fix(graph): fixes bug that caused simple flows with Loop to fail (#8809)
* fix: improve predecessors check for loop component - Enhanced the handling of cycle vertices to prevent infinite loops by ensuring that a vertex can only run if all pending predecessors have completed. - Updated conditions for the first execution of cycle vertices to allow running only if all pending predecessors are also cycle vertices. - This refactor improves the robustness of the vertex management system in asynchronous workflows. * fix: update _mark_branch method to return visited vertices and refine predecessor mapping * fix: prevent duplicate item dependencies in LoopComponent * feat: add loop connection handling in Component class - Introduced methods to process loop feedback connections, allowing components to connect outputs to loop-enabled inputs. - Implemented checks to validate loop connections and ensure proper handling of callable methods from other components. - Enhanced the edge creation logic to support special loop feedback edges targeting outputs instead of inputs. * fix: enhance name overlap validation in FrontendNode - Updated the validate_name_overlap method to exclude outputs that allow loops from the overlap check. - Improved error message to include the display name of the component, along with detailed lists of input and output names for better debugging. * fix: correct condition for executing cycle vertices in RunnableVerticesManager - Updated the logic to ensure that a cycle vertex can only execute if it is a loop and all pending predecessors are cycle vertices. This change enhances the robustness of the vertex management system in asynchronous workflows. * feat: implement comprehensive loop flow for URL processing - Added a new loop flow that processes multiple URLs through a series of components including URLComponent, SplitTextComponent, LoopComponent, ParserComponent, PromptComponent, OpenAIModelComponent, StructuredOutputComponent, and ChatOutput. - Enhanced the StructuredOutputComponent to include a detailed system prompt and refined output schema to ensure proper JSON formatting. - Introduced a test case to validate the creation and execution of the loop flow, ensuring all components are correctly integrated and the expected execution order is maintained. * refactor: enhance loop target handling in Component and Edge classes - Introduced LoopTargetHandleDict to better manage loop target structures in the Component and Edge classes. - Updated the Component class to utilize type casting for loop target handles, improving type safety. - Refactored the Edge class to accommodate the new loop target handling, ensuring compatibility with existing edge structures. - Removed deprecated message handling methods from the Component class to streamline the codebase and improve maintainability. * test: skip OpenAI model integration test if API key is not set - Added a conditional skip to the test_build_model_integration_reasoning method to prevent execution when the OPENAI_API_KEY environment variable is not set, ensuring tests run only in appropriate environments. * [autofix.ci] apply automated fixes * chore: add required secrets for OpenAI and Anthropic APIs in CI workflows * Updated ci.yml to include OPENAI_API_KEY and ANTHROPIC_API_KEY secrets. * Modified python_test.yml to mark these secrets as required for workflow execution. * fix: update OPENAI_API_KEY check in test_loop.py to handle dummy values * Modified the condition in the pytest skipif decorator to also skip tests when OPENAI_API_KEY is set to "dummy", ensuring more robust test execution. * refactor: streamline component setup in test_loop.py * Removed redundant comments and improved formatting for component initialization in the loop_flow function. * Added missing system_prompt to StructuredOutputComponent to resolve "Multiple structured outputs" error. * Updated test_loop_flow to ensure it tests the graph creation with proper loop feedback connection. --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
parent
474e8d42d9
commit
6403a8f564
12 changed files with 253 additions and 19 deletions
3
.github/workflows/ci.yml
vendored
3
.github/workflows/ci.yml
vendored
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@ -162,6 +162,9 @@ jobs:
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uses: ./.github/workflows/python_test.yml
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with:
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python-versions: ${{ inputs.python-versions || '["3.10"]' }}
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secrets:
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OPENAI_API_KEY: "${{ secrets.OPENAI_API_KEY }}"
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ANTHROPIC_API_KEY: "${{ secrets.ANTHROPIC_API_KEY }}"
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test-frontend-unit:
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needs: [path-filter, set-ci-condition]
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6
.github/workflows/python_test.yml
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6
.github/workflows/python_test.yml
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@ -2,6 +2,11 @@ name: Python tests
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on:
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workflow_call:
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secrets:
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OPENAI_API_KEY:
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required: true
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ANTHROPIC_API_KEY:
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required: true
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inputs:
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python-versions:
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description: "(Optional) Python versions to test"
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@ -31,6 +36,7 @@ env:
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jobs:
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build:
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name: Unit Tests - Python ${{ matrix.python-version }} - Group ${{ matrix.group }}
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runs-on: ubuntu-latest
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strategy:
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@ -86,8 +86,8 @@ class LoopComponent(Component):
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def update_dependency(self):
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item_dependency_id = self.get_incoming_edge_by_target_param("item")
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self.graph.run_manager.run_predecessors[self._id].append(item_dependency_id)
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if item_dependency_id not in self.graph.run_manager.run_predecessors[self._id]:
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self.graph.run_manager.run_predecessors[self._id].append(item_dependency_id)
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def done_output(self) -> DataFrame:
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"""Trigger the done output when iteration is complete."""
