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### Description * Update all existing connector docs to use new pipeline approach ### Additional changes: * Some defaults were set for the runners to match those in the configs to make those easy to handle, i.e. the biomed runner: ```python max_retries: int = 5, max_request_time: int = 45, decay: float = 0.3, ```
121 lines
4.1 KiB
ReStructuredText
121 lines
4.1 KiB
ReStructuredText
Reddit
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==========
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Connect Reddit 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 Reddit dependencies as shown here.
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.. code:: shell
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pip install "unstructured[reddit]"
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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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reddit \
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--subreddit-name machinelearning \
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--client-id "<client id here>" \
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--client-secret "<client secret here>" \
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--user-agent "Unstructured Ingest Subreddit fetcher by \u\..." \
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--search-query "Unstructured" \
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--num-posts 10 \
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--output-dir reddit-ingest-output \
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--num-processes 2 \
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--verbose
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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, ProcessorConfig, ReadConfig
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from unstructured.ingest.runner import RedditRunner
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if __name__ == "__main__":
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runner = RedditRunner(
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processor_config=ProcessorConfig(
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verbose=True,
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output_dir="reddit-ingest-output",
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num_processes=2,
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),
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read_config=ReadConfig(),
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partition_config=PartitionConfig(),
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)
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runner.run(
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subreddit_name="machinelearning",
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client_id="<client id here>",
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client_secret="<client secret here>",
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user_agent=r"Unstructured Ingest Subreddit fetcher by \\u\...",
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search_query="Unstructured",
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num_posts=10,
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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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reddit \
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--subreddit-name machinelearning \
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--client-id "<client id here>" \
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--client-secret "<client secret here>" \
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--user-agent "Unstructured Ingest Subreddit fetcher by \u\..." \
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--search-query "Unstructured" \
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--num-posts 10 \
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--output-dir reddit-ingest-output \
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--num-processes 2 \
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--verbose \
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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, ProcessorConfig, ReadConfig
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from unstructured.ingest.runner import RedditRunner
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if __name__ == "__main__":
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runner = RedditRunner(
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processor_config=ProcessorConfig(
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verbose=True,
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output_dir="reddit-ingest-output",
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num_processes=2,
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),
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read_config=ReadConfig(),
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partition_config=PartitionConfig(
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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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)
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runner.run(
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subreddit_name="machinelearning",
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client_id="<client id here>",
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client_secret="<client secret here>",
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user_agent=r"Unstructured Ingest Subreddit fetcher by \\u\...",
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search_query="Unstructured",
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num_posts=10,
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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 reddit --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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