I am using pinecone vector database for storing the veactors of the text chunks, then I am imlementing similarity search to get similar document after that I am loading my llm model and using the retrieveralQA chain I am trying to use “from langchain.chains import RetrievalQA ” for my chatbot but I am always getting a validation error in RetrieveralQA.from_chain_type.

        #Initializing the Pinecone
    
        from langchain_pinecone import PineconeVectorStore
        from pinecone import Pinecone
        from langchain.chains import RetrievalQA 
    
        pc = Pinecone(api_key="1e094edb-8730-46a7-8178-615a08ca303b")
        index = pc.Index("medical-chatbot")
    
        # index_name="medical-chatbot"
    
        #Creating Embeddings for Each of The Text Chunks & storing
        docsearch=PineconeVectorStore.from_texts([t.page_content for t in text_chunks], embeddings, index_name='medical-chatbot')
    
        #If we already have an index we can load it like this
        index_name="medical-chatbot"
        docsearch=PineconeVectorStore.from_existing_index(index_name, embeddings)
    
        query = "What are Salivary Gland Disease"
    
        docs=docsearch.similarity_search(query, k=3)
    
        print("Result", docs)
    
        prompt_template="""
        Use the following pieces of information to answer the user's question.
        If you don't know the answer, just say that you don't know, don't try to make up an answer.
    
        Context: {context}
        Question: {question}
    
        Only return the helpful answer below and nothing else.
        Helpful answer:
        """
    
        PROMPT=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
        chain_type_kwargs={"prompt": PROMPT}
    
        llm=CTransformers(model="../model/llama-2-7b-chat.ggmlv3.q4_0.bin",
                      model_type="llama",
                      config={'max_new_tokens':512,
                              'temperature':0.8})
    
    
        # retriever = docsearch.as_retriever()
    
    
        qa=RetrievalQA.from_chain_type(
            llm=llm, 
        chain_type="stuff", 
        retriever=docsearch.as_retriever(search_kwargs={'k': 2}),
        return_source_documents=True, 
            chain_type_kwargs=chain_type_kwargs)

this is the error Message

    ValidationError Traceback (most recent call last)
    Cell In[49], line 5
    1 from langchain.chains import RetrievalQA
    2 # retriever = docsearch.as_retriever()
    ----> 5 qa=RetrievalQA.from_chain_type(
    6 llm=llm,
    7 chain_type="stuff",
    8 retriever=docsearch.as_retriever(search_kwargs={'k': 2}),
    9 return_source_documents=True,
    10 chain_type_kwargs=chain_type_kwargs)
    
    File c:End-to-end-Medical-Chatbot-using-Llama2-mainmyenvlibsite-packageslangchainchainsretrieval_qabase.py:95, in BaseRetrievalQA.from_chain_type(cls, llm, chain_type, chain_type_kwargs, **kwargs)
    91 _chain_type_kwargs = chain_type_kwargs or {}
    92 combine_documents_chain = load_qa_chain(
    93 llm, chain_type=chain_type, **_chain_type_kwargs
    94 )
    ---> 95 return cls(combine_documents_chain=combine_documents_chain, **kwargs)
    
    File c:End-to-end-Medical-Chatbot-using-Llama2-mainmyenvlibsite-packageslangchainloadserializable.py:74, in Serializable.init(self, **kwargs)
    73 def init(self, **kwargs: Any) -> None:
    ---> 74 super().init(**kwargs)
    75 self._lc_kwargs = kwargs
    
    File c:End-to-end-Medical-Chatbot-using-Llama2-mainmyenvlibsite-packagespydanticmain.py:341, in pydantic.main.BaseModel.init()
    
    ValidationError: 1 validation error for RetrievalQA
    retriever
    Can't instantiate abstract class BaseRetriever with abstract methods _aget_relevant_documents, _get_relevant_documents (type=type_error)