Scrapegraph-ai/examples/huggingfacehub/smart_scraper_multi_huggingfacehub.py
Marco Vinciguerra 0b4cfd6522 fix: abstract_graph and removed unused embeddings
Co-Authored-By: Federico Aguzzi <62149513+f-aguzzi@users.noreply.github.com>
2024-08-01 14:38:50 +02:00

49 lines
1.4 KiB
Python

"""
Basic example of scraping pipeline using SmartScraper
"""
import os, json
from dotenv import load_dotenv
from scrapegraphai.graphs import SmartScraperMultiGraph
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"
)
graph_config = {
"llm": {"model_instance": llm_model_instance},
}
# *******************************************************
# Create the SmartScraperMultiGraph instance and run it
# *******************************************************
multiple_search_graph = SmartScraperMultiGraph(
prompt="Who is Marco Perini?",
source= [
"https://perinim.github.io/",
"https://perinim.github.io/cv/"
],
schema=None,
config=graph_config
)
result = multiple_search_graph.run()
print(json.dumps(result, indent=4))