Enhance interview functionality in Report Agent and Zep Tools
- Updated the "interview_agents" tool in the Report Agent to utilize the OASIS interview API for real-time agent interviews across Twitter and Reddit, providing authentic responses. - Improved the ZepToolsService to streamline agent selection, question generation, and interview processing, ensuring a more efficient and structured interview workflow. - Enhanced documentation to reflect the new interview capabilities, including updated usage instructions and important operational notes regarding the OASIS environment. - Removed deprecated methods related to simulated interviews, focusing on real API interactions for improved accuracy and reliability.
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2 changed files with 145 additions and 116 deletions
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@ -248,28 +248,31 @@ class ReportAgent:
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},
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"interview_agents": {
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"name": "interview_agents",
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"description": """【深度采访 - 多视角观点采集】
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采访模拟中的Agent角色，获取来自不同视角的深度观点。这是获取模拟结果中各方声音的最佳方式！
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"description": """【深度采访 - 真实Agent采访（双平台）】
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调用OASIS模拟环境的采访API，对正在运行的模拟Agent进行真实采访！
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这不是LLM模拟，而是调用真实的采访接口获取模拟Agent的原始回答。
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默认在Twitter和Reddit两个平台同时采访，获取更全面的观点。
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功能流程：
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1. 自动读取人设文件，了解所有模拟Agent
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2. 智能选择与采访主题最相关的Agent（如学生、媒体、官方等）
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3. 模拟采访每个选中的Agent，获取符合其人设的回答
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4. 整合所有采访结果，提供多视角分析
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3. 自动生成采访问题
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4. 调用 /api/simulation/interview/batch 接口在双平台进行真实采访
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5. 整合所有采访结果，提供多视角分析
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【使用场景】
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- 需要从不同角色视角了解事件看法（学生怎么看？媒体怎么看？官方怎么说？）
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- 需要收集多方意见和立场
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- 需要获取模拟Agent的直接引言和观点
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- 需要获取模拟Agent的真实回答（来自OASIS模拟环境）
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- 想让报告更生动，包含"采访实录"
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【返回内容】
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- 被采访Agent的身份信息
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- 各Agent的采访回答（符合其人设的原创内容）
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- 各Agent在Twitter和Reddit两个平台的采访回答
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- 关键引言（可直接引用）
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- 采访摘要和观点对比
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【重要】这是获取模拟Agent"真实声音"的唯一方式！""",
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【重要】需要OASIS模拟环境正在运行才能使用此功能！""",
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"parameters": {
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"interview_topic": "采访主题或需求描述（如：'了解学生对宿舍甲醛事件的看法'）",
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"max_agents": "最多采访的Agent数量（可选，默认5）"
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@ -334,7 +337,7 @@ class ReportAgent:
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return result.to_text()
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elif tool_name == "interview_agents":
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# 深度采访 - 采访模拟Agent获取多视角观点
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# 深度采访 - 调用真实的OASIS采访API获取模拟Agent的回答（双平台）
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interview_topic = parameters.get("interview_topic", parameters.get("query", ""))
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max_agents = parameters.get("max_agents", 5)
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if isinstance(max_agents, str):
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@ -1256,19 +1256,22 @@ class ZepToolsService:
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"""
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【InterviewAgents - 深度采访】
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采访模拟中的Agent，获取多视角的深度观点：
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调用真实的OASIS采访API，采访模拟中正在运行的Agent：
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1. 自动读取人设文件，了解所有模拟Agent
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2. 使用LLM分析采访需求，智能选择最相关的Agent
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3. 模拟采访每个选中的Agent，获取符合其人设的回答
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4. 整合所有采访结果，生成采访报告
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3. 使用LLM生成采访问题
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4. 调用 /api/simulation/interview/batch 接口进行真实采访（双平台同时采访）
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5. 整合所有采访结果，生成采访报告
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【重要】此功能需要模拟环境处于运行状态（OASIS环境未关闭）
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【使用场景】
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- 需要从不同角色视角了解事件看法
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- 需要收集多方意见和观点
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- 需要模拟真实采访获取第一手资料
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- 需要获取模拟Agent的真实回答（非LLM模拟）
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Args:
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simulation_id: 模拟ID（用于定位人设文件）
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simulation_id: 模拟ID（用于定位人设文件和调用采访API）
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interview_requirement: 采访需求描述（非结构化，如"了解学生对事件的看法"）
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simulation_requirement: 模拟需求背景（可选）
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max_agents: 最多采访的Agent数量
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@ -1277,8 +1280,9 @@ class ZepToolsService:
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Returns:
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InterviewResult: 采访结果
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"""
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import os
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logger.info(f"InterviewAgents 深度采访: {interview_requirement[:50]}...")
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from .simulation_runner import SimulationRunner
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logger.info(f"InterviewAgents 深度采访（真实API）: {interview_requirement[:50]}...")
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result = InterviewResult(
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interview_topic=interview_requirement,
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@ -1296,8 +1300,8 @@ class ZepToolsService:
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result.total_agents = len(profiles)
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logger.info(f"加载到 {len(profiles)} 个Agent人设")
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# Step 2: 使用LLM选择要采访的Agent
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selected_agents, selection_reasoning = self._select_agents_for_interview(
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# Step 2: 使用LLM选择要采访的Agent（返回agent_id列表）
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selected_agents, selected_indices, selection_reasoning = self._select_agents_for_interview(
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profiles=profiles,
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interview_requirement=interview_requirement,
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simulation_requirement=simulation_requirement,
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@ -1306,7 +1310,7 @@ class ZepToolsService:
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result.selected_agents = selected_agents
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result.selection_reasoning = selection_reasoning
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logger.info(f"选择了 {len(selected_agents)} 个Agent进行采访")
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logger.info(f"选择了 {len(selected_agents)} 个Agent进行采访: {selected_indices}")
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# Step 3: 生成采访问题（如果没有提供）
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if not result.interview_questions:
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@ -1317,25 +1321,113 @@ class ZepToolsService:
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)
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logger.info(f"生成了 {len(result.interview_questions)} 个采访问题")
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# Step 4: 对每个选中的Agent进行采访
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for agent in selected_agents:
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interview = self._conduct_interview(
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agent=agent,
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questions=result.interview_questions,
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interview_requirement=interview_requirement,
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simulation_requirement=simulation_requirement
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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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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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try:
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# 构建批量采访列表（不指定platform，双平台采访）
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interviews_request = []
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for agent_idx in selected_indices:
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interviews_request.append({
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"agent_id": agent_idx,
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"prompt": optimized_prompt # 使用优化后的prompt
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# 不指定platform，API会在twitter和reddit两个平台都采访
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})
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logger.info(f"调用批量采访API（双平台）: {len(interviews_request)} 个Agent")
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# 调用 SimulationRunner 的批量采访方法（不传platform，双平台采访）
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api_result = SimulationRunner.interview_agents_batch(
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simulation_id=simulation_id,
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interviews=interviews_request,
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platform=None, # 不指定platform，双平台采访
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timeout=180.0 # 双平台需要更长超时
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)
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result.interviews.append(interview)
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logger.info(f"采访API返回: {api_result.get('interviews_count', 0)} 个结果, success={api_result.get('success')}")
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# 检查API调用是否成功
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if not api_result.get("success", False):
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error_msg = api_result.get("error", "未知错误")
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logger.warning(f"采访API返回失败: {error_msg}")
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result.summary = f"采访API调用失败：{error_msg}。请检查OASIS模拟环境状态。"
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return result
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# Step 5: 解析API返回结果，构建AgentInterview对象
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# 双平台模式返回格式: {"twitter_0": {...}, "reddit_0": {...}, "twitter_1": {...}, ...}
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api_data = api_result.get("result", {})
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results_dict = api_data.get("results", {}) if isinstance(api_data, dict) else {}
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for i, agent_idx in enumerate(selected_indices):
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agent = selected_agents[i]
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agent_name = agent.get("realname", agent.get("username", f"Agent_{agent_idx}"))
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agent_role = agent.get("profession", "未知")
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agent_bio = agent.get("bio", "")
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# 获取该Agent在两个平台的采访结果
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twitter_result = results_dict.get(f"twitter_{agent_idx}", {})
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reddit_result = results_dict.get(f"reddit_{agent_idx}", {})
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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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# 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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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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interview = AgentInterview(
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agent_name=agent_name,
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agent_role=agent_role,
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agent_bio=agent_bio[:150],
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question=combined_prompt,
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response=response_text,
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key_quotes=key_quotes[:5]
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)
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result.interviews.append(interview)
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result.interviewed_count = len(result.interviews)
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except ValueError as e:
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# 模拟环境未运行
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logger.warning(f"采访API调用失败（环境未运行？）: {e}")
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result.summary = f"采访失败：{str(e)}。模拟环境可能已关闭，请确保OASIS环境正在运行。"
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return result
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except Exception as e:
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logger.error(f"采访API调用异常: {e}")
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import traceback
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logger.error(traceback.format_exc())
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result.summary = f"采访过程发生错误：{str(e)}"
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return result
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result.interviewed_count = len(result.interviews)
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# Step 6: 生成采访摘要
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if result.interviews:
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result.summary = self._generate_interview_summary(
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interviews=result.interviews,
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interview_requirement=interview_requirement
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)
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# Step 5: 生成采访摘要
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result.summary = self._generate_interview_summary(
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interviews=result.interviews,
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interview_requirement=interview_requirement
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)
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logger.info(f"InterviewAgents完成: 采访了 {result.interviewed_count} 个Agent")
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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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def _load_agent_profiles(self, simulation_id: str) -> List[Dict[str, Any]]:
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@ -1391,7 +1483,15 @@ class ZepToolsService:
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simulation_requirement: str,
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max_agents: int
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) -> tuple:
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"""使用LLM选择要采访的Agent"""
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"""
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使用LLM选择要采访的Agent
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Returns:
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tuple: (selected_agents, selected_indices, reasoning)
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- selected_agents: 选中Agent的完整信息列表
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- selected_indices: 选中Agent的索引列表（用于API调用）
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- reasoning: 选择理由
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"""
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# 构建Agent摘要列表
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agent_summaries = []
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@ -1444,17 +1544,20 @@ class ZepToolsService:
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# 获取选中的Agent完整信息
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selected_agents = []
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valid_indices = []
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for idx in selected_indices:
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if 0 <= idx < len(profiles):
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selected_agents.append(profiles[idx])
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valid_indices.append(idx)
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return selected_agents, reasoning
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return selected_agents, valid_indices, reasoning
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except Exception as e:
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logger.warning(f"LLM选择Agent失败，使用默认选择: {e}")
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# 降级：随机选择前N个
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# 降级：选择前N个
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selected = profiles[:max_agents]
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return selected, "使用默认选择策略"
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indices = list(range(min(max_agents, len(profiles))))
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return selected, indices, "使用默认选择策略"
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def _generate_interview_questions(
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self,
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@ -1503,83 +1606,6 @@ class ZepToolsService:
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"您认为应该如何解决或改进这个问题？"
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]
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def _conduct_interview(
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self,
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agent: Dict[str, Any],
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questions: List[str],
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interview_requirement: str,
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simulation_requirement: str
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) -> AgentInterview:
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"""模拟采访单个Agent"""
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agent_name = agent.get("realname", agent.get("username", "未知"))
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agent_role = agent.get("profession", "未知")
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agent_bio = agent.get("bio", "")
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agent_persona = agent.get("persona", agent_bio)
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# 将多个问题合并为一次采访
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questions_text = "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)])
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system_prompt = f"""你现在扮演以下角色进行采访：
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【角色名称】{agent_name}
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【角色身份】{agent_role}
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【角色简介】{agent_bio}
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【详细人设】
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{agent_persona[:2000]}
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【重要】
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1. 你必须完全代入这个角色，用第一人称回答
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2. 你的回答必须符合角色的身份、立场、性格和说话风格
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3. 引用角色人设中的具体观点和经历
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4. 语言风格要符合角色特征（如：学生更随性，官方更正式）
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5. 表达真实的情感和态度"""
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user_prompt = f"""采访背景：{simulation_requirement if simulation_requirement else interview_requirement}
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记者提问：
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{questions_text}
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请以【{agent_name}】的身份回答以上问题。回答要体现角色的独特视角和立场。"""
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try:
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response = self.llm.chat(
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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],
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temperature=0.7,
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max_tokens=2000
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)
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# 提取关键引言（包含引号的句子）
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import re
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key_quotes = re.findall(r'[""「」『』]([^""「」『』]{10,100})[""「」『』]', response)
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if not key_quotes:
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# 提取有力的陈述句
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sentences = response.split('。')
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key_quotes = [s.strip() + '。' for s in sentences if len(s.strip()) > 20][:3]
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return AgentInterview(
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agent_name=agent_name,
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agent_role=agent_role,
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agent_bio=agent_bio[:150],
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question=questions_text,
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response=response,
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key_quotes=key_quotes[:5]
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)
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except Exception as e:
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logger.error(f"采访 {agent_name} 失败: {e}")
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return AgentInterview(
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agent_name=agent_name,
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agent_role=agent_role,
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||||
agent_bio=agent_bio[:150],
|
||||
question=questions_text,
|
||||
response=f"[采访失败: {str(e)}]",
|
||||
key_quotes=[]
|
||||
)
|
||||
|
||||
def _generate_interview_summary(
|
||||
self,
|
||||
interviews: List[AgentInterview],
|
||||
|
|
@ -1602,7 +1628,7 @@ class ZepToolsService:
|
|||
2. 指出观点的共识和分歧
|
||||
3. 突出有价值的引言
|
||||
4. 客观中立，不偏袒任何一方
|
||||
5. 控制在300-500字"""
|
||||
5. 控制在1000字内"""
|
||||
|
||||
user_prompt = f"""采访主题：{interview_requirement}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue