{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-mxbai-embed-large","slug":"mxbai-embed-large","name":"MXBAI Embed Large (335M)","type":"model","url":"https://ollama.com/library/mxbai-embed-large","page_url":"https://unfragile.ai/mxbai-embed-large","categories":["rag-knowledge","testing-quality"],"tags":["ollama","open-source","embeddings","mixedbread","embedding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-mxbai-embed-large__cap_0","uri":"capability://memory.knowledge.dense.vector.embedding.generation.with.mteb.optimized.architecture","name":"dense vector embedding generation with mteb-optimized architecture","description":"Generates high-dimensional dense vector representations of arbitrary-length text inputs using a Bert-large-sized (335M parameter) architecture trained without MTEB benchmark data leakage. The model accepts raw text strings and outputs numerical embedding vectors optimized for semantic similarity and retrieval tasks, with inference available through Ollama's REST API, Python SDK, and JavaScript SDK for local or cloud execution.","intents":["Generate embeddings for semantic search and RAG systems without relying on commercial APIs","Build retrieval-augmented generation pipelines with locally-hosted embeddings for privacy-sensitive applications","Create vector representations for document similarity, clustering, and recommendation systems","Embed text at scale for knowledge base indexing with predictable latency and cost"],"best_for":["Teams building RAG systems requiring local model control and no API dependencies","Developers prioritizing privacy and data residency over cloud-based embedding services","Researchers benchmarking embedding model performance on MTEB tasks","Solo developers prototyping semantic search without OpenAI/Anthropic API costs"],"limitations":["Context window hard-capped at 512 tokens (~350 words), requiring text truncation or chunking for longer documents","Embedding dimensionality not documented, preventing optimization of vector storage and similarity computation","No multimodal support — text-only input, cannot embed images, audio, or structured data","Inference latency not characterized — actual throughput and time-to-first-embedding unknown for different hardware","Training data composition undocumented, limiting ability to assess domain-specific performance or bias","No official model card with detailed evaluation metrics, failure modes, or per-domain performance breakdown"],"requires":["Ollama runtime (any recent version supporting mxbai-embed-large model)","670MB disk space for model download and storage","Python 3.7+ for ollama Python SDK, or Node.js 14+ for JavaScript SDK","For cloud deployment: Ollama Pro or Max tier subscription (Free tier limited to 1 concurrent model)"],"input_types":["Plain text strings","Optionally prefixed with retrieval task prompt (e.g., 'Represent this sentence for searching relevant passages: [text]')"],"output_types":["Numerical embedding vectors (array of floats)","JSON response via REST API with embedding array and model metadata"],"categories":["memory-knowledge","embedding-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_1","uri":"capability://tool.use.integration.local.rest.api.embedding.service.with.multi.sdk.support","name":"local rest api embedding service with multi-sdk support","description":"Exposes embedding inference through Ollama's standardized REST API endpoint (http://localhost:11434/api/embeddings) with native language bindings for Python and JavaScript, enabling seamless integration into existing applications without custom HTTP client code. The API abstracts model loading, inference execution, and vector serialization, supporting both local execution and cloud deployment through Ollama's subscription tiers.","intents":["Integrate embeddings into Python/JavaScript applications without writing custom model loading or inference code","Deploy embeddings as a microservice accessible via standard HTTP without containerization overhead","Scale embedding inference across multiple concurrent requests using Ollama's cloud tier concurrency limits","Switch between local and cloud embedding execution without application code changes"],"best_for":["Full-stack developers building Python/JavaScript applications requiring embeddings","Teams deploying embeddings as a shared microservice across multiple applications","Startups needing to scale from local development to cloud without architectural refactoring","Developers prioritizing minimal operational overhead (no Docker, Kubernetes, or model serving infrastructure)"],"limitations":["REST API response schema not documented — no official specification for error codes, rate limits, or response structure","Cloud deployment concurrency limits: 1 model (Free), 3 models (Pro), 10 models (Max) — insufficient for high-throughput production workloads","No built-in request batching or async queue — each API call blocks until embedding completes","Inference latency not characterized per hardware tier — no SLA or performance guarantees for cloud execution","No authentication/authorization mechanism documented for cloud API access","Python/JavaScript SDKs may lag behind REST API capabilities or have version compatibility issues"],"requires":["Ollama runtime installed and running (any recent version)","For Python: ollama Python package (pip install ollama)","For