1. 统一为使用基于pydantic的.env环境变量管理配置
2. 全项目基于loguru进行日志管理
This commit is contained in:
Doiiars
+114
-119
@@ -8,6 +8,7 @@ import os
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import re
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from datetime import datetime
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from typing import Optional, Dict, Any, List, Union
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from loguru import logger
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from .llms import LLMClient
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from .nodes import (
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@@ -20,32 +21,25 @@ from .nodes import (
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)
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from .state import State
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from .tools import MediaCrawlerDB, DBResponse, keyword_optimizer, multilingual_sentiment_analyzer
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from .utils import Config, load_config, format_search_results_for_prompt
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from .utils.config import settings, Settings
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from .utils import format_search_results_for_prompt
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class DeepSearchAgent:
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"""Deep Search Agent主类"""
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def __init__(self, config: Optional[Config] = None):
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def __init__(self, config: Optional[Settings] = None):
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"""
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初始化Deep Search Agent
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Args:
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config: 配置对象，如果不提供则自动加载
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config: 可选配置对象（不填则用全局settings）
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"""
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# 加载配置
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self.config = config or load_config()
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self.config = config or settings
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# 初始化LLM客户端
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self.llm_client = self._initialize_llm()
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# 设置数据库环境变量
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os.environ["DB_HOST"] = self.config.db_host or ""
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os.environ["DB_USER"] = self.config.db_user or ""
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os.environ["DB_PASSWORD"] = self.config.db_password or ""
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os.environ["DB_NAME"] = self.config.db_name or ""
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os.environ["DB_PORT"] = str(self.config.db_port)
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os.environ["DB_CHARSET"] = self.config.db_charset
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# 初始化搜索工具集
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self.search_agency = MediaCrawlerDB()
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@@ -60,19 +54,19 @@ class DeepSearchAgent:
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self.state = State()
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# 确保输出目录存在
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os.makedirs(self.config.output_dir, exist_ok=True)
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os.makedirs(self.config.OUTPUT_DIR, exist_ok=True)
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print(f"Insight Agent已初始化")
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print(f"使用LLM: {self.llm_client.get_model_info()}")
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print(f"搜索工具集: MediaCrawlerDB (支持5种本地数据库查询工具)")
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print(f"情感分析: WeiboMultilingualSentiment (支持22种语言的情感分析)")
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logger.info(f"Insight Agent已初始化")
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logger.info(f"使用LLM: {self.llm_client.get_model_info()}")
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logger.info(f"搜索工具集: MediaCrawlerDB (支持5种本地数据库查询工具)")
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logger.info(f"情感分析: WeiboMultilingualSentiment (支持22种语言的情感分析)")
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def _initialize_llm(self) -> LLMClient:
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"""初始化LLM客户端"""
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return LLMClient(
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api_key=self.config.llm_api_key,
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model_name=self.config.llm_model_name,
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base_url=self.config.llm_base_url,
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api_key=self.config.INSIGHT_ENGINE_API_KEY,
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model_name=self.config.INSIGHT_ENGINE_MODEL_NAME,
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base_url=self.config.INSIGHT_ENGINE_BASE_URL,
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)
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def _initialize_nodes(self):
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@@ -127,7 +121,7 @@ class DeepSearchAgent:
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Returns:
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DBResponse对象（可能包含情感分析结果）
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"""
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print(f" → 执行数据库查询工具: {tool_name}")
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logger.info(f" → 执行数据库查询工具: {tool_name}")
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# 对于热点内容搜索，不需要关键词优化（因为不需要query参数）
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if tool_name == "search_hot_content":
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@@ -138,12 +132,12 @@ class DeepSearchAgent:
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# 检查是否需要进行情感分析
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enable_sentiment = kwargs.get("enable_sentiment", True)
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if enable_sentiment and response.results and len(response.results) > 0:
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print(f" 🎭 开始对热点内容进行情感分析...")
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logger.info(f" 🎭 开始对热点内容进行情感分析...")
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sentiment_analysis = self._perform_sentiment_analysis(response.results)
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if sentiment_analysis:
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# 将情感分析结果添加到响应的parameters中
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response.parameters["sentiment_analysis"] = sentiment_analysis
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print(f" ✅ 情感分析完成")
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logger.info(f" ✅ 情感分析完成")
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return response
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@@ -170,32 +164,32 @@ class DeepSearchAgent:
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context=f"使用{tool_name}工具进行查询"
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)
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print(f" 🔍 原始查询: '{query}'")
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print(f" ✨ 优化后关键词: {optimized_response.optimized_keywords}")
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logger.info(f" 🔍 原始查询: '{query}'")
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logger.info(f" ✨ 优化后关键词: {optimized_response.optimized_keywords}")
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# 使用优化后的关键词进行多次查询并整合结果
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all_results = []
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total_count = 0
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for keyword in optimized_response.optimized_keywords:
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print(f" 查询关键词: '{keyword}'")
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logger.info(f" 查询关键词: '{keyword}'")
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try:
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if tool_name == "search_topic_globally":
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# 使用配置文件中的默认值，忽略agent提供的limit_per_table参数
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limit_per_table = self.config.default_search_topic_globally_limit_per_table
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limit_per_table = self.config.DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE
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response = self.search_agency.search_topic_globally(topic=keyword, limit_per_table=limit_per_table)
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elif tool_name == "search_topic_by_date":
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start_date = kwargs.get("start_date")
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end_date = kwargs.get("end_date")
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# 使用配置文件中的默认值，忽略agent提供的limit_per_table参数
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limit_per_table = self.config.default_search_topic_by_date_limit_per_table
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limit_per_table = self.config.DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE
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if not start_date or not end_date:
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raise ValueError("search_topic_by_date工具需要start_date和end_date参数")
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response = self.search_agency.search_topic_by_date(topic=keyword, start_date=start_date, end_date=end_date, limit_per_table=limit_per_table)
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elif tool_name == "get_comments_for_topic":
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# 使用配置文件中的默认值，按关键词数量分配，但保证最小值
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limit = self.config.default_get_comments_for_topic_limit // len(optimized_response.optimized_keywords)
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limit = self.config.DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT // len(optimized_response.optimized_keywords)
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limit = max(limit, 50)
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response = self.search_agency.get_comments_for_topic(topic=keyword, limit=limit)
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elif tool_name == "search_topic_on_platform":
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@@ -203,30 +197,30 @@ class DeepSearchAgent:
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start_date = kwargs.get("start_date")
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end_date = kwargs.get("end_date")
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# 使用配置文件中的默认值，按关键词数量分配，但保证最小值
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limit = self.config.default_search_topic_on_platform_limit // len(optimized_response.optimized_keywords)
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limit = self.config.DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT // len(optimized_response.optimized_keywords)
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limit = max(limit, 30)
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if not platform:
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raise ValueError("search_topic_on_platform工具需要platform参数")
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response = self.search_agency.search_topic_on_platform(platform=platform, topic=keyword, start_date=start_date, end_date=end_date, limit=limit)
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else:
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print(f" 未知的搜索工具: {tool_name}，使用默认全局搜索")
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response = self.search_agency.search_topic_globally(topic=keyword, limit_per_table=self.config.default_search_topic_globally_limit_per_table)
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logger.info(f" 未知的搜索工具: {tool_name}，使用默认全局搜索")
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response = self.search_agency.search_topic_globally(topic=keyword, limit_per_table=self.config.DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE)
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# 收集结果
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if response.results:
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print(f" 找到 {len(response.results)} 条结果")
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logger.info(f" 找到 {len(response.results)} 条结果")
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all_results.extend(response.results)
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total_count += len(response.results)
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else:
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print(f" 未找到结果")
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logger.info(f" 未找到结果")
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except Exception as e:
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print(f" 查询'{keyword}'时出错: {str(e)}")
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logger.error(f" 查询'{keyword}'时出错: {str(e)}")
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continue
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# 去重和整合结果
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unique_results = self._deduplicate_results(all_results)
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print(f" 总计找到 {total_count} 条结果，去重后 {len(unique_results)} 条")
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logger.info(f" 总计找到 {total_count} 条结果，去重后 {len(unique_results)} 条")
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# 构建整合后的响应
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integrated_response = DBResponse(
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@@ -244,12 +238,12 @@ class DeepSearchAgent:
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# 检查是否需要进行情感分析
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enable_sentiment = kwargs.get("enable_sentiment", True)
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if enable_sentiment and unique_results and len(unique_results) > 0:
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print(f" 🎭 开始对搜索结果进行情感分析...")
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logger.info(f" 🎭 开始对搜索结果进行情感分析...")
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sentiment_analysis = self._perform_sentiment_analysis(unique_results)
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if sentiment_analysis:
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# 将情感分析结果添加到响应的parameters中
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integrated_response.parameters["sentiment_analysis"] = sentiment_analysis
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print(f" ✅ 情感分析完成")
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logger.info(f" ✅ 情感分析完成")
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return integrated_response
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@@ -282,11 +276,11 @@ class DeepSearchAgent:
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try:
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# 初始化情感分析器（如果尚未初始化且未被禁用）
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if not self.sentiment_analyzer.is_initialized and not self.sentiment_analyzer.is_disabled:
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print(" 初始化情感分析模型...")
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logger.info(" 初始化情感分析模型...")
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if not self.sentiment_analyzer.initialize():
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print(" 情感分析模型初始化失败，将直接透传原始文本")
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logger.info(" 情感分析模型初始化失败，将直接透传原始文本")
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elif self.sentiment_analyzer.is_disabled:
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print(" 情感分析功能已禁用，直接透传原始文本")
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logger.info(" 情感分析功能已禁用，直接透传原始文本")
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# 将查询结果转换为字典格式
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results_dict = []
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@@ -310,7 +304,7 @@ class DeepSearchAgent:
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return sentiment_analysis.get("sentiment_analysis")
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except Exception as e:
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print(f" ❌ 情感分析过程中发生错误: {str(e)}")
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logger.exception(f" ❌ 情感分析过程中发生错误: {str(e)}")
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return None
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def analyze_sentiment_only(self, texts: Union[str, List[str]]) -> Dict[str, Any]:
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@@ -323,16 +317,16 @@ class DeepSearchAgent:
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Returns:
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情感分析结果
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"""
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print(f" → 执行独立情感分析")
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logger.info(f" → 执行独立情感分析")
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try:
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# 初始化情感分析器（如果尚未初始化且未被禁用）
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if not self.sentiment_analyzer.is_initialized and not self.sentiment_analyzer.is_disabled:
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print(" 初始化情感分析模型...")
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logger.info(" 初始化情感分析模型...")
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if not self.sentiment_analyzer.initialize():
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print(" 情感分析模型初始化失败，将直接透传原始文本")
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logger.info(" 情感分析模型初始化失败，将直接透传原始文本")
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elif self.sentiment_analyzer.is_disabled:
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print(" 情感分析功能已禁用，直接透传原始文本")
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logger.warning(" 情感分析功能已禁用，直接透传原始文本")
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# 执行分析
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if isinstance(texts, str):
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@@ -368,7 +362,7 @@ class DeepSearchAgent:
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return response
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except Exception as e:
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print(f" ❌ 情感分析过程中发生错误: {str(e)}")
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logger.exception(f" ❌ 情感分析过程中发生错误: {str(e)}")
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return {
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"success": False,
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"error": str(e),
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@@ -386,9 +380,9 @@ class DeepSearchAgent:
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Returns:
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最终报告内容
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"""
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print(f"\n{'='*60}")
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print(f"开始深度研究: {query}")
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print(f"{'='*60}")
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logger.info(f"\n{'='*60}")
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logger.info(f"开始深度研究: {query}")
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logger.info(f"{'='*60}")
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try:
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# Step 1: 生成报告结构
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@@ -403,20 +397,18 @@ class DeepSearchAgent:
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# Step 4: 保存报告
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if save_report:
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self._save_report(final_report)
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print(f"\n{'='*60}")
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print("深度研究完成！")
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print(f"{'='*60}")
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logger.info("深度研究完成！")
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return final_report
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except Exception as e:
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print(f"研究过程中发生错误: {str(e)}")
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logger.exception(f"研究过程中发生错误: {str(e)}")
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raise e
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def _generate_report_structure(self, query: str):
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"""生成报告结构"""
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print(f"\n[步骤 1] 生成报告结构...")
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logger.info(f"\n[步骤 1] 生成报告结构...")
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# 创建报告结构节点
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report_structure_node = ReportStructureNode(self.llm_client, query)
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@@ -424,17 +416,18 @@ class DeepSearchAgent:
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# 生成结构并更新状态
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self.state = report_structure_node.mutate_state(state=self.state)
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print(f"报告结构已生成，共 {len(self.state.paragraphs)} 个段落:")
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_message = f"报告结构已生成，共 {len(self.state.paragraphs)} 个段落:"
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for i, paragraph in enumerate(self.state.paragraphs, 1):
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print(f" {i}. {paragraph.title}")
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_message += f"\n {i}. {paragraph.title}"
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logger.info(_message)
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def _process_paragraphs(self):
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"""处理所有段落"""
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total_paragraphs = len(self.state.paragraphs)
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for i in range(total_paragraphs):
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print(f"\n[步骤 2.{i+1}] 处理段落: {self.state.paragraphs[i].title}")
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print("-" * 50)
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logger.info(f"\n[步骤 2.{i+1}] 处理段落: {self.state.paragraphs[i].title}")
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logger.info("-" * 50)
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# 初始搜索和总结
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self._initial_search_and_summary(i)
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@@ -446,7 +439,7 @@ class DeepSearchAgent:
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self.state.paragraphs[i].research.mark_completed()
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progress = (i + 1) / total_paragraphs * 100
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print(f"段落处理完成 ({progress:.1f}%)")
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logger.info(f"段落处理完成 ({progress:.1f}%)")
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def _initial_search_and_summary(self, paragraph_index: int):
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"""执行初始搜索和总结"""
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@@ -459,18 +452,18 @@ class DeepSearchAgent:
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}
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# 生成搜索查询和工具选择
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print(" - 生成搜索查询...")
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logger.info(" - 生成搜索查询...")
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search_output = self.first_search_node.run(search_input)
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search_query = search_output["search_query"]
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search_tool = search_output.get("search_tool", "search_topic_globally") # 默认工具
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reasoning = search_output["reasoning"]
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print(f" - 搜索查询: {search_query}")
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print(f" - 选择的工具: {search_tool}")
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print(f" - 推理: {reasoning}")
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logger.info(f" - 搜索查询: {search_query}")
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logger.info(f" - 选择的工具: {search_tool}")
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logger.info(f" - 推理: {reasoning}")
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# 执行搜索
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print(" - 执行数据库查询...")
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logger.info(" - 执行数据库查询...")
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# 处理特殊参数
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search_kwargs = {}
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@@ -485,13 +478,13 @@ class DeepSearchAgent:
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if self._validate_date_format(start_date) and self._validate_date_format(end_date):
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search_kwargs["start_date"] = start_date
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search_kwargs["end_date"] = end_date
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print(f" - 时间范围: {start_date} 到 {end_date}")
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logger.info(f" - 时间范围: {start_date} 到 {end_date}")
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else:
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print(f" 日期格式错误（应为YYYY-MM-DD），改用全局搜索")
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print(f" 提供的日期: start_date={start_date}, end_date={end_date}")
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logger.info(f" 日期格式错误（应为YYYY-MM-DD），改用全局搜索")
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logger.info(f" 提供的日期: start_date={start_date}, end_date={end_date}")
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search_tool = "search_topic_globally"
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elif search_tool == "search_topic_by_date":
|
||||
print(f" search_topic_by_date工具缺少时间参数，改用全局搜索")
|
||||
logger.info(f" search_topic_by_date工具缺少时间参数，改用全局搜索")
|
||||
search_tool = "search_topic_globally"
|
||||
|
||||
# 处理需要平台参数的工具
|
||||
@@ -499,28 +492,28 @@ class DeepSearchAgent:
|
||||
platform = search_output.get("platform")
|
||||
if platform:
|
||||
search_kwargs["platform"] = platform
|
||||
print(f" - 指定平台: {platform}")
|
||||
logger.info(f" - 指定平台: {platform}")
|
||||
else:
|
||||
print(f" search_topic_on_platform工具缺少平台参数，改用全局搜索")
|
||||
logger.warning(f" search_topic_on_platform工具缺少平台参数，改用全局搜索")
|
||||
search_tool = "search_topic_globally"
|
||||
|
||||
# 处理限制参数，使用配置文件中的默认值而不是agent提供的参数
|
||||
if search_tool == "search_hot_content":
|
||||
time_period = search_output.get("time_period", "week")
|
||||
limit = self.config.default_search_hot_content_limit
|
||||
limit = self.config.DEFAULT_SEARCH_HOT_CONTENT_LIMIT
|
||||
search_kwargs["time_period"] = time_period
|
||||
search_kwargs["limit"] = limit
|
||||
elif search_tool in ["search_topic_globally", "search_topic_by_date"]:
|
||||
if search_tool == "search_topic_globally":
|
||||
limit_per_table = self.config.default_search_topic_globally_limit_per_table
|
||||
limit_per_table = self.config.DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE
|
||||
else: # search_topic_by_date
|
||||
limit_per_table = self.config.default_search_topic_by_date_limit_per_table
|
||||
limit_per_table = self.config.DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE
|
||||
search_kwargs["limit_per_table"] = limit_per_table
|
||||
elif search_tool in ["get_comments_for_topic", "search_topic_on_platform"]:
|
||||
if search_tool == "get_comments_for_topic":
|
||||
limit = self.config.default_get_comments_for_topic_limit
|
||||
limit = self.config.DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT
|
||||
else: # search_topic_on_platform
|
||||
limit = self.config.default_search_topic_on_platform_limit
|
||||
limit = self.config.DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT
|
||||
search_kwargs["limit"] = limit
|
||||
|
||||
search_response = self.execute_search_tool(search_tool, search_query, **search_kwargs)
|
||||
@@ -529,8 +522,8 @@ class DeepSearchAgent:
|
||||
search_results = []
|
||||
if search_response and search_response.results:
|
||||
# 使用配置文件控制传递给LLM的结果数量，0表示不限制
|
||||
if self.config.max_search_results_for_llm > 0:
|
||||
max_results = min(len(search_response.results), self.config.max_search_results_for_llm)
|
||||
if self.config.MAX_SEARCH_RESULTS_FOR_LLM > 0:
|
||||
max_results = min(len(search_response.results), self.config.MAX_SEARCH_RESULTS_FOR_LLM)
|
||||
else:
|
||||
max_results = len(search_response.results) # 不限制，传递所有结果
|
||||
for result in search_response.results[:max_results]:
|
||||
@@ -548,24 +541,25 @@ class DeepSearchAgent:
|
||||
})
|
||||
|
||||
if search_results:
|
||||
print(f" - 找到 {len(search_results)} 个搜索结果")
|
||||
_message = f" - 找到 {len(search_results)} 个搜索结果"
|
||||
for j, result in enumerate(search_results, 1):
|
||||
date_info = f" (发布于: {result.get('published_date', 'N/A')})" if result.get('published_date') else ""
|
||||
print(f" {j}. {result['title'][:50]}...{date_info}")
|
||||
_message += f"\n {j}. {result['title'][:50]}...{date_info}"
|
||||
logger.info(_message)
|
||||
else:
|
||||
print(" - 未找到搜索结果")
|
||||
logger.info(" - 未找到搜索结果")
|
||||
|
||||
# 更新状态中的搜索历史
|
||||
paragraph.research.add_search_results(search_query, search_results)
|
||||
|
||||
# 生成初始总结
|
||||
print(" - 生成初始总结...")
|
||||
logger.info(" - 生成初始总结...")
|
||||
summary_input = {
|
||||
"title": paragraph.title,
|
||||
"content": paragraph.content,
|
||||
"search_query": search_query,
|
||||
"search_results": format_search_results_for_prompt(
|
||||
search_results, self.config.max_content_length
|
||||
search_results, self.config.MAX_CONTENT_LENGTH
|
||||
)
|
||||
}
|
||||
|
||||
@@ -574,14 +568,14 @@ class DeepSearchAgent:
|
||||
summary_input, self.state, paragraph_index
|
||||
)
|
||||
|
||||
print(" - 初始总结完成")
|
||||
logger.info(" - 初始总结完成")
|
||||
|
||||
def _reflection_loop(self, paragraph_index: int):
|
||||
"""执行反思循环"""
|
||||
paragraph = self.state.paragraphs[paragraph_index]
|
||||
|
||||
for reflection_i in range(self.config.max_reflections):
|
||||
print(f" - 反思 {reflection_i + 1}/{self.config.max_reflections}...")
|
||||
for reflection_i in range(self.config.MAX_REFLECTIONS):
|
||||
logger.info(f" - 反思 {reflection_i + 1}/{self.config.MAX_REFLECTIONS}...")
|
||||
|
||||
# 准备反思输入
|
||||
reflection_input = {
|
||||
@@ -596,9 +590,9 @@ class DeepSearchAgent:
|
||||
search_tool = reflection_output.get("search_tool", "search_topic_globally") # 默认工具
|
||||
reasoning = reflection_output["reasoning"]
|
||||
|
||||
print(f" 反思查询: {search_query}")
|
||||
print(f" 选择的工具: {search_tool}")
|
||||
print(f" 反思推理: {reasoning}")
|
||||
logger.info(f" 反思查询: {search_query}")
|
||||
logger.info(f" 选择的工具: {search_tool}")
|
||||
logger.info(f" 反思推理: {reasoning}")
|
||||
|
||||
# 执行反思搜索
|
||||
# 处理特殊参数
|
||||
@@ -614,13 +608,13 @@ class DeepSearchAgent:
|
||||
if self._validate_date_format(start_date) and self._validate_date_format(end_date):
|
||||
search_kwargs["start_date"] = start_date
|
||||
search_kwargs["end_date"] = end_date
|
||||
print(f" 时间范围: {start_date} 到 {end_date}")
|
||||
logger.info(f" 时间范围: {start_date} 到 {end_date}")
|
||||
else:
|
||||
print(f" 日期格式错误（应为YYYY-MM-DD），改用全局搜索")
|
||||
print(f" 提供的日期: start_date={start_date}, end_date={end_date}")
|
||||
logger.info(f" 日期格式错误（应为YYYY-MM-DD），改用全局搜索")
|
||||
logger.info(f" 提供的日期: start_date={start_date}, end_date={end_date}")
|
||||
search_tool = "search_topic_globally"
|
||||
elif search_tool == "search_topic_by_date":
|
||||
print(f" search_topic_by_date工具缺少时间参数，改用全局搜索")
|
||||
logger.warning(f" search_topic_by_date工具缺少时间参数，改用全局搜索")
|
||||
search_tool = "search_topic_globally"
|
||||
|
||||
# 处理需要平台参数的工具
|
||||
@@ -628,31 +622,31 @@ class DeepSearchAgent:
|
||||
platform = reflection_output.get("platform")
|
||||
if platform:
|
||||
search_kwargs["platform"] = platform
|
||||
print(f" 指定平台: {platform}")
|
||||
logger.info(f" 指定平台: {platform}")
|
||||
else:
|
||||
print(f" search_topic_on_platform工具缺少平台参数，改用全局搜索")
|
||||
logger.warning(f" search_topic_on_platform工具缺少平台参数，改用全局搜索")
|
||||
search_tool = "search_topic_globally"
|
||||
|
||||
# 处理限制参数
|
||||
if search_tool == "search_hot_content":
|
||||
time_period = reflection_output.get("time_period", "week")
|
||||
# 使用配置文件中的默认值，不允许agent控制limit参数
|
||||
limit = self.config.default_search_hot_content_limit
|
||||
limit = self.config.DEFAULT_SEARCH_HOT_CONTENT_LIMIT
|
||||
search_kwargs["time_period"] = time_period
|
||||
search_kwargs["limit"] = limit
|
||||
elif search_tool in ["search_topic_globally", "search_topic_by_date"]:
|
||||
# 使用配置文件中的默认值，不允许agent控制limit_per_table参数
|
||||
if search_tool == "search_topic_globally":
|
||||
limit_per_table = self.config.default_search_topic_globally_limit_per_table
|
||||
limit_per_table = self.config.DEFAULT_SEARCH_TOPIC_GLOBALLY_LIMIT_PER_TABLE
|
||||
else: # search_topic_by_date
|
||||
limit_per_table = self.config.default_search_topic_by_date_limit_per_table
|
||||
limit_per_table = self.config.DEFAULT_SEARCH_TOPIC_BY_DATE_LIMIT_PER_TABLE
|
||||
search_kwargs["limit_per_table"] = limit_per_table
|
||||
elif search_tool in ["get_comments_for_topic", "search_topic_on_platform"]:
|
||||
# 使用配置文件中的默认值，不允许agent控制limit参数
|
||||
if search_tool == "get_comments_for_topic":
|
||||
limit = self.config.default_get_comments_for_topic_limit
|
||||
limit = self.config.DEFAULT_GET_COMMENTS_FOR_TOPIC_LIMIT
|
||||
else: # search_topic_on_platform
|
||||
limit = self.config.default_search_topic_on_platform_limit
|
||||
limit = self.config.DEFAULT_SEARCH_TOPIC_ON_PLATFORM_LIMIT
|
||||
search_kwargs["limit"] = limit
|
||||
|
||||
search_response = self.execute_search_tool(search_tool, search_query, **search_kwargs)
|
||||
@@ -661,8 +655,8 @@ class DeepSearchAgent:
|
||||
search_results = []
|
||||
if search_response and search_response.results:
|
||||
# 使用配置文件控制传递给LLM的结果数量，0表示不限制
|
||||
if self.config.max_search_results_for_llm > 0:
|
||||
max_results = min(len(search_response.results), self.config.max_search_results_for_llm)
|
||||
if self.config.MAX_SEARCH_RESULTS_FOR_LLM > 0:
|
||||
max_results = min(len(search_response.results), self.config.MAX_SEARCH_RESULTS_FOR_LLM)
|
||||
else:
|
||||
max_results = len(search_response.results) # 不限制，传递所有结果
|
||||
for result in search_response.results[:max_results]:
|
||||
@@ -680,12 +674,13 @@ class DeepSearchAgent:
|
||||
})
|
||||
|
||||
if search_results:
|
||||
print(f" 找到 {len(search_results)} 个反思搜索结果")
|
||||
_message = f" 找到 {len(search_results)} 个反思搜索结果"
|
||||
for j, result in enumerate(search_results, 1):
|
||||
date_info = f" (发布于: {result.get('published_date', 'N/A')})" if result.get('published_date') else ""
|
||||
print(f" {j}. {result['title'][:50]}...{date_info}")
|
||||
_message += f"\n {j}. {result['title'][:50]}...{date_info}"
|
||||
logger.info(_message)
|
||||
else:
|
||||
print(" 未找到反思搜索结果")
|
||||
logger.info(" 未找到反思搜索结果")
|
||||
|
||||
# 更新搜索历史
|
||||
paragraph.research.add_search_results(search_query, search_results)
|
||||
@@ -696,7 +691,7 @@ class DeepSearchAgent:
|
||||
"content": paragraph.content,
|
||||
"search_query": search_query,
|
||||
"search_results": format_search_results_for_prompt(
|
||||
search_results, self.config.max_content_length
|
||||
search_results, self.config.MAX_CONTENT_LENGTH
|
||||
),
|
||||
"paragraph_latest_state": paragraph.research.latest_summary
|
||||
}
|
||||
@@ -706,11 +701,11 @@ class DeepSearchAgent:
|
||||
reflection_summary_input, self.state, paragraph_index
|
||||
)
|
||||
|
||||
print(f" 反思 {reflection_i + 1} 完成")
|
||||
logger.info(f" 反思 {reflection_i + 1} 完成")
|
||||
|
||||
def _generate_final_report(self) -> str:
|
||||
"""生成最终报告"""
|
||||
print(f"\n[步骤 3] 生成最终报告...")
