{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-fastembed","slug":"pypi-fastembed","name":"fastembed","type":"repo","url":"https://pypi.org/project/fastembed/","page_url":"https://unfragile.ai/pypi-fastembed","categories":["rag-knowledge"],"tags":["vector","embedding","neural","search","qdrant","sentence-transformers"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-fastembed__cap_0","uri":"capability://data.processing.analysis.dense.text.embedding.generation.with.onnx.runtime.acceleration","name":"dense text embedding generation with onnx runtime acceleration","description":"Generates dense vector representations of text using the TextEmbedding class, which leverages ONNX Runtime for CPU-optimized inference instead of PyTorch. The library automatically downloads and caches pre-trained models (default: BAAI/bge-small-en-v1.5), applies tokenization and pooling strategies (mean, cls, last-token), and supports batch processing with data parallelism for efficient multi-document embedding at scale.","intents":["I need to embed large document collections quickly without GPU overhead","I want semantic search capabilities with minimal dependencies in serverless environments","I need to generate embeddings comparable to OpenAI Ada-002 but self-hosted and faster"],"best_for":["Teams building RAG systems with strict latency requirements","Developers deploying embeddings in resource-constrained environments (Lambda, Cloud Functions)","Organizations needing local, privacy-preserving embedding generation without cloud APIs"],"limitations":["ONNX Runtime CPU inference is slower than GPU acceleration for very large batches (>10k documents)","Model caching directory must be writable; no in-memory-only mode for ephemeral deployments","Pooling strategies are fixed at model load time; cannot switch strategies per-batch without reloading"],"requires":["Python 3.8+","onnxruntime package (auto-installed)","~500MB disk space per model for caching","Writable filesystem for model cache (default: ~/.cache/fastembed)"],"input_types":["text (strings or lists of strings)"],"output_types":["numpy arrays (float32 vectors, shape: [batch_size, embedding_dim])"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-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 vector representations using the SparseTextEmbedding class, supporting multiple sparse embedding strategies (SPLADE, BM25, BM42) that produce high-dimensional vectors with mostly zero values. These sparse embeddings are designed to integrate with traditional keyword-based search systems, enabling hybrid search by combining dense semantic vectors with sparse lexical matching in a single retrieval pipeline.","intents":["I need to combine semantic and keyword search in a single query without maintaining two separate indices","I want to preserve exact term matching while adding semantic understanding to my search","I need to migrate from BM25-only search to hybrid without reindexing existing data"],"best_for":["Teams implementing hybrid search combining dense + sparse vectors","Organizations with existing BM25/Elasticsearch infrastructure wanting semantic augmentation","Developers building domain-specific search where exact term matching is critical"],"limitations":["Sparse embeddings require significantly more storage than dense vectors (10-100x larger indices)","SPLADE models are slower to generate than dense embeddings due to vocabulary expansion","Sparse vector support in vector databases is less mature than dense; Qdrant has native support but others may require custom indexing"],"requires":["Python 3.8+","fastembed package with sparse embedding models","Vector database with sparse vector support (Qdrant recommended) or custom sparse indexing layer"],"input_types":["text (strings or lists of strings)"],"output_types":["sparse vectors (dict format with token_id: weight pairs, or scipy sparse matrices)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fastembed__cap_10","uri":"capability://automation.workflow.minimal.dependency.footprint.for.serverless.and.edge.deployment","name":"minimal dependency footprint for serverless and edge deployment","description":"Designed with minimal external dependencies (primarily ONNX Runtime and numpy), avoiding heavy frameworks like PyTorch or TensorFlow. This lightweight design enables deployment in resource-constrained environments such as AWS Lambda, Google Cloud Functions, and edge devices where package size and memory limits are strict. The library's total package size is <50MB, compared to 500MB+ for PyTorch-based alternatives.","intents":["I need to deploy embeddings in AWS Lambda or similar serverless functions with size constraints","I want to run embeddings on edge devices with limited memory and storage","I need to minimize cold start time for serverless embedding services"],"best_for":["Teams deploying embeddings in serverless architectures (Lambda, Cloud Functions, Cloud Run)","Developers building edge AI applications on resource-constrained devices","Organizations optimizing deployment package size and cold start latency"],"limitations":["Minimal dependencies means fewer optimization options; cannot leverage PyTorch's advanced features","ONNX Runtime has less community support than PyTorch for custom operations or model architectures","Some advanced models may not have ONNX versions available; conversion from PyTorch requires custom tooling","Serverless deployment still requires model caching strategy; default ~/.cache/fastembed won't work in ephemeral filesystems"],"requires":["Python 3.8+","fastembed package (~50MB total)","onnxruntime (~20MB)","numpy (~10MB)","Serverless runtime with Python 3.8+ support"],"input_types":["text strings or images"],"output_types":["numpy arrays of embeddings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-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 embeddings for each token in a document rather than a single aggregate embedding. This enables fine-grained matching where query tokens are compared against all document tokens, allowing relevance scoring based on the best token-pair matches rather than document-level similarity.","intents":["I need more granular relevance matching than document-level similarity provides","I want to implement ColBERT-style retrieval for improved ranking precision","I need to match specific phrases or entities within documents without losing context"],"best_for":["Teams building high-precision retrieval systems where token-level matching improves ranking","Developers implementing advanced RAG with ColBERT reranking","Organizations needing phrase-aware search beyond semantic similarity"],"limitations":["Token-level embeddings require 10-100x more storage than dense document embeddings (one vector per token)","Similarity computation is O(query_tokens × document_tokens) instead of O(1), increasing latency for large documents","Requires specialized vector database support for efficient token-level similarity search; standard dense vector DBs are inefficient"],"requires":["Python 3.8+","fastembed with ColBERT model support","Vector database optimized for token-level search (Qdrant with custom indexing or specialized systems)"],"input_types":["text (strings or lists of strings)"],"output_types":["token-level embeddings (2D arrays: [num_tokens, embedding_dim])"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fastembed__cap_3","uri":"capability://image.visual.image.embedding.generation.with.clip.based.models","name":"image embedding generation with clip-based models","description":"Generates dense vector representations of images using the ImageEmbedding class, which leverages CLIP and similar vision-language models via ONNX Runtime. The class handles image loading, preprocessing (resizing, normalization), and batch inference to produce embeddings that capture visual semantics in a shared embedding space with text embeddings, enabling cross-modal search.","intents":["I need to search images by text queries or find similar images without manual tagging","I want to build a multimodal search system combining text and image retrieval","I need to embed product images for visual similarity recommendations"],"best_for":["Teams building e-commerce or content discovery platforms with visual search","Developers implementing multimodal RAG combining document images and text","Organizations needing cross-modal retrieval without cloud vision APIs"],"limitations":["Image preprocessing adds latency (~50-200ms per image for resizing and normalization)","CLIP embeddings are less specialized than fine-tuned vision models for domain-specific image types","Batch processing requires images to be loaded into memory; very large image collections need streaming/chunking"],"requires":["Python 3.8+","fastembed with image embedding models","PIL/Pillow for image loading and preprocessing","~1GB disk space for CLIP model cache"],"input_types":["image files (JPEG, PNG, etc.) or PIL Image objects"],"output_types":["numpy arrays (float32 vectors, shape: [batch_size, embedding_dim], typically 512 or 768)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-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 embeddings for document images using the LateInteractionMultimodalEmbedding class, implementing the ColPali architecture to produce per-patch embeddings from document images (PDFs, scans). This enables fine-grained matching where query tokens are compared against visual patches in documents, supporting retrieval of specific content within document images without OCR.","intents":["I need to search within scanned documents or PDFs without running OCR","I want to find specific information in document images using natural language queries","I need to index large document collections with mixed text and image content"],"best_for":["Teams processing scanned documents, invoices, or forms at scale","Organizations building document retrieval systems without OCR infrastructure","Developers implementing RAG over PDF/image-heavy knowledge bases"],"limitations":["Patch-level embeddings for document images require 100-1000x more storage than single document embeddings","Document image preprocessing (page splitting, resizing) adds significant latency (~500ms-2s per page)","Requires GPU acceleration for practical throughput; CPU inference is too slow for production document indexing","ColPali models are newer and less battle-tested than dense text embeddings; fewer optimizations available"],"requires":["Python 3.8+","fastembed with ColPali model support","fastembed-gpu package for GPU acceleration (strongly recommended)","PDF processing library (pypdf or similar) for multi-page documents","CUDA 11.8+ and compatible GPU for practical performance"],"input_types":["document images (JPEG, PNG), PDF files (via preprocessing)"],"output_types":["patch-level embeddings (3D arrays: [num_patches, embedding_dim]), typically [num_patches, 128]"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-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 pairs of texts (query-document, question-answer) using the TextCrossEncoder class, which applies transformer models that jointly encode both texts to produce relevance scores. Unlike bi-encoders that embed texts independently, cross-encoders directly model the relationship between text pairs, enabling accurate reranking of retrieval results or scoring of candidate answers without embedding the entire candidate set.","intents":["I need to rerank search results