feat:llm数据生成

.vscode/settings.json

+{
+    "python-envs.defaultEnvManager": "ms-python.python:conda",
+    "python-envs.defaultPackageManager": "ms-python.python:conda",
+    "python-envs.pythonProjects": []
+}
\ No newline at end of file

README.md

@@ -11,3 +11,16 @@
 
 
 ## 4、训练
 ## 4、训练
 基于数据集进行SFT训练
 基于数据集进行SFT训练
+
+## 5、统一导出
+合并 `merge_excel.py` 与 `merge_json.py` 的导出内容：
+
+```bash
+OPENAI_API_KEY=your_key python main.py --excel datasets/excel --json-dir datasets/json --out datasets/merged/merged_all.json
+```
+
+说明：`main.py` 会使用每条数据的 `prompt` 作为 system、`original_text` 作为 user 调用 OpenAI 接口；
+解析返回 JSON 中的 `result` 与 `ground_truth` 对比，不一致会重试，默认最多 `10` 次。
+可选参数：`--model`、`--max-retries`、`--openai-base-url`。
+
+依赖安装：`pip install openai tenacity pandas`
\ No newline at end of file

datasets/merged/merged_all.json

+{
+  "交付时间审查": [
+    {
+      "review_item": "交付时间审查",
+      "original_text": "2025.09.30",
+      "suggest": "建议修改为：'技术协议签订后X天内完成交货'（具体天数需根据实际履约周期确定），或补充'方案确认后X天内交货'的表述，确保交货时间与前置条件挂钩。\n",
+      "__source_file__": "今麦郎合同审核.xlsx",
+      "ground_truth": "不合格",
+      "__source_type__": "excel",
+      "prompt": "你是乙方（供方、卖方）法律部门的合同审查助手                  
# 交付时间审查
1、合同必须明确交货时间，且满足以下任一约定方式：
- 约定以“方案确认后XX天”作为交付时限
- 约定以“技术协议签订后XX天”作为交付时限
2、本审查只需输出一个审查结果项，如果有合格项，则优先输出合格项，如果全是不合格项，优先输出信息量最丰富最完整的不合格项
# 审查不合格建议
1、交付要提及方案确认或技术协议签订为起始时间点
# 审查约束
- 输出包括审查的句子、详情、结果、建议以及句子所在的合同分段
- 审查结果为合格/不合格，合格的审查结果无需输出建议
- 审查句子严格提取关键且详细的句子内容，保留换行、空格、标点符号等特殊字符
- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"审查句子\",\"details\":\"审查详情\",\"result\":\"不合格\",\"suggest\":\"审查建议\"}]```
依据审查要点，遵循审查约束，完成合同审查，一步步仔细思考。"
+    }
+  ],
+  "安装调试与指导审查": [
+    {
+      "review_item": "安装调试与指导审查",
+      "original_text": "为向定作人提供合格的满足本工程正常运行所需的产品，承揽人还应向定作人及时提供所有与之相关的技术服务，包括但不限于：承揽人根据附件2《技术协议》和本合同规定负责产品的设计、制造、运输、安装和调试指导、试车、性能考核和验收、质保期内问题处理和技术服务。",
+      "suggest": "删除'指导'相关表述，修改为'调试配合'或'技术支持'，例如：'负责产品的设计、制造、运输、安装和调试配合、试车、性能考核和验收'。\n\n",
+      "__source_file__": "今麦郎合同审核.xlsx",
+      "ground_truth": "不合格",
+      "__source_type__": "excel",
+      "prompt": "你是乙方（供方、卖方）法律部门的合同审查助手                  
# 安装调试与指导审查
1、针对产品的安装/安装调试/安装指导/指导/技术服务，主体为乙方，动作为提供/负责/参与/指导/进行/培训，审查不合格；如果主体是甲方，或者动作为配合/协助/支持，审查合格
# 审查不合格建议
1、协商修改，修改安装调试条款为配合/协助
2、删除指导相关条款或者修改为配合、技术支持
# 审查约束
- 输出包括审查的句子、详情、结果、建议以及句子所在的合同分段
- 审查结果为合格/不合格，合格的审查结果无需输出建议
- 审查句子严格提取关键且详细的句子内容，保留换行、空格、标点符号等特殊字符
- 结果以JSON数组的格式返回,例如```json [{\"original_text\":\"审查句子\",\"details\":\"审查详情\",\"result\":\"不合格\",\"suggest\":\"审查建议\"}]```
依据审查要点，遵循审查约束，完成合同审查，一步步仔细思考。"
+    }
+  ]
+}
\ No newline at end of file

