620 lines
24 KiB
Python
620 lines
24 KiB
Python
"""
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TavilySearch 联网搜索插件
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基于 Tavily API 的联网搜索功能，仅作为 LLM Tool 供 AIChat 调用
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支持多 API Key 轮询，搜索结果返回给 AIChat 的 AI 处理（带上下文和人设）
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"""
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import tomllib
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import aiohttp
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import uuid
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import asyncio
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import re
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from pathlib import Path
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from typing import List, Optional
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from loguru import logger
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from utils.plugin_base import PluginBase
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class TavilySearch(PluginBase):
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"""Tavily 联网搜索插件 - 仅作为 LLM Tool"""
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description = "Tavily 联网搜索 - 支持多 Key 轮询的搜索工具"
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author = "Assistant"
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version = "1.0.0"
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def __init__(self):
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super().__init__()
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self.config = None
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self.api_keys = []
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self.current_key_index = 0
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self.temp_dir: Optional[Path] = None
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async def async_init(self):
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"""异步初始化"""
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try:
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config_path = Path(__file__).parent / "config.toml"
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if not config_path.exists():
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logger.error(f"TavilySearch 配置文件不存在: {config_path}")
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return
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with open(config_path, "rb") as f:
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self.config = tomllib.load(f)
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self.temp_dir = Path(__file__).parent / "temp"
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self.temp_dir.mkdir(exist_ok=True)
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self.api_keys = self._load_api_keys()
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if not self.api_keys:
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logger.warning("TavilySearch: 未配置有效的 API Key")
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else:
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logger.success(f"TavilySearch 已加载，共 {len(self.api_keys)} 个 API Key")
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except Exception as e:
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logger.error(f"TavilySearch 初始化失败: {e}")
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self.config = None
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def _load_api_keys(self) -> List[str]:
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"""从配置加载 API Keys（兼容 api_key / api_keys）"""
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if not self.config:
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return []
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tavily_config = self.config.get("tavily", {})
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keys: List[str] = []
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raw_keys = tavily_config.get("api_keys", [])
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if isinstance(raw_keys, str):
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keys.extend([k.strip() for k in raw_keys.replace("\n", ",").split(",")])
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elif isinstance(raw_keys, list):
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keys.extend([str(k).strip() for k in raw_keys])
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single_key = str(tavily_config.get("api_key", "")).strip()
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if single_key:
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keys.append(single_key)
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cleaned = []
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seen = set()
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for k in keys:
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if not k or k.startswith("#"):
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continue
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if k in seen:
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continue
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seen.add(k)
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cleaned.append(k)
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return cleaned
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def _get_next_api_key(self) -> str:
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"""轮询获取下一个 API Key"""
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if not self.api_keys:
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return ""
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key = self.api_keys[self.current_key_index]
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self.current_key_index = (self.current_key_index + 1) % len(self.api_keys)
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return key
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def _clean_query_text(self, text: str) -> str:
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"""清洗查询文本"""
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cleaned = str(text or "").strip()
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if not cleaned:
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return ""
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cleaned = cleaned.replace("【当前消息】", "").strip()
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cleaned = re.sub(r"^(?:@\S+\s*)+", "", cleaned)
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cleaned = re.sub(
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r"^(?:请|帮我|麻烦|请帮我)?(?:搜索|搜|查|查询|检索|搜一下|查一下|搜索下|搜下)\s*",
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"",
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cleaned,
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)
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return cleaned.strip()
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def _extract_topic_hint(self, query: str) -> str:
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"""提取主题前缀，用于补全后续子问题上下文"""
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text = self._clean_query_text(query)
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if not text:
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return ""
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first_part = text
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for sep in ("和", "以及", "并且", "还有", "同时", "，", ",", "；", ";", "。"):
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idx = first_part.find(sep)
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if idx > 0:
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first_part = first_part[:idx].strip()
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break
