"""语义向量通道（可选增强）：本地 BGE-M3 权重 或 联网 Embedding API 双模式。

- provider=local：sentence-transformers 加载本地权重（如 models/bge-m3）；
- provider=api：OpenAI 兼容 /embeddings 接口（默认阿里 DashScope 兼容模式，
  模型 text-embedding-v4，填 API Key 即可，无需下载权重）；
- 任一模式就绪即启用语义召回；均不可用时回退词表归一化，行为与无此模块一致；
- 依赖与权重均为可选安装，不进入 requirements（"零重依赖、离线优先"约束）。
"""
from __future__ import annotations

import importlib.util
import logging
from pathlib import Path

import httpx

logger = logging.getLogger(__name__)

# BGE-M3 目录的就绪判定：配置文件 + 任一权重文件 + 分词器
_REQUIRED_CONFIG = "config.json"
_WEIGHT_FILES = ("model.safetensors", "pytorch_model.bin")
_TOKENIZER_FILES = ("tokenizer.json", "vocab.txt")

DEFAULT_API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1"
DEFAULT_API_MODEL = "text-embedding-v4"

_model = None          # 懒加载的本地 SentenceTransformer 实例
_model_path = ""
_skill_emb = None      # 词表技能向量缓存
_skill_names: list[str] = []
_emb_cache_key = ""    # 缓存对应的通道标识（provider+路径/模型）


def deps_installed() -> bool:
    """sentence-transformers 是否已安装。"""
    return importlib.util.find_spec("sentence_transformers") is not None


def resolve_weights_path(path: str | Path) -> Path:
    """权重路径解析：相对路径相对工程根目录解析，绝对路径原样返回（兼容既有配置）。"""
    from app.config import ROOT
    p = Path(str(path or ""))
    return p if p.is_absolute() else ROOT / p


def weights_present(path: str | Path) -> bool:
    """权重目录是否包含 BGE-M3 所需文件。"""
    p = Path(str(path or ""))
    if not p.is_dir() or not (p / _REQUIRED_CONFIG).exists():
        return False
    if not any((p / f).exists() for f in _WEIGHT_FILES):
        return False
    return any((p / f).exists() for f in _TOKENIZER_FILES)


def status(cfg: dict) -> dict:
    """语义通道状态：供 /api/health 与 /api/config 展示。"""
    provider = str(cfg.get("embed_provider") or "local").strip() or "local"
    path = str(cfg.get("embed_model_path") or "").strip()
    api_key = str(cfg.get("embed_api_key") or "").strip()
    present = weights_present(resolve_weights_path(path)) if path else False
    deps = deps_installed()
    local_ready = bool(path and present and deps)
    api_ready = bool(api_key)
    ready = api_ready if provider == "api" else local_ready
    return {
        "provider": provider,
        "configured": bool(path) if provider == "local" else api_ready,
        "path": path,
        "weights_present": present,
        "deps_installed": deps,
        "local_ready": local_ready,
        "api_key_masked": ("••••" + api_key[-4:]) if api_key else "",
        "api_base_url": str(cfg.get("embed_api_base_url") or DEFAULT_API_BASE),
        "api_model": str(cfg.get("embed_api_model") or DEFAULT_API_MODEL),
        "ready": bool(ready),
    }


# ---------------------------------------------------------------- 本地模式
def _load_local(path: str):
    """懒加载本地模型；不可用时返回 None。"""
    global _model, _model_path
    if _model is not None and _model_path == path:
        return _model
    try:
        from sentence_transformers import SentenceTransformer  # 可选依赖
        _model = SentenceTransformer(path)
        _model_path = path
        logger.info("语义向量模型已加载（本地）: %s", path)
    except Exception as exc:  # noqa: BLE001 - 模型加载失败必须可回退
        logger.warning("本地语义模型加载失败，回退词表归一化: %s", exc)
        _model = None
        return None
    return _model


def _embed_local(texts: list[str], path: str):
    model = _load_local(path)
    if model is None:
        return None
    return model.encode(texts, normalize_embeddings=True)


# ---------------------------------------------------------------- 联网模式
def _embed_api(texts: list[str], cfg: dict):
    """调用 OpenAI 兼容 embeddings 接口（DashScope 等），返回向量列表。"""
    st = status(cfg)
    try:
        resp = httpx.post(
            f"{st['api_base_url'].rstrip('/')}/embeddings",
            headers={"Authorization": f"Bearer {cfg.get('embed_api_key')}"},
            json={"model": st["api_model"], "input": texts},
            timeout=30,
        )
        resp.raise_for_status()
        data = resp.json()["data"]
        data.sort(key=lambda d: d["index"])
        import math
        vecs = []
        for d in data:
            v = d["embedding"]
            norm = math.sqrt(sum(x * x for x in v)) or 1.0
            vecs.append([x / norm for x in v])
        return vecs
    except Exception as exc:  # noqa: BLE001 - 网络/鉴权失败必须可回退
        logger.warning("Embedding API 调用失败，回退词表归一化: %s", exc)
        return None


# ---------------------------------------------------------------- 统一入口
def _embed(texts: list[str], cfg: dict):
    """按 provider 取向量（已归一化）；失败返回 None。"""
    provider = str(cfg.get("embed_provider") or "local").strip() or "local"
    if provider == "api":
        return _embed_api(texts, cfg)
    return _embed_local(texts, str(resolve_weights_path(
        str(cfg.get("embed_model_path") or "").strip())))


def most_similar(name: str, cfg: dict, lexicon: dict,
                 threshold: float = 0.62) -> tuple[str, float] | None:
    """返回词表中与 name 语义最近的技能 (标准名, 相似度)；未过阈值或不可用返回 None。"""
    global _skill_emb, _skill_names, _emb_cache_key
    st = status(cfg)
    if not st["ready"]:
        return None
    cache_key = f"{st['provider']}|{st['path'] or st['api_model']}"
    if _skill_emb is None or cache_key != _emb_cache_key:
        _skill_names = [s["name"] for s in lexicon["skills"]]
        _skill_emb = _embed(_skill_names, cfg)
        if _skill_emb is None:
            return None
        _emb_cache_key = cache_key
    vecs = _embed([name], cfg)
    if vecs is None:
        return None
    vec = vecs[0]
    scores = [sum(a * b for a, b in zip(row, vec)) for row in _skill_emb]
    best_i = max(range(len(scores)), key=lambda i: scores[i])
    if scores[best_i] >= threshold:
        return _skill_names[best_i], round(scores[best_i], 4)
    return None