{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-faiss-cpu","slug":"pypi-faiss-cpu","name":"faiss-cpu","type":"repo","url":"https://pypi.org/project/faiss-cpu/","page_url":"https://unfragile.ai/pypi-faiss-cpu","categories":["model-training"],"tags":["faiss","similarity","search","clustering","machine","learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-faiss-cpu__cap_0","uri":"capability://search.retrieval.dense.vector.similarity.search.with.multiple.index.types","name":"dense-vector similarity search with multiple index types","description":"Implements approximate nearest neighbor (ANN) search across dense vector spaces using multiple indexing strategies (flat, IVF, HNSW, PQ) that trade off between speed, memory, and accuracy. The library uses quantization and hierarchical clustering techniques to enable sub-linear search time on billion-scale datasets without loading entire indices into memory. Supports both exact and approximate search modes with configurable recall-vs-speed tradeoffs.","intents":["Find semantically similar vectors in a large corpus without scanning every vector","Build a recommendation system that retrieves top-K similar items from millions of candidates in milliseconds","Implement semantic search over embeddings from language models or vision encoders","Scale vector similarity operations from millions to billions of vectors efficiently"],"best_for":["ML engineers building semantic search or recommendation systems","Teams implementing RAG pipelines with large embedding collections","Researchers prototyping ANN algorithms and index structures","Production systems requiring sub-millisecond latency on billion-scale vector datasets"],"limitations":["Approximate indices (IVF, HNSW, PQ) have configurable but non-zero recall loss — exact search requires flat indices which don't scale beyond ~100M vectors","Index construction is CPU-bound and can take hours for billion-scale datasets; no incremental indexing for most index types","Quantization (PQ, OPQ) reduces vector dimensionality and precision, requiring careful tuning of codebook size and training data","No built-in distributed indexing — scaling across multiple machines requires manual sharding or external orchestration","CPU version has no GPU acceleration; GPU operations require separate faiss-gpu package with CUDA/cuDNN dependencies"],"requires":["Python 3.6+","NumPy for array handling","C++ compiler for building from source (wheels available for common platforms)","Dense vector embeddings as float32 arrays (other dtypes require conversion)"],"input_types":["dense vectors (float32 numpy arrays)","vector batches (2D arrays of shape [N, D])","index configuration parameters (nlist, nprobe, code_size)"],"output_types":["integer indices of nearest neighbors","distance scores (L2, inner product, cosine)","serialized index objects (binary format)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_1","uri":"capability://data.processing.analysis.inverted.file.index.construction.with.clustering","name":"inverted-file index construction with clustering","description":"Builds IVF (Inverted File) indices by partitioning the vector space into Voronoi cells using k-means clustering, then storing vectors in inverted lists keyed by their nearest cluster centroid. During search, only vectors in nearby clusters are examined, reducing search complexity from O(N) to O(N/nlist + nprobe*nlist/k). Supports training on a subset of data and adding vectors incrementally to pre-trained indices.","intents":["Partition a large vector collection into searchable clusters for faster retrieval","Train an index on a representative sample, then add new vectors without retraining","Balance search speed and accuracy by tuning the number of clusters (nlist) and probes (nprobe)"],"best_for":["Production systems with streaming vector ingestion where retraining is expensive","Applications requiring tunable recall-vs-latency tradeoffs","Teams with limited GPU resources needing CPU-based indexing"],"limitations":["k-means training is sensitive to initialization and may converge to local optima; requires multiple restarts or careful seed selection","IVF search quality degrades with high-dimensional vectors (>1000 dims) due to curse of dimensionality; requires dimensionality reduction or product quantization","Cluster imbalance can occur if data distribution is skewed, leading to uneven inverted list sizes and suboptimal search performance","Adding vectors after training doesn't update cluster centroids, so new vectors may be assigned to suboptimal clusters"],"requires":["Training vectors (typically 10x-100x the number of clusters)","Pre-specified number of clusters (nlist parameter)","Vector dimensionality must be consistent across training and search"],"input_types":["training vectors (float32 numpy array of shape [N_train, D])","query vectors (float32 numpy array of shape [N_query, D])","nlist parameter (number of clusters, typically 100-10000)"],"output_types":["trained IVF index object","cluster centroid coordinates","inverted lists (vector IDs grouped by cluster)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_10","uri":"capability://search.retrieval.range.search.and.threshold.based.retrieval","name":"range search and threshold-based retrieval","description":"Retrieves all vectors within a specified distance threshold (radius search) rather than top-K nearest neighbors. Useful for clustering, outlier detection, and similarity thresholding. Supports both exact and approximate range search with configurable recall tradeoffs.","intents":["Find all vectors similar to a query within a distance threshold","Implement clustering or grouping based on similarity thresholds","Detect outliers or anomalies by finding vectors with no nearby neighbors"],"best_for":["Applications with similarity thresholds rather than fixed top-K requirements","Clustering and grouping tasks","Anomaly detection based on neighborhood density"],"limitations":["Range search results are variable-sized; difficult to predict result count or memory requirements","Approximate range search may miss vectors near the threshold boundary","No built-in result sorting; results are returned in arbitrary order","Performance depends heavily on threshold value; very loose thresholds return many results, causing latency spikes"],"requires":["Distance threshold (radius parameter)","Query vectors"],"input_types":["query vectors (float32 numpy arrays)","radius parameter (distance threshold)"],"output_types":["variable-length lists of neighbor indices","distance scores for each neighbor"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_11","uri":"capability://automation.workflow.index.cloning.and.copying","name":"index cloning and copying","description":"Creates independent copies of trained indices, enabling parallel search operations or index modification without affecting the original. Supports both shallow copies (shared data structures) and deep copies (independent data). Useful for A/B testing different index configurations or maintaining multiple versions.","intents":["Create independent copies of indices for parallel search without contention","Maintain multiple index versions for A/B testing or gradual rollout","Modify an index without affecting the original"],"best_for":["Multi-threaded or distributed systems requiring parallel index access","A/B testing different index configurations","Gradual index updates with fallback to previous versions"],"limitations":["Deep copying large indices is memory-intensive; requires 2x the index size in memory","Shallow copies share underlying data; modifications to one copy affect all copies","No automatic synchronization between copies; users must manage consistency"],"requires":["Trained index object","Sufficient memory for copied index"],"input_types":["index object to copy"],"output_types":["cloned index object"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_2","uri":"capability://data.processing.analysis.product.quantization.vector.compression","name":"product-quantization vector compression","description":"Compresses high-dimensional vectors into compact codes by decomposing the vector space into M subspaces, quantizing each subspace independently to K centroids, and storing only the centroid indices (typically 8-16 bits per subspace). Enables distance computation in compressed space using lookup tables, reducing memory footprint by 10-100x while maintaining approximate search accuracy. Supports both PQ (product quantization) and OPQ (optimized PQ with learned rotation).","intents":["Reduce memory footprint of billion-scale vector indices from gigabytes to megabytes","Enable fast distance computation using lookup tables instead of full vector operations","Trade vector precision for memory efficiency in memory-constrained deployments"],"best_for":["Mobile and edge deployments with strict memory budgets","Large-scale production systems where index size directly impacts cost","Applications where 95%+ recall is acceptable and memory savings justify precision loss"],"limitations":["Quantization introduces approximation error; recall degrades with aggressive compression (e.g., 8-bit codes lose ~5-15% recall vs float32)","OPQ requires training on representative data and learning an optimal rotation matrix; training is computationally expensive and sensitive to data distribution","Subspace decomposition assumes independence between subspaces, which may not hold for correlated dimensions","Distance computation in compressed space is faster but less accurate than full-vector computation; cannot be used for exact nearest neighbor search"],"requires":["Training vectors for learning codebooks (typically 100K-1M vectors)","Specification of M (number of subspaces, typically 8-64) and K (codebook size, typically 256)","Vector dimensionality must be divisible by M"],"input_types":["training vectors (float32 numpy array)","query vectors (float32 numpy array)","M parameter (number of subspaces)","K parameter (codebook size per subspace)"],"output_types":["compressed codes (uint8 arrays of shape [N, M])","codebook lookup tables (float32 arrays of shape [M, K, D/M])","distance estimates (float32 scores computed from lookup tables)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_3","uri":"capability://search.retrieval.hierarchical.navigable.small.world.graph.indexing","name":"hierarchical-navigable-small-world