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from llama_index.core import VectorStoreIndex, Document
from smolagents import tool
from langchain_community.embeddings import HuggingFaceEmbeddings
#from all_datasets import *
from level_classifier_tool_2 import (
classify_levels_phrases,
HFEmbeddingBackend,
build_phrase_index
)
from phrases import BLOOMS_PHRASES, DOK_PHRASES
#Retriever tool
@tool
def QuestionRetrieverTool(subject: str, topic: str, grade: str) -> dict:
""" Retrieve up to 5 closely-related example Q&A pairs from the source datasets.
description = (
"Retrieve up to 5 closely-related example Q&A pairs from the source datasets."
)
Args:
subject: The subject area (e.g., "Math", "Science"). type: "string",
topic: The specific topic within the subject (e.g., "Algebra", "Biology"). type: "string",
grade: The grade level (e.g., "5th", "8th"). type: "string"
Returns:
A dictionary with:
closest questions found for: dict with subject, topic, grade
questions: List of up to 5 dicts with question text.
}"""
query = f"{topic} question for {grade} of the {subject}"
results = index.as_retriever(similarity_top_k=5).retrieve(query)
question_texts = [r.node.text for r in results]
return {"closest questions found for": {
"subject": subject,
"topic": topic,
"grade": grade,},
"questions": [{"text": question} for question in question_texts]
}
# Scoring tool
@tool
def classify_and_score(
question: str,
target_bloom: str,
target_dok: str,
agg: str = "max"
) -> dict:
"""Classify a question against Bloom’s and DOK targets and return guidance.
Args:
question: The question text to evaluate for cognitive demand.
target_bloom: Target Bloom’s level or range. Accepts exact (e.g., "Analyze")
or plus form (e.g., "Apply+") meaning that level or higher.
target_dok: Target DOK level or range. Accepts exact (e.g., "DOK3")
or span (e.g., "DOK2-DOK3").
agg: Aggregation method over phrase similarities within a level
(choices: "mean", "max", "topk_mean").
Returns:
A dictionary with:
ok: True if both Bloom’s and DOK match the targets.
measured: Dict with best levels and per-level scores for Bloom’s and DOK.
feedback: Brief guidance describing how to adjust the question to hit targets.
"""
res = classify_levels_phrases(
question,
BLOOMS_PHRASES,
DOK_PHRASES,
backend=_backend,
prebuilt_bloom_index=_BLOOM_INDEX,
prebuilt_dok_index=_DOK_INDEX,
agg=agg,
return_phrase_matches=True
)
def _parse_target_bloom(t: str):
order = ["Remember","Understand","Apply","Analyze","Evaluate","Create"]
if t.endswith("+"):
base = t[:-1]
return set(order[order.index(base):])
return {t}
def _parse_target_dok(t: str):
order = ["DOK1","DOK2","DOK3","DOK4"]
if "-" in t:
lo, hi = t.split("-")
return set(order[order.index(lo):order.index(hi)+1])
return {t}
bloom_target_set = _parse_target_bloom(target_bloom)
dok_target_set = _parse_target_dok(target_dok)
bloom_best = res["blooms"]["best_level"]
dok_best = res["dok"]["best_level"]
bloom_ok = bloom_best in bloom_target_set
dok_ok = dok_best in dok_target_set
feedback_parts = []
if not bloom_ok:
feedback_parts.append(
f"Shift Bloom’s from {bloom_best} toward {sorted(bloom_target_set)}. "
f"Top cues: {res['blooms']['top_phrases'].get(bloom_best, [])[:3]}"
)
if not dok_ok:
feedback_parts.append(
f"Shift DOK from {dok_best} toward {sorted(dok_target_set)}. "
f"Top cues: {res['dok']['top_phrases'].get(dok_best, [])[:3]}"
)
return {
"ok": bool(bloom_ok and dok_ok),
"measured": {
"bloom_best": bloom_best,
"bloom_scores": res["blooms"]["scores"],
"dok_best": dok_best,
"dok_scores": res["dok"]["scores"],
},
"feedback": " ".join(feedback_parts) if feedback_parts else "On target.",
} |