Source code for langchain_community.embeddings.self_hosted_hugging_face

import importlib
import logging
from typing import Any, Callable, List, Optional

from langchain_community.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. " "Provide device={deviceId} to `from_model_id` to use available" "GPUs for execution. deviceId is -1 for CPU and " "can be a positive integer associated with CUDA device id.", cuda_device_count, ) client = client.to(device) return client
[docs] class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings): """HuggingFace embedding models on self-hosted remote hardware. Supported hardware includes auto-launched instances on AWS, GCP, Azure, and Lambda, as well as servers specified by IP address and SSH credentials (such as on-prem, or another cloud like Paperspace, Coreweave, etc.). To use, you should have the ``runhouse`` python package installed. Example: .. code-block:: python from langchain_community.embeddings import SelfHostedHuggingFaceEmbeddings import runhouse as rh model_id = "sentence-transformers/all-mpnet-base-v2" gpu = rh.cluster(name="rh-a10x", instance_type="A100:1") hf = SelfHostedHuggingFaceEmbeddings(model_id=model_id, hardware=gpu) """ client: Any #: :meta private: model_id: str = DEFAULT_MODEL_NAME """Model name to use.""" model_reqs: List[str] = ["./", "sentence_transformers", "torch"] """Requirements to install on hardware to inference the model.""" hardware: Any """Remote hardware to send the inference function to.""" model_load_fn: Callable = load_embedding_model """Function to load the model remotely on the server.""" load_fn_kwargs: Optional[dict] = None """Keyword arguments to pass to the model load function.""" inference_fn: Callable = _embed_documents """Inference function to extract the embeddings.""" def __init__(self, **kwargs: Any): """Initialize the remote inference function.""" load_fn_kwargs = kwargs.pop("load_fn_kwargs", {}) load_fn_kwargs["model_id"] = load_fn_kwargs.get("model_id", DEFAULT_MODEL_NAME) load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", False) load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0) super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs] class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings): """HuggingFace InstructEmbedding models on self-hosted remote hardware. Supported hardware includes auto-launched instances on AWS, GCP, Azure, and Lambda, as well as servers specified by IP address and SSH credentials (such as on-prem, or another cloud like Paperspace, Coreweave, etc.). To use, you should have the ``runhouse`` python package installed. Example: .. code-block:: python from langchain_community.embeddings import SelfHostedHuggingFaceInstructEmbeddings import runhouse as rh model_name = "hkunlp/instructor-large" gpu = rh.cluster(name='rh-a10x', instance_type='A100:1') hf = SelfHostedHuggingFaceInstructEmbeddings( model_name=model_name, hardware=gpu) """ # noqa: E501 model_id: str = DEFAULT_INSTRUCT_MODEL """Model name to use.""" embed_instruction: str = DEFAULT_EMBED_INSTRUCTION """Instruction to use for embedding documents.""" query_instruction: str = DEFAULT_QUERY_INSTRUCTION """Instruction to use for embedding query.""" model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"] """Requirements to install on hardware to inference the model.""" def __init__(self, **kwargs: Any): """Initialize the remote inference function.""" load_fn_kwargs = kwargs.pop("load_fn_kwargs", {}) load_fn_kwargs["model_id"] = load_fn_kwargs.get( "model_id", DEFAULT_INSTRUCT_MODEL ) load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", True) load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0) super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace instruct model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ instruction_pairs = [] for text in texts: instruction_pairs.append([self.embed_instruction, text]) embeddings = self.client(self.pipeline_ref, instruction_pairs) return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]: """Compute query embeddings using a HuggingFace instruct model. Args: text: The text to embed. Returns: Embeddings for the text. """ instruction_pair = [self.query_instruction, text] embedding = self.client(self.pipeline_ref, [instruction_pair])[0] return embedding.tolist()