doc_parser_skill: - New: verify_flowchart.py (flowchart validation) - Updated: LLM.py (multi-provider: DeepSeek + DashScope) - Updated: image_parser.py (logic tree support, external prompts) - Updated: SKILL.md, prompts/image_prompt.md conflict_detection_skill: - Updated: LLM.py (multi-provider sync) - Updated: detect_conflicts.py (logic tree text conversion) ir_generation_skill: - Replaced old scripts/LLM.py + ir_generator.py with standalone project - New: main.py, config.py, step1-3_*.py, ensemble_merge.py - New: prompts/, tests/ subdirectories tests: - New: acceptance/ test suite with schema validation - Fixed: conftest no longer globally skips non-acceptance tests - Updated: test_sample.py for new ir_generation structure Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""Verify flowchart logic trees for structural correctness and consistency.
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Usage::
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python verify_flowchart.py <parsed.json|flowchart.json> [--llm] [--output-report REPORT.md]
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Performs three levels of checks:
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1. **Structural validation** — tree integrity, node uniqueness, leaf types
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2. **Path extraction** — renders all root-to-leaf paths as readable text
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3. **LLM consistency check** (opt-in with ``--llm``) — compares extracted paths
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against the original text description for logical inconsistencies
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Outputs PASS/FAIL and a detailed report.
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"""
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import argparse
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import json
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import logging
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from image_parser import ImageParser
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from LLM import LLMClient
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Prompt for LLM path-vs-description consistency check
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# ---------------------------------------------------------------------------
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PROMPT_VERIFY_PATHS = """你是一个流程图审核专家。以下内容来自同一张流程图的解析结果：
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## 流程图路径（从嵌套逻辑树提取的所有根到叶路径）
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```
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{paths_text}
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```
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## 原始文字描述
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```
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{description}
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```
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## 你的任务
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逐条检查每条路径是否与文字描述一致。重点关注：
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1. **分支方向错误**：路径中的判断分支走向是否与文字描述矛盾？
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例如：文字说"满足条件后退出"，但路径中"是"分支走向了"不受限"。
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2. **缺失步骤**：路径中是否缺少文字描述中提到的关键步骤？
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3. **冗余步骤**：路径中是否包含文字描述未提及的多余步骤？
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4. **条件颠倒**：判断条件的"是/否"分支是否与文字描述相反？
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## 输出格式
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如果**所有路径一致**，只输出：
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```
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[[PATHS_CONSISTENT]]
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```
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如果**发现不一致**，输出 JSON 数组：
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```json
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[
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{{
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"path_index": 1,
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"issue_type": "branch_error|missing_step|redundant_step|condition_reversed",
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"severity": "high|medium|low",
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"description": "用中文说明具体问题"
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}}
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]
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```
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注意：输出必须是严格合法的 JSON 数组，不要有尾随逗号，不要包含代码块包裹符号。
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"""
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# ---------------------------------------------------------------------------
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# Core verification logic
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# ---------------------------------------------------------------------------
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def verify_parsed_json(parsed_path: str, *, use_llm: bool = False) -> dict:
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"""Load _parsed.json and verify all flowchart logic trees.
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Returns a report dict with keys:
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- total_flowcharts: int
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- passed: int
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- failed: int
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- results: list of per-flowchart results
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"""
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with open(parsed_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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image_analysis = data.get("image_analysis", [])
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flowcharts = [img for img in image_analysis if img.get("type") == "flowchart"]
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report = {
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"total_flowcharts": len(flowcharts),
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"passed": 0,
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"failed": 0,
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"results": [],
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}
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llm = LLMClient() if use_llm else None
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for img in flowcharts:
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rid = img.get("rid", "unknown")
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logger.info("Verifying flowchart: rid=%s", rid)
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result = _verify_single(img, llm)
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report["results"].append(result)
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if result["structural_ok"] and (not use_llm or result.get("llm_ok", True)):
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report["passed"] += 1
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else:
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report["failed"] += 1
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return report
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def verify_flowchart_file(filepath: str, *, use_llm: bool = False) -> dict:
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"""Load a standalone flowchart JSON file and verify it."""
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with open(filepath, "r", encoding="utf-8") as f:
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tree = json.load(f)
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img = {"logic_tree_nested": tree, "description": "", "rid": os.path.basename(filepath)}
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llm = LLMClient() if use_llm else None
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result = _verify_single(img, llm)
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return {
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"total_flowcharts": 1,
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"passed": 1 if result["structural_ok"] else 0,
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"failed": 0 if result["structural_ok"] else 1,
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"results": [result],
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}
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def _verify_single(img: dict, llm: LLMClient | None) -> dict:
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"""Verify a single flowchart image analysis entry."""
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rid = img.get("rid", "unknown")
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description = img.get("description", "").strip()
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# Try nested format first, fall back to flat format
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tree = img.get("logic_tree_nested") or img.get("logic_tree")
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if tree is None:
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return {
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"rid": rid,
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"structural_ok": False,
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"errors": ["No logic_tree found"],
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"paths_text": "",
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"llm_issues": [],
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}
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# Check if it's the new nested format or old flat format
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is_nested = "children" in tree and isinstance(tree.get("children"), list)
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# --- Level 1: Structural validation ---
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structural_ok = True
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errors: list[str] = []
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if is_nested:
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ok, err = ImageParser._validate_flowchart(tree)
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if not ok:
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structural_ok = False
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errors.append(f"Structure: {err}")
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# Extract paths
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paths = ImageParser.extract_paths(tree)
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paths_text = ImageParser.paths_to_text(paths)
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errors.append(f"Path count: {len(paths)}")
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else:
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# Old flat format — basic check
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nodes = tree.get("nodes", [])
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ids = [n.get("id", "") for n in nodes]
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if len(ids) != len(set(ids)):
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structural_ok = False
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errors.append("Structure: duplicate node ids in flat format")
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# Build simple path-like text for flat format
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paths_text = _flat_to_text(tree)
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# --- Level 2: Path count sanity check ---
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if is_nested and len(paths) == 0:
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structural_ok = False
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errors.append("No paths extracted from tree")
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# --- Level 3: LLM consistency check ---
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llm_issues: list[dict] = []
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llm_ok = True
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if llm and description and paths_text:
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prompt = PROMPT_VERIFY_PATHS.format(
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paths_text=paths_text,
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description=description,
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)
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try:
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raw = llm.chat(
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model=LLMClient.TEXT_MODEL,
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messages=[{"role": "user", "content": prompt}],
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)
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llm_issues = _parse_llm_issues(raw)
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if llm_issues:
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llm_ok = False
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errors.append(f"LLM found {len(llm_issues)} issue(s)")
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except RuntimeError as e:
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errors.append(f"LLM check failed: {e}")
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return {
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"rid": rid,
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"structural_ok": structural_ok,
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"errors": errors,
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"paths_text": paths_text,
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"llm_ok": llm_ok,
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"llm_issues": llm_issues,
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}
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def _flat_to_text(tree: dict) -> str:
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"""Build path-like text from old flat-format logic_tree."""
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nodes = tree.get("nodes", [])
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root = tree.get("root", "")
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lines = [f"Root: {root}"]
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node_map = {n["id"]: n for n in nodes}
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def _trace(node_id: str, visited: set, path: list[str]) -> list[str]:
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if node_id in visited:
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path.append(f"[循环] {node_id}")
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return path
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visited.add(node_id)
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node = node_map.get(node_id)
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if node is None:
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path.append(f"[缺失] {node_id}")
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return path
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ntype = node.get("type", "")
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if ntype == "decision":
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cond = node.get("condition", "")
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for b in node.get("branches", []):
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val = b.get("value", "")
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tgt = b.get("target", "")
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new_path = path + [f"[判断] {cond} → {val}"]
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_trace(tgt, visited.copy(), new_path)
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elif ntype == "end":
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path.append(f"[结束] {node.get('description', '')}")
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lines.append(" -> ".join(path))
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else:
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path.append(f"[{ntype}] {node.get('description', '')}")
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# Flat format doesn't have explicit children for non-decision nodes
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# so we can't trace further
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lines.append(" -> ".join(path))
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return path
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# Try to find start nodes
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starts = [n for n in nodes if n.get("type") == "start"]
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if starts:
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for s in starts:
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_trace(s["id"], set(), [])
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else:
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lines.append("(Cannot trace: no start node in flat format)")
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return "\n".join(lines)
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def _parse_llm_issues(content: str) -> list[dict]:
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"""Parse LLM response for path consistency issues."""
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stripped = content.strip()
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if "[[PATHS_CONSISTENT]]" in stripped:
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return []
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# Remove markdown code fences
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if "```json" in stripped:
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stripped = stripped.split("```json", 1)[1]
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if "```" in stripped:
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stripped = stripped.split("```", 1)[0]
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elif "```" in stripped:
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stripped = stripped.split("```", 1)[1]
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if "```" in stripped:
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stripped = stripped.split("```", 1)[0]
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stripped = stripped.strip()
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if not stripped:
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return []
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try:
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issues = json.loads(stripped)
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if isinstance(issues, list):
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return issues
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return []
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except json.JSONDecodeError:
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logger.debug("Failed to parse LLM issues: %s", stripped[:200])
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return []
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# ---------------------------------------------------------------------------
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# Report rendering
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# ---------------------------------------------------------------------------
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def print_report(report: dict) -> str:
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"""Print a human-readable verification report and return it as a string."""
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lines: list[str] = []
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lines.append("=" * 60)
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lines.append("流程图校验报告")
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lines.append("=" * 60)
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lines.append(f"流程图总数: {report['total_flowcharts']}")
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lines.append(f"通过: {report['passed']}")
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lines.append(f"失败: {report['failed']}")
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overall = "PASS" if report["failed"] == 0 else "FAIL"
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lines.append(f"总体结果: {overall}")
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lines.append("")
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for i, r in enumerate(report["results"], 1):
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rid = r["rid"]
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status = "[PASS]" if r["structural_ok"] else "[FAIL]"
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lines.append(f"[{i}] rid={rid} {status}")
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for err in r.get("errors", []):
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lines.append(f" - {err}")
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if r.get("paths_text"):
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lines.append(" 路径:")
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for path_line in r["paths_text"].split("\n"):
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lines.append(f" {path_line}")
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llm_issues = r.get("llm_issues", [])
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if llm_issues:
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lines.append(" LLM发现的问题:")
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for issue in llm_issues:
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lines.append(f" [{issue.get('severity', '?')}] {issue.get('description', '')}")
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lines.append("")
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report_text = "\n".join(lines)
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print(report_text)
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return report_text
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def main():
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parser = argparse.ArgumentParser(
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description="Verify flowchart logic trees for correctness.",
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)
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parser.add_argument(
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"input", metavar="FILE",
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help="Path to _parsed.json or standalone flowchart JSON",
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)
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parser.add_argument(
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"--llm", action="store_true",
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help="Run LLM consistency check (compares paths against text description)",
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)
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parser.add_argument(
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"--output-report", metavar="PATH",
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help="Save verification report to a file",
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)
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args = parser.parse_args()
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# Determine input type
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with open(args.input, "r", encoding="utf-8") as f:
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data = json.load(f)
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if "image_analysis" in data:
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report = verify_parsed_json(args.input, use_llm=args.llm)
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else:
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report = verify_flowchart_file(args.input, use_llm=args.llm)
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report_text = print_report(report)
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if args.output_report:
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with open(args.output_report, "w", encoding="utf-8") as f:
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f.write(report_text)
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logger.info("Report saved: %s", args.output_report)
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# Exit code: 0 for PASS, 1 for FAIL
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if report["failed"] > 0:
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sys.exit(1)
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user