BA-Chatbot_Ali_Thesis/backend/module_recommendation.py

from typing import List
from haystack.schema import Document
from reranker import ReRanker
from reader import Reader
from retriever.retriever import Retriever
from haystack.nodes import FARMReader


class WPMRecommendation:
    def __init__(
        self,
        retriever: Retriever,
        reader: Reader,
        reRanker: ReRanker,
        farm_reader: FARMReader,
    ) -> None:
        """
        Initializes the WPMRecommendation class with required components for retrieving, reranking, and reading documents.
        Args:
            retriever (Retriever): An instance of Retriever for fetching relevant documents.
            reader (Reader): An instance of Reader for interpreting and processing documents.
            reRanker (ReRanker): An instance of ReRanker for reranking documents based on relevance.
            farm_reader (FARMReader): An instance of FARMReader for additional reading capabilities.
        """
        self.retriever = retriever
        self.reader = reader
        self.reranker = reRanker
        self.farm_reader = farm_reader

    def _filter_wpms(self, documents: List[Document]):
        """
        Filters documents to include only those marked as Wahlpflichtmodule (WPM).

        Args:
            documents (List[Document]): A list of documents to be filtered.

        Returns:
            List[Document]: Filtered documents marked as WPM.
        """
        return [doc for doc in documents if doc.meta.get("is_wpm") is True]

    def _build_query_for_prompt(
        self, interets: str, future_carrer: str, previous_courses: str
    ):
        """
        Constructs a query based on the user's interests, future career plans, and previously taken courses.

        Args:
            interets (str): User's interests.
            future_carrer (str): User's future career aspirations.
            previous_courses (str): Previously taken courses by the user.

        Returns:
            str: A constructed query based on the provided information.
        """

        query = ""
        if interets:
            query += f"Ich habe folgende Interessen: \n{interets}.\n"
        if future_carrer:
            query += f"Zudem möchte ich zukünftig im folgenden Bereich arbeiten:\n{future_carrer}.\n"
        if previous_courses:
            query += f"Ich habe bereits schon folgenden Wahlplfichtmodule belegt:\n{previous_courses}.\n"
        return query

    def recommend_wpms(
        self,
        interets: str,
        future_carrer: str,
        previous_courses: str,
        retrieval_model_or_method="mpnet",
        recommendation_method: str = "get_retrieved_results",
        rerank_retrieved_results=True,
    ):
        """
        Recommends Wahlpflichtmodule (WPM) based on the user's interests, future career plans, and previous courses.

        Args:
            interets (str): User's interests.
            future_carrer (str): User's future career aspirations.
            previous_courses (str): Previously taken courses by the user.
            retrieval_model_or_method (str, optional): The retrieval model or method to use. Defaults to "mpnet".
            recommendation_method (str, optional): The method for generating recommendations. Defaults to "get_retrieved_results".
            rerank_retrieved_results (bool, optional): Flag to determine if reranking should be done on retrieved results. Defaults to True.

        Returns:
            Varies: Returns different types of outputs based on the recommendation method chosen.
        """
        top_k_docs = self.retriever.get_top_k_passages(
            query=interets, index="ib", method=retrieval_model_or_method
        )["documents"]
        retrieved_wpms = self._filter_wpms(top_k_docs)
        final_references = retrieved_wpms
        query = self._build_query_for_prompt(
            interets=interets,
            future_carrer=future_carrer,
            previous_courses=previous_courses,
        )
        if rerank_retrieved_results:
            reranked_top_k = self.reranker.rerank_documents_with_gpt35(
                documents=retrieved_wpms, query=query
            )
            final_references = self.reranker.get_final_references(
                reranked_documents=reranked_top_k, retrieved_documents=retrieved_wpms
            )
        if recommendation_method == "generate_llm_answer":
            return self.reader.get_gpt_wpm_recommendation(
                query=query, top_k_wpms=final_references
            )
        if recommendation_method == "generate_farm_reader_answer":
            pass

        return final_references