Arrowspace

ArrowSpace

ArrowSpace is a vector database and search library that augments nearest-neighbour search with spectral graph features. It computes a Laplacian over the item graph and uses the Rayleigh quotient to produce a $$λτ$$ (lambda-tau) score per item, enabling search that respects both semantic similarity and structural role.

When to Activate

• You need vector similarity search that goes beyond cosine / L2
• Your dataset has latent structure that proximity metrics miss
• You want to characterise the spectral properties of an embedding space
• You need graph-based retrieval with spectral awareness

Installation

pip install "arrowspace>=0.28"

Or from source: see pyarrowspace.

Core API

Build an ArrowSpace index

from arrowspace import ArrowSpaceBuilder
import numpy as np

items = np.array([[...], [...], ...], dtype=np.float64)
params = {"eps": 0.5, "k": 12, "topk": 6, "p": 2.0, "sigma": None}
aspace, gl = ArrowSpaceBuilder().build(params, items)

Since 0.28 every key has a default (eps=0.5, k=12, topk=6, p=2.0, sigma=eps); a partial dict is accepted. Defaults are a starting point — to compute parameters fitted to your dataset, use arrowspace_tuner:

import arrowspace_tuner
from arrowspace import ArrowSpaceBuilder

graph_params = arrowspace_tuner.tune(items)   # discovers eps, k, topk, p, sigma
aspace, gl = ArrowSpaceBuilder().build(graph_params, items)

gl is the graph Laplacian containing the Laplacian matrix (accessed via gl.to_dense() or gl.to_csr()).

Two pipelines: EigenMaps vs EnergyMaps

EnergyMaps pipeline

Build with build_energy(items, energy_params, graph_params). Both dicts are required positional arguments; every energy_params key is optional (defaults shown):

energy_params = {
    "optical_tokens": 50,   # target centroids after optical compression; None disables compression
    "trim_quantile": 0.1,   # fraction of high-norm items trimmed per spatial bin
    "eta": 0.1,             # diffusion step size for heat-flow smoothing over L0
    "steps": 4,             # number of diffusion iterations
    "split_quantile": 0.9,  # quantile threshold for splitting high-dispersion centroids
    "neighbor_k": 20,       # neighbourhood size for dispersion / local statistics
    "split_tau": 0.15,      # offset magnitude when splitting centroids along the local gradient
    "w_lambda": 1.0,        # weight of the lambda-proximity term in the energy distance
    "w_disp": 0.5,          # weight of the dispersion-difference term
    "w_dirichlet": 0.25,    # weight of the Rayleigh-Dirichlet term over feature deltas
    "candidate_m": 32,      # candidates evaluated before selecting the k nearest (M >= k)
}
aspace, gl = ArrowSpaceBuilder().build_energy(items, energy_params, graph_params)

Edge weights combine three terms weighted by w_lambda / w_disp / w_dirichlet: the lambda delta (spectral proximity), the dispersion (graph) delta, and a Dirichlet term over feature deltas. optical_tokens compresses the corpus into centroids by spatial binning with low-activation pooling; diffusion (eta, steps) smooths the compressed graph and high-dispersion centroids are split into sub-centroids (split_quantile, split_tau, neighbor_k). Query with aspace.search_energy(q, gl, k); search_batch raises NotImplementedError for energy indexes — use single-row search, search_hybrid, or search_linear_sorted instead.

Per-item $$λτ$$ scores

After building, the ArrowSpace instance exposes spectral scores for every item:

# Indexed by item insertion order: result[i] is score for i-th item
scores = aspace.lambdas()

# Sorted ascending: list of (score, item_index) tuples
ranked = aspace.lambdas_sorted()

• lambdas() — scores array aligned by item index (item 0, item 1, ...)
• lambdas_sorted() — (score, index) pairs sorted from least to most coherent

These are per-item spectral signatures, distinct from graph eigenvalues.

Search

query = np.array([...], dtype=np.float64)
hits = aspace.search(query, gl, tau=1.0)
# Returns list of (index, score) tuples; pass k=... to override topk

Search variants

Contract: search, search_hybrid, and the batch path raise ValueError (not PanicException) on degenerate (lambda ~0), non-finite, or dimension-mismatched queries. search_batch is per-row failure tolerant — non-finite or mismatched batches still raise, since those affect every row.

Parameters

Note: instead of hand-picking eps/k/tau, arrowspace_tuner can discover them from your corpus (label-free, Optuna-based) — pip install arrowspace-tuner, then arrowspace_tuner.tune(items).

Tau ($$λτ$$ blend)

tau is the alpha-beta blend weight between cosine similarity and the spectral $$λτ$$ score: tau weights the cosine term, 1 - tau the spectral term. tau=1.0 is pure cosine ranking; lower values blend in structural coherence. Start at tau=1.0 and lower it (e.g. 0.5–0.75) when retrieval should respect structural role. Values outside $$[0, 1]$$ extrapolate the blend and are not recommended.

After building, check for degenerate λτ scores — np.sum(np.abs(aspace.lambdas()) < 1e-12) should be ~0. Many zeros mean eps is too low: raise it and rebuild.

Sequencing and motif/subgraph analysis

Order items along the graph spectrum, or spot motifs and subgraphs on the Laplacian:

from arrowspace import sequence_by_lambda, sequence_by_graph

seq = sequence_by_lambda(aspace.lambdas())   # spectral curriculum, ascending
seq.order        # node indices in sequence order
seq.positions    # per-step coordinate: lambda score (sequence_by_lambda),
                 # DFS discovery depth within its component (sequence_by_graph)
seq.components   # connected components traversed

seriated = sequence_by_graph(gl)   # MST-chain seriation of the Laplacian

Motif and subgraph spotting (cfg dicts optional; defaults apply per key):

# EigenMaps builds: motifs on the F x F bootstrap Laplacian — returned ids
# are FEATURE dimensions, not items (node-space contract)
motifs = aspace.spot_motives_eigen(gl, {"top_l": 16, "min_triangles": 2})

# EigenMaps builds: item-space motifs on the centroid graph; every item must
# carry a cluster assignment (outlier-free) and n_clusters >= 2;
# ids are item indices in 0..nitems
motifs = aspace.spot_motives_eigen_items(gl, {"top_l": 16, "min_triangles": 2})

# EnergyMaps builds: motifs on the subcentroid graph, returned as item indices
motifs = aspace.spot_motives_energy(gl, {"top_l": 16, "min_triangles": 2})

# EnergyMaps builds: motif-anchored subgraphs — dicts with node_indices, item_indices,
# rayleigh (computed only when cfg sets rayleigh_max), nnodes, nfeatures
subgs = aspace.spot_subg_motives(gl, {"min_size": 4, "rayleigh_max": None})

# Centroid hierarchy — dicts with level, node_indices, root_indices, nnodes, nfeatures;
# cfg accepts graph params plus hierarchy keys min_centroids (8) and max_depth (2)
levels = aspace.spot_subg_centroids(gl, {"max_depth": 2, "min_centroids": 2})

Mode requirements: spot_motives_eigen / spot_motives_eigen_items need an EigenMaps build; spot_motives_energy / spot_subg_motives need an EnergyMaps build (build_energy) and raise ValueError otherwise.

Config keys (defaults): motif cfg top_l=16 (prune to top-L strongest neighbours), min_triangles=2, min_clust=0.4, max_motif_size=32, max_sets=256, jaccard_dedup=0.8; subgraph cfg min_size=3, rayleigh_max=None; centroid-hierarchy cfg additionally accepts min_centroids=8, max_depth=2, and the graph-param keys — unknown keys are rejected.

Rust API surface (arrowspace-rs 0.28)

For Rust users (docs.rs/arrowspace); the Python bindings wrap the same core:

• Builder defaults match the 0.28 defaults above (eps=0.5, k=12, topk=6, p=2.0, sigma=None meaning σ := eps); override via with_lambda_graph(eps, k, topk, p, sigma_override).
• TauMode synthesis policies for the per-item lambda-tau score (distinct from the query-time alpha-beta blend tau): Median (default), Mean, Percentile(f64), Fixed(f64).
• Fallible variants try_prepare_query_item / try_search_lambda_aware return ArrowSpaceError::DegenerateLambda instead of panicking; the panicking twins prepare_query_item / search_lambda_aware are deprecated since 0.27.
• range_search for radius queries.
• add_items / mul_items / scale_item mutate items and recompute lambdas.
• build_for_persistence(data, PipelineKind) — PipelineKind is Eigen or Energy(EnergyParams) and parses from "eigen" / "energy" via FromStr.
• from_config reconstructs an index from a typed config map.
• degenerate_lambda_count() — Rust-side quality gate mirroring the lambda check above.
• Sequencing and motif analysis are available Rust-side under the analysis modules (sequencing, motives, subgraphs).

Resources

• JOSS paper
• Design article: Semantic Basins
• Presentation: Spectral Indexing
• Presentation: Deep-dive
• Complex examples: pyarrowspace test suite
• Hyperparameter tuning: arrowspace_tuner
• Rust source
• Rust API docs
• Python source

See also

skills/arrowspace-core.md, skills/arrowspace-search.md, skills/arrowspace-spectral.md for focused skill definitions.