""" 总结节点实现 负责根据搜索结果生成和更新段落内容 """ import json from typing import Dict, Any, List from json.decoder import JSONDecodeError from .base_node import StateMutationNode from ..state.state import State from ..prompts import SYSTEM_PROMPT_FIRST_SUMMARY, SYSTEM_PROMPT_REFLECTION_SUMMARY from ..utils.text_processing import ( remove_reasoning_from_output, clean_json_tags, extract_clean_response, fix_incomplete_json, format_search_results_for_prompt ) # 导入论坛读取工具 import sys import os sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) try: from utils.forum_reader import get_latest_host_speech, format_host_speech_for_prompt FORUM_READER_AVAILABLE = True except ImportError: FORUM_READER_AVAILABLE = False print("警告: 无法导入forum_reader模块，将跳过HOST发言读取功能") class FirstSummaryNode(StateMutationNode): """根据搜索结果生成段落首次总结的节点""" def __init__(self, llm_client): """ 初始化首次总结节点 Args: llm_client: LLM客户端 """ super().__init__(llm_client, "FirstSummaryNode") def validate_input(self, input_data: Any) -> bool: """验证输入数据""" if isinstance(input_data, str): try: data = json.loads(input_data) required_fields = ["title", "content", "search_query", "search_results"] return all(field in data for field in required_fields) except JSONDecodeError: return False elif isinstance(input_data, dict): required_fields = ["title", "content", "search_query", "search_results"] return all(field in input_data for field in required_fields) return False def run(self, input_data: Any, **kwargs) -> str: """ 调用LLM生成段落总结 Args: input_data: 包含title、content、search_query和search_results的数据 **kwargs: 额外参数 Returns: 段落总结内容 """ try: if not self.validate_input(input_data): raise ValueError("输入数据格式错误") # 准备输入数据 if isinstance(input_data, str): data = json.loads(input_data) else: data = input_data.copy() if isinstance(input_data, dict) else input_data # 读取最新的HOST发言（如果可用） if FORUM_READER_AVAILABLE: try: host_speech = get_latest_host_speech() if host_speech: # 将HOST发言添加到输入数据中 data['host_speech'] = host_speech self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符") except Exception as e: self.log_info(f"读取HOST发言失败: {str(e)}") # 转换为JSON字符串 message = json.dumps(data, ensure_ascii=False) # 如果有HOST发言，添加到消息前面作为参考 if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']: formatted_host = format_host_speech_for_prompt(data['host_speech']) message = formatted_host + "\n" + message self.log_info("正在生成首次段落总结") # 调用LLM，增加max_tokens以支持更长的总结 response = self.llm_client.invoke( SYSTEM_PROMPT_FIRST_SUMMARY, message, max_tokens=8192 # 支持更长的总结内容 ) # 处理响应 processed_response = self.process_output(response) self.log_info("成功生成首次段落总结") return processed_response except Exception as e: self.log_error(f"生成首次总结失败: {str(e)}") raise e def process_output(self, output: str) -> str: """ 处理LLM输出，提取段落内容 Args: output: LLM原始输出 Returns: 段落内容 """ try: # 清理响应文本 cleaned_output = remove_reasoning_from_output(output) cleaned_output = clean_json_tags(cleaned_output) # 记录清理后的输出用于调试 self.log_info(f"清理后的输出: {cleaned_output}") # 解析JSON try: result = json.loads(cleaned_output) self.log_info("JSON解析成功") except JSONDecodeError as e: self.log_info(f"JSON解析失败: {str(e)}") # 尝试修复JSON fixed_json = fix_incomplete_json(cleaned_output) if fixed_json: try: result = json.loads(fixed_json) self.log_info("JSON修复成功") except JSONDecodeError: self.log_info("JSON修复失败，直接使用清理后的文本") # 如果不是JSON格式，直接返回清理后的文本 return cleaned_output else: self.log_info("无法修复JSON，直接使用清理后的文本") # 如果不是JSON格式，直接返回清理后的文本 return cleaned_output # 提取段落内容 if isinstance(result, dict): paragraph_content = result.get("paragraph_latest_state", "") if paragraph_content: return paragraph_content # 如果提取失败，返回原始清理后的文本 return cleaned_output except Exception as e: self.log_error(f"处理输出失败: {str(e)}") return "段落总结生成失败" def mutate_state(self, input_data: Any, state: State, paragraph_index: int, **kwargs) -> State: """ 更新段落的最新总结到状态 Args: input_data: 输入数据 state: 当前状态 paragraph_index: 段落索引 **kwargs: 额外参数 Returns: 更新后的状态 """ try: # 生成总结 summary = self.run(input_data, **kwargs) # 更新状态 if 0 <= paragraph_index < len(state.paragraphs): state.paragraphs[paragraph_index].research.latest_summary = summary self.log_info(f"已更新段落 {paragraph_index} 的首次总结") else: raise ValueError(f"段落索引 {paragraph_index} 超出范围") state.update_timestamp() return state except Exception as e: self.log_error(f"状态更新失败: {str(e)}") raise e class ReflectionSummaryNode(StateMutationNode): """根据反思搜索结果更新段落总结的节点""" def __init__(self, llm_client): """ 初始化反思总结节点 Args: llm_client: LLM客户端 """ super().