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Where to Next

Now that you’ve completed the hello cognee tutorial, you’re ready to explore more advanced features and concepts. Here are your next steps:

Core Concepts - Deep Dive into Cognee

Learn the fundamentals of how cognee transforms data into searchable memory:

Data to Memory

  • Chunking: Understanding how data is broken into manageable pieces
  • Node Sets: Organizing data into meaningful groups
  • Ontologies: Defining knowledge structures
  • Adding Data: Best practices for different data types (text, documents, structured data)

Memory Processing

Search Memory

  • Search Types: Different ways to query your knowledge graph
  • Search Strategies: Optimizing retrieval for your use case

How-to Guides - Practical Implementation

Get hands-on with specific implementations and configurations:

Cognee SDK

Database Setup

Cognee UI

Advanced Features

Choose Your Path

New to knowledge graphs? → Start with Core Concepts to understand the theory

Ready to build? → Jump to How-to Guides for specific implementations

Want to see examples? → Check out our Use Cases for inspiration

Common Confusion Points Explained

Many users get confused about these key concepts. Here’s where to learn more:

“Why does cognee use both graph AND vector databases?”
→ Read Architecture to understand how dual storage works together for intelligent search

“What’s the difference between tasks, pipelines, and datapoints?”
→ Start with Core Concepts for the big picture, then dive into Memory Processing

“How does my text become a searchable knowledge graph?”
→ Follow the complete flow in Data to Memory and see Pipelines

“When should I use different search types?”
→ Explore Search Types and Search Memory

“How do I customize cognee for my specific use case?”
→ Check out Custom Pipelines and Custom Tasks

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Run from source (SDK, community adapters and your own tasks)Core Concepts