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49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
"""
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Basic example of scraping pipeline using SmartScraper
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"""
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import os, json
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from dotenv import load_dotenv
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from scrapegraphai.graphs import SmartScraperMultiGraph
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from langchain_community.llms import HuggingFaceEndpoint
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from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
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load_dotenv()
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
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repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
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llm_model_instance = HuggingFaceEndpoint(
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repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
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)
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embedder_model_instance = HuggingFaceInferenceAPIEmbeddings(
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api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2"
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)
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graph_config = {
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"llm": {"model_instance": llm_model_instance},
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}
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# *******************************************************
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# Create the SmartScraperMultiGraph instance and run it
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# *******************************************************
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multiple_search_graph = SmartScraperMultiGraph(
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prompt="Who is Marco Perini?",
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source= [
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"https://perinim.github.io/",
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"https://perinim.github.io/cv/"
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],
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schema=None,
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config=graph_config
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)
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result = multiple_search_graph.run()
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print(json.dumps(result, indent=4))
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