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update:1.更新mcp插件功能，目前只支持remote调用

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This commit is contained in:
xiamuceer
2025-11-07 22:14:20 +08:00
parent 1e998920e3
commit 88115a45c5
26 changed files with 4088 additions and 138 deletions
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backend/app/api/chapters.py
+63 -3
View File
@@ -823,12 +823,14 @@ async def generate_chapter_content_stream(
请求体参数：
- style_id: 可选，指定使用的写作风格ID。不提供则不使用任何风格
- target_word_count: 可选，目标字数，默认3000字，范围500-10000字
- enable_mcp: 可选，是否启用MCP工具增强，默认True
注意：此函数不使用依赖注入的db，而是在生成器内部创建独立的数据库会话
以避免流式响应期间的连接泄漏问题
"""
style_id = generate_request.style_id
target_word_count = generate_request.target_word_count or 3000
enable_mcp = generate_request.enable_mcp if hasattr(generate_request, 'enable_mcp') else True
# 预先验证章节存在性（使用临时会话）
async for temp_db in get_db(request):
try:
@@ -1002,7 +1004,60 @@ async def generate_chapter_content_stream(
# 发送开始事件
yield f"data: {json.dumps({'type': 'start', 'message': '开始AI创作...'}, ensure_ascii=False)}\n\n"
# 根据是否有前置内容选择不同的提示词，并应用写作风格和记忆增强
# 🔧 MCP工具增强：收集章节参考资料
mcp_reference_materials = ""
if enable_mcp and current_user_id:
try:
yield f"data: {json.dumps({'type': 'progress', 'message': '🔍 尝试使用MCP工具收集参考资料...', 'progress': 28}, ensure_ascii=False)}\n\n"
# 构建资料收集提示词
planning_prompt = f"""你正在为小说《{project.title}》创作第{current_chapter.chapter_number}章《{current_chapter.title}》。
【章节大纲】
{outline.content if outline else current_chapter.summary or '暂无大纲'}
【小说信息】
- 题材：{project.genre or '未设定'}
- 主题：{project.theme or '未设定'}
- 时代背景：{project.world_time_period or '未设定'}
- 地理位置：{project.world_location or '未设定'}
【任务】
请使用可用工具搜索相关背景资料，帮助创作更真实、更有深度的章节内容。
你可以查询：
1. 该章节涉及的历史事件或时代背景
2. 地理环境和场景描写参考
3. 相关领域的专业知识（如武术、科技、魔法等）
4. 文化习俗和生活细节
请根据章节内容，有针对性地查询1-2个最关键的问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_prompt,
user_id=current_user_id,
db_session=db_session,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
tool_count = planning_result["tool_calls_made"]
yield f"data: {json.dumps({'type': 'progress', 'message': f'✅ MCP工具调用成功（{tool_count}次）', 'progress': 32}, ensure_ascii=False)}\n\n"
mcp_reference_materials = planning_result.get("content", "")
logger.info(f"📚 MCP工具收集参考资料：{len(mcp_reference_materials)} 字符")
else:
yield f"data: {json.dumps({'type': 'progress', 'message': 'ℹ️ 未使用MCP工具（无可用工具或不需要）', 'progress': 32}, ensure_ascii=False)}\n\n"
except Exception as e:
logger.warning(f"MCP工具调用失败（降级处理）: {e}")
yield f"data: {json.dumps({'type': 'progress', 'message': '⚠️ MCP工具暂时不可用，使用基础模式', 'progress': 32}, ensure_ascii=False)}\n\n"
# 根据是否有前置内容选择不同的提示词，并应用写作风格、记忆增强和MCP参考资料
if previous_content:
prompt = prompt_service.get_chapter_generation_with_context_prompt(
title=project.title,
@@ -1021,7 +1076,8 @@ async def generate_chapter_content_stream(
chapter_outline=outline.content if outline else current_chapter.summary or '暂无大纲',
style_content=style_content,
target_word_count=target_word_count,
memory_context=memory_context
memory_context=memory_context,
mcp_references=mcp_reference_materials
)
else:
prompt = prompt_service.get_chapter_generation_prompt(
@@ -1040,9 +1096,13 @@ async def generate_chapter_content_stream(
chapter_outline=outline.content if outline else current_chapter.summary or '暂无大纲',
style_content=style_content,
target_word_count=target_word_count,
memory_context=memory_context
memory_context=memory_context,
mcp_references=mcp_reference_materials
)
if mcp_reference_materials:
logger.info(f"📖 已整合MCP参考资料（{len(mcp_reference_materials)}字符）到章节生成提示词")
logger.info(f"开始AI流式创作章节 {chapter_id}")
# 流式生成内容
backend/app/api/characters.py
+31 -6
View File
@@ -1,5 +1,5 @@
"""角色管理API"""
from fastapi import APIRouter, Depends, HTTPException
from fastapi import APIRouter, Depends, HTTPException, Request
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select, func
import json
@@ -221,6 +221,7 @@ async def delete_character(
@router.post("/generate", response_model=CharacterResponse, summary="AI生成角色")
async def generate_character(
request: CharacterGenerateRequest,
http_request: Request,
db: AsyncSession = Depends(get_db),
user_ai_service: AIService = Depends(get_user_ai_service)
):
@@ -294,18 +295,42 @@ async def generate_character(
user_input=user_input
)
# 调用AI生成角色
logger.info(f"🎯 开始为项目 {request.project_id} 生成角色")
# 获取user_id用于MCP工具调用
user_id = http_request.state.user_id if hasattr(http_request.state, 'user_id') else 'default_user'
# 调用AI生成角色（支持MCP工具）
logger.info(f"🎯 开始为项目 {request.project_id} 生成角色（启用MCP）")
logger.info(f" - 角色名：{request.name or 'AI生成'}")
logger.info(f" - 角色定位：{request.role_type}")
logger.info(f" - 背景设定：{request.background or '无'}")
logger.info(f" - AI提供商：{user_ai_service.api_provider}")
logger.info(f" - AI模型：{user_ai_service.default_model}")
logger.info(f" - Prompt长度：{len(prompt)} 字符")
logger.info(f" - 用户ID：{user_id}")
try:
ai_response = await user_ai_service.generate_text(prompt=prompt)
logger.info(f"✅ AI响应接收完成，长度：{len(ai_response) if ai_response else 0} 字符")
# 使用支持MCP的生成方法
result = await user_ai_service.generate_text_with_mcp(
prompt=prompt,
user_id=user_id,
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None, # 使用AIService初始化时的配置
model=None # 使用AIService初始化时的配置
)
# 提取内容
if isinstance(result, dict):
ai_response = result.get('content', '')
logger.info(f"✅ AI响应接收完成（MCP增强），长度：{len(ai_response)} 字符")
if result.get('tool_calls'):
logger.info(f" - 工具调用：{len(result['tool_calls'])} 次")
else:
ai_response = result
logger.info(f"✅ AI响应接收完成，长度：{len(ai_response) if ai_response else 0} 字符")
except Exception as ai_error:
logger.error(f"❌ AI服务调用异常：{str(ai_error)}")
raise HTTPException(
@@ -559,7 +584,7 @@ async def generate_character(
history = GenerationHistory(
project_id=request.project_id,
prompt=prompt,
generated_content=ai_response,
generated_content=json.dumps(result, ensure_ascii=False) if isinstance(result, dict) else ai_response,
model=user_ai_service.default_model
)
db.add(history)
backend/app/api/mcp_plugins.py
+862
View File
@@ -0,0 +1,862 @@
"""MCP插件管理API"""
from fastapi import APIRouter, HTTPException, Depends, Query, Request
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
from typing import List, Optional
from datetime import datetime
from app.database import get_db
from app.models.mcp_plugin import MCPPlugin
from app.schemas.mcp_plugin import (
MCPPluginCreate,
MCPPluginSimpleCreate,
MCPPluginUpdate,
MCPPluginResponse,
MCPToolCall,
MCPTestResult
)
import json
from app.user_manager import User
from app.mcp.registry import mcp_registry
from app.logger import get_logger
from app.services.ai_service import create_user_ai_service
from app.models.settings import Settings as UserSettings
logger = get_logger(__name__)
router = APIRouter(prefix="/mcp/plugins", tags=["MCP插件管理"])
def require_login(request: Request) -> User:
"""依赖：要求用户已登录"""
if not hasattr(request.state, "user") or not request.state.user:
raise HTTPException(status_code=401, detail="需要登录")
return request.state.user
@router.get("", response_model=List[MCPPluginResponse])
async def list_plugins(
enabled_only: bool = Query(False, description="只返回启用的插件"),
category: Optional[str] = Query(None, description="按分类筛选"),
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
获取用户的所有MCP插件
"""
query = select(MCPPlugin).where(MCPPlugin.user_id == user.user_id)
if enabled_only:
query = query.where(MCPPlugin.enabled == True)
if category:
query = query.where(MCPPlugin.category == category)
query = query.order_by(MCPPlugin.sort_order, MCPPlugin.created_at)
result = await db.execute(query)
plugins = result.scalars().all()
logger.info(f"用户 {user.user_id} 查询插件列表，共 {len(plugins)} 个")
return plugins
@router.post("", response_model=MCPPluginResponse)
async def create_plugin(
data: MCPPluginCreate,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
创建新的MCP插件
"""
# 检查插件名是否已存在
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.user_id == user.user_id,
MCPPlugin.plugin_name == data.plugin_name
)
)
existing = result.scalar_one_or_none()
if existing:
raise HTTPException(status_code=400, detail=f"插件名已存在: {data.plugin_name}")
# 创建插件数据
plugin_data = data.model_dump()
# 如果没有提供display_name，使用plugin_name作为默认值
if not plugin_data.get("display_name"):
plugin_data["display_name"] = plugin_data["plugin_name"]
# 创建插件
plugin = MCPPlugin(
user_id=user.user_id,
**plugin_data
)
db.add(plugin)
await db.commit()
await db.refresh(plugin)
# 如果启用，加载到注册表
if plugin.enabled:
success = await mcp_registry.load_plugin(plugin)
if success:
plugin.status = "active"
else:
plugin.status = "error"
plugin.last_error = "加载失败"
await db.commit()
await db.refresh(plugin)
logger.info(f"用户 {user.user_id} 创建插件: {plugin.plugin_name}")
return plugin
@router.post("/simple", response_model=MCPPluginResponse)
async def create_plugin_simple(
data: MCPPluginSimpleCreate,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
通过标准MCP配置JSON创建或更新插件（简化版）
接受格式：
{
"config_json": '{"mcpServers": {"exa": {"type": "http", "url": "...", "headers": {}}}}',
"category": "search"
}
自动从mcpServers中提取插件名称（取第一个键）
如果插件已存在，则更新；否则创建新插件
"""
try:
# 解析配置JSON
config = json.loads(data.config_json)
# 验证格式
if "mcpServers" not in config:
raise HTTPException(status_code=400, detail="配置JSON必须包含mcpServers字段")
servers = config["mcpServers"]
if not servers or len(servers) == 0:
raise HTTPException(status_code=400, detail="mcpServers不能为空")
# 自动提取第一个插件名称
plugin_name = list(servers.keys())[0]
server_config = servers[plugin_name]
logger.info(f"从配置中提取插件名称: {plugin_name}")
# 提取配置
server_type = server_config.get("type", "http")
if server_type not in ["http", "stdio"]:
raise HTTPException(status_code=400, detail=f"不支持的服务器类型: {server_type}")
# 检查插件名是否已存在
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.user_id == user.user_id,
MCPPlugin.plugin_name == plugin_name
)
)
existing = result.scalar_one_or_none()
# 构建插件数据
plugin_data = {
"plugin_name": plugin_name,
"display_name": plugin_name,
"plugin_type": server_type,
"enabled": data.enabled,
"category": data.category,
"sort_order": 0
}
if server_type == "http":
plugin_data["server_url"] = server_config.get("url")
plugin_data["headers"] = server_config.get("headers", {})
if not plugin_data["server_url"]:
raise HTTPException(status_code=400, detail="HTTP类型插件必须提供url字段")
elif server_type == "stdio":
plugin_data["command"] = server_config.get("command")
plugin_data["args"] = server_config.get("args", [])
plugin_data["env"] = server_config.get("env", {})
if not plugin_data["command"]:
raise HTTPException(status_code=400, detail="Stdio类型插件必须提供command字段")
if existing:
# 更新现有插件
logger.info(f"插件 {plugin_name} 已存在，执行更新操作")
# 先卸载旧插件
if existing.enabled:
await mcp_registry.unload_plugin(user.user_id, existing.plugin_name)
# 更新字段
for key, value in plugin_data.items():
setattr(existing, key, value)
plugin = existing
await db.commit()
await db.refresh(plugin)
# 如果启用，重新加载
if plugin.enabled:
success = await mcp_registry.load_plugin(plugin)
if success:
plugin.status = "active"
plugin.last_error = None
else:
plugin.status = "error"
plugin.last_error = "加载失败"
await db.commit()
await db.refresh(plugin)
logger.info(f"用户 {user.user_id} 更新插件: {plugin_name}")
else:
# 创建新插件
plugin = MCPPlugin(
user_id=user.user_id,
**plugin_data
)
db.add(plugin)
await db.commit()
await db.refresh(plugin)
# 如果启用，加载到注册表
if plugin.enabled:
success = await mcp_registry.load_plugin(plugin)
if success:
plugin.status = "active"
else:
plugin.status = "error"
plugin.last_error = "加载失败"
await db.commit()
await db.refresh(plugin)
logger.info(f"用户 {user.user_id} 通过简化配置创建插件: {plugin_name}")
return plugin
except json.JSONDecodeError as e:
raise HTTPException(status_code=400, detail=f"配置JSON格式错误: {str(e)}")
except HTTPException:
raise
except Exception as e:
logger.error(f"创建插件失败: {str(e)}")
raise HTTPException(status_code=500, detail=f"创建插件失败: {str(e)}")
@router.get("/{plugin_id}", response_model=MCPPluginResponse)
async def get_plugin(
plugin_id: str,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
获取插件详情
"""
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.id == plugin_id,
MCPPlugin.user_id == user.user_id
)
)
plugin = result.scalar_one_or_none()
if not plugin:
raise HTTPException(status_code=404, detail="插件不存在")
return plugin
@router.put("/{plugin_id}", response_model=MCPPluginResponse)
async def update_plugin(
plugin_id: str,
data: MCPPluginUpdate,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
更新插件配置
"""
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.id == plugin_id,
MCPPlugin.user_id == user.user_id
)
)
plugin = result.scalar_one_or_none()
if not plugin:
raise HTTPException(status_code=404, detail="插件不存在")
# 更新字段
update_data = data.model_dump(exclude_unset=True)
for key, value in update_data.items():
setattr(plugin, key, value)
await db.commit()
await db.refresh(plugin)
# 如果插件已启用，重新加载
if plugin.enabled:
await mcp_registry.reload_plugin(plugin)
logger.info(f"用户 {user.user_id} 更新插件: {plugin.plugin_name}")
return plugin
@router.delete("/{plugin_id}")
async def delete_plugin(
plugin_id: str,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
删除插件
"""
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.id == plugin_id,
MCPPlugin.user_id == user.user_id
)
)
plugin = result.scalar_one_or_none()
if not plugin:
raise HTTPException(status_code=404, detail="插件不存在")
# 从注册表卸载
await mcp_registry.unload_plugin(user.user_id, plugin.plugin_name)
# 删除数据库记录
await db.delete(plugin)
await db.commit()
logger.info(f"用户 {user.user_id} 删除插件: {plugin.plugin_name}")
return {"message": "插件已删除", "plugin_name": plugin.plugin_name}
@router.post("/{plugin_id}/toggle", response_model=MCPPluginResponse)
async def toggle_plugin(
plugin_id: str,
enabled: bool = Query(..., description="启用或禁用"),
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
启用或禁用插件
"""
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.id == plugin_id,
MCPPlugin.user_id == user.user_id
)
)
plugin = result.scalar_one_or_none()
if not plugin:
raise HTTPException(status_code=404, detail="插件不存在")
plugin.enabled = enabled
if enabled:
# 启用：加载到注册表
success = await mcp_registry.load_plugin(plugin)
if success:
plugin.status = "active"
plugin.last_error = None
else:
plugin.status = "error"
plugin.last_error = "加载失败"
else:
# 禁用：从注册表卸载
await mcp_registry.unload_plugin(user.user_id, plugin.plugin_name)
plugin.status = "inactive"
await db.commit()
await db.refresh(plugin)
action = "启用" if enabled else "禁用"
logger.info(f"用户 {user.user_id} {action}插件: {plugin.plugin_name}")
return plugin
@router.post("/{plugin_id}/test", response_model=MCPTestResult)
async def test_plugin(
plugin_id: str,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
测试插件连接并调用工具验证功能
测试流程:
1. 测试MCP服务器连接
2. 获取工具列表
3. 自动选择一个工具进行实际调用测试
4. 返回完整测试结果
"""
import time
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.id == plugin_id,
MCPPlugin.user_id == user.user_id
)
)
plugin = result.scalar_one_or_none()
if not plugin:
raise HTTPException(status_code=404, detail="插件不存在")
if not plugin.enabled:
return MCPTestResult(
success=False,
message="插件未启用",
error="请先启用插件",
suggestions=["点击开关按钮启用插件"]
)
start_time = time.time()
try:
# 1. 确保插件已加载
if not mcp_registry.get_client(user.user_id, plugin.plugin_name):
success = await mcp_registry.load_plugin(plugin)
if not success:
return MCPTestResult(
success=False,
message="插件加载失败",
error="无法创建MCP客户端",
suggestions=["请检查插件配置", "请确认服务器URL正确"]
)
# 2. 测试连接并获取工具列表
test_result = await mcp_registry.test_plugin(user.user_id, plugin.plugin_name)
if not test_result["success"]:
plugin.status = "error"
plugin.last_error = test_result.get("error", "连接测试失败")
plugin.last_test_at = datetime.now()
await db.commit()
return MCPTestResult(**test_result)
tools = test_result.get("tools", [])
if not tools:
plugin.status = "error"
plugin.last_error = "插件没有提供任何工具"
plugin.last_test_at = datetime.now()
await db.commit()
return MCPTestResult(
success=False,
message="插件没有提供任何工具",
error="工具列表为空",
response_time_ms=test_result.get("response_time_ms"),
suggestions=["请检查插件配置", "请确认MCP服务器正常运行"]
)
# 3. 使用AI智能选择工具并生成测试参数
logger.info(f"使用AI分析工具并生成测试计划...")