JavaScript: ollama JavaScript package (npm install ollama)","For REST API: curl, httpx, or any HTTP client library","For cloud: Ollama Pro or Max subscription with valid API credentials"],"input_types":["Text string (single embedding request)","Array of text strings (batch embedding via REST API)"],"output_types":["JSON object with 'embedding' array (float values) and model metadata","HTTP status codes (200 success, 4xx client error, 5xx server error — specific codes undocumented)"],"categories":["tool-use-integration","api-service"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_2","uri":"capability://search.retrieval.semantic.similarity.computation.for.retrieval.and.ranking","name":"semantic similarity computation for retrieval and ranking","description":"Leverages the model's MTEB-optimized dense embeddings to compute cosine similarity between query and document vectors, enabling semantic search, document ranking, and relevance scoring without explicit similarity computation code. The embedding space is trained to maximize similarity between semantically related texts across diverse domains, supporting both exact-match and semantic-fuzzy retrieval patterns.","intents":["Implement semantic search that finds relevant documents by meaning rather than keyword matching","Rank search results by semantic relevance to user queries in RAG pipelines","Detect duplicate or near-duplicate documents by embedding similarity thresholds","Build recommendation systems that surface similar content based on semantic relationships"],"best_for":["Teams building search functionality for unstructured text corpora (documentation, knowledge bases, research papers)","RAG system developers needing retrieval that understands query intent beyond keywords","Content platforms implementing 'similar items' or 'related articles' features","Researchers evaluating semantic similarity metrics across diverse text domains"],"limitations":["Similarity computation requires pre-computed embeddings for all documents — no on-the-fly embedding of new documents without re-indexing","512-token context window limits document length before truncation, potentially losing semantic information in long texts","No built-in vector database or similarity search index — requires external storage (Pinecone, Weaviate, Milvus, or in-memory solutions)","Embedding dimensionality unknown, preventing optimization of similarity computation speed and storage efficiency","No domain-specific fine-tuning — performance on specialized domains (medical, legal, code) not characterized","Similarity threshold tuning requires manual experimentation; no guidance on optimal thresholds for different use cases"],"requires":["Pre-computed embeddings for all documents in the corpus (generated via mxbai-embed-large)","Vector similarity library (numpy for cosine similarity, or specialized vector DB client)","Vector storage backend (optional but recommended for >1000 documents): Pinecone, Weaviate, Milvus, Qdrant, or in-memory solution","Query text embedding via mxbai-embed-large API before similarity computation"],"input_types":["Query text string (embedded via mxbai-embed-large)","Document text strings (pre-embedded and stored as vectors)","Similarity threshold (float between 0 and 1, default typically 0.5-0.7)"],"output_types":["Ranked list of documents with similarity scores (float values)","Boolean relevance judgment (above/below threshold)","Similarity matrix (for batch queries against document corpus)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_3","uri":"capability://automation.workflow.local.model.execution.with.automatic.hardware.optimization","name":"local model execution with automatic hardware optimization","description":"Ollama runtime automatically detects available hardware (GPU/CPU) and optimizes model inference execution without manual CUDA/PyTorch configuration. The model is distributed in GGUF quantized format, enabling efficient inference on consumer GPUs (likely <4GB VRAM) and CPU fallback, with transparent model loading and caching managed by Ollama's daemon process.","intents":["Run embeddings locally without GPU setup or CUDA/cuDNN installation complexity","Deploy embeddings on resource-constrained hardware (laptops, edge devices) with automatic CPU fallback","Avoid cloud API latency and cost by executing inference on local hardware","Maintain data privacy by keeping embeddings computation entirely on-device"],"best_for":["Solo developers and small teams without DevOps infrastructure for model serving","Organizations with strict data residency requirements (healthcare, finance, government)","Developers prototyping RAG systems locally before cloud deployment","Edge computing scenarios requiring embeddings on resource-constrained devices"],"limitations":["Inference latency not characterized — actual throughput on different hardware (M1/M2 Mac, RTX 3090, CPU-only) unknown","GPU memory requirements not documented — model may not fit on older GPUs with <2GB VRAM","CPU inference speed likely slow for production workloads — no benchmarks provided for CPU-only execution","Ollama daemon must remain running — adds background process overhead and memory consumption","No explicit GPU memory management — potential out-of-memory errors if multiple models loaded simultaneously","Hardware detection may fail on exotic setups (custom CUDA builds, non-standard GPU