feat(parallel-exeuction): parallel asyncio event loop dispatcher

examples/openai/search_graph_openai.py

@@ -28,7 +28,7 @@
 # ************************************************
 
 search_graph = SearchGraph(
-    prompt="List me the Chioggia typical dishes",
+    prompt="List me Chioggia's famous dishes",
     config=graph_config
 )
 

scrapegraphai/nodes/graph_iterator_node.py

@@ -2,12 +2,18 @@
 GraphIterator Module
 """
 
-from typing import List, Optional
+import asyncio
 import copy
-from tqdm import tqdm
+from typing import List, Optional
+
+from tqdm.asyncio import tqdm
+
 from .base_node import BaseNode
 
 
+_default_batchsize = 16
+
+
 class GraphIteratorNode(BaseNode):
     """
     A node responsible for instantiating and running multiple graph instances in parallel.
@@ -23,12 +29,20 @@ class GraphIteratorNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "Parse".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None, node_name: str = "GraphIterator"):
+    def __init__(
+        self,
+        input: str,
+        output: List[str],
+        node_config: Optional[dict] = None,
+        node_name: str = "GraphIterator",
+    ):
         super().__init__(node_name, "node", input, output, 2, node_config)
 
-        self.verbose = False if node_config is None else node_config.get("verbose", False)
+        self.verbose = (
+            False if node_config is None else node_config.get("verbose", False)
+        )
 
-    def execute(self,  state: dict) -> dict:
+    def execute(self, state: dict) -> dict:
         """
         Executes the node's logic to instantiate and run multiple graph instances in parallel.
 
@@ -43,37 +57,78 @@ def execute(self,  state: dict) -> dict:
             KeyError: If the input keys are not found in the state, indicating that the
                         necessary information for running the graph instances is missing.
         """
+        batchsize = self.node_config.get("batchsize", _default_batchsize)
 
         if self.verbose:
-            print(f"--- Executing {self.node_name} Node ---")
+            print(f"--- Executing {self.node_name} Node with batchsize {batchsize} ---")
+
+        try:
+            eventloop = asyncio.get_event_loop()
+        except RuntimeError:
+            eventloop = None
+
+        if eventloop and eventloop.is_running():
+            state = eventloop.run_until_complete(self._async_execute(state, batchsize))
+        else:
+            state = asyncio.run(self._async_execute(state, batchsize))
+
+        return state
+
+    async def _async_execute(self, state: dict, batchsize: int) -> dict:
+        """asynchronously executes the node's logic with multiple graph instances
+        running in parallel, using a semaphore of some size for concurrency regulation
+
+        Args:
+            state: The current state of the graph.
+            batchsize: The maximum number of concurrent instances allowed.
+
+        Returns:
+            The updated state with the output key containing the results
+            aggregated out of all parallel graph instances.
 
-        # Interpret input keys based on the provided input expression
+        Raises:
+            KeyError: If the input keys are not found in the state.
+        """
+
+        # interprets input keys based on the provided input expression
         input_keys = self.get_input_keys(state)
 
-        # Fetching data from the state based on the input keys
+        # fetches data from the state based on the input keys
         input_data = [state[key] for key in input_keys]
 
         user_prompt = input_data[0]
         urls = input_data[1]
 
         graph_instance = self.node_config.get("graph_instance", None)
+
         if graph_instance is None:
-            raise ValueError("Graph instance is required for graph iteration.")
-        
-        # set the prompt and source for each url
+            raise ValueError("graph instance is required for concurrent execution")
+
+        # sets the prompt for the graph instance
         graph_instance.prompt = user_prompt
-        graphs_instances = []
+
+        participants = []
+
+        # semaphore to limit the number of concurrent tasks
+        semaphore = asyncio.Semaphore(batchsize)
+
+        async def _async_run(graph):
+            async with semaphore:
+                return await asyncio.to_thread(graph.run)
+
+        # creates a deepcopy of the graph instance for each endpoint
         for url in urls:
-            # make a copy of the graph instance
-            copy_graph_instance = copy.copy(graph_instance)
-            copy_graph_instance.source = url
-            graphs_instances.append(copy_graph_instance)
-
-        # run the graph for each url and use tqdm for progress bar
-        graphs_answers = []
-        for graph in tqdm(graphs_instances, desc="Processing Graph Instances", disable=not self.verbose):
-            result = graph.run()
-            graphs_answers.append(result)
-
-        state.update({self.output[0]: graphs_answers})
+            instance = copy.copy(graph_instance)
+            instance.source = url
+
+            participants.append(instance)
+
+        futures = [_async_run(graph) for graph in participants]
+
+        answers = await tqdm.gather(
+            *futures, desc="processing graph instances", disable=not self.verbose
+        )
+
+        state.update({self.output[0]: answers})
+
         return state