# 获取用户的AI设置
settings_result = await db.execute(
select(UserSettings).where(UserSettings.user_id == user.user_id)
)
user_settings = settings_result.scalar_one_or_none()
if not user_settings or not user_settings.api_key:
# 如果没有AI配置，回退到简单测试
logger.warning("用户未配置AI服务，使用简单连接测试")
plugin.status = "active"
plugin.last_error = None
plugin.last_test_at = datetime.now()
plugin.tools = tools
await db.commit()
return MCPTestResult(
success=True,
message=f"✅ 连接测试成功（未配置AI，跳过工具调用测试）",
response_time_ms=test_result.get("response_time_ms"),
tools_count=len(tools),
suggestions=[
f"连接测试: 成功",
f"可用工具数: {len(tools)}",
"提示: 配置AI服务后可进行智能工具调用测试"
]
)
# 使用AI的标准Function Calling机制选择工具
ai_service = create_user_ai_service(
api_provider=user_settings.api_provider,
api_key=user_settings.api_key,
api_base_url=user_settings.api_base_url,
model_name=user_settings.llm_model,
temperature=0.3,
max_tokens=1000
)
# 将MCP工具格式转换为OpenAI Function Calling格式
openai_tools = []
for tool in tools:
openai_tool = {
"type": "function",
"function": {
"name": tool["name"],
"description": tool.get("description", ""),
}
}
# 将 inputSchema 转换为 parameters
if "inputSchema" in tool:
openai_tool["function"]["parameters"] = tool["inputSchema"]
openai_tools.append(openai_tool)
logger.info(f"转换了 {len(openai_tools)} 个MCP工具为OpenAI格式")
logger.info(f"工具列表: {[t['function']['name'] for t in openai_tools]}")
# 使用标准的Function Calling，将转换后的工具传递给AI
prompt = f"""你是MCP插件测试助手，需要测试插件 '{plugin.plugin_name}' 的功能。
请选择一个合适的工具进行测试，优先选择搜索、查询类工具。
生成真实有效的测试参数（例如搜索"人工智能最新进展"而不是"test"）。
现在开始测试这个插件。"""
system_prompt = "你是专业的API测试工具。当给定工具列表时，选择一个工具并使用合适的参数调用它。"
# 调用AI的Function Calling
logger.info(f"📞 准备调用AI Function Calling")
logger.info(f" - Provider: {user_settings.api_provider}")
logger.info(f" - Model: {user_settings.llm_model}")
logger.info(f" - Tools count: {len(openai_tools)}")
logger.debug(f" - Tools: {json.dumps(openai_tools, ensure_ascii=False, indent=2)}")
ai_response = await ai_service.generate_text(
prompt=prompt,
system_prompt=system_prompt,
tools=openai_tools, # 传递转换后的OpenAI格式工具
tool_choice="required" # 要求AI必须选择一个工具
)
logger.info(f"📥 收到AI响应")
logger.info(f" - Response keys: {list(ai_response.keys())}")
logger.debug(f" - Full response: {json.dumps(ai_response, ensure_ascii=False, indent=2)}")
# 检查AI是否请求调用工具
if not ai_response.get("tool_calls"):
# AI未调用工具，记录详细信息
logger.error(f"❌ AI未返回工具调用")
logger.error(f" - Response: {ai_response}")
logger.error(f" - Content: {ai_response.get('content', 'N/A')}")
logger.error(f" - Finish reason: {ai_response.get('finish_reason', 'N/A')}")
plugin.status = "error"
plugin.last_error = "AI未返回工具调用请求"
plugin.last_test_at = datetime.now()
await db.commit()
return MCPTestResult(
success=False,
message="❌ AI Function Calling失败",
error=f"AI未返回工具调用请求。响应: {ai_response.get('content', 'N/A')[:200]}",
tools_count=len(tools),
suggestions=[
"请确认使用的AI模型支持Function Calling",
"OpenAI: 需要gpt-4, gpt-3.5-turbo等模型",
"Anthropic: 需要claude-3系列模型",
f"当前Provider: {user_settings.api_provider}",
f"当前模型: {user_settings.llm_model}",
f"AI返回内容: {ai_response.get('content', 'N/A')[:100]}"
]
)
# 获取第一个工具调用
tool_call = ai_response["tool_calls"][0]
function = tool_call["function"]
tool_name = function["name"]
test_arguments = function["arguments"]
# AI返回的arguments可能是JSON字符串，需要解析
if isinstance(test_arguments, str):
try:
test_arguments = json.loads(test_arguments)
logger.info(f"✅ 解析AI返回的JSON字符串参数")
except json.JSONDecodeError as e:
logger.error(f"❌ 解析AI参数失败: {e}")
return MCPTestResult(
success=False,
message="❌ AI返回的参数格式错误",
error=f"无法解析参数JSON: {str(e)}",
tools_count=len(tools),
suggestions=["AI返回的参数不是有效的JSON格式"]
)
logger.info(f"🤖 AI通过Function Calling选择的工具: {tool_name}")
logger.info(f"📝 AI生成的参数: {test_arguments}")
logger.info(f"📝 参数类型: {type(test_arguments).__name__}")
# 4. 使用AI选择的工具和参数调用MCP工具
call_start = time.time()
try:
tool_result = await mcp_registry.call_tool(
user.user_id,
plugin.plugin_name,
tool_name,
test_arguments
)
call_end = time.time()
call_time = round((call_end - call_start) * 1000, 2)
total_time = round((call_end - start_time) * 1000, 2)
# 6. 测试成功，更新插件状态
plugin.status = "active"
plugin.last_error = None
plugin.last_test_at = datetime.now()
plugin.tools = tools # 缓存工具列表
await db.commit()
# 格式化工具结果用于显示
result_str = str(tool_result)
# 如果结果太长，截取前800字符
if len(result_str) > 800:
result_preview = result_str[:800] + "\n...(结果已截断，完整结果请查看日志)"
else:
result_preview = result_str
return MCPTestResult(
success=True,
message=f"✅ Function Calling测试成功！工具 '{tool_name}' 调用正常",
response_time_ms=total_time,
tools_count=len(tools),
suggestions=[
f"🤖 AI (Function Calling) 选择: {tool_name}",
f"📝 AI生成的参数: {json.dumps(test_arguments, ensure_ascii=False)}",
f"⏱️ 调用耗时: {call_time}ms",
f"📊 返回结果:\n{result_preview}"
]
)
except Exception as call_error:
call_end = time.time()
total_time = round((call_end - start_time) * 1000, 2)
logger.warning(f"工具调用失败: {tool_name}, 错误: {call_error}")
# 工具调用失败,但连接成功
plugin.status = "active" # 仍标记为active,因为连接是成功的
plugin.last_error = f"工具调用测试失败: {str(call_error)}"
plugin.last_test_at = datetime.now()
plugin.tools = tools
await db.commit()
return MCPTestResult(
success=True, # 连接成功就算测试通过
message=f"⚠️ 连接成功，但工具调用失败",
response_time_ms=total_time,
tools_count=len(tools),
error=f"工具 '{tool_name}' 调用失败: {str(call_error)}",
suggestions=[
f"✅ 连接测试: 成功",
f"❌ 工具调用测试: 失败",
f"🤖 AI (Function Calling) 选择: {tool_name}",
f"📝 AI生成的参数: {json.dumps(test_arguments, ensure_ascii=False)}",
f"❌ 错误: {str(call_error)}",
"💡 可能原因: API Key无效、参数错误或服务限制"
]
)
except Exception as e:
end_time = time.time()
total_time = round((end_time - start_time) * 1000, 2)
logger.error(f"测试插件失败: {plugin.plugin_name}, 错误: {e}")
plugin.status = "error"
plugin.last_error = str(e)
plugin.last_test_at = datetime.now()
await db.commit()
return MCPTestResult(
success=False,
message="❌ 测试失败",
response_time_ms=total_time,
error=str(e),
error_type=type(e).__name__,
suggestions=["请检查服务器是否在线", "请确认配置正确", "请检查API Key是否有效"]
)
def _build_test_arguments(tool_name: str, input_schema: dict, plugin_name: str) -> dict:
"""
根据工具schema智能构造测试参数
Args:
tool_name: 工具名称
input_schema: 输入schema
plugin_name: 插件名称
Returns:
测试参数字典
"""
# 针对常见MCP工具的默认测试参数
test_cases = {
# Exa搜索工具
"search": {
"query": "AI technology",
"num_results": 3
},
"search_and_contents": {
"query": "artificial intelligence",
"num_results": 2
},
# Brave搜索
"brave_web_search": {
"query": "AI news",
"count": 3
},
# Filesystem工具
"read_file": {
"path": "README.md"
},
"list_directory": {
"path": "."
},
}
# 如果有针对特定工具的测试用例，使用它
if tool_name in test_cases:
logger.info(f"使用预定义测试参数: {test_cases[tool_name]}")
return test_cases[tool_name]
# 否则根据schema自动构造
properties = input_schema.get("properties", {})
required = input_schema.get("required", [])
test_args = {}
for prop_name, prop_schema in properties.items():
# 只填充必需的参数
if prop_name not in required:
continue
prop_type = prop_schema.get("type", "string")
# 根据参数名称和类型猜测合适的测试值
if "query" in prop_name.lower() or "search" in prop_name.lower():
test_args[prop_name] = "test query"
elif "url" in prop_name.lower():
test_args[prop_name] = "https://example.com"
elif "path" in prop_name.lower():
test_args[prop_name] = "."
elif "count" in prop_name.lower() or "limit" in prop_name.lower() or "num" in prop_name.lower():
test_args[prop_name] = 3
elif prop_type == "string":
test_args[prop_name] = "test"
elif prop_type == "number" or prop_type == "integer":
test_args[prop_name] = 1
elif prop_type == "boolean":
test_args[prop_name] = True
elif prop_type == "array":
test_args[prop_name] = []
elif prop_type == "object":
test_args[prop_name] = {}
logger.info(f"自动构造测试参数: {test_args}")
return test_args
@router.get("/{plugin_id}/tools")
async def get_plugin_tools(
plugin_id: str,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
获取插件提供的工具列表
"""
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.id == plugin_id,
MCPPlugin.user_id == user.user_id
)
)
plugin = result.scalar_one_or_none()
if not plugin:
raise HTTPException(status_code=404, detail="插件不存在")
if not plugin.enabled:
raise HTTPException(status_code=400, detail="插件未启用")
try:
tools = await mcp_registry.get_plugin_tools(user.user_id, plugin.plugin_name)
# 更新缓存
plugin.tools = tools
await db.commit()
return {
"plugin_name": plugin.plugin_name,
"tools": tools,
"count": len(tools)
}
except Exception as e:
logger.error(f"获取工具列表失败: {plugin.plugin_name}, 错误: {e}")
raise HTTPException(status_code=500, detail=f"获取工具列表失败: {str(e)}")
@router.post("/call")
async def call_mcp_tool(
data: MCPToolCall,
user: User = Depends(require_login),
db: AsyncSession = Depends(get_db)
):
"""
调用MCP工具
"""
# 获取插件
result = await db.execute(
select(MCPPlugin).where(
MCPPlugin.id == data.plugin_id,
MCPPlugin.user_id == user.user_id
)
)
plugin = result.scalar_one_or_none()
if not plugin:
raise HTTPException(status_code=404, detail="插件不存在")
if not plugin.enabled:
raise HTTPException(status_code=400, detail="插件未启用")
try:
# 调用工具
result = await mcp_registry.call_tool(
user.user_id,
plugin.plugin_name,
data.tool_name,
data.arguments
)
return {
"success": True,
"plugin_name": plugin.plugin_name,
"tool_name": data.tool_name,
"result": result
}
except Exception as e:
logger.error(f"调用工具失败: {plugin.plugin_name}.{data.tool_name}, 错误: {e}")
raise HTTPException(status_code=500, detail=f"工具调用失败: {str(e)}")
backend/app/api/organizations.py
+1
View File
@@ -38,6 +38,7 @@ class OrganizationGenerateRequest(BaseModel):
organization_type: Optional[str] = Field(None, description="组织类型")
background: Optional[str] = Field(None, description="组织背景")
requirements: Optional[str] = Field(None, description="特殊要求")
enable_mcp: bool = Field(True, description="是否启用MCP工具增强（搜索组织架构参考）")
@router.get("/project/{project_id}", response_model=List[OrganizationDetailResponse], summary="获取项目的所有组织")
backend/app/api/outlines.py
+255 -23
View File
@@ -404,8 +404,8 @@ async def _generate_new_outline(
db: AsyncSession,
user_ai_service: AIService
) -> OutlineListResponse:
"""全新生成大纲"""
logger.info(f"全新生成大纲 - 项目: {project.id}, keep_existing: {request.keep_existing}")
"""全新生成大纲（MCP增强版）"""
logger.info(f"全新生成大纲 - 项目: {project.id}, enable_mcp: {request.enable_mcp}")
# 获取角色信息
characters_result = await db.execute(
@@ -418,7 +418,59 @@ async def _generate_new_outline(
for char in characters
])
# 使用完整提示词
# 🔍 MCP工具增强：收集情节设计参考资料
mcp_reference_materials = ""
if request.enable_mcp:
try:
logger.info(f"🔍 尝试使用MCP工具收集大纲设计参考资料...")
# 构建资料收集查询
planning_query = f"""你正在为小说《{project.title}》设计完整大纲。
项目信息：
- 主题：{request.theme or project.theme}
- 类型：{request.genre or project.genre}
- 章节数：{request.chapter_count}
- 叙事视角：{request.narrative_perspective}
- 目标字数：{request.target_words}
世界观设定：
- 时间背景：{project.world_time_period or '未设定'}
- 地理位置：{project.world_location or '未设定'}
- 氛围基调：{project.world_atmosphere or '未设定'}
角色信息：
{characters_info or '暂无角色'}
请搜索：
1. 该类型小说的经典情节结构和套路
2. 适合该主题的冲突设计思路
3. 符合世界观的情节元素和场景设计灵感
请有针对性地查询1-2个最关键的问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_query,
user_id="system", # 全新生成时可能没有用户上下文
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
mcp_reference_materials = planning_result.get("content", "")
logger.info(f"📚 MCP工具收集参考资料：{len(mcp_reference_materials)} 字符")
else:
logger.info(f"ℹ️ MCP工具未进行调用，继续正常生成")
except Exception as e:
logger.warning(f"⚠️ MCP工具调用失败，继续使用常规模式: {str(e)}")
mcp_reference_materials = ""
# 使用完整提示词（插入MCP参考资料）
prompt = prompt_service.get_complete_outline_prompt(
title=project.title,
theme=request.theme or project.theme or "未设定",
@@ -431,18 +483,22 @@ async def _generate_new_outline(
atmosphere=project.world_atmosphere or "未设定",
rules=project.world_rules or "未设定",
characters_info=characters_info or "暂无角色信息",
requirements=request.requirements or ""
requirements=request.requirements or "",
mcp_references=mcp_reference_materials
)
# 调用AI
# 调用AI生成大纲
ai_response = await user_ai_service.generate_text(
prompt=prompt,
provider=request.provider,
model=request.model
)
# 提取内容（generate_text返回字典）
ai_content = ai_response.get("content", "") if isinstance(ai_response, dict) else ai_response
# 解析响应
outline_data = _parse_ai_response(ai_response)
outline_data = _parse_ai_response(ai_content)
# 全新生成模式：必须删除旧大纲和章节
# 注意：这是"new"模式的核心逻辑，应该始终删除旧数据
@@ -463,7 +519,7 @@ async def _generate_new_outline(
history = GenerationHistory(
project_id=project.id,
prompt=prompt,
generated_content=ai_response,
generated_content=json.dumps(ai_response, ensure_ascii=False) if isinstance(ai_response, dict) else ai_response,
model=request.model or "default"
)
db.add(history)
@@ -571,8 +627,8 @@ async def _continue_outline(
user_ai_service: AIService,
user_id: str = "system"
) -> OutlineListResponse:
"""续写大纲 - 分批生成，每批5章（记忆增强版）"""
logger.info(f"续写大纲 - 项目: {project.id}, 已有: {len(existing_outlines)} 章")
"""续写大纲 - 分批生成，每批5章（记忆+MCP增强版）"""
logger.info(f"续写大纲 - 项目: {project.id}, 已有: {len(existing_outlines)} 章, enable_mcp: {request.enable_mcp}")
# 分析已有大纲
current_chapter_count = len(existing_outlines)
@@ -664,7 +720,57 @@ async def _continue_outline(
logger.warning(f"⚠️ 记忆上下文构建失败，继续不使用记忆: {str(e)}")
memory_context = None
# 使用标准续写提示词模板（支持记忆增强）
# 🔍 MCP工具增强：收集续写参考资料
mcp_reference_materials = ""
if request.enable_mcp:
try:
logger.info(f"🔍 第{batch_num + 1}批：尝试使用MCP工具收集续写参考资料...")
