"""
LLM 模型配置 API
"""
from typing import Optional

from fastapi import APIRouter, Depends, HTTPException, Query
from pydantic import BaseModel, Field, field_validator, model_validator
from sqlalchemy import func, or_, select, update
from sqlalchemy.ext.asyncio import AsyncSession

from app.core.database import get_db
from app.core.config import settings
from app.core.deps import get_current_user
from app.models.llm_model_config import LLMModelConfig
from app.models.user import User
from app.schemas.response import success_response
from app.services.llm_provider_service import LLMProviderService
from app.services.local_embedding_service import LocalEmbeddingService

router = APIRouter()

TYPE_SPECIFIC_FIELDS = {
    "llm_temperature",
    "llm_top_p",
    "llm_max_tokens",
    "llm_system_prompt",
    "embedding_dimension",
    "chunk_size",
    "chunk_overlap",
}


class LLMModelConfigUpsertRequest(BaseModel):
    """模型配置新增/编辑请求"""

    model_code: Optional[str] = None
    model_name: Optional[str] = None
    model_type: str = Field("chat", pattern="^(chat|embedding)$")
    provider: str = Field(..., min_length=1, max_length=64)
    endpoint_url: Optional[str] = Field(None, max_length=512)
    api_key: Optional[str] = Field(None, max_length=512)
    llm_model_name: str = Field(..., min_length=1, max_length=128)
    llm_timeout: int = Field(120, ge=5, le=600)
    llm_temperature: float = Field(0.70, ge=0, le=2)
    llm_top_p: float = Field(0.90, ge=0, le=1)
    llm_max_tokens: int = Field(8192, ge=1, le=32768)
    llm_system_prompt: Optional[str] = None
    embedding_dimension: Optional[int] = Field(None, ge=1, le=8192)
    chunk_size: int = Field(settings.CHUNK_SIZE, ge=100, le=10000)
    chunk_overlap: int = Field(settings.CHUNK_OVERLAP, ge=0, le=5000)
    description: Optional[str] = Field(None, max_length=500)
    is_active: bool = True
    is_default: bool = False

    @field_validator(
        "model_code",
        "model_name",
        "provider",
        "endpoint_url",
        "api_key",
        "llm_model_name",
        "llm_system_prompt",
        "description",
        mode="before",
    )
    @classmethod
    def strip_string_fields(cls, value):
        if isinstance(value, str):
            value = value.strip()
            return value or None
        return value

    @model_validator(mode="after")
    def validate_embedding_options(self):
        if self.model_type == "embedding" and self.chunk_overlap >= self.chunk_size:
            raise ValueError("分块重叠字符数必须小于分块字符数")
        return self


class LLMModelConfigTestRequest(LLMModelConfigUpsertRequest):
    """模型测试请求"""


def serialize_model_config(config: LLMModelConfig, include_api_key: bool = False) -> dict:
    """序列化模型配置"""
    api_key = config.api_key or ""
    data = {
        "config_id": config.config_id,
        "model_code": config.model_code,
        "model_name": config.model_name,
        "model_type": config.model_type or "chat",
        "provider": config.provider,
        "endpoint_url": config.endpoint_url,
        "llm_model_name": config.llm_model_name,
        "llm_timeout": config.llm_timeout,
        "llm_temperature": float(config.llm_temperature or 0),
        "llm_top_p": float(config.llm_top_p or 0),
        "llm_max_tokens": config.llm_max_tokens,
        "llm_system_prompt": config.llm_system_prompt,
        "embedding_dimension": config.embedding_dimension,
        "chunk_size": config.chunk_size,
        "chunk_overlap": config.chunk_overlap,
        "description": config.description,
        "is_active": bool(config.is_active),
        "is_default": bool(config.is_default),
        "has_api_key": bool(api_key),
        "api_key_masked": mask_api_key(api_key),
        "created_at": config.created_at.isoformat() if config.created_at else None,
        "updated_at": config.updated_at.isoformat() if config.updated_at else None,
    }
    if include_api_key:
        data["api_key"] = api_key
    return data


