179 lines
6.7 KiB
Python
179 lines
6.7 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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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
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T = TypeVar("T", bound=BaseModel)
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class AgentRunResultProxy:
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"""``Agent.run`` 结果的轻量代理：把已解析的结构化对象暴露为 ``.data`` / ``.output``。"""
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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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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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from pydantic import TypeAdapter
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schema_dict = TypeAdapter(self.output_schema).json_schema()
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schema_str = json.dumps(schema_dict, ensure_ascii=False)
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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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"""从模型自由文本中抽取 ```json 块并按 ``output_schema`` 校验为对象。"""
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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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from pydantic import TypeAdapter
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adapter = TypeAdapter(self.output_schema)
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return adapter.validate_json(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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def __getattr__(self, item):
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# Delegate any unknown attributes (like .system_prompt, .tool) to the underlying pydantic_ai Agent
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return getattr(self.agent, item)
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async def run(
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self, user_prompt: str, deps: Any = None, message_history: list = None, **kwargs
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) -> Any:
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# Custom retry loop
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"""运行一次 deepseek-reasoner 推理：失败时根据错误反馈让模型重试，最多 ``self.retries`` 轮。"""
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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, deps=deps, message_history=current_history, **kwargs
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)
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raw_text = (
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result.data
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if hasattr(result, "data")
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else getattr(result, "output", str(result))
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)
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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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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 = (
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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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)
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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(
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f"Exceeded maximum retries ({self.retries}) for output validation. Last error: {last_exception}"
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)
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