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64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
"""
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Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
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"""
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import os
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from dotenv import load_dotenv
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import pandas as pd
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from scrapegraphai.graphs import CSVScraperMultiGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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load_dotenv()
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# ************************************************
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# Read the CSV file
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# ************************************************
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FILE_NAME = "inputs/username.csv"
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curr_dir = os.path.dirname(os.path.realpath(__file__))
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file_path = os.path.join(curr_dir, FILE_NAME)
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text = pd.read_csv(file_path)
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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fireworks_api_key = os.getenv("FIREWORKS_APIKEY")
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graph_config = {
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"llm": {
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"api_key": fireworks_api_key,
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"model": "fireworks/accounts/fireworks/models/mixtral-8x7b-instruct"
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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# "base_url": "http://localhost:11434", # set ollama URL arbitrarily
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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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# Create the CSVScraperMultiGraph instance and run it
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# ************************************************
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csv_scraper_graph = CSVScraperMultiGraph(
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prompt="List me all the last names",
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source=[str(text), str(text)],
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config=graph_config
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)
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result = csv_scraper_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = csv_scraper_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json or csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")
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