108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
"""
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Optimized image classification prompts for MinerU with caching support.
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These prompts are designed to maximize prompt caching efficiency by:
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1. Keeping static content at the beginning
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2. Placing variable content at the end
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3. Separating simple and context-aware versions for better cache hits
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"""
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# Base prompt for image classification (static part)
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IMAGE_CLASSIFICATION_BASE = """你是一个专业的文档分析助手。请根据图片内容进行分析。
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# 任务要求：
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请分析这张图片并输出JSON格式结果。输出需要包含以下字段：
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1. type: 图片类型分类
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- "structured_content": 包含可提取结构的内容（文档、表格、图表、公式等）
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- "brief_description": 有明确主题但无法提取结构（照片、插图等）
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- "meaningless": 无实质内容（装饰、分隔线等）
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2. title: 图片的简短标题（10字以内）
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- 应该是语义化的标题，例如："系统架构图"、"流程图"、"数据表格"、"统计图表"、"代码截图"等
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3. description: 图片内容的详细描述
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- 描述图片的主要内容和视觉元素
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4. ocr_content: 仅对structured_content类型，提取图片中的文字内容
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# 重要禁止项：
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- 绝对禁止输出任何 base64 编码的图片数据
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- 禁止输出类似 "data:image/png;base64,..." 或 "data:image/jpeg;base64,..." 的内容
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- 禁止输出任何 markdown 格式的图片标签
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- 违反以上规则会导致输出无效
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# 输出格式：
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```json
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{
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"type": "分类类型",
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"title": "简短标题",
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"description": "详细描述",
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"ocr_content": "提取的文字内容（如适用）"
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}
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```"""
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# Simple classification prompt (without context)
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IMAGE_CLASSIFICATION_SIMPLE = IMAGE_CLASSIFICATION_BASE + """
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# 具体要求：
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- description字段请控制在50-100字以内
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- 仅基于图片内容本身进行分析"""
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# Context-aware classification prompt (with context)
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IMAGE_CLASSIFICATION_CONTEXT_BASE = IMAGE_CLASSIFICATION_BASE + """
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# 具体要求：
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- description字段请控制在100-200字以内，需要：
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- 解释图片与周围文本的关系
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- 说明图片在文档中的作用
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# 上下文信息：
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"""
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# Context template suffix (variable part - placed at the end)
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CONTEXT_SUFFIX_TEMPLATE = """页面位置：第 {page_idx} 页
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页面类型：{page_type}
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{page_title_info}
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# 周围文本内容：
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{surrounding_text}"""
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def format_image_classification_prompt(context=None, language_code=None, has_text_content=True):
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"""
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Format the image classification prompt optimized for caching.
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Args:
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context: Optional context information dict with keys:
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page_idx, page_type, page_title_info, surrounding_text
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language_code: Optional language code for output language matching
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has_text_content: Whether there's text content available for language detection
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Returns:
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Formatted prompt string
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"""
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# Add language instruction based on content availability
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language_instruction = ""
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if has_text_content and language_code:
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# Have text content and detected language
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from ..language_detector import LanguageDetector
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language_instruction = f"\n\n# 语言要求：\n{LanguageDetector.get_language_instruction(language_code)}\n"
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elif not has_text_content:
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# No text content, use image-based language detection
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from ..language_detector import LanguageDetector
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language_instruction = f"\n\n# 语言要求：\n{LanguageDetector.get_image_language_instruction(language_code)}\n"
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if context:
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# Build context suffix
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context_suffix = CONTEXT_SUFFIX_TEMPLATE.format(
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page_idx=context.get('page_idx', 1),
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page_type=context.get('page_type', '未知'),
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page_title_info=context.get('page_title_info', ''),
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surrounding_text=context.get('surrounding_text', '无')
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)
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# Return context-aware prompt with variable content at the end
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return IMAGE_CLASSIFICATION_CONTEXT_BASE + language_instruction + context_suffix
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else:
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# Return simple prompt with language instruction
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return IMAGE_CLASSIFICATION_SIMPLE + language_instruction |