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from dotenv import load_dotenv

load_dotenv()

from llama_index.core import VectorStoreIndex

from llama_index.readers.web import TrafilaturaWebReader

import chromadb

def create_embedding_store(name):

chroma_client = chromadb.EphemeralClient()

return chroma_client.get_or_create_collection(name)

def query_pages(collection, urls, questions):

docs = TrafilaturaWebReader().load_data(urls)

index = VectorStoreIndex.from_documents(docs, chroma_collection=collection)

query_engine = index.as_query_engine()

for question in questions:

print(f"Question: {question} \n")

print(f"Answer: {query_engine.query(question)}")

if __name__ == "__main__":

url_list = ["https://supertype.ai", "https://supertype.ai/about-us"]

questions = [

"Who are the members of Supertype.ai",

"What problems are they trying to solve?",

"What are the important values at the company?"

]

collection = create_embedding_store("supertype")

query_pages(

collection,

url_list,

questions

)