cognee/docs/ko/core-concepts/further-concepts/node-sets.md

NodeSets

Tagging and grouping data in Cognee

What are NodeSets?

A NodeSet lets you group parts of your AI memory at the dataset level. You create them as a simple list of tags when adding data to Cognee: await cognee.add(..., node_set=["projectA","finance"]) These tags travel with your data into the knowledge graph, where they become first-class nodes connected with belongs_to_set edges — and you can later filter searches to only those subsets.

How they flow through Cognee

• Add:
  • NodeSets are attached as simple tags to datasets or documents
  • This happens when you first ingest data
• Cognify:
  • carried into Documents and Chunks
  • materialized as real NodeSet nodes in the graph
  • connected with belongs_to_set edges
• Search:
  • NodeSets act as entry points into the graph
  • Queries can be scoped to only nodes linked to specific NodeSets
  • This lets you search within a tagged subset of your data

Why they matter

• Provide a lightweight way to organize and tag your data
• Enable graph-based filtering, traversal, and reporting
• Ideal for creating project-, domain-, or user-defined subsets of your knowledge graph

Example

import asyncio
import cognee

async def main():
    # reset Cognee’s memory and metadata for a clean run
    await cognee.prune.prune_data()
    await cognee.prune.prune_system(metadata=True)

    # add a document linked only to the "AI_Memory" node set
    await cognee.add(
        "Cognee builds AI memory from raw documents.",
        node_set=["AI_Memory"]
    )

    # add a document linked to both "AI_Memory" and "Graph_RAG" node sets
    await cognee.add(
        "Cognee combines vector search with graph reasoning.",
        node_set=["AI_Memory", "Graph_RAG"]
    )

    # build the knowledge graph by extracting entities and relationships
    await cognee.cognify()

if __name__ == "__main__":
    asyncio.run(main())

What just happened?

• You reset Cognee’s memory so you’re working with a clean graph.
• You added two documents, each tagged with one or more NodeSet labels.
  • The first document is only linked to AI_Memory.
  • The second document is linked to both AI_Memory and Graph_RAG.
• When you ran cognify(), Cognee:
  • Created NodeSet nodes (AI_Memory, Graph_RAG) in the graph.
  • Attached each document to the corresponding NodeSets.
  • Extracted entities and relationships from the documents, then linked those entities back to the same NodeSets.

This means the tags you add flow down into the extracted entities:

• “Cognee” appears in both documents → connects to both NodeSets.
• “AI memory” appears only in the first → connects only to AI_Memory.
• “Vector search” appears only in the second → connects to both since that document belongs to AI_Memory and Graph_RAG.

Your NodeSets now unlock powerful search and navigation capabilities:

• You can filter searches by NodeSet.
• You can scope queries to specific NodeSets.
• You can navigate data by project or domain using NodeSets.

Where NodeSets are first attached How NodeSets are promoted into graph nodes Use NodeSets as anchors in queries

---

To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt