refactor: 重构知识库工具并集成新工具
- 新增 KnowledgeBaseMiddleware，提供通用知识库工具（list_kbs、get_mindmap、query_kb） - 新增 debug/tools.py 调试工具集 - 重构 kbs/tools.py，分离通用工具与 MySQL 工具 - 移除旧的 toolkits registry 简化架构 - API 变更：get_databases_by_user → get_databases_by_user_id - 前端拆分 KnowledgeBaseTool.vue 为多个专用工具组件
This commit is contained in:
Wenjie Zhang
parent
b4ea2ab948
commit
76656ab95e
@ -77,7 +77,8 @@ class MyAgentContext(BaseContext):
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```python
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from src.agents.common import BaseContext, gen_tool_info
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from src.agents.common.tools import get_buildin_tools
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from src.agents.common.toolkits.buildin import calculator, query_knowledge_graph
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from src.agents.common.toolkits.buildin.tools import _create_tavily_search
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from src.agents.common.toolkits.mysql import get_mysql_tools
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@dataclass(kw_only=True)
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@ -86,7 +87,9 @@ class ReporterContext(BaseContext):
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default_factory=lambda: [t.name for t in get_mysql_tools()],
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metadata={
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"name": "工具",
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"options": lambda: gen_tool_info(get_buildin_tools() + get_mysql_tools()),
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"options": lambda: gen_tool_info(
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[calculator, query_knowledge_graph, _create_tavily_search()] + get_mysql_tools()
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),
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"description": "包含内置工具和 MySQL 工具包。",
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},
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)
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@ -551,8 +551,7 @@ async def save_agent_config(
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if "knowledges" in config and config["knowledges"]:
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# 获取用户有权访问的知识库名称
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try:
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user_info = {"role": current_user.role, "department_id": current_user.department_id}
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accessible_databases = await knowledge_base.get_databases_by_user(user_info)
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accessible_databases = await knowledge_base.get_databases_by_user_id(current_user.user_id)
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accessible_kb_names = {
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db.get("name") for db in accessible_databases.get("databases", []) if db.get("name")
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}
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@ -89,8 +89,7 @@ async def _ensure_database_not_dify(db_id: str, operation: str) -> None:
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async def get_databases(current_user: User = Depends(get_admin_user)):
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"""获取所有知识库（根据用户权限过滤）"""
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try:
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user_info = {"role": current_user.role, "department_id": current_user.department_id}
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return await knowledge_base.get_databases_by_user(user_info)
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return await knowledge_base.get_databases_by_user_id(current_user.user_id)
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except Exception as e:
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logger.error(f"获取数据库列表失败 {e}, {traceback.format_exc()}")
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return {"message": f"获取数据库列表失败 {e}", "databases": []}
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@ -180,8 +179,7 @@ async def create_database(
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async def get_accessible_databases(current_user: User = Depends(get_required_user)):
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"""获取当前用户有权访问的知识库列表（用于智能体配置）"""
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try:
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user_info = {"role": current_user.role, "department_id": current_user.department_id}
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databases = await knowledge_base.get_databases_by_user(user_info)
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databases = await knowledge_base.get_databases_by_user_id(current_user.user_id)
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accessible = [
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{
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@ -664,10 +662,10 @@ async def batch_delete_documents(db_id: str, file_ids: list[str] = Body(...), cu
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"""批量删除文档或文件夹"""
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logger.debug(f"BATCH DELETE documents {file_ids} in {db_id}")
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await _ensure_database_not_dify(db_id, "批量文档删除")
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deleted_count = 0
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failed_items = []
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for doc_id in file_ids:
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try:
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file_meta_info = await knowledge_base.get_file_basic_info(db_id, doc_id)
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@ -700,7 +698,7 @@ async def batch_delete_documents(db_id: str, file_ids: list[str] = Body(...), cu
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if deleted_count == 0:
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raise HTTPException(status_code=400, detail=f"批量删除失败: 所有 {len(failed_items)} 个文件均未删除。")
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return {
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"message": f"部分删除成功: 已删除 {deleted_count} 个文件，失败 {len(failed_items)} 个",
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"message": f"部分删除成功: 已删除 {deleted_count} 个文件，失败 {len(failed_items)} 个",
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"deleted_count": deleted_count,
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"failed_items": failed_items
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}
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@ -286,8 +286,7 @@ async def get_databases_overview(current_user: User = Depends(get_admin_user)):
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知识库列表
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"""
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try:
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user_info = {"role": current_user.role, "department_id": current_user.department_id}
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databases = await knowledge_base.get_databases_by_user(user_info)
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databases = await knowledge_base.get_databases_by_user_id(current_user.user_id)
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# databases["databases"] 是一个列表，每个元素已经包含了基本信息
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db_list_raw = databases.get("databases", [])
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@ -12,6 +12,7 @@ from src.agents.common.middlewares import (
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RuntimeConfigMiddleware,
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save_attachments_to_fs,
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)
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from src.agents.common.middlewares.knowledge_base_middleware import KnowledgeBaseMiddleware
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from src.services.mcp_service import get_tools_from_all_servers
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@ -43,7 +44,8 @@ class ChatbotAgent(BaseAgent):
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middleware=[
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save_attachments_to_fs, # 附件注入提示词
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FilesystemMiddleware(backend=_create_fs_backend), # 文件系统后端
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RuntimeConfigMiddleware(extra_tools=all_mcp_tools), # 运行时配置应用（模型/工具/知识库/MCP/提示词）
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KnowledgeBaseMiddleware(), # 知识库工具
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RuntimeConfigMiddleware(extra_tools=all_mcp_tools), # 运行时配置应用（模型/工具/MCP/提示词）
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ModelRetryMiddleware(), # 模型重试中间件
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TodoListMiddleware(),
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PatchToolCallsMiddleware(),
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@ -7,7 +7,6 @@ from src.agents.common.models import load_chat_model
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from src.agents.common.state import BaseState
