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refactor(core): decouple actors and remove workflow templates (#67)

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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>
This commit is contained in:
朝夕
2026-05-06 15:05:47 +08:00
committed by GitHub
parent b3ea4cd8d9
commit 209ba45477
97 changed files with 1872 additions and 1498 deletions
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pretor/adapter/model_adapter/deepseek_reasoner.py
+51 -39
View File
@@ -18,25 +18,29 @@ from typing import Type, TypeVar, Any, Generic
from pydantic import BaseModel, ValidationError
from pydantic_ai import Agent
T = TypeVar('T', bound=BaseModel)
T = TypeVar("T", bound=BaseModel)
class AgentRunResultProxy:
"""AgentRunResultProxy 核心组件类。
这是一个领域数据模型或功能封装类，承载了 AgentRunResultProxy 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。 """
这是一个领域数据模型或功能封装类，承载了 AgentRunResultProxy 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。"""
def __init__(self, original, parsed):
self._original = original
self._parsed = parsed
def __getattr__(self, name):
"""检索并获取特定的 getattr 数据集合或实例对象。
根据提供的查询条件或上下文凭证，从数据库、缓存或第三方服务中读取对应的资源状态。
Args: name: 赋予该实体的人类可读名称或标题字符串，主要用于前端 UI 展示、日志记录或模糊检索。
Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。 """
if name == 'data':
Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
if name == "data":
return self._parsed
if name == 'output':
if name == "output":
return self._parsed
return getattr(self._original, name)
class DeepSeekReasonerAgent(Generic[T]):
"""
专为 DeepSeek-V4/R1 设计的适配器。
@@ -44,15 +48,15 @@ class DeepSeekReasonerAgent(Generic[T]):
"""
def __init__(
self,
model,
name,
output_type: Any = str,
system_prompt: str = "",
deps_type: Type[Any] = None,
tools: list = None,
retries: int = 3,
**kwargs
self,
model,
name,
output_type: Any = str,
system_prompt: str = "",
deps_type: Type[Any] = None,
tools: list = None,
retries: int = 3,
**kwargs,
):
self.output_schema = output_type
self.has_custom_output = output_type is not str and output_type is not None
@@ -63,6 +67,7 @@ class DeepSeekReasonerAgent(Generic[T]):
if self.has_custom_output:
try:
from pydantic import TypeAdapter
schema_dict = TypeAdapter(self.output_schema).json_schema()
schema_str = json.dumps(schema_dict, ensure_ascii=False)
format_instruction = (
@@ -77,14 +82,14 @@ class DeepSeekReasonerAgent(Generic[T]):
if self.tools:
tool_descs = []
for t in self.tools:
desc = getattr(t, '__name__', str(t))
if hasattr(t, '__doc__') and t.__doc__:
desc = getattr(t, "__name__", str(t))
if hasattr(t, "__doc__") and t.__doc__:
desc += f": {t.__doc__.strip()}"
tool_descs.append(f"- {desc}")
tool_instruction = (
"\n\n系统为您提供了以下工具。由于当前处于结构化降级模式，无法原生调用。"
"但如果您在思考过程中判断必须使用这些工具，请在返回的结构中（或如果是自由文本）注明意图，由外层逻辑进行调度：\n" +
"\n".join(tool_descs)
"但如果您在思考过程中判断必须使用这些工具，请在返回的结构中（或如果是自由文本）注明意图，由外层逻辑进行调度：\n"
+ "\n".join(tool_descs)
)
self.agent = Agent(
@@ -93,40 +98,41 @@ class DeepSeekReasonerAgent(Generic[T]):
output_type=str, # Force native agent to return str to disable function calling
system_prompt=system_prompt + format_instruction + tool_instruction,
deps_type=deps_type,
**kwargs
**kwargs,
)
def _parse_output(self, text: str) -> Any:
"""执行与 parse output 相关的核心业务流转操作。
该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
Args: text (str): 控制逻辑流向的具体字符串参数，指定了期望的 text 内容。
Returns: (Any): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。 """
Returns: (Any): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
