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202 | class OpenVINORerank(BaseNodePostprocessor):
model_id_or_path: str = Field(description="Huggingface model id or local path.")
top_n: int = Field(description="Number of nodes to return sorted by score.")
keep_retrieval_score: bool = Field(
default=False,
description="Whether to keep the retrieval score in metadata.",
)
_model: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
def __init__(
self,
top_n: int = 3,
model_id_or_path: str = "BAAI/bge-reranker-large",
device: Optional[str] = "auto",
model_kwargs: Dict[str, Any] = {},
keep_retrieval_score: Optional[bool] = False,
):
device = infer_torch_device() if device is None else device
super().__init__(
top_n=top_n,
model_id_or_path=model_id_or_path,
device=device,
keep_retrieval_score=keep_retrieval_score,
)
try:
from huggingface_hub import HfApi
except ImportError as e:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with: "
"`pip install -U huggingface_hub`."
) from e
def require_model_export(
model_id: str, revision: Any = None, subfolder: Any = None
) -> bool:
model_dir = Path(model_id)
if subfolder is not None:
model_dir = model_dir / subfolder
if model_dir.is_dir():
return (
not (model_dir / "openvino_model.xml").exists()
or not (model_dir / "openvino_model.bin").exists()
)
hf_api = HfApi()
try:
model_info = hf_api.model_info(model_id, revision=revision or "main")
normalized_subfolder = (
None if subfolder is None else Path(subfolder).as_posix()
)
model_files = [
file.rfilename
for file in model_info.siblings
if normalized_subfolder is None
or file.rfilename.startswith(normalized_subfolder)
]
ov_model_path = (
"openvino_model.xml"
if subfolder is None
else f"{normalized_subfolder}/openvino_model.xml"
)
return (
ov_model_path not in model_files
or ov_model_path.replace(".xml", ".bin") not in model_files
)
except Exception:
return True
if require_model_export(model_id_or_path):
# use remote model
self._model = OVModelForSequenceClassification.from_pretrained(
model_id_or_path, export=True, device=device, **model_kwargs
)
else:
# use local model
self._model = OVModelForSequenceClassification.from_pretrained(
model_id_or_path, device=device, **model_kwargs
)
self._tokenizer = AutoTokenizer.from_pretrained(model_id_or_path)
@classmethod
def class_name(cls) -> str:
return "OpenVINORerank"
@staticmethod
def create_and_save_openvino_model(
model_name_or_path: str,
output_path: str,
export_kwargs: Optional[dict] = None,
) -> None:
export_kwargs = export_kwargs or {}
model = OVModelForSequenceClassification.from_pretrained(
model_name_or_path, export=True, compile=False, **export_kwargs
)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model.save_pretrained(output_path)
tokenizer.save_pretrained(output_path)
print(
f"Saved OpenVINO model to {output_path}. Use it with "
f"`embed_model = OpenVINORerank(model='{output_path}')`."
)
def _postprocess_nodes(
self,
nodes: List[NodeWithScore],
query_bundle: Optional[QueryBundle] = None,
) -> List[NodeWithScore]:
dispatcher.event(
ReRankStartEvent(
query=query_bundle,
nodes=nodes,
top_n=self.top_n,
model_name=self.model_id_or_path,
)
)
if query_bundle is None:
raise ValueError("Missing query bundle in extra info.")
if len(nodes) == 0:
return []
nodes_text_list = [
str(node.node.get_content(metadata_mode=MetadataMode.EMBED))
for node in nodes
]
with self.callback_manager.event(
CBEventType.RERANKING,
payload={
EventPayload.NODES: nodes,
EventPayload.MODEL_NAME: self.model_id_or_path,
EventPayload.QUERY_STR: query_bundle.query_str,
EventPayload.TOP_K: self.top_n,
},
) as event:
query_pairs = [[query_bundle.query_str, text] for text in nodes_text_list]
length = self._model.request.inputs[0].get_partial_shape()[1]
if length.is_dynamic:
input_tensors = self._tokenizer(
query_pairs, padding=True, truncation=True, return_tensors="pt"
)
else:
input_tensors = self._tokenizer(
query_pairs,
padding="max_length",
max_length=length.get_length(),
truncation=True,
return_tensors="pt",
)
outputs = self._model(**input_tensors, return_dict=True)
logits = outputs[0]
if logits.shape[1] == 1:
scores = 1 / (1 + np.exp(-logits.flatten()))
else:
exp_logits = np.exp(logits)
scores = exp_logits[:, 1] / np.sum(exp_logits, axis=1)
assert len(scores) == len(nodes)
for node, score in zip(nodes, scores):
if self.keep_retrieval_score:
# keep the retrieval score in metadata
node.node.metadata["retrieval_score"] = node.score
node.score = score
new_nodes = sorted(nodes, key=lambda x: -x.score if x.score else 0)[
: self.top_n
]
event.on_end(payload={EventPayload.NODES: new_nodes})
dispatcher.event(ReRankEndEvent(nodes=new_nodes[: self.top_n]))
return new_nodes
|
