The final report agent has been largely completed.
This commit is contained in:
戒酒的李白
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"""
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Report Engine节点处理模块
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实现报告生成的各个处理步骤
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"""
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from .base_node import BaseNode, StateMutationNode
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from .template_selection_node import TemplateSelectionNode
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from .html_generation_node import HTMLGenerationNode
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__all__ = [
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"BaseNode",
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"StateMutationNode",
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"TemplateSelectionNode",
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"HTMLGenerationNode"
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]
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"""
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Report Engine节点基类
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定义所有处理节点的基础接口
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"""
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import logging
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from abc import ABC, abstractmethod
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from typing import Any, Dict, Optional
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from ..llms.base import BaseLLM
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from ..state.state import ReportState
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class BaseNode(ABC):
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"""节点基类"""
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def __init__(self, llm_client: BaseLLM, node_name: str = ""):
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"""
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初始化节点
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Args:
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llm_client: LLM客户端
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node_name: 节点名称
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"""
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self.llm_client = llm_client
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self.node_name = node_name or self.__class__.__name__
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self.logger = logging.getLogger('ReportEngine')
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@abstractmethod
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def run(self, input_data: Any, **kwargs) -> Any:
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"""
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执行节点处理逻辑
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Args:
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input_data: 输入数据
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**kwargs: 额外参数
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Returns:
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处理结果
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"""
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pass
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def validate_input(self, input_data: Any) -> bool:
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"""
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验证输入数据
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Args:
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input_data: 输入数据
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Returns:
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验证是否通过
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"""
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return True
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def process_output(self, output: Any) -> Any:
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"""
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处理输出数据
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Args:
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output: 原始输出
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Returns:
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处理后的输出
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"""
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return output
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def log_info(self, message: str):
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"""记录信息日志"""
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formatted_message = f"[{self.node_name}] {message}"
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self.logger.info(formatted_message)
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def log_error(self, message: str):
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"""记录错误日志"""
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formatted_message = f"[{self.node_name}] {message}"
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self.logger.error(formatted_message)
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class StateMutationNode(BaseNode):
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"""带状态修改功能的节点基类"""
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@abstractmethod
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def mutate_state(self, input_data: Any, state: ReportState, **kwargs) -> ReportState:
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"""
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修改状态
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Args:
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input_data: 输入数据
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state: 当前状态
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**kwargs: 额外参数
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Returns:
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修改后的状态
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"""
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pass
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"""
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HTML生成节点
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将整合后的内容转换为美观的HTML报告
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"""
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import json
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from datetime import datetime
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from typing import Dict, Any
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from .base_node import StateMutationNode
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from ..llms.base import BaseLLM
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from ..state.state import ReportState
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from ..prompts import SYSTEM_PROMPT_HTML_GENERATION
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# 不再需要text_processing依赖
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class HTMLGenerationNode(StateMutationNode):
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"""HTML生成处理节点"""
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def __init__(self, llm_client: BaseLLM):
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"""
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初始化HTML生成节点
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Args:
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llm_client: LLM客户端
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"""
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super().__init__(llm_client, "HTMLGenerationNode")
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def run(self, input_data: Dict[str, Any], **kwargs) -> str:
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"""
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执行HTML生成
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Args:
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input_data: 包含报告数据的字典
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- query: 原始查询
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- query_engine_report: QueryEngine报告内容
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- media_engine_report: MediaEngine报告内容
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- insight_engine_report: InsightEngine报告内容
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- forum_logs: 论坛日志内容
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- selected_template: 选择的模板内容
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Returns:
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生成的HTML内容
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"""
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self.log_info("开始生成HTML报告...")
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try:
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# 准备LLM输入数据
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llm_input = {
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"query": input_data.get('query', ''),
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"query_engine_report": input_data.get('query_engine_report', ''),
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"media_engine_report": input_data.get('media_engine_report', ''),
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"insight_engine_report": input_data.get('insight_engine_report', ''),
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"forum_logs": input_data.get('forum_logs', ''),
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"selected_template": input_data.get('selected_template', '')
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}
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# 转换为JSON格式
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message = json.dumps(llm_input, ensure_ascii=False, indent=2)
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# 调用LLM生成HTML
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response = self.llm_client.invoke(SYSTEM_PROMPT_HTML_GENERATION, message)
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# 处理响应
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processed_response = self.process_output(response)
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self.log_info("HTML报告生成完成")
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return processed_response
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except Exception as e:
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self.log_error(f"HTML生成失败: {str(e)}")
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# 返回备用HTML
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return self._generate_fallback_html(input_data)
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def mutate_state(self, input_data: Dict[str, Any], state: ReportState, **kwargs) -> ReportState:
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"""
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修改报告状态，添加生成的HTML内容
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Args:
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input_data: 输入数据
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state: 当前报告状态
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**kwargs: 额外参数
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Returns:
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更新后的报告状态
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"""
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# 生成HTML
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html_content = self.run(input_data, **kwargs)
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# 更新状态
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state.html_content = html_content
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state.mark_completed()
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return state
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def process_output(self, output: str) -> str:
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"""
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处理LLM输出，提取HTML内容
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Args:
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output: LLM原始输出
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Returns:
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清理后的HTML内容
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"""
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try:
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self.log_info(f"处理LLM原始输出，长度: {len(output)} 字符")
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html_content = ""
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# 尝试解析JSON响应
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try:
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result = json.loads(output)
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html_content = result.get('html_content', '')
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self.log_info("成功从JSON中提取html_content")
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except json.JSONDecodeError:
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self.log_info("不是JSON格式，直接使用原始输出")
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html_content = output
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# 如果还是没有内容，尝试其他提取方法
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if not html_content.strip():
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# 查找HTML标记
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if '<!DOCTYPE html>' in output:
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start_idx = output.find('<!DOCTYPE html>')
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html_content = output[start_idx:]
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elif '<html' in output:
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start_idx = output.find('<html')
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html_content = output[start_idx:]
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else:
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html_content = output
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# 清理markdown代码块标记
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if html_content.startswith('```html'):
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html_content = html_content.replace('```html', '').replace('```', '').strip()
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elif html_content.startswith('```'):
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html_content = html_content.replace('```', '').strip()
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# 处理转义字符
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html_content = html_content.replace('\\n', '\n')
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html_content = html_content.replace('\\t', '\t')
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html_content = html_content.replace('\\r', '\r')
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html_content = html_content.replace('\\"', '"')
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html_content = html_content.replace("\\'", "'")
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# 验证HTML内容
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if not html_content.strip():
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raise ValueError("生成的HTML内容为空")
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# 确保HTML有基本结构
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if not html_content.strip().startswith('<!DOCTYPE') and not html_content.strip().startswith('<html'):
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self.log_info("HTML缺少基本结构，添加包装")
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html_content = f"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>智能舆情分析报告</title>
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</head>
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<body>
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{html_content}
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</body>
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</html>"""
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self.log_info(f"HTML处理完成，最终长度: {len(html_content)} 字符")
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return html_content.strip()
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except Exception as e:
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self.log_error(f"处理HTML输出失败: {str(e)}")
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return self._generate_error_html(str(e))
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def _generate_fallback_html(self, input_data: Dict[str, Any]) -> str:
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"""
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生成备用HTML报告（当LLM失败时使用）
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Args:
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input_data: 输入数据
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Returns:
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备用HTML内容
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"""
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self.log_info("使用备用HTML生成方法")
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query = input_data.get('query', '智能舆情分析报告')
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query_report = input_data.get('query_engine_report', '')
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media_report = input_data.get('media_engine_report', '')
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insight_report = input_data.get('insight_engine_report', '')
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forum_logs = input_data.get('forum_logs', '')
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generation_time = datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")
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html_content = f"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>{query} - 智能舆情分析报告</title>
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<style>
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body {{
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
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line-height: 1.6;
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color: #333;
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max-width: 1200px;
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margin: 0 auto;
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padding: 20px;
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background: #f5f5f5;
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}}
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.container {{
