mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
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107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
"""
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SearchInternetNode Module
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"""
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from typing import List, Optional
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from langchain_core.output_parsers import CommaSeparatedListOutputParser
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from langchain_core.prompts import PromptTemplate
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from tqdm import tqdm
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from ..prompts import (
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TEMPLATE_SEARCH_WITH_CONTEXT_CHUNKS,
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TEMPLATE_SEARCH_WITH_CONTEXT_NO_CHUNKS,
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)
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from .base_node import BaseNode
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class SearchLinksWithContext(BaseNode):
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"""
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A node that generates a search query based on the user's input and searches the internet
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for relevant information. The node constructs a prompt for the language model, submits it,
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and processes the output to generate a search query. It then uses the search query to find
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relevant information on the internet and updates the state with the generated answer.
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Attributes:
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llm_model: An instance of the language model client used for generating search queries.
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verbose (bool): A flag indicating whether to show print statements during execution.
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Args:
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input (str): Boolean expression defining the input keys needed from the state.
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output (List[str]): List of output keys to be updated in the state.
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node_config (dict): Additional configuration for the node.
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node_name (str): The unique identifier name for the node,
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defaulting to "SearchLinksWithContext".
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"""
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def __init__(
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self,
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input: str,
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output: List[str],
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node_config: Optional[dict] = None,
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node_name: str = "SearchLinksWithContext",
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):
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super().__init__(node_name, "node", input, output, 2, node_config)
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self.llm_model = node_config["llm_model"]
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self.verbose = (
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True if node_config is None else node_config.get("verbose", False)
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)
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def execute(self, state: dict) -> dict:
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"""
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Generates an answer by constructing a prompt from the user's input and the scraped
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content, querying the language model, and parsing its response.
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Args:
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state (dict): The current state of the graph. The input keys will be used
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to fetch the correct data from the state.
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Returns:
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dict: The updated state with the output key containing the generated answer.
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Raises:
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KeyError: If the input keys are not found in the state, indicating
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that the necessary information for generating an answer is missing.
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"""
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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input_keys = self.get_input_keys(state)
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input_data = [state[key] for key in input_keys]
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doc = input_data[1]
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output_parser = CommaSeparatedListOutputParser()
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format_instructions = output_parser.get_format_instructions()
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result = []
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for i, chunk in enumerate(
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tqdm(doc, desc="Processing chunks", disable=not self.verbose)
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):
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if len(doc) == 1:
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prompt = PromptTemplate(
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template=TEMPLATE_SEARCH_WITH_CONTEXT_CHUNKS,
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input_variables=["question"],
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partial_variables={
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"context": chunk.page_content,
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"format_instructions": format_instructions,
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},
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)
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else:
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prompt = PromptTemplate(
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template=TEMPLATE_SEARCH_WITH_CONTEXT_NO_CHUNKS,
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input_variables=["question"],
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partial_variables={
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"context": chunk.page_content,
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"chunk_id": i + 1,
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"format_instructions": format_instructions,
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},
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)
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result.extend(prompt | self.llm_model | output_parser)
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state["urls"] = result
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return state
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