ref：分离know模块和cli模块

src/cli/__init__.py

@@ -0,0 +1,3 @@
+"""
+CLI startup and interaction package.
+"""

src/cli/console.py

@@ -1,4 +1,4 @@
-"""MaiSaka 终端输出组件。"""
+"""MaiSaka terminal console helpers."""
 
 from rich.console import Console
 from rich.theme import Theme

src/cli/input_reader.py

@@ -1,12 +1,12 @@
 """
-MaiSaka - 异步输入读取器
+MaiSaka asynchronous stdin reader for CLI interaction.
-将阻塞的标准输入读取放到后台线程中，供 asyncio 循环安全消费。
 """
 
+from typing import Optional
+
 import asyncio
 import sys
 import threading
-from typing import Optional
 
 
 class InputReader:

src/cli/maisaka_cli.py

@@ -8,24 +8,23 @@ from typing import Optional
 
 import asyncio
 import os
+import time
 
 from rich import box
 from rich.markdown import Markdown
 from rich.panel import Panel
 from rich.text import Text
 
+from src.know_u.knowledge import KnowledgeLearner, retrieve_relevant_knowledge
+from src.know_u.knowledge_store import get_knowledge_store
 from src.chat.message_receive.message import SessionMessage
 from src.chat.replyer.maisaka_generator import MaisakaReplyGenerator
 from src.config.config import config_manager, global_config
 from src.mcp_module import MCPManager
 
-from .chat_loop_service import MaisakaChatLoopService
+from src.maisaka.chat_loop_service import MaisakaChatLoopService
-from .console import console
+from src.maisaka.message_adapter import build_message, format_speaker_content, remove_last_perception
-from .input_reader import InputReader
+from src.maisaka.tool_handlers import (
-from .knowledge import retrieve_relevant_knowledge
-from .knowledge_store import get_knowledge_store
-from .message_adapter import build_message, format_speaker_content, remove_last_perception
-from .tool_handlers import (
     ToolHandlerContext,
     handle_mcp_tool,
     handle_stop,
@@ -33,6 +32,9 @@ from .tool_handlers import (
     handle_wait,
 )
 
+from .console import console
+from .input_reader import InputReader
+
 
 class BufferCLI:
     """Maisaka 命令行交互入口。"""
@@ -43,6 +45,10 @@ class BufferCLI:
         self._reader = InputReader()
         self._chat_history: Optional[list[SessionMessage]] = None
         self._knowledge_store = get_knowledge_store()
+        self._knowledge_learner = KnowledgeLearner("maisaka_cli")
+        self._knowledge_min_messages_for_extraction = 10
+        self._knowledge_min_extraction_interval = 30
+        self._last_knowledge_extraction_time = 0.0
 
         knowledge_stats = self._knowledge_store.get_stats()
         if knowledge_stats["total_items"] > 0:
@@ -112,6 +118,7 @@ class BufferCLI:
             self._chat_start_time = now
             self._last_assistant_response_time = None
             self._chat_history = self._chat_loop_service.build_chat_context(user_text)
+            self._trigger_knowledge_learning([self._chat_history[-1]])
         else:
             self._chat_history.append(
                 build_message(
@@ -123,9 +130,37 @@ class BufferCLI:
                     ),
                 )
             )
+            self._trigger_knowledge_learning([self._chat_history[-1]])
 
         await self._run_llm_loop(self._chat_history)
 
+    def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None:
+        """在 CLI 会话中按批次触发 knowledge 学习。"""
+        if not global_config.maisaka.enable_knowledge_module:
+            return
+
+        self._knowledge_learner.add_messages(messages)
+
+        elapsed = time.monotonic() - self._last_knowledge_extraction_time
+        if elapsed < self._knowledge_min_extraction_interval:
+            return
+
+        cache_size = self._knowledge_learner.get_cache_size()
+        if cache_size < self._knowledge_min_messages_for_extraction:
+            return
+
+        self._last_knowledge_extraction_time = time.monotonic()
+        asyncio.create_task(self._run_knowledge_learning())
+
+    async def _run_knowledge_learning(self) -> None:
+        """后台执行 knowledge 学习，避免阻塞主对话。"""
+        try:
+            added_count = await self._knowledge_learner.learn()
+            if added_count > 0 and global_config.maisaka.show_thinking:
+                console.print(f"[muted]Knowledge learning added {added_count} item(s).[/muted]")
+        except Exception as exc:
+            console.print(f"[warning]Knowledge learning failed: {exc}[/warning]")
+
     async def _run_llm_loop(self, chat_history: list[SessionMessage]) -> None:
         """
         Main inner loop for the Maisaka planner.

src/know_u/__init__.py

@@ -0,0 +1,3 @@
+"""
+Knowledge utilities package for Maisaka.
+"""

src/know_u/knowledge.py

@@ -0,0 +1,239 @@
+"""
+Maisaka knowledge retrieval and learning helpers.
+"""
+
+from typing import Any, Dict, List
+
+import asyncio
+import json
+
+from src.chat.message_receive.message import SessionMessage
+from src.chat.utils.utils import is_bot_self
+from src.common.data_models.llm_service_data_models import LLMGenerationOptions
+from src.common.logger import get_logger
+from src.services.llm_service import LLMServiceClient
+
+from src.know_u.knowledge_store import KNOWLEDGE_CATEGORIES, get_knowledge_store
+from src.maisaka.message_adapter import get_message_role, get_message_text, parse_speaker_content
+
+logger = get_logger("maisaka_knowledge")
+
+NO_RESULT_KEYWORDS = [
+    "无",
+    "没有",
+    "不适用",
+    "无需",
+    "无相关",
+]
+
+
+def extract_category_ids_from_result(result: str) -> List[str]:
+    """Extract valid category ids from an LLM result string."""
+    if not result:
+        return []
+
+    normalized = result.strip()
+    if not normalized:
+        return []
+
+    lowered = normalized.lower()
+    if any(keyword in lowered for keyword in ["none", "no relevant", "no_need", "no need"]):
+        return []
+    if any(keyword in normalized for keyword in NO_RESULT_KEYWORDS):
+        return []
+
+    category_ids: List[str] = []
+    for part in normalized.replace(",", " ").replace("，", " ").replace("\n", " ").split():
+        candidate = part.strip()
+        if candidate in KNOWLEDGE_CATEGORIES and candidate not in category_ids:
+            category_ids.append(candidate)
+
+    return category_ids
+
+
+async def retrieve_relevant_knowledge(
+    knowledge_analyzer: Any,
+    chat_history: List[SessionMessage],
+) -> str:
+    """Retrieve formatted knowledge snippets relevant to the current chat history."""
+    store = get_knowledge_store()
+    categories_summary = store.get_categories_summary()
+
+    try:
+        category_ids = await knowledge_analyzer.analyze_knowledge_need(chat_history, categories_summary)
+        if not category_ids:
+            return ""
+        return store.get_formatted_knowledge(category_ids)
+    except Exception:
+        logger.exception("Failed to retrieve relevant knowledge")
+        return ""
+
+
+class KnowledgeLearner:
+    """
+    从最近对话中提取用户画像类知识并写入知识库。
+    """
+
+    def __init__(self, session_id: str) -> None:
+        self._session_id = session_id
+        self._store = get_knowledge_store()
+        self._llm = LLMServiceClient(task_name="utils", request_type="maisaka.knowledge.learn")
+        self._learning_lock = asyncio.Lock()
+        self._messages_cache: List[SessionMessage] = []
+
+    def add_messages(self, messages: List[SessionMessage]) -> None:
+        """缓存待学习的消息。"""
+        self._messages_cache.extend(messages)
+
+    def get_cache_size(self) -> int:
+        """获取缓存消息数量。"""
+        return len(self._messages_cache)
+
+    async def learn(self) -> int:
+        """
+        从缓存消息中提取知识并落库。
+
+        Returns:
+            新增入库的知识条数
+        """
+        if not self._messages_cache:
+            return 0
+
+        async with self._learning_lock:
+            chat_excerpt = self._build_chat_excerpt()
+            if not chat_excerpt:
+                return 0
+
+            prompt = self._build_learning_prompt(chat_excerpt)
+            try:
+                result = await self._llm.generate_response(
+                    prompt=prompt,
+                    options=LLMGenerationOptions(
+                        temperature=0.1,
+                        max_tokens=512,
+                    ),
+                )
+            except Exception:
+                logger.exception("Knowledge learning model call failed")
+                return 0
+
+            knowledge_items = self._parse_learning_result(result.response or "")
+            if not knowledge_items:
+                logger.debug("Knowledge learning finished without extracted entries")
+                return 0
+
+            added_count = 0
