QuestionRetrieverTool.py

from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from smolagents import Tool
from langchain_community.retrievers import BM25Retriever
from smolagents import CodeAgent, InferenceClientModel
from datasets import load_dataset
import re
import pandas as pd
#%%
class QuestionRetrieverTool(Tool):
    name = "Question_retriever"
    description = "Uses semantic search to retrieve relevant question given the class, difficulty, and topic inputs by the user."
    inputs = {
        "query": {
            "type": "string",
            "description": "This tool returns relevant question and answer pairs based on the provided context.",
        }
    }
    output_type = "string"

    def __init__(self, docs, **kwargs):
        super().__init__(**kwargs)
        self.retriever = BM25Retriever.from_documents(
            docs, k=5  # Retrieve the top 5 documents
        )

    def forward(self, query: str) -> str:
        assert isinstance(query, str), "Your search query must be a string"

        docs = self.retriever.invoke(
            query,
        )
        return "\nRetrieved example question and answer pairs:\n" + "".join(
            [
                f"\n\n===== Q and A pairs {str(i)} =====\n" + doc.page_content
                for i, doc in enumerate(docs)
            ]
        )