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Uses recursive folder traversal to handle nested directory structures without manual file selection, enabling hands-off indexing of large document collections.","intents":["I want to index all my company documents automatically without manually selecting files","I need to process mixed document types (PDFs, spreadsheets, Word docs) in a single indexing pass","I want to set up document indexing once and have it handle new files added to a folder"],"best_for":["enterprises with large document repositories needing privacy-preserving search","teams migrating from cloud-based document search to on-premises solutions","organizations with compliance requirements preventing cloud data transfer"],"limitations":["No incremental indexing — full re-indexing required for updates, not delta-based","OCR not supported for scanned PDFs — text extraction only from digital documents","Large document collections (>100GB) may require significant disk space for embeddings storage","No built-in deduplication — duplicate documents will be indexed separately"],"requires":["Local filesystem access with read permissions to document directory","Python 3.8+ for text extraction libraries (pdfplumber, python-docx, openpyxl)","Sufficient disk space for vector embeddings (typically 1-2GB per 10,000 documents)"],"input_types":["PDF files","Microsoft Word documents (.docx)","Excel spreadsheets (.xls, .xlsx)","Plain text files (.txt)","Markdown files (.md)","CSV files"],"output_types":["Extracted text chunks","Vector embeddings (float arrays)","Metadata (filename, path, document type)"],"categories":["data-processing-analysis","document-ingestion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_1","uri":"capability://data.processing.analysis.sentence.transformer.embedding.generation.with.configurable.models","name":"sentence-transformer embedding generation with configurable models","description":"Generates dense vector embeddings for document chunks using Sentence Transformers (BAAI models by default), converting text into high-dimensional vectors suitable for semantic similarity search. Supports model selection via environment configuration, allowing users to choose embeddings optimized for their domain (e.g., multilingual, domain-specific fine-tuned models) without code changes.","intents":["I want to convert my documents into embeddings for semantic search without managing ML infrastructure","I need to choose an embedding model optimized for my specific domain or language","I want embeddings generated locally without sending data to external APIs"],"best_for":["teams requiring data privacy and on-premises ML inference","organizations with domain-specific documents needing specialized embedding models","developers building RAG systems with strict data residency requirements"],"limitations":["Embedding generation is CPU-bound and slow for large collections (typically 50-200 documents/minute on standard hardware)","Model size varies (100MB-500MB) and must fit in available RAM during inference","No GPU acceleration built-in — CPU-only by default, requiring manual CUDA configuration for speedup","Fixed embedding dimension per model (typically 384-768 dims) — cannot customize output size"],"requires":["Python 3.8+","sentence-transformers library (pip install sentence-transformers)","4GB+ RAM for model loading and inference","Internet connection for first-time model download from Hugging Face Hub"],"input_types":["Text strings (document chunks, typically 256-512 tokens)"],"output_types":["Dense float vectors (384-768 dimensions depending on model)","Embedding metadata (model name, dimension count)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_2","uri":"capability://memory.knowledge.qdrant.vector.database.storage.and.semantic.search","name":"qdrant vector database storage and semantic search","description":"Stores generated embeddings in Qdrant vector database and performs approximate nearest neighbor (ANN) search to retrieve semantically similar documents for a given query. Uses vector similarity metrics (cosine, Euclidean) to rank documents by relevance without keyword matching, enabling natural language search across document collections.","intents":["I want to search my documents using natural language questions instead of keywords","I need fast semantic similarity search across thousands of documents","I want to retrieve contextually relevant documents for LLM augmentation"],"best_for":["organizations building semantic search over proprietary documents","RAG systems requiring sub-second retrieval latency for interactive applications","teams needing vector database with on-premises deployment options"],"limitations":["Qdrant instance must be running separately — requires Docker or standalone deployment","No built-in persistence across container restarts without volume mounting","Search quality depends entirely on embedding model quality — poor embeddings = poor retrieval","Reranking is optional and adds latency — without it, top-k results may miss relevant documents"],"requires":["Qdrant server (Docker image: qdrant/qdrant:latest or standalone binary)","Network connectivity to Qdrant instance (localhost:6333 by default)","Persistent volume for Qdrant data storage (if using Docker)"],"input_types":["Query embeddings (float vectors matching indexed embedding dimension)","Search parameters (top_k, similarity threshold)"],"output_types":["Ranked list of document chunks with similarity scores","Metadata (document ID, filename, chunk position)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_3","uri":"capability://search.retrieval.semantic.reranking.with.baai.models.for.result.refinement","name":"semantic reranking with baai models for result refinement","description":"Applies a second-stage ranking model (typically BAAI cross-encoder) to refine the top-k results from vector search, re-scoring documents based on semantic relevance to the original query. This two-stage retrieval pattern (retrieve-then-rerank) improves precision by filtering out false positives from the initial ANN search without requiring full dataset re-scoring.","intents":["I want to improve search result quality beyond basic vector similarity","I need to filter out semantically irrelevant documents from the top-k results","I want to rank results by actual query relevance, not just embedding similarity"],"best_for":["RAG systems where result quality is critical (legal, medical, financial documents)","applications with strict latency requirements where reranking only top-k is acceptable","teams needing to improve search precision without reindexing all documents"],"limitations":["Reranking adds 100-500ms latency per query depending on top-k size and model","Reranker model must fit in memory alongside embeddings model — typically requires 2-4GB additional RAM","Reranking is applied only to top-k results — cannot recover documents ranked outside initial retrieval","No A/B testing framework built-in — difficult to measure reranking impact empirically"],"requires":["BAAI cross-encoder model (auto-downloaded from Hugging Face on first use)","2-4GB additional RAM for reranker model inference","Python 3.8+ with sentence-transformers library"],"input_types":["Query text","List of candidate documents (from vector search)","Top-k parameter (number of results to rerank)"],"output_types":["Reranked list of documents with updated relevance scores","Relevance scores (0-1 range from cross-encoder)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_4","uri":"capability://tool.use.integration.multi.llm.backend.integration.with.pluggable.providers","name":"multi-llm backend integration with pluggable providers","description":"Abstracts LLM interaction behind a provider interface supporting Ollama (local), OpenAI (ChatGPT), and Anthropic (Claude) without code changes. Uses environment configuration to select the active LLM backend, enabling users to switch between fully local inference and cloud LLMs based on deployment mode, privacy requirements, or cost considerations.","intents":["I want to use a local LLM for complete data privacy but fall back to cloud LLMs if needed","I need to switch between different LLM providers without rewriting my RAG application","I want to compare results from different LLMs (Ollama vs ChatGPT vs Claude) on the same documents"],"best_for":["enterprises with strict data residency requirements who want fallback to cloud LLMs","developers building LLM applications that need provider flexibility","teams evaluating different LLMs for RAG quality without architectural changes"],"limitations":["Each LLM provider has different API contracts — prompt formatting and parameter names vary","Local Ollama requires significant hardware (8GB+ RAM, GPU recommended) for reasonable inference speed","Cloud LLM integrations require valid API keys and internet connectivity — no offline fallback","Response latency varies dramatically (Ollama: 5-30s, ChatGPT: 1-5s, Claude: 2-8s) — no latency normalization"],"requires":["For Ollama: Ollama installed locally (ollama.ai) with model pulled (e.g., ollama pull mistral)","For OpenAI: OPENAI_API_KEY environment variable with valid ChatGPT API key","For Anthropic: ANTHROPIC_API_KEY environment variable with valid Claude API key","Network connectivity for cloud LLM providers"],"input_types":["System prompt (instructions for LLM behavior)","Retrieved context (document chunks from vector search)","User query (natural language question)"],"output_types":["LLM-generated text response","Response metadata (model name, tokens used, latency)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_5","uri":"capability://tool.use.integration.mcp.server.protocol.implementation.for.tool.integration","name":"mcp server protocol implementation for tool integration","description":"Exposes Minima's RAG capabilities as a Model Context Protocol (MCP) server, allowing external LLM clients (Claude Desktop, other MCP-compatible applications) to invoke document search and retrieval as remote tools. Implements MCP's request-response protocol for tool discovery, invocation, and result streaming without requiring direct API integration.","intents":["I want to use Claude Desktop to search my local documents without building a custom UI","I need to expose my RAG system as a tool that other MCP-compatible applications can call","I want my documents searchable from Claude