{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-quivrhq--quivr","slug":"quivrhq--quivr","name":"quivr","type":"mcp","url":"https://core.quivr.com","page_url":"https://unfragile.ai/quivrhq--quivr","categories":["rag-knowledge"],"tags":["ai","api","chatbot","chatgpt","database","docker","framework","frontend","groq","html","javascript","llm","openai","postgresql","privacy","rag","react","security","typescript","vector"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-quivrhq--quivr__cap_0","uri":"capability://data.processing.analysis.multi.format.document.ingestion.with.automatic.chunking","name":"multi-format document ingestion with automatic chunking","description":"Ingests diverse document types (PDF, TXT, Markdown, DOCX) through Brain.from_files() and automatically chunks content into semantically meaningful segments for vector storage. Uses configurable chunking strategies that preserve document structure while optimizing for retrieval performance. Handles file parsing, text extraction, and pre-processing in a unified pipeline before embedding.","intents":["I want to load multiple document types into my RAG system without writing custom parsers","I need to automatically split large documents into retrieval-optimized chunks","I want to preserve document structure (headings, sections) during ingestion"],"best_for":["Teams building knowledge bases from heterogeneous document sources","Developers integrating RAG into existing document management systems","Non-technical users uploading files without preprocessing"],"limitations":["Chunking strategy is fixed per configuration — no dynamic chunk size adjustment based on content type","No built-in OCR for scanned PDFs; requires pre-processing for image-based documents","Large files (>100MB) may require external streaming ingestion; in-memory processing limits apply"],"requires":["Python 3.9+","quivr-core package installed from PyPI","Supported file formats (PDF, TXT, Markdown, DOCX)","LLM endpoint configured for embedding generation"],"input_types":["PDF files","Plain text files","Markdown documents","DOCX files","File paths or file objects"],"output_types":["Chunked text segments","Vector embeddings (stored in configured vector store)","Metadata (source, chunk index, page numbers)"],"categories":["data-processing-analysis","document-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_1","uri":"capability://memory.knowledge.vector.embedding.and.storage.with.pluggable.backends","name":"vector embedding and storage with pluggable backends","description":"Abstracts vector storage through a configurable backend system supporting PGVector (PostgreSQL), FAISS (local), and other vector databases. Automatically generates embeddings using configured LLM endpoints and persists vectors with metadata. The Brain class manages the lifecycle of vector store initialization, document indexing, and retrieval without exposing backend-specific APIs to the user.","intents":["I want to switch vector databases (FAISS to PGVector) without changing application code","I need to store embeddings with document metadata for filtering and ranking","I want to use local vector storage for privacy or cloud-based storage for scalability"],"best_for":["Teams evaluating different vector databases for production RAG","Developers building privacy-first applications requiring local-only storage","Organizations with existing PostgreSQL infrastructure wanting to leverage PGVector"],"limitations":["Vector store abstraction adds ~50-100ms overhead per operation due to adapter layer","No built-in vector store replication or failover; requires external orchestration","Embedding model is fixed per Brain instance — cannot mix embeddings from different models in same store"],"requires":["Python 3.9+","Vector store backend installed (pgvector extension for PostgreSQL, or FAISS library)","LLM endpoint configured for embedding generation","Database credentials if using cloud vector stores"],"input_types":["Text chunks (from document ingestion)","Metadata dictionaries","Query strings for retrieval"],"output_types":["Vector embeddings (float arrays)","Stored vectors with metadata in chosen backend","Retrieved chunks ranked by similarity"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_10","uri":"capability://memory.knowledge.brain.persistence.and.state.management","name":"brain persistence and state management","description":"Provides the Brain class as a stateful container for RAG operations, managing document ingestion, vector store lifecycle, conversation history, and pipeline configuration. Brain instances can be serialized and persisted to disk or external storage, enabling recovery of RAG state across application restarts. Supports both in-memory and persistent backends.","intents":["I want to save and restore RAG state (documents, vectors, conversation) across sessions","I need to manage multiple independent RAG instances for different knowledge domains","I want to version control RAG configurations and document sets"],"best_for":["Production RAG systems requiring state persistence","Multi-user systems with per-user or per-tenant RAG instances","Teams managing RAG deployments with configuration versioning"],"limitations":["Brain serialization includes full vector store — large knowledge bases produce large serialized objects","No built-in