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background: white;
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padding: 40px;
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border-radius: 8px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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}}
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h1 {{
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color: #2c3e50;
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border-bottom: 3px solid #3498db;
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padding-bottom: 10px;
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}}
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h2 {{
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color: #34495e;
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margin-top: 30px;
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margin-bottom: 15px;
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}}
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.section {{
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margin-bottom: 30px;
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padding: 20px;
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border-left: 4px solid #3498db;
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background: #f8f9fa;
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}}
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.meta {{
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background: #e9ecef;
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padding: 15px;
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border-radius: 5px;
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margin-bottom: 20px;
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}}
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.footer {{
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margin-top: 40px;
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padding-top: 20px;
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border-top: 1px solid #eee;
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text-align: center;
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color: #666;
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}}
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pre {{
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background: #f4f4f4;
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padding: 15px;
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border-radius: 5px;
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overflow-x: auto;
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white-space: pre-wrap;
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}}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>{query}</h1>
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<div class="meta">
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<strong>报告生成时间:</strong> {generation_time}<br>
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<strong>数据来源:</strong> QueryEngine、MediaEngine、InsightEngine、ForumEngine<br>
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<strong>报告类型:</strong> 综合舆情分析报告
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</div>
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<h2>执行摘要</h2>
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<div class="section">
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本报告整合了多个分析引擎的研究结果，为您提供全面的舆情分析洞察。
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通过对查询主题"{query}"的深度分析，我们从多个维度展现了当前的舆情态势。
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</div>
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{f'<h2>QueryEngine分析结果</h2><div class="section"><pre>{query_report}</pre></div>' if query_report else ''}
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{f'<h2>MediaEngine分析结果</h2><div class="section"><pre>{media_report}</pre></div>' if media_report else ''}
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{f'<h2>InsightEngine分析结果</h2><div class="section"><pre>{insight_report}</pre></div>' if insight_report else ''}
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{f'<h2>论坛监控数据</h2><div class="section"><pre>{forum_logs[:2000]}{"..." if len(forum_logs) > 2000 else ""}</pre></div>' if forum_logs else ''}
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<h2>综合结论</h2>
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<div class="section">
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基于多个分析引擎的综合研究，我们对"{query}"主题进行了全面分析。
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各引擎从不同角度提供了深入洞察，为决策提供了重要参考。
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</div>
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<div class="footer">
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<p>本报告由智能舆情分析平台自动生成</p>
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<p>ReportEngine v1.0 | 生成时间: {generation_time}</p>
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</div>
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</div>
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</body>
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</html>"""
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return html_content
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def _generate_error_html(self, error_message: str) -> str:
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"""
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生成错误HTML页面
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Args:
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error_message: 错误信息
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Returns:
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错误HTML内容
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"""
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return f"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>报告生成失败</title>
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<style>
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body {{
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font-family: Arial, sans-serif;
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text-align: center;
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padding: 50px;
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background: #f8f9fa;
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}}
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.error-container {{
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background: white;
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padding: 40px;
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border-radius: 8px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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max-width: 600px;
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margin: 0 auto;
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}}
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.error-title {{
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color: #e74c3c;
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font-size: 24px;
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margin-bottom: 20px;
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}}
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.error-message {{
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color: #666;
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margin-bottom: 20px;
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}}
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</style>
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</head>
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<body>
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<div class="error-container">
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<div class="error-title">报告生成失败</div>
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<div class="error-message">错误信息: {error_message}</div>
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<p>请检查输入数据或稍后重试。</p>
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</div>
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</body>
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</html>"""
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@@ -0,0 +1,274 @@
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"""
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模板选择节点
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根据查询内容和可用模板选择最合适的报告模板
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"""
|
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|
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import os
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import json
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from typing import Dict, Any, List, Optional
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|
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from .base_node import BaseNode
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from ..prompts import SYSTEM_PROMPT_TEMPLATE_SELECTION
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|
||||
|
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class TemplateSelectionNode(BaseNode):
|
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"""模板选择处理节点"""
|
||||
|
||||
def __init__(self, llm_client, template_dir: str = "ReportEngine/report_template"):
|
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"""
|
||||
初始化模板选择节点
|
||||
|
||||
Args:
|
||||
llm_client: LLM客户端
|
||||
template_dir: 模板目录路径
|
||||
"""
|
||||
super().__init__(llm_client, "TemplateSelectionNode")
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self.template_dir = template_dir
|
||||
|
||||
def run(self, input_data: Dict[str, Any], **kwargs) -> Dict[str, Any]:
|
||||
"""
|
||||
执行模板选择
|
||||
|
||||
Args:
|
||||
input_data: 包含查询和报告内容的字典
|
||||
- query: 原始查询
|
||||
- reports: 三个子agent的报告列表
|
||||
- forum_logs: 论坛日志内容
|
||||
|
||||
Returns:
|
||||
选择的模板信息
|
||||
"""
|
||||
self.log_info("开始模板选择...")
|
||||
|
||||
query = input_data.get('query', '')
|
||||
reports = input_data.get('reports', [])
|
||||
forum_logs = input_data.get('forum_logs', '')
|
||||
|
||||
# 获取可用模板
|
||||
available_templates = self._get_available_templates()
|
||||
|
||||
if not available_templates:
|
||||
self.log_info("未找到预设模板，使用内置默认模板")
|
||||
return self._get_fallback_template()
|
||||
|
||||
# 首先尝试简单关键词匹配
|
||||
simple_match = self._simple_keyword_matching(query, available_templates)
|
||||
if simple_match:
|
||||
self.log_info(f"通过关键词匹配选择模板: {simple_match['template_name']}")
|
||||
return simple_match
|
||||
|
||||
# 如果关键词匹配失败，尝试LLM选择
|
||||
try:
|
||||
llm_result = self._llm_template_selection(query, reports, forum_logs, available_templates)
|
||||
if llm_result:
|
||||
return llm_result
|
||||
except Exception as e:
|
||||
self.log_error(f"LLM模板选择失败: {str(e)}")
|
||||
|
||||
# 所有方法都失败，使用默认的社会热点事件模板
|
||||
default_template = self._get_default_social_event_template(available_templates)
|
||||
if default_template:
|
||||
return default_template
|
||||
|
||||
# 最后备选方案
|
||||
return self._get_fallback_template()
|
||||
|
||||
def _simple_keyword_matching(self, query: str, available_templates: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
|
||||
"""基于关键词的简单模板匹配"""
|
||||
query_lower = query.lower()
|
||||
|
||||
# 关键词映射
|
||||
keyword_mapping = {
|
||||
'企业': ['企业品牌'],
|
||||
'品牌': ['企业品牌'],
|
||||
'声誉': ['企业品牌'],
|
||||
'市场': ['市场竞争'],
|
||||
'竞争': ['市场竞争'],
|
||||
'格局': ['市场竞争'],
|
||||
'政策': ['政策', '行业'],
|
||||
'行业': ['政策', '行业'],
|
||||
'动态': ['政策', '行业'],
|
||||
'突发': ['突发事件', '危机'],
|
||||
'危机': ['突发事件', '危机'],
|
||||
'公关': ['突发事件', '危机'],
|
||||
'日常': ['日常', '定期'],
|
||||
'定期': ['日常', '定期'],
|
||||
'监测': ['日常', '定期'],
|
||||
'热点': ['社会公共热点'],
|
||||
'社会': ['社会公共热点'],
|
||||
'事件': ['社会公共热点'],
|
||||
}
|
||||
|
||||
# 检查查询中的关键词
|
||||
for keyword, template_keywords in keyword_mapping.items():
|
||||
if keyword in query_lower:
|
||||
# 查找匹配的模板
|
||||
for template in available_templates:
|
||||
for template_keyword in template_keywords:
|
||||
if template_keyword in template['name']:
|
||||
return {
|
||||
'template_name': template['name'],
|
||||
'template_content': template['content'],
|
||||
'selection_reason': f'基于关键词"{keyword}"匹配选择'
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
def _llm_template_selection(self, query: str, reports: List[Any], forum_logs: str,
|
||||
available_templates: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
|
||||
"""使用LLM进行模板选择"""
|
||||
self.log_info("尝试使用LLM进行模板选择...")
|
||||
|
||||
# 构建模板列表
|
||||
template_list = "\n".join([f"- {t['name']}: {t['description']}" for t in available_templates])
|
||||
|
||||
user_message = f"""查询内容: {query}
|
||||
|
||||
报告数量: {len(reports)} 个分析引擎报告
|
||||
论坛日志: {'有' if forum_logs else '无'}
|
||||
|
||||
可用模板:
|
||||
{template_list}
|
||||
|
||||
请选择最合适的模板。"""
|
||||
|
||||
# 调用LLM
|
||||
response = self.llm_client.invoke(SYSTEM_PROMPT_TEMPLATE_SELECTION, user_message)
|
||||
|
||||
# 检查响应是否为空
|
||||
if not response or not response.strip():
|
||||
self.log_error("LLM返回空响应")
|
||||
return None
|
||||
|
||||
self.log_info(f"LLM原始响应: {response[:200]}...")
