First work around ArContext documentation.

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diff --git a/docs/user_guide/pipeline_input_context/configuration_and_connection.md b/docs/user_guide/pipeline_input_context/configuration_and_connection.md
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+Load and connect a context
+==========================
+
+Once an [ArContext is defined](context_definition.md), it have to be connected to a pipeline.
+
+# Load JSON configuration file
+
+An [ArContext](../../argaze.md/#argaze.ArFeatures.ArContext) can be loaded from a JSON configuration file thanks to the argaze.load package method.
+
+Here is a JSON configuration file related to the [previously defined Example context](context_definition.md):
+
+```json
+{
+    "my_context.Example": {
+        "name": "My example context",
+        "parameter": ...,
+        "pipeline": "pipeline.json"
+    }
+}
+```
+
+Then, here is how to load the JSON file:
+
+```python
+import argaze
+
+# Load ArContext
+with argaze.load('./configuration.json') as ar_context:
+
+    # Do something with ArContext
+    ...
+```
+
+!!! note
+    There is nothing to do to execute a loaded context as it is handled inside its own **__enter__** method.
diff --git a/docs/user_guide/pipeline_input_context/context_definition.md b/docs/user_guide/pipeline_input_context/context_definition.md
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+Define a context class
+======================
+
+The [ArContext](../../argaze.md/#argaze.ArFeatures.ArContext) class defines a generic class interface to handle pipeline inputs according to [Python context manager feature](https://docs.python.org/3/reference/datamodel.html#context-managers).
+
+# Write Python context file
+
+A specific [ArContext](../../argaze.md/#argaze.ArFeatures.ArContext) can be defined into a Python file.
+
+Here is an example context defined into *my_context.py* file:
+
+```python
+from argaze import ArFeatures, DataFeatures
+
+class Example(ArFeatures.ArContext):
+
+	@DataFeatures.PipelineStepInit
+	def __init__(self, **kwargs):
+
+		# Init ArContext class
+		super().__init__()
+
+		# Init private attribute
+		self.__parameter = ...
+
+	@property
+	def parameter(self):
+		"""Any required parameter."""
+		return self.__parameter
+
+	@parameter.setter
+	def parameter(self, parameter):
+		self.__parameter = parameter
+	
+	@DataFeatures.PipelineStepEnter
+	def __enter__(self):
+
+		# Start context according any required parameter
+		... self.parameter
+
+		# Assuming that timestamp, x and y values are available
+		...
+
+			# Process timestamped gaze position
+			self._process_gaze_position(timestamp = timestamp, x = x, y = y)
+
+		return self
+
+	@DataFeatures.PipelineStepExit
+	def __exit__(self, exception_type, exception_value, exception_traceback):
+
+		# End context
+		...
+```
+
+!!! note ""
+
+	The next chapter explains how to [load a context to connect it with a pipeline](configuration_and_connection.md).
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diff --git a/docs/user_guide/pipeline_input_context/introduction.md b/docs/user_guide/pipeline_input_context/introduction.md
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+Overview
+========
+
+This section explains how to wrap any pipeline detailled in previous sections into various context. 
+
+First, let's look at the schema below: it gives an overview of the main notions involved in the following chapters.
+
+![Pipeline input context](../../img/pipeline_input_context.png)
+
+To build your own input context, you need to know:
+
+* [How to define a context class](context_definition.md),
+* [How to load and connect a context](configuration_and_connection.md),
+* [How to visualize a context](visualization.md),
+* [How to pause and resume a context](pause_and_resume.md)