249 lines
10 KiB
Python
249 lines
10 KiB
Python
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# ============================================================
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# AI 精确解析（moduleId: domain-drawing-ai, 可重生 ✅）
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# 优先 LLM 结构化提取；未配置/失败/非 JSON -> fail-closed 回退确定性算法，绝不编造。
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# ============================================================
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from __future__ import annotations
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import json
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import re
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from typing import Any
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AI_VERSION = "drawing-ai.v1"
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_SYSTEM_PROMPT = """你是工业图纸结构化解析器。只能根据用户提供的图纸证据提取信息，禁止编造。
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输出严格 JSON 对象：
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{
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"drawingNumber": str|null,
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"revision": str|null,
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"title": str|null,
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"scale": str|null,
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"format": str|null,
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"material": str|null,
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"surfaceTreatment": str|null,
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"processSteps": [{"code": str, "name": str, "description": str, "confidence": number}],
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"bomRows": [{"position": str|null, "reference": str|null, "name": str|null, "quantity": number|null, "unit": str|null}],
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"details": {"thread": str|null, "roughness": str|null, "tolerance": str|null, "depthMm": number|null},
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"blocks": [{"blockCode": str, "blockName": str|null}],
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"assemblies": [{"assemblyName": str}],
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"locations": [{"location": str}],
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"sequenceSteps": [{"stepIndex": number, "sentence": str, "markers": [str]}],
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"confidence": number,
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"warnings": [str]
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}
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提取不到的值一律 null；数量/工时/资源没有证据时不得猜测。"""
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def _evidence_text(parsed: dict[str, Any]) -> str:
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parts: list[str] = []
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asset = parsed.get("asset") or {}
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drawing = parsed.get("drawing") or {}
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parts.append(f"文件名: {asset.get('filename')}")
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parts.append(f"图号: {drawing.get('drawingNumber')} 版本: {drawing.get('revision')}")
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for row in parsed.get("fieldCandidates") or []:
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parts.append(f"字段 {row.get('field')}: {row.get('value')} (置信度 {row.get('confidence')})")
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parts.append("文本:")
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for row in (parsed.get("texts") or [])[:200]:
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parts.append(str(row.get("text") or ""))
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refs = parsed.get("itemReferences") or []
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if refs:
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parts.append("零件引用: " + ", ".join(str(row.get("reference")) for row in refs[:100]))
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return "\n".join(parts)
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def _deterministic_summary(parsed: dict[str, Any]) -> dict[str, Any]:
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fields = {row.get("field"): row.get("value") for row in parsed.get("fieldCandidates") or []}
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from server.aps_domain.drawing_process import recognize_process_candidates
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process = recognize_process_candidates(parsed)
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return {
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"drawingNumber": fields.get("drawingNumber"),
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"revision": fields.get("revision"),
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"title": fields.get("title"),
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"scale": fields.get("scale"),
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"format": fields.get("format"),
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"material": fields.get("material") or fields.get("materialSpecification"),
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"processSteps": [
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{
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"code": row["operationCode"],
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"name": row["operationName"],
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"description": (row.get("evidence") or {}).get("text"),
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"confidence": row["confidence"],
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}
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for row in process.get("operations") or []
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],
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"bomRows": [],
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"details": {
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"thread": fields.get("threadSpecification"),
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"roughness": fields.get("surfaceRoughness"),
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"tolerance": fields.get("tolerance"),
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"depthMm": fields.get("depthMm"),
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},
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"blocks": [{"blockCode": row["blockCode"], "blockName": row.get("blockName")} for row in process.get("blocks") or []],
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"assemblies": [{"assemblyName": row["assemblyName"]} for row in process.get("assemblies") or []],
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"locations": [{"location": row["location"]} for row in process.get("locations") or []],
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"sequenceSteps": [
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{"stepIndex": row["stepIndex"], "sentence": row["sentence"], "markers": row.get("markers") or []}
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for row in process.get("sequenceSteps") or []
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],
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"confidence": 0.0,
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"warnings": ["AI 未启用或不可用，使用确定性解析结果；字段仍需人工复核。"],
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}
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def _coerce_analysis(raw: Any) -> dict[str, Any] | None:
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"""宽容解析模型返回：dict / JSON 文本 / markdown 围栏 / 双重编码 JSON。"""
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if isinstance(raw, dict):
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return raw
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if not isinstance(raw, str):
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return None
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text = re.sub(r"^```(?:json)?\s*|\s*```$", "", raw.strip(), flags=re.IGNORECASE).strip()
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attempts: list[Any] = [text]
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try:
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attempts.append(json.loads(text))
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except Exception: # noqa: BLE001, S110 - 交给后续解析
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pass
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match = re.search(r"\{.*\}", text, re.DOTALL)
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if match:
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attempts.append(match.group(0))
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for candidate in attempts:
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if isinstance(candidate, str):
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try:
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candidate = json.loads(candidate)
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except Exception: # noqa: BLE001, S112 - 继续尝试
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continue
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if isinstance(candidate, dict):
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return candidate
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return None
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def _candidate_evidence(
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parsed: dict[str, Any],
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candidate_type: str,
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row: dict[str, Any],
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) -> dict[str, Any]:
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asset = parsed.get("asset") or {}
