from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
[docs]
class HuggingFaceEmbeddings(BaseModel, Embeddings):
"""HuggingFace sentence_transformers embedding models.
To use, you should have the ``sentence_transformers`` python package installed.
Example:
.. code-block:: python
from langchain_huggingface import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
hf = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
model_name: str = DEFAULT_MODEL_NAME
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the Sentence Transformer model, such as `device`,
`prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method for the documents of
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
`precision`, `normalize_embeddings`, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
query_encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method for the query of
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
`precision`, `normalize_embeddings`, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
multi_process: bool = False
"""Run encode() on multiple GPUs."""
show_progress: bool = False
"""Whether to show a progress bar."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers # type: ignore[import]
except ImportError as exc:
raise ImportError(
"Could not import sentence_transformers python package. "
"Please install it with `pip install sentence-transformers`."
) from exc
self._client = sentence_transformers.SentenceTransformer(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
model_config = ConfigDict(
extra="forbid",
protected_namespaces=(),
)
def _embed(
self, texts: list[str], encode_kwargs: Dict[str, Any]
) -> List[List[float]]:
"""
Embed a text using the HuggingFace transformer model.
Args:
texts: The list of texts to embed.
encode_kwargs: Keyword arguments to pass when calling the
`encode` method for the documents of the SentenceTransformer
encode method.
Returns:
List of embeddings, one for each text.
"""
import sentence_transformers # type: ignore[import]
texts = list(map(lambda x: x.replace("\n", " "), texts))
if self.multi_process:
pool = self._client.start_multi_process_pool()
embeddings = self._client.encode_multi_process(texts, pool)
sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
else:
embeddings = self._client.encode(
texts,
show_progress_bar=self.show_progress,
**encode_kwargs, # type: ignore
)
if isinstance(embeddings, list):
raise TypeError(
"Expected embeddings to be a Tensor or a numpy array, "
"got a list instead."
)
return embeddings.tolist()
[docs]
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
return self._embed(texts, self.encode_kwargs)
[docs]
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embed_kwargs = (
self.query_encode_kwargs
if len(self.query_encode_kwargs) > 0
else self.encode_kwargs
)
return self._embed([text], embed_kwargs)[0]
