Enhance interview prompt handling and update README.md
- Introduced a prefix to optimize interview prompts, ensuring agents respond directly with text without invoking tools. - Updated the simulation API to utilize the optimized prompts for individual and batch interviews. - Modified the `get_interview_history` function to allow for flexible platform querying, returning results from both Reddit and Twitter when no platform is specified. - Enhanced README.md to include new prompt optimization details and updated API usage examples for clarity.
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3 changed files with 120 additions and 33 deletions
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@ -672,6 +672,12 @@ Flask后端 模拟脚本
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> **注意**: 所有Interview接口的参数都通过请求体(JSON)传递，包括simulation_id。
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>
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> **双平台模式说明**: 当不指定`platform`参数时，双平台模拟会同时采访两个平台并返回整合结果。
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>
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> **Prompt自动优化**: 系统会自动在用户提供的prompt前添加说明前缀，避免Agent调用工具：
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> ```
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> 原始prompt: "武汉大学发布撤销处分通告后你有什么看法"
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> 优化后: "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：武汉大学发布撤销处分通告后你有什么看法"
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> ```
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#### 1. 采访单个Agent
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@ -860,11 +866,11 @@ Flask后端 模拟脚本
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| 参数 | 类型 | 必填 | 默认值 | 说明 |
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|------|------|------|--------|------|
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| simulation_id | String | 是 | - | 模拟ID |
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| platform | String | 否 | reddit | 平台类型（reddit/twitter） |
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| agent_id | Integer | 否 | - | 过滤Agent ID |
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| platform | String | 否 | null | 平台类型（reddit/twitter），不指定则返回两个平台的所有历史 |
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| agent_id | Integer | 否 | - | 只获取该Agent的采访历史 |
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| limit | Integer | 否 | 100 | 返回数量限制 |
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**返回示例**:
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**返回示例（不指定platform，返回双平台历史）**:
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```json
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{
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"success": true,
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@ -875,7 +881,14 @@ Flask后端 模拟脚本
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"agent_id": 0,
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"response": "我认为...",
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"prompt": "你对这件事有什么看法？",
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"timestamp": "2025-12-08T10:00:00",
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"timestamp": "2025-12-08T10:00:02",
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"platform": "twitter"
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},
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{
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"agent_id": 0,
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"response": "从Reddit角度来看...",
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"prompt": "你对这件事有什么看法？",
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"timestamp": "2025-12-08T10:00:01",
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"platform": "reddit"
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},
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...
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@ -19,6 +19,29 @@ from ..models.project import ProjectManager
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logger = get_logger('mirofish.api.simulation')
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# Interview prompt 优化前缀
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# 添加此前缀可以避免Agent调用工具，直接用文本回复
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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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def optimize_interview_prompt(prompt: str) -> str:
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"""
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优化Interview提问，添加前缀避免Agent调用工具
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Args:
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prompt: 原始提问
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Returns:
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优化后的提问
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"""
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if not prompt:
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return prompt
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# 避免重复添加前缀
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if prompt.startswith(INTERVIEW_PROMPT_PREFIX):
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return prompt
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return f"{INTERVIEW_PROMPT_PREFIX}{prompt}"
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# ============== 实体读取接口 ==============
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@simulation_bp.route('/entities/<graph_id>', methods=['GET'])
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@ -1819,14 +1842,17 @@ def interview_agent():
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"error": "模拟环境未运行或已关闭。请确保模拟已完成并进入等待命令模式。"
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}), 400
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# 优化prompt，添加前缀避免Agent调用工具
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optimized_prompt = optimize_interview_prompt(prompt)
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result = SimulationRunner.interview_agent(
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simulation_id=simulation_id,
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agent_id=agent_id,
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prompt=prompt,
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prompt=optimized_prompt,
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platform=platform,
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timeout=timeout
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)
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return jsonify({
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"success": result.get("success", False),
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"data": result
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@ -1951,9 +1977,16 @@ def interview_agents_batch():
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"error": "模拟环境未运行或已关闭。请确保模拟已完成并进入等待命令模式。"
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}), 400
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# 优化每个采访项的prompt，添加前缀避免Agent调用工具
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optimized_interviews = []
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for interview in interviews:
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optimized_interview = interview.copy()
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optimized_interview['prompt'] = optimize_interview_prompt(interview.get('prompt', ''))
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optimized_interviews.append(optimized_interview)
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result = SimulationRunner.interview_agents_batch(
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simulation_id=simulation_id,
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interviews=interviews,
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interviews=optimized_interviews,
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platform=platform,
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timeout=timeout
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)
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@ -2051,9 +2084,12 @@ def interview_all_agents():
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"error": "模拟环境未运行或已关闭。请确保模拟已完成并进入等待命令模式。"
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}), 400
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# 优化prompt，添加前缀避免Agent调用工具
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optimized_prompt = optimize_interview_prompt(prompt)
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result = SimulationRunner.interview_all_agents(
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simulation_id=simulation_id,
