MCP-서버 리팩토링 후 P&ID 추출 테스트전 다른 기능 확인 후 커밋
This commit is contained in:
windpacer
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mcp-server/worker/pid_worker_py.md
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526
mcp-server/worker/pid_worker_py.md
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❯ pid_worker.py를 만들어줘
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● 먼저 현재 디렉토리 구조를 확인하고 파일을 작성합니다.
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Listed 1 directory (ctrl+o to expand)
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● Bash(mkdir -p /home/windpacer/projects/ExperionCrawler/mcp-server/worker)
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⎿ Done
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● Write(mcp-server/worker/pid_worker.py)
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────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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Create file
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mcp-server/worker/pid_worker.py
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╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
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1 #!/usr/bin/env python3
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2 """P&ID 파싱 전용 워커 프로세스
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3
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4 Usage: python pid_worker.py <port>
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5
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6 담당 도구:
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7 extract_pid_tags, match_pid_tags,
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8 parse_pid_dxf, parse_pid_pdf, parse_pid_drawing,
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9 build_pid_graph_parallel, analyze_pid_impact
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10 """
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11
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12 from __future__ import annotations
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13 import sys
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14 import os
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15
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16 # mcp-server 디렉토리를 Python 경로에 추가 (pipeline 패키지 접근)
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17 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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18
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19 import io
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20 import json
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21 import asyncio
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22 import signal
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23 import logging
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24 import re
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25 from functools import lru_cache
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26
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27 from fastapi import FastAPI, Request
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28 import uvicorn
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29
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30 # ── 설정 ─────────────────────────────────────────────────────────────────────
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31
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32 VLLM_BASE_URL = "http://localhost:8000/v1"
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33 VLLM_MODEL = "Qwen/Qwen3-Coder-Next-FP8"
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34 DB_CONNECTION_STRING = "postgresql://postgres:postgres@localhost:5432/iiot_platform"
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35 DB_TIMEOUT = 10
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36
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37 _SERVER_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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38 STORAGE_DIR = os.path.join(_SERVER_DIR, "storage")
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39
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40 logging.basicConfig(
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41 level=logging.INFO,
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42 stream=sys.stderr,
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43 format="%(asctime)s [pid_worker] %(levelname)s %(message)s",
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44 )
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45
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46 app = FastAPI()
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47
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48 # ── 싱글톤 ───────────────────────────────────────────────────────────────────
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49
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50 @lru_cache(maxsize=1)
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51 def _llm():
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52 from openai import OpenAI
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53 return OpenAI(base_url=VLLM_BASE_URL, api_key="dummy")
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54
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55
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56 @lru_cache(maxsize=1)
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57 def _ocr():
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58 from paddleocr import PaddleOCR
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59 use_gpu = os.environ.get("PADDLE_USE_GPU", "true").lower() == "true"
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60 try:
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61 return PaddleOCR(use_angle_cls=True, lang="korean", use_gpu=use_gpu, show_log=False)
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62 except Exception:
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63 if use_gpu:
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64 os.environ["PADDLE_USE_GPU"] = "false"
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65 return _ocr()
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66 raise
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67
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68 # ── DB ───────────────────────────────────────────────────────────────────────
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69
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70 def _get_db_connection():
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71 import psycopg
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72 return psycopg.connect(DB_CONNECTION_STRING, connect_timeout=DB_TIMEOUT)
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73
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74 # ── 텍스트 추출 ──────────────────────────────────────────────────────────────
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75
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76 def _extract_text_from_dxf(filepath: str) -> str:
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77 import ezdxf
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78 from ezdxf.tools.text import plain_mtext
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79 doc = ezdxf.readfile(filepath)
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80 msp = doc.modelspace()
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81 texts = []
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82 for entity in msp:
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83 if entity.dxftype() == "TEXT":
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84 texts.append(entity.dxf.text)
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85 elif entity.dxftype() == "MTEXT":
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86 try:
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87 plain = plain_mtext(entity.dxf.text)
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88 if plain.strip():
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89 texts.append(plain)
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90 except Exception:
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91 pass
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92 return "\n".join(texts)
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93
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94
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95 def _extract_text_from_pdf(filepath: str) -> str:
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96 import fitz
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97 doc = fitz.open(filepath)
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98 return "\n".join(page.get_text() for page in doc)
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99
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100
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101 def _extract_text_from_pdf_ocr(filepath: str) -> str:
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102 import fitz
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103 from PIL import Image
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104 import numpy as np
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105 doc = fitz.open(filepath)
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106 all_texts = []
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107 for page in doc:
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108 mat = fitz.Matrix(300 / 72)
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109 pix = page.get_pixmap(matrix=mat)
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110 img = Image.open(io.BytesIO(pix.tobytes("png")))
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111 result = _ocr().ocr(np.array(img), cls=True)
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112 if result and result[0]:
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113 all_texts.extend(line[1][0] for line in result[0])
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114 return "\n".join(all_texts)
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115
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116 # ── JSON 배열 파싱 유틸 ───────────────────────────────────────────────────────
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117
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118 def _parse_json_array(raw: str, finish_reason: str = "") -> list:
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119 """LLM 출력에서 JSON 배열 추출. finish_reason=length 잘림 복구 포함."""