main.py

+from __future__ import annotations
+
+import json_repair
+import argparse
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+from openai import OpenAI
+from tenacity import Retrying, stop_after_attempt, wait_fixed
+
+from merge_excel import read_excel_rows_from_path
+from merge_json import apply_field_mapping, read_json_lists_from_dir
+
+base_url = 'http://192.168.252.71:9002/v1'
+model= 'Qwen2-72B-Instruct'
+key = 'none'
+
+
+# base_url = 'http://192.168.252.71:18088/api/v1'
+# model = 'Qwen2-72B-Instruct'
+# key = 'fastgpt-eSZPKebnYpOrsm2jsA6iithc81AseS7nknOjOavElp4s57HrTYSo8ZXW'
+
+REVIEW_ITEM_MAP: dict[str, str] = {
+	"发票审查-csc": "发票审查",
+	"发票审查-merge": "发票审查",
+	"价款内容审查-merge": "价款内容审查",
+	"运输保险审查-merge": "运输保险审查",
+	"变更取消责任审查-csc": "变更取消责任审查",
+	"安装调试审查": "安装调试与指导审查",
+	"标的物内容审查": "标的物审查",
+	"技术指导审查": "安装调试与指导审查",
+	"履行义务审查": "第三方审查",
+	"三方贷款审查": "第三方审查",
+	"三方货款审查": "第三方审查",
+	"违约审查": "违约与延期审查",
+	"违约条款审查": "违约与延期审查",
+	"延期审查": "违约与延期审查",
+	"责任规定审查": "违约与延期审查",
+}
+
+
+def normalize_review_item(review_item: Any) -> str:
+	review_item_text = str(review_item).strip()
+	mapped_review_item = REVIEW_ITEM_MAP.get(review_item_text)
+	if mapped_review_item is not None:
+		return mapped_review_item
+
+	if review_item_text.endswith(".json"):
+		review_item_text = review_item_text[:-5]
+
+	mapped_review_item = REVIEW_ITEM_MAP.get(review_item_text)
+	if mapped_review_item is not None:
+		return mapped_review_item
+
+	return review_item_text
+
+
+def merge_export_rows(
+	excel_path: str | Path,
+	json_dir: str | Path,
+	sheet_name: str | int | None = 0,
+) -> list[dict[str, Any]]:
+	excel_rows = read_excel_rows_from_path(excel_path, sheet_name=sheet_name)
+	for row in excel_rows:
+		row["__source_type__"] = "excel"
+
+	json_rows = read_json_lists_from_dir(json_dir)
+	json_rows = apply_field_mapping(json_rows)
+	for row in json_rows:
+		row["__source_type__"] = "json"
+
+	return excel_rows + json_rows
+
+
+def build_review_item_mapping_from_sheet(
+	excel_path: str | Path,
+	sheet_name: str = "提取审查",
+) -> dict[str, Any]:
+	path = Path(excel_path)
+	files = sorted(path.glob("*.xlsx")) if path.is_dir() else [path]
+	mapping: dict[str, Any] = {}
+
+	for file_path in files:
+		try:
+			df = pd.read_excel(file_path, sheet_name=sheet_name)
+		except Exception:
+			continue
+
+		if df.empty or len(df.columns) < 2:
+			continue
+
+		key_col = "审查要点" if "审查要点" in df.columns else df.columns[0]
+		value_col = "审查提示词" if "审查提示词" in df.columns else df.columns[1]
+  
+		for _, row in df.iterrows():
+			key = row.get(key_col)
+			if pd.isna(key):
+				continue
+			value = row.get(value_col)
+			if pd.isna(value):
+				continue
+			mapping[str(key).strip()] = value
+
+	return mapping
+
+
+def apply_review_item_mapping(rows: list[dict[str, Any]], mapping: dict[str, Any]) -> None:
+	unmatched_review_items: set[str] = set()
+	for row in rows:
+		review_item = row.get("review_item")
+		if review_item is None:
+			unmatched_review_items.add("<None>")
+			continue
+		review_item_text = normalize_review_item(review_item)
+		row["review_item"] = review_item_text
+		mapped_value = mapping.get(review_item_text)
+		if mapped_value is not None:
+			row["prompt"] = mapped_value
+		else:
+			unmatched_review_items.add(review_item_text)
+
+	if unmatched_review_items:
+		print(f"[mapping_unmatched_dedup] review_items={sorted(unmatched_review_items)}")