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# Tools - 核心工具函数
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from src.agents.common.toolkits.buildin import get_buildin_tools
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from src.agents.common.toolkits.utils import gen_tool_info
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# MCP - Agent 层统一入口（自动过滤 disabled_tools）
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@ -21,7 +20,6 @@ __all__ = [
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# Model utilities
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"load_chat_model",
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# Core tools
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"get_buildin_tools",
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"gen_tool_info",
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# Core MCP
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"get_enabled_mcp_tools",
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@ -12,9 +12,6 @@ from src import config as sys_config
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from src.services.mcp_service import get_mcp_server_names
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from src.utils import logger
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from .toolkits.buildin import get_buildin_tools
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from .toolkits.utils import gen_tool_info
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@dataclass(kw_only=True)
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class BaseContext:
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@ -66,7 +63,6 @@ class BaseContext:
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default_factory=list,
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metadata={
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"name": "工具",
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"options": lambda: gen_tool_info(get_buildin_tools()),
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"description": "内置的工具。",
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},
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)
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26
src/agents/common/middlewares/knowledge_base_middleware.py
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26
src/agents/common/middlewares/knowledge_base_middleware.py
Normal file
@ -0,0 +1,26 @@
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"""知识库中间件 - 提供通用知识库工具"""
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from collections.abc import Callable
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from typing import Any
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from langchain.agents.middleware import AgentMiddleware, ModelRequest, ModelResponse
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from langchain.tools.tool_node import ToolCallRequest
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from src.agents.common.toolkits.kbs import get_common_kb_tools
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from src.utils.logging_config import logger
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class KnowledgeBaseMiddleware(AgentMiddleware):
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"""知识库中间件 - 提供通用知识库工具
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提供 3 个通用工具：
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- list_kbs: 列出用户可访问的知识库
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- get_mindmap: 获取指定知识库的思维导图
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- query_kb: 在指定知识库中检索
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"""
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def __init__(self):
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super().__init__()
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# 预加载通用知识库工具
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self.kb_tools = get_common_kb_tools()
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self.tools = self.kb_tools
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logger.debug(f"Initialized KnowledgeBaseMiddleware with {len(self.kb_tools)} tools")
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@ -12,8 +12,7 @@ from langchain_core.messages import SystemMessage, ToolMessage
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from langgraph.types import Command
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from src.agents.common import load_chat_model
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from src.agents.common.toolkits.buildin import get_buildin_tools
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from src.agents.common.toolkits.kbs import get_kb_based_tools
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from src.agents.common.toolkits import get_all_tool_instances
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from src.services.mcp_service import get_enabled_mcp_tools
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from src.services.skill_resolver import (
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SkillSessionSnapshot,
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@ -48,10 +47,9 @@ class RuntimeConfigState(AgentState):
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class RuntimeConfigMiddleware(AgentMiddleware):
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"""运行时配置中间件 - 应用模型/工具/知识库/MCP/提示词配置
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"""运行时配置中间件 - 应用模型/工具/MCP/提示词配置
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注意：所有可能用到的知识库工具必须在初始化时预加载并注册到 self.tools
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运行时根据配置从 self.tools 中筛选工具，不能动态添加新工具
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知识库工具已移至独立的 KnowledgeBaseMiddleware
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支持自定义上下文字段名称，以便在不同场景（如主智能体/子智能体）使用不同的配置字段
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"""
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@ -106,10 +104,10 @@ class RuntimeConfigMiddleware(AgentMiddleware):
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self.tools: list[Any] = []
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# 预加载工具列表（仅当启用工具覆盖时）
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# 注意：知识库工具已移至独立的 KnowledgeBaseMiddleware
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if self.enable_tools_override:
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self.kb_tools = get_kb_based_tools()
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self.buildin_tools = get_buildin_tools()
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self.tools = self.kb_tools + self.buildin_tools + (extra_tools or [])
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self.base_tools = get_all_tool_instances()
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self.tools = self.base_tools + (extra_tools or [])
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elif extra_tools:
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logger.warning(
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"RuntimeConfigMiddleware: extra_tools 参数已提供，但 enable_tools_override=False，"
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@ -239,13 +237,7 @@ class RuntimeConfigMiddleware(AgentMiddleware):
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continue
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logger.warning(f"RuntimeConfigMiddleware: tool dependency not found, skip: {tool_name}")
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# 2. 知识库工具
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knowledges = getattr(context, self.knowledges_context_name, None)
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if knowledges:
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kb_tools = get_kb_based_tools(db_names=knowledges)
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selected_tools.extend(kb_tools)
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# 3. MCP 工具（使用统一入口，自动过滤 disabled_tools）
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# 2. MCP 工具（使用统一入口，自动过滤 disabled_tools）
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mcps = getattr(context, self.mcps_context_name, None) or []
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all_mcp_names: list[str] = []
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for server_name in mcps:
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@ -4,51 +4,23 @@ from .registry import (
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get_all_extra_metadata,
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get_all_tool_instances,
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get_extra_metadata,
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register_tool,
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tool,
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)
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# 工具获取函数
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from .buildin import get_buildin_tools
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from .kbs import get_kb_based_tools
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from src.services.mcp_service import get_enabled_mcp_tools
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from .kbs import get_common_kb_tools
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async def get_tools_from_context(context, extra_tools=None):
