Scrapegraph-ai/examples/azure/xml_scraper_graph_multi_azure.py
Marco Vinciguerra 1dde43cdeb add new examples
2024-06-03 21:03:13 +02:00

65 lines
2.0 KiB
Python

"""
Basic example of scraping pipeline using XMLScraperMultiGraph from XML documents
"""
import os
from dotenv import load_dotenv
from scrapegraphai.graphs import XMLScraperMultiGraph
from langchain_openai import AzureChatOpenAI
from langchain_openai import AzureOpenAIEmbeddings
from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
load_dotenv()
# ************************************************
# Read the XML file
# ************************************************
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()
# ************************************************
# Define the configuration for the graph
# ************************************************
llm_model_instance = AzureChatOpenAI(
openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]
)
embedder_model_instance = AzureOpenAIEmbeddings(
azure_deployment=os.environ["AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME"],
openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)
graph_config = {
"llm": {"model_instance": llm_model_instance},
"embeddings": {"model_instance": embedder_model_instance}
}
# ************************************************
# Create the XMLScraperMultiGraph instance and run it
# ************************************************
xml_scraper_graph = XMLScraperMultiGraph(
prompt="List me all the authors, title and genres of the books",
source=[text, text], # Pass the content of the file, not the file object
config=graph_config
)
result = xml_scraper_graph.run()
print(result)
# ************************************************
# Get graph execution info
# ************************************************
graph_exec_info = xml_scraper_graph.get_execution_info()
print(prettify_exec_info(graph_exec_info))
# Save to json or csv
convert_to_csv(result, "result")
convert_to_json(result, "result")