+            for item in knowledge_items:
+                category_id = str(item.get("category_id", "")).strip()
+                content = str(item.get("content", "")).strip()
+                if not category_id or not content:
+                    continue
+
+                if self._store.add_knowledge(
+                    category_id=category_id,
+                    content=content,
+                    metadata={
+                        "session_id": self._session_id,
+                        "source": "maisaka_learning",
+                    },
+                ):
+                    added_count += 1
+
+            if added_count > 0:
+                logger.info(
+                    f"Maisaka knowledge learning finished: session_id={self._session_id} added={added_count}"
+                )
+            else:
+                logger.debug(
+                    f"Maisaka knowledge learning finished without new entries: session_id={self._session_id}"
+                )
+
+            return added_count
+
+    def _build_chat_excerpt(self) -> str:
+        """
+        构建适合画像提取的对话片段，只保留用户可见文本。
+        """
+        lines: List[str] = []
+        for message in self._messages_cache[-30:]:
+            if get_message_role(message) == "assistant":
+                continue
+            if get_message_role(message) == "tool":
+                continue
+            if is_bot_self(message.platform, message.message_info.user_info.user_id):
+                continue
+
+            raw_text = get_message_text(message).strip()
+            if not raw_text:
+                continue
+
+            speaker_name, body = parse_speaker_content(raw_text)
+            visible_text = (body or raw_text).strip()
+            if not visible_text:
+                continue
+
+            speaker = speaker_name or message.message_info.user_info.user_nickname or "用户"
+            lines.append(f"{speaker}: {visible_text}")
+
+        return "\n".join(lines)
+
+    def _build_learning_prompt(self, chat_excerpt: str) -> str:
+        """构建知识提取提示词。"""
+        categories_text = "\n".join(
+            f"{category_id}. {category_name}" for category_id, category_name in KNOWLEDGE_CATEGORIES.items()
+        )
+        return (
+            "你是一个用户画像知识提取器，需要从聊天记录里提取稳定、可复用的用户事实。\n"
+            "只提取用户明确表达或高置信度可归纳的信息，不要猜测，不要提取一次性情绪，不要重复表述。\n"
+            "如果没有可提取内容，返回空数组 []。\n"
+            "输出必须是 JSON 数组，每项格式为 "
+            '{"category_id":"分类编号","content":"简洁中文陈述"}。\n'
+            "分类如下：\n"
+            f"{categories_text}\n\n"
+            "聊天记录：\n"
+            f"{chat_excerpt}"
+        )
+
+    def _parse_learning_result(self, result: str) -> List[Dict[str, str]]:
+        """解析模型返回的知识条目。"""
+        normalized = result.strip()
+        if not normalized:
+            return []
+
+        if "```" in normalized:
+            normalized = normalized.replace("```json", "").replace("```JSON", "").replace("```", "").strip()
+
+        try:
+            parsed = json.loads(normalized)
+        except json.JSONDecodeError:
+            logger.warning("Knowledge learning result is not valid JSON")
+            return []
+
+        if not isinstance(parsed, list):
+            return []
+
+        normalized_items: List[Dict[str, str]] = []
+        seen_pairs: set[tuple[str, str]] = set()
+        for item in parsed:
+            if not isinstance(item, dict):
+                continue
+
+            category_id = str(item.get("category_id", "")).strip()
+            content = " ".join(str(item.get("content", "")).strip().split())
+            if category_id not in KNOWLEDGE_CATEGORIES:
+                continue
+            if not content:
+                continue
+
+            pair = (category_id, content)
+            if pair in seen_pairs:
+                continue
+            seen_pairs.add(pair)
+            normalized_items.append(
+                {
+                    "category_id": category_id,
+                    "content": content,
+                }
+            )
+
+        return normalized_items

