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

The Necessity of Vector Databases

Understand why traditional databases fall short for semantic search and how vector databases address this gap in RAG.

Beyond Keyword Search

Imagine you're looking for documents about "fast cars" but some documents use "speedy automobiles." A simple keyword search might miss these!

Traditional databases are great for exact matches, but struggle with understanding the meaning behind words.

How Traditional Databases Search

Most traditional databases (like SQL or NoSQL) rely on exact keyword matching or predefined indexes.

  • SQL Databases: Use structured queries to find data matching specific values.
  • NoSQL Databases: Offer more flexibility but often still depend on keys or keyword indexes.

They're like a librarian who only finds books if you know the exact title.

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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