Source code for langchain.embeddings.self_hosted_hugging_face
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
    "Represent the question for retrieving supporting documents: "
)
logger = logging.getLogger(__name__)
def _embed_documents(client: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
    """Inference function to send to the remote hardware.
    Accepts a sentence_transformer model_id and
    returns a list of embeddings for each document in the batch.
    """
    return client.encode(*args, **kwargs)
[docs]def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0) -> Any:
    """Load the embedding model."""
    if not instruct:
        import sentence_transformers
        client = sentence_transformers.SentenceTransformer(model_id)
    else:
        from InstructorEmbedding import INSTRUCTOR
        client = INSTRUCTOR(model_id)
    if importlib.util.find_spec("torch") is not None:
        import torch
        cuda_device_count = torch.cuda.device_count()
        if device < -1 or (device >= cuda_device_count):
            raise ValueError(
                f"Got device=={device}, "
                f"device is required to be within [-1, {cuda_device_count})"
            )
        if device < 0 and cuda_device_count > 0:
            logger.warning(
                "Device has %d GPUs available. "