wip:优化individual，加了基本的启动页面
This commit is contained in:
parent
2e5b2bce3a
commit
dc857cbff7
|
|
@ -0,0 +1 @@
|
|||
version: v0.1
|
||||
7
main.py
7
main.py
|
|
@ -0,0 +1,7 @@
|
|||
from pretor.utils.banner import print_banner
|
||||
|
||||
def main():
|
||||
print_banner()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -1,49 +0,0 @@
|
|||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.models.google import GoogleModel
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.providers.openai import OpenAIProvider
|
||||
from pydantic_ai.providers.google import GoogleProvider
|
||||
from pydantic_ai.providers.anthropic import AnthropicProvider
|
||||
from pathlib import Path
|
||||
|
||||
class AgentFactory:
|
||||
def __init__(self):
|
||||
self._models_mapping = {"openai": (OpenAIChatModel, OpenAIProvider), "gemini": (GoogleModel, GoogleProvider), "claude": (AnthropicModel, AnthropicProvider)}
|
||||
|
||||
def _load_agent_protocol(self):
|
||||
pass
|
||||
|
||||
def create_model(self, protocol_name: str, api_key: str, url: str | None, model_id: str):
|
||||
"""
|
||||
创建agent的模型对象
|
||||
|
||||
Args:
|
||||
protocol_name: 协议名称，如openai,gemini等，应当为_model_mapping的键
|
||||
api_key: api调用令牌
|
||||
url: api调用的url
|
||||
model_id: 模型名
|
||||
|
||||
Returns:
|
||||
应当返回一个可以作为Agent类model参数的对象
|
||||
|
||||
Raises:
|
||||
当protocol_name在_models_mapping中不存在，抛出ValueError错误
|
||||
"""
|
||||
if protocol_name not in self._models_mapping:
|
||||
raise ValueError(f"不支持的协议类型: {protocol_name}")
|
||||
model_class, provider_class = self._models_mapping[protocol_name]
|
||||
return model_class(model_id, provider_class(api_key = api_key, url = url))
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from pydantic_ai import Agent
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.models.google import GoogleModel
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.providers.openai import OpenAIProvider
|
||||
from pydantic_ai.providers.google import GoogleProvider
|
||||
from pydantic_ai.providers.anthropic import AnthropicProvider
|
||||
from pretor.core.global_state_machine.model_provider import Provider
|
||||
from pretor.utils.agent_model import ResponseModel, DepsModel
|
||||
from pretor.utils.error import ModelNotExistError
|
||||
|
||||
class AgentFactory:
|
||||
def __init__(self):
|
||||
self._models_mapping = {"openai": (OpenAIChatModel, OpenAIProvider), "gemini": (GoogleModel, GoogleProvider), "claude": (AnthropicModel, AnthropicProvider)}
|
||||
|
||||
def create_agent(self,
|
||||
provider: Provider,
|
||||
model_id: str,
|
||||
output_type: ResponseModel,
|
||||
system_prompt: str,
|
||||
deps_type: DepsModel,
|
||||
agent_name: str) -> Agent:
|
||||
"""
|
||||
create_agent方法，将输入的provider对象实例化为一个pydantic-ai的agent对象
|
||||
|
||||
Args:
|
||||
provider: Provider对象，从global_state_machine中获取
|
||||
model_id: 模型名
|
||||
output_type: 输出格式
|
||||
system_prompt: 系统提示词
|
||||
deps_type: 依赖类型，在agent运行时动态输入的格式化消息
|
||||
agent_name: agent的名字
|
||||
|
||||
Returns:
|
||||
返回被实例化的pydantic-ai的Agent对象
|
||||
"""
|
||||
if model_id not in provider.provider_models:
|
||||
raise ModelNotExistError("模型不存在")
|
||||
if provider.provider_type not in self._models_mapping:
|
||||
raise ValueError(f"不支持的协议类型: {provider.provider_type}")
|
||||
model_class, provider_class = self._models_mapping[provider.provider_type]
|
||||
model = model_class(model_id, provider_class(api_key=provider.api_key, url=provider.url))
|
||||
agent = Agent(model=model,
|
||||
name=agent_name,
|
||||
system_prompt=system_prompt,
|
||||
output_type=output_type,
|
||||
deps_type=deps_type)
|
||||
return agent
|
||||
|
|
@ -1,54 +0,0 @@
|
|||
|
||||
|
||||
|
||||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
def create_agent(self, agent_name: str,
|
||||
system_prompt: str,
|
||||
provider_title: str,
|
||||
model_id: str,
|
||||
output_type: ResponseModel,
|
||||
deps_type: DepsModel) -> Agent:
|
||||
"""
|
||||
create_agent方法，将保存的适配器转化为agent对象并返回
|
||||
|
||||
Args:
|
||||
agent_name: agent名字，代表实例化个体起的名字
|
||||
system_prompt: 系统提示词，给llm的系统提示词
|
||||
provider_title: 供应商名称
|
||||
model_id: 模型Id，实例化agent所输入的model_id
|
||||
output_type: 输出格式，实例化agent后，对应llm所应当输出的格式
|
||||
deps_type: 依赖格式，输入llm的格式
|
||||
Returns:
|
||||
一个pydanticAI的Agent对象，包含对应的apikey,url,model_id等信息，应当挂载到individual类的agent属性下
|
||||
Raises:
|
||||
ProviderNotExistError 当在provider_register属性里找不到供应商的自定义名称时抛出
|
||||
ModelNotExistError 在获取的provider的模型列表中找不到输入的model_id抛出
|
||||
"""
|
||||
if provider_title not in self.provider_register:
|
||||
raise ProviderNotExistError("提供商不存在")
|
||||
provider = self.provider_register[provider_title]
|
||||
if model_id not in provider.provider_models:
|
||||
raise ModelNotExistError("模型不存在")
|
||||
model = self._agent_factory.create_model(provider.provider_type,
|
||||
provider.provider_apikey,
|
||||
provider.provider_url,
|
||||
model_id)
|
||||
agent = Agent(model=model,
|
||||
name=agent_name,
|
||||
system_prompt=system_prompt,
|
||||
output_type=output_type,
|
||||
deps_type=deps_type)
|
||||
return agent
|
||||
|
|
@ -21,10 +21,8 @@ from typing import Dict, Literal
|
|||
from pretor.core.database.postgres import PostgresDatabase
|
||||
from pretor.api.platform.event import PretorEvent
|
||||
import asyncio
|
||||
|
||||
from pretor.core.workflow.workflow import PretorWorkflow
|
||||
|
||||
|
||||
@ray.remote
|
||||
class GlobalStateMachine:
|
||||
def __init__(self, postgres_database: PostgresDatabase):
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ from abc import ABC, abstractmethod
|
|||
from pydantic import BaseModel
|
||||
from typing import List
|
||||
from enum import Enum
|
||||
|
||||
class ProviderStatus(Enum, str):
|
||||
UP = "up"
|
||||
DOWN = "down"
|
||||
|
|
|
|||
|
|
@ -13,42 +13,124 @@
|
|||
# limitations under the License.
|
||||
|
||||
import ray
|
||||
from pydantic_ai import Agent
|
||||
from pretor.core.workflow.workflow import PretorWorkflow, WorkStep, WorkerGroup
|
||||
from typing import Union, overload
|
||||
from pretor.core.individual.consciousness_node.template import (ConsciousnessNodeDeps, ForSupervisoryNode, ForWorkflow,\
|
||||
ForWorkflowEngine, ForWorkflowInput, ForSupervisoryInput, ForWorkflowEngineInput)
|
||||
from pydantic_ai import Agent, RunContext
|
||||
from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
|
||||
from pretor.core.global_state_machine.model_provider.base_provider import Provider
|
||||
from pretor.adapter.model_adapter.agent_factory import AgentFactory
|
||||
|
||||
@ray.remote
|
||||
class ConsciousnessNode:
|
||||
def __init__(self, agent: Agent):
|
||||
self.agent = agent
|
||||
def __init__(self) -> None:
|
||||
self.agent: None | Agent = None
|
||||
|
||||
async def generate_workflow(self, template: dict, task_description: str) -> PretorWorkflow:
|
||||
prompt = f"Given the template {template} and task '{task_description}', generate a list of actionable steps."
|
||||
# Simulated parsing logic: in a real implementation we would parse structured output from the agent
|
||||
# response = await self.agent.run(prompt)
|
||||
@self.agent.system_prompt
|
||||
async def dynamic_prompt(ctx: RunContext[ConsciousnessNodeDeps]):
|
||||
return f"Context: original_command: {ctx.deps.original_command}, workflow_template: {ctx.deps.workflow_template}"
|
||||
|
||||
wg = WorkerGroup(
|
||||
name="default_squad",
|
||||
primary_individual={"coder": 1},
|
||||
composite_individual={}
|
||||
)
|
||||
def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str) -> None:
|
||||
"""
|
||||
create_agent方法，将agent对象装配到ConsciousnessNode的属性内
|
||||
该方法通过provider_title从global_state_machine中获取provider对象，然后从provider对象中取出供应商形象，装配为pydantic_ai的
|
||||
Agent实例，
|
||||
并挂载到self.agent属性
|
||||
Args:
|
||||
global_state_machine: 全局状态机
|
||||
provider_title: 供应商名
|
||||
model_id: 模型id
|
||||
|
||||
steps = [
|
||||
WorkStep(step=1, node="consciousness_node", action="analyze", desc="Analyze task details: " + task_description),
|
||||
WorkStep(step=2, node="control_node", action="execute", desc="Execute default execution logic", input=["1"])
|
||||
]
|
||||
Returns:
|
||||
无返回
|
||||
"""
|
||||
system_prompt: str = ""
|
||||
output_type = Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]
|
||||
provider: Provider = global_state_machine.get_provider.remote(provider_title)
|
||||
agent_factory = AgentFactory()
|
||||
self.agent = agent_factory.create_agent(provider=provider,
|
||||
model_id=model_id,
|
||||
output_type=output_type,
|
||||
system_prompt=system_prompt,
|
||||
deps_type=ConsciousnessNodeDeps,
|
||||
agent_name="consciousness_node")
|
||||
|
||||
return PretorWorkflow(
|
||||
title=f"Workflow for {task_description[:10]}",
|
||||
workgroup_list=[wg],
|
||||
work_link=steps
|
||||
)
|
||||
async def running(self, payload: Union[ForWorkflowEngineInput, ForWorkflowInput, ForSupervisoryInput]) -> str:
|
||||
result: Union[ForWorkflowEngine, ForWorkflow, ForSupervisoryNode] = await self._run(payload)
|
||||
if isinstance(result, ForWorkflowEngine):
|
||||
return result
|
||||
|
||||
async def check_task(self, task_status: dict) -> bool:
|
||||
if task_status.get("status") == "completed":
|
||||
return True
|
||||
return False
|
||||
elif isinstance(result, ForWorkflow):
|
||||
return result
|
||||
|
||||
async def process_complex_transaction(self, transaction_data: dict) -> dict:
|
||||
prompt = f"Process the following complex transaction data and extract key entities: {transaction_data}"
|
||||
result = await self.agent.run(prompt)
|
||||
return {"processed": True, "analysis": result.data}
|
||||
elif isinstance(result, ForSupervisoryInput):
|
||||
return result
|
||||
|
||||
else:
|
||||
return None
|
||||
|
||||
@overload
|
||||
async def _run(self, payload: ForWorkflowEngineInput) -> ForWorkflowEngine:
|
||||
"""
|
||||
_run方法
|
||||
该分支应当在supervisory_node简单处理用户命令后，工作流创建前调用！
|
||||
Args:
|
||||
payload: 应当包含workflow_template和event对象
|
||||
|
||||
Returns:
|
||||
ForWorkflowEngine对象，将被放到全局状态机后丢入WorkflowEngine的异步队列
|
||||
"""
|
||||
pass
|
||||
|
||||
@overload
|
||||
async def _run(self, payload: ForWorkflow) -> ForWorkflow:
|
||||
"""
|
||||
_run方法
|
||||
该分支应当在workflow运行时，由WorkflowEngine进行调用！
|
||||
Args:
|
||||
payload: 应当包含workflow中的WorkStep对象
|
||||
|
