import importlib.util
import os
from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict
[docs]
class QuantizedBgeEmbeddings(BaseModel, Embeddings):
"""Leverage Itrex runtime to unlock the performance of compressed NLP models.
Please ensure that you have installed intel-extension-for-transformers.
Input:
model_name: str = Model name.
max_seq_len: int = The maximum sequence length for tokenization. (default 512)
pooling_strategy: str =
"mean" or "cls", pooling strategy for the final layer. (default "mean")
query_instruction: Optional[str] =
An instruction to add to the query before embedding. (default None)
document_instruction: Optional[str] =
An instruction to add to each document before embedding. (default None)
padding: Optional[bool] =
Whether to add padding during tokenization or not. (default True)
model_kwargs: Optional[Dict] =
Parameters to add to the model during initialization. (default {})
encode_kwargs: Optional[Dict] =
Parameters to add during the embedding forward pass. (default {})
onnx_file_name: Optional[str] =
File name of onnx optimized model which is exported by itrex.
(default "int8-model.onnx")
Example:
.. code-block:: python
from langchain_community.embeddings import QuantizedBgeEmbeddings
model_name = "Intel/bge-small-en-v1.5-sts-int8-static-inc"
encode_kwargs = {'normalize_embeddings': True}
hf = QuantizedBgeEmbeddings(
model_name,
encode_kwargs=encode_kwargs,
query_instruction="Represent this sentence for searching relevant passages: "
)
""" # noqa: E501
def __init__(
self,
model_name: str,
*,
max_seq_len: int = 512,
pooling_strategy: str = "mean", # "mean" or "cls"
query_instruction: Optional[str] = None,
document_instruction: Optional[str] = None,
padding: bool = True,
model_kwargs: Optional[Dict] = None,
encode_kwargs: Optional[Dict] = None,
onnx_file_name: Optional[str] = "int8-model.onnx",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
# check sentence_transformers python package
if importlib.util.find_spec("intel_extension_for_transformers") is None:
raise ImportError(
"Could not import intel_extension_for_transformers python package. "
"Please install it with "
"`pip install -U intel-extension-for-transformers`."
)
# check torch python package
if importlib.util.find_spec("torch") is None:
raise ImportError(
"Could not import torch python package. "
"Please install it with `pip install -U torch`."
)
# check onnx python package
if importlib.util.find_spec("onnx") is None:
raise ImportError(
"Could not import onnx python package. "
"Please install it with `pip install -U onnx`."
)
self.model_name_or_path = model_name
self.max_seq_len = max_seq_len
self.pooling = pooling_strategy
self.padding = padding
self.encode_kwargs = encode_kwargs or {}
self.model_kwargs = model_kwargs or {}
self.normalize = self.encode_kwargs.get("normalize_embeddings", False)
self.batch_size = self.encode_kwargs.get("batch_size", 32)
self.query_instruction = query_instruction
self.document_instruction = document_instruction
self.onnx_file_name = onnx_file_name
self.load_model()
[docs]
def load_model(self) -> None:
from huggingface_hub import hf_hub_download
from intel_extension_for_transformers.transformers import AutoModel
from transformers import AutoConfig, AutoTokenizer
self.hidden_size = AutoConfig.from_pretrained(
self.model_name_or_path
).hidden_size
self.transformer_tokenizer = AutoTokenizer.from_pretrained(
self.model_name_or_path,
)
onnx_model_path = os.path.join(self.model_name_or_path, self.onnx_file_name) # type: ignore[arg-type]
if not os.path.exists(onnx_model_path):
onnx_model_path = hf_hub_download(
self.model_name_or_path, filename=self.onnx_file_name
)
self.transformer_model = AutoModel.from_pretrained(
onnx_model_path, use_embedding_runtime=True
)
model_config = ConfigDict(
extra="allow",
protected_namespaces=(),
)
def _embed(self, inputs: Any) -> Any:
import torch
engine_input = [value for value in inputs.values()]
outputs = self.transformer_model.generate(engine_input)
if "last_hidden_state:0" in outputs:
last_hidden_state = outputs["last_hidden_state:0"]
else:
last_hidden_state = [out for out in outputs.values()][0]
last_hidden_state = torch.tensor(last_hidden_state).reshape(
inputs["input_ids"].shape[0], inputs["input_ids"].shape[1], self.hidden_size
)
if self.pooling == "mean":
emb = self._mean_pooling(last_hidden_state, inputs["attention_mask"])
elif self.pooling == "cls":
emb = self._cls_pooling(last_hidden_state)
else:
raise ValueError("pooling method no supported")
if self.normalize:
emb = torch.nn.functional.normalize(emb, p=2, dim=1)
return emb
@staticmethod
def _cls_pooling(last_hidden_state: Any) -> Any:
return last_hidden_state[:, 0]
@staticmethod
def _mean_pooling(last_hidden_state: Any, attention_mask: Any) -> Any:
try:
import torch
except ImportError as e:
raise ImportError(
"Unable to import torch, please install with `pip install -U torch`."
) from e
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()
)
sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return sum_embeddings / sum_mask
def _embed_text(self, texts: List[str]) -> List[List[float]]:
inputs = self.transformer_tokenizer(
texts,
max_length=self.max_seq_len,
truncation=True,
padding=self.padding,
return_tensors="pt",
)
return self._embed(inputs).tolist()
[docs]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of text documents using the Optimized Embedder model.
Input:
texts: List[str] = List of text documents to embed.
Output:
List[List[float]] = The embeddings of each text document.
"""
try:
import pandas as pd
except ImportError as e:
raise ImportError(
"Unable to import pandas, please install with `pip install -U pandas`."
) from e
docs = [
self.document_instruction + d if self.document_instruction else d
for d in texts
]
# group into batches
text_list_df = pd.DataFrame(docs, columns=["texts"]).reset_index()
# assign each example with its batch
text_list_df["batch_index"] = text_list_df["index"] // self.batch_size
# create groups
batches = list(text_list_df.groupby(["batch_index"])["texts"].apply(list))
vectors = []
for batch in batches:
vectors += self._embed_text(batch)
return vectors
[docs]
def embed_query(self, text: str) -> List[float]:
if self.query_instruction:
text = self.query_instruction + text
return self._embed_text([text])[0]
