Turn your Documentation into a Queryable Knowledge Graph for High retrieval accuracy and low hallucinations

Why Knowledge Graphs matter (and why RAG isn't enough)

The RAG problem every data engineer knows: You ask "What authentication does our API use?" and get back chunks about OAuth from three different services mixed together. Your system can't tell the difference between a database table and a REST endpoint. Traditional RAG systems fail because they:

• Treat everything as unstructured text (even your structured data)
• Rely on vector similarity that misses context
• Can't preserve relationships between entities
• Give you the "most similar" answer, not the correct one

Knowledge graphs solve this by:

• Understanding relationships: Knows that "User" entity in Slack docs relates to "User" in TicketMaster docs
• Preserving context: Distinguishes between different types of authentication across APIs
• Preventing hallucinations: Returns actual documented information, not AI guesses
• Enabling precise retrieval: Query specific subsets of your knowledge base

We created a workshop where we demonstrate how we use Cognee+dlt to implement knowledge graphs.

What our workshop shows

I demonstrate the graph creation.

Using dlt + Cognee, I took API docs from Slack, PayPal, and TicketMaster and built a knowledge graph that:

• Understands context: Knows the difference between "authentication" and "endpoint" in your domain
• Connects related concepts: Automatically links similar patterns across different APIs
• Prevents hallucinations: Retrieves actual documented information, not AI guesses
• Enables precise queries: "What pagination does TicketMaster use?" → Gets the exact method from their docs

The ontology approach eliminates guesswork. Instead of hoping your RAG system understands what you mean by "endpoint," you define it once and the system builds everything around your definitions.

What you can build along

Follow our Colab notebooks to turn your messy documentation into:

• Smart documentation that answers specific technical questions
• Queryable knowledge bases from your existing docs
• Cross-reference systems that find connections between different documentation sources

In the Workshop

You will find the code needed to follow along in the video description.

• Demo 1: How dlt transforms structured data → knowledge graph (NYC taxi dataset)
• Demo 2: API documentation → queryable graph with ontologies
• Live troubleshooting when things don't work as expected
• Production deployment strategies and cost analysis

Resources

🎥 Full Workshop (90 min) - Colab notebooks in description
🔗 Cognee for reliable memory

Delivered through DataTalks.Club LLM Zoomcamp - thanks to Alexey for providing the platform and community that makes these workshops possible.

If you're tired of documentation that's impossible to search and RAG systems that make stuff up, this workshop shows you how dlt + Cognee can build knowledge graphs that actually understand your domain.