{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-all-minilm","slug":"all-minilm","name":"All-MiniLM (22M, 33M)","type":"model","url":"https://ollama.com/library/all-minilm","page_url":"https://unfragile.ai/all-minilm","categories":["rag-knowledge"],"tags":["ollama","open-source","embeddings","sentence-transformers","embedding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-all-minilm__cap_0","uri":"capability://memory.knowledge.dense.vector.embedding.generation.for.semantic.similarity","name":"dense vector embedding generation for semantic similarity","description":"Generates fixed-dimensional dense vector embeddings from input text using self-supervised contrastive learning trained on large sentence-level datasets. The model encodes semantic meaning into a continuous vector space, enabling downstream similarity computations via cosine distance or dot product. Embeddings are computed locally via Ollama's inference runtime, with REST API and language-specific client bindings (Python, JavaScript) for integration.","intents":["I need to convert text into vectors for semantic search in my RAG pipeline","I want to compute similarity between two sentences without calling a cloud API","I need embeddings for clustering or similarity-based retrieval in my knowledge base","I'm building a local semantic search engine and need fast, lightweight embeddings"],"best_for":["Developers building RAG systems with local-first or privacy-sensitive requirements","Teams needing lightweight embeddings for resource-constrained environments (edge devices, mobile backends)","Researchers prototyping semantic search without cloud API costs or latency","Organizations requiring on-device inference for compliance or data residency"],"limitations":["Fixed 512-token context window — cannot embed documents or passages longer than ~400 words; requires chunking for longer texts","Embedding dimensionality unknown from documentation — cannot optimize vector storage or similarity computation without reverse-engineering model output","No explicit multilingual support documented — unclear if model generalizes across languages or is English-only","No quantization or precision details provided — actual inference latency and memory footprint depend on undocumented deployment format","Contrastive learning approach may produce embeddings less semantically rich than larger models (e.g., OpenAI text-embedding-3) for specialized domains"],"requires":["Ollama 0.1.26 or later installed locally or via Ollama Cloud","Python 3.6+ with ollama library (pip install ollama) for Python integration, or Node.js 14+ for JavaScript","Minimum ~100MB disk space for model download (22M variant ~46MB, 33M variant ~67MB)","For cloud deployment: Ollama Cloud account with appropriate concurrency tier (Free: 1 model, Pro: 3 models, Max: 10 models)"],"input_types":["Plain text strings (sentences, paragraphs, document chunks)","UTF-8 encoded text up to 512 tokens"],"output_types":["Dense vector embeddings (dimensionality unspecified in documentation)","Returned as JSON array in REST API response or native array in client libraries"],"categories":["memory-knowledge","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-all-minilm__cap_1","uri":"capability://tool.use.integration.local.inference.via.ollama.rest.api.with.multi.language.client.support","name":"local inference via ollama rest api with multi-language client support","description":"Exposes embedding generation through Ollama's standardized REST API endpoint (POST /api/embeddings) and language-specific client libraries (Python ollama.embeddings(), JavaScript ollama.embeddings()). Requests are routed to a locally-running Ollama daemon, which manages model loading, GPU/CPU inference, and response serialization. No authentication or API keys required for local deployment; cloud-hosted Ollama Cloud requires account credentials.","intents":["I want to call the embedding model from Python or JavaScript without managing model loading myself","I need a simple HTTP endpoint to integrate embeddings into my existing microservices architecture","I'm deploying embeddings to Ollama Cloud and need to know what concurrency limits apply","I want to batch embed multiple texts efficiently through the REST API"],"best_for":["Full-stack developers integrating embeddings into web applications or microservices","Teams using Ollama as a unified local LLM/embedding inference platform","Organizations deploying to Ollama Cloud for managed inference without self-hosting","Polyglot teams needing consistent embedding APIs across Python and JavaScript codebases"],"limitations":["REST API is synchronous only — no streaming or async response support documented; each request blocks until embedding is computed","Concurrency limits on Ollama Cloud tier-dependent (Free: 1 concurrent model, Pro: 3, Max: 10) — high-throughput embedding pipelines may require Pro/Max tier","No built-in batching API — multiple embeddings require sequential HTTP requests; batching must be implemented at application layer","Local Ollama deployment requires manual model management and server lifecycle (startup, shutdown, resource allocation)","No documented rate limiting or quota management for Ollama Cloud — unclear if free tier has throughput caps"],"requires":["Ollama daemon running locally (ollama serve) or Ollama Cloud account with API credentials","For local: Ollama 0.1.26+ installed on Linux, macOS, or Windows","For Python: ollama Python package (pip install ollama)","For JavaScript: ollama JavaScript library (npm install ollama)","Network connectivity to localhost:11434 (local) or Ollama Cloud endpoint (cloud)"],"input_types":["JSON payload with 'model' and 'prompt' fields via POST /api/embeddings","Text string in 'prompt' field (up to 512 tokens)"],"output_types":["JSON response with 'embedding' array (vector values) and optional metadata","Native array/list in Python and JavaScript client libraries"],"categories":["tool-use-integration","api-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-all-minilm__cap_2","uri":"capability://memory.knowledge.lightweight.model.variants.optimized.for.resource.constrained.deployment","name":"lightweight model variants optimized for resource-constrained deployment","description":"Provides two parameter-efficient model variants (22M and 33M parameters) designed for edge devices, mobile backends, and resource-constrained environments. Both variants fit in <100MB disk space and are quantized/optimized for Ollama's GGUF format (exact quantization method undocumented). The 22M variant prioritizes minimal footprint; the 33M variant trades slightly larger size for potentially improved semantic quality. Model selection is transparent to the API — clients specify 'all-minilm:22m' or 'all-minilm:33m' in requests.","intents":["I need embeddings on a Raspberry Pi or edge device with <500MB RAM","I want to minimize model download size and inference latency for a mobile backend","I'm running multiple embedding models on a single GPU and need to fit them in VRAM","I need to choose between semantic quality and resource consumption for my use case"],"best_for":["Edge computing and IoT deployments with strict memory/storage constraints","Mobile app backends requiring on-device or lightweight cloud inference","Multi-model inference systems where VRAM is shared across embeddings, LLMs, and other models","Cost-sensitive deployments where model size directly impacts infrastructure costs"],"limitations":["Exact parameter counts (22M, 33M) are inferred from model size and naming — not officially documented, making it impossible to verify actual architecture","Quantization method and precision (e.g., int8, fp16, fp32) are unknown — actual inference speed and memory footprint depend on undocumented deployment format","No semantic quality benchmarks comparing 22M vs. 33M variants — unclear which variant is appropriate for specific use cases","Both variants share the same 512-token context window — no size/quality tradeoff for longer sequences","No documented hardware requirements (GPU VRAM, CPU cores) — actual deployability on edge devices is untested"],"requires":["Ollama 0.1.26+ with support for model variants","~46MB disk space for 22M variant, ~67MB for 33M variant","Sufficient RAM for model loading (exact requirement unknown; likely 200-500MB based on parameter count)","Optional: GPU with VRAM for accelerated inference (VRAM requirement undocumented)"],"input_types":["Text strings via 'all-minilm:22m' or 'all-minilm:33m' model selector"],"output_types":["Dense vector embeddings (dimensionality unspecified)"],"categories":["memory-knowledge","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-all-minilm__cap_3","uri":"capability://search.retrieval.semantic.similarity.computation.via.vector.distance.metrics","name":"semantic similarity computation via vector distance metrics","description":"Embeddings generated by All-MiniLM are designed for semantic similarity computation using standard distance metrics (cosine similarity, dot product, Euclidean distance). The model's contrastive learning training objective aligns semantically similar texts to have high dot product in the embedding space. Similarity computation is performed client-side using standard linear algebra libraries (numpy, torch, etc.) — the model itself only generates embeddings; similarity scoring is the responsibility of the application layer.","intents":["I want to find the most similar documents in my knowledge base for a given query","I need to compute pairwise similarity between multiple texts for clustering or deduplication","I'm building a semantic search engine and need to rank results by relevance","I want to detect near-duplicate or paraphrased content in a document corpus"],"best_for":["RAG systems where query-to-document relevance ranking is critical","Content deduplication and near-duplicate detection pipelines","Semantic clustering and topic modeling applications","Search and recommendation systems with text-based similarity"],"limitations":["Similarity computation is not built into the model — requires client-side implementation using numpy, scipy, or torch; no server-side similarity API provided","Contrastive learning training may produce embeddings optimized for sentence-level similarity but suboptimal for document-level or cross-domain similarity","No documented similarity thresholds or calibration guidance — developers must empirically determine appropriate cutoffs for their use case","Cosine similarity assumes normalized embeddings — if embeddings are not L2-normalized, dot product may not be comparable across different text lengths","No built-in handling of semantic drift or domain shift — embeddings trained on general sentence datasets may not generalize to specialized domains (medical, legal, code)"],"requires":["Embeddings generated by All-MiniLM model","Linear algebra library: numpy (Python), torch (PyTorch), or equivalent for vector operations","Optional: scipy.spatial.distance for efficient similarity computation at scale","Knowledge of similarity metric choice (cosine vs. dot product vs. Euclidean) and implications"],"input_types":["Two or more dense vector embeddings (output from All-MiniLM)"],"output_types":["Scalar similarity score (typically 0-1 for cosine similarity, unbounded for dot product)","Ranked list of similar items (for search/retrieval use cases)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-all-minilm__cap_4","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.context.embedding.for.knowledge.bases","name":"retrieval-augmented generation (rag) context embedding for knowledge bases","description":"All-MiniLM is specifically designed for RAG pipelines where documents are pre-embedded and stored in a vector database, and user queries are embedded at runtime to retrieve semantically similar documents. The model encodes both documents and queries into the same embedding space, enabling direct similarity-based retrieval without fine-tuning. Integration with vector databases (Pinecone, Weaviate, Milvus, etc.) is application-layer responsibility — the model provides only embedding generation.","intents":["I want to embed a large document corpus once and reuse embeddings for multiple queries","I need to build a semantic search layer on top of my knowledge base without fine-tuning","I'm implementing RAG for a chatbot and need to retrieve relevant documents for context","I want to reduce hallucinations in LLM responses by grounding them in retrieved documents"],"best_for":["Teams building RAG systems with local-first or privacy-sensitive requirements","Knowledge base search applications where query-document relevance is critical","Chatbot and Q&A systems that need to retrieve context before generating responses","Organizations with large document corpora requiring efficient semantic search"],"limitations":["512-token context window limits document chunk size — long documents must be split into overlapping chunks, increasing storage and retrieval complexity","No query-document asymmetry — embeddings are symmetric, unlike specialized query/document embedding models (e.g., ColBERT) that may produce better retrieval quality","Embedding dimensionality is undocumented — cannot optimize vector database indexing (e.g., HNSW, IVF) without knowing exact vector size","No built-in relevance calibration or threshold guidance — developers must empirically determine cutoffs for document retrieval","Contrastive learning may not generalize to specialized domains (medical, legal, code) — no fine-tuning or domain adaptation documented"],"requires":["All-MiniLM model deployed via Ollama","Vector database (Pinecone, Weaviate, Milvus, Chroma, FAISS, etc.) for storing and retrieving embeddings","Document chunking strategy (e.g., fixed-size chunks, semantic chunking) to handle 512-token limit","Application code to orchestrate: document embedding → storage → query embedding → retrieval → LLM context injection"],"input_types":["Document chunks (text, up to 512 tokens)","User queries (text, up to 512 tokens)"],"output_types":["Dense vector embeddings for storage in vector database","Retrieved document chunks ranked by similarity to query"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-all-minilm__cap_5","uri":"capability://tool.use.integration.ollama.cloud.managed.inference.with.tier.based.concurrency.scaling","name":"ollama cloud managed inference with tier-based concurrency scaling","description":"All-MiniLM is available on Ollama Cloud, a managed inference platform that abstracts infrastructure management and provides API-based access without self-hosting. Concurrency limits are tier-based: Free tier allows 1 concurrent model, Pro tier allows 3, and Max tier allows 10. Billing is per-model-minute