# 构建资料收集查询
latest_summary = latest_outlines[-1].content if latest_outlines else ""
planning_query = f"""你正在为小说《{project.title}》续写大纲。
当前进度：已有{len(latest_outlines)}章，即将续写第{current_start_chapter}-{current_start_chapter + current_batch_size - 1}章
项目信息：
- 主题：{request.theme or project.theme}
- 类型：{request.genre or project.genre}
- 叙事视角：{request.narrative_perspective}
- 情节阶段：{request.plot_stage}
- 故事发展方向：{request.story_direction or '自然延续'}
最近章节概要：
{latest_summary[:200]}
请搜索：
1. 该情节阶段的经典处理手法和技巧
2. 适合该发展方向的情节转折和冲突设计
3. 符合类型特点的场景设计和剧情元素
请有针对性地查询1-2个最关键的问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_query,
user_id=user_id,
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
mcp_reference_materials = planning_result.get("content", "")
logger.info(f"📚 第{batch_num + 1}批MCP工具收集参考资料：{len(mcp_reference_materials)} 字符")
else:
logger.info(f"ℹ️ 第{batch_num + 1}批MCP工具未进行调用，继续正常生成")
except Exception as e:
logger.warning(f"⚠️ 第{batch_num + 1}批MCP工具调用失败，继续使用常规模式: {str(e)}")
mcp_reference_materials = ""
# 使用标准续写提示词模板（支持记忆+MCP增强）
prompt = prompt_service.get_outline_continue_prompt(
title=project.title,
theme=request.theme or project.theme or "未设定",
@@ -683,7 +789,8 @@ async def _continue_outline(
start_chapter=current_start_chapter,
story_direction=request.story_direction or "自然延续",
requirements=request.requirements or "",
memory_context=memory_context
memory_context=memory_context,
mcp_references=mcp_reference_materials
)
# 调用AI生成当前批次
@@ -694,8 +801,11 @@ async def _continue_outline(
model=request.model
)
# 提取内容（generate_text返回字典）
ai_content = ai_response.get("content", "") if isinstance(ai_response, dict) else ai_response
# 解析响应
outline_data = _parse_ai_response(ai_response)
outline_data = _parse_ai_response(ai_content)
# 保存当前批次的大纲
batch_outlines = await _save_outlines(
@@ -706,7 +816,7 @@ async def _continue_outline(
history = GenerationHistory(
project_id=project.id,
prompt=f"[批次{batch_num + 1}/{total_batches}] {str(prompt)[:500]}",
generated_content=ai_response,
generated_content=json.dumps(ai_response, ensure_ascii=False) if isinstance(ai_response, dict) else ai_response,
model=request.model or "default"
)
db.add(history)
@@ -820,7 +930,7 @@ async def new_outline_generator(
db: AsyncSession,
user_ai_service: AIService
) -> AsyncGenerator[str, None]:
"""全新生成大纲SSE生成器"""
"""全新生成大纲SSE生成器（MCP增强版）"""
db_committed = False
try:
yield await SSEResponse.send_progress("开始生成大纲...", 5)
@@ -828,6 +938,7 @@ async def new_outline_generator(
project_id = data.get("project_id")
# 确保chapter_count是整数（前端可能传字符串）
chapter_count = int(data.get("chapter_count", 10))
enable_mcp = data.get("enable_mcp", True)
# 验证项目
yield await SSEResponse.send_progress("加载项目信息...", 10)
@@ -852,7 +963,61 @@ async def new_outline_generator(
for char in characters
])
# 使用完整提示词
# 🔍 MCP工具增强：收集情节设计参考资料
mcp_reference_materials = ""
if enable_mcp:
try:
yield await SSEResponse.send_progress("🔍 使用MCP工具收集参考资料...", 18)
logger.info(f"🔍 尝试使用MCP工具收集大纲设计参考资料...")
# 构建资料收集查询
planning_query = f"""你正在为小说《{project.title}》设计完整大纲。
项目信息：
- 主题：{data.get('theme') or project.theme}
- 类型：{data.get('genre') or project.genre}
- 章节数：{chapter_count}
- 叙事视角：{data.get('narrative_perspective') or '第三人称'}
- 目标字数：{data.get('target_words') or project.target_words or 100000}
世界观设定：
- 时间背景：{project.world_time_period or '未设定'}
- 地理位置：{project.world_location or '未设定'}
- 氛围基调：{project.world_atmosphere or '未设定'}
角色信息：
{characters_info or '暂无角色'}
请搜索：
1. 该类型小说的经典情节结构和套路
2. 适合该主题的冲突设计思路
3. 符合世界观的情节元素和场景设计灵感
请有针对性地查询1-2个最关键的问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_query,
user_id="system",
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
mcp_reference_materials = planning_result.get("content", "")
logger.info(f"📚 MCP工具收集参考资料：{len(mcp_reference_materials)} 字符")
yield await SSEResponse.send_progress(f"📚 MCP收集到参考资料 ({len(mcp_reference_materials)}字符)", 19)
else:
logger.info(f"ℹ️ MCP工具未进行调用，继续正常生成")
except Exception as e:
logger.warning(f"⚠️ MCP工具调用失败，继续使用常规模式: {str(e)}")
mcp_reference_materials = ""
# 使用完整提示词（插入MCP参考资料）
yield await SSEResponse.send_progress("准备AI提示词...", 20)
prompt = prompt_service.get_complete_outline_prompt(
title=project.title,
@@ -866,7 +1031,8 @@ async def new_outline_generator(
atmosphere=project.world_atmosphere or "未设定",
rules=project.world_rules or "未设定",
characters_info=characters_info or "暂无角色信息",
requirements=data.get("requirements") or ""
requirements=data.get("requirements") or "",
mcp_references=mcp_reference_materials
)
# 调用AI
@@ -879,8 +1045,11 @@ async def new_outline_generator(
yield await SSEResponse.send_progress("✅ AI生成完成，正在解析...", 70)
# 提取内容（generate_text返回字典）
ai_content = ai_response.get("content", "") if isinstance(ai_response, dict) else ai_response
# 解析响应
outline_data = _parse_ai_response(ai_response)
outline_data = _parse_ai_response(ai_content)
# 删除旧大纲和章节
yield await SSEResponse.send_progress("清理旧数据...", 75)
@@ -902,7 +1071,7 @@ async def new_outline_generator(
history = GenerationHistory(
project_id=project_id,
prompt=prompt,
generated_content=ai_response,
generated_content=json.dumps(ai_response, ensure_ascii=False) if isinstance(ai_response, dict) else ai_response,
model=data.get("model") or "default"
)
db.add(history)
@@ -957,7 +1126,7 @@ async def continue_outline_generator(
user_ai_service: AIService,
user_id: str = "system"
) -> AsyncGenerator[str, None]:
"""大纲续写SSE生成器 - 分批生成，推送进度（记忆增强版）"""
"""大纲续写SSE生成器 - 分批生成，推送进度（记忆+MCP增强版）"""
db_committed = False
try:
yield await SSEResponse.send_progress("开始续写大纲...", 5)
@@ -1090,13 +1259,72 @@ async def continue_outline_generator(
except Exception as e:
logger.warning(f"⚠️ 记忆上下文构建失败: {str(e)}")
memory_context = None
# 🔍 MCP工具增强：收集续写参考资料
mcp_reference_materials = ""
enable_mcp = data.get("enable_mcp", True)
if enable_mcp:
try:
yield await SSEResponse.send_progress(
f"🔍 第{str(batch_num + 1)}批：使用MCP工具收集参考资料...",
batch_progress + 4
)
logger.info(f"🔍 第{batch_num + 1}批：尝试使用MCP工具收集续写参考资料...")
# 构建资料收集查询
latest_summary = latest_outlines[-1].content if latest_outlines else ""
planning_query = f"""你正在为小说《{project.title}》续写大纲。
当前进度：已有{len(latest_outlines)}章，即将续写第{current_start_chapter}-{current_start_chapter + current_batch_size - 1}章
项目信息：
- 主题：{data.get('theme') or project.theme}
- 类型：{data.get('genre') or project.genre}
- 叙事视角：{data.get('narrative_perspective') or project.narrative_perspective or '第三人称'}
- 情节阶段：{data.get('plot_stage', 'development')}
- 故事发展方向：{data.get('story_direction', '自然延续')}
最近章节概要：
{latest_summary[:200]}
请搜索：
1. 该情节阶段的经典处理手法和技巧
2. 适合该发展方向的情节转折和冲突设计
3. 符合类型特点的场景设计和剧情元素
请有针对性地查询1-2个最关键的问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_query,
user_id=user_id,
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
mcp_reference_materials = planning_result.get("content", "")
logger.info(f"📚 第{batch_num + 1}批MCP工具收集参考资料：{len(mcp_reference_materials)} 字符")
yield await SSEResponse.send_progress(
f"📚 第{str(batch_num + 1)}批收集到参考资料 ({len(mcp_reference_materials)}字符)",
batch_progress + 4.5
)
else:
logger.info(f"ℹ️ 第{batch_num + 1}批MCP工具未进行调用，继续正常生成")
except Exception as e:
logger.warning(f"⚠️ 第{batch_num + 1}批MCP工具调用失败，继续使用常规模式: {str(e)}")
mcp_reference_materials = ""
yield await SSEResponse.send_progress(
f" 调用AI生成第{str(batch_num + 1)}批...",
batch_progress + 5
)
# 使用标准续写提示词模板（支持记忆增强）
# 使用标准续写提示词模板（支持记忆+MCP增强）
prompt = prompt_service.get_outline_continue_prompt(
title=project.title,
theme=data.get("theme") or project.theme or "未设定",
@@ -1115,7 +1343,8 @@ async def continue_outline_generator(
start_chapter=current_start_chapter,
story_direction=data.get("story_direction", "自然延续"),
requirements=data.get("requirements", ""),
memory_context=memory_context
memory_context=memory_context,
mcp_references=mcp_reference_materials
)
# 调用AI生成当前批次
@@ -1130,8 +1359,11 @@ async def continue_outline_generator(
batch_progress + 10
)
# 提取内容（generate_text返回字典）
ai_content = ai_response.get("content", "") if isinstance(ai_response, dict) else ai_response
# 解析响应
outline_data = _parse_ai_response(ai_response)
outline_data = _parse_ai_response(ai_content)
# 保存当前批次的大纲
batch_outlines = await _save_outlines(
@@ -1142,7 +1374,7 @@ async def continue_outline_generator(
history = GenerationHistory(
project_id=project_id,
prompt=f"[续写批次{batch_num + 1}/{total_batches}] {str(prompt)[:500]}",
generated_content=ai_response,
generated_content=json.dumps(ai_response, ensure_ascii=False) if isinstance(ai_response, dict) else ai_response,
model=data.get("model") or "default"
)
db.add(history)
backend/app/api/settings.py
+5 -2
View File
@@ -359,7 +359,10 @@ async def test_api_connection(data: ApiTestRequest):
logger.info(f"✅ API 测试成功")
logger.info(f" - 响应时间: {response_time}ms")
logger.info(f" - 响应内容: {response[:100] if response else 'N/A'}")
# 安全地处理响应内容（确保是字符串）
response_str = str(response) if response else 'N/A'
logger.info(f" - 响应内容: {response_str[:100]}")
return {
"success": True,
@@ -367,7 +370,7 @@ async def test_api_connection(data: ApiTestRequest):
"response_time_ms": response_time,
"provider": provider,
"model": llm_model,
"response_preview": response[:100] if response and len(response) > 100 else response,
"response_preview": response_str[:100] if len(response_str) > 100 else response_str,
"details": {
"api_available": True,
"model_accessible": True,
backend/app/api/wizard_stream.py
+234 -17
View File
@@ -1,5 +1,5 @@
"""项目创建向导流式API - 使用SSE避免超时"""
from fastapi import APIRouter, Depends, HTTPException
from fastapi import APIRouter, Depends, HTTPException, Request
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
from typing import Dict, Any, AsyncGenerator
@@ -15,6 +15,7 @@ from app.models.relationship import CharacterRelationship, Organization, Organiz
from app.models.writing_style import WritingStyle
from app.models.project_default_style import ProjectDefaultStyle
from app.services.ai_service import AIService
from app.services.mcp_tool_service import MCPToolService
from app.services.prompt_service import prompt_service
from app.logger import get_logger
from app.utils.sse_response import SSEResponse, create_sse_response
@@ -29,7 +30,7 @@ async def world_building_generator(
db: AsyncSession,
user_ai_service: AIService
) -> AsyncGenerator[str, None]:
"""世界构建流式生成器"""
"""世界构建流式生成器 - 支持MCP工具增强"""
# 标记数据库会话是否已提交
db_committed = False
try:
@@ -47,27 +48,94 @@ async def world_building_generator(
character_count = data.get("character_count")
provider = data.get("provider")
model = data.get("model")
enable_mcp = data.get("enable_mcp", True) # 默认启用MCP
user_id = data.get("user_id") # 从中间件注入
if not title or not description or not theme or not genre:
yield await SSEResponse.send_error("title、description、theme 和 genre 是必需的参数", 400)
return
# 获取提示词
yield await SSEResponse.send_progress("准备AI提示词...", 20)
prompt = prompt_service.get_world_building_prompt(
# 获取基础提示词
yield await SSEResponse.send_progress("准备AI提示词...", 15)
base_prompt = prompt_service.get_world_building_prompt(
title=title,
theme=theme,
genre=genre
)
# 流式调用AI
yield await SSEResponse.send_progress("正在调用AI生成...", 30)
# MCP工具增强：收集参考资料
reference_materials = ""
if enable_mcp and user_id:
try:
yield await SSEResponse.send_progress("🔍 尝试使用MCP工具收集参考资料...", 18)
# 直接调用MCP增强的AI，内部会自动检查和加载工具
# 构建资料收集提示词
planning_prompt = f"""你正在为小说《{title}》设计世界观。
【小说信息】
- 题材：{genre}
- 主题：{theme}
- 简介：{description}
【任务】
请使用可用工具搜索相关背景资料，帮助构建更真实、更有深度的世界观设定。
你可以查询：
1. 历史背景（如果是历史题材）
2. 地理环境和文化特征
3. 相关领域的专业知识
4. 类似作品的设定参考
请根据题材特点，有针对性地查询2-3个关键问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_prompt,
user_id=user_id,
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
yield await SSEResponse.send_progress(
f"✅ MCP工具调用成功（{planning_result['tool_calls_made']}次）",
25
)
reference_materials = planning_result.get("content", "")
else:
yield await SSEResponse.send_progress("ℹ️ 未使用MCP工具（无可用工具或不需要）", 25)
except Exception as e:
logger.warning(f"MCP工具调用失败（降级处理）: {e}")
yield await SSEResponse.send_progress("⚠️ MCP工具暂时不可用，使用基础模式", 25)
# 构建增强提示词
if reference_materials:
enhanced_prompt = f"""{base_prompt}
【参考资料】
以下是通过MCP工具收集的真实背景资料，请参考这些信息构建更真实的世界观：
{reference_materials}
请结合上述资料，生成符合历史/现实的世界观设定。"""
final_prompt = enhanced_prompt
yield await SSEResponse.send_progress("💡 已整合参考资料，开始生成世界观...", 30)
else:
final_prompt = base_prompt
yield await SSEResponse.send_progress("正在调用AI生成...", 30)
# 流式生成世界观
accumulated_text = ""
chunk_count = 0
async for chunk in user_ai_service.generate_text_stream(
prompt=prompt,
prompt=final_prompt,
provider=provider,
model=model
):
@@ -190,6 +258,7 @@ async def world_building_generator(
@router.post("/world-building", summary="流式生成世界构建")
async def generate_world_building_stream(
request: Request,
data: Dict[str, Any],
db: AsyncSession = Depends(get_db),
user_ai_service: AIService = Depends(get_user_ai_service)
@@ -198,6 +267,10 @@ async def generate_world_building_stream(
使用SSE流式生成世界构建，避免超时
前端使用EventSource接收实时进度和结果
"""
# 从中间件注入user_id到data中
if hasattr(request.state, 'user_id'):
data['user_id'] = request.state.user_id
return create_sse_response(world_building_generator(data, db, user_ai_service))
@@ -206,7 +279,7 @@ async def characters_generator(
db: AsyncSession,
user_ai_service: AIService
) -> AsyncGenerator[str, None]:
"""角色批量生成流式生成器 - 优化版:分批+重试"""
"""角色批量生成流式生成器 - 优化版:分批+重试+MCP工具增强"""
db_committed = False
try:
yield await SSEResponse.send_progress("开始生成角色...", 5)
@@ -219,6 +292,8 @@ async def characters_generator(
requirements = data.get("requirements", "")
provider = data.get("provider")
model = data.get("model")
enable_mcp = data.get("enable_mcp", True) # 默认启用MCP
user_id = data.get("user_id") # 从中间件注入
# 验证项目
yield await SSEResponse.send_progress("验证项目...", 10)
@@ -239,6 +314,57 @@ async def characters_generator(
"rules": project.world_rules or "未设定"
}
# MCP工具增强：收集角色参考资料
character_reference_materials = ""
if enable_mcp and user_id:
try:
yield await SSEResponse.send_progress("🔍 尝试使用MCP工具收集角色参考资料...", 8)
# 构建角色资料收集提示词
planning_prompt = f"""你正在为小说《{project.title}》设计角色。
【小说信息】
- 题材：{genre or project.genre}
- 主题：{theme or project.theme}
- 时代背景：{world_context.get('time_period', '未设定')}
- 地理位置：{world_context.get('location', '未设定')}
【任务】
请使用可用工具搜索相关参考资料，帮助设计更真实、更有深度的角色。
你可以查询：
1. 该时代/地域的真实历史人物特征
2. 文化背景和社会习俗
3. 职业特点和生活方式
4. 相关领域的人物原型
请根据题材特点，有针对性地查询1-2个关键问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_prompt,
user_id=user_id,
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
yield await SSEResponse.send_progress(
f"✅ MCP工具调用成功（{planning_result['tool_calls_made']}次）",
12
)
character_reference_materials = planning_result.get("content", "")
else:
yield await SSEResponse.send_progress("ℹ️ 未使用MCP工具（无可用工具或不需要）", 12)
except Exception as e:
logger.warning(f"MCP工具调用失败（降级处理）: {e}")
yield await SSEResponse.send_progress("⚠️ MCP工具暂时不可用，使用基础模式", 12)
# 优化的分批策略:每批生成3个,平衡效率和成功率
BATCH_SIZE = 3 # 每批生成3个角色
MAX_RETRIES = 3 # 每批最多重试3次
@@ -291,7 +417,8 @@ async def characters_generator(
else:
batch_requirements += "\n主要是配角(supporting)和反派(antagonist)"
prompt = prompt_service.get_characters_batch_prompt(
# 构建基础提示词
base_prompt = prompt_service.get_characters_batch_prompt(
count=current_batch_size, # 传递精确数量
time_period=world_context.get("time_period", ""),
location=world_context.get("location", ""),
@@ -302,6 +429,19 @@ async def characters_generator(
requirements=batch_requirements
)
# 如果有MCP参考资料，增强提示词
if character_reference_materials:
prompt = f"""{base_prompt}
【参考资料】
以下是通过MCP工具收集的真实背景资料，请参考这些信息设计更真实的角色：
{character_reference_materials}
请结合上述资料，设计符合历史/文化背景的角色。"""
else:
prompt = base_prompt
# 流式生成
accumulated_text = ""
async for chunk in user_ai_service.generate_text_stream(
@@ -708,13 +848,19 @@ async def characters_generator(
@router.post("/characters", summary="流式批量生成角色")
async def generate_characters_stream(
request: Request,
data: Dict[str, Any],
db: AsyncSession = Depends(get_db),
user_ai_service: AIService = Depends(get_user_ai_service)
):
"""
使用SSE流式批量生成角色，避免超时
支持MCP工具增强
"""
# 从中间件注入user_id到data中
if hasattr(request.state, 'user_id'):
data['user_id'] = request.state.user_id
return create_sse_response(characters_generator(data, db, user_ai_service))
@@ -1071,7 +1217,7 @@ async def regenerate_world_building_generator(
db: AsyncSession,
user_ai_service: AIService
) -> AsyncGenerator[str, None]:
"""重新生成世界观流式生成器"""
"""重新生成世界观流式生成器 - 支持MCP工具增强"""
db_committed = False
try:
yield await SSEResponse.send_progress("开始重新生成世界观...", 10)
@@ -1087,23 +1233,89 @@ async def regenerate_world_building_generator(
provider = data.get("provider")
model = data.get("model")
enable_mcp = data.get("enable_mcp", True) # 默认启用MCP
user_id = data.get("user_id") # 从中间件注入
# 获取世界构建提示词
yield await SSEResponse.send_progress("准备AI提示词...", 20)
prompt = prompt_service.get_world_building_prompt(
# 获取基础提示词
yield await SSEResponse.send_progress("准备AI提示词...", 15)
base_prompt = prompt_service.get_world_building_prompt(
title=project.title,
theme=project.theme or "",
genre=project.genre or ""
)
# 流式调用AI
yield await SSEResponse.send_progress("正在调用AI生成...", 30)
# MCP工具增强：收集参考资料
reference_materials = ""
if enable_mcp and user_id:
try:
yield await SSEResponse.send_progress("🔍 尝试使用MCP工具收集参考资料...", 18)
# 直接调用MCP增强的AI，内部会自动检查和加载工具
# 构建资料收集提示词
planning_prompt = f"""你正在为小说《{project.title}》重新设计世界观。
【小说信息】
- 题材：{project.genre or '未设定'}
- 主题：{project.theme or '未设定'}
【任务】
请使用可用工具搜索相关背景资料，帮助构建更真实、更有深度的世界观设定。
你可以查询：
1. 历史背景（如果是历史题材）
2. 地理环境和文化特征
3. 相关领域的专业知识
4. 类似作品的设定参考
请根据题材特点，有针对性地查询2-3个关键问题。"""
# 调用MCP增强的AI（非流式，最多2轮工具调用）
planning_result = await user_ai_service.generate_text_with_mcp(
prompt=planning_prompt,
user_id=user_id,
db_session=db,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
provider=None,
model=None
)
# 提取参考资料
if planning_result.get("tool_calls_made", 0) > 0:
yield await SSEResponse.send_progress(
f"✅ MCP工具调用成功（{planning_result['tool_calls_made']}次）",
25
)
reference_materials = planning_result.get("content", "")
else:
yield await SSEResponse.send_progress("ℹ️ 未使用MCP工具（无可用工具或不需要）", 25)
except Exception as e:
logger.warning(f"MCP工具调用失败（降级处理）: {e}")
yield await SSEResponse.send_progress("⚠️ MCP工具暂时不可用，使用基础模式", 25)
# 构建增强提示词
if reference_materials:
enhanced_prompt = f"""{base_prompt}
【参考资料】
以下是通过MCP工具收集的真实背景资料，请参考这些信息构建更真实的世界观：
{reference_materials}
请结合上述资料，生成符合历史/现实的世界观设定。"""
final_prompt = enhanced_prompt
yield await SSEResponse.send_progress("💡 已整合参考资料，开始重新生成世界观...", 30)
else:
final_prompt = base_prompt
yield await SSEResponse.send_progress("正在调用AI生成...", 30)
# 流式生成世界观
accumulated_text = ""
chunk_count = 0
async for chunk in user_ai_service.generate_text_stream(
prompt=prompt,
prompt=final_prompt,
provider=provider,
model=model
):
@@ -1187,6 +1399,7 @@ async def regenerate_world_building_generator(
@router.post("/world-building/{project_id}/regenerate", summary="流式重新生成世界观")
async def regenerate_world_building_stream(
request: Request,
project_id: str,
data: Dict[str, Any],
db: AsyncSession = Depends(get_db),
@@ -1200,6 +1413,10 @@ async def regenerate_world_building_stream(
"model": "模型名称（可选）"
}
"""
# 从中间件注入user_id到data中
if hasattr(request.state, 'user_id'):
data['user_id'] = request.state.user_id
return create_sse_response(regenerate_world_building_generator(project_id, data, db, user_ai_service))
backend/app/main.py
+9 -1
View File
@@ -28,7 +28,13 @@ async def lifespan(app: FastAPI):
"""应用生命周期管理"""
logger.info("应用启动，等待用户登录...")
# 导入MCP注册表
from app.mcp.registry import mcp_registry
yield
# 清理MCP插件
await mcp_registry.cleanup_all()
await close_db()
logger.info("应用已关闭")
@@ -114,7 +120,8 @@ async def db_session_stats():
from app.api import (
projects, outlines, characters, chapters,
wizard_stream, relationships, organizations,
auth, users, settings, writing_styles, memories
auth, users, settings, writing_styles, memories,
mcp_plugins
)
app.include_router(auth.router, prefix="/api")
@@ -130,6 +137,7 @@ app.include_router(relationships.router, prefix="/api")
app.include_router(organizations.router, prefix="/api")
app.include_router(writing_styles.router, prefix="/api")
app.include_router(memories.router) # 记忆管理API (已包含/api前缀)
app.include_router(mcp_plugins.router, prefix="/api") # MCP插件管理API
static_dir = Path(__file__).parent.parent / "static"
if static_dir.exists():
backend/app/mcp/__init__.py
+4
View File
@@ -0,0 +1,4 @@
"""MCP插件系统"""
from .registry import mcp_registry
__all__ = ["mcp_registry"]
backend/app/mcp/http_client.py
+345
View File
@@ -0,0 +1,345 @@
"""HTTP MCP客户端 - 实现JSON-RPC 2.0协议"""
import httpx
from typing import Dict, Any, List, Optional
from app.logger import get_logger
import time
logger = get_logger(__name__)
class MCPError(Exception):
"""MCP错误"""
pass
class HTTPMCPClient:
"""HTTP模式MCP客户端（类似Cursor/Claude Code实现）"""
def __init__(
self,
url: str,
headers: Optional[Dict[str, str]] = None,
env: Optional[Dict[str, str]] = None,
timeout: float = 60.0,
http_client: Optional[httpx.AsyncClient] = None
):
"""
初始化HTTP MCP客户端
Args:
url: MCP服务器URL
headers: HTTP请求头
env: 环境变量（用于API Key等）
timeout: 超时时间（秒）
http_client: 可选的共享HTTP客户端（用于连接池复用）
"""
self.url = url.rstrip('/')
self.headers = headers or {}
self.env = env or {}
self.timeout = timeout
# 设置MCP必需的Accept头
# MCP服务器要求客户端必须接受 application/json 和 text/event-stream
if 'Accept' not in self.headers:
self.headers['Accept'] = 'application/json, text/event-stream'
# 设置Content-Type
if 'Content-Type' not in self.headers:
self.headers['Content-Type'] = 'application/json'
# 如果env中有API Key，添加到headers
if 'API_KEY' in self.env:
self.headers['Authorization'] = f'Bearer {self.env["API_KEY"]}'
# 使用共享客户端或创建新客户端
self._owns_client = http_client is None
if http_client:
self.client = http_client
else:
self.client = httpx.AsyncClient(
timeout=httpx.Timeout(timeout),
headers=self.headers
)
self._request_id = 0
def _next_request_id(self) -> int:
"""获取下一个请求ID"""
self._request_id += 1
return self._request_id
async def _call_jsonrpc(
self,
method: str,
params: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
"""
调用JSON-RPC 2.0方法
Args:
method: 方法名
params: 参数
Returns:
响应结果
Raises:
MCPError: 调用失败时抛出
"""
request_id = self._next_request_id()
payload = {
"jsonrpc": "2.0",
"id": request_id,
"method": method,
"params": params or {}
}
try:
logger.debug(f"MCP请求: {method} -> {self.url}")
response = await self.client.post(
self.url,
json=payload,
headers=self.headers # 显式传递headers（对于共享客户端很重要）
)
response.raise_for_status()
# 获取响应内容
response_text = response.text
content_type = response.headers.get('content-type', '')
# 如果是空响应
if not response_text or response_text.strip() == '':
raise MCPError("服务器返回空响应")
# 处理SSE格式响应
if 'text/event-stream' in content_type or response_text.startswith('event:'):
logger.debug("检测到SSE格式响应，开始解析")
data = self._parse_sse_response(response_text)
else:
# 标准JSON响应
try:
data = response.json()
except ValueError as e:
logger.error(f"JSON解析失败，响应内容: {response_text[:500]}")
raise MCPError(f"无法解析JSON响应: {str(e)}")
# 检查JSON-RPC错误
if "error" in data:
error = data["error"]
error_msg = error.get("message", "Unknown error")
error_code = error.get("code", -1)
logger.error(f"MCP错误 [{error_code}]: {error_msg}")
raise MCPError(f"[{error_code}] {error_msg}")
if "result" not in data:
raise MCPError("响应中缺少result字段")
return data["result"]
except httpx.HTTPStatusError as e:
logger.error(f"HTTP错误 {e.response.status_code}: {e.response.text}")
raise MCPError(f"HTTP错误 {e.response.status_code}: {e.response.text}")
except httpx.RequestError as e:
logger.error(f"请求错误: {str(e)}")
raise MCPError(f"请求错误: {str(e)}")
except MCPError:
raise
except Exception as e:
logger.error(f"未知错误: {str(e)}")
raise MCPError(f"未知错误: {str(e)}")
def _parse_sse_response(self, sse_text: str) -> Dict[str, Any]:
"""
解析SSE格式的响应
SSE格式示例:
event: message
data: {"result": {...}}
Args:
sse_text: SSE格式的文本
Returns:
解析后的JSON数据
"""
import json
lines = sse_text.strip().split('\n')
data_lines = []
for line in lines:
line = line.strip()
if line.startswith('data:'):
# 提取data后面的内容
data_content = line[5:].strip()
data_lines.append(data_content)
if not data_lines:
raise MCPError("SSE响应中没有找到data字段")
# 合并所有data行（某些SSE可能分多行）
full_data = ''.join(data_lines)
try:
return json.loads(full_data)
except json.JSONDecodeError as e:
logger.error(f"解析SSE data失败: {full_data[:200]}")
raise MCPError(f"SSE data不是有效的JSON: {str(e)}")
async def list_tools(self) -> List[Dict[str, Any]]:
"""
列举可用工具
Returns:
工具列表
"""
try:
result = await self._call_jsonrpc("tools/list")
tools = result.get("tools", [])
logger.info(f"获取到 {len(tools)} 个工具")
return tools
except Exception as e:
logger.error(f"获取工具列表失败: {e}")
raise
async def call_tool(
self,
tool_name: str,
arguments: Dict[str, Any]
) -> Any:
"""
调用工具
Args:
tool_name: 工具名称
arguments: 工具参数
Returns:
工具执行结果
"""
try:
logger.info(f"调用工具: {tool_name}")
logger.debug(f"参数: {arguments}")
result = await self._call_jsonrpc(
"tools/call",
{
"name": tool_name,
"arguments": arguments
}
)
# MCP返回的result通常包含content数组
if isinstance(result, dict) and "content" in result:
content = result["content"]
if isinstance(content, list) and len(content) > 0:
# 提取第一个content项的text
first_content = content[0]
if isinstance(first_content, dict) and "text" in first_content:
return first_content["text"]
return first_content
return content
return result
except Exception as e:
logger.error(f"调用工具失败: {tool_name}, 错误: {e}")
raise
async def list_resources(self) -> List[Dict[str, Any]]:
"""
列举可用资源
Returns:
资源列表
"""
try:
result = await self._call_jsonrpc("resources/list")
resources = result.get("resources", [])
logger.info(f"获取到 {len(resources)} 个资源")
return resources
except Exception as e:
logger.error(f"获取资源列表失败: {e}")
raise
async def read_resource(self, uri: str) -> Any:
"""
读取资源
Args:
uri: 资源URI
Returns:
资源内容
"""
try:
result = await self._call_jsonrpc(
"resources/read",
{"uri": uri}
)
return result
except Exception as e:
logger.error(f"读取资源失败: {uri}, 错误: {e}")
raise
async def test_connection(self) -> Dict[str, Any]:
"""
测试连接
Returns:
测试结果
"""
start_time = time.time()
try:
# 尝试列举工具来测试连接
tools = await self.list_tools()
end_time = time.time()
response_time = round((end_time - start_time) * 1000, 2)
return {
"success": True,
"message": "连接测试成功",
"response_time_ms": response_time,
"tools_count": len(tools),
"tools": tools
}
except MCPError as e:
end_time = time.time()
response_time = round((end_time - start_time) * 1000, 2)
return {
"success": False,
"message": "连接测试失败",
"response_time_ms": response_time,
"error": str(e),
"error_type": "MCPError",
"suggestions": [
"请检查服务器URL是否正确",
"请确认API Key是否有效",
"请检查网络连接"
]
}
except Exception as e:
end_time = time.time()
response_time = round((end_time - start_time) * 1000, 2)
return {
"success": False,
"message": "连接测试失败",
"response_time_ms": response_time,
"error": str(e),
"error_type": type(e).__name__,
"suggestions": [
"请检查服务器是否在线",
"请确认配置是否正确"
]
}
async def close(self):
"""关闭客户端（仅在拥有客户端所有权时关闭）"""
if self._owns_client and self.client:
await self.client.aclose()
backend/app/mcp/registry.py
+349
View File
@@ -0,0 +1,349 @@
"""MCP插件注册表 - 管理运行时插件实例"""
import asyncio
import time
import httpx
from typing import Dict, Optional, Any, List, Tuple
from collections import OrderedDict
from app.mcp.http_client import HTTPMCPClient, MCPError
from app.models.mcp_plugin import MCPPlugin
from app.logger import get_logger
logger = get_logger(__name__)
class MCPPluginRegistry:
"""MCP插件注册表 - 管理运行时插件实例（多用户优化版）"""
def __init__(self, max_clients: int = 1000, client_ttl: int = 3600):
"""
初始化注册表
Args:
max_clients: 最大缓存客户端数量
client_ttl: 客户端过期时间（秒），默认1小时
"""
# 存储格式: {plugin_id: (client, last_access_time)}
self._clients: OrderedDict[str, Tuple[HTTPMCPClient, float]] = OrderedDict()
# 细粒度锁：每个用户一个锁
self._user_locks: Dict[str, asyncio.Lock] = {}
self._locks_lock = asyncio.Lock() # 保护locks字典本身
# 配置参数
self._max_clients = max_clients
self._client_ttl = client_ttl
# 共享HTTP客户端池（用于所有MCP HTTP请求）
self._shared_http_client = httpx.AsyncClient(
limits=httpx.Limits(
max_keepalive_connections=100,
max_connections=200,
keepalive_expiry=30.0
),
timeout=httpx.Timeout(connect=10.0, read=60.0, write=10.0, pool=5.0),
headers={
"User-Agent": "MuMuAINovel-MCP-Client/1.0"
}
)
# 启动后台清理任务
self._cleanup_task = None
self._start_cleanup_task()
def _start_cleanup_task(self):
"""启动后台清理任务"""
if self._cleanup_task is None:
self._cleanup_task = asyncio.create_task(self._cleanup_loop())
logger.info("✅ MCP插件注册表后台清理任务已启动")
async def _cleanup_loop(self):
"""后台清理过期客户端"""
while True:
try:
await asyncio.sleep(300) # 每5分钟清理一次
await self._cleanup_expired_clients()
except asyncio.CancelledError:
break
except Exception as e:
logger.error(f"清理任务异常: {e}")
async def _cleanup_expired_clients(self):
"""清理过期的客户端"""
now = time.time()
expired_ids = []
# 收集过期的plugin_id
for plugin_id, (client, last_access) in list(self._clients.items()):
if now - last_access > self._client_ttl:
expired_ids.append(plugin_id)
if expired_ids:
logger.info(f"🧹 清理 {len(expired_ids)} 个过期的MCP客户端")
for plugin_id in expired_ids:
# 提取user_id来获取对应的锁
user_id = plugin_id.split(':', 1)[0]
user_lock = await self._get_user_lock(user_id)
async with user_lock:
if plugin_id in self._clients:
await self._unload_plugin_unsafe(plugin_id)
async def _get_user_lock(self, user_id: str) -> asyncio.Lock:
"""
获取用户专属的锁（细粒度锁）
Args:
user_id: 用户ID
