Vector Databases Explained: RAG for Business Data

The Problem RAG Solves

LLMs have a knowledge cutoff and don't know anything about your business. RAG fixes this by retrieving relevant documents from your data and including them in the prompt.

How Vector Databases Work

Text is converted to numerical vectors (embeddings) that capture semantic meaning. Similar concepts have similar vectors. When you ask a question, the system finds documents with similar vectors and feeds them to the LLM.

Popular Options

• Qdrant: Fast, Rust-based, great for production
• Weaviate: Feature-rich, good for complex use cases
• pgvector: PostgreSQL extension, simple if you're already using Postgres
• ChromaDB: Easy to start with, good for prototyping

What We Deploy

Most of our OpenClaw installations include Qdrant for vector storage. It runs locally, handles millions of documents, and integrates seamlessly with the agent framework.