from pathlib import Path
from typing import Any, Dict, List
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field
DEFAULT_QUERY_INSTRUCTION = (
"Represent the question for retrieving supporting documents: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
"Represent this question for searching relevant passages: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："
[docs]class OpenVINOEmbeddings(BaseModel, Embeddings):
"""OpenVINO embedding models.
Example:
.. code-block:: python
from langchain_community.embeddings import OpenVINOEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'CPU'}
encode_kwargs = {'normalize_embeddings': True}
ov = OpenVINOEmbeddings(
model_name_or_path=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
ov_model: Any
"""OpenVINO model object."""
tokenizer: Any
"""Tokenizer for embedding model."""
model_name_or_path: str
"""HuggingFace model id."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the model."""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method of the model."""
show_progress: bool = False
"""Whether to show a progress bar."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
from optimum.intel.openvino import OVModelForFeatureExtraction
except ImportError as e:
raise ImportError(
"Could not import optimum-intel python package. "
"Please install it with: "
"pip install -U 'optimum[openvino,nncf]'"
) from e
try:
from huggingface_hub import HfApi
except ImportError as e:
raise ImportError(
"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(self.model_name_or_path):
# use remote model
self.ov_model = OVModelForFeatureExtraction.from_pretrained(
self.model_name_or_path, export=True, **self.model_kwargs
)
else:
# use local model
self.ov_model = OVModelForFeatureExtraction.from_pretrained(
self.model_name_or_path, **self.model_kwargs
)
try:
from transformers import AutoTokenizer
except ImportError as e:
raise ImportError(
"Unable to import transformers, please install with "
"`pip install -U transformers`."
) from e
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
def _text_length(self, text: Any) -> int:
"""
Help function to get the length for the input text. Text can be either
a list of ints (which means a single text as input), or a tuple of list of ints
(representing several text inputs to the model).
"""
if isinstance(text, dict): # {key: value} case
return len(next(iter(text.values())))
elif not hasattr(text, "__len__"): # Object has no len() method
return 1
# Empty string or list of ints
elif len(text) == 0 or isinstance(text[0], int):
return len(text)
else:
# Sum of length of individual strings
return sum([len(t) for t in text])
[docs] def encode(
self,
sentences: Any,
batch_size: int = 4,
show_progress_bar: bool = False,
convert_to_numpy: bool = True,
convert_to_tensor: bool = False,
mean_pooling: bool = False,
normalize_embeddings: bool = True,
) -> Any:
"""
Computes sentence embeddings.
:param sentences: the sentences to embed.
:param batch_size: the batch size used for the computation.
:param show_progress_bar: Whether to output a progress bar.
:param convert_to_numpy: Whether the output should be a list of numpy vectors.
:param convert_to_tensor: Whether the output should be one large tensor.
:param mean_pooling: Whether to pool returned vectors.
:param normalize_embeddings: Whether to normalize returned vectors.
:return: By default, a 2d numpy array with shape [num_inputs, output_dimension].
"""
try:
import numpy as np
except ImportError as e:
raise ImportError(
"Unable to import numpy, please install with " "`pip install -U numpy`."
) from e
try:
from tqdm import trange
except ImportError as e:
raise ImportError(
"Unable to import tqdm, please install with " "`pip install -U tqdm`."
) from e
try:
import torch
except ImportError as e:
raise ImportError(
"Unable to import torch, please install with " "`pip install -U torch`."
) from e
def run_mean_pooling(model_output: Any, attention_mask: Any) -> Any:
token_embeddings = model_output[
0
] # First element of model_output contains all token embeddings
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
if convert_to_tensor:
convert_to_numpy = False
input_was_string = False
if isinstance(sentences, str) or not hasattr(
sentences, "__len__"
): # Cast an individual sentence to a list with length 1
sentences = [sentences]
input_was_string = True
all_embeddings: Any = []
length_sorted_idx = np.argsort([-self._text_length(sen) for sen in sentences])
sentences_sorted = [sentences[idx] for idx in length_sorted_idx]
for start_index in trange(
0, len(sentences), batch_size, desc="Batches", disable=not show_progress_bar
):
sentences_batch = sentences_sorted[start_index : start_index + batch_size]
length = self.ov_model.request.inputs[0].get_partial_shape()[1]
if length.is_dynamic:
features = self.tokenizer(
sentences_batch, padding=True, truncation=True, return_tensors="pt"
)
else:
features = self.tokenizer(
sentences_batch,
padding="max_length",
max_length=length.get_length(),
truncation=True,
return_tensors="pt",
)
out_features = self.ov_model(**features)
if mean_pooling:
embeddings = run_mean_pooling(out_features, features["attention_mask"])
else:
embeddings = out_features[0][:, 0]
if normalize_embeddings:
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
# fixes for #522 and #487 to avoid oom problems on gpu with large datasets
if convert_to_numpy:
embeddings = embeddings.cpu()
all_embeddings.extend(embeddings)
all_embeddings = [all_embeddings[idx] for idx in np.argsort(length_sorted_idx)]
if convert_to_tensor:
if len(all_embeddings):
all_embeddings = torch.stack(all_embeddings)
else:
all_embeddings = torch.Tensor()
elif convert_to_numpy:
all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])
if input_was_string:
all_embeddings = all_embeddings[0]
return all_embeddings
class Config:
extra = "forbid"
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.encode(
texts, show_progress_bar=self.show_progress, **self.encode_kwargs
)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self.embed_documents([text])[0]
[docs] def save_model(
self,
model_path: str,
) -> bool:
self.ov_model.half()
self.ov_model.save_pretrained(model_path)
self.tokenizer.save_pretrained(model_path)
return True
[docs]class OpenVINOBgeEmbeddings(OpenVINOEmbeddings):
"""OpenVNO BGE embedding models.
Bge Example:
.. code-block:: python
from langchain_community.embeddings import OpenVINOBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'CPU'}
encode_kwargs = {'normalize_embeddings': True}
ov = OpenVINOBgeEmbeddings(
model_name_or_path=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
"""Instruction to use for embedding query."""
embed_instruction: str = ""
"""Instruction to use for embedding document."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
if "-zh" in self.model_name_or_path:
self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = [self.embed_instruction + t.replace("\n", " ") for t in texts]
embeddings = self.encode(texts, **self.encode_kwargs)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embedding = self.encode(self.query_instruction + text, **self.encode_kwargs)
return embedding.tolist()