from a retriever to improve final ranking quality","I want to score question-answer pairs to find the best answer from multiple candidates","I need to filter low-relevance results without embedding every candidate"],"best_for":["Teams implementing multi-stage retrieval pipelines (retriever → reranker)","Developers building QA systems that need to score candidate answers","Organizations optimizing search quality without increasing embedding storage"],"limitations":["Cross-encoder inference is O(k) where k is number of candidates to score; cannot scale to scoring millions of candidates","Requires both query and document in memory simultaneously; batch processing is limited by GPU/CPU memory","Scoring latency is higher than dense similarity lookup; typically 10-100ms per pair depending on model size","Not suitable for initial retrieval stage; must be used after dense retrieval to score top-k results"],"requires":["Python 3.8+","fastembed with cross-encoder models","Pre-computed dense embeddings or retrieval results to rerank","GPU recommended for latency-sensitive applications (CPU inference adds 100-500ms per batch)"],"input_types":["text pairs (tuples of strings: (query, document) or (question, answer))"],"output_types":["relevance scores (float32 arrays, typically 0-1 range or unbounded depending on model)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fastembed__cap_6","uri":"capability://memory.knowledge.automatic.model.downloading.and.caching.with.hugging.face.integration","name":"automatic model downloading and caching with hugging face integration","description":"Automatically downloads pre-trained embedding models from Hugging Face Model Hub and caches them locally using a configurable cache directory. The system handles model versioning, integrity checking, and lazy loading, allowing developers to specify models by name (e.g., 'BAAI/bge-small-en-v1.5') without manual download management. Cache location defaults to ~/.cache/fastembed but is configurable for containerized or restricted-filesystem environments.","intents":["I want to use different embedding models without manually downloading and managing model files","I need to deploy embeddings in containers where I can't rely on persistent filesystem","I want to ensure reproducibility by pinning specific model versions"],"best_for":["Teams deploying embeddings across multiple environments (dev, staging, prod)","Developers building containerized applications requiring model isolation","Organizations needing to audit and control which models are used"],"limitations":["First model load requires network access to Hugging Face; no offline-first mode for air-gapped environments","Cache directory must be writable; ephemeral filesystems (Lambda, Cloud Functions) require custom cache backends","Model versioning is implicit via Hugging Face commit hash; no built-in version pinning mechanism beyond model name","Cache cleanup is manual; no automatic eviction policy for disk space management"],"requires":["Python 3.8+","Network access to huggingface.co (or custom mirror)","Writable filesystem for cache (default: ~/.cache/fastembed)","~500MB-2GB disk space per model depending on model size"],"input_types":["model identifier strings (e.g., 'BAAI/bge-small-en-v1.5')"],"output_types":["loaded model objects ready for inference"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fastembed__cap_7","uri":"capability://automation.workflow.batch.processing.with.data.parallelism.for.embedding.generation","name":"batch processing with data parallelism for embedding generation","description":"Processes large batches of documents efficiently using data parallelism, where the library automatically splits input batches across available CPU cores or GPU devices. The implementation uses ONNX Runtime's built-in parallelism and optional multi-threading to maximize throughput, allowing developers to embed thousands of documents with a single function call while the library handles batching, device allocation, and result aggregation.","intents":["I need to embed a large corpus of documents efficiently without writing custom batching logic","I want to maximize CPU/GPU utilization when embedding millions of documents","I need to process documents in streaming fashion without loading entire corpus into memory"],"best_for":["Teams building initial indexing pipelines for large document collections","Developers implementing batch embedding jobs in data pipelines","Organizations optimizing embedding throughput for cost-sensitive applications"],"limitations":["Batch size must be tuned per hardware; too large batches cause OOM, too small batches underutilize hardware","Data parallelism overhead is significant for small batches (<100 documents); not suitable for real-time single-document embedding","Memory usage scales linearly with batch size; very large batches (>10k documents) may require external streaming/chunking","Parallelism is transparent; no fine-grained control over thread/process allocation or device placement"],"requires":["Python 3.8+","fastembed package","Sufficient RAM for batch size (typically 1-2GB for batch_size=1000 with dense embeddings)","Optional: GPU with CUDA support for GPU-accelerated batching via fastembed-gpu"],"input_types":["lists of text strings or image objects"],"output_types":["numpy arrays of embeddings (shape: [batch_size, embedding_dim])"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fastembed__cap_8","uri":"capability://automation.workflow.gpu.acceleration.with.optional.fastembed.gpu.package","name":"gpu acceleration with optional fastembed-gpu package","description":"Provides optional GPU acceleration through a separate fastembed-gpu package that replaces CPU ONNX Runtime with CUDA-optimized