---
title: Build Semantic Search with LLM Embeddings
url: https://www.dataloco.com/en/build-semantic-search-with-llm-embeddings
published: 2026-09-16T03:10:58+00:00
language: en
section: Developers
source: https://machinelearningmastery.com/build-semantic-search-with-llm-embeddings/
publisher: Dataloco
---

# Build Semantic Search with LLM Embeddings

A new article explains how to construct a simple semantic search engine using sentence embeddings and nearest neighbors. Traditional search engines primarily depend on keyword matching, which may disregard important semantic nuances and lead to the omission of relevant documents. Semantic search, contrastingly, emphasizes meaning over exact wording, enhancing the retrieval process. Large language models (LLMs) are integral to this approach, as they convert text into numerical vector representations known as embeddings that encapsulate the text's semantic information. For instance, similar texts will yield similar embedding vectors, while unrelated concepts will produce markedly different vectors.

The article provides a practical guide for building an efficient semantic search engine, starting with necessary imports and utilizing a public dataset called "ag_news" which includes texts from news articles. The process involves loading the dataset, extracting the "text" column, and printing a sample article to inspect the data. Embedding vectors for the texts are obtained using Hugging Face sentence transformer models, such as "all-MiniLM-L6-v2", which are commonly used for this purpose.

Additionally, a NearestNeighbors object is initialized to identify the closest vectors based on cosine similarity. The core function of the search engine takes a plain-text query, specifies the number of top results to retrieve, and computes the query embedding while retrieving the nearest neighbors from the index. The results are ranked by similarity, showcasing the potential of this simple semantic search engine as a foundational layer in modern architectures that integrate semantic search with LLMs.

This foundational knowledge may encourage further exploration into more complex retrieval augmented generation systems.