feat: Agent间知识共享 — 全局知识池（去重+置信度+过期）

- GlobalKnowledge 模型新增 confidence 和 expires_at 字段
- save_global_knowledge 增加 MD5 去重、置信度评分、TTL过期
- _global_knowledge_search 增加过期过滤、置信度优先排序
- run()/run_stream() 所有完成路径补齐知识提取调用
- 新增 Alembic 迁移 010_add_global_knowledge

Cl  oses #9

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/agent_runtime/core.py

@@ -226,6 +226,7 @@ class AgentRuntime:
                 # LLM 直接返回文本 → 结束
                 self.context.add_assistant_message(content)
                 final_text = content or "（模型未返回有效内容）"
+                review_score = 0.0
 
                 # 输出质量自检（默认关闭，Agent 节点可开启）
                 if self.config.self_review_enabled and not _self_review_attempted:
@@ -239,6 +240,7 @@ class AgentRuntime:
                         tool_result=json.dumps(review, ensure_ascii=False),
                     ))
                     if review["passed"]:
+                        review_score = review["score"]
                         logger.info("self_review 通过 (%.2f >= %.2f)", review["score"], review["threshold"])
                     else:
                         logger.info("self_review 未通过 (%.2f < %.2f)，追加修正", review["score"], review["threshold"])
@@ -269,7 +271,7 @@ class AgentRuntime:
                         tool_calls_made=self.context.tool_calls_made,
                     )
                 # 提取知识到全局知识池（Agent 间知识共享）
-                await self._extract_global_knowledge(user_input, final_text, steps)
+                await self._extract_global_knowledge(user_input, final_text, steps, review_score)
                 return AgentResult(
                     success=True,
                     content=final_text,
@@ -393,6 +395,9 @@ class AgentRuntime:
                 iterations_used=self.context.iteration,
                 tool_calls_made=self.context.tool_calls_made,
             )
+        # 提取知识到全局知识池（即便截断，工具调用序列仍有参考价值）
+        if last_content:
+            await self._extract_global_knowledge(user_input, last_content, steps)
         if last_content:
             steps.append(AgentStep(
                 iteration=self.context.iteration,
@@ -512,6 +517,7 @@ class AgentRuntime:
                 # LLM 直接返回文本 → 结束
                 self.context.add_assistant_message(content)
                 final_text = content or "（模型未返回有效内容）"
+                review_score = 0.0
 
                 # 输出质量自检（默认关闭）
                 if self.config.self_review_enabled and not _self_review_attempted:
@@ -524,6 +530,7 @@ class AgentRuntime:
                         "session_id": self.context.session_id,
                     }
                     if review["passed"]:
+                        review_score = review["score"]
                         logger.info("self_review 通过 (%.2f >= %.2f)", review["score"], review["threshold"])
                     else:
                         logger.info("self_review 未通过 (%.2f < %.2f)，追加修正", review["score"], review["threshold"])
@@ -560,6 +567,8 @@ class AgentRuntime:
                         iterations_used=self.context.iteration,
                         tool_calls_made=self.context.tool_calls_made,
                     )
+                # 提取知识到全局知识池（Agent 间知识共享）
+                await self._extract_global_knowledge(user_input, final_text, steps, review_score)
                 return
 
             # 有工具调用 → 先记录 assistant 消息
@@ -706,6 +715,9 @@ class AgentRuntime:
                 iterations_used=self.context.iteration,
                 tool_calls_made=self.context.tool_calls_made,
             )
+        # 提取知识到全局知识池（即便截断，工具调用序列仍有参考价值）
+        if last_content:
+            await self._extract_global_knowledge(user_input, last_content, steps)
         yield {
             "type": "final",
             "content": last_content or "已达最大迭代次数，但模型未返回最终回答。",
@@ -778,6 +790,7 @@ class AgentRuntime:
 
     async def _extract_global_knowledge(
         self, user_input: str, final_answer: str, steps: List[AgentStep],
+        self_review_score: float = 0.0,
     ) -> None:
         """从 Agent 执行结果中提取知识，写入全局知识池（Agent 间共享）。"""
         # 提取工具调用名称作为 tags
@@ -798,11 +811,24 @@ class AgentRuntime:
         source_agent_id = self.config.name if self.config.name != "default_agent" else ""
         source_user_id = self.config.user_id or ""
 
+        # 置信度评估：基于 self_review 评分和工具执行成功数
+        confidence = "medium"
+        if self_review_score >= 0.8:
+            confidence = "high"
+        elif self_review_score > 0 and self_review_score < 0.5:
+            confidence = "low"
+        elif tool_names and len(tool_names) >= 2:
+            confidence = "high"  # 多工具协作通常质量更高
+        # TTL: 高置信度知识有效期更长
+        ttl_hours = 720 if confidence == "high" else 168 if confidence == "medium" else 24
+
         await self.memory.save_global_knowledge(
             content=content,
             source_agent_id=source_agent_id,
             source_user_id=source_user_id,
             tags=tags,
+            confidence=confidence,
+            ttl_hours=ttl_hours,
         )
 
     async def _self_review(self, content: str, task_context: str = "") -> dict: