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/memory.py

@@ -178,13 +178,21 @@ class AgentMemory:
 
     async def _global_knowledge_search(self, query: str = "") -> str:
         """从 GlobalKnowledge 表检索相关的全局知识条目。"""
+        from datetime import datetime
         from app.models.agent import GlobalKnowledge
 
         db: Optional[Session] = None
         try:
             db = SessionLocal()
+            now = datetime.utcnow()
+
+            # 查询未过期的知识（expires_at IS NULL 或 expires_at > now）
             rows = (
                 db.query(GlobalKnowledge)
+                .filter(
+                    (GlobalKnowledge.expires_at.is_(None))
+                    | (GlobalKnowledge.expires_at > now)
+                )
                 .order_by(GlobalKnowledge.created_at.desc())
                 .limit(50)
                 .all()
@@ -212,6 +220,7 @@ class AgentMemory:
                             "metadata": {
                                 "source_agent_id": row.source_agent_id,
                                 "tags": row.tags or [],
+                                "confidence": row.confidence or "medium",
                             },
                         })
 
@@ -225,17 +234,22 @@ class AgentMemory:
                             lines = ["## 全局知识库"]
                             for i, m in enumerate(matched, 1):
                                 tags = m.get("metadata", {}).get("tags", [])
+                                conf = m.get("metadata", {}).get("confidence", "medium")
                                 tag_str = f" [{', '.join(tags[:3])}]" if tags else ""
-                                lines.append(f"{i}.{tag_str} {m.get('content_text', '')[:500]}")
+                                conf_str = f" (置信度:{conf})" if conf != "medium" else ""
+                                lines.append(f"{i}.{tag_str}{conf_str} {m.get('content_text', '')[:500]}")
                             return "\n".join(lines)
             else:
-                # 无 query，返回最近 5 条全局知识
-                recent = rows[:5]
+                # 无 query，返回最近 5 条全局知识（优先高置信度）
+                recent = sorted(rows, key=lambda r: (
+                    0 if r.confidence == "high" else 1 if r.confidence == "medium" else 2
+                ))[:5]
                 if recent:
                     lines = ["## 全局知识库（最近）"]
                     for i, row in enumerate(recent, 1):
                         tag_str = f" [{(', '.join(row.tags[:3]))}]" if row.tags else ""
-                        lines.append(f"{i}.{tag_str} {row.content[:500]}")
+                        conf_str = f" (置信度:{row.confidence})" if row.confidence and row.confidence != "medium" else ""
+                        lines.append(f"{i}.{tag_str}{conf_str} {row.content[:500]}")
                     return "\n".join(lines)
 
             return ""
@@ -249,8 +263,14 @@ class AgentMemory:
     async def save_global_knowledge(
         self, content: str, source_agent_id: str = "",
         source_user_id: str = "", tags: Optional[List[str]] = None,
+        confidence: str = "medium", ttl_hours: int = 0,
     ) -> None:
-        """将知识条目写入全局知识池。"""
+        """将知识条目写入全局知识池（带去重、置信度、过期时间）。
+
+        去重策略：对 content 取哈希，若已有相同哈希的条目则跳过。
+        过期策略：ttl_hours > 0 时设置 expires_at；0 表示永不过期。
+        """
+        from datetime import datetime, timedelta
         from app.models.agent import GlobalKnowledge
 
         if not content or len(content) < 20:
@@ -260,7 +280,26 @@ class AgentMemory:
         try:
             db = SessionLocal()
 
-            # 生成 embedding
+            # 去重：用 content 的 MD5 哈希检查是否已存在
+            import hashlib
+            content_hash = hashlib.md5(content[:500].encode()).hexdigest()
+
+            # 查询最近 200 条，检查是否有相同哈希的条目
+            recent = (
+                db.query(GlobalKnowledge)
+                .order_by(GlobalKnowledge.created_at.desc())
+                .limit(200)
+                .all()
+            )
+            for existing in recent:
+                existing_hash = hashlib.md5(
+                    (existing.content or "")[:500].encode()
+                ).hexdigest()
+                if existing_hash == content_hash:
+                    logger.info("全局知识去重：已存在相同条目，跳过写入")
+                    return
+
+            # 嵌入向量
             embedding_json = ""
             try:
                 emb = await embedding_service.generate_embedding(content)
@@ -269,18 +308,26 @@ class AgentMemory:
             except Exception:
                 pass
 
+            # 过期时间
+            expires_at = None
+            if ttl_hours > 0:
+                expires_at = datetime.utcnow() + timedelta(hours=ttl_hours)
+
             record = GlobalKnowledge(
                 content=content[:2000],
                 embedding=embedding_json or None,
                 source_agent_id=source_agent_id or "",
                 source_user_id=source_user_id or "",
                 tags=tags or [],
+                confidence=confidence or "medium",
+                expires_at=expires_at,
                 scope_kind=self.scope_kind,
                 scope_id=self.scope_id or "global",
             )
             db.add(record)
             db.commit()
-            logger.info("已写入全局知识: agent=%s tags=%s", source_agent_id, tags)
+            logger.info("已写入全局知识: agent=%s tags=%s confidence=%s",
+                        source_agent_id, tags, confidence)
         except Exception as e:
             logger.warning("保存全局知识失败: %s", e)
             if db: