"""Embedding-based skill candidate retrieval.

当前实现使用 OpenAI-compatible `/v1/embeddings` 接口调用
阿里云百炼 `text-embedding-v4` 做最小语义召回：
1. 复用当前 provider 的 `api_key/api_base`
2. 先用 embedding 相似度召回一小批候选
3. 再交给上层 LLM selector 做最终技能选择
"""

from __future__ import annotations

import asyncio
import math
import os
import json
from urllib import request
from typing import Any


class SkillEmbeddingRetriever:
    """用 OpenAI-compatible embeddings API 为 skill 选择做候选召回。"""

    def __init__(
        self,
        *,
        api_key_env: str = "OPENAI_API_KEY",
        api_base_env: str = "OPENAI_API_BASE",
        model: str = "text-embedding-v4",
        timeout_seconds: float = 20.0,
    ) -> None:
        self.api_key_env = api_key_env
        self.api_base_env = api_base_env
        self.model = model
        self.timeout_seconds = timeout_seconds

    async def retrieve(
        self,
        *,
        query: str,
        candidates: list[dict[str, str]],
        top_k: int = 12,
        api_key: str | None = None,
        api_base: str | None = None,
        model: str | None = None,
    ) -> list[dict[str, str]]:
        """按 embedding 相似度召回 top-k 候选。

        如果没有可用的 API Key / base URL，或者 embedding 调用失败，
        当前阶段先退回到“全部候选交给 LLM selector”。
        """

        if not candidates:
            return []

        resolved_api_key = api_key or os.getenv(self.api_key_env)
        resolved_api_base = api_base or os.getenv(self.api_base_env)
        if not resolved_api_key or not resolved_api_base:
            return candidates

        try:
            query_embedding = await self._embed_texts(
                api_key=resolved_api_key,
                api_base=resolved_api_base,
                texts=[query],
                model=model or self.model,
            )
            candidate_texts = [self._candidate_text(item) for item in candidates]
            candidate_embeddings = await self._embed_texts(
                api_key=resolved_api_key,
                api_base=resolved_api_base,
                texts=candidate_texts,
                model=model or self.model,
            )
        except Exception:
            return candidates

        if not query_embedding or not query_embedding[0] or len(candidate_embeddings) != len(candidates):
            return candidates

        query_vector = query_embedding[0]
        scored: list[tuple[float, dict[str, str]]] = []
        for candidate, vector in zip(candidates, candidate_embeddings, strict=False):
            if not vector:
                continue
            scored.append((self._cosine_similarity(query_vector, vector), candidate))

        scored.sort(key=lambda item: item[0], reverse=True)
        return [item[1] for item in scored[:top_k]]

    async def _embed_texts(
        self,
        *,
        api_key: str,
        api_base: str,
        texts: list[str],
        model: str,
    ) -> list[list[float]]:
        """调用 OpenAI-compatible embeddings 接口。

        当前对齐的是你们实际在用的网关配置：
        - `POST {api_base}/embeddings`
        - `model=text-embedding-v4`
        - `encoding_format=float`
        """

        all_vectors: list[list[float]] = []
        endpoint = self._normalize_embeddings_endpoint(api_base)
        for start in range(0, len(texts), 10):
            batch = texts[start:start + 10]
            payload = await self._post_embeddings(
                endpoint=endpoint,
                api_key=api_key,
                model=model,
                texts=batch,
            )
            embeddings = payload.get("data") or []
            embeddings = sorted(embeddings, key=lambda item: item.get("index", 0))
            all_vectors.extend([list(item.get("embedding") or []) for item in embeddings])
        return all_vectors

    async def _post_embeddings(
        self,
        *,
        endpoint: str,
        api_key: str,
        model: str,
        texts: list[str],
    ) -> dict[str, Any]:
        return await asyncio.to_thread(
            self._post_embeddings_sync,
            endpoint=endpoint,
            api_key=api_key,
            model=model,
            texts=texts,
        )

    def _post_embeddings_sync(
        self,
        *,
        endpoint: str,
        api_key: str,
        model: str,
        texts: list[str],
    ) -> dict[str, Any]:
        body = json.dumps(
            {
                "model": model,
                "input": texts if len(texts) > 1 else texts[0],
                "encoding_format": "float",
            }
        ).encode("utf-8")
        req = request.Request(
            endpoint,
            data=body,
            headers={
                "Authorization": f"Bearer {api_key}",
                "Content-Type": "application/json",
            },
            method="POST",
        )
        with request.urlopen(req, timeout=self.timeout_seconds) as response:
            return json.loads(response.read().decode("utf-8"))

    @staticmethod
    def _candidate_text(candidate: dict[str, str]) -> str:
        name = (candidate.get("name") or "").strip()
        description = (candidate.get("description") or "").strip()
        return f"{name}\n{description}".strip()

    @staticmethod
    def _normalize_embeddings_endpoint(api_base: str) -> str:
        base = api_base.rstrip("/")
        if base.endswith("/embeddings"):
            return base
        if base.endswith("/v1"):
            return f"{base}/embeddings"
        return f"{base}/v1/embeddings"

    @staticmethod
    def _cosine_similarity(left: list[float], right: list[float]) -> float:
        if not left or not right or len(left) != len(right):
            return -1.0
        dot = sum(a * b for a, b in zip(left, right, strict=False))
        left_norm = math.sqrt(sum(a * a for a in left))
        right_norm = math.sqrt(sum(b * b for b in right))
        if left_norm == 0 or right_norm == 0:
            return -1.0
        return dot / (left_norm * right_norm)