Vector Stores for Retrieval
Learn to use vector databases to store and efficiently retrieve relevant document chunks based on semantic similarity for RAG (Retrieval Augmented Generation).
Intro to Vector Stores
Welcome to the final lesson on Data Loading & Retrieval! Today, we'll dive into Vector Stores, a crucial component for building intelligent AI agents.
Think of vector stores as specialized databases designed to store and efficiently search through numerical representations of information, called embeddings.
Why Vector Stores for RAG?
Vector stores are the backbone of Retrieval Augmented Generation (RAG). RAG allows Large Language Models (LLMs) to access external, up-to-date information, overcoming their inherent limitations like:
- Knowledge cutoffs: LLMs only know what they were trained on.
- Hallucinations: Making up facts when uncertain.
Vector stores provide the relevant context for the LLM to generate accurate responses.
All lessons in this course
- Document Loaders Explained
- Text Splitters & Embeddings
- Vector Stores for Retrieval
- Retrievers & Contextual Compression