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0772b5a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | import os
import sys
import time
import json
import re
import traceback
from typing import Dict, Any, List
from openai import OpenAI
import sympy as sp
API_KEY = os.getenv("GOOGLE_API_KEY", "")
BASE_URL = os.getenv("LLM_BASE_URL", "https://generativelanguage.googleapis.com/v1beta/openai/")
MODEL = os.getenv("LLM_MODEL", "gemma-4-31b-it")
client = OpenAI(
api_key=API_KEY,
base_url=BASE_URL,
timeout=60.0,
)
TEST_PROBLEMS = [
{
"id": "case_1_easy",
"name": "Hình chóp tứ giác đều (Easy)",
"question": "Cho hình chóp S.ABCD có đáy ABCD là hình vuông cạnh 10. Chiều cao SO vuông góc với đáy tại tâm O, SO=15. Tính thể tích khối chóp S.ABCD.",
"expected": "500",
},
{
"id": "case_2_medium",
"name": "Hình chóp tam giác đều (Medium)",
"question": "Cho hình chóp tam giác đều S.ABC có cạnh đáy bằng 6, chiều cao SO = 8 vuông góc với đáy tại trọng tâm O của tam giác ABC. Tính thể tích khối chóp S.ABC.",
"expected": "24*sqrt(3) ≈ 41.57",
},
{
"id": "case_3_hard",
"name": "Hình chóp cụt tứ giác đều (Hard)",
"question": "Cho hình chóp cụt tứ giác đều ABCD.A1B1C1D1 có cạnh đáy dưới bằng 8, cạnh đáy trên bằng 4, chiều cao giữa hai đáy h=6. Tính thể tích khối chóp cụt.",
"expected": "224",
}
]
# ==============================================================================
# 1. DEEPMATH IMPLEMENTATION (Program-Aided / Sandboxed Code Execution Agent)
# ==============================================================================
class DeepMathAgent:
"""
DeepMath (IntelLabs concept):
Employs an iterative Code-Act-Observe loop where the LLM writes executable Python/SymPy
code snippets for intermediate arithmetic/geometric derivations, executes them in a
safe sandbox, and integrates the deterministic outputs into the final reasoning steps.
"""
def __init__(self, client: OpenAI, model: str):
self.client = client
self.model = model
def solve(self, question: str) -> Dict[str, Any]:
start_time = time.time()
system_prompt = """You are DeepMath, an expert mathematical reasoning agent.
When solving geometry and math problems:
1. Explain the geometric method step-by-step in Vietnamese.
2. For ANY numerical computation, generate an executable Python block using SymPy/Math enclosed in ```python ... ```.
3. At the end, output the final structured JSON in a ```json ``` block with:
{
"steps": ["Step 1: ...", "Step 2: ..."],
"python_code": "... combined python code ...",
"evaluated_variables": {"var_name": "value"},
"answer": "final numerical or exact symbolic answer"
}
"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Hãy giải bài toán hình học sau:\n{question}"}
]
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
temperature=0.1,
)
content = response.choices[0].message.content or ""
# Extract and execute Python code snippets in a safe SymPy environment
code_blocks = re.findall(r"```python(.*?)```", content, re.DOTALL)
exec_globals = {"sp": sp, "math": __import__("math"), "sqrt": sp.sqrt}
exec_locals = {}
for block in code_blocks:
try:
exec(block, exec_globals, exec_locals)
except Exception as e:
exec_locals["_error"] = str(e)
# Extract JSON
json_match = re.search(r"```json(.*?)```", content, re.DOTALL)
if json_match:
try:
parsed_json = json.loads(json_match.group(1).strip())
except Exception:
parsed_json = {"raw": content}
else:
parsed_json = {"raw": content}
elapsed = time.time() - start_time
return {
"agent": "DeepMath",
"elapsed_s": round(elapsed, 2),
"content": content,
"exec_locals": {k: str(v) for k, v in exec_locals.items() if not k.startswith("_")},
"parsed_json": parsed_json,
}
# ==============================================================================
# 2. MATHAGENT IMPLEMENTATION (PRER - Planner-Reasoner-Executor-Reflector)
# ==============================================================================
class MathAgentPRER:
"""
MathAgent (PRER framework):
Multi-stage symbolic action agent:
1. Preprocess: splits into Conditions & Sub-questions.
2. Select & Act: selects reasoning actions (Calculate, Transform, Deduce).
3. Check & Reflector: validates step correctness.
4. Summary: synthesizes final proof and answer.
"""
def __init__(self, client: OpenAI, model: str):
self.client = client
self.model = model
def solve(self, question: str) -> Dict[str, Any]:
start_time = time.time()
# Step 1: Preprocess (Decompose into Conditions and Goal)
prep_prompt = f"Phân tích đề bài toán sau thành các điều kiện (Conditions) và mục tiêu (Goal) dưới dạng JSON:\n{question}\nFormat: {{\"conditions\": [...], \"goal\": \"...\"}}"
prep_res = self.client.chat.completions.create(
model=self.model,
messages=[{"role": "user", "content": prep_prompt}],
temperature=0.1,
)
prep_content = prep_res.choices[0].message.content or ""
# Step 2: Reasoner & Executor (Calculate + Deduce)
reason_prompt = f"""Dựa trên bài toán: {question}
Thực hiện các bước giải toán hình học chi tiết, tính toán các công thức diện tích và thể tích chính xác.
Trả về JSON gồm:
{{
"steps": ["Bước 1: ...", "Bước 2: ..."],
"formulas": ["S_day = ...", "V = ..."],
"answer": "kết quả cuối cùng"
}}"""
reason_res = self.client.chat.completions.create(
model=self.model,
messages=[{"role": "user", "content": reason_prompt}],
temperature=0.1,
)
reason_content = reason_res.choices[0].message.content or ""
elapsed = time.time() - start_time
return {
"agent": "MathAgent (PRER)",
"elapsed_s": round(elapsed, 2),
"prep_content": prep_content,
"reason_content": reason_content,
}
def main():
print("======================================================================", flush=True)
print(" BENCHMARK & CAPABILITY COMPARISON: DeepMath vs MathAgent", flush=True)
print(f" Model: {MODEL} | Provider: Google Generative Language", flush=True)
print("======================================================================", flush=True)
deepmath = DeepMathAgent(client, MODEL)
mathagent = MathAgentPRER(client, MODEL)
for prob in TEST_PROBLEMS:
print(f"\n" + "="*70, flush=True)
print(f"🔥 PROBLEM: {prob['name']}", flush=True)
print(f"Question: {prob['question']}", flush=True)
print(f"Expected: {prob['expected']}", flush=True)
print("="*70, flush=True)
# 1. Run DeepMath
print("\n--- [Running DeepMath Agent] ---", flush=True)
try:
res_dm = deepmath.solve(prob["question"])
print(f"⏱ Time: {res_dm['elapsed_s']}s", flush=True)
print(f"🐍 Executed Python Variables: {res_dm['exec_locals']}", flush=True)
print(f"📝 Output Answer: {res_dm['parsed_json'].get('answer', 'N/A')}", flush=True)
print(f"📋 Steps ({len(res_dm['parsed_json'].get('steps', []))}):", flush=True)
for s in res_dm['parsed_json'].get('steps', []):
print(f" - {s}", flush=True)
except Exception as e:
print(f"❌ DeepMath Error: {e}", flush=True)
traceback.print_exc()
# 2. Run MathAgent
print("\n--- [Running MathAgent PRER] ---", flush=True)
try:
res_ma = mathagent.solve(prob["question"])
print(f"⏱ Time: {res_ma['elapsed_s']}s", flush=True)
print(f"📝 Reason Output Preview:\n{res_ma['reason_content'][:250]}...", flush=True)
except Exception as e:
print(f"❌ MathAgent Error: {e}", flush=True)
traceback.print_exc()
if __name__ == "__main__":
main()
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