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"""Wrapper around TensorflowHub embedding models.""" | |
from typing import Any, List | |
from pydantic import BaseModel, Extra | |
from langchain.embeddings.base import Embeddings | |
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3" | |
class TensorflowHubEmbeddings(BaseModel, Embeddings): | |
"""Wrapper around tensorflow_hub embedding models. | |
To use, you should have the ``tensorflow_text`` python package installed. | |
Example: | |
.. code-block:: python | |
from langchain.embeddings import TensorflowHubEmbeddings | |
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3" | |
tf = TensorflowHubEmbeddings(model_url=url) | |
""" | |
embed: Any #: :meta private: | |
model_url: str = DEFAULT_MODEL_URL | |
"""Model name to use.""" | |
def __init__(self, **kwargs: Any): | |
"""Initialize the tensorflow_hub and tensorflow_text.""" | |
super().__init__(**kwargs) | |
try: | |
import tensorflow_hub | |
import tensorflow_text # noqa | |
self.embed = tensorflow_hub.load(self.model_url) | |
except ImportError as e: | |
raise ValueError( | |
"Could not import some python packages." "Please install them." | |
) from e | |
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 TensorflowHub embedding 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.embed(texts).numpy() | |
return embeddings.tolist() | |
def embed_query(self, text: str) -> List[float]: | |
"""Compute query embeddings using a TensorflowHub embedding model. | |
Args: | |
text: The text to embed. | |
Returns: | |
Embeddings for the text. | |
""" | |
text = text.replace("\n", " ") | |
embedding = self.embed(text).numpy()[0] | |
return embedding.tolist() | |