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29	29	Simply use the API endpoint at [[https:~~/~~/spatial.kg.ebrains.eu/api/>>https://spatial.kg.ebrains.eu/api/]] by running queries according to the following examples:
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	31	+== Get started: Query by a "bounding box" (hyperrectangle) ==
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32		-==== Get started: Query by a "bounding box" (hyperrectangle) ====
33		-
34	34	{{code language="bash" layout="LINENUMBERS"}}
35	35	curl -X 'POST' \
36	36	'https://spatial.kg.ebrains.eu/spatial-search/cores/ebrains/spatial_objects' \
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44	44	As you can see, you're sending a POST request to the endpoint at **https:~/~/spatial.kg.ebrains.eu/spatial-search/cores/ebrains/spatial_objects  **with a payload defining a geometry of interest. In this case, we're looking for all objects that are **inside **a **hyperrectangle **defined by its lowest and highest point in the coordinate system of the coordinate space **AMB-CCF_v3-RAS**. This query will return you an array of ids of the objects located within the geometry which you then can use to conveniently query the KG either through the [[Instance API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/2%20Advanced/getInstancesByIds]] or the [[Query API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/1%20Basic/runDynamicQuery]] to access detailed meta information.
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	45	+== Query by hypersphere ==
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47		-==== Query by hypersphere ====
48		-
49	49	Alongside the possibility to use hyperrectangles for querying the spatial search, you can also use hyperspheres:
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51	51	{{code language="javascript" layout="LINENUMBERS"}}
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57	57	It is defined by the center of the sphere with its coordinates, the radius in coordinate units and the coordinate space of the given coordinates (in this case again AMB-CCF_v3-RAS)
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	57	+== Advanced use: Union geometries for complex queries ==
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60		-
61		-==== Advanced use: Union geometries for complex queries ====
62		-
63	63	To build arbitrarily complex geometries, it is possible to combine hyperrectangles and hyperspheres via (nested) unions:
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65	65	{{code language="javascript" layout="LINENUMBERS"}}
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69	69	{{/code}}
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	68	+= Technical details of the ingestion pipeline =
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	70	+The current pipeline is regularly parsing locareJSON files registered in the KG and translates complex geometries via the [[vtk>>https://pypi.org/project/vtk/]] library into a point cloud of a density of 1 coordinate unit (by ray-casting on an obb-tree) and uploads the resulting files to the [[bucket of this collab>>https://wiki.ebrains.eu/bin/view/Collabs/kg-spatial-search/Bucket]]. A cron job on the spatial search server pulls the files from the repo and rebuilds the databases index.
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	72	+
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75	75	)))
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