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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Vector Embeddings and Similarity Search

Grasp the concepts of vector embeddings, their generation, and how similarity search enables relevant document retrieval.

Vectors for Meaning

Welcome! In the world of Large Language Models (LLMs), understanding text isn't just about words. It's about meaning.

Computers naturally work with numbers, not human language. How do we bridge this gap to help LLMs understand the meaning of text?

What are Vector Embeddings?

Vector embeddings are numerical representations of text (or images, audio, etc.). Think of them as a list of numbers that capture the 'essence' or 'meaning' of a piece of information.

  • Each piece of text (a word, sentence, or document) gets its own unique vector.
  • These vectors are usually long lists of floating-point numbers (e.g., [0.123, -0.456, 0.789, ...]).

All lessons in this course

  1. The Necessity of Vector Databases
  2. Vector Embeddings and Similarity Search
  3. Integrating with a Vector Database
  4. Indexing, Filtering & Hybrid Search
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