"""LLM / embedding provider configuration for students without local models.

Resolution order: CLI args  >  environment variables  >  config/llm.json  >  built-in presets.

Copy `config.example.json` to `config/llm.json` and fill in your key, or just export env vars.
"""

import json
import os
import urllib.error
import urllib.request
from pathlib import Path

ROOT = Path(__file__).resolve().parent.parent

# Built-in presets for common OpenAI-compatible endpoints (China-friendly first).
PRESETS = {
    "deepseek": {
        "base_url": "https://api.deepseek.com/v1",
        "model": "deepseek-chat",
        "embedding_model": None,
    },
    "siliconflow": {
        "base_url": "https://api.siliconflow.cn/v1",
        "model": "deepseek-ai/DeepSeek-V3",
        "embedding_model": "BAAI/bge-m3",
    },
    "moonshot": {
        "base_url": "https://api.moonshot.cn/v1",
        "model": "moonshot-v1-8k",
        "embedding_model": None,
    },
    "zhipu": {
        "base_url": "https://open.bigmodel.cn/api/paas/v4",
        "model": "glm-4-flash",
        "embedding_model": "embedding-3",
    },
    "qwen": {
        "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "model": "qwen-plus",
        "embedding_model": "text-embedding-v3",
    },
    "volcengine": {
        "base_url": "https://ark.cn-beijing.volces.com/api/v3",
        "model": "<your-endpoint-id>",
        "embedding_model": None,
    },
    "openai": {
        "base_url": "https://api.openai.com/v1",
        "model": "gpt-4o-mini",
        "embedding_model": "text-embedding-3-small",
    },
    "ollama": {
        "base_url": "http://localhost:11434/v1",
        "model": "qwen2.5:7b",
        "embedding_model": "bge-m3",
    },
}


def load_config():
    p = ROOT / "config" / "llm.json"
    if p.exists():
        try:
            return json.loads(p.read_text(encoding="utf-8"))
        except Exception:  # noqa: BLE001
            return {}
    return {}


def available_presets():
    return sorted(PRESETS)


def resolve_llm(provider=None, base_url=None, api_key=None, model=None):
    cfg = load_config()
    name = provider or os.environ.get("LLM_PROVIDER") or cfg.get("provider")
    preset = PRESETS.get(name, {}) if name else {}
    merged = {**preset, **(cfg.get("llm") or {})}
    return {
        "provider": name,
        "base_url": (
            base_url or os.environ.get("OPENAI_BASE_URL") or merged.get("base_url") or "https://api.openai.com/v1"
        ).rstrip("/"),
        "api_key": api_key or os.environ.get("OPENAI_API_KEY") or merged.get("api_key") or "",
        "model": model or os.environ.get("OPENAI_MODEL") or merged.get("model") or "gpt-4o-mini",
    }


def resolve_embedding(provider=None, base_url=None, api_key=None, model=None):
    cfg = load_config()
    name = provider or os.environ.get("EMBEDDING_PROVIDER") or cfg.get("provider")
    preset = PRESETS.get(name, {}) if name else {}
    merged = {**preset, **(cfg.get("embedding") or {})}
    return {
        "provider": name,
        "base_url": (
            base_url or os.environ.get("EMBEDDING_BASE_URL") or merged.get("base_url") or "https://api.openai.com/v1"
        ).rstrip("/"),
        "api_key": api_key or os.environ.get("EMBEDDING_API_KEY") or os.environ.get("OPENAI_API_KEY") or merged.get("api_key") or "",
        "model": model or os.environ.get("EMBEDDING_MODEL") or merged.get("embedding_model") or "text-embedding-3-small",
    }


def chat(messages, temperature=0.2, timeout=180, provider=None, base_url=None, api_key=None, model=None):
    """Call any OpenAI-compatible /chat/completions endpoint."""
    conf = resolve_llm(provider, base_url, api_key, model)
    if not conf["api_key"]:
        raise RuntimeError(
            "未配置 API Key。请设置 OPENAI_API_KEY 环境变量，或把 config.example.json 复制为 config/llm.json 并填入 key。"
        )
    body = {"model": conf["model"], "messages": messages, "temperature": temperature}
    req = urllib.request.Request(
        f"{conf['base_url']}/chat/completions",
        data=json.dumps(body).encode("utf-8"),
        headers={"Content-Type": "application/json", "Authorization": f"Bearer {conf['api_key']}"},
    )
    with urllib.request.urlopen(req, timeout=timeout) as resp:
        data = json.loads(resp.read().decode("utf-8"))
    return data["choices"][0]["message"]["content"], conf


def http_error_hint(exc):
    if isinstance(exc, urllib.error.HTTPError):
        try:
            detail = exc.read().decode("utf-8")[:300]
        except Exception:  # noqa: BLE001
            detail = ""
        return f"HTTP {exc.code}: {detail or exc.reason}"
    return str(exc)