"""
AWOOOI — Knowledge RAG Service (Phase 33, ADR-067)
==================================================
本地 RAG 知識庫：bge-m3 1024維向量 + pgvector

索引策略:
  - 初期 < 100 筆: 線性搜尋
  - 超過 100 筆: 執行 CREATE INDEX ivfflat (手動觸發)

向量模型: bge-m3 (GCP-A/GCP-B/111 Ollama lane, 1024維)
生成模型: qwen2.5:7b-instruct (Ollama 111)

leWOOOgo: Service 層只處理業務邏輯，DB 存取委派 rag_chunk_repository
架構審查 C1 修正: 2026-04-10 Claude Sonnet 4.6 Asia/Taipei
"""
from __future__ import annotations

from pathlib import Path

import httpx
import structlog

import src.repositories.rag_chunk_repository as rag_repo
from src.core.config import settings
from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint

logger = structlog.get_logger(__name__)

_EMBED_MODEL = "bge-m3:latest"
_GEN_MODEL = "qwen2.5:7b-instruct"
_TOP_K = 5

_INDEX_SOURCES = [
    Path("docs/runbooks"),
    Path("docs/adr"),
    Path("docs"),
    Path(".agents/skills"),
]


class KnowledgeRAGService:
    """RAG 知識庫服務 — leWOOOgo 合規: DB 存取全部委派 rag_chunk_repository"""

    def __init__(self) -> None:
        self._http: httpx.AsyncClient | None = None

    async def _get_http(self) -> httpx.AsyncClient:
        if self._http is None or self._http.is_closed:
            self._http = httpx.AsyncClient(timeout=httpx.Timeout(60.0, connect=10.0))
        return self._http

    # ------------------------------------------------------------------
    # 公開 API
    # ------------------------------------------------------------------

    async def query(self, question: str, top_k: int = _TOP_K) -> str:
        """RAG 查詢：embedding → pgvector knn → 生成回答"""
        embedding = await self._embed(question)
        if embedding is None:
            return "⚠️ 無法生成向量，RAG 查詢失敗"

        chunks = await rag_repo.search_chunks(embedding, top_k)
        if not chunks:
            return "📭 知識庫尚無相關資料，請先執行索引建立"

        context = "\n\n---\n\n".join(
            f"[{c.get('source','?')}] {c.get('title','')}\n{c.get('chunk_text','')}"
            for c in chunks
        )
        return await self._generate_answer(question, context)

    async def index_document(
        self,
        source: str,
        source_id: str,
        title: str,
        text: str,
        metadata: dict | None = None,
    ) -> bool:
        """
        將文件向量化並儲存到 pgvector
        自動分段 (每段 500 字, overlap 100)，索引前先刪舊版本去重
        """
        await rag_repo.delete_by_source_id(source_id)
        chunks = self._chunk_text(text, chunk_size=500, overlap=100)
        success = 0
        for chunk in chunks:
            emb = await self._embed(chunk)
            if emb:
                ok = await rag_repo.insert_chunk(source, source_id, title, chunk, emb, metadata or {})
                if ok:
                    success += 1
        logger.info("rag_indexed", source=source, source_id=source_id, chunks=success)
        return success > 0

    async def index_all_sources(self) -> int:
        """
        掃描所有知識來源並向量化（供 /rag/index 端點呼叫）
        來源: docs/runbooks/, docs/adr/, docs/, .agents/skills/
        """
        total = 0
        for source_dir in _INDEX_SOURCES:
            if not source_dir.exists():
                continue
            for md_file in source_dir.rglob("*.md"):
                try:
                    text = md_file.read_text(encoding="utf-8", errors="ignore")
                    ok = await self.index_document(
                        source=source_dir.parts[-1],
                        source_id=str(md_file),
                        title=md_file.stem,
                        text=text,
                    )
                    if ok:
                        total += 1
                    logger.debug("rag_source_indexed", file=str(md_file))
                except Exception as e:
                    logger.warning("rag_source_index_failed", file=str(md_file), error=str(e))
        logger.info("rag_index_all_complete", total_docs=total)
        return total

    async def get_stats(self) -> dict:
        """RAG 知識庫統計"""
        return await rag_repo.get_stats()

    # ------------------------------------------------------------------
    # 向量化 + 生成
    # ------------------------------------------------------------------

    async def _embed(self, text: str) -> list[float] | None:
        try:
            http = await self._get_http()
            resp = await http.post(
                f"{resolve_ollama_endpoint('embedding')}/api/embeddings",
                json={
                    "model": getattr(settings, "OLLAMA_EMBEDDING_MODEL", _EMBED_MODEL),
                    "prompt": text,
                },
            )
            if resp.status_code == 200:
                return resp.json().get("embedding")
        except Exception as e:
            logger.warning("rag_embed_failed", error=str(e))
        return None

    async def _generate_answer(self, question: str, context: str) -> str:
        prompt = (
            "你是 AWOOOI AIOps 知識庫助手，請用繁體中文根據以下資料回答問題。\n"
            "如果資料不足以回答，明確說「資料不足」，不要猜測。\n\n"
            f"=== 相關資料 ===\n{context[:6000]}\n\n"
            f"=== 問題 ===\n{question}"
        )
        try:
            http = await self._get_http()
            resp = await http.post(
                f"{resolve_ollama_endpoint('rag')}/api/generate",
                json={
                    "model": _GEN_MODEL,
                    "prompt": prompt,
                    "stream": False,
                    "options": {"num_predict": 512, "temperature": 0.2},
                },
                timeout=httpx.Timeout(90.0, connect=10.0),
            )
            if resp.status_code == 200:
                return resp.json().get("response", "").strip()
        except Exception as e:
            logger.error("rag_generate_failed", error=str(e))
        return "⚠️ RAG 生成失敗，請稍後再試"

    # ------------------------------------------------------------------
    # 工具
    # ------------------------------------------------------------------

    @staticmethod
    def _chunk_text(text: str, chunk_size: int = 500, overlap: int = 100) -> list[str]:
        """簡單字元分段，帶 overlap"""
        chunks = []
        start = 0
        while start < len(text):
            end = start + chunk_size
            chunks.append(text[start:end])
            start += chunk_size - overlap
        return [c for c in chunks if c.strip()]

    async def close(self) -> None:
        if self._http and not self._http.is_closed:
            await self._http.aclose()


_instance: KnowledgeRAGService | None = None


def get_knowledge_rag_service() -> KnowledgeRAGService:
    global _instance
    if _instance is None:
        _instance = KnowledgeRAGService()
    return _instance


def set_knowledge_rag_service(svc: KnowledgeRAGService) -> None:
    global _instance
    _instance = svc