115 lines
4.8 KiB
Python
115 lines
4.8 KiB
Python
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"""
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Shared prompt templates for AI Service.
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Centralized prompt configuration for consistent AI behavior across all endpoints.
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"""
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SYSTEM_PROMPT = """你是一名经验丰富的客服专员，名字叫"小N"。请遵循以下准则：
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像朋友一样聊天：使用自然、口语化的中文，语气亲切有礼貌，就像在和一位老朋友聊天。多使用"咱们"、"您看"、"是不是"这样拉近距离的表达。
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避免生硬罗列：介绍多个信息时，不要用"第一、第二"或分点列举，而是用流畅的段落把它们串联起来。比如："我们这边有几类课程挺受欢迎的，一个是……另外还有……您对哪方面更感兴趣呀？"
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主动引导对话：不要一次性把知识库里的所有内容都倒出来。可以先简单概括，然后问问客户具体想了解什么，比如："您是想提升职场英语，还是有留学方面的打算呢？"
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委婉处理未知：如果知识库里没有答案，别直接说"不知道"，可以说："您稍等，我帮您确认一下"、"这个问题我需要问问同事，马上给您回复"。
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隐藏机器身份：绝不要提及"作为AI"、"根据算法"等词，也避免使用过于官方的术语。就像普通人一样用"我"、"我们"。
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多用语气词和情感表达：适当加入"呢"、"哦"、"哈"、"呀"等语气词，根据情况使用表情符号（😊）。表达理解和关心，比如"我明白您的意思"、"别着急，我们一起看看"。
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保持简洁但有温度：回答一般控制在3-5句话，如果需要详细说明，可以分成小段落，但每段都要口语化，读起来不累。
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隐私与安全提醒：如果客户问到敏感信息（如密码、转账），要温和地引导至人工渠道："为了您的信息安全，建议您拨打官方电话400-xxx-xxxx咨询会更稳妥哦。"""
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def format_evidence_for_prompt(
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retrieval_results: list,
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max_results: int = 5,
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max_content_length: int = 500
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) -> str:
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"""
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Format retrieval results as evidence text for prompts.
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Args:
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retrieval_results: List of retrieval hits. Can be:
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- dict format: {'content', 'score', 'source', 'metadata'}
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- RetrievalHit object: with .text, .score, .source, .metadata attributes
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max_results: Maximum number of results to include
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max_content_length: Maximum length of each content snippet
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Returns:
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Formatted evidence text
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"""
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if not retrieval_results:
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return ""
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evidence_parts = []
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for i, hit in enumerate(retrieval_results[:max_results]):
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if hasattr(hit, 'text'):
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content = hit.text
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score = hit.score
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source = getattr(hit, 'source', '知识库')
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metadata = getattr(hit, 'metadata', {}) or {}
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else:
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content = hit.get('content', '')
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score = hit.get('score', 0)
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source = hit.get('source', '知识库')
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metadata = hit.get('metadata', {}) or {}
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if len(content) > max_content_length:
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content = content[:max_content_length] + '...'
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nested_meta = metadata.get('metadata', {})
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source_doc = nested_meta.get('source_doc', source) if nested_meta else source
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category = nested_meta.get('category', '') if nested_meta else ''
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department = nested_meta.get('department', '') if nested_meta else ''
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header = f"[文档{i+1}]"
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if source_doc and source_doc != "知识库":
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header += f" 来源：{source_doc}"
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if category:
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header += f" | 类别：{category}"
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if department:
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header += f" | 部门：{department}"
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evidence_parts.append(f"{header}\n相关度：{score:.2f}\n内容：{content}")
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return "\n\n".join(evidence_parts)
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def build_system_prompt_with_evidence(evidence_text: str) -> str:
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"""
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Build system prompt with knowledge base evidence.
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Args:
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evidence_text: Formatted evidence from retrieval results
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Returns:
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Complete system prompt
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"""
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if not evidence_text:
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return SYSTEM_PROMPT
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return f"""{SYSTEM_PROMPT}
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知识库参考内容：
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{evidence_text}"""
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def build_user_prompt_with_evidence(query: str, evidence_text: str) -> str:
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"""
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Build user prompt with knowledge base evidence (for single-message format).
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Args:
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query: User's question
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evidence_text: Formatted evidence from retrieval results
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Returns:
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Complete user prompt
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"""
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if not evidence_text:
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return f"""用户问题：{query}
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未找到相关检索结果，请基于通用知识回答用户问题。"""
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return f"""【系统指令】
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{SYSTEM_PROMPT}
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【知识库内容】
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{evidence_text}
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【用户问题】
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{query}"""
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