+
+
+def group_rows_by_review_item_with_dedup(
+	rows: list[dict[str, Any]],
+) -> dict[str, list[dict[str, Any]]]:
+	grouped_rows: dict[str, list[dict[str, Any]]] = {}
+	seen_original_texts: dict[str, set[Any]] = {}
+
+	for row in rows:
+		review_item = row.get("review_item")
+		review_item_key = "<None>" if review_item is None else str(review_item).strip()
+		if review_item_key not in grouped_rows:
+			grouped_rows[review_item_key] = []
+			seen_original_texts[review_item_key] = set()
+
+		original_text = row.get("original_text")
+		original_text_key = original_text.strip() if isinstance(original_text, str) else original_text
+		if original_text_key in seen_original_texts[review_item_key]:
+			continue
+
+		seen_original_texts[review_item_key].add(original_text_key)
+		grouped_rows[review_item_key].append(row)
+
+	return dict(sorted(grouped_rows.items(), key=lambda item: item[0]))
+
+
+def print_grouped_review_item_stats(grouped_rows: dict[str, list[dict[str, Any]]]) -> None:
+	total_rows = sum(len(group_rows) for group_rows in grouped_rows.values())
+	print(f"grouped_review_item_count={len(grouped_rows)}")
+	print(f"grouped_total_rows={total_rows}")
+	for review_item, group_rows in grouped_rows.items():
+		print(f"[group_stats] review_item={review_item}, count={len(group_rows)}")
+
+
+def call_openai_chat_completion(
+	client: OpenAI,
+	model: str,
+	system_prompt: str,
+	user_text: str,
+) -> str:
+    extra_body = None
+    if system_prompt:
+        extra_body = {
+			'variables': {
+				'system': system_prompt 
+			}
+    	}
+    resp = client.chat.completions.create(
+		model=model,
+		temperature=1.2,
+		messages=[
+			{"role": "system", "content": system_prompt},
+			{"role": "user", "content": user_text},
+		],
+  		 extra_body=extra_body
+	)
+    content = resp.choices[0].message.content if resp.choices else None
+    reasoning_content = resp.choices[0].message.reasoning_content if resp.choices else None
+    if not isinstance(content, str) or not content.strip():
+        raise ValueError("OpenAI响应缺少message.content")
+    return reasoning_content,content
+
+
+def filter_rows_by_llm_result(
+	rows: list[dict[str, Any]],
+	client: OpenAI,
+	model: str,
+	max_retries: int = 10,
+) -> list[dict[str, Any]]:
+	kept_rows: list[dict[str, Any]] = []
+
+	for index, row in enumerate(rows, start=1):
+		system_prompt = row.get("prompt")
+		user_text = row.get("original_text")
+		ground_truth = row.get("ground_truth")
+
+		if not isinstance(system_prompt, str) or not system_prompt.strip():
+			continue
+		if not isinstance(user_text, str) or not user_text.strip():
+			continue
+		if ground_truth is None:
+			continue
+
+		ground_truth_text = str(ground_truth).strip()
+		ground_truth_text = '合格'
+
+		try:
+			for attempt in Retrying(wait=wait_fixed(1), stop=stop_after_attempt(max_retries), reraise=True):
+				with attempt:
+					reasoning_content,content = call_openai_chat_completion(
+						client=client,
+						model=model,
+						system_prompt=system_prompt,
+						user_text=user_text,
+					)
+					result_text = json_repair.loads(content)
+					if result_text is None:
+						print(f"[llm_parse_failed] row={index}, attempt={attempt.retry_state.attempt_number}")
+						raise ValueError("llm response json parse failed")
+					final_result = result_text[0].get("result")
