I am building a RAG application using Groq API, langchain and Streamlit. It uses OllamaEmbeddings for embedding the text and Groq as the large language model. Here is the code:

import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_groq import ChatGroq
from htmlTemplates import css, bot_template, user_template

def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator="n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks

def get_vectorstore(text_chunks):
    embeddings = OllamaEmbeddings(model='nomic-embed-text')
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore

def get_conversation_chain(vectorstore):
    llm = ChatGroq(model="mixtral-8x7b-32768")
    memory = ConversationBufferMemory(
        memory_key='chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )
    return conversation_chain

def handle_userinput(user_question):
    response = 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:
            st.write(bot_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)

def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with PDFs", page_icon=":books:")
    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with PDFs :books:")
    user_question = st.text_input("Ask a question about your document:")
    if user_question:
        handle_userinput(user_question)

    st.write(user_template.replace("{{MSG}}", "Hello"), unsafe_allow_html=True)
    st.write(bot_template.replace("{{MSG}}", "Hello"), unsafe_allow_html=True)

    with st.sidebar:
        st.subheader("Your documents")
        pdf_docs = st.file_uploader("Upload your PDF here and click on Process", accept_multiple_files=True)
        if st.button("Process"): 
            with st.spinner("Processing"):
                # Get pdf text
                raw_text = get_pdf_text(pdf_docs)

                # Get text chunks
                text_chunks = get_text_chunks(raw_text)

                # Create vector store
                vector_store = get_vectorstore(text_chunks)

                # Create conversation chain
                st.session_state.conversation = get_conversation_chain(vector_store)

if __name__ == '__main__':
    main()

It generates the following error when I try to process the pdf document:

ValidationError: 1 validation error for LLMChain llm Can't instantiate abstract class BaseLanguageModel with abstract methods agenerate_prompt, apredict, apredict_messages, generate_prompt, predict, predict_messages (type=type_error)
Traceback:
File "D:DeerwalkSEM VIProject IIGroqvenvLibsite-packagesstreamlitruntimescriptrunnerscript_runner.py", line 589, in _run_script
    exec(code, module.__dict__)
File "D:DeerwalkSEM VIProject IIGroqapp1.py", line 93, in <module>
    main()
File "D:DeerwalkSEM VIProject IIGroqapp1.py", line 90, in main
    st.session_state.conversation = get_conversation_chain(vector_store)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:DeerwalkSEM VIProject IIGroqapp1.py", line 39, in get_conversation_chain
    conversation_chain = ConversationalRetrievalChain.from_llm(
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:DeerwalkSEM VIProject IIGroqvenvLibsite-packageslangchainchainsconversational_retrievalbase.py", line 213, in from_llm
    doc_chain = load_qa_chain(
                ^^^^^^^^^^^^^^
File "D:DeerwalkSEM VIProject IIGroqvenvLibsite-packageslangchainchainsquestion_answering__init__.py", line 238, in load_qa_chain
    return loader_mapping[chain_type](
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:DeerwalkSEM VIProject IIGroqvenvLibsite-packageslangchainchainsquestion_answering__init__.py", line 70, in _load_stuff_chain
    llm_chain = LLMChain(
                ^^^^^^^^^
File "D:DeerwalkSEM VIProject IIGroqvenvLibsite-packageslangchainloadserializable.py", line 64, in __init__
    super().__init__(**kwargs)
File "pydanticmain.py", line 341, in pydantic.main.BaseModel.__init__

I am new to this. So, I don’t know what to try and couldn’t find helpful resources online.