aiagent/backend/app/services/scene_templates.py

"""
场景模板注册：路线图「客服 / 研发 / 运维」三类最小可运行工作流，供 API 与脚本一键创建 Agent。
"""
from __future__ import annotations

from typing import Any, Callable, Dict, List, Optional

PromptBuilder = Callable[[Dict[str, Any]], str]


def _default_prompt_cs(params: Dict[str, Any]) -> str:
    extra = (params.get("extra_instructions") or "").strip()
    base = """你是企业客服场景 Agent。根据用户问题给出清晰、可执行的回答；不确定时先澄清。
用户输入可能包含：{{input}} 或来自前序节点的合并字段。保持礼貌、简洁。"""
    if extra:
        return f"{base}\n\n【额外说明】\n{extra}"
    return base


def _default_prompt_dev(params: Dict[str, Any]) -> str:
    extra = (params.get("extra_instructions") or "").strip()
    lang = params.get("preferred_language") or "任意合适语言"
    base = f"""你是研发辅助 Agent，负责代码与设计说明。优先给出可运行示例与步骤；涉及安全/生产变更时明确风险。
偏好语言/栈：{lang}。
用户诉求：{{input}}"""
    if extra:
        return f"{base}\n\n【额外说明】\n{extra}"
    return base


def _default_prompt_ops(params: Dict[str, Any]) -> str:
    extra = (params.get("extra_instructions") or "").strip()
    base = """你是运维/日志分析场景 Agent。帮助用户解读日志片段、定位可能原因与下一步排查；不要编造未提供的日志内容。
用户输入：{{input}}"""
    if extra:
        return f"{base}\n\n【额外说明】\n{extra}"
    return base


def _build_minimal_workflow(
    prompt: str,
    *,
    temperature: float = 0.3,
    enable_tools: bool = False,
    tools: Optional[List[str]] = None,
) -> Dict[str, Any]:
    tools = tools or []
    return {
        "nodes": [
            {
                "id": "start-1",
                "type": "start",
                "position": {"x": 80, "y": 120},
                "data": {},
            },
            {
                "id": "llm-1",
                "type": "llm",
                "position": {"x": 320, "y": 120},
                "data": {
                    "prompt": prompt,
                    "temperature": float(temperature),
                    "enable_tools": enable_tools,
                    "tools": tools,
                    "selected_tools": tools,
                },
            },
            {
                "id": "end-1",
                "type": "end",
                "position": {"x": 560, "y": 120},
                "data": {},
            },
        ],
        "edges": [
            {
                "id": "e_start_llm",
                "source": "start-1",
                "target": "llm-1",
                "sourceHandle": "right",
                "targetHandle": "left",
            },
            {
                "id": "e_llm_end",
                "source": "llm-1",
                "target": "end-1",
                "sourceHandle": "right",
                "targetHandle": "left",
            },
        ],
    }


def build_workflow_for_template(template_id: str, parameters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
    """
    根据模板 ID 与参数生成 workflow_config（nodes+edges）。

    通用参数：temperature, enable_tools, tools(list[str]), extra_instructions, preferred_language(dev)
    """
    parameters = dict(parameters or {})
    meta = SCENE_TEMPLATE_REGISTRY.get(template_id)
    if not meta:
        raise ValueError(f"未知模板: {template_id}")

    temperature = float(parameters.get("temperature", meta.get("default_temperature", 0.3)))
    enable_tools = bool(parameters.get("enable_tools", False))
    tools = parameters.get("tools")
    if tools is not None and not isinstance(tools, list):
        tools = []
    elif tools is None:
        tools = list(meta.get("default_tools") or [])

    prompt_fn: PromptBuilder = meta["prompt_builder"]
    prompt = prompt_fn(parameters)
    return _build_minimal_workflow(
        prompt,
        temperature=temperature,
        enable_tools=enable_tools,
        tools=tools if enable_tools else [],
    )


SCENE_TEMPLATE_REGISTRY: Dict[str, Dict[str, Any]] = {
    "template_customer_service": {
        "title": "客服场景",
        "description": "通用客服问答与澄清（最小 LLM 链）。",
        "category": "customer_service",
        "default_temperature": 0.35,
        "default_tools": [],
        "prompt_builder": _default_prompt_cs,
    },
    "template_dev_codegen": {
        "title": "研发 / 代码助手",
        "description": "代码与设计说明辅助（最小 LLM 链）。",
        "category": "dev",
        "default_temperature": 0.25,
        "default_tools": [],
        "prompt_builder": _default_prompt_dev,
    },
    "template_ops_log_analysis": {
        "title": "运维 / 日志分析",
        "description": "日志解读与排查建议（最小 LLM 链）。",
        "category": "ops",
        "default_temperature": 0.3,
        "default_tools": [],
        "prompt_builder": _default_prompt_ops,
    },
}


def list_scene_template_meta() -> List[Dict[str, Any]]:
    """供 GET 接口返回（不含 prompt_builder）。"""
    out: List[Dict[str, Any]] = []
    for tid, meta in SCENE_TEMPLATE_REGISTRY.items():
        out.append(
            {
                "id": tid,
                "title": meta["title"],
                "description": meta["description"],
                "category": meta.get("category"),
                "default_temperature": meta.get("default_temperature"),
                "parameter_hints": [
                    "temperature",
                    "enable_tools",
                    "tools",
                    "extra_instructions",
                    "preferred_language（仅研发模板）",
                ],
            }
        )
    return out