{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fastembed","slug":"fastembed","name":"FastEmbed","type":"repo","url":"https://github.com/qdrant/fastembed","page_url":"https://unfragile.ai/fastembed","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"fastembed__cap_0","uri":"capability://data.processing.analysis.dense.text.embedding.generation.with.onnx.runtime.inference","name":"dense text embedding generation with onnx runtime inference","description":"Generates fixed-size dense vector representations for text using the TextEmbedding class, which loads pre-trained models (default: BAAI/bge-small-en-v1.5) via ONNX Runtime for CPU-based inference. The architecture uses automatic model downloading with local caching, supports configurable pooling strategies (mean, max, cls token), and implements data parallelism across CPU cores for batch processing without requiring GPU hardware.","intents":["Generate embeddings for semantic search over document collections without cloud API calls","Build RAG systems with locally-hosted embedding models for privacy-sensitive applications","Embed large document batches efficiently on CPU-only infrastructure like serverless functions","Compare text similarity using dense vector representations for clustering or deduplication"],"best_for":["Teams building RAG systems requiring on-premise embedding generation","Developers deploying to serverless/edge environments without GPU access","Organizations with privacy requirements preventing cloud embedding APIs","Solo developers prototyping semantic search without infrastructure overhead"],"limitations":["Dense embeddings alone lack interpretability compared to sparse methods — token-level matching not available","ONNX Runtime CPU inference slower than GPU-accelerated alternatives for very large batches (>100k documents)","Default model (BAAI/bge-small-en-v1.5) optimized for English; multilingual support requires different model selection","Fixed embedding dimension (384 for default model) cannot be customized post-training"],"requires":["Python 3.8+","ONNX Runtime library (auto-installed via pip)","~500MB disk space for default model download and cache","Minimum 2GB RAM for batch processing"],"input_types":["plain text strings","lists of text documents","variable-length text (tokenization handled internally)"],"output_types":["numpy arrays (shape: [batch_size, embedding_dim])","float32 dense vectors (384-dim for default model)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_1","uri":"capability://data.processing.analysis.sparse.text.embedding.generation.for.hybrid.search","name":"sparse text embedding generation for hybrid search","description":"Generates sparse token-weighted embeddings using the SparseTextEmbedding class, supporting multiple sparse embedding strategies (SPLADE, BM25, BM42) that produce high-dimensional vectors with mostly zero values. These embeddings preserve exact token matching information and integrate seamlessly with traditional full-text search systems, enabling hybrid search by combining dense and sparse representations in a single query.","intents":["Build hybrid search systems combining semantic understanding with exact keyword matching","Integrate embedding-based retrieval with existing Elasticsearch or Lucene-based search infrastructure","Improve recall on domain-specific terminology where dense embeddings may fail","Enable interpretable search results by exposing which tokens contributed to relevance scores"],"best_for":["Teams migrating from BM25-only search to semantic search without abandoning keyword matching","Applications requiring both semantic and lexical relevance (e.g., legal document search, medical records)","Systems needing explainable retrieval where token contributions are visible","Hybrid search implementations using Qdrant or Elasticsearch with sparse vector support"],"limitations":["Sparse embeddings consume more storage than dense vectors (typically 10-100x larger on disk despite sparsity)","SPLADE and BM42 models require more computational resources than dense embedding inference","Sparse embeddings less effective for semantic similarity on short queries or out-of-vocabulary terms","Integration with vector databases requires explicit sparse vector support (not all databases provide this)"],"requires":["Python 3.8+","ONNX Runtime library","Vector database with sparse vector support (Qdrant 1.7+, Elasticsearch 8.0+, or Weaviate)","~1GB disk space for SPLADE model download"],"input_types":["plain text strings","lists of documents","variable-length text"],"output_types":["sparse vectors (dict format: {token_id: weight, ...