|
||||
|
||||
# 尝试解析JSON响应
|
||||
try:
|
||||
# 清理响应文本
|
||||
cleaned_response = self._clean_llm_response(response)
|
||||
result = json.loads(cleaned_response)
|
||||
|
||||
# 验证选择的模板是否存在
|
||||
selected_template_name = result.get('template_name', '')
|
||||
for template in available_templates:
|
||||
if template['name'] == selected_template_name or selected_template_name in template['name']:
|
||||
self.log_info(f"LLM选择模板: {selected_template_name}")
|
||||
return {
|
||||
'template_name': template['name'],
|
||||
'template_content': template['content'],
|
||||
'selection_reason': result.get('selection_reason', 'LLM智能选择')
|
||||
}
|
||||
|
||||
self.log_error(f"LLM选择的模板不存在: {selected_template_name}")
|
||||
return None
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
self.log_error(f"JSON解析失败: {str(e)}")
|
||||
# 尝试从文本响应中提取模板信息
|
||||
return self._extract_template_from_text(response, available_templates)
|
||||
|
||||
def _clean_llm_response(self, response: str) -> str:
|
||||
"""清理LLM响应"""
|
||||
# 移除可能的markdown代码块标记
|
||||
if '```json' in response:
|
||||
response = response.split('```json')[1].split('```')[0]
|
||||
elif '```' in response:
|
||||
response = response.split('```')[1].split('```')[0]
|
||||
|
||||
# 移除前后空白
|
||||
response = response.strip()
|
||||
|
||||
return response
|
||||
|
||||
def _extract_template_from_text(self, response: str, available_templates: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
|
||||
"""从文本响应中提取模板信息"""
|
||||
self.log_info("尝试从文本响应中提取模板信息")
|
||||
|
||||
# 查找响应中是否包含模板名称
|
||||
for template in available_templates:
|
||||
template_name_variants = [
|
||||
template['name'],
|
||||
template['name'].replace('.md', ''),
|
||||
template['name'].replace('模板', ''),
|
||||
]
|
||||
|
||||
for variant in template_name_variants:
|
||||
if variant in response:
|
||||
self.log_info(f"在响应中找到模板: {template['name']}")
|
||||
return {
|
||||
'template_name': template['name'],
|
||||
'template_content': template['content'],
|
||||
'selection_reason': '从文本响应中提取'
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
def _get_available_templates(self) -> List[Dict[str, Any]]:
|
||||
"""获取可用的模板列表"""
|
||||
templates = []
|
||||
|
||||
if not os.path.exists(self.template_dir):
|
||||
self.log_error(f"模板目录不存在: {self.template_dir}")
|
||||
return templates
|
||||
|
||||
# 查找所有markdown模板文件
|
||||
for filename in os.listdir(self.template_dir):
|
||||
if filename.endswith('.md'):
|
||||
template_path = os.path.join(self.template_dir, filename)
|
||||
try:
|
||||
with open(template_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
template_name = filename.replace('.md', '')
|
||||
description = self._extract_template_description(template_name)
|
||||
|
||||
templates.append({
|
||||
'name': template_name,
|
||||
'path': template_path,
|
||||
'content': content,
|
||||
'description': description
|
||||
})
|
||||
except Exception as e:
|
||||
self.log_error(f"读取模板文件失败 {filename}: {str(e)}")
|
||||
|
||||
return templates
|
||||
|
||||
def _extract_template_description(self, template_name: str) -> str:
|
||||
"""根据模板名称生成描述"""
|
||||
if '企业品牌' in template_name:
|
||||
return "适用于企业品牌声誉和形象分析"
|
||||
elif '市场竞争' in template_name:
|
||||
return "适用于市场竞争格局和对手分析"
|
||||
elif '日常' in template_name or '定期' in template_name:
|
||||
return "适用于日常监测和定期汇报"
|
||||
elif '政策' in template_name or '行业' in template_name:
|
||||
return "适用于政策影响和行业动态分析"
|
||||
elif '热点' in template_name or '社会' in template_name:
|
||||
return "适用于社会热点和公共事件分析"
|
||||
elif '突发' in template_name or '危机' in template_name:
|
||||
return "适用于突发事件和危机公关"
|
||||
|
||||
return "通用报告模板"
|
||||
|
||||
def _get_default_social_event_template(self, available_templates: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
|
||||
"""获取默认的社会热点事件分析模板"""
|
||||
# 查找社会热点事件分析模板
|
||||
for template in available_templates:
|
||||
if '社会公共热点事件' in template['name'] or '热点' in template['name']:
|
||||
self.log_info(f"使用默认模板: {template['name']}")
|
||||
return {
|
||||
'template_name': template['name'],
|
||||
'template_content': template['content'],
|
||||
'selection_reason': '默认使用社会热点事件分析模板'
|
||||
}
|
||||
return None
|
||||
|
||||
def _get_fallback_template(self) -> Dict[str, Any]:
|
||||
"""获取备用默认模板（空模板，让LLM自行发挥）"""
|
||||
self.log_info("未找到合适模板，使用空模板让LLM自行发挥")
|
||||
|
||||
return {
|
||||
'template_name': '自由发挥模板',
|
||||
'template_content': '',
|
||||
'selection_reason': '未找到合适的预设模板，让LLM根据内容自行设计报告结构'
|
||||
}
|
||||
Reference in New Issue
Block a user