Returns:
该用户的锁对象
"""
async with self._locks_lock:
if user_id not in self._user_locks:
self._user_locks[user_id] = asyncio.Lock()
return self._user_locks[user_id]
def _touch_client(self, plugin_id: str):
"""
更新客户端的最后访问时间（LRU）
Args:
plugin_id: 插件ID
"""
if plugin_id in self._clients:
client, _ = self._clients[plugin_id]
self._clients[plugin_id] = (client, time.time())
# 移到末尾（LRU）
self._clients.move_to_end(plugin_id)
async def _evict_lru_client(self):
"""驱逐最久未使用的客户端（当达到max_clients限制时）"""
if len(self._clients) >= self._max_clients:
# 获取最旧的plugin_id
oldest_id = next(iter(self._clients))
logger.info(f"📤 达到最大客户端数量限制，驱逐: {oldest_id}")
await self._unload_plugin_unsafe(oldest_id)
async def load_plugin(self, plugin: MCPPlugin) -> bool:
"""
从配置加载插件
Args:
plugin: 插件配置
Returns:
是否加载成功
"""
# 使用细粒度锁（只锁定当前用户）
user_lock = await self._get_user_lock(plugin.user_id)
async with user_lock:
try:
plugin_id = f"{plugin.user_id}:{plugin.plugin_name}"
# 如果已加载，先卸载
if plugin_id in self._clients:
await self._unload_plugin_unsafe(plugin_id)
# 检查是否需要驱逐LRU客户端
await self._evict_lru_client()
# 目前只支持HTTP类型
if plugin.plugin_type == "http":
if not plugin.server_url:
logger.error(f"HTTP插件缺少server_url: {plugin.plugin_name}")
return False
# 使用共享HTTP连接池创建客户端
client = HTTPMCPClient(
url=plugin.server_url,
headers=plugin.headers or {},
env=plugin.env or {},
timeout=plugin.config.get('timeout', 60.0) if plugin.config else 60.0,
http_client=self._shared_http_client # 传入共享连接池
)
# 存储客户端和当前时间戳
self._clients[plugin_id] = (client, time.time())
logger.info(f"✅ 加载MCP插件: {plugin_id}")
return True
else:
logger.warning(f"暂不支持的插件类型: {plugin.plugin_type}")
return False
except Exception as e:
logger.error(f"加载插件失败 {plugin.plugin_name}: {e}")
return False
async def unload_plugin(self, user_id: str, plugin_name: str):
"""
卸载插件
Args:
user_id: 用户ID
plugin_name: 插件名称
"""
# 使用细粒度锁（只锁定当前用户）
user_lock = await self._get_user_lock(user_id)
async with user_lock:
plugin_id = f"{user_id}:{plugin_name}"
await self._unload_plugin_unsafe(plugin_id)
async def _unload_plugin_unsafe(self, plugin_id: str):
"""卸载插件（不加锁，内部使用）"""
if plugin_id in self._clients:
client, _ = self._clients[plugin_id] # 解包 (client, timestamp)
try:
await client.close()
except Exception as e:
logger.error(f"关闭插件客户端失败 {plugin_id}: {e}")
del self._clients[plugin_id]
logger.info(f"卸载MCP插件: {plugin_id}")
async def reload_plugin(self, plugin: MCPPlugin) -> bool:
"""
重新加载插件
Args:
plugin: 插件配置
Returns:
是否重载成功
"""
await self.unload_plugin(plugin.user_id, plugin.plugin_name)
return await self.load_plugin(plugin)
def get_client(self, user_id: str, plugin_name: str) -> Optional[HTTPMCPClient]:
"""
获取插件客户端（支持LRU访问时间更新）
Args:
user_id: 用户ID
plugin_name: 插件名称
Returns:
客户端实例或None
"""
plugin_id = f"{user_id}:{plugin_name}"
entry = self._clients.get(plugin_id)
if entry:
# 更新访问时间（LRU）
self._touch_client(plugin_id)
return entry[0] # 返回客户端对象
return None
async def call_tool(
self,
user_id: str,
plugin_name: str,
tool_name: str,
arguments: Dict[str, Any]
) -> Any:
"""
调用插件工具
Args:
user_id: 用户ID
plugin_name: 插件名称
tool_name: 工具名称
arguments: 工具参数
Returns:
工具执行结果
Raises:
ValueError: 插件不存在或未启用
MCPError: 工具调用失败
"""
client = self.get_client(user_id, plugin_name)
if not client:
raise ValueError(f"插件未加载: {plugin_name}")
try:
result = await client.call_tool(tool_name, arguments)
logger.info(f"✅ 工具调用成功: {plugin_name}.{tool_name}")
# logger.info(f"✅ 工具返回内容: {result}")
return result
except Exception as e:
logger.error(f"❌ 工具调用失败: {plugin_name}.{tool_name}, 错误: {e}")
raise
async def get_plugin_tools(
self,
user_id: str,
plugin_name: str
) -> List[Dict[str, Any]]:
"""
获取插件的工具列表
Args:
user_id: 用户ID
plugin_name: 插件名称
Returns:
工具列表
"""
client = self.get_client(user_id, plugin_name)
if not client:
raise ValueError(f"插件未加载: {plugin_name}")
try:
tools = await client.list_tools()
return tools
except Exception as e:
logger.error(f"获取工具列表失败: {plugin_name}, 错误: {e}")
raise
async def test_plugin(
self,
user_id: str,
plugin_name: str
) -> Dict[str, Any]:
"""
测试插件连接
Args:
user_id: 用户ID
plugin_name: 插件名称
Returns:
测试结果
"""
client = self.get_client(user_id, plugin_name)
if not client:
raise ValueError(f"插件未加载: {plugin_name}")
return await client.test_connection()
async def cleanup_all(self):
"""清理所有插件和资源"""
# 停止后台清理任务
if self._cleanup_task:
self._cleanup_task.cancel()
try:
await self._cleanup_task
except asyncio.CancelledError:
pass
# 清理所有客户端
plugin_ids = list(self._clients.keys())
for plugin_id in plugin_ids:
user_id = plugin_id.split(':', 1)[0]
user_lock = await self._get_user_lock(user_id)
async with user_lock:
await self._unload_plugin_unsafe(plugin_id)
# 关闭共享HTTP客户端
try:
await self._shared_http_client.aclose()
except Exception as e:
logger.error(f"关闭共享HTTP客户端失败: {e}")
logger.info("✅ 已清理所有MCP插件和资源")
# 全局注册表实例
mcp_registry = MCPPluginRegistry()
backend/app/models/__init__.py
+17 -20
View File
@@ -1,37 +1,34 @@
"""数据库模型"""
"""数据模型导出"""
from app.models.project import Project
from app.models.outline import Outline
from app.models.character import Character
from app.models.chapter import Chapter
from app.models.character import Character
from app.models.relationship import CharacterRelationship, Organization, OrganizationMember, RelationshipType
from app.models.generation_history import GenerationHistory
from app.models.settings import Settings
from app.models.writing_style import WritingStyle
from app.models.project_default_style import ProjectDefaultStyle
from app.models.relationship import (
RelationshipType,
CharacterRelationship,
Organization,
OrganizationMember
)
from app.models.memory import StoryMemory, PlotAnalysis
from app.models.analysis_task import AnalysisTask
from app.models.batch_generation_task import BatchGenerationTask
from app.models.settings import Settings
from app.models.memory import StoryMemory, PlotAnalysis
from app.models.writing_style import WritingStyle
from app.models.project_default_style import ProjectDefaultStyle
from app.models.mcp_plugin import MCPPlugin
__all__ = [
"Project",
"Outline",
"Character",
"Chapter",
"GenerationHistory",
"Settings",
"WritingStyle",
"ProjectDefaultStyle",
"RelationshipType",
"Character",
"CharacterRelationship",
"Organization",
"OrganizationMember",
"StoryMemory",
"PlotAnalysis",
"RelationshipType",
"GenerationHistory",
"AnalysisTask",
"BatchGenerationTask",
"Settings",
"StoryMemory",
"PlotAnalysis",
"WritingStyle",
"ProjectDefaultStyle",
"MCPPlugin"
]
backend/app/models/mcp_plugin.py
+52
View File
@@ -0,0 +1,52 @@
"""MCP插件配置数据模型"""
from sqlalchemy import Column, String, Text, Boolean, Integer, DateTime, Index, JSON
from sqlalchemy.sql import func
from app.database import Base
import uuid
class MCPPlugin(Base):
"""MCP插件配置表"""
__tablename__ = "mcp_plugins"
id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
user_id = Column(String(50), nullable=False, index=True, comment="用户ID")
# 插件基本信息
plugin_name = Column(String(100), nullable=False, comment="插件名称（唯一标识）")
display_name = Column(String(200), nullable=False, comment="显示名称")
description = Column(Text, comment="插件描述")
plugin_type = Column(String(50), default="http", comment="插件类型：http/stdio")
# 连接配置
server_url = Column(String(500), comment="服务器URL（HTTP类型）")
command = Column(String(500), comment="启动命令（stdio类型）")
args = Column(JSON, comment="命令参数（stdio类型）")
env = Column(JSON, comment="环境变量")
headers = Column(JSON, comment="HTTP请求头")
# 插件配置
config = Column(JSON, comment="插件特定配置（JSON）")
tools = Column(JSON, comment="提供的工具列表")
# 状态管理
enabled = Column(Boolean, default=True, comment="是否启用")
status = Column(String(50), default="inactive", comment="状态：active/inactive/error")
last_error = Column(Text, comment="最后错误信息")
last_test_at = Column(DateTime, comment="最后测试时间")
# 排序和分组
category = Column(String(100), default="general", comment="分类")
sort_order = Column(Integer, default=0, comment="排序顺序")
# 时间戳
created_at = Column(DateTime, server_default=func.now(), comment="创建时间")
updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now(), comment="更新时间")
__table_args__ = (
Index('idx_user_plugin', 'user_id', 'plugin_name', unique=True),
Index('idx_user_enabled', 'user_id', 'enabled'),
)
def __repr__(self):
return f"<MCPPlugin(id={self.id}, name={self.plugin_name}, enabled={self.enabled})>"
backend/app/schemas/chapter.py
+2
View File
@@ -66,6 +66,7 @@ class ChapterGenerateRequest(BaseModel):
ge=500, # 最小500字
le=10000 # 最大10000字
)
enable_mcp: bool = Field(True, description="是否启用MCP工具增强（搜索参考资料）")
class BatchGenerateRequest(BaseModel):
@@ -80,6 +81,7 @@ class BatchGenerateRequest(BaseModel):
le=10000
)
enable_analysis: bool = Field(False, description="是否启用同步分析")
enable_mcp: bool = Field(True, description="是否启用MCP工具增强（搜索参考资料）")
max_retries: int = Field(3, description="每个章节的最大重试次数", ge=0, le=5)
backend/app/schemas/character.py
+1
View File
@@ -63,6 +63,7 @@ class CharacterGenerateRequest(BaseModel):
role_type: Optional[str] = Field(None, description="角色类型")
background: Optional[str] = Field(None, description="角色背景")
requirements: Optional[str] = Field(None, description="特殊要求")
enable_mcp: bool = Field(True, description="是否启用MCP工具增强（搜索人物原型参考）")
class CharacterListResponse(BaseModel):
backend/app/schemas/mcp_plugin.py
+105
View File
@@ -0,0 +1,105 @@
"""MCP插件Pydantic模式"""
from pydantic import BaseModel, Field
from typing import Optional, Dict, Any, List
from datetime import datetime
class MCPToolSchema(BaseModel):
"""MCP工具定义"""
name: str
description: Optional[str] = None
inputSchema: Optional[Dict[str, Any]] = None
category: Optional[str] = None
class MCPPluginBase(BaseModel):
"""插件基础模式"""
plugin_name: str = Field(..., description="插件唯一标识")
display_name: Optional[str] = Field(None, description="显示名称")
description: Optional[str] = Field(None, description="插件描述")
plugin_type: str = Field(default="http", description="插件类型：http/stdio")
category: str = Field(default="general", description="分类")
sort_order: int = Field(default=0, description="排序顺序")
class MCPPluginCreate(MCPPluginBase):
"""创建插件"""
server_url: Optional[str] = Field(None, description="服务器URL（HTTP类型）")
command: Optional[str] = Field(None, description="启动命令（stdio类型）")
args: Optional[List[str]] = Field(None, description="命令参数")
env: Optional[Dict[str, str]] = Field(None, description="环境变量")
headers: Optional[Dict[str, str]] = Field(None, description="HTTP请求头")
config: Optional[Dict[str, Any]] = Field(None, description="插件特定配置")
enabled: bool = Field(default=True, description="是否启用")
class MCPPluginSimpleCreate(BaseModel):
"""简化的插件创建（通过标准MCP配置JSON）"""
config_json: str = Field(..., description="标准MCP配置JSON字符串")
enabled: bool = Field(default=True, description="是否启用")
category: str = Field(default="general", description="插件分类")
class MCPPluginUpdate(BaseModel):
"""更新插件"""
display_name: Optional[str] = None
description: Optional[str] = None
server_url: Optional[str] = None
command: Optional[str] = None
args: Optional[List[str]] = None
env: Optional[Dict[str, str]] = None
headers: Optional[Dict[str, str]] = None
config: Optional[Dict[str, Any]] = None
enabled: Optional[bool] = None
category: Optional[str] = None
sort_order: Optional[int] = None
class MCPPluginResponse(BaseModel):
"""插件响应 - 优化后只返回必要字段"""
id: str
plugin_name: str
display_name: str
description: Optional[str] = None
plugin_type: str
category: str
# HTTP类型字段
server_url: Optional[str] = None
headers: Optional[Dict[str, str]] = None
# Stdio类型字段
command: Optional[str] = None
args: Optional[List[str]] = None
env: Optional[Dict[str, str]] = None
# 状态字段
enabled: bool
status: str
last_error: Optional[str] = None
last_test_at: Optional[datetime] = None
# 时间戳
created_at: datetime
class Config:
from_attributes = True
class MCPToolCall(BaseModel):
"""工具调用请求"""
plugin_id: str = Field(..., description="插件ID")
tool_name: str = Field(..., description="工具名称")
arguments: Dict[str, Any] = Field(default_factory=dict, description="工具参数")
class MCPTestResult(BaseModel):
"""测试结果"""
success: bool
message: str
response_time_ms: Optional[float] = None
tools_count: Optional[int] = None
tools: Optional[List[MCPToolSchema]] = None
error: Optional[str] = None
error_type: Optional[str] = None
suggestions: Optional[List[str]] = None
backend/app/schemas/outline.py
+1
View File
@@ -61,6 +61,7 @@ class OutlineGenerateRequest(BaseModel):
story_direction: Optional[str] = Field(None, description="故事发展方向提示(续写时使用)")
plot_stage: str = Field("development", description="情节阶段: development(发展), climax(高潮), ending(结局)")
keep_existing: bool = Field(False, description="是否保留现有大纲(续写时)")
enable_mcp: bool = Field(True, description="是否启用MCP工具增强（搜索情节设计参考）")
class ChapterOutlineGenerateRequest(BaseModel):
backend/app/services/ai_service.py
+410 -8
View File
@@ -5,6 +5,7 @@ from anthropic import AsyncAnthropic
from app.config import settings as app_settings
from app.logger import get_logger
import httpx
import json
logger = get_logger(__name__)
@@ -126,10 +127,12 @@ class AIService:
model: Optional[str] = None,
temperature: Optional[float] = None,
max_tokens: Optional[int] = None,
system_prompt: Optional[str] = None
) -> str:
system_prompt: Optional[str] = None,
tools: Optional[List[Dict[str, Any]]] = None,
tool_choice: Optional[str] = None
) -> Dict[str, Any]:
"""
生成文本
生成文本（支持工具调用）
Args:
prompt: 用户提示词
@@ -138,9 +141,14 @@ class AIService:
temperature: 温度参数
max_tokens: 最大token数
system_prompt: 系统提示词
tools: 可用工具列表（MCP工具格式）
tool_choice: 工具选择策略 (auto/required/none)
Returns:
生成的文本
Dict包含:
- content: 文本内容（如果没有工具调用）
- tool_calls: 工具调用列表（如果AI决定调用工具）
- finish_reason: 完成原因
"""
provider = provider or self.api_provider
model = model or self.default_model
@@ -148,12 +156,12 @@ class AIService:
max_tokens = max_tokens or self.default_max_tokens
if provider == "openai":
return await self._generate_openai(
prompt, model, temperature, max_tokens, system_prompt
return await self._generate_openai_with_tools(
prompt, model, temperature, max_tokens, system_prompt, tools, tool_choice
)
elif provider == "anthropic":
return await self._generate_anthropic(
prompt, model, temperature, max_tokens, system_prompt
return await self._generate_anthropic_with_tools(
prompt, model, temperature, max_tokens, system_prompt, tools, tool_choice
)
else:
raise ValueError(f"不支持的AI提供商: {provider}")
@@ -247,6 +255,7 @@ class AIService:
logger.info(f"✅ OpenAI API调用成功")
logger.info(f" - 响应ID: {data.get('id', 'N/A')}")
logger.info(f" - 选项数量: {len(data.get('choices', []))}")
logger.debug(f" - 完整API响应: {data}")
if not data.get('choices'):
logger.error("❌ OpenAI返回的choices为空")
@@ -294,6 +303,173 @@ class AIService:
logger.error(f" - 模型: {model}")