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"""从上下文配置中获取工具列表"""
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# 1. 基础工具 (从 context.tools 中筛选)
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all_basic_tools = get_buildin_tools() + (extra_tools or [])
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selected_tools = []
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if context.tools:
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# 创建工具映射表
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tools_map = {t.name: t for t in all_basic_tools}
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for tool_name in context.tools:
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if tool_name in tools_map:
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selected_tools.append(tools_map[tool_name])
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# 2. 知识库工具
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if context.knowledges:
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kb_tools = get_kb_based_tools(db_names=context.knowledges)
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selected_tools.extend(kb_tools)
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# 3. MCP 工具（使用统一入口，自动过滤 disabled_tools）
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if context.mcps:
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for server_name in context.mcps:
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mcp_tools = await get_enabled_mcp_tools(server_name)
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selected_tools.extend(mcp_tools)
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return selected_tools
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# 触发各模块的 @tool 装饰器执行，自动注册工具
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from . import buildin
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from . import debug
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from . import mysql
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__all__ = [
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"register_tool",
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"get_extra_metadata",
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"get_all_extra_metadata",
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"get_all_tool_instances",
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"ToolExtraMetadata",
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"tool",
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"get_buildin_tools",
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"get_kb_based_tools",
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"get_tools_from_context",
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"get_common_kb_tools",
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]
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@ -1,7 +1,7 @@
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# buildin 工具包
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from .tools import get_buildin_tools, query_knowledge_graph
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from .tools import calculator, query_knowledge_graph
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__all__ = [
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"get_buildin_tools",
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"calculator",
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"query_knowledge_graph",
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]
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@ -1,33 +1,48 @@
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import os
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import traceback
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import uuid
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from typing import Annotated, Any
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import requests
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from langgraph.types import interrupt
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from src import config, graph_base
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from src.agents.common.toolkits import tool
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from src.storage.minio import aupload_file_to_minio
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from src.agents.common.toolkits.registry import _all_tool_instances, _extra_registry, ToolExtraMetadata
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from src.utils import logger
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# Lazy initialization for TavilySearch (only when TAVILY_API_KEY is available)
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# Lazy initialization for TavilySearch (only when API key is available)
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_tavily_search_instance = None
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def get_tavily_search():
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"""Get TavilySearch instance lazily, only when API key is available."""
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def _create_tavily_search():
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"""Create and register TavilySearch tool with metadata."""
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global _tavily_search_instance
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if _tavily_search_instance is None and config.enable_web_search:
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if _tavily_search_instance is None:
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from langchain_tavily import TavilySearch
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_tavily_search_instance = TavilySearch()
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_tavily_search_instance.metadata = {"name": "Tavily 网页搜索", "category": "buildin", "tags": ["搜索"]}
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# 即使没有配置 API_KEY 也返回实例，调用时会出错
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return _tavily_search_instance
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# 注册 TavilySearch 工具（延迟初始化）
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def _register_tavily_tool():
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"""Register TavilySearch tool with extra metadata."""
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tavily_instance = _create_tavily_search()
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# 手动注册到全局注册表
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_extra_registry["tavily_search"] = ToolExtraMetadata(
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category="buildin",
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tags=["搜索"],
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display_name="Tavily 网页搜索",
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)
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# 添加到工具实例列表
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_all_tool_instances.append(tavily_instance)
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# 模块加载时注册
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if config.enable_web_search:
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try:
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_register_tavily_tool()
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except Exception as e:
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logger.warning(f"Failed to register TavilySearch tool: {e}")
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@tool(category="buildin", tags=["计算"], display_name="计算器")
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def calculator(a: float, b: float, operation: str) -> float:
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"""计算器：对给定的2个数字进行基本数学运算"""
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@ -49,81 +64,6 @@ def calculator(a: float, b: float, operation: str) -> float:
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raise
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@tool(category="buildin", tags=["图片", "测试"], display_name="文生图测试")
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async def text_to_img_demo(text: str) -> str:
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"""【测试用】使用模型生成图片， 会返回图片的URL"""
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url = "https://api.siliconflow.cn/v1/images/generations"
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payload = {
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"model": "Qwen/Qwen-Image",
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"prompt": text,
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}
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headers = {"Authorization": f"Bearer {os.getenv('SILICONFLOW_API_KEY')}", "Content-Type": "application/json"}
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try:
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response = requests.post(url, json=payload, headers=headers)
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response_json = response.json()
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except Exception as e:
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logger.error(f"Failed to generate image with: {e}")
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raise ValueError(f"Image generation failed: {e}")
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try:
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image_url = response_json["images"][0]["url"]
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except (KeyError, IndexError, TypeError) as e:
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logger.error(f"Failed to parse image URL from response: {e}, {response_json=}")
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raise ValueError(f"Image URL extraction failed: {e}")
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# 2. Upload to MinIO (Simplified)
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response = requests.get(image_url)
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file_data = response.content
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||||
file_name = f"{uuid.uuid4()}.jpg"
|
||||
image_url = await aupload_file_to_minio(
|
||||
bucket_name="generated-images", file_name=file_name, data=file_data, file_extension="jpg"
|
||||
)
|
||||
logger.info(f"Image uploaded. URL: {image_url}")
|
||||
return image_url
|
||||
|
||||
|
||||
@tool(category="debug", tags=["内置", "审批"], display_name="人工审批")
|
||||
def get_approved_user_goal(
|
||||
operation_description: str,
|
||||
) -> dict:
|
||||
"""
|
||||
请求人工审批，在执行重要操作前获得人类确认。
|
||||
|
||||
Args:
|
||||
operation_description: 需要审批的操作描述，例如 "调用知识库工具"
|
||||
Returns:
|
||||
dict: 包含审批结果的字典，格式为 {"approved": bool, "message": str}
|
||||
"""
|
||||
# 构建详细的中断信息
|
||||
interrupt_info = {
|
||||
"question": "是否批准以下操作？",
|
||||
"operation": operation_description,
|
||||
}
|
||||
|
||||
# 触发人工审批
|
||||
is_approved = interrupt(interrupt_info)
|
||||
|
||||
# 返回审批结果
|
||||
if is_approved:
|
||||
result = {
|
||||
"approved": True,
|
||||
"message": f"✅ 操作已批准：{operation_description}",
|
||||
}
|
||||
print(f"✅ 人工审批通过: {operation_description}")
|
||||
else:
|
||||
result = {
|
||||
"approved": False,
|
||||
"message": f"❌ 操作被拒绝：{operation_description}",
|
||||
}
|
||||
print(f"❌ 人工审批被拒绝: {operation_description}")
|
||||
|
||||
return result
|
||||
|
||||
|
||||
KG_QUERY_DESCRIPTION = """
|
||||
使用这个工具可以查询知识图谱中包含的三元组信息。
|
||||
关键词（query），使用可能帮助回答这个问题的关键词进行查询，不要直接使用用户的原始输入去查询。
|
||||
@ -144,18 +84,3 @@ def query_knowledge_graph(query: Annotated[str, "The keyword to query knowledge
|
||||
except Exception as e:
|
||||
logger.error(f"Knowledge graph query error: {e}, {traceback.format_exc()}")
|
||||
return f"知识图谱查询失败: {str(e)}"
|
||||
|
||||
|
||||
def get_buildin_tools() -> list:
|
||||
"""获取内置工具列表"""
|
||||
static_tools = [
|
||||
query_knowledge_graph,
|
||||
get_approved_user_goal,
|
||||
calculator,
|
||||
text_to_img_demo,
|
||||
]
|
||||
|
||||
# 始终添加 tavily_search，无论是否配置 API_KEY（调用时会出错）
|
||||
static_tools.append(get_tavily_search())
|
||||
|
||||
return static_tools
|
||||
|
||||
7
src/agents/common/toolkits/debug/__init__.py
Normal file
7
src/agents/common/toolkits/debug/__init__.py
Normal file
@ -0,0 +1,7 @@
|
||||
# debug 工具包
|
||||
from .tools import get_approved_user_goal, text_to_img_demo
|
||||
|
||||
__all__ = [
|
||||
"get_approved_user_goal",
|
||||
"text_to_img_demo",
|
||||
]
|
||||
86
src/agents/common/toolkits/debug/tools.py
Normal file
86
src/agents/common/toolkits/debug/tools.py
Normal file
@ -0,0 +1,86 @@
|
||||
import os
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
import requests
|
||||
from langgraph.types import interrupt
|
||||
|
||||
from src import config
|
||||
from src.agents.common.toolkits import tool
|
||||
from src.storage.minio import aupload_file_to_minio
|
||||
from src.utils import logger
|
||||
|
||||
|
||||
@tool(category="debug", tags=["图片", "测试"], display_name="文生图测试")
|
||||
async def text_to_img_demo(text: str) -> str:
|
||||
"""【测试用】使用模型生成图片， 会返回图片的URL"""
|
||||
|
||||
url = "https://api.siliconflow.cn/v1/images/generations"
|
||||
|
||||
payload = {
|
||||
"model": "Qwen/Qwen-Image",
|
||||
"prompt": text,
|
||||
}
|
||||
headers = {"Authorization": f"Bearer {os.getenv('SILICONFLOW_API_KEY')}", "Content-Type": "application/json"}
|
||||
|
||||
try:
|
||||
response = requests.post(url, json=payload, headers=headers)
|
||||
response_json = response.json()
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to generate image with: {e}")
|
||||
raise ValueError(f"Image generation failed: {e}")
|
||||
|
||||
try:
|
||||
image_url = response_json["images"][0]["url"]
|
||||
except (KeyError, IndexError, TypeError) as e:
|
||||
logger.error(f"Failed to parse image URL from response: {e}, {response_json=}")
|
||||
raise ValueError(f"Image URL extraction failed: {e}")
|
||||
|
||||
# 2. Upload to MinIO (Simplified)
|
||||
response = requests.get(image_url)
|
||||
file_data = response.content
|
||||
|
||||
file_name = f"{uuid.uuid4()}.jpg"
|
||||
image_url = await aupload_file_to_minio(
|
||||
bucket_name="generated-images", file_name=file_name, data=file_data, file_extension="jpg"
|
||||
)
|
||||
logger.info(f"Image uploaded. URL: {image_url}")
|
||||
return image_url
|
||||
|
||||
|
||||
@tool(category="debug", tags=["内置", "审批"], display_name="人工审批")
|
||||
def get_approved_user_goal(
|
||||
operation_description: str,
|
||||
) -> dict:
|
||||
"""
|
||||
请求人工审批，在执行重要操作前获得人类确认。
|
||||
|
||||
Args:
|
||||
operation_description: 需要审批的操作描述，例如 "调用知识库工具"
|
||||
Returns:
|
||||
dict: 包含审批结果的字典，格式为 {"approved": bool, "message": str}
|
||||
"""
|
||||
# 构建详细的中断信息
|
||||
interrupt_info = {
|
||||
"question": "是否批准以下操作？",
|
||||
"operation": operation_description,
|
||||
}
|
||||
|
||||
# 触发人工审批
|
||||
is_approved = interrupt(interrupt_info)
|
||||
|
||||
# 返回审批结果
|
||||
if is_approved:
|
||||
result = {
|
||||
"approved": True,
|
||||
"message": f"✅ 操作已批准：{operation_description}",
|
||||
}
|
||||
print(f"✅ 人工审批通过: {operation_description}")
|
||||
else:
|
||||
result = {
|
||||
"approved": False,
|
||||
"message": f"❌ 操作被拒绝：{operation_description}",
|
||||
}
|
||||
print(f"❌ 人工审批被拒绝: {operation_description}")
|
||||
|
||||
return result
|
||||
@ -1,3 +1,5 @@
|
||||
from .tools import get_kb_based_tools
|
||||
from .tools import (
|
||||
get_common_kb_tools,
|
||||
)
|
||||
|
||||
__all__ = ["get_kb_based_tools"]
|
||||
__all__ = ["get_common_kb_tools"]
|
||||
|
||||
@ -1,159 +1,224 @@
|
||||
"""知识库工具模块"""
|
||||
import inspect
|
||||
import traceback
|
||||
from typing import Any
|
||||
|
||||
from langchain_core.tools import StructuredTool
|
||||
from langchain_core.tools import tool
|
||||
from langgraph.prebuilt.tool_node import ToolRuntime
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from src import knowledge_base
|
||||
from src.utils import logger
|
||||
|
||||
|
||||
class KnowledgeRetrieverModel(BaseModel):
|
||||
query_text: str | None = Field(
|
||||
default=None,
|
||||
# ========== 通用知识库工具函数 ==========
|
||||
|
||||
|
||||
class ListKBsInput(BaseModel):
|
||||
"""列出用户可访问的知识库输入模型"""
|
||||
# Langchain 的 runtime 注入机制要求必须有参数
|
||||
dummy: str = Field(default="", description="Dummy parameter - ignore") # Add this
|
||||
|
||||
|
||||
@tool(args_schema=ListKBsInput)
|
||||
async def list_kbs(dummy: str, runtime: ToolRuntime) -> str: # Now has 2 params
|
||||
"""列出当前用户可访问的知识库列表
|
||||
|
||||
返回用户基于权限可访问的知识库名称列表。这个列表是根据用户的角色和部门信息过滤后的结果，
|
||||
但不包括用户在当前对话中未启用的知识库。
|
||||
|
||||
Returns:
|
||||
用户可访问的知识库名称列表（字符串格式）
|
||||
"""
|
||||
# 从 runtime.context 获取用户信息
|
||||
runtime_context = runtime.context
|
||||
user_id = getattr(runtime_context, "user_id", None)
|
||||
if not user_id:
|
||||
return "无法获取用户信息"
|
||||
|
||||
# 打印 runtime—context 中的所有信息以进行调试
|
||||
logger.debug(f"Runtime context: {runtime_context.__dict__}")
|
||||
|
||||
# 获取用户在当前对话中启用的知识库列表
|
||||
enabled_kb_names = getattr(runtime_context, "knowledges", []) or []
|
||||
|
||||
# 获取用户可访问的知识库列表（包含名称和描述）
|
||||
try:
|
||||
result = await knowledge_base.get_databases_by_raw_id(user_id)
|
||||
all_kbs = result.get("databases", [])
|
||||
except Exception as e:
|
||||
logger.error(f"获取用户知识库列表失败: {e}")
|
||||
return f"获取知识库列表失败: {str(e)}"
|
||||
|
||||
all_kb_names = [kb["name"] for kb in all_kbs]
|
||||
|
||||
logger.debug(f"用户 {user_id} 可访问的知识库列表: {all_kb_names}")
|
||||
logger.debug(f"用户 {user_id} 当前对话启用的知识库列表: {enabled_kb_names}")
|
||||
|
||||
# 与启用的知识库取交集
|
||||
available_kbs = [kb for kb in all_kbs if kb["name"] in enabled_kb_names]
|
||||
|
||||
if not available_kbs:
|
||||
return "当前没有可访问的知识库"
|
||||
|
||||
# 格式化输出（包含名称和描述）
|
||||
kb_list = []
|
||||
for kb in available_kbs:
|
||||
name = kb.get("name", "")
|
||||
desc = kb.get("description") or "无描述"
|
||||
kb_list.append({"name": name, "description": desc})
|
||||
|
||||
return kb_list
|
||||
|
||||
|
||||
class GetMindmapInput(BaseModel):
|
||||
"""获取思维导图输入模型"""
|
||||
|
||||
kb_name: str = Field(
|
||||
description="知识库名称，用于指定要获取思维导图的知识库"
|
||||
)
|
||||
|
||||
|
||||
@tool(args_schema=GetMindmapInput)
|
||||
async def get_mindmap(kb_name: str, runtime: ToolRuntime) -> str:
|
||||
"""获取指定知识库的思维导图结构
|
||||
|
||||
当用户想要了解知识库的整体结构、文件分类、知识架构时使用此工具。
|
||||
返回知识库的思维导图层级结构。
|
||||
|
||||
Args:
|
||||
kb_name: 知识库名称
|
||||
|
||||
Returns:
|
||||
知识库的思维导图结构（文本格式）
|
||||
"""
|
||||
if not kb_name:
|
||||
return "请提供知识库名称"
|
||||
|
||||
# 获取所有检索器
|
||||
retrievers = knowledge_base.get_retrievers()
|
||||
|
||||
# 查找对应的知识库
|
||||
target_db_id = None
|
||||
target_info = None
|
||||
for db_id, info in retrievers.items():
|
||||
if info["name"] == kb_name:
|
||||
target_db_id = db_id
|
||||
target_info = info
|
||||
break
|
||||
|
||||
if not target_db_id:
|
||||
return f"知识库 '{kb_name}' 不存在"
|
||||
|
||||
try:
|
||||
from src.repositories.knowledge_base_repository import KnowledgeBaseRepository
|
||||
|
||||
kb_repo = KnowledgeBaseRepository()
|
||||
kb = await kb_repo.get_by_id(target_db_id)
|
||||
|
||||
if kb is None:
|
||||
return f"知识库 {target_info['name']} 不存在"
|
||||
|
||||
mindmap_data = kb.mindmap
|
||||
|
||||
if not mindmap_data:
|
||||
return f"知识库 {target_info['name']} 还没有生成思维导图。"
|
||||
|
||||
# 将思维导图数据转换为文本格式
|
||||
def mindmap_to_text(node, level=0):
|
||||
"""递归将思维导图JSON转换为层级文本"""
|
||||
indent = " " * level
|
||||
text = f"{indent}- {node.get('content', '')}\n"
|
||||
for child in node.get("children", []):
|
||||
text += mindmap_to_text(child, level + 1)
|
||||
return text
|
||||
|
||||
mindmap_text = f"知识库 {target_info['name']} 的思维导图结构：\n\n"
|
||||
mindmap_text += mindmap_to_text(mindmap_data)
|
||||
|
||||
return mindmap_text
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取思维导图失败: {e}")
|
||||
return f"获取思维导图失败: {str(e)}"
|
||||
|
||||
|
||||
class QueryKBInput(BaseModel):
|
||||
"""知识库检索输入模型"""
|
||||
|
||||
kb_name: str = Field(
|
||||
description="知识库名称，用于指定要在哪个知识库中检索"
|
||||
)
|
||||
query_text: str = Field(
|
||||
description=(
|
||||
"查询的关键词，查询的时候，应该尽量以可能帮助回答这个问题的关键词进行查询，不要直接使用用户的原始输入去查询。"
|
||||
"查询的关键词，查询的时候，应该尽量以可能帮助回答这个问题的关键词进行查询，"
|
||||
"不要直接使用用户的原始输入去查询。"
|
||||
)
|
||||
)
|
||||
operation: str = Field(
|
||||
default="search",
|
||||
description=(
|
||||
"操作类型：'search' 表示检索知识库内容，'get_mindmap' 表示获取知识库的思维导图结构。"
|
||||
"当用户询问知识库的整体结构、文件分类、知识架构时，使用 'get_mindmap'。"
|
||||
"当用户需要查询具体内容时，使用 'search'。"
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class CommonKnowledgeRetriever(KnowledgeRetrieverModel):
|
||||
"""Common knowledge retriever model."""
|
||||
|
||||
file_name: str | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"（非必要不启用此参数，留空即可）当操作类型为 'search' 且已经读取思维导图之后，可以指定文件关键词，支持模糊匹配。\n"
|
||||
"（非必要不启用此参数，留空即可）当已经读取思维导图之后，可以指定文件关键词，支持模糊匹配。\n"
|
||||
"仅当检索结果过多且不相关，需要进一步缩小范围时使用。"
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def get_kb_based_tools(db_names: list[str] | None = None) -> list:
|
||||
"""获取所有知识库基于的工具"""
|
||||
# 获取所有知识库
|
||||
kb_tools = []
|
||||
@tool(args_schema=QueryKBInput)
|
||||
async def query_kb(kb_name: str, query_text: str, file_name: str | None = None, runtime: ToolRuntime = None) -> Any:
|
||||
"""在指定知识库中检索内容
|
||||
|
||||
当用户需要查询具体内容时使用此工具。根据关键词在知识库中检索相关文档片段。
|
||||
|
||||
Args:
|
||||
kb_name: 知识库名称
|
||||
query_text: 查询的关键词
|
||||
file_name: （可选）文件名称过滤
|
||||
|
||||
Returns:
|
||||
检索结果
|
||||
"""
|
||||
if not kb_name:
|
||||
return "请提供知识库名称"
|
||||
if not query_text:
|
||||
return "请提供查询内容"
|
||||
|
||||
# 获取所有检索器
|
||||
retrievers = knowledge_base.get_retrievers()
|
||||
if db_names is None:
|
||||
db_ids = None
|
||||
else:
|
||||
db_ids = [kb_id for kb_id, kb in retrievers.items() if kb["name"] in db_names]
|
||||
|
||||
def _create_retriever_wrapper(db_id: str, retriever_info: dict[str, Any]):
|
||||
"""创建检索器包装函数的工厂函数，避免闭包变量捕获问题"""
|
||||
# 查找对应的知识库
|
||||
target_info = None
|
||||
for db_id, info in retrievers.items():
|
||||
if info["name"] == kb_name:
|
||||
target_info = info
|
||||
break
|
||||
|
||||
async def async_retriever_wrapper(
|
||||
query_text: str, operation: str = "search", file_name: str | None = None
|
||||
) -> Any:
|
||||
"""异步检索器包装函数，支持检索和获取思维导图"""
|
||||
if not target_info:
|
||||
return f"知识库 '{kb_name}' 不存在"
|
||||
|
||||
# 获取思维导图
|
||||
if operation == "get_mindmap":
|
||||
try:
|
||||
logger.debug(f"Getting mindmap for database {db_id}")
|
||||
try:
|
||||
retriever = target_info["retriever"]
|
||||
kwargs = {}
|
||||
if file_name:
|
||||
kwargs["file_name"] = file_name
|
||||
|
||||
from src.repositories.knowledge_base_repository import KnowledgeBaseRepository
|
||||
if inspect.iscoroutinefunction(retriever):
|
||||
result = await retriever(query_text, **kwargs)
|
||||
else:
|
||||
result = retriever(query_text, **kwargs)
|
||||
|
||||
kb_repo = KnowledgeBaseRepository()
|
||||
kb = await kb_repo.get_by_id(db_id)
|
||||
return result
|
||||
|
||||
if kb is None:
|
||||
return f"知识库 {retriever_info['name']} 不存在"
|
||||
except Exception as e:
|
||||
logger.error(f"检索失败: {e}")
|
||||
return f"检索失败: {str(e)}"
|
||||
|
||||
mindmap_data = kb.mindmap
|
||||
|
||||
if not mindmap_data:
|
||||
return f"知识库 {retriever_info['name']} 还没有生成思维导图。"
|
||||
def get_common_kb_tools() -> list:
|
||||
"""获取通用知识库工具列表
|
||||
|
||||
# 将思维导图数据转换为文本格式，便于AI理解
|
||||
def mindmap_to_text(node, level=0):
|
||||
"""递归将思维导图JSON转换为层级文本"""
|
||||
indent = " " * level
|
||||
text = f"{indent}- {node.get('content', '')}\n"
|
||||
for child in node.get("children", []):
|
||||
text += mindmap_to_text(child, level + 1)
|
||||
return text
|
||||
|
||||
mindmap_text = f"知识库 {retriever_info['name']} 的思维导图结构：\n\n"
|
||||
mindmap_text += mindmap_to_text(mindmap_data)
|
||||
|
||||
logger.debug(f"Successfully retrieved mindmap for {db_id}")
|
||||
return mindmap_text
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting mindmap for {db_id}: {e}")
|
||||
return f"获取思维导图失败: {str(e)}"
|
||||
|
||||
# 默认：检索知识库
|
||||
retriever = retriever_info["retriever"]
|
||||
try:
|
||||
logger.debug(f"Retrieving from database {db_id} with query: {query_text}")
|
||||
kwargs = {}
|
||||
if file_name:
|
||||
kwargs["file_name"] = file_name
|
||||
|
||||
if inspect.iscoroutinefunction(retriever):
|
||||
result = await retriever(query_text, **kwargs)
|
||||
else:
|
||||
result = retriever(query_text, **kwargs)
|
||||
logger.debug(f"Retrieved {len(result) if isinstance(result, list) else 'N/A'} results from {db_id}")
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"Error in retriever {db_id}: {e}")
|
||||
return f"检索失败: {str(e)}"
|
||||
|
||||
return async_retriever_wrapper
|
||||
|
||||
for db_id, retrieve_info in retrievers.items():
|
||||
if db_ids is not None and db_id not in db_ids:
|
||||
continue
|
||||
|
||||
try:
|
||||
# 构建工具描述
|
||||
description = (
|
||||
f"使用 {retrieve_info['name']} 知识库的多功能工具。\n"
|
||||
f"知识库描述：{retrieve_info['description'] or '没有描述。'}\n\n"
|
||||
f"支持的操作：\n"
|
||||
f"1. 'search' - 检索知识库内容：根据关键词查询相关文档片段\n"
|
||||
f"2. 'get_mindmap' - 获取思维导图：查看知识库的整体结构和文件分类\n\n"
|
||||
f"使用建议：\n"
|
||||
f"- 需要查询具体内容时，使用 operation='search'\n"
|
||||
f"- 想了解知识库结构、文件分类时，使用 operation='get_mindmap'"
|
||||
)
|
||||
|
||||
# 使用工厂函数创建检索器包装函数，避免闭包问题
|
||||
retriever_wrapper = _create_retriever_wrapper(db_id, retrieve_info)
|
||||
|
||||
safename = retrieve_info["name"].replace(" ", "_")[:20]
|
||||
|
||||
args_schema = KnowledgeRetrieverModel
|
||||
if retrieve_info["metadata"]["kb_type"] in ["milvus"]:
|
||||
args_schema = CommonKnowledgeRetriever
|
||||
|
||||
# 使用 StructuredTool.from_function 创建异步工具
|
||||
tool = StructuredTool.from_function(
|
||||
coroutine=retriever_wrapper,
|
||||
name=safename,
|
||||
description=description,
|
||||
args_schema=args_schema,
|
||||
metadata=retrieve_info["metadata"] | {"tag": ["knowledgebase"]},
|
||||
)
|
||||
|
||||
kb_tools.append(tool)
|
||||
# logger.debug(f"Successfully created tool {tool_id} for database {db_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create tool for database {db_id}: {e}, \n{traceback.format_exc()}")
|
||||
continue
|
||||
|
||||
return kb_tools
|
||||
返回 3 个通用工具：
|
||||
- list_kbs: 列出用户可访问的知识库
|
||||
- get_mindmap: 获取指定知识库的思维导图
|
||||
- query_kb: 在指定知识库中检索
|
||||
"""
|
||||
return [list_kbs, get_mindmap, query_kb]
|
||||
|
||||
@ -19,36 +19,6 @@ _extra_registry: dict[str, ToolExtraMetadata] = {}
|
||||
_all_tool_instances: list = []
|
||||
|
||||
|
||||
def register_tool(
|
||||
category: str,
|
||||
tags: list[str] = None,
|
||||
display_name: str = "",
|
||||
icon: str = "",
|
||||
):
|
||||
"""装饰器：注册工具附加元数据
|
||||
|
||||
使用方式:
|
||||
@register_tool(category="buildin", tags=["内置"], display_name="计算器")
|
||||
def calculator(...):
|
||||
...
|
||||
"""
|
||||
|
||||
def decorator(func_or_tool: Callable) -> Callable:
|
||||
# 获取工具名称（优先用 metadata.name）
|
||||
tool_name = getattr(func_or_tool, "name", None) or getattr(
|
||||
func_or_tool, "__name__", ""
|
||||
)
|
||||
_extra_registry[tool_name] = ToolExtraMetadata(
|
||||
category=category,
|
||||
tags=tags or [],
|
||||
display_name=display_name,
|
||||
icon=icon,
|
||||
)
|
||||
return func_or_tool
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def get_extra_metadata(tool_name: str) -> ToolExtraMetadata | None:
|
||||
"""获取工具附加元数据"""
|
||||
return _extra_registry.get(tool_name)
|
||||
|
||||
@ -11,8 +11,9 @@ from langchain.agents.middleware import (
|
||||
|
||||
from src.agents.common import BaseAgent, load_chat_model
|
||||
from src.agents.common.backends import create_agent_composite_backend
|
||||
from src.agents.common.middlewares.knowledge_base_middleware import KnowledgeBaseMiddleware
|
||||
from src.agents.common.middlewares import RuntimeConfigMiddleware, SummaryOffloadMiddleware, save_attachments_to_fs
|
||||
from src.agents.common.toolkits.buildin.tools import get_tavily_search
|
||||
from src.agents.common.toolkits.buildin.tools import _create_tavily_search
|
||||
from src.services.mcp_service import get_tools_from_all_servers
|
||||
from src.utils import logger
|
||||
|
||||
@ -79,19 +80,16 @@ class DeepAgent(BaseAgent):
|
||||
|
||||
async def get_tools(self):
|
||||
"""返回 Deep Agent 的专用工具"""
|
||||
tools = []
|
||||
tavily_search = get_tavily_search()
|
||||
if tavily_search:
|
||||
tools.append(tavily_search)
|
||||
from src import config
|
||||
|
||||
tools = []
|
||||
if config.enable_web_search:
|
||||
tavily = _create_tavily_search()
|
||||
if tavily:
|
||||
tools.append(tavily)
|
||||
|
||||
# # Assert that search tool is available for DeepAgent
|
||||
# assert tools, (
|
||||
# "DeepAgent requires at least one search tool. "
|
||||
# "Please configure TAVILY_API_KEY environment variable to enable web search."
|
||||
# )
|
||||
if not tools:
|
||||
logger.warning("No search tools configured, DeepAgent will work without web search")
|
||||
tools = []
|
||||
return tools
|
||||
|
||||
async def get_graph(self, **kwargs):
|
||||
@ -159,6 +157,7 @@ class DeepAgent(BaseAgent):
|
||||
save_attachments_to_fs, # 附件注入提示词
|
||||
TodoListMiddleware(),
|
||||
PatchToolCallsMiddleware(),
|
||||
KnowledgeBaseMiddleware(), # 知识库工具
|
||||
subagents_middleware,
|
||||
summary_middleware,
|
||||
# 工具调用限制：tavily_search 总调用最多 20 次
|
||||
|
||||
@ -7,6 +7,7 @@ from src.knowledge.chunking.ragflow_like.presets import (
|
||||
ensure_chunk_defaults_in_additional_params,
|
||||
)
|
||||
from src.knowledge.factory import KnowledgeBaseFactory
|
||||
from src.storage.postgres.models_business import User
|
||||
from src.utils import logger
|
||||
from src.utils.datetime_utils import utc_isoformat
|
||||
|
||||
@ -243,19 +244,46 @@ class KnowledgeBaseManager:
|
||||
|
||||
return user_department_id in accessible_departments
|
||||
|
||||
async def get_databases_by_user(self, user: dict) -> dict:
|
||||
"""根据用户权限获取知识库列表
|
||||
async def get_databases_by_raw_id(self, user_id: int) -> dict:
|
||||
"""根据用户ID获取知识库列表（原始ID版本，兼容旧接口）"""
|
||||
from src.repositories.user_repository import UserRepository
|
||||
|
||||
Args:
|
||||
user: 用户信息字典，包含 role 和 department_id
|
||||
# 通过数据库获取用户信息
|
||||
user_repo = UserRepository()
|
||||
user: User | None = await user_repo.get_by_id(id=int(user_id))
|
||||
if not user:
|
||||
logger.warning(f"User not found: {user_id}")
|
||||
return {"databases": []}
|
||||
return await self.get_databases_by_user(user)
|
||||
|
||||
|
||||
async def get_databases_by_user_id(self, user_id: str) -> dict:
|
||||
"""根据用户ID获取知识库列表（字符串ID版本）"""
|
||||
from src.repositories.user_repository import UserRepository
|
||||
|
||||
# 通过数据库获取用户信息
|
||||
user_repo = UserRepository()
|
||||
user: User | None = await user_repo.get_by_user_id(user_id)
|
||||
if not user:
|
||||
logger.warning(f"User not found: {user_id}")
|
||||
return {"databases": []}
|
||||
return await self.get_databases_by_user(user)
|
||||
|
||||
async def get_databases_by_user(self, user: User) -> dict:
|
||||
"""根据用户权限获取知识库列表"""
|
||||
|
||||
# 构建用户信息字典
|
||||
user_info = {
|
||||
"role": user.role,
|
||||
"department_id": user.department_id,
|
||||
}
|
||||
|
||||
logger.info(f"Getting databases for user {user.id} with role {user.role} and department {user.department_id}")
|
||||
|
||||
Returns:
|
||||
过滤后的知识库列表
|
||||
"""
|
||||
all_databases = (await self.get_databases()).get("databases", [])
|
||||
|
||||
# 超级管理员可以看到所有知识库
|
||||
if user.get("role") == "superadmin":
|
||||
if user_info.get("role") == "superadmin":
|
||||
return {"databases": all_databases}
|
||||
|
||||
filtered_databases = []
|
||||
@ -265,7 +293,7 @@ class KnowledgeBaseManager:
|
||||
if not db_id:
|
||||
continue
|
||||
|
||||
if await self.check_accessible(user, db_id):
|
||||
if await self.check_accessible(user_info, db_id):
|
||||
filtered_databases.append(db)
|
||||
|
||||
return {"databases": filtered_databases}
|
||||
|
||||
@ -379,28 +379,6 @@ async def stream_agent_chat(
|
||||
# 先构建 langgraph_config
|
||||
langgraph_config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}
|
||||
|
||||
# 注意：LangGraph 会自动从 checkpointer 恢复 state（包括 attachments 和 files）
|
||||
# 无需手动加载或传递
|
||||
|
||||
# 根据用户权限过滤知识库
|
||||
requested_knowledge_names = input_context["agent_config"].get("knowledges")
|
||||
logger.info(f"Requesting knowledges: {requested_knowledge_names}")
|
||||
if requested_knowledge_names and isinstance(requested_knowledge_names, list) and requested_knowledge_names:
|
||||
user_info = {"role": "user", "department_id": department_id}
|
||||
accessible_databases = await knowledge_base.get_databases_by_user(user_info)
|
||||
accessible_kb_names = {
|
||||
db.get("name")
|
||||
for db in accessible_databases.get("databases", [])
|
||||
if isinstance(db, dict) and db.get("name")
|
||||
}
|
||||
logger.info(f"Accessible knowledges: {accessible_kb_names}")
|
||||
|
||||
filtered_knowledge_names = [kb for kb in requested_knowledge_names if kb in accessible_kb_names]
|
||||
blocked_knowledge_names = [kb for kb in requested_knowledge_names if kb not in accessible_kb_names]
|
||||
if blocked_knowledge_names:
|
||||
logger.warning(f"用户 {user_id} 无权访问知识库: {blocked_knowledge_names}, 已自动过滤")
|
||||
input_context["agent_config"]["knowledges"] = filtered_knowledge_names
|
||||
|
||||
full_msg = None
|
||||
accumulated_content = []
|
||||
async for msg, metadata in agent.stream_messages(messages, input_context=input_context):
|
||||
|
||||
@ -78,13 +78,6 @@ def validate_skill_slug(slug: str) -> str:
|
||||
raise ValueError("无效 skill slug")
|
||||
return normalized
|
||||
|
||||
|
||||
def _get_buildin_tool_names() -> list[str]:
|
||||
from src.agents.common.toolkits.buildin import get_buildin_tools
|
||||
|
||||
return [tool.name for tool in get_buildin_tools()]
|
||||
|
||||
|
||||
def get_skills_root_dir() -> Path:
|
||||
root = Path(sys_config.save_dir) / "skills"
|
||||
root.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
@ -242,11 +242,6 @@ async def test_awrap_tool_call_denies_invisible_skill():
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_model_call_injects_dependency_tools_and_mcps_after_activation(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setattr(
|
||||
runtime_middleware,
|
||||
"get_buildin_tools",
|
||||
lambda: [_FakeTool(name="calculator"), _FakeTool(name="dep-tool")],
|
||||
)
|
||||
monkeypatch.setattr(runtime_middleware, "get_kb_based_tools", lambda db_names=None: [])
|
||||
|
||||
snapshot = _build_snapshot(
|
||||
|
||||
@ -5,11 +5,17 @@
|
||||
<!-- 网页搜索 -->
|
||||
<WebSearchTool v-else-if="isWebSearchResult" :tool-call="toolCall" />
|
||||
|
||||
<!-- 知识库 -->
|
||||
<KnowledgeBaseTool v-else-if="isKnowledgeBaseResult" :tool-call="toolCall" />
|
||||
<!-- 知识库列表 -->
|
||||
<ListKbsTool v-else-if="toolName === 'list_kbs'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 思维导图 -->
|
||||
<GetMindmapTool v-else-if="toolName === 'get_mindmap'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 知识库检索 -->
|
||||
<QueryKbTool v-else-if="toolName === 'query_kb'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 待办事项 -->
|
||||
<TodoListTool v-else-if="isTodoListResult" :tool-call="toolCall" />
|
||||
<TodoListTool v-else-if="toolName === 'write_todos'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 计算器 -->
|
||||
<CalculatorTool v-else-if="isCalculatorResult" :tool-call="toolCall" />
|
||||
@ -21,31 +27,31 @@
|
||||
<TaskTool v-else-if="isTaskResult" :tool-call="toolCall" />
|
||||
|
||||
<!-- 写文件 -->
|
||||
<WriteFileTool v-else-if="isWriteFileResult" :tool-call="toolCall" />
|
||||
<WriteFileTool v-else-if="toolName === 'write_file'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 读文件 -->
|
||||
<ReadFileTool v-else-if="isReadFileResult" :tool-call="toolCall" />
|
||||
<ReadFileTool v-else-if="toolName === 'read_file'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 列目录 -->
|
||||
<ListDirectoryTool v-else-if="isListDirectoryResult" :tool-call="toolCall" />
|
||||
<ListDirectoryTool v-else-if="toolName === 'list_directory' || toolName === 'ls'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 搜索文件内容 -->
|
||||
<SearchFileContentTool v-else-if="isSearchFileContentResult" :tool-call="toolCall" />
|
||||
<SearchFileContentTool v-else-if="toolName === 'search_file_content'" :tool-call="toolCall" />
|
||||
|
||||
<!-- Glob 搜索 -->
|
||||
<GlobTool v-else-if="isGlobResult" :tool-call="toolCall" />
|
||||
<GlobTool v-else-if="toolName === 'glob'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 编辑文件 -->
|
||||
<EditFileTool v-else-if="isEditFileResult" :tool-call="toolCall" />
|
||||
<EditFileTool v-else-if="toolName === 'edit_file' || toolName === 'replace'" :tool-call="toolCall" />
|
||||
|
||||
<!-- MySQL 查询 -->
|
||||
<MysqlQueryTool v-else-if="isMysqlQueryResult" :tool-call="toolCall" />
|
||||
<MysqlQueryTool v-else-if="toolName === 'mysql_query'" :tool-call="toolCall" />
|
||||
|
||||
<!-- MySQL 描述表 -->
|
||||
<MysqlDescribeTableTool v-else-if="isMysqlDescribeTableResult" :tool-call="toolCall" />
|
||||
<MysqlDescribeTableTool v-else-if="toolName === 'mysql_describe_table'" :tool-call="toolCall" />
|
||||
|
||||
<!-- MySQL 列出表 -->
|
||||
<MysqlListTablesTool v-else-if="isMysqlListTablesResult" :tool-call="toolCall" />
|
||||
<MysqlListTablesTool v-else-if="toolName === 'mysql_list_tables'" :tool-call="toolCall" />
|
||||
|
||||
<!-- 默认展示 -->
|
||||
<BaseToolCall v-else :tool-call="toolCall" />
|
||||
@ -54,11 +60,11 @@
|
||||
<script setup>
|
||||
import { computed, ref } from 'vue'
|
||||
import BaseToolCall from './BaseToolCall.vue'
|
||||
import { useAgentStore } from '@/stores/agent'
|
||||
import { useDatabaseStore } from '@/stores/database'
|
||||
|
||||
import WebSearchTool from './tools/WebSearchTool.vue'
|
||||
import KnowledgeBaseTool from './tools/KnowledgeBaseTool.vue'
|
||||
import ListKbsTool from './tools/ListKbsTool.vue'
|
||||
import GetMindmapTool from './tools/GetMindmapTool.vue'
|
||||
import QueryKbTool from './tools/QueryKbTool.vue'
|
||||
import KnowledgeGraphTool from './tools/KnowledgeGraphTool.vue'
|
||||
import CalculatorTool from './tools/CalculatorTool.vue'
|
||||
import TodoListTool from './tools/TodoListTool.vue'
|
||||
@ -81,17 +87,7 @@ const props = defineProps({
|
||||
}
|
||||
})
|
||||
|
||||
const agentStore = useAgentStore()
|
||||
const databaseStore = useDatabaseStore()
|
||||
|
||||
const toolName = computed(() => props.toolCall.name || props.toolCall.function?.name || '')
|
||||
const tool = computed(() => {
|
||||
const toolsList = agentStore?.availableTools ? Object.values(agentStore.availableTools) : []
|
||||
const tool = toolsList.find((t) => t.name === toolName.value)
|
||||
return tool || null
|
||||
})
|
||||
|
||||
const databases = computed(() => databaseStore.databases || [])
|
||||
|
||||
const parseData = (content) => {
|
||||
if (typeof content === 'string') {
|
||||
@ -122,14 +118,6 @@ const isTaskResult = computed(() => {
|
||||
return args && typeof args === 'object' && 'subagent_type' in args
|
||||
})
|
||||
|
||||
const isKnowledgeBaseResult = computed(() => {
|
||||
const databaseInfo = databases.value.find((db) => db.name === toolName.value)
|
||||
if (databaseInfo && databaseInfo.kb_type !== 'lightrag') {
|
||||
return true
|
||||
}
|
||||
return false
|
||||
})
|
||||
|
||||
const isKnowledgeGraphResult = computed(() => {
|
||||
const name = toolName.value.toLowerCase()
|
||||
const hasGraphKeyword = name.includes('graph') || name.includes('图谱') || name.includes('kg')
|
||||
@ -142,10 +130,6 @@ const isKnowledgeGraphResult = computed(() => {
|
||||
)
|
||||
})
|
||||
|
||||
const isTodoListResult = computed(() => {
|
||||
return toolName.value === 'write_todos'
|
||||
})
|
||||
|
||||
const isCalculatorResult = computed(() => {
|
||||
const name = toolName.value.toLowerCase()
|
||||
return name.includes('calculator') || name.includes('calc') || name.includes('math')
|
||||
@ -158,42 +142,6 @@ const isImageResult = computed(() => {
|
||||
return data && typeof data === 'string' && data.startsWith('http')
|
||||
})
|
||||
|
||||
const isWriteFileResult = computed(() => {
|
||||
return toolName.value === 'write_file'
|
||||
})
|
||||
|
||||
const isReadFileResult = computed(() => {
|
||||
return toolName.value === 'read_file'
|
||||
})
|
||||
|
||||
const isListDirectoryResult = computed(() => {
|
||||
return toolName.value === 'list_directory' || toolName.value === 'ls'
|
||||
})
|
||||
|
||||
const isSearchFileContentResult = computed(() => {
|
||||
return toolName.value === 'search_file_content'
|
||||
})
|
||||
|
||||
const isGlobResult = computed(() => {
|
||||
return toolName.value === 'glob'
|
||||
})
|
||||
|
||||
const isEditFileResult = computed(() => {
|
||||
return toolName.value === 'edit_file' || toolName.value === 'replace'
|
||||
})
|
||||
|
||||
const isMysqlQueryResult = computed(() => {
|
||||
return toolName.value === 'mysql_query'
|
||||
})
|
||||
|
||||
const isMysqlDescribeTableResult = computed(() => {
|
||||
return toolName.value === 'mysql_describe_table'
|
||||
})
|
||||
|
||||
const isMysqlListTablesResult = computed(() => {
|
||||
return toolName.value === 'mysql_list_tables'
|
||||
})
|
||||
|
||||
// 处理知识图谱刷新
|
||||
const graphToolCallRef = ref(null)
|
||||
const refreshGraph = () => {
|
||||
|
||||
@ -4,7 +4,9 @@ export { default as ToolCallRenderer } from './ToolCallRenderer.vue'
|
||||
|
||||
// 专用工具组件导出
|
||||
export { default as WebSearchTool } from './tools/WebSearchTool.vue'
|
||||
export { default as KnowledgeBaseTool } from './tools/KnowledgeBaseTool.vue'
|
||||
export { default as ListKbsTool } from './tools/ListKbsTool.vue'
|
||||
export { default as GetMindmapTool } from './tools/GetMindmapTool.vue'
|
||||
export { default as QueryKbTool } from './tools/QueryKbTool.vue'
|
||||
export { default as KnowledgeGraphTool } from './tools/KnowledgeGraphTool.vue'
|
||||
export { default as CalculatorTool } from './tools/CalculatorTool.vue'
|
||||
export { default as TodoListTool } from './tools/TodoListTool.vue'
|
||||
|
||||
@ -0,0 +1,71 @@
|
||||
<template>
|
||||
<BaseToolCall :tool-call="toolCall" :hide-params="true">
|
||||
<template #header>
|
||||
<div class="sep-header">
|
||||
<span class="note">{{ operationLabel }}</span>
|
||||
<span class="separator" v-if="kbName">|</span>
|
||||
<span class="description" v-if="kbName">知识库: {{ kbName }}</span>
|
||||
</div>
|
||||
</template>
|
||||
<template #result="{ resultContent }">
|
||||
<div class="get-mindmap-result">
|
||||
<pre class="mindmap-content">{{ formatMindmapResult(resultContent) }}</pre>
|
||||
</div>
|
||||
</template>
|
||||
</BaseToolCall>
|
||||
</template>
|
||||
|
||||
<script setup>
|
||||
import { computed } from 'vue'
|
||||
import BaseToolCall from '../BaseToolCall.vue'
|
||||
|
||||
const props = defineProps({
|
||||
toolCall: {
|
||||
type: Object,
|
||||
required: true
|
||||
}
|
||||
})
|
||||
|
||||
const args = computed(() => {
|
||||
const value = props.toolCall.args || props.toolCall.function?.arguments
|
||||
if (!value) return {}
|
||||
if (typeof value === 'object') return value
|
||||
try {
|
||||
return JSON.parse(value)
|
||||
} catch {
|
||||
return {}
|
||||
}
|
||||
})
|
||||
|
||||
const toolName = computed(() => props.toolCall.name || props.toolCall.function?.name || '思维导图')
|
||||
|
||||
const operationLabel = computed(() => toolName.value)
|
||||
|
||||
const kbName = computed(() => args.value.kb_name || '')
|
||||
|
||||
const formatMindmapResult = (content) => {
|
||||
if (typeof content === 'string') return content
|
||||
if (typeof content === 'object') return JSON.stringify(content, null, 2)
|
||||
return String(content)
|
||||
}
|
||||
</script>
|
||||
|
||||
<style scoped lang="less">
|
||||
.get-mindmap-result {
|
||||
background: var(--gray-0);
|
||||
border-radius: 8px;
|
||||
padding: 12px 16px;
|
||||
max-height: 300px;
|
||||
overflow-y: auto;
|
||||
|
||||
.mindmap-content {
|
||||
margin: 0;
|
||||
font-size: 13px;
|
||||
line-height: 1.6;
|
||||
color: var(--gray-700);
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
@ -1,127 +0,0 @@
|
||||
<template>
|
||||
<BaseToolCall :tool-call="toolCall" :hide-params="true">
|
||||
<template #header>
|
||||
<div class="sep-header">
|
||||
<span class="note">{{ operationLabel }}</span>
|
||||
<span class="separator" v-if="queryText">|</span>
|
||||
<span class="description">{{ queryText }}</span>
|
||||
<span class="separator" v-if="fileName">|</span>
|
||||
<span class="description" v-if="fileName">文件: {{ fileName }}</span>
|
||||
</div>
|
||||
</template>
|
||||
<template #result="{ resultContent }">
|
||||
<div v-if="operation === 'get_mindmap'" class="knowledge-base-result">