graph indexing","description":"Builds HNSW (Hierarchical Navigable Small World) indices by constructing a multi-layer graph where each layer is a navigable small-world network with logarithmic diameter. Search navigates from top layers (sparse, long-range connections) to bottom layers (dense, local connections), achieving O(log N) search complexity. Supports incremental insertion of new vectors without retraining, making it suitable for streaming workloads.","intents":["Build an index that supports fast incremental vector insertion without full retraining","Achieve logarithmic search complexity on dynamic, growing vector collections","Implement approximate nearest neighbor search with tunable recall via ef parameter"],"best_for":["Streaming and real-time systems where vectors arrive continuously","Applications requiring dynamic index updates without downtime","Use cases where recall requirements are high (>95%) and latency is critical"],"limitations":["HNSW memory overhead is higher than IVF due to graph structure storage (~50-100 bytes per vector for graph pointers)","Search quality is sensitive to M parameter (max connections per node); no automatic tuning, requires manual experimentation","Graph construction is inherently sequential; insertion of new vectors requires graph updates that cannot be easily parallelized","No built-in support for vector deletion; removing vectors requires index reconstruction","Performance degrades in very high dimensions (>1000) due to curse of dimensionality affecting graph connectivity"],"requires":["M parameter (max connections per node, typically 5-48)","ef_construction parameter (size of dynamic candidate list during construction, typically 200-400)","ef parameter for search (controls search breadth, typically 100-1000)"],"input_types":["vectors to insert (float32 numpy arrays)","query vectors (float32 numpy arrays)","M and ef parameters"],"output_types":["HNSW graph structure (adjacency lists per layer)","nearest neighbor indices","distance scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_4","uri":"capability://search.retrieval.composite.index.chaining.with.automatic.routing","name":"composite-index chaining with automatic routing","description":"Chains multiple index types together (e.g., IVF→PQ, HNSW→PQ) where the first index coarsely filters candidates and the second refines results, enabling automatic routing of queries through the pipeline. Supports index composition via IndexIVFPQ, IndexHNSWPQ, and custom composite indices. Allows fine-grained control over filtering thresholds and refinement strategies.","intents":["Combine coarse filtering (fast, approximate) with fine refinement (slower, accurate) for optimal speed-accuracy tradeoff","Build memory-efficient indices by composing clustering with quantization","Implement multi-stage retrieval pipelines without manual orchestration"],"best_for":["Production systems requiring strict latency SLAs with high recall","Memory-constrained deployments needing aggressive compression","Teams wanting to experiment with index composition without custom code"],"limitations":["Composite indices are less flexible than custom pipelines; limited to predefined compositions (IVF+PQ, HNSW+PQ, etc.)","Tuning multiple index parameters (nlist, nprobe, M, K) creates a high-dimensional hyperparameter space with complex interactions","Index composition adds latency from multiple stages; total search time is sum of filtering + refinement, not parallelizable","No automatic parameter selection; requires manual experimentation or grid search to find optimal configurations"],"requires":["Specification of both index types and their parameters","Training data for both stages (clustering for first stage, quantization for second)","Understanding of tradeoffs between filtering threshold and refinement cost"],"input_types":["training vectors for both index stages","query vectors","parameters for both index types"],"output_types":["composite index object","nearest neighbor indices","distance scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_5","uri":"capability://data.processing.analysis.batch.vector.addition.with.automatic.index.updates","name":"batch vector addition with automatic index updates","description":"Adds multiple vectors to an index in batches, automatically updating internal data structures (cluster assignments, quantization codebooks, graph connections) without full index reconstruction. Supports both exact indices (flat, IVF) and approximate indices (HNSW, PQ) with different update semantics. Provides options for synchronous updates (immediate consistency) or asynchronous updates (deferred consistency for throughput).","intents":["Ingest new vectors into an index without stopping search operations","Maintain index consistency while handling streaming vector arrivals","Balance update latency against search performance impact"],"best_for":["Real-time systems with continuous vector ingestion","Applications where index downtime is unacceptable","Streaming ML pipelines that need to index embeddings as they're generated"],"limitations":["IVF indices don't update cluster centroids