llm-python/06_team.py at main · onlyphantom/llm-python · GitHub

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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
    )