Comparison
Vector Database vs Knowledge Graph
Similarity retrieval versus explicit relationship mapping.
Overview
Vector databases retrieve by semantic closeness; knowledge graphs preserve explicit relationships between entities, processes, tools, and decisions. Mature AI knowledge systems often use both.
Differences
| Dimension | Option A | Option B |
|---|---|---|
| Retrieval | Similarity search | Relationship traversal |
| Strength | Finding relevant text | Mapping dependencies |
| Weakness | Opaque relationships | Requires structure |
| Best for | RAG grounding | System navigation |
Use Cases
- →Support answers → Vector database
- →Onexial node network → Knowledge graph
Recommendation
Use vectors to find source material and a graph to expose relationships, navigation, and system-level understanding.
Related Workflows
Related Tool Stacks
↳ connected nodes
Workflow↳ linked
RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
Workflow↳ linked
Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
Tool Stack↳ linked
RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
Tool Stack↳ linked
Knowledge Graph Stack
Relationship layer that maps concepts, workflows, prompts, tools, and cases.