||||
Returns:
|
||||
ForWorkflow对象，作为ConsciousnessNode执行Workflow中的WorkStep的结果
|
||||
"""
|
||||
pass
|
||||
|
||||
@overload
|
||||
async def _run(self, payload: ForSupervisoryInput) -> ForSupervisoryNode:
|
||||
"""
|
||||
_run方法
|
||||
该分支应当在workflow运行完全结束后，由WorkflowEngine进行调用！
|
||||
Args:
|
||||
payload: 应当包含整个Workflow的情况
|
||||
|
||||
Returns:
|
||||
ForSupervisory对象，作为ConsciousnessNode对于全工作流的技术性总结，返回给SupervisoryNode
|
||||
"""
|
||||
pass
|
||||
|
||||
async def _run(self, payload: Union[ForSupervisoryInput, ForWorkflowInput, ForWorkflowEngineInput])\
|
||||
-> Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]:
|
||||
|
||||
if isinstance(payload, ForWorkflowEngineInput):
|
||||
deps = ConsciousnessNodeDeps(original_command=payload.original_command,
|
||||
workflow_template=payload.workflow_template,
|
||||
command="拆解原始命令变成一个工作流")
|
||||
result = await self.agent.run(f"根据original_command制定workflow，可以学习workflow_template",
|
||||
deps=deps,
|
||||
output_type=ForWorkflowEngine,)
|
||||
return result.output
|
||||
elif isinstance(payload, ForWorkflowInput):
|
||||
deps = ConsciousnessNodeDeps(original_command=payload.original_command,
|
||||
command="完成workflowstep的任务")
|
||||
result = await self.agent.run(payload.workflow_step.model_dump_json(),
|
||||
deps=deps,
|
||||
output_type=ForWorkflow)
|
||||
return result.output
|
||||
elif isinstance(payload, ForSupervisoryInput):
|
||||
deps = ConsciousnessNodeDeps(original_command=payload.original_command,
|
||||
command="对于结果进行检查，并且生成一份技术性的总结报告")
|
||||
result = await self.agent.run(payload.workflow.model_dump_json(),
|
||||
deps=deps,
|
||||
output_type=ForSupervisoryNode)
|
||||
return result.output
|
||||
return None
|
||||
|
|
@ -1,34 +0,0 @@
|
|||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from pydantic import Field
|
||||
from pretor.core.workflow.workflow import PretorWorkflow
|
||||
from pretor.utils.agent_model import ResponseModel, DepsModel
|
||||
|
||||
class ConsciousnessNodeResponse(ResponseModel):
|
||||
pass
|
||||
|
||||
class ForControlNode(ConsciousnessNodeResponse):
|
||||
|
||||
|
||||
class ForSystem(ConsciousnessNodeResponse):
|
||||
workflow: PretorWorkflow
|
||||
|
||||
class ForSupervisoryNode(ConsciousnessNodeResponse):
|
||||
pass
|
||||
|
||||
class SupervisoryNodeDeps(DepsModel):
|
||||
platform: str
|
||||
user_name: str
|
||||
time: str
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from pretor.core.workflow.workflow import PretorWorkflow, WorkStep
|
||||
from pretor.utils.agent_model import ResponseModel, DepsModel
|
||||
|
||||
|
||||
#意识节点回复类
|
||||
class ConsciousnessNodeResponse(ResponseModel):
|
||||
"""Consciousness response model,是意识节点所有回复类型的父类"""
|
||||
pass
|
||||
|
||||
|
||||
class ForWorkflowEngine(ConsciousnessNodeResponse):
|
||||
"""生成workflow并放入WorkflowEngine"""
|
||||
workflow: PretorWorkflow
|
||||
|
||||
|
||||
class ForWorkflow(ConsciousnessNodeResponse):
|
||||
"""处理workflow中需要ConsciousnessNode的工作"""
|
||||
output: str
|
||||
|
||||
|
||||
class ForSupervisoryNode(ConsciousnessNodeResponse):
|
||||
"""工作流完成后进行校验并返回给SupervisoryNode"""
|
||||
output: str
|
||||
|
||||
|
||||
class ConsciousnessNodeDeps(DepsModel):
|
||||
original_command: str
|
||||
workflow_template: str | None = None
|
||||
command: str
|
||||
|
||||
|
||||
class ConsciousnessNodeInput(BaseModel):
