Update Argilla integration for v2.x SDK (#2915)

* update argilla version

* Update flavor

* update the ArgillaAnnotator

* Update docs

* fix formatting

* Update docs/book/component-guide/annotators/argilla.md

Co-authored-by: Alex Strick van Linschoten <[email protected]>

* Update docs/book/component-guide/annotators/argilla.md

Co-authored-by: Alex Strick van Linschoten <[email protected]>

* update type hinting

* update paragraph

* fix: add  deprecation validator

* fix: use logger

* Add argilla to list of ignored integrations

* Update registration command

---------

Co-authored-by: Alex Strick van Linschoten <[email protected]>
Co-authored-by: Alexej Penner <[email protected]>
Co-authored-by: Alexej Penner <[email protected]>

Author: 3 people

docs/book/.gitbook/assets/argilla_annotator.png

@@ -55,7 +55,7 @@ The core parts of the annotation workflow include:
 ### List of available annotators
 
 For production use cases, some more flavors can be found in specific `integrations` modules. In terms of annotators,
-ZenML features integrations with `label_studio` and `pigeon`.
+ZenML features integrations with the following tools.
 
 | Annotator                               | Flavor         | Integration    | Notes                                                                |
 |-----------------------------------------|----------------|----------------|----------------------------------------------------------------------|

docs/book/component-guide/annotators/annotators.md

@@ -4,12 +4,7 @@ description: Annotating data using Argilla.
 
 # Argilla
 
-[Argilla](https://github.com/argilla-io/argilla) is an open-source data curation
-platform designed to enhance the development of both small and large language
-models (LLMs) and NLP tasks in general. It enables users to build robust
-language models through faster data curation using both human and machine
-feedback, providing support for each step in the MLOps cycle, from data labeling
-to model monitoring.
+[Argilla](https://github.com/argilla-io/argilla) is a collaboration tool for AI engineers and domain experts who need to build high-quality datasets for their projects. It enables users to build robust language models through faster data curation using both human and machine feedback, providing support for each step in the MLOps cycle, from data labeling to model monitoring.
 
 ![Argilla Annotator](../../.gitbook/assets/argilla_annotator.png)
 
@@ -31,7 +26,7 @@ of Argilla as well as a deployed instance of Argilla. There is an easy way to
 deploy Argilla as a [Hugging Face
 Space](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla), for
 instance, which is documented in the [Argilla
-documentation](https://docs.argilla.io/en/latest/getting_started/installation/deployments/huggingface-spaces.html).
+documentation](https://docs.argilla.io/latest/getting_started/quickstart/).
 
 ### How to deploy it?
 
@@ -59,16 +54,16 @@ zenml secret create argilla_secrets --api_key="<your_argilla_api_key>"
 Then register your annotator with ZenML:
 
 ```shell
-zenml annotator register argilla --flavor argilla --authentication_secret=argilla_secrets
+zenml annotator register argilla --flavor argilla --authentication_secret=argilla_secrets --port=6900
 ```
 
 When using a deployed instance of Argilla, the instance URL must be specified
 without any trailing `/` at the end. If you are using a Hugging Face Spaces
 instance and its visibility is set to private, you must also set the
-`extra_headers` parameter which would include a Hugging Face token. For example:
+`headers` parameter which would include a Hugging Face token. For example:
 
 ```shell
-zenml annotator register argilla --flavor argilla --authentication_secret=argilla_secrets --instance_url="https://[your-owner-name]-[your_space_name].hf.space" --extra_headers="{"Authorization": f"Bearer {<your_hugging_face_token>}"}"
+zenml annotator register argilla --flavor argilla --authentication_secret=argilla_secrets --instance_url="https://[your-owner-name]-[your_space_name].hf.space" --headers='{"Authorization": "Bearer {[your_hugging_face_token]}"}'
 ```
 
 Finally, add all these components to a stack and set it as your active stack.
@@ -95,9 +90,8 @@ functionality via the ZenML SDK.
 
 You can access information about the datasets you're using with the `zenml
 annotator dataset list`. To work on annotation for a particular dataset, you can
-run `zenml annotator dataset annotate <dataset_name>`. What follows is an
-overview of some key components to the Argilla integration and how it can be
-used.
+run `zenml annotator dataset annotate <dataset_name>`. This will open the Argilla
+web interface for you to start annotating the dataset.
 
 #### Argilla Annotator Stack Component
 

docs/book/component-guide/annotators/argilla.md

@@ -229,10 +229,7 @@
     "xgboost",
     "argilla",
     "argilla.client",
-    "argilla.client.client",
-    "argilla.client.sdk",
-    "argilla.client.sdk.commons",
-    "argilla.client.sdk.commons.errors",
+    "argilla._exceptions._api",
     "peewee",
     "prodigy",
     "prodigy.components",

docs/mocked_libs.json

@@ -36,7 +36,7 @@ install_integrations() {
     # figure out the python version
     python_version=$(python -c "import sys; print('.'.join(map(str, sys.version_info[:2])))")
 
-    ignore_integrations="feast label_studio bentoml seldon pycaret skypilot_aws skypilot_gcp skypilot_azure pigeon prodigy"
+    ignore_integrations="feast label_studio bentoml seldon pycaret skypilot_aws skypilot_gcp skypilot_azure pigeon prodigy argilla"
 
     # Ignore tensorflow and deepchecks only on Python 3.12
     if [ "$python_version" = "3.12" ]; then

scripts/install-zenml-dev.sh

@@ -26,7 +26,7 @@ class ArgillaIntegration(Integration):
 
     NAME = ARGILLA
     REQUIREMENTS = [
-        "argilla>=1.20.0,<2",
+        "argilla>=2.0.0",
     ]
 
     @classmethod