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62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
"""
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Basic example of scraping pipeline using ScriptCreatorGraph
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"""
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import os
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from dotenv import load_dotenv
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from scrapegraphai.graphs import ScriptCreatorMultiGraph
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from scrapegraphai.utils import prettify_exec_info
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from langchain_openai import AzureChatOpenAI
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from langchain_openai import AzureOpenAIEmbeddings
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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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llm_model_instance = AzureChatOpenAI(
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openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
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azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]
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)
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embedder_model_instance = AzureOpenAIEmbeddings(
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azure_deployment=os.environ["AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME"],
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openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
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)
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graph_config = {
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"llm": {"model_instance": llm_model_instance},
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"embeddings": {"model_instance": embedder_model_instance},
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"library": "beautifulsoup"
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}
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# ************************************************
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# Create the ScriptCreatorGraph instance and run it
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# ************************************************
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urls=[
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"https://schultzbergagency.com/emil-raste-karlsen/",
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"https://schultzbergagency.com/johanna-hedberg/",
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]
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# ************************************************
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# Create the ScriptCreatorGraph instance and run it
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# ************************************************
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script_creator_graph = ScriptCreatorMultiGraph(
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prompt="Find information about actors",
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# also accepts a string with the already downloaded HTML code
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source=urls,
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config=graph_config
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)
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result = script_creator_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 = script_creator_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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