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395651c | 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 | import json
import logging
import sympy as sp
from typing import Dict, Any, List
from app.llm_client import get_llm_client
logger = logging.getLogger(__name__)
class SolverAgent:
def __init__(self):
self.llm = get_llm_client()
async def solve(self, semantic_data: Dict[str, Any], engine_result: Dict[str, Any]) -> Dict[str, Any]:
"""
Solves the geometric problem based on coordinates and the target question.
Returns a 'solution' dictionary with answer, steps, and symbolic_expression.
"""
target_question = semantic_data.get("target_question")
if not target_question:
# If no question, just return an empty solution structure
return {
"answer": None,
"steps": [],
"symbolic_expression": None
}
logger.info(f"==[SolverAgent] Solving for: '{target_question}'==")
input_text = semantic_data.get("input_text", "")
coordinates = engine_result.get("coordinates", {})
# We provide the coordinates and semantic context to the LLM to help it reason.
# The LLM is tasked with generating the solution structure directly.
system_prompt = """
You are a Geometry Solver Agent. Your goal is to provide a step-by-step solution for a specific geometric question.
=== DATA PROVIDED ===
1. Target Question: The specific question to answer.
2. Geometry Data: Entities and values extracted from the problem.
3. Coordinates: Calculated coordinates for all points.
=== REQUIREMENTS ===
- Provide the solution in the SAME LANGUAGE as the user's input.
- Use SymPy concepts if appropriate.
- Steps should be clear, concise, and logical.
- The final answer should be numerically or symbolically accurate based on the coordinates and geometric properties.
- For geometric proofs (e.g., "Is AB perpendicular to AC?"), explain the reasoning based on the data.
Output ONLY a JSON object with this structure:
{
"answer": "Chuỗi văn bản kết quả cuối cùng (kèm đơn vị nếu có)",
"steps": [
"Bước 1: ...",
"Bước 2: ...",
...
],
"symbolic_expression": "Biểu thức toán học rút gọn (LaTeX format optional)"
}
"""
user_content = f"""
INPUT_TEXT: {input_text}
TARGET_QUESTION: {target_question}
SEMANTIC_DATA: {json.dumps(semantic_data, ensure_ascii=False)}
COORDINATES: {json.dumps(coordinates)}
"""
logger.debug("[SolverAgent] Requesting solution from LLM...")
try:
raw = await self.llm.chat_completions_create(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_content}
],
response_format={"type": "json_object"}
)
clean_raw = raw.strip()
# Handle potential markdown code blocks if the response_format wasn't strictly honored
if clean_raw.startswith("```"):
import re
match = re.search(r"```(?:json)?\s*(.*?)\s*```", clean_raw, re.DOTALL)
if match:
clean_raw = match.group(1).strip()
try:
solution = json.loads(clean_raw)
except json.JSONDecodeError:
# Last resort: try to find anything between { and }
import re
json_match = re.search(r'(\{.*\})', clean_raw, re.DOTALL)
if json_match:
solution = json.loads(json_match.group(1))
else:
raise
logger.info("[SolverAgent] Solution generated successfully.")
return solution
except Exception as e:
logger.error(f"[SolverAgent] Error generating solution: {e}")
logger.debug(f"[SolverAgent] Raw LLM output was: \n{raw if 'raw' in locals() else 'N/A'}")
return {
"answer": "Không thể tính toán lời giải tại thời điểm này.",
"steps": ["Đã xảy ra lỗi trong quá trình xử lý lời giải."],
"symbolic_expression": None
}
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