B5记忆压缩

code/b4_local_agent_llm.py

@@ -47,17 +47,6 @@ def _extract_tool_result(message: dict) -> dict:
 
 
 def _three_points(text: str) -> list[str]:
-    """
-    从工具返回的文本内容中提取最多3个关键要点。
-
-    处理逻辑：
-    1. 按换行符或中文标点（句号、感叹号、问号）分割文本
-    2. 去除每个片段的空白字符和末尾标点
-    3. 去重并保留最多3个不同的片段
-    4. 如果不足3个，用默认提示填充
-
-    用于将工具执行结果转换为简洁的3条中文摘要。
-    """
     parts = [part.strip(" \t\r\n。") for part in re.split(r"\n+|(?<=[。！？!?])", text) if part.strip()]
     points = []
     for part in parts:

code/b5_memory.py

@@ -216,6 +216,37 @@ def _keyword_search_memory(
               
 
       return docs
+def _summarize_messages(messages: list, trace: dict, answer: str, max_chars: int = 600) -> str:
+      """Generate a deterministic extractive summary of a conversation."""
+      parts = []
+      user_questions = []
+      tool_calls_made = []
+
+      for msg in messages:
+          role = msg.get("role", "")
+          content = msg.get("content", "")
+          if role == "user" and content:
+              user_questions.append(content[:200])
+          elif role == "assistant":
+              tcs = msg.get("tool_calls", [])
+              for tc in tcs:
+                  tool_calls_made.append(tc.get("name", "unknown"))
+
+      if user_questions:
+          parts.append("用户提问:")
+          for q in user_questions:
+              parts.append(f"  - {q}")
+
+      if tool_calls_made:
+          tools_unique = list(dict.fromkeys(tool_calls_made))
+          parts.append(f"调用工具: {', '.join(tools_unique)}")
+
+      parts.append(f"LLM 调用 {trace.get('llm_call_count', '?')} 次，"
+                   f"工具回合 {trace.get('tool_rounds_used', '?')} 次")
+      parts.append(f"最终回答: {answer[:200]}")
+
+      text = "\n".join(parts)
+      return text[:max_chars]
 def _safe_conversation_id(conversation_id: str) -> str:
     if not isinstance(conversation_id, str) or not re.fullmatch(r"[A-Za-z0-9_.-]+", conversation_id):
         raise ValueError("conversation_id may only contain letters, numbers, dot, underscore, and hyphen")
@@ -247,12 +278,14 @@ def save_memory(
     target_path = Path(target_dir) / f"{conversation_id}.md"
     relative_path = f"{relative_dir}/{conversation_id}.md"
     title = f"{save_type.title()} {conversation_id}"
-    summary = answer[:200]
+    compressed = _summarize_messages(messages, trace, answer)
     markdown = (
         f"# {title}\n\n"
         f"- memory_id: `{memory_id}`\n"
         f"- conversation_id: `{conversation_id}`\n"
         f"- created_or_updated_at: `{now}`\n\n"
+        "## Conversation Summary\n\n"
+        f"{compressed}\n\n"
         "## Final Answer\n\n"
         f"{answer}\n\n"
         "## Messages\n\n```json\n"
@@ -268,7 +301,7 @@ def save_memory(
         "memory_id": memory_id,
         "memory_type": save_type,
         "title": title,
-        "summary": summary,
+        "summary": compressed,
         "path": relative_path,
         "conversation_id": conversation_id,
         "created_at": created_at,
@@ -281,7 +314,7 @@ def save_memory(
         "memory_type": save_type,
         "conversation_id": conversation_id,
         "title": title,
-        "summary": summary,
+        "summary": compressed,
         "path": relative_path,
         "index_path": Path(paths["index"]).name,
         "created_at": created_at,