feat：maisaka使用主程序的message格式，优化逻辑，移除冗余模块

src/maisaka/knowledge.py

@@ -1,171 +1,58 @@
 """
-MaiSaka - 了解模块
-负责从对话中提取和存储用户个人特征信息。
+MaiSaka knowledge retrieval helpers.
 """
 
 from typing import List
+
+from src.common.data_models.mai_message_data_model import MaiMessage
+
 from .knowledge_store import KNOWLEDGE_CATEGORIES, get_knowledge_store
 
-
-def build_knowledge_summary() -> str:
-    """
-    构建了解分类摘要，用于 LLM 请求。
-
-    Returns:
-        格式化的分类列表文本
-    """
-    store = get_knowledge_store()
-    return store.get_categories_summary()
+NO_RESULT_KEYWORDS = [
+    "\u65e0",
+    "\u6ca1\u6709",
+    "\u4e0d\u9002\u7528",
+    "\u65e0\u9700",
+    "\u65e0\u76f8\u5173",
+]
 
 
 def extract_category_ids_from_result(result: str) -> List[str]:
-    """
-    从 LLM 返回结果中提取分类编号。
-
-    Args:
-        result: LLM 返回的结果文本
-
-    Returns:
-        分类编号列表
-    """
+    """Extract valid category ids from an LLM result string."""
     if not result:
         return []
 
-    # 检查是否表示"无相关内容"
-    if any(keyword in result for keyword in ["无", "没有", "不适用", "无需", "无相关"]):
+    normalized = result.strip()
+    if not normalized:
         return []
 
-    # 解析编号（支持逗号分隔、空格分隔、换行分隔）
-    category_ids = []
-    for part in result.replace(",", " ").replace("，", " ").replace("\n", " ").split():
-        part = part.strip()
-        if part in KNOWLEDGE_CATEGORIES:
-            category_ids.append(part)
+    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
 
 
-def format_context_for_memory(context_messages: List[dict]) -> str:
-    """
-    格式化上下文消息为文本，用于记忆分析。
-
-    Args:
-        context_messages: 上下文消息列表
-
-    Returns:
-        格式化后的文本
-    """
-    parts = []
-    for msg in context_messages:
-        role = msg.get("role", "")
-        content = msg.get("content", "")
-
-        if role == "user":
-            parts.append(f"用户: {content}")
-        elif role == "assistant":
-            # 跳过感知消息
-            if "【AI 感知】" not in content:
-                parts.append(f"助手: {content}")
-
-    return "\n".join(parts)
-
-
-async def store_knowledge_from_context(
-    llm_service,
-    context_messages: List[dict],
-    store_result_callback=None,
-) -> int:
-    """
-    记忆部分：从上下文中提取并存储了解信息。
-
-    在上下文裁切时触发：
-    1. 请求 LLM 分析聊天内容涉及哪些分类
-    2. 为每个分类创建 subAgent 提取相关内容
-    3. 存入了解列表
-
-    Args:
-        llm_service: LLM 服务实例
-        context_messages: 需要分析的上下文消息
-        store_result_callback: 存储结果回调函数
-
-    Returns:
-        成功存储的了解信息数量
-    """
-    store = get_knowledge_store()
-    context_text = format_context_for_memory(context_messages)
-    categories_summary = build_knowledge_summary()
-
-    if not context_text:
-        return 0
-
-    try:
-        # 第一步：分析涉及哪些分类
-        category_ids = await llm_service.analyze_knowledge_categories(context_messages, categories_summary)
-
-        if not category_ids:
-            return 0
-
-        # 第二步：为每个分类提取内容并存储
-        stored_count = 0
-        for category_id in category_ids:
-            try:
-                # 提取该分类的相关内容
-                extracted_content = await llm_service.extract_knowledge_for_category(
-                    context_messages, category_id, store.get_category_name(category_id)
-                )
-
-                if extracted_content:
-                    # 存储到了解列表
-                    success = store.add_knowledge(
-                        category_id=category_id, content=extracted_content, metadata={"source": "context_compression"}
-                    )
-                    if success:
-                        stored_count += 1
-                        if store_result_callback:
-                            store_result_callback(category_id, store.get_category_name(category_id), extracted_content)
-            except Exception:
-                # 单个分类失败不影响其他分类
-                continue
-
-        return stored_count
-
-    except Exception:
-        return 0
-
-
 async def retrieve_relevant_knowledge(
     llm_service,
-    chat_history: List[dict],
+    chat_history: List[MaiMessage],
 ) -> str:
-    """
-    提取部分：根据当前上下文检索相关的了解信息。
-
-    在每次对话前触发（EQ 模块和 timing 模块位置）：
-    1. 请求 LLM 分析需要哪些分类的了解内容
-    2. 提取对应分类的所有内容并拼接
-    3. 返回格式化后的了解内容
-
-    Args:
-        llm_service: LLM 服务实例
-        chat_history: 当前对话历史
-
-    Returns:
-        格式化后的了解内容文本
-    """
+    """Retrieve formatted knowledge snippets relevant to the current chat history."""
     store = get_knowledge_store()
     categories_summary = store.get_categories_summary()
 
     try:
-        # 分析需要哪些分类
         category_ids = await llm_service.analyze_knowledge_need(chat_history, categories_summary)
-
         if not category_ids:
             return ""
-
-        # 获取并格式化了解内容
-        formatted_knowledge = store.get_formatted_knowledge(category_ids)
-
-        return formatted_knowledge
-
+        return store.get_formatted_knowledge(category_ids)
     except Exception:
         return ""