chat-ai-api/app/routers/chat_tools.py

from fastapi import APIRouter, HTTPException
from datetime import datetime

from ..core.ark_client import get_client
from ..config.config import Config
from ..schemas.chat import ChatMessage, ChatResponse, CommentRequest, CirclePromptConfig, HistoricalFigure, RephraseRequest, FigureUpsert
from ..db.souyue_mongo import get_mblog_by_id
from ..db.mongo import get_circle_prompt, upsert_circle_prompt, get_all_figures, get_figure_by_id, insert_figure, update_figure, delete_figure


router = APIRouter()


def _build_prompt(product_text: str, prompt_config: dict) -> str:
    name = prompt_config.get("name", "兴趣圈")
    role = prompt_config.get("role", "活跃用户")
    style = prompt_config.get("style", "自然亲切，有活人感")
    keywords: list = prompt_config.get("keywords") or []
    forbidden: list = prompt_config.get("forbidden") or []
    extraInstruction = prompt_config.get("extra_instruction")

    lines = [
        f"你是{role}，活跃在{name}兴趣圈。",
        "请根据以下帖子信息，生成一条10-30字的评论，要求：",
        "1. 内容指向性强，结合帖子具体内容",
        f"2. 风格：{style}",
    ]
    seq = 3
    if keywords:
        lines.append(f"{seq}. 适当融入关键词（自然使用）：{', '.join(keywords)}")
        seq += 1
    if forbidden:
        lines.append(f"{seq}. 禁止使用以下词语：{', '.join(forbidden)}")
        seq += 1
    lines.append(f"{seq}. 语言自然，不要暴露你是AI")

    # 额外要求
    if extraInstruction:
        lines.append(f"【额外要求】{','.join(extraInstruction)}")

    lines.append(f"\n帖子内容：{product_text}")

    return "\n".join(lines)


# 存储/更新兴趣圈提示词模版（appName 已存在则覆盖）
@router.post("/prompt")
async def save_circle_prompt(promptcfg: CirclePromptConfig):
    try:
        upsert_circle_prompt(promptcfg.model_dump())
        return {"message": "保存成功", "appName": promptcfg.appName}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"保存失败: {str(e)}")


# 评论帖子的马甲机器人，无状态，支持批量对多个帖子智能回复
@router.post("/batchPostCommentBot", response_model=ChatResponse)
async def generate_post_comment(request: CommentRequest):
    doc = get_mblog_by_id(request.id)
    if not doc:
        raise HTTPException(status_code=404, detail="帖子不存在")

    title = doc.get("title", "")
    brief = doc.get("brief", "")
    nickname = doc.get("nickname", "")
    app_name = doc.get("appName", "")
    images: list = doc.get("images") or []

    product_text = f"主题：{title}\n摘要：{brief}\n发布者：{nickname}"
    file_list = []
    if images:
        for img_url in images:
            file_list.append({"type": "input_image", "image_url": img_url})


    prompt_config = get_circle_prompt(app_name)
    input_text = _build_prompt(product_text, prompt_config)

    content = file_list + [{"type": "input_text", "text": input_text}]
    print(f"concat text: {content}")
    client = get_client(app_name)
    response = client.responses.create(
        model=Config.MODEL_NAME,
        input=[{"role": "user", "content": content}],

    )

    message_content = ""
    for item in response.output:
        if hasattr(item, 'type') and item.type == 'message' and hasattr(item, 'content'):
            if isinstance(item.content, list):
                for content_item in item.content:
                    if hasattr(content_item, 'text'):
                        message_content += content_item.text
            else:
                message_content += str(item.content)

    if not message_content:
        raise HTTPException(status_code=500, detail="AI未能生成评论")

    return ChatResponse(
        message=ChatMessage(role="assistant", content=message_content, timestamp=datetime.now()),
        model=response.model,
        usage=response.usage.model_dump() if response.usage else None,
    )


# ===================== 历史人物管理 =====================
# 获取历史人物列表
@router.get("/figures", response_model=list[HistoricalFigure])
async def list_figures():
    return get_all_figures()

# 获取单个历史人物
@router.get("/figures/{id}", response_model=HistoricalFigure)
async def get_figure(id: str):
    doc = get_figure_by_id(id)
    if not doc:
        raise HTTPException(status_code=404, detail="历史人物不存在")
    return doc

# 新增历史人物
@router.post("/figures", response_model=HistoricalFigure)
async def create_figure(figure: FigureUpsert):
    inserted_id = insert_figure(figure.model_dump())
    if not inserted_id:
        raise HTTPException(status_code=500, detail="新增失败")
    return {**figure.model_dump(), "_id": inserted_id}

# 修改历史人物
@router.put("/figures/{id}", response_model=HistoricalFigure)
async def modify_figure(id: str, figure: FigureUpsert):
    matched = update_figure(id, figure.model_dump())
    if not matched:
        raise HTTPException(status_code=404, detail="历史人物不存在")
    return {**figure.model_dump(), "_id": id}

# 删除历史人物
@router.delete("/figures/{id}")
async def remove_figure(id: str):
    deleted = delete_figure(id)
    if not deleted:
        raise HTTPException(status_code=404, detail="历史人物不存在")
    return {"message": "删除成功", "id": id}


# ===================== 润色接口 =====================
# 润色接口
@router.post("/rephrase")
async def rephrase_as_figure(request: RephraseRequest):
    figure = get_figure_by_id(request.figureId)
    if not figure:
        raise HTTPException(status_code=404, detail="历史人物不存在")

    prompt = (
        f"你是{figure['name']}，{figure['description']}，生活在{figure['era']}。\n"
        f"请将以下话语改写成{figure['name']}的说话风格，保留原意，体现其性格特点（{figure['prompt']}）。\n"
        f"只输出改写后的内容，不要解释、不要加引号。\n"
        f"原文：{request.text}"
    )

    client = get_client()
    response = client.responses.create(
        model=Config.MODEL_NAME,
        input=[{"role": "user", "content": prompt}],
        stream=False,
        store=False,
        # thinking={"type":"auto"},
    )

    rephrased = ""
    for item in response.output:
        if hasattr(item, 'type') and item.type == 'message' and hasattr(item, 'content'):
            if isinstance(item.content, list):
                for content_item in item.content:
                    if hasattr(content_item, 'text'):
                        rephrased += content_item.text
            else:
                rephrased += str(item.content)

    if not rephrased:
        raise HTTPException(status_code=500, detail="AI未能生成润色结果")

    return {
        "original": request.text,
        "rephrased": rephrased,
        "figure": figure["name"],
    }