How can I use Pinecone namespaces with LangChain's ConversationalRetrievalChain for managing multiple backstories?

I have set up Pinecone as a vector database to store embeddings generated by OpenAI’s text-embedding-ada-002 and built a LangChain ConversationalRetrievalChain using gpt-3.5-turbo as the LLM. The goal is to store the backstories of different fictional company employees in a single Pinecone index and query them separately. I believe namespaces can enable this separation, but I haven't found clear instructions in the LangChain documentation for Pinecone namespaces.

I’m looking for advice on how to modify my code to insert and retrieve data from three different namespaces. Below is a snippet of my current code:

PYTHONCopied
import streamlit as st
from dotenv import load_dotenv
import re, os
import pinecone
from langchain.embeddings import OpenAIEmbeddings 
from langchain.vectorstores import Pinecone
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.callbacks import get_openai_callback

from langchain.prompts import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)

def initializePinecone():
    PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
    PINECONE_ENVIRONMENT = os.environ.get("PINECONE_ENVIRONMENT")

    pinecone.init(api_key=PINECONE_API_KEY, 
              environment=PINECONE_ENVIRONMENT)

    # Set the index name
    index_name = "fictionalemployees"

    # check if the extractive-question-answering index exists
    if index_name not in pinecone.list_indexes():
        # create the index if it does not exist
        pinecone.create_index(
            index_name,
            dimension=1536,
            metric="cosine"
        )

    return index_name

def get_pineconeVectorStore(text_chunks, indexName):
    embeddings = OpenAIEmbeddings(deployment="text-embedding-ada-002", model="text-embedding-ada-002", chunk_size=10)
    pineconeVectorStore = Pinecone.from_texts(
                            text_chunks, 
                            embeddings, 
                            index_name=indexName)
    return pineconeVectorStore

def get_conversation_chain(vectorstore):
    llm = ChatOpenAI(model="gpt-3.5-turbo", max_tokens=50)

    general_system_template = r"""
    You are John, a Customer Service Officer at the company. You are answering questions regarding your job at helping with the rental application process.
    ----
    {context}
    ----
    """
    general_user_template = "Question:{question}"
    messages = [
                SystemMessagePromptTemplate.from_template(general_system_template),
                HumanMessagePromptTemplate.from_template(general_user_template)
    ]
    qa_prompt = ChatPromptTemplate.from_messages( messages )

    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)

    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory,
        combine_docs_chain_kwargs={"prompt": qa_prompt}
    )

    return conversation_chain

def handle_userinput(user_question):
    response = count_tokens(
                st.session_state.conversation,
                {'question': user_question})
    st.session_state.chat_history = response['chat_history']

    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(user_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)
        else:
            message.content = truncate_at_last_sentence(replace_list_format(message.content))
            st.write(bot_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)

# Somewhere in main:
  #create Pinecone vector store
  pineconeVectorStore = get_pineconeVectorStore(
  text_chunks, pineconeIndexName)

  # create conversation chain using Pinecone
  st.session_state.conversation = get_conversation_chain(
                                                                          pineconeVectorStore)

Any guidance or examples on adjusting this code to work with namespaces for storing and querying separate employee backstories would be greatly appreciated.