316 lines
12 KiB
Python
316 lines
12 KiB
Python
"""
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AI服务层
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封装对R1和V3智能体的调用
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"""
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import logging
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from typing import Dict, List, Any, Optional, Tuple
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from app.agents.r1_agent import R1Agent
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from app.agents.v3_agent import V3Agent
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from app.models.search_result import SearchResult, SearchImportance
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from config import Config
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logger = logging.getLogger(__name__)
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class AIService:
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"""AI服务统一接口"""
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def __init__(self):
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self.r1_agent = R1Agent()
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self.v3_agent = V3Agent()
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# 设置调试器
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try:
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from app import socketio
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from app.utils.debug_logger import ai_debug_logger
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ai_debug_logger.set_socketio(socketio)
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logger.info("AI调试器已初始化")
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except Exception as e:
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logger.warning(f"无法初始化调试器: {e}")
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# ========== 问题分析阶段 (R1) ==========
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def analyze_question_type(self, question: str) -> str:
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"""分析问题类型"""
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try:
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return self.r1_agent.analyze_question_type(question)
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except Exception as e:
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logger.error(f"分析问题类型失败: {e}")
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return "exploratory" # 默认值
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def refine_questions(self, question: str, question_type: str) -> List[str]:
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"""细化问题"""
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try:
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return self.r1_agent.refine_questions(question, question_type)
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except Exception as e:
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logger.error(f"细化问题失败: {e}")
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return [question] # 返回原问题
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def create_research_approach(self, question: str, question_type: str,
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refined_questions: List[str]) -> str:
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"""制定研究思路"""
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try:
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return self.r1_agent.create_research_approach(
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question, question_type, refined_questions
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)
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except Exception as e:
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logger.error(f"制定研究思路失败: {e}")
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return "采用系统化的方法深入研究这个问题。"
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# ========== 大纲制定阶段 (R1) ==========
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def create_outline(self, question: str, question_type: str,
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refined_questions: List[str], research_approach: str) -> Dict[str, Any]:
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"""创建研究大纲"""
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try:
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return self.r1_agent.create_outline(
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question, question_type, refined_questions, research_approach
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)
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except Exception as e:
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logger.error(f"创建大纲失败: {e}")
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# 返回基本大纲
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return {
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"main_topic": question,
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"research_questions": refined_questions[:3],
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"sub_topics": [
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{
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"topic": "核心分析",
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"explain": "对问题进行深入分析",
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"priority": "high",
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"related_questions": refined_questions[:2]
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}
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]
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}
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def validate_outline(self, outline: Dict[str, Any]) -> str:
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"""验证大纲"""
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try:
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return self.r1_agent.validate_outline(outline)
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except Exception as e:
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logger.error(f"验证大纲失败: {e}")
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return "大纲结构合理。"
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def modify_outline(self, original_outline: Dict[str, Any],
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user_feedback: str, validation_issues: str = "") -> Dict[str, Any]:
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"""修改大纲"""
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try:
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return self.r1_agent.modify_outline(
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original_outline, user_feedback, validation_issues
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)
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except Exception as e:
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logger.error(f"修改大纲失败: {e}")
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return original_outline
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# ========== 搜索阶段 (V3 + R1) ==========
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def generate_search_queries(self, subtopic: str, explanation: str,
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related_questions: List[str], priority: str) -> List[str]:
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"""生成搜索查询（V3）"""
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# 根据优先级确定搜索数量
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count_map = {
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"high": Config.MAX_SEARCHES_HIGH_PRIORITY,
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"medium": Config.MAX_SEARCHES_MEDIUM_PRIORITY,
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"low": Config.MAX_SEARCHES_LOW_PRIORITY
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}
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count = count_map.get(priority, 10)
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try:
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return self.v3_agent.generate_search_queries(
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subtopic, explanation, related_questions, count
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)
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except Exception as e:
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logger.error(f"生成搜索查询失败: {e}")
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# 返回基本查询
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return [subtopic, f"{subtopic} {explanation}"][:count]
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def evaluate_search_results(self, subtopic: str,
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search_results: List[SearchResult]) -> List[SearchResult]:
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"""评估搜索结果重要性（R1）"""
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evaluated_results = []
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for result in search_results:
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try:
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importance = self.r1_agent.evaluate_search_result(
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subtopic,
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result.title,
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result.url,
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result.snippet
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)
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result.importance = SearchImportance(importance)
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evaluated_results.append(result)
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except Exception as e:
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logger.error(f"评估搜索结果失败: {e}")
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result.importance = SearchImportance.MEDIUM
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evaluated_results.append(result)
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return evaluated_results
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# ========== 信息反思阶段 (R1) ==========
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def reflect_on_information(self, subtopic: str,
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search_results: List[SearchResult]) -> List[Dict[str, str]]:
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"""信息反思，返回需要深入的要点"""
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# 生成搜索摘要
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summary = self._generate_search_summary(search_results)
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try:
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return self.r1_agent.reflect_on_information(subtopic, summary)
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except Exception as e:
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logger.error(f"信息反思失败: {e}")
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return []
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def generate_refined_queries(self, key_points: List[Dict[str, str]]) -> Dict[str, List[str]]:
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"""为关键点生成细化查询（V3）"""
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refined_queries = {}
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for point in key_points:
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try:
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queries = self.v3_agent.generate_refined_queries(
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point["key_info"],
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point["detail_needed"]
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)
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refined_queries[point["key_info"]] = queries
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except Exception as e:
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logger.error(f"生成细化查询失败: {e}")
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refined_queries[point["key_info"]] = [point["key_info"]]
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return refined_queries
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# ========== 信息整合阶段 (R1) ==========
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def integrate_information(self, subtopic: str,
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all_search_results: List[SearchResult]) -> Dict[str, Any]:
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"""整合信息"""
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# 格式化搜索结果
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formatted_results = self._format_search_results_for_integration(all_search_results)
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try:
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return self.r1_agent.integrate_information(subtopic, formatted_results)
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except Exception as e:
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logger.error(f"整合信息失败: {e}")
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# 返回基本结构
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return {
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"key_points": [],
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"themes": []
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}
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# ========== 报告撰写阶段 (R1) ==========
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def write_subtopic_report(self, subtopic: str, integrated_info: Dict[str, Any]) -> str:
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"""撰写子主题报告"""
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try:
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return self.r1_agent.write_subtopic_report(subtopic, integrated_info)
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except Exception as e:
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logger.error(f"撰写子主题报告失败: {e}")
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return f"## {subtopic}\n\n撰写报告时发生错误。"
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# ========== 幻觉检测阶段 (R1 + V3) ==========
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def detect_and_fix_hallucinations(self, report: str,
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original_sources: Dict[str, str]) -> Tuple[str, List[Dict]]:
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"""检测并修复幻觉内容"""
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hallucinations = []
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fixed_report = report
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# 提取报告中的所有URL引用
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url_references = self._extract_url_references(report)
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for url, content in url_references.items():
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if url in original_sources:
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try:
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# 检测幻觉（R1）
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result = self.r1_agent.detect_hallucination(
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content, url, original_sources[url]
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)
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if result.get("is_hallucination", False):
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hallucinations.append({
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"url": url,
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"content": content,
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"type": result.get("hallucination_type", "未知"),
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"explanation": result.get("explanation", "")
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})
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# 重写内容（V3）
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try:
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new_content = self.v3_agent.rewrite_hallucination(
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content, original_sources[url]
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)
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fixed_report = fixed_report.replace(content, new_content)
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except Exception as e:
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logger.error(f"重写幻觉内容失败: {e}")
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except Exception as e:
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logger.error(f"检测幻觉失败: {e}")
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return fixed_report, hallucinations
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# ========== 最终报告阶段 (R1) ==========
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def generate_final_report(self, main_topic: str, research_questions: List[str],
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subtopic_reports: Dict[str, str]) -> str:
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"""生成最终报告"""
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try:
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return self.r1_agent.generate_final_report(
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main_topic, research_questions, subtopic_reports
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)
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except Exception as e:
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logger.error(f"生成最终报告失败: {e}")
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# 返回基本报告
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reports_text = "\n\n---\n\n".join(subtopic_reports.values())
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return f"# {main_topic}\n\n## 研究报告\n\n{reports_text}"
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# ========== 辅助方法 ==========
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def _generate_search_summary(self, search_results: List[SearchResult]) -> str:
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"""生成搜索结果摘要"""
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high_count = sum(1 for r in search_results if r.importance == SearchImportance.HIGH)
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medium_count = sum(1 for r in search_results if r.importance == SearchImportance.MEDIUM)
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low_count = sum(1 for r in search_results if r.importance == SearchImportance.LOW)
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summary_lines = [
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f"共找到 {len(search_results)} 条搜索结果",
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f"高重要性: {high_count} 条",
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f"中重要性: {medium_count} 条",
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f"低重要性: {low_count} 条",
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"",
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"主要发现："
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]
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# 添加高重要性结果的摘要
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for result in search_results[:10]: # 最多10条
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if result.importance == SearchImportance.HIGH:
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summary_lines.append(f"- {result.title}: {result.snippet[:100]}...")
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return '\n'.join(summary_lines)
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def _format_search_results_for_integration(self, search_results: List[SearchResult]) -> str:
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"""格式化搜索结果用于整合"""
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formatted_lines = []
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for i, result in enumerate(search_results, 1):
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formatted_lines.extend([
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f"{i}. 来源: {result.url}",
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f" 标题: {result.title}",
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f" 内容: {result.snippet}",
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f" 重要性: {result.importance.value if result.importance else '未评估'}",
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""
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])
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return '\n'.join(formatted_lines)
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def _extract_url_references(self, report: str) -> Dict[str, str]:
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"""从报告中提取URL引用及其对应内容"""
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# 简单实现，实际可能需要更复杂的解析
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import re
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url_references = {}
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# 匹配模式: 内容（来源：URL）
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pattern = r'([^（]+)（来源：([^）]+)）'
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matches = re.finditer(pattern, report)
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for match in matches:
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content = match.group(1).strip()
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url = match.group(2).strip()
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url_references[url] = content
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return url_references |