chore: initial commit for Pretor v0.1.0-alpha
正式发布 Pretor 平台的首个 alpha 版本。本项目旨在构建一个基于分布式架构的多智能体协同工作流水线。 核心功能实现： 1. 建立基于 BaseIndividual 的动态插件加载机制。 2. 实现三类核心 worker_individual 子个体。 3. 集成 Ray 框架支持分布式集群调度。 4. 基于 PostgreSQL 的全量持久化存储方案。 5. 提供完整的 FastAPI 后端与 React 前端交互界面。
This commit is contained in:
@@ -0,0 +1,150 @@
|
||||
import re
|
||||
import json
|
||||
from typing import Type, TypeVar, Any, Generic
|
||||
from pydantic import BaseModel, ValidationError
|
||||
from pydantic_ai import Agent, RunContext
|
||||
from pydantic_ai.run import AgentRunResult
|
||||
|
||||
T = TypeVar('T', bound=BaseModel)
|
||||
|
||||
class AgentRunResultProxy:
|
||||
def __init__(self, original, parsed):
|
||||
self._original = original
|
||||
self._parsed = parsed
|
||||
def __getattr__(self, name):
|
||||
if name == 'data':
|
||||
return self._parsed
|
||||
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。
|
||||
将结构化输出降级为文本解析模式，并支持重试逻辑以确保系统兼容性。
|
||||
"""
|
||||
|
||||
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.output_schema = output_type
|
||||
self.has_custom_output = output_type is not str and output_type is not None
|
||||
self.tools = tools or []
|
||||
self.retries = retries
|
||||
|
||||
format_instruction = ""
|
||||
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 = (
|
||||
f"\n\nCRITICAL: 你必须输出且只能输出一段纯 JSON 格式的数据，"
|
||||
f"并包裹在 ```json 和 ``` 之间。格式必须符合以下 JSON Schema 结构（或对应数据类型）：\n"
|
||||
f"{schema_str}"
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
tool_instruction = ""
|
||||
if self.tools:
|
||||
tool_descs = []
|
||||
for t in self.tools:
|
||||
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)
|
||||
)
|
||||
|
||||
self.agent = Agent(
|
||||
model=model,
|
||||
name=name,
|
||||
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
|
||||
)
|
||||
|
||||
def _parse_output(self, text: str) -> Any:
|
||||
if not self.has_custom_output:
|
||||
return text
|
||||
|
||||
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('[')
|
||||
start = -1
|
||||
end = -1
|
||||
if start_obj != -1 and (start_arr == -1 or start_obj < start_arr):
|
||||
start = start_obj
|
||||
end = json_str.rfind('}')
|
||||
elif start_arr != -1:
|
||||
start = start_arr
|
||||
end = json_str.rfind(']')
|
||||
|
||||
if start != -1 and end != -1 and end > start:
|
||||
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:
|
||||
raise ValueError(f"返回的 JSON 无法匹配所需结构：{e}")
|
||||
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
|
||||
return getattr(self.agent, item)
|
||||
|
||||
async def run(self, user_prompt: str, deps: Any = None, message_history: list = None, **kwargs) -> Any:
|
||||
# Custom retry loop
|
||||
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
|
||||
)
|
||||
|
||||
raw_text = result.data if hasattr(result, 'data') else getattr(result, 'output', str(result))
|
||||
|
||||
try:
|
||||
parsed_data = self._parse_output(raw_text)
|
||||
|
||||
# Proxy the result to inject the parsed data seamlessly
|
||||
return AgentRunResultProxy(result, parsed_data)
|
||||
|
||||
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。"
|
||||
|
||||
# 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'):
|
||||
current_history = result.all_messages()
|
||||
|
||||
raise ValueError(f"Exceeded maximum retries ({self.retries}) for output validation. Last error: {last_exception}")
|
||||
Reference in New Issue
Block a user