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match = re.match(r"^(.{2,40}?)(?:的|是|有哪些|包括|改动|更新|介绍|详情|内容|情况)", first_part)
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topic_hint = match.group(1).strip() if match else ""
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if not topic_hint and len(first_part) <= 40:
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topic_hint = first_part
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topic_hint = re.sub(r"(是什么|有哪些|有啥|是什么样).*$", "", topic_hint).strip()
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return topic_hint
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def _split_multi_queries(self, query: str, tavily_config: dict) -> List[str]:
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"""将复合问题拆分为多个子查询"""
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split_debug_log = bool(tavily_config.get("split_debug_log", False))
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raw = self._clean_query_text(query)
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if not raw:
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return []
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if split_debug_log:
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logger.info(f"[TavilySplit] 原始查询: {query}")
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logger.info(f"[TavilySplit] 清洗后查询: {raw}")
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max_sub_queries = int(tavily_config.get("max_sub_queries", 4) or 4)
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split_min_chars = int(tavily_config.get("split_min_chars", 6) or 6)
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prepend_context = bool(tavily_config.get("prepend_context_for_sub_query", True))
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normalized = raw
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normalized = re.sub(r"(另外|此外|同时|并且|还有|以及|然后|再者|顺便)", "｜", normalized)
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normalized = re.sub(r"[；;。！？!?\n\r]+", "｜", normalized)
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parts = [
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p.strip(" ，,、|｜")
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for p in normalized.split("｜")
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if p.strip(" ，,、|｜")
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]
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if split_debug_log:
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logger.info(f"[TavilySplit] 初步拆分片段: {parts}")
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if len(parts) == 1:
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single = parts[0]
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if "和" in single and len(single) >= split_min_chars * 2:
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candidate = re.split(r"\s*和\s*", single, maxsplit=1)
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if len(candidate) == 2:
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left = candidate[0].strip()
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right = candidate[1].strip()
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if len(left) >= split_min_chars and len(right) >= split_min_chars:
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parts = [left, right]
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if split_debug_log:
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logger.info(f"[TavilySplit] 通过“和”二次拆分: {parts}")
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# 语义拆分兜底：即使没有明显连接词，也尽量把“版本改动 + 英雄技能介绍”拆开
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if len(parts) == 1:
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single = parts[0].strip()
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change_keywords = ("改动", "更新", "变更", "调整", "改版", "平衡")
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hero_keywords = ("新英雄", "英雄", "技能", "机制", "天赋", "介绍", "详解")
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change_pos = min([single.find(k) for k in change_keywords if k in single] or [-1])
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hero_pos = min([single.find(k) for k in hero_keywords if k in single] or [-1])
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if change_pos >= 0 and hero_pos >= 0 and hero_pos > change_pos:
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left = single[:hero_pos].strip(" ，,、")
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right = single[hero_pos:].strip(" ，,、")
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if len(left) >= split_min_chars and len(right) >= split_min_chars:
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topic_hint = self._extract_topic_hint(left or single)
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if topic_hint and topic_hint not in right:
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right = f"{topic_hint} {right}".strip()
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parts = [left, right]
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if split_debug_log:
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logger.info(f"[TavilySplit] 语义兜底拆分: {parts}")
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deduped: List[str] = []
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seen = set()
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for p in parts:
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if len(p) < split_min_chars:
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continue
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if p in seen:
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continue
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seen.add(p)
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deduped.append(p)
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parts = deduped[:max_sub_queries] if deduped else [raw]
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if split_debug_log:
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logger.info(f"[TavilySplit] 去重截断后: {parts}")
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if prepend_context and len(parts) > 1:
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topic_hint = self._extract_topic_hint(parts[0] or raw)
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if topic_hint:
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with_context: List[str] = []
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for idx, p in enumerate(parts):
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item = p
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if idx > 0 and topic_hint not in item:
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item = f"{topic_hint} {item}".strip()
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with_context.append(item)
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parts = with_context
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if split_debug_log:
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logger.info(f"[TavilySplit] 主题前缀: {topic_hint}")
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logger.info(f"[TavilySplit] 前缀补全后: {parts}")
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if split_debug_log:
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logger.info(f"[TavilySplit] 最终子查询({len(parts)}): {parts}")
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return parts
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def _truncate_text(self, text: str, max_chars: int) -> str:
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"""按字符数截断文本"""
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content = str(text or "").strip()
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if max_chars <= 0 or len(content) <= max_chars:
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return content
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return content[:max_chars].rstrip() + "..."
