Refactor replyer retrieval to async and add memory formatter

Changed get_replyer and related calls to async across multiple modules for proper coroutine handling. Added format_memories_bracket_style utility for memory formatting. Improved video analysis caching logic and type annotations. Updated error logging for message processing.

src/chat/memory_system/__init__.py

@@ -30,6 +30,7 @@ from .memory_system import MemorySystem, MemorySystemConfig, get_memory_system,
 
 # Vector DB存储系统
 from .vector_memory_storage_v2 import VectorMemoryStorage, VectorStorageConfig, get_vector_memory_storage
+from .memory_formatter import format_memories_bracket_style
 
 __all__ = [
     # 核心数据结构
@@ -62,6 +63,8 @@ __all__ = [
     "MemoryActivator",
     "memory_activator",
     "enhanced_memory_activator",  # 兼容性别名
+    # 格式化工具
+    "format_memories_bracket_style",
 ]
 
 # 版本信息

src/chat/memory_system/memory_formatter.py

@@ -0,0 +1,118 @@
+"""记忆格式化工具
+
+提供统一的记忆块格式化函数，供构建 Prompt 时使用。
+
+当前使用的函数: format_memories_bracket_style
+输入: list[dict] 其中每个元素包含：
+    - display: str  记忆可读内容
+    - memory_type: str  记忆类型 (personal_fact/opinion/preference/event 等)
+    - metadata: dict 可选，包括
+        - confidence: 置信度 (str|float)
+        - importance: 重要度 (str|float)
+        - timestamp: 时间戳 (float|str)
+        - source: 来源 (str)
+        - relevance_score: 相关度 (float)
+
+返回: 适合直接嵌入提示词的大段文本；若无有效记忆返回空串。
+"""
+from __future__ import annotations
+
+from typing import Any, Iterable
+import time
+
+
+def _format_timestamp(ts: Any) -> str:
+    try:
+        if ts in (None, ""):
+            return ""
+        if isinstance(ts, (int, float)) and ts > 0:
+            return time.strftime("%Y-%m-%d %H:%M", time.localtime(float(ts)))
+        return str(ts)
+    except Exception:
+        return ""
+
+
+def _coerce_str(v: Any) -> str:
+    if v is None:
+        return ""
+    return str(v)
+
+
+def format_memories_bracket_style(
+    memories: Iterable[dict[str, Any]] | None,
+    query_context: str | None = None,
+    max_items: int = 15,
+) -> str:
+    """以方括号 + 标注字段的方式格式化记忆列表。
+
+    例子输出:
+        ## 相关记忆回顾
+        - [类型:personal_fact|重要:高|置信:0.83|相关:0.72] 他喜欢黑咖啡 (来源: chat, 2025-10-05 09:30)
+
+    Args:
+        memories: 记忆字典迭代器
+        query_context: 当前查询/用户的消息，用于在首行提示（可选）
+        max_items: 最多输出的记忆条数
+    Returns:
+        str: 格式化文本；若无内容返回空串
+    """
+    if not memories:
+        return ""
+
+    lines: list[str] = ["## 相关记忆回顾"]
+    if query_context:
+        lines.append(f"（与当前消息相关：{query_context[:60]}{'...' if len(query_context) > 60 else ''}）")
+    lines.append("")
+
+    count = 0
+    for mem in memories:
+        if count >= max_items:
+            break
+        if not isinstance(mem, dict):
+            continue
+        display = _coerce_str(mem.get("display", "")).strip()
+        if not display:
+            continue
+
+        mtype = _coerce_str(mem.get("memory_type", "fact")) or "fact"
+        meta = mem.get("metadata", {}) if isinstance(mem.get("metadata"), dict) else {}
+        confidence = _coerce_str(meta.get("confidence", ""))
+        importance = _coerce_str(meta.get("importance", ""))
+        source = _coerce_str(meta.get("source", ""))
+        rel = meta.get("relevance_score")
+        try:
+            rel_str = f"{float(rel):.2f}" if rel is not None else ""
+        except Exception:
+            rel_str = ""
+        ts = _format_timestamp(meta.get("timestamp"))
+
+        # 构建标签段
+        tags: list[str] = [f"类型:{mtype}"]
+        if importance:
+            tags.append(f"重要:{importance}")
+        if confidence:
+            tags.append(f"置信:{confidence}")
+        if rel_str:
+            tags.append(f"相关:{rel_str}")
+
+        tag_block = "|".join(tags)
+        suffix_parts = []
+        if source:
+            suffix_parts.append(source)
+        if ts:
+            suffix_parts.append(ts)
+        suffix = (" (" + ", ".join(suffix_parts) + ")") if suffix_parts else ""
+
+        lines.append(f"- [{tag_block}] {display}{suffix}")
+        count += 1
+
+    if count == 0:
+        return ""
+
+    if count >= max_items:
+        lines.append(f"\n(已截断，仅显示前 {max_items} 条相关记忆)")
+
+    return "\n".join(lines)
+
+
+__all__ = ["format_memories_bracket_style"]

src/chat/memory_system/memory_system.py

@@ -997,6 +997,7 @@ class MemorySystem:
             from src.chat.message_receive.chat_stream import get_chat_manager
 
             chat_manager = get_chat_manager()
+            # get_stream 为异步方法，需要 await
             chat_stream = await chat_manager.get_stream(stream_id)
 
             if not chat_stream or not hasattr(chat_stream, "context_manager"):
@@ -1111,6 +1112,7 @@ class MemorySystem:
                 from src.chat.message_receive.chat_stream import get_chat_manager
 
                 chat_manager = get_chat_manager()
+                # ChatManager.get_stream 是异步方法，需要 await，否则会产生 "coroutine was never awaited" 警告
                 chat_stream = await chat_manager.get_stream(stream_id)
                 if chat_stream and hasattr(chat_stream, "context_manager"):
                     history_limit = self._determine_history_limit(context)