fix: memory scope_id column overflow and API read mismatch

AgentRuntime writes scope_id as {user_id}:{agent_id} (73 chars) but: - DB columns were CHAR(36)/VARCHAR(36), causing Data too long errors - Memory API queried with agent_id only, returning empty results - Add _mem_scope() helper to build correct scope_id format - Update all CRUD queries in agent_memory.py to use _mem_scope() - Enhance login failure logging in auth.py Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-07-01 08:05:23 +08:00
parent e33aa6e0b7
commit f1326bff95
3 changed files with 78 additions and 40 deletions
--- a/backend/app/api/agent_memory.py
+++ b/backend/app/api/agent_memory.py
@@ -11,7 +11,6 @@ from typing import List, Optional
 from fastapi import APIRouter, Depends, HTTPException, Query
 from pydantic import BaseModel, Field
 from sqlalchemy.orm import Session
-from sqlalchemy import or_

 from app.core.database import get_db
 from app.api.auth import get_current_user
@@ -30,6 +29,11 @@ router = APIRouter(
 SCOPE_AGENT = "agent"


+def _mem_scope(user: User, agent_id: str) -> str:
+    """构建记忆 scope_id，与 AgentRuntime 写入的格式保持一致（{user_id}:{agent_id}）。"""
+    return f"{user.id}:{agent_id}"
+
+
 def _check_agent(db: Session, agent_id: str, user: User):
    agent = db.query(Agent).filter(Agent.id == agent_id).first()
    if not agent:
@@ -218,9 +222,10 @@ def list_global_knowledge(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    q = db.query(GlobalKnowledge).filter(
        GlobalKnowledge.scope_kind == SCOPE_AGENT,
-        GlobalKnowledge.scope_id == agent_id,
+        GlobalKnowledge.scope_id == scope_id,
    )
    if search:
        q = q.filter(GlobalKnowledge.content.contains(search))
@@ -237,13 +242,14 @@ def create_global_knowledge(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    gk = GlobalKnowledge(
        id=str(uuid.uuid4()),
        content=body.content,
        tags=body.tags,
        confidence=body.confidence,
        scope_kind=SCOPE_AGENT,
-        scope_id=agent_id,
+        scope_id=scope_id,
        source_user_id=current_user.id,
    )
    db.add(gk)
@@ -261,10 +267,11 @@ def update_global_knowledge(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    gk = db.query(GlobalKnowledge).filter(
        GlobalKnowledge.id == knowledge_id,
        GlobalKnowledge.scope_kind == SCOPE_AGENT,
-        GlobalKnowledge.scope_id == agent_id,
+        GlobalKnowledge.scope_id == scope_id,
    ).first()
    if not gk:
        raise HTTPException(status_code=404, detail="知识条目不存在")
@@ -287,10 +294,11 @@ def delete_global_knowledge(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    gk = db.query(GlobalKnowledge).filter(
        GlobalKnowledge.id == knowledge_id,
        GlobalKnowledge.scope_kind == SCOPE_AGENT,
-        GlobalKnowledge.scope_id == agent_id,
+        GlobalKnowledge.scope_id == scope_id,
    ).first()
    if not gk:
        raise HTTPException(status_code=404, detail="知识条目不存在")
@@ -312,9 +320,10 @@ def list_knowledge_entities(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    q = db.query(KnowledgeEntity).filter(
        KnowledgeEntity.scope_kind == SCOPE_AGENT,
-        KnowledgeEntity.scope_id == agent_id,
+        KnowledgeEntity.scope_id == scope_id,
    )
    if search:
        q = q.filter(
@@ -334,6 +343,7 @@ def create_knowledge_entity(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    ke = KnowledgeEntity(
        id=str(uuid.uuid4()),
        name=body.name,
@@ -343,7 +353,7 @@ def create_knowledge_entity(
        confidence=body.confidence,
        metadata_=body.tags,
        scope_kind=SCOPE_AGENT,
-        scope_id=agent_id,
+        scope_id=scope_id,
        user_id=current_user.id,
    )
    db.add(ke)
@@ -361,10 +371,11 @@ def update_knowledge_entity(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    ke = db.query(KnowledgeEntity).filter(
        KnowledgeEntity.id == entity_id,
        KnowledgeEntity.scope_kind == SCOPE_AGENT,
-        KnowledgeEntity.scope_id == agent_id,
+        KnowledgeEntity.scope_id == scope_id,
    ).first()
    if not ke:
        raise HTTPException(status_code=404, detail="实体不存在")
@@ -391,10 +402,11 @@ def delete_knowledge_entity(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    ke = db.query(KnowledgeEntity).filter(
        KnowledgeEntity.id == entity_id,
        KnowledgeEntity.scope_kind == SCOPE_AGENT,
-        KnowledgeEntity.scope_id == agent_id,
+        KnowledgeEntity.scope_id == scope_id,
    ).first()
    if not ke:
        raise HTTPException(status_code=404, detail="实体不存在")
@@ -420,9 +432,10 @@ def list_learning_patterns(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    q = db.query(AgentLearningPattern).filter(
        AgentLearningPattern.scope_kind == SCOPE_AGENT,
-        AgentLearningPattern.scope_id == agent_id,
+        AgentLearningPattern.scope_id == scope_id,
    )
    total = q.count()
    items = q.order_by(AgentLearningPattern.updated_at.desc()).offset(skip).limit(limit).all()
@@ -457,10 +470,11 @@ def delete_learning_pattern(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    lp = db.query(AgentLearningPattern).filter(
        AgentLearningPattern.id == pattern_id,
        AgentLearningPattern.scope_kind == SCOPE_AGENT,
-        AgentLearningPattern.scope_id == agent_id,
+        AgentLearningPattern.scope_id == scope_id,
    ).first()
    if not lp:
        raise HTTPException(status_code=404, detail="学习模式不存在")
@@ -482,15 +496,10 @@ def list_vector_memories(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
-    uid = current_user.id
-    # scope_id may be: agent_id, or {user_id}:{agent_id}, or {user_id}:{agent_id}:{session_id}
+    scope_id = _mem_scope(current_user, agent_id)
    q = db.query(AgentVectorMemory).filter(
        AgentVectorMemory.scope_kind == SCOPE_AGENT,
-        or_(
-            AgentVectorMemory.scope_id == agent_id,
-            AgentVectorMemory.scope_id.like(f"%:{agent_id}"),
-            AgentVectorMemory.scope_id.like(f"{uid}:%"),
-        ),
+        AgentVectorMemory.scope_id == scope_id,
    )
    if search:
        q = q.filter(AgentVectorMemory.content_text.contains(search))
@@ -507,17 +516,13 @@ def delete_vector_memory(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
-    uid = current_user.id
+    scope_id = _mem_scope(current_user, agent_id)
    vm = (
        db.query(AgentVectorMemory)
        .filter(
            AgentVectorMemory.id == memory_id,
            AgentVectorMemory.scope_kind == SCOPE_AGENT,
-            or_(
-                AgentVectorMemory.scope_id == agent_id,
-                AgentVectorMemory.scope_id.like(f"%:{agent_id}"),
-                AgentVectorMemory.scope_id.like(f"{uid}:%"),
-            ),
+            AgentVectorMemory.scope_id == scope_id,
        )
        .first()
    )
@@ -549,35 +554,32 @@ def export_memory(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    gk_list = (
        db.query(GlobalKnowledge)
-        .filter(GlobalKnowledge.scope_kind == SCOPE_AGENT, GlobalKnowledge.scope_id == agent_id)
+        .filter(GlobalKnowledge.scope_kind == SCOPE_AGENT, GlobalKnowledge.scope_id == scope_id)
        .all()
    )
    ke_list = (
        db.query(KnowledgeEntity)
-        .filter(KnowledgeEntity.scope_kind == SCOPE_AGENT, KnowledgeEntity.scope_id == agent_id)
+        .filter(KnowledgeEntity.scope_kind == SCOPE_AGENT, KnowledgeEntity.scope_id == scope_id)
        .all()
    )
    kr_list = (
        db.query(KnowledgeRelation)
-        .filter(KnowledgeRelation.scope_kind == SCOPE_AGENT, KnowledgeRelation.scope_id == agent_id)
+        .filter(KnowledgeRelation.scope_kind == SCOPE_AGENT, KnowledgeRelation.scope_id == scope_id)
        .all()
    )
    lp_list = (
        db.query(AgentLearningPattern)
-        .filter(AgentLearningPattern.scope_kind == SCOPE_AGENT, AgentLearningPattern.scope_id == agent_id)
+        .filter(AgentLearningPattern.scope_kind == SCOPE_AGENT, AgentLearningPattern.scope_id == scope_id)
        .all()
    )
    vm_list = (
        db.query(AgentVectorMemory)
        .filter(
            AgentVectorMemory.scope_kind == SCOPE_AGENT,
-            or_(
-                AgentVectorMemory.scope_id == agent_id,
-                AgentVectorMemory.scope_id.like(f"%:{agent_id}"),
-                AgentVectorMemory.scope_id.like(f"{current_user.id}:%"),
-            ),
+            AgentVectorMemory.scope_id == scope_id,
        )
        .all()
    )
@@ -600,6 +602,7 @@ def import_memory(
    current_user: User = Depends(get_current_user),
 ):
    _check_agent(db, agent_id, current_user)
+    scope_id = _mem_scope(current_user, agent_id)
    imported = {"global_knowledge": 0, "knowledge_entities": 0, "knowledge_relations": 0, "learning_patterns": 0}

    for item in body.global_knowledge:
@@ -609,7 +612,7 @@ def import_memory(
            tags=item.get("tags"),
            confidence=item.get("confidence", "medium"),
            scope_kind=SCOPE_AGENT,
-            scope_id=agent_id,
+            scope_id=scope_id,
            source_agent_id=item.get("source_agent_id"),
            source_user_id=current_user.id,
        )
@@ -626,7 +629,7 @@ def import_memory(
            confidence=item.get("confidence", "medium"),
            metadata_=item.get("metadata") or item.get("tags"),
            scope_kind=SCOPE_AGENT,
-            scope_id=agent_id,
+            scope_id=scope_id,
            user_id=current_user.id,
        )
        db.add(ke)
@@ -641,7 +644,7 @@ def import_memory(
            description=item.get("description"),
            weight=item.get("weight", "1.0"),
            scope_kind=SCOPE_AGENT,
-            scope_id=agent_id,
+            scope_id=scope_id,
        )
        db.add(kr)
        imported["knowledge_relations"] += 1
@@ -650,7 +653,7 @@ def import_memory(
        lp = AgentLearningPattern(
            id=str(uuid.uuid4()),
            scope_kind=SCOPE_AGENT,
-            scope_id=agent_id,
+            scope_id=scope_id,
            task_category=item.get("task_category", "general"),
            task_keywords=item.get("task_keywords", ""),
            suggested_tools=item.get("suggested_tools", "[]"),
@@ -668,7 +671,7 @@ def import_memory(
        vm = AgentVectorMemory(
            id=str(uuid.uuid4()),
            scope_kind=SCOPE_AGENT,
-            scope_id=agent_id,
+            scope_id=scope_id,
            session_key=item.get("session_key", ""),
            content_text=item.get("content_text", ""),
            metadata_=item.get("metadata"),