__init__(llm_client, "ReflectionSummaryNode") def validate_input(self, input_data: Any) -> bool: """验证输入数据""" if isinstance(input_data, str): try: data = json.loads(input_data) required_fields = ["title", "content", "search_query", "search_results", "paragraph_latest_state"] return all(field in data for field in required_fields) except JSONDecodeError: return False elif isinstance(input_data, dict): required_fields = ["title", "content", "search_query", "search_results", "paragraph_latest_state"] return all(field in input_data for field in required_fields) return False def run(self, input_data: Any, **kwargs) -> str: """ 调用LLM更新段落内容 Args: input_data: 包含完整反思信息的数据 **kwargs: 额外参数 Returns: 更新后的段落内容 """ try: if not self.validate_input(input_data): raise ValueError("输入数据格式错误") # 准备输入数据 if isinstance(input_data, str): data = json.loads(input_data) else: data = input_data.copy() if isinstance(input_data, dict) else input_data # 读取最新的HOST发言（如果可用） if FORUM_READER_AVAILABLE: try: host_speech = get_latest_host_speech() if host_speech: # 将HOST发言添加到输入数据中 data['host_speech'] = host_speech self.log_info(f"已读取HOST发言，长度: {len(host_speech)}字符") except Exception as e: self.log_info(f"读取HOST发言失败: {str(e)}") # 转换为JSON字符串 message = json.dumps(data, ensure_ascii=False) # 如果有HOST发言，添加到消息前面作为参考 if FORUM_READER_AVAILABLE and 'host_speech' in data and data['host_speech']: formatted_host = format_host_speech_for_prompt(data['host_speech']) message = formatted_host + "\n" + message self.log_info("正在生成反思总结") # 调用LLM，增加max_tokens以支持更长的总结 response = self.llm_client.invoke( SYSTEM_PROMPT_REFLECTION_SUMMARY, message, max_tokens=8192 # 支持更长的总结内容 ) # 处理响应 processed_response = self.process_output(response) self.log_info("成功生成反思总结") return processed_response except Exception as e: self.log_error(f"生成反思总结失败: {str(e)}") raise e def process_output(self, output: str) -> str: """ 处理LLM输出，提取更新后的段落内容 Args: output: LLM原始输出 Returns: 更新后的段落内容 """ try: # 清理响应文本 cleaned_output = remove_reasoning_from_output(output) cleaned_output = clean_json_tags(cleaned_output) # 记录清理后的输出用于调试 self.log_info(f"清理后的输出: {cleaned_output}") # 解析JSON try: result = json.loads(cleaned_output) self.log_info("JSON解析成功") except JSONDecodeError as e: self.log_info(f"JSON解析失败: {str(e)}") # 尝试修复JSON fixed_json = fix_incomplete_json(cleaned_output) if fixed_json: try: result = json.loads(fixed_json) self.log_info("JSON修复成功") except JSONDecodeError: self.log_info("JSON修复失败，直接使用清理后的文本") # 如果不是JSON格式，直接返回清理后的文本 return cleaned_output else: self.log_info("无法修复JSON，直接使用清理后的文本") # 如果不是JSON格式，直接返回清理后的文本 return cleaned_output # 提取更新后的段落内容 if isinstance(result, dict): updated_content = result.get("updated_paragraph_latest_state", "") if updated_content: return updated_content # 如果提取失败，返回原始清理后的文本 return cleaned_output except Exception as e: self.log_error(f"处理输出失败: {str(e)}") return "反思总结生成失败" def mutate_state(self, input_data: Any, state: State, paragraph_index: int, **kwargs) -> State: """ 将更新后的总结写入状态 Args: input_data: 输入数据 state: 当前状态 paragraph_index: 段落索引 **kwargs: 额外参数 Returns: 更新后的状态 """ try: # 生成更新后的总结 updated_summary = self.run(input_data, **kwargs) # 更新状态 if 0 <= paragraph_index < len(state.paragraphs): state.paragraphs[paragraph_index].research.latest_summary = updated_summary state.paragraphs[paragraph_index].research.increment_reflection() self.log_info(f"已更新段落 {paragraph_index} 的反思总结") else: raise ValueError(f"段落索引 {paragraph_index} 超出范围") state.update_timestamp() return state except Exception as e: self.log_error(f"状态更新失败: {str(e)}") raise e