# kNowlEdge grAph Transformer (NEAT)
[](https://github.com/cognitedata/neat/actions/workflows/release.yaml)
[](https://cognite-neat.readthedocs-hosted.com/en/latest/?badge=latest)
[](https://github.com/cognitedata/neat)
[](https://pypi.org/project/cognite-neat/)
[](https://pypistats.org/packages/cognite-neat)
[](https://hub.docker.com/r/cognite/neat)
[](https://github.com/cognitedata/neat/blob/master/LICENSE)
[](https://github.com/ambv/black)
[](https://github.com/astral-sh/ruff)
[](http://mypy-lang.org)
NEAT is a domain expert centric and developer friendly solution for rapid:
- semantic data modeling
- creation, transformation and enrichment of knowledge graphs
- and ingestion of the models and graphs into [Cognite Data Fusion](https://www.cognite.com/en/product/cognite_data_fusion_industrial_dataops_platform)
NEAT is using open and globally recognized standards maintained by the [World Wide Web Consortium (W3C)](https://www.w3.org/RDF/).
NEAT represents an essential tool for creation of standardized, machine-actionable, linked and semantic (meta)data.
> NEAT is an acronym derived from k**N**owl**Ed**ge gr**A**ph **T**ransformer hallucinated by GenAI.
## Installation
```bash
pip install cognite-neat
```
## Usage
The user interface for `NEAT` is a notebook-based environment. Once you have set up your notebook
environment, you start by creating a `CogniteClient` and instantiate a `NeatSession` object.
```python
from cognite.neat import NeatSession, get_cognite_client
client = get_cognite_client(".env")
neat = NeatSession(client)
neat.read.cdf.data_model(("my_space", "MyDataModel", "v1"))
```
## Documentation
For more information, see the [documentation](https://cognite-neat.readthedocs-hosted.com/en/latest/)
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"description": "# kNowlEdge grAph Transformer (NEAT)\n\n[](https://github.com/cognitedata/neat/actions/workflows/release.yaml)\n[](https://cognite-neat.readthedocs-hosted.com/en/latest/?badge=latest)\n[](https://github.com/cognitedata/neat)\n[](https://pypi.org/project/cognite-neat/)\n[](https://pypistats.org/packages/cognite-neat)\n[](https://hub.docker.com/r/cognite/neat)\n[](https://github.com/cognitedata/neat/blob/master/LICENSE)\n[](https://github.com/ambv/black)\n[](https://github.com/astral-sh/ruff)\n[](http://mypy-lang.org)\n\nNEAT is a domain expert centric and developer friendly solution for rapid:\n\n- semantic data modeling\n- creation, transformation and enrichment of knowledge graphs\n- and ingestion of the models and graphs into [Cognite Data Fusion](https://www.cognite.com/en/product/cognite_data_fusion_industrial_dataops_platform)\n\nNEAT is using open and globally recognized standards maintained by the [World Wide Web Consortium (W3C)](https://www.w3.org/RDF/).\nNEAT represents an essential tool for creation of standardized, machine-actionable, linked and semantic (meta)data.\n\n> NEAT is an acronym derived from k**N**owl**Ed**ge gr**A**ph **T**ransformer hallucinated by GenAI.\n\n## Installation\n\n```bash\npip install cognite-neat\n```\n\n## Usage\n\nThe user interface for `NEAT` is a notebook-based environment. Once you have set up your notebook\nenvironment, you start by creating a `CogniteClient` and instantiate a `NeatSession` object.\n\n```python\nfrom cognite.neat import NeatSession, get_cognite_client\n\nclient = get_cognite_client(\".env\")\n\nneat = NeatSession(client)\n\nneat.read.cdf.data_model((\"my_space\", \"MyDataModel\", \"v1\"))\n```\n\n## Documentation\n\nFor more information, see the [documentation](https://cognite-neat.readthedocs-hosted.com/en/latest/)\n\n",
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