Turn Text into Vectors

Generate text embeddings and inspect their dimensions to understand how a pretrained model represents text as numbers.

Search by Meaning

Compare embeddings and build semantic search that finds related support articles even when they use different words.

Store and Filter Embeddings

Store embeddings in a persistent vector database, retrieve saved records, and combine metadata filters with similarity search.

About the course

Through AICC’s partnership with Linuxademy.com, this course introduces the foundations of vectors, embeddings, and vector databases through small, practical projects. Start with manually defined vectors, then generate text embeddings, measure similarity, and search short support articles by meaning. Learn to store and retrieve embeddings, verify persistence, and combine metadata filtering with similarity search. Use simple Python, Google Colab, a pretrained embedding model from Hugging Face, and a vector database. No GPU or paid APIs are required. Follow along with the projects or watch to learn. Linuxademy’s mission is to help people build, understand, and operate independently. Finish by combining these foundations into a small semantic search system, preparing you for later AI application and RAG courses.

Curriculum

1 Chapter · 2 Lessons

    1. (Included in full purchase)

      Understand Vectors & Embeddings

    2. (Included in full purchase)

      Create Your First Text Embeddings

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