fix: add tests to cycles in Graph and improve error handling (#3628)
* Add cycle detection and handling in graph edge building process - Introduced `cycles` property to detect cycles in the graph. - Modified `_build_edges` and `build_edge` methods to differentiate between `CycleEdge` and `Edge`. - Updated imports and type hints to support new functionality. * Add cycle detection and handling in graph processing - Introduced `is_cyclic` property to check for cycles in the graph. - Added `_snapshot` method for capturing the current state of the graph. - Modified `layered_topological_sort` to handle cyclic graphs by starting from a specified start component. - Updated imports and type hints for better code clarity and functionality. * Refactor tests and components for improved caching and data handling - Updated `test_vector_store_rag.py` to use `set_on_output` with `cache=True` and simplified assertions. - Enhanced `test_memory_chatbot.py` with additional assertions for graph structure and caching. - Simplified `to_data` method in `base.py` to directly return `_data` without JSON serialization. * Add unit tests for detecting cycles in graph - Introduce `test_cycle_in_graph` to verify cyclic behavior in the graph. - Add `test_cycle_in_graph_max_iterations` to ensure max iterations limit is respected. - Implement `Concatenate` component for testing purposes. * Disable output cache in graph tests to allow loops to work * Refactor: Update VertexStates enum values to uppercase and optimize imports in base.py * Refactor type hints and improve error handling in `Vertex` class - Replace `ValueError` with `NoComponentInstance` exception for missing component instances. - Add `target_handle_name` parameter to `_get_result` method for better result retrieval. - Refactor type hints to use `collections.abc` for `AsyncIterator`, `Generator`, and `Iterator`. - Update type hints for `extract_messages_from_artifacts` and `successors_ids` methods to use generic `dict` and `list`.
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
parent
751edcf5dc
commit
bc6e918f49
6 changed files with 222 additions and 55 deletions
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@ -4,24 +4,31 @@ import json
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import uuid
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import warnings
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from collections import defaultdict, deque
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from collections.abc import Generator, Iterable
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from datetime import datetime, timezone
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from functools import partial
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from itertools import chain
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from typing import TYPE_CHECKING, Any, Optional
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from collections.abc import Generator
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from typing import TYPE_CHECKING, Any, Optional, cast
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import nest_asyncio
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from loguru import logger
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from langflow.exceptions.component import ComponentBuildException
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from langflow.graph.edge.base import CycleEdge
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from langflow.graph.edge.base import CycleEdge, Edge
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from langflow.graph.edge.schema import EdgeData
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from langflow.graph.graph.constants import Finish, lazy_load_vertex_dict
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from langflow.graph.graph.runnable_vertices_manager import RunnableVerticesManager
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from langflow.graph.graph.schema import GraphData, GraphDump, StartConfigDict, VertexBuildResult
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from langflow.graph.graph.state_manager import GraphStateManager
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from langflow.graph.graph.state_model import create_state_model_from_graph
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from langflow.graph.graph.utils import find_start_component_id, process_flow, should_continue, sort_up_to_vertex
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from langflow.graph.graph.utils import (
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find_all_cycle_edges,
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find_start_component_id,
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has_cycle,
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process_flow,
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should_continue,
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sort_up_to_vertex,
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)
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from langflow.graph.schema import InterfaceComponentTypes, RunOutputs
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from langflow.graph.vertex.base import Vertex, VertexStates
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from langflow.graph.vertex.schema import NodeData
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@ -279,6 +286,15 @@ class Graph:
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raise ValueError("Max iterations reached")
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def _snapshot(self):
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return {
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"_run_queue": self._run_queue.copy(),
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"_first_layer": self._first_layer.copy(),
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"vertices_layers": copy.deepcopy(self.vertices_layers),
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"vertices_to_run": copy.deepcopy(self.vertices_to_run),
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"run_manager": copy.deepcopy(self.run_manager.to_dict()),
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}
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def __apply_config(self, config: StartConfigDict):
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for vertex in self.vertices:
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if vertex._custom_component is None:
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@ -460,6 +476,23 @@ class Graph:
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raise ValueError("Graph not prepared. Call prepare() first.")
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return self._first_layer
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@property
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def is_cyclic(self):
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"""
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Check if the graph has any cycles.
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Returns:
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bool: True if the graph has any cycles, False otherwise.
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"""
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if self._is_cyclic is None:
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vertices = [vertex.id for vertex in self.vertices]
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try:
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edges = [(e["data"]["sourceHandle"]["id"], e["data"]["targetHandle"]["id"]) for e in self._edges]
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except KeyError:
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edges = [(e["source"], e["target"]) for e in self._edges]
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self._is_cyclic = has_cycle(vertices, edges)
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return self._is_cyclic
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@property
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def run_id(self):
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"""
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@ -1380,7 +1413,7 @@ class Graph:
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def find_next_runnable_vertices(self, vertex_id: str, vertex_successors_ids: list[str]) -> list[str]:
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next_runnable_vertices = set()
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for v_id in vertex_successors_ids:
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for v_id in sorted(vertex_successors_ids):
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if not self.is_vertex_runnable(v_id):
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next_runnable_vertices.update(self.find_runnable_predecessors_for_successor(v_id))
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else:
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@ -1536,21 +1569,31 @@ class Graph:
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neighbors[neighbor] += 1
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return neighbors
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@property
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def cycles(self):
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if self._cycles is None:
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if self._start is None:
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self._cycles = []
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else:
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entry_vertex = self._start._id
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edges = [(e["data"]["sourceHandle"]["id"], e["data"]["targetHandle"]["id"]) for e in self._edges]
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self._cycles = find_all_cycle_edges(entry_vertex, edges)
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return self._cycles
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def _build_edges(self) -> list[CycleEdge]:
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"""Builds the edges of the graph."""
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# Edge takes two vertices as arguments, so we need to build the vertices first
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# and then build the edges
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# if we can't find a vertex, we raise an error
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edges: set[CycleEdge] = set()
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edges: set[CycleEdge | Edge] = set()
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for edge in self._edges:
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new_edge = self.build_edge(edge)
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edges.add(new_edge)
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if self.vertices and not edges:
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warnings.warn("Graph has vertices but no edges")
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return list(edges)
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return list(cast(Iterable[CycleEdge], edges))
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def build_edge(self, edge: EdgeData) -> CycleEdge:
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def build_edge(self, edge: EdgeData) -> CycleEdge | Edge:
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source = self.get_vertex(edge["source"])
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target = self.get_vertex(edge["target"])
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@ -1558,7 +1601,10 @@ class Graph:
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raise ValueError(f"Source vertex {edge['source']} not found")
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if target is None:
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raise ValueError(f"Target vertex {edge['target']} not found")
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new_edge = CycleEdge(source, target, edge)
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if (source.id, target.id) in self.cycles:
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new_edge: CycleEdge | Edge = CycleEdge(source, target, edge)
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else:
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new_edge = Edge(source, target, edge)
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return new_edge
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def _get_vertex_class(self, node_type: str, node_base_type: str, node_id: str) -> type["Vertex"]:
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@ -1608,7 +1654,6 @@ class Graph:
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if stop_component_id and start_component_id:
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raise ValueError("You can only provide one of stop_component_id or start_component_id")
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self.validate_stream()
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self.edges = self._build_edges()
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if stop_component_id or start_component_id:
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try:
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@ -1658,12 +1703,25 @@ class Graph:
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"""Performs a layered topological sort of the vertices in the graph."""
