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 +1,107 @@
+"""
+HtmlAnalyzerNode Module
+"""
+
+from typing import List, Optional
+
+from langchain_core.prompts import PromptTemplate
+from langchain_ollama import ChatOllama
+from langchain_core.output_parsers import StrOutputParser
+
+from ..prompts import TEMPLATE_HTML_ANALYSIS, TEMPLATE_HTML_ANALYSIS_WITH_CONTEXT
+from ..utils import reduce_html
+from .base_node import BaseNode
+
+
+class HtmlAnalyzerNode(BaseNode):
+    """
+    A node that generates an analysis of the provided HTML code based on the wanted infromations to be extracted.
+
+    Attributes:
+        llm_model: An instance of a language model client, configured for generating answers.
+        verbose (bool): A flag indicating whether to show print statements during execution.
+
+    Args:
+        input (str): Boolean expression defining the input keys needed from the state.
+        output (List[str]): List of output keys to be updated in the state.
+        node_config (dict): Additional configuration for the node.
+        node_name (str): The unique identifier name for the node, defaulting to "GenerateAnswer".
+    """
+
+    def __init__(
+        self,
+        input: str,
+        output: List[str],
+        node_config: Optional[dict] = None,
+        node_name: str = "HtmlAnalyzer",
+    ):
+        super().__init__(node_name, "node", input, output, 2, node_config)
+
+        self.llm_model = node_config["llm_model"]
+
+        if isinstance(node_config["llm_model"], ChatOllama):
+            self.llm_model.format = "json"
+
+        self.verbose = (
+            True if node_config is None else node_config.get("verbose", False)
+        )
+        self.force = False if node_config is None else node_config.get("force", False)
+        self.script_creator = (
+            False if node_config is None else node_config.get("script_creator", False)
+        )
+        self.is_md_scraper = (
+            False if node_config is None else node_config.get("is_md_scraper", False)
+        )
+
+        self.additional_info = node_config.get("additional_info")
+
+    def execute(self, state: dict) -> dict:
+        """
+        Generates an analysis of the provided HTML code based on the wanted infromations to be extracted.
+
+        Args:
+            state (dict): The current state of the graph. The input keys will be used
+                            to fetch the correct data from the state.
+
+        Returns:
+            dict: The updated state with the output key containing the generated answer.
+
+        Raises:
+            KeyError: If the input keys are not found in the state, indicating
+                      that the necessary information for generating an answer is missing.
+        """
+        self.logger.info(f"--- Executing {self.node_name} Node ---")
+
+        input_keys = self.get_input_keys(state)
+        input_data = [state[key] for key in input_keys]
+        refined_prompt = input_data[0]
+        html = input_data[1]
+        reduced_html = reduce_html(
+            html[0].page_content, self.node_config.get("reduction", 0)
+        )
+
+        if self.additional_info is not None:
+            prompt = PromptTemplate(
+                template=TEMPLATE_HTML_ANALYSIS_WITH_CONTEXT,
+                partial_variables={
+                    "initial_analysis": refined_prompt,
+                    "html_code": reduced_html,
+                    "additional_context": self.additional_info,
+                },
+            )
+        else:
+            prompt = PromptTemplate(
+                template=TEMPLATE_HTML_ANALYSIS,
+                partial_variables={
+                    "initial_analysis": refined_prompt,
+                    "html_code": reduced_html,
+                },
+            )
+
+        output_parser = StrOutputParser()
+
+        chain = prompt | self.llm_model | output_parser
+        html_analysis = chain.invoke({})
+
+        state.update({self.output[0]: html_analysis, self.output[1]: reduced_html})
+        return state