|
||||
logger.info(f"\n[步骤 3] 生成最终报告...")
|
||||
|
||||
# 准备报告数据
|
||||
report_data = []
|
||||
@@ -724,7 +719,7 @@ class DeepSearchAgent:
|
||||
try:
|
||||
final_report = self.report_formatting_node.run(report_data)
|
||||
except Exception as e:
|
||||
print(f"LLM格式化失败，使用备用方法: {str(e)}")
|
||||
logger.exception(f"LLM格式化失败，使用备用方法: {str(e)}")
|
||||
final_report = self.report_formatting_node.format_report_manually(
|
||||
report_data, self.state.report_title
|
||||
)
|
||||
@@ -733,7 +728,7 @@ class DeepSearchAgent:
|
||||
self.state.final_report = final_report
|
||||
self.state.mark_completed()
|
||||
|
||||
print("最终报告生成完成")
|
||||
logger.info("最终报告生成完成")
|
||||
return final_report
|
||||
|
||||
def _save_report(self, report_content: str):
|
||||
@@ -744,20 +739,20 @@ class DeepSearchAgent:
|
||||
query_safe = query_safe.replace(' ', '_')[:30]
|
||||
|
||||
filename = f"deep_search_report_{query_safe}_{timestamp}.md"
|
||||
filepath = os.path.join(self.config.output_dir, filename)
|
||||
filepath = os.path.join(self.config.OUTPUT_DIR, filename)
|
||||
|
||||
# 保存报告
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
f.write(report_content)
|
||||
|
||||
print(f"报告已保存到: {filepath}")
|
||||
logger.info(f"报告已保存到: {filepath}")
|
||||
|
||||
# 保存状态（如果配置允许）
|
||||
if self.config.save_intermediate_states:
|
||||
if self.config.SAVE_INTERMEDIATE_STATES:
|
||||
state_filename = f"state_{query_safe}_{timestamp}.json"
|
||||
state_filepath = os.path.join(self.config.output_dir, state_filename)
|
||||
state_filepath = os.path.join(self.config.OUTPUT_DIR, state_filename)
|
||||
self.state.save_to_file(state_filepath)
|
||||
print(f"状态已保存到: {state_filepath}")
|
||||
logger.info(f"状态已保存到: {state_filepath}")
|
||||
|
||||
def get_progress_summary(self) -> Dict[str, Any]:
|
||||
"""获取进度摘要"""
|
||||
@@ -766,12 +761,12 @@ class DeepSearchAgent:
|
||||
def load_state(self, filepath: str):
|
||||
"""从文件加载状态"""
|
||||
self.state = State.load_from_file(filepath)
|
||||
print(f"状态已从 {filepath} 加载")
|
||||
logger.info(f"状态已从 {filepath} 加载")
|
||||
|
||||
def save_state(self, filepath: str):
|
||||
"""保存状态到文件"""
|
||||
self.state.save_to_file(filepath)
|
||||
print(f"状态已保存到 {filepath}")
|
||||
logger.info(f"状态已保存到 {filepath}")
|
||||
|
||||
|
||||
def create_agent(config_file: Optional[str] = None) -> DeepSearchAgent:
|
||||
@@ -784,5 +779,5 @@ def create_agent(config_file: Optional[str] = None) -> DeepSearchAgent:
|
||||
Returns:
|
||||
DeepSearchAgent实例
|
||||
"""
|
||||
config = load_config(config_file)
|
||||
config = settings
|
||||
return DeepSearchAgent(config)
|
||||
|
||||
Reference in New Issue
Block a user