Deep Dive: Vector Stores vs. Knowledge Graphs

"RAG" (Retrieval Augmented Generation) is the buzzword of the year. But not all RAG is created equal.

Most systems use Vector Stores. They chunk text, embed it, and retrieve "similar" chunks. This is great for fuzzy matching.

• Query: "What did we discuss about pricing?"
• Vector: Finds meeting notes with "pricing", "cost", "budget".

But Vector Stores fail at Structure.

• Query: "Who is the manager of the person who wrote the pricing doc?"
• Vector: 🤷‍♂️

Enter the Knowledge Graph

A Knowledge Graph stores entities and relationships. (Alice)-[MANAGED_BY]->(Bob) (Alice)-[WROTE]->(PricingDoc)

Now the query is a simple graph traversal.

The Bothive Hybrid Approach

We use Hybrid Memory. We use Vectors to find the "context neighborhood" and Graphs to navigate the precise "facts." This gives our agents the intuition of an LLM with the precision of a database.

1-- A simplified view of our memory schema
2SELECT entity.name 
3FROM entities 
4JOIN relationships ON entities.id = relationships.source_id
5WHERE relationships.type = 'MANAGED_BY' 
6  AND relationships.target = 'Bob';

By combining these two technologies, we create a memory system that feels truly human-like in its recall ability.