Vector Databases for Prompting
Explore how vector databases store and retrieve relevant information to feed into RAG prompts effectively.
What are Vector Databases?
Welcome to the world of Vector Databases (VDBs)! These specialized databases are game-changers for working with Large Language Models (LLMs).
VDBs store information as numerical vectors, which are just lists of numbers. This allows them to quickly find data that is "semantically similar" – meaning it has a similar underlying meaning, not just matching keywords.
Language as Numbers: Embeddings
Before data enters a VDB, it's transformed into an embedding. An embedding is a numerical representation of text, where words or phrases with similar meanings are close together in a multi-dimensional space.
- Text to Vector: An embedding model converts your text into a vector.
- Meaning Preserved: These vectors capture the semantic meaning of the text.
- LLM's Language: This is how LLMs "understand" and process language internally.
All lessons in this course
- Prompting with External Data
- Retrieval Augmented Generation (RAG)
- Vector Databases for Prompting