How to Build Vector Search From Scratch in Python

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In this article, the concept of vector search is introduced and implemented using Python’s NumPy library. Vector search helps bridge the gap between finding exact words and understanding the true meaning behind a query, transforming the documents and queries into numerical vectors called embeddings in high-dimensional space. The similarity between vectors, determined by cosine similarity which measures their angular not linear distance, is more important than their actual proximity in understanding semantics. The tutorial sets up a mock dataset consisting of short product descriptions that are transformed into 8-dimensional vectors and then normalized to implement indexing and querying. The effectiveness of the search is demonstrated by conducting several search queries, wherein the resulting most relevant products closely resemble the constructed query. These processes are visualized in a 2D PCA projection to reveal the grouping based on cluster similarities.

Key Points:
– Vector search represents text as high-dimensional embeddings to find semantic similarity.
– Documents and queries are converted to embeddings which are then normalized.
– Cosine similarity, based on the angle between vectors, is used to determine semantic closeness.
– A mock dataset is implemented with controlled random data reflecting three clusters: electronics, clothing, and furniture.
– A vector index is created which supports basic search functionality based on the nearest embeddings.