feat(vectordb): 添加向量数据库支持及集成功能
新增向量数据库管理器模块，支持文本的存储、检索和相似度查询 添加知识库插件和AI聊天插件，利用向量数据库实现记忆功能 优化跨平台翻译模块，集成向量数据库存储历史翻译记录 改进消息处理逻辑，优先使用用户显示名称
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113
plugins/ai_chat.py
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113
plugins/ai_chat.py
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# -*- coding: utf-8 -*-
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"""
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AI 聊天插件，支持向量数据库记忆功能
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"""
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import time
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import uuid
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from core.managers.command_manager import matcher
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from models.events.message import GroupMessageEvent, PrivateMessageEvent
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from core.managers.vectordb_manager import vectordb_manager
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from core.utils.logger import ModuleLogger
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from core.config_loader import global_config
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logger = ModuleLogger("AIChat")
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# 尝试导入 OpenAI 客户端
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try:
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from openai import AsyncOpenAI
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OPENAI_AVAILABLE = True
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except ImportError:
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OPENAI_AVAILABLE = False
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async def get_ai_response(user_id: int, group_id: int, user_message: str) -> str:
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"""获取 AI 回复，包含向量数据库记忆"""
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if not OPENAI_AVAILABLE:
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return "请先安装 openai 库: pip install openai"
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# 从配置中获取 DeepSeek API 配置（复用跨平台插件的配置或全局配置）
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api_key = getattr(global_config.cross_platform, 'deepseek_api_key', None) or "your-api-key"
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api_url = getattr(global_config.cross_platform, 'deepseek_api_url', "https://api.deepseek.com/v1")
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model = getattr(global_config.cross_platform, 'deepseek_model', "deepseek-chat")
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if api_key == "your-api-key":
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return "请先在配置中设置 DeepSeek API Key"
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# 1. 从向量数据库检索相关记忆
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collection_name = f"chat_memory_{user_id}"
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memory_context = ""
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try:
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results = vectordb_manager.query_texts(
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collection_name=collection_name,
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query_texts=[user_message],
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n_results=3
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)
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if results and results.get("documents") and results["documents"][0]:
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memory_context = "\n\n相关历史记忆：\n"
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for i, doc in enumerate(results["documents"][0], 1):
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memory_context += f"{i}. {doc}\n"
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except Exception as e:
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logger.error(f"检索聊天记忆失败: {e}")
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# 2. 构建 Prompt
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system_prompt = f"""你是一个友好的 AI 助手。请根据用户的输入进行回复。
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如果提供了相关历史记忆，请参考这些记忆来保持对话的连贯性。{memory_context}"""
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try:
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client = AsyncOpenAI(
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api_key=api_key,
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base_url=api_url.replace("/chat/completions", "")
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)
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response = await client.chat.completions.create(
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model=model,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message}
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],
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temperature=0.7,
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max_tokens=1000
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)
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ai_reply = response.choices[0].message.content
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# 3. 将本次对话存入向量数据库
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if ai_reply:
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try:
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doc_id = str(uuid.uuid4())
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text_to_embed = f"用户: {user_message}\nAI: {ai_reply}"
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metadata = {
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"user_id": user_id,
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"group_id": group_id,
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"timestamp": int(time.time())
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}
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vectordb_manager.add_texts(
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collection_name=collection_name,
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texts=[text_to_embed],
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metadatas=[metadata],
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ids=[doc_id]
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)
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except Exception as e:
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logger.error(f"保存聊天记忆失败: {e}")
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return ai_reply
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except Exception as e:
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logger.error(f"AI 聊天请求失败: {e}")
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return f"请求失败: {str(e)}"
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@matcher.command("chat", "聊天")
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async def chat_command(event: GroupMessageEvent | PrivateMessageEvent, args: list[str]):
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"""AI 聊天命令"""
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if not args:
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await event.reply("请提供要聊天的内容，例如：/chat 你好")
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return
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user_message = " ".join(args)
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user_id = event.user_id
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group_id = getattr(event, 'group_id', 0)
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await event.reply("正在思考中...")
