mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
synced 2026-06-15 21:00:30 +08:00
101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
"""
|
|
KnowledgeGraphNode Module
|
|
"""
|
|
|
|
# Imports from standard library
|
|
from typing import List, Optional
|
|
from tqdm import tqdm
|
|
|
|
# Imports from Langchain
|
|
from langchain.prompts import PromptTemplate
|
|
from langchain_core.output_parsers import JsonOutputParser
|
|
|
|
# Imports from the library
|
|
from .base_node import BaseNode
|
|
from ..utils import create_graph, add_customizations, create_interactive_graph
|
|
|
|
class KnowledgeGraphNode(BaseNode):
|
|
"""
|
|
A node responsible for generating a knowledge graph from a dictionary.
|
|
|
|
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 = "KnowledgeGraph"):
|
|
super().__init__(node_name, "node", input, output, 2, node_config)
|
|
|
|
self.llm_model = node_config["llm_model"]
|
|
self.verbose = False if node_config is None else node_config.get(
|
|
"verbose", False)
|
|
|
|
def execute(self, state: dict) -> dict:
|
|
"""
|
|
Executes the node's logic to create a knowledge graph from a dictionary.
|
|
|
|
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.
|
|
"""
|
|
|
|
if self.verbose:
|
|
print(f"--- Executing {self.node_name} Node ---")
|
|
|
|
# Interpret 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
|
|
input_data = [state[key] for key in input_keys]
|
|
|
|
user_prompt = input_data[0]
|
|
answer_dict = input_data[1]
|
|
|
|
# Build the graph
|
|
graph = create_graph(answer_dict)
|
|
# Create the interactive graph
|
|
create_interactive_graph(graph, output_file='knowledge_graph.html')
|
|
|
|
# output_parser = JsonOutputParser()
|
|
# format_instructions = output_parser.get_format_instructions()
|
|
|
|
# template_merge = """
|
|
# You are a website scraper and you have just scraped some content from multiple websites.\n
|
|
# You are now asked to provide an answer to a USER PROMPT based on the content you have scraped.\n
|
|
# You need to merge the content from the different websites into a single answer without repetitions (if there are any). \n
|
|
# The scraped contents are in a JSON format and you need to merge them based on the context and providing a correct JSON structure.\n
|
|
# OUTPUT INSTRUCTIONS: {format_instructions}\n
|
|
# USER PROMPT: {user_prompt}\n
|
|
# WEBSITE CONTENT: {website_content}
|
|
# """
|
|
|
|
# prompt_template = PromptTemplate(
|
|
# template=template_merge,
|
|
# input_variables=["user_prompt"],
|
|
# partial_variables={
|
|
# "format_instructions": format_instructions,
|
|
# "website_content": answers_str,
|
|
# },
|
|
# )
|
|
|
|
# merge_chain = prompt_template | self.llm_model | output_parser
|
|
# answer = merge_chain.invoke({"user_prompt": user_prompt})
|
|
|
|
# Update the state with the generated answer
|
|
state.update({self.output[0]: graph})
|
|
return state
|