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update links in integrations.rst (#1418)

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A number of the links in integrations.rst don't seem to lead to the
intended section in the unstructured documentation.

For example:
```See the `stage_for_weaviate <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-weaviate>`_ docs for details```

It seems this link should direct to here instead: https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-weaviate
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docs/source/integrations.rst
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@ -8,7 +8,7 @@ which take a list of ``Element`` objects as input and return formatted dictionar
``Integration with Argilla``
----------------------------
You can convert a list of ``Text`` elements to an `Argilla <https://www.argilla.io/>`_ ``Dataset`` using the `stage_for_argilla <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-argilla>`_ staging brick. Specify the type of dataset to be generated using the ``argilla_task`` parameter. Valid values are ``"text_classification"``, ``"token_classification"``, and ``"text2text"``. Follow the link for more details on usage.
You can convert a list of ``Text`` elements to an `Argilla <https://www.argilla.io/>`_ ``Dataset`` using the `stage_for_argilla <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-argilla>`_ staging brick. Specify the type of dataset to be generated using the ``argilla_task`` parameter. Valid values are ``"text_classification"``, ``"token_classification"``, and ``"text2text"``. Follow the link for more details on usage.
``Integration with Baseplate``
@ -16,31 +16,31 @@ You can convert a list of ``Text`` elements to an `Argilla <https://www.argilla.
`Baseplate <https://docs.baseplate.ai/introduction>`_ is a backend optimized for use with LLMs that has an easy to use spreadsheet
interface. The ``unstructured`` library offers a staging brick to convert a list of ``Element`` objects into the
`rows format <https://docs.baseplate.ai/api-reference/documents/overview>`_ required by the Baseplate API. See the
`stage_for_baseplate <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-baseplate>`_ documentation for
`stage_for_baseplate <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-baseplate>`_ documentation for
information on how to stage elements for ingestion into Baseplate.
``Integration with Datasaur``
------------------------------
You can format a list of ``Text`` elements as input to token based tasks in `Datasaur <https://datasaur.ai/>`_ using the `stage_for_datasaur <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-datasaur>`_ staging brick. You will obtain a list of dictionaries indexed by the keys ``"text"`` with the content of the element, and ``"entities"`` with an empty list. Follow the link to learn how to customise your entities and for more details on usage.
You can format a list of ``Text`` elements as input to token based tasks in `Datasaur <https://datasaur.ai/>`_ using the `stage_for_datasaur <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-datasaur>`_ staging brick. You will obtain a list of dictionaries indexed by the keys ``"text"`` with the content of the element, and ``"entities"`` with an empty list. Follow the link to learn how to customise your entities and for more details on usage.
``Integration with Hugging Face``
----------------------------------
You can prepare ``Text`` elements for processing in Hugging Face `Transformers <https://huggingface.co/docs/transformers/index>`_
pipelines by splitting the elements into chunks that fit into the model's attention window using the `stage_for_transformers <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-transformers>`_ staging brick. You can customise the transformation by defining
pipelines by splitting the elements into chunks that fit into the model's attention window using the `stage_for_transformers <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-transformers>`_ staging brick. You can customise the transformation by defining
the ``buffer`` and ``window_size``, the ``split_function`` and the ``chunk_separator``. if you need to operate on
text directly instead of ``unstructured`` ``Text`` objects, use the `chunk_by_attention_window <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-transformers>`_ helper function. Follow the links for more details on usage.
text directly instead of ``unstructured`` ``Text`` objects, use the `chunk_by_attention_window <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-transformers>`_ helper function. Follow the links for more details on usage.
``Integration with Labelbox``
------------------------------
You can format your outputs for use with `LabelBox <https://labelbox.com/>`_ using the `stage_for_label_box <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-label-box>`_ staging brick. LabelBox accepts cloud-hosted data and does not support importing text directly. With this integration you can stage the data files in the ``output_directory`` to be uploaded to a cloud storage service (such as S3 buckets) and get a config of type ``List[Dict[str, Any]]`` that can be written to a ``.json`` file and imported into LabelBox. Follow the link to see how to generate the ``config.json`` file that can be used with LabelBox, how to upload the staged data files to an S3 bucket, and for more details on usage.
You can format your outputs for use with `LabelBox <https://labelbox.com/>`_ using the `stage_for_label_box <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-label-box>`_ staging brick. LabelBox accepts cloud-hosted data and does not support importing text directly. With this integration you can stage the data files in the ``output_directory`` to be uploaded to a cloud storage service (such as S3 buckets) and get a config of type ``List[Dict[str, Any]]`` that can be written to a ``.json`` file and imported into LabelBox. Follow the link to see how to generate the ``config.json`` file that can be used with LabelBox, how to upload the staged data files to an S3 bucket, and for more details on usage.
``Integration with Label Studio``
----------------------------------
You can format your outputs for upload to `Label Studio <https://labelstud.io/>`_ using the `stage_for_label_studio <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-label-studio>`_ staging brick. After running ``stage_for_label_studio``, you can write the results
You can format your outputs for upload to `Label Studio <https://labelstud.io/>`_ using the `stage_for_label_studio <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-label-studio>`_ staging brick. After running ``stage_for_label_studio``, you can write the results
to a JSON folder that is ready to be included in a new Label Studio project. You can also include pre-annotations and predictions
as part of your upload.
@ -85,12 +85,12 @@ See `here <https://llamahub.ai/>`_ for more LlamaHub examples.
``Integration with Pandas``
----------------------------
You can convert a list of ``Element`` objects to a Pandas dataframe with columns for
the text from each element and their types such as ``NarrativeText`` or ``Title`` using the `convert_to_dataframe <https://unstructured-io.github.io/unstructured/bricks.html#convert-to-dataframe>`_ staging brick. Follow the link for more details on usage.
the text from each element and their types such as ``NarrativeText`` or ``Title`` using the `convert_to_dataframe <https://unstructured-io.github.io/unstructured/bricks/staging.html#convert-to-dataframe>`_ staging brick. Follow the link for more details on usage.
``Integration with Prodigy``
-----------------------------
You can format your JSON or CSV outputs for use with `Prodigy <https://prodi.gy/docs/api-loaders>`_ using the `stage_for_prodigy <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-prodigy>`_ and `stage_csv_for_prodigy <https://unstructured-io.github.io/unstructured/bricks.html#stage-csv-for-prodigy>`_ staging bricks. After running ``stage_for_prodigy`` |
You can format your JSON or CSV outputs for use with `Prodigy <https://prodi.gy/docs/api-loaders>`_ using the `stage_for_prodigy <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-prodigy>`_ and `stage_csv_for_prodigy <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-csv-for-prodigy>`_ staging bricks. After running ``stage_for_prodigy`` |
``stage_csv_for_prodigy``, you can write the results to a ``.json`` | ``.jsonl`` or a ``.csv`` file that is ready to be used with Prodigy. Follow the links for more details on usage.
@ -99,6 +99,6 @@ You can format your JSON or CSV outputs for use with `Prodigy <https://prodi.gy/
`Weaviate <https://weaviate.io/>`_ is an open-source vector database that allows you to store data objects and vector embeddings
from a variety of ML models. Storing text and embeddings in a vector database such as Weaviate is a key component of the
`emerging LLM tech stack <https://medium.com/@unstructured-io/llms-and-the-emerging-ml-tech-stack-bdb189c8be5c>`_.
See the `stage_for_weaviate <https://unstructured-io.github.io/unstructured/bricks.html#stage-for-weaviate>`_ docs for details
See the `stage_for_weaviate <https://unstructured-io.github.io/unstructured/bricks/staging.html#stage-for-weaviate>`_ docs for details
on how to upload ``unstructured`` outputs to Weaviate. An example notebook is also available
`here <https://github.com/Unstructured-IO/unstructured/tree/main/examples/weaviate>`_.