feat: implement ScrapeGraph class for only web scraping automation

scrapegraphai/graphs/scrape_graph.py

@@ -0,0 +1,98 @@
+"""
+SmartScraperGraph Module
+"""
+from typing import Optional
+from pydantic import BaseModel
+from .base_graph import BaseGraph
+from .abstract_graph import AbstractGraph
+from ..nodes import (
+    FetchNode,
+    ParseNode,
+)
+
+class ScrapeGraph(AbstractGraph):
+    """
+    ScrapeGraph is a scraping pipeline that automates the process of 
+    extracting information from web pages.
+
+    Attributes:
+        prompt (str): The prompt for the graph.
+        source (str): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (BaseModel): The schema for the graph output.
+        verbose (bool): A flag indicating whether to show print statements during execution.
+        headless (bool): A flag indicating whether to run the graph in headless mode.
+
+    Args:
+        prompt (str): The prompt for the graph.
+        source (str): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (BaseModel): The schema for the graph output.
+
+    Example:
+        >>> scraper = ScraperGraph(
+        ...     "https://en.wikipedia.org/wiki/Chioggia",
+        ...     {"llm": {"model": "openai/gpt-3.5-turbo"}}
+        ... )
+        >>> result = smart_scraper.run()
+        )
+    """
+
+    def __init__(self, source: str, config: dict, prompt: str = "", schema: Optional[BaseModel] = None):
+        super().__init__(prompt, config, source, schema)
+
+        self.input_key = "url" if source.startswith("http") else "local_dir"
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping workflow.
+        """
+        fetch_node = FetchNode(
+            input="url| local_dir",
+            output=["doc"],
+            node_config={
+                "llm_model": self.llm_model,
+                "force": self.config.get("force", False),
+                "cut": self.config.get("cut", True),
+                "loader_kwargs": self.config.get("loader_kwargs", {}),
+                "browser_base": self.config.get("browser_base"),
+                "scrape_do": self.config.get("scrape_do")
+            }
+        )
+
+        parse_node = ParseNode(
+            input="doc",
+            output=["parsed_doc"],
+            node_config={
+                "llm_model": self.llm_model,
+                "chunk_size": self.model_token
+            }
+        )
+
+        return BaseGraph(
+            nodes=[
+                fetch_node,
+                parse_node,
+            ],
+            edges=[
+                (fetch_node, parse_node),
+            ],
+            entry_point=fetch_node,
+            graph_name=self.__class__.__name__
+        )
+
+    def run(self) -> str:
+        """
+        Executes the scraping process and returns the scraping content.
+
+        Returns:
+            str: The scraping content.
+        """
+
+        inputs = {"user_prompt": self.prompt, self.input_key: self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("parsed_doc", "No document found.")