VectorD_umB

Store and Query Vectors in SQLite

VectorD_umB is a Python script that provides a simple way to store and query vectors in an SQLite database. It leverages the power of NumPy for vector manipulation and SQLite for efficient storage and retrieval of data.

Features

Efficient storage of vectors as NumPy arrays in an SQLite database.

Fast nearest neighbor search for query vectors based on Euclidean distance.

Easy-to-use and extendable with support for custom database tables and queries.

Support for SentenceTransformer embeddings, enabling semantic similarity searches.

Prerequisites

To use VectorD_umB, you need to have the following installed on your system:

1. Python (>=3.6)
2. NumPy (>=1.19)
3. SQLite (usually included with Python distributions)

Getting Started

Follow these steps to get started with the sqlite_vector project:

1. Clone the repository to your local machine:

git clone https://github.com/DaveOkpare/sqlite_vector

2. Install numpy:

cd sqlite_vector

pip install -r requirements.txt

# OR

pip install numpy

# Import the necessary modules in your Python script:
from vector import VectorDB
import numpy

# Create random nd.array datasets
array1 = np.arange(10)
array2 = np.arange(11, 20)
array3 = np.arange(21, 30)

# Initialize the VectorDB class and create a collection name
db = VectorDB("students")
db.create()

# Insert the arrays into the database
db.insert([array1, array2, array3])

# Search for a specific value in the database
query = np.arange(2, 11, 2) 

output = db.search(query=query, num_results=1)

print(f"Results: {output}")

"""
Results: [0 1 2 3 4 5 6 7 8 9]
"""

License

Feel free to explore and adapt the provided example to suit your needs! For more details, check out the source code in the repository.

This project is licensed under the MIT License