deploy: ba6f855

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 8a1100ee3fd8a3a485e4cdcbcf429e7a
+tags: d77d1c0d9ca2f4c8421862c7c5a0d620

CNAME

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+burr.dagworks.io

_sources/concepts/actions.rst.txt

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+=======
+Actions
+=======
+
+.. _actions:
+
+
+Actions do the heavy-lifting in a workflow. They should contain all complex compute. You can define actions
+either through a class-based or function-based API. If actions implement ``async def run`` then will be run in an
+asynchronous context (and thus require one of the async application functions).
+
+Actions have two primary responsibilities:
+
+1. ``run`` -- compute a result
+2. ``update`` -- modify the state
+
+The ``run`` method should return a dictionary of the result and the ``update`` method should return
+the updated state. They declare their dependencies so the framework knows *which* state variables they read and write. This allows the
+framework to optimize the execution of the workflow. We call (1) a ``Function`` and (2) a ``Reducer`` (similar to `Redux <https://redux.js.org/>`_, if you're familiar with frontend UI technology).
+
+.. _inputref:
+
+--------------
+Runtime Inputs
+--------------
+
+Actions can declare inputs that are not part of the state. This is for the case that you want to pause workflow execution for human input.
+
+For instance, say you have a chatbot. The first step will declare the ``input`` parameter ``prompt`` -- it will take that, process it, and put
+it in the state. The subsequent steps will read the result of that from state.
+
+There are two APIs for defining actions: class-based and function-based. They are largely equivalent, but differ in use.
+
+- use the function-based API when you want to write something quick and terse that reads from a fixed set of state variables
+- use the class-based API when you want to leverage inheritance or parameterize the action in more powerful ways
+
+-------------------
+Class-Based Actions
+-------------------
+
+You can define an action by implementing the :py:class:`Action <burr.core.action.Action>` class:
+
+.. code-block:: python
+
+    from burr.core import Action, State
+
+    class CustomAction(Action):
+        @property
+        def reads(self) -> list[str]:
+            return ["var_from_state"]
+
+        def run(self, state: State) -> dict:
+            return {"var_to_update": state["var_from_state"] + 1}
+
+        @property
+        def writes(self) -> list[str]:
+            return ["var_to_update"]
+
+        def update(self, result: dict, state: State) -> State:
+            return state.update(**result)
+
+You then pass the action to the :py:class:`ApplicationBuilder <burr.core.application.ApplicationBuilder>`:
+
+.. code-block:: python
+
+    from burr.core import ApplicationBuilder
+
+    app = ApplicationBuilder().with_actions(
+        custom_action=CustomAction()
+    )...
+
+
+Note that if the action has inputs, you have to define the optional ``inputs`` property:
+
+.. code-block:: python
+
+    from burr.core import Action, State
+
+    class CustomAction(Action):
+        @property
+        def reads(self) -> list[str]:
+            return ["var_from_state"]
+
+        def run(self, state: State, increment_by: int) -> dict:
+            return {"var_to_update": state["var_from_state"] + increment_by}
+
+        @property
+        def writes(self) -> list[str]:
+            return ["var_to_update"]
+
+        def update(self, result: dict, state: State) -> State:
+            return state.update(**result)
+
+        @property
+        def inputs(self) -> list[str]:
+            return ["increment_by"]
+
+
+----------------------
+Function-based actions
+----------------------
+
+You can also define actions by decorating a function with the :py:func:`@action <burr.core.action.action>` decorator:
+
+.. code-block:: python
+
+    from burr.core import action, State
+
+    @action(reads=["var_from_state"], writes=["var_to_update"])
+    def custom_action(state: State) -> Tuple[dict, State]:
+        result = {"var_to_update": state["var_from_state"] + 1}
+        return result, state.update(**result)
+
+    app = ApplicationBuilder().with_actions(
+        custom_action=custom_action
+    )...
+
+Function-based actions can take in parameters which are akin to passing in constructor parameters. This is done through the :py:meth:`bind <burr.core.action.bind>` method:
+
+.. code-block:: python
+
+    @action(reads=["var_from_state"], writes=["var_to_update"])
+    def custom_action(state: State, increment_by: int) -> Tuple[dict, State]:
+        result = {"var_to_update": state["var_from_state"] + increment_by}
+        return result, state.update(**result)
+
+    app = ApplicationBuilder().with_actions(
+        custom_action=custom_action.bind(increment_by=2)
+    )...
+
+This is the same as ``functools.partial``, but it is more explicit and easier to read. If an action has parameters that are not
+bound, they will be referred to as inputs. For example:
+
+
+.. code-block:: python
+
+    @action(reads=["var_from_state"], writes=["var_to_update"])
+    def custom_action(state: State, increment_by: int) -> Tuple[dict, State]:
+        result = {"var_to_update": state["var_from_state"] + increment_by}
+        return result, state.update(**result)
+
+    app = ApplicationBuilder().with_actions(
+        custom_action=custom_action
+    )...
+
+Will require the inputs to be passed in at runtime.
+
+Note that these combine the ``update`` and ``run`` methods into a single function, and they're both executed at the same time.
+
+-----------
+``Inputs``
+-----------
+
+If you simply want a node to take in inputs and pass them to the state, you can use the `Input` action:
+
+.. code-block:: python
+
+    app = ApplicationBuilder().with_actions(
+        get_input=Input("var_from_state")
+    )...
+
+This will look for the `var_from_state` in the inputs and pass it to the state. Note this is just syntactic sugar
+for declaring inputs through one of the other APIs and adding it to state -- if you want to do anything more complex
+with the input, you should use other APIs.
+
+-----------
+``Results``
+-----------
+
+If you just want to fill a result from the state, you can use the `Result` action:
+
+.. code-block:: python
+
+    app = ApplicationBuilder().with_actions(
+        get_result=Result("var_from_state")
+    )...
+
+
+This simply grabs the value from the state and returns it as the result. It is purely a placeholder
+for an action that should just use the result, although you do not need it.
+
+Refer to :ref:`actions <actions>` for documentation.

