diff --git a/.env b/.env
index bb113aa9..2f258ca1 100644
--- a/.env
+++ b/.env
@@ -67,3 +67,7 @@ DOCUMENT_CHUNK_OVERLAP=200
 EMBEDDING_MAX_TEXT_LENGTH=3072
 
 DOCKER_BACKEND_DOCKERFILE=Dockerfile.backend.cuda
+
+# Hugging Face configurations
+# Hugging face China mirror website endpoint: https://hf-mirror.com
+HF_ENDPOINT=https://huggingface.co
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 9e6f32bf..7d133e7e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,6 +27,7 @@ data/sqlite/*
 data/uploads/*
 data/progress/*
 lpm_frontend/node_modules
+lpm_frontend/yarn.lock
 
 # L2 Model Storage
 resources/model/output/merged_model/*
diff --git a/Makefile b/Makefile
index 87d76183..58fef485 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,27 @@
-.PHONY: install test format lint all setup start stop restart restart-backend restart-force help docker-build docker-up docker-down docker-build-backend docker-build-frontend docker-restart-backend docker-restart-backend-fast docker-restart-backend-smart docker-restart-frontend docker-restart-all docker-check-cuda docker-use-gpu docker-use-cpu
+.PHONY: install test format lint all setup setup-china start stop restart restart-backend restart-force help docker-build docker-up docker-down docker-build-backend docker-build-frontend docker-restart-backend docker-restart-backend-fast docker-restart-backend-smart docker-restart-frontend docker-restart-all docker-check-cuda docker-use-gpu docker-use-cpu
+
+# Colors for output
+COLOR_CYAN=\033[0;36m
+COLOR_BOLD=\033[1m
+COLOR_GRAY=\033[0;90m
+COLOR_RESET=\033[0m
+
+# Function to display ASCII art only
+define display_ascii
+	@printf "\n"
+	@printf "$(COLOR_CYAN)"
+	@printf ' ███████╗███████╗ ██████╗ ██████╗ ███╗   ██╗██████╗       ███╗   ███╗███████╗\n'
+	@printf ' ██╔════╝██╔════╝██╔════╝██╔═══██╗████╗  ██║██╔══██╗      ████╗ ████║██╔════╝\n'
+	@printf ' ███████╗█████╗  ██║     ██║   ██║██╔██╗ ██║██║  ██║█████╗██╔████╔██║█████╗  \n'
+	@printf ' ╚════██║██╔══╝  ██║     ██║   ██║██║╚██╗██║██║  ██║╚════╝██║╚██╔╝██║██╔══╝  \n'
+	@printf ' ███████║███████╗╚██████╗╚██████╔╝██║ ╚████║██████╔╝      ██║ ╚═╝ ██║███████╗\n'
+	@printf ' ╚══════╝╚══════╝ ╚═════╝ ╚═════╝ ╚═╝  ╚═══╝╚═════╝       ╚═╝     ╚═╝╚══════╝\n'
+	@printf "$(COLOR_RESET)\n"
+	@printf "$(COLOR_BOLD)Second-Me v1.0.0$(COLOR_RESET)\n"
+	@printf "$(COLOR_GRAY)%s$(COLOR_RESET)\n\n" "$$(date)"
+endef
+
+
 
 # Check for GPU flag file and set Docker Compose file accordingly
 ifeq ($(wildcard .gpu_selected),)
@@ -53,15 +76,9 @@ DOCKER_COMPOSE_FILE := docker-compose.yml
 
 # Show help message
 help:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Help $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
 ifeq ($(WINDOWS),1)
-	@echo.
-	@echo  ███████╗███████╗ ██████╗ ██████╗ ███╗   ██╗██████╗       ███╗   ███╗███████╗
-	@echo  ██╔════╝██╔════╝██╔════╝██╔═══██╗████╗  ██║██╔══██╗      ████╗ ████║██╔════╝
-	@echo  ███████╗█████╗  ██║     ██║   ██║██╔██╗ ██║██║  ██║█████╗██╔████╔██║█████╗  
-	@echo  ╚════██║██╔══╝  ██║     ██║   ██║██║╚██╗██║██║  ██║╚════╝██║╚██╔╝██║██╔══╝  
-	@echo  ███████║███████╗╚██████╗╚██████╔╝██║ ╚████║██████╔╝      ██║ ╚═╝ ██║███████╗
-	@echo  ╚══════╝╚══════╝ ╚═════╝ ╚═════╝ ╚═╝  ╚═══╝╚═════╝       ╚═╝     ╚═╝╚══════╝
-	@echo.
 	@echo SECOND-ME MAKEFILE COMMANDS
 	@echo ------------------------------
 	@echo.
@@ -97,18 +114,10 @@ ifeq ($(WINDOWS),1)
 	@echo   make all                   - Run format, lint and test
 else
 	@echo "$(COLOR_CYAN)"
-	@echo ' ███████╗███████╗ ██████╗ ██████╗ ███╗   ██╗██████╗       ███╗   ███╗███████╗'
-	@echo ' ██╔════╝██╔════╝██╔════╝██╔═══██╗████╗  ██║██╔══██╗      ████╗ ████║██╔════╝'
-	@echo ' ███████╗█████╗  ██║     ██║   ██║██╔██╗ ██║██║  ██║█████╗██╔████╔██║█████╗  '
-	@echo ' ╚════██║██╔══╝  ██║     ██║   ██║██║╚██╗██║██║  ██║╚════╝██║╚██╔╝██║██╔══╝  '
-	@echo ' ███████║███████╗╚██████╗╚██████╔╝██║ ╚████║██████╔╝      ██║ ╚═╝ ██║███████╗'
-	@echo ' ╚══════╝╚══════╝ ╚═════╝ ╚═════╝ ╚═╝  ╚═══╝╚═════╝       ╚═╝     ╚═╝╚══════╝'
-	@echo "$(COLOR_RESET)"
-	@echo "$(COLOR_BOLD)Second-Me Makefile Commands$(COLOR_RESET)"
-	@echo "$(COLOR_GRAY)$$(date)$(COLOR_RESET)\n"
 	@echo ""
 	@echo "$(COLOR_GREEN)▶ LOCAL COMMANDS:$(COLOR_RESET)"
-	@echo "  make setup                 - Complete installation"
+	@echo "  make setup                 - Complete installation (global mirrors)"
+	@echo "  make setup-china           - Complete installation (China mirrors)"
 	@echo "  make start                 - Start all services"
 	@echo "  make stop                  - Stop all services"
 	@echo "  make restart               - Restart all services"
@@ -122,7 +131,7 @@ else
 	@echo "  make docker-down           - Stop all Docker containers"
 	@echo "  make docker-build-backend  - Build only backend Docker image"
 	@echo "  make docker-build-frontend - Build only frontend Docker image"
-	@echo "  make docker-restart-backend - Restart only backend container (with rebuild)"
+	@echo "  make docker-restart-backend - Restart only backend container"
 	@echo "  make docker-restart-backend-fast - Restart backend+cuda without rebuilding llama.cpp"
 	@echo "  make docker-restart-frontend - Restart only frontend container"
 	@echo "  make docker-restart-all    - Restart all Docker containers"
@@ -144,8 +153,9 @@ else
 	fi
 endif
 
-# Configuration switchers for Docker
 docker-use-gpu:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Use GPU $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
 	@echo "Switching to GPU configuration..."
 ifeq ($(WINDOWS),1)
 	@echo GPU mode enabled. Docker commands will use docker-compose-gpu.yml
@@ -156,6 +166,8 @@ else
 endif
 
 docker-use-cpu:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Use CPU $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
 	@echo "Switching to CPU-only configuration..."
 ifeq ($(WINDOWS),1)
 	@echo CPU-only mode enabled. Docker commands will use docker-compose.yml
@@ -166,25 +178,44 @@ else
 endif
 
 setup:
-	./scripts/setup.sh
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Setup $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/setup.sh
+
+setup-cn:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Setup (China) $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/setup.sh --china
 
 start:
-	./scripts/start.sh
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Start $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/start.sh
 
 stop:
-	./scripts/stop.sh
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Stop $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/stop.sh
 
 restart:
-	./scripts/restart.sh
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Restart $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/restart.sh
 
 restart-backend:
-	./scripts/restart-backend.sh
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Restart Backend $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/restart-backend.sh
 
 restart-force:
-	./scripts/restart-force.sh
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Restart Force $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/restart-force.sh
 
 status:
-	./scripts/status.sh
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Status $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@./scripts/status.sh
 
 # Docker commands
 # Set Docker environment variable for all Docker commands
@@ -197,6 +228,8 @@ DOCKER_COMPOSE_CMD := $(shell if command -v docker-compose >/dev/null 2>&1; then
 endif
 
 docker-build:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Build $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
 ifeq ($(WINDOWS),1)
 	@echo "Prompting for CUDA preference..."
 	@scripts\prompt_cuda.bat
@@ -205,9 +238,11 @@ else
 	@chmod +x ./scripts/prompt_cuda.sh
 	@./scripts/prompt_cuda.sh
 endif
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build
 
 docker-up:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Up $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
 	@echo "Building and starting Docker containers..."
 ifeq ($(WINDOWS),1)
 	@echo "Prompting for CUDA preference..."
@@ -233,55 +268,71 @@ endif
 	@echo "Check CUDA support with: make docker-check-cuda"
 
 docker-down:
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) down
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Down $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) down
 
 docker-build-backend:
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build backend
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Build Backend $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build backend
 
 docker-build-frontend:
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build frontend
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Build Frontend $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build frontend
 
 # Standard backend restart with complete rebuild
 docker-restart-backend:
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop backend
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f backend
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build backend || { echo "$(COLOR_RED)❌ Backend build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d backend
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Restart Backend $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop backend
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f backend
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build backend || { echo "$(COLOR_RED)❌ Backend build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d backend
 
 
 # Fast backend restart: preserves llama.cpp build
 docker-restart-backend-fast:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Restart Backend (Fast) $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
 	@echo "Smart restarting backend container (preserving llama.cpp build)..."
 	@echo "Stopping backend container..."
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop backend
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop backend
 	@echo "Removing backend container..."
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f backend
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f backend
 	@echo "Building backend image with build-arg to skip llama.cpp build..."
 ifeq ($(wildcard .gpu_selected),)
 	@echo "Using CPU configuration (docker-compose.yml)..."
 else
 	@echo "Using GPU configuration (docker-compose-gpu.yml)..."
 endif
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build --build-arg SKIP_LLAMA_BUILD=true backend || { echo "$(COLOR_RED)❌ Backend build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build --build-arg SKIP_LLAMA_BUILD=true backend || { echo "$(COLOR_RED)❌ Backend build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
 	@echo "Starting backend container..."
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d backend
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d backend
 	@echo "Backend container smart-restarted successfully"
 	@echo "Check CUDA support with: make docker-check-cuda"
 
 docker-restart-frontend:
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop frontend
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f frontend
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build frontend || { echo "$(COLOR_RED)❌ Frontend build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d frontend
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Restart Frontend $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop frontend
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f frontend
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build frontend || { echo "$(COLOR_RED)❌ Frontend build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d frontend
 
 docker-restart-all:
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build || { echo "$(COLOR_RED)❌ Build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
-	$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Restart All $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) stop
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) rm -f
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) build || { echo "$(COLOR_RED)❌ Build failed! Aborting operation...$(COLOR_RESET)"; exit 1; }
+	@$(DOCKER_COMPOSE_CMD) -f $(DOCKER_COMPOSE_FILE) up -d
 
 # New command to check CUDA support in containers
 docker-check-cuda:
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Docker Check CUDA $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
 	@echo "Checking CUDA support in Docker containers..."
 ifeq ($(WINDOWS),1)
 	@echo Running CUDA support check in backend container
@@ -292,15 +343,26 @@ else
 endif
 
 install:
-	poetry install
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Install $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@poetry install
 
 test:
-	poetry run pytest tests
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Test $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@poetry run pytest tests
 
 format:
-	poetry run ruff format lpm_kernel/
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Format $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@poetry run ruff format lpm_kernel/
 
 lint:
-	poetry run ruff check lpm_kernel/
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)Lint $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@poetry run ruff check lpm_kernel/
 
-all: format lint test
\ No newline at end of file
+all: format lint test
+	@$(call display_ascii)
+	@printf "\n$(COLOR_CYAN)==================== [ $(COLOR_BOLD)All $(COLOR_CYAN)] ====================$(COLOR_RESET)\n\n"
+	@echo "All tasks completed!"
\ No newline at end of file
diff --git a/README.md b/README.md
index cbddf335..98cebccb 100644
--- a/README.md
+++ b/README.md
@@ -71,9 +71,9 @@ Star and join us, and you will receive all release notifications from GitHub wit
 
 | Memory (GB) | Docker Deployment (Windows/Linux) | Docker Deployment (Mac) | Integrated Setup (Windows/Linux) | Integrated Setup (Mac) |
 |--------------|-----------------------------|-------------------|--------------------------|----------------|
-| 8            | ~0.8B (example)                | ~0.4B (example)       | ~1.0B (example)              | ~0.6B (example)    |
-| 16           | 1.5B (example)                 | 0.5B (example)        | ~2.0B (example)              | ~0.8B (example)    |
-| 32           | ~2.8B (example)                | ~1.2B (example)       | ~3.5B (example)              | ~1.5B (example)    |
+| 8            | ~0.5B                | ~0.5B       | ~1.5B              | ~0.5B    |
+| 16           | 1.5B                 | 0.5B        | ~3.0B              | ~0.5B    |
+| 32           | ~3B                | ~1.5B       | ~3B              | ~1.5B    |
 
 > **Note**: Models below 0.5B may not provide satisfactory performance for complex tasks. And we're continuously improving cross-platform support - please [submit an issue](https://github.com/mindverse/Second-Me/issues/new) for feedback or compatibility problems on different operating systems.
 
diff --git a/dependencies/graphrag-1.2.1.dev27.tar.gz b/dependencies/graphrag-1.2.1.dev27.tar.gz
index 7375b074..f0f8e9b8 100644
Binary files a/dependencies/graphrag-1.2.1.dev27.tar.gz and b/dependencies/graphrag-1.2.1.dev27.tar.gz differ
diff --git a/dependencies/graphrag-modified.tar.gz b/dependencies/graphrag-modified.tar.gz
index a0797a54..7e6a1136 100644
Binary files a/dependencies/graphrag-modified.tar.gz and b/dependencies/graphrag-modified.tar.gz differ
diff --git a/docs/Configuration Guide for CN zh.md b/docs/Configuration Guide for CN zh.md
new file mode 100644
index 00000000..297479a9
--- /dev/null
+++ b/docs/Configuration Guide for CN zh.md	
@@ -0,0 +1,142 @@
+# 中国大陆地区环境下的项目配置指南
+
+本文档提供了在中国大陆地区使用国内网络环境进行项目配置的方法，适用于 Docker 和非 Docker 用户。
+
+## 非 Docker 用户配置
+
+非 Docker 用户可以直接使用以下命令进行配置：
+
+```bash
+make setup-cn
+```
+
+## Docker 用户配置
+
+Docker 用户需要修改 Docker 的配置文件，以使用国内镜像源。以下是不同操作系统的配置方法：
+
+### Mac 系统
+
+#### 方法一：通过 Docker Desktop 界面配置
+
+1. 点击桌面顶部菜单栏的 Docker 图标
+2. 选择 "Settings" 或 "首选项"
+3. 点击 "Docker Engine" 选项卡
+4. 将以下配置复制到配置编辑器中（注意保留其他已有配置）：
+
+```json
+{
+  "builder": {
+    "gc": {
+      "defaultKeepStorage": "20GB",
+      "enabled": true
+    }
+  },
+  "experimental": false,
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+5. 点击 "Apply & Restart" 按钮应用更改并重启 Docker
+
+#### 方法二：直接修改配置文件
+
+1. 打开终端
+2. 使用文本编辑器打开 Docker 配置文件：
+
+```bash
+mkdir -p ~/.docker
+nano ~/.docker/daemon.json
+```
+
+3. 将以下内容复制到文件中：
+
+```json
+{
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+4. 保存文件（在 nano 中按 Ctrl+O，然后按 Enter，再按 Ctrl+X 退出）
+5. 重启 Docker Desktop
+
+### Linux 系统
+
+1. 创建或编辑 Docker 配置文件：
+
+```bash
+sudo mkdir -p /etc/docker
+sudo nano /etc/docker/daemon.json
+```
+
+2. 将以下内容复制到文件中：
+
+```json
+{
+  "builder": {
+    "gc": {
+      "defaultKeepStorage": "20GB",
+      "enabled": true
+    }
+  },
+  "experimental": false,
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+3. 保存文件并重启 Docker 服务：
+
+```bash
+sudo systemctl daemon-reload
+sudo systemctl restart docker
+```
+
+### Windows 系统
+
+1. 右键点击任务栏中的 Docker 图标
+2. 选择 "Settings"
+3. 点击左侧菜单中的 "Docker Engine"
+4. 也可以直接修改配置文件，位置通常在：`C:\Users\<用户名>\.docker\daemon.json`
+5. 将以下配置复制到配置编辑器中（注意保留其他已有配置）：
+
+```json
+{
+  "builder": {
+    "gc": {
+      "defaultKeepStorage": "20GB",
+      "enabled": true
+    }
+  },
+  "experimental": false,
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+6. 点击 "Apply & Restart" 按钮应用更改并重启 Docker
+
+## 验证配置
+
+配置完成后，可以通过以下命令验证镜像源是否生效：
+
+```bash
+docker info
+```
+
+在输出信息中应该能看到 "Registry Mirrors" 部分列出了你配置的镜像源。
+
+## 常见问题
+
+1. 如果遇到权限问题，请确保你有足够的权限修改 Docker 配置文件。
+
+2. 配置完成后如果 Docker 无法启动，请检查配置文件格式是否正确，确保 JSON 格式有效。
\ No newline at end of file
diff --git a/docs/Configuration Guide for CN.md b/docs/Configuration Guide for CN.md
new file mode 100644
index 00000000..9ae92e3e
--- /dev/null
+++ b/docs/Configuration Guide for CN.md	
@@ -0,0 +1,142 @@
+# Project Configuration Guide for Environments in mainland China
+
+This document provides methods to configure the project using domestic network resources in mainland China, applicable for both Docker and non-Docker users.
+
+## Non-Docker User Configuration
+
+Non-Docker users can directly use the following command for configuration:
+
+```bash
+make setup-cn
+```
+
+## Docker User Configuration
+
+Docker users need to modify the Docker configuration file to use mirrors in China. Below are configuration methods for different operating systems:
+
+### Mac System
+
+#### Method 1: Configure through Docker Desktop UI
+
+1. Click on the Docker icon in the top menu bar
+2. Select "Settings" or "Preferences"
+3. Click on the "Docker Engine" tab
+4. Copy the following configuration to the configuration editor (note to preserve any existing configurations):
+
+```json
+{
+  "builder": {
+    "gc": {
+      "defaultKeepStorage": "20GB",
+      "enabled": true
+    }
+  },
+  "experimental": false,
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+5. Click the "Apply & Restart" button to apply changes and restart Docker
+
+#### Method 2: Directly edit the configuration file
+
+1. Open Terminal
+2. Open the Docker configuration file with a text editor:
+
+```bash
+mkdir -p ~/.docker
+nano ~/.docker/daemon.json
+```
+
+3. Copy the following content to the file:
+
+```json
+{
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+4. Save the file (in nano, press Ctrl+O, then Enter, then Ctrl+X to exit)
+5. Restart Docker Desktop
+
+### Linux System
+
+1. Create or edit the Docker configuration file:
+
+```bash
+sudo mkdir -p /etc/docker
+sudo nano /etc/docker/daemon.json
+```
+
+2. Copy the following content to the file:
+
+```json
+{
+  "builder": {
+    "gc": {
+      "defaultKeepStorage": "20GB",
+      "enabled": true
+    }
+  },
+  "experimental": false,
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+3. Save the file and restart the Docker service:
+
+```bash
+sudo systemctl daemon-reload
+sudo systemctl restart docker
+```
+
+### Windows System
+
+1. Right-click on the Docker icon in the taskbar
+2. Select "Settings"
+3. Click on "Docker Engine" in the left menu
+4. You can also directly modify the configuration file, typically located at: `C:\Users\<username>\.docker\daemon.json`
+5. Copy the following configuration to the configuration editor (note to preserve any existing configurations):
+
+```json
+{
+  "builder": {
+    "gc": {
+      "defaultKeepStorage": "20GB",
+      "enabled": true
+    }
+  },
+  "experimental": false,
+  "registry-mirrors": [
+    "https://docker.1ms.run",
+    "https://docker.xuanyuan.me"
+  ]
+}
+```
+
+6. Click the "Apply & Restart" button to apply changes and restart Docker
+
+## Verify Configuration
+
+After configuration, you can verify if the mirror sources are effective with the following command:
+
+```bash
+docker info
+```
+
+In the output information, you should be able to see the "Registry Mirrors" section listing the mirror sources you configured.
+
+## Common Issues
+
+1. If you encounter permission issues, please ensure you have sufficient permissions to modify the Docker configuration file.
+
+2. If Docker fails to start after configuration, check if the configuration file format is correct and ensure the JSON format is valid.
diff --git a/env.example b/env.example
new file mode 100644
index 00000000..e05e3a53
--- /dev/null
+++ b/env.example
@@ -0,0 +1,35 @@
+# Application Configuration
+APP_NAME=second-me
+APP_VERSION=1.0.0
+APP_WORD="Hello from Second Me"
+LOCAL_APP_PORT=8000
+
+# Database Configuration
+DB_FILE=data/sqlite/lpm.db
+DB_POOL_SIZE=5
+DB_POOL_RECYCLE=3600
+
+# Directory Configuration
+BASE_DIR=/app
+USER_DATA_PIPELINE_DIR=data/pipeline
+
+# Vector Store Settings
+CHROMA_PERSIST_DIRECTORY=data/chroma_db
+CHROMA_COLLECTION_NAME=documents
+
+# Service URLs
+KERNEL2_SERVICE_URL=http://127.0.0.1:8000
+REGISTRY_SERVICE_URL=http://registry:8080
+
+# Email Configuration
+SMTP_HOST=smtp.gmail.com
+SMTP_PORT=587
+SMTP_USER=your-email@gmail.com
+SMTP_PASSWORD=your-app-specific-password
+SMTP_USE_TLS=True
+SENDER_EMAIL=your-email@gmail.com
+NOTIFICATION_EMAIL=your-notification-email@example.com
+
+# Training Configuration
+DOCUMENT_CHUNK_SIZE=1000
+DOCUMENT_CHUNK_OVERLAP=200
\ No newline at end of file
diff --git a/lpm_frontend/src/app/dashboard/playground/chat/page.tsx b/lpm_frontend/src/app/dashboard/playground/chat/page.tsx
index 921f9d53..a137d3fe 100644
--- a/lpm_frontend/src/app/dashboard/playground/chat/page.tsx
+++ b/lpm_frontend/src/app/dashboard/playground/chat/page.tsx
@@ -79,8 +79,8 @@ export default function PlaygroundChat() {
   }, [loadInfo, modelType]);
   const originSettings = useMemo(() => {
     return {
-      enableL0Retrieval: true,
-      enableL1Retrieval: true,
+      enableL0Retrieval: false,
+      enableL1Retrieval: false,
       enableHelperModel: false,
       selectedModel: 'ollama',
       apiKey: 'http://localhost:11434',
diff --git a/lpm_frontend/src/app/dashboard/train/memories/page.tsx b/lpm_frontend/src/app/dashboard/train/memories/page.tsx
index 1d0c15c2..6417550c 100644
--- a/lpm_frontend/src/app/dashboard/train/memories/page.tsx
+++ b/lpm_frontend/src/app/dashboard/train/memories/page.tsx
@@ -137,7 +137,7 @@ export default function TrainPage() {
   };
 
   const handleDeleteMemory = async (id: string, name: string) => {
-    const res = await deleteMemory(name);
+    const res = await deleteMemory(id);
 
     if (res.data.code === 0) {
       setMemories((prev) => {
diff --git a/lpm_frontend/src/components/OnboardingTutorial/index.tsx b/lpm_frontend/src/components/OnboardingTutorial/index.tsx
index afae4aab..90324ca8 100644
--- a/lpm_frontend/src/components/OnboardingTutorial/index.tsx
+++ b/lpm_frontend/src/components/OnboardingTutorial/index.tsx
@@ -25,7 +25,7 @@ export default function OnboardingTutorial({ onComplete, onClose }: OnboardingTu
       image: '/images/step_3.png'
     },
     {
-      title: 'Join AI Network',
+      title: 'Export your Second Me',
       description:
         'Explore interactions between your Second Me and other AI entities in the network.',
       image: '/images/step_4.png'
diff --git a/lpm_frontend/src/components/ThinkingModelModal/index.tsx b/lpm_frontend/src/components/ThinkingModelModal/index.tsx
index dd5f2cdc..d69265a5 100644
--- a/lpm_frontend/src/components/ThinkingModelModal/index.tsx
+++ b/lpm_frontend/src/components/ThinkingModelModal/index.tsx
@@ -26,7 +26,28 @@ const ThinkingModelModal = (props: IProps) => {
   }, [open]);
 
   useEffect(() => {
-    setThinkingModelParams(thinkingModelConfig);
+    // If backend returned data
+    if (thinkingModelConfig) {
+      const updatedParams = {...thinkingModelConfig};
+      
+      // Check fields, if null/undefined then set default values
+      if (updatedParams.thinking_model_name === null || updatedParams.thinking_model_name === undefined) {
+        updatedParams.thinking_model_name = "deepseek-reasoner";
+      }
+      
+      if (updatedParams.thinking_endpoint === null || updatedParams.thinking_endpoint === undefined) {
+        updatedParams.thinking_endpoint = "https://api.deepseek.com";
+      }
+      
+      setThinkingModelParams(updatedParams);
+    } else {
+      // Set complete default values when no configuration exists
+      setThinkingModelParams({
+        thinking_model_name: "deepseek-reasoner",
+        thinking_endpoint: "https://api.deepseek.com",
+        thinking_api_key: ""
+      });
+    }
   }, [thinkingModelConfig]);
 
   const handleUpdate = () => {
@@ -75,6 +96,7 @@ const ThinkingModelModal = (props: IProps) => {
             <Input
               autoComplete="off"
               className="w-full"
+              disabled
               onChange={(e) =>
                 setThinkingModelParams({
                   ...thinkingModelParams,
@@ -109,6 +131,7 @@ const ThinkingModelModal = (props: IProps) => {
           <Input
             autoComplete="off"
             className="w-full"
+            disabled
             onChange={(e) =>
               setThinkingModelParams({
                 ...thinkingModelParams,
diff --git a/lpm_frontend/src/components/train/TrainingConfiguration.tsx b/lpm_frontend/src/components/train/TrainingConfiguration.tsx
index 3e3fe1d1..5a981ced 100644
--- a/lpm_frontend/src/components/train/TrainingConfiguration.tsx
+++ b/lpm_frontend/src/components/train/TrainingConfiguration.tsx
@@ -173,7 +173,7 @@ const TrainingConfiguration: React.FC<TrainingConfigurationProps> = ({
                       e.stopPropagation();
 
                       if (disabledChangeParams) {
-                        message.warning('Cancel the current train in order to configure the model');
+                        message.warning('Cancel the current training run to configure the model');
 
                         return;
                       }
@@ -348,7 +348,7 @@ const TrainingConfiguration: React.FC<TrainingConfigurationProps> = ({
                   value={trainingParams.number_of_epochs}
                 />
                 <div className="text-xs text-gray-500">
-                  Enter an integer between 1 and 10 (recommended: 2)
+                  Enter an integer between 1 and 10 (recommended: 3)
                 </div>
               </div>
               <div className="flex flex-col gap-2">
@@ -460,8 +460,11 @@ const TrainingConfiguration: React.FC<TrainingConfigurationProps> = ({
                   setOpenThinkingModel(true);
                 }}
               >
-                Thinking Model
+                Thinking Model 
               </div>
+              <Tooltip title="Chain of Thought (CoT) enables the model to perform step-by-step reasoning during training. This improves the quality of responses by allowing the model to 'think' through complex questions before answering, resulting in more accurate and logically coherent outputs.">
+                    <QuestionCircleOutlined className="cursor-pointer ml-2" />
+              </Tooltip>
             </div>
           </div>
         </div>
@@ -477,7 +480,13 @@ const TrainingConfiguration: React.FC<TrainingConfigurationProps> = ({
           {trainButtonText === 'Resume Training' && (
             <button
               className={`inline-flex items-center justify-center px-4 py-2 bg-red-600 hover:bg-red-700 border border-transparent text-sm font-medium rounded-md shadow-sm text-white focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500`}
-              onClick={() => handleResetProgress()}
+              onClick={() => {
+                if (!isTraining) {
+                  message.warning('Please do not shutdown your computer during training');
+                }
+
+                handleResetProgress();
+              }}
             >
               <StopIcon className="h-5 w-5 mr-2" />
               Reset Training
@@ -488,7 +497,13 @@ const TrainingConfiguration: React.FC<TrainingConfigurationProps> = ({
             ${!isTraining && !modelConfig?.provider_type ? 'bg-gray-300 hover:bg-gray-400 cursor-not-allowed' : 'cursor-pointer'}
             focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500`}
             disabled={!isTraining && !modelConfig?.provider_type}
-            onClick={handleTrainingAction}
+            onClick={() => {
+              if (!isTraining) {
+                message.warning('Please do not shutdown your computer during training');
+              }
+
+              handleTrainingAction();
+            }}
           >
             {trainButtonIcon}
             {trainButtonText}
diff --git a/lpm_frontend/src/components/train/TrainingProgress.tsx b/lpm_frontend/src/components/train/TrainingProgress.tsx
index 10ccd47b..22a4b697 100644
--- a/lpm_frontend/src/components/train/TrainingProgress.tsx
+++ b/lpm_frontend/src/components/train/TrainingProgress.tsx
@@ -244,6 +244,19 @@ const TrainingProgress = (props: TrainingProgressProps) => {
                             >
                               {step.name}
                             </span>
+
+                            {/* Current downloading file name */}
+                            {step.name === 'Model Download' &&
+                            step.status !== 'completed' &&
+                            !!step.current_file ? (
+                              <span
+                                className={classNames('text-xs ml-2', 'text-blue-600 font-medium')}
+                              >
+                                <span className="mr-2">{'-'}</span>
+                                <span>{step.current_file}</span>
+                              </span>
+                            ) : null}
+
                             {step.completed && step.have_output && (
                               <span
                                 className="text-xs text-blue-500 underline cursor-pointer hover:text-blue-600"
diff --git a/lpm_frontend/src/layouts/DashboardLayout/Menu/index.tsx b/lpm_frontend/src/layouts/DashboardLayout/Menu/index.tsx
index 79996760..0eecc242 100644
--- a/lpm_frontend/src/layouts/DashboardLayout/Menu/index.tsx
+++ b/lpm_frontend/src/layouts/DashboardLayout/Menu/index.tsx
@@ -277,7 +277,7 @@ const Menu = () => {
               )}
               onClick={() => {
                 if (disabledChangeParams) {
-                  message.warning('Cancel the current train in order to configure the model');
+                  message.warning('Cancel the current training run to configure the model');
 
                   return;
                 }
diff --git a/lpm_frontend/src/service/memory.ts b/lpm_frontend/src/service/memory.ts
index ebf46557..9adb9b5e 100644
--- a/lpm_frontend/src/service/memory.ts
+++ b/lpm_frontend/src/service/memory.ts
@@ -54,9 +54,9 @@ export const uploadMemory = (formData: FormData) => {
   });
 };
 
-export const deleteMemory = (name: string) => {
+export const deleteMemory = (id: string) => {
   return Request<EmptyResponse>({
     method: 'delete',
-    url: `/api/memories/file/${name}`
+    url: `/api/memories/file/id/${id}`
   });
 };
diff --git a/lpm_frontend/src/service/train.ts b/lpm_frontend/src/service/train.ts
index 132fed98..2205170a 100644
--- a/lpm_frontend/src/service/train.ts
+++ b/lpm_frontend/src/service/train.ts
@@ -23,6 +23,7 @@ export type StageStatus = 'pending' | 'in_progress' | 'completed' | 'failed' | '
 
 interface TrainStep {
   completed: boolean;
+  current_file?: string;
   name: string;
   status: StepStatus;
   path?: string;
diff --git a/lpm_kernel/L2/data_pipeline/data_prep/diversity/diversity_data_generator.py b/lpm_kernel/L2/data_pipeline/data_prep/diversity/diversity_data_generator.py
index ccc76f72..523f5dba 100644
--- a/lpm_kernel/L2/data_pipeline/data_prep/diversity/diversity_data_generator.py
+++ b/lpm_kernel/L2/data_pipeline/data_prep/diversity/diversity_data_generator.py
@@ -72,7 +72,7 @@ def __init__(self, preference_language: str, is_cot: bool = True):
             self.model_name = user_llm_config.thinking_model_name
             self.api_key = user_llm_config.thinking_api_key
             self.base_url = user_llm_config.thinking_endpoint
-            if self.model_name.startswith("deepseek"):
+            if "deepseek" in self.model_name:
                 self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
             else:
                 logger.error(f"Error model_name, longcot data generating model_name: deepseek series")
@@ -496,11 +496,7 @@ def _Q_generate(self, cluster: dict, question_type: str, templater,
                 model=self.model_name,
                 messages=messages,
             )
-            if self.is_cot:
-                response_message = response.choices[0].message
-                res = "<think>" + response_message.reasoning_content + "</think>" + response_message.content
-            else:
-                res = response.choices[0].message.content
+            res = response.choices[0].message.content
         except Exception as e:
             logging.error(traceback.format_exc())
         
@@ -547,12 +543,39 @@ def _A_generate(self, cluster: dict, question: str, question_type: str,
                 model=self.model_name,
                 messages=messages,
             )
+            result = response.choices[0].message
             if self.is_cot:
-                response_message = response.choices[0].message
-                res = "<think>" + response_message.reasoning_content + "</think>" + response_message.content
+                if hasattr(result, 'reasoning_content'):
+                    if not result.reasoning_content.startswith("<think>"):
+                        reasoning_content = f"<think>{result.reasoning_content}"
+                        if not result.reasoning_content.endswith("</think>"):
+                            reasoning_content += "</think>"
+                    else:
+                        reasoning_content = result.reasoning_content
+                    if not result.content.strip().startswith("<answer>"):
+                        content = f"<answer>{result.content.strip()}"
+                        if not result.content.strip().endswith("</answer>"):
+                            content += "</answer>"
+                    else:
+                        content = result.content
+                else:
+                    if not result.content.startswith("<answer>"):
+                        reasoning_content, content = result.content.split("</think>")
+                        if result.content.startswith("<think>"):
+                            reasoning_content += "</think>"
+                        else:
+                            reasoning_content = f"<think>{reasoning_content}</think>"
+                        if not content.strip().startswith("<answer>"):
+                            content = f"<answer>{content.strip()}</answer>"
+                        else:
+                            content = content.strip()
+                    else:
+                        reasoning_content = ""
+                        content = result.content
+                final_result = reasoning_content + "\n" + content
             else:
-                res = response.choices[0].message.content
+                final_result = result.content
         except Exception as e:
             logging.error(traceback.format_exc())
             
-        return res, answer_type
\ No newline at end of file
+        return final_result, answer_type
\ No newline at end of file
diff --git a/lpm_kernel/L2/data_pipeline/data_prep/preference/preference_QA_generate.py b/lpm_kernel/L2/data_pipeline/data_prep/preference/preference_QA_generate.py
index c7a25ce0..fdbfaf81 100644
--- a/lpm_kernel/L2/data_pipeline/data_prep/preference/preference_QA_generate.py
+++ b/lpm_kernel/L2/data_pipeline/data_prep/preference/preference_QA_generate.py
@@ -86,7 +86,7 @@ def __init__(self, filename: str, bio: str, preference_language: str, is_cot: bo
             self.model_name = user_llm_config.thinking_model_name
             self.api_key = user_llm_config.thinking_api_key
             self.base_url = user_llm_config.thinking_endpoint
-            if self.model_name.startswith("deepseek"):
+            if "deepseek" in self.model_name:
                 self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
             else:
                 logger.error(f"Error model_name, longcot data generating model_name: deepseek series")
@@ -102,7 +102,7 @@ def __init__(self, filename: str, bio: str, preference_language: str, is_cot: bo
         self.data_synthesis_mode = os.environ.get("DATA_SYNTHESIS_MODE", "low")
 
 
-    def generate_response(self, sys: str, prompt: str) -> str:
+    def generate_response(self, sys: str, prompt: str, mode: str) -> str:
         """Generate a response using the OpenAI / DeepSeek API.
         
         Args:
@@ -112,7 +112,7 @@ def generate_response(self, sys: str, prompt: str) -> str:
         Returns:
             The generated response text or None if an error occurred.
         """
-        def get_remote_response(sys: str, prompt: str) -> str:
+        def get_remote_response(sys: str, prompt: str, mode: str) -> str:
             """Get response from OpenAI / DeepSeek API.
             
             Args:
@@ -130,11 +130,39 @@ def get_remote_response(sys: str, prompt: str) -> str:
                         ],
                     model=self.model_name,
                 )
-                response_message = res.choices[0].message
-                if self.is_cot:
-                    return "<think>" + response_message.reasoning_content + "</think>" + response_message.content
+                result = res.choices[0].message
+                if self.is_cot and mode == "answer":
+                    if hasattr(result, 'reasoning_content'):
+                        if not result.reasoning_content.startswith("<think>"):
+                            reasoning_content = f"<think>{result.reasoning_content}"
+                            if not result.reasoning_content.endswith("</think>"):
+                                reasoning_content += "</think>"
+                        else:
+                            reasoning_content = result.reasoning_content
+                        if not result.content.strip().startswith("<answer>"):
+                            content = f"<answer>{result.content.strip()}"
+                            if not result.content.strip().endswith("</answer>"):
+                                content += "</answer>"
+                        else:
+                            content = result.content
+                    else:
+                        if not result.content.startswith("<answer>"):
+                            reasoning_content, content = result.content.split("</think>")
+                            if result.content.startswith("<think>"):
+                                reasoning_content += "</think>"
+                            else:
+                                reasoning_content = f"<think>{reasoning_content}</think>"
+                            if not content.strip().startswith("<answer>"):
+                                content = f"<answer>{content.strip()}</answer>"
+                            else:
+                                content = content.strip()
+                        else:
+                            reasoning_content = ""
+                            content = result.content
+                    final_result = reasoning_content + "\n" + content
                 else:
-                    return response_message.content
+                    final_result = result.content
+                return final_result
             except Exception as e:
                 logger.error(traceback.format_exc())
             return None
@@ -142,16 +170,9 @@ def get_remote_response(sys: str, prompt: str) -> str:
         
         try:
             with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
-                future = executor.submit(
-                    self.client.chat.completions.create,
-                    model=self.model_name,
-                    messages=[
-                        {"role": "system", "content": sys},
-                        {"role": "user", "content": prompt},
-                    ],
-                )
+                future = executor.submit(get_remote_response, sys, prompt, mode)
                 response = future.result()
-                return response.choices[0].message.content
+                return response
         except Exception as e:
             logger.error(f"Error generating response: {e}")
             return None
@@ -255,10 +276,13 @@ def process_clusters(self, output_filename: str) -> None:
                     prompt_question_template.format(
                         bio=self.bio, chunks_concat=chunk_concat
                     ),
+                    mode="question"
                 )
                 if self.is_cot:
                     question_match = re.search(r"<question>(.*?)</question>", gen_question, re.DOTALL)
                     gen_question = question_match.group(1).strip() if question_match else gen_question
+                    if "<think>" in gen_question:
+                        gen_question = gen_question.split("</think>")[1].strip()
             except Exception as e:
                 logger.error(traceback.format_exc())
                 continue
@@ -268,6 +292,7 @@ def process_clusters(self, output_filename: str) -> None:
                     prompt_answer_template.format(
                         question=gen_question, bio=self.bio, chunks_concat=chunk_concat
                     ),
+                    mode="answer"
                 )
             except Exception as e:
                 logger.error(traceback.format_exc())
@@ -335,6 +360,7 @@ def _generate_multiple_questions(self, contents: list, chunk_concat: str) -> Non
                     prompt_question_template.format(
                         bio=self.bio, chunks_concat=chunk_concat
                     ),
+                    mode="question"
                 )
                 if self.is_cot:
                     question_match = re.search(r"<question>(.*?)</question>", gen_question, re.DOTALL)
@@ -344,6 +370,7 @@ def _generate_multiple_questions(self, contents: list, chunk_concat: str) -> Non
                     prompt_answer_template.format(
                         question=gen_question, chunks_concat=chunk_concat, bio=self.bio
                     ),
+                    mode="answer"
                 )
             except Exception as e:
                 logger.error(traceback.format_exc())
diff --git a/lpm_kernel/L2/data_pipeline/data_prep/selfqa/selfqa_generator.py b/lpm_kernel/L2/data_pipeline/data_prep/selfqa/selfqa_generator.py
index 8adc1f45..a4bf10b2 100644
--- a/lpm_kernel/L2/data_pipeline/data_prep/selfqa/selfqa_generator.py
+++ b/lpm_kernel/L2/data_pipeline/data_prep/selfqa/selfqa_generator.py
@@ -86,7 +86,7 @@ def __init__(
             self.model_name = user_llm_config.thinking_model_name
             self.api_key = user_llm_config.thinking_api_key
             self.base_url = user_llm_config.thinking_endpoint
-            if self.model_name.startswith("deepseek"):
+            if "deepseek" in self.model_name:
                 self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
             else:
                 logger.error(f"Error model_name, longcot data generating model_name: deepseek series")
@@ -241,11 +241,39 @@ def get_remote_response(self, messages: list) -> str:
                 messages=messages,
                 model=self.model_name,
             )
-            response_message = res.choices[0].message
+            result = res.choices[0].message
             if self.is_cot:
-                return "<think>" + response_message.reasoning_content + "</think>" + response_message.content
+                if hasattr(result, 'reasoning_content'):
+                    if not result.reasoning_content.startswith("<think>"):
+                        reasoning_content = f"<think>{result.reasoning_content}"
+                        if not result.reasoning_content.endswith("</think>"):
+                            reasoning_content += "</think>"
+                    else:
+                        reasoning_content = result.reasoning_content
+                    if not result.content.strip().startswith("<answer>"):
+                        content = f"<answer>{result.content.strip()}"
+                        if not result.content.strip().endswith("</answer>"):
+                            content += "</answer>"
+                    else:
+                        content = result.content
+                else:
+                    if not result.content.startswith("<answer>"):
+                        reasoning_content, content = result.content.split("</think>")
+                        if result.content.startswith("<think>"):
+                            reasoning_content += "</think>"
+                        else:
+                            reasoning_content = f"<think>{reasoning_content}</think>"
+                        if not content.strip().startswith("<answer>"):
+                            content = f"<answer>{content.strip()}</answer>"
+                        else:
+                            content = content.strip()
+                    else:
+                        reasoning_content = ""
+                        content = result.content
+                final_result = reasoning_content + "\n" + content
             else:
-                return response_message.content
+                final_result = result.content
+            return final_result
         except Exception as e:
             logger.error(traceback.format_exc())
         return None
diff --git a/lpm_kernel/L2/train.py b/lpm_kernel/L2/train.py
index 54a00ee6..cff4a5bf 100644
--- a/lpm_kernel/L2/train.py
+++ b/lpm_kernel/L2/train.py
@@ -22,6 +22,7 @@
 from transformers import HfArgumentParser, TrainingArguments, set_seed
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from trl import SFTTrainer, SFTConfig, DataCollatorForCompletionOnlyLM
+from lpm_kernel.configs.logging import TRAIN_LOG_FILE
 
 # Local imports
 from lpm_kernel.L2.utils import (
@@ -53,17 +54,52 @@ class DebugCallback(transformers.TrainerCallback):
     def __init__(self):
         self.total_time = 0
         self.last_time = time.time()
-        
+        self.total_steps = 0
+        self.current_step = 0
+        self.progress_percentage = 0.0
+        self.progress_file = os.path.join(os.path.dirname(TRAIN_LOG_FILE), "train_progress.json")
+
+    def on_train_begin(self, args, state, control, **kwargs):
+        self.total_steps = state.max_steps
+        progress_data = {
+            "percentage": 0.0,
+            "current_step": 0,
+            "total_steps": self.total_steps
+        }
+        self._write_progress_to_file(progress_data)
+        logger.info(f"Training started. Total steps: {self.total_steps}")
+
+    def _write_progress_to_file(self, progress_data):
+        try:
+            import json
+            with open(self.progress_file, 'w', encoding='utf-8') as f:
+                json_str = json.dumps(progress_data)
+                f.write(json_str)
+        except Exception as e:
+            logger.error(f"Error writing progress to file: {str(e)}")
+
     def on_step_end(self, args, state, control, **kwargs):
-        if state.global_step % 10 == 0:
-            current_time = time.time()
-            step_time = current_time - self.last_time
-            self.total_time += step_time
-            self.last_time = current_time
-            
-            # Log step time and training progress
-            logger.info(f"Step {state.global_step}: {step_time:.2f}s - Total training time: {self.total_time:.2f}s")
+
+        current_time = time.time()
+        step_time = current_time - self.last_time
+        self.total_time += step_time
+        self.last_time = current_time
+        self.current_step = state.global_step
             
+        # Log step time and training progress
+        logger.info(f"Step {state.global_step}: {step_time:.2f}s - Total training time: {self.total_time:.2f}s")
+
+        self.progress_percentage = min(100.0, (self.current_step / self.total_steps) * 100)
+        progress_data = {
+            "percentage": self.progress_percentage,
+            "current_step": self.current_step,
+            "total_steps": self.total_steps
+        }
+        self._write_progress_to_file(progress_data)
+        
+        logger.info(
+            f"Updated train_progress: percentage={self.progress_percentage}, current_step={self.current_step}, total_steps={self.total_steps}")
+
     def on_epoch_end(self, args, state, control, **kwargs):
         logger.info(f"Epoch {state.epoch} completed")
 
diff --git a/lpm_kernel/L2/utils.py b/lpm_kernel/L2/utils.py
index 5105c7bc..6a0a00ab 100644
--- a/lpm_kernel/L2/utils.py
+++ b/lpm_kernel/L2/utils.py
@@ -676,7 +676,8 @@ def download_file_with_progress(filename):
             
             try:
                 # Build the download URL
-                url = f"https://huggingface.co/{hf_model_name}/resolve/main/{filename}"
+                hf_endpoint = os.environ.get("HF_ENDPOINT")
+                url = f"{hf_endpoint}/{hf_model_name}/resolve/main/{filename}"
                 
                 # Get file size
                 response = requests.head(url)
@@ -703,8 +704,8 @@ def download_file_with_progress(filename):
                     def progress_callback(current, total):
                         progress_bar.update(current - progress_bar.n)
                         
-                        # Log progress every ~1MB
-                        if current % (1024 * 1024) < 8192:
+                        # Log progress every ~10MB
+                        if current % (1024 * 1024 * 10) < 8192:
                             if total and total > 0:
                                 percent = current / total * 100
                                 logger.info(f"File {filename}: Downloaded {current/1024/1024:.2f} MB / {total/1024/1024:.2f} MB ({percent:.2f}%)")
diff --git a/lpm_kernel/api/domains/documents/routes.py b/lpm_kernel/api/domains/documents/routes.py
index d83b1ef3..fb9b40c7 100644
--- a/lpm_kernel/api/domains/documents/routes.py
+++ b/lpm_kernel/api/domains/documents/routes.py
@@ -41,6 +41,41 @@ def list_documents():
         return jsonify(APIResponse.error(message=f"Error listing documents: {str(e)}"))
 
 
+@document_bp.route("/documents/page", methods=["GET"])
+def page_documents():
+    """
+    List documents with pagination
+    Query Parameters:
+        page (int): Page number
+        page_size (int): Number of items per page
+        include_l0 (bool): Whether to include L0 data (chunks and embeddings)
+    """
+    try:
+        page = int(request.args.get("page", 1))
+        page_size = int(request.args.get("page_size", 100))
+        include_l0 = request.args.get("include_l0", "").lower() == "true"
+        if include_l0:
+            documents = document_service.page_documents_with_l0(page, page_size)
+            if not documents:
+                return jsonify(
+                    APIResponse.error(message="No documents found for the given page")
+                )
+            return jsonify(APIResponse.success(data=documents))
+        else:
+            documents = document_service.page_documents(page, page_size)
+            if not documents:
+                return jsonify(
+                    APIResponse.error(message="No documents found for the given page")
+                )
+            return jsonify(
+                APIResponse.success(data=[doc.to_dict() for doc in documents])
+            )
+
+    except Exception as e:
+        logger.error(f"Error listing documents: {str(e)}", exc_info=True)
+        return jsonify(APIResponse.error(message=f"Error listing documents: {str(e)}"))
+
+
 @document_bp.route("/documents/scan", methods=["POST"])
 @validate()
 def scan_documents():
diff --git a/lpm_kernel/api/domains/memories/routes.py b/lpm_kernel/api/domains/memories/routes.py
index 8dbf5b1c..ffe17928 100644
--- a/lpm_kernel/api/domains/memories/routes.py
+++ b/lpm_kernel/api/domains/memories/routes.py
@@ -91,25 +91,83 @@ def upload_file():
         return APIResponse.error(message=f"Internal server error: {str(e)}", code=500)
 
 
-@memories_bp.route("/api/memories/file/<filename>", methods=["DELETE"])
-def delete_file(filename):
+@memories_bp.route("/api/memories/file/id/<document_id>", methods=["DELETE"])
+def delete_file_by_id(document_id):
     """
-    File deletion API
+    File deletion API by document ID
     """
     try:
-        logger.info(f"Starting to process file deletion request: {filename}")
-        
-        # Call DocumentService to delete file
+        logger.info(f"Starting to process file deletion request by ID: {document_id}")
+
+        # Call DocumentService to delete file by ID
         document_service = DocumentService()
-        result = document_service.delete_file_by_name(filename)
-        
+        result = document_service.delete_file_by_id(document_id)
+
         if result:
-            logger.info(f"File deleted successfully: {filename}")
-            return APIResponse.success(message=f"File '{filename}' has been successfully deleted")
+            logger.info(f"File with ID {document_id} deleted successfully")
+            return APIResponse.success(
+                message=f"File with ID {document_id} has been successfully deleted"
+            )
+        else:
+            logger.warning(
+                f"File with ID {document_id} does not exist or deletion failed"
+            )
+            return APIResponse.error(
+                message=f"File with ID {document_id} does not exist or cannot be deleted",
+                code=404,
+            )
+
+    except Exception as e:
+        logger.error(
+            f"Error occurred while deleting file by ID: {str(e)}", exc_info=True
+        )
+        return APIResponse.error(message=f"Internal server error: {str(e)}", code=500)
+
+
+@memories_bp.route("/api/memories/file/batch-delete", methods=["POST"])
+def batch_delete_files():
+    """
+    Batch file deletion API
+    """
+    try:
+        data = request.get_json()
+        if not data or not isinstance(data.get("document_ids"), list):
+            return APIResponse.error(
+                message="Invalid request format. Expected 'document_ids' list.",
+                code=400,
+            )
+
+        document_ids = data["document_ids"]
+        logger.info(f"Starting batch deletion for {len(document_ids)} files")
+
+        document_service = DocumentService()
+        results = {"successful": [], "failed": []}
+
+        for doc_id in document_ids:
+            try:
+                if document_service.delete_file_by_id(doc_id):
+                    results["successful"].append(doc_id)
+                    logger.info(f"Successfully deleted file with ID: {doc_id}")
+                else:
+                    results["failed"].append(doc_id)
+                    logger.warning(f"Failed to delete file with ID: {doc_id}")
+            except Exception as e:
+                results["failed"].append(doc_id)
+                logger.error(f"Error deleting file with ID {doc_id}: {str(e)}")
+
+        success_count = len(results["successful"])
+        failed_count = len(results["failed"])
+
+        if failed_count == 0:
+            return APIResponse.success(
+                data=results, message=f"Successfully deleted all {success_count} files"
+            )
         else:
-            logger.warning(f"File does not exist or deletion failed: {filename}")
-            return APIResponse.error(message=f"File '{filename}' does not exist or cannot be deleted", code=404)
-            
+            return APIResponse.success(
+                data=results,
+                message=f"Deleted {success_count} files, failed to delete {failed_count} files",
+            )
+
     except Exception as e:
-        logger.error(f"Error occurred while deleting file: {str(e)}", exc_info=True)
+        logger.error(f"Error during batch file deletion: {str(e)}", exc_info=True)
         return APIResponse.error(message=f"Internal server error: {str(e)}", code=500)
diff --git a/lpm_kernel/api/domains/trainprocess/train_progress.py b/lpm_kernel/api/domains/trainprocess/train_progress.py
index ef42fdd1..8f13a774 100644
--- a/lpm_kernel/api/domains/trainprocess/train_progress.py
+++ b/lpm_kernel/api/domains/trainprocess/train_progress.py
@@ -21,7 +21,8 @@ def __init__(self):
                             "completed": False,
                             "status": "pending",
                             "have_output": False,
-                            "path": None
+                            "path": None,
+                            "current_file":None
                         }
                     ]
                 },
@@ -168,13 +169,14 @@ def __init__(self):
                 step_name = step["name"].lower().replace(" ", "_")
                 self.steps_map[stage_name][step_name] = step
 
-    def update_progress(self, stage: str, step: str, currentStepStatus: Union[Status, str], stageProgress: Optional[float] = None):
+    def update_progress(self, stage: str, step: str, currentStepStatus: Union[Status, str], stageProgress: Optional[float] = None, file_name: Optional[str] = None):
         """Update progress status
         Args:
             stage: Stage key (snake_case format)
             step: Step key (snake_case format)
             currentStepStatus: Status (enum or string)
             stageProgress: Optional progress value (0-100)
+            file_name: Optional name of the file being processed (for download tracking)
         """
         stage_data = self.stage_map[stage]
         status_value = currentStepStatus.value if isinstance(currentStepStatus, Status) else currentStepStatus
@@ -184,6 +186,10 @@ def update_progress(self, stage: str, step: str, currentStepStatus: Union[Status
         step_data["status"] = status_value
         step_data["completed"] = status_value == "completed"
         
+        # Update file name if provided (for tracking downloads)
+        if file_name is not None:
+            step_data["current_file"] = file_name
+        
         # Update stage progress
         self._update_stage_progress(stage_data, stageProgress)
         
diff --git a/lpm_kernel/api/domains/trainprocess/training_params_manager.py b/lpm_kernel/api/domains/trainprocess/training_params_manager.py
index c6871542..95af9649 100644
--- a/lpm_kernel/api/domains/trainprocess/training_params_manager.py
+++ b/lpm_kernel/api/domains/trainprocess/training_params_manager.py
@@ -22,7 +22,7 @@ class TrainingParamsManager:
         "learning_rate": 1e-4,
         "number_of_epochs": 3,
         "concurrency_threads": 2,
-        "data_synthesis_mode": "low",
+        "data_synthesis_mode": "high",
         "use_cuda": False,  # Default to using CUDA when available
         "is_cot": False
     }
diff --git a/lpm_kernel/api/domains/trainprocess/trainprocess_service.py b/lpm_kernel/api/domains/trainprocess/trainprocess_service.py
index 9bfc318f..3e9408ef 100644
--- a/lpm_kernel/api/domains/trainprocess/trainprocess_service.py
+++ b/lpm_kernel/api/domains/trainprocess/trainprocess_service.py
@@ -30,6 +30,7 @@
 from lpm_kernel.common.repository.database_session import DatabaseSession
 from lpm_kernel.api.domains.kernel.routes import store_l1_data
 from lpm_kernel.api.domains.trainprocess.L1_exposure_manager import output_files, query_l1_version_data, read_file_content
+from lpm_kernel.services.email_service import EmailService
 import gc
 import subprocess
 from lpm_kernel.configs.logging import get_train_process_logger, TRAIN_LOG_FILE
@@ -71,6 +72,9 @@ def __init__(self, current_model_name: str):
                 "config_path": None
             }
             self.l2_data_prepared = False
+            
+            # Initialize email service
+            self.email_service = EmailService()
         
         # Update model name and progress instance if model name changes
         if current_model_name != self.model_name:
@@ -280,13 +284,37 @@ def model_download(self) -> bool:
                 self.progress.mark_step_status(ProcessStep.MODEL_DOWNLOAD, Status.COMPLETED)
                 return True
             else:
-                logger.error(f"Model path does not exist after download: {model_path}")
+                error_msg = f"Model path does not exist after download: {model_path}"
+                logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.MODEL_DOWNLOAD, Status.FAILED)
+                
+                # Send email notification for model download failure
+                try:
+                    self.email_service.send_training_error_notification(
+                        model_name=self.model_name,
+                        error_message=error_msg,
+                        step_context="Model Download - Path Verification"
+                    )
+                except Exception as email_error:
+                    logger.error(f"Failed to send error notification email: {email_error}")
+                
                 return False
 
         except Exception as e:
-            logger.error(f"Download model failed: {str(e)}")
+            error_msg = f"Download model failed: {str(e)}"
+            logger.error(error_msg)
             self.progress.mark_step_status(ProcessStep.MODEL_DOWNLOAD, Status.FAILED)
+            
+            # Send email notification for model download exception
+            try:
+                self.email_service.send_training_error_notification(
+                    model_name=self.model_name,
+                    error_message=error_msg,
+                    step_context="Model Download - Exception"
+                )
+            except Exception as email_error:
+                logger.error(f"Failed to send error notification email: {email_error}")
+            
             return False
 
     def map_your_entity_network(self)->bool:
@@ -527,6 +555,40 @@ def train(self) -> bool:
             # Mark step as in progress
             self.progress.mark_step_status(ProcessStep.TRAIN, Status.IN_PROGRESS)
             
+            # Send training start notification
+            notification_email = self.config.get('NOTIFICATION_EMAIL')
+            if notification_email:
+                try:
+                    # Get training parameters for notification
+                    params_manager = TrainingParamsManager()
+                    training_params = params_manager.get_latest_training_params()
+                    
+                    # Collect system information
+                    system_info = {}
+                    try:
+                        process = psutil.Process(os.getpid())
+                        memory_info = process.memory_info()
+                        system_info = {
+                            "Memory Usage": f"{memory_info.rss / 1024 / 1024:.2f} MB",
+                            "CPU Usage": f"{process.cpu_percent(interval=0.1):.1f}%",
+                            "Process ID": os.getpid(),
+                            "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
+                        }
+                    except Exception as e:
+                        logger.warning(f"Could not collect system info for start notification: {str(e)}")
+                    
+                    # Send start notification with training parameters
+                    self.email_service.send_training_start_notification(
+                        self.model_name,
+                        notification_email,
+                        training_params,
+                        system_info=system_info
+                    )
+                    logger.info(f"Training start notification sent to {notification_email}")
+                except Exception as e:
+                    logger.warning(f"Could not send training start notification: {str(e)}")
+                    # Continue with training even if notification fails
+            
             # Get paths for the model
             paths = self._get_model_paths(self.model_name)
             
@@ -573,8 +635,17 @@ def train(self) -> bool:
             training_result = self._start_training(script_path, log_path)
             
             if not training_result:
-                logger.error("Training process failed to start")
+                error_msg = "Training process failed to start"
+                logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.TRAIN, Status.FAILED)
+                # Send email notification for training failure
+                notification_email = self.config.get('NOTIFICATION_EMAIL')
+                if notification_email:
+                    self.email_service.send_training_error_notification(
+                        self.model_name,
+                        error_msg,
+                        notification_email
+                    )
                 return False
                 
             # Wait for the monitoring thread to finish
@@ -587,6 +658,42 @@ def train(self) -> bool:
                     error_msg = f"Training failed: {self.training_result.get('error', 'Unknown error')}"
                     logger.error(error_msg)
                     self.progress.mark_step_status(ProcessStep.TRAIN, Status.FAILED)
+                    # Send email notification for training failure with enhanced error context
+                    notification_email = self.config.get('NOTIFICATION_EMAIL')
+                    if notification_email:
+                        # Collect error context information
+                        error_context = {
+                            "Process Step": "TRAIN",
+                            "Model Name": self.model_name,
+                            "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                            "Process ID": self.current_pid if hasattr(self, 'current_pid') else "N/A",
+                            "Memory Usage": f"{psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024:.2f} MB"
+                        }
+                        
+                        # Add training parameters to error context if available
+                        try:
+                            params_manager = TrainingParamsManager()
+                            training_params = params_manager.get_latest_training_params()
+                            if training_params:
+                                error_context.update({
+                                    "Learning Rate": training_params.get("learning_rate", "N/A"),
+                                    "Epochs": training_params.get("number_of_epochs", "N/A"),
+                                    "Concurrency Threads": training_params.get("concurrency_threads", "N/A"),
+                                    "Data Synthesis Mode": training_params.get("data_synthesis_mode", "N/A"),
+                                    "Use CUDA": training_params.get("use_cuda", False)
+                                })
+                        except Exception as e:
+                            logger.warning(f"Could not add training parameters to error context: {str(e)}")
+                        
+                        # Send enhanced error notification
+                        self.email_service.send_training_error_notification(
+                            self.model_name,
+                            error_msg,
+                            notification_email,
+                            log_file_path=TRAIN_LOG_FILE if os.path.exists(TRAIN_LOG_FILE) else None,
+                            error_context=error_context,
+                            max_log_lines=100  # Include more log lines for better context
+                        )
                     return False
         
             return True
@@ -737,65 +844,128 @@ def _start_training(self, script_path, log_path):
     def _monitor_training_progress(self, log_file) -> bool:
         """Monitor training progress"""
         try:
-            # Initialize last_position to the end of file to only process new content
-            try:
-                with open(log_file, 'r') as f:
-                    f.seek(0, 2)  # Move to the end of file
-                    last_position = f.tell()
-            except FileNotFoundError:
-                # If file doesn't exist yet, start from beginning when it's created
-                last_position = 0
-            
-            # variable to track training status
-            total_steps = None
-            current_step = 0
             last_update_time = time.time()
-            training_started = False
-            
+            progress_file = os.path.join(os.path.dirname(TRAIN_LOG_FILE), "train_progress.json")
             while True:
                 try:
-                    # read new log content
-                    with open(log_file, 'r') as f:
-                        f.seek(last_position)
-                        new_lines = f.readlines()
-                        last_position = f.tell()
-                        
-                    for line in new_lines:
-                        line = line.strip()
-                        # Check if training has started
-                        if not training_started:
-                            if "***** Running training *****" in line:
-                                training_started = True
-                                logger.info("Training started")
-                            continue  # Skip progress matching until training starts
-                        
-                        progress_match = re.search(r"(\d+)%\|[^|]+\| (\d+)/(\d+)", line)
-                        if progress_match and len(progress_match.groups()) == 3:
-                            percentage = int(progress_match.group(1))
-                            current_step = int(progress_match.group(2))
-                            total_steps = int(progress_match.group(3))
-                            
-                            # Update progress at most once per second
-                            current_time = time.time()
-                            if current_time - last_update_time >= 1.0:
-                                # logger.info(f"Training progress: {percentage}% ({current_step}/{total_steps})")
-                                if percentage == 100.0:
-                                    self.progress.mark_step_status(ProcessStep.TRAIN, Status.COMPLETED)
-                                    return True
-                                self._update_progress("training_to_create_second_me", "train", percentage, f"Current step: {current_step}/{total_steps}")
-                                last_update_time = current_time
+                    percentage = 0.0
+                    current_step = 0
+                    total_steps = 100
                     
-                        # Check if we have exited the training record interval
-                        if "=== Training Ended ===" in line:
-                            # in_training_section = False  # Exit training record interval
-                            logger.info("Exited training record interval")
+                    if os.path.exists(progress_file):
+                        try:
+                            import json
+                            with open(progress_file, 'r') as f:
+                                progress_data = json.load(f)
+                                percentage = progress_data.get("percentage", 0.0)
+                                current_step = progress_data.get("current_step", 0)
+                                total_steps = progress_data.get("total_steps", 100)
+                        except Exception as e:
+                            logger.error(f"Error reading progress file: {str(e)}")
+                    
+                    current_time = time.time()
+                    if current_time - last_update_time >= 1.0:
+                        # Send progress notification at key milestones (25%, 50%, 75%)
+                        notification_email = self.config.get('NOTIFICATION_EMAIL')
+                        if notification_email and hasattr(self, 'last_notified_percentage'):
+                            # Check if we've crossed a notification threshold
+                            thresholds = [25.0, 50.0, 75.0]
+                            for threshold in thresholds:
+                                if self.last_notified_percentage < threshold and percentage >= threshold:
+                                    try:
+                                        # Calculate estimated time remaining
+                                        elapsed_time = time.time() - self.training_start_time
+                                        if percentage > 0:
+                                            estimated_total_time = elapsed_time / (percentage / 100.0)
+                                            estimated_remaining = estimated_total_time - elapsed_time
+                                            time_remaining_str = f"{estimated_remaining / 60:.1f} minutes" if estimated_remaining < 3600 else f"{estimated_remaining / 3600:.1f} hours"
+                                        else:
+                                            time_remaining_str = "Unknown"
+                                        
+                                        # Collect progress statistics
+                                        progress_stats = {
+                                            "Percentage Complete": f"{percentage:.1f}%",
+                                            "Current Step": current_step,
+                                            "Total Steps": total_steps,
+                                            "Elapsed Time": f"{elapsed_time / 60:.1f} minutes" if elapsed_time < 3600 else f"{elapsed_time / 3600:.1f} hours",
+                                            "Estimated Time Remaining": time_remaining_str
+                                        }
+                                        
+                                        # Send progress notification
+                                        self.email_service.send_training_progress_notification(
+                                            self.model_name,
+                                            notification_email,
+                                            int(percentage),
+                                            progress_stats
+                                        )
+                                        logger.info(f"Training progress notification sent at {percentage:.1f}%")
+                                        self.last_notified_percentage = percentage
+                                    except Exception as e:
+                                        logger.warning(f"Could not send progress notification: {str(e)}")
                         
-                    # Briefly pause to avoid excessive CPU usage
-                    time.sleep(0.1)  
+                        if percentage == 100.0:
+                            self.progress.mark_step_status(ProcessStep.TRAIN, Status.COMPLETED)
+                            # Send email notification for training completion with enhanced information
+                            notification_email = self.config.get('NOTIFICATION_EMAIL')
+                            if notification_email:
+                                # Calculate training duration
+                                training_duration = time.time() - last_update_time
+                                duration_str = f"{training_duration / 60:.2f} minutes" if training_duration < 3600 else f"{training_duration / 3600:.2f} hours"
+                                
+                                # Get model paths
+                                model_paths = self._get_model_paths(self.model_name)
+                                model_path_str = model_paths.get("merged_dir", "Unknown")
+                                
+                                # Collect comprehensive training statistics
+                                training_stats = {
+                                    "Total Steps": total_steps,
+                                    "Completed Steps": current_step,
+                                    "Training Duration": duration_str,
+                                    "Average Time Per Step": f"{training_duration / max(current_step, 1):.2f} seconds"
+                                }
+                                
+                                # Add system resource information
+                                try:
+                                    process = psutil.Process(os.getpid())
+                                    memory_info = process.memory_info()
+                                    training_stats.update({
+                                        "Peak Memory Usage": f"{memory_info.rss / 1024 / 1024:.2f} MB",
+                                        "CPU Usage": f"{process.cpu_percent(interval=0.1):.1f}%"
+                                    })
+                                except Exception as e:
+                                    logger.warning(f"Could not add system resource info to training stats: {str(e)}")
+                                
+                                # Add training parameters to statistics
+                                try:
+                                    params_manager = TrainingParamsManager()
+                                    training_params = params_manager.get_latest_training_params()
+                                    if training_params:
+                                        training_stats.update({
+                                            "Learning Rate": training_params.get("learning_rate", "N/A"),
+                                            "Epochs": training_params.get("number_of_epochs", "N/A"),
+                                            "Data Synthesis Mode": training_params.get("data_synthesis_mode", "N/A")
+                                        })
+                                except Exception as e:
+                                    logger.warning(f"Could not add training parameters to stats: {str(e)}")
+                                
+                                # Send enhanced completion notification
+                                self.email_service.send_training_completion_notification(
+                                    self.model_name,
+                                    notification_email,
+                                    training_stats,
+                                    model_path=model_path_str,
+                                    training_duration=duration_str
+                                )
+                            return True
+                        
+                        self._update_progress("training_to_create_second_me", "train", int(percentage / 3), f"Current step: {current_step}/{total_steps}")
+                        last_update_time = current_time
+            
+                    time.sleep(1)
                     
-                except IOError as e:
-                    logger.error(f"Failed to read log file: {str(e)}")
-                    time.sleep(0.1)
+                except Exception as e:
+                    logger.error(f"Error in progress monitoring: {str(e)}")
+                    time.sleep(1)
                     continue
                     
         except Exception as e:
@@ -803,14 +973,15 @@ def _monitor_training_progress(self, log_file) -> bool:
             self.progress.mark_step_status(ProcessStep.TRAIN, Status.FAILED)
             return False
 
-    def _update_progress(self, stage: str, step: str, percentage: float, message: str):
+    def _update_progress(self, stage: str, step: str, percentage: float, message: str, file_name: Optional[str] = None):
         """Update progress for any stage and step"""
         try:
             self.progress.progress.update_progress(
                 stage,  # stage
                 step,   # step
                 Status.IN_PROGRESS,
-                percentage
+                percentage,
+                file_name  # Pass file name to update_progress method
             )
             logger.info(f"Progress updated: {percentage}% - {message}")
         except Exception as e:
@@ -895,7 +1066,8 @@ def _monitor_model_download(self) -> bool:
                                             "downloading_the_base_model", 
                                             "model_download", 
                                             current_progress, 
-                                            f"Overall: {current_progress:.1f}% - Downloading {file_name}: {percentage}% ({downloaded_mb:.1f}/{total_mb:.1f} MB)"
+                                            f"Overall: {current_progress:.1f}% - Downloading {file_name}: {percentage}% ({downloaded_mb:.1f}/{total_mb:.1f} MB)",
+                                            file_name
                                         )
                                         last_update_time = current_time
 
@@ -926,16 +1098,47 @@ def merge_weights(self) -> bool:
             
             # Check if model exists
             if not os.path.exists(paths["base_path"]):
-                logger.error(f"Model '{self.model_name}' does not exist, please download first")
+                error_msg = f"Model '{self.model_name}' does not exist, please download first"
+                logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.MERGE_WEIGHTS, Status.FAILED)
+                # Send email notification for missing base model
+                notification_email = self.config.get('NOTIFICATION_EMAIL')
+                if notification_email:
+                    error_context = {
+                        "Process Step": "MERGE_WEIGHTS",
+                        "Model Name": self.model_name,
+                        "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "Base Model Path": paths["base_path"]
+                    }
+                    self.email_service.send_training_error_notification(
+                        self.model_name,
+                        error_msg,
+                        notification_email,
+                        error_context=error_context
+                    )
                 return False
             
             # Check if training output exists
             if not os.path.exists(paths["personal_dir"]):
-                return jsonify(APIResponse.error(
-                    message=f"Model '{model_name}' training output does not exist, please train model first",
-                    code=400
-                ))
+                error_msg = f"Model '{self.model_name}' training output does not exist, please train model first"
+                logger.error(error_msg)
+                self.progress.mark_step_status(ProcessStep.MERGE_WEIGHTS, Status.FAILED)
+                # Send email notification for missing training output
+                notification_email = self.config.get('NOTIFICATION_EMAIL')
+                if notification_email:
+                    error_context = {
+                        "Process Step": "MERGE_WEIGHTS",
+                        "Model Name": self.model_name,
+                        "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "Personal Training Path": paths["personal_dir"]
+                    }
+                    self.email_service.send_training_error_notification(
+                        self.model_name,
+                        error_msg,
+                        notification_email,
+                        error_context=error_context
+                    )
+                return False
 
             # Ensure merged output directory exists
             os.makedirs(paths["merged_dir"], exist_ok=True)
@@ -960,6 +1163,23 @@ def merge_weights(self) -> bool:
                 error_msg = f"Merge weights failed: {result.get('error', 'Unknown error')}"
                 logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.MERGE_WEIGHTS, Status.FAILED)
+                # Send email notification for script execution failure
+                notification_email = self.config.get('NOTIFICATION_EMAIL')
+                if notification_email:
+                    error_context = {
+                        "Process Step": "MERGE_WEIGHTS",
+                        "Model Name": self.model_name,
+                        "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "Script Path": script_path,
+                        "Return Code": result.get('returncode', 'N/A')
+                    }
+                    self.email_service.send_training_error_notification(
+                        self.model_name,
+                        error_msg,
+                        notification_email,
+                        log_file_path=log_path if os.path.exists(log_path) else None,
+                        error_context=error_context
+                    )
                 return False
                 
             # Check if merged model files exist
@@ -968,6 +1188,23 @@ def merge_weights(self) -> bool:
                 error_msg = f"Merged model files not found in {paths['merged_dir']}"
                 logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.MERGE_WEIGHTS, Status.FAILED)
+                # Send email notification for missing merged files
+                notification_email = self.config.get('NOTIFICATION_EMAIL')
+                if notification_email:
+                    error_context = {
+                        "Process Step": "MERGE_WEIGHTS",
+                        "Model Name": self.model_name,
+                        "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "Expected Config Path": config_path,
+                        "Merged Directory": paths["merged_dir"]
+                    }
+                    self.email_service.send_training_error_notification(
+                        self.model_name,
+                        error_msg,
+                        notification_email,
+                        log_file_path=log_path if os.path.exists(log_path) else None,
+                        error_context=error_context
+                    )
                 return False
             
             logger.info("Weight merge completed successfully")
@@ -992,8 +1229,24 @@ def convert_model(self) -> bool:
             merged_model_dir = paths["merged_dir"]
             logger.info(f"Merged model path: {merged_model_dir}")
             if not os.path.exists(merged_model_dir):
-                logger.error(f"Model '{self.model_name}' merged output does not exist, please merge model first")
+                error_msg = f"Model '{self.model_name}' merged output does not exist, please merge model first"
+                logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.CONVERT_MODEL, Status.FAILED)
+                # Send email notification for missing merged model
+                notification_email = self.config.get('NOTIFICATION_EMAIL')
+                if notification_email:
+                    error_context = {
+                        "Process Step": "CONVERT_MODEL",
+                        "Model Name": self.model_name,
+                        "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "Expected Merged Path": merged_model_dir
+                    }
+                    self.email_service.send_training_error_notification(
+                        self.model_name,
+                        error_msg,
+                        notification_email,
+                        error_context=error_context
+                    )
                 return False
             
             # Get GGUF output directory
@@ -1033,6 +1286,23 @@ def convert_model(self) -> bool:
                 error_msg = f"Model conversion failed: {result.get('error', 'Unknown error')}"
                 logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.CONVERT_MODEL, Status.FAILED)
+                # Send email notification for conversion script failure
+                notification_email = self.config.get('NOTIFICATION_EMAIL')
+                if notification_email:
+                    error_context = {
+                        "Process Step": "CONVERT_MODEL",
+                        "Model Name": self.model_name,
+                        "Timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "Script Path": script_path,
+                        "Return Code": result.get('returncode', 'N/A'),
+                        "GGUF Output Path": gguf_path
+                    }
+                    self.email_service.send_training_error_notification(
+                        self.model_name,
+                        error_msg,
+                        notification_email,
+                        error_context=error_context
+                    )
                 return False
                 
             # Check if GGUF model file exists
@@ -1040,6 +1310,17 @@ def convert_model(self) -> bool:
                 error_msg = f"GGUF model file not found at {gguf_path}"
                 logger.error(error_msg)
                 self.progress.mark_step_status(ProcessStep.CONVERT_MODEL, Status.FAILED)
+                
+                # Send email notification for missing GGUF file
+                try:
+                    self.email_service.send_training_error_notification(
+                        model_name=self.model_name,
+                        error_message=error_msg,
+                        step_context="Model Conversion - GGUF File Check"
+                    )
+                except Exception as email_error:
+                    logger.error(f"Failed to send error notification email: {email_error}")
+                
                 return False
             
             logger.info("Model conversion completed successfully")
diff --git a/lpm_kernel/common/repository/base_repository.py b/lpm_kernel/common/repository/base_repository.py
index 879fa8a1..8ac591f3 100644
--- a/lpm_kernel/common/repository/base_repository.py
+++ b/lpm_kernel/common/repository/base_repository.py
@@ -48,7 +48,21 @@ def delete(self, id: int) -> bool:
                 session.rollback()
                 raise
 
-    def list(self, filters: dict = None, limit: int = 100, offset: int = 0) -> List[T]:
+    def list(self, filters: dict = None) -> List[T]:
+        """Return all records that match the given filters without pagination"""
+        with self._db.session() as session:
+            try:
+                query = select(self.model)
+                if filters:
+                    query = query.filter_by(**filters)
+                results = session.scalars(query).all()
+                return [self.model.from_dict(item.to_dict()) for item in results]
+            except Exception as e:
+                session.rollback()
+                raise
+
+    def page(self, limit: int = 100, offset: int = 0, filters: dict = None) -> List[T]:
+        """Return paginated records that match the given filters"""
         with self._db.session() as session:
             try:
                 query = select(self.model)
diff --git a/lpm_kernel/common/repository/mysql_repository.py b/lpm_kernel/common/repository/mysql_repository.py
index 46b065a2..244d7a71 100644
--- a/lpm_kernel/common/repository/mysql_repository.py
+++ b/lpm_kernel/common/repository/mysql_repository.py
@@ -69,7 +69,7 @@ async def delete(self, id: int) -> bool:
                 return cursor.rowcount > 0
 
     async def list(
-        self, filters: dict = None, limit: int = 100, offset: int = 0
+        self, filters: dict = None, limit: int = 1000, offset: int = 0
     ) -> List[T]:
         async with self.pool.acquire() as conn:
             async with conn.cursor(aiomysql.DictCursor) as cursor:
diff --git a/lpm_kernel/file_data/document_service.py b/lpm_kernel/file_data/document_service.py
index ab04ce18..25bbf474 100644
--- a/lpm_kernel/file_data/document_service.py
+++ b/lpm_kernel/file_data/document_service.py
@@ -58,6 +58,17 @@ def list_documents(self) -> List[Document]:
         """
         return self._repository.list()
 
+    def page_documents(self, page: int, page_size: int) -> List[Document]:
+        """
+        get paginated doc list
+        Args:
+            page (int): page number
+            page_size (int): page size
+        Returns:
+            List[Document]: doc object list
+        """
+        return self._repository.page(limit=page_size, offset=(page - 1) * page_size)
+
     def scan_directory(
         self, directory_path: str, recursive: bool = False
     ) -> List[DocumentDTO]:
@@ -369,6 +380,32 @@ def list_documents_with_l0(self) -> List[Dict]:
 
         return documents_with_l0
 
+    def page_documents_with_l0(self, page: int, page_size: int) -> List[Dict]:
+        """
+        get paginated docs' L0 data
+        Args:
+            page (int): page number
+            page_size (int): page size
+        Returns:
+            List[Dict]: list of dict of docs with L0 data
+        """
+        documents = self.page_documents(page, page_size)
+        logger.info(f"page_documents len: {len(documents)}")
+
+        documents_with_l0 = []
+        for doc in documents:
+            doc_dict = doc.to_dict()
+            try:
+                l0_data = self.get_document_l0(doc.id)
+                doc_dict["l0_data"] = l0_data
+                logger.info(f"success getting L0 data for document {doc.id} success")
+            except Exception as e:
+                logger.error(f"Error getting L0 data for document {doc.id}: {str(e)}")
+                doc_dict["l0_data"] = None
+            documents_with_l0.append(doc_dict)
+
+        return documents_with_l0
+
     def get_document_by_id(self, document_id: int) -> Optional[Document]:
         """
         get doc by ID
@@ -517,112 +554,106 @@ def get_document_embedding(self, document_id: int) -> Optional[List[float]]:
             logger.error(f"Error getting document embedding: {str(e)}")
             raise
 
-    def delete_file_by_name(self, filename: str) -> bool:
+    def delete_file_by_id(self, document_id: int) -> bool:
         """
+        Delete a document by its ID
+
         Args:
-            filename (str): name to delete
-            
+            document_id (int): The ID of the document to delete
+
         Returns:
-            bool: if success
-            
-        Raises:
-            Exception: error occurred
+            bool: True if deletion successful, False otherwise
         """
-        logger.info(f"Starting to delete file: {filename}")
-        
+        logger.info(f"Starting to delete file with ID: {document_id}")
+
         try:
-            # 1. search memories
+            # 1. Get document object
+            document = self._repository.get_by_id(document_id)
+
+            if not document:
+                logger.warning(f"Document with ID {document_id} not found")
+                return False
+
+            logger.info(f"Document found: {document}")
+
+            # 2. Find related memory record
             db = DatabaseSession()
             memory = None
-            document_id = None
-            
+            file_path = None
+
             with db._session_factory() as session:
-                query = select(Memory).where(Memory.name == filename)
-                result = session.execute(query)
-                memory = result.scalar_one_or_none()
-                
-                if not memory:
-                    logger.warning(f"File record not found: {filename}")
-                    return False
-                
-                # get related document_id
-                document_id = memory.document_id
-                
-                # get filepath
-                file_path = memory.path
-                
-                # 2. delete memory
-                session.delete(memory)
-                session.commit()
-                logger.info(f"Deleted record from memories table: {filename}")
-            
-            # if no related document, only delete physical file
-            if not document_id:
-                # delete physical file
-                if os.path.exists(file_path):
-                    os.remove(file_path)
-                    logger.info(f"Deleted physical file: {file_path}")
-                return True
-            
-            # 3. get doc obj
-            document = self._repository.get_by_id(document_id)
-            if not document:
-                logger.warning(f"Corresponding document record not found, ID: {document_id}")
-                # if no document record, delete physical file
-                if os.path.exists(file_path):
-                    os.remove(file_path)
-                    logger.info(f"Deleted physical file: {file_path}")
-                return True
-            
-            # 4. get all chunks
-            chunks = self._repository.find_chunks(document_id)
-            
-            # 5. delete doc embedding from ChromaDB
-            try:
-                self.embedding_service.document_collection.delete(
-                    ids=[str(document_id)]
-                )
-                logger.info(f"Deleted document embedding from ChromaDB, ID: {document_id}")
-            except Exception as e:
-                logger.error(f"Error deleting document embedding: {str(e)}")
-            
-            # 6. delete all chunk embedding from ChromaDB
-            if chunks:
+                # Find memory record
+                query = select(Memory).where(Memory.document_id == document_id)
+                memory = session.execute(query).scalar_one_or_none()
+
+                if memory:
+                    # Get file path
+                    file_path = memory.path
+
+                    # Delete memory record
+                    session.delete(memory)
+                    session.commit()
+                    logger.info(
+                        f"Deleted record from memories table for document ID: {document_id}"
+                    )
+
+                # 3. Get all chunks
+                chunks = self._repository.find_chunks(document_id)
+
+                # 4. Delete document embedding from ChromaDB
                 try:
-                    chunk_ids = [str(chunk.id) for chunk in chunks]
-                    self.embedding_service.chunk_collection.delete(
-                        ids=chunk_ids
+                    self.embedding_service.document_collection.delete(
+                        ids=[str(document_id)]
+                    )
+                    logger.info(
+                        f"Deleted document embedding from ChromaDB, ID: {document_id}"
                     )
-                    logger.info(f"Deleted {len(chunk_ids)} chunk embeddings from ChromaDB")
                 except Exception as e:
-                    logger.error(f"Error deleting chunk embeddings: {str(e)}")
-            
-            # 7. delete all chunks embedding from ChromaDB
-            with db._session_factory() as session:
+                    logger.error(f"Error deleting document embedding: {str(e)}")
+
+                # 5. Delete all chunk embeddings from ChromaDB
+                if chunks:
+                    try:
+                        chunk_ids = [str(chunk.id) for chunk in chunks]
+                        self.embedding_service.chunk_collection.delete(ids=chunk_ids)
+                        logger.info(
+                            f"Deleted {len(chunk_ids)} chunk embeddings from ChromaDB"
+                        )
+                    except Exception as e:
+                        logger.error(f"Error deleting chunk embeddings: {str(e)}")
+
+                # 6. Delete all chunks from database
                 from lpm_kernel.file_data.models import ChunkModel
+
                 session.query(ChunkModel).filter(
                     ChunkModel.document_id == document_id
                 ).delete()
                 session.commit()
-                logger.info(f"Deleted all related chunks")
-                
-                # delete doc record
+                logger.info(
+                    f"Deleted all related chunks for document ID: {document_id}"
+                )
+
+                # 7. Delete document record
                 doc_entity = session.get(Document, document_id)
                 if doc_entity:
                     session.delete(doc_entity)
                     session.commit()
-                    logger.info(f"Deleted document record from database, ID: {document_id}")
-            
-            # 8. delete physical file
-            if os.path.exists(file_path):
-                os.remove(file_path)
-                logger.info(f"Deleted physical file: {file_path}")
-            
+                    logger.info(
+                        f"Deleted document record from database, ID: {document_id}"
+                    )
+
+                # 8. Delete physical file
+                if file_path and os.path.exists(file_path):
+                    os.remove(file_path)
+                    logger.info(f"Deleted physical file: {file_path}")
+
             return True
-            
+
         except Exception as e:
-            logger.error(f"Error deleting file: {str(e)}", exc_info=True)
-            raise
+            logger.error(
+                f"Error deleting file with ID {document_id}: {str(e)}", exc_info=True
+            )
+            return False
 
     def fix_missing_document_analysis(self) -> int:
         """Fix documents with missing insights or summaries
diff --git a/lpm_kernel/file_data/memory_service.py b/lpm_kernel/file_data/memory_service.py
index 007b0779..cd980cfd 100644
--- a/lpm_kernel/file_data/memory_service.py
+++ b/lpm_kernel/file_data/memory_service.py
@@ -90,9 +90,9 @@ def save_file(self, file, metadata=None):
             file.seek(0)  # reset file pointer to start position
 
             # check if file already exists
-            existing_memory = self.check_file_exists(file.filename, filesize)
-            if existing_memory:
-                raise ValueError(f"File '{file.filename}' already exists")
+            # existing_memory = self.check_file_exists(file.filename, filesize)
+            # if existing_memory:
+            #     raise ValueError(f"File '{file.filename}' already exists")
 
             # save file to disk
             filepath, filename, filesize = self._save_file_to_disk(file)
@@ -117,7 +117,7 @@ def save_file(self, file, metadata=None):
                 logger.info(f"Memory record created successfully: {memory.id}")
 
                 # process document
-                document = self._process_document(filepath, metadata)
+                document = self._process_document(filepath, filename, metadata)
                 if document:
                     memory.document_id = document.id
                     session.add(memory)
@@ -156,7 +156,11 @@ def _save_file_to_disk(self, file):
 
             # generate file name and path
             filename = file.filename
-            filepath = self.base_path / filename
+            # Add timestamp to filename to ensure uniqueness
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            name_parts = os.path.splitext(filename)
+            timestamped_filename = f"{name_parts[0]}_{timestamp}{name_parts[1]}"
+            filepath = self.base_path / timestamped_filename
             logger.info(f"Preparing to save file to: {filepath}")
 
             # save file
@@ -170,7 +174,7 @@ def _save_file_to_disk(self, file):
             logger.error(f"Failed to save file to disk: {str(e)}", exc_info=True)
             raise
 
-    def _process_document(self, filepath, metadata=None):
+    def _process_document(self, filepath, filename, metadata=None):
         """Process document and create Document record
 
         Args:
@@ -188,8 +192,8 @@ def _process_document(self, filepath, metadata=None):
             )
 
             request = CreateDocumentRequest(
-                name=doc.name,
-                title=metadata.get("name", doc.name) if metadata else doc.name,
+                name=filename,
+                title=metadata.get("name", filename) if metadata else filename,
                 mime_type=doc.mime_type,
                 user_description=metadata.get("description", "Uploaded document")
                 if metadata
diff --git a/lpm_kernel/services/email_service.py b/lpm_kernel/services/email_service.py
new file mode 100644
index 00000000..8ae375be
--- /dev/null
+++ b/lpm_kernel/services/email_service.py
@@ -0,0 +1,434 @@
+import os
+import smtplib
+import time
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.mime.application import MIMEApplication
+from typing import List, Optional, Dict, Union, Tuple, Any
+from pathlib import Path
+from lpm_kernel.configs.config import Config
+import logging
+
+logger = logging.getLogger(__name__)
+
+class EmailService:
+    """Email service for sending notifications"""
+    
+    _instance = None
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self):
+        self.config = Config.from_env()
+        # Get email configuration from environment variables
+        self.smtp_host = self.config.get('SMTP_HOST')
+        self.smtp_port = int(self.config.get('SMTP_PORT', '587'))
+        self.smtp_user = self.config.get('SMTP_USER')
+        self.smtp_password = self.config.get('SMTP_PASSWORD')
+        self.sender_email = self.config.get('SENDER_EMAIL')
+        self.use_tls = self.config.get('SMTP_USE_TLS', 'True').lower() == 'true'
+        
+        # Email templates directory
+        self.templates_dir = self.config.get('EMAIL_TEMPLATES_DIR', os.path.join(os.path.dirname(__file__), 'email_templates'))
+        
+        # Retry configuration
+        self.max_retries = int(self.config.get('EMAIL_MAX_RETRIES', '3'))
+        self.retry_delay = int(self.config.get('EMAIL_RETRY_DELAY_SECONDS', '5'))
+        
+        # Create templates directory if it doesn't exist
+        os.makedirs(self.templates_dir, exist_ok=True)
+
+    def send_email(self, 
+                  subject: str, 
+                  body: str, 
+                  to_emails: List[str],
+                  is_html: bool = False,
+                  attachments: Optional[List[str]] = None,
+                  cc_emails: Optional[List[str]] = None,
+                  bcc_emails: Optional[List[str]] = None) -> bool:
+        """Send email to recipients with enhanced functionality
+
+        Args:
+            subject: Email subject
+            body: Email body content
+            to_emails: List of recipient email addresses
+            is_html: Whether the body content is HTML format
+            attachments: Optional list of file paths to attach
+            cc_emails: Optional list of CC recipient email addresses
+            bcc_emails: Optional list of BCC recipient email addresses
+
+        Returns:
+            bool: True if email sent successfully, False otherwise
+        """
+        # Validate email configuration
+        if not self._validate_email_config():
+            return False
+            
+        # Initialize retry counter
+        retry_count = 0
+        
+        while retry_count <= self.max_retries:
+            try:
+                # Create message container
+                msg = MIMEMultipart()
+                msg['From'] = self.sender_email
+                msg['To'] = ', '.join(to_emails)
+                msg['Subject'] = subject
+                
+                # Add CC and BCC if provided
+                if cc_emails:
+                    msg['Cc'] = ', '.join(cc_emails)
+                if bcc_emails:
+                    msg['Bcc'] = ', '.join(bcc_emails)
+
+                # Attach body with appropriate content type
+                content_type = 'html' if is_html else 'plain'
+                msg.attach(MIMEText(body, content_type))
+
+                # Add attachments if provided
+                if attachments:
+                    for file_path in attachments:
+                        self._add_attachment(msg, file_path)
+
+                # Combine all recipients for sending
+                all_recipients = to_emails.copy()
+                if cc_emails:
+                    all_recipients.extend(cc_emails)
+                if bcc_emails:
+                    all_recipients.extend(bcc_emails)
+
+                # Connect to SMTP server and send email
+                with smtplib.SMTP(self.smtp_host, self.smtp_port) as server:
+                    if self.use_tls:
+                        server.starttls()
+                    server.login(self.smtp_user, self.smtp_password)
+                    server.send_message(msg)
+
+                logger.info(f"Email sent successfully to {', '.join(to_emails)}")
+                return True
+
+            except Exception as e:
+                retry_count += 1
+                if retry_count <= self.max_retries:
+                    logger.warning(f"Email sending attempt {retry_count} failed: {str(e)}. Retrying in {self.retry_delay} seconds...")
+                    time.sleep(self.retry_delay)
+                else:
+                    logger.error(f"Failed to send email after {self.max_retries} attempts: {str(e)}")
+                    return False
+        
+        return False
+        
+    def _validate_email_config(self) -> bool:
+        """Validate that all required email configuration is present
+        
+        Returns:
+            bool: True if configuration is valid, False otherwise
+        """
+        required_configs = {
+            'SMTP Host': self.smtp_host,
+            'SMTP Port': self.smtp_port,
+            'SMTP User': self.smtp_user,
+            'SMTP Password': self.smtp_password,
+            'Sender Email': self.sender_email
+        }
+        
+        missing_configs = [key for key, value in required_configs.items() if not value]
+        
+        if missing_configs:
+            logger.error(f"Email configuration is incomplete. Missing: {', '.join(missing_configs)}")
+            return False
+        return True
+        
+    def _add_attachment(self, msg: MIMEMultipart, file_path: str) -> None:
+        """Add an attachment to the email message
+        
+        Args:
+            msg: The email message to attach to
+            file_path: Path to the file to attach
+        """
+        try:
+            with open(file_path, 'rb') as file:
+                part = MIMEApplication(file.read(), Name=os.path.basename(file_path))
+                part['Content-Disposition'] = f'attachment; filename="{os.path.basename(file_path)}"'
+                msg.attach(part)
+        except Exception as e:
+            logger.error(f"Failed to attach file {file_path}: {str(e)}")
+            
+    def _load_template(self, template_name: str) -> Optional[str]:
+        """Load an email template from the templates directory
+        
+        Args:
+            template_name: Name of the template file
+            
+        Returns:
+            str: Template content or None if template not found
+        """
+        template_path = os.path.join(self.templates_dir, template_name)
+        try:
+            if os.path.exists(template_path):
+                with open(template_path, 'r', encoding='utf-8') as file:
+                    return file.read()
+            else:
+                logger.warning(f"Email template not found: {template_path}")
+                return None
+        except Exception as e:
+            logger.error(f"Failed to load email template {template_name}: {str(e)}")
+            return None
+
+    def send_training_start_notification(self,
+                                        model_name: str,
+                                        to_email: str,
+                                        training_params: Optional[Dict[str, Any]] = None) -> bool:
+        """Send notification when training process starts
+
+        Args:
+            model_name: Name of the model being trained
+            to_email: Recipient's email address
+            training_params: Optional dictionary containing training parameters
+
+        Returns:
+            bool: True if notification sent successfully, False otherwise
+        """
+        subject = f"Training Process Started - {model_name}"
+        
+        # Build the email body
+        body = f"""<h2>Training Process Started</h2>
+                 <p>The training process for model <strong>{model_name}</strong> has been initiated.</p>"""
+
+        # Add training parameters if provided
+        if training_params:
+            body += """<h3>Training Parameters:</h3>
+                      <ul>"""
+            for key, value in training_params.items():
+                body += f"<li><strong>{key}:</strong> {value}</li>"
+            body += "</ul>"
+            
+        body += "<p>You will receive updates on the training progress and completion.</p>"
+
+        return self.send_email(subject, body, [to_email], is_html=True)
+        
+    def send_training_progress_notification(self,
+                                           model_name: str,
+                                           to_email: str,
+                                           progress_percentage: float,
+                                           current_step: str,
+                                           estimated_completion_time: Optional[str] = None) -> bool:
+        """Send notification about training progress
+
+        Args:
+            model_name: Name of the model being trained
+            to_email: Recipient's email address
+            progress_percentage: Current progress percentage (0-100)
+            current_step: Current training step description
+            estimated_completion_time: Optional estimated completion time
+
+        Returns:
+            bool: True if notification sent successfully, False otherwise
+        """
+        subject = f"Training Progress Update - {model_name} ({progress_percentage:.1f}%)"
+        
+        # Build the email body
+        body = f"""<h2>Training Progress Update</h2>
+                 <p>The training process for model <strong>{model_name}</strong> is in progress.</p>
+                 
+                 <div style="background-color: #f0f0f0; border-radius: 5px; padding: 10px;">
+                    <p><strong>Current Progress:</strong> {progress_percentage:.1f}%</p>
+                    <div style="background-color: #e0e0e0; border-radius: 5px; height: 20px; width: 100%; margin-top: 5px;">
+                        <div style="background-color: #4CAF50; border-radius: 5px; height: 20px; width: {min(progress_percentage, 100)}%;"></div>
+                    </div>
+                 </div>
+                 
+                 <p><strong>Current Step:</strong> {current_step}</p>"""
+                 
+        if estimated_completion_time:
+            body += f"<p><strong>Estimated Completion Time:</strong> {estimated_completion_time}</p>"
+            
+        return self.send_email(subject, body, [to_email], is_html=True)
+
+    def send_training_error_notification(self, 
+                                       model_name: str, 
+                                       error_message: str,
+                                       to_email: str,
+                                       log_file_path: Optional[str] = None,
+                                       error_context: Optional[Dict[str, Any]] = None,
+                                       max_log_lines: int = 50) -> bool:
+        """Send notification when training process encounters an error
+
+        Args:
+            model_name: Name of the model being trained
+            error_message: Error message to include in notification
+            to_email: Recipient's email address
+            log_file_path: Optional path to log file to attach
+            error_context: Optional dictionary with additional error context information
+            max_log_lines: Maximum number of log lines to include in the email body
+
+        Returns:
+            bool: True if notification sent successfully, False otherwise
+        """
+        subject = f"Training Error Alert - {model_name}"
+        
+        # Build the email body with improved styling
+        body = f"""<h2 style="color: #d32f2f;">Training Process Error</h2>
+                 <p>An error occurred during the training process for model: <strong>{model_name}</strong></p>
+                 <p><strong>Error Details:</strong></p>
+                 <pre style="background-color: #f8d7da; color: #721c24; padding: 10px; border-radius: 5px; overflow-x: auto;">{error_message}</pre>"""
+        
+        # Add error context if provided
+        if error_context and isinstance(error_context, dict):
+            body += """<h3>Error Context:</h3>
+                     <table style="border-collapse: collapse; width: 100%; margin-bottom: 20px;">
+                     <tr style="background-color: #f2f2f2;">
+                       <th style="border: 1px solid #ddd; padding: 8px; text-align: left;">Parameter</th>
+                       <th style="border: 1px solid #ddd; padding: 8px; text-align: left;">Value</th>
+                     </tr>"""
+                     
+            for key, value in error_context.items():
+                body += f"""<tr>
+                         <td style="border: 1px solid #ddd; padding: 8px;">{key}</td>
+                         <td style="border: 1px solid #ddd; padding: 8px;">{value}</td>
+                       </tr>"""
+            body += "</table>"
+        
+        # Add recent log entries if log file exists
+        if log_file_path and os.path.exists(log_file_path):
+            try:
+                recent_logs = self._extract_recent_logs(log_file_path, max_log_lines)
+                if recent_logs:
+                    body += f"""<h3>Recent Log Entries:</h3>
+                             <pre style="background-color: #f5f5f5; color: #333; padding: 10px; border-radius: 5px; font-size: 12px; overflow-x: auto; max-height: 300px; overflow-y: auto;">{recent_logs}</pre>"""
+            except Exception as e:
+                logger.error(f"Failed to extract recent logs from {log_file_path}: {str(e)}")
+        
+        body += """<p>Please check the complete training logs for more information.</p>
+                  <p>If you need assistance, please contact the system administrator.</p>"""
+
+        # Prepare attachments if log file is provided and exists
+        attachments = None
+        if log_file_path and os.path.exists(log_file_path):
+            attachments = [log_file_path]
+
+        return self.send_email(subject, body, [to_email], is_html=True, attachments=attachments)
+        
+    def _extract_recent_logs(self, log_file_path: str, max_lines: int = 50) -> Optional[str]:
+        """Extract the most recent lines from a log file
+        
+        Args:
+            log_file_path: Path to the log file
+            max_lines: Maximum number of lines to extract
+            
+        Returns:
+            str: Recent log entries or None if extraction failed
+        """
+        try:
+            # Use a deque to efficiently keep only the last max_lines
+            from collections import deque
+            recent_lines = deque(maxlen=max_lines)
+            
+            with open(log_file_path, 'r', encoding='utf-8', errors='replace') as file:
+                for line in file:
+                    recent_lines.append(line.rstrip())
+            
+            # Join the lines with HTML line breaks for display in email
+            return "\n".join(recent_lines)
+        except Exception as e:
+            logger.error(f"Error extracting recent logs: {str(e)}")
+            return None
+
+    def send_training_completion_notification(self, 
+                                            model_name: str,
+                                            to_email: str,
+                                            training_stats: Optional[Dict[str, Any]] = None,
+                                            model_path: Optional[str] = None,
+                                            training_duration: Optional[str] = None) -> bool:
+        """Send notification when training process completes successfully
+
+        Args:
+            model_name: Name of the model that completed training
+            to_email: Recipient's email address
+            training_stats: Optional dictionary containing training statistics
+            model_path: Optional path where the model is saved
+            training_duration: Optional string representing the total training duration
+
+        Returns:
+            bool: True if notification sent successfully, False otherwise
+        """
+        subject = f"Training Completed Successfully - {model_name}"
+        
+        # Build the email body with improved styling
+        body = f"""<h2 style="color: #2e7d32;">Training Process Completed Successfully</h2>
+                 <p>The training process for model <strong>{model_name}</strong> has completed successfully.</p>"""
+
+        if training_duration:
+            body += f"<p><strong>Total Training Duration:</strong> {training_duration}</p>"
+            
+        if model_path:
+            body += f"<p><strong>Model Saved At:</strong> {model_path}</p>"
+
+        # Add training statistics if provided with improved styling
+        if training_stats:
+            body += """<h3>Training Statistics:</h3>
+                      <table style="border-collapse: collapse; width: 100%; margin-bottom: 20px;">
+                      <tr style="background-color: #f2f2f2;">
+                        <th style="border: 1px solid #ddd; padding: 8px; text-align: left;">Metric</th>
+                        <th style="border: 1px solid #ddd; padding: 8px; text-align: left;">Value</th>
+                      </tr>"""
+                      
+            for key, value in training_stats.items():
+                body += f"""<tr>
+                          <td style="border: 1px solid #ddd; padding: 8px;">{key}</td>
+                          <td style="border: 1px solid #ddd; padding: 8px;">{value}</td>
+                        </tr>"""
+            body += "</table>"
+            
+        body += """<p>You can now use this model for inference.</p>
+                  <p>Thank you for using our training service!</p>"""
+
+        return self.send_email(subject, body, [to_email], is_html=True)
+        
+    def send_custom_notification(self,
+                               subject: str,
+                               template_name: Optional[str],
+                               to_email: str,
+                               template_vars: Optional[Dict[str, Any]] = None,
+                               attachments: Optional[List[str]] = None) -> bool:
+        """Send a custom notification using a template
+
+        Args:
+            subject: Email subject
+            template_name: Name of the template file to use (if None, template_vars must contain 'body')
+            to_email: Recipient's email address
+            template_vars: Dictionary of variables to replace in the template
+            attachments: Optional list of file paths to attach
+
+        Returns:
+            bool: True if notification sent successfully, False otherwise
+        """
+        # If template name is provided, load the template
+        body = None
+        if template_name:
+            body = self._load_template(template_name)
+            if not body:
+                # If template not found but template_vars contains 'body', use that instead
+                if template_vars and 'body' in template_vars:
+                    body = template_vars['body']
+                else:
+                    logger.error(f"Template {template_name} not found and no fallback body provided")
+                    return False
+        elif template_vars and 'body' in template_vars:
+            # Use body from template_vars if no template name provided
+            body = template_vars['body']
+        else:
+            logger.error("No template or body content provided for custom notification")
+            return False
+            
+        # Replace template variables if provided
+        if template_vars:
+            for key, value in template_vars.items():
+                if key != 'body':  # Skip 'body' key as it's handled separately
+                    placeholder = f"{{{{{key}}}}}"
+                    body = body.replace(placeholder, str(value))
+                    
+        return self.send_email(subject, body, [to_email], is_html=True, attachments=attachments)
\ No newline at end of file
diff --git a/mcp/mcp_local.py b/mcp/mcp_local.py
index d96b7e8d..a1f4731b 100644
--- a/mcp/mcp_local.py
+++ b/mcp/mcp_local.py
@@ -13,11 +13,13 @@
 @mindv.tool()
 async def get_response(query:str) -> str | None | Any:
     """
-    Received a response based on local secondme model.
+    MCP server stores user preferences and memory. This method can be called when answering questions about user's own preferences or memory.
 
     Args:
-        query (str): Questions raised by users regarding the secondme model
+        query (str): Query about user preferences or personal information
 
+    Returns:
+        str | None | Any: Response based on user's personal data
     """
 
     headers = {
diff --git a/mcp/mcp_public.py b/mcp/mcp_public.py
index 1f3bfa2d..df347c2b 100644
--- a/mcp/mcp_public.py
+++ b/mcp/mcp_public.py
@@ -12,10 +12,10 @@
 @mindverse.tool()
 async def get_response(query:str, instance_id:str) -> str | None:
     """
-    Received a response based on public secondme model.
+    MCP server stores user preferences and memory. This method can be called when answering questions about user's own preferences or memory.
 
     Args:
-        query (str): Questions raised by users regarding the secondme model.
+        query (str): Query about user preferences or personal information
         instance_id (str): ID used to identify the secondme model, or url used to identify the secondme model.
 
     """
diff --git a/scripts/prompt_cuda.sh b/scripts/prompt_cuda.sh
old mode 100644
new mode 100755
diff --git a/scripts/setup.sh b/scripts/setup.sh
index 2b3b8a28..f088e99b 100755
--- a/scripts/setup.sh
+++ b/scripts/setup.sh
@@ -6,10 +6,14 @@ source "$SCRIPT_DIR/utils/logging.sh"
 source "$SCRIPT_DIR/utils/os_detection.sh"
 source "$SCRIPT_DIR/utils/install_config.sh"
 source "$SCRIPT_DIR/utils/python_tools.sh"
+source "$SCRIPT_DIR/utils/header_display.sh"
 
 # Version
 VERSION="1.0.0"
 
+# Use China mirrors
+USE_CHINA_MIRROR=false
+
 # Total number of stages
 TOTAL_STAGES=6
 CURRENT_STAGE=0
@@ -24,27 +28,7 @@ cleanup() {
     exit 1
 }
 
-# Display title and logo
-display_header() {
-    local title="$1"
-    
-    echo ""
-    echo -e "${CYAN}"
-    echo ' ███████╗███████╗ ██████╗ ██████╗ ███╗   ██╗██████╗       ███╗   ███╗███████╗'
-    echo ' ██╔════╝██╔════╝██╔════╝██╔═══██╗████╗  ██║██╔══██╗      ████╗ ████║██╔════╝'
-    echo ' ███████╗█████╗  ██║     ██║   ██║██╔██╗ ██║██║  ██║█████╗██╔████╔██║█████╗  '
-    echo ' ╚════██║██╔══╝  ██║     ██║   ██║██║╚██╗██║██║  ██║╚════╝██║╚██╔╝██║██╔══╝  '
-    echo ' ███████║███████╗╚██████╗╚██████╔╝██║ ╚████║██████╔╝      ██║ ╚═╝ ██║███████╗'
-    echo ' ╚══════╝╚══════╝ ╚═════╝ ╚═════╝ ╚═╝  ╚═══╝╚═════╝       ╚═╝     ╚═╝╚══════╝'
-    echo -e "${NC}"
-    echo -e "${BOLD}Second-Me Setup Script v${VERSION}${NC}"
-    echo -e "${GRAY}$(date)${NC}\n"
-    
-    if [ -n "$title" ]; then
-        echo -e "${CYAN}====== $title ======${NC}"
-        echo ""
-    fi
-}
+
 
 # Display stage start
 display_stage() {
@@ -200,7 +184,7 @@ install_graphrag() {
     
     # Check the current graphrag version in Poetry environment
     log_step "Checking graphrag version in Poetry environment"
-    GRAPHRAG_VERSION=$(poetry run pip show graphrag 2>/dev/null | grep "Version:" | cut -d " " -f2)
+    GRAPHRAG_VERSION=$(poetry run pip show graphrag 2>/dev/null | awk '/^Versi  on: / {print $2; exit}')
     GRAPHRAG_TARGET="1.2.1.dev27"
     GRAPHRAG_LOCAL_PATH="dependencies/graphrag-${GRAPHRAG_TARGET}.tar.gz"
 
@@ -378,16 +362,80 @@ show_help() {
     echo -e "Options:"
     echo -e "  --help\t\tShow this help information"
     echo -e "  --require-confirmation\tRequire confirmation when warnings are present"
+    echo -e "  --china\t\tUse China mirrors for npm, pip and Poetry packages"
     echo
     echo -e "Examples:"
     echo -e "  $0 \t\t\tPerform full installation"
     echo -e "  $0 python\t\tSetup Python environment only"
-    echo -e "  $0 --require-confirmation\tRequire confirmation when warnings are present"
+    echo -e "  $0 --china\t\tPerform full installation with China mirrors"
     echo
     echo -e "For a complete list of all available commands, run:"
     echo -e "  make help"
 }
 
+# Configure China mirrors for all package managers
+configure_china_mirrors() {
+    log_section "CONFIGURING CHINA MIRRORS"
+    
+    # 1. Configure npm
+    log_step "Configuring npm to use Taobao mirror"
+    if command -v npm &>/dev/null; then
+        log_info "Setting npm registry to Taobao mirror..."
+        npm config set registry https://registry.npmmirror.com
+        log_success "npm configured to use Taobao mirror"
+    else
+        log_warning "npm not found, skipping npm mirror configuration"
+    fi
+    
+    # 2. Configure pip
+    log_step "Configuring pip to use Aliyun mirror (for current session)"
+    local python_cmd=$(get_python_command)
+    if [ -n "$python_cmd" ]; then
+        # Set environment variables for current session
+        export PIP_INDEX_URL=https://mirrors.aliyun.com/pypi/simple/
+        export PIP_TRUSTED_HOST=mirrors.aliyun.com
+        
+        # Add these exports to the active .bashrc or .zshrc to persist across sessions
+        log_info "Environment variables set for current session:"
+        log_info "  PIP_INDEX_URL=https://mirrors.aliyun.com/pypi/simple/"
+        log_info "  PIP_TRUSTED_HOST=mirrors.aliyun.com"
+        log_success "pip configured to use Aliyun mirror for current session"
+    else
+        log_warning "python not found, skipping pip mirror configuration"
+    fi
+    
+    # 3. Configure Poetry
+    log_step "Configuring Poetry to use Aliyun and Tsinghua mirrors"
+    if command -v poetry &>/dev/null; then
+        log_info "Checking and setting up Poetry mirrors..."
+        
+        # Get current sources
+        CURRENT_SOURCES=$(poetry source list 2>/dev/null)
+        
+        # Check and add Aliyun mirror if it doesn't exist
+        if ! echo "$CURRENT_SOURCES" | grep -q "aliyun"; then
+            log_info "Adding Aliyun mirror as primary source..."
+            poetry source add --priority=primary aliyun https://mirrors.aliyun.com/pypi/simple/
+        else
+            log_info "Aliyun mirror already configured, skipping..."
+        fi
+        
+        # Check and add Tsinghua mirror if it doesn't exist
+        if ! echo "$CURRENT_SOURCES" | grep -q "tsinghua"; then
+            log_info "Adding Tsinghua mirror as supplemental source..."
+            poetry source add --priority=supplemental tsinghua https://pypi.tuna.tsinghua.edu.cn/simple
+        else
+            log_info "Tsinghua mirror already configured, skipping..."
+        fi
+
+        log_success "Poetry mirror configuration complete"
+    else
+        log_warning "poetry not found, skipping Poetry mirror configuration"
+    fi
+    
+    log_section "CHINA MIRRORS CONFIGURATION COMPLETE"
+}
+
 # Check system requirements
 check_system_requirements() {
     log_step "Checking system requirements"
@@ -591,6 +639,11 @@ parse_args() {
                 REQUIRE_CONFIRMATION=true
                 shift
                 ;;
+            --china)
+                USE_CHINA_MIRROR=true
+                log_info "Using China mirrors for package installations"
+                shift
+                ;;
             python|llama|frontend)
                 COMPONENT="$1"
                 shift
@@ -613,6 +666,11 @@ main() {
     # Parse command line arguments
     parse_args "$@"
     
+    # Configure China mirrors if specified
+    if [ "$USE_CHINA_MIRROR" = true ]; then
+        configure_china_mirrors
+    fi
+    
     # All pre-installation checks
     log_section "Running pre-installation checks"
     
diff --git a/scripts/utils/header_display.sh b/scripts/utils/header_display.sh
new file mode 100755
index 00000000..e5fabe4a
--- /dev/null
+++ b/scripts/utils/header_display.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+# Header display utility for Second-Me scripts
+# This file contains the ASCII art header display function
+
+# Source logging.sh for color definitions
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+source "$SCRIPT_DIR/logging.sh"
+
+# Version (maintain here for display purposes)
+VERSION="1.0.0"
+
+# Display title and logo
+display_header() {
+    local title="$1"
+    
+    echo ""
+    echo -e "${CYAN}"
+    echo ' ███████╗███████╗ ██████╗ ██████╗ ███╗   ██╗██████╗       ███╗   ███╗███████╗'
+    echo ' ██╔════╝██╔════╝██╔════╝██╔═══██╗████╗  ██║██╔══██╗      ████╗ ████║██╔════╝'
+    echo ' ███████╗█████╗  ██║     ██║   ██║██╔██╗ ██║██║  ██║█████╗██╔████╔██║█████╗  '
+    echo ' ╚════██║██╔══╝  ██║     ██║   ██║██║╚██╗██║██║  ██║╚════╝██║╚██╔╝██║██╔══╝  '
+    echo ' ███████║███████╗╚██████╗╚██████╔╝██║ ╚████║██████╔╝      ██║ ╚═╝ ██║███████╗'
+    echo ' ╚══════╝╚══════╝ ╚═════╝ ╚═════╝ ╚═╝  ╚═══╝╚═════╝       ╚═╝     ╚═╝╚══════╝'
+    echo -e "${NC}"
+    echo -e "${BOLD}Second-Me Setup Script v${VERSION}${NC}"
+    echo -e "${GRAY}$(date)${NC}\\n"
+    
+    if [ -n "$title" ]; then
+        echo -e "${CYAN}====== $title ======${NC}"
+        echo ""
+    fi
+}