without sending data to Anthropic"],"best_for":["Claude Desktop users wanting local document search without cloud data transfer","teams building MCP-compatible applications that need RAG capabilities","organizations integrating Minima with existing MCP tool ecosystems"],"limitations":["MCP server requires separate process/port — adds deployment complexity vs embedded library","Tool discovery and invocation adds network latency (typically 50-200ms per request)","MCP protocol is still evolving — breaking changes possible in future versions","Limited to MCP-compatible clients — cannot be used with non-MCP LLM applications"],"requires":["MCP server running (typically via docker-compose-mcp.yml)","MCP-compatible client (Claude Desktop, or other MCP tools)","Network connectivity between client and MCP server"],"input_types":["MCP tool invocation requests (JSON-RPC format)","Tool parameters (query string, top-k results)"],"output_types":["MCP tool results (JSON-formatted document chunks with metadata)","Tool execution status and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_6","uri":"capability://automation.workflow.web.ui.and.electron.desktop.application.interfaces","name":"web ui and electron desktop application interfaces","description":"Provides dual user interfaces for document search and RAG interaction: a web-based UI (accessible via browser) and a native Electron desktop application. Both interfaces connect to the same backend services (indexer, vector database, LLM) and support chat-style interaction with retrieved context, enabling non-technical users to search documents without CLI or API knowledge.","intents":["I want a user-friendly interface for searching documents without technical setup","I need a desktop application that works offline with local Ollama","I want to share document search capability with non-technical team members"],"best_for":["non-technical users needing document search without CLI knowledge","teams deploying Minima to end-users who expect desktop/web applications","organizations wanting both web and native app options for different use cases"],"limitations":["Web UI requires running web server — adds deployment complexity vs CLI-only","Electron app is platform-specific — separate builds needed for Windows, macOS, Linux","UI state is not persisted — conversation history lost on page refresh or app restart","No user authentication built-in — assumes single-user or trusted network deployment"],"requires":["Node.js 14+ for web UI and Electron app","Running backend services (indexer, Qdrant, LLM provider)","Web browser (for web UI) or Electron runtime (for desktop app)"],"input_types":["Natural language queries (text input)","Chat messages (multi-turn conversation)"],"output_types":["HTML-rendered search results with document snippets","Chat interface with LLM responses and source citations"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_7","uri":"capability://automation.workflow.environment.based.configuration.management","name":"environment-based configuration management","description":"Centralizes all system configuration through environment variables (.env file), including document paths, embedding models, vector database endpoints, LLM providers, and API keys. Eliminates need for code changes when switching deployment modes, models, or providers — configuration is purely declarative and environment-specific.","intents":["I want to configure Minima for different environments (dev, staging, prod) without code changes","I need to switch between local Ollama and cloud LLMs by changing environment variables","I want to manage API keys and sensitive configuration securely without hardcoding"],"best_for":["DevOps teams managing multiple Minima deployments across environments","developers switching between local and cloud configurations frequently","organizations with security policies requiring externalized configuration"],"limitations":["No validation of environment variables at startup — invalid configs only fail at runtime","No built-in secrets management — API keys stored in plaintext .env files (requires external secret store)","Configuration changes require service restart — no hot-reload capability","Limited documentation of all available environment variables — discovery requires code inspection"],"requires":[".env file in application root directory","Environment variables set before application startup"],"input_types":["Environment variable key-value pairs"],"output_types":["Parsed configuration object used by all services"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_8","uri":"capability://automation.workflow.docker.compose.orchestration.for.multi.service.deployment","name":"docker compose orchestration for multi-service deployment","description":"Provides three pre-configured Docker Compose files (docker-compose-ollama.yml, docker-compose-chatgpt.yml, docker-compose-mcp.yml) that orchestrate all required services (indexer, web UI, Qdrant, LLM provider) as containers. Eliminates manual service startup and dependency management — single docker-compose up command deploys entire RAG system with correct networking and volume configuration.","intents":["I want to deploy Minima with all services (Qdrant, LLM, UI) in one command","I need to switch between deployment modes (local Ollama vs ChatGPT vs MCP) easily","I want reproducible deployments across development and production environments"],"best_for":["DevOps teams deploying Minima to production or staging environments","developers wanting quick local setup without manual service configuration","teams using Docker/Kubernetes for infrastructure management"],"limitations":["Requires Docker and Docker Compose installation — adds dependency for non-containerized deployments","Each deployment mode has separate Compose file — no single file supporting all modes","Volume mounting for document indexing requires host path configuration — not portable across machines","Resource limits not pre-configured — Ollama container may consume excessive memory without limits"],"requires":["Docker 20.10+","Docker Compose 1.29+","Sufficient disk space for Qdrant data and model downloads (10GB+ recommended)","For Ollama mode: GPU support (optional but recommended for performance)"],"input_types":["Docker Compose YAML configuration","Environment variables for service configuration"],"output_types":["Running containerized services (indexer, web UI, Qdrant, LLM)","Network connectivity between services"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-minima__cap_9","uri":"capability://data.processing.analysis.incremental.document.indexing.with.change.detection","name":"incremental document indexing with change detection","description":"Monitors local document directory for new or modified files and updates the vector database incrementally without full re-indexing. Tracks file modification timestamps and checksums to detect changes, re-embedding only affected documents while preserving existing embeddings for unchanged files. Reduces indexing time and computational cost for large document collections with frequent updates.","intents":["I want to add new documents to my index without re-indexing everything","I need to update embeddings when documents change without full re-processing","I want to keep my search index current with minimal computational overhead"],"best_for":["organizations with large document repositories (>10GB) receiving frequent updates","teams needing near-real-time document search with minimal indexing latency","systems with resource constraints where full re-indexing is prohibitively expensive"],"limitations":["Change detection relies on filesystem timestamps — may miss changes if timestamps are not updated","No distributed change tracking — only works with local filesystem, not network shares or cloud storage","Deleted documents are not automatically removed from index — requires manual cleanup","Chunk boundaries may shift when documents are updated — orphaned embeddings may remain"],"requires":["Filesystem with reliable modification timestamp tracking","Write access to document directory for change detection","Persistent storage for change tracking metadata"],"input_types":["Document directory path","File modification timestamps"],"output_types":["List of changed/new documents","Updated embeddings for changed documents"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Local filesystem access with read permissions to document directory","Python 3.8+ for text extraction libraries (pdfplumber, python-docx, openpyxl)","Sufficient disk space for vector embeddings (typically 1-2GB per 10,000 documents)","Python 3.8+","sentence-transformers library (pip install sentence-transformers)","4GB+ RAM for model loading and inference","Internet connection for first-time model download from Hugging Face Hub","Qdrant server (Docker image: qdrant/qdrant:latest or standalone binary)","Network connectivity to Qdrant instance (localhost:6333 by default)","Persistent volume for Qdrant data storage (if using Docker)"],"failure_modes":["No incremental indexing — full re-indexing required for updates, not delta-based","OCR not supported for scanned PDFs — text extraction only from digital documents","Large document collections (>100GB) may require significant disk space for embeddings storage","No built-in deduplication — duplicate documents will be indexed separately","Embedding generation is CPU-bound and slow for large collections (typically 50-200 documents/minute on standard hardware)","Model size varies (100MB-500MB) and must fit in available RAM during inference","No GPU acceleration built-in — CPU-only by default, requiring manual CUDA configuration for speedup","Fixed embedding dimension per model (typically 384-768 dims) — cannot customize output size","Qdrant instance must be running separately — requires Docker or standalone deployment","No built-in persistence across container restarts without volume mounting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=minima","compare_url":"https://unfragile.ai/compare?artifact=minima"}},"signature":"8v1w+4lS02RI+7lbFN5enmy4NdX6tePKeT0+jI0y7raJoUEa6cRNBR+YZpTzW06TnLw8nXnD8Nmz2p0McTv8Bg==","signedAt":"2026-06-20T10:40:51.722Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/minima","artifact":"https://unfragile.ai/minima","verify":"https://unfragile.ai/api/v1/verify?slug=minima","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"}}