Learn how to turn text into embeddings, measure semantic similarity, and visualize your corpus using sentence-transformers and Python.

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Embeddings transform text into numerical vectors that capture semantic meaning, enabling search by concept rather than keyword matching. This tutorial demonstrates building a semantic search system using sentence-transformers in Python: generating 384-dimensional embeddings from text, measuring similarity via cosine distance, visualizing clusters with PCA, and persisting artifacts for retrieval. The implementation covers corpus loading, model configuration, similarity ranking, and 2D projection—forming the foundation for retrieval-augmented generation (RAG) pipelines.

TF-IDF vs. Embeddings: From Keywords to Semantic Search

What Are Vector Databases and Why They Matter

Understanding Embeddings: Turning Language into Geometry

Implementation Walkthrough: Configuration and Directory Setup

Embedding Utilities (embeddings_utils.py)

Driver Script Walkthrough (01_intro_to_embeddings.py)