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### Description Updating the python version of the example docs to show how to run the same code that the CLI runs, but using python. Rather than copying the same command that would be run via the terminal and using the subprocess library to run it, this updates it to use the supported code exposed in the inference directory. For now only the wikipedia one has been updated to get some opinions on this before updating all other connector docs. Would close out https://github.com/Unstructured-IO/unstructured/issues/1445
102 lines
3.7 KiB
ReStructuredText
102 lines
3.7 KiB
ReStructuredText
Elasticsearch
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==============
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Connect Elasticsearch to your preprocessing pipeline, and batch process all your documents using ``unstructured-ingest`` to store structured outputs locally on your filesystem.
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First you'll need to install the Elasticsearch dependencies as shown here.
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.. code:: shell
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pip install "unstructured[elasticsearch]"
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Run Locally
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-----------
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.. tabs::
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.. tab:: Shell
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.. code:: shell
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unstructured-ingest \
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elasticsearch \
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--metadata-exclude filename,file_directory,metadata.data_source.date_processed \
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--url http://localhost:9200 \
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--index-name movies \
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--jq-query '{ethnicity, director, plot}' \
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--output-dir elasticsearch-ingest-output \
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--num-processes 2
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.. tab:: Python
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.. code:: python
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from unstructured.ingest.interfaces import PartitionConfig, ReadConfig
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from unstructured.ingest.runner.elasticsearch import elasticsearch
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if __name__ == "__main__":
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elasticsearch(
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verbose=True,
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read_config=ReadConfig(),
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partition_config=PartitionConfig(
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output_dir="elasticsearch-ingest-output",
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num_processes=2,
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metadata_exclude=["filename", "file_directory", "metadata.data_source.date_processed"],
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),
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url="http://localhost:9200",
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index_name="movies",
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jq_query="{ethnicity, director, plot}",
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)
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Run via the API
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---------------
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You can also use upstream connectors with the ``unstructured`` API. For this you'll need to use the ``--partition-by-api`` flag and pass in your API key with ``--api-key``.
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.. tabs::
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.. tab:: Shell
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.. code:: shell
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unstructured-ingest \
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elasticsearch \
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--metadata-exclude filename,file_directory,metadata.data_source.date_processed \
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--url http://localhost:9200 \
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--index-name movies \
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--jq-query '{ethnicity, director, plot}' \
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--output-dir elasticsearch-ingest-output \
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--num-processes 2
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--partition-by-api \
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--api-key "<UNSTRUCTURED-API-KEY>"
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.. tab:: Python
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.. code:: python
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import os
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from unstructured.ingest.interfaces import PartitionConfig, ReadConfig
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from unstructured.ingest.runner.elasticsearch import elasticsearch
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if __name__ == "__main__":
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elasticsearch(
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verbose=True,
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read_config=ReadConfig(),
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partition_config=PartitionConfig(
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output_dir="elasticsearch-ingest-output",
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num_processes=2,
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metadata_exclude=["filename", "file_directory", "metadata.data_source.date_processed"],
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partition_by_api=True,
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api_key=os.getenv("UNSTRUCTURED_API_KEY"),
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),
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url="http://localhost:9200",
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index_name="movies",
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jq_query="{ethnicity, director, plot}",
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)
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Additionally, you will need to pass the ``--partition-endpoint`` if you're running the API locally. You can find more information about the ``unstructured`` API `here <https://github.com/Unstructured-IO/unstructured-api>`_.
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For a full list of the options the CLI accepts check ``unstructured-ingest elasticsearch --help``.
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NOTE: Keep in mind that you will need to have all the appropriate extras and dependencies for the file types of the documents contained in your data storage platform if you're running this locally. You can find more information about this in the `installation guide <https://unstructured-io.github.io/unstructured/installing.html>`_.
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