"""Shared plumbing for rules that ask an LLM for a 0-10 score.

llm_score 与 fluency 共用的直连 LLM 调用与评分解析——两处各自实现一遍的
重复逻辑收敛到这里（v1.3.1 Phase 3 代码质量项）。
"""

import httpx

from agenteval.utils.llm import extract_content_from_llm_response, parse_json_from_llm_text


def parse_scored_content(content: str) -> tuple[float, str]:
    """Parse an LLM reply into a clamped 0-10 score plus reason."""
    parsed = parse_json_from_llm_text(content)
    score = float(parsed["score"])
    return max(0.0, min(10.0, score)), parsed.get("reason", "")


async def call_scored_llm(
    api_url: str,
    api_key: str | None,
    model: str,
    system_prompt: str,
    user_prompt: str,
    timeout: float = 60.0,
) -> tuple[float | None, str]:
    """POST a chat request to a raw OpenAI-compatible endpoint and parse the score.

    Returns (score, reason); score is None with an error message on failure.
    """
    headers = {"Content-Type": "application/json"}
    if api_key:
        headers["Authorization"] = f"Bearer {api_key}"
    payload = {
        "model": model,
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ],
        "temperature": 0.2,
    }
    try:
        async with httpx.AsyncClient(timeout=timeout) as client:
            resp = await client.post(api_url, headers=headers, json=payload)
        resp.raise_for_status()
        content = extract_content_from_llm_response(resp.json())
        if not content:
            return None, "LLM 返回内容为空"
        return parse_scored_content(content)
    except Exception as exc:
        return None, str(exc)