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examples/integrations/indexify_node_example.py
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examples/integrations/indexify_node_example.py
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"""
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Basic example of scraping pipeline using SmartScraper with schema
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"""
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import os, json
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from typing import List
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from dotenv import load_dotenv
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load_dotenv()
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from pydantic import BaseModel, Field
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from scrapegraphai.graphs import SmartScraperGraph
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from scrapegraphai.integrations import IndexifyNode
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# ************************************************
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# Define the output schema for the graph
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# ************************************************
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class Image(BaseModel):
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url: str = Field(description="The url of the image")
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class Images(BaseModel):
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images: List[Image]
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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openai_key = os.getenv("OPENAI_APIKEY")
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graph_config = {
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"llm": {
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"api_key":openai_key,
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"model": "gpt-3.5-turbo",
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},
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"verbose": True,
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"headless": False,
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}
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# ************************************************
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# Define the custom nodes for the graph
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# ************************************************
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indexify_node = IndexifyNode(
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input="answer & img_urls",
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output=["is_indexed"],
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node_config={
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"verbose": True
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}
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)
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# ************************************************
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# Create the SmartScraperGraph instance
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# ************************************************
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smart_scraper_graph = SmartScraperGraph(
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prompt="List me all the images with their url",
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source="https://giphy.com/",
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schema=Images,
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config=graph_config
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)
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# Add the custom node to the graph
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smart_scraper_graph.append_node(indexify_node)
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# ************************************************
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# Run the SmartScraperGraph
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# ************************************************
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result = smart_scraper_graph.run()
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print(json.dumps(result, indent=2))
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Init file for integrations module
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Init file for integrations module
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"""
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"""
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from .burr_bridge import BurrBridge
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from .burr_bridge import BurrBridge
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from .indexify_node import IndexifyNode
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scrapegraphai/integrations/indexify_node.py
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scrapegraphai/integrations/indexify_node.py
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"""
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IndexifyNode Module
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"""
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from typing import List, Optional
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from ..utils.logging import get_logger
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from ..nodes.base_node import BaseNode
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# try:
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# import indexify
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# except ImportError:
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# raise ImportError("indexify package is not installed. Please install it with 'pip install scrapegraphai[indexify]'")
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class IndexifyNode(BaseNode):
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"""
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A node responsible for indexing the content present in the state.
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Attributes:
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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, defaulting to "Parse".
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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 = "Indexify",
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):
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super().__init__(node_name, "node", input, output, 2, node_config)
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self.verbose = (
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False 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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Executes the node's logic to index the content present in the state.
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Args:
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state (dict): The current state of the graph. The input keys will be used to fetch the
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correct data from the state.
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Returns:
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dict: The updated state with the output key containing the parsed content chunks.
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Raises:
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KeyError: If the input keys are not found in the state, indicating that the
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necessary information for parsing the content is missing.
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"""
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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# Interpret input keys based on the provided input expression
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# input_keys length matches the min_input_len parameter in the __init__ method
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# e.g. "answer & parsed_doc" or "answer | img_urls"
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input_keys = self.get_input_keys(state)
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# Fetching data from the state based on the input keys
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input_data = [state[key] for key in input_keys]
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answer = input_data[0]
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img_urls = input_data[1]
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# Indexify the content
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# ...
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isIndexified = True
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state.update({self.output[0]: isIndexified})
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return state
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