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prompt=prompt,
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prompt=optimized_prompt,
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platform=platform,
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timeout=timeout
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)
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@ -2094,8 +2130,9 @@ def get_interview_history():
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请求（JSON）：
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{
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"simulation_id": "sim_xxxx", // 必填，模拟ID
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"platform": "reddit", // 可选，平台类型（reddit/twitter），默认reddit
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"agent_id": 0, // 可选，过滤Agent ID
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"platform": "reddit", // 可选，平台类型（reddit/twitter）
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// 不指定则返回两个平台的所有历史
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"agent_id": 0, // 可选，只获取该Agent的采访历史
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"limit": 100 // 可选，返回数量，默认100
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}
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@ -2121,7 +2158,7 @@ def get_interview_history():
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data = request.get_json() or {}
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simulation_id = data.get('simulation_id')
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platform = data.get('platform', 'reddit')
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platform = data.get('platform') # 不指定则返回两个平台的历史
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agent_id = data.get('agent_id')
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limit = data.get('limit', 100)
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@ -1279,30 +1279,16 @@ class SimulationRunner:
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}
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@classmethod
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def get_interview_history(
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def _get_interview_history_from_db(
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cls,
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simulation_id: str,
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platform: str = "reddit",
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db_path: str,
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platform_name: str,
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agent_id: Optional[int] = None,
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limit: int = 100
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) -> List[Dict[str, Any]]:
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"""
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获取Interview历史记录（从数据库读取）
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Args:
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simulation_id: 模拟ID
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platform: 平台类型（reddit/twitter）
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agent_id: 过滤Agent ID（可选）
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limit: 返回数量限制
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Returns:
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Interview历史记录列表
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"""
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"""从单个数据库获取Interview历史"""
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import sqlite3
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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db_path = os.path.join(sim_dir, f"{platform}_simulation.db")
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if not os.path.exists(db_path):
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return []
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@ -1312,8 +1298,6 @@ class SimulationRunner:
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# 构建查询
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# 注意：ActionType.INTERVIEW.value 应该是字符串形式
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if agent_id is not None:
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cursor.execute("""
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SELECT user_id, info, created_at
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@ -1342,13 +1326,66 @@ class SimulationRunner:
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"response": info.get("response", info),
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"prompt": info.get("prompt", ""),
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"timestamp": created_at,
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"platform": platform
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"platform": platform_name
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})
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conn.close()
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except Exception as e:
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logger.error(f"读取Interview历史失败: {e}")
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logger.error(f"读取Interview历史失败 ({platform_name}): {e}")
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return results
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@classmethod
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def get_interview_history(
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cls,
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simulation_id: str,
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platform: str = None,
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agent_id: Optional[int] = None,
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limit: int = 100
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) -> List[Dict[str, Any]]:
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"""
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获取Interview历史记录（从数据库读取）
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Args:
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simulation_id: 模拟ID
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platform: 平台类型（reddit/twitter/None）
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- "reddit": 只获取Reddit平台的历史
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- "twitter": 只获取Twitter平台的历史
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- None: 获取两个平台的所有历史
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agent_id: 指定Agent ID（可选，只获取该Agent的历史）
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limit: 每个平台返回数量限制
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Returns:
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Interview历史记录列表
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"""
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sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id)
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results = []
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# 确定要查询的平台
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if platform in ("reddit", "twitter"):
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platforms = [platform]
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else:
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# 不指定platform时，查询两个平台
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platforms = ["twitter", "reddit"]
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for p in platforms:
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db_path = os.path.join(sim_dir, f"{p}_simulation.db")
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platform_results = cls._get_interview_history_from_db(
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db_path=db_path,
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platform_name=p,
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agent_id=agent_id,
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limit=limit
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)
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results.extend(platform_results)
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# 按时间降序排序
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results.sort(key=lambda x: x.get("timestamp", ""), reverse=True)
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# 如果查询了多个平台，限制总数
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if len(platforms) > 1 and len(results) > limit:
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results = results[:limit]
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return results
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