feat(indexify-node): add example
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Marco Perini 2024-06-05 18:45:37 +02:00
parent 5c9843f141
commit 5d1fbf806a
3 changed files with 153 additions and 1 deletions

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"""
Basic example of scraping pipeline using SmartScraper with schema
"""
import os, json
from typing import List
from dotenv import load_dotenv
load_dotenv()
from pydantic import BaseModel, Field
from scrapegraphai.graphs import SmartScraperGraph
from scrapegraphai.integrations import IndexifyNode
# ************************************************
# Define the output schema for the graph
# ************************************************
class Image(BaseModel):
url: str = Field(description="The url of the image")
class Images(BaseModel):
images: List[Image]
# ************************************************
# Define the configuration for the graph
# ************************************************
openai_key = os.getenv("OPENAI_APIKEY")
graph_config = {
"llm": {
"api_key":openai_key,
"model": "gpt-3.5-turbo",
},
"verbose": True,
"headless": False,
}
# ************************************************
# Define the custom nodes for the graph
# ************************************************
indexify_node = IndexifyNode(
input="answer & img_urls",
output=["is_indexed"],
node_config={
"verbose": True
}
)
# ************************************************
# Create the SmartScraperGraph instance
# ************************************************
smart_scraper_graph = SmartScraperGraph(
prompt="List me all the images with their url",
source="https://giphy.com/",
schema=Images,
config=graph_config
)
# Add the custom node to the graph
smart_scraper_graph.append_node(indexify_node)
# ************************************************
# Run the SmartScraperGraph
# ************************************************
result = smart_scraper_graph.run()
print(json.dumps(result, indent=2))

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Init file for integrations module
"""
from .burr_bridge import BurrBridge
from .burr_bridge import BurrBridge
from .indexify_node import IndexifyNode

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"""
IndexifyNode Module
"""
from typing import List, Optional
from ..utils.logging import get_logger
from ..nodes.base_node import BaseNode
# try:
# import indexify
# except ImportError:
# raise ImportError("indexify package is not installed. Please install it with 'pip install scrapegraphai[indexify]'")
class IndexifyNode(BaseNode):
"""
A node responsible for indexing the content present in the state.
Attributes:
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 "Parse".
"""
def __init__(
self,
input: str,
output: List[str],
node_config: Optional[dict] = None,
node_name: str = "Indexify",
):
super().__init__(node_name, "node", input, output, 2, node_config)
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 index the content present in the state.
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 parsed content chunks.
Raises:
KeyError: If the input keys are not found in the state, indicating that the
necessary information for parsing the content is missing.
"""
self.logger.info(f"--- Executing {self.node_name} Node ---")
# Interpret input keys based on the provided input expression
# input_keys length matches the min_input_len parameter in the __init__ method
# e.g. "answer & parsed_doc" or "answer | img_urls"
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]
answer = input_data[0]
img_urls = input_data[1]
# Indexify the content
# ...
isIndexified = True
state.update({self.output[0]: isIndexified})
return state