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120 if raw.startswith("```"):
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121 lines = raw.splitlines()
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122 raw = "\n".join(lines[1:-1] if lines and lines[-1].strip() == "```" else lines[1:]).strip()
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123
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124 if finish_reason == "length":
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125 last_close = raw.rfind("}")
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126 if last_close != -1:
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127 raw = raw[:last_close + 1] + "]"
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128
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129 # 가장 긴 균형 잡힌 [...] 추출
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130 depth = 0; start = -1; best = ""
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131 for i, c in enumerate(raw):
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132 if c == "[":
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133 if depth == 0:
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134 start = i
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135 depth += 1
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136 elif c == "]":
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137 depth -= 1
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138 if depth == 0 and start >= 0:
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139 cand = raw[start:i + 1]
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140 if len(cand) > len(best):
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141 best = cand
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142 raw = best if best else "[]"
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143
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144 try:
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145 return json.loads(raw)
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146 except json.JSONDecodeError:
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147 data = []
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148 for obj in re.findall(r"\{[^{}]*\}", raw, re.DOTALL):
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149 try:
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150 data.append(json.loads(obj))
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151 except json.JSONDecodeError:
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152 pass
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153 return data
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154
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155 # ── 태그 추출/매핑 도구 ───────────────────────────────────────────────────────
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156
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157 def _extract_pid_tags(text: str, source_type: str) -> str:
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158 system = (
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159 "You are a P&ID (Piping and Instrumentation Diagram) expert.\n"
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160 "Extract all instrument and equipment tags from the provided text.\n"
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161 "Return ONLY a valid JSON array. Each element must have exactly these fields:\n"
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162 '{"tagNo":"FCV-101","equipmentName":null,"instrumentType":"FCV",'
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163 '"lineNumber":null,"pidDrawingNo":null,"confidence":0.95}\n'
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164 "Rules:\n"
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165 "- tagNo: any token matching [LETTERS]-[DIGITS] or [LETTERS]-[DIGITS]-[SUFFIX]\n"
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166 " Examples: FCV-101, P-10101, T-10100, VG-6203-15A-F1A-n, BT-6200, DP-10101\n"
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167 "- instrumentType: leading letters of tagNo\n"
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168 "- equipmentName: descriptive name if present near tag, else null\n"
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169 "- lineNumber/pidDrawingNo: null unless explicitly associated\n"
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170 "- confidence: 0.95 for clear tags, lower for ambiguous\n"
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171 "- Output ONLY the JSON array, no markdown, no explanation.\n"
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172 "- If no tags found, return: []\n"
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173 )
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174 truncated = text[:100000]
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175 resp = _llm().chat.completions.create(
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176 model=VLLM_MODEL,
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177 messages=[
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178 {"role": "system", "content": system},
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179 {"role": "user", "content": f"Source: {source_type}\n\nText:\n{truncated}"},
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180 ],
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181 max_tokens=32768,
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182 temperature=0.1,
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183 extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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184 )
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185 raw = (resp.choices[0].message.content or "").strip()
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186 data = _parse_json_array(raw, resp.choices[0].finish_reason)
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187 logging.info(f"extract_pid_tags source={source_type} count={len(data)}")
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188 return json.dumps({"success": True, "count": len(data), "tags": data},
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189 ensure_ascii=False, indent=2)
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190
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191
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192 def _match_pid_tags(pid_tags: list, experion_tags: list) -> str:
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193 system = (
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194 "You are a P&ID to Experion tag matching expert.\n"
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195 "Match P&ID tags to Experion tags based on similarity.\n"
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196 "Return ONLY a JSON array:\n"
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197 '[{"pidTag":"FT-101","experionTag":"ft-101.pv","confidence":0.92},...]\n'
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198 "- If no good match: confidence < 0.5, experionTag null\n"
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199 "- Output ONLY the JSON array.\n"
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200 )
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201 resp = _llm().chat.completions.create(
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202 model=VLLM_MODEL,
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203 messages=[
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204 {"role": "system", "content": system},
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205 {"role": "user", "content": (
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206 f"P&ID Tags:\n{chr(10).join(pid_tags)}\n\n"
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207 f"Experion Tags:\n{chr(10).join(experion_tags)}"
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208 )},
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209 ],
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210 max_tokens=16384,
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211 temperature=0.1,
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212 extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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213 )
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214 raw = (resp.choices[0].message.content or "").strip()
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215 data = _parse_json_array(raw, resp.choices[0].finish_reason)
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216 return json.dumps({"success": True, "count": len(data), "mappings": data},
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217 ensure_ascii=False, indent=2)
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218
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219 # ── 도면 파싱 도구 ────────────────────────────────────────────────────────────
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220
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221 _TAG_EXTRACT_SYSTEM = (
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222 "You are a P&ID (Piping and Instrumentation Diagram) expert.\n"
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223 "Extract instrument and equipment tags from the provided text.\n"
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224 "Return ONLY a JSON array:\n"
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225 '[{"tagNo":"FIT-10115","equipmentName":"Flow Transmitter","instrumentType":"FIT",'
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226 '"lineNumber":"L-101","pidDrawingNo":"P&ID-001","confidence":0.95},...]\n'
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227 "Rules:\n"
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228 "- tagNo: Instrument [Function]-[Number], Equipment [Type]-[Number]\n"
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229 "- instrumentType: first 2-4 letters of tagNo\n"
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230 "- equipmentName/lineNumber/pidDrawingNo: null if not present\n"
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231 "- confidence: 0.0 to 1.0\n"
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232 "- Output ONLY the JSON array, no markdown.\n"
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233 "- If no tags found, return: []\n"
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234 )
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235
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236
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237 def _parse_pid_dxf(filepath: str) -> str:
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238 text = _extract_text_from_dxf(filepath)
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239 if not text.strip():
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240 return json.dumps({"success": True, "text": "", "count": 0, "tags": []},
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241 ensure_ascii=False, indent=2)
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242
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243 resp = _llm().chat.completions.create(
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244 model=VLLM_MODEL,
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245 messages=[
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246 {"role": "system", "content": _TAG_EXTRACT_SYSTEM},
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247 {"role": "user", "content": f"Source: dxf\n\nText:\n{text[:12000]}"},
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248 ],
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249 max_tokens=4096,
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250 temperature=0.1,
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251 )
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252 raw = (resp.choices[0].message.content or "").strip()
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253 data = _parse_json_array(raw, resp.choices[0].finish_reason)
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254 if not isinstance(data, list):
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255 data = []
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256 return json.dumps({"success": True, "text": text[:10000], "count": len(data), "tags": data},
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257 ensure_ascii=False, indent=2)
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258
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259
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260 def _parse_pid_pdf(filepath: str, use_ocr: bool = True) -> str:
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261 text = _extract_text_from_pdf_ocr(filepath) if use_ocr else _extract_text_from_pdf(filepath)
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262 if not text.strip():
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263 return json.dumps({"success": True, "text": "", "count": 0, "tags": []},
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264 ensure_ascii=False, indent=2)
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265
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266 resp = _llm().chat.completions.create(
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267 model=VLLM_MODEL,
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268 messages=[
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269 {"role": "system", "content": _TAG_EXTRACT_SYSTEM},
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270 {"role": "user", "content": f"Source: pdf\n\nText:\n{text[:12000]}"},
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271 ],
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272 max_tokens=4096,
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273 temperature=0.1,
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274 )
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275 raw = (resp.choices[0].message.content or "").strip()
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276 data = _parse_json_array(raw, resp.choices[0].finish_reason)
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277 if not isinstance(data, list):
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278 data = []
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279 return json.dumps({"success": True, "text": text[:10000], "count": len(data), "tags": data},
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280 ensure_ascii=False, indent=2)
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281
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282
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283 def _parse_pid_drawing(filepath: str) -> str:
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284 ext = os.path.splitext(filepath)[1].lower()
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285 if ext == ".dxf":
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286 return _parse_pid_dxf(filepath)
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287 elif ext == ".pdf":
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288 return _parse_pid_pdf(filepath)
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289 elif ext == ".dwg":
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290 return json.dumps({
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291 "success": False,
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292 "error": "DWG 파일은 직접 파싱할 수 없습니다. DXF로 변환 후 사용하세요.",
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293 }, ensure_ascii=False)
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294 else:
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295 return json.dumps({
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296 "success": False,
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297 "error": f"지원하지 않는 형식: {ext}. 지원: .dxf, .pdf",
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298 }, ensure_ascii=False)
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299
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300 # ── 그래프 도구 ───────────────────────────────────────────────────────────────
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301
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302 async def _build_pid_graph_parallel(filepath: str) -> str:
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303 from pipeline.extractor import PidGeometricExtractor
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304 from pipeline.topology import PidTopologyBuilder
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305 from pipeline.mapper import IntelligentMapper
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306 from openai import AsyncOpenAI
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307
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308 os.makedirs(STORAGE_DIR, exist_ok=True)
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309
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310 # Phase 1: 기하 추출
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311 extractor = PidGeometricExtractor(filepath)
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312 geo_data_path = os.path.join(STORAGE_DIR, os.path.basename(filepath) + "_geo.json")
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313 extractor.extract_and_save(geo_data_path)
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314 with open(geo_data_path, "r", encoding="utf-8") as f:
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315 geo_data = json.load(f)
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316
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317 # 시스템 태그 조회
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318 system_tags: list[str] = []
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319 try:
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320 conn = _get_db_connection()
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321 with conn.cursor() as cur:
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322 cur.execute("SELECT tagname FROM realtime_table")
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323 system_tags = [r[0] for r in cur.fetchall()]
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324 except Exception as e:
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325 logging.warning(f"시스템 태그 조회 실패: {e}")
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326
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327 # Phase 2: 1차 위상 빌더 (Mapper용 그래프)
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328 builder = PidTopologyBuilder(geo_data)
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329 builder.build_graph()
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330
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331 # Phase 3: 병렬 LLM 매핑
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332 api_client = AsyncOpenAI(base_url=VLLM_BASE_URL, api_key="dummy")
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333 mapper = IntelligentMapper(builder.G, system_tags, api_client=api_client)
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334
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335 transmitter_nodes = [
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336 n for n, d in builder.G.nodes(data=True)
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337 if d.get("value", "").upper() in {"FIT", "FT", "LT", "PT", "TE"}
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338 ]
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339 valve_nodes = [
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340 n for n, d in builder.G.nodes(data=True)
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341 if d.get("value", "").upper() in {"FCV", "LCV", "TCV", "PCV", "XV"}
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342 ]
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343 equipment_nodes = [
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344 n for n, d in builder.G.nodes(data=True)
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345 if d.get("type") not in {"TEXT", "LINE", "LWPOLYLINE"}
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346 ]
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347
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348 extracted_results = await asyncio.gather(
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349 mapper.extract_transmitters(transmitter_nodes),
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350 mapper.extract_valves(valve_nodes),
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351 mapper.extract_equipment(equipment_nodes),
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352 )
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353
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354 # 매핑 결과 통합
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355 all_mapped_tags = []
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356 for res_dict in extracted_results:
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357 for node_id, mapping in res_dict.items():
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358 if mapping.resolved_tag != "UNKNOWN":
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359 node_data = builder.G.nodes[node_id]
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360 all_mapped_tags.append({
|
||||
361 "entity_id": node_id,
|
||||
362 "tagName": mapping.resolved_tag,
|
||||
363 "bbox": (
|
||||
364 node_data["bbox"].bounds
|
||||
365 if hasattr(node_data["bbox"], "bounds")
|
||||
366 else node_data["bbox"]
|
||||
367 ),
|
||||
368 "clean_value": mapping.resolved_tag,
|
||||
369 })
|
||||
370
|
||||
371 # Phase 4: 최종 위상 모델링 + 저장
|
||||
372 final_builder = PidTopologyBuilder(geo_data, all_extracted_tags=all_mapped_tags)
|
||||
373 final_builder.build_graph()
|
||||
374
|
||||
375 graph_id = os.path.basename(filepath).replace(".dxf", "_graph.json")
|
||||
376 graph_path = os.path.join(STORAGE_DIR, graph_id)
|
||||
377 final_builder.save_graph(graph_path)
|
||||
378
|
||||
379 logging.info(f"build_pid_graph_parallel graph_id={graph_id} "
|
||||
380 f"nodes={final_builder.G.number_of_nodes()} "
|
||||
381 f"edges={final_builder.G.number_of_edges()}")
|
||||
382 return json.dumps({
|
||||
383 "success": True,
|
||||
384 "graph_id": graph_id,
|
||||
385 "graph_path": graph_path,
|
||||
386 "nodes": final_builder.G.number_of_nodes(),
|
||||
387 "edges": final_builder.G.number_of_edges(),
|
||||
388 }, ensure_ascii=False)
|
||||
389
|
||||
390
|
||||
391 def _analyze_pid_impact(graph_id: str, start_node_id: str) -> str:
|
||||
392 from pipeline.analyzer import PidAnalysisEngine
|
||||
393
|
||||
394 graph_path = os.path.join(STORAGE_DIR, graph_id)
|
||||
395 mapping_path = graph_path.replace("_graph.json", "_mapping.json")
|
||||
396 analyzer = PidAnalysisEngine(graph_path, mapping_path)
|
||||
397 result = analyzer.analyze_impact(start_node_id)
|
||||
398 return json.dumps(result, ensure_ascii=False, indent=2)
|
||||
399
|
||||
400 # ── 요청 디스패처 ─────────────────────────────────────────────────────────────
|
||||
401
|
||||
402 async def _dispatch(tool: str, params: dict) -> str:
|
||||
403 try:
|
||||
404 match tool:
|
||||
405 case "extract_pid_tags":
|
||||
406 return _extract_pid_tags(**params)
|
||||
407 case "match_pid_tags":
|
||||
408 return _match_pid_tags(**params)
|
||||
409 case "parse_pid_dxf":
|
||||
410 return _parse_pid_dxf(**params)
|
||||
411 case "parse_pid_pdf":
|
||||
412 return _parse_pid_pdf(**params)
|
||||
413 case "parse_pid_drawing":
|
||||
414 return _parse_pid_drawing(**params)
|
||||
415 case "build_pid_graph_parallel":
|
||||
416 return await _build_pid_graph_parallel(**params)
|
||||
417 case "analyze_pid_impact":
|
||||
418 return _analyze_pid_impact(**params)
|
||||
419 case _:
|
||||
420 return json.dumps({"success": False, "error": f"알 수 없는 도구: {tool}"},
|
||||
421 ensure_ascii=False)
|
||||
422 except Exception as e:
|
||||
423 logging.error(f"dispatch error tool={tool}: {e}", exc_info=True)
|
||||
424 return json.dumps({"success": False, "error": str(e)}, ensure_ascii=False)
|
||||
425
|
||||
426 # ── 종료 예약 ─────────────────────────────────────────────────────────────────
|
||||
427
|
||||
428 def _schedule_shutdown():
|
||||
429 """응답 전송 완료 후 0.5초 뒤 프로세스 종료 예약."""
|
||||
430 async def _do():
|
||||
431 await asyncio.sleep(0.5)
|
||||
432 os.kill(os.getpid(), signal.SIGTERM)
|
||||
433 asyncio.create_task(_do())
|
||||
434
|
||||
435 # ── HTTP 엔드포인트 ───────────────────────────────────────────────────────────
|
||||
436
|
||||
437 @app.get("/health")
|
||||
438 async def health():
|
||||
439 return {"status": "ok"}
|
||||
├─────┼───────────────────────────────┼────────┤
|
||||
│ 9 │ atexit 정리 훅 │ 쉬움 │
|
||||
└─────┴───────────────────────────────┴────────┘
|
||||
|
||||
실제 워커 파일(worker/rag_worker.py 등)을 작성할까요, 아니면 계획서를 수정 반영하는 것으로 마무리할까요?
|
||||
|
||||
✻ Brewed for 1m 12s
|
||||
|
||||
※ recap: MCP 서버 병렬 아키텍처 설계 문서 진단 및 수정 방향 제시 완료. 다음 단계로 실제 워커 파일 작성 또는 계획서 수정
|
||||
반영 중 선택이 필요합니다. (disable recaps in /config)
|
||||
|
||||
❯ pid_worker.py를 만들어줘
|
||||
|
||||
● 먼저 현재 디렉토리 구조를 확인하고 파일을 작성합니다.
|
||||
|
||||
Listed 1 directory (ctrl+o to expand)
|
||||
|
||||
● Bash(mkdir -p /home/windpacer/projects/ExperionCrawler/mcp-server/worker)
|
||||
⎿ Done
|
||||
|
||||
● Write(mcp-server/worker/pid_worker.py)
|
||||
⎿ Wrote 461 lines to mcp-server/worker/pid_worker.py
|
||||
1 #!/usr/bin/env python3
|
||||
2 """P&ID 파싱 전용 워커 프로세스
|
||||
3
|
||||
4 Usage: python pid_worker.py <port>
|
||||
5
|
||||
6 담당 도구:
|
||||
7 extract_pid_tags, match_pid_tags,
|
||||
8 parse_pid_dxf, parse_pid_pdf, parse_pid_drawing,
|
||||
9 build_pid_graph_parallel, analyze_pid_impact
|
||||
10 """
|
||||
… +451 lines (ctrl+o to expand)
|
||||
|
||||
● 문법 오류 없는지 확인합니다.
|
||||
|
||||
● Bash(cd /home/windpacer/projects/ExperionCrawler/mcp-server && .venv/bin/python -m py_compile worker/pid_worker.py &&
|
||||
echo "OK")
|
||||
⎿ OK
|
||||
|
||||
● 문법 오류 없음. 완성된 파일을 확인합니다.
|
||||
|
||||
Read 1 file (ctrl+o to expand)
|
||||
|
||||
● mcp-server/worker/pid_worker.py (461줄) 생성 완료. 진단에서 발견한 수정 사항이 모두 반영됐습니다.
|
||||
|
||||
---
|
||||
적용된 수정 목록
|
||||
|
||||
┌──────────┬────────────────────────────────────────────────────────────────────────────────────┐
|
||||
│ Fix │ 내용 │
|
||||
├──────────┼────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ ✅ Fix 1 │ RAG 워커 혼입 없음 — P&ID 도구만 포함 │
|
||||
├──────────┼────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ ✅ Fix 2 │ app = FastAPI() 선언 │
|
||||
├──────────┼────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ ✅ Fix 3 │ _schedule_shutdown() — asyncio.create_task로 0.5초 지연 후 SIGTERM, 응답 먼저 반환 │
|
||||
├──────────┼────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ ✅ Fix 4 │ Request.json() 바디 파싱 │
|
||||
├──────────┼────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ ✅ Fix 7 │ /health 엔드포인트 추가 (메인 서버 헬스체크 루프용) │
|
||||
├──────────┼────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ ✅ Fix 8 │ 워커는 stdout/stderr 제어 없음, 메인 서버에서 DEVNULL로 실행 │
|
||||
└──────────┴────────────────────────────────────────────────────────────────────────────────────┘
|
||||
|
||||
구조 요약
|
||||
|
||||
/execute → 일반 요청 (워커 유지)
|
||||
/execute/one_shot → P&ID 요청 후 자동 종료
|
||||
/health → 워커 준비 확인용
|
||||
|
||||
pipeline 모듈은 sys.path 조작으로 mcp-server/pipeline/을 자동 인식합니다. 실행: python worker/pid_worker.py 5004
|
||||
Reference in New Issue
Block a user