AttributeError: module 'pinecone' has no attribute 'Index'

Hi I'm trying to create a pinecone index and write embeddings to it as per below code, but i'm keep getting AttributeError: module 'pinecone' has no attribute 'Index' error, i checked and found that its due to langchain and pinecone version miss matches so tried to install lesser version of pinecone-client==2.2.4 that also not working, please help.

import os
from dotenv import load_dotenv,find_dotenv
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

load_dotenv(find_dotenv(),override=True)

from pinecone import Pinecone, ServerlessSpec  # if you're initializing a new index

from dotenv import load_dotenv, find_dotenv

pc = Pinecone()


# loading PDF, DOCX and TXT files as LangChain Documents
def load_document(file):
    import os
    name, extension = os.path.splitext(file)

    if extension == '.pdf':
        from langchain.document_loaders import PyPDFLoader
        print(f'Loading {file}')
        loader = PyPDFLoader(file)
    elif extension == '.docx':
        from langchain.document_loaders import Docx2txtLoader
        print(f'Loading {file}')
        loader = Docx2txtLoader(file)
    elif extension == '.txt':
        # from langchain.document_loaders import TextLoader
        from langchain_community.document_loaders import TextLoader
        loader = TextLoader(file)
    else:
        print('Document format is not supported!')
        return None

    data = loader.load()
    return data 

data = load_document('file.txt')

def chunk_data(data, chunk_size=256):
    from langchain.text_splitter import RecursiveCharacterTextSplitter
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0)
    chunks = text_splitter.split_documents(data)
    return chunks

def insert_or_fetch_embeddings(index_name, chunks):
    # importing the necessary libraries and initializing the Pinecone client
    import pinecone
    from langchain_community.vectorstores import Pinecone
    from langchain_openai import OpenAIEmbeddings
    from pinecone import ServerlessSpec

    pc = pinecone.Pinecone()

    embeddings = OpenAIEmbeddings(model='text-embedding-3-small', dimensions=1536)  # 512 works as well

    # loading from existing index
    if index_name in pc.list_indexes().names():
        print(f'Index {index_name} already exists. Loading embeddings ... ', end='')
        vector_store = Pinecone.from_existing_index(index_name, embeddings)
        print('Ok')
    else:
        # creating the index and embedding the chunks into the index
        print(f'Creating index {index_name} and embeddings ...', end='')

        # creating a new index
        pc.create_index(
            name=index_name,
            dimension=1536,
            metric='cosine',
            spec=ServerlessSpec(
                cloud="aws",
                region="us-east-1"
            )
        )

        # processing the input documents, generating embeddings using the provided `OpenAIEmbeddings` instance,
        # inserting the embeddings into the index and returning a new Pinecone vector store object.
        vector_store = Pinecone.from_documents(chunks, embeddings, index_name=index_name)
        print('Ok')

        return vector_store

index_name = 'askadocument'
vector_store = insert_or_fetch_embeddings(index_name=index_name, chunks=chunks)

llm = ChatOpenAI(model="gpt-4.1-mini", temperature=0.3)

retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 2})

chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)

answer = chain.invoke(query)

requirements:
openai
langchain
langchain_openai
langchain_experimental
langchainhub
pinecone
python-dotenv
tiktoken
docx2txt
pypdf
pinecone-client==2.2.4

p.s: please don't down vote the question.