Most RAG setups look good in demos — until things get slightly complex.
You ask a question, it retrieves “relevant” chunks, and everything seems fine.
But as soon as your system spans multiple documents — APIs, billing, infra, workflows — things start breaking down.
Not because the information isn’t there.
But because the relationships between them are lost.
The problem with RAG
RAG works by retrieving chunks based on similarity.
That means:
- It finds text that looks relevant
- But doesn’t understand how pieces connect
- And can’t reconstruct system behavior
So you end up with answers that are:
- technically correct
- but incomplete
- and often misleading
Real systems aren’t flat
In real systems:
- a deploy triggers a pipeline
- the pipeline applies changes to Kubernetes
- monitoring evaluates the rollout
- failures trigger rollback logic
None of this lives in a single document.
And RAG doesn’t connect these dots.
What I built instead
I built Mindex:
https://usemindex.dev/
Instead of just retrieving chunks, it builds a knowledge graph on top of your documents.
So your AI can:
- connect documents
- follow relationships
- reconstruct flows
Not just match text.
RAG vs Graph-based context
Here’s a simplified comparison:
❌ Naive RAG
- Returns a flat list of documents
- No relationships
- No ordering
- No system understanding
✅ Mindex (GraphRAG)
- Connects documents
- Traverses relationships
- Infers flows (cause → effect)
- Provides structured context
Why this matters
The difference is subtle at first.
But when you're working with:
- internal documentation
- APIs
- distributed systems
It becomes critical.
You don’t just need relevant text.
You need to understand how things work together.
How it works
Mindex combines:
- semantic search
- a knowledge graph layer
- relationship traversal
It’s available via:
- CLI
- MCP (works with tools like Claude Code, Cursor, etc.)
- REST API
Try it
You can try it here:
Feedback welcome
I’m especially interested in feedback from people:
- building with RAG
- working with internal knowledge bases
- building AI dev tools
Curious to hear how you're handling this today.
