Pipeline state dump and load

CHANGELOG.md

@@ -6,6 +6,7 @@
 
 - Added support for automatic schema extraction from text using LLMs. In the `SimpleKGPipeline`, when the user provides no schema, the automatic schema extraction is enabled by default.
 - Added ability to return a user-defined message if context is empty in GraphRAG (which skips the LLM call).
+- Added pipeline execution control with state management (`dump_state()` and `load_state()` methods) and partial execution support in the `run()` method (with `until` and `from_` parameters), enabling pipeline state dump and resumption of long-running pipelines, debugging workflows, and incremental processing.
 
 ### Fixed
 

docs/source/user_guide_pipeline.rst

@@ -204,3 +204,33 @@ This will send an `TASK_PROGRESS` event to the pipeline callback.
 .. note::
 
     In a future release, the `context_` parameter will be added to the `run` method.
+
+
+*************************
+Pipeline State Management
+*************************
+
+Pipelines support state management to enable saving and restoring execution state, which is useful for debugging, resuming long-running pipelines, or incremental processing workflows.
+
+Saving and Loading State
+========================
+
+You can save the current state of a pipeline execution using the `dump_state()` method and restore it with `load_state()`. The pipeline also supports partial execution using the `until` and `from_` parameters:
+
+- **`until`**: Stop execution after a specific component completes
+- **`from_`**: Start execution from a specific component instead of from the beginning
+
+.. code:: python
+
+    # Run pipeline and save state
+    result = await pipeline.run(..., until="a")
+    state = pipeline.dump_state(result.run_id)
+    # The user could save the state to a JSON file
+
+    # Resuming pipeline, could be from another run
+    loaded_run_id = pipeline.load_state(state)
+    new_result = await pipeline.run(..., from_="b", previous_run_id=loaded_run_id)
+
+.. warning:: State Compatibility
+
+    When loading state, the current pipeline must have at least all the components that were present when the state was saved. Additional components are allowed, but missing components will cause a validation error.

src/neo4j_graphrag/experimental/pipeline/orchestrator.py

@@ -19,7 +19,7 @@
 import uuid
 import warnings
 from functools import partial
-from typing import TYPE_CHECKING, Any, AsyncGenerator
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Optional
 
 from neo4j_graphrag.experimental.pipeline.types.context import RunContext
 from neo4j_graphrag.experimental.pipeline.exceptions import (
@@ -46,16 +46,31 @@ class Orchestrator:
     - finding the next tasks to execute
     - building the inputs for each task
     - calling the run method on each task
+    - optionally stopping after a specified component
+    - optionally starting from a specified component
 
     Once a TaskNode is done, it calls the `on_task_complete` callback
     that will save the results, find the next tasks to be executed
     (checking that all dependencies are met), and run them.
+
+    Partial execution is supported through:
+    - stop_after: Stop execution after this component completes
+    - start_from: Start execution from this component instead of roots
     """
 
-    def __init__(self, pipeline: Pipeline):
+    def __init__(
+        self,
+        pipeline: Pipeline,
+        stop_after: Optional[str] = None,
+        start_from: Optional[str] = None,
+        previous_run_id: Optional[str] = None,
+    ):
         self.pipeline = pipeline
         self.event_notifier = EventNotifier(pipeline.callbacks)
         self.run_id = str(uuid.uuid4())
+        self.previous_run_id = previous_run_id  # useful for pipeline resumption
+        self.stop_after = stop_after
+        self.start_from = start_from
 
     async def run_task(self, task: TaskPipelineNode, data: dict[str, Any]) -> None:
         """Get inputs and run a specific task. Once the task is done,
@@ -129,7 +144,10 @@ async def on_task_complete(
         await self.add_result_for_component(
             task.name, res_to_save, is_final=task.is_leaf()
         )
-        # then get the next tasks to be executed
+        # stop if this is the stop_after node
+        if self.stop_after and task.name == self.stop_after:
+            return
+        # otherwise, get the next tasks to be executed
         # and run them in //
         await asyncio.gather(*[self.run_task(n, data) async for n in self.next(task)])
 
