diff --git a/ai-service/migrations/003_user_memories.py b/ai-service/migrations/003_user_memories.py
new file mode 100644
index 0000000..c1999c6
--- /dev/null
+++ b/ai-service/migrations/003_user_memories.py
@@ -0,0 +1,75 @@
+"""
+Database Migration: User Memories Table.
+[AC-IDMP-14] 用户级记忆滚动摘要表
+
+创建时间: 2025-03-08
+变更说明:
+- 新增 user_memories 表用于存储滚动摘要与事实/偏好/未解决问题
+
+执行方式:
+- SQLModel 会自动创建表（通过 init_db）
+- 此脚本用于手动迁移或回滚
+
+SQL DDL:
+```sql
+CREATE TABLE user_memories (
+ id UUID PRIMARY KEY,
+ tenant_id VARCHAR NOT NULL,
+ user_id VARCHAR NOT NULL,
+ summary TEXT,
+ facts JSON,
+ preferences JSON,
+ open_issues JSON,
+ summary_version INTEGER NOT NULL DEFAULT 1,
+ last_turn_id VARCHAR,
+ expires_at TIMESTAMP,
+ created_at TIMESTAMP NOT NULL DEFAULT NOW(),
+ updated_at TIMESTAMP NOT NULL DEFAULT NOW()
+);
+
+CREATE INDEX ix_user_memories_tenant_user ON user_memories(tenant_id, user_id);
+CREATE INDEX ix_user_memories_tenant_user_updated ON user_memories(tenant_id, user_id, updated_at);
+```
+
+回滚 SQL:
+```sql
+DROP TABLE IF EXISTS user_memories;
+```
+"""
+
+USER_MEMORIES_DDL = """
+CREATE TABLE IF NOT EXISTS user_memories (
+ id UUID PRIMARY KEY,
+ tenant_id VARCHAR NOT NULL,
+ user_id VARCHAR NOT NULL,
+ summary TEXT,
+ facts JSON,
+ preferences JSON,
+ open_issues JSON,
+ summary_version INTEGER NOT NULL DEFAULT 1,
+ last_turn_id VARCHAR,
+ expires_at TIMESTAMP,
+ created_at TIMESTAMP NOT NULL DEFAULT NOW(),
+ updated_at TIMESTAMP NOT NULL DEFAULT NOW()
+);
+"""
+
+USER_MEMORIES_INDEXES = """
+CREATE INDEX IF NOT EXISTS ix_user_memories_tenant_user ON user_memories(tenant_id, user_id);
+CREATE INDEX IF NOT EXISTS ix_user_memories_tenant_user_updated ON user_memories(tenant_id, user_id, updated_at);
+"""
+
+USER_MEMORIES_ROLLBACK = """
+DROP TABLE IF EXISTS user_memories;
+"""
+
+
+async def upgrade(conn):
+ """执行迁移"""
+ await conn.execute(USER_MEMORIES_DDL)
+ await conn.execute(USER_MEMORIES_INDEXES)
+
+
+async def downgrade(conn):
+ """回滚迁移"""
+ await conn.execute(USER_MEMORIES_ROLLBACK)
diff --git a/ai-service/scripts/clear_kb_vectors.py b/ai-service/scripts/clear_kb_vectors.py
new file mode 100644
index 0000000..7e148b5
--- /dev/null
+++ b/ai-service/scripts/clear_kb_vectors.py
@@ -0,0 +1,178 @@
+"""
+Script to cleanup vector data for a specific knowledge base.
+Clears the Qdrant collection for the given KB ID, allowing re-indexing.
+"""
+
+import asyncio
+import logging
+import sys
+
+sys.path.insert(0, "Q:\\agentProject\\ai-robot-core\\ai-service")
+
+from app.core.config import get_settings
+from app.core.qdrant_client import get_qdrant_client
+from app.core.database import get_session
+from app.models.entities import KnowledgeBase, Document
+from sqlalchemy import select
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+async def get_knowledge_base_info(kb_id: str) -> dict | None:
+ """Get knowledge base information from database."""
+ async for session in get_session():
+ stmt = select(KnowledgeBase).where(KnowledgeBase.id == kb_id)
+ result = await session.execute(stmt)
+ kb = result.scalar_one_or_none()
+
+ if kb:
+ doc_stmt = select(Document).where(Document.kb_id == kb_id)
+ doc_result = await session.execute(doc_stmt)
+ documents = doc_result.scalars().all()
+
+ return {
+ "id": str(kb.id),
+ "tenant_id": kb.tenant_id,
+ "name": kb.name,
+ "doc_count": len(documents),
+ "document_ids": [str(doc.id) for doc in documents]
+ }
+ return None
+
+
+async def list_kb_collections(tenant_id: str, kb_id: str) -> list[str]:
+ """List all collections that might be related to the KB."""
+ client = await get_qdrant_client()
+ qdrant = await client.get_client()
+
+ collections = await qdrant.get_collections()
+ all_names = [c.name for c in collections.collections]
+
+ safe_tenant = tenant_id.replace('@', '_')
+ safe_kb = kb_id.replace('-', '_')[:8]
+
+ matching = [
+ name for name in all_names
+ if safe_kb in name or kb_id.replace('-', '')[:8] in name.replace('_', '')
+ ]
+
+ return matching
+
+
+async def clear_kb_vector_data(tenant_id: str, kb_id: str, delete_docs: bool = False) -> bool:
+ """
+ Clear vector data for a specific knowledge base.
+
+ Args:
+ tenant_id: Tenant identifier
+ kb_id: Knowledge base ID
+ delete_docs: Whether to also delete document records from database
+
+ Returns:
+ True if successful
+ """
+ client = await get_qdrant_client()
+ qdrant = await client.get_client()
+
+ collection_name = client.get_kb_collection_name(tenant_id, kb_id)
+
+ try:
+ exists = await qdrant.collection_exists(collection_name)
+ if exists:
+ await qdrant.delete_collection(collection_name=collection_name)
+ logger.info(f"Deleted Qdrant collection: {collection_name}")
+ else:
+ logger.info(f"Collection {collection_name} does not exist")
+
+ if delete_docs:
+ async for session in get_session():
+ doc_stmt = select(Document).where(Document.kb_id == kb_id)
+ doc_result = await session.execute(doc_stmt)
+ documents = doc_result.scalars().all()
+
+ for doc in documents:
+ await session.delete(doc)
+
+ stmt = select(KnowledgeBase).where(KnowledgeBase.id == kb_id)
+ result = await session.execute(stmt)
+ kb = result.scalar_one_or_none()
+ if kb:
+ kb.doc_count = 0
+ kb.updated_at = datetime.utcnow()
+
+ await session.commit()
+ logger.info(f"Deleted {len(documents)} document records from database")
+ break
+
+ return True
+
+ except Exception as e:
+ logger.error(f"Failed to clear KB vector data: {e}")
+ return False
+
+
+async def main(kb_id: str, delete_docs: bool = False):
+ """Main function to clear KB vector data."""
+ logger.info(f"Starting cleanup for knowledge base: {kb_id}")
+
+ kb_info = await get_knowledge_base_info(kb_id)
+
+ if not kb_info:
+ logger.error(f"Knowledge base not found: {kb_id}")
+ return False
+
+ logger.info(f"Found knowledge base:")
+ logger.info(f" - ID: {kb_info['id']}")
+ logger.info(f" - Name: {kb_info['name']}")
+ logger.info(f" - Tenant: {kb_info['tenant_id']}")
+ logger.info(f" - Document count: {kb_info['doc_count']}")
+
+ matching_collections = await list_kb_collections(kb_info['tenant_id'], kb_id)
+ if matching_collections:
+ logger.info(f" - Related collections: {matching_collections}")
+
+ print()
+ print("=" * 60)
+ print("WARNING: This will delete all vector data for this knowledge base!")
+ print(f"Collection to delete: kb_{kb_info['tenant_id'].replace('@', '_')}_{kb_id.replace('-', '_')[:8]}")
+ if delete_docs:
+ print("Document records in database will also be deleted!")
+ print("=" * 60)
+ print()
+
+ confirm = input("Continue? (yes/no): ")
+ if confirm.lower() != "yes":
+ print("Cancelled")
+ return False
+
+ success = await clear_kb_vector_data(
+ tenant_id=kb_info['tenant_id'],
+ kb_id=kb_id,
+ delete_docs=delete_docs
+ )
+
+ if success:
+ logger.info(f"Successfully cleared vector data for KB: {kb_id}")
+ logger.info("You can now re-index the knowledge base documents.")
+ else:
+ logger.error(f"Failed to clear vector data for KB: {kb_id}")
+
+ return success
+
+
+if __name__ == "__main__":
+ import argparse
+ from datetime import datetime
+
+ parser = argparse.ArgumentParser(description="Clear vector data for a knowledge base")
+ parser.add_argument("kb_id", help="Knowledge base ID to clear")
+ parser.add_argument("--delete-docs", action="store_true",
+ help="Also delete document records from database")
+
+ args = parser.parse_args()
+
+ asyncio.run(main(args.kb_id, args.delete_docs))
diff --git a/ai-service/svg/kefu.svg b/ai-service/svg/kefu.svg
new file mode 100644
index 0000000..715c3c5
--- /dev/null
+++ b/ai-service/svg/kefu.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/ai-service/svg/user.svg b/ai-service/svg/user.svg
new file mode 100644
index 0000000..c34afaa
--- /dev/null
+++ b/ai-service/svg/user.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/ai-service/svg/用户1.svg b/ai-service/svg/用户1.svg
new file mode 100644
index 0000000..7c83ed9
--- /dev/null
+++ b/ai-service/svg/用户1.svg
@@ -0,0 +1 @@
+
\ No newline at end of file