numpy-vector-store 0.1.0

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

NumPy Vector Store

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

• High-performance index-free O(n) cosine similarity searches
• Vectorized metadata queries with NumPy operations
• Easy persistence to and from compressed NumPy binary files (.npz)
• Zero dependency on external services or data stores

Why?

This library is purpose-built for small to medium-scale vector search tasks and offers a simple, lightweight alternative to heavyweight solutions like Pinecone, Qdrant, Weaviate, Postgres + pgvector, or Azure AI Search—no complex setup or infrastructure required.

Sometimes you don't need a sledgehammer to crack a nut.

When/Where?

Below are benchmark results for the module's search method 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

⚠️ Pending submission to PyPI

uv add numpy-vector-store

Quick Start

import numpy as np
from numpy_vector_store import VectorStore

# Load your vector store
store = VectorStore(dimensions=1536, file_path='vectors.npz')
store.load()

# Embed your search query
query = np.array([0.2, 0.3, 0.4, ...])

# Search using cosine similarity
results = store.search(query, top_k=3)

# Compare the results
for index, similarity, meta in results:
    print(f"{meta['title']}: {similarity:.3f}")

Usage Examples

Adding Vectors

Adding Vectors in Batch

The add_vectors method takes a 2D NumPy array where each row is a vector, and a 1D NumPy array of metadata objects.

# 2D np.array of vectors
embeddings = np.array([
    [0.1, 0.2, 0.3, ...],  # Text embedding 1
    [0.4, 0.5, 0.6, ...],  # Text embedding 2
    [0.7, 0.8, 0.9, ...]   # Text embedding 3
])

# 1D np.array of metadata objects
metadata = np.array([
    {"title": "AI Overview", "word_count": 12},
    {"title": "Python Guide", "word_count": 10},
    {"title": "Vector DBs", "word_count": 8}
])

# Vectors and metadata added using efficient vectorized NumPy operations
store.add_vectors(embeddings, metadata)

Adding Vectors Individually

Batch operations are generally preferable and more efficient. But individual vectors can be added with the same method.

store.add_vectors(
    np.array([new_embedding]), # or np.atleast_2d(new_embedding)
    np.array([{"title": "Neural Networks Paper", "word_count": 15}])
)

Save the Vector Store

Save to a gzip compressed NumPy binary file directly

store.save()

Or use context manager for automatic persistence

with VectorStore(dimensions=3, file_path="vectors.npz") as store:
    store.add_vectors(vectors_2d, metadata_array)

# Automatically saves when exiting the context

Working with Metadata

Work with flexible, unstructured metadata using standard Python operations. No schema required - perfect for getting started quickly:

# Access individual metadata
first_metadata = store.metadata[0]
print(f"First entry: {first_metadata}")

# Iterate through all metadata
for i, metadata in enumerate(store.metadata):
    print(f"Entry {i}: {metadata}")

Advanced: Structured Metadata for Performance

If you define a homogeneous NumPy schema upfront, you get significant performance improvements for metadata operations. Instead of Python loops and dictionary lookups, you get vectorized NumPy operations that are orders of magnitude faster.

Learn more: NumPy Structured Arrays Documentation

# Define schema for performance
store = VectorStore(
    dimensions=512,
    metadata_schema={'title': 'U200', 'year': 'i4', 'citations': 'i4'}
)
store.add_vectors(
    np.array([vector1, vector2]),
    np.array([
        {"title": "Paper 1", "year": 2023, "citations": 100},
        {"title": "Paper 2", "year": 2022, "citations": 50}
    ])
)

# Perform vectorized NumPy operations on metadata
recent_mask = store.metadata['year'] == 2023
recent_vectors = np.array(store.vectors)[recent_mask]

# Sort by citations
sorted_indices = np.argsort(store.metadata['citations'])[::-1]
top_vectors = np.array(store.vectors)[sorted_indices]

# Complex filtering
high_impact = store.metadata['citations'] > 100
recent = store.metadata['year'] > 2020
combined_mask = high_impact & recent
filtered_vectors = np.array(store.vectors)[combined_mask]

Contributing

Setup Development Environment

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

Before Submitting a Pull Request

Please ensure:

1. Code Quality: Run uv run ruff check - should show no issues
2. Formatting: Run uv run ruff format - should show "files left unchanged"
3. Type Checking: Run uv run mypy src/ - should show no errors
4. Tests: Run uv run pytest - all tests should pass

License

MIT License - see LICENSE file for details.