chore: import upstream snapshot with attribution

2026-07-13 12:18:10 +08:00
commit fbfefa28d3
384 changed files with 46941 additions and 0 deletions
@@ -0,0 +1,99 @@
+"""
+ScriptCreatorMultiGraph Module
+"""
+
+from copy import deepcopy
+from typing import List, Optional, Type
+
+from pydantic import BaseModel
+
+from ..nodes import GraphIteratorNode, MergeGeneratedScriptsNode
+from ..utils.copy import safe_deepcopy
+from .abstract_graph import AbstractGraph
+from .base_graph import BaseGraph
+from .script_creator_graph import ScriptCreatorGraph
+
+
+class ScriptCreatorMultiGraph(AbstractGraph):
+    """
+    ScriptCreatorMultiGraph is a scraping pipeline that scrapes a list
+    of URLs generating web scraping scripts.
+    It only requires a user prompt and a list of URLs.
+    Attributes:
+        prompt (str): The user prompt to search the internet.
+        llm_model (dict): The configuration for the language model.
+        embedder_model (dict): The configuration for the embedder model.
+        headless (bool): A flag to run the browser in headless mode.
+        verbose (bool): A flag to display the execution information.
+        model_token (int): The token limit for the language model.
+    Args:
+        prompt (str): The user prompt to search the internet.
+        source (List[str]): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (Optional[BaseModel]): The schema for the graph output.
+    Example:
+        >>> script_graph = ScriptCreatorMultiGraph(
+        ...     "What is Chioggia famous for?",
+        ...     source=[],
+        ...     config={"llm": {"model": "openai/gpt-3.5-turbo"}}
+        ...     schema={}
+        ... )
+        >>> result = script_graph.run()
+    """
+
+    def __init__(
+        self,
+        prompt: str,
+        source: List[str],
+        config: dict,
+        schema: Optional[Type[BaseModel]] = None,
+    ):
+        self.copy_config = safe_deepcopy(config)
+        self.copy_schema = deepcopy(schema)
+        super().__init__(prompt, config, source, schema)
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping and searching.
+        Returns:
+            BaseGraph: A graph instance representing the web scraping and searching workflow.
+        """
+
+        graph_iterator_node = GraphIteratorNode(
+            input="user_prompt & urls",
+            output=["scripts"],
+            node_config={
+                "graph_instance": ScriptCreatorGraph,
+                "scraper_config": self.copy_config,
+            },
+            schema=self.copy_schema,
+        )
+
+        merge_scripts_node = MergeGeneratedScriptsNode(
+            input="user_prompt & scripts",
+            output=["merged_script"],
+            node_config={"llm_model": self.llm_model, "schema": self.schema},
+        )
+
+        return BaseGraph(
+            nodes=[
+                graph_iterator_node,
+                merge_scripts_node,
+            ],
+            edges=[
+                (graph_iterator_node, merge_scripts_node),
+            ],
+            entry_point=graph_iterator_node,
+            graph_name=self.__class__.__name__,
+        )
+
+    def run(self) -> str:
+        """
+        Executes the web scraping and searching process.
+        Returns:
+            str: The answer to the prompt.
+        """
+
+        inputs = {"user_prompt": self.prompt, "urls": self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+        return self.final_state.get("merged_script", "Failed to generate the script.")