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"""Wrapper around HuggingFace Hub embedding models.""" | |
from typing import Any, Dict, List, Optional | |
from pydantic import BaseModel, Extra, root_validator | |
from langchain.embeddings.base import Embeddings | |
from langchain.utils import get_from_dict_or_env | |
DEFAULT_REPO_ID = "sentence-transformers/all-mpnet-base-v2" | |
VALID_TASKS = ("feature-extraction",) | |
class HuggingFaceHubEmbeddings(BaseModel, Embeddings): | |
"""Wrapper around HuggingFaceHub embedding models. | |
To use, you should have the ``huggingface_hub`` python package installed, and the | |
environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass | |
it as a named parameter to the constructor. | |
Example: | |
.. code-block:: python | |
from langchain.embeddings import HuggingFaceHubEmbeddings | |
repo_id = "sentence-transformers/all-mpnet-base-v2" | |
hf = HuggingFaceHubEmbeddings( | |
repo_id=repo_id, | |
task="feature-extraction", | |
huggingfacehub_api_token="my-api-key", | |
) | |
""" | |
client: Any #: :meta private: | |
repo_id: str = DEFAULT_REPO_ID | |
"""Model name to use.""" | |
task: Optional[str] = "feature-extraction" | |
"""Task to call the model with.""" | |
model_kwargs: Optional[dict] = None | |
"""Key word arguments to pass to the model.""" | |
huggingfacehub_api_token: Optional[str] = None | |
class Config: | |
"""Configuration for this pydantic object.""" | |
extra = Extra.forbid | |
def validate_environment(cls, values: Dict) -> Dict: | |
"""Validate that api key and python package exists in environment.""" | |
huggingfacehub_api_token = get_from_dict_or_env( | |
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN" | |
) | |
try: | |
from huggingface_hub.inference_api import InferenceApi | |
repo_id = values["repo_id"] | |
if not repo_id.startswith("sentence-transformers"): | |
raise ValueError( | |
"Currently only 'sentence-transformers' embedding models " | |
f"are supported. Got invalid 'repo_id' {repo_id}." | |
) | |
client = InferenceApi( | |
repo_id=repo_id, | |
token=huggingfacehub_api_token, | |
task=values.get("task"), | |
) | |
if client.task not in VALID_TASKS: | |
raise ValueError( | |
f"Got invalid task {client.task}, " | |
f"currently only {VALID_TASKS} are supported" | |
) | |
values["client"] = client | |
except ImportError: | |
raise ValueError( | |
"Could not import huggingface_hub python package. " | |
"Please it install it with `pip install huggingface_hub`." | |
) | |
return values | |
def embed_documents(self, texts: List[str]) -> List[List[float]]: | |
"""Call out to HuggingFaceHub's embedding endpoint for embedding search docs. | |
Args: | |
texts: The list of texts to embed. | |
Returns: | |
List of embeddings, one for each text. | |
""" | |
# replace newlines, which can negatively affect performance. | |
texts = [text.replace("\n", " ") for text in texts] | |
_model_kwargs = self.model_kwargs or {} | |
responses = self.client(inputs=texts, params=_model_kwargs) | |
return responses | |
def embed_query(self, text: str) -> List[float]: | |
"""Call out to HuggingFaceHub's embedding endpoint for embedding query text. | |
Args: | |
text: The text to embed. | |
Returns: | |
Embeddings for the text. | |
""" | |
response = self.embed_documents([text])[0] | |
return response | |