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reply = await get_ai_response(user_id, group_id, user_message)
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await event.reply(reply)
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@@ -148,7 +148,7 @@ async def handle_qq_group_message(event: GroupMessageEvent):
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group_name = f"群{group_id}"
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await handle_qq_message(
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nickname=event.sender.nickname or event.sender.card or str(event.user_id),
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nickname=event.sender.card or event.sender.nickname or str(event.user_id),
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user_id=event.user_id,
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group_name=group_name,
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group_id=group_id,
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@@ -2,8 +2,11 @@
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"""
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跨平台消息互通插件翻译模块
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"""
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import time
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import uuid
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from typing import Dict, List
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from core.utils.logger import ModuleLogger
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from core.managers.vectordb_manager import vectordb_manager
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from .config import config
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# 创建模块专用日志记录器
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@@ -19,7 +22,7 @@ def get_translation_context(channel_id: int, direction: str) -> List[Dict[str, s
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return TRANSLATION_CONTEXT_CACHE.get(cache_key, [])
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def add_translation_context(channel_id: int, direction: str, original: str, translated: str):
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"""添加翻译到上下文缓存"""
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"""添加翻译到上下文缓存和向量数据库"""
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cache_key = f"{channel_id}_{direction}"
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if cache_key not in TRANSLATION_CONTEXT_CACHE:
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TRANSLATION_CONTEXT_CACHE[cache_key] = []
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@@ -31,6 +34,59 @@ def add_translation_context(channel_id: int, direction: str, original: str, tran
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if len(TRANSLATION_CONTEXT_CACHE[cache_key]) > MAX_CONTEXT_MESSAGES:
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TRANSLATION_CONTEXT_CACHE[cache_key] = TRANSLATION_CONTEXT_CACHE[cache_key][-MAX_CONTEXT_MESSAGES:]
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# 将翻译记录保存到向量数据库
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try:
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collection_name = f"translation_memory_{channel_id}"
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doc_id = str(uuid.uuid4())
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# 将原文和译文组合作为向量化文本
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text_to_embed = f"原文: {original}\n译文: {translated}"
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metadata = {
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"channel_id": channel_id,
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"direction": direction,
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"original": original,
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"translated": translated,
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"timestamp": int(time.time())
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}
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vectordb_manager.add_texts(
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collection_name=collection_name,
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texts=[text_to_embed],
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metadatas=[metadata],
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ids=[doc_id]
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)
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logger.debug(f"[CrossPlatform] 翻译记录已保存到向量数据库: {collection_name}")
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except Exception as e:
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logger.error(f"[CrossPlatform] 保存翻译记录到向量数据库失败: {e}")
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def get_similar_translations(channel_id: int, text: str, direction: str, limit: int = 3) -> str:
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"""从向量数据库检索相似的翻译记录"""
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try:
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collection_name = f"translation_memory_{channel_id}"
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# 检索相似文本
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results = vectordb_manager.query_texts(
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collection_name=collection_name,
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query_texts=[text],
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n_results=limit,
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where={"direction": direction}
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)
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if not results or not results.get("documents") or not results["documents"][0]:
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return ""
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context_ref = "\n\n参考历史相似翻译（向量检索）：\n"
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for i, metadata in enumerate(results["metadatas"][0], 1):
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original = metadata.get("original", "")
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translated = metadata.get("translated", "")
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context_ref += f"{i}. 原文: {original[:100]}\n 译文: {translated[:100]}\n"
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return context_ref
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except Exception as e:
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logger.error(f"[CrossPlatform] 从向量数据库检索翻译记录失败: {e}")
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return ""
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async def translate_with_deepseek(
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text: str,
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@@ -51,11 +107,17 @@ async def translate_with_deepseek(
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messages = []
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context_ref = ""
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if channel_id > 0:
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# 1. 获取最近的上下文缓存
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context = get_translation_context(channel_id, direction)
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if context:
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context_ref = "\n\n参考之前的翻译：\n"
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context_ref = "\n\n参考最近的翻译：\n"
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for i, ctx in enumerate(context[-5:], 1):
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context_ref += f"{i}. 原文: {ctx['original'][:100]}\n 译文: {ctx['translated'][:100]}\n"
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# 2. 从向量数据库检索相似的历史翻译
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similar_context = get_similar_translations(channel_id, text, direction)
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if similar_context:
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context_ref += similar_context
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system_prompt = f"""你是一个专业的翻译助手。请将以下文本翻译成{lang_name}。
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只返回翻译后的文本，不要添加任何解释、注释或其他内容。避免翻译出仇视言论以及违反中国大陆相关法律法规的内容。如果有，请在翻译后有敏感的词语中把文本替换成井号（#）
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@@ -115,11 +177,17 @@ async def translate_with_deepseek_sync(
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context_ref = ""
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if channel_id > 0:
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# 1. 获取最近的上下文缓存
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context = get_translation_context(channel_id, direction)
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if context:
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context_ref = "\n\n参考之前的翻译：\n"
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context_ref = "\n\n参考最近的翻译：\n"
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for i, ctx in enumerate(context[-5:], 1):
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context_ref += f"{i}. 原文: {ctx['original'][:100]}\n 译文: {ctx['translated'][:100]}\n"
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# 2. 从向量数据库检索相似的历史翻译
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similar_context = get_similar_translations(channel_id, text, direction)
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if similar_context:
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context_ref += similar_context
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system_prompt = f"""你是一个专业的翻译助手。请将以下文本翻译成{lang_name}。
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只返回翻译后的文本，不要添加任何解释、注释或其他内容。避免翻译出仇视言论以及违反中国大陆相关法律法规的内容。如果有，请在翻译后有敏感的词语中把文本替换成井号（#）
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86
plugins/knowledge_base.py
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plugins/knowledge_base.py
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# -*- coding: utf-8 -*-
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"""
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群聊知识库插件，支持向量数据库检索
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"""
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import time
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import uuid
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from core.managers.command_manager import matcher
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from models.events.message import GroupMessageEvent
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from core.managers.vectordb_manager import vectordb_manager
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from core.utils.logger import ModuleLogger
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from core.permission import Permission
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logger = ModuleLogger("GroupKnowledgeBase")
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@matcher.command("kb_add", "添加知识库", permission=Permission.ADMIN)
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async def kb_add_command(event: GroupMessageEvent, args: list[str]):
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"""添加知识库条目"""
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if len(args) < 2:
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await event.reply("用法: /kb_add <问题> <答案>")
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return
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question = args[0]
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answer = " ".join(args[1:])
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group_id = event.group_id
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try:
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collection_name = f"knowledge_base_{group_id}"
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doc_id = str(uuid.uuid4())
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text_to_embed = f"问题: {question}\n答案: {answer}"
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metadata = {
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"group_id": group_id,
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"question": question,
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"answer": answer,
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"added_by": event.user_id,
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"timestamp": int(time.time())
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}
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success = vectordb_manager.add_texts(
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collection_name=collection_name,
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texts=[text_to_embed],
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metadatas=[metadata],
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ids=[doc_id]
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)
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if success:
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await event.reply(f"知识库条目添加成功！\n问题: {question}")
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else:
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await event.reply("知识库条目添加失败，请查看日志。")
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except Exception as e:
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logger.error(f"添加知识库失败: {e}")
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await event.reply(f"添加失败: {str(e)}")
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@matcher.command("kb_search", "搜索知识库")
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async def kb_search_command(event: GroupMessageEvent, args: list[str]):
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"""搜索知识库条目"""
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if not args:
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await event.reply("用法: /kb_search <关键词>")
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return
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query = " ".join(args)
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group_id = event.group_id
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try:
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collection_name = f"knowledge_base_{group_id}"
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results = vectordb_manager.query_texts(
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collection_name=collection_name,
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query_texts=[query],
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n_results=3
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)
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if not results or not results.get("documents") or not results["documents"][0]:
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await event.reply("未找到相关的知识库条目。")
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return
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reply_msg = f"为您找到以下相关知识：\n"
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for i, metadata in enumerate(results["metadatas"][0], 1):
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question = metadata.get("question", "")
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answer = metadata.get("answer", "")
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reply_msg += f"\n{i}. Q: {question}\n A: {answer}"
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await event.reply(reply_msg)
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except Exception as e:
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logger.error(f"搜索知识库失败: {e}")
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await event.reply(f"搜索失败: {str(e)}")
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