| 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 |
| ) |
|
|
| 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) |
| ] |
| ) |
|
|
