Vector search is a way to find related items based on their meaning rather than just matching words. Instead of relying on exact keyword matches, it uses numerical vectors to capture the essence of text. This makes it possible to find items that are similar in meaning even if they don’t share the same words. In this guide, you’ll learn how to build a basic vector search engine from scratch in Python using only NumPy.

Understanding Vector Search and How It Works

Traditional search methods look for exact word matches, which can miss the true intent behind a query. Vector search, on the other hand, converts text into high-dimensional vectors called embeddings. These embeddings represent the semantic meaning of the text. When two pieces of text have similar meanings, their vectors will be close together in this high-dimensional space.

The key to this approach is measuring how close two vectors are. The most common method is cosine similarity, which looks at the angle between two vectors rather than their actual distance. This makes the comparison scale-invariant, meaning it focuses on the direction of the vectors, which correlates to their meaning. The closer the vectors, the more similar the texts are considered.

Setting Up Sample Data and Embeddings

To demonstrate, imagine a small catalog of product descriptions from an online store. These descriptions are simplified into 8-dimensional vectors to simulate real embeddings. In a real-world scenario, these vectors would be generated using models like sentence-transformers, which process the text and produce meaningful embeddings. Here, random data with a clear cluster structure is used to mimic different categories like electronics, clothing, and furniture.

The code creates three cluster centers, each representing a category, and adds some noise to simulate variation within each group. This results in a set of 15 product descriptions with their corresponding embeddings. Each description doesn’t need to be stored in the search engine; only the vectors are necessary, along with labels for identification.

Building the Index for Fast Search

The core of the search engine is the index, which stores normalized vectors. Normalization scales each vector to unit length, making cosine similarity calculations equivalent to dot products. This simplifies the computation and speeds up the search process.

A simple class is created to manage the index. It has methods to add vectors and labels, normalize vectors, and perform searches. When a search is performed, the query vector is normalized, and its dot product with all stored vectors is calculated. These scores indicate how similar each stored item is to the query. The top results are then sorted and returned based on their scores.

This approach is straightforward and efficient for small datasets. For larger datasets, more advanced indexing techniques might be needed, but this simple setup provides a clear understanding of the fundamentals behind vector search.

Building a vector search engine from scratch helps demystify how semantic search works. By understanding the role of embeddings, normalization, and similarity metrics, it becomes easier to see how modern search systems provide relevant results based on meaning rather than just keywords.

Inspired by

• https://www.kdnuggets.com/how-to-build-vector-search-from-scratch-in-python

Sources

• addtoany.com
• ibm.com
• elastic.co
• numpy.org
• github.com