|
||||
pass
|
||||
|
||||
|
||||
class ForWorkflowEngineInput(ConsciousnessNodeInput):
|
||||
workflow_template: str
|
||||
original_command: str
|
||||
|
||||
|
||||
class ForWorkflowInput(ConsciousnessNodeInput):
|
||||
workflow_step: WorkStep
|
||||
original_command: str
|
||||
|
||||
|
||||
class ForSupervisoryInput(ConsciousnessNodeInput):
|
||||
workflow: PretorWorkflow
|
||||
original_command: str
|
||||
|
|
@ -14,39 +14,111 @@
|
|||
|
||||
import datetime
|
||||
import ray
|
||||
from typing import Union
|
||||
from typing import Union, overload
|
||||
from pretor.api.platform.event import PretorEvent
|
||||
|
||||
from pretor.core.individual.supervisory_node.response import ForConsciousnessNode, ForUser, SupervisoryNodeDeps
|
||||
from pydantic_ai import RunContext
|
||||
from pretor.adapter.model_adapter.agent_factory import AgentFactory
|
||||
from pretor.core.global_state_machine.global_state_machine import GlobalStateMachine
|
||||
from pretor.core.global_state_machine.model_provider import Provider
|
||||
from pretor.core.individual.supervisory_node.template import ForConsciousnessNode, ForUser, SupervisoryNodeDeps, TerminationMessage
|
||||
from pydantic_ai import RunContext, Agent
|
||||
|
||||
@ray.remote
|
||||
class SupervisoryNode:
|
||||
def __init__(self, provider_manager: ProviderManager, provider_title: str, model_id: str) -> None:
|
||||
def __init__(self) -> None:
|
||||
self.agent: None | Agent = None
|
||||
|
||||
async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str) -> None:
|
||||
"""
|
||||
create_agent方法，将agent对象装配到SupervisoryNode的属性内
|
||||
该方法通过provider_title从global_state_machine中获取provider对象，然后从provider对象中取出供应商形象，装配为pydantic_ai的Agent实例，
|
||||
并挂载到self.agent属性
|
||||
Args:
|
||||
global_state_machine: 全局状态机
|
||||
provider_title: 供应商名
|
||||
model_id: 模型id
|
||||
|
||||
Returns:
|
||||
无返回
|
||||
"""
|
||||
system_prompt: str = ""
|
||||
output_type = Union[ForConsciousnessNode, ForUser]
|
||||
self.agent = provider_manager.create_agent(agent_name="supervisory",
|
||||
system_prompt=system_prompt,
|
||||
provider_title=provider_title,
|
||||
model_id=model_id,
|
||||
output_type=output_type,
|
||||
deps_type=SupervisoryNodeDeps)
|
||||
provider: Provider = await global_state_machine.get_provider.remote(provider_title)
|
||||
agent_factory = AgentFactory()
|
||||
self.agent = agent_factory.create_agent(provider=provider,
|
||||
model_id=model_id,
|
||||
output_type=output_type,
|
||||
system_prompt=system_prompt,
|
||||
deps_type=SupervisoryNodeDeps,
|
||||
agent_name="supervisory_node")
|
||||
|
||||
@self.agent.system_prompt
|
||||
async def dynamic_prompt(ctx: RunContext[SupervisoryNodeDeps]):
|
||||
return f"Context: Platform={ctx.deps.platform}, User={ctx.deps.user_name}, Time={ctx.deps.time}"
|
||||
|
||||
async def working(self, event: PretorEvent):
|
||||
deps = SupervisoryNodeDeps(platform=event.platform,
|
||||
user_name=event.user_name,
|
||||
time=datetime.datetime.now())
|
||||
result = await self.agent.run(event.message,deps=deps)
|
||||
if isinstance(result.data, ForConsciousnessNode):
|
||||
###工作函数
|
||||
async def working(self, payload: Union[PretorEvent, TerminationMessage]) -> str:
|
||||
"""
|
||||
working方法，是节点唯一的调用方法，对于_run函数的结果进行判断并实现最终回复
|
||||
Args:
|
||||
payload: 消息载荷，包含所有信息
|
||||
|
||||