@@ -252,10 +270,24 @@ async def add_result_for_component(
             )
 
     async def get_results_for_component(self, name: str) -> Any:
+        # when resuming, check previous run_id, otherwise check current run_id
+        if self.previous_run_id:
+            return await self.pipeline.store.get_result_for_component(
+                self.previous_run_id, name
+            )
         return await self.pipeline.store.get_result_for_component(self.run_id, name)
 
     async def get_status_for_component(self, name: str) -> RunStatus:
-        status = await self.pipeline.store.get_status_for_component(self.run_id, name)
+        # when resuming, check previous run_id, otherwise check current run_id
+        if self.previous_run_id:
+            status = await self.pipeline.store.get_status_for_component(
+                self.previous_run_id, name
+            )
+        else:
+            status = await self.pipeline.store.get_status_for_component(
+                self.run_id, name
+            )
+
         if status is None:
             return RunStatus.UNKNOWN
         return RunStatus(status)
@@ -266,7 +298,13 @@ async def run(self, data: dict[str, Any]) -> None:
         will handle the task dependencies.
         """
         await self.event_notifier.notify_pipeline_started(self.run_id, data)
-        tasks = [self.run_task(root, data) for root in self.pipeline.roots()]
+        # start from a specific node if requested, otherwise from roots
+        if self.start_from:
+            start_nodes = [self.pipeline.get_node_by_name(self.start_from)]
+        else:
+            start_nodes = self.pipeline.roots()
+
+        tasks = [self.run_task(root, data) for root in start_nodes]
         await asyncio.gather(*tasks)
         await self.event_notifier.notify_pipeline_finished(
             self.run_id, await self.pipeline.get_final_results(self.run_id)

src/neo4j_graphrag/experimental/pipeline/pipeline.py

@@ -18,7 +18,7 @@
 import warnings
 from collections import defaultdict
 from timeit import default_timer
-from typing import Any, Optional, AsyncGenerator
+from typing import Any, Optional, AsyncGenerator, Dict
 import asyncio
 
 from neo4j_graphrag.utils.logging import prettify
@@ -390,22 +390,34 @@ def validate_parameter_mapping(self) -> None:
             self.validate_parameter_mapping_for_task(task)
         self.is_validated = True
 
-    def validate_input_data(self, data: dict[str, Any]) -> bool:
+    def validate_input_data(
+        self, data: dict[str, Any], from_: Optional[str] = None
+    ) -> bool:
         """Performs parameter and data validation before running the pipeline:
         - Check parameters defined in the connect method
         - Make sure the missing parameters are present in the input `data` dict.
 
         Args:
             data (dict[str, Any]): input data to use for validation
                 (usually from Pipeline.run)
+            from_ (Optional[str]): If provided, only validate components that will actually execute
+                starting from this component
 
         Raises:
             PipelineDefinitionError if any parameter mapping is invalid or if a
                 parameter is missing.
         """
         if not self.is_validated:
             self.validate_parameter_mapping()
+
+        # determine which components need validation
+        components_to_validate = self._get_components_to_validate(from_)
+
         for task in self._nodes.values():
+            # skip validation for components that won't execute
+            if task.name not in components_to_validate:
+                continue
+
             if task.name not in self.param_mapping:
                 self.validate_parameter_mapping_for_task(task)
             missing_params = self.missing_inputs[task.name]
@@ -417,6 +429,37 @@ def validate_input_data(self, data: dict[str, Any]) -> bool:
                     )
         return True
 
+    def _get_components_to_validate(self, from_: Optional[str] = None) -> set[str]:
+        """Determine which components need validation based on execution context.
+
+        Args:
+            from_ (Optional[str]): Starting component for execution
+
+        Returns:
+            set[str]: Set of component names that need validation
+        """
+        if from_ is None:
+            # no from_ specified, validate all components
+            return set(self._nodes.keys())
+
+        # when from_ is specified, only validate components that will actually execute
+        # this includes the from_ component and all its downstream dependencies
+        components_to_validate = set()
+
+        def add_downstream_components(component_name: str) -> None:
+            """Recursively add a component and all its downstream dependencies"""
+            if component_name in components_to_validate:
+                return  # Already processed
+            components_to_validate.add(component_name)
+
+            # add all components that depend on this one
+            for edge in self.next_edges(component_name):
+                add_downstream_components(edge.end)
+
+        # start from the specified component and add all downstream
+        add_downstream_components(from_)
+        return components_to_validate
+
     def validate_parameter_mapping_for_task(self, task: TaskPipelineNode) -> bool:
         """Make sure that all the parameter mapping for a given task are valid.
         Does not consider user input yet.
@@ -563,19 +606,131 @@ async def event_stream(event: Event) -> None:
             if event_queue_getter_task and not event_queue_getter_task.done():
                 event_queue_getter_task.cancel()
 
-    async def run(self, data: dict[str, Any]) -> PipelineResult:
+    async def run(
+        self,
+        data: dict[str, Any],
+        from_: Optional[str] = None,
+        until: Optional[str] = None,
+        previous_run_id: Optional[str] = None,
+    ) -> PipelineResult:
+        """Run the pipeline, optionally from a specific component or until a specific component.
+
+        Args:
+            data (dict[str, Any]): The input data for the pipeline
+            from_ (str | None, optional): If provided, start execution from this component. Defaults to None.
+            until (str | None, optional): If provided, stop execution after this component. Defaults to None.
+            previous_run_id (str | None, optional): If provided, resume from this previous run_id. Defaults to None.
+
+        Returns:
+            PipelineResult: The result of the pipeline execution
+        """
         logger.debug("PIPELINE START")
         start_time = default_timer()
         self.invalidate()
-        self.validate_input_data(data)
-        orchestrator = Orchestrator(self)
+        self.validate_input_data(data, from_)
+
+        # create orchestrator with appropriate start_from and stop_after params
+        orchestrator = Orchestrator(
+            self, stop_after=until, start_from=from_, previous_run_id=previous_run_id
+        )
+
         logger.debug(f"PIPELINE ORCHESTRATOR: {orchestrator.run_id}")
         await orchestrator.run(data)
+
         end_time = default_timer()
         logger.debug(
             f"PIPELINE FINISHED {orchestrator.run_id} in {end_time - start_time}s"
         )
+
         return PipelineResult(
             run_id=orchestrator.run_id,
             result=await self.get_final_results(orchestrator.run_id),
         )
+
+    def dump_state(self, run_id: str) -> Dict[str, Any]:
+        """Dump the current state of the pipeline to a serializable dictionary.
+
+        Args:
+            run_id (str): The run_id to dump state for
+
+        Returns:
+            Dict[str, Any]: A serializable dictionary containing the pipeline state
+
+        Raises:
+            ValueError: If run_id is None or empty
+        """
+        if not run_id:
+            raise ValueError("run_id cannot be None or empty")
+
+        pipeline_state: Dict[str, Any] = {
+            "run_id": run_id,
+            "store": self.store.dump(run_id),
+        }
+        return pipeline_state
+
+    def load_state(self, state: Dict[str, Any]) -> str:
+        """Load pipeline state from a serialized dictionary.
+
+        Args:
+            state (dict[str, Any]): Previously serialized pipeline state
+
+        Returns:
+            str: The run_id from the loaded state
+
+        Raises:
+            ValueError: If the state is invalid or incompatible with current pipeline
+        """
+        if "run_id" not in state:
+            raise ValueError("Invalid state: missing run_id")
+
+        run_id: str = state["run_id"]
+
+        # validate pipeline compatibility
+        self._validate_state_compatibility(state)
+
+        # load store data
+        if "store" in state:
+            self.store.load(state["store"])
+
+        return run_id
+
+    def _validate_state_compatibility(self, state: Dict[str, Any]) -> None:
+        """Validate that the loaded state is compatible with the current pipeline.
+
+        This checks that the components defined in the pipeline match those
+        that were present when the state was saved.
+
+        Args:
+            state (dict[str, Any]): The state to validate
+
+        Raises:
+            ValueError: If the state is incompatible with the current pipeline
+        """
+        if "store" not in state:
+            return  # no store data to validate
+
+        store_data = state["store"]
+        if not store_data:
+            return  # empty store, nothing to validate
+
+        # extract component names from the store keys
+        # keys are in format: "run_id:component_name" or "run_id:component_name:suffix"
+        stored_components = set()
+        for key in store_data.keys():
+            parts = key.split(":")
+            if len(parts) >= 2:
+                component_name = parts[1]
+                stored_components.add(component_name)
+
+        # get current pipeline component names
+        current_components = set(self._nodes.keys())
+
+        # check if stored components are a subset of current components
+        # this allows for the pipeline to have additional components, but not missing ones
+        missing_components = stored_components - current_components
+        if missing_components:
+            raise ValueError(
+                f"State is incompatible with current pipeline. "
+                f"Missing components: {sorted(missing_components)}. "
+                f"Current pipeline components: {sorted(current_components)}"
+            )