raise
async def _generate_openai_with_tools(
self,
prompt: str,
model: str,
temperature: float,
max_tokens: int,
system_prompt: Optional[str],
tools: Optional[List[Dict[str, Any]]] = None,
tool_choice: Optional[str] = None
) -> Dict[str, Any]:
"""使用OpenAI生成文本（支持工具调用）"""
if not self.openai_http_client:
raise ValueError("OpenAI客户端未初始化，请检查API key配置")
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": prompt})
try:
logger.info(f"🔵 开始调用OpenAI API（支持工具调用）")
logger.info(f" - 模型: {model}")
logger.info(f" - 工具数量: {len(tools) if tools else 0}")
url = f"{self.openai_base_url}/chat/completions"
headers = {
"Authorization": f"Bearer {self.openai_api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
# 添加工具参数
if tools:
payload["tools"] = tools
if tool_choice:
if tool_choice == "required":
payload["tool_choice"] = "required"
elif tool_choice == "auto":
payload["tool_choice"] = "auto"
elif tool_choice == "none":
payload["tool_choice"] = "none"
response = await self.openai_http_client.post(url, headers=headers, json=payload)
response.raise_for_status()
data = response.json()
logger.info(f"✅ OpenAI API调用成功")
logger.debug(f" - 完整API响应: {data}")
if not data.get('choices'):
logger.error(f"❌ API返回的choices为空")
logger.error(f" - 完整响应: {data}")
logger.error(f" - 响应键: {list(data.keys())}")
raise ValueError(f"API返回的响应格式错误：choices字段为空。完整响应: {data}")
choice = data['choices'][0]
message = choice.get('message', {})
finish_reason = choice.get('finish_reason')
# 检查是否有工具调用
tool_calls = message.get('tool_calls')
if tool_calls:
logger.info(f"🔧 AI请求调用 {len(tool_calls)} 个工具")
return {
"tool_calls": tool_calls,
"content": message.get('content', ''),
"finish_reason": finish_reason
}
# 没有工具调用，返回普通内容
content = message.get('content', '')
if content:
return {
"content": content,
"finish_reason": finish_reason
}
else:
raise ValueError(f"AI返回了空内容（finish_reason: {finish_reason}）")
except httpx.HTTPStatusError as e:
logger.error(f"❌ OpenAI API调用失败 (HTTP {e.response.status_code})")
logger.error(f" - 错误信息: {e.response.text}")
raise Exception(f"API返回错误 ({e.response.status_code}): {e.response.text}")
except Exception as e:
logger.error(f"❌ OpenAI API调用失败: {str(e)}")
raise
async def _generate_anthropic_with_tools(
self,
prompt: str,
model: str,
temperature: float,
max_tokens: int,
system_prompt: Optional[str],
tools: Optional[List[Dict[str, Any]]] = None,
tool_choice: Optional[str] = None
) -> Dict[str, Any]:
"""使用Anthropic生成文本（支持工具调用）"""
if not self.anthropic_client:
raise ValueError("Anthropic客户端未初始化，请检查API key配置")
try:
logger.info(f"🔵 开始调用Anthropic API（支持工具调用）")
logger.info(f" - 模型: {model}")
logger.info(f" - 工具数量: {len(tools) if tools else 0}")
kwargs = {
"model": model,
"max_tokens": max_tokens,
"temperature": temperature,
"messages": [{"role": "user", "content": prompt}]
}
if system_prompt:
kwargs["system"] = system_prompt
# 添加工具参数
if tools:
kwargs["tools"] = tools
if tool_choice == "required":
kwargs["tool_choice"] = {"type": "any"}
elif tool_choice == "auto":
kwargs["tool_choice"] = {"type": "auto"}
response = await self.anthropic_client.messages.create(**kwargs)
# 检查是否有工具调用
tool_calls = []
content_text = ""
for block in response.content:
if block.type == "tool_use":
tool_calls.append({
"id": block.id,
"type": "function",
"function": {
"name": block.name,
"arguments": block.input
}
})
elif block.type == "text":
content_text += block.text
if tool_calls:
logger.info(f"🔧 AI请求调用 {len(tool_calls)} 个工具")
return {
"tool_calls": tool_calls,
"content": content_text,
"finish_reason": response.stop_reason
}
return {
"content": content_text,
"finish_reason": response.stop_reason
}
except Exception as e:
logger.error(f"❌ Anthropic API调用失败: {str(e)}")
raise
async def _generate_openai_stream(
self,
prompt: str,
@@ -457,6 +633,232 @@ class AIService:
logger.error(f" - 错误类型: {type(e).__name__}")
raise
async def generate_text_with_mcp(
self,
prompt: str,
user_id: str,
db_session,
enable_mcp: bool = True,
max_tool_rounds: int = 3,
tool_choice: str = "auto",
**kwargs
) -> Dict[str, Any]:
"""
支持MCP工具的AI文本生成（非流式）
Args:
prompt: 用户提示词
user_id: 用户ID，用于获取MCP工具
db_session: 数据库会话
enable_mcp: 是否启用MCP增强
max_tool_rounds: 最大工具调用轮次
tool_choice: 工具选择策略（auto/required/none）
**kwargs: 其他AI参数（provider, model, temperature等）
Returns:
{
"content": "AI生成的最终文本",
"tool_calls_made": 2, # 实际调用的工具次数
"tools_used": ["exa_search", "filesystem_read"],
"finish_reason": "stop",
"mcp_enhanced": True
}
"""
from app.services.mcp_tool_service import mcp_tool_service, MCPToolServiceError
# 初始化返回结果
result = {
"content": "",
"tool_calls_made": 0,
"tools_used": [],
"finish_reason": "",
"mcp_enhanced": False
}
# 1. 获取MCP工具（如果启用）
tools = None
if enable_mcp:
try:
tools = await mcp_tool_service.get_user_enabled_tools(
user_id=user_id,
db_session=db_session
)
if tools:
logger.info(f"MCP增强: 加载了 {len(tools)} 个工具")
result["mcp_enhanced"] = True
except MCPToolServiceError as e:
logger.error(f"获取MCP工具失败，降级为普通生成: {e}")
tools = None
# 2. 工具调用循环
conversation_history = [
{"role": "user", "content": prompt}
]
for round_num in range(max_tool_rounds):
logger.info(f"MCP工具调用轮次: {round_num + 1}/{max_tool_rounds}")
# 调用AI
ai_response = await self.generate_text(
prompt=conversation_history[-1]["content"],
tools=tools if round_num == 0 else None, # 只在第一轮传递工具
tool_choice=tool_choice if round_num == 0 else None,
**kwargs
)
# 检查是否有工具调用
tool_calls = ai_response.get("tool_calls", [])
if not tool_calls:
# AI返回最终内容
result["content"] = ai_response.get("content", "")
result["finish_reason"] = ai_response.get("finish_reason", "stop")
break
# 3. 执行工具调用
logger.info(f"AI请求调用 {len(tool_calls)} 个工具")
try:
tool_results = await mcp_tool_service.execute_tool_calls(
user_id=user_id,
tool_calls=tool_calls,
db_session=db_session
)
# 记录使用的工具
for tool_call in tool_calls:
tool_name = tool_call["function"]["name"]
if tool_name not in result["tools_used"]:
result["tools_used"].append(tool_name)
result["tool_calls_made"] += len(tool_calls)
# 4. 构建工具上下文
tool_context = await mcp_tool_service.build_tool_context(
tool_results,
format="markdown"
)
# 5. 更新对话历史
conversation_history.append({
"role": "assistant",
"content": ai_response.get("content", ""),
"tool_calls": tool_calls
})
for tool_result in tool_results:
conversation_history.append({
"role": "tool",
"tool_call_id": tool_result["tool_call_id"],
"content": tool_result["content"]
})
# 6. 构建下一轮提示
next_prompt = (
f"{prompt}\n\n"
f"{tool_context}\n\n"
f"请基于以上工具查询结果，继续完成任务。"
)
conversation_history.append({
"role": "user",
"content": next_prompt
})
except Exception as e:
logger.error(f"执行MCP工具失败: {e}", exc_info=True)
# 降级：返回当前AI响应
result["content"] = ai_response.get("content", "")
result["finish_reason"] = "tool_error"
break
else:
# 达到最大轮次
logger.warning(f"达到MCP最大调用轮次 {max_tool_rounds}")
result["content"] = conversation_history[-1].get("content", "")
result["finish_reason"] = "max_rounds"
return result
async def generate_text_stream_with_mcp(
self,
prompt: str,
user_id: str,
db_session,
enable_mcp: bool = True,
mcp_planning_prompt: Optional[str] = None,
**kwargs
) -> AsyncGenerator[str, None]:
"""
支持MCP工具的AI流式文本生成（两阶段模式）
Args:
prompt: 用户提示词
user_id: 用户ID
db_session: 数据库会话
enable_mcp: 是否启用MCP增强
mcp_planning_prompt: MCP规划阶段的提示词（可选）
**kwargs: 其他AI参数
Yields:
流式文本chunk
"""
from app.services.mcp_tool_service import mcp_tool_service
# 阶段1: 工具调用阶段（非流式）
enhanced_prompt = prompt
if enable_mcp:
try:
# 获取MCP工具
tools = await mcp_tool_service.get_user_enabled_tools(
user_id=user_id,
db_session=db_session
)
if tools:
logger.info(f"MCP增强（流式）: 加载了 {len(tools)} 个工具")
# 使用规划提示让AI决定需要查询什么
if not mcp_planning_prompt:
mcp_planning_prompt = (
f"任务: {prompt}\n\n"
f"请分析这个任务，决定是否需要查询外部信息。"
f"如果需要，请调用相应的工具获取信息。"
)
# 非流式调用获取工具结果
planning_result = await self.generate_text_with_mcp(
prompt=mcp_planning_prompt,
user_id=user_id,
db_session=db_session,
enable_mcp=True,
max_tool_rounds=2,
tool_choice="auto",
**kwargs
)
# 如果有工具调用，将结果融入提示
if planning_result["tool_calls_made"] > 0:
enhanced_prompt = (
f"{prompt}\n\n"
f"【参考资料】\n"
f"{planning_result.get('content', '')}"
)
logger.info(
f"MCP工具规划完成，调用了 "
f"{planning_result['tool_calls_made']} 次工具"
)
except Exception as e:
logger.error(f"MCP工具规划失败，使用原始提示: {e}")
# 阶段2: 内容生成阶段（流式）
async for chunk in self.generate_text_stream(
prompt=enhanced_prompt,
**kwargs
):
yield chunk
# 创建全局AI服务实例
ai_service = AIService()
backend/app/services/mcp_tool_service.py
+355
View File
@@ -0,0 +1,355 @@
"""MCP工具服务 - 统一管理MCP工具的注入和执行"""
from typing import List, Dict, Any, Optional
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
import asyncio
import json
from datetime import datetime
from app.models.mcp_plugin import MCPPlugin
from app.mcp.registry import mcp_registry
from app.logger import get_logger
logger = get_logger(__name__)
class MCPToolServiceError(Exception):
"""MCP工具服务异常"""
pass
class MCPToolService:
"""MCP工具服务 - 统一管理MCP工具的注入和执行"""
def __init__(self):
self._tool_cache = {} # 工具定义缓存
self._result_cache = {} # 工具结果缓存（可选）
async def get_user_enabled_tools(
self,
user_id: str,
db_session: AsyncSession,
category: Optional[str] = None
) -> List[Dict[str, Any]]:
"""
获取用户启用的MCP工具列表
Args:
user_id: 用户ID
db_session: 数据库会话
category: 工具类别筛选（search/analysis/filesystem等）
Returns:
工具定义列表，格式符合OpenAI Function Calling规范
"""
try:
# 1. 查询用户启用的插件（enabled=True即可，不强制要求status=active）
# 因为新启用的插件status可能还是inactive，需要给它机会被调用
query = select(MCPPlugin).where(
MCPPlugin.user_id == user_id,
MCPPlugin.enabled == True
)
if category:
query = query.where(MCPPlugin.category == category)
result = await db_session.execute(query)
plugins = result.scalars().all()
if not plugins:
logger.info(f"用户 {user_id} 没有启用的MCP插件")
return []
# 2. 获取所有工具定义
all_tools = []
for plugin in plugins:
try:
# 确保插件已加载到注册表
if not mcp_registry.get_client(user_id, plugin.plugin_name):
logger.info(f"插件 {plugin.plugin_name} 未加载，尝试加载...")
success = await mcp_registry.load_plugin(plugin)
if not success:
logger.warning(f"插件 {plugin.plugin_name} 加载失败，跳过")
continue
# 从registry获取该插件的工具列表
plugin_tools = await mcp_registry.get_plugin_tools(
user_id=user_id,
plugin_name=plugin.plugin_name
)
# 格式化为Function Calling格式
formatted_tools = self._format_tools_for_ai(
plugin_tools,
plugin.plugin_name # ✅ 修复：使用正确的属性名plugin_name
)
all_tools.extend(formatted_tools)
logger.info(
f"从插件 {plugin.plugin_name} 加载了 "
f"{len(formatted_tools)} 个工具"
)
except Exception as e:
logger.error(
f"获取插件 {plugin.plugin_name} 的工具失败: {e}",
exc_info=True
)
continue
logger.info(f"用户 {user_id} 共加载 {len(all_tools)} 个MCP工具")
return all_tools
except Exception as e:
logger.error(f"获取用户MCP工具失败: {e}", exc_info=True)
raise MCPToolServiceError(f"获取MCP工具失败: {str(e)}")
def _format_tools_for_ai(
self,
plugin_tools: List[Dict[str, Any]],
plugin_name: str
) -> List[Dict[str, Any]]:
"""
将MCP工具定义格式化为AI Function Calling格式
Args:
plugin_tools: MCP插件的工具列表
plugin_name: 插件名称
Returns:
格式化后的工具列表
"""
formatted_tools = []
for tool in plugin_tools:
formatted_tool = {
"type": "function",
"function": {
"name": f"{plugin_name}_{tool['name']}", # 加插件前缀避免冲突
"description": tool.get("description", ""),
"parameters": tool.get("inputSchema", {
"type": "object",
"properties": {},
"required": []
})
}
}
formatted_tools.append(formatted_tool)
return formatted_tools
async def execute_tool_calls(
self,
user_id: str,
tool_calls: List[Dict[str, Any]],
db_session: AsyncSession,
timeout: float = 60.0
) -> List[Dict[str, Any]]:
"""
批量执行AI请求的工具调用（并行执行）
Args:
user_id: 用户ID
tool_calls: AI返回的工具调用列表
db_session: 数据库会话
timeout: 单个工具调用的超时时间（秒，默认30秒）
Returns:
工具调用结果列表
"""
if not tool_calls:
return []
logger.info(f"开始执行 {len(tool_calls)} 个工具调用")
# 创建异步任务列表
tasks = [
self._execute_single_tool(
user_id=user_id,
tool_call=tool_call,
db_session=db_session,
timeout=timeout
)
for tool_call in tool_calls
]
# 并行执行所有工具调用
results = await asyncio.gather(*tasks, return_exceptions=True)
# 处理结果
formatted_results = []
for i, result in enumerate(results):
tool_call = tool_calls[i]
if isinstance(result, Exception):
# 工具调用异常
formatted_results.append({
"tool_call_id": tool_call.get("id", f"call_{i}"),
"role": "tool",
"name": tool_call["function"]["name"],
"content": f"工具调用失败: {str(result)}",
"success": False,
"error": str(result)
})
else:
formatted_results.append(result)
return formatted_results
async def _execute_single_tool(
self,
user_id: str,
tool_call: Dict[str, Any],
db_session: AsyncSession,
timeout: float
) -> Dict[str, Any]:
"""
执行单个工具调用
Args:
user_id: 用户ID
tool_call: 工具调用信息
db_session: 数据库会话
timeout: 超时时间
Returns:
工具调用结果
"""
tool_call_id = tool_call.get("id", "unknown")
function_name = tool_call["function"]["name"]
try:
# 解析插件名和工具名
if "_" in function_name:
plugin_name, tool_name = function_name.split("_", 1)
else:
raise ValueError(f"无效的工具名称格式: {function_name}")
# 解析参数
arguments_str = tool_call["function"]["arguments"]
if isinstance(arguments_str, str):
arguments = json.loads(arguments_str)
else:
arguments = arguments_str
logger.info(
f"执行工具: {plugin_name}.{tool_name}, "
f"参数: {arguments}"
)
# 设置超时
try:
result = await asyncio.wait_for(
mcp_registry.call_tool(
user_id=user_id,
plugin_name=plugin_name,
tool_name=tool_name,
arguments=arguments
),
timeout=timeout
)
# 成功返回
return {
"tool_call_id": tool_call_id,
"role": "tool",
"name": function_name,
"content": json.dumps(result, ensure_ascii=False),
"success": True,
"error": None
}
except asyncio.TimeoutError:
raise MCPToolServiceError(
f"工具调用超时（>{timeout}秒）"
)
except Exception as e:
logger.error(
f"工具 {function_name} 调用失败: {e}",
exc_info=True
)
return {
"tool_call_id": tool_call_id,
"role": "tool",
"name": function_name,
"content": f"工具调用失败: {str(e)}",
"success": False,
"error": str(e)
}
async def build_tool_context(
self,
tool_results: List[Dict[str, Any]],
format: str = "markdown"
) -> str:
"""
将工具调用结果格式化为上下文文本
Args:
tool_results: 工具调用结果列表
format: 输出格式（markdown/json/plain）
Returns:
格式化的上下文字符串
"""
if not tool_results:
return ""
if format == "markdown":
return self._build_markdown_context(tool_results)
elif format == "json":
return json.dumps(tool_results, ensure_ascii=False, indent=2)
else: # plain
return self._build_plain_context(tool_results)
def _build_markdown_context(
self,
tool_results: List[Dict[str, Any]]
) -> str:
"""构建Markdown格式的工具上下文"""
lines = ["## 🔧 工具调用结果\n"]
for i, result in enumerate(tool_results, 1):
tool_name = result.get("name", "unknown")
success = result.get("success", False)
content = result.get("content", "")
status_emoji = "✅" if success else "❌"
lines.append(f"### {status_emoji} {i}. {tool_name}\n")
if success:
# 尝试美化JSON内容
try:
content_obj = json.loads(content)