distributed state management; Brain instances are single-process only","Persistence format is implementation-specific; no standard export format for portability"],"requires":["Python 3.9+","Storage backend (local filesystem or external database)","Sufficient disk space for serialized Brain objects"],"input_types":["Brain configuration (LLM endpoint, vector store, RAG pipeline)","Document collection (ingested files)","Conversation history"],"output_types":["Serialized Brain object (pickle or JSON)","Persisted vector store","Conversation state"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_11","uri":"capability://text.generation.language.prompt.templating.and.customization.system","name":"prompt templating and customization system","description":"Provides configurable prompt templates for each RAG pipeline step (query rewriting, retrieval, generation) that can be customized via configuration files or programmatically. Templates support variable substitution for query, context, and conversation history. Enables fine-tuning of LLM behavior without code changes.","intents":["I want to customize system prompts for query rewriting and answer generation","I need to adapt prompts for different domains or languages","I want to experiment with different prompt strategies without code changes"],"best_for":["Teams optimizing RAG quality through prompt engineering","Multi-domain RAG systems with domain-specific prompts","Organizations experimenting with different generation strategies"],"limitations":["Prompt quality depends on manual engineering — no automated prompt optimization","Template variables are fixed; no dynamic prompt generation based on context","No built-in prompt versioning or A/B testing framework"],"requires":["Python 3.9+","YAML configuration with prompt templates OR programmatic template definition"],"input_types":["Prompt template (string with variables)","Template variables (query, context, history)","Configuration file"],"output_types":["Rendered prompt (string with variables substituted)","LLM input"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_12","uri":"capability://tool.use.integration.fastapi.backend.service.with.rest.api","name":"fastapi backend service with rest api","description":"Provides a production-ready FastAPI backend that exposes Quivr RAG capabilities through REST endpoints. Handles authentication, request validation, error handling, and response formatting. Integrates with Supabase for user management and document storage. Enables deployment of RAG as a scalable web service.","intents":["I want to expose RAG capabilities as a REST API for web/mobile clients","I need authentication and multi-user support for RAG","I want to deploy RAG as a scalable backend service"],"best_for":["Teams building web/mobile applications with RAG backends","Organizations deploying RAG as a shared service","Developers requiring REST API compatibility"],"limitations":["REST API adds latency compared to direct Python library usage","Authentication overhead (Supabase integration) adds complexity","Streaming responses over HTTP have higher overhead than direct library calls"],"requires":["Python 3.9+","FastAPI framework","Supabase account for authentication and storage","Docker for containerized deployment"],"input_types":["HTTP requests (JSON payloads)","Query parameters","File uploads (for document ingestion)"],"output_types":["JSON responses","Streaming responses (Server-Sent Events)","File downloads"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_13","uri":"capability://automation.workflow.next.js.frontend.application.with.chat.ui","name":"next.js frontend application with chat ui","description":"Provides a production-ready Next.js frontend application with a chat interface for interacting with RAG. Includes real-time message streaming, conversation history display, document upload, and configuration management. Integrates with the FastAPI backend and provides a reference implementation for RAG UI patterns.","intents":["I want a ready-to-use chat interface for RAG without building from scratch","I need real-time streaming display of RAG responses","I want a reference implementation for RAG UI patterns"],"best_for":["Teams building RAG applications and needing a UI starting point","Developers learning RAG UI patterns and best practices","Organizations deploying Quivr as a complete application"],"limitations":["Frontend is tightly coupled to Quivr backend API — customization requires forking","No built-in analytics or usage tracking","Mobile responsiveness may require additional customization"],"requires":["Node.js 18+","Next.js framework","Connection to Quivr FastAPI backend"],"input_types":["User text input","File uploads","Configuration changes"],"output_types":["Rendered chat UI","Streamed response display","Conversation history"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_2","uri":"capability://planning.reasoning.langgraph.orchestrated.rag.pipeline.with.multi.step.workflow","name":"langgraph-orchestrated rag pipeline with multi-step workflow","description":"Implements a sophisticated RAG workflow using LangGraph that chains together four key steps: filter_history (conversation context management), rewrite (query optimization), retrieve (semantic search), and generate_rag (LLM-based answer generation). Each step is a discrete node in a directed acyclic graph, enabling conditional routing, error handling, and extensibility. The QuivrQARAGLangGraph class manages state transitions and data flow between steps.","intents":["I want to improve query quality before retrieval through automatic rewriting","I need to maintain conversation context across multiple turns without token explosion","I want to customize the RAG pipeline by adding/removing steps or changing their order"],"best_for":["Teams building multi-turn conversational RAG systems","Developers needing fine-grained control over RAG pipeline stages","Organizations requiring custom retrieval or generation logic at specific pipeline points"],"limitations":["LangGraph orchestration adds ~200-300ms latency per pipeline execution due to state management and node transitions","Conversation history filtering is stateless — no built-in persistence; requires external session store for multi-user scenarios","Pipeline is sequential; no parallel execution of independent steps (e.g., retrieve and web search simultaneously)"],"requires":["Python 3.9+","LangGraph library (included in quivr-core dependencies)","LLM endpoint configured for query rewriting and generation","Vector store backend for retrieval step"],"input_types":["User query (string)","Conversation history (list of messages)","Retrieved context (text chunks with metadata)"],"output_types":["Generated answer (string)","Pipeline state (intermediate results from each step)","Streaming tokens (if using ask_streaming())"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_3","uri":"capability://planning.reasoning.query.rewriting.for.improved.retrieval","name":"query rewriting for improved retrieval","description":"Automatically rewrites user queries using an LLM before retrieval to improve semantic matching and reduce ambiguity. The rewrite step in the RAG pipeline transforms natural language queries into optimized forms that better align with document content and retrieval model expectations. This step operates within the LangGraph pipeline and uses the configured LLM endpoint.","intents":["I want to handle vague or ambiguous user queries by expanding them with context","I need to improve retrieval quality without modifying the vector index","I want to normalize queries across different phrasings of the same intent"],"best_for":["Applications with diverse user query patterns and vocabulary","Teams dealing with short or ambiguous user inputs","Systems where retrieval quality is critical and query optimization is cost-effective"],"limitations":["Query rewriting adds ~500ms-1s latency per query (LLM inference time)","Rewriting quality depends entirely on LLM capability — weaker models may produce worse queries","No feedback loop to learn from retrieval failures and improve rewriting over time"],"requires":["Python 3.9+","LLM endpoint configured and accessible","Sufficient LLM context window to process query + system prompt"],"input_types":["User query (string)","Conversation history (optional, for context)"],"output_types":["Rewritten query (string)","Query metadata (confidence, alternative phrasings)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_4","uri":"capability://search.retrieval.semantic.search.with.conversation.history.filtering","name":"semantic search with conversation history filtering","description":"Performs semantic similarity search against the vector store to retrieve relevant document chunks, with optional filtering based on conversation history to avoid redundant or contradictory context. The retrieve step uses the rewritten query to find top-k similar chunks, and the filter_history step prunes conversation history to fit within token budgets while preserving semantic continuity. Both steps operate within the LangGraph pipeline.","intents":["I want to find relevant documents based on semantic similarity, not keyword matching","I need to manage conversation context size to avoid exceeding LLM token limits","I want to avoid retrieving duplicate or contradictory information across turns"],"best_for":["Multi-turn conversational systems with long interaction histories","Applications requiring semantic understanding beyond keyword search","Teams managing token budgets for expensive LLM APIs"],"limitations":["Semantic search quality depends on embedding model quality — weak embeddings produce poor retrieval","History filtering is heuristic-based (token counting) — may drop important context or retain irrelevant context","No built-in deduplication of retrieved chunks; similar chunks from different documents are all returned"],"requires":["Python 3.9+","Vector store backend with similarity search capability","Embedding model configured for query encoding","Token counter for conversation history management"],"input_types":["Query embedding (vector)","Conversation history (list of messages with token counts)","Metadata filters (optional, for document filtering)"],"output_types":["Retrieved chunks (list of text segments with similarity scores)","Filtered conversation history (pruned messages within token budget)","Retrieval metadata (source documents, chunk indices, similarity scores)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.endpoint.abstraction","name":"multi-provider llm endpoint abstraction","description":"Abstracts LLM provider integrations through the LLMEndpoint