src/know_u/knowledge_store.py

@@ -0,0 +1,197 @@
+"""
+MaiSaka knowledge store.
+"""
+
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import json
+
+# 数据目录位于项目根目录下的 mai_knowledge
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+KNOWLEDGE_DATA_DIR = PROJECT_ROOT / "mai_knowledge"
+KNOWLEDGE_FILE = KNOWLEDGE_DATA_DIR / "knowledge.json"
+
+
+KNOWLEDGE_CATEGORIES = {
+    "1": "性别",
+    "2": "性格",
+    "3": "饮食口味",
+    "4": "交友喜好",
+    "5": "情绪/理性倾向",
+    "6": "兴趣爱好",
+    "7": "职业/专业",
+    "8": "生活习惯",
+    "9": "价值观",
+    "10": "沟通风格",
+    "11": "学习方式",
+    "12": "压力应对方式",
+}
+
+
+class KnowledgeStore:
+    """
+    简单的 Maisaka 知识存储。
+
+    特性：
+    - 持久化到 JSON 文件
+    - 按分类存储用户画像类知识
+    - 支持基础去重
+    """
+
+    def __init__(self) -> None:
+        """初始化知识存储。"""
+        self._knowledge: Dict[str, List[Dict[str, Any]]] = {
+            category_id: [] for category_id in KNOWLEDGE_CATEGORIES
+        }
+        self._ensure_data_dir()
+        self._load()
+
+    def _ensure_data_dir(self) -> None:
+        """确保数据目录存在。"""
+        KNOWLEDGE_DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+    def _load(self) -> None:
+        """从文件加载知识数据。"""
+        if not KNOWLEDGE_FILE.exists():
+            self._knowledge = {category_id: [] for category_id in KNOWLEDGE_CATEGORIES}
+            return
+
+        try:
+            with open(KNOWLEDGE_FILE, "r", encoding="utf-8") as file:
+                loaded = json.load(file)
+
+            normalized_knowledge: Dict[str, List[Dict[str, Any]]] = {
+                category_id: [] for category_id in KNOWLEDGE_CATEGORIES
+            }
+            for category_id in KNOWLEDGE_CATEGORIES:
+                category_items = loaded.get(category_id, [])
+                if isinstance(category_items, list):
+                    normalized_knowledge[category_id] = [
+                        item for item in category_items if isinstance(item, dict)
+                    ]
+            self._knowledge = normalized_knowledge
+        except Exception:
+            self._knowledge = {category_id: [] for category_id in KNOWLEDGE_CATEGORIES}
+
+    def _save(self) -> None:
+        """保存知识数据到文件。"""
+        with open(KNOWLEDGE_FILE, "w", encoding="utf-8") as file:
+            json.dump(self._knowledge, file, ensure_ascii=False, indent=2)
+
+    @staticmethod
+    def _normalize_content(content: str) -> str:
+        """标准化知识内容，便于去重。"""
+        return " ".join(str(content).strip().split())
+
+    def add_knowledge(
+        self,
+        category_id: str,
+        content: str,
+        metadata: Optional[Dict[str, Any]] = None,
+    ) -> bool:
+        """
+        添加一条知识信息。
+
+        Args:
+            category_id: 分类编号
+            content: 知识内容
+            metadata: 附加元数据
+
+        Returns:
+            是否新增成功；若命中去重则返回 False
+        """
+        if category_id not in KNOWLEDGE_CATEGORIES:
+            return False
+
+        normalized_content = self._normalize_content(content)
+        if not normalized_content:
+            return False
+
+        existing_items = self._knowledge.get(category_id, [])
+        for item in existing_items:
+            existing_content = self._normalize_content(str(item.get("content", "")))
+            if existing_content == normalized_content:
+                return False
+
+        knowledge_item = {
+            "id": f"know_{category_id}_{datetime.now().timestamp()}",
+            "content": normalized_content,
+            "metadata": metadata or {},
+            "created_at": datetime.now().isoformat(),
+        }
+        self._knowledge[category_id].append(knowledge_item)
+        self._save()
+        return True
+
+    def get_category_knowledge(self, category_id: str) -> List[Dict[str, Any]]:
+        """获取某个分类下的所有知识。"""
+        return self._knowledge.get(category_id, [])
+
+    def get_all_knowledge(self) -> Dict[str, List[Dict[str, Any]]]:
+        """获取全部知识。"""
+        return self._knowledge
+
+    def get_category_name(self, category_id: str) -> str:
+        """获取分类名称。"""
+        return KNOWLEDGE_CATEGORIES.get(category_id, "未知分类")
+
+    def get_categories_summary(self) -> str:
+        """获取分类摘要，供模型判断是否需要检索。"""
+        lines: List[str] = []
+        for category_id, category_name in KNOWLEDGE_CATEGORIES.items():
+            count = len(self._knowledge.get(category_id, []))
+            count_text = f"{count}条" if count > 0 else "无数据"
+            lines.append(f"{category_id}. {category_name} ({count_text})")
+        return "\n".join(lines)
+
+    def get_formatted_knowledge(self, category_ids: List[str], limit_per_category: int = 5) -> str:
+        """
+        获取指定分类的格式化知识内容。
+
+        Args:
+            category_ids: 分类编号列表
+            limit_per_category: 每个分类最多返回多少条
+
+        Returns:
+            格式化后的知识内容
+        """
+        parts: List[str] = []
+        for category_id in category_ids:
+            items = self.get_category_knowledge(category_id)
+            if not items:
+                continue
+
+            category_name = self.get_category_name(category_id)
+            parts.append(f"【{category_name}】")
+
+            recent_items = items[-limit_per_category:]
+            for item in recent_items:
+                content = str(item.get("content", "")).strip()
+                if content:
+                    parts.append(f"- {content}")
+
+        return "\n".join(parts)
+
+    def get_stats(self) -> Dict[str, Any]:
+        """获取知识数据统计。"""
+        total_items = sum(len(items) for items in self._knowledge.values())
+        return {
+            "total_categories": len(KNOWLEDGE_CATEGORIES),
+            "total_items": total_items,
+            "data_file": str(KNOWLEDGE_FILE),
+            "data_exists": KNOWLEDGE_FILE.exists(),
+            "data_size_kb": KNOWLEDGE_FILE.stat().st_size / 1024 if KNOWLEDGE_FILE.exists() else 0,
+        }
+
+
+_knowledge_store_instance: Optional[KnowledgeStore] = None
+
+
+def get_knowledge_store() -> KnowledgeStore:
+    """获取知识存储单例。"""
+    global _knowledge_store_instance
+    if _knowledge_store_instance is None:
+        _knowledge_store_instance = KnowledgeStore()
+    return _knowledge_store_instance

