--- title: Indexer keywords: fastai sidebar: home_sidebar nb_path: "nbs/indexers.indexer.ipynb" ---
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class IndexerBase[source]

IndexerBase(indexerClass=None, *args, **kwargs) :: Indexer

Provides a base class for all items. All items in the schema inherit from this class, and it provides some basic functionality for consistency and to enable easier usage.

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class IndexerData[source]

IndexerData(**kwargs)

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get_indexer_run_data[source]

get_indexer_run_data(client, indexer_run)

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test_registration[source]

test_registration(integrator)

Check whether an integrator is registred. Registration is necessary to be able to load the right indexer when retrieving it from the database.

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Running your own indexer

When we run an indexer we have four steps. 1) Get the indexer and indexer run based on the run uid. 2) run the indexer 3) populate the graph with the new information. To mock that, first we create a client and add some toy data.

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from integrators.indexers.geo.geo_indexer import GeoIndexer

client = PodClient()

location = Location.from_data(latitude=-37.81, longitude=144.96)
address = Address.from_data()
indexer = Indexer.from_data(indexerClass="GeoIndexer", name="GeoIndexer")
indexer_run = IndexerRun.from_data(progress=0, targetDataType="Address")

for x in [location, address, indexer, indexer_run]: client.create(x)
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edge_success = client.create_edge(Edge(indexer_run, indexer, "indexer"))
edge_success2 = client.create_edge(Edge(location, address, "location"))

assert edge_success and edge_success2
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Before we can move on, we need to make sure that our indexer is registred. This hold for any integrator that we create.

{% include important.html content='Note that before running an indexer, it needs to be registered. We can do this by importing the file in integrators.indexer_registry.py.' %}

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test_registration(GeoIndexer)
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Now we start with the setting we would normally have: some memri client makes a call to the pod to execute an indexer run. Lets start by getting the indexer and the indexer run.

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indexer_run = client.get(indexer_run.uid)
indexer = indexer_run.indexer[0]
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Next, we retrieve the data, which was specified in the client by the targetDataType.

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data = indexer.get_data(client, indexer_run)
1 items found to index
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updated_items, new_items = indexer.index(data, indexer_run, client)
indexing 1 items
Loading formatted geocoded file...
updating IndexerRun (#4)
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indexer.populate(client, updated_items, new_items)
creating Country (#None)
updating Address (#2)
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