Local Similarity Search Engine

This project is a high-performance similarity search engine designed to run entirely on your local machine. It allows you to find the most similar items to a given query from a large dataset, without needing to send your data to the cloud.

Features

• Privacy-Focused: All data and computations stay on your machine, ensuring privacy and security.
• High Performance: Optimized for fast similarity searches on large datasets.
• Simple API: An easy-to-use interface for indexing data and performing searches.

Getting Started

Follow these instructions to get the project set up on your local machine.

Prerequisites

Make sure you have Python 3.8 or higher installed on your system.

Installation

1. Clone the repository:

   git clone <your-repository-url>

2. Navigate into the project directory:

   cd <repository-name>

3. Install the necessary dependencies:

   pip install -r requirements.txt

Usage

Here is a basic example of how to use the similarity search engine.

# main.py
import numpy as np
from similarity_engine import SimilarityEngine

# Initialize the engine with the vector dimension
engine = SimilarityEngine(dimension=128)

# Create some random vectors to index
num_vectors = 1000
vector_dim = 128
vectors = np.random.rand(num_vectors, vector_dim).astype('float32')
ids = [f"id_{i}" for i in range(num_vectors)]

# Index the vectors
engine.index(vectors, ids)

# Create a query vector
query_vector = np.random.rand(1, vector_dim).astype('float32')

# Perform a search
results = engine.search(query_vector, top_k=5)

print("Top 5 most similar items:")
for item_id, score in results:
    print(f"ID: {item_id}, Score: {score:.4f}")

To run the example, execute the following command in your terminal:

python main.py

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for