src/maisaka/chat_loop_service.py

@@ -15,18 +15,18 @@ from rich.pretty import Pretty
 from rich.text import Text
 
 from src.chat.message_receive.message import SessionMessage
+from src.cli.console import console
 from src.common.data_models.llm_service_data_models import LLMGenerationOptions
 from src.common.logger import get_logger
 from src.common.prompt_i18n import load_prompt
 from src.config.config import global_config
+from src.know_u.knowledge import extract_category_ids_from_result
 from src.llm_models.model_client.base_client import BaseClient
 from src.llm_models.payload_content.message import Message, MessageBuilder, RoleType
 from src.llm_models.payload_content.tool_option import ToolCall, ToolDefinitionInput, ToolOption, normalize_tool_options
 from src.services.llm_service import LLMServiceClient
 
 from .builtin_tools import get_builtin_tools
-from .console import console
-from .knowledge import extract_category_ids_from_result
 from .message_adapter import (
     build_message,
     format_speaker_content,

src/maisaka/knowledge.py

@@ -1,58 +0,0 @@
-"""
-MaiSaka knowledge retrieval helpers.
-"""
-
-from typing import List
-
-from src.chat.message_receive.message import SessionMessage
-
-from .knowledge_store import KNOWLEDGE_CATEGORIES, get_knowledge_store
-
-NO_RESULT_KEYWORDS = [
-    "\u65e0",
-    "\u6ca1\u6709",
-    "\u4e0d\u9002\u7528",
-    "\u65e0\u9700",
-    "\u65e0\u76f8\u5173",
-]
-
-
-def extract_category_ids_from_result(result: str) -> List[str]:
-    """Extract valid category ids from an LLM result string."""
-    if not result:
-        return []
-
-    normalized = result.strip()
-    if not normalized:
-        return []
-
-    lowered = normalized.lower()
-    if any(keyword in lowered for keyword in ["none", "no relevant", "no_need", "no need"]):
-        return []
-    if any(keyword in normalized for keyword in NO_RESULT_KEYWORDS):
-        return []
-
-    category_ids: List[str] = []
-    for part in normalized.replace(",", " ").replace("\uff0c", " ").replace("\n", " ").split():
-        candidate = part.strip()
-        if candidate in KNOWLEDGE_CATEGORIES and candidate not in category_ids:
-            category_ids.append(candidate)
-
-    return category_ids
-
-
-async def retrieve_relevant_knowledge(
-    knowledge_analyzer,
-    chat_history: List[SessionMessage],
-) -> str:
-    """Retrieve formatted knowledge snippets relevant to the current chat history."""
-    store = get_knowledge_store()
-    categories_summary = store.get_categories_summary()
-
-    try:
-        category_ids = await knowledge_analyzer.analyze_knowledge_need(chat_history, categories_summary)
-        if not category_ids:
-            return ""
-        return store.get_formatted_knowledge(category_ids)
-    except Exception:
-        return ""

src/maisaka/knowledge_store.py

@@ -1,190 +0,0 @@
-"""
-MaiSaka - 了解列表持久化存储
-存储用户个人特征信息，支持层级结构和本地持久化。
-"""
-
-import json
-import os
-from pathlib import Path
-from typing import Dict, List, Optional, Any
-from datetime import datetime
-
-# 数据目录 - 项目根目录下的 mai_knowledge
-PROJECT_ROOT = Path(os.path.dirname(os.path.abspath(__file__)))
-KNOWLEDGE_DATA_DIR = PROJECT_ROOT / "mai_knowledge"
-KNOWLEDGE_FILE = KNOWLEDGE_DATA_DIR / "knowledge.json"
-
-
-# 个人特征分类列表（预定义）
-KNOWLEDGE_CATEGORIES = {
-    "1": "性别",
-    "2": "性格",
-    "3": "饮食口味",
-    "4": "交友喜好",
-    "5": "情绪/理性倾向",
-    "6": "兴趣爱好",
-    "7": "职业/专业",
-    "8": "生活习惯",
-    "9": "价值观",
-    "10": "沟通风格",
-    "11": "学习方式",
-    "12": "压力应对方式",
-}
-
-
-class KnowledgeStore:
-    """
-    了解列表存储。
-
-    特性：
-    - 持久化到 JSON 文件
-    - 层级结构存储（按分类）
-    - 支持增量更新
-    - 启动时自动加载
-    """
-
-    def __init__(self):
-        """初始化了解存储"""
-        self._knowledge: Dict[str, List[Dict[str, Any]]] = {category_id: [] for category_id in KNOWLEDGE_CATEGORIES}
-        self._ensure_data_dir()
-        self._load()
-
-    def _ensure_data_dir(self):
-        """确保数据目录存在"""
-        KNOWLEDGE_DATA_DIR.mkdir(parents=True, exist_ok=True)
-
-    def _load(self):
-        """从文件加载了解数据"""
-        if not KNOWLEDGE_FILE.exists():
-            self._knowledge = {category_id: [] for category_id in KNOWLEDGE_CATEGORIES}
-            return
-
-        try:
-            with open(KNOWLEDGE_FILE, "r", encoding="utf-8") as f:
-                loaded = json.load(f)
-                # 确保所有分类都存在
-                for category_id in KNOWLEDGE_CATEGORIES:
-                    if category_id not in loaded:
-                        loaded[category_id] = []
-                self._knowledge = loaded
-        except Exception as e:
-            print(f"[warning]加载了解数据失败: {e}[/warning]")
-            self._knowledge = {category_id: [] for category_id in KNOWLEDGE_CATEGORIES}
-
-    def _save(self):
-        """保存了解数据到文件"""
-        try:
-            with open(KNOWLEDGE_FILE, "w", encoding="utf-8") as f:
-                json.dump(self._knowledge, f, ensure_ascii=False, indent=2)
-        except Exception as e:
-            print(f"[warning]保存了解数据失败: {e}[/warning]")
-
-    def add_knowledge(
-        self,
-        category_id: str,
-        content: str,
-        metadata: Optional[Dict[str, Any]] = None,
-    ) -> bool:
-        """
-        添加一条了解信息。
-
-        Args:
-            category_id: 分类编号
-            content: 了解内容
-            metadata: 元数据
-
-        Returns:
-            是否添加成功
-        """
-        if category_id not in KNOWLEDGE_CATEGORIES:
-            return False
-
-        try:
-            knowledge_item = {
-                "id": f"know_{category_id}_{datetime.now().timestamp()}",
-                "content": content,
-                "metadata": metadata or {},
-                "created_at": datetime.now().isoformat(),
-            }
-            self._knowledge[category_id].append(knowledge_item)
-            self._save()
-            return True
-        except Exception:
-            return False
-
-    def get_category_knowledge(self, category_id: str) -> List[Dict[str, Any]]:
-        """
-        获取某个分类的所有了解信息。
-
-        Args:
-            category_id: 分类编号
-
-        Returns:
-            该分类的所有了解信息
-        """
-        return self._knowledge.get(category_id, [])
-
-    def get_all_knowledge(self) -> Dict[str, List[Dict[str, Any]]]:
-        """获取所有了解信息"""
-        return self._knowledge
-
-    def get_category_name(self, category_id: str) -> str:
-        """获取分类名称"""
-        return KNOWLEDGE_CATEGORIES.get(category_id, "未知分类")
-
-    def get_categories_summary(self) -> str:
-        """获取所有分类的摘要（用于 LLM 展示）"""
-        lines = []
-        for category_id, category_name in KNOWLEDGE_CATEGORIES.items():
-            count = len(self._knowledge.get(category_id, []))
-            if count > 0:
-                lines.append(f"{category_id}. {category_name} ({count}条)")
-            else:
-                lines.append(f"{category_id}. {category_name} (无数据)")
-        return "\n".join(lines)
-
-    def get_formatted_knowledge(self, category_ids: List[str]) -> str:
-        """
-        获取指定分类的了解内容，格式化为文本。
-
-        Args:
-            category_ids: 分类编号列表
-
-        Returns:
-            格式化后的了解内容文本
-        """
-        parts = []
-        for category_id in category_ids:
-            category_name = self.get_category_name(category_id)
-            items = self.get_category_knowledge(category_id)
-
-            if items:
-                parts.append(f"【{category_name}】")
-                for item in items:
-                    content = item.get("content", "")
-                    parts.append(f"  - {content}")
-
-        return "\n".join(parts) if parts else "暂无相关了解信息"
-
-    def get_stats(self) -> Dict[str, Any]:
-        """获取了解数据统计信息"""
-        total_items = sum(len(items) for items in self._knowledge.values())
-        return {
-            "total_categories": len(KNOWLEDGE_CATEGORIES),
-            "total_items": total_items,
-            "data_file": str(KNOWLEDGE_FILE),
-            "data_exists": KNOWLEDGE_FILE.exists(),
-            "data_size_kb": KNOWLEDGE_FILE.stat().st_size / 1024 if KNOWLEDGE_FILE.exists() else 0,
-        }
-
-
-# 全局单例
-_knowledge_store_instance: Optional[KnowledgeStore] = None
-
-
-def get_knowledge_store() -> KnowledgeStore:
-    """获取了解存储实例（单例模式）"""
-    global _knowledge_store_instance
-    if _knowledge_store_instance is None:
-        _knowledge_store_instance = KnowledgeStore()
-    return _knowledge_store_instance

src/maisaka/runtime.py

@@ -13,9 +13,10 @@ from src.common.data_models.mai_message_data_model import GroupInfo, UserInfo
 from src.common.logger import get_logger
 from src.common.utils.utils_config import ExpressionConfigUtils
 from src.config.config import global_config
-from src.mcp_module import MCPManager
+from src.know_u.knowledge import KnowledgeLearner
 from src.learners.expression_learner import ExpressionLearner
 from src.learners.jargon_miner import JargonMiner
+from src.mcp_module import MCPManager
 
 from .chat_loop_service import MaisakaChatLoopService
 from .reasoning_engine import MaisakaReasoningEngine
@@ -66,9 +67,11 @@ class MaisakaHeartFlowChatting:
         self._enable_jargon_learning = jargon_learn
         self._min_messages_for_extraction = 10
         self._min_extraction_interval = 30
-        self._last_extraction_time = 0.0
+        self._last_expression_extraction_time = 0.0
+        self._last_knowledge_extraction_time = 0.0
         self._expression_learner = ExpressionLearner(session_id)
         self._jargon_miner = JargonMiner(session_id, session_name=session_name)
+        self._knowledge_learner = KnowledgeLearner(session_id)
 
         self._reasoning_engine = MaisakaReasoningEngine(self)
 
@@ -157,7 +160,7 @@ class MaisakaHeartFlowChatting:
                     if not cached_messages:
                         break
                     await self._internal_turn_queue.put(cached_messages)
-                    asyncio.create_task(self._trigger_expression_learning(cached_messages))
+                    asyncio.create_task(self._trigger_batch_learning(cached_messages))
         except asyncio.CancelledError:
             logger.info(f"{self.log_prefix} Maisaka runtime loop cancelled")
 
@@ -223,6 +226,18 @@ class MaisakaHeartFlowChatting:
         self._agent_state = self._STATE_STOP
         self._wait_until = None
 
+    async def _trigger_batch_learning(self, messages: list[SessionMessage]) -> None:
+        """按同一批消息触发表达方式、黑话和 knowledge 学习。"""
+        expression_result, knowledge_result = await asyncio.gather(
+            self._trigger_expression_learning(messages),
+            self._trigger_knowledge_learning(messages),
+            return_exceptions=True,
+        )
+        if isinstance(expression_result, Exception):
+            logger.error(f"{self.log_prefix} expression learning task crashed: {expression_result}")
+        if isinstance(knowledge_result, Exception):
+            logger.error(f"{self.log_prefix} knowledge learning task crashed: {knowledge_result}")
+
     async def _trigger_expression_learning(self, messages: list[SessionMessage]) -> None:
         """Trigger expression learning from the newly collected batch."""
         self._expression_learner.add_messages(messages)
@@ -231,7 +246,7 @@ class MaisakaHeartFlowChatting:
             logger.debug(f"{self.log_prefix} expression learning disabled, skip this batch")
             return
 
-        elapsed = time.time() - self._last_extraction_time
+        elapsed = time.time() - self._last_expression_extraction_time
         if elapsed < self._min_extraction_interval:
             logger.debug(
                 f"{self.log_prefix} expression learning interval not reached: "
@@ -248,7 +263,7 @@ class MaisakaHeartFlowChatting:
             )
             return
 
-        self._last_extraction_time = time.time()
+        self._last_expression_extraction_time = time.time()
         logger.info(
             f"{self.log_prefix} starting expression learning: "
             f"new_batch={len(messages)} learner_cache={cache_size} "
@@ -266,6 +281,47 @@ class MaisakaHeartFlowChatting:
         except Exception:
             logger.exception(f"{self.log_prefix} expression learning failed")
 
+    async def _trigger_knowledge_learning(self, messages: list[SessionMessage]) -> None:
+        """Trigger knowledge learning from the newly collected batch."""
+        self._knowledge_learner.add_messages(messages)
+
+        if not global_config.maisaka.enable_knowledge_module:
+            logger.debug(f"{self.log_prefix} knowledge learning disabled, skip this batch")
+            return
+
+        elapsed = time.time() - self._last_knowledge_extraction_time
+        if elapsed < self._min_extraction_interval:
+            logger.debug(
+                f"{self.log_prefix} knowledge learning interval not reached: "
+                f"elapsed={elapsed:.2f}s threshold={self._min_extraction_interval}s"
+            )
+            return
+
+        cache_size = self._knowledge_learner.get_cache_size()
+        if cache_size < self._min_messages_for_extraction:
+            logger.debug(
+                f"{self.log_prefix} knowledge learning skipped due to cache size: "
+                f"learner_cache={cache_size} threshold={self._min_messages_for_extraction} "
+                f"message_cache_total={len(self.message_cache)}"
+            )
+            return
+
+        self._last_knowledge_extraction_time = time.time()
+        logger.info(
+            f"{self.log_prefix} starting knowledge learning: "
+            f"new_batch={len(messages)} learner_cache={cache_size} "
+            f"message_cache_total={len(self.message_cache)}"
+        )
+
+        try:
+            added_count = await self._knowledge_learner.learn()
+            if added_count > 0:
+                logger.info(f"{self.log_prefix} knowledge learning finished: added={added_count}")
+            else:
+                logger.debug(f"{self.log_prefix} knowledge learning finished without usable result")
+        except Exception:
+            logger.exception(f"{self.log_prefix} knowledge learning failed")
+
     async def _init_mcp(self) -> None:
         """Initialize MCP tools and inject them into the planner."""
         config_path = Path(__file__).resolve().parents[2] / "config" / "mcp_config.json"

src/maisaka/tool_handlers.py

@@ -12,10 +12,10 @@ import os
 from rich.panel import Panel
 
 from src.chat.message_receive.message import SessionMessage
+from src.cli.console import console
+from src.cli.input_reader import InputReader
 from src.llm_models.payload_content.tool_option import ToolCall
 
-from .console import console
-from .input_reader import InputReader
 from .message_adapter import build_message
 
 if TYPE_CHECKING:

src/mcp_module/config.py

@@ -27,7 +27,7 @@ from typing import Optional
 import json
 import os
 
-from src.maisaka.console import console
+from src.cli.console import console
 
 
 DEFAULT_MCP_CONFIG_PATH = Path(__file__).resolve().parents[2] / "config" / "mcp_config.json"

src/mcp_module/connection.py

@@ -6,7 +6,7 @@ MaiSaka - 单个 MCP 服务器连接管理
 from contextlib import AsyncExitStack
 from typing import Any, Optional
 
-from src.maisaka.console import console
+from src.cli.console import console
 
 from .config import MCPServerConfig
 

src/mcp_module/manager.py

@@ -5,7 +5,7 @@ MaiSaka - MCP 管理器
 
 from typing import Optional
 
-from src.maisaka.console import console
+from src.cli.console import console
 
 from .config import DEFAULT_MCP_CONFIG_PATH, MCPServerConfig, load_mcp_config
 from .connection import MCPConnection, MCP_AVAILABLE