class, supporting OpenAI, Anthropic Claude, Mistral, and local models via Ollama. Provides a unified interface for model inference, streaming, and function calling across providers with automatic fallback and error handling. Configuration-driven provider selection allows switching models without code changes.","intents":["I want to use different LLM providers (OpenAI, Anthropic, local) interchangeably","I need to switch models for cost optimization or performance tuning","I want to run models locally for privacy while maintaining the same application code"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Organizations with privacy requirements needing local model support","Developers building multi-tenant systems with per-user model selection"],"limitations":["LLMEndpoint abstraction adds ~50-100ms overhead per inference due to provider-specific request formatting","Function calling schemas vary across providers — complex tool use may require provider-specific tuning","Streaming implementation differs by provider; some providers have higher latency to first token"],"requires":["Python 3.9+","API keys for chosen LLM providers (OpenAI, Anthropic, Mistral) OR local Ollama instance","Network connectivity to provider APIs (unless using local Ollama)"],"input_types":["Prompt (string or message list)","Model configuration (provider, model name, temperature, max_tokens)","Function definitions (for tool calling)"],"output_types":["Generated text (string)","Streaming tokens (iterator)","Function call results (structured data)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_6","uri":"capability://text.generation.language.streaming.response.generation.with.token.by.token.output","name":"streaming response generation with token-by-token output","description":"Provides ask_streaming() method that returns tokens incrementally as the LLM generates them, enabling real-time response display in user interfaces. Implements streaming across the entire RAG pipeline, from query rewriting through final answer generation. Handles provider-specific streaming protocols (Server-Sent Events for OpenAI, etc.) and normalizes them into a unified token stream.","intents":["I want to display LLM responses in real-time as they're generated","I need to reduce perceived latency by showing tokens immediately","I want to allow users to interrupt generation mid-stream"],"best_for":["Web and mobile applications with real-time UI requirements","Teams building chatbot interfaces with low-latency expectations","Applications where user engagement depends on immediate feedback"],"limitations":["Streaming adds complexity to error handling — failures mid-stream may leave partial responses","Token-by-token output prevents batch optimizations; throughput is lower than non-streaming","Some LLM providers have higher latency to first token in streaming mode"],"requires":["Python 3.9+","LLM provider with streaming support (OpenAI, Anthropic, Mistral, Ollama)","Client capable of consuming streaming responses (WebSocket or Server-Sent Events)"],"input_types":["User query (string)","Conversation history (list of messages)","Streaming configuration (chunk size, timeout)"],"output_types":["Token stream (iterator of strings)","Metadata (token count, generation time)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_7","uri":"capability://automation.workflow.configuration.driven.rag.customization.via.yaml.workflows","name":"configuration-driven rag customization via yaml workflows","description":"Enables RAG pipeline customization through YAML configuration files that define workflow steps, LLM endpoints, vector stores, and tool integrations without code changes. The configuration system parses YAML specs and instantiates the corresponding Brain and RAG pipeline components. Supports conditional routing, tool definitions, and prompt templates within the configuration layer.","intents":["I want to customize RAG behavior (prompts, models, tools) without redeploying code","I need to manage multiple RAG configurations for different use cases","I want non-technical users to adjust RAG parameters through configuration files"],"best_for":["Teams managing multiple RAG deployments with different configurations","Organizations with non-technical stakeholders who need to tune RAG behavior","Developers building multi-tenant systems with per-tenant RAG customization"],"limitations":["YAML configuration complexity grows quickly with advanced customizations; large configs become hard to maintain","No built-in validation of configuration correctness — invalid configs fail at runtime, not parse time","Configuration changes require application restart; no hot-reload capability"],"requires":["Python 3.9+","YAML configuration file with valid schema","All referenced LLM endpoints and vector stores must be accessible"],"input_types":["YAML configuration file","Environment variables for secrets (API keys, credentials)"],"output_types":["Instantiated Brain object","Configured RAG pipeline","Tool registry"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_8","uri":"capability://tool.use.integration.tool.integration.and.function.calling.framework","name":"tool integration and function calling framework","description":"Provides a framework for integrating external tools (web search, APIs, custom functions) into the RAG pipeline through a tool registry and function calling interface. Tools are defined declaratively with schemas and can be invoked by the LLM during generation or as separate pipeline steps. Includes built-in web search tools and supports custom tool definitions.","intents":["I want to augment RAG with real-time information (web search, APIs)","I need to call external functions based on LLM reasoning","I want to extend RAG with domain-specific tools without modifying core pipeline"],"best_for":["Applications requiring real-time information beyond training data","Teams building specialized RAG systems with domain-specific tools","Developers creating agentic RAG systems that reason about tool use"],"limitations":["Tool invocation adds latency — web search tools add 2-5s per call","LLM function calling quality varies by model; weaker models may misuse tools","No built-in tool result validation or error recovery; failed tool calls propagate to user"],"requires":["Python 3.9+","Tool definitions with JSON schemas","LLM provider supporting function calling (OpenAI, Anthropic, Mistral)","External tool endpoints (for web search, APIs, etc.)"],"input_types":["Tool definitions (JSON schema)","Tool parameters (from LLM function calls)","User query (for tool selection)"],"output_types":["Tool results (structured data or text)","Function call logs (for debugging)","Final answer incorporating tool results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-quivrhq--quivr__cap_9","uri":"capability://memory.knowledge.conversation.memory.management.with.context.windowing","name":"conversation memory management with context windowing","description":"Manages multi-turn conversation state through the filter_history pipeline step, which maintains conversation history while respecting token budgets and semantic coherence. Implements heuristic-based history pruning that removes older messages while preserving recent context and key information. Conversation state is tracked in the Brain object and passed through the RAG pipeline.","intents":["I want to maintain conversation context across multiple turns without exceeding token limits","I need to prevent context explosion in long conversations","I want to preserve important context while pruning redundant messages"],"best_for":["Multi-turn conversational RAG systems","Applications with cost-sensitive LLM usage","Teams building chatbots with long conversation histories"],"limitations":["History filtering is stateless — no learning from conversation patterns; same heuristics apply to all conversations","No built-in persistence; conversation history is lost on application restart unless externally stored","Pruning heuristics may drop important context if token budget is aggressive"],"requires":["Python 3.9+","Token counter for LLM (e.g., tiktoken for OpenAI models)","Configured token budget (max_tokens parameter)"],"input_types":["Conversation history (list of messages with roles and content)","Token budget (integer)","Current query (for context relevance)"],"output_types":["Filtered conversation history (pruned messages)","Pruning metadata (removed messages, token savings)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","quivr-core package installed from PyPI","Supported file formats (PDF, TXT, Markdown, DOCX)","LLM endpoint configured for embedding generation","Vector store backend installed (pgvector extension for PostgreSQL, or FAISS library)","Database credentials if using cloud vector stores","Storage backend (local filesystem or external database)","Sufficient disk space for serialized Brain objects","YAML configuration with prompt templates OR programmatic template definition","FastAPI framework"],"failure_modes":["Chunking strategy is fixed per configuration — no dynamic chunk size adjustment based on content type","No built-in OCR for scanned PDFs; requires pre-processing for image-based documents","Large files (>100MB) may require external streaming ingestion; in-memory processing limits apply","Vector store abstraction adds ~50-100ms overhead per operation due to adapter layer","No built-in vector store replication or failover; requires external orchestration","Embedding model is fixed per Brain instance — cannot mix embeddings from different models in same store","Brain serialization includes full vector store — large knowledge bases produce large serialized objects","No built-in distributed state management; Brain instances are single-process only","Persistence format is implementation-specific; no standard export format for portability","Prompt quality depends on manual engineering — no automated prompt optimization","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8003232551858542,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:26.976Z","last_commit":"2025-07-09T12:55:23Z"},"community":{"stars":39124,"forks":3752,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=quivrhq--quivr","compare_url":"https://unfragile.ai/compare?artifact=quivrhq--quivr"}},"signature":"H8ffaIPG8R80NWv5cgitCJcpUZySnq0lJcyaRbqUZx9jUMYS/53L7l6VNOYrhZPzcR0JgXnZK0d0Rg2gLexUAg==","signedAt":"2026-06-22T10:00:03.491Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/quivrhq--quivr","artifact":"https://unfragile.ai/quivrhq--quivr","verify":"https://unfragile.ai/api/v1/verify?slug=quivrhq--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"}}