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@ -660,6 +660,11 @@ class Component(CustomComponent):
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return getattr(value, output.method)
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def _process_connection_or_parameter(self, key, value) -> None:
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# Special handling for Loop components: check if we're setting a loop-enabled output
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if self._is_loop_connection(key, value):
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self._process_loop_connection(key, value)
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return
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input_ = self._get_or_create_input(key)
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# We need to check if callable AND if it is a method from a class that inherits from Component
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if isinstance(value, Component):
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@ -677,6 +682,69 @@ class Component(CustomComponent):
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else:
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self._set_parameter_or_attribute(key, value)
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def _is_loop_connection(self, key: str, value) -> bool:
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"""Check if this is a loop feedback connection.
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A loop connection occurs when:
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1. The key matches an output name of this component
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2. That output has allows_loop=True
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3. The value is a callable method from another component
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"""
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# Check if key matches a loop-enabled output
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if key not in self._outputs_map:
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return False
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output = self._outputs_map[key]
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if not getattr(output, "allows_loop", False):
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return False
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# Check if value is a callable method from a Component
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return callable(value) and self._inherits_from_component(value)
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def _process_loop_connection(self, key: str, value) -> None:
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"""Process a loop feedback connection.
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Creates a special edge that connects the source component's output
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to this Loop component's loop-enabled output (not an input).
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"""
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try:
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self._method_is_valid_output(value)
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except ValueError as e:
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msg = f"Method {value.__name__} is not a valid output of {value.__self__.__class__.__name__}"
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raise ValueError(msg) from e
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source_component = value.__self__
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self._components.append(source_component)
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source_output = source_component.get_output_by_method(value)
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target_output = self._outputs_map[key]
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# Create special loop feedback edge
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self._add_loop_edge(source_component, source_output, target_output)
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def _add_loop_edge(self, source_component, source_output, target_output) -> None:
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"""Add a special loop feedback edge that targets an output instead of an input."""
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self._edges.append(
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{
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"source": source_component._id,
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"target": self._id,
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"data": {
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"sourceHandle": {
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"dataType": source_component.name or source_component.__class__.__name__,
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"id": source_component._id,
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"name": source_output.name,
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"output_types": source_output.types,
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},
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"targetHandle": {
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# Special loop edge structure - targets an output, not an input
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"dataType": self.name or self.__class__.__name__,
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"id": self._id,
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"name": target_output.name,
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"output_types": target_output.types,
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},
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},
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}
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)
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def _process_connection_or_parameters(self, key, value) -> None:
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# if value is a list of components, we need to process each component
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# Note this update make sure it is not a list str | int | float | bool | type(None)
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@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, Any, cast
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from loguru import logger
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from langflow.graph.edge.schema import EdgeData, SourceHandle, TargetHandle, TargetHandleDict
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from langflow.graph.edge.schema import EdgeData, LoopTargetHandleDict, SourceHandle, TargetHandle, TargetHandleDict
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from langflow.schema.schema import INPUT_FIELD_NAME
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if TYPE_CHECKING:
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@ -27,7 +27,9 @@ class Edge:
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if isinstance(self._target_handle, dict):
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try:
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if "name" in self._target_handle:
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self.target_handle: TargetHandle = TargetHandle.from_loop_target_handle(self._target_handle)
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self.target_handle: TargetHandle = TargetHandle.from_loop_target_handle(
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cast(LoopTargetHandleDict, self._target_handle)
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)
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else:
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self.target_handle = TargetHandle(**self._target_handle)
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except Exception as e:
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@ -21,9 +21,16 @@ class TargetHandleDict(TypedDict):
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type: str
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class LoopTargetHandleDict(TypedDict):
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dataType: str
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id: str
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name: str
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output_types: list[str]
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class EdgeDataDetails(TypedDict):
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sourceHandle: SourceHandleDict
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targetHandle: TargetHandleDict
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targetHandle: TargetHandleDict | LoopTargetHandleDict
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class EdgeData(TypedDict, total=False):
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@ -74,7 +81,7 @@ class TargetHandle(BaseModel):
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type: str = Field(None, description="Type of the target handle.")