or subscription-based (exact pricing model undocumented). Cloud deployment uses the same REST API as local Ollama, enabling seamless migration from local to cloud without code changes.","intents":["I want to deploy embeddings to production without managing Ollama infrastructure","I need to scale embedding inference from development (local) to production (cloud) without code changes","I want to use Ollama Cloud's managed infrastructure to avoid GPU/server costs","I need to understand concurrency limits and scaling options for my embedding workload"],"best_for":["Teams without DevOps expertise who want managed inference without self-hosting","Startups and small teams seeking cost-effective cloud inference without long-term commitments","Organizations migrating from local Ollama development to cloud production","Applications with moderate, predictable embedding throughput (not high-volume real-time systems)"],"limitations":["Concurrency limits are tier-based and may be insufficient for high-throughput systems — Free tier (1 model) cannot handle concurrent requests; Pro/Max tiers have hard limits","Pricing model is undocumented — unclear if billing is per-request, per-minute, or subscription-based; no cost comparison vs. self-hosting","No documented SLA, uptime guarantees, or latency SLOs — reliability and performance characteristics are unknown","Vendor lock-in risk — migrating away from Ollama Cloud requires re-implementing embedding infrastructure elsewhere","No documented data residency or privacy guarantees — unclear if embeddings are logged, retained, or used for model improvement"],"requires":["Ollama Cloud account with API credentials (signup required)","Appropriate tier selection: Free (1 model, development only), Pro (3 models, small production), Max (10 models, high-concurrency production)","Network connectivity to Ollama Cloud API endpoint","Same client libraries as local Ollama (Python ollama, JavaScript ollama)"],"input_types":["Same as local Ollama: JSON payload with 'model' and 'prompt' fields"],"output_types":["Same as local Ollama: JSON response with 'embedding' array"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["Ollama 0.1.26 or later installed locally or via Ollama Cloud","Python 3.6+ with ollama library (pip install ollama) for Python integration, or Node.js 14+ for JavaScript","Minimum ~100MB disk space for model download (22M variant ~46MB, 33M variant ~67MB)","For cloud deployment: Ollama Cloud account with appropriate concurrency tier (Free: 1 model, Pro: 3 models, Max: 10 models)","Ollama daemon running locally (ollama serve) or Ollama Cloud account with API credentials","For local: Ollama 0.1.26+ installed on Linux, macOS, or Windows","For Python: ollama Python package (pip install ollama)","For JavaScript: ollama JavaScript library (npm install ollama)","Network connectivity to localhost:11434 (local) or Ollama Cloud endpoint (cloud)","Ollama 0.1.26+ with support for model variants"],"failure_modes":["Fixed 512-token context window — cannot embed documents or passages longer than ~400 words; requires chunking for longer texts","Embedding dimensionality unknown from documentation — cannot optimize vector storage or similarity computation without reverse-engineering model output","No explicit multilingual support documented — unclear if model generalizes across languages or is English-only","No quantization or precision details provided — actual inference latency and memory footprint depend on undocumented deployment format","Contrastive learning approach may produce embeddings less semantically rich than larger models (e.g., OpenAI text-embedding-3) for specialized domains","REST API is synchronous only — no streaming or async response support documented; each request blocks until embedding is computed","Concurrency limits on Ollama Cloud tier-dependent (Free: 1 concurrent model, Pro: 3, Max: 10) — high-throughput embedding pipelines may require Pro/Max tier","No built-in batching API — multiple embeddings require sequential HTTP requests; batching must be implemented at application layer","Local Ollama deployment requires manual model management and server lifecycle (startup, shutdown, resource allocation)","No documented rate limiting or quota management for Ollama Cloud — unclear if free tier has throughput caps","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.45,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","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=all-minilm","compare_url":"https://unfragile.ai/compare?artifact=all-minilm"}},"signature":"Q2/Wx4FIusihfhTytg5ja7v3E2kMPiYiXwLiV7dfIT5K4m+2enn5Hv4P+SVBYY8S7X2n179K6IdL7TXumIZ2CA==","signedAt":"2026-06-22T07:56:44.805Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/all-minilm","artifact":"https://unfragile.ai/all-minilm","verify":"https://unfragile.ai/api/v1/verify?slug=all-minilm","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"}}