during batch addition; new vectors may be assigned to suboptimal clusters, degrading search quality over time","HNSW insertion is sequential and cannot be parallelized; batch insertion throughput is limited by single-threaded graph updates","No built-in batching across multiple machines; distributed ingestion requires external coordination","Batch size affects update latency; very large batches (>1M vectors) may cause temporary search latency spikes"],"requires":["Pre-trained index (for IVF, HNSW) or empty flat index","Vectors to add (float32 numpy arrays)","Batch size parameter (typically 10K-1M vectors)"],"input_types":["vectors to add (float32 numpy arrays of shape [batch_size, D])","optional vector IDs (integer array)"],"output_types":["updated index object","vector IDs assigned to new vectors"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_6","uri":"capability://automation.workflow.index.serialization.and.persistence","name":"index serialization and persistence","description":"Serializes trained indices to disk in a binary format optimized for fast loading, preserving all internal structures (cluster centroids, quantization codebooks, graph connections). Supports both complete index serialization and partial serialization (e.g., codebooks only). Enables index sharing across processes and machines via file transfer or network protocols.","intents":["Save a trained index to disk and reload it without retraining","Share indices across multiple processes or machines","Version control indices alongside model checkpoints"],"best_for":["Production deployments where index training is expensive and must be amortized","Multi-process or distributed systems requiring index sharing","ML pipelines with reproducibility requirements"],"limitations":["Serialized index format is Faiss-specific and not human-readable; no standard interchange format (e.g., no JSON or Parquet support)","Index files are binary and version-dependent; indices trained with older Faiss versions may not load in newer versions","No built-in compression for serialized indices; file sizes can be large (gigabytes for billion-scale indices)","Serialization doesn't include vector data, only index structure; vectors must be stored separately"],"requires":["Trained index object","Writable filesystem or network storage","Same Faiss version for loading as was used for saving (or compatible versions)"],"input_types":["trained index object","file path or file-like object"],"output_types":["binary index file","loaded index object"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_7","uri":"capability://data.processing.analysis.distance.metric.selection.and.custom.metrics","name":"distance metric selection and custom metrics","description":"Supports multiple distance metrics (L2 Euclidean, inner product, cosine similarity, Hamming distance) and allows custom metric definition via metric_type parameter. Metrics are used consistently across all index types and search operations. Enables metric-specific optimizations (e.g., SIMD acceleration for L2 distance).","intents":["Choose the distance metric appropriate for the embedding space (e.g., cosine for normalized embeddings, L2 for unnormalized)","Optimize search performance for specific metrics using hardware-accelerated implementations","Implement custom similarity functions for domain-specific use cases"],"best_for":["Applications with specific similarity requirements (e.g., cosine for text embeddings, L2 for image embeddings)","Teams needing metric flexibility without reimplementing search logic","Performance-critical systems where metric-specific optimizations matter"],"limitations":["Custom metrics require C++ implementation; Python-only custom metrics are not supported","Some index types have limited metric support (e.g., HNSW works best with L2 and inner product, less optimized for cosine)","Metric choice affects index training; changing metrics requires retraining","No automatic metric selection; users must choose appropriate metric for their embedding space"],"requires":["Specification of metric_type parameter (METRIC_L2, METRIC_INNER_PRODUCT, METRIC_COSINE, etc.)","Consistent metric usage across training and search"],"input_types":["metric_type parameter (string or enum)","vectors (float32 numpy arrays)"],"output_types":["distance scores computed using specified metric"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_8","uri":"capability://data.processing.analysis.k.means.clustering.with.batch.updates","name":"k-means clustering with batch updates","description":"Implements k-means clustering optimized for large-scale vector data using batch updates, Faiss-specific initializations (k-means++, random), and convergence criteria. Used internally for IVF index training but also exposed as a standalone API. Supports GPU acceleration (in GPU version) and multi-threaded CPU execution.","intents":["Cluster large vector collections for exploratory analysis or data organization","Train cluster centroids for IVF index construction","Partition vectors into groups for downstream processing"],"best_for":["Unsupervised learning tasks requiring fast clustering of large embeddings","IVF index training