971a0851
 tangwang
补充reranker-jina，探...
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"""
Jina reranker v3 backend using the model card's recommended AutoModel API.
Reference: https://huggingface.co/jinaai/jina-reranker-v3
Requires: transformers, torch.
"""
from __future__ import annotations
import logging
import threading
import time
from typing import Any, Dict, List, Tuple
import torch
from transformers import AutoModel
logger = logging.getLogger("reranker.backends.jina_reranker_v3")
class JinaRerankerV3Backend:
"""
jina-reranker-v3 backend using `AutoModel(..., trust_remote_code=True)`.
The official model card recommends calling:
model = AutoModel.from_pretrained(..., trust_remote_code=True)
model.rerank(query, documents, top_n=...)
Config from services.rerank.backends.jina_reranker_v3.
"""
def __init__(self, config: Dict[str, Any]) -> None:
self._config = config or {}
self._model_name = str(
self._config.get("model_name") or "jinaai/jina-reranker-v3"
)
self._cache_dir = self._config.get("cache_dir") or "./model_cache"
self._dtype = str(self._config.get("dtype") or "auto")
self._device = self._config.get("device")
self._batch_size = max(1, int(self._config.get("batch_size", 64)))
self._return_embeddings = bool(self._config.get("return_embeddings", False))
self._trust_remote_code = bool(self._config.get("trust_remote_code", True))
self._lock = threading.Lock()
logger.info(
"[Jina_Reranker_V3] Loading model %s (dtype=%s, device=%s, batch=%s)",
self._model_name,
self._dtype,
self._device,
self._batch_size,
)
load_kwargs: Dict[str, Any] = {
"trust_remote_code": self._trust_remote_code,
"cache_dir": self._cache_dir,
"dtype": self._dtype,
}
self._model = AutoModel.from_pretrained(self._model_name, **load_kwargs)
self._model.eval()
if self._device is not None:
self._model = self._model.to(self._device)
elif torch.cuda.is_available():
self._device = "cuda"
self._model = self._model.to(self._device)
else:
self._device = "cpu"
logger.info(
"[Jina_Reranker_V3] Model ready | model=%s device=%s",
self._model_name,
self._device,
)
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, docs, normalize=normalize, top_n=None)
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": "jina_reranker_v3",
"normalize": normalize,
"normalize_note": "jina_reranker_v3 returns model relevance scores directly",
}
unique_texts: List[str] = []
unique_first_indices: List[int] = []
text_to_unique_idx: Dict[str, int] = {}
for orig_idx, text in indexed:
unique_idx = text_to_unique_idx.get(text)
if unique_idx is None:
unique_idx = len(unique_texts)
text_to_unique_idx[text] = unique_idx
unique_texts.append(text)
unique_first_indices.append(orig_idx)
effective_top_n = min(top_n, len(unique_texts)) if top_n is not None else None
unique_scores = self._rerank_unique(
query=query,
docs=unique_texts,
top_n=effective_top_n,
)
for orig_idx, text in indexed:
unique_idx = text_to_unique_idx[text]
output_scores[orig_idx] = float(unique_scores[unique_idx])
elapsed_ms = (time.time() - start_ts) * 1000.0
dedup_ratio = 1.0 - (len(unique_texts) / float(len(indexed))) if indexed else 0.0
meta = {
"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": "jina_reranker_v3",
"device": self._device,
"dtype": self._dtype,
"batch_size": self._batch_size,
"normalize": normalize,
"normalize_note": "jina_reranker_v3 returns model relevance scores directly",
}
if effective_top_n is not None:
meta["top_n"] = effective_top_n
if len(unique_texts) > self._batch_size:
meta["top_n_note"] = (
"Applied as a request hint only; full scores were computed because "
"global top_n across multiple local batches would be lossy."
)
return output_scores, meta
def _rerank_unique(
self,
query: str,
docs: List[str],
top_n: int | None,
) -> List[float]:
if not docs:
return []
unique_scores: List[float] = [0.0] * len(docs)
with self._lock:
for start in range(0, len(docs), self._batch_size):
batch_docs = docs[start : start + self._batch_size]
batch_top_n = None
if top_n is not None and len(docs) <= self._batch_size:
batch_top_n = min(top_n, len(batch_docs))
results = self._model.rerank(
query,
batch_docs,
top_n=batch_top_n,
return_embeddings=self._return_embeddings,
)
for item in results:
batch_index = int(item["index"])
unique_scores[start + batch_index] = float(item["relevance_score"])
return unique_scores
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