"""
Basic example of scraping pipeline using XMLScraperGraph from XML documents
"""
import os
from dotenv import load_dotenv
from scrapegraphai.graphs import XMLScraperGraph
from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
load_dotenv()
FILE_NAME = "inputs/books.xml"
curr_dir = os.path.dirname(os.path.realpath(__file__))
file_path = os.path.join(curr_dir, FILE_NAME)
with open(file_path, 'r', encoding="utf-8") as file:
text = file.read()
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"
)
graph_config = {
"llm": {"model_instance": llm_model_instance},
}
xml_scraper_graph = XMLScraperGraph(
prompt="List me all the authors, title and genres of the books",
source=text,
config=graph_config
)
result = xml_scraper_graph.run()
print(result)
graph_exec_info = xml_scraper_graph.get_execution_info()
print(prettify_exec_info(graph_exec_info))
convert_to_csv(result, "result")
convert_to_json(result, "result")