dynamic adjustment based on conversation importance","No built-in conversation persistence — requires external database to survive application restarts","Token limit management is reactive (truncate when exceeding limit) rather than proactive (estimate tokens before generation)"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","Vector database for document retrieval","Optional: conversation storage backend (PostgreSQL, Redis)"],"input_types":["user message (text)","conversation history (list of messages)","session/user ID"],"output_types":["LLM response (text)","retrieved document references","updated conversation history"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_4","uri":"capability://text.generation.language.llm.provider.abstraction.and.model.selection","name":"llm provider abstraction and model selection","description":"Abstracts LLM interactions behind a provider-agnostic interface supporting OpenAI, Anthropic, Hugging Face, and local models (via Ollama or similar). Handles API authentication, request formatting, response parsing, and error handling for each provider. Allows runtime model selection and parameter tuning (temperature, max_tokens, top_p) without code changes, enabling cost optimization and model experimentation.","intents":["I want to switch between LLM providers (OpenAI to Anthropic) without rewriting my application","I need to experiment with different models and parameters to optimize cost vs quality","I want to use local models for privacy-sensitive applications without cloud dependencies"],"best_for":["developers building LLM applications who want provider flexibility","teams optimizing for cost (switching between expensive and cheap models)","enterprises with data privacy requirements needing on-premise model support"],"limitations":["API differences between providers (e.g., function calling syntax) require provider-specific adapters — not fully transparent","Model-specific features (e.g., vision, tool use) are not uniformly supported across all providers","Switching models mid-application may require prompt tuning due to different model behaviors"],"requires":["API key for selected LLM provider (OpenAI, Anthropic, Hugging Face)","Or: local model server (Ollama, vLLM) for on-premise deployment","Python 3.9+"],"input_types":["prompt (text)","model parameters (temperature, max_tokens, etc.)","optional: function/tool definitions"],"output_types":["LLM response (text)","token usage metrics","optional: structured outputs (JSON)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_5","uri":"capability://text.generation.language.prompt.templating.and.dynamic.context.injection","name":"prompt templating and dynamic context injection","description":"Provides templating system for constructing prompts with dynamic placeholders for user queries, retrieved documents, conversation history, and system instructions. Templates support conditional logic (e.g., include history only if conversation length > N) and formatting options (e.g., numbered lists, markdown). At runtime, the system injects retrieved context, user input, and metadata into templates before sending to LLM.","intents":["I want to define reusable prompt templates that adapt to different contexts (RAG vs pure chat)","I need to control how retrieved documents are formatted in prompts (citations, summaries, full text)","I want to A/B test different prompt structures without code changes"],"best_for":["teams building RAG systems where prompt engineering is critical","applications requiring consistent prompt formatting across multiple use cases","non-technical users who want to customize prompts without code"],"limitations":["Template syntax is custom — requires learning a new DSL rather than using standard Jinja2 or similar","No built-in prompt optimization or validation — template quality depends on manual testing","Complex conditional logic in templates can become hard to maintain and debug"],"requires":["Python 3.9+","Template definitions (YAML, JSON, or Python dicts)"],"input_types":["template string with placeholders","context dict (user query, documents, history, metadata)"],"output_types":["rendered prompt (text)","token count estimate"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_6","uri":"capability://memory.knowledge.document.source.attribution.and.citation.generation","name":"document source attribution and citation generation","description":"Tracks the source and location (page number, chunk ID, document name) of each retrieved chunk and automatically generates citations in LLM responses. When the LLM references retrieved content, the system can append source metadata (e.g., '[Source: document.pdf, page 5]') or generate formatted citations (APA, MLA, Chicago style). Enables traceability of where information came from in the knowledge base.","intents":["I want users to know which documents the AI used to answer their question","I need to generate properly formatted citations for academic or professional use","I want to audit which documents are being used most frequently in responses"],"best_for":["applications requiring transparency and auditability (legal, medical, academic)","teams building research assistants or knowledge management systems","enterprises needing to track information lineage for compliance"],"limitations":["Citation accuracy depends on LLM faithfully referencing retrieved sources — no guarantee LLM won't hallucinate sources","Automatic citation formatting is basic — complex citation styles require manual post-processing","No built-in deduplication of sources — if multiple chunks from same document are retrieved, citations may be redundant"],"requires":["Document metadata (source name, page number, chunk ID) stored with embeddings","LLM that supports structured output or reliable citation markers in responses"],"input_types":["retrieved document chunks with metadata","LLM response (text)"],"output_types":["response with inline citations","source list (document name, page, relevance score)","formatted bibliography (optional)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_7","uri":"capability://tool.use.integration.user.and.workspace.management.with.multi.tenancy","name":"user and workspace management with multi-tenancy","description":"Provides user authentication, workspace isolation, and role-based access control (RBAC) to support multi-tenant deployments. Each user has isolated document collections, conversation histories, and vector embeddings. The system manages user credentials, API keys, and workspace settings, enabling self-hosted or SaaS deployments where multiple organizations can use the same instance without data leakage.","intents":["I want to deploy Quivr as a SaaS product where each customer has isolated data","I need to control which users can upload documents or access specific knowledge bases","I want to track usage per user or workspace for billing or analytics"],"best_for":["teams building multi-tenant SaaS products on top of Quivr","enterprises deploying Quivr internally with multiple departments","developers who want to offer Quivr as a white-label solution"],"limitations":["RBAC is basic (user, admin, viewer) — no fine-grained permission control at document level","No built-in audit logging — requires external logging system to track data access","Workspace isolation is logical (database filters) not physical — requires careful query construction to prevent data leakage"],"requires":["Authentication backend (JWT, OAuth2, or built-in user database)","Database for user and workspace metadata (PostgreSQL recommended)","Session management (Redis or similar for token storage)"],"input_types":["user credentials (email, password, or OAuth token)","workspace ID","role (user, admin, viewer)"],"output_types":["authentication token (JWT)","user profile (email, workspace, role)","workspace metadata (name, document count, usage)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_8","uri":"capability://automation.workflow.batch.document.processing.and.async.ingestion","name":"batch document processing and async ingestion","description":"Supports uploading multiple documents simultaneously and processes them asynchronously in the background, with progress tracking and error handling. Uses job queues (Celery, RQ, or similar) to distribute parsing, chunking, and embedding across workers, preventing blocking of the main application. Provides webhooks or polling endpoints to track ingestion status and retrieve results when complete.","intents":["I want to upload 1000 documents without waiting for all of them to be processed","I need to know when document ingestion completes so I can notify users","I want to scale document processing across multiple workers for faster throughput"],"best_for":["applications with large initial document uploads (data migration, knowledge base setup)","teams needing to process documents continuously without blocking user interactions","deployments requiring horizontal scaling of ingestion workloads"],"limitations":["Async processing adds complexity — requires job queue infrastructure (Redis, RabbitMQ)","Error handling in background jobs is harder to debug — failed ingestions may go unnoticed without monitoring","Progress tracking is approximate — actual completion time depends on queue depth and worker availability"],"requires":["Job queue system (Celery, RQ, or similar)","Message broker (Redis, RabbitMQ)","Worker processes (separate from main application)","Database for job status tracking"],"input_types":["file uploads (batch)","document metadata (optional)"],"output_types":["job ID (for tracking)","progress updates (percentage complete)","completion status (success/failure)","error messages (if processing fails)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quivr__cap_9","uri":"capability://automation.workflow.knowledge.base.versioning.and.document.history","name":"knowledge base versioning and document history","description":"Maintains version history for uploaded documents, allowing users to revert to previous versions or compare changes. When a document is updated, the system stores the new version alongside metadata (upload timestamp, uploader, change summary) and optionally re-embeds only changed chunks to avoid redundant computation. Enables rollback if a document is accidentally corrupted or outdated.","intents":["I want to track changes to my knowledge base over time and see who updated what","I need to revert a document to a previous version if it was accidentally modified","I want to understand how my knowledge base evolved (audit trail)"],"best_for":["teams managing living knowledge bases that change frequently","enterprises with compliance requirements for document audit trails","collaborative environments where multiple users edit the same knowledge base"],"limitations":["Version storage can consume significant disk space — requires cleanup policies for old versions","Incremental re-embedding (only changed chunks) is complex to implement correctly — may require full re-embedding as fallback","No built-in diff visualization — comparing versions requires manual inspection"],"requires":["Document storage backend with versioning support (S3 with versioning, Git, or database)","Metadata database to track version history","Optional: diff library for comparing document versions"],"input_types":["document file (updated version)","change summary (optional)","user ID (who made the change)"],"output_types":["version ID","version history (list of all versions with timestamps)","diff (changes between versions)","rollback confirmation"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","File system access with read permissions","Optional: OCR library (Tesseract) for scanned PDFs","API key for embedding service (OpenAI, Hugging Face, or local model server)","Vector database instance (Supabase, Pinecone, Weaviate, or local Chroma)","Network access to embedding service","Analytics backend (Mixpanel, Segment, custom database)","Time-series database for metrics (InfluxDB, Prometheus, or similar)","Dashboard tool (Grafana, Metabase, or custom)","Pre-computed embeddings in vector database"],"failure_modes":["No native support for image-heavy PDFs or scanned documents — requires OCR preprocessing","Chunking strategy is fixed per document type — no dynamic adjustment based on content density","Large files (>100MB) may require manual splitting to avoid memory overhead during parsing","Embedding model selection is fixed at initialization — switching models requires re-embedding all documents","No built-in deduplication of semantically similar chunks — may store redundant embeddings","Vector database choice locks you into that provider's ecosystem (Supabase pgvector vs Pinecone have different scaling characteristics)","Analytics collection adds latency to every request — requires careful instrumentation to avoid performance impact","Privacy concerns with tracking user queries — requires anonymization or explicit consent","Cost tracking is approximate — depends on accurate token counting and pricing data from LLM providers","Retrieval quality depends entirely on embedding model quality — poor embeddings produce poor search results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:23.056Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=quivr","compare_url":"https://unfragile.ai/compare?artifact=quivr"}},"signature":"l4bKIjXPM2L1ApCz7nXPMAdl95vcC9uyHKHc/NS0AQD6wwJWaIXeQbG0CPteivr79SGa3Tvz5wwdhAgPAgxzCQ==","signedAt":"2026-06-21T15:43:50.986Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/quivr","artifact":"https://unfragile.ai/quivr","verify":"https://unfragile.ai/api/v1/verify?slug=quivr","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"}}