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 state management with `run_until`, `resume_from`, `dump_state`, and `load_state` methods, enabling pipeline execution checkpointing and resumption.
 
 ### Fixed
 

docs/source/user_guide_pipeline.rst

@@ -133,6 +133,36 @@ can be added to the visualization by setting `hide_unused_outputs` to `False`:
     webbrowser.open("pipeline_full.html")
 
 
+*************************
+Pipeline State Management
+*************************
+
+Pipelines support checkpointing and resumption through state management features:
+
+.. code:: python
+
+    # Run pipeline until a specific component
+    state = await pipeline.run_until(data, stop_after="component_name", state_file="state.json")
+
+    # Resume pipeline from a specific component
+    result = await pipeline.resume_from(state, data, start_from="component_name")
+
+    # Alternatively, load state from file
+    result = await pipeline.resume_from(None, data, start_from="component_name", state_file="state.json")
+
+The state contains:
+- Pipeline configuration (parameter mappings between components and validation state)
+- Execution results (outputs from completed components stored in the ResultStore)
+- Final pipeline results from previous runs
+- Component-specific state (interface available but not yet implemented by components)
+
+This enables:
+- Checkpointing long-running pipelines
+- Debugging pipeline execution
+- Resuming failed pipelines from the last successful component
+- Comparing different component implementations with deterministic inputs by saving the state before the component and reusing it, avoiding non-deterministic results from preceding components
+
+
 ************************
 Adding an Event Callback
 ************************

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 +46,30 @@ 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,
+        run_id: Optional[str] = None,
+    ):
         self.pipeline = pipeline
         self.event_notifier = EventNotifier(pipeline.callbacks)
-        self.run_id = str(uuid.uuid4())
+        self.run_id = run_id or str(uuid.uuid4())
+        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 +143,10 @@ 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)])
 
@@ -266,7 +283,12 @@ 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
@@ -140,6 +140,7 @@ def __init__(
         }
         """
         self.missing_inputs: dict[str, list[str]] = defaultdict()
+        self._current_run_id: Optional[str] = None
 
     @classmethod
     def from_template(
@@ -390,22 +391,34 @@ 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 +430,37 @@ 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 +607,134 @@ 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,
+    ) -> 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.
+
+        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
+        # if current run_id exists (from loaded state), use it to continue the same run
+        orchestrator = Orchestrator(
+            self, stop_after=until, start_from=from_, run_id=self._current_run_id
+        )
+
+        # Track the current run_id
+        self._current_run_id = orchestrator.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) -> Dict[str, Any]:
+        """Dump the current state of the pipeline to a serializable dictionary.
+
+        Returns:
+            Dict[str, Any]: A serializable dictionary containing the pipeline state
+
+        Raises:
+            ValueError: If no pipeline run has been executed yet
+        """
+        if self._current_run_id is None:
+            raise ValueError(
+                "No pipeline run has been executed yet. Cannot dump state without a run_id."
+            )
+
+        pipeline_state: Dict[str, Any] = {
+            "run_id": self._current_run_id,
+            "store": self.store.dump(self._current_run_id),
+        }
+        return pipeline_state
+
+    def load_state(self, state: Dict[str, Any]) -> None:
+        """Load pipeline state from a serialized dictionary.
+
+        Args:
+            state (dict[str, Any]): Previously serialized pipeline 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 = state["run_id"]
+
+        # validate pipeline compatibility
+        self._validate_state_compatibility(state)
+
+        # set the current run_id
+        self._current_run_id = run_id
+
+        # load pipeline state attributes
+        if "is_validated" in state:
+            self.is_validated = state["is_validated"]
+
+        # load store data
+        if "store" in state:
+            self.store.load(run_id, state["store"])
+
+    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)}"
+            )