_sources/concepts/additional-visibility.rst.txt

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+=====================
+Additional Visibility
+=====================
+
+Burr comes with the ability to see inside your actions. This is a very pluggable framework
+that comes with the default tracking client.
+
+
+-------
+Tracing
+-------
+
+Burr comes with a tracing capability to see recursive spans inside an action. This is similar to
+the `OpenTelemetry <https://opentelemetry.io/>`_ sdk, although it is simplified significantly.
+
+To add the tracing capability, the action first has to declare a ``__tracer`` input. This is a
+an object that instantiates spans and tracks state.
+
+Then, using the `__tracer` as a callable, you can instantiate spans and track state.
+
+For the function-based API, this would look as follows:
+
+.. code-block:: python
+
+    from burr.visibility import TracingFactory
+    from burr.core import action
+
+    @action(reads=['input_var'], writes=['output_var'])
+    def my_action(state: State, __tracer: TracingFactory) -> Tuple[dict, State]:
+        with __tracer('process_data'):
+            initial_data = _process_data(state['input_var'])
+            with __tracer('validate_data'):
+                _validate(initial_data)
+        with __tracer('transform_data', dependencies=['process_data']):
+            transformed_data = _transform(initial_data)
+        return {'output_var': transformed_data}, state.update({'output_var': transformed_data})
+
+
+This would create the following traces:
+
+#. ``process_data``
+#. ``validate_data`` as a child of ``process_data``
+#. ``transform_data`` as a causal dependent of ``process_data``
+
+Dependencies are used to express [dag](-style structures of spans within actions. This is useful for gaining visibility into the internal structure
+of an action, but is likely best used with integrations with micro-orchestration systems for implementating actions, such as Hamilton or Lanchain.
+This maps to the `span link <https://opentelemetry.io/docs/concepts/signals/traces/#span-links>`_ concept in OpenTelemetry.
+
+Note that, on the surface, this doesn't actually *do* anything. It has to be paired with the appropriate hooks.
+These just function as callbacks (on enter/exit). The :py:class:`LocalTrackingClient <burr.tracking.LocalTrackingClient>`, used by the
+:ref:`tracking <tracking>` feature forms one of these hooks, but we will be adding more, including:
+
+1. An OpenTelemetry client
+2. A DataDog client
+
+.. note::
+
+    The class-based API can leverage this by declaring ``inputs`` as ``__tracer`` and then using the ``__tracer`` inside the ``run`` method.
+
+------------
+Observations
+------------
+
+(This is a work in progress, and is not complete)
+
+You can make observations on the state by calling out to the `log_artifact` method on the `__tracer` context manager.
+For instance:
+
+.. code-block:: python
+
+    from burr.visibility import TracingFactory, ArtifactLogger
+    from burr.core import action
+
+    @action(reads=['input_var'], writes=['output_var'])
+    def my_action(
+        state: State,
+        __tracer: TracingFactory,
+        __logger: ArtifactLogger
+        ) -> Tuple[dict, State]:
+        with __tracer('process_data'):
+            initial_data = _process_data(state['input_var'])
+            with __tracer('validate_data'):
+                validation_results = _validate(initial_data)
+                t.log_artifact(validation_results=validation_results)
+        with __tracer('transform_data', dependencies=['process_data'])
+            transformed_data = _transform(initial_data)
+            __logger.log_artifact(transformed_data_size=len(transformed_data))
+
+        return {'output_var': transformed_data}, state.update({'output_var': transformed_data})
+
+The output can be any "json-dumpable" object (or pydantic model). This will be stored along with the span and can be used for debugging or analysis.
+
+You can read more in the :ref:`reference documentation <visibility>`.