})","float32 token weights","variable-length output (number of non-zero dimensions depends on text)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_10","uri":"capability://automation.workflow.gpu.acceleration.via.optional.fastembed.gpu.package","name":"gpu acceleration via optional fastembed-gpu package","description":"Provides optional GPU acceleration through a separate fastembed-gpu package that replaces ONNX CPU inference with CUDA-accelerated inference. The architecture maintains API compatibility with CPU-based FastEmbed while delegating inference to GPU runtimes, enabling 5-20x speedup for large-scale embedding generation without code changes.","intents":["Accelerate embedding generation for large-scale indexing jobs using available GPU hardware","Reduce embedding latency for real-time search applications with GPU resources","Scale embedding throughput for high-traffic inference servers","Maintain code compatibility while switching between CPU and GPU inference"],"best_for":["Teams with GPU infrastructure (NVIDIA GPUs with CUDA support) wanting to accelerate embedding generation","High-throughput search systems requiring sub-100ms embedding latency","Large-scale indexing jobs where GPU acceleration provides ROI","Organizations with existing GPU infrastructure (ML training clusters, inference servers)"],"limitations":["Requires NVIDIA GPU with CUDA support; no AMD or Intel GPU support","fastembed-gpu package adds complexity; requires separate installation and CUDA toolkit setup","GPU memory constraints limit batch sizes; OOM errors possible with large batches on smaller GPUs","GPU acceleration overhead (~100-200ms) makes it inefficient for small batches (<100 items)","CUDA driver and toolkit version mismatches can cause runtime failures"],"requires":["Python 3.8+","NVIDIA GPU with CUDA Compute Capability 3.5+ (Tesla K40 or newer)","CUDA Toolkit 11.0+ installed and configured","cuDNN library matching CUDA version","fastembed-gpu package (separate from fastembed)","Sufficient GPU memory (4GB+ for typical models)"],"input_types":["same as CPU version (text, images, batches)"],"output_types":["same as CPU version (embeddings, scores)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_11","uri":"capability://data.processing.analysis.multi.language.embedding.support.with.language.specific.models","name":"multi-language embedding support with language-specific models","description":"Supports embedding generation for multiple languages through language-specific pre-trained models (e.g., multilingual BERT variants, language-specific BGE models). The framework allows selection of appropriate models for target languages, with automatic tokenization and inference handling language-specific text processing requirements.","intents":["Build semantic search systems supporting multiple languages in a single index","Generate embeddings for non-English documents without language-specific preprocessing","Create multilingual RAG systems that retrieve relevant documents across language boundaries","Support language-specific retrieval where English-only models would fail"],"best_for":["Global organizations with multilingual document collections","Search systems serving users in multiple languages","Multilingual RAG systems requiring cross-language retrieval","Teams building international applications with language diversity"],"limitations":["Language-specific models often smaller and less capable than English-only models","Multilingual models (e.g., multilingual BERT) have lower quality than language-specific alternatives","Cross-language retrieval (e.g., English query on French documents) requires multilingual models with quality tradeoffs","Limited model selection for low-resource languages; many languages unsupported","Language detection required for automatic model selection; errors propagate to retrieval quality"],"requires":["Python 3.8+","Language-specific model selection (requires knowledge of available models)","Appropriate model downloaded for target language(s)","Optional: language detection library for automatic model selection"],"input_types":["text in target language","language identifier (optional, for model selection)","lists of multilingual documents"],"output_types":["embeddings compatible with language-specific model","numpy arrays (shape: [batch_size, embedding_dim])"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_12","uri":"capability://data.processing.analysis.model.evaluation.and.benchmarking.utilities","name":"model evaluation and benchmarking utilities","description":"Provides utilities for evaluating embedding model quality on standard benchmarks (MTEB, BEIR) and comparing model performance across different architectures and sizes. The framework includes built-in benchmark datasets and scoring metrics, enabling developers to quantify embedding quality before deployment.","intents":["Evaluate embedding model quality on standard benchmarks before deployment","Compare different embedding models to select optimal model for specific use case","Measure embedding quality improvements from model updates or fine-tuning","Benchmark embedding generation speed and resource consumption across models"],"best_for":["Researchers and teams selecting embedding models for production systems","Organizations evaluating embedding quality impact on retrieval metrics","Teams monitoring embedding model performance over time","Development teams validating model updates before deployment"],"limitations":["Benchmark datasets may not reflect real-world retrieval patterns; benchmark quality ≠ production quality","Evaluation requires significant computation time (hours for full MTEB benchmark)","Limited to standard benchmarks; custom domain-specific evaluation requires additional work","Benchmark results sensitive to hyperparameters (pooling strategy, batch size); reproducibility requires careful documentation"],"requires":["Python 3.8+","Benchmark datasets (auto-downloaded on first use)","Significant computation time (hours for full evaluation)","Optional: GPU for faster evaluation"],"input_types":["embedding models (model identifiers)","benchmark dataset names (MTEB, BEIR, etc.)","optional: custom evaluation datasets"],"output_types":["benchmark scores (NDCG, MRR, MAP, etc.)","performance metrics (latency, throughput)","comparison tables across models","detailed evaluation reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_2","uri":"capability://data.processing.analysis.late.interaction.token.level.embedding.with.colbert","name":"late interaction token-level embedding with colbert","description":"Generates token-level embeddings using the LateInteractionTextEmbedding class, which implements the ColBERT architecture to produce per-token dense vectors instead of a single document vector. Late interaction enables fine-grained matching at query time by computing similarity between individual query tokens and document tokens, allowing relevance scoring based on token-level alignment rather than aggregate document similarity.","intents":["Implement advanced retrieval systems where query tokens match against document tokens individually","Build reranking systems that score documents based on token-level relevance patterns","Create retrieval systems with better recall on multi-faceted queries with diverse token requirements","Enable interpretable retrieval by exposing which document tokens matched which query tokens"],"best_for":["Information retrieval researchers and teams building state-of-the-art search systems","Applications requiring fine-grained relevance matching (e.g., question-answering, legal search)","Teams willing to trade increased storage and compute for improved retrieval quality","Systems using Qdrant or other vector databases supporting variable-length embeddings"],"limitations":["Produces variable-length embeddings (one per token), requiring ~10-50x more storage than dense embeddings","Query-time computation more expensive than dense embeddings due to token-level similarity computation","Requires specialized vector database support for variable-length embeddings and MaxSim scoring","ColBERT models larger than dense alternatives, increasing download and memory footprint"],"requires":["Python 3.8+","ONNX Runtime library","Vector database with support for variable-length embeddings (Qdrant 1.7+)","~2GB disk space for ColBERT model download","Understanding of late interaction retrieval concepts"],"input_types":["plain text strings","lists of documents","variable-length text"],"output_types":["variable-length token embeddings (shape: [num_tokens, embedding_dim])","float32 dense vectors per token","metadata: token count and positions"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_3","uri":"capability://image.visual.image.embedding.generation.with.clip.and.multimodal.models","name":"image embedding generation with clip and multimodal models","description":"Generates dense vector representations for images using the ImageEmbedding class, which loads pre-trained vision models (CLIP, ViT-based architectures) via ONNX Runtime. The implementation handles image preprocessing (resizing, normalization), batch processing across CPU cores, and produces embeddings in the same vector space as text embeddings when using multimodal models, enabling cross-modal search.","intents":["Build image search systems that retrieve similar images based on visual content","Implement cross-modal search combining text queries with image databases","Generate embeddings for image clustering, deduplication, or recommendation systems","Create multimodal RAG systems where images and text are indexed in a shared embedding space"],"best_for":["Teams building image search or visual similarity applications","Multimodal RAG systems requiring text-to-image and image-to-image retrieval","E-commerce platforms implementing visual search without cloud vision APIs","Content moderation or duplicate detection systems working with image collections"],"limitations":["Image preprocessing adds latency (~50-200ms per image) compared to text embedding","CLIP embeddings less effective for fine-grained visual attributes (color, texture) compared to specialized vision models","Requires image files in memory or on disk; streaming/URL-based images need preprocessing","Cross-modal embeddings (text + image in same space) have lower quality than single-modality embeddings"],"requires":["Python 3.8+","ONNX Runtime library","PIL/Pillow for image preprocessing","~1-2GB disk space for CLIP model download","Images in standard formats (JPEG, PNG, WebP)"],"input_types":["PIL Image objects","numpy arrays (shape: [height, width, 3])","file paths to image files","image bytes"],"output_types":["numpy arrays (shape: [batch_size, embedding_dim])","float32 dense vectors (512-dim for CLIP models)","embeddings in same space as text embeddings for multimodal models"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_4","uri":"capability://image.visual.multimodal.late.interaction.embedding.for.document.images","name":"multimodal late interaction embedding for document images","description":"Generates token-level multimodal embeddings using the LateInteractionMultimodalEmbedding class, implementing the ColPali architecture for document image understanding. This capability produces per-token embeddings from document images (PDFs, scans) that preserve spatial and semantic information, enabling fine-grained matching between text queries and document regions at the token level.","intents":["Build document search systems that understand both text content and visual layout in PDFs and scanned documents","Implement retrieval over document image collections where text extraction is unreliable or unavailable","Create systems that match text queries against document regions with spatial awareness","Enable document understanding that preserves formatting, tables, and visual structure information"],"best_for":["Teams processing document collections with mixed text and visual content (PDFs, scans)","Legal or financial document search systems requiring layout-aware retrieval","Organizations with large archives of scanned documents needing semantic search","Research teams building advanced document understanding systems"],"limitations":["ColPali models significantly larger than text-only models, requiring 3-5GB disk space and more memory","Document image processing slower than text embedding (100-500ms per page depending on resolution)","Requires high-quality document images; heavily compressed or low-resolution images degrade performance","Variable-length output (tokens per page) requires specialized vector database support","Limited model selection compared to text embeddings; primarily ColPali architecture available"],"requires":["Python 3.8+","ONNX Runtime library","PIL/Pillow for image preprocessing","~3-5GB disk space for ColPali model download","Document images in standard formats (JPEG, PNG, PDF via conversion)","Vector database supporting variable-length embeddings (Qdrant 1.7+)"],"input_types":["PIL Image objects of document pages","numpy arrays (shape: [height, width, 3])","file paths to document images","PDF pages converted to images"],"output_types":["variable-length token embeddings (shape: [num_tokens, embedding_dim])","float32 dense vectors per document region/token","metadata: token positions and document structure information"],"categories":["image-visual","data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_5","uri":"capability://data.processing.analysis.text.pair.scoring.and.reranking.with.cross.encoders","name":"text pair scoring and reranking with cross-encoders","description":"Scores relevance of text pairs using the TextCrossEncoder class, which loads pre-trained cross-encoder models via ONNX Runtime to compute similarity scores between query-document pairs. Unlike embedding-based retrieval, cross-encoders process both texts jointly, enabling more accurate relevance judgments for reranking retrieved candidates or scoring question-answer pairs.","intents":["Rerank search results from dense or sparse retrieval to improve final ranking quality","Score question-answer pairs for QA systems or fact verification","Compute semantic similarity between text pairs with higher accuracy than embedding-based methods","Implement multi-stage retrieval pipelines where cross-encoders refine initial retrieval results"],"best_for":["Teams implementing multi-stage retrieval pipelines with initial retrieval + reranking","QA systems requiring accurate question-answer pair scoring","Search systems where ranking quality is critical and compute budget allows reranking","Applications needing higher accuracy than embedding-based similarity at the cost of latency"],"limitations":["Cross-encoders require processing each query-document pair independently, scaling O(n) with candidate count vs O(1) for embedding similarity","Latency per pair ~10-50ms on CPU, making reranking of large result sets expensive","Cannot be used for initial retrieval (no pre-computed embeddings); must follow dense/sparse retrieval stage","Model selection limited compared to embedding models; fewer pre-trained cross-encoders available"],"requires":["Python 3.8+","ONNX Runtime library","~500MB disk space for cross-encoder model download","Initial retrieval results to rerank (dense or sparse embeddings)","Acceptable latency budget for scoring (typically 10-100ms per pair)"],"input_types":["query-document text pairs (tuples or lists)","variable-length text strings","batch of pairs for parallel scoring"],"output_types":["float32 similarity scores (typically 0-1 range)","numpy arrays (shape: [batch_size] for single scores or [batch_size, num_labels] for multi-class)","ranked lists of candidates with scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_6","uri":"capability://automation.workflow.automatic.model.downloading.and.local.caching.with.version.management","name":"automatic model downloading and local caching with version management","description":"Manages model lifecycle including automatic downloading from Hugging Face Hub, local caching with version tracking, and cache invalidation. The architecture uses a configurable cache directory, supports model versioning via git revisions, and implements atomic downloads to prevent corruption. Models are cached locally after first download, eliminating repeated network calls and enabling offline operation after initial setup.","intents":["Deploy embedding systems without manual model management or version control","Enable offline operation after initial model