NumPy Vector Store

A fast, lightweight, zero-setup in-memory vector store powered by NumPy.

• Tiny local vector search for projects that do not need a vector database
• Fast exact vector search using vectorized NumPy operations
• Simple typed API returning VectorHit(index, value, metadata)
• Composable filtering by passing prefiltered row indexes with within_rows
• Portable persistence as trusted local .npz files with vectors + metadata
• No framework opinions: bring your own embeddings, chunking, async, and metadata model

Why?

This library is purpose-built for small to medium-scale vector search tasks and offers a simple alternative to heavyweight vector databases when you do not need network services, indexing infrastructure, ingestion pipelines, or domain-specific metadata filtering.

When/Where?

Below are benchmark results for cosine similarity search to help you assess its suitability for your use case.

Embedding Type	Dimensions	~5ms	~25ms	~100ms	~500ms
Sentence Transformers	384	1K vectors 1.5MB	10K vectors 15MB	100K vectors 147MB	500K vectors 732MB
OpenAI Small	1536	500 vectors 3MB	5K vectors 29MB	25K vectors 147MB	100K vectors 586MB
OpenAI Large	3072	200 vectors 2MB	2.5K vectors 29MB	5K vectors 59MB	25K vectors 293MB

Benchmarks performed on Apple M2 hardware.

Installation

uv add numpy-vector-store

Quick Start

import numpy as np
from numpy_vector_store import VectorStore

store = VectorStore[dict[str, str]](dimensions=3)

store.add(
    vectors=np.array([
        [1.0, 0.0, 0.0],
        [0.0, 1.0, 0.0],
        [0.0, 0.0, 1.0],
    ]),
    metadata=[
        {"title": "x-axis"},
        {"title": "y-axis"},
        {"title": "z-axis"},
    ],
)

hits = store.cosine_search(
    query=np.array([0.9, 0.1, 0.0]),
    top_k=2,
)

for hit in hits:
    print(f"{hit.metadata['title']}: {hit.value:.3f}")

metadata is an opaque row payload returned with hits. It can be a dict, dataclass, string, integer row ID, or any other Python object that fits your application.

Normalization

VectorStore defaults to normalize=True, which scales each stored vector to length 1. Normalization preserves vector direction while discarding magnitude:

[3.0, 4.0] -> [0.6, 0.8]

This is the default because it makes cosine similarity fast and direction-only, which is the common case for semantic embeddings. Use normalize=False when vector length matters, such as when magnitude encodes strength, confidence, counts, scale, or raw geometry.

Zero vectors are rejected in both modes because cosine similarity is undefined for zero-norm vectors.

Method	normalize=True default	normalize=False
cosine_search	True cosine similarity over stored unit vectors; fastest/default path for embeddings	True cosine similarity over raw vectors; computes vector norms during search
dot_search	Dot product of unit vectors, effectively equivalent to cosine similarity	True dot product over original vectors; use when magnitude should affect ranking
euclidean_search	Distance between normalized directions; useful only when direction-normalized distance is intended	True Euclidean distance over original vectors; use for geometric/feature-space nearest neighbors
get	Returns normalized vectors	Returns original vectors
save	Saves normalized vectors	Saves raw vectors
load	Loads and normalizes vectors	Loads vectors exactly as stored

Search Methods

Use cosine_search for semantic embeddings and direction-only similarity:

hits = store.cosine_search(query, top_k=10, min_value=0.75)

Use dot_search with normalize=False when larger-magnitude vectors should rank higher:

store = VectorStore[dict[str, str]](dimensions=3, normalize=False)
store.add(vectors, metadata)
hits = store.dot_search(query, top_k=10, min_value=0.0)

Use euclidean_search with normalize=False for raw coordinate or feature-space nearest-neighbor search:

store = VectorStore[dict[str, str]](dimensions=3, normalize=False)
store.add(vectors, metadata)
hits = store.euclidean_search(query, top_k=10, max_value=1.5)

Prefiltering

The store does not implement a metadata query language. To filter by metadata, produce row indexes first, then pass them with within_rows.

rows = [
    i
    for i, metadata in enumerate(store.metadata)
    if metadata["title"].startswith("x")
]

hits = store.cosine_search(query, top_k=10, within_rows=rows)

For structured NumPy metadata, use NumPy to produce the row indexes:

metadata_table = np.array(
    [
        ("intro", "A", 2024),
        ("setup", "A", 2023),
        ("guide", "B", 2024),
    ],
    dtype=[("title", "U20"), ("product", "U10"), ("year", "i4")],
)

store = VectorStore[int](dimensions=3)
store.add(vectors, metadata=np.arange(len(metadata_table)))

mask = (metadata_table["product"] == "A") & (metadata_table["year"] >= 2024)
rows = np.flatnonzero(mask)

hits = store.cosine_search(query, within_rows=rows)

for hit in hits:
    row = metadata_table[hit.metadata]
    print(row["title"], hit.value)

Persistence

Pass a file_path and call save() / load() explicitly:

store = VectorStore[dict[str, str]](dimensions=1536, file_path="vectors.npz")
store.add(embeddings, metadata)
store.save()

loaded = VectorStore[dict[str, str]](dimensions=1536, file_path="vectors.npz")
loaded.load()

If you save with normalize=False, load with normalize=False too:

store = VectorStore[dict[str, str]](
    dimensions=1536,
    file_path="raw-vectors.npz",
    normalize=False,
)
store.add(raw_vectors, metadata)
store.save()

loaded = VectorStore[dict[str, str]](
    dimensions=1536,
    file_path="raw-vectors.npz",
    normalize=False,
)
loaded.load()

Context manager usage auto-saves on exit:

with VectorStore[dict[str, str]](dimensions=1536, file_path="vectors.npz") as store:
    store.add(embeddings, metadata)

Persistence uses a minimal NumPy .npz contract with vectors and metadata arrays. The .npz file does not encode the normalize setting; choose the same setting when loading that you used when saving. Loading validates shape, dimensions, row counts, and zero-norm vectors. It also uses allow_pickle=True for flexible Python metadata payloads, so only load files generated by your own application or another trusted local process. Loading untrusted .npz files is not a supported security model.

Compatibility

This project is still pre-1.0, so occasional breaking changes are expected while the API stabilizes. Breaking changes are documented in GitHub release notes. Deprecated APIs will keep warning for at least one point release before removal.

Contributing

git clone https://github.com/tvanreenen/numpy-vector-store.git
cd numpy-vector-store
uv sync --frozen --group dev

Before submitting a pull request:

1. Run uv run ruff check
2. Run uv run ruff format --check
3. Run uv run mypy src/
4. Run uv run pytest

License

MIT License - see LICENSE file for details.