forked from Archives/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
"""Wrapper around HuggingFace embedding models."""
|
|
from typing import Any, List
|
|
|
|
from pydantic import BaseModel, Extra
|
|
|
|
from langchain.embeddings.base import Embeddings
|
|
|
|
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
|
|
|
|
|
|
class HuggingFaceEmbeddings(BaseModel, Embeddings):
|
|
"""Wrapper around sentence_transformers embedding models.
|
|
|
|
To use, you should have the ``sentence_transformers`` python package installed.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain.embeddings import HuggingFaceEmbeddings
|
|
model_name = "sentence-transformers/all-mpnet-base-v2"
|
|
hf = HuggingFaceEmbeddings(model_name=model_name)
|
|
"""
|
|
|
|
client: Any #: :meta private:
|
|
model_name: str = DEFAULT_MODEL_NAME
|
|
"""Model name to use."""
|
|
|
|
def __init__(self, **kwargs: Any):
|
|
"""Initialize the sentence_transformer."""
|
|
super().__init__(**kwargs)
|
|
try:
|
|
import sentence_transformers
|
|
|
|
self.client = sentence_transformers.SentenceTransformer(self.model_name)
|
|
except ImportError:
|
|
raise ValueError(
|
|
"Could not import sentence_transformers python package. "
|
|
"Please install it with `pip install sentence_transformers`."
|
|
)
|
|
|
|
class Config:
|
|
"""Configuration for this pydantic object."""
|
|
|
|
extra = Extra.forbid
|
|
|
|
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.
|
|
"""
|
|
texts = list(map(lambda x: x.replace("\n", " "), texts))
|
|
embeddings = self.client.encode(texts)
|
|
return embeddings
|
|
|
|
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.
|
|
"""
|
|
text = text.replace("\n", " ")
|
|
embedding = self.client.encode(text)
|
|
return embedding
|