112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
"""
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基础 Agent 模块
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提供所有 Agent 的基类，内置：
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- 防提示词注入检测
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- 模型调用耗时日志
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"""
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import time
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import logging
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from google.adk.agents import LlmAgent
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from google.adk.models.llm_response import LlmResponse
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from google.genai import types
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from common.logger import after_model_callback
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from common.prompt_guard import check_prompt_injection
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logger = logging.getLogger("adk.agent")
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# ============================================================
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# 防注入的 before_model_callback
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# ============================================================
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async def _before_model_callback(callback_context, llm_request):
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"""在模型调用前检测提示词注入，并记录开始时间"""
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# 1. 防提示词注入检测：检查最新的用户消息
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contents = getattr(llm_request, "contents", None) or []
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# 找到最后一条 role="user" 的消息
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last_user_content = None
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for content in reversed(contents):
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if getattr(content, "role", None) == "user":
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last_user_content = content
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break
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if last_user_content:
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text = ""
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parts = getattr(last_user_content, "parts", None) or []
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for part in parts:
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part_text = getattr(part, "text", None)
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if part_text:
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text += part_text
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injection = check_prompt_injection(text)
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if injection:
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logger.warning(
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"检测到提示词注入 | agent=%s pattern='%s'",
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callback_context.agent_name,
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injection,
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)
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return LlmResponse(
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content=types.Content(
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role="model",
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parts=[types.Part(text="抱歉，我无法处理该请求。")],
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),
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turn_complete=True,
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)
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# 2. 记录开始时间（供 after_model_callback 使用）
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callback_context.state["_log_model_call_start_time"] = time.perf_counter()
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return None
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# ============================================================
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# HuiYuBaseAgent 基类
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# ============================================================
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class HuiYuBaseAgent(LlmAgent):
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"""
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所有 Agent 的基类，自动集成：
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- 防提示词注入检测
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- 模型调用耗时日志
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用法：
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agent = HuiYuBaseAgent(
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name="my_agent",
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model=model,
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instruction="...",
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)
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如果需要自定义回调，仍然可以传入 before_model_callback / after_model_callback，
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它们会在防注入检测和日志记录之间执行。
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"""
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def __init__(self, **kwargs):
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# 获取用户传入的回调
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user_before = kwargs.pop("before_model_callback", None)
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user_after = kwargs.pop("after_model_callback", None)
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# 构建回调链：防注入 → 用户回调 → 日志
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before_chain = [_before_model_callback]
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if user_before:
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before_chain.append(user_before)
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after_chain = []
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if user_after:
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after_chain.append(user_after)
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after_chain.append(after_model_callback)
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kwargs["before_model_callback"] = before_chain
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kwargs["after_model_callback"] = after_chain
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# 强制模型用中文思考：在 instruction 前附加系统级要求
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instruction = kwargs.get("instruction", "")
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if instruction:
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kwargs["instruction"] = (
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"【系统要求：你的所有思考过程、分析推理、内心独白必须使用中文，"
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"禁止在思考中使用英文。你的最终回复也使用中文。】\n\n"
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+ instruction
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)
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super().__init__(**kwargs)
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