if not self.has_custom_output:
return text
match = re.search(r'```json\s*(.*?)\s*```', text, re.DOTALL)
match = re.search(r"```json\s*(.*?)\s*```", text, re.DOTALL)
json_str = match.group(1).strip() if match else text
if not json_str.startswith('{') and not json_str.startswith('['):
start_obj = json_str.find('{')
start_arr = json_str.find('[')
if not json_str.startswith("{") and not json_str.startswith("["):
start_obj = json_str.find("{")
start_arr = json_str.find("[")
start = -1
end = -1
if start_obj != -1 and (start_arr == -1 or start_obj < start_arr):
start = start_obj
end = json_str.rfind('}')
end = json_str.rfind("}")
elif start_arr != -1:
start = start_arr
end = json_str.rfind(']')
end = json_str.rfind("]")
if start != -1 and end != -1 and end > start:
json_str = json_str[start:end+1]
json_str = json_str[start : end + 1]
if not json_str:
raise ValueError("未找到有效的 JSON 块。请将结果包装在 ```json 中。")
try:
from pydantic import TypeAdapter
adapter = TypeAdapter(self.output_schema)
return adapter.validate_json(json_str)
except ValidationError as e:
@@ -134,33 +140,35 @@ class DeepSeekReasonerAgent(Generic[T]):
except json.JSONDecodeError as e:
raise ValueError(f"返回的不是合法的 JSON：{e}")
def __getattr__(self, item):
# Delegate any unknown attributes (like .system_prompt, .tool) to the underlying pydantic_ai Agent
"""检索并获取特定的 getattr 数据集合或实例对象。
根据提供的查询条件或上下文凭证，从数据库、缓存或第三方服务中读取对应的资源状态。
Args: item: 参与 getattr 逻辑运算或数据构建的上下文依赖对象。
Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。 """
Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
return getattr(self.agent, item)
async def run(self, user_prompt: str, deps: Any = None, message_history: list = None, **kwargs) -> Any:
async def run(
self, user_prompt: str, deps: Any = None, message_history: list = None, **kwargs
) -> Any:
# Custom retry loop
"""执行与 run 相关的核心业务流转操作。
该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
Args: user_prompt (str): 控制逻辑流向的具体字符串参数，指定了期望的 user prompt 内容。 deps (Any): 参与 run 逻辑运算或数据构建的上下文依赖对象。 message_history (list): 批量操作所需的列表集合，囊括了需要统一处理的多个 message history 元素。
Returns: (Any): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。 """
Returns: (Any): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
current_history = message_history or []
last_exception = None
for attempt in range(self.retries + 1):
result = await self.agent.run(
user_prompt,
deps=deps,
message_history=current_history,
**kwargs
user_prompt, deps=deps, message_history=current_history, **kwargs
)
raw_text = result.data if hasattr(result, 'data') else getattr(result, 'output', str(result))
raw_text = (
result.data
if hasattr(result, "data")
else getattr(result, "output", str(result))
)
try:
parsed_data = self._parse_output(raw_text)
@@ -171,11 +179,15 @@ class DeepSeekReasonerAgent(Generic[T]):
except ValueError as e:
last_exception = e
# Prepare retry prompt
user_prompt = f"你的上一次输出解析失败，错误原因是: {e}\n请修正格式后重新输出。"
user_prompt = (
f"你的上一次输出解析失败，错误原因是: {e}\n请修正格式后重新输出。"
)
# We need to maintain history manually so the model sees what it did wrong
# Actually, pydantic-ai manages history inside the result. Let's use the all_messages from result
if hasattr(result, 'all_messages'):
if hasattr(result, "all_messages"):
current_history = result.all_messages()
raise ValueError(f"Exceeded maximum retries ({self.retries}) for output validation. Last error: {last_exception}")
raise ValueError(
f"Exceeded maximum retries ({self.retries}) for output validation. Last error: {last_exception}"
)