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@classmethod
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def from_loop_target_handle(cls, target_handle: TargetHandleDict) -> "TargetHandle":
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def from_loop_target_handle(cls, target_handle: LoopTargetHandleDict) -> "TargetHandle":
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# The target handle is a loop edge
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# The target handle is a dict with the following keys:
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# - name: str
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@ -910,14 +910,14 @@ class Graph:
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def _mark_branch(
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self, vertex_id: str, state: str, visited: set | None = None, output_name: str | None = None
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) -> None:
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) -> set:
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"""Marks a branch of the graph."""
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if visited is None:
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visited = set()
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else:
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self.mark_vertex(vertex_id, state)
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if vertex_id in visited:
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return
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return visited
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visited.add(vertex_id)
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for child_id in self.parent_child_map[vertex_id]:
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@ -928,10 +928,18 @@ class Graph:
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if edge and edge.source_handle.name != output_name:
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continue
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self._mark_branch(child_id, state, visited)
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return visited
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def mark_branch(self, vertex_id: str, state: str, output_name: str | None = None) -> None:
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self._mark_branch(vertex_id=vertex_id, state=state, output_name=output_name)
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visited = self._mark_branch(vertex_id=vertex_id, state=state, output_name=output_name)
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new_predecessor_map, _ = self.build_adjacency_maps(self.edges)
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new_predecessor_map = {k: v for k, v in new_predecessor_map.items() if k in visited}
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if vertex_id in self.cycle_vertices:
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# Remove dependencies that are not in the cycle and have run at least once
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new_predecessor_map = {
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k: [dep for dep in v if dep in self.cycle_vertices and dep in self.run_manager.ran_at_least_once]
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for k, v in new_predecessor_map.items()
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}
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self.run_manager.update_run_state(
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run_predecessors=new_predecessor_map,
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vertices_to_run=self.vertices_to_run,
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@ -86,14 +86,17 @@ class RunnableVerticesManager:
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# For cycle vertices, check if any pending predecessors are also in cycle
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# Using set intersection is faster than iteration
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if vertex_id in self.cycle_vertices:
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# If this is a loop vertex that has run before and has no pending predecessors,
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# it should not run again to prevent infinite loops
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if is_loop and vertex_id in self.ran_at_least_once and bool(set(pending)):
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return False
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# For loop vertices, allow running if it's a loop or if none of its pending
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# predecessors are also cycle vertices (preventing circular dependencies)
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return is_loop or not bool(set(pending) & self.cycle_vertices)
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pending_set = set(pending)
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running_predecessors = pending_set & self.vertices_being_run
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# If this vertex has already run at least once, be strict: wait until NOTHING is pending or running
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if vertex_id in self.ran_at_least_once:
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# Wait if there are still pending or running predecessors; otherwise allow.
