mirror of
https://github.com/hwchase17/langchain.git
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24605bcdb6
- [x] **PR message**:
- **Description:** Fixes warning messages raised due to missing
`protected_namespaces` parameter in `ConfigDict`.
- **Issue:** https://github.com/langchain-ai/langchain/issues/27609
- **Dependencies:** No dependencies
- **Twitter handle:** @gawbul
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
from typing import Any, List
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from langchain_core.embeddings import Embeddings
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from pydantic import BaseModel, ConfigDict
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DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
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class TensorflowHubEmbeddings(BaseModel, Embeddings):
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"""TensorflowHub embedding models.
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To use, you should have the ``tensorflow_text`` python package installed.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import TensorflowHubEmbeddings
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url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
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tf = TensorflowHubEmbeddings(model_url=url)
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"""
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embed: Any = None #: :meta private:
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model_url: str = DEFAULT_MODEL_URL
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"""Model name to use."""
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def __init__(self, **kwargs: Any):
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"""Initialize the tensorflow_hub and tensorflow_text."""
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super().__init__(**kwargs)
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try:
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import tensorflow_hub
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except ImportError:
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raise ImportError(
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"Could not import tensorflow-hub python package. "
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"Please install it with `pip install tensorflow-hub``."
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)
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try:
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import tensorflow_text # noqa
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except ImportError:
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raise ImportError(
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"Could not import tensorflow_text python package. "
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"Please install it with `pip install tensorflow_text``."
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)
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self.embed = tensorflow_hub.load(self.model_url)
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model_config = ConfigDict(
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extra="forbid",
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protected_namespaces=(),
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)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a TensorflowHub embedding model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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embeddings = self.embed(texts).numpy()
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return embeddings.tolist()
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a TensorflowHub embedding model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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text = text.replace("\n", " ")
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embedding = self.embed([text]).numpy()[0]
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return embedding.tolist()
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