Source code for langchain_community.embeddings.optimum_intel

from typing import Any, Dict, List, Optional

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra


[docs]class QuantizedBiEncoderEmbeddings(BaseModel, Embeddings): """Quantized bi-encoders embedding models. Please ensure that you have installed optimum-intel and ipex. 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 {}) Example: from langchain_community.embeddings import QuantizedBiEncoderEmbeddings model_name = "Intel/bge-small-en-v1.5-rag-int8-static" encode_kwargs = {'normalize_embeddings': True} hf = QuantizedBiEncoderEmbeddings( model_name, encode_kwargs=encode_kwargs, query_instruction="Represent this sentence for searching relevant passages: " ) """ 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, **kwargs: Any, ) -> None: super().__init__(**kwargs) 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.load_model()
[docs] def load_model(self) -> None: try: from transformers import AutoTokenizer except ImportError as e: raise ImportError( "Unable to import transformers, please install with " "`pip install -U transformers`." ) from e try: from optimum.intel import IPEXModel self.transformer_model = IPEXModel.from_pretrained( self.model_name_or_path, **self.model_kwargs ) except Exception as e: raise Exception( f""" Failed to load model {self.model_name_or_path}, due to the following error: {e} Please ensure that you have installed optimum-intel and ipex correctly,using: pip install optimum[neural-compressor] pip install intel_extension_for_pytorch For more information, please visit: * Install optimum-intel as shown here: https://github.com/huggingface/optimum-intel. * Install IPEX as shown here: https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=cpu&version=v2.2.0%2Bcpu. """ ) self.transformer_tokenizer = AutoTokenizer.from_pretrained( pretrained_model_name_or_path=self.model_name_or_path, ) self.transformer_model.eval()
class Config: """Configuration for this pydantic object.""" extra = Extra.allow def _embed(self, inputs: Any) -> Any: try: import torch except ImportError as e: raise ImportError( "Unable to import torch, please install with `pip install -U torch`." ) from e with torch.inference_mode(): outputs = self.transformer_model(**inputs) if self.pooling == "mean": emb = self._mean_pooling(outputs, inputs["attention_mask"]) elif self.pooling == "cls": emb = self._cls_pooling(outputs) 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(outputs: Any) -> Any: if isinstance(outputs, dict): token_embeddings = outputs["last_hidden_state"] else: token_embeddings = outputs[0] return token_embeddings[:, 0] @staticmethod def _mean_pooling(outputs: 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 if isinstance(outputs, dict): token_embeddings = outputs["last_hidden_state"] else: # First element of model_output contains all token embeddings token_embeddings = outputs[0] input_mask_expanded = ( attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() ) sum_embeddings = torch.sum(token_embeddings * 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 try: from tqdm import tqdm except ImportError as e: raise ImportError( "Unable to import tqdm, please install with `pip install -U tqdm`." ) 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 tqdm(batches, desc="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]