inference. When installed, the library automatically detects available GPUs and routes inference to GPU devices, providing 5-10x speedup for embedding generation. The GPU implementation maintains API compatibility with CPU version, requiring only package installation change without code modifications.","intents":["I need to embed millions of documents quickly for initial indexing","I want to reduce embedding latency for real-time search applications","I need to maximize throughput in high-volume embedding pipelines"],"best_for":["Teams with GPU infrastructure (on-prem or cloud) embedding large document collections","Organizations building real-time embedding services with latency requirements","Developers optimizing cost per embedding in high-volume scenarios"],"limitations":["GPU acceleration requires CUDA 11.8+ and compatible NVIDIA GPU; not available for AMD or Intel GPUs","fastembed-gpu package adds significant dependency overhead (CUDA runtime, cuDNN); increases deployment complexity","GPU memory is limited; batch sizes must be smaller than CPU to fit in VRAM (typically 256-1024 vs 10k+ on CPU)","GPU acceleration is most beneficial for large batches; small batches (<100 docs) may not justify GPU overhead"],"requires":["Python 3.8+","fastembed-gpu package (separate install from base fastembed)","NVIDIA GPU with CUDA Compute Capability 7.0+ (V100, A100, RTX series, etc.)","CUDA 11.8+ and cuDNN 8.0+ installed and in system PATH","Sufficient GPU VRAM for batch size (typically 2-8GB for batch_size=256-1024)"],"input_types":["lists of text strings or image objects"],"output_types":["numpy arrays of embeddings (shape: [batch_size, embedding_dim])"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-fastembed__cap_9","uri":"capability://memory.knowledge.multi.model.embedding.support.with.unified.interface","name":"multi-model embedding support with unified interface","description":"Provides a unified Python interface supporting 50+ pre-trained embedding models across multiple architectures (dense, sparse, late-interaction, multimodal) without requiring model-specific code. The library abstracts model differences through consistent class APIs (TextEmbedding, ImageEmbedding, etc.), allowing developers to swap models by changing a single parameter while maintaining identical inference code. Supported models include BAAI BGE, Sentence Transformers, SPLADE, ColBERT, CLIP, and ColPali variants.","intents":["I want to experiment with different embedding models without rewriting code","I need to compare embedding quality across multiple models for my use case","I want to use domain-specific models (medical, legal, code) without custom integration"],"best_for":["Teams evaluating embedding models for production deployment","Researchers comparing embedding architectures and quality","Organizations building model-agnostic embedding infrastructure"],"limitations":["Not all models are equally optimized; some models have slower ONNX conversion or larger file sizes","Model quality varies significantly; library doesn't provide automated model selection or quality metrics","Swapping models requires reindexing existing embeddings; no compatibility layer for different embedding dimensions","Model availability depends on Hugging Face community; unsupported models require custom ONNX conversion"],"requires":["Python 3.8+","fastembed package","Model identifier from supported models list","Disk space for selected models (~500MB-2GB per model)"],"input_types":["text strings, images, or document images depending on model type"],"output_types":["numpy arrays of embeddings with model-specific dimensions"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","onnxruntime package (auto-installed)","~500MB disk space per model for caching","Writable filesystem for model cache (default: ~/.cache/fastembed)","fastembed package with sparse embedding models","Vector database with sparse vector support (Qdrant recommended) or custom sparse indexing layer","fastembed package (~50MB total)","onnxruntime (~20MB)","numpy (~10MB)","Serverless runtime with Python 3.8+ support"],"failure_modes":["ONNX Runtime CPU inference is slower than GPU acceleration for very large batches (>10k documents)","Model caching directory must be writable; no in-memory-only mode for ephemeral deployments","Pooling strategies are fixed at model load time; cannot switch strategies per-batch without reloading","Sparse embeddings require significantly more storage than dense vectors (10-100x larger indices)","SPLADE models are slower to generate than dense embeddings due to vocabulary expansion","Sparse vector support in vector databases is less mature than dense; Qdrant has native support but others may require custom indexing","Minimal dependencies means fewer optimization options; cannot leverage PyTorch's advanced features","ONNX Runtime has less community support than PyTorch for custom operations or model architectures","Some advanced models may not have ONNX versions available; conversion from PyTorch requires custom tooling","Serverless deployment still requires model caching strategy; default ~/.cache/fastembed won't work in ephemeral filesystems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.5800000000000001,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:17.402Z","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=pypi-fastembed","compare_url":"https://unfragile.ai/compare?artifact=pypi-fastembed"}},"signature":"5AUuj+eWJzm3QNBtz5Lt4fHYUwUpswA7KuPr1afF1VyHMAUDz6gIr9LcK2IW1x3B38qC/y3BacsbSnfrJwdiDg==","signedAt":"2026-06-20T23:51:26.244Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-fastembed","artifact":"https://unfragile.ai/pypi-fastembed","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}