feat(report_agent): enhance interview text processing and response handling; improve quote extraction and formatting for better clarity
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666ghj
@@ -308,7 +308,30 @@ class AgentInterview:
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if self.key_quotes:
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text += "\n**关键引言:**\n"
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for quote in self.key_quotes:
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text += f"> \"{quote}\"\n"
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# 清理各种引号
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clean_quote = quote.replace('\u201c', '').replace('\u201d', '').replace('"', '')
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clean_quote = clean_quote.replace('\u300c', '').replace('\u300d', '')
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clean_quote = clean_quote.strip()
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# 去掉开头的标点
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while clean_quote and clean_quote[0] in '，,；;：:、。！？\n\r\t ':
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clean_quote = clean_quote[1:]
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# 过滤包含问题编号的垃圾内容（问题1-9）
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skip = False
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for d in '123456789':
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if f'\u95ee\u9898{d}' in clean_quote:
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skip = True
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break
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if skip:
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continue
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# 截断过长内容（按句号截断，而非硬截断）
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if len(clean_quote) > 150:
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dot_pos = clean_quote.find('\u3002', 80)
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if dot_pos > 0:
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clean_quote = clean_quote[:dot_pos + 1]
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else:
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clean_quote = clean_quote[:147] + "..."
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if clean_quote and len(clean_quote) >= 10:
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text += f'> "{clean_quote}"\n'
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return text
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@@ -350,27 +373,26 @@ class InterviewResult:
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def to_text(self) -> str:
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"""转换为详细的文本格式，供LLM理解和报告引用"""
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text_parts = [
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f"## 🎤 深度采访报告",
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"## 深度采访报告",
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f"**采访主题:** {self.interview_topic}",
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f"**采访人数:** {self.interviewed_count} / {self.total_agents} 位模拟Agent",
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f"\n### 采访对象选择理由",
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f"{self.selection_reasoning}",
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f"\n---"
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"\n### 采访对象选择理由",
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self.selection_reasoning or "（自动选择）",
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"\n---",
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"\n### 采访实录",
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]
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# 各Agent的采访内容
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if self.interviews:
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text_parts.append(f"\n### 采访实录")
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for i, interview in enumerate(self.interviews, 1):
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text_parts.append(f"\n#### 采访 #{i}: {interview.agent_name}")
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text_parts.append(interview.to_text())
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text_parts.append("\n---")
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# 采访摘要
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if self.summary:
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text_parts.append(f"\n### 采访摘要与核心观点")
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text_parts.append(self.summary)
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else:
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text_parts.append("（无采访记录）\n\n---")
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text_parts.append("\n### 采访摘要与核心观点")
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text_parts.append(self.summary or "（无摘要）")
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return "\n".join(text_parts)
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@@ -1329,8 +1351,18 @@ class ZepToolsService:
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# 将问题合并为一个采访prompt
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combined_prompt = "\n".join([f"{i+1}. {q}" for i, q in enumerate(result.interview_questions)])
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# 添加优化前缀，避免Agent调用工具而直接回复文本
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INTERVIEW_PROMPT_PREFIX = "结合你的人设、所有的过往记忆与行动，不调用任何工具直接用文本回复我："
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# 添加优化前缀，约束Agent回复格式
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INTERVIEW_PROMPT_PREFIX = (
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"你正在接受一次采访。请结合你的人设、所有的过往记忆与行动，"
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"以纯文本方式直接回答以下问题。\n"
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"回复要求：\n"
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"1. 直接用自然语言回答，不要调用任何工具\n"
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"2. 不要返回JSON格式或工具调用格式\n"
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"3. 不要使用Markdown标题（如#、##、###）\n"
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"4. 按问题编号逐一回答，每个回答以「问题X：」开头（X为问题编号）\n"
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"5. 每个问题的回答之间用空行分隔\n"
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"6. 回答要有实质内容，每个问题至少回答2-3句话\n\n"
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)
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optimized_prompt = f"{INTERVIEW_PROMPT_PREFIX}{combined_prompt}"
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# Step 4: 调用真实的采访API（不指定platform，默认双平台同时采访）
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@@ -1380,26 +1412,43 @@ class ZepToolsService:
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twitter_response = twitter_result.get("response", "")
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reddit_response = reddit_result.get("response", "")
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# 合并两个平台的回答
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response_parts = []
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if twitter_response:
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response_parts.append(f"【Twitter平台回答】\n{twitter_response}")
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if reddit_response:
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response_parts.append(f"【Reddit平台回答】\n{reddit_response}")
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if response_parts:
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response_text = "\n\n".join(response_parts)
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else:
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response_text = "[无回复]"
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# 清理可能的工具调用 JSON 包裹
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twitter_response = self._clean_tool_call_response(twitter_response)
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reddit_response = self._clean_tool_call_response(reddit_response)
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# 始终输出双平台标记
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twitter_text = twitter_response if twitter_response else "（该平台未获得回复）"
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reddit_text = reddit_response if reddit_response else "（该平台未获得回复）"
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response_text = f"【Twitter平台回答】\n{twitter_text}\n\n【Reddit平台回答】\n{reddit_text}"
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# 提取关键引言（从两个平台的回答中）
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import re
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combined_responses = f"{twitter_response} {reddit_response}"
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key_quotes = re.findall(r'[""「」『』]([^""「」『』]{10,100})[""「」『』]', combined_responses)
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# 清理响应文本：去掉标记、编号、Markdown 等干扰
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clean_text = re.sub(r'#{1,6}\s+', '', combined_responses)
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clean_text = re.sub(r'\{[^}]*tool_name[^}]*\}', '', clean_text)
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clean_text = re.sub(r'[*_`|>~\-]{2,}', '', clean_text)
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clean_text = re.sub(r'问题\d+[：:]\s*', '', clean_text)
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clean_text = re.sub(r'【[^】]+】', '', clean_text)
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# 策略1（主）: 提取完整的有实质内容的句子
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sentences = re.split(r'[。！？]', clean_text)
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meaningful = [
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s.strip() for s in sentences
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if 20 <= len(s.strip()) <= 150
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and not re.match(r'^[\s\W，,；;：:、]+', s.strip())
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and not s.strip().startswith(('{', '问题'))
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]
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meaningful.sort(key=len, reverse=True)
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key_quotes = [s + "。" for s in meaningful[:3]]
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# 策略2（补充）: 正确配对的中文引号「」内长文本
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if not key_quotes:
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sentences = combined_responses.split('。')
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key_quotes = [s.strip() + '。' for s in sentences if len(s.strip()) > 20][:3]
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paired = re.findall(r'\u201c([^\u201c\u201d]{15,100})\u201d', clean_text)
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paired += re.findall(r'\u300c([^\u300c\u300d]{15,100})\u300d', clean_text)
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key_quotes = [q for q in paired if not re.match(r'^[，,；;：:、]', q)][:3]
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interview = AgentInterview(
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agent_name=agent_name,
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@@ -1435,6 +1484,27 @@ class ZepToolsService:
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logger.info(f"InterviewAgents完成: 采访了 {result.interviewed_count} 个Agent（双平台）")
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return result
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@staticmethod
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def _clean_tool_call_response(response: str) -> str:
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"""清理 Agent 回复中的 JSON 工具调用包裹，提取实际内容"""
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if not response or not response.strip().startswith('{'):
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return response
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text = response.strip()
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if 'tool_name' not in text[:80]:
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return response
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import re as _re
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try:
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data = json.loads(text)
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if isinstance(data, dict) and 'arguments' in data:
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for key in ('content', 'text', 'body', 'message', 'reply'):
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if key in data['arguments']:
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return str(data['arguments'][key])
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except (json.JSONDecodeError, KeyError, TypeError):
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match = _re.search(r'"content"\s*:\s*"((?:[^"\\]|\\.)*)"', text)
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if match:
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return match.group(1).replace('\\n', '\n').replace('\\"', '"')
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return response
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def _load_agent_profiles(self, simulation_id: str) -> List[Dict[str, Any]]:
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"""加载模拟的Agent人设文件"""
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import os
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@@ -1581,6 +1651,8 @@ class ZepToolsService:
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2. 针对不同角色可能有不同答案
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3. 涵盖事实、观点、感受等多个维度
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4. 语言自然，像真实采访一样
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5. 每个问题控制在50字以内，简洁明了
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6. 直接提问，不要包含背景说明或前缀
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返回JSON格式：{"questions": ["问题1", "问题2", ...]}"""
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@@ -1633,7 +1705,14 @@ class ZepToolsService:
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2. 指出观点的共识和分歧
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3. 突出有价值的引言
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4. 客观中立，不偏袒任何一方
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5. 控制在1000字内"""
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5. 控制在1000字内
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格式约束（必须遵守）：
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- 使用纯文本段落，用空行分隔不同部分
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- 不要使用Markdown标题（如#、##、###）
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- 不要使用分割线（如---、***）
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- 引用受访者原话时使用中文引号「」
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- 可以使用**加粗**标记关键词，但不要使用其他Markdown语法"""
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user_prompt = f"""采访主题：{interview_requirement}
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