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146 | class IpexLLMEmbedding(BaseEmbedding):
max_length: int = Field(
default=DEFAULT_HUGGINGFACE_LENGTH, description="Maximum length of input.", gt=0
)
normalize: bool = Field(default=True, description="Normalize embeddings or not.")
query_instruction: Optional[str] = Field(
description="Instruction to prepend to query text."
)
text_instruction: Optional[str] = Field(
description="Instruction to prepend to text."
)
cache_folder: Optional[str] = Field(
description="Cache folder for Hugging Face files."
)
_model: Any = PrivateAttr()
_device: str = PrivateAttr()
def __init__(
self,
model_name: str = DEFAULT_HUGGINGFACE_EMBEDDING_MODEL,
max_length: Optional[int] = None,
query_instruction: Optional[str] = None,
text_instruction: Optional[str] = None,
normalize: bool = True,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
cache_folder: Optional[str] = None,
trust_remote_code: bool = False,
device: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
**model_kwargs,
):
self._device = device or infer_torch_device()
cache_folder = cache_folder or get_cache_dir()
if model_name is None:
raise ValueError("The `model_name` argument must be provided.")
if not is_listed_model(model_name, BGE_MODELS):
bge_model_list_str = ", ".join(BGE_MODELS)
logger.warning(
"IpexLLMEmbedding currently only provides optimization for "
f"Hugging Face BGE models, which are: {bge_model_list_str}"
)
self._model = SentenceTransformer(
model_name,
device=self._device,
cache_folder=cache_folder,
trust_remote_code=trust_remote_code,
prompts={
"query": query_instruction
or get_query_instruct_for_model_name(model_name),
"text": text_instruction
or get_text_instruct_for_model_name(model_name),
},
**model_kwargs,
)
if self._device == "cpu":
self._model = _optimize_pre(self._model)
self._model = _optimize_post(self._model)
# TODO: optimize using ipex-llm optimize_model
elif self._device == "xpu":
self._model = _optimize_pre(self._model)
self._model = _optimize_post(self._model)
self._model = self._model.half().to(self._device)
if max_length:
self._model.max_seq_length = max_length
else:
max_length = self._model.max_seq_length
super().__init__(
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
model_name=model_name,
max_length=max_length,
normalize=normalize,
query_instruction=query_instruction,
text_instruction=text_instruction,
)
@classmethod
def class_name(cls) -> str:
return "IpexLLMEmbedding"
def _embed(
self,
sentences: List[str],
prompt_name: Optional[str] = None,
) -> List[List[float]]:
"""Embed sentences."""
return self._model.encode(
sentences,
batch_size=self.embed_batch_size,
prompt_name=prompt_name,
normalize_embeddings=self.normalize,
).tolist()
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._embed(query, prompt_name="query")
async def _aget_query_embedding(self, query: str) -> List[float]:
"""Get query embedding async."""
return self._get_query_embedding(query)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Get text embedding async."""
return self._get_text_embedding(text)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
return self._embed(text, prompt_name="text")
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
return self._embed(texts, prompt_name="text")
|