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* Updated Metadata page: add common and additional metadata fields by document types and connectors * Updated specific installation extra by document types and connectors * Added embedding brick page in Sphinx TOC * Fixed Sphinx warnings in new pages
84 lines
2.8 KiB
ReStructuredText
84 lines
2.8 KiB
ReStructuredText
Azure Cognitive Search
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======================
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Batch process all your records using ``unstructured-ingest`` to store structured outputs locally on your filesystem and upload those local files to an Azure Cognitive Search index.
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First you'll need to install the azure cognitive search dependencies as shown here.
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.. code:: shell
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pip install "unstructured[azure-cognitive-search]"
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Run Locally
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-----------
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The upstream connector can be any of the ones supported, but for convenience here, showing a sample command using the
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upstream s3 connector.
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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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s3 \
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--remote-url s3://utic-dev-tech-fixtures/small-pdf-set/ \
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--anonymous \
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--output-dir s3-small-batch-output-to-azure \
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--num-processes 2 \
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--verbose \
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--strategy fast \
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azure-cognitive-search \
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--key "$AZURE_SEARCH_API_KEY" \
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--endpoint "$AZURE_SEARCH_ENDPOINT" \
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--index utic-test-ingest-fixtures-output
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.. tab:: Python
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.. code:: python
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import os
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import subprocess
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command = [
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"unstructured-ingest",
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"s3",
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"--remote-url", "s3://utic-dev-tech-fixtures/small-pdf-set/",
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"--anonymous",
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"--output-dir", "s3-small-batch-output-to-azure",
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"--num-processes", "2",
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"--verbose",
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"--strategy", "fast",
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"azure-cognitive-search",
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"--key", os.getenv("AZURE_SEARCH_API_KEY"),
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"--endpoint", os.getenv("$AZURE_SEARCH_ENDPOINT"),
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"--index", "utic-test-ingest-fixtures-output",
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]
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# Run the command
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process = subprocess.Popen(command, stdout=subprocess.PIPE)
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output, error = process.communicate()
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# Print output
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if process.returncode == 0:
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print("Command executed successfully. Output:")
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print(output.decode())
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else:
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print("Command failed. Error:")
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print(error.decode())
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For a full list of the options the CLI accepts check ``unstructured-ingest <upstream connector> azure-cognitive-search --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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Sample Index Schema
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-------------------
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To make sure the schema of the index matches the data being written to it, a sample schema json can be used:
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.. literalinclude:: azure_cognitive_sample_index_schema.json
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:language: json
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:linenos:
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:caption: Object description
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