Complete Code

import bs4
from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader(
    web_paths=("https://andersen.sdu.dk/vaerk/hersholt/TheEmperorsNewClothes_e.html",),
    bs_kwargs=dict(
        parse_only=bs4.SoupStrainer(
            class_=("post-content", "post-title", "post-header")
        )
    ),
)
documents = loader.load()

from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = splitter.split_documents(documents)


from langchain_huggingface.embeddings import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(
    model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
)


import os
import getpass
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_SERVICE_KEY = os.getenv("SUPABASE_SERVICE_KEY")

from langchain_community.vectorstores import SupabaseVectorStore
from supabase.client import Client, create_client
supabase_url = SUPABASE_URL
supabase_key = SUPABASE_SERVICE_KEY
supabase_client = create_client(supabase_url, supabase_key)

# create a new collection
vectorstore = SupabaseVectorStore.from_documents(
    chunks,
    embedding = embedding,
    client = supabase_client,
    table_name = "documents",
    query_name = "match_documents",
    chunk_size = 500,
)

from langchain_community.vectorstores import SupabaseVectorStore
from supabase.client import Client, create_client
supabase_url = "http://127.0.0.1:8000"
supabase_key = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE"
supabase_client = create_client(supabase_url, supabase_key)

vectorstore = SupabaseVectorStore(
    embedding=embedding,
    client=supabase_client,
    table_name="documents",
    query_name="match_documents",
)
retriever = vectorstore.as_retriever()


from langchain_ollama import ChatOllama
llm = ChatOllama(
    model="gemma:2b",
    # model="mistral",
    temperature=0.5,
)


from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder,
)
from langchain.chains.history_aware_retriever import create_history_aware_retriever

contextualize_q_system_prompt = (
    "Given a chat history and the latest user question "
    "which might reference context in the chat history, "
    "formulate a standalone question which can be understood "
    "without the chat history. Do NOT answer the question, "
    "just reformulate it if needed and otherwise return it as is."
)
contextualize_q_prompt = ChatPromptTemplate(
    [
        ("system", contextualize_q_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}"),
    ]
)
history_aware_retriever = create_history_aware_retriever(
    llm, retriever, contextualize_q_prompt
)


from langchain.chains.retrieval import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

system_prompt = (
    "You are an assistant for question-answering tasks. "
    "Use the following pieces of retrieved context to answer "
    "the question. If you don't know the answer, say that you "
    "don't know. Use three sentences maximum and keep the "
    "answer concise."
    "\n\n"
    "{context}"
)
qa_prompt = ChatPromptTemplate(
    [
        ("system", system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}"),
    ]
)


question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)

rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)

from langchain.globals import set_debug, set_verbose
set_debug(True)
set_verbose(True)

from langchain_core.messages import AIMessage, HumanMessage

chat_history = []

def process_question(question):
    ai_msg = rag_chain.invoke({"input": question, "chat_history": chat_history})
    chat_history.extend(
       [
           HumanMessage(content=question),
           AIMessage(content=ai_msg["answer"]),
       ]
    )
    return ai_msg["answer"]


from pprint import pprint
while True:
    user_input = input("Enter your question: ")
    if user_input == "exit":
        break
    else:
        pprint(process_question(user_input))

Enter your question: how does the emperor think about his cloth
[outputs]
(' order?\n'
 'Assistant: The emperor believes that wearing such special clothes will help '
 "him identify the capable and incapable men in his empire. He's convinced "
 'that this material will distinguish the clever from the stupid, and so he '
 'urgently orders the weavers to begin making the cloth for him.')

Enter your question: how do the emperor's people think about the emperor
[outputs]
("'s cloth?\n"
 'AI:  desire and curiosity.\n'
 "Assistant: The people in the emperor's city are intrigued and excited about "
 "the new fabric. They've heard stories about its beauty and the emperor's "
 'desire to see it firsthand. This has created a buzz in the community, and '
 'many are eager to witness the cloth for themselves.')

Enter your question: what was my first question
{'output_text': 'The question is: How does the emperor think about his cloth?\n'
                '\n'
                'The final answer is: The emperor thinks about his cloth and '
                'wants to see it appear as though it was in his pretty finery.'}
Enter your question:

{'output_text': 'The passage does not specify how the emperor thinks about his '
                'cloth, so I cannot answer this question from the provided '
                'context.'}

ollama run gemma:2b