NodeSet Grouping

A minimal guide to grouping memories with node sets. Use it when one dataset holds several topics and you want each memory labeled — including memories that belong to more than one group — so you can see and later query those slices separately.

Before You Start

Complete Quickstart to understand basic operations
Ensure you have LLM Providers and Embedding Providers configured
Read NodeSets for how tags become graph nodes
No data is required up front — the script ingests its own passages, but it starts with cognee.forget(everything=True), which wipes all existing Cognee data; run it against a setup you can afford to reset

Code in Action

import asyncio
import os

import cognee
from cognee import visualize_graph
from cognee.shared.logging_utils import ERROR, setup_logging

text_a = """
    AI is revolutionizing financial services through intelligent fraud detection
    and automated customer service platforms.
    """

text_b = """
    Advances in AI are enabling smarter systems that learn and adapt over time.
    """

text_c = """
    MedTech startups have seen significant growth in recent years, driven by innovation
    in digital health and medical devices.
    """

node_set_a = ["AI", "FinTech"]
node_set_b = ["AI"]
node_set_c = ["MedTech"]


async def main():
    await cognee.forget(everything=True)

    await cognee.remember(text_a, node_set=node_set_a, self_improvement=False)
    await cognee.remember(text_b, node_set=node_set_b, self_improvement=False)
    await cognee.remember(text_c, node_set=node_set_c, self_improvement=False)

    visualization_path = os.path.join(
        os.path.dirname(__file__), ".artifacts", "nodeset_grouping.html"
    )
    await visualize_graph(visualization_path)


if __name__ == "__main__":
    logger = setup_logging(log_level=ERROR)
    asyncio.run(main())

What Just Happened

Step 1: Plan the Groups

node_set_a = ["AI", "FinTech"]
node_set_b = ["AI"]
node_set_c = ["MedTech"]

Each list is the set of labels one passage carries. text_a sits in two groups at once (AI and FinTech), text_b only in AI, and text_c only in MedTech — node sets overlap freely, so a memory does not have to pick a single home.

Step 2: Tag Each Memory on Ingest

await cognee.remember(text_a, node_set=node_set_a, self_improvement=False)
await cognee.remember(text_b, node_set=node_set_b, self_improvement=False)
await cognee.remember(text_c, node_set=node_set_c, self_improvement=False)

node_set is applied while the graph is built, so the labels are materialized as NodeSet nodes and attached to the derived chunks and entities with belongs_to_set edges. self_improvement=False keeps the run to plain ingestion instead of also running improve().

Step 3: Render the Grouped Graph

visualization_path = os.path.join(
    os.path.dirname(__file__), ".artifacts", "nodeset_grouping.html"
)
await visualize_graph(visualization_path)

The render writes a self-contained HTML file next to the script under .artifacts/. Open it and recolor the nodes by Node set: each document contributes one key, so text_a’s nodes show as AI, FinTech while text_b shows as AI and text_c as MedTech. To see the overlap itself, follow the belongs_to_set edges out of the AI NodeSet node — they reach the entities extracted from both text_a and text_b.

Advanced Usage

The labels the script wrote at ingest time are read back at query time. Each option below builds on the same AI, FinTech, and MedTech groups the script just created.

Scope Recall to One Group

The same names you passed to remember() scope a later recall() through node_name. This is the payoff of tagging: one dataset holds all three topics, but a query can be grounded in a single slice of it.

from cognee import SearchType

# Recall only within the FinTech subset
results = await cognee.recall(
    "What is happening in financial services?",
    query_type=SearchType.GRAPH_COMPLETION,
    node_name=["FinTech"],
)

In practice this lets you keep one shared dataset while still asking targeted questions — “only the finance material”, “just the MedTech passages” — without splitting everything into separate datasets.

Combine Several Node Sets

Pass several names to widen the slice. node_name_filter_operator controls how they combine: the default OR returns results connected to any of the listed names, while AND requires results to belong to all of them at once.

from cognee import SearchType

# OR (default) — anything tagged AI or MedTech
results = await cognee.recall(
    "What are the key topics?",
    query_type=SearchType.GRAPH_COMPLETION,
    node_name=["AI", "MedTech"],
    node_name_filter_operator="OR",
)

# AND — only what sits in both AI and FinTech, i.e. text_a
results = await cognee.recall(
    "How is AI used in finance?",
    query_type=SearchType.GRAPH_COMPLETION,
    node_name=["AI", "FinTech"],
    node_name_filter_operator="AND",
)

Node-set filtering works with graph-completion search types (GRAPH_COMPLETION, GRAPH_COMPLETION_COT, GRAPH_COMPLETION_CONTEXT_EXTENSION, GRAPH_SUMMARY_COMPLETION, TEMPORAL, RAG_COMPLETION, TRIPLET_COMPLETION, CHUNKS). It has no effect on SUMMARIES, CYPHER, or NATURAL_LANGUAGE.

Navigate Data by Project or Domain

Because node sets become first-class graph nodes, they can act as anchors for exploration as well as filtering. A project-level label like project_alpha or a domain-level label like compliance gives you a stable entry point into the related documents, chunks, and entities.This makes node sets a lightweight way to organize one knowledge graph around the mental model your team already uses: project, customer, topic, workflow, or domain.

# Tag documents by project and domain during ingestion
await cognee.remember(
    "Project Alpha must satisfy EU compliance requirements.",
    node_set=["project_alpha", "compliance"],
)

await cognee.remember(
    "Project Alpha rollout depends on infrastructure readiness.",
    node_set=["project_alpha", "operations"],
)

After the remember() workflow finishes, project_alpha, compliance, and operations become graph anchors you can use to explore related information by project or by domain.

NodeSets

How tags become first-class graph nodes you can filter on.

Graph Visualization

Render your knowledge graph to an interactive HTML file.

remember()

Every parameter remember() accepts, including node_set.

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

Rust SDK

TypeScript SDK

OSS

Before You Start

Code in Action

What Just Happened

Step 1: Plan the Groups

Step 2: Tag Each Memory on Ingest

Step 3: Render the Grouped Graph

Advanced Usage

NodeSets

Graph Visualization

remember()

​Before You Start

​Code in Action

​What Just Happened

​Step 1: Plan the Groups

​Step 2: Tag Each Memory on Ingest

​Step 3: Render the Grouped Graph

​Advanced Usage

NodeSets

Graph Visualization

remember()

Before You Start

Code in Action

What Just Happened

Step 1: Plan the Groups

Step 2: Tag Each Memory on Ingest

Step 3: Render the Grouped Graph

Advanced Usage