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