181341db48
- 新增 HumanApprovalModal 组件，用于处理用户对关键操作的审批。 - 引入 useApproval 可组合项，用于管理审批状态和逻辑。 - 更新 AgentChatComponent 以显示审批模态框并处理审批操作。 - 增强消息处理功能，支持工具调用合并并改进对 AI 消息块的处理。 - 重构各种组件和 API，以整合新的审批流程，确保代理交互期间的流畅用户体验。
222 lines
8.0 KiB
Python
222 lines
8.0 KiB
Python
import asyncio
|
||
import traceback
|
||
from typing import Annotated, Any
|
||
|
||
from langchain.tools import tool
|
||
from langchain_core.tools import StructuredTool
|
||
from langchain_tavily import TavilySearch
|
||
from langgraph.types import interrupt
|
||
from pydantic import BaseModel, Field
|
||
|
||
from src import config, graph_base, knowledge_base
|
||
from src.utils import logger
|
||
|
||
|
||
# TODO[修改建议]:前端需要通过interrupt进行交互，点击是或否来批准执行
|
||
# 返回中断点：
|
||
# is_approved : bool = True 或者 False
|
||
# resume_command = Command(resume=is_approved)
|
||
# stream = graph.stream(resume_command, config=config, stream_mode="messages")
|
||
# graph.invoke(resume_command, config=config)
|
||
@tool(name_or_callable="人工审批工具", description="请求人工审批工具，用于在执行重要操作前获得人类确认。")
|
||
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
|
||
|
||
|
||
@tool(name_or_callable="查询知识图谱", description="使用这个工具可以查询知识图谱中包含的三元组信息。")
|
||
def query_knowledge_graph(query: Annotated[str, "The keyword to query knowledge graph."]) -> Any:
|
||
"""Use this to query knowledge graph, which include some food domain knowledge."""
|
||
try:
|
||
logger.debug(f"Querying knowledge graph with: {query}")
|
||
result = graph_base.query_node(query, hops=2, return_format="triples")
|
||
logger.debug(
|
||
f"Knowledge graph query returned "
|
||
f"{len(result.get('triples', [])) if isinstance(result, dict) else 'N/A'} triples"
|
||
)
|
||
return result
|
||
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_static_tools() -> list:
|
||
"""注册静态工具"""
|
||
static_tools = [query_knowledge_graph, get_approved_user_goal]
|
||
|
||
# 检查是否启用网页搜索
|
||
if config.enable_web_search:
|
||
search = TavilySearch(max_results=10)
|
||
search.metadata = {"name": "Tavily 网页搜索"}
|
||
static_tools.append(search)
|
||
|
||
return static_tools
|
||
|
||
|
||
class KnowledgeRetrieverModel(BaseModel):
|
||
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。"
|
||
)
|
||
)
|
||
|
||
|
||
def get_kb_based_tools() -> list:
|
||
"""获取所有知识库基于的工具"""
|
||
# 获取所有知识库
|
||
kb_tools = []
|
||
retrievers = knowledge_base.get_retrievers()
|
||
|
||
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"""
|
||
|
||
async def async_retriever_wrapper(query_text: str) -> Any:
|
||
"""异步检索器包装函数"""
|
||
retriever = retriever_info["retriever"]
|
||
try:
|
||
logger.debug(f"Retrieving from database {db_id} with query: {query_text}")
|
||
if asyncio.iscoroutinefunction(retriever):
|
||
result = await retriever(query_text)
|
||
else:
|
||
result = retriever(query_text)
|
||
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():
|
||
try:
|
||
# 构建工具描述
|
||
description = (
|
||
f"使用 {retrieve_info['name']} 知识库进行检索。\n"
|
||
f"下面是这个知识库的描述：\n{retrieve_info['description'] or '没有描述。'} "
|
||
)
|
||
|
||
# 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]
|
||
|
||
# 使用 StructuredTool.from_function 创建异步工具
|
||
tool = StructuredTool.from_function(
|
||
coroutine=retriever_wrapper,
|
||
name=safename,
|
||
description=description,
|
||
args_schema=KnowledgeRetrieverModel,
|
||
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
|
||
|
||
|
||
def get_buildin_tools() -> list:
|
||
"""获取所有可运行的工具（给大模型使用）"""
|
||
tools = []
|
||
|
||
try:
|
||
# 获取所有知识库基于的工具
|
||
tools.extend(get_kb_based_tools())
|
||
tools.extend(get_static_tools())
|
||
|
||
from src.agents.common.toolkits.mysql.tools import get_mysql_tools
|
||
|
||
tools.extend(get_mysql_tools())
|
||
|
||
except Exception as e:
|
||
logger.error(f"Failed to get knowledge base retrievers: {e}")
|
||
|
||
return tools
|
||
|
||
|
||
def gen_tool_info(tools) -> list[dict[str, Any]]:
|
||
"""获取所有工具的信息（用于前端展示）"""
|
||
tools_info = []
|
||
|
||
try:
|
||
# 获取注册的工具信息
|
||
for tool_obj in tools:
|
||
try:
|
||
metadata = getattr(tool_obj, "metadata", {}) or {}
|
||
info = {
|
||
"id": tool_obj.name,
|
||
"name": metadata.get("name", tool_obj.name),
|
||
"description": tool_obj.description,
|
||
"metadata": metadata,
|
||
"args": [],
|
||
# "is_async": is_async # Include async information
|
||
}
|
||
|
||
if hasattr(tool_obj, "args_schema") and tool_obj.args_schema:
|
||
if isinstance(tool_obj.args_schema, dict):
|
||
schema = tool_obj.args_schema
|
||
else:
|
||
schema = tool_obj.args_schema.schema()
|
||
|
||
for arg_name, arg_info in schema.get("properties", {}).items():
|
||
info["args"].append(
|
||
{
|
||
"name": arg_name,
|
||
"type": arg_info.get("type", ""),
|
||
"description": arg_info.get("description", ""),
|
||
}
|
||
)
|
||
|
||
tools_info.append(info)
|
||
# logger.debug(f"Successfully processed tool info for {tool_obj.name}")
|
||
|
||
except Exception as e:
|
||
logger.error(
|
||
f"Failed to process tool {getattr(tool_obj, 'name', 'unknown')}: {e}\n{traceback.format_exc()}. "
|
||
f"Details: {dict(tool_obj.__dict__)}"
|
||
)
|
||
continue
|
||
|
||
except Exception as e:
|
||
logger.error(f"Failed to get tools info: {e}\n{traceback.format_exc()}")
|
||
return []
|
||
|
||
logger.info(f"Successfully extracted info for {len(tools_info)} tools")
|
||
return tools_info
|