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async def _search_tavily(self, query: str) -> Optional[dict]:
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"""调用 Tavily API 进行搜索"""
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tavily_config = self.config["tavily"]
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proxy_config = self.config.get("proxy", {})
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proxy = None
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if proxy_config.get("enabled", False):
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proxy_type = proxy_config.get("type", "http")
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proxy_host = proxy_config.get("host", "127.0.0.1")
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proxy_port = proxy_config.get("port", 7890)
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proxy = f"{proxy_type}://{proxy_host}:{proxy_port}"
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try:
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import ssl
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timeout = aiohttp.ClientTimeout(total=30)
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# SSL 配置
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ssl_config = self.config.get("ssl", {})
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ssl_verify = ssl_config.get("verify", True)
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connector = None
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if not ssl_verify:
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# 跳过 SSL 验证
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ssl_context = ssl.create_default_context()
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ssl_context.check_hostname = False
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ssl_context.verify_mode = ssl.CERT_NONE
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connector = aiohttp.TCPConnector(ssl=ssl_context)
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if not self.api_keys:
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logger.error("没有可用的 Tavily API Key")
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return None
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max_attempts = min(len(self.api_keys), tavily_config.get("max_key_attempts", len(self.api_keys)))
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async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:
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for attempt in range(max_attempts):
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api_key = self._get_next_api_key()
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if not api_key:
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logger.error("没有可用的 Tavily API Key")
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return None
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payload = {
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"api_key": api_key,
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"query": query,
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"search_depth": tavily_config.get("search_depth", "basic"),
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"max_results": tavily_config.get("max_results", 5),
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"include_raw_content": (
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tavily_config.get("include_raw_content", False)
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or tavily_config.get("use_raw_content_in_result", False)
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),
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"include_images": tavily_config.get("include_images", False),
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}
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async with session.post(
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"https://api.tavily.com/search",
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json=payload,
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proxy=proxy
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) as resp:
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if resp.status == 200:
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result = await resp.json()
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logger.info(f"Tavily 搜索成功: {query[:30]}...")
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logger.info(f"Tavily 原始返回: {result}")
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return result
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error_text = await resp.text()
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logger.warning(
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f"Tavily API 错误: {resp.status}, 尝试 key {attempt + 1}/{max_attempts}, "
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f"body={error_text[:200]}"
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)
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if resp.status in {401, 403, 429}:
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continue
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return None
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except Exception as e:
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logger.error(f"Tavily 搜索失败: {e}")
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return None
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def _extract_image_urls(self, results: dict) -> List[str]:
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"""从搜索结果中提取图片 URL"""
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if not results:
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return []
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images = results.get("images", [])
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urls: List[str] = []
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for item in images:
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if isinstance(item, str):
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url = item.strip()
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elif isinstance(item, dict):
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url = (item.get("url") or item.get("image") or item.get("src") or "").strip()
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else:
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url = ""
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if url:
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urls.append(url)
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return urls
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async def _download_image_with_session(
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self,
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session: aiohttp.ClientSession,
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url: str,
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proxy: Optional[str],
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max_retries: int = 1
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) -> Optional[str]:
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"""下载图片到本地临时目录（复用 session）"""
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if not self.temp_dir:
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return None
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for attempt in range(max_retries + 1):
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try:
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async with session.get(url, proxy=proxy) as resp:
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if resp.status != 200:
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if attempt >= max_retries:
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return None
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await asyncio.sleep(0.5 * (attempt + 1))
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continue
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content = await resp.read()
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ext = Path(url).suffix.lower()
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if ext not in {".jpg", ".jpeg", ".png", ".webp"}:
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ext = ".jpg"
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filename = f"tavily_{uuid.uuid4().hex}{ext}"
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save_path = self.temp_dir / filename
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with open(save_path, "wb") as f:
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f.write(content)
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return str(save_path)
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except Exception as e:
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if attempt < max_retries:
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await asyncio.sleep(0.5 * (attempt + 1))
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continue
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logger.warning(f"下载图片失败: {url} -> {e}")
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return None
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async def _download_image(self, url: str) -> Optional[str]:
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"""下载图片到本地临时目录（兼容旧调用）"""
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if not self.temp_dir:
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return None
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try:
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import ssl
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timeout = aiohttp.ClientTimeout(total=30)
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proxy_config = self.config.get("proxy", {}) if self.config else {}
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proxy = None
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if proxy_config.get("enabled", False):
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proxy_type = proxy_config.get("type", "http")
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proxy_host = proxy_config.get("host", "127.0.0.1")
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proxy_port = proxy_config.get("port", 7890)
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proxy = f"{proxy_type}://{proxy_host}:{proxy_port}"
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ssl_config = self.config.get("ssl", {}) if self.config else {}
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ssl_verify = ssl_config.get("verify", True)
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ssl_context = None
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if not ssl_verify:
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ssl_context = ssl.create_default_context()
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ssl_context.check_hostname = False
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ssl_context.verify_mode = ssl.CERT_NONE
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connector = aiohttp.TCPConnector(ssl=ssl_context) if ssl_context else None
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async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:
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return await self._download_image_with_session(session, url, proxy, max_retries=1)
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except Exception as e:
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logger.warning(f"下载图片失败: {url} -> {e}")
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return None
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def _format_search_results(
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self,
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results: dict,
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*,
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include_raw_content: bool = False,
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raw_content_max_chars: int = 1800,
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section_title: Optional[str] = None,
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) -> str:
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"""格式化搜索结果供 AI 处理"""
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if not results or "results" not in results:
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if section_title:
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return f"{section_title}\n未找到相关搜索结果"
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return "未找到相关搜索结果"
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formatted = []
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if section_title:
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formatted.append(section_title)
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for i, item in enumerate(results["results"], 1):
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title = item.get("title", "无标题")
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content = item.get("content", "")
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url = item.get("url", "")
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block = [
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f"【结果 {i}】",
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f"标题: {title}",
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f"内容: {content}",
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f"来源: {url}",
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]
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if include_raw_content:
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raw_content = self._truncate_text(item.get("raw_content", ""), raw_content_max_chars)
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if raw_content:
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block.append(f"原文摘录: {raw_content}")
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formatted.append("\n".join(block) + "\n")
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return "\n".join(formatted)
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def get_llm_tools(self) -> List[dict]:
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"""返回 LLM 工具定义"""
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if not self.config or not self.config["behavior"]["enabled"]:
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return []
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return [
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{
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"type": "function",
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"function": {
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"name": "tavily_web_search",
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"description": (
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"执行联网检索并返回可引用的信息来源。"
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"仅在用户明确要求查资料、最新信息、权威来源或需要事实核实时调用；"
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"可直接回答的问题不要触发该工具。"
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),
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "检索问题或关键词。应简洁、明确，避免口语噪声。"
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}
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},
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"required": ["query"],
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"additionalProperties": False
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}
|
||
}
|
||
}
|
||
]
|
||
|
||
async def execute_llm_tool(self, tool_name: str, arguments: dict, bot, from_wxid: str) -> dict:
|
||
"""
|
||
执行 LLM 工具调用
|
||
|
||
只负责搜索，返回结果给 AIChat 的 AI 处理（带上下文和人设）
|
||
"""
|
||
if tool_name != "tavily_web_search":
|
||
return None
|
||
|
||
if not self.config or not self.config["behavior"]["enabled"]:
|
||
return {"success": False, "message": "TavilySearch 插件未启用"}
|
||
|
||
if not self.api_keys:
|
||
return {"success": False, "message": "未配置 Tavily API Key"}
|
||
|
||
query = arguments.get("query", "")
|
||
if not query:
|
||
return {"success": False, "message": "搜索关键词不能为空"}
|
||
|
||
tavily_config = self.config.get("tavily", {})
|
||
multi_query_split = bool(tavily_config.get("multi_query_split", True))
|
||
use_raw_content_in_result = bool(tavily_config.get("use_raw_content_in_result", False))
|
||
raw_content_max_chars = int(tavily_config.get("raw_content_max_chars", 1800) or 1800)
|
||
|
||
try:
|
||
logger.info(f"开始 Tavily 搜索: {query}")
|
||
|
||
split_debug_log = bool(tavily_config.get("split_debug_log", False))
|
||
|
||
if multi_query_split:
|
||
sub_queries = self._split_multi_queries(query, tavily_config)
|
||
else:
|
||
cleaned_query = self._clean_query_text(query)
|
||
sub_queries = [cleaned_query] if cleaned_query else [str(query).strip()]
|
||
|
||
if not sub_queries:
|
||
return {"success": False, "message": "搜索关键词不能为空"}
|
||
|
||
if split_debug_log:
|
||
logger.info(f"Tavily 子问题拆分完成，共 {len(sub_queries)} 个: {sub_queries}")
|
||
else:
|
||
logger.info(f"Tavily 子问题拆分完成，共 {len(sub_queries)} 个")
|
||
|
||
search_batches = []
|
||
failed_queries = []
|
||
for sub_query in sub_queries:
|
||
result = await self._search_tavily(sub_query)
|
||
if result:
|
||
search_batches.append((sub_query, result))
|
||
else:
|
||
failed_queries.append(sub_query)
|
||
|
||
if not search_batches:
|
||
return {"success": False, "message": "搜索失败，请稍后重试"}
|
||
|
||
# 发送搜索图片（若开启 include_images）
|
||
if tavily_config.get("include_images", False):
|
||
image_urls = []
|
||
for _sub_query, sub_result in search_batches:
|
||
image_urls.extend(self._extract_image_urls(sub_result))
|
||
|
||
if image_urls:
|
||
image_urls = list(dict.fromkeys(image_urls))
|
||
|
||
max_images = int(tavily_config.get("max_images", 3) or 3)
|
||
download_concurrency = int(tavily_config.get("image_download_concurrency", 3) or 3)
|
||
download_retries = int(tavily_config.get("image_download_retries", 1) or 1)
|
||
download_timeout = int(tavily_config.get("image_download_timeout", 30) or 30)
|
||
|
||
import ssl
|
||
timeout = aiohttp.ClientTimeout(total=download_timeout)
|
||
proxy_config = self.config.get("proxy", {}) if self.config else {}
|
||
proxy = None
|
||
if proxy_config.get("enabled", False):
|
||
proxy_type = proxy_config.get("type", "http")
|
||
proxy_host = proxy_config.get("host", "127.0.0.1")
|
||
proxy_port = proxy_config.get("port", 7890)
|
||
proxy = f"{proxy_type}://{proxy_host}:{proxy_port}"
|
||
|
||
ssl_config = self.config.get("ssl", {}) if self.config else {}
|
||
ssl_verify = ssl_config.get("verify", True)
|
||
ssl_context = None
|
||
if not ssl_verify:
|
||
ssl_context = ssl.create_default_context()
|
||
ssl_context.check_hostname = False
|
||
ssl_context.verify_mode = ssl.CERT_NONE
|
||
|
||
connector = aiohttp.TCPConnector(ssl=ssl_context) if ssl_context else None
|
||
semaphore = asyncio.Semaphore(max(1, download_concurrency))
|
||
|
||
async with aiohttp.ClientSession(timeout=timeout, connector=connector) as session:
|
||
async def fetch_image(url: str) -> Optional[str]:
|
||
async with semaphore:
|
||
return await self._download_image_with_session(
|
||
session,
|
||
url,
|
||
proxy,
|
||
max_retries=download_retries
|
||
)
|
||
|
||
tasks = [fetch_image(url) for url in image_urls[:max_images]]
|
||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||
|
||
sent = 0
|
||
for result in results:
|
||
if sent >= max_images:
|
||
break
|
||
if isinstance(result, str) and result:
|
||
await bot.send_image(from_wxid, result)
|
||
sent += 1
|
||
|
||
# 格式化搜索结果
|
||
if len(search_batches) == 1:
|
||
formatted_results = self._format_search_results(
|
||
search_batches[0][1],
|
||
include_raw_content=use_raw_content_in_result,
|
||
raw_content_max_chars=raw_content_max_chars,
|
||
)
|
||
else:
|
||
sections = []
|
||
for idx, (sub_query, sub_result) in enumerate(search_batches, 1):
|
||
sections.append(
|
||
self._format_search_results(
|
||
sub_result,
|
||
include_raw_content=use_raw_content_in_result,
|
||
raw_content_max_chars=raw_content_max_chars,
|
||
section_title=f"【子问题 {idx}】{sub_query}",
|
||
)
|
||
)
|
||
formatted_results = "\n\n".join(sections)
|
||
|
||
if failed_queries:
|
||
failed_text = "\n".join([f"- {q}" for q in failed_queries])
|
||
formatted_results = (
|
||
f"{formatted_results}\n\n"
|
||
f"【未检索成功的子问题】\n{failed_text}"
|
||
)
|
||
|
||
logger.success(f"Tavily 搜索完成: {query[:30]}...")
|
||
|
||
# 返回搜索结果，标记需要 AI 继续处理
|
||
return {
|
||
"success": True,
|
||
"message": formatted_results,
|
||
"need_ai_reply": True # 标记需要 AI 基于此结果继续回复
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"Tavily 搜索执行失败: {e}")
|
||
import traceback
|
||
logger.error(traceback.format_exc())
|
||
return {"success": False, "message": f"搜索失败: {str(e)}"}
|