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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 | import os
import json
import logging
from openai import AsyncOpenAI
from typing import Dict, Any
from dotenv import load_dotenv
load_dotenv()
logger = logging.getLogger(__name__)
from app.url_utils import openai_compatible_api_key, sanitize_env
from app.llm_client import get_llm_client
class ParserAgent:
def __init__(self):
self.llm = get_llm_client()
async def process(self, text: str, feedback: str = None, context: Dict[str, Any] = None) -> Dict[str, Any]:
logger.info(f"==[ParserAgent] Processing input (len={len(text)})==")
if feedback:
logger.warning(f"[ParserAgent] Feedback from previous attempt: {feedback}")
if context:
logger.info(f"[ParserAgent] Using previous context (dsl_len={len(context.get('geometry_dsl', ''))})")
system_prompt = """
You are a Geometry Parser Agent. Extract geometric entities and constraints from Vietnamese/LaTeX math problem text.
=== CONTEXT AWARENESS ===
If previous context is provided, it means this is a follow-up request.
- Combine old entities with new ones.
- Update 'analysis' to reflect the entire problem state.
Output ONLY a JSON object with this EXACT structure (no extra keys, no markdown):
{
"entities": ["Point A", "Point B", ...],
"type": "pyramid|prism|sphere|rectangle|triangle|circle|parallelogram|trapezoid|square|rhombus|general",
"values": {"AB": 5, "SO": 15, "radius": 3},
"target_question": "Câu hỏi cụ thể cần giải (ví dụ: 'Tính diện tích tam giác ABC'). NẾU KHÔNG CÓ CÂU HỎI THÌ ĐỂ null.",
"analysis": "Tóm tắt ngắn gọn toàn bộ bài toán sau khi đã cập nhật các yêu cầu mới bằng tiếng Việt."
}
Rules:
- "analysis" MUST be a meaningful and UP-TO-DATE summary of the problem in Vietnamese.
- "target_question" must be concise.
- Include midpoints, auxiliary points in "entities" if mentioned.
- If feedback is provided, correct your previous output accordingly.
"""
user_content = f"Text: {text}"
if context:
user_content = f"PREVIOUS ANALYSIS: {context.get('analysis')}\nNEW REQUEST: {text}"
if feedback:
user_content += f"\nFeedback from previous attempt: {feedback}. Please correct the constraints."
logger.debug("[ParserAgent] Calling LLM (Multi-Layer)...")
raw = await self.llm.chat_completions_create(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_content}
],
response_format={"type": "json_object"}
)
# Pre-process raw string: extract the JSON block if present
import re
clean_raw = raw.strip()
# Handle potential markdown code blocks
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:
result = json.loads(clean_raw)
except json.JSONDecodeError as e:
logger.error(f"[ParserAgent] JSON Parse Error: {e}. Attempting regex fallback...")
import re
json_match = re.search(r'(\{.*\})', clean_raw, re.DOTALL)
if json_match:
try:
# Handle single quotes if present (common LLM failure)
json_str = json_match.group(1)
if "'" in json_str and '"' not in json_str:
json_str = json_str.replace("'", '"')
result = json.loads(json_str)
except:
result = None
else:
result = None
if not result:
# Fallback for critical failure
result = {
"entities": [],
"type": "general",
"values": {},
"target_question": None,
"analysis": text
}
logger.info(f"[ParserAgent] LLM response received.")
logger.debug(f"[ParserAgent] Parsed JSON: {json.dumps(result, ensure_ascii=False, indent=2)}")
return result
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