llm_aided.py
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# Copyright (c) Opendatalab. All rights reserved.
from loguru import logger
from openai import OpenAI
import json_repair
from mineru.backend.pipeline.pipeline_middle_json_mkcontent import merge_para_with_text
def llm_aided_title(page_info_list, title_aided_config):
client = OpenAI(
api_key=title_aided_config["api_key"],
base_url=title_aided_config["base_url"],
)
title_dict = {}
origin_title_list = []
i = 0
for page_info in page_info_list:
blocks = page_info["para_blocks"]
for block in blocks:
if block["type"] == "title":
origin_title_list.append(block)
title_text = merge_para_with_text(block)
if 'line_avg_height' in block:
line_avg_height = block['line_avg_height']
else:
title_block_line_height_list = []
for line in block['lines']:
bbox = line['bbox']
title_block_line_height_list.append(int(bbox[3] - bbox[1]))
if len(title_block_line_height_list) > 0:
line_avg_height = sum(title_block_line_height_list) / len(title_block_line_height_list)
else:
line_avg_height = int(block['bbox'][3] - block['bbox'][1])
title_dict[f"{i}"] = [title_text, line_avg_height, int(page_info['page_idx']) + 1]
i += 1
# logger.info(f"Title list: {title_dict}")
title_optimize_prompt = f"""输入的内容是一篇文档中所有标题组成的字典,请根据以下指南优化标题的结果,使结果符合正常文档的层次结构:
1. 字典中每个value均为一个list,包含以下元素:
- 标题文本
- 文本行高是标题所在块的平均行高
- 标题所在的页码
2. 保留原始内容:
- 输入的字典中所有元素都是有效的,不能删除字典中的任何元素
- 请务必保证输出的字典中元素的数量和输入的数量一致
3. 保持字典内key-value的对应关系不变
4. 优化层次结构:
- 根据标题内容的语义为每个标题元素添加适当的层次结构
- 行高较大的标题一般是更高级别的标题
- 标题从前至后的层级必须是连续的,不能跳过层级
- 标题层级最多为4级,不要添加过多的层级
- 优化后的标题只保留代表该标题的层级的整数,不要保留其他信息
5. 合理性检查与微调:
- 在完成初步分级后,仔细检查分级结果的合理性
- 根据上下文关系和逻辑顺序,对不合理的分级进行微调
- 确保最终的分级结果符合文档的实际结构和逻辑
IMPORTANT:
请直接返回优化过的由标题层级组成的字典,格式为{{标题id:标题层级}},如下:
{{
0:1,
1:2,
2:2,
3:3
}}
不需要对字典格式化,不需要返回任何其他信息。
Input title list:
{title_dict}
Corrected title list:
"""
#5.
#- 字典中可能包含被误当成标题的正文,你可以通过将其层级标记为 0 来排除它们
retry_count = 0
max_retries = 3
dict_completion = None
while retry_count < max_retries:
try:
completion = client.chat.completions.create(
model=title_aided_config["model"],
messages=[
{'role': 'user', 'content': title_optimize_prompt}],
extra_body={"enable_thinking": False},
temperature=0.7,
stream=True,
)
content_pieces = []
for chunk in completion:
if chunk.choices and chunk.choices[0].delta.content is not None:
content_pieces.append(chunk.choices[0].delta.content)
content = "".join(content_pieces).strip()
# logger.info(f"Title completion: {content}")
if "</think>" in content:
idx = content.index("</think>") + len("</think>")
content = content[idx:].strip()
dict_completion = json_repair.loads(content)
dict_completion = {int(k): int(v) for k, v in dict_completion.items()}
# logger.info(f"len(dict_completion): {len(dict_completion)}, len(title_dict): {len(title_dict)}")
if len(dict_completion) == len(title_dict):
for i, origin_title_block in enumerate(origin_title_list):
origin_title_block["level"] = int(dict_completion[i])
break
else:
logger.warning(
"The number of titles in the optimized result is not equal to the number of titles in the input.")
retry_count += 1
except Exception as e:
logger.exception(e)
retry_count += 1
if dict_completion is None:
logger.error("Failed to decode dict after maximum retries.")