""" Basic example of scraping pipeline using ScriptCreatorGraph """ import os from dotenv import load_dotenv from scrapegraphai.graphs import ScriptCreatorMultiGraph from scrapegraphai.utils import prettify_exec_info from langchain_community.llms import HuggingFaceEndpoint from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings load_dotenv() # ************************************************ # Define the configuration for the graph # ************************************************ HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN') repo_id = "mistralai/Mistral-7B-Instruct-v0.2" llm_model_instance = HuggingFaceEndpoint( repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN ) embedder_model_instance = HuggingFaceInferenceAPIEmbeddings( api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2" ) # ************************************************ # Create the SmartScraperGraph instance and run it # ************************************************ graph_config = { "llm": {"model_instance": llm_model_instance}, } # ************************************************ # Create the ScriptCreatorGraph instance and run it # ************************************************ urls=[ "https://schultzbergagency.com/emil-raste-karlsen/", "https://schultzbergagency.com/johanna-hedberg/", ] # ************************************************ # Create the ScriptCreatorGraph instance and run it # ************************************************ script_creator_graph = ScriptCreatorMultiGraph( prompt="Find information about actors", # also accepts a string with the already downloaded HTML code source=urls, config=graph_config ) result = script_creator_graph.run() print(result) # ************************************************ # Get graph execution info # ************************************************ graph_exec_info = script_creator_graph.get_execution_info() print(prettify_exec_info(graph_exec_info))