Update ReportAgent to enhance report retrieval and streamline tool call process
- Reduced maximum tool calls per chat from 5 to 2 for improved efficiency. - Simplified system prompt to focus on concise responses and report content. - Implemented report content retrieval with length limitation to prevent context overflow. - Adjusted tool call execution to limit to one call per iteration, enhancing clarity in responses. - Updated user message prompts to encourage concise answers based on retrieved data.
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1 changed files with 41 additions and 82 deletions
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@ -499,7 +499,7 @@ class ReportAgent:
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MAX_REFLECTION_ROUNDS = 3
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# 对话中的最大工具调用次数
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MAX_TOOL_CALLS_PER_CHAT = 5
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MAX_TOOL_CALLS_PER_CHAT = 2
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def __init__(
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self,
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@ -1656,62 +1656,45 @@ class ReportAgent:
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chat_history = chat_history or []
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system_prompt = f"""你是一个拥有「上帝视角」的未来预测助手，负责回答关于模拟预测结果的问题。
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# 获取已生成的报告内容
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report_content = ""
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try:
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report = ReportManager.get_report_by_simulation(self.simulation_id)
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if report and report.markdown_content:
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# 限制报告长度，避免上下文过长
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report_content = report.markdown_content[:15000]
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if len(report.markdown_content) > 15000:
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report_content += "\n\n... [报告内容已截断] ..."
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except Exception as e:
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logger.warning(f"获取报告内容失败: {e}")
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═══════════════════════════════════════════════════════════════
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【预测场景背景】
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═══════════════════════════════════════════════════════════════
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预测条件（模拟需求）: {self.simulation_requirement}
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模拟世界ID: {self.graph_id}
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# 构建系统提示
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system_prompt = f"""你是一个简洁高效的模拟预测助手。
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模拟世界是对未来的预演。你的任务是帮助用户理解：在设定条件下，未来会发生什么？各类人群会如何反应？
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【背景】
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预测条件: {self.simulation_requirement}
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═══════════════════════════════════════════════════════════════
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【最重要的规则 - 必须遵守】
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═══════════════════════════════════════════════════════════════
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【已生成的分析报告】
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{report_content if report_content else "（暂无报告）"}
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1. 【必须调用工具获取预测数据】
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- 你的回答必须基于模拟世界中的预测结果
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- 禁止使用你自己的知识来回答问题
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- 每次回答前至少调用1次工具获取预测数据
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2. 【必须引用Agent的原始言行】
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- Agent的发言和行为是对未来人群行为的预测
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- 在回答中使用引用格式展示这些预测，例如：
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> "某类人群会表示：原文内容..."
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- 这些引用是预测的核心证据
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3. 【忠实呈现预测结果】
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- 回答必须反映模拟世界中的预测结果
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- 不要添加模拟中不存在的信息
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- 如果某方面信息不足，如实说明
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═══════════════════════════════════════════════════════════════
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【可用检索工具】（最多调用3次）
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═══════════════════════════════════════════════════════════════
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【规则】
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1. 优先基于上述报告内容回答问题
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2. 直接回答问题，避免冗长的思考论述
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3. 仅在报告内容不足以回答时，才调用工具检索更多数据
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4. 回答要简洁、清晰、有条理
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【可用工具】（仅在需要时使用，最多调用1-2次）
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{self._get_tools_description()}
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【工具使用建议】
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- insight_forge: 用于深度分析，会自动分解问题并多维度检索
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- panorama_search: 用于了解全貌和演变过程
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- quick_search: 用于快速验证某个具体信息
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- interview_agents: 用于采访模拟Agent，获取不同角色的真实观点和看法
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【工具调用格式】
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<tool_call>
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{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}}
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</tool_call>
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═══════════════════════════════════════════════════════════════
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【回答要求】
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═══════════════════════════════════════════════════════════════
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1. 先调用工具获取模拟数据，再回答问题
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2. 大量引用检索到的原文
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3. 使用 > 格式引用重要内容
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4. 如果信息不足，如实说明限制
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5. 保持专业和客观"""
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【回答风格】
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- 简洁直接，不要长篇大论
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- 使用 > 格式引用关键内容
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- 优先给出结论，再解释原因"""
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# 构建消息
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messages = [{"role": "system", "content": system_prompt}]
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@ -1720,48 +1703,27 @@ class ReportAgent:
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for h in chat_history[-10:]: # 限制历史长度
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messages.append(h)
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# 添加用户消息，强调需要先检索
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# 添加用户消息
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messages.append({
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"role": "user",
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"content": f"""{message}
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【提醒】请先调用工具获取模拟数据，再回答问题。推荐使用 insight_forge 进行深度检索。"""
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"content": message
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})
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# ReACT循环
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# ReACT循环（简化版）
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tool_calls_made = []
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max_iterations = 5 # 最大迭代轮数
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min_tool_calls = 1 # 最少工具调用次数
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max_iterations = 2 # 减少迭代轮数
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for iteration in range(max_iterations):
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response = self.llm.chat(
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messages=messages,
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temperature=0.5,
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max_tokens=2048
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temperature=0.5
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)
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# 解析工具调用
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tool_calls = self._parse_tool_calls(response)
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if not tool_calls:
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# 没有工具调用
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if len(tool_calls_made) < min_tool_calls and iteration < 2:
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# 还没有调用过工具，强烈提示需要先检索
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messages.append({"role": "assistant", "content": response})
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messages.append({
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"role": "user",
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"content": f"""【重要】你还没有调用工具获取模拟数据！
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请先调用工具检索相关信息：
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<tool_call>
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{{"name": "insight_forge", "parameters": {{"query": "{message[:100]}"}}}}
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</tool_call>
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【记住】回答必须基于模拟结果，不能使用你自己的知识！"""
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})
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continue
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# 已有工具调用，清理响应并返回
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# 没有工具调用，直接返回响应
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clean_response = re.sub(r'<tool_call>.*?</tool_call>', '', response, flags=re.DOTALL)
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clean_response = re.sub(r'\[TOOL_CALL\].*?\)', '', clean_response)
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@ -1771,33 +1733,30 @@ class ReportAgent:
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"sources": [tc.get("parameters", {}).get("query", "") for tc in tool_calls_made]
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}
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# 执行工具调用
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# 执行工具调用（限制数量）
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tool_results = []
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for call in tool_calls:
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for call in tool_calls[:1]: # 每轮最多执行1次工具调用
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if len(tool_calls_made) >= self.MAX_TOOL_CALLS_PER_CHAT:
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break
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result = self._execute_tool(call["name"], call.get("parameters", {}))
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tool_results.append({
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"tool": call["name"],
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"result": result[:2000] # 增加结果长度限制
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"result": result[:1500] # 限制结果长度
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})
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tool_calls_made.append(call)
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# 将结果添加到消息
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messages.append({"role": "assistant", "content": response})
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observation = "═══ 检索结果 ═══\n" + "\n\n".join([
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f"【{r['tool']}】\n{r['result']}" for r in tool_results
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])
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observation = "\n".join([f"[{r['tool']}结果]\n{r['result']}" for r in tool_results])
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messages.append({
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"role": "user",
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"content": observation + "\n\n请基于以上模拟数据回答问题。\n【重要】请在回答中引用检索到的原文，使用 > 格式。"
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"content": observation + "\n\n请简洁回答问题。"
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})
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# 达到最大迭代，获取最终响应
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final_response = self.llm.chat(
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messages=messages,
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temperature=0.5,
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max_tokens=2048
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temperature=0.5
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)
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# 清理响应
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