150 lines
6.0 KiB
Python
150 lines
6.0 KiB
Python
import re
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import json
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from typing import Type, TypeVar, Any, Generic
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from pydantic import BaseModel, ValidationError
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from pydantic_ai import Agent, RunContext
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from pydantic_ai.run import AgentRunResult
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T = TypeVar('T', bound=BaseModel)
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class DeepSeekReasonerAgent(Generic[T]):
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"""
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专为 DeepSeek-V4/R1 设计的适配器。
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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。
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"""
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def __init__(
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self,
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model,
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name,
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output_type: Any = str,
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system_prompt: str = "",
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deps_type: Type[Any] = None,
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tools: list = None,
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retries: int = 3,
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**kwargs
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):
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self.output_schema = output_type
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self.has_custom_output = output_type is not str and output_type is not None
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self.tools = tools or []
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self.retries = retries
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format_instruction = ""
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if self.has_custom_output:
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try:
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if hasattr(self.output_schema, 'model_json_schema'):
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schema_str = json.dumps(self.output_schema.model_json_schema(), ensure_ascii=False)
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else:
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# Don't inject <class 'dict'> into prompt
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schema_str = str(getattr(self.output_schema, '__name__', str(self.output_schema)))
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format_instruction = (
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f"\n\nCRITICAL: 你必须输出且只能输出一段纯 JSON 格式的数据，"
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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"
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f"{schema_str}"
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)
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except Exception:
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pass
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tool_instruction = ""
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if self.tools:
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tool_descs = []
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for t in self.tools:
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desc = getattr(t, '__name__', str(t))
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if hasattr(t, '__doc__') and t.__doc__:
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desc += f": {t.__doc__.strip()}"
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tool_descs.append(f"- {desc}")
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tool_instruction = (
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"\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。"
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"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" +
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"\n".join(tool_descs)
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)
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self.agent = Agent(
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model=model,
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name=name,
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output_type=str, # Force native agent to return str to disable function calling
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system_prompt=system_prompt + format_instruction + tool_instruction,
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deps_type=deps_type,
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**kwargs
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)
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def _parse_output(self, text: str) -> Any:
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if not self.has_custom_output:
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return text
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match = re.search(r'```json\s*(.*?)\s*```', text, re.DOTALL)
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json_str = match.group(1).strip() if match else text
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if not json_str.startswith('{') and not json_str.startswith('['):
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start_obj = json_str.find('{')
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start_arr = json_str.find('[')
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start = -1
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end = -1
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if start_obj != -1 and (start_arr == -1 or start_obj < start_arr):
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start = start_obj
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end = json_str.rfind('}')
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elif start_arr != -1:
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start = start_arr
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end = json_str.rfind(']')
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if start != -1 and end != -1 and end > start:
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json_str = json_str[start:end+1]
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if not json_str:
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raise ValueError("未找到有效的 JSON 块。请将结果包装在 ```json 中。")
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try:
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if hasattr(self.output_schema, 'model_validate_json'):
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return self.output_schema.model_validate_json(json_str)
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else:
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return json.loads(json_str)
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except ValidationError as e:
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raise ValueError(f"返回的 JSON 无法匹配所需结构：{e}")
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except json.JSONDecodeError as e:
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raise ValueError(f"返回的不是合法的 JSON：{e}")
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async def run(self, user_prompt: str, deps: Any = None, message_history: list = None, **kwargs) -> Any:
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# Custom retry loop
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current_history = message_history or []
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last_exception = None
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for attempt in range(self.retries + 1):
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result = await self.agent.run(
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user_prompt,
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deps=deps,
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message_history=current_history,
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**kwargs
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)
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raw_text = result.data if hasattr(result, 'data') else getattr(result, 'output', str(result))
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try:
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parsed_data = self._parse_output(raw_text)
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# Proxy the result to inject the parsed data seamlessly
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class AgentRunResultProxy:
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def __init__(self, original, parsed):
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self._original = original
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self._parsed = parsed
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def __getattr__(self, name):
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if name == 'data':
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return self._parsed
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if name == 'output':
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return self._parsed
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return getattr(self._original, name)
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return AgentRunResultProxy(result, parsed_data)
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except ValueError as e:
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last_exception = e
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# Prepare retry prompt
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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。"
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# We need to maintain history manually so the model sees what it did wrong
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# Actually, pydantic-ai manages history inside the result. Let's use the all_messages from result
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if hasattr(result, 'all_messages'):
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current_history = result.all_messages()
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raise ValueError(f"Exceeded maximum retries ({self.retries}) for output validation. Last error: {last_exception}")
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