d31c7f65
 tangwang
补充云服务reranker
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"""
DashScope cloud reranker backend (OpenAI-compatible reranks API).
Reference:
- https://dashscope.aliyuncs.com/compatible-api/v1/reranks
- Use region-specific domains when needed:
- China: https://dashscope.aliyuncs.com
- Singapore: https://dashscope-intl.aliyuncs.com
- US: https://dashscope-us.aliyuncs.com
"""
from __future__ import annotations
import json
import logging
import math
import os
import time
from typing import Any, Dict, List, Tuple
from urllib import error as urllib_error
from urllib import request as urllib_request
from reranker.backends.batching_utils import deduplicate_with_positions
logger = logging.getLogger("reranker.backends.dashscope_rerank")
class DashScopeRerankBackend:
"""
DashScope cloud reranker backend.
Config from services.rerank.backends.dashscope_rerank:
- model_name: str, default "qwen3-rerank"
- endpoint: str, default "https://dashscope.aliyuncs.com/compatible-api/v1/reranks"
- api_key: optional str (or env DASHSCOPE_API_KEY)
- timeout_sec: float, default 15.0
- top_n_cap: int, optional cap; 0 means use all docs in request
- instruct: optional str
- max_retries: int, default 1
- retry_backoff_sec: float, default 0.2
Env overrides:
- DASHSCOPE_API_KEY
- RERANK_DASHSCOPE_ENDPOINT
- RERANK_DASHSCOPE_MODEL
- RERANK_DASHSCOPE_TIMEOUT_SEC
- RERANK_DASHSCOPE_TOP_N_CAP
"""
def __init__(self, config: Dict[str, Any]) -> None:
self._config = config or {}
self._model_name = str(
os.getenv("RERANK_DASHSCOPE_MODEL")
or self._config.get("model_name")
or "qwen3-rerank"
)
self._endpoint = str(
os.getenv("RERANK_DASHSCOPE_ENDPOINT")
or self._config.get("endpoint")
or "https://dashscope.aliyuncs.com/compatible-api/v1/reranks"
).strip()
self._api_key = str(
os.getenv("DASHSCOPE_API_KEY")
or self._config.get("api_key")
or ""
).strip().strip('"').strip("'")
self._timeout_sec = float(
os.getenv("RERANK_DASHSCOPE_TIMEOUT_SEC")
or self._config.get("timeout_sec")
or 15.0
)
self._top_n_cap = int(
os.getenv("RERANK_DASHSCOPE_TOP_N_CAP")
or self._config.get("top_n_cap")
or 0
)
self._instruct = str(self._config.get("instruct") or "").strip()
self._max_retries = int(self._config.get("max_retries", 1))
self._retry_backoff_sec = float(self._config.get("retry_backoff_sec", 0.2))
if not self._endpoint:
raise ValueError("dashscope_rerank endpoint is required")
if not self._api_key:
raise ValueError(
"dashscope_rerank api_key is required (set services.rerank.backends.dashscope_rerank.api_key "
"or env DASHSCOPE_API_KEY)"
)
if self._timeout_sec <= 0:
raise ValueError(f"dashscope_rerank timeout_sec must be > 0, got {self._timeout_sec}")
if self._top_n_cap < 0:
raise ValueError(f"dashscope_rerank top_n_cap must be >= 0, got {self._top_n_cap}")
if self._max_retries <= 0:
raise ValueError(f"dashscope_rerank max_retries must be > 0, got {self._max_retries}")
if self._retry_backoff_sec < 0:
raise ValueError(
f"dashscope_rerank retry_backoff_sec must be >= 0, got {self._retry_backoff_sec}"
)
logger.info(
"DashScope reranker ready | endpoint=%s model=%s timeout_sec=%s top_n_cap=%s",
self._endpoint,
self._model_name,
self._timeout_sec,
self._top_n_cap,
)
def _http_post_json(self, payload: Dict[str, Any]) -> Dict[str, Any]:
body = json.dumps(payload, ensure_ascii=False).encode("utf-8")
req = urllib_request.Request(
url=self._endpoint,
method="POST",
data=body,
headers={
"Authorization": f"Bearer {self._api_key}",
"Content-Type": "application/json",
},
)
with urllib_request.urlopen(req, timeout=self._timeout_sec) as resp:
raw = resp.read().decode("utf-8", errors="replace")
try:
data = json.loads(raw)
except json.JSONDecodeError as exc:
raise RuntimeError(f"DashScope response is not valid JSON: {raw[:512]}") from exc
if not isinstance(data, dict):
raise RuntimeError(f"DashScope response must be JSON object, got: {type(data).__name__}")
return data
def _post_rerank(self, query: str, docs: List[str], top_n: int) -> Dict[str, Any]:
payload: Dict[str, Any] = {
"model": self._model_name,
"query": query,
"documents": docs,
"top_n": top_n,
}
if self._instruct:
payload["instruct"] = self._instruct
last_exc: Exception | None = None
for attempt in range(1, self._max_retries + 1):
try:
return self._http_post_json(payload)
except urllib_error.HTTPError as exc:
body = ""
try:
body = exc.read().decode("utf-8", errors="replace")
except Exception:
body = ""
last_exc = RuntimeError(
f"DashScope rerank HTTP {exc.code} (attempt {attempt}/{self._max_retries}): {body[:512]}"
)
except urllib_error.URLError as exc:
last_exc = RuntimeError(
f"DashScope rerank network error (attempt {attempt}/{self._max_retries}): {exc}"
)
except Exception as exc: # pragma: no cover - defensive
last_exc = RuntimeError(
f"DashScope rerank unexpected error (attempt {attempt}/{self._max_retries}): {exc}"
)
if attempt < self._max_retries and self._retry_backoff_sec > 0:
time.sleep(self._retry_backoff_sec * attempt)
raise RuntimeError(str(last_exc) if last_exc else "DashScope rerank failed with unknown error")
@staticmethod
def _extract_results(data: Dict[str, Any]) -> List[Dict[str, Any]]:
# Compatible API style: {"results":[...]}
results = data.get("results")
if isinstance(results, list):
return [x for x in results if isinstance(x, dict)]
# Native style fallback: {"output":{"results":[...]}}
output = data.get("output")
if isinstance(output, dict):
output_results = output.get("results")
if isinstance(output_results, list):
return [x for x in output_results if isinstance(x, dict)]
return []
@staticmethod
def _coerce_score(raw_score: Any, normalize: bool) -> float:
try:
score = float(raw_score)
except (TypeError, ValueError):
return 0.0
if not normalize:
return score
# DashScope relevance_score is typically already in [0,1]; keep it.
if 0.0 <= score <= 1.0:
return score
# Fallback when provider returns logits/raw scores.
if score > 60:
return 1.0
if score < -60:
return 0.0
return 1.0 / (1.0 + math.exp(-score))
def score_with_meta_topn(
self,
query: str,
docs: List[str],
normalize: bool = True,
top_n: int | None = None,
) -> Tuple[List[float], Dict[str, Any]]:
start_ts = time.time()
total_docs = len(docs) if docs else 0
output_scores: List[float] = [0.0] * total_docs
query = "" if query is None else str(query).strip()
indexed: List[Tuple[int, str]] = []
for i, doc in enumerate(docs or []):
if doc is None:
continue
text = str(doc).strip()
if not text:
continue
indexed.append((i, text))
if not query or not indexed:
elapsed_ms = (time.time() - start_ts) * 1000.0
return output_scores, {
"input_docs": total_docs,
"usable_docs": len(indexed),
"unique_docs": 0,
"dedup_ratio": 0.0,
"elapsed_ms": round(elapsed_ms, 3),
"model": self._model_name,
"backend": "dashscope_rerank",
"normalize": normalize,
"top_n": 0,
}
indexed_texts = [text for _, text in indexed]
unique_texts, position_to_unique = deduplicate_with_positions(indexed_texts)
top_n_effective = len(unique_texts)
if top_n is not None and int(top_n) > 0:
top_n_effective = min(top_n_effective, int(top_n))
if self._top_n_cap > 0:
top_n_effective = min(top_n_effective, self._top_n_cap)
response = self._post_rerank(query=query, docs=unique_texts, top_n=top_n_effective)
results = self._extract_results(response)
unique_scores: List[float] = [0.0] * len(unique_texts)
for rank, item in enumerate(results):
raw_idx = item.get("index", rank)
try:
idx = int(raw_idx)
except (TypeError, ValueError):
continue
if idx < 0 or idx >= len(unique_scores):
continue
raw_score = item.get("relevance_score", item.get("score"))
unique_scores[idx] = self._coerce_score(raw_score, normalize=normalize)
for (orig_idx, _), unique_idx in zip(indexed, position_to_unique):
output_scores[orig_idx] = float(unique_scores[unique_idx])
elapsed_ms = (time.time() - start_ts) * 1000.0
dedup_ratio = 0.0
if indexed:
dedup_ratio = 1.0 - (len(unique_texts) / float(len(indexed)))
return output_scores, {
"input_docs": total_docs,
"usable_docs": len(indexed),
"unique_docs": len(unique_texts),
"dedup_ratio": round(dedup_ratio, 4),
"elapsed_ms": round(elapsed_ms, 3),
"model": self._model_name,
"backend": "dashscope_rerank",
"normalize": normalize,
"top_n": top_n_effective,
"requested_top_n": int(top_n) if top_n is not None else None,
"response_results": len(results),
"endpoint": self._endpoint,
}
def score_with_meta(
self,
query: str,
docs: List[str],
normalize: bool = True,
) -> Tuple[List[float], Dict[str, Any]]:
return self.score_with_meta_topn(query=query, docs=docs, normalize=normalize, top_n=None)
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