Notebooks let you use all four Cognee operations: add(), cognify(), memify(), and search(). You’ll need an API key to get started.

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Open or create a notebook

  • Expand Notebooks in the sidebar.
  • Open the example notebook (Python Development with Cognee Tutorial) or create a new one with +.

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Using operations

  • add() and cognify() can also be run in the UI, but in notebooks you can call them programmatically.
  • memify() and search() are available only in notebooks.
We’ll cover each operation below, and to see them in action, you can run the example notebook that is available when you sign up for Cogwit.

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Add data

Upload files or text content into your dataset: 
# Add a single text string
await cognee.add("Your text content here", node_set=["my_data"])

# Add from a local file
await cognee.add(
    "/path/to/your/local/file.txt",
    node_set=["guido_data"]
)
You can only add .txt files in notebooks. In particular, you should convert PDFs to text before adding them.

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Cognify data

Transform your data into a knowledge graph: 
# Basic cognify
await cognee.cognify()

# With temporal processing
await cognee.cognify(temporal_cognify=True)

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Memify data

Enrich your knowledge graph with semantic relationships: 
# Run memify to infer new connections
await cognee.memify()

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Search your data

Query your knowledge graph with natural language: 
# Basic search
results = await cognee.search("What does our product aim to deliver?")
print(results[0])

# Graph completion search
results = await cognee.search(
    "What Python type hinting challenges did I face?",
    query_type=cognee.SearchType.GRAPH_COMPLETION
)

# Temporal search
results = await cognee.search(
    "What can we learn from recent contributions?",
    query_type=cognee.SearchType.TEMPORAL
)

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Next steps

  • Explore memify to infer new relationships.
  • Automate ingestion and analysis with the Cogwit SDK.

Cogwit UI

Learn to manage datasets and upload files through the UI.

Cogwit SDK

Explore advanced SDK examples and automation patterns.