app/rag_chain.py

from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from app.models.model import LLM
from app.managers import vector_manager as vm
from langchain import hub
from app.utils.prompts import prompts
from app.utils import pdf2vector

def get_qa_chain(language: str = "Deutsch"):
    db = vm.load_vectorstore(language)
    retriever = db.as_retriever(search_kwargs={"k": 5})
    # qa_chain = RetrievalQA.from_chain_type(
    #     llm=LLM,
    #     retriever=retriever,
    #     return_source_documents=True
    # )

    system_prompt = prompts[language]

    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_prompt),
            ("human", "{input}"),
        ]
    )
    # prompt = hub.pull("langchain-ai/retrieval-qa-chat")
    question_answer_chain = create_stuff_documents_chain(LLM, prompt)
    chain = create_retrieval_chain(retriever, question_answer_chain)

    # chain.invoke({"input": query}) 

    return chain