Vicinity is a light-weight, low-dependency vector store. It provides a simple and intuitive interface for nearest neighbor search, with support for different backends and evaluation. There are many nearest neighbors packages and methods out there. However, we found it difficult to compare them. Every package has its own interface, quirks, and limitations, and learning a new package can be time-consuming. In addition to that, how do you effectively evaluate different packages? How do you know which one is the best for your use case? This is where Vicinity comes in. Instead of learning a new interface for each new package or backend, Vicinity provides a unified interface for all backends. This allows you to easily experiment with different indexing methods and distance metrics and choose the best one for your use case. Vicinity also provides a simple way to evaluate the performance of different backends, allowing you to measure the queries per second and recall.

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Quick Start

To install Vicinity, you can use the following command: 
pip install vicinity
Then, you can use it like this: 
import numpy as np
from vicinity import Vicinity, Backend, Metric

# Create some dummy data as strings or other serializable objects
items = ["triforce", "master sword", "hylian shield", "boomerang", "hookshot"]
vectors = np.random.rand(len(items), 128)

# Initialize the Vicinity instance (using the basic backend and cosine metric)
vicinity = Vicinity.from_vectors_and_items(
    vectors=vectors,
    items=items,
    backend_type=Backend.BASIC,
    metric=Metric.COSINE
)

# Create a query vector
query_vector = np.random.rand(128)

# Query for nearest neighbors with a top-k search
results = vicinity.query(query_vector, k=3)

# Query for nearest neighbors with a threshold search
results = vicinity.query_threshold(query_vector, threshold=0.9)

# Query with a list of query vectors
query_vectors = np.random.rand(5, 128)
results = vicinity.query(query_vectors, k=3)