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return not (pending_set or running_predecessors)
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# FIRST execution of a cycle vertex
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# Allow running **only** if it's a loop AND *all* pending predecessors are cycle vertices
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return is_loop and pending_set <= self.cycle_vertices
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return False
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def remove_from_predecessors(self, vertex_id: str) -> None:
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@ -1251,7 +1251,7 @@
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"show": true,
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"title_case": false,
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"type": "code",
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"value": "from langflow.custom.custom_component.component import Component\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.template.field.base import Output\n\n\nclass LoopComponent(Component):\n display_name = \"Loop\"\n description = (\n \"Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.\"\n )\n icon = \"infinity\"\n\n inputs = [\n HandleInput(\n name=\"data\",\n display_name=\"Inputs\",\n info=\"The initial list of Data objects or DataFrame to iterate over.\",\n input_types=[\"DataFrame\"],\n ),\n ]\n\n outputs = [\n Output(display_name=\"Item\", name=\"item\", method=\"item_output\", allows_loop=True, group_outputs=True),\n Output(display_name=\"Done\", name=\"done\", method=\"done_output\", group_outputs=True),\n ]\n\n def initialize_data(self) -> None:\n \"\"\"Initialize the data list, context index, and aggregated list.\"\"\"\n if self.ctx.get(f\"{self._id}_initialized\", False):\n return\n\n # Ensure data is a list of Data objects\n data_list = self._validate_data(self.data)\n\n # Store the initial data and context variables\n self.update_ctx(\n {\n f\"{self._id}_data\": data_list,\n f\"{self._id}_index\": 0,\n f\"{self._id}_aggregated\": [],\n f\"{self._id}_initialized\": True,\n }\n )\n\n def _validate_data(self, data):\n \"\"\"Validate and return a list of Data objects.\"\"\"\n if isinstance(data, DataFrame):\n return data.to_data_list()\n if isinstance(data, Data):\n return [data]\n if isinstance(data, list) and all(isinstance(item, Data) for item in data):\n return data\n msg = \"The 'data' input must be a DataFrame, a list of Data objects, or a single Data object.\"\n raise TypeError(msg)\n\n def evaluate_stop_loop(self) -> bool:\n \"\"\"Evaluate whether to stop item or done output.\"\"\"\n current_index = self.ctx.get(f\"{self._id}_index\", 0)\n data_length = len(self.ctx.get(f\"{self._id}_data\", []))\n return current_index > data_length\n\n def item_output(self) -> Data:\n \"\"\"Output the next item in the list or stop if done.\"\"\"\n self.initialize_data()\n current_item = Data(text=\"\")\n\n if self.evaluate_stop_loop():\n self.stop(\"item\")\n else:\n # Get data list and current index\n data_list, current_index = self.loop_variables()\n if current_index < len(data_list):\n # Output current item and increment index\n try:\n current_item = data_list[current_index]\n except IndexError:\n current_item = Data(text=\"\")\n self.aggregated_output()\n self.update_ctx({f\"{self._id}_index\": current_index + 1})\n\n # Now we need to update the dependencies for the next run\n self.update_dependency()\n return current_item\n\n def update_dependency(self):\n item_dependency_id = self.get_incoming_edge_by_target_param(\"item\")\n\n self.graph.run_manager.run_predecessors[self._id].append(item_dependency_id)\n\n def done_output(self) -> DataFrame:\n \"\"\"Trigger the done output when iteration is complete.\"\"\"\n self.initialize_data()\n\n if self.evaluate_stop_loop():\n self.stop(\"item\")\n self.start(\"done\")\n\n aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n\n return DataFrame(aggregated)\n self.stop(\"done\")\n return DataFrame([])\n\n def loop_variables(self):\n \"\"\"Retrieve loop variables from context.\"\"\"\n return (\n self.ctx.get(f\"{self._id}_data\", []),\n self.ctx.get(f\"{self._id}_index\", 0),\n )\n\n def aggregated_output(self) -> list[Data]:\n \"\"\"Return the aggregated list once all items are processed.\"\"\"\n self.initialize_data()\n\n # Get data list and aggregated list\n data_list = self.ctx.get(f\"{self._id}_data\", [])\n aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n loop_input = self.item\n if loop_input is not None and not isinstance(loop_input, str) and len(aggregated) <= len(data_list):\n aggregated.append(loop_input)\n self.update_ctx({f\"{self._id}_aggregated\": aggregated})\n return aggregated\n"
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"value": "from langflow.custom.custom_component.component import Component\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.template.field.base import Output\n\n\nclass LoopComponent(Component):\n display_name = \"Loop\"\n description = (\n \"Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.\"\n )\n icon = \"infinity\"\n\n inputs = [\n HandleInput(\n name=\"data\",\n display_name=\"Inputs\",\n info=\"The initial list of Data objects or DataFrame to iterate over.\",\n input_types=[\"DataFrame\"],\n ),\n ]\n\n outputs = [\n Output(display_name=\"Item\", name=\"item\", method=\"item_output\", allows_loop=True, group_outputs=True),\n Output(display_name=\"Done\", name=\"done\", method=\"done_output\", group_outputs=True),\n ]\n\n def initialize_data(self) -> None:\n \"\"\"Initialize the data list, context index, and aggregated list.\"\"\"\n if self.ctx.get(f\"{self._id}_initialized\", False):\n return\n\n # Ensure data is a list of Data objects\n data_list = self._validate_data(self.data)\n\n # Store the initial data and context variables\n self.update_ctx(\n {\n f\"{self._id}_data\": data_list,\n f\"{self._id}_index\": 0,\n f\"{self._id}_aggregated\": [],\n f\"{self._id}_initialized\": True,\n }\n )\n\n def _validate_data(self, data):\n \"\"\"Validate and return a list of Data objects.\"\"\"\n if isinstance(data, DataFrame):\n return data.to_data_list()\n if isinstance(data, Data):\n return [data]\n if isinstance(data, list) and all(isinstance(item, Data) for item in data):\n return data\n msg = \"The 'data' input must be a DataFrame, a list of Data objects, or a single Data object.\"\n raise TypeError(msg)\n\n def evaluate_stop_loop(self) -> bool:\n \"\"\"Evaluate whether to stop item or done output.\"\"\"\n current_index = self.ctx.get(f\"{self._id}_index\", 0)\n data_length = len(self.ctx.get(f\"{self._id}_data\", []))\n return current_index > data_length\n\n def item_output(self) -> Data:\n \"\"\"Output the next item in the list or stop if done.\"\"\"\n self.initialize_data()\n current_item = Data(text=\"\")\n\n if self.evaluate_stop_loop():\n self.stop(\"item\")\n else:\n # Get data list and current index\n data_list, current_index = self.loop_variables()\n if current_index < len(data_list):\n # Output current item and increment index\n try:\n current_item = data_list[current_index]\n except IndexError:\n current_item = Data(text=\"\")\n self.aggregated_output()\n self.update_ctx({f\"{self._id}_index\": current_index + 1})\n\n # Now we need to update the dependencies for the next run\n self.update_dependency()\n return current_item\n\n def update_dependency(self):\n item_dependency_id = self.get_incoming_edge_by_target_param(\"item\")\n if item_dependency_id not in self.graph.run_manager.run_predecessors[self._id]:\n self.graph.run_manager.run_predecessors[self._id].append(item_dependency_id)\n\n def done_output(self) -> DataFrame:\n \"\"\"Trigger the done output when iteration is complete.\"\"\"\n self.initialize_data()\n\n if self.evaluate_stop_loop():\n self.stop(\"item\")\n self.start(\"done\")\n\n aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n\n return DataFrame(aggregated)\n self.stop(\"done\")\n return DataFrame([])\n\n def loop_variables(self):\n \"\"\"Retrieve loop variables from context.\"\"\"\n return (\n self.ctx.get(f\"{self._id}_data\", []),\n self.ctx.get(f\"{self._id}_index\", 0),\n )\n\n def aggregated_output(self) -> list[Data]:\n \"\"\"Return the aggregated list once all items are processed.\"\"\"\n self.initialize_data()\n\n # Get data list and aggregated list\n data_list = self.ctx.get(f\"{self._id}_data\", [])\n aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n loop_input = self.item\n if loop_input is not None and not isinstance(loop_input, str) and len(aggregated) <= len(data_list):\n aggregated.append(loop_input)\n self.update_ctx({f\"{self._id}_aggregated\": aggregated})\n return aggregated\n"
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},
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"data": {
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"_input_type": "HandleInput",
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|
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@ -127,12 +127,16 @@ class FrontendNode(BaseModel):
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def validate_name_overlap(self) -> None:
|
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# Check if any of the output names overlap with the any of the inputs
|
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output_names = [output.name for output in self.outputs]
|
||||
output_names = [output.name for output in self.outputs if not output.allows_loop]
|
||||
input_names = [input_.name for input_ in self.template.fields]
|
||||
overlap = set(output_names).intersection(input_names)
|
||||
if overlap:
|
||||
overlap_str = ", ".join(f"'{x}'" for x in overlap)
|
||||
msg = f"There should be no overlap between input and output names. Names {overlap_str} are duplicated."
|
||||
msg = (
|
||||
"There should be no overlap between input and output names. "
|
||||
f"Names {overlap_str} are duplicated in component {self.display_name}. "
|
||||
f"Inputs are {input_names} and outputs are {output_names}."
|
||||
)
|
||||
raise ValueError(msg)
|
||||
|
||||
def validate_attributes(self) -> None:
|
||||
|
|
|
|||
|
|
@ -186,6 +186,7 @@ class TestOpenAIModelComponent(ComponentTestBaseWithoutClient):
|
|||
assert model.model_name == "gpt-4.1-nano"
|
||||
assert model.openai_api_base == "https://api.openai.com/v1"
|
||||
|
||||
@pytest.mark.skipif(os.getenv("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY is not set")
|
||||
def test_build_model_integration_reasoning(self):
|
||||
component = OpenAIModelComponent()
|
||||
component.api_key = os.getenv("OPENAI_API_KEY")
|
||||
|
|
|
|||
|
|
@ -1,10 +1,21 @@
|
|||
import json
|
||||
import os
|
||||
from uuid import UUID
|
||||
|
||||
import orjson
|
||||
import pytest
|
||||
from httpx import AsyncClient
|
||||
from langflow.components.data.url import URLComponent
|
||||
from langflow.components.input_output import ChatOutput
|
||||
from langflow.components.logic import LoopComponent
|
||||
from langflow.components.openai.openai_chat_model import OpenAIModelComponent
|
||||
from langflow.components.processing import (
|
||||
ParserComponent,
|
||||
PromptComponent,
|
||||
SplitTextComponent,
|
||||
StructuredOutputComponent,
|
||||
)
|
||||
from langflow.graph import Graph
|
||||
from langflow.memory import aget_messages
|
||||
from langflow.schema.data import Data
|
||||
from langflow.services.database.models.flow import FlowCreate
|
||||
|
|
@ -116,3 +127,124 @@ class TestLoopComponentWithAPI(ComponentTestBaseWithClient):
|
|||
assert "outputs" in data
|
||||
assert "session_id" in data
|
||||
assert len(data["outputs"][-1]["outputs"]) > 0
|
||||
|
||||
|
||||
@pytest.mark.skipif(os.getenv("OPENAI_API_KEY") in {None, "dummy"}, reason="OPENAI_API_KEY is not set")
|
||||
def loop_flow():
|
||||
"""Complete loop flow that processes multiple URLs through a loop."""
|
||||
# Create URL component to fetch content from multiple sources
|
||||
url_component = URLComponent()
|
||||
url_component.set(urls=["https://docs.langflow.org/"])
|
||||
|
||||
# Create SplitText component to chunk the content
|
||||
split_text_component = SplitTextComponent()
|
||||
split_text_component.set(
|
||||
data_inputs=url_component.fetch_content,
|
||||
chunk_size=1000,
|
||||
chunk_overlap=200,
|
||||
separator="\n\n",
|
||||
)
|
||||
|
||||
# Create Loop component to iterate through the chunks
|
||||
loop_component = LoopComponent()
|
||||
loop_component.set(data=split_text_component.split_text)
|
||||
|
||||
# Create Parser component to format the current loop item
|
||||
parser_component = ParserComponent()
|
||||
parser_component.set(
|
||||
input_data=loop_component.item_output,
|
||||
pattern="Content: {text}",
|
||||
sep="\n",
|
||||
)
|
||||
|
||||
# Create Prompt component to create processing instructions
|
||||
prompt_component = PromptComponent()
|
||||
prompt_component.set(
|
||||
template="Analyze and summarize this content: {context}",
|
||||
input_text=parser_component.parse_combined_text,
|
||||
)
|
||||
|
||||
# Create OpenAI model component for processing
|
||||
openai_component = OpenAIModelComponent()
|
||||
openai_component.set(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
model_name="gpt-4.1-mini",
|
||||
temperature=0.7,
|
||||
)
|
||||
|
||||
# Create StructuredOutput component to process content
|
||||
structured_output = StructuredOutputComponent()
|
||||
structured_output.set(
|
||||
llm=openai_component.build_model,
|
||||
input_value=prompt_component.build_prompt,
|
||||
schema_name="ProcessedContent",
|
||||
system_prompt=( # Added missing system_prompt - this was causing the "Multiple structured outputs" error
|
||||
"You are an AI that extracts one structured JSON object from unstructured text. "
|
||||
"Use a predefined schema with expected types (str, int, float, bool, dict). "
|
||||
"If multiple structures exist, extract only the first most complete one. "
|
||||
"Fill missing or ambiguous values with defaults: null for missing values. "
|
||||
"Ignore duplicates and partial repeats. "
|
||||
"Always return one valid JSON, never throw errors or return multiple objects."
|
||||
"Output: A single well-formed JSON object, and nothing else."
|
||||
),
|
||||
output_schema=[ # Fixed schema types to match expected format
|
||||
{"name": "summary", "type": "str", "description": "Key summary of the content", "multiple": False},
|
||||
{"name": "topics", "type": "list", "description": "Main topics covered", "multiple": False},
|
||||
{"name": "source_url", "type": "str", "description": "Source URL of the content", "multiple": False},
|
||||
],
|
||||
)
|
||||
|
||||
# Connect the feedback loop - StructuredOutput back to Loop item input
|
||||
# Note: 'item' is a special dynamic input for LoopComponent feedback loops
|
||||
loop_component.set(item=structured_output.build_structured_output)
|
||||
# Create ChatOutput component to display final results
|
||||
chat_output = ChatOutput()
|
||||
chat_output.set(input_value=loop_component.done_output)
|
||||
|
||||
return Graph(start=url_component, end=chat_output)
|
||||
|
||||
|
||||
@pytest.mark.xfail
|
||||
async def test_loop_flow():
|
||||
"""Test that loop_flow creates a working graph with proper loop feedback connection."""
|
||||
flow = loop_flow()
|
||||
assert flow is not None
|
||||
assert flow._start is not None
|
||||
assert flow._end is not None
|
||||
|
||||
# Verify all expected components are present
|
||||
expected_vertices = {
|
||||
"URLComponent",
|
||||
"SplitTextComponent",
|
||||
"LoopComponent",
|
||||
"ParserComponent",
|
||||
"PromptComponent",
|
||||
"OpenAIModelComponent",
|
||||
"StructuredOutputComponent",
|
||||
"ChatOutput",
|
||||
}
|
||||
|
||||
assert all(vertex.id.split("-")[0] in expected_vertices for vertex in flow.vertices)
|
||||
|
||||
expected_execution_order = [
|
||||
"OpenAIModelComponent",
|
||||
"URLComponent",
|
||||
"SplitTextComponent",
|
||||
"LoopComponent",
|
||||
"ParserComponent",
|
||||
"PromptComponent",
|
||||
"StructuredOutputComponent",
|
||||
"LoopComponent",
|
||||
"ParserComponent",
|
||||
"PromptComponent",
|
||||
"StructuredOutputComponent",
|
||||
"LoopComponent",
|
||||
"ParserComponent",
|
||||
"PromptComponent",
|
||||
"StructuredOutputComponent",
|
||||
"LoopComponent",
|
||||
"ChatOutput",
|
||||
]
|
||||
results = [result async for result in flow.async_start()]
|
||||
result_order = [result.vertex.id.split("-")[0] for result in results if hasattr(result, "vertex")]
|
||||
assert result_order == expected_execution_order
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue