from pathlib import Path from typing import Any, Dict, List from langchain_core.embeddings import Embeddings from langchain_core.pydantic_v1 import BaseModel, Extra, 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：" 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 ValueError( "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 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(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]) 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] features = self.tokenizer( sentences_batch, padding=True, 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: """Configuration for this pydantic object.""" extra = Extra.forbid 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() 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] class OpenVINOBgeEmbeddings(OpenVINOEmbeddings): """OpenVNO BGE embedding models. Bge Example: .. code-block:: python from langchain_community.embeddings import OpenVINOBgeEmbeddings model_name_or_path = "BAAI/bge-large-en" 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 ) """ 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.""" 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 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() 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()