I’m new to LLM and confronted with problems I have no clue how to solve them or why they’re caused.

One of them being as_retriever() not being recognized by VSCode, if that makes sense. It’s just white.

Another problem is the one you will see below:

ValidationError: 1 validation error for StuffDocumentsChain __root__ document_variable_name context was not found in llm_chain input_variables: ['', 'question'] (type=value_error)
Traceback:
File "C:UsersDELLAppDataLocalProgramsPythonPython310libsite-packagesstreamlitruntimescriptrunnerscript_runner.py", line 584, in _run_script
    exec(code, module.__dict__)
File "C:UsersDELLOneDriveBureaubladRamroop AIapp.py", line 70, in <module>
    response = final_result(prompt)
File "C:UsersDELLOneDriveBureaubladRamroop AIapp.py", line 55, in final_result
    qa_result = qa_bot()
File "C:UsersDELLOneDriveBureaubladRamroop AIapp.py", line 49, in qa_bot
    qa = retrieval_qa_chain(llm, qa_prompt, db)
File "C:UsersDELLOneDriveBureaubladRamroop AIapp.py", line 33, in retrieval_qa_chain
    qa_chain = RetrievalQA.from_chain_type(
File "C:UsersDELLAppDataLocalProgramsPythonPython310libsite-packageslangchainchainsretrieval_qabase.py", line 105, in from_chain_type
    combine_documents_chain = load_qa_chain(
File "C:UsersDELLAppDataLocalProgramsPythonPython310libsite-packageslangchainchainsquestion_answering__init__.py", line 249, in load_qa_chain
    return loader_mapping[chain_type](
File "C:UsersDELLAppDataLocalProgramsPythonPython310libsite-packageslangchainchainsquestion_answering__init__.py", line 81, in _load_stuff_chain
    return StuffDocumentsChain(
File "C:UsersDELLAppDataLocalProgramsPythonPython310libsite-packageslangchain_coreloadserializable.py", line 120, in __init__
    super().__init__(**kwargs)
File "C:UsersDELLAppDataLocalProgramsPythonPython310libsite-packagespydanticv1main.py", line 341, in __init__
    raise validation_error

These are the 2 problems I’m currently facing. Any help will greatly appreciated. I’m adding my entire for your convenience.

My code:

import streamlit as st
from langchain_core.prompts import PromptTemplate
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS 
from langchain_community.llms.ctransformers import CTransformers 
from langchain.chains.retrieval_qa.base import RetrievalQA


DB_FAISS_PATH = "vectorstoresdb"

custom_promt_template = """You are a helpful, respectful and honest assistant. 
Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. 
Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. 
If you don't know the answer to a question, please don't share false information.

Context: {}
Question: {question}

"""

def set_custom_promt():
    prompt = PromptTemplate(template=custom_promt_template, input_variables=['', 'question'])

    return prompt


def load_llm():
    llm = CTransformers(model= "llama-2-7b-chat.ggmlv3.q4_0.bin", model_type= "llama", max_new_tokens= 512, temperature= 0.7)

    return llm

def retrieval_qa_chain(llm, prompt, db):
    qa_chain = RetrievalQA.from_chain_type(
        llm= llm,
        chain_type= "stuff",
        retriever = db.as_retriever(search_kwargs={'k': 2}),
        return_source_documents = True,
        chain_type_kwargs= {'prompt': prompt}
    )

    return qa_chain


def qa_bot():
    embeddings = HuggingFaceEmbeddings(model_name= 'sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
    llm = load_llm()
    qa_prompt = set_custom_promt()
    qa = retrieval_qa_chain(llm, qa_prompt, db)

    return qa


def final_result(query):
    qa_result = qa_bot()
    response = qa_result({'query': query})

    return response



# Create centered main title
st.title('???? QA Bot')

# Create a text input box for the user
prompt = st.text_input('Input your question here')

# If the user hits enter
if prompt:
    response = final_result(prompt)
    # Writing it out to the screen
    st.write(response)

    # Displaying source text
    with st.expander('Source Text'):
        st.write(response['source_text'])