configurations)"],"requires":["Ollama runtime (latest version recommended, minimum version unknown)","670MB free disk space for model download","For GPU acceleration: NVIDIA CUDA-capable GPU (RTX/GTX series) or Apple Silicon (M1/M2/M3), or AMD GPU with ROCm support","For CPU-only: x86-64 or ARM64 processor (inference will be slow)","4GB+ RAM for Ollama daemon and model inference"],"input_types":["Text strings (any length up to 512 tokens)"],"output_types":["Embedding vectors (float arrays)","Ollama daemon logs (for debugging hardware detection)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_4","uri":"capability://automation.workflow.cloud.hosted.embedding.service.with.tiered.concurrency.limits","name":"cloud-hosted embedding service with tiered concurrency limits","description":"Ollama offers cloud deployment of mxbai-embed-large through subscription tiers (Free, Pro, Max) with increasing concurrent model limits (1, 3, 10 respectively), enabling elastic scaling without managing infrastructure. Cloud execution uses the same API and SDK as local deployment, allowing transparent migration from local to cloud without application code changes.","intents":["Scale embedding inference from local development to cloud without architectural refactoring","Handle variable embedding workloads with elastic concurrency limits","Deploy embeddings as a managed service without operating Ollama infrastructure","Reduce operational overhead by outsourcing model serving and hardware management"],"best_for":["Startups scaling from prototype to production without DevOps team","Teams needing elastic embedding capacity without fixed infrastructure costs","Developers wanting managed service simplicity without self-hosting complexity","Organizations with variable embedding workloads (bursty traffic patterns)"],"limitations":["Concurrency limits (1/3/10 models) insufficient for high-throughput production (>100 concurrent requests/sec)","Pricing model not documented — unclear if charged per-request, per-model, or per-subscription tier","No SLA or latency guarantees documented — cloud inference speed not characterized","No authentication/authorization mechanism documented — security model unclear","Data retention policy not documented — unknown if embeddings/queries logged or retained","Vendor lock-in risk — no documented export mechanism for switching to alternative cloud providers","Rate limiting not documented — unclear if requests throttled or queued when concurrency limits exceeded"],"requires":["Ollama Pro or Max subscription (Free tier limited to 1 concurrent model, insufficient for production)","Valid Ollama cloud API credentials","Network connectivity to Ollama cloud infrastructure","Same Python/JavaScript SDK as local deployment (no code changes required)"],"input_types":["Text strings (same as local API)"],"output_types":["JSON embedding response (same as local API)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_5","uri":"capability://memory.knowledge.multi.domain.semantic.generalization.without.benchmark.contamination","name":"multi-domain semantic generalization without benchmark contamination","description":"The model is trained without MTEB benchmark data leakage, enabling fair evaluation and generalization across diverse domains, tasks, and text lengths. This training approach ensures embeddings capture genuine semantic relationships rather than overfitting to specific benchmark tasks, supporting robust performance on out-of-distribution text (medical, legal, code, social media, etc.).","intents":["Deploy embeddings on specialized domains (medical, legal, code) with confidence in generalization","Evaluate embedding quality fairly without benchmark contamination bias","Build RAG systems that handle diverse text types (documentation, research, user-generated content) uniformly","Avoid overfitting to specific retrieval tasks when building search systems"],"best_for":["Researchers benchmarking embedding models with fair evaluation methodology","Teams building domain-specific RAG systems requiring robust generalization","Organizations deploying embeddings across heterogeneous text corpora","Developers prioritizing semantic understanding over task-specific optimization"],"limitations":["Domain-specific performance not characterized — no per-domain MTEB scores or failure analysis","Training data composition undocumented — unclear which domains/languages represented in training set","No comparison of generalization vs. task-specific fine-tuned models — unclear if domain-specific fine-tuning would improve performance","Benchmark contamination claim unverified — no independent audit of training data vs. MTEB test sets","No guidance on domain-specific prompt engineering — unclear if retrieval task prompt ('Represent this sentence for searching relevant passages') optimal for all domains"],"requires":["Understanding of MTEB benchmark and data leakage risks (for researchers)","Evaluation methodology that tests on out-of-distribution domains (for validation)"],"input_types":["Text from any domain (medical, legal, code, social media, research, etc.)"],"output_types":["Embeddings with consistent semantic properties across domains"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_6","uri":"capability://data.processing.analysis.batch.embedding.generation.via.rest.api","name":"batch embedding generation via rest api","description":"The Ollama REST API supports embedding multiple text strings in a single request, enabling efficient batch processing of documents without per-text API overhead. Batch requests reduce network latency and allow the inference engine to optimize computation across multiple inputs, improving throughput for large-scale embedding tasks.","intents":["Embed large document corpora efficiently by batching requests instead of individual API calls","Reduce network latency and API overhead when indexing thousands of documents","Optimize inference throughput by processing multiple texts in parallel","Build efficient data pipelines for bulk embedding of knowledge bases or search indexes"],"best_for":["Data engineers building ETL pipelines for embedding large document collections","Teams indexing knowledge bases or search corpora during initial setup","Developers optimizing embedding throughput for batch processing workloads","Organizations with periodic bulk embedding tasks (daily/weekly knowledge base updates)"],"limitations":["Batch size limits not documented — unclear if there's a maximum number of texts per request","Batch API response schema not documented — unclear how results are ordered or structured","No streaming response support documented — entire batch must complete before response returned","Memory overhead of batch processing not characterized — large batches may cause OOM errors","No progress tracking or partial failure handling — unclear if one failed text fails entire batch","Batch optimization benefits not quantified — no benchmarks showing throughput improvement vs. sequential requests"],"requires":["Ollama REST API endpoint (local or cloud)","HTTP client supporting POST requests with JSON arrays","Array of text strings (batch size limits unknown)"],"input_types":["Array of text strings (JSON format)"],"output_types":["Array of embedding vectors (JSON format, ordering relative to input unknown)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_7","uri":"capability://memory.knowledge.prompt.based.task.adaptation.for.retrieval.optimization","name":"prompt-based task adaptation for retrieval optimization","description":"The model supports optional task-specific prompting to optimize embeddings for different use cases, with documented guidance for retrieval tasks: 'Represent this sentence for searching relevant passages: [text]'. This prompt engineering approach adapts the embedding space without fine-tuning, enabling semantic search optimization while maintaining generalization across other tasks.","intents":["Optimize embeddings for semantic search by prefixing queries with retrieval-specific prompts","Adapt embeddings to different tasks (search, clustering, classification) without model retraining","Improve retrieval relevance by signaling task intent to the embedding model","Experiment with task-specific prompts to tune embedding quality for specific use cases"],"best_for":["RAG developers optimizing retrieval quality without fine-tuning","Teams experimenting with different embedding tasks (search, clustering, recommendation)","Researchers studying prompt engineering effects on embedding quality","Developers building multi-task systems requiring task-specific embeddings"],"limitations":["Prompt engineering guidance limited to retrieval task — no documented prompts for clustering, classification, or other tasks","Optimal prompt format not specified — unclear if prompt placement (prefix vs. suffix) or phrasing affects quality","No quantified impact of prompting on retrieval quality — unclear how much improvement to expect","Prompt engineering may not generalize across domains — optimal prompts for medical/legal/code domains unknown","No ablation studies on prompt components — unclear which parts of the retrieval prompt are essential","Prompting adds token overhead — longer prompts reduce effective context window for text content"],"requires":["Understanding of task-specific prompt engineering","Experimentation to find optimal prompts for specific use cases","Evaluation methodology to measure prompt impact on embedding quality"],"input_types":["Text string with optional task-specific prompt prefix"],"output_types":["Embeddings optimized for specified task"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_8","uri":"capability://tool.use.integration.cross.platform.sdk.support.with.unified.api","name":"cross-platform sdk support with unified api","description":"The model is accessible through native Python and JavaScript SDKs with identical API signatures, enabling seamless integration across full-stack applications without language-specific wrapper code. Both SDKs abstract Ollama's REST API and support local/cloud execution with transparent fallback and error handling.","intents":["Integrate embeddings into Python backend services without custom HTTP client code","Embed text in JavaScript/Node.js applications with native SDK support","Build full-stack applications with consistent embedding API across frontend and backend","Switch between Python and JavaScript implementations without API refactoring"],"best_for":["Full-stack developers building Python/JavaScript applications","Teams with polyglot codebases requiring consistent embedding APIs","Developers prioritizing SDK convenience over raw HTTP client control","Organizations standardizing on Ollama across multiple language ecosystems"],"limitations":["SDK documentation not provided — unclear if SDKs have feature parity with REST API","SDK version compatibility not documented — unclear if Python/JavaScript SDKs stay synchronized","Error handling differences between SDKs unknown — exception types and messages may differ","SDK performance characteristics not documented — overhead of SDK wrapper vs. raw HTTP unknown","Limited to Python and JavaScript — no official SDKs for Go, Rust, Java, or other languages","SDK dependency management — Python/JavaScript package versions may conflict with application dependencies"],"requires":["Python 3.7+ with ollama package (pip install ollama)","Node.js 14+ with ollama package (npm install ollama)","Ollama runtime (local or cloud)"],"input_types":["Text strings (Python: str, JavaScript: string)"],"output_types":["Embedding vectors (Python: list/numpy array, JavaScript: array)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-mxbai-embed-large__cap_9","uri":"capability://automation.workflow.model.distribution.and.versioning.via.ollama.registry","name":"model distribution and versioning via ollama registry","description":"The model is distributed through Ollama's centralized model registry with version tagging (mxbai-embed-large:latest, mxbai-embed-large:335m), enabling one-command installation and automatic updates. The registry handles model download, verification, and caching, with 9.9M downloads indicating widespread adoption and community validation.","intents":["Install mxbai-embed-large with a single command without manual model downloads","Manage model versions and updates through Ollama's version tagging system","Verify model integrity through Ollama's registry validation","Track model adoption and community feedback through download metrics"],"best_for":["Developers new to embedding models seeking simple installation","Teams standardizing on Ollama for model distribution and versioning","Organizations wanting centralized model management without custom infrastructure","Community-driven projects benefiting from shared model registry"],"limitations":["Version tagging scheme not documented — unclear how versions are selected or deprecated","No explicit model versioning or changelog — unclear what changed between versions","Registry availability not guaranteed — no SLA or uptime commitment documented","No model signing or cryptographic verification documented — security of registry unclear","Download metrics (9.9M) not timestamped — unclear if current or historical","No alternative distribution channels documented — unclear if model available outside Ollama registry"],"requires":["Ollama runtime installed","Network connectivity to Ollama registry (ollama.com)","Command: ollama pull mxbai-embed-large"],"input_types":["Model name and optional version tag (e.g., 'mxbai-embed-large:latest')"],"output_types":["Downloaded model file (670MB GGUF format)","Installation status and verification logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (any recent version supporting mxbai-embed-large model)","670MB disk space for model download and storage","Python 3.7+ for ollama Python SDK, or Node.js 14+ for JavaScript SDK","For cloud deployment: Ollama Pro or Max tier subscription (Free tier limited to 1 concurrent model)","Ollama runtime installed and running (any recent version)","For Python: ollama Python package (pip install ollama)","For JavaScript: ollama JavaScript package (npm install ollama)","For REST API: curl, httpx, or any HTTP client library","For cloud: Ollama Pro or Max subscription with valid API credentials","Pre-computed embeddings for all documents in the corpus (generated via mxbai-embed-large)"],"failure_modes":["Context window hard-capped at 512 tokens (~350 words), requiring text truncation or chunking for longer documents","Embedding dimensionality not documented, preventing optimization of vector storage and similarity computation","No multimodal support — text-only input, cannot embed images, audio, or structured data","Inference latency not characterized — actual throughput and time-to-first-embedding unknown for different hardware","Training data composition undocumented, limiting ability to assess domain-specific performance or bias","No official model card with detailed evaluation metrics, failure modes, or per-domain performance breakdown","REST API response schema not documented — no official specification for error codes, rate limits, or response structure","Cloud deployment concurrency limits: 1 model (Free), 3 models (Pro), 10 models (Max) — insufficient for high-throughput production workloads","No built-in request batching or async queue — each API call blocks until embedding completes","Inference latency not characterized per hardware tier — no SLA or performance guarantees for cloud execution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.55,"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=mxbai-embed-large","compare_url":"https://unfragile.ai/compare?artifact=mxbai-embed-large"}},"signature":"nWesvM6outwrxYZxyuM3ETdrCXQ8SxAcRPliWSnbKCOoj8v6Lj3EEpxHYiBesAcBsqvfL00G7a7U16iBCi14DQ==","signedAt":"2026-06-20T20:01:32.564Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mxbai-embed-large","artifact":"https://unfragile.ai/mxbai-embed-large","verify":"https://unfragile.ai/api/v1/verify?slug=mxbai-embed-large","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"}}