_sources/concepts/hooks.rst.txt

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+=====
+Hooks
+=====
+
+.. _hooks:
+
+Burr has a system of lifecycle adapters (adapted from the similar `Hamilton <https://github.com/dagworks-inc/hamilton>`_ concept), which allow you to run tooling before and after
+various places in a node's execution. For instance, you could:
+
+1. Log every step as a trace in datadog
+2. Add a time-delay to your steps to allow for rendering
+3. Add a print statement to every step to see what happened (E.G. implement the printline in cowsay above)
+4. Synchronize state/updates to an external database
+5. Put results on a queue to feed to some monitoring system
+
+Note some of the above are yet to be implemented.
+
+To implement hooks, you subclass any number of the :ref:`available lifecycle hooks <hooksref>`.
+These have synchronous and asynchronous versions, and your hook can subclass as many as you want
+(as long as it doesn't do both the synchronous and asynchronous versions of the same hook).
+
+To use them, you pass them into the :py:class:`ApplicationBuilder <burr.core.application.ApplicationBuilder>` as a ``*args`` list of hooks. For instance,
+a hook that prints out the nodes name during execution looks like this.
+We implement the pre/post run step hooks.
+
+.. code-block:: python
+
+    class PrintLnHook(PostRunStepHook, PreRunStepHook):
+        def pre_run_step(self, *, state: "State", action: "Action", **future_kwargs: Any):
+            print(f"Starting action: {action.node.name}")
+
+        def post_run_step(
+            self,
+            *,
+            state: "State",
+            action: "Action",
+            result: Optional[dict],
+            sequence_id: int,
+            exception: Exception,
+            **future_kwargs: Any,
+        ):
+            print(f"Finishing action: {action.node.name}")
+
+To include this in the application, you pass it into the :py:meth:`with_hooks <burr.core.application.ApplicationBuilder.with_hooks>` method.
+
+.. code-block:: python
+
+    app = (
+        ApplicationBuilder()
+        .with_hooks(PrintLnHook())
+        ...
+        .build())
+
+.. note::
+
+    There are synchronous and asynchronous hooks. Synchronous hooks will be called with both synchronous and asynchronous run methods
+    (all of ``step``, ``astep``, ``iterate``, ``aiterate``, ``run``, and ``arun``), whereas synchronous hooks will only be called with
+    the asynchronous methods (``astep``, ``aiterate``, ``arun``).
+
+.. warning::
+    Hook order is currently undefined -- they happen to be called now in the order in which they are defined. We will likely
+    alter them to be called in the order they are defined (for `pre...`) hooks and in reverse order for `post...` hooks,
+    but this is not yet implemented.
+
+Read more about the hook API in the :ref:`hooks section <hooksref>`.

_sources/concepts/index.rst.txt

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+====================
+Concepts
+====================
+
+Overview of the concepts -- read these to get a mental model for how Burr works.
+
+.. _concepts:
+
+.. toctree::
+    :maxdepth: 2
+
+    state-machine
+    state
+    actions
+    transitions
+    hooks
+    tracking
+    state-persistence
+    streaming-actions
+    additional-visibility
+    planned-capabilities