"""

  Reranker service - unified /rerank API backed by pluggable backends
  (BGE, Qwen3-vLLM, Qwen3-Transformers, DashScope cloud rerank).

  
  POST /rerank

  Request: { "query": "...", "docs": ["doc1", "doc2", ...], "normalize": optional bool }
  Response: { "scores": [float], "meta": {...} }
  

  Backend selected via config: services.rerank.instances.<name>.backend

  (bge | jina_reranker_v3 | qwen3_vllm | qwen3_vllm_score | qwen3_transformers | qwen3_transformers_packed | qwen3_gguf | qwen3_gguf_06b | dashscope_rerank), env RERANK_BACKEND.

  """
  
  import logging

  import os

  import time
  from typing import Any, Dict, List, Optional
  
  from fastapi import FastAPI, HTTPException
  from pydantic import BaseModel, Field
  

  from config.services_config import get_rerank_backend_config
  from reranker.backends import RerankBackendProtocol, get_rerank_backend

  from reranker.config import CONFIG
  
  logging.basicConfig(
      level=logging.INFO,
      format="%(asctime)s %(levelname)s %(name)s | %(message)s",
  )
  logger = logging.getLogger("reranker.service")
  

  app = FastAPI(title="saas-search Reranker Service", version="1.0.0")

  _reranker: Optional[RerankBackendProtocol] = None
  _backend_name: str = ""

  _LOG_DOC_PREVIEW_COUNT = max(1, int(os.getenv("RERANK_LOG_DOC_PREVIEW_COUNT", "3")))
  _LOG_TEXT_PREVIEW_CHARS = max(32, int(os.getenv("RERANK_LOG_TEXT_PREVIEW_CHARS", "180")))
  
  
  def _compact_preview(text: str, max_chars: int) -> str:
      compact = " ".join((text or "").split())
      if len(compact) <= max_chars:
          return compact
      return compact[:max_chars] + "..."
  
  
  def _preview_docs(docs: List[str], max_items: int, max_chars: int) -> List[Dict[str, Any]]:
      previews: List[Dict[str, Any]] = []
      for idx, doc in enumerate(docs[:max_items]):
          previews.append(
              {
                  "idx": idx,
                  "len": len(doc),
                  "preview": _compact_preview(doc, max_chars),
              }
          )
      return previews

  
  
  class RerankRequest(BaseModel):
      query: str = Field(..., description="Search query")
      docs: List[str] = Field(..., description="Documents/passages to rerank")
      normalize: Optional[bool] = Field(
          default=CONFIG.NORMALIZE, description="Apply sigmoid normalization"
      )

      top_n: Optional[int] = Field(
          default=None,
          description="Optional top_n hint for backends that support partial ranking",
      )

  
  
  class RerankResponse(BaseModel):
      scores: List[float] = Field(..., description="Scores aligned to input docs order")
      meta: Dict[str, Any] = Field(default_factory=dict)
  
  
  @app.on_event("startup")
  def load_model() -> None:

      global _reranker, _backend_name

      logger.info("Starting reranker service | instance=%s port=%s", CONFIG.INSTANCE, CONFIG.PORT)

      try:

          backend_name, backend_cfg = get_rerank_backend_config(CONFIG.INSTANCE)

          _backend_name = backend_name
          _reranker = get_rerank_backend(backend_name, backend_cfg)
          model_info = getattr(_reranker, "_model_name", None) or backend_cfg.get("model_name", backend_name)

          logger.info(

              "Reranker ready | instance=%s backend=%s model=%s",
              CONFIG.INSTANCE,

              _backend_name,
              model_info,

          )
      except Exception as exc:
          logger.error("Failed to initialize reranker: %s", exc, exc_info=True)
          raise
  
  
  @app.get("/health")
  def health() -> Dict[str, Any]:

      model_info = ""
      if _reranker is not None:
          model_info = getattr(_reranker, "_model_name", None) or getattr(
              _reranker, "_config", {}
          ).get("model_name", _backend_name)

      payload: Dict[str, Any] = {

          "status": "ok" if _reranker is not None else "unavailable",

          "instance": CONFIG.INSTANCE,

          "model_loaded": _reranker is not None,

          "model": model_info,
          "backend": _backend_name,

      }

      if _reranker is not None:
          _fmt = getattr(_reranker, "_instruction_format", None)
          if _fmt is not None:
              payload["instruction_format"] = _fmt
      return payload

  
  
  @app.post("/rerank", response_model=RerankResponse)
  def rerank(request: RerankRequest) -> RerankResponse:
      if _reranker is None:
          raise HTTPException(status_code=503, detail="Reranker model not loaded")
  
      query = (request.query or "").strip()
      if not query:
          raise HTTPException(status_code=400, detail="query cannot be empty")
  
      if request.docs is None or len(request.docs) == 0:
          raise HTTPException(status_code=400, detail="docs cannot be empty")
  
      if len(request.docs) > CONFIG.MAX_DOCS:
          raise HTTPException(
              status_code=400,
              detail=f"Too many docs: {len(request.docs)} > {CONFIG.MAX_DOCS}",
          )

      if request.top_n is not None and int(request.top_n) <= 0:
          raise HTTPException(status_code=400, detail="top_n must be > 0")

  
      normalize = CONFIG.NORMALIZE if request.normalize is None else bool(request.normalize)

      top_n = int(request.top_n) if request.top_n is not None else None

  
      start_ts = time.time()
      logger.info(

          "Rerank request | docs=%d normalize=%s query_len=%d query=%r doc_preview=%s",

          len(request.docs),
          normalize,

          len(query),
          _compact_preview(query, _LOG_TEXT_PREVIEW_CHARS),
          _preview_docs(request.docs, _LOG_DOC_PREVIEW_COUNT, _LOG_TEXT_PREVIEW_CHARS),

      )

      if top_n is not None and hasattr(_reranker, "score_with_meta_topn"):
          scores, meta = getattr(_reranker, "score_with_meta_topn")(
              query,
              request.docs,
              normalize=normalize,
              top_n=top_n,
          )
      else:
          scores, meta = _reranker.score_with_meta(query, request.docs, normalize=normalize)

      meta = dict(meta)

      if top_n is not None:
          meta.setdefault("requested_top_n", top_n)

      meta.update({"service_elapsed_ms": round((time.time() - start_ts) * 1000.0, 3)})

      score_preview = [round(float(s), 6) for s in scores[:_LOG_DOC_PREVIEW_COUNT]]

      logger.info(

          "Rerank done | docs=%d unique=%s dedup=%s elapsed_ms=%s batches=%s batchsize=%s batch_concurrency=%s query=%r score_preview=%s",

          meta.get("input_docs"),
          meta.get("unique_docs"),
          meta.get("dedup_ratio"),
          meta.get("service_elapsed_ms"),

          meta.get("batches"),
          meta.get("batchsize"),
          meta.get("batch_concurrency"),

          _compact_preview(query, _LOG_TEXT_PREVIEW_CHARS),
          score_preview,

      )
  
      return RerankResponse(scores=scores, meta=meta)
  
  
  if __name__ == "__main__":
      import uvicorn
  
      uvicorn.run(
          "reranker.server:app",
          host=CONFIG.HOST,
          port=CONFIG.PORT,
          reload=False,
          log_level="info",
      )