[Python] ImportError: cannot import name 'Client' from 'pinecone' (unknown location)

The problem with this piece of code is that I am unable to import Client from the pinecone library. I tried to uninstalling and reinstalling different versions none of them worked. I also tried it with the pinecone-client library but none of it works. Any advice would be appreciated.

Context of the code: I building a simple RAG which reads a txt file about Costco which provides an output depending on the input provided to the model.I am using the API's Pinecone and Huggingface.

import os
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Pinecone
from langchain_community.llms import HuggingFaceHub
from langchain_core.prompts import PromptTemplate
from langchain.chains import RetrievalQA # This is still in langchain core
from dotenv import load_dotenv

import pinecone
print(pinecone.__version__)
from pinecone import * # Importing the new Pinecone client
print(pinecone.Client)

# Initialize Pinecone client with new SDK
pinecone_api_key = os.getenv("PINECONE_API_KEY")
pinecone_environment = "us-east-1" # Your Pinecone environment (must match your index region)

client = Client(api_key=pinecone_api_key, environment=pinecone_environment)

index_name = "gentlecat"

# Check if index exists, else create
if index_name not in [idx.name for idx in client.list_indexes()]:
client.create_index(name=index_name, dimension=384, metric="cosine")

# Connect to the index
index = client.index(index_name)

# Load & split documents
loader = TextLoader(r"filepath", encoding="utf-8")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=4)
docs = text_splitter.split_documents(documents)

# Setup embeddings (make sure model outputs 384 dims)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# Create Pinecone vectorstore with new client
vectorstore = Pinecone.from_documents(docs, embeddings, index_name=index_name, client=client)

# Setup LLM
llm = HuggingFaceHub(
repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
model_kwargs={"temperature": 0.8, "top_p": 0.8, "top_k": 50},
huggingfacehub_api_token=os.getenv("HUGGINGFACE_API_KEY"),
)

# Setup prompt template
template = """You are a helpful assistant. Answer based on the Costco business context.

Context: {context}
Question: {question}
Answer:"""

prompt = PromptTemplate(template=template, input_variables=["context", "question"])

# Create RetrievalQA chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(),
chain_type_kwargs={"prompt": prompt},
)

# Example query
query = "What is Costco's primary business model?"
answer = qa_chain.run(query)
print("Answer:", answer)


Output:

7.3.0
Traceback (most recent call last):
File "c:\filepath\main.py", line 14, in <module>
print(pinecone.Client)
File "C:\Users\FilePath\Python\Lib\site-packages\pinecone\utils\lazy_imports.py", line 65, in __getattr__
raise AttributeError(f"module '{self._original_module.__name__}' has no attribute '{name}'")
AttributeError: module 'pinecone' has no attribute 'Client'```

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