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vertices_ids = {vertex.id for vertex in vertices}
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# Queue for vertices with no incoming edges
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queue = deque(
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vertex.id
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for vertex in vertices
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# if filter_graphs then only vertex.is_input will be considered
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if self.in_degree_map[vertex.id] == 0 and (not filter_graphs or vertex.is_input)
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)
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in_degree_map = self.in_degree_map.copy()
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if self.is_cyclic and all(in_degree_map.values()):
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# This means we have a cycle because all vertex have in_degree_map > 0
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# because of this we set the queue to start on the ._start if it exists
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if self._start is not None:
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queue = deque([self._start._id])
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else:
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# Find the chat input component
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chat_input = find_start_component_id(vertices_ids)
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if chat_input is None:
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raise ValueError("No input component found and no start component provided")
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queue = deque([chat_input])
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else:
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queue = deque(
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vertex.id
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for vertex in vertices
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# if filter_graphs then only vertex.is_input will be considered
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if in_degree_map[vertex.id] == 0 and (not filter_graphs or vertex.is_input)
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)
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layers: list[list[str]] = []
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visited = set(queue)
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@ -1684,13 +1742,13 @@ class Graph:
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if neighbor not in vertices_ids:
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continue
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self.in_degree_map[neighbor] -= 1 # 'remove' edge
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if self.in_degree_map[neighbor] == 0 and neighbor not in visited:
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in_degree_map[neighbor] -= 1 # 'remove' edge
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if in_degree_map[neighbor] == 0 and neighbor not in visited:
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queue.append(neighbor)
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# if > 0 it might mean not all predecessors have added to the queue
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# so we should process the neighbors predecessors
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elif self.in_degree_map[neighbor] > 0:
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elif in_degree_map[neighbor] > 0:
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for predecessor in self.predecessor_map[neighbor]:
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if predecessor not in queue and predecessor not in visited:
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queue.append(predecessor)
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@ -4,10 +4,9 @@ import inspect
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import os
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import traceback
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import types
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import json
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from collections.abc import AsyncIterator, Callable, Iterator, Mapping
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from enum import Enum
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from typing import TYPE_CHECKING, Any, Optional
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from collections.abc import AsyncIterator, Callable, Iterator, Mapping
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import pandas as pd
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from loguru import logger
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@ -37,9 +36,9 @@ if TYPE_CHECKING:
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class VertexStates(str, Enum):
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"""Vertex are related to it being active, inactive, or in an error state."""
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ACTIVE = "active"
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INACTIVE = "inactive"
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ERROR = "error"
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ACTIVE = "ACTIVE"
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INACTIVE = "INACTIVE"
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ERROR = "ERROR"
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class Vertex:
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@ -105,12 +104,7 @@ class Vertex:
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self._custom_component._set_input_value(name, value)
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def to_data(self):
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try:
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data = json.loads(json.dumps(self._data, default=str))
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except TypeError:
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data = self._data
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return data
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return self._data
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def add_component_instance(self, component_instance: "Component"):
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component_instance.set_vertex(self)
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@ -102,8 +102,6 @@ class ComponentVertex(Vertex):
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)
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for edge in self.get_edge_with_target(requester.id):
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# We need to check if the edge is a normal edge
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# or a contract edge
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if edge.is_cycle and edge.target_param:
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return requester.get_value_from_template_dict(edge.target_param)
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111
src/backend/tests/unit/graph/graph/test_cycles.py
Normal file
111
src/backend/tests/unit/graph/graph/test_cycles.py
Normal file
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@ -0,0 +1,111 @@
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import pytest
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from langflow.components.inputs.ChatInput import ChatInput
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from langflow.components.outputs.ChatOutput import ChatOutput
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from langflow.components.outputs.TextOutput import TextOutputComponent
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from langflow.components.prototypes.ConditionalRouter import ConditionalRouterComponent
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from langflow.custom.custom_component.component import Component
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from langflow.graph.graph.base import Graph
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from langflow.io import MessageTextInput, Output
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from langflow.schema.message import Message
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@pytest.fixture
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def client():
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pass
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class Concatenate(Component):
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display_name = "Concatenate"
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description = "Concatenates two strings"
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inputs = [
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MessageTextInput(name="text", display_name="Text", required=True),
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]
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outputs = [
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Output(display_name="Text", name="some_text", method="concatenate"),
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]
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def concatenate(self) -> Message:
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return Message(text=f"{self.text}{self.text}" or "test")
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def test_cycle_in_graph():
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chat_input = ChatInput(_id="chat_input")
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router = ConditionalRouterComponent(_id="router")
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chat_input.set(input_value=router.false_response)
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concat_component = Concatenate(_id="concatenate")
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concat_component.set(text=chat_input.message_response)
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router.set(
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input_text=chat_input.message_response,
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match_text="testtesttesttest",
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operator="equals",
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message=concat_component.concatenate,
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)
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text_output = TextOutputComponent(_id="text_output")
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text_output.set(input_value=router.true_response)
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chat_output = ChatOutput(_id="chat_output")
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chat_output.set(input_value=text_output.text_response)
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graph = Graph(chat_input, chat_output)
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assert graph.is_cyclic is True
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# Run queue should contain chat_input and not router
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assert "chat_input" in graph._run_queue
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assert "router" not in graph._run_queue
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results = []
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max_iterations = 20
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snapshots = [graph._snapshot()]
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for result in graph.start(max_iterations=max_iterations, config={"output": {"cache": False}}):
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snapshots.append(graph._snapshot())
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results.append(result)
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results_ids = [result.vertex.id for result in results if hasattr(result, "vertex")]
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assert results_ids[-2:] == ["text_output", "chat_output"]
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assert len(results_ids) > len(graph.vertices), snapshots
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# Check that chat_output and text_output are the last vertices in the results
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assert results_ids == [
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"chat_input",
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"concatenate",
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"router",
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"chat_input",
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"concatenate",
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"router",
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"chat_input",
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"concatenate",
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"router",
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"chat_input",
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"concatenate",
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"router",
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"text_output",
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"chat_output",
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], f"Results: {results_ids}"
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def test_cycle_in_graph_max_iterations():
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chat_input = ChatInput(_id="chat_input")
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router = ConditionalRouterComponent(_id="router")
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chat_input.set(input_value=router.false_response)
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concat_component = Concatenate(_id="concatenate")
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concat_component.set(text=chat_input.message_response)
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router.set(
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input_text=chat_input.message_response,
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match_text="testtesttesttest",
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operator="equals",
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message=concat_component.concatenate,
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)
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text_output = TextOutputComponent(_id="text_output")
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text_output.set(input_value=router.true_response)
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chat_output = ChatOutput(_id="chat_output")
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chat_output.set(input_value=text_output.text_response)
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graph = Graph(chat_input, chat_output)
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assert graph.is_cyclic is True
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# Run queue should contain chat_input and not router
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assert "chat_input" in graph._run_queue
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assert "router" not in graph._run_queue
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results = []
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with pytest.raises(ValueError, match="Max iterations reached"):
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for result in graph.start(max_iterations=2, config={"output": {"cache": False}}):
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results.append(result)
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@ -30,23 +30,35 @@ AI: """
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openai_component.set(
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input_value=prompt_component.build_prompt, max_tokens=100, temperature=0.1, api_key="test_api_key"
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)
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openai_component.get_output("text_output").value = "Mock response"
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openai_component.set_on_output(name="text_output", value="Mock response", cache=True)
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chat_output = ChatOutput(_id="chat_output")
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chat_output.set(input_value=openai_component.text_response)
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graph = Graph(chat_input, chat_output)
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assert graph.in_degree_map == {"chat_output": 1, "prompt": 2, "openai": 1, "chat_input": 0, "chat_memory": 0}
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return graph
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def test_memory_chatbot(memory_chatbot_graph):
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# Now we run step by step
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expected_order = deque(["chat_input", "chat_memory", "prompt", "openai", "chat_output"])
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assert memory_chatbot_graph.in_degree_map == {
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"chat_output": 1,
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"prompt": 2,
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"openai": 1,
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"chat_input": 0,
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"chat_memory": 0,
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}
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assert memory_chatbot_graph.vertices_layers == [["prompt"], ["openai"], ["chat_output"]]
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assert memory_chatbot_graph.first_layer == ["chat_input", "chat_memory"]
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for step in expected_order:
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result = memory_chatbot_graph.step()
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if isinstance(result, Finish):
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break
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assert step == result.vertex.id
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assert step == result.vertex.id, (memory_chatbot_graph.in_degree_map, memory_chatbot_graph.vertices_layers)
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def test_memory_chatbot_dump_structure(memory_chatbot_graph: Graph):
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@ -1,5 +1,4 @@
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import copy
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from collections import Counter, defaultdict
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from textwrap import dedent
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import pytest
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@ -15,7 +14,6 @@ from langflow.components.prompts.Prompt import PromptComponent
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from langflow.components.vectorstores.AstraDB import AstraVectorStoreComponent
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from langflow.graph.graph.base import Graph
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from langflow.graph.graph.constants import Finish
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from langflow.graph.graph.schema import VertexBuildResult
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from langflow.schema.data import Data
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@ -29,7 +27,7 @@ def ingestion_graph():
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# Ingestion Graph
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file_component = FileComponent(_id="file-123")
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file_component.set(path="test.txt")
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file_component.set_on_output("data", value=Data(text="This is a test file."))
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file_component.set_on_output(name="data", value=Data(text="This is a test file."), cache=True)
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text_splitter = SplitTextComponent(_id="text-splitter-123")
|
||||
text_splitter.set(data_inputs=file_component.load_file)
|
||||
openai_embeddings = OpenAIEmbeddingsComponent(_id="openai-embeddings-123")
|
||||
|
|
@ -43,9 +41,10 @@ def ingestion_graph():
|
|||
api_endpoint="https://astra.example.com",
|
||||
token="token",
|
||||
)
|
||||
vector_store.set_on_output("vector_store", value="mock_vector_store")
|
||||
vector_store.set_on_output("base_retriever", value="mock_retriever")
|
||||
vector_store.set_on_output("search_results", value=[Data(text="This is a test file.")])
|
||||
vector_store.set_on_output(name="vector_store", value="mock_vector_store", cache=True)
|
||||
vector_store.set_on_output(name="base_retriever", value="mock_retriever", cache=True)
|
||||
vector_store.set_on_output(name="search_results", value=[Data(text="This is a test file.")], cache=True)
|
||||
|
||||
ingestion_graph = Graph(file_component, vector_store)
|
||||
return ingestion_graph
|
||||
|
||||
|
|
@ -65,14 +64,15 @@ def rag_graph():
|
|||
)
|
||||
# Mock search_documents
|
||||
rag_vector_store.set_on_output(
|
||||
"search_results",
|
||||
name="search_results",
|
||||
value=[
|
||||
Data(data={"text": "Hello, world!"}),
|
||||
Data(data={"text": "Goodbye, world!"}),
|
||||
],
|
||||
cache=True,
|
||||
)
|
||||
rag_vector_store.set_on_output("base_retriever", value="mock_retriever")
|
||||
rag_vector_store.set_on_output("vector_store", value="mock_vector_store")
|
||||
rag_vector_store.set_on_output(name="vector_store", value="mock_vector_store", cache=True)
|
||||
rag_vector_store.set_on_output(name="base_retriever", value="mock_retriever", cache=True)
|
||||
parse_data = ParseDataComponent(_id="parse-data-123")
|
||||
parse_data.set(data=rag_vector_store.search_documents)
|
||||
prompt_component = PromptComponent(_id="prompt-123")
|
||||
|
|
@ -88,7 +88,7 @@ def rag_graph():
|
|||
|
||||
openai_component = OpenAIModelComponent(_id="openai-123")
|
||||
openai_component.set(api_key="sk-123", openai_api_base="https://api.openai.com/v1")
|
||||
openai_component.set_on_output("text_output", value="Hello, world!")
|
||||
openai_component.set_on_output(name="text_output", value="Hello, world!", cache=True)
|
||||
openai_component.set(input_value=prompt_component.build_prompt)
|
||||
|
||||
chat_output = ChatOutput(_id="chatoutput-123")
|
||||
|
|
@ -98,7 +98,7 @@ def rag_graph():
|
|||
return graph
|
||||
|
||||
|
||||
def test_vector_store_rag(ingestion_graph: Graph, rag_graph: Graph):
|
||||
def test_vector_store_rag(ingestion_graph, rag_graph):
|
||||
assert ingestion_graph is not None
|
||||
ingestion_ids = [
|
||||
"file-123",
|
||||
|
|
@ -117,17 +117,11 @@ def test_vector_store_rag(ingestion_graph: Graph, rag_graph: Graph):
|
|||
"openai-embeddings-124",
|
||||
]
|
||||
for ids, graph, len_results in zip([ingestion_ids, rag_ids], [ingestion_graph, rag_graph], [5, 8]):
|
||||
results: list[VertexBuildResult] = []
|
||||
ids_count = Counter(ids)
|
||||
results_id_count: dict[str, int] = defaultdict(int)
|
||||
for result in graph.start(config={"output": {"cache": True}}):
|
||||
results = []
|
||||
for result in graph.start():
|
||||
results.append(result)
|
||||
if hasattr(result, "vertex"):
|
||||
results_id_count[result.vertex.id] += 1
|
||||
|
||||
assert (
|
||||
len(results) == len_results
|
||||
), f"Counts: {ids_count} != {results_id_count}, Diff: {set(ids_count.keys()) - set(results_id_count.keys())}"
|
||||
assert len(results) == len_results
|
||||
vids = [result.vertex.id for result in results if hasattr(result, "vertex")]
|
||||
assert all(vid in ids for vid in vids), f"Diff: {set(vids) - set(ids)}"
|
||||
assert results[-1] == Finish()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue