Error: 'HuggingFaceBgeEmbeddings' object has no attribute 'show_progress'

[tamris]

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Example Code

import pickle
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain.prompts import ChatPromptTemplate 
from langchain.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq


def initialize_llm():
    groq_api_key = "my_api"  
    if not groq_api_key:
        raise ValueError("GROQ_API_KEY is not set in environment variables")
    llm = ChatGroq(temperature=0, model_name="llama3-8b-8192", groq_api_key=groq_api_key)
    return llm

def create_rag_chain(retriever, llm):
    system_prompt = (
        "Anda adalah asisten untuk tugas menjawab pertanyaan yang bernama TikAI. "
        "Gunakan konteks yang diambil untuk menjawab. "
        "Menjawab menggunakan bahasa Indonesia. "
        "Jika Anda tidak ada jawaban pada konteks, katakan saja 'saya tidak tahu' dan berikan jawaban yang sesuai. "
        "Gunakan maksimal empat kalimat dan pertahankan jawaban singkat.\n\n"
        "{context}"
    )

    chat_prompt = ChatPromptTemplate.from_messages([
        ("system", system_prompt),
        ("human", "{question}")
    ])

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

llm = initialize_llm()

from tqdm import tqdm
tqdm._instances.clear()  # Menonaktifkan progress bar

# Membuat objek embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

# Memuat vectorstore
with open('models/chatbot/vectorstore.pkl', 'rb') as f:
    vectorstore = pickle.load(f)

retriever = vectorstore.as_retriever()
rag_chain = create_rag_chain(retriever, llm)

@app.route('/chat_bot', methods=['POST'])
def chat_bot():
    try:
        data = request.get_json()
        question = data.get('question')

        if not question:
            return jsonify({"error": "Question is required"}), 400

        print(f"Question received: {question}")
        result = rag_chain.invoke({"query": question})
        print(f"Result: {result}")

        if isinstance(result, dict) and 'result' in result:
            answer = result['result']
        else:
            answer = 'Saya tidak tahu'

        return jsonify({"answer": answer})
    except Exception as e:
        print(f"Error: {e}")
        return jsonify({"error": "Internal Server Error"}), 500

Description

so I'm creating a chatbot function and when it's in the embeding process it gets an error like this:
Error: 'HuggingFaceBgeEmbeddings' object has no attribute 'show_progress'

System Info

Python Version: 3.11.2

langchain_core: 0.3.29
langchain: 0.3.14
langchain_community: 0.3.14
langsmith: 0.1.147
langchain_groq: 0.2.2
langchain_huggingface: 0.1.2
langchain_text_splitters: 0.3.4