download for air-gapped environments","Manage multiple model versions without manual file organization","Simplify CI/CD pipelines by automating model provisioning"],"best_for":["Teams deploying to serverless/containerized environments requiring reproducible model versions","Organizations with air-gapped or low-bandwidth networks needing offline operation","Development teams wanting automatic model provisioning without manual setup","Systems requiring model version pinning for reproducibility"],"limitations":["First-time model download requires internet connectivity and can take 1-5 minutes depending on model size","Cache directory must have sufficient disk space (500MB-5GB depending on models used)","No built-in cache cleanup; old model versions persist on disk unless manually deleted","Model updates from Hugging Face Hub require manual cache invalidation or version specification"],"requires":["Python 3.8+","Internet connectivity for initial model download","Writable disk space for cache directory (~500MB-5GB per model)","Hugging Face Hub access (public models only by default)"],"input_types":["model identifiers (string, e.g., 'BAAI/bge-small-en-v1.5')","optional git revision/branch specification","optional custom cache directory path"],"output_types":["loaded ONNX models ready for inference","model metadata (dimension, architecture)","cache location information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_7","uri":"capability://automation.workflow.parallel.batch.processing.with.cpu.thread.pool.optimization","name":"parallel batch processing with cpu thread pool optimization","description":"Processes multiple documents/images in parallel using thread pools to distribute work across CPU cores, implemented via ONNX Runtime's built-in parallelism and FastEmbed's batch processing layer. The architecture automatically determines optimal batch sizes and thread counts based on available CPU cores, enabling efficient utilization of multi-core systems without explicit GPU acceleration.","intents":["Embed large document collections efficiently on CPU-only infrastructure","Maximize throughput when processing batches of texts or images","Utilize multi-core CPUs effectively without GPU hardware","Reduce total embedding time for batch operations by 5-10x vs sequential processing"],"best_for":["Batch embedding jobs processing thousands of documents offline","Serverless functions with multi-core CPU allocation (AWS Lambda with 3GB+ memory)","Data pipeline stages requiring high-throughput embedding generation","Teams without GPU infrastructure but with multi-core CPU availability"],"limitations":["Thread pool overhead adds ~50-100ms latency for small batches (<10 items); optimal for batches >100","GIL contention in Python limits effective parallelism; actual speedup typically 3-6x on 8-core CPUs vs theoretical 8x","Memory usage scales with batch size; large batches can exceed available RAM on memory-constrained systems","Optimal batch size and thread count require tuning; defaults may not match all hardware configurations"],"requires":["Python 3.8+","Multi-core CPU (2+ cores; benefits increase with more cores)","ONNX Runtime with threading support","Sufficient RAM for batch processing (typically 2-4GB for 1000-item batches)"],"input_types":["lists of text documents","lists of images","variable-length batches"],"output_types":["numpy arrays with embeddings for all batch items","shape: [batch_size, embedding_dim]"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_8","uri":"capability://data.processing.analysis.configurable.pooling.strategies.for.dense.embeddings","name":"configurable pooling strategies for dense embeddings","description":"Supports multiple pooling methods to aggregate token-level representations into fixed-size document embeddings, including mean pooling, max pooling, and CLS token extraction. The pooling strategy is configurable per model and affects the semantic properties of the resulting embeddings, with different strategies optimized for different retrieval scenarios.","intents":["Customize embedding generation to match specific retrieval requirements (e.g., max pooling for rare term matching)","Experiment with different pooling strategies to optimize retrieval quality","Adapt embeddings to domain-specific similarity metrics","Fine-tune embedding properties without retraining models"],"best_for":["Researchers experimenting with embedding properties and retrieval quality","Teams optimizing retrieval systems for specific domains or query types","Systems requiring different pooling strategies for different document types","Applications where embedding semantics significantly impact downstream tasks"],"limitations":["Pooling strategy choice requires domain knowledge; no universal optimal strategy","Different pooling methods produce incompatible embeddings; cannot mix strategies in same index","Limited documentation on when to use each strategy; requires empirical evaluation","Pooling strategy must be consistent between indexing and query time; mismatches degrade retrieval"],"requires":["Python 3.8+","Understanding of pooling strategies and their effects on embeddings","Ability to re-index documents if changing pooling strategy"],"input_types":["pooling strategy name (string: 'mean', 'max', 'cls')","model configuration"],"output_types":["fixed-size dense embeddings with selected pooling applied","numpy arrays (shape: [batch_size, embedding_dim])"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__cap_9","uri":"capability://tool.use.integration.integration.with.qdrant.vector.database.for.semantic.search","name":"integration with qdrant vector database for semantic search","description":"Provides native integration with Qdrant vector database, enabling seamless indexing of FastEmbed embeddings and execution of semantic search queries. The integration handles embedding generation, vector upload, and query execution in a unified workflow, with support for both dense and sparse embeddings, late interaction models, and hybrid search configurations.","intents":["Build end-to-end semantic search systems using FastEmbed embeddings and Qdrant storage","Index large document collections with automatic embedding generation and vector storage","Execute semantic search queries with minimal boilerplate code","Implement hybrid search combining dense, sparse, and late interaction embeddings in Qdrant"],"best_for":["Teams building RAG systems with Qdrant as the vector store","Organizations standardizing on Qdrant for vector search infrastructure","Developers wanting integrated embedding + search without separate systems","Systems requiring both semantic and keyword search in a single platform"],"limitations":["Integration specific to Qdrant; requires Qdrant instance (self-hosted or cloud)","Network latency between FastEmbed and Qdrant adds overhead (~10-50ms per operation)","Qdrant collection schema must match embedding type (dense vs sparse vs late interaction); schema mismatches cause errors","Limited documentation on advanced Qdrant features (filtering, reranking) with FastEmbed"],"requires":["Python 3.8+","Qdrant instance (self-hosted or Qdrant Cloud)","Qdrant Python client library","Network connectivity to Qdrant server","Qdrant collection pre-created with compatible schema"],"input_types":["documents (text strings or lists)","images (for image search)","Qdrant collection names","search queries (text or images)"],"output_types":["search results with scores and metadata","retrieved documents ranked by relevance","Qdrant point IDs for result tracking"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastembed__headline","uri":"capability://data.processing.analysis.lightweight.embedding.generation.library","name":"lightweight embedding generation library","description":"FastEmbed is a fast and lightweight library for generating high-quality embeddings from text and images, optimized for low-latency inference without requiring a GPU. It integrates ONNX Runtime for efficient performance, making it suitable for various applications in semantic search and multimodal retrieval.","intents":["best lightweight embedding library","embedding generation for text and images","fast embedding solutions for low-latency inference","ONNX Runtime embedding library comparison","embedding library for resource-constrained environments"],"best_for":["resource-constrained environments","low-latency applications"],"limitations":[],"requires":[],"input_types":["text","image"],"output_types":["embeddings"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","ONNX Runtime library (auto-installed via pip)","~500MB disk space for default model download and cache","Minimum 2GB RAM for batch processing","ONNX Runtime library","Vector database with sparse vector support (Qdrant 1.7+, Elasticsearch 8.0+, or Weaviate)","~1GB disk space for SPLADE model download","NVIDIA GPU with CUDA Compute Capability 3.5+ (Tesla K40 or newer)","CUDA Toolkit 11.0+ installed and configured","cuDNN library matching CUDA version"],"failure_modes":["Dense embeddings alone lack interpretability compared to sparse methods — token-level matching not available","ONNX Runtime CPU inference slower than GPU-accelerated alternatives for very large batches (>100k documents)","Default model (BAAI/bge-small-en-v1.5) optimized for English; multilingual support requires different model selection","Fixed embedding dimension (384 for default model) cannot be customized post-training","Sparse embeddings consume more storage than dense vectors (typically 10-100x larger on disk despite sparsity)","SPLADE and BM42 models require more computational resources than dense embedding inference","Sparse embeddings less effective for semantic similarity on short queries or out-of-vocabulary terms","Integration with vector databases requires explicit sparse vector support (not all databases provide this)","Requires NVIDIA GPU with CUDA support; no AMD or Intel GPU support","fastembed-gpu package adds complexity; requires separate installation and CUDA toolkit setup","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"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=fastembed","compare_url":"https://unfragile.ai/compare?artifact=fastembed"}},"signature":"sYzBDvb8m4ICu8QkHMdDMnHw9lvhEPM7cWsojmCpscNI8LW/vo5t9x+kWZdQRylmh7tL2Wt/3QOk235YmqTIBA==","signedAt":"2026-06-20T14:36:42.373Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fastembed","artifact":"https://unfragile.ai/fastembed","verify":"https://unfragile.ai/api/v1/verify?slug=fastembed","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"}}