85afabcd94
- 扩展 LLM client 协议，支持普通对话、日志分析和单 action 解析 - chat 非内置输入默认进入 LLM 普通对话，不再本地拦截问候 - 新增 ask、log analyze、action propose、action run 等交互命令 - 单 action 执行前强制人工确认，并复用现有 ActionRouter、审核、事件和 checkpoint - 日志分析默认读取尾部内容并脱敏后再提交给 LLM - 更新 README、发布包 README 和 run.sh help - 补充 LLM 与 chat 交互相关测试
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
"""LLM client 需要实现的共享协议。"""
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from __future__ import annotations
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from typing import Any, Protocol
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from pam_deploy_graph.models import (
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ActionResult,
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ExecutionStrategy,
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LlmActionAnalysis,
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LlmDeployPlan,
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LlmIntentResult,
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LlmParamResult,
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LlmSingleActionProposal,
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)
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class LlmClient(Protocol):
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"""Agent 使用的最小 LLM 能力接口。"""
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def understand_request(self, text: str) -> LlmIntentResult:
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"""识别用户自然语言请求的意图。"""
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...
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def extract_params(self, text: str, base_params: dict[str, Any] | None = None) -> LlmParamResult:
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"""从自然语言中抽取部署参数。"""
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...
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def generate_plan(
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self,
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*,
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params: dict[str, Any],
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intent: str,
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strategy: ExecutionStrategy,
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) -> LlmDeployPlan:
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"""根据参数和意图生成部署计划。"""
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...
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def analyze_action_result(
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self,
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*,
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action: str,
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result: ActionResult,
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) -> LlmActionAnalysis:
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"""分析 action 执行结果，并给出是否允许继续执行的建议。"""
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...
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def chat(self, text: str, context: dict[str, Any] | None = None) -> str:
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"""进行普通自然语言对话，不触发部署 workflow。"""
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...
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def analyze_log(self, log_text: str, question: str | None = None, source_path: str = "") -> str:
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"""分析日志文本并给出异常摘要、原因和建议。"""
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...
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def propose_action(
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self,
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text: str,
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allowed_actions: list[str],
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params: dict[str, Any],
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state_summary: dict[str, Any] | None = None,
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) -> LlmSingleActionProposal:
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"""把自然语言解析为单次 action 调用建议。"""
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...
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