Returns:
|
||||
str,监控节点对于用户的回复
|
||||
"""
|
||||
result = await self._run(payload)
|
||||
if isinstance(result, ForConsciousnessNode):
|
||||
|
||||
return "任务已创建"
|
||||
elif isinstance(result.date, ForUser):
|
||||
return result.data.content
|
||||
|
||||
elif isinstance(result, ForUser):
|
||||
return result.content
|
||||
|
||||
else:
|
||||
return "未知响应类型"
|
||||
|
||||
async def router(self, ):
|
||||
@overload
|
||||
async def _run(self, payload: PretorEvent) -> Union[ForConsciousnessNode, ForUser]:
|
||||
"""
|
||||
_run方法
|
||||
Args:
|
||||
payload: PretorEvent的实例，是用户输入时对于消息的封装
|
||||
|
||||
Returns:
|
||||
ForUser对象，监控节点对于用户进行的简单回答
|
||||
ForConsciousnessNode对象，监控节点将用户的请求判断为复杂任务，将PretorEvent传递给意识节点，并且给选择好的工作流模板
|
||||
"""
|
||||
pass
|
||||
|
||||
@overload
|
||||
async def _run(self, payload: TerminationMessage) -> ForUser:
|
||||
"""
|
||||
_run方法
|
||||
Args:
|
||||
payload: Termination的实例，是工作流结束后到达监控节点的最后结果
|
||||
|
||||
Returns:
|
||||
ForUser对象，工作流结束后给用户的返回
|
||||
"""
|
||||
pass
|
||||
|
||||
async def _run(self, payload: Union[PretorEvent, TerminationMessage]) -> Union[ForConsciousnessNode, ForUser]:
|
||||
"""
|
||||
_run方法，将payload转化为对llm发送的消息并发送
|
||||
Args:
|
||||
payload: 消息载荷
|
||||
|
||||
Returns:
|
||||
ForConsciousnessNode对象，对意识节点发送的消息
|
||||
ForUser对象，对用户发送到消息
|
||||
"""
|
||||
if isinstance(payload, PretorEvent):
|
||||
deps = SupervisoryNodeDeps(platform=payload.platform,
|
||||
user_name=payload.user_name,
|
||||
time=datetime.datetime.now())
|
||||
result = await self.agent.run(payload.message, deps=deps)
|
||||
else:
|
||||
deps = SupervisoryNodeDeps(platform=payload.platform,
|
||||
user_name=payload.user_name,
|
||||
time=datetime.datetime.now())
|
||||
result = await self.agent.run(payload.message, deps=deps)
|
||||
return result.output
|
||||
|
|
@ -14,6 +14,7 @@
|
|||
|
||||
from pydantic import Field
|
||||
from pretor.utils.agent_model import ResponseModel, DepsModel
|
||||
from pydantic import BaseModel
|
||||
|
||||
class SupervisoryNodeResponse(ResponseModel):
|
||||
pass
|
||||
|
|
@ -24,6 +25,11 @@ class ForUser(SupervisoryNodeResponse):
|
|||
class ForConsciousnessNode(SupervisoryNodeResponse):
|
||||
workflow_template: str = Field(..., description="选择的工作流模板，应当为对应模板的name字段")
|
||||
|
||||
class TerminationMessage(BaseModel):
|
||||
platform: str
|
||||
user_name: str
|
||||
message: str
|
||||
|
||||
class SupervisoryNodeDeps(DepsModel):
|
||||
platform: str
|
||||
user_name: str
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from typing import List, Optional, Union, Dict, Any, Literal
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
# --- 1. 给 Individual (LLM/Agent) 的具体需求 ---
|
||||
class IndividualDemand(BaseModel):
|
||||
role_prompt: str = Field(..., description="赋予该个体的角色定义")
|
||||
task_goal: str = Field(..., description="该个体的具体执行目标")
|
||||
expected_output: str = Field(..., description="期望产出的数据结构或格式描述")
|
||||
|
||||
# --- 2. 给 Tool (插件/函数调用) 的具体需求 ---
|
||||
class ToolDemand(BaseModel):
|
||||
method: str = Field(..., description="插件调用的具体方法名")
|
||||
args: Dict[str, Any] = Field(default_factory=dict, description="传递给插件的参数")
|
||||
|
||||
# --- 3. 给 System (系统/物理资源) 的具体需求 ---
|
||||
class SystemDemand(BaseModel):
|
||||
operation: Literal["allocate_resource", "docker_manage", "file_io", "network"]
|
||||
params: Dict[str, Any] = Field(..., description="操作所需的物理参数，如 GPU 核心数、路径等")
|
||||
|
||||
# --- 4. 统一需求入口 (裁判官协议体) ---
|
||||
class DemandProtocol(BaseModel):
|
||||
variety: Literal["individual", "tool", "system"]
|
||||
name: str = Field(..., description="目标名称（如：python_expert, pytest_tool, docker_engine）")
|
||||
content: Union[IndividualDemand, ToolDemand, SystemDemand] = Field(..., description="需求的具体参数细节")
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
# Copyright 2026 zhaoxi826
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from abc import ABC,abstractmethod
|
||||
from pretor.core.workflow.workflow import PretorWorkflow
|
||||
|
||||
|
||||
class RunnableObject(ABC):
|
||||
@abstractmethod
|
||||
def __init__(self, **kwargs):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def run(self, workflow: PretorWorkflow) -> None:
|
||||
pass
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
from rich.console import Console
|
||||
from rich.text import Text
|
||||
import yaml
|
||||
def print_banner() -> None:
|
||||
with open("config/config.yml","r") as config:
|
||||
config = yaml.load(config, Loader=yaml.FullLoader)
|
||||
version = config.get("version", "unknown")
|
||||
pretor_banner = """
|
||||
██████╗ ██████╗ ███████╗████████╗ ██████╗ ██████╗
|
||||
██╔══██╗██╔══██╗██╔════╝╚══██╔══╝██╔═══██╗██╔══██╗
|
||||
██████╔╝██████╔╝█████╗ ██║ ██║ ██║██████╔╝
|
||||
██╔═══╝ ██╔══██╗██╔══╝ ██║ ██║ ██║██╔══██╗
|
||||
██║ ██║ ██║███████╗ ██║ ╚██████╔╝██║ ██║
|
||||
╚═╝ ╚═╝ ╚═╝╚══════╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝
|
||||
"""
|
||||
console = Console()
|
||||
banner_colored = Text(pretor_banner, style="gold3 bold")
|
||||
console.print(banner_colored)
|
||||
console.print("=" * 40, style="dim") # dim=灰色，低调
|
||||
console.print("🚀 Multi-Agent Orchestration Platform", style="blue")
|
||||
console.print(f"📦 Version: {version}", style="green")
|
||||
console.print(f"👤 Author: zhaoxi826", style="yellow")
|
||||
console.print(f"📜 License: Apache 2.0", style="magenta")
|
||||
console.print(f"🐙 github: https://github.com/zhaoxi826/pretor", style="yellow")
|
||||
console.print("=" * 40, style="dim")
|
||||
|
|
@ -19,16 +19,19 @@ T = TypeVar("T", bound=Type[BaseModel])
|
|||
|
||||
def pickle(cls: T) -> T:
|
||||
"""
|
||||
这是一个类装饰器，用来接管 Pydantic 对象的 Pickle 序列化流程。
|
||||
它强迫 Pickle 使用 Pydantic 经过 Rust 优化的 JSON 导出/导入逻辑。
|
||||
类装饰器pickle
|
||||
通过装饰继承了BaseModel的类，用pydantic的高效序列化替代python原生__reduce__魔术方法,实现ray在通讯时的高效序列化
|
||||
Args:
|
||||
cls: 继承了BaseModel类的类，需要被装饰的对象
|
||||
|
||||
Returns:
|
||||
返回被重写了__reduce__魔术方法的cls类
|
||||
"""
|
||||
def __reduce__(self):
|
||||
# 1. 序列化：触发 Pydantic-core (Rust) 的极速序列化
|
||||
data = self.model_dump_json()
|
||||
# 2. 反序列化：告诉 Pickle 重建时调用 cls.model_validate_json
|
||||
return cls.model_validate_json, (data,)
|
||||
|
||||
# 动态把这个魔术方法“缝”到类上
|
||||
cls.__reduce__ = __reduce__
|
||||
return cls
|
||||
|
||||
|
|
|
|||
|
|
@ -13,9 +13,12 @@ dependencies = [
|
|||
"loguru>=0.7.3",
|
||||
"passlib[bcrypt]>=1.7.4",
|
||||
"pydantic-ai>=1.73.0",
|
||||
"pyfiglet>=1.0.4",
|
||||
"pytest>=9.0.3",
|
||||
"python-ulid>=3.1.0",
|
||||
"ray[default,serve]>=2.54.0",
|
||||
"rich>=14.3.3",
|
||||
"sqlmodel>=0.0.37",
|
||||
"types-docutils==0.22.3.20260408",
|
||||
"vllm>=0.11.0",
|
||||
]
|
||||
|
|
|
|||
Loading…
Reference in New Issue