def mask_api_key(api_key: str) -> str:
    """脱敏 API Key"""
    if not api_key:
        return ""
    if len(api_key) <= 8:
        return "*" * len(api_key)
    return f"{api_key[:4]}{'*' * (len(api_key) - 8)}{api_key[-4:]}"


def normalize_payload(payload: LLMModelConfigUpsertRequest) -> dict:
    """补齐自动生成字段"""
    data = payload.model_dump()
    provider = data["provider"]
    llm_model_name = data["llm_model_name"]
    model_type = data.get("model_type") or "chat"
    data["model_type"] = model_type
    data["model_name"] = data.get("model_name") or LLMProviderService.build_model_name(provider, llm_model_name)
    base_code = data.get("model_code") or LLMProviderService.build_model_code(provider, llm_model_name)
    # embedding 类型加前缀，避免与同名对话模型编码冲突
    if model_type == "embedding" and not base_code.startswith("emb_"):
        base_code = f"emb_{base_code}"
    data["model_code"] = base_code
    data["endpoint_url"] = data.get("endpoint_url") or LLMProviderService.get_default_endpoint_url(provider)
    if data["is_default"]:
        data["is_active"] = True
    if model_type == "embedding":
        data["llm_system_prompt"] = None
    return data


def to_storage_payload(payload: dict) -> dict:
    """将接口扁平字段归并为按模型类型区分的 JSON 参数。"""
    data = dict(payload)
    if data["model_type"] == "embedding":
        type_config = {
            "chunk_size": data["chunk_size"],
            "chunk_overlap": data["chunk_overlap"],
        }
        if data.get("embedding_dimension") is not None:
            type_config["dimension"] = data["embedding_dimension"]
    else:
        type_config = {
            "temperature": data["llm_temperature"],
            "top_p": data["llm_top_p"],
            "max_tokens": data["llm_max_tokens"],
        }
        if data.get("llm_system_prompt"):
            type_config["system_prompt"] = data["llm_system_prompt"]

    for field in TYPE_SPECIFIC_FIELDS:
        data.pop(field, None)
    data["type_config"] = type_config
    return data


async def ensure_default_config(
    db: AsyncSession,
    model_type: str,
    preferred_config_id: Optional[int] = None,
):
    """确保指定模型类型始终存在一个默认启用配置。"""
    default_result = await db.execute(
        select(LLMModelConfig.config_id)
        .where(
            LLMModelConfig.model_type == model_type,
            LLMModelConfig.is_default == True,
            LLMModelConfig.is_active == True,
        )
        .limit(1)
    )
    if default_result.scalar_one_or_none():
        return

    candidate_id = None
    if preferred_config_id:
        candidate_result = await db.execute(
            select(LLMModelConfig.config_id).where(
                LLMModelConfig.config_id == preferred_config_id,
                LLMModelConfig.model_type == model_type,
                LLMModelConfig.is_active == True,
            )
        )
        candidate_id = candidate_result.scalar_one_or_none()

    if candidate_id is None:
        fallback_result = await db.execute(
            select(LLMModelConfig.config_id)
            .where(
                LLMModelConfig.model_type == model_type,
                LLMModelConfig.is_active == True,
            )
            .order_by(LLMModelConfig.updated_at.desc(), LLMModelConfig.config_id.desc())
            .limit(1)
        )
        candidate_id = fallback_result.scalar_one_or_none()

    if candidate_id is None:
        return

    await db.execute(
        update(LLMModelConfig)
        .where(LLMModelConfig.model_type == model_type)
        .values(is_default=False)
    )
    await db.execute(
        update(LLMModelConfig)
        .where(LLMModelConfig.config_id == candidate_id)
        .values(is_default=True)
    )


@router.get("/providers", response_model=dict)
async def get_provider_catalog(
    current_user: User = Depends(get_current_user),
):
    """获取模型提供方目录"""
    catalog = [dict(item) for item in LLMProviderService.get_provider_catalog()]
    for item in catalog:
        item["default_chunk_size"] = settings.CHUNK_SIZE
        item["default_chunk_overlap"] = settings.CHUNK_OVERLAP
        if item["value"] == "local":
            item["models"] = LocalEmbeddingService.list_available_models()
    return success_response(data=catalog)


@router.get("/", response_model=dict)
async def get_llm_model_configs(
    page: int = Query(1, ge=1),
    page_size: int = Query(10, ge=1, le=100),
    keyword: Optional[str] = Query(None, description="搜索关键词（模型名称、编码、模型名）"),
    provider: Optional[str] = Query(None, description="提供方筛选"),
    model_type: Optional[str] = Query(None, description="模型类型筛选: chat/embedding"),
    is_active: Optional[bool] = Query(None, description="启用状态筛选"),
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """获取模型配置列表"""

    conditions = []
    if keyword:
        conditions.append(
            or_(
                LLMModelConfig.model_name.like(f"%{keyword}%"),
                LLMModelConfig.model_code.like(f"%{keyword}%"),
                LLMModelConfig.llm_model_name.like(f"%{keyword}%"),
            )
        )
    if provider:
        conditions.append(LLMModelConfig.provider == provider)
    if model_type:
        conditions.append(LLMModelConfig.model_type == model_type)
    if is_active is not None:
        conditions.append(LLMModelConfig.is_active == is_active)

    count_query = select(func.count(LLMModelConfig.config_id))
    if conditions:
        count_query = count_query.where(*conditions)
    total_result = await db.execute(count_query)
    total = total_result.scalar() or 0

    query = select(LLMModelConfig).order_by(
        LLMModelConfig.is_default.desc(),
        LLMModelConfig.updated_at.desc(),
        LLMModelConfig.config_id.desc(),
    )
    if conditions:
        query = query.where(*conditions)
    query = query.offset((page - 1) * page_size).limit(page_size)

    result = await db.execute(query)
    configs = result.scalars().all()

    return {
        "code": 200,
        "message": "success",
        "data": [serialize_model_config(item) for item in configs],
        "total": total,
        "page": page,
        "page_size": page_size,
    }


@router.get("/{config_id}", response_model=dict)
async def get_llm_model_config_detail(
    config_id: int,
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """获取模型配置详情"""
    result = await db.execute(
        select(LLMModelConfig).where(LLMModelConfig.config_id == config_id)
    )
    config = result.scalar_one_or_none()
    if not config:
        raise HTTPException(status_code=404, detail="模型配置不存在")

    return success_response(data=serialize_model_config(config, include_api_key=True))


@router.post("/", response_model=dict)
async def create_llm_model_config(
    request_data: LLMModelConfigUpsertRequest,
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """创建模型配置"""
    payload = to_storage_payload(normalize_payload(request_data))

    existing_code_result = await db.execute(
        select(LLMModelConfig).where(LLMModelConfig.model_code == payload["model_code"])
    )
    if existing_code_result.scalar_one_or_none():
        raise HTTPException(status_code=400, detail="模型编码已存在")

    new_config = LLMModelConfig(**payload)
    db.add(new_config)
    await db.flush()

    if payload["is_default"]:
        await db.execute(
            update(LLMModelConfig)
            .where(
                LLMModelConfig.model_type == new_config.model_type,
                LLMModelConfig.config_id != new_config.config_id,
            )
            .values(is_default=False)
        )

    await ensure_default_config(
        db,
        new_config.model_type,
        preferred_config_id=new_config.config_id,
    )
    await db.commit()
    await db.refresh(new_config)

    return success_response(
        data=serialize_model_config(new_config),
        message="模型配置创建成功",
    )


@router.put("/{config_id}", response_model=dict)
async def update_llm_model_config(
    config_id: int,
    request_data: LLMModelConfigUpsertRequest,
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """更新模型配置"""
    result = await db.execute(
        select(LLMModelConfig).where(LLMModelConfig.config_id == config_id)
    )
    config = result.scalar_one_or_none()
    if not config:
        raise HTTPException(status_code=404, detail="模型配置不存在")

    previous_model_type = config.model_type or "chat"
    payload = to_storage_payload(normalize_payload(request_data))
    existing_code_result = await db.execute(
        select(LLMModelConfig).where(
            LLMModelConfig.model_code == payload["model_code"],
            LLMModelConfig.config_id != config_id,
        )
    )
    if existing_code_result.scalar_one_or_none():
        raise HTTPException(status_code=400, detail="模型编码已被其他配置使用")

    for key, value in payload.items():
        setattr(config, key, value)

    await db.flush()

    if config.is_default:
        await db.execute(
            update(LLMModelConfig)
            .where(
                LLMModelConfig.model_type == config.model_type,
                LLMModelConfig.config_id != config.config_id,
            )
            .values(is_default=False)
        )

    await ensure_default_config(
        db,
        config.model_type,
        preferred_config_id=config.config_id,
    )
    if previous_model_type != config.model_type:
        await ensure_default_config(db, previous_model_type)
    await db.commit()
    await db.refresh(config)

    return success_response(
        data=serialize_model_config(config),
        message="模型配置更新成功",
    )


@router.put("/{config_id}/status", response_model=dict)
async def update_llm_model_config_status(
    config_id: int,
    is_active: bool = Query(..., description="是否启用"),
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """更新模型配置启用状态"""
    result = await db.execute(
        select(LLMModelConfig).where(LLMModelConfig.config_id == config_id)
    )
    config = result.scalar_one_or_none()
    if not config:
        raise HTTPException(status_code=404, detail="模型配置不存在")

    config.is_active = is_active
    if not is_active and config.is_default:
        config.is_default = False

    await db.flush()
    await ensure_default_config(db, config.model_type)
    await db.commit()
    await db.refresh(config)

    return success_response(
        data=serialize_model_config(config),
        message="模型状态更新成功",
    )


@router.put("/{config_id}/default", response_model=dict)
async def set_default_llm_model_config(
    config_id: int,
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """设为默认模型配置"""
    result = await db.execute(
        select(LLMModelConfig).where(LLMModelConfig.config_id == config_id)
    )
    config = result.scalar_one_or_none()
    if not config:
        raise HTTPException(status_code=404, detail="模型配置不存在")

    config.is_active = True
    config.is_default = True
    await db.flush()
    await db.execute(
        update(LLMModelConfig)
        .where(
            LLMModelConfig.model_type == config.model_type,
            LLMModelConfig.config_id != config.config_id,
        )
        .values(is_default=False)
    )
    await db.commit()
    await db.refresh(config)

    return success_response(
        data={
            **serialize_model_config(config),
            "requires_revectorization": config.model_type == "embedding",
        },
        message="默认模型切换成功",
    )


@router.delete("/{config_id}", response_model=dict)
async def delete_llm_model_config(
    config_id: int,
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """删除模型配置"""
    result = await db.execute(
        select(LLMModelConfig).where(LLMModelConfig.config_id == config_id)
    )
    config = result.scalar_one_or_none()
    if not config:
        raise HTTPException(status_code=404, detail="模型配置不存在")

    was_default = bool(config.is_default)
    model_type = config.model_type or "chat"
    await db.delete(config)
    await db.flush()

    if was_default:
        await ensure_default_config(db, model_type)

    await db.commit()
    return success_response(message="模型配置删除成功")


@router.post("/test", response_model=dict)
async def test_llm_model_config(
    request_data: LLMModelConfigTestRequest,
    current_user: User = Depends(get_current_user),
):
    """测试模型连接（按类型分流：chat 走对话补全，embedding 走向量接口）"""
    payload = normalize_payload(request_data)
    try:
        if payload.get("model_type") == "embedding":
            test_result = await LLMProviderService.test_embedding_connection(payload)
        else:
            test_result = await LLMProviderService.test_model_connection(payload)
    except ValueError as exc:
        raise HTTPException(status_code=400, detail=str(exc)) from exc
    return success_response(data=test_result, message="模型测试成功")