+					if final_result != ground_truth_text:
+						print(
+							f"[llm_mismatch] row={index}, attempt={attempt.retry_state.attempt_number}, "
+							f"original_text={user_text},ground_truth={ground_truth_text},result={result_text}"
+						)
+						raise ValueError("llm result mismatch")
+					print(f"[llm_match] row={index}, attempt={attempt.retry_state.attempt_number},result={result_text}")
+					row['rsp'] = f'<think>{reasoning_content}</think>{result_text}'
+		except Exception as exc:
+			row['rsp'] = ''
+			print(f"[llm_drop] row={index}, error={exc}")
+		finally:
+			kept_rows.append(row)
+
+	print(f"llm_filtered_rows={len(kept_rows)}/{len(rows)}")
+	return kept_rows
+
+
+def main() -> None:
+	parser = argparse.ArgumentParser(description="合并Excel与JSON导出内容")
+	parser.add_argument("--know", default="prompts/know.xlsx", help="提示词目录路径")
+	parser.add_argument("--excel", default="datasets/excel", help="Excel文件路径或目录")
+	parser.add_argument("--json-dir", default="datasets/json", help="JSON目录路径")
+	parser.add_argument("--sheet", default=0, help="sheet名称或索引，默认0")
+	parser.add_argument("--model", default=model, help="OpenAI模型名称")
+	parser.add_argument("--max-retries", type=int, default=10, help="每条数据最大重试次数")
+	parser.add_argument("--openai-base-url", default=base_url, help="OpenAI接口基础URL")
+	parser.add_argument("--out", default="datasets/merged/merged_all.json", help="输出JSON文件路径")
+	args = parser.parse_args()
+
+	sheet: str | int | None
+	if isinstance(args.sheet, str) and args.sheet.isdigit():
+		sheet = int(args.sheet)
+	else:
+		sheet = args.sheet
+
+	rows = merge_export_rows(args.excel, args.json_dir, sheet_name=sheet)
+	mapping = build_review_item_mapping_from_sheet(args.know, sheet_name="提取审查")
+	apply_review_item_mapping(rows, mapping)
+
+	api_key = os.getenv("OPENAI_API_KEY", key).strip()
+	if not api_key:
+		raise ValueError("缺少环境变量 OPENAI_API_KEY")
+	client = OpenAI(api_key=api_key, base_url=args.openai_base_url)
+
+	# rows = rows[:2]
+	rows = filter_rows_by_llm_result(
+		rows=rows,
+		client=client,
+		model=args.model,
+		max_retries=max(1, args.max_retries),
+	)
+	grouped_rows = group_rows_by_review_item_with_dedup(rows)
+
+	out_path = Path(args.out)
+	out_path.parent.mkdir(parents=True, exist_ok=True)
+	out_path.write_text(json.dumps(grouped_rows, ensure_ascii=False, indent=2), encoding="utf-8")
+
+	print(f"len(rows)={len(rows)}")
+	print(f"mapped_review_item_count={len(mapping)}")
+	print_grouped_review_item_stats(grouped_rows)
+	print(f"saved to: {out_path}")
+
+
+if __name__ == "__main__":
+	main()

merge_json.py

@@ -42,7 +42,7 @@ def read_json_lists_from_dir(json_dir: str | Path) -> list[dict[str, Any]]:
 	for file_path in sorted(path.glob("*.json")):
 	for file_path in sorted(path.glob("*.json")):
 		rows = read_json_list(file_path)
 		rows = read_json_list(file_path)
 		for row in rows:
 		for row in rows:
-			row['review_item'] = file_path.name.split('-')[0]
+			row['review_item'] = file_path.name.split('.')[0]
 			row["__source_file__"] = file_path.name
 			row["__source_file__"] = file_path.name
 		all_rows.extend(rows)
 		all_rows.extend(rows)
 
 
@@ -87,7 +87,7 @@ def main() -> None:
 
 
 	print(f'len(rows)={len(rows)}')
 	print(f'len(rows)={len(rows)}')
 	# print(rows[0])
 	# print(rows[0])
-	show_ds(rows)
+	# show_ds(rows)
 
 
 
 
 if __name__ == "__main__":
 if __name__ == "__main__":