src/semantic_searcher.py

import torch
from sentence_transformers import SentenceTransformer


class SemanticSearcher:
    def __init__(self, df_counsel_chat_topic, df_counsel_chat):
        self.df_counsel_chat_topic = df_counsel_chat_topic
        self.df_counsel_chat = df_counsel_chat
        self.embedder = SentenceTransformer("all-MiniLM-L6-v2")
        self.question_embeddings = self.embedder.encode(
            self.df_counsel_chat_topic["questionCombined"].tolist(),
            show_progress_bar=True,
            convert_to_tensor=True,
        )

    def retrieve_relevant_qna(self, question: str, question_context: str = None):
        if question_context is None:
            question_context = ""
        query = question + "\n" + question_context
        query_embedding = self.embedder.encode(query, convert_to_tensor=True)

        # We use cosine-similarity and torch.topk to find the highest 5 scores
        similarity_scores = self.embedder.similarity(
            query_embedding, self.question_embeddings
        )[0]
        _, indices = torch.topk(similarity_scores, k=1)
        index = indices.tolist()
        question_id = self.df_counsel_chat_topic.loc[index, "questionID"].values[0]
        relevant_qna = (
            self.df_counsel_chat.loc[self.df_counsel_chat["questionID"] == question_id]
            .sort_values(by=["upvotes", "views"], ascending=False)
            .head(3)[[
                "questionTitle",
                "topic",
                "therapistInfo",
                "therapistURL",
                "answerText",
                "upvotes",
                "views",
            ]]
        )
        return relevant_qna