|
||||
<div class="mindmap-result">
|
||||
<pre class="mindmap-content">{{ formatMindmapResult(resultContent) }}</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div v-else-if="isArrayResult(resultContent)" class="knowledge-base-result">
|
||||
<KbResultGroupedList :chunks="parsedData(resultContent)" />
|
||||
</div>
|
||||
|
||||
<div v-else class="knowledge-base-result">
|
||||
<div class="plain-text-result">
|
||||
<pre class="plain-text-content">{{ resultContent }}</pre>
|
||||
</div>
|
||||
</div>
|
||||
</template>
|
||||
</BaseToolCall>
|
||||
</template>
|
||||
|
||||
<script setup>
|
||||
import { computed } from 'vue'
|
||||
import BaseToolCall from '../BaseToolCall.vue'
|
||||
import KbResultGroupedList from '@/components/sources/KbResultGroupedList.vue'
|
||||
|
||||
const props = defineProps({
|
||||
toolCall: {
|
||||
type: Object,
|
||||
required: true
|
||||
}
|
||||
})
|
||||
|
||||
const args = computed(() => {
|
||||
const value = props.toolCall.args || props.toolCall.function?.arguments
|
||||
if (!value) return {}
|
||||
if (typeof value === 'object') return value
|
||||
try {
|
||||
return JSON.parse(value)
|
||||
} catch {
|
||||
return {}
|
||||
}
|
||||
})
|
||||
|
||||
const toolName = computed(() => props.toolCall.name || props.toolCall.function?.name || '知识库')
|
||||
const operation = computed(() => args.value.operation || 'search')
|
||||
|
||||
const operationLabel = computed(() => {
|
||||
const labels = {
|
||||
search: `${toolName.value} 搜索`,
|
||||
get_mindmap: toolName.value
|
||||
}
|
||||
return labels[operation.value] || operation.value
|
||||
})
|
||||
|
||||
const queryText = computed(() => args.value.query_text || '')
|
||||
const fileName = computed(() => args.value.file_name || '')
|
||||
|
||||
const parseData = (content) => {
|
||||
if (typeof content === 'string') {
|
||||
try {
|
||||
return JSON.parse(content)
|
||||
} catch {
|
||||
return []
|
||||
}
|
||||
}
|
||||
return content || []
|
||||
}
|
||||
|
||||
const parsedData = (content) => parseData(content)
|
||||
|
||||
const isArrayResult = (content) => {
|
||||
if (Array.isArray(content)) return true
|
||||
if (typeof content === 'string') {
|
||||
try {
|
||||
return Array.isArray(JSON.parse(content))
|
||||
} catch {
|
||||
return false
|
||||
}
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
const formatMindmapResult = (content) => {
|
||||
if (typeof content === 'string') return content
|
||||
if (typeof content === 'object') return JSON.stringify(content, null, 2)
|
||||
return String(content)
|
||||
}
|
||||
</script>
|
||||
|
||||
<style scoped lang="less">
|
||||
.knowledge-base-result {
|
||||
background: var(--gray-0);
|
||||
border-radius: 8px;
|
||||
|
||||
.mindmap-result,
|
||||
.plain-text-result {
|
||||
padding: 12px 16px;
|
||||
max-height: 300px;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.mindmap-content,
|
||||
.plain-text-content {
|
||||
margin: 0;
|
||||
font-size: 13px;
|
||||
line-height: 1.6;
|
||||
color: var(--gray-700);
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
}
|
||||
|
||||
.mindmap-content {
|
||||
font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
92
web/src/components/ToolCallingResult/tools/ListKbsTool.vue
Normal file
92
web/src/components/ToolCallingResult/tools/ListKbsTool.vue
Normal file
@ -0,0 +1,92 @@
|
||||
<template>
|
||||
<BaseToolCall :tool-call="toolCall" :hide-params="true">
|
||||
<template #header>
|
||||
<div class="sep-header">
|
||||
<span class="note">{{ operationLabel }}</span>
|
||||
</div>
|
||||
</template>
|
||||
<template #result="{ resultContent }">
|
||||
<div class="list-kbs-result">
|
||||
<div class="kb-count">共 {{ kbList.length }} 个知识库</div>
|
||||
<div class="kb-list">
|
||||
<div v-for="kb in kbList" :key="kb.name" class="kb-item">
|
||||
<div class="kb-name">{{ kb.name }}</div>
|
||||
<div class="kb-description">{{ kb.description || '无描述' }}</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</template>
|
||||
</BaseToolCall>
|
||||
</template>
|
||||
|
||||
<script setup>
|
||||
import { computed } from 'vue'
|
||||
import BaseToolCall from '../BaseToolCall.vue'
|
||||
|
||||
const props = defineProps({
|
||||
toolCall: {
|
||||
type: Object,
|
||||
required: true
|
||||
}
|
||||
})
|
||||
|
||||
const toolName = computed(() => props.toolCall.name || props.toolCall.function?.name || '知识库')
|
||||
|
||||
const operationLabel = computed(() => `${toolName.value} 列表`)
|
||||
|
||||
const parseData = (content) => {
|
||||
if (typeof content === 'string') {
|
||||
try {
|
||||
return JSON.parse(content)
|
||||
} catch {
|
||||
return []
|
||||
}
|
||||
}
|
||||
return content || []
|
||||
}
|
||||
|
||||
const kbList = computed(() => {
|
||||
const resultContent = props.toolCall.tool_call_result?.content
|
||||
const data = parseData(resultContent)
|
||||
return Array.isArray(data) ? data : []
|
||||
})
|
||||
</script>
|
||||
|
||||
<style scoped lang="less">
|
||||
.list-kbs-result {
|
||||
background: var(--gray-0);
|
||||
border-radius: 8px;
|
||||
padding: 12px 16px;
|
||||
|
||||
.kb-count {
|
||||
font-size: 12px;
|
||||
color: var(--gray-700);
|
||||
margin-bottom: 12px;
|
||||
}
|
||||
|
||||
.kb-list {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.kb-item {
|
||||
padding: 10px 12px;
|
||||
background: var(--gray-10);
|
||||
border-radius: 6px;
|
||||
border: 1px solid var(--gray-100);
|
||||
|
||||
.kb-name {
|
||||
font-size: 13px;
|
||||
font-weight: 500;
|
||||
color: var(--gray-700);
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
.kb-description {
|
||||
font-size: 12px;
|
||||
color: var(--gray-600);
|
||||
}
|
||||
}
|
||||
}
|
||||
</style>
|
||||
70
web/src/components/ToolCallingResult/tools/QueryKbTool.vue
Normal file
70
web/src/components/ToolCallingResult/tools/QueryKbTool.vue
Normal file
@ -0,0 +1,70 @@
|
||||
<template>
|
||||
<BaseToolCall :tool-call="toolCall" :hide-params="true">
|
||||
<template #header>
|
||||
<div class="sep-header">
|
||||
<span class="note">{{ operationLabel }}</span>
|
||||
<span class="separator" v-if="kbName">|</span>
|
||||
<span class="description" v-if="kbName">知识库: {{ kbName }}</span>
|
||||
<span class="separator" v-if="queryText">|</span>
|
||||
<span class="description">{{ queryText }}</span>
|
||||
</div>
|
||||
</template>
|
||||
<template #result="{ resultContent }">
|
||||
<div class="query-kb-result">
|
||||
<KbResultGroupedList :chunks="parsedData(resultContent)" />
|
||||
</div>
|
||||
</template>
|
||||
</BaseToolCall>
|
||||
</template>
|
||||
|
||||
<script setup>
|
||||
import { computed } from 'vue'
|
||||
import BaseToolCall from '../BaseToolCall.vue'
|
||||
import KbResultGroupedList from '@/components/sources/KbResultGroupedList.vue'
|
||||
|
||||
const props = defineProps({
|
||||
toolCall: {
|
||||
type: Object,
|
||||
required: true
|
||||
}
|
||||
})
|
||||
|
||||
const args = computed(() => {
|
||||
const value = props.toolCall.args || props.toolCall.function?.arguments
|
||||
if (!value) return {}
|
||||
if (typeof value === 'object') return value
|
||||
try {
|
||||
return JSON.parse(value)
|
||||
} catch {
|
||||
return {}
|
||||
}
|
||||
})
|
||||
|
||||
const toolName = computed(() => props.toolCall.name || props.toolCall.function?.name || '知识库')
|
||||
|
||||
const operationLabel = computed(() => `${toolName.value} 搜索`)
|
||||
|
||||
const kbName = computed(() => args.value.kb_name || '')
|
||||
const queryText = computed(() => args.value.query_text || '')
|
||||
|
||||
const parseData = (content) => {
|
||||
if (typeof content === 'string') {
|
||||
try {
|
||||
return JSON.parse(content)
|
||||
} catch {
|
||||
return []
|
||||
}
|
||||
}
|
||||
return content || []
|
||||
}
|
||||
|
||||
const parsedData = (content) => parseData(content)
|
||||
</script>
|
||||
|
||||
<style scoped lang="less">
|
||||
.query-kb-result {
|
||||
background: var(--gray-0);
|
||||
border-radius: 8px;
|
||||
padding: 4px;
|
||||
}
|
||||
</style>
|
||||
Loading…
Reference in New Issue
Block a user