mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
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135 lines
4.3 KiB
Python
135 lines
4.3 KiB
Python
"""
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GraphIterator Module
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"""
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import asyncio
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import copy
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from typing import List, Optional
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from tqdm.asyncio import tqdm
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from .base_node import BaseNode
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_default_batchsize = 16
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class GraphIteratorNode(BaseNode):
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"""
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A node responsible for instantiating and running multiple graph instances in parallel.
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It creates as many graph instances as the number of elements in the input list.
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Attributes:
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verbose (bool): A flag indicating whether to show print statements during execution.
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Args:
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input (str): Boolean expression defining the input keys needed from the state.
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output (List[str]): List of output keys to be updated in the state.
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node_config (dict): Additional configuration for the node.
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node_name (str): The unique identifier name for the node, defaulting to "Parse".
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"""
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def __init__(
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self,
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input: str,
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output: List[str],
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node_config: Optional[dict] = None,
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node_name: str = "GraphIterator",
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):
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super().__init__(node_name, "node", input, output, 2, node_config)
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self.verbose = (
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False if node_config is None else node_config.get("verbose", False)
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)
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def execute(self, state: dict) -> dict:
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"""
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Executes the node's logic to instantiate and run multiple graph instances in parallel.
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Args:
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state (dict): The current state of the graph. The input keys will be used to fetch
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the correct data from the state.
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Returns:
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dict: The updated state with the output key containing the results of the graph instances.
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Raises:
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KeyError: If the input keys are not found in the state, indicating that the
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necessary information for running the graph instances is missing.
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"""
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batchsize = self.node_config.get("batchsize", _default_batchsize)
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if self.verbose:
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print(f"--- Executing {self.node_name} Node with batchsize {batchsize} ---")
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try:
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eventloop = asyncio.get_event_loop()
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except RuntimeError:
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eventloop = None
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if eventloop and eventloop.is_running():
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state = eventloop.run_until_complete(self._async_execute(state, batchsize))
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else:
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state = asyncio.run(self._async_execute(state, batchsize))
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return state
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async def _async_execute(self, state: dict, batchsize: int) -> dict:
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"""asynchronously executes the node's logic with multiple graph instances
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running in parallel, using a semaphore of some size for concurrency regulation
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Args:
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state: The current state of the graph.
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batchsize: The maximum number of concurrent instances allowed.
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Returns:
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The updated state with the output key containing the results
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aggregated out of all parallel graph instances.
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Raises:
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KeyError: If the input keys are not found in the state.
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"""
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# interprets input keys based on the provided input expression
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input_keys = self.get_input_keys(state)
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# fetches data from the state based on the input keys
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input_data = [state[key] for key in input_keys]
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user_prompt = input_data[0]
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urls = input_data[1]
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graph_instance = self.node_config.get("graph_instance", None)
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if graph_instance is None:
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raise ValueError("graph instance is required for concurrent execution")
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# sets the prompt for the graph instance
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graph_instance.prompt = user_prompt
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participants = []
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# semaphore to limit the number of concurrent tasks
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semaphore = asyncio.Semaphore(batchsize)
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async def _async_run(graph):
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async with semaphore:
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return await asyncio.to_thread(graph.run)
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# creates a deepcopy of the graph instance for each endpoint
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for url in urls:
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instance = copy.copy(graph_instance)
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instance.source = url
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participants.append(instance)
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futures = [_async_run(graph) for graph in participants]
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answers = await tqdm.gather(
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*futures, desc="processing graph instances", disable=not self.verbose
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)
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state.update({self.output[0]: answers})
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return state
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