content = json.dumps(content_obj, ensure_ascii=False, indent=2)
except:
pass
lines.append(f"```json\n{content}\n```\n")
else:
lines.append(f"**错误**: {content}\n")
return "\n".join(lines)
def _build_plain_context(
self,
tool_results: List[Dict[str, Any]]
) -> str:
"""构建纯文本格式的工具上下文"""
lines = ["=== 工具调用结果 ===\n"]
for i, result in enumerate(tool_results, 1):
tool_name = result.get("name", "unknown")
success = result.get("success", False)
content = result.get("content", "")
status = "成功" if success else "失败"
lines.append(f"{i}. {tool_name} - {status}")
lines.append(f" 结果: {content}\n")
return "\n".join(lines)
# 全局单例
mcp_tool_service = MCPToolService()
backend/app/services/plot_analyzer.py
+15 -1
View File
@@ -225,8 +225,22 @@ class PlotAnalyzer:
temperature=0.3 # 降低温度以获得更稳定的JSON输出
)
# 🔍 添加调试日志：查看AI返回的原始内容
logger.info(f"🔍 AI返回类型: {type(response)}")
logger.info(f"🔍 AI返回内容(前500字符): {str(response)}")
# 从返回的字典中提取content字段
if isinstance(response, dict):
response_text = response.get('content', '')
if not response_text:
logger.error("❌ AI返回的字典中没有content字段或content为空")
return None
else:
# 兼容旧的字符串返回格式
response_text = response
# 解析JSON结果
analysis_result = self._parse_analysis_response(response)
analysis_result = self._parse_analysis_response(response_text)
if analysis_result:
logger.info(f"✅ 第{chapter_number}章分析完成")
backend/app/services/prompt_service.py
+57 -9
View File
@@ -282,6 +282,8 @@ class PromptService:
角色信息：
{characters_info}
{mcp_references}
其他要求：{requirements}
整体要求：
@@ -356,6 +358,8 @@ class PromptService:
{memory_context}
{mcp_references}
【续写指导】
- 当前情节阶段：{plot_stage_instruction}
- 起始章节编号：第{start_chapter}章
@@ -836,8 +840,17 @@ class PromptService:
chapter_count: int, narrative_perspective: str,
target_words: int, time_period: str, location: str,
atmosphere: str, rules: str, characters_info: str,
requirements: str = "") -> str:
"""获取向导大纲生成提示词"""
requirements: str = "",
mcp_references: str = "") -> str:
"""获取向导大纲生成提示词（支持MCP增强）"""
# 格式化MCP参考资料
mcp_text = ""
if mcp_references:
mcp_text = "【📚 MCP工具搜索 - 情节设计参考】\n"
mcp_text += "以下是通过MCP工具搜索到的情节设计参考资料，可用于设计大纲结构和情节发展：\n\n"
mcp_text += mcp_references
mcp_text += "\n"
return cls.format_prompt(
cls.COMPLETE_OUTLINE_GENERATION,
title=title,
@@ -851,6 +864,7 @@ class PromptService:
atmosphere=atmosphere,
rules=rules,
characters_info=characters_info,
mcp_references=mcp_text,
requirements=requirements or "无特殊要求"
)
@@ -862,7 +876,8 @@ class PromptService:
chapter_number: int, chapter_title: str,
chapter_outline: str, style_content: str = "",
target_word_count: int = 3000,
memory_context: dict = None) -> str:
memory_context: dict = None,
mcp_references: str = "") -> str:
"""
获取章节完整创作提示词
@@ -870,6 +885,7 @@ class PromptService:
style_content: 写作风格要求内容，如果提供则会追加到提示词中
target_word_count: 目标字数，默认3000字
memory_context: 记忆上下文（可选）
mcp_references: MCP工具搜索的参考资料（可选）
"""
# 计算最大字数（目标字数+1000）
max_word_count = target_word_count + 1000
@@ -884,6 +900,14 @@ class PromptService:
memory_text += "\n" + memory_context.get('character_states', '')
memory_text += "\n" + memory_context.get('plot_points', '')
# 格式化MCP参考资料
mcp_text = ""
if mcp_references:
mcp_text = "\n【📚 MCP工具搜索 - 参考资料】\n"
mcp_text += "以下是通过MCP工具搜索到的相关参考资料，可用于丰富情节和细节：\n\n"
mcp_text += mcp_references
mcp_text += "\n"
base_prompt = cls.format_prompt(
cls.CHAPTER_GENERATION,
title=title,
@@ -903,11 +927,17 @@ class PromptService:
max_word_count=max_word_count
)
# 插入记忆上下文
# 插入记忆上下文和MCP参考资料
insert_text = ""
if memory_text:
insert_text += memory_text
if mcp_text:
insert_text += mcp_text
if insert_text:
base_prompt = base_prompt.replace(
"本章信息：",
memory_text + "\n\n本章信息："
insert_text + "\n\n本章信息："
)
# 如果有风格要求，应用到提示词中
@@ -925,7 +955,8 @@ class PromptService:
chapter_title: str, chapter_outline: str,
style_content: str = "",
target_word_count: int = 3000,
memory_context: dict = None) -> str:
memory_context: dict = None,
mcp_references: str = "") -> str:
"""
获取章节完整创作提示词（带前置章节上下文和记忆增强）
@@ -933,6 +964,7 @@ class PromptService:
style_content: 写作风格要求内容，如果提供则会追加到提示词中
target_word_count: 目标字数，默认3000字
memory_context: 记忆上下文（可选）
mcp_references: MCP工具搜索的参考资料（可选）
"""
# 计算最大字数（目标字数+1000）
max_word_count = target_word_count + 1000
@@ -948,6 +980,12 @@ class PromptService:
else:
memory_text = "暂无相关记忆"
# 格式化MCP参考资料
if mcp_references:
memory_text += "\n\n【📚 MCP工具搜索 - 参考资料】\n"
memory_text += "以下是通过MCP工具搜索到的相关参考资料，可用于丰富情节和细节：\n\n"
memory_text += mcp_references
base_prompt = cls.format_prompt(
cls.CHAPTER_GENERATION_WITH_CONTEXT,
title=title,
@@ -996,8 +1034,9 @@ class PromptService:
recent_plot: str, plot_stage_instruction: str,
start_chapter: int, story_direction: str,
requirements: str = "",
memory_context: dict = None) -> str:
"""获取大纲续写提示词（支持记忆增强）"""
memory_context: dict = None,
mcp_references: str = "") -> str:
"""获取大纲续写提示词（支持记忆+MCP增强）"""
end_chapter = start_chapter + chapter_count - 1
# 格式化记忆上下文
@@ -1011,6 +1050,14 @@ class PromptService:
else:
memory_text = "暂无相关记忆（可能是首次续写或记忆库为空）"
# 格式化MCP参考资料
mcp_text = ""
if mcp_references:
mcp_text = "\n\n【📚 MCP工具搜索 - 续写参考资料】\n"
mcp_text += "以下是通过MCP工具搜索到的续写参考资料，可用于丰富情节发展和冲突设计：\n\n"
mcp_text += mcp_references
mcp_text += "\n"
return cls.format_prompt(
cls.OUTLINE_CONTINUE_GENERATION,
title=title,
@@ -1031,7 +1078,8 @@ class PromptService:
end_chapter=end_chapter,
story_direction=story_direction,
requirements=requirements or "无特殊要求",
memory_context=memory_text
memory_context=memory_text,
mcp_references=mcp_text
)
@classmethod
frontend/src/App.tsx
+2
View File
@@ -14,6 +14,7 @@ import ChapterReader from './pages/ChapterReader';
import ChapterAnalysis from './pages/ChapterAnalysis';
import WritingStyles from './pages/WritingStyles';
import Settings from './pages/Settings';
import MCPPlugins from './pages/MCPPlugins';
// import Polish from './pages/Polish';
import Login from './pages/Login';
import AuthCallback from './pages/AuthCallback';
@@ -36,6 +37,7 @@ function App() {
<Route path="/" element={<ProtectedRoute><ProjectList /></ProtectedRoute>} />
<Route path="/wizard" element={<ProtectedRoute><ProjectWizardNew /></ProtectedRoute>} />
<Route path="/settings" element={<ProtectedRoute><Settings /></ProtectedRoute>} />
<Route path="/mcp-plugins" element={<ProtectedRoute><MCPPlugins /></ProtectedRoute>} />
<Route path="/chapters/:chapterId/reader" element={<ProtectedRoute><ChapterReader /></ProtectedRoute>} />
<Route path="/project/:projectId" element={<ProtectedRoute><ProjectDetail /></ProtectedRoute>}>
<Route index element={<Navigate to="world-setting" replace />} />
frontend/src/pages/MCPPlugins.tsx
+708
View File
@@ -0,0 +1,708 @@
import { useState, useEffect } from 'react';
import { useNavigate } from 'react-router-dom';
import {
Card,
Button,
Space,
Typography,
Modal,
Form,
Input,
Switch,
Select,
message,
Tag,
Tooltip,
Spin,
Empty,
Alert,
Descriptions,
Layout,
} from 'antd';
import {
PlusOutlined,
EditOutlined,
DeleteOutlined,
CheckCircleOutlined,
CloseCircleOutlined,
ThunderboltOutlined,
InfoCircleOutlined,
ToolOutlined,
ArrowLeftOutlined,
} from '@ant-design/icons';
import { mcpPluginApi } from '../services/api';
import type { MCPPlugin, MCPTool } from '../types';
const { Paragraph, Text, Title } = Typography;
const { TextArea } = Input;
const { Header, Content } = Layout;
export default function MCPPluginsPage() {
const navigate = useNavigate();
const isMobile = window.innerWidth <= 768;
const [form] = Form.useForm();
const [loading, setLoading] = useState(false);
const [plugins, setPlugins] = useState<MCPPlugin[]>([]);
const [modalVisible, setModalVisible] = useState(false);
const [editingPlugin, setEditingPlugin] = useState<MCPPlugin | null>(null);
const [testingPluginId, setTestingPluginId] = useState<string | null>(null);
const [viewingTools, setViewingTools] = useState<{ pluginId: string; tools: MCPTool[] } | null>(null);
useEffect(() => {
loadPlugins();
}, []);
const loadPlugins = async () => {
setLoading(true);
try {
const data = await mcpPluginApi.getPlugins();
setPlugins(data);
} catch (error) {
message.error('加载插件列表失败');
} finally {
setLoading(false);
}
};
const handleCreate = () => {
setEditingPlugin(null);
form.resetFields();
form.setFieldsValue({
enabled: true,
category: 'search',
config_json: `{
"mcpServers": {
"exa": {
"type": "http",
"url": "https://mcp.exa.ai/mcp?exaApiKey=YOUR_API_KEY",
"headers": {}
}
}
}`
});
setModalVisible(true);
};
const handleEdit = (plugin: MCPPlugin) => {
setEditingPlugin(plugin);
// 重构为标准MCP配置格式
const mcpConfig: any = {
mcpServers: {
[plugin.plugin_name]: {
type: plugin.plugin_type || 'http'
}
}
};
if (plugin.plugin_type === 'http') {
mcpConfig.mcpServers[plugin.plugin_name].url = plugin.server_url;
mcpConfig.mcpServers[plugin.plugin_name].headers = plugin.headers || {};
} else {
mcpConfig.mcpServers[plugin.plugin_name].command = plugin.command;
mcpConfig.mcpServers[plugin.plugin_name].args = plugin.args || [];
mcpConfig.mcpServers[plugin.plugin_name].env = plugin.env || {};
}
form.setFieldsValue({
config_json: JSON.stringify(mcpConfig, null, 2),
enabled: plugin.enabled,
category: plugin.category || 'general',
});
setModalVisible(true);
};
const handleDelete = (plugin: MCPPlugin) => {
Modal.confirm({
title: '删除插件',
content: `确定要删除插件 "${plugin.display_name || plugin.plugin_name}" 吗？`,
okText: '确定',
cancelText: '取消',
okType: 'danger',
onOk: async () => {
try {
await mcpPluginApi.deletePlugin(plugin.id);
message.success('插件已删除');
loadPlugins();
} catch (error) {
message.error('删除插件失败');
}
},
});
};
const handleToggle = async (plugin: MCPPlugin, enabled: boolean) => {
try {
await mcpPluginApi.togglePlugin(plugin.id, enabled);
message.success(enabled ? '插件已启用' : '插件已禁用');
loadPlugins();
} catch (error) {
message.error('切换插件状态失败');
}
};
const handleTest = async (pluginId: string) => {
setTestingPluginId(pluginId);
try {
const result = await mcpPluginApi.testPlugin(pluginId);
// 测试完成后，无论成功失败都刷新插件列表以更新状态
await loadPlugins();
if (result.success) {
Modal.success({
title: '✅ 测试成功',
width: 700,
content: (
<div>
<p style={{ marginBottom: 16, fontSize: '15px', fontWeight: 500 }}>{result.message}</p>
{/* 显示详细的测试结果 */}
{result.suggestions && result.suggestions.length > 0 && (
<div style={{ marginTop: 16 }}>
<Text strong style={{ fontSize: '14px' }}>测试详情:</Text>
<div style={{
marginTop: 8,
padding: 12,
background: '#f5f5f5',
borderRadius: 4,
fontSize: '13px',
fontFamily: 'monospace',
maxHeight: '400px',
overflowY: 'auto'
}}>
{result.suggestions.map((suggestion: string, index: number) => (
<div key={index} style={{
marginBottom: index < (result.suggestions?.length || 0) - 1 ? 8 : 0,
whiteSpace: 'pre-wrap',
wordBreak: 'break-word',
lineHeight: 1.6
}}>
{suggestion}
</div>
))}
</div>
</div>
)}
{/* 显示工具数量 */}
{result.tools_count !== undefined && (
<div style={{ marginTop: 12 }}>
<Text type="secondary">🔧 可用工具数: <strong>{result.tools_count}</strong></Text>
</div>
)}
{/* 显示响应时间 */}
{result.response_time_ms !== undefined && (
<div style={{ marginTop: 4 }}>
<Text type="secondary">⏱️ 响应时间: <strong>{result.response_time_ms}ms</strong></Text>
</div>
)}
<div style={{
marginTop: 16,
padding: 12,
background: '#f6ffed',
border: '1px solid #b7eb8f',
borderRadius: 4
}}>
<Text type="success" style={{ fontSize: '13px' }}>
✓ 插件状态已自动更新为"运行中"
</Text>
</div>
</div>
),
});
} else {
Modal.error({
title: '❌ 测试失败',
width: 700,
content: (
<div>
<p style={{ marginBottom: 16, fontSize: '15px', fontWeight: 500 }}>{result.message}</p>
{/* 显示错误信息 */}
{result.error && (
<div style={{ marginTop: 16 }}>
<Text strong style={{ fontSize: '14px' }}>错误信息:</Text>
<div style={{
marginTop: 8,
padding: 12,
background: '#fff2f0',
borderRadius: 4,
color: '#cf1322',
fontSize: '13px',
fontFamily: 'monospace',
whiteSpace: 'pre-wrap',
wordBreak: 'break-word',
maxHeight: '300px',
overflowY: 'auto'
}}>
{result.error}
</div>
</div>
)}
{/* 显示建议 */}
{result.suggestions && result.suggestions.length > 0 && (
<div style={{ marginTop: 16 }}>
<Text strong style={{ fontSize: '14px' }}>💡 建议:</Text>
<ul style={{ marginTop: 8, marginBottom: 0, paddingLeft: 20 }}>
{result.suggestions.map((suggestion: string, index: number) => (
<li key={index} style={{ marginBottom: 6, fontSize: '13px' }}>
{suggestion}
</li>
))}
</ul>
</div>
)}
<div style={{
marginTop: 16,
padding: 12,
background: '#fff7e6',
border: '1px solid #ffd591',
borderRadius: 4
}}>
<Text style={{ fontSize: '13px', color: '#ad6800' }}>
⚠️ 插件状态已更新，请检查配置后重试
</Text>
</div>
</div>
),
});
}
} catch (error: any) {
message.error('测试插件失败');
} finally {
setTestingPluginId(null);
}
};
const handleViewTools = async (pluginId: string) => {
try {
const result = await mcpPluginApi.getPluginTools(pluginId);
setViewingTools({ pluginId, tools: result.tools });
} catch (error) {
message.error('获取工具列表失败');
}
};
const handleSubmit = async (values: any) => {
setLoading(true);
try {
// 验证JSON格式
try {
JSON.parse(values.config_json);
} catch (e) {
message.error('配置JSON格式错误，请检查');
setLoading(false);
return;
}
const data = {
config_json: values.config_json,
enabled: values.enabled,
category: values.category || 'general',
};
// 统一使用简化API，后端会自动判断是创建还是更新
await mcpPluginApi.createPluginSimple(data);
message.success(editingPlugin ? '插件已更新' : '插件已创建');
setModalVisible(false);
form.resetFields();
loadPlugins();
} catch (error: any) {
const errorMsg = error?.response?.data?.detail || '操作失败';
message.error(errorMsg);
} finally {
setLoading(false);
}
};
const getStatusTag = (plugin: MCPPlugin) => {
if (!plugin.enabled) {
return <Tag color="default">已禁用</Tag>;
}
switch (plugin.status) {
case 'active':
return <Tag color="success" icon={<CheckCircleOutlined />}>运行中</Tag>;
case 'error':
return (
<Tooltip title={plugin.last_error}>
<Tag color="error" icon={<CloseCircleOutlined />}>错误</Tag>
</Tooltip>
);
default:
return <Tag color="default">未激活</Tag>;
}
};
return (
<Layout style={{ minHeight: '100vh', background: '#f0f2f5' }}>
{/* 顶部导航栏 */}
<Header style={{
background: 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
padding: isMobile ? '0 16px' : '0 24px',
display: 'flex',
alignItems: 'center',
justifyContent: 'space-between',
boxShadow: '0 2px 8px rgba(0,0,0,0.15)',
position: 'fixed',
top: 0,
left: 0,
right: 0,
zIndex: 1000,
height: isMobile ? 56 : 64
}}>
<Space size={isMobile ? 12 : 16}>
<Button
type="text"
icon={<ArrowLeftOutlined />}
onClick={() => navigate('/')}
style={{
color: '#fff',
fontSize: isMobile ? 16 : 18,
display: 'flex',
alignItems: 'center'
}}
>
{!isMobile && '返回'}
</Button>
<Title level={isMobile ? 4 : 3} style={{
margin: 0,
color: '#fff',
fontSize: isMobile ? 18 : 24
}}>
MCP插件管理
</Title>
</Space>
</Header>
{/* 主内容区 */}
<Content style={{
marginTop: isMobile ? 56 : 64,
padding: isMobile ? '16px' : '24px',
maxWidth: 1400,
width: '100%',
margin: `${isMobile ? 56 : 64}px auto 0`,
}}>
<Card
variant="borderless"
style={{
borderRadius: isMobile ? 8 : 12,
boxShadow: '0 2px 8px rgba(0,0,0,0.06)',
marginBottom: isMobile ? 16 : 24
}}
>
<div style={{
display: 'flex',
justifyContent: 'space-between',
alignItems: 'center',
marginBottom: isMobile ? 16 : 20
}}>
<Title level={isMobile ? 5 : 4} style={{ margin: 0 }}>
我的插件
</Title>
<Button
type="primary"
icon={<PlusOutlined />}
onClick={handleCreate}
size={isMobile ? 'middle' : 'large'}
style={{
borderRadius: 8,
background: 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)',
border: 'none'
}}
>
添加插件
</Button>
</div>
<Alert
message="什么是 MCP 插件？"
description={
<div style={{ fontSize: isMobile ? '12px' : '14px' }}>
<p style={{ margin: '8px 0' }}>
MCP (Model Context Protocol) 是一个标准化的协议，允许 AI 调用外部工具获取数据。
</p>
<p style={{ margin: '8px 0 0 0' }}>
通过添加 MCP 插件，AI 可以访问搜索引擎、数据库、API 等外部服务，增强创作能力。
</p>
</div>
}
type="info"
showIcon
icon={<InfoCircleOutlined />}
style={{ marginBottom: isMobile ? 16 : 20 }}
/>
</Card>
{/* 插件列表 */}
<Spin spinning={loading}>
{plugins.length === 0 ? (
<Empty
description="还没有添加任何插件"
image={Empty.PRESENTED_IMAGE_SIMPLE}
style={{ padding: isMobile ? '40px 0' : '60px 0' }}
>
<Button type="primary" icon={<PlusOutlined />} onClick={handleCreate}>
添加第一个插件
</Button>
</Empty>
) : (
<Space direction="vertical" size="middle" style={{ width: '100%' }}>
{plugins.map((plugin) => (
<Card
key={plugin.id}
size="small"
style={{
borderRadius: 8,
border: '1px solid #f0f0f0',
}}
>
<div
style={{
display: 'flex',
justifyContent: 'space-between',
alignItems: 'flex-start',
gap: '16px',
flexWrap: isMobile ? 'wrap' : 'nowrap',
}}
>
<div style={{ flex: 1, minWidth: 0 }}>
<Space direction="vertical" size="small" style={{ width: '100%' }}>
<div style={{ display: 'flex', alignItems: 'center', gap: '8px', flexWrap: 'wrap' }}>
<Text strong style={{ fontSize: isMobile ? '14px' : '16px' }}>
{plugin.display_name || plugin.plugin_name}
</Text>
{getStatusTag(plugin)}
<Tag color={plugin.plugin_type === 'http' ? 'blue' : 'cyan'}>
{plugin.plugin_type?.toUpperCase() || 'UNKNOWN'}
</Tag>
{plugin.category && plugin.category !== 'general' && (
<Tag color="purple">{plugin.category}</Tag>
)}
</div>
{plugin.description && (
<Paragraph
type="secondary"
style={{
margin: 0,
fontSize: isMobile ? '12px' : '13px',
}}
ellipsis={{ rows: 2 }}
>
{plugin.description}
</Paragraph>
)}
{/* 只显示有值的URL或命令，脱敏处理敏感信息 */}
{plugin.plugin_type === 'http' && plugin.server_url && (
<div style={{ fontSize: isMobile ? '11px' : '12px' }}>
<Text type="secondary" code>
{(() => {
// 脱敏处理：隐藏URL中的API Key
const url = plugin.server_url;
try {
const urlObj = new URL(url);
// 替换查询参数中的敏感信息
const params = new URLSearchParams(urlObj.search);
let maskedUrl = `${urlObj.protocol}//${urlObj.host}${urlObj.pathname}`;
const sensitiveKeys = ['apiKey', 'api_key', 'key', 'token', 'secret', 'password', 'auth'];
let hasParams = false;
params.forEach((value, key) => {
const isSensitive = sensitiveKeys.some(k => key.toLowerCase().includes(k.toLowerCase()));
const maskedValue = isSensitive ? '***' : value;
maskedUrl += (hasParams ? '&' : '?') + `${key}=${maskedValue}`;
hasParams = true;
});
return maskedUrl;
} catch {
// 如果URL解析失败，尝试简单替换
return url.replace(/([?&])(apiKey|api_key|key|token|secret|password|auth)=([^&]+)/gi, '$1$2=***');
}
})()}
</Text>
</div>
)}
{plugin.plugin_type === 'stdio' && plugin.command && (
<div style={{ fontSize: isMobile ? '11px' : '12px' }}>
<Text type="secondary" code>
{plugin.command} {plugin.args?.join(' ')}
</Text>
</div>
)}
{/* 显示最后错误信息 */}
{plugin.last_error && (
<Text type="danger" style={{ fontSize: isMobile ? '11px' : '12px' }}>
错误: {plugin.last_error}
</Text>
)}
</Space>
</div>
<Space size="small" wrap>
<Tooltip title={plugin.enabled ? '禁用插件' : '启用插件'}>
<Switch
checked={plugin.enabled}
onChange={(checked) => handleToggle(plugin, checked)}
size={isMobile ? 'small' : 'default'}
/>
</Tooltip>
<Tooltip title="测试连接">
<Button
icon={<ThunderboltOutlined />}
onClick={() => handleTest(plugin.id)}
loading={testingPluginId === plugin.id}
size={isMobile ? 'small' : 'middle'}
/>
</Tooltip>
<Tooltip title="查看工具">
<Button
icon={<ToolOutlined />}
onClick={() => handleViewTools(plugin.id)}
disabled={!plugin.enabled || plugin.status !== 'active'}
size={isMobile ? 'small' : 'middle'}
/>
</Tooltip>
<Tooltip title="编辑">
<Button
icon={<EditOutlined />}
onClick={() => handleEdit(plugin)}
size={isMobile ? 'small' : 'middle'}
/>
</Tooltip>
<Tooltip title="删除">
<Button
danger
icon={<DeleteOutlined />}
onClick={() => handleDelete(plugin)}
size={isMobile ? 'small' : 'middle'}
/>
</Tooltip>
</Space>
</div>
</Card>
))}
</Space>
)}
</Spin>
</Content>
{/* 创建/编辑插件模态框 */}
<Modal
title={editingPlugin ? '编辑插件' : '添加插件'}
open={modalVisible}
onCancel={() => {
setModalVisible(false);
form.resetFields();
}}
onOk={() => form.submit()}
width={isMobile ? '100%' : 600}
confirmLoading={loading}
okText="保存"
cancelText="取消"
>
<Form form={form} layout="vertical" onFinish={handleSubmit}>
<Form.Item
label="MCP配置JSON"
name="config_json"
rules={[{ required: true, message: '请输入配置JSON' }]}
extra="粘贴标准MCP配置，系统自动提取插件名称。支持HTTP和Stdio类型"
>
<TextArea
rows={16}
placeholder={`示例：
{
"mcpServers": {
"exa": {
"type": "http",
"url": "https://mcp.exa.ai/mcp?exaApiKey=YOUR_API_KEY",
"headers": {}
}
}
}`}
style={{ fontFamily: 'monospace', fontSize: '13px' }}
/>
</Form.Item>
<Form.Item
label="插件分类"
name="category"
rules={[{ required: true, message: '请选择插件分类' }]}
extra="选择插件的功能类别，用于AI智能匹配使用场景"
>
<Select placeholder="请选择分类">
<Select.Option value="search">搜索类 (Search) - 网络搜索、信息查询</Select.Option>
<Select.Option value="analysis">分析类 (Analysis) - 数据分析、文本处理</Select.Option>
<Select.Option value="filesystem">文件系统 (FileSystem) - 文件读写操作</Select.Option>
<Select.Option value="database">数据库 (Database) - 数据库查询</Select.Option>
<Select.Option value="api">API调用 (API) - 第三方服务接口</Select.Option>
<Select.Option value="generation">生成类 (Generation) - 内容生成工具</Select.Option>
<Select.Option value="general">通用 (General) - 其他功能</Select.Option>
</Select>
</Form.Item>
</Form>
</Modal>
{/* 查看工具列表模态框 */}
<Modal
title="可用工具列表"
open={!!viewingTools}
onCancel={() => setViewingTools(null)}
footer={[
<Button key="close" onClick={() => setViewingTools(null)}>
关闭
</Button>,
]}
width={isMobile ? '100%' : 700}
>
{viewingTools && (
<Space direction="vertical" size="middle" style={{ width: '100%' }}>
{viewingTools.tools.length === 0 ? (
<Empty description="该插件没有提供任何工具" />
) : (
viewingTools.tools.map((tool, index) => (
<Card key={index} size="small" style={{ borderRadius: 8 }}>
<Descriptions column={1} size="small">
<Descriptions.Item label="工具名称">
<Text code strong>
{tool.name}
</Text>
</Descriptions.Item>
{tool.description && (
<Descriptions.Item label="描述">{tool.description}</Descriptions.Item>
)}
{tool.inputSchema && (
<Descriptions.Item label="输入参数">
<pre
style={{
margin: 0,
padding: 8,
background: '#f5f5f5',
borderRadius: 4,
fontSize: isMobile ? '11px' : '12px',
overflow: 'auto',
}}
>
{JSON.stringify(tool.inputSchema, null, 2)}
</pre>
</Descriptions.Item>
)}
</Descriptions>
</Card>
))
)}
</Space>
)}
</Modal>
</Layout>
);
}
frontend/src/pages/ProjectList.tsx
+70 -48
View File
@@ -1,7 +1,7 @@
import { useEffect, useState } from 'react';
import { useNavigate } from 'react-router-dom';
import { Card, Button, Empty, Modal, message, Spin, Row, Col, Statistic, Space, Tag, Progress, Typography, Tooltip, Badge, Alert, Upload, Checkbox, Divider, Switch } from 'antd';
import { EditOutlined, DeleteOutlined, BookOutlined, RocketOutlined, CalendarOutlined, FileTextOutlined, TrophyOutlined, FireOutlined, SettingOutlined, InfoCircleOutlined, CloseOutlined, UploadOutlined, DownloadOutlined } from '@ant-design/icons';
import { Card, Button, Empty, Modal, message, Spin, Row, Col, Statistic, Space, Tag, Progress, Typography, Tooltip, Badge, Alert, Upload, Checkbox, Divider, Switch, Dropdown } from 'antd';
import { EditOutlined, DeleteOutlined, BookOutlined, RocketOutlined, CalendarOutlined, FileTextOutlined, TrophyOutlined, FireOutlined, SettingOutlined, InfoCircleOutlined, CloseOutlined, UploadOutlined, DownloadOutlined, ApiOutlined, MoreOutlined } from '@ant-design/icons';
import { projectApi } from '../services/api';
import { useStore } from '../store';
import { useProjectSync } from '../store/hooks';
@@ -388,10 +388,25 @@ export default function ProjectList() {
>
导入
</Button>
<Button
type="default"
size="middle"
icon={<ApiOutlined />}
onClick={() => navigate('/mcp-plugins')}
style={{
flex: 1,
borderRadius: 8,
borderColor: '#722ed1',
color: '#722ed1',
boxShadow: '0 2px 8px rgba(114, 46, 209, 0.2)'
}}
>
MCP
</Button>
</Space>
</Space>
) : (
// PC端：原有布局
// PC端：优化后的布局 - 主要按钮 + 下拉菜单
<Space size={12} style={{ display: 'flex', justifyContent: 'flex-end' }}>
<Button
type="primary"
@@ -407,51 +422,6 @@ export default function ProjectList() {
>
向导创建
</Button>
<Button
type="default"
size="large"
icon={<DownloadOutlined />}
onClick={handleOpenExportModal}
disabled={exportableProjects.length === 0}
style={{
borderRadius: 8,
borderColor: '#1890ff',
color: '#1890ff',
boxShadow: '0 2px 8px rgba(24, 144, 255, 0.2)'
}}
onMouseEnter={(e) => {
e.currentTarget.style.borderColor = '#1890ff';
e.currentTarget.style.background = '#e6f7ff';
}}
onMouseLeave={(e) => {
e.currentTarget.style.borderColor = '#1890ff';
e.currentTarget.style.background = 'transparent';
}}
>
导出项目
</Button>
<Button
type="default"
size="large"
icon={<UploadOutlined />}
onClick={() => setImportModalVisible(true)}
style={{
borderRadius: 8,
borderColor: '#52c41a',
color: '#52c41a',
boxShadow: '0 2px 8px rgba(82, 196, 26, 0.2)'
}}
onMouseEnter={(e) => {
e.currentTarget.style.borderColor = '#52c41a';
e.currentTarget.style.background = '#f6ffed';
}}
onMouseLeave={(e) => {
e.currentTarget.style.borderColor = '#52c41a';
e.currentTarget.style.background = 'transparent';
}}
>
导入项目
</Button>
<Button
type="default"
size="large"
@@ -476,6 +446,58 @@ export default function ProjectList() {
>
API设置
</Button>
<Dropdown
menu={{
items: [
{
key: 'export',
label: '导出项目',
icon: <DownloadOutlined />,
onClick: handleOpenExportModal,
disabled: exportableProjects.length === 0
},
{
key: 'import',
label: '导入项目',
icon: <UploadOutlined />,
onClick: () => setImportModalVisible(true)
},
{
type: 'divider'
},
{
key: 'mcp',
label: 'MCP插件',
icon: <ApiOutlined />,
onClick: () => navigate('/mcp-plugins')
}
]
}}
placement="bottomRight"
>
<Button
size="large"
icon={<MoreOutlined />}
style={{
borderRadius: 8,
borderColor: '#d9d9d9',
boxShadow: '0 2px 8px rgba(0, 0, 0, 0.08)',
transition: 'all 0.3s ease'
}}
onMouseEnter={(e) => {
e.currentTarget.style.borderColor = '#1890ff';
e.currentTarget.style.color = '#1890ff';
e.currentTarget.style.boxShadow = '0 2px 12px rgba(24, 144, 255, 0.3)';
}}
onMouseLeave={(e) => {
e.currentTarget.style.borderColor = '#d9d9d9';
e.currentTarget.style.color = 'rgba(0, 0, 0, 0.88)';
e.currentTarget.style.boxShadow = '0 2px 8px rgba(0, 0, 0, 0.08)';
}}
>
更多
</Button>
</Dropdown>
<UserMenu />
</Space>
)}
frontend/src/services/api.ts
+54
View File
@@ -1,4 +1,9 @@
import axios from 'axios';
interface MCPPluginSimpleCreate {
config_json: string;
enabled: boolean;
}
import { message } from 'antd';
import { ssePost } from '../utils/sseClient';
import type { SSEClientOptions } from '../utils/sseClient';
@@ -30,6 +35,13 @@ import type {
WritingStyleUpdate,
PresetStyle,
WritingStyleListResponse,
MCPPlugin,
MCPPluginCreate,
MCPPluginUpdate,
MCPTestResult,
MCPTool,
MCPToolCallRequest,
MCPToolCallResponse,
} from '../types';
const api = axios.create({
@@ -429,3 +441,45 @@ export const wizardStreamApi = {
options
),
};
export const mcpPluginApi = {
// 获取所有插件
getPlugins: () =>
api.get<unknown, MCPPlugin[]>('/mcp/plugins'),
// 获取单个插件
getPlugin: (id: string) =>
api.get<unknown, MCPPlugin>(`/mcp/plugins/${id}`),
// 创建插件
createPlugin: (data: MCPPluginCreate) =>
api.post<unknown, MCPPlugin>('/mcp/plugins', data),
// 简化创建插件（通过标准MCP配置JSON）
createPluginSimple: (data: MCPPluginSimpleCreate) =>
api.post<unknown, MCPPlugin>('/mcp/plugins/simple', data),
// 更新插件
updatePlugin: (id: string, data: MCPPluginUpdate) =>
api.put<unknown, MCPPlugin>(`/mcp/plugins/${id}`, data),
// 删除插件
deletePlugin: (id: string) =>
api.delete<unknown, { message: string }>(`/mcp/plugins/${id}`),
// 启用/禁用插件
togglePlugin: (id: string, enabled: boolean) =>
api.post<unknown, MCPPlugin>(`/mcp/plugins/${id}/toggle`, null, { params: { enabled } }),
// 测试插件连接
testPlugin: (id: string) =>
api.post<unknown, MCPTestResult>(`/mcp/plugins/${id}/test`),
// 获取插件工具列表
getPluginTools: (id: string) =>
api.get<unknown, { tools: MCPTool[] }>(`/mcp/plugins/${id}/tools`),
// 调用工具
callTool: (data: MCPToolCallRequest) =>
api.post<unknown, MCPToolCallResponse>('/mcp/call', data),
};
frontend/src/types/index.ts
+81
View File
@@ -523,3 +523,84 @@ export interface TriggerAnalysisResponse {
status: string;
message: string;
}
// MCP 插件类型定义 - 优化后只包含必要字段
export interface MCPPlugin {
id: string;
plugin_name: string;
display_name: string;
description?: string;
plugin_type: 'http' | 'stdio';
category: string;
// HTTP类型字段
server_url?: string;
headers?: Record<string, string>;
// Stdio类型字段
command?: string;
args?: string[];
env?: Record<string, string>;
// 状态字段
enabled: boolean;
status: 'active' | 'inactive' | 'error';
last_error?: string;
last_test_at?: string;
// 时间戳
created_at: string;
}
export interface MCPPluginCreate {
plugin_name: string;
display_name?: string;
description?: string;
server_type: 'http' | 'stdio';
server_url?: string;
command?: string;
args?: string[];
env?: Record<string, string>;
headers?: Record<string, string>;
enabled?: boolean;
}
export interface MCPPluginUpdate {
display_name?: string;
description?: string;
server_url?: string;
command?: string;
args?: string[];
env?: Record<string, string>;
headers?: Record<string, string>;
enabled?: boolean;
}
export interface MCPTool {
name: string;
description?: string;
inputSchema?: Record<string, unknown>;
}
export interface MCPTestResult {
success: boolean;
message: string;
tools?: MCPTool[];
tools_count?: number;
response_time_ms?: number;
error?: string;
error_type?: string;
suggestions?: string[];
}
export interface MCPToolCallRequest {
plugin_id: string;
tool_name: string;
arguments: Record<string, unknown>;
}
export interface MCPToolCallResponse {
success: boolean;
result?: unknown;
error?: string;
}