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drawing = parsed.get("drawing") or {}
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evidence = {
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"source": "drawingAiAnalysis",
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"candidateType": candidate_type,
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"drawingAssetId": asset.get("id"),
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"sourceSha256": asset.get("sha256"),
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"filename": asset.get("filename"),
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"drawingNumber": drawing.get("drawingNumber"),
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"revision": drawing.get("revision"),
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}
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supplied = row.get("evidence")
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if supplied:
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evidence["modelEvidence"] = supplied
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return {key: value for key, value in evidence.items() if value not in (None, "")}
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def _normalize_review_candidates(
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parsed: dict[str, Any],
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analysis: dict[str, Any],
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) -> dict[str, Any]:
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"""把模型抽取统一降为有证据、待审核且不自动补工时/资源的候选。"""
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normalized = dict(analysis)
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def normalize_group(
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key: str,
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candidate_type: str,
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*,
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prohibit_schedule_values: bool = False,
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) -> list[dict[str, Any]]:
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rows: list[dict[str, Any]] = []
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for raw_row in normalized.get(key) or []:
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if not isinstance(raw_row, dict):
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continue
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row = dict(raw_row)
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row["candidateType"] = candidate_type
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row["status"] = "PENDING_REVIEW"
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row["reviewRequired"] = True
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if prohibit_schedule_values:
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row["standardTime"] = None
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row["resourceCode"] = None
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unknown = list(row.get("unknownFields") or [])
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for field in ("standardTime", "resourceCode"):
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if field not in unknown:
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unknown.append(field)
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row["unknownFields"] = unknown
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row["evidence"] = _candidate_evidence(parsed, candidate_type, row)
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rows.append(row)
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normalized[key] = rows
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return rows
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normalize_group("processSteps", "ROUTING_OPERATION", prohibit_schedule_values=True)
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if "routingOperations" in normalized:
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normalize_group("routingOperations", "ROUTING_OPERATION", prohibit_schedule_values=True)
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normalize_group("bomRows", "BOM_ROW")
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if "bomCandidates" in normalized:
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normalize_group("bomCandidates", "BOM_ROW")
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material_rows = normalized.get("materialCandidates")
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if not isinstance(material_rows, list):
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material_rows = normalized.get("materials")
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if not isinstance(material_rows, list):
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material = normalized.get("material")
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material_rows = [{"value": material}] if material not in (None, "") else []
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normalized["materialCandidates"] = material_rows
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normalize_group("materialCandidates", "MATERIAL")
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if "materials" in normalized:
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normalized["materials"] = [dict(row) for row in normalized["materialCandidates"]]
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normalized["status"] = "PENDING_REVIEW"
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normalized["reviewRequired"] = True
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normalized["writesMasterData"] = False
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normalized["prohibitedAutoFill"] = ["routing.standardTime", "routing.resourceCode"]
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return normalized
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async def analyze_drawing_ai(
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parsed: dict[str, Any],
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*,
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provider: Any | None = None,
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) -> dict[str, Any]:
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deterministic = _normalize_review_candidates(parsed, _deterministic_summary(parsed))
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if provider is None or not getattr(provider, "enabled", False):
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return {
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"contractVersion": AI_VERSION,
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"status": "AI_UNAVAILABLE",
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"reason": "ai-provider-not-configured",
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"message": "未配置 AI 模型服务，已展示确定性解析结果",
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"analysis": deterministic,
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"deterministic": True,
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}
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provider_error: str | None = None
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analysis: Any = None
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try:
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analysis = await provider.chat_json(_SYSTEM_PROMPT, _evidence_text(parsed), timeout=30.0)
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except Exception as exc: # noqa: BLE001 - provider 异常一律 fail-closed
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provider_error = f"provider-error:{type(exc).__name__}"
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analysis = _coerce_analysis(analysis)
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if not isinstance(analysis, dict) and hasattr(provider, "chat_text"):
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try:
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raw_text = await provider.chat_text(_SYSTEM_PROMPT, _evidence_text(parsed), timeout=45.0)
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analysis = _coerce_analysis(raw_text)
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except Exception: # noqa: BLE001 - 兜底失败继续回退
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analysis = None
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if not isinstance(analysis, dict):
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return {
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"contractVersion": AI_VERSION,
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"status": "AI_UNAVAILABLE",
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"reason": provider_error or "provider-invalid-json",
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"message": "模型返回格式无效或服务异常，已回退确定性解析结果",
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"analysis": deterministic,
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"deterministic": True,
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}
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analysis = _normalize_review_candidates(parsed, analysis)
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analysis.setdefault("warnings", [])
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analysis.setdefault("confidence", 0.0)
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return {
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"contractVersion": AI_VERSION,
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"status": "AI_ANALYZED",
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"analysis": analysis,
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"deterministic": False,
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}
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__all__ = ["AI_VERSION", "analyze_drawing_ai"]
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