zhaoxi
209ba45477
Removes the deprecated `workflow_template` concept entirely across both backend API routers, internal logic handling within the `supervisory_node` and `consciousness_node`, and front-end components. Enables `consciousness_node` to work autonomously. Also refactors core package structure to enforce the "one python package, one Ray Actor" architectural rule. `GlobalWorkflowManager`, `WorkflowRunningEngine`, `PostgresDatabase`, and `WorkerCluster` have been moved to their own top-level decoupled package directories with properly exported `__init__.py` modules. Test suites have been relocated and import paths updated across the system. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>
94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pydantic_ai import Agent
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.models.anthropic import AnthropicModel
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from pydantic_ai.providers.openai import OpenAIProvider
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from pydantic_ai.providers.anthropic import AnthropicProvider
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from pretor.adapter.model_adapter.deepseek_reasoner import DeepSeekReasonerAgent
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from pretor.core.global_state_machine.model_provider import Provider
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from pretor.utils.agent_model import ResponseModel, DepsModel
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from pretor.utils.error import ModelNotExistError
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class AgentFactory:
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"""AgentFactory 核心组件类。
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这是一个领域数据模型或功能封装类，承载了 AgentFactory 相关的内聚属性定义与状态维护。它的存在隔离了局部的业务复杂性，并对外提供了类型安全的访问接口。"""
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def __init__(self):
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self._models_mapping = {
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"openai": (OpenAIChatModel, OpenAIProvider),
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"claude": (AnthropicModel, AnthropicProvider),
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"deepseek": (OpenAIChatModel, OpenAIProvider),
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}
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def create_agent(
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self,
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provider: Provider,
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model_id: str,
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output_type: ResponseModel,
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system_prompt: str,
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deps_type: DepsModel,
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agent_name: str,
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tools: list = None,
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) -> Agent:
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"""
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create_agent方法，将输入的provider对象实例化为一个pydantic-ai的agent对象
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Args:
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provider: Provider对象，从global_state_machine中获取
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model_id: 模型名
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output_type: 输出格式
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system_prompt: 系统提示词
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deps_type: 依赖类型，在agent运行时动态输入的格式化消息
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agent_name: agent的名字
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tools: 工具列表
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Returns:
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返回被实例化的pydantic-ai的Agent对象
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"""
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if model_id not in provider.provider_models:
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raise ModelNotExistError("模型不存在")
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if provider.provider_type not in self._models_mapping:
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raise ValueError(f"不支持的协议类型: {provider.provider_type}")
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model_class, provider_class = self._models_mapping[provider.provider_type]
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model = model_class(
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model_id,
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provider=provider_class(
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api_key=provider.provider_apikey, base_url=provider.provider_url
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),
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)
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match provider.provider_type:
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case "deepseek":
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agent = DeepSeekReasonerAgent(
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model=model,
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name=agent_name,
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output_type=output_type,
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deps_type=deps_type,
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system_prompt=system_prompt,
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tools=tools,
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retries=3,
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)
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case _:
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agent = Agent(
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model=model,
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name=agent_name,
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system_prompt=system_prompt,
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output_type=output_type,
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deps_type=deps_type,
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tools=tools,
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)
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return agent
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