where clustering quality directly impacts search performance","Data exploration and analysis of embedding spaces"],"limitations":["k-means is sensitive to initialization and may converge to local optima; requires multiple restarts or careful seed selection","Convergence is slow for very high-dimensional data (>1000 dims); dimensionality reduction recommended","No automatic k selection; users must specify number of clusters","Cluster imbalance can occur with skewed data distributions, leading to empty clusters or very large clusters"],"requires":["Training vectors (float32 numpy arrays)","Number of clusters (k parameter)","Number of iterations or convergence threshold"],"input_types":["training vectors (float32 numpy arrays of shape [N, D])","k parameter (number of clusters)","niter parameter (number of iterations)","seed parameter (random seed for initialization)"],"output_types":["cluster centroids (float32 array of shape [k, D])","cluster assignments (integer array of shape [N])","cluster sizes (integer array of shape [k])"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-faiss-cpu__cap_9","uri":"capability://data.processing.analysis.vector.normalization.and.preprocessing","name":"vector normalization and preprocessing","description":"Provides utilities for normalizing vectors (L2 normalization, unit normalization) and applying transformations (PCA, whitening) before indexing. Supports both in-memory and streaming preprocessing. Transformations can be applied consistently to both training and query vectors.","intents":["Normalize embeddings to unit length for cosine similarity search","Apply dimensionality reduction (PCA) to reduce index size and improve search quality","Whiten vectors to improve clustering quality for IVF indices"],"best_for":["Preprocessing pipelines for embedding normalization","Dimensionality reduction for high-dimensional embeddings","Improving IVF clustering quality through whitening"],"limitations":["PCA requires training on representative data; transformation is not invertible without storing the original vectors","Whitening can amplify noise in low-variance dimensions; requires careful tuning of regularization","Preprocessing adds latency to both index training and search; must be applied consistently to all vectors","No automatic preprocessing selection; users must choose appropriate transformations for their data"],"requires":["Training vectors for learning transformations (PCA, whitening)","Specification of transformation parameters (number of components for PCA, regularization for whitening)"],"input_types":["vectors to normalize or transform (float32 numpy arrays)","transformation parameters"],"output_types":["normalized or transformed vectors (float32 numpy arrays)","transformation objects (for applying to new vectors)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+","NumPy for array handling","C++ compiler for building from source (wheels available for common platforms)","Dense vector embeddings as float32 arrays (other dtypes require conversion)","Training vectors (typically 10x-100x the number of clusters)","Pre-specified number of clusters (nlist parameter)","Vector dimensionality must be consistent across training and search","Distance threshold (radius parameter)","Query vectors","Trained index object"],"failure_modes":["Approximate indices (IVF, HNSW, PQ) have configurable but non-zero recall loss — exact search requires flat indices which don't scale beyond ~100M vectors","Index construction is CPU-bound and can take hours for billion-scale datasets; no incremental indexing for most index types","Quantization (PQ, OPQ) reduces vector dimensionality and precision, requiring careful tuning of codebook size and training data","No built-in distributed indexing — scaling across multiple machines requires manual sharding or external orchestration","CPU version has no GPU acceleration; GPU operations require separate faiss-gpu package with CUDA/cuDNN dependencies","k-means training is sensitive to initialization and may converge to local optima; requires multiple restarts or careful seed selection","IVF search quality degrades with high-dimensional vectors (>1000 dims) due to curse of dimensionality; requires dimensionality reduction or product quantization","Cluster imbalance can occur if data distribution is skewed, leading to uneven inverted list sizes and suboptimal search performance","Adding vectors after training doesn't update cluster centroids, so new vectors may be assigned to suboptimal clusters","Range search results are variable-sized; difficult to predict result count or memory requirements","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"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-faiss-cpu","compare_url":"https://unfragile.ai/compare?artifact=pypi-faiss-cpu"}},"signature":"Phn+9oiu7tgBr9izwfEDFq1VDS5suIv+ZfxSYd5sraIA9Lei6Y2AlBpcJ3rEOCoOCUmhYqog2x+UlGONnJWHDQ==","signedAt":"2026-06-21T17:23:21.515Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-faiss-cpu","artifact":"https://unfragile.ai/pypi-faiss-cpu","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-faiss-cpu","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"}}