notebook_tutor/retrieval.py

from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
from operator import itemgetter
from prompt_templates import PromptTemplates


class RetrievalManager:
    """
    RetrievalManager class.

    This class represents a retrieval manager that processes questions using a retrieval-augmented QA chain and returns the response.

    Attributes:
        retriever (object): The retriever object used for retrieval.
        chat_model (object): The ChatOpenAI object representing the OpenAI Chat model.

    Methods:
        notebook_QA(question):
            Processes a question using the retrieval-augmented QA chain and returns the response.
    """
    def __init__(self, retriever):
        self.retriever = retriever
        self.chat_model = ChatOpenAI(model="gpt-4-turbo", temperature=0.1)
        self.prompts = PromptTemplates()

    def notebook_QA(self, question):
        """
        Processes a question using the retrieval-augmented QA chain and returns the response.

        Parameters:
            question (str): The question to be processed.

        Returns:
            str: The response generated by the retrieval-augmented QA chain.
        """
        retrieval_augmented_qa_chain = (
            {"context": itemgetter("question") | self.retriever, "question": itemgetter("question")}
            | RunnablePassthrough.assign(context=itemgetter("context"))
            | {"response": self.prompts.get_rag_qa_prompt() | self.chat_model, "context": itemgetter("context")}
        )

        response = retrieval_augmented_qa_chain.invoke({"question": question})

        return response["response"].content

    def get_RAG_QA_chain(self):
        return (
            {"context": itemgetter("question") | self.retriever, "question": itemgetter("question")}
            | RunnablePassthrough.assign(context=itemgetter("context"))
            | {"response": self.